def read_als_832(fname, ind_tomo=None, normalized=False, proj=None, sino=None):
"""
Read ALS 8.3.2 standard data format.
Parameters
----------
fname : str
Path to file name without indices and extension.
ind_tomo : list of int, optional
Indices of the projection files to read.
normalized : boolean, optional
If False, darks and flats will not be read. This should
only be used for cases where tomo is already normalized.
8.3.2 has a plugin that normalization is preferred to be
done with prior to tomopy reconstruction.
proj : {sequence, int}, optional
Specify projections to read. (start, end, step)
sino : {sequence, int}, optional
Specify sinograms to read. (start, end, step)
Returns
-------
ndarray
3D tomographic data.
ndarray
3D flat field data.
ndarray
3D dark field data.
"""
# File definitions.
fname = os.path.abspath(fname)
if not normalized:
tomo_name = fname + '_0000_0000.tif'
flat_name = fname + 'bak_0000.tif'
dark_name = fname + 'drk_0000.tif'
log_file = fname + '.sct'
else:
if "output" not in fname:
raise Exception(
'Please provide the normalized output directory as input')
tomo_name = fname + '_0.tif'
fname = fname.split('output')[0] + fname.split('/')[
len(fname.split('/')) - 1]
log_file = fname + '.sct'
# Read metadata from ALS log file.
contents = open(log_file, 'r')
for line in contents:
if '-nangles' in line:
nproj = int(re.findall(r'\d+', line)[0])
if '-num_bright_field' in line:
nflat = int(re.findall(r'\d+', line)[0])
if '-i0cycle' in line:
inter_bright = int(re.findall(r'\d+', line)[1])
if '-num_dark_fields' in line:
ndark = int(re.findall(r'\d+', line)[0])
contents.close()
if ind_tomo is None:
ind_tomo = list(range(0, nproj))
if proj is not None:
ind_tomo = ind_tomo[slice(*proj)]
if not normalized:
ind_flat = list(range(0, nflat))
if inter_bright > 0:
ind_flat = list(range(0, nproj, inter_bright))
flat_name = fname + 'bak_0000_0000.tif'
ind_dark = list(range(0, ndark))
# Read image data from tiff stack.
tomo = dxreader.read_tiff_stack(tomo_name,
ind=ind_tomo,
digit=4,
slc=(sino, None))
if not normalized:
# Adheres to 8.3.2 flat/dark naming conventions:
# ----Flats----
# root_namebak_xxxx_yyyy
# For datasets that take flat at the start and end of its scan,
# xxxx is in incrementals of one, and yyyy is either 0000 or the
# last projection. For datasets that take flat while they scan
# (when the beam fluctuates during scans),
# xxxx is always 0000, and yyyy is in intervals given by log file.
if inter_bright == 0:
a = [0, nproj - 1]
list_flat = dxreader._list_file_stack(flat_name, ind_flat, digit=4)
for x in ind_flat:
body = os.path.splitext(list_flat[x])[0] + "_"
ext = os.path.splitext(list_flat[x])[1]
for y, z in enumerate(a):
y = body + '{0:0={1}d}'.format(z, 4) + ext
if z == 0:
list_flat[x] = y
else:
list_flat.append(y)
list_flat = sorted(list_flat)
for m, image in enumerate(list_flat):
_arr = dxreader.read_tiff(image)
if m == 0:
dx = len(ind_flat * 2)
dy, dz = _arr.shape
flat = np.zeros((dx, dy, dz))
flat[m] = _arr
flat = dxreader._slice_array(flat, (None, sino))
else:
flat = dxreader.read_tiff_stack(flat_name,
ind=ind_flat,
digit=4,
slc=(sino, None))
# Adheres to 8.3.2 flat/dark naming conventions:
# ----Darks----
# root_namedrk_xxxx_yyyy
# All datasets thus far that take darks at the start and end of
# its scan, so xxxx is in incrementals of one, and yyyy is either
# 0000 or the last projection.
list_dark = dxreader._list_file_stack(dark_name, ind_dark, digit=4)
for x in ind_dark:
body = os.path.splitext(list_dark[x])[0] + '_'
ext = os.path.splitext(list_dark[x])[1]
body = body + '{0:0={1}d}'.format(nproj - 1, 4) + ext
list_dark[x] = body
list_dark = sorted(list_dark)
for m, image in enumerate(list_dark):
_arr = dxreader.read_tiff(image)
if m == 0:
dx = len(ind_dark)
dy, dz = _arr.shape
dark = np.zeros((dx, dy, dz))
dark[m] = _arr
dark = dxreader._slice_array(dark, (None, sino))
else:
flat = np.ones(1)
dark = np.zeros(1)
return tomo, flat, dark
def read_als_832(fname, ind_tomo=None, normalized=False, proj=None, sino=None):
"""
Read ALS 8.3.2 standard data format.
Parameters
----------
fname : str
Path to file name without indices and extension.
ind_tomo : list of int, optional
Indices of the projection files to read.
normalized : boolean, optional
If False, darks and flats will not be read. This should
only be used for cases where tomo is already normalized.
8.3.2 has a plugin that normalization is preferred to be
done with prior to tomopy reconstruction.
proj : {sequence, int}, optional
Specify projections to read. (start, end, step)
sino : {sequence, int}, optional
Specify sinograms to read. (start, end, step)
Returns
-------
ndarray
3D tomographic data.
ndarray
3D flat field data.
ndarray
3D dark field data.
"""
# File definitions.
fname = os.path.abspath(fname)
if not normalized:
tomo_name = fname + '_0000_0000.tif'
flat_name = fname + 'bak_0000.tif'
dark_name = fname + 'drk_0000.tif'
log_file = fname + '.sct'
else:
if "output" not in fname:
raise Exception(
'Please provide the normalized output directory as input')
tomo_name = fname + '_0.tif'
fname = fname.split(
'output')[0] + fname.split('/')[len(fname.split('/')) - 1]
log_file = fname + '.sct'
# Read metadata from ALS log file.
contents = open(log_file, 'r')
for line in contents:
if '-nangles' in line:
nproj = int(re.findall(r'\d+', line)[0])
if '-num_bright_field' in line:
nflat = int(re.findall(r'\d+', line)[0])
if '-i0cycle' in line:
inter_bright = int(re.findall(r'\d+', line)[1])
if '-num_dark_fields' in line:
ndark = int(re.findall(r'\d+', line)[0])
contents.close()
if ind_tomo is None:
ind_tomo = list(range(0, nproj))
if proj is not None:
ind_tomo = ind_tomo[slice(*proj)]
if not normalized:
ind_flat = list(range(0, nflat))
if inter_bright > 0:
ind_flat = list(range(0, nproj, inter_bright))
flat_name = fname + 'bak_0000_0000.tif'
ind_dark = list(range(0, ndark))
# Read image data from tiff stack.
tomo = dxreader.read_tiff_stack(tomo_name, ind=ind_tomo, digit=4,
slc=(sino, None))
if not normalized:
# Adheres to 8.3.2 flat/dark naming conventions:
# ----Flats----
# root_namebak_xxxx_yyyy
# For datasets that take flat at the start and end of its scan,
# xxxx is in incrementals of one, and yyyy is either 0000 or the
# last projection. For datasets that take flat while they scan
# (when the beam fluctuates during scans),
# xxxx is always 0000, and yyyy is in intervals given by log file.
if inter_bright == 0:
a = [0, nproj - 1]
list_flat = dxreader._list_file_stack(flat_name, ind_flat, digit=4)
for x in ind_flat:
body = os.path.splitext(list_flat[x])[0] + "_"
ext = os.path.splitext(list_flat[x])[1]
for y, z in enumerate(a):
y = body + '{0:0={1}d}'.format(z, 4) + ext
if z == 0:
list_flat[x] = y
else:
list_flat.append(y)
list_flat = sorted(list_flat)
for m, image in enumerate(list_flat):
_arr = dxreader.read_tiff(image)
if m == 0:
dx = len(ind_flat * 2)
dy, dz = _arr.shape
flat = np.zeros((dx, dy, dz))
flat[m] = _arr
flat = dxreader._slice_array(flat, (None, sino))
else:
flat = dxreader.read_tiff_stack(flat_name, ind=ind_flat, digit=4,
slc=(sino, None))
# Adheres to 8.3.2 flat/dark naming conventions:
# ----Darks----
# root_namedrk_xxxx_yyyy
# All datasets thus far that take darks at the start and end of
# its scan, so xxxx is in incrementals of one, and yyyy is either
# 0000 or the last projection.
list_dark = dxreader._list_file_stack(dark_name, ind_dark, digit=4)
for x in ind_dark:
body = os.path.splitext(list_dark[x])[0] + '_'
ext = os.path.splitext(list_dark[x])[1]
body = body + '{0:0={1}d}'.format(nproj - 1, 4) + ext
list_dark[x] = body
list_dark = sorted(list_dark)
for m, image in enumerate(list_dark):
_arr = dxreader.read_tiff(image)
if m == 0:
dx = len(ind_dark)
dy, dz = _arr.shape
dark = np.zeros((dx, dy, dz))
dark[m] = _arr
dark = dxreader._slice_array(dark, (None, sino))
else:
flat = np.ones(1)
dark = np.zeros(1)
return tomo, flat, dark
def test_slice_array_slices_when_given_a_slice_range(self):
test_array = reader._slice_array(np.array([[1, 2, 3], [1, 2, 3]]),
[1, 2])
np.testing.utils.assert_equal(test_array, np.array([[1, 2]]))
def test_slice_array_does_not_slice_when_not_given_a_slice_range(self):
test_array = [1, 2, 3]
test_array = reader._slice_array(test_array, None)
self.assertEqual(test_array, [1, 2, 3])
def test_slice_array_slices_when_given_a_slice_range(self):
test_array = reader._slice_array(
np.array([[1, 2, 3], [1, 2, 3]]), [1, 2])
np.testing.utils.assert_equal(test_array, np.array([[1, 2]]))
def test_slice_array_does_not_slice_when_not_given_a_slice_range(self):
test_array = [1, 2, 3]
test_array = reader._slice_array(test_array, None)
self.assertEqual(test_array, [1, 2, 3])
