def test_get_acquisition_data_subset(self):
nr = NexusReader(self.filename)
key = nr.get_image_keys()
sl = nr.get_acquisition_data_subset(0, 10)
data = nr.get_acquisition_data().subset(['vertical', 'horizontal'])
self.assertTrue(sl.shape, (10, data.shape[1]))
def testAll(self):
if has_wget:
# def testGetDimensions(self):
nr = NexusReader(self.filename)
self.assertEqual(nr.get_sinogram_dimensions(), (135, 91, 160),
"Sinogram dimensions are not correct")
# def testGetProjectionDimensions(self):
nr = NexusReader(self.filename)
self.assertEqual(nr.get_projection_dimensions(), (91, 135, 160),
"Projection dimensions are not correct")
# def testLoadProjectionWithoutDimensions(self):
nr = NexusReader(self.filename)
projections = nr.load_projection()
self.assertEqual(
projections.shape, (91, 135, 160),
"Loaded projection data dimensions are not correct")
# def testLoadProjectionWithDimensions(self):
nr = NexusReader(self.filename)
projections = nr.load_projection(
(slice(0, 1), slice(0, 135), slice(0, 160)))
self.assertEqual(
projections.shape, (1, 135, 160),
"Loaded projection data dimensions are not correct")
# def testLoadProjectionCompareSingle(self):
nr = NexusReader(self.filename)
projections_full = nr.load_projection()
projections_part = nr.load_projection(
(slice(0, 1), slice(0, 135), slice(0, 160)))
numpy.testing.assert_array_equal(projections_part,
projections_full[0:1, :, :])
# def testLoadProjectionCompareMulti(self):
nr = NexusReader(self.filename)
projections_full = nr.load_projection()
projections_part = nr.load_projection(
(slice(0, 3), slice(0, 135), slice(0, 160)))
numpy.testing.assert_array_equal(projections_part,
projections_full[0:3, :, :])
# def testLoadProjectionCompareRandom(self):
nr = NexusReader(self.filename)
projections_full = nr.load_projection()
projections_part = nr.load_projection(
(slice(1, 8), slice(5, 10), slice(8, 20)))
numpy.testing.assert_array_equal(projections_part,
projections_full[1:8, 5:10, 8:20])
# def testLoadProjectionCompareFull(self):
nr = NexusReader(self.filename)
projections_full = nr.load_projection()
projections_part = nr.load_projection(
(slice(None, None), slice(None, None), slice(None, None)))
numpy.testing.assert_array_equal(projections_part,
projections_full[:, :, :])
# def testLoadFlatCompareFull(self):
nr = NexusReader(self.filename)
flats_full = nr.load_flat()
flats_part = nr.load_flat(
(slice(None, None), slice(None, None), slice(None, None)))
numpy.testing.assert_array_equal(flats_part, flats_full[:, :, :])
# def testLoadDarkCompareFull(self):
nr = NexusReader(self.filename)
darks_full = nr.load_dark()
darks_part = nr.load_dark(
(slice(None, None), slice(None, None), slice(None, None)))
numpy.testing.assert_array_equal(darks_part, darks_full[:, :, :])
# def testProjectionAngles(self):
nr = NexusReader(self.filename)
angles = nr.get_projection_angles()
self.assertEqual(
angles.shape, (91, ),
"Loaded projection number of angles are not correct")
# def test_get_acquisition_data_subset(self):
nr = NexusReader(self.filename)
key = nr.get_image_keys()
sl = nr.get_acquisition_data_subset(0, 10)
data = nr.get_acquisition_data()
print(data.geometry)
print(data.geometry.dimension_labels)
print(data.dimension_labels)
rdata = data.subset(channel=0)
#
self.assertTrue(sl.shape, (10, rdata.shape[1]))
try:
data.subset(['vertical', 'horizontal'])
self.assertTrue(False)
except ValueError as ve:
print("Exception catched", ve)
self.assertTrue(True)
else:
# skips all tests if module wget is not present
self.assertFalse(has_wget)
os.path.join("..", "..", "..", "..", "CCPi-ReconstructionFramework",
"data", "24737_fd.nxs"))
# Read and print the dimensions of the raw projections
dims = reader.get_projection_dimensions()
print(dims)
# Load and average all flat and dark images in preparation for normalising data.
flat = avg_img(reader.load_flat())
dark = avg_img(reader.load_dark())
# Set up normaliser object for normalising data by flat and dark images.
norm = Normalizer(flat_field=flat, dark_field=dark)
# Load the raw projections and pass as input to the normaliser.
norm.set_input(reader.get_acquisition_data())
# Set up CenterOfRotationFinder object to center data.
cor = CenterOfRotationFinder()
# Set the output of the normaliser as the input and execute to determine center.
cor.set_input(norm.get_output())
center_of_rotation = cor.get_output()
# Set up AcquisitionDataPadder to pad data for centering using the computed
# center, set the output of the normaliser as input and execute to produce
# padded/centered data.
padder = AcquisitionDataPadder(center_of_rotation=center_of_rotation)
padder.set_input(norm.get_output())
padded_data = padder.get_output()
