def setUp(self):
# Input
self.data = hdf5.read_data(os.path.join(DATA_DIR, "example_input.h5"))
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.small_data = self.data[0][350:400, 325:375]
# Input drifted by known value
self.data_drifted = hdf5.read_data(
os.path.join(DATA_DIR, "example_drifted.h5"))
C, T, Z, Y, X = self.data_drifted[0].shape
self.data_drifted[0].shape = Y, X
# Input drifted by random value
z = 1j # imaginary unit
self.deltar = numpy.random.uniform(-100, 100)
self.deltac = numpy.random.uniform(-100, 100)
nr, nc = self.data[0].shape
array_nr = numpy.arange(-numpy.fix(nr / 2), numpy.ceil(nr / 2))
array_nc = numpy.arange(-numpy.fix(nc / 2), numpy.ceil(nc / 2))
Nr = fft.ifftshift(array_nr)
Nc = fft.ifftshift(array_nc)
[Nc, Nr] = numpy.meshgrid(Nc, Nr)
self.data_random_drifted = fft.ifft2(
fft.fft2(self.data[0]) * numpy.power(
math.e, z * 2 * math.pi *
(self.deltar * Nr / nr + self.deltac * Nc / nc)))
# Noisy inputs
noise = random.normal(0, 3000, self.data[0].size)
noise_array = noise.reshape(self.data[0].shape[0],
self.data[0].shape[1])
self.data_noisy = self.data[0] + noise_array
self.data_drifted_noisy = self.data_drifted[0] + noise_array
self.data_random_drifted_noisy = self.data_random_drifted + noise_array
# Small input drifted by random value
self.small_deltar = numpy.random.uniform(-10, 10)
self.small_deltac = numpy.random.uniform(-10, 10)
nr, nc = self.small_data.shape
array_nr = numpy.arange(-numpy.fix(nr / 2), numpy.ceil(nr / 2))
array_nc = numpy.arange(-numpy.fix(nc / 2), numpy.ceil(nc / 2))
Nr = fft.ifftshift(array_nr)
Nc = fft.ifftshift(array_nc)
[Nc, Nr] = numpy.meshgrid(Nc, Nr)
self.small_data_random_drifted = fft.ifft2(
fft.fft2(self.small_data) * numpy.power(
math.e, z * 2 * math.pi *
(self.small_deltar * Nr / nr + self.small_deltac * Nc / nc)))
# Small noisy inputs
small_noise = random.normal(0, 3000, self.small_data.size)
small_noise_array = small_noise.reshape(self.small_data.shape[0],
self.small_data.shape[1])
self.small_data_noisy = self.small_data + small_noise_array
self.small_data_random_drifted_noisy = self.small_data_random_drifted + small_noise_array
def setUp(self):
data = hdf5.read_data("ar-example-input.h5")
self.data = data
# test also for different polar parameters
data_mini = hdf5.read_data("ar-example-minimirror-input.h5")
self.data_mini = data_mini
white_data_512 = model.DataArray(numpy.empty((512, 512), dtype="uint16"))
white_data_512[...] = 255
white_mag_512 = 0.4917
white_spxs_512 = (13e-6, 13e-6)
white_binning_512 = (2, 2)
white_data_512.metadata[model.MD_AR_POLE] = (283, 259)
white_data_512.metadata[model.MD_AR_XMAX] = 13.25e-3
white_data_512.metadata[model.MD_AR_HOLE_DIAMETER] = 0.6e-3
white_data_512.metadata[model.MD_AR_FOCUS_DISTANCE] = 0.5e-3
white_data_512.metadata[model.MD_AR_PARABOLA_F] = 2.5e-3
white_pxs_512 = (white_spxs_512[0] * white_binning_512[0] / white_mag_512,
white_spxs_512[1] * white_binning_512[1] / white_mag_512)
white_data_512.metadata[model.MD_PIXEL_SIZE] = white_pxs_512
self.white_data_512 = white_data_512
white_data_1024 = model.DataArray(numpy.empty((1024, 1024), dtype="uint16"))
white_data_1024[...] = 255
white_mag_1024 = 0.4917
white_spxs_1024 = (13e-6, 13e-6)
white_binning_1024 = (2, 2)
white_data_1024.metadata[model.MD_AR_POLE] = (283, 259)
white_data_1024.metadata[model.MD_AR_XMAX] = 13.25e-3
white_data_1024.metadata[model.MD_AR_HOLE_DIAMETER] = 0.6e-3
white_data_1024.metadata[model.MD_AR_FOCUS_DISTANCE] = 0.5e-3
white_data_1024.metadata[model.MD_AR_PARABOLA_F] = 2.5e-3
white_pxs_1024 = (white_spxs_1024[0] * white_binning_1024[0] / white_mag_1024,
white_spxs_1024[1] * white_binning_1024[1] / white_mag_1024)
white_data_1024.metadata[model.MD_PIXEL_SIZE] = white_pxs_1024
self.white_data_1024 = white_data_1024
white_data_2500 = model.DataArray(numpy.empty((2560, 2160), dtype="uint16"))
white_data_2500[...] = 255
white_mag_2500 = 0.4917
white_spxs_2500 = (13e-6, 13e-6)
white_binning_2500 = (2, 2)
white_data_2500.metadata[model.MD_AR_POLE] = (283, 259)
white_data_2500.metadata[model.MD_AR_XMAX] = 13.25e-3
white_data_2500.metadata[model.MD_AR_HOLE_DIAMETER] = 0.6e-3
white_data_2500.metadata[model.MD_AR_FOCUS_DISTANCE] = 0.5e-3
white_data_2500.metadata[model.MD_AR_PARABOLA_F] = 2.5e-3
# These values makes the computation much harder:
# white_mag_2500 = 0.53
# white_spxs_2500 = (6.5e-6, 6.5e-6)
# white_binning_2500 = (1, 1)
# white_data_2500.metadata[model.MD_AR_POLE] = (1480, 1129)
white_pxs_2500 = (white_spxs_2500[0] * white_binning_2500[0] / white_mag_2500,
white_spxs_2500[1] * white_binning_2500[1] / white_mag_2500)
white_data_2500.metadata[model.MD_PIXEL_SIZE] = white_pxs_2500
self.white_data_2500 = white_data_2500
def setUp(self):
# Input
self.data = hdf5.read_data("example_input.h5")
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.small_data = self.data[0][350:400, 325:375]
# Input drifted by known value
self.data_drifted = hdf5.read_data("example_drifted.h5")
C, T, Z, Y, X = self.data_drifted[0].shape
self.data_drifted[0].shape = Y, X
# Input drifted by random value
z = 1j # imaginary unit
self.deltar = numpy.random.uniform(-100, 100)
self.deltac = numpy.random.uniform(-100, 100)
nr, nc = self.data[0].shape
array_nr = numpy.arange(-numpy.fix(nr / 2), numpy.ceil(nr / 2))
array_nc = numpy.arange(-numpy.fix(nc / 2), numpy.ceil(nc / 2))
Nr = fft.ifftshift(array_nr)
Nc = fft.ifftshift(array_nc)
[Nc, Nr] = numpy.meshgrid(Nc, Nr)
self.data_random_drifted = fft.ifft2(fft.fft2(self.data[0]) * numpy.power(math.e,
z * 2 * math.pi * (self.deltar * Nr / nr + self.deltac * Nc / nc)))
# Noisy inputs
noise = random.normal(0, 3000, self.data[0].size)
noise_array = noise.reshape(self.data[0].shape[0], self.data[0].shape[1])
self.data_noisy = self.data[0] + noise_array
self.data_drifted_noisy = self.data_drifted[0] + noise_array
self.data_random_drifted_noisy = self.data_random_drifted + noise_array
# Small input drifted by random value
self.small_deltar = numpy.random.uniform(-10, 10)
self.small_deltac = numpy.random.uniform(-10, 10)
nr, nc = self.small_data.shape
array_nr = numpy.arange(-numpy.fix(nr / 2), numpy.ceil(nr / 2))
array_nc = numpy.arange(-numpy.fix(nc / 2), numpy.ceil(nc / 2))
Nr = fft.ifftshift(array_nr)
Nc = fft.ifftshift(array_nc)
[Nc, Nr] = numpy.meshgrid(Nc, Nr)
self.small_data_random_drifted = fft.ifft2(fft.fft2(self.small_data) * numpy.power(math.e,
z * 2 * math.pi * (self.small_deltar * Nr / nr + self.small_deltac * Nc / nc)))
# Small noisy inputs
small_noise = random.normal(0, 3000, self.small_data.size)
small_noise_array = small_noise.reshape(self.small_data.shape[0], self.small_data.shape[1])
self.small_data_noisy = self.small_data + small_noise_array
self.small_data_random_drifted_noisy = self.small_data_random_drifted + small_noise_array
def test_find_center_big(self):
"""
Test FindCenterCoordinates on large data
"""
# Note: it's not very clear why, but the center is not exactly the same
# as with the original data.
expected_coordinates = [(-0.0003367114224783442, -0.022941682748052378),
(0.42179351215760619, -0.25668360673638801),
(0.054153514028894206, -0.046475569488448026),
(0.15117193581594143, 0.20813363301021551),
(0.1963834856403108, -0.18329597166583256),
(0.23159684275306583, 1.3670166271550004),
(-1.3363782613242998, 0.20192181693837058),
(-0.14978764151902624, 0.66067572281822606),
(-0.058984235874285897, 0.13071737132569164),
(0.021009283646695891, -0.007037802630523865)]
for i in range(10):
data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "image" + str(i + 1) + ".h5"))[0]
data.shape = data.shape[-2:]
Y, X = data.shape
databig = numpy.zeros((200 + Y, 200 + X), data.dtype)
databig += numpy.min(data)
# We put it right at the center, so shouldn't change expected coordinates
databig[100:100 + Y:, 100: 100 + X] = data
spot_coordinates = spot.FindCenterCoordinates(databig)
numpy.testing.assert_almost_equal(spot_coordinates, expected_coordinates[i], 3)
def test_find_center(self):
"""
Test FindCenterCoordinates
"""
data = []
subimages = []
for i in xrange(10):
data.append(hdf5.read_data("image" + str(i + 1) + ".h5"))
C, T, Z, Y, X = data[i][0].shape
data[i][0].shape = Y, X
subimages.append(data[i][0])
spot_coordinates = coordinates.FindCenterCoordinates(subimages)
expected_coordinates = [(-0.00019439548586790034,
-0.023174120210179554),
(0.41813787193469681, -0.77556146879261101),
(0.05418032832973009, -0.046573726263258203),
(0.15117173005078957, 0.20813259555303279),
(0.15372338817998937, -0.071307409462406962),
(0.22214464176322843, 1.5448851668913044),
(-1.3567379189595801, 0.20634334863259929),
(-0.068717256379618827, 0.76902400758882417),
(-0.064496044288789064, 0.14000630665134439),
(0.020941736978718473, -0.0071056828496776324)]
numpy.testing.assert_almost_equal(spot_coordinates,
expected_coordinates, 3)
def test_find_center_big(self):
"""
Test FindCenterCoordinates on large data
"""
# Note: it's not very clear why, but the center is not exactly the same
# as with the original data.
expected_coordinates = [(-0.0003367114224783442,
-0.022941682748052378),
(0.42179351215760619, -0.25668360673638801),
(0.054153514028894206, -0.046475569488448026),
(0.15117193581594143, 0.20813363301021551),
(0.1963834856403108, -0.18329597166583256),
(0.23159684275306583, 1.3670166271550004),
(-1.3363782613242998, 0.20192181693837058),
(-0.14978764151902624, 0.66067572281822606),
(-0.058984235874285897, 0.13071737132569164),
(0.021009283646695891, -0.007037802630523865)]
for i in range(10):
data = hdf5.read_data(
os.path.join(TEST_IMAGE_PATH, "image" + str(i + 1) + ".h5"))[0]
data.shape = data.shape[-2:]
Y, X = data.shape
databig = numpy.zeros((200 + Y, 200 + X), data.dtype)
databig += numpy.min(data)
# We put it right at the center, so shouldn't change expected coordinates
databig[100:100 + Y:, 100:100 + X] = data
spot_coordinates = spot.FindCenterCoordinates(databig)
numpy.testing.assert_almost_equal(spot_coordinates,
expected_coordinates[i], 3)
def setUp(self):
self.data = hdf5.read_data(os.path.dirname(__file__) + "/grid_10x10.h5")
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.fake_img = self.data[0]
if self.backend_was_running:
self.skipTest("Running backend found")
def setUp(self):
self.data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "one_spot.h5"))
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.fake_img = self.data[0]
if self.backend_was_running:
self.skipTest("Running backend found")
def setUp(self):
self.data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "one_spot.h5"))
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.fake_img = self.data[0]
if self.backend_was_running:
self.skipTest("Running backend found")
def setUp(self):
self.data = hdf5.read_data("grid_10x10.h5")
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.fake_img = self.data[0]
if self.backend_was_running:
self.skipTest("Running backend found")
def setUp(self):
self.data = hdf5.read_data("grid_10x10.h5")
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.fake_img = self.data[0]
if self.backend_was_running:
self.skipTest("Running backend found")
def setUp(self):
data = hdf5.read_data("ar-example-input.h5")
mag = 0.4917
spxs = (13e-6, 13e-6)
binning = (4, 4)
# data[0].metadata[model.MD_BASELINE] = 820
data[0].metadata[model.MD_BINNING] = binning
data[0].metadata[model.MD_SENSOR_PIXEL_SIZE] = spxs
data[0].metadata[model.MD_LENS_MAG] = mag
data[0].metadata[model.MD_AR_POLE] = (141, 255 - 139.449038462)
mag = data[0].metadata[model.MD_LENS_MAG]
pxs = (spxs[0] * binning[0] / mag, spxs[1] * binning[1] / mag)
data[0].metadata[model.MD_PIXEL_SIZE] = pxs
self.data = data
white_data_512 = model.DataArray(
numpy.empty((512, 512), dtype="uint16"))
white_data_512[...] = 255
white_mag_512 = 0.4917
white_spxs_512 = (13e-6, 13e-6)
white_binning_512 = (2, 2)
white_data_512.metadata[model.MD_AR_POLE] = (283, 259)
white_pxs_512 = (white_spxs_512[0] * white_binning_512[0] /
white_mag_512, white_spxs_512[1] *
white_binning_512[1] / white_mag_512)
white_data_512.metadata[model.MD_PIXEL_SIZE] = white_pxs_512
self.white_data_512 = white_data_512
white_data_1024 = model.DataArray(
numpy.empty((1024, 1024), dtype="uint16"))
white_data_1024[...] = 255
white_mag_1024 = 0.4917
white_spxs_1024 = (13e-6, 13e-6)
white_binning_1024 = (2, 2)
white_data_1024.metadata[model.MD_AR_POLE] = (283, 259)
white_pxs_1024 = (white_spxs_1024[0] * white_binning_1024[0] /
white_mag_1024, white_spxs_1024[1] *
white_binning_1024[1] / white_mag_1024)
white_data_1024.metadata[model.MD_PIXEL_SIZE] = white_pxs_1024
self.white_data_1024 = white_data_1024
white_data_2500 = model.DataArray(
numpy.empty((2560, 2160), dtype="uint16"))
white_data_2500[...] = 255
white_mag_2500 = 0.4917
white_spxs_2500 = (13e-6, 13e-6)
white_binning_2500 = (2, 2)
white_data_2500.metadata[model.MD_AR_POLE] = (283, 259)
# These values makes the computation much harder:
# white_mag_2500 = 0.53
# white_spxs_2500 = (6.5e-6, 6.5e-6)
# white_binning_2500 = (1, 1)
# white_data_2500.metadata[model.MD_AR_POLE] = (1480, 1129)
white_pxs_2500 = (white_spxs_2500[0] * white_binning_2500[0] /
white_mag_2500, white_spxs_2500[1] *
white_binning_2500[1] / white_mag_2500)
white_data_2500.metadata[model.MD_PIXEL_SIZE] = white_pxs_2500
self.white_data_2500 = white_data_2500
def setUp(self):
data = hdf5.read_data(os.path.join(PATH, "spectrum_fitting.h5"))[1]
data = numpy.squeeze(data)
self.data = data
self.wl_in_meters = numpy.linspace(470e-9, 1030e-9, 167)
max_bw = data.shape[0] // 2
min_bw = (max_bw - data.shape[0]) + 1
self.wl_in_pixels = list(range(min_bw, max_bw + 1))
self._peak_fitter = peak.PeakFitter()
def test_no_hole(self):
"""
Test FindCircleCenter raises exception
"""
data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "blank_image.h5"))
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
self.assertRaises(LookupError, delphi.FindCircleCenter, data[0], 0.02032, 3)
def setUp(self):
if self.backend_was_running:
self.skipTest("Running backend found")
# image for FakeCCD
self.data = hdf5.read_data("../align/test/one_spot.h5")
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.fake_img = self.data[0]
def setUp(self):
if self.backend_was_running:
self.skipTest("Running backend found")
# image for FakeCCD
self.data = hdf5.read_data("../align/test/one_spot.h5")
C, T, Z, Y, X = self.data[0].shape
self.data[0].shape = Y, X
self.fake_img = self.data[0]
def test_no_hole(self):
"""
Test FindCircleCenter raises exception
"""
data = hdf5.read_data("blank_image.h5")
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
self.assertRaises(IOError, delphi.FindCircleCenter, data[0], 0.02032, 3)
def test_no_hole(self):
"""
Test FindCircleCenter raises exception
"""
data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "blank_image.h5"))
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
self.assertRaises(LookupError, delphi.FindCircleCenter, data[0], 0.02032, 3)
def test_no_hole(self):
"""
Test FindCircleCenter raises exception
"""
data = hdf5.read_data("blank_image.h5")
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
self.assertRaises(IOError, delphi.FindCircleCenter, data[0], 0.02032,
3)
def test_find_lens_center(self):
"""
Test FindCircleCenter for lenses
"""
data = hdf5.read_data("navcam-calib2.h5")
Z, Y, X = data[0].shape
lens_coordinates = delphi.FindCircleCenter(data[0][0], delphi.LENS_RADIUS, 6)
expected_coordinates = (450.5, 445.5)
numpy.testing.assert_almost_equal(lens_coordinates, expected_coordinates)
def test_precomputed(self):
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = polar.AngleResolved2Polar(data[0], 201)
desired_output = hdf5.read_data("desired201x201image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_precomputed(self):
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = angleres.AngleResolved2Polar(data[0], 201)
desired_output = hdf5.read_data("desired201x201image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_precomputed_mini(self):
data_mini = self.data_mini
C, T, Z, Y, X = data_mini[0].shape
data_mini[0].shape = Y, X
result = angleres.AngleResolved2Polar(data_mini[0], 201)
desired_output = hdf5.read_data("desired201x201imagemini.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], atol=1e-07)
def test_find_hole_center(self):
"""
Test FindCircleCenter for holes
"""
data = hdf5.read_data("sem_hole.h5")
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
hole_coordinates = delphi.FindCircleCenter(data[0], 0.02032, 3)
expected_coordinates = (390.5, 258.5)
numpy.testing.assert_almost_equal(hole_coordinates, expected_coordinates)
def test_precomputed(self):
# These are example data (computer generated)
data = hdf5.read_data("image1.h5")[0]
data.shape = data.shape[-2:]
si = spot.SpotIntensity(data) # guessed background
self.assertAlmostEqual(si, 0.713582339927869)
# Same thing, with some static background
background = numpy.zeros(data.shape, dtype=data.dtype)
background += 50
si = spot.SpotIntensity(data, background)
self.assertAlmostEqual(si, 0.8621370728816907)
def test_find_lens_center(self):
"""
Test FindCircleCenter for lenses
"""
data = hdf5.read_data("navcam-calib2.h5")
Z, Y, X = data[0].shape
lens_coordinates = delphi.FindCircleCenter(data[0][0],
delphi.LENS_RADIUS, 6)
expected_coordinates = (450.5, 445.5)
numpy.testing.assert_almost_equal(lens_coordinates,
expected_coordinates)
def test_find_lens_center(self):
"""
Test FindRingCenter for lenses
"""
data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "navcam-calib2.h5"))
imgs = img.RGB2Greyscale(img.ensureYXC(data[0]))
# lens_coordinates = delphi.FindCircleCenter(data[0][0], delphi.LENS_RADIUS, 5)
# expected_coordinates = (-5.9703947e-05, 1.5257675e-04) # (451.5, 445.5) px
lens_coordinates = delphi.FindRingCenter(imgs)
expected_coordinates = (-1.6584835e-05, 1.3084411e-04) # 454.75, 446.1) px
numpy.testing.assert_almost_equal(lens_coordinates, expected_coordinates)
def test_find_hole_center(self):
"""
Test FindCircleCenter for holes
"""
data = hdf5.read_data("sem_hole.h5")
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
hole_coordinates = delphi.FindCircleCenter(data[0], 0.02032, 3)
expected_coordinates = (390.5, 258.5)
numpy.testing.assert_almost_equal(hole_coordinates,
expected_coordinates)
def test_1024x1024(self):
"""
Test for 1024x1024 white image input
"""
white_data_1024 = self.white_data_1024
result = angleres.AngleResolved2Polar(white_data_1024, 201)
desired_output = hdf5.read_data("desired_white_1024.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_100x100(self):
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = angleres.AngleResolved2Polar(data[0], 101)
desired_output = hdf5.read_data("desired100x100image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def testReadMDOutWlBands(self):
"""
Checks that we hand MD_OUT_WL if it contains multiple bands.
OME supports only one value, so it's ok to discard some info.
"""
metadata = [
{
model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: ((630e-9, 660e-9), (675e-9, 690e-9)), # m
model.MD_USER_TINT: (255, 0, 65), # purple
model.MD_LIGHT_POWER: 100e-3, # W
}
]
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md.copy())
a[i, i] = i # "watermark" it
ldata.append(a)
# export
hdf5.export(FILENAME, ldata)
# check data
rdata = hdf5.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
im = rdata[0]
emd = metadata[0].copy()
rmd = im.metadata
img.mergeMetadata(emd)
img.mergeMetadata(rmd)
self.assertEqual(rmd[model.MD_DESCRIPTION], emd[model.MD_DESCRIPTION])
iwl = rmd[model.MD_IN_WL] # nm
self.assertTrue((emd[model.MD_IN_WL][0] <= iwl[0] and iwl[1] <= emd[model.MD_IN_WL][-1]))
# It should be within at least one of the bands
owl = rmd[model.MD_OUT_WL] # nm
for eowl in emd[model.MD_OUT_WL]:
if eowl[0] <= owl[0] and owl[1] <= eowl[-1]:
break
else:
self.fail("Out wl %s is not within original metadata" % (owl,))
def test_100x100(self):
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = polar.AngleResolved2Polar(data[0], 101)
desired_output = hdf5.read_data("desired100x100image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_1024x1024(self):
"""
Test for 1024x1024 white image input
"""
white_data_1024 = self.white_data_1024
result = angleres.AngleResolved2Polar(white_data_1024, 201)
desired_output = hdf5.read_data("desired_white_1024.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def setUp(self):
data = hdf5.read_data("ar-example-input.h5")
mag = 0.4917
spxs = (13e-6, 13e-6)
binning = (4, 4)
# data[0].metadata[model.MD_BASELINE] = 820
data[0].metadata[model.MD_BINNING] = binning
data[0].metadata[model.MD_SENSOR_PIXEL_SIZE] = spxs
data[0].metadata[model.MD_LENS_MAG] = mag
data[0].metadata[model.MD_AR_POLE] = (141, 255 - 139.449038462)
mag = data[0].metadata[model.MD_LENS_MAG]
pxs = (spxs[0] * binning[0] / mag,
spxs[1] * binning[1] / mag)
data[0].metadata[model.MD_PIXEL_SIZE] = pxs
self.data = data
white_data_512 = model.DataArray(numpy.empty((512, 512), dtype="uint16"))
white_data_512[...] = 255
white_mag_512 = 0.4917
white_spxs_512 = (13e-6, 13e-6)
white_binning_512 = (2, 2)
white_data_512.metadata[model.MD_AR_POLE] = (283, 259)
white_pxs_512 = (white_spxs_512[0] * white_binning_512[0] / white_mag_512,
white_spxs_512[1] * white_binning_512[1] / white_mag_512)
white_data_512.metadata[model.MD_PIXEL_SIZE] = white_pxs_512
self.white_data_512 = white_data_512
white_data_1024 = model.DataArray(numpy.empty((1024, 1024), dtype="uint16"))
white_data_1024[...] = 255
white_mag_1024 = 0.4917
white_spxs_1024 = (13e-6, 13e-6)
white_binning_1024 = (2, 2)
white_data_1024.metadata[model.MD_AR_POLE] = (283, 259)
white_pxs_1024 = (white_spxs_1024[0] * white_binning_1024[0] / white_mag_1024,
white_spxs_1024[1] * white_binning_1024[1] / white_mag_1024)
white_data_1024.metadata[model.MD_PIXEL_SIZE] = white_pxs_1024
self.white_data_1024 = white_data_1024
white_data_2500 = model.DataArray(numpy.empty((2560, 2160), dtype="uint16"))
white_data_2500[...] = 255
white_mag_2500 = 0.4917
white_spxs_2500 = (13e-6, 13e-6)
white_binning_2500 = (2, 2)
white_data_2500.metadata[model.MD_AR_POLE] = (283, 259)
# These values makes the computation much harder:
# white_mag_2500 = 0.53
# white_spxs_2500 = (6.5e-6, 6.5e-6)
# white_binning_2500 = (1, 1)
# white_data_2500.metadata[model.MD_AR_POLE] = (1480, 1129)
white_pxs_2500 = (white_spxs_2500[0] * white_binning_2500[0] / white_mag_2500,
white_spxs_2500[1] * white_binning_2500[1] / white_mag_2500)
white_data_2500.metadata[model.MD_PIXEL_SIZE] = white_pxs_2500
self.white_data_2500 = white_data_2500
def test_precomputed(self):
# These are example data (computer generated)
data = hdf5.read_data("image1.h5")[0]
data.shape = data.shape[-2:]
si = spot.SpotIntensity(data) # guessed background
self.assertAlmostEqual(si, 0.713582339927869)
# Same thing, with some static background
background = numpy.zeros(data.shape, dtype=data.dtype)
background += 50
si = spot.SpotIntensity(data, background)
self.assertAlmostEqual(si, 0.8621370728816907)
def test_find_lens_center(self):
"""
Test FindRingCenter for lenses
"""
data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "navcam-calib2.h5"))
imgs = img.RGB2Greyscale(img.ensureYXC(data[0]))
# lens_coordinates = delphi.FindCircleCenter(data[0][0], delphi.LENS_RADIUS, 5)
# expected_coordinates = (-5.9703947e-05, 1.5257675e-04) # (451.5, 445.5) px
lens_coordinates = delphi.FindRingCenter(imgs)
expected_coordinates = (-1.6584835e-05, 1.3084411e-04) # 454.75, 446.1) px
numpy.testing.assert_almost_equal(lens_coordinates, expected_coordinates)
def testExportRead(self):
"""
Checks that we can read back an image and a thumbnail
"""
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400)
] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
white = (12, 52) # non symmetric position
ldata = []
num = 2
# TODO: check support for combining channels when same data shape
for i in range(num):
a = model.DataArray(numpy.zeros(sizes[i][::-1], dtype))
a[white[::-1]] = 1027
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 0] += 255 # red
blue = (12, 22) # non symmetric position
thumbnail[blue[::-1]] = [0, 0, 255]
thumbnail.metadata[model.MD_POS] = (0.1, -2)
thumbnail.metadata[model.MD_PIXEL_SIZE] = (13e-6, 13e-6)
# export
hdf5.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = hdf5.read_data(FILENAME)
self.assertEqual(len(rdata), num)
for i, im in enumerate(rdata):
subim = im[0, 0, 0] # remove C,T,Z dimensions
self.assertEqual(subim.shape, sizes[i][-1::-1])
self.assertEqual(subim[white[-1:-3:-1]], ldata[i][white[-1:-3:-1]])
# check thumbnail
rthumbs = hdf5.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [255, 0, 0])
self.assertEqual(im[blue[::-1]].tolist(), [0, 0, 255])
self.assertAlmostEqual(im.metadata[model.MD_POS],
thumbnail.metadata[model.MD_POS])
def testReadMDOutWlBands(self):
"""
Checks that we hand MD_OUT_WL if it contains multiple bands.
OME supports only one value, so it's ok to discard some info.
"""
metadata = [{model.MD_SW_VERSION: "1.0-test",
model.MD_HW_NAME: "fake hw",
model.MD_DESCRIPTION: "blue dye",
model.MD_ACQ_DATE: time.time() + 1,
model.MD_BPP: 12,
model.MD_BINNING: (1, 1), # px, px
model.MD_PIXEL_SIZE: (1e-6, 1e-6), # m/px
model.MD_POS: (13.7e-3, -30e-3), # m
model.MD_EXP_TIME: 1.2, # s
model.MD_IN_WL: (500e-9, 520e-9), # m
model.MD_OUT_WL: ((630e-9, 660e-9), (675e-9, 690e-9)), # m
model.MD_USER_TINT: (255, 0, 65), # purple
model.MD_LIGHT_POWER: 100e-3 # W
},
]
size = (512, 256)
dtype = numpy.dtype("uint16")
ldata = []
for i, md in enumerate(metadata):
a = model.DataArray(numpy.zeros(size[::-1], dtype), md.copy())
a[i, i] = i # "watermark" it
ldata.append(a)
# export
hdf5.export(FILENAME, ldata)
# check data
rdata = hdf5.read_data(FILENAME)
self.assertEqual(len(rdata), len(ldata))
im = rdata[0]
emd = metadata[0].copy()
rmd = im.metadata
img.mergeMetadata(emd)
img.mergeMetadata(rmd)
self.assertEqual(rmd[model.MD_DESCRIPTION], emd[model.MD_DESCRIPTION])
iwl = rmd[model.MD_IN_WL] # nm
self.assertTrue((emd[model.MD_IN_WL][0] <= iwl[0] and
iwl[1] <= emd[model.MD_IN_WL][-1]))
# It should be within at least one of the bands
owl = rmd[model.MD_OUT_WL] # nm
for eowl in emd[model.MD_OUT_WL]:
if (eowl[0] <= owl[0] and owl[1] <= eowl[-1]):
break
else:
self.fail("Out wl %s is not within original metadata" % (owl,))
def test_512x512(self):
"""
Test for 512x512 white image input
"""
white_data_512 = self.white_data_512
Y, X = white_data_512.shape
result = polar.AngleResolved2Polar(white_data_512, 201)
desired_output = hdf5.read_data("desired_white_512.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_2560x2160(self):
"""
Test for 2560x2160 white image input
"""
white_data_2500 = self.white_data_2500
Y, X = white_data_2500.shape
result = polar.AngleResolved2Polar(white_data_2500, 2000, dtype=numpy.float16)
desired_output = hdf5.read_data("desired_white_2500.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_find_hole_center(self):
"""
Test FindCircleCenter for holes
"""
# Note: this hole image has a better contrast and less noise than typical
# hole images on the DELPHI
data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "sem_hole.h5"))
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
hole_coordinates = delphi.FindCircleCenter(data[0], 0.02032, 6, darkest=True)
expected_coordinates = (0.0052705, -0.0018415) # (391.5, 257.5) px
numpy.testing.assert_almost_equal(hole_coordinates, expected_coordinates)
def test_find_hole_center(self):
"""
Test FindCircleCenter for holes
"""
# Note: this hole image has a better contrast and less noise than typical
# hole images on the DELPHI
data = hdf5.read_data(os.path.join(TEST_IMAGE_PATH, "sem_hole.h5"))
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
hole_coordinates = delphi.FindCircleCenter(data[0], 0.02032, 6, darkest=True)
expected_coordinates = (0.0052705, -0.0018415) # (391.5, 257.5) px
numpy.testing.assert_almost_equal(hole_coordinates, expected_coordinates)
def test_512x512(self):
"""
Test for 512x512 white image input
"""
white_data_512 = self.white_data_512
Y, X = white_data_512.shape
result = polar.AngleResolved2Polar(white_data_512, 201)
desired_output = hdf5.read_data("desired_white_512.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def testExportRead(self):
"""
Checks that we can read back an image and a thumbnail
"""
# create 2 simple greyscale images
sizes = [(512, 256), (500, 400)] # different sizes to ensure different acquisitions
dtype = numpy.dtype("uint16")
white = (12, 52) # non symmetric position
ldata = []
num = 2
# TODO: check support for combining channels when same data shape
for i in range(num):
a = model.DataArray(numpy.zeros(sizes[i][::-1], dtype))
a[white[::-1]] = 1027
ldata.append(a)
# thumbnail : small RGB completely red
tshape = (sizes[0][1] // 8, sizes[0][0] // 8, 3)
tdtype = numpy.uint8
thumbnail = model.DataArray(numpy.zeros(tshape, tdtype))
thumbnail[:, :, 0] += 255 # red
blue = (12, 22) # non symmetric position
thumbnail[blue[::-1]] = [0, 0, 255]
thumbnail.metadata[model.MD_POS] = (0.1, -2)
thumbnail.metadata[model.MD_PIXEL_SIZE] = (13e-6, 13e-6)
# export
hdf5.export(FILENAME, ldata, thumbnail)
# check it's here
st = os.stat(FILENAME) # this test also that the file is created
self.assertGreater(st.st_size, 0)
# check data
rdata = hdf5.read_data(FILENAME)
self.assertEqual(len(rdata), num)
for i, im in enumerate(rdata):
subim = im[0, 0, 0] # remove C,T,Z dimensions
self.assertEqual(subim.shape, sizes[i][-1::-1])
self.assertEqual(subim[white[-1:-3:-1]], ldata[i][white[-1:-3:-1]])
# check thumbnail
rthumbs = hdf5.read_thumbnail(FILENAME)
self.assertEqual(len(rthumbs), 1)
im = rthumbs[0]
self.assertEqual(im.shape, tshape)
self.assertEqual(im[0, 0].tolist(), [255, 0, 0])
self.assertEqual(im[blue[::-1]].tolist(), [0, 0, 255])
self.assertAlmostEqual(im.metadata[model.MD_POS], thumbnail.metadata[model.MD_POS])
def test_uint16_input(self):
"""
Tests for input of DataArray with uint16 ndarray.
"""
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = polar.AngleResolved2Polar(data[0], 201)
desired_output = hdf5.read_data("desired201x201image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_autofocus_spect(self):
"""
Test AutoFocus on 1 line CCD for example spectrum.
"""
# Make sure the image is the example spectrum image, in case this test runs after test_autofocus_slit.
data = hdf5.read_data(os.path.dirname(odemis.__file__) + "/driver/sparc-spec-sim.h5")
new_img = img.ensure2DImage(data[0])
self.ccd.set_image(new_img)
self.focus.moveAbs({"z": self._good_focus - 200e-6}).result()
f = align.AutoFocus(self.spectrometer, None, self.focus, method=MTD_BINARY)
foc_pos, foc_lev = f.result(timeout=900)
logging.debug("Found focus at {} good focus at {}".format(foc_pos, self._good_focus))
# The focus step size is 10.9e-6, the tolerance is set to 2.5e-5; approximately two focus steps.
numpy.testing.assert_allclose(foc_pos, self._good_focus, atol=2.5e-5)
def test_uint16_input(self):
"""
Tests for input of DataArray with uint16 ndarray.
"""
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = polar.AngleResolved2Polar(data[0], 201)
desired_output = hdf5.read_data("desired201x201image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_2560x2160(self):
"""
Test for 2560x2160 white image input
"""
white_data_2500 = self.white_data_2500
result = angleres.AngleResolved2Polar(white_data_2500, 2000)
desired_output = hdf5.read_data("desired_white_2500.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_divide_and_find_center_grid(self):
"""
Test DivideInNeighborhoods combined with FindCenterCoordinates
"""
grid_data = hdf5.read_data("grid_10x10.h5")
C, T, Z, Y, X = grid_data[0].shape
grid_data[0].shape = Y, X
subimages, subimage_coordinates = coordinates.DivideInNeighborhoods(grid_data[0], (10, 10), 40)
spot_coordinates = [spot.FindCenterCoordinates(i) for i in subimages]
optical_coordinates = coordinates.ReconstructCoordinates(subimage_coordinates, spot_coordinates)
self.assertEqual(len(subimages), 100)
def test_background_substraction_precomputed(self):
"""
Test clean up before polar conversion
"""
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
clean_data = angleres.ARBackgroundSubtract(data[0])
result = angleres.AngleResolved2Polar(clean_data, 201)
desired_output = hdf5.read_data("substracted_background_image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_1000x1000(self):
data = self.data
data[0] = data[0].astype(numpy.int64)
data[0] = numpy.right_shift(data[0], 8)
data[0] = data[0].astype(numpy.int8)
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = angleres.AngleResolved2Polar(data[0], 1001)
desired_output = hdf5.read_data("desired1000x1000image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1)
def test_float_input(self):
"""
Tests for input of DataArray with float ndarray.
"""
data = self.data
data[0] = data[0].astype(numpy.float)
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = angleres.AngleResolved2Polar(data[0], 201)
desired_output = hdf5.read_data("desired201x201image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_measure_focus(self):
"""
Test MeasureFocus
"""
data = hdf5.read_data(os.path.dirname(__file__) + "/grid_10x10.h5")
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
input = data[0]
prev_res = autofocus.MeasureSEMFocus(input)
for i in range(1, 10, 1):
blur = ndimage.gaussian_filter(input, sigma=i)
res = autofocus.MeasureSEMFocus(blur)
self.assertGreater(prev_res, res)
prev_res = res
def test_float_input(self):
"""
Tests for input of DataArray with float ndarray.
"""
data = self.data
data[0] = data[0].astype(numpy.float)
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = angleres.AngleResolved2Polar(data[0], 201)
desired_output = hdf5.read_data("desired201x201image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_1000x1000(self):
data = self.data
data[0] = data[0].astype(numpy.int64)
data[0] = numpy.right_shift(data[0], 8)
data[0] = data[0].astype(numpy.int8)
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
result = angleres.AngleResolved2Polar(data[0], 1001)
desired_output = hdf5.read_data("desired1000x1000image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1)
def test_measure_focus(self):
"""
Test MeasureFocus
"""
data = hdf5.read_data(os.path.dirname(__file__) + "/grid_10x10.h5")
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
input = data[0]
prev_res = autofocus.MeasureSEMFocus(input)
for i in range(1, 10, 1):
blur = ndimage.gaussian_filter(input, sigma=i)
res = autofocus.MeasureSEMFocus(blur)
self.assertGreater(prev_res, res)
prev_res = res
def test_2560x2160(self):
"""
Test for 2560x2160 white image input
"""
white_data_2500 = self.white_data_2500
Y, X = white_data_2500.shape
result = polar.AngleResolved2Polar(white_data_2500, 2000, dtype=numpy.float16)
desired_output = hdf5.read_data("desired_white_2500.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
# FIXME: doesn't seem to pass on 64 bits ?! floating point computation differences?
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_background_substraction_precomputed(self):
"""
Test clean up before polar conversion
"""
data = self.data
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
clean_data = angleres.ARBackgroundSubtract(data[0])
result = angleres.AngleResolved2Polar(clean_data, 201)
desired_output = hdf5.read_data("substracted_background_image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_background_substraction_float_input(self):
"""
Tests for input of DataArray with float ndarray.
"""
data = self.data
data[0] = data[0].astype(numpy.float)
C, T, Z, Y, X = data[0].shape
data[0].shape = Y, X
clean_data = angleres.ARBackgroundSubtract(data[0])
result = angleres.AngleResolved2Polar(clean_data, 201)
desired_output = hdf5.read_data("substracted_background_image.h5")
C, T, Z, Y, X = desired_output[0].shape
desired_output[0].shape = Y, X
numpy.testing.assert_allclose(result, desired_output[0], rtol=1e-04)
def test_devide_and_find_center_grid(self):
"""
Test DivideInNeighborhoods combined with FindCenterCoordinates
"""
grid_data = hdf5.read_data("grid_10x10.h5")
C, T, Z, Y, X = grid_data[0].shape
grid_data[0].shape = Y, X
subimages, subimage_coordinates = coordinates.DivideInNeighborhoods(
grid_data[0], (10, 10), 40)
spot_coordinates = coordinates.FindCenterCoordinates(subimages)
optical_coordinates = coordinates.ReconstructCoordinates(
subimage_coordinates, spot_coordinates)
self.assertEqual(len(subimages), 100)
