def get_projection_dimensions(self):
'''
Return the dimensions of the dataset
'''
if not h5pyAvailable:
raise Exception("Error: h5py is not installed")
if self.filename is None:
return
try:
with NexusFile(self.filename, 'r') as file:
try:
projections = file[self.data_path]
except KeyError as ke:
raise KeyError('Error: data path {0} not found\n{1}'\
.format(self.data_path,
ke.args[0]))
#image_keys = np.array(file[self.key_path])
image_keys = self.get_image_keys()
dims = list(projections.shape)
dims[0] = np.sum(image_keys == 0)
return tuple(dims)
except:
print(
"Warning: Error reading image_keys trying accessing data on ",
self.data_path)
with NexusFile(self.filename, 'r') as file:
dims = file[self.data_path].shape
return tuple(dims)
def load(self, dimensions=None, image_key_id=0):
'''
This is generic loading function of flat field, dark field and projection data.
'''
if not h5pyAvailable:
raise Exception("Error: h5py is not installed")
if self.filename is None:
return
try:
with NexusFile(self.filename, 'r') as file:
image_keys = np.array(file[self.key_path])
projections = None
if dimensions == None:
projections = np.array(file[self.data_path])
result = projections[image_keys == image_key_id]
return result
else:
#When dimensions are specified they need to be mapped to image_keys
index_array = np.where(image_keys == image_key_id)
projection_indexes = index_array[0][dimensions[0]]
new_dimensions = list(dimensions)
new_dimensions[0] = projection_indexes
new_dimensions = tuple(new_dimensions)
result = np.array(file[self.data_path][new_dimensions])
return result
except:
print("Error reading nexus file")
raise
def list_file_content(self):
try:
with NexusFile(self.filename, 'r') as file:
file.visit(print)
except:
print("Error reading nexus file")
raise
def get_acquisition_data_whole(self):
with NexusFile(self.filename, 'r') as file:
try:
dims = self.get_projection_dimensions()
except KeyError:
print("Warning: ")
dims = file[self.data_path].shape
ymin = 0
ymax = dims[1] - 1
return self.get_acquisition_data_subset(ymin=ymin, ymax=ymax)
def get_projection_angles(self):
'''
This function returns the projection angles
'''
if not h5pyAvailable:
raise Exception("Error: h5py is not installed")
if self.filename is None:
return
try:
with NexusFile(self.filename, 'r') as file:
angles = np.array(file[self.angle_path], np.float32)
image_keys = np.array(file[self.key_path])
return angles[image_keys == 0]
except:
print("get_projection_angles Error reading nexus file")
raise
def get_sinogram_dimensions(self):
'''
Return the dimensions of the dataset
'''
if not h5pyAvailable:
raise Exception("Error: h5py is not installed")
if self.filename is None:
return
try:
with NexusFile(self.filename, 'r') as file:
projections = file[self.data_path]
image_keys = np.array(file[self.key_path])
dims = list(projections.shape)
dims[0] = dims[1]
dims[1] = np.sum(image_keys == 0)
return tuple(dims)
except:
print("Error reading nexus file")
raise
def get_image_keys(self):
try:
with NexusFile(self.filename, 'r') as file:
return np.array(file[self.key_path])
except KeyError as ke:
raise KeyError("get_image_keys: ", ke.args[0], self.key_path)
def get_acquisition_data_batch(self, bmin=None, bmax=None):
if not h5pyAvailable:
raise Exception("Error: h5py is not installed")
if self.filename is None:
return
try:
with NexusFile(self.filename, 'r') as file:
try:
dims = self.get_projection_dimensions()
except KeyError:
dims = file[self.data_path].shape
if bmin is None or bmax is None:
raise ValueError(
'get_acquisition_data_batch: please specify fastest index batch limits'
)
if bmin >= 0 and bmin < bmax and bmax <= dims[0]:
data = np.array(file[self.data_path][bmin:bmax])
else:
raise ValueError(
'get_acquisition_data_batch: bmin {0}>0 bmax {1}<{2}'.
format(bmin, bmax, dims[0]))
except:
print("Error reading nexus file")
raise
try:
angles = self.get_projection_angles()[bmin:bmax]
except KeyError as ke:
n = data.shape[0]
angles = np.linspace(0, n, n + 1, dtype=np.float32)[bmin:bmax]
if bmax - bmin > 1:
geometry = AcquisitionGeometry(
'parallel',
'3D',
angles,
pixel_num_h=dims[2],
pixel_size_h=1,
pixel_num_v=bmax - bmin,
pixel_size_v=1,
dist_source_center=None,
dist_center_detector=None,
channels=1,
dimension_labels=['angle', 'vertical', 'horizontal'])
out = geometry.allocate()
out.fill(data)
return out
elif bmax - bmin == 1:
geometry = AcquisitionGeometry(
'parallel',
'2D',
angles,
pixel_num_h=dims[2],
pixel_size_h=1,
dist_source_center=None,
dist_center_detector=None,
channels=1,
dimension_labels=['angle', 'horizontal'])
out = geometry.allocate()
out.fill(data.squeeze())
return out
def get_acquisition_data_subset(self, ymin=None, ymax=None):
'''
This method load the acquisition data and given dimension and returns an AcquisitionData Object
'''
if not h5pyAvailable:
raise Exception("Error: h5py is not installed")
if self.filename is None:
return
try:
with NexusFile(self.filename, 'r') as file:
try:
dims = self.get_projection_dimensions()
except KeyError:
pass
dims = file[self.data_path].shape
if ymin is None and ymax is None:
try:
image_keys = self.get_image_keys()
print("image_keys", image_keys)
projections = np.array(file[self.data_path])
data = projections[image_keys == 0]
except KeyError as ke:
print(ke)
data = np.array(file[self.data_path])
else:
image_keys = self.get_image_keys()
print("image_keys", image_keys)
projections = np.array(
file[self.data_path])[image_keys == 0]
if ymin is None:
ymin = 0
if ymax > dims[1]:
raise ValueError('ymax out of range')
data = projections[:, :ymax, :]
elif ymax is None:
ymax = dims[1]
if ymin < 0:
raise ValueError('ymin out of range')
data = projections[:, ymin:, :]
else:
if ymax > dims[1]:
raise ValueError('ymax out of range')
if ymin < 0:
raise ValueError('ymin out of range')
data = projections[:, ymin:ymax, :]
except:
print("Error reading nexus file")
raise
try:
angles = self.get_projection_angles()
except KeyError as ke:
n = data.shape[0]
angles = np.linspace(0, n, n + 1, dtype=np.float32)
if ymax - ymin > 1:
geometry = AcquisitionGeometry(
'parallel',
'3D',
angles,
pixel_num_h=dims[2],
pixel_size_h=1,
pixel_num_v=ymax - ymin,
pixel_size_v=1,
dist_source_center=None,
dist_center_detector=None,
channels=1,
dimension_labels=['angle', 'vertical', 'horizontal'])
out = geometry.allocate()
out.fill(data)
return out
elif ymax - ymin == 1:
geometry = AcquisitionGeometry(
'parallel',
'2D',
angles,
pixel_num_h=dims[2],
pixel_size_h=1,
dist_source_center=None,
dist_center_detector=None,
channels=1,
dimension_labels=['angle', 'horizontal'])
out = geometry.allocate()
out.fill(data.squeeze())
return out