savu.plugins.reconstructions.tomobar.tomobar_recon_3D.TomobarRecon3d.process_frames() - Code Metrics - Inspection of "Merge pull request #913 from DiamondLightSource/4_..." - DiamondLightSource/Savu - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 26b3f2...a95b01 )

by Daniil

created 2022-06-16 15:39 UTC

TomobarRecon3d.process_frames() D

↳ Parent: savu.plugins.reconstructions.tomobar.tomobar_recon_3D

Complexity

Conditions

Size

Total Lines	75
Code Lines	55

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	12
eloc	55
nop	2
dl	0
loc	75
rs	4.8
c	0
b	0
f	0

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Long Method

Small methods make your code easier to understand, in particular if combined with a good name. Besides, if your method is small, finding a good name is usually much easier.

For example, if you find yourself adding comments to a method's body, this is usually a good sign to extract the commented part to a new method, and use the comment as a starting point when coming up with a good name for this new method.

Commonly applied refactorings include:

If many parameters/temporary variables are present:
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If you have long conditionals: Decompose Conditional
Otherwise: Extract method

Complexity

Complex classes like savu.plugins.reconstructions.tomobar.tomobar_recon_3D.TomobarRecon3d.process_frames() often do a lot of different things. To break such a class down, we need to identify a cohesive component within that class. A common approach to find such a component is to look for fields/methods that share the same prefixes, or suffixes.

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# Copyright 2019 Diamond Light Source Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
.. module:: tomobar_recon_3D
   :platform: Unix
   :synopsis: A wrapper around TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software \
   for direct and advanced iterative image reconstruction using _3D_ capabilities of regularisation. \
   This plugin will divide 3D projection data into overlapping subsets using padding.

.. moduleauthor:: Daniil Kazantsev <[email protected]>
"""

from savu.plugins.reconstructions.base_recon import BaseRecon
from savu.plugins.driver.gpu_plugin import GpuPlugin

import numpy as np
from tomobar.methodsIR import RecToolsIR
from tomobar.methodsDIR import RecToolsDIR
from savu.plugins.utils import register_plugin
from savu.core.iterate_plugin_group_utils import enable_iterative_loop, \
    check_if_end_plugin_in_iterate_group, setup_extra_plugin_data_padding


@register_plugin
class TomobarRecon3d(BaseRecon, GpuPlugin):

    def __init__(self):
        super(TomobarRecon3d, self).__init__("TomobarRecon3d")
        self.Vert_det = None
        self.pad = None

    @setup_extra_plugin_data_padding

    def set_filter_padding(self, in_pData, out_pData):
        self.pad = self.parameters['padding']
        in_data = self.get_in_datasets()[0]
        det_y = in_data.get_data_dimension_by_axis_label('detector_y')
        pad_det_y = '%s.%s' % (det_y, self.pad)
        pad_dict = {'pad_directions': [pad_det_y], 'pad_mode': 'edge'}
        in_pData[0].padding = pad_dict
        out_pData[0].padding = pad_dict
        if len(self.get_in_datasets()) > 1:
            in_pData[1].padding = pad_dict

    @enable_iterative_loop
    def setup(self):
        in_dataset = self.get_in_datasets()[0]
        procs = self.exp.meta_data.get("processes")
        procs = len([i for i in procs if 'GPU' in i]) # calculates the total number of GPU processes
        dim = in_dataset.get_data_dimension_by_axis_label('detector_y')
        nSlices = int(np.ceil(in_dataset.get_shape()[dim] / float(procs)))
        # calculate the amount of slices than would fit the GPU memory
        gpu_available_mb = self.get_gpu_memory()[0]/procs  # get the free GPU memory of a first device if many
        det_x_dim = in_dataset.get_shape()[in_dataset.get_data_dimension_by_axis_label('detector_x')]
        rot_angles_dim = in_dataset.get_shape()[in_dataset.get_data_dimension_by_axis_label('rotation_angle')]
        slice_size_mbbytes = int(np.ceil(((det_x_dim * det_x_dim) * 1024 * 4) / (1024 ** 3)))

        if self.parameters['reconstruction_method'] == 'FISTA3D':
            # calculate the GPU memory required based on 3D regularisation restrictions (avoiding CUDA-error)
            if 'ROF_TV' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 8
            if 'FGP_TV' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 12
            if 'SB_TV' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 10
            if 'PD_TV' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 8
            if 'LLT_ROF' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 12
            if 'TGV' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 15
            if 'NDF' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 5
            if 'Diff4th' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 5
            if 'NLTV' in self.parameters['regularisation_method']:
                slice_size_mbbytes *= 8
        if self.parameters['reconstruction_method'] == 'SIRT3D' or self.parameters['reconstruction_method'] == 'CGLS3D':
            slice_size_mbbytes *= 3

        slices_fit_total = int(gpu_available_mb / slice_size_mbbytes) - 2*self.parameters['padding']
        if nSlices > slices_fit_total:
            nSlices = slices_fit_total
        if nSlices < self.parameters['padding']:
            print("The padding value is larger than the number of slices in the 3D slab")
        self._set_max_frames(nSlices)
        # get experimental metadata of projection_shifts
        if 'projection_shifts' in list(self.exp.meta_data.dict.keys()):
            self.projection_shifts = self.exp.meta_data.dict['projection_shifts']
        super(TomobarRecon3d, self).setup()

    def pre_process(self):
        in_pData = self.get_plugin_in_datasets()[0]
        self.det_dimX_ind = in_pData.get_data_dimension_by_axis_label('detector_x')
        try:
            self.det_dimY_ind = in_pData.get_data_dimension_by_axis_label('detector_y')
        except ValueError:
            raise ValueError('<<<The dimension of the given projection data is 2D, while 3D is required! >>>')
        #  getting the value for padded vertical detector
        self.Vert_det = in_pData.get_shape()[self.det_dimY_ind] + 2 * self.pad

        # extract given parameters into dictionaries suitable for ToMoBAR input
        self._data_ = {'OS_number': self.parameters['algorithm_ordersubsets'],
                       'huber_threshold': self.parameters['data_Huber_thresh'],
                       'ringGH_lambda': self.parameters['data_full_ring_GH'],
                       'ringGH_accelerate': self.parameters['data_full_ring_accelerator_GH']}

        self._algorithm_ = {'iterations': self.parameters['algorithm_iterations'],
                            'nonnegativity': self.parameters['algorithm_nonnegativity'],
                            'mask_diameter': self.parameters['algorithm_mask'],
                            'verbose': self.parameters['algorithm_verbose']}

        self._regularisation_ = {'method': self.parameters['regularisation_method'],
                                 'regul_param': self.parameters['regularisation_parameter'],
                                 'iterations': self.parameters['regularisation_iterations'],
                                 'device_regulariser': self.parameters['regularisation_device'],
                                 'edge_threhsold': self.parameters['regularisation_edge_thresh'],
                                 'time_marching_step': self.parameters['regularisation_timestep'],
                                 'regul_param2': self.parameters['regularisation_parameter2'],
                                 'PD_LipschitzConstant': self.parameters['regularisation_PD_lip'],
                                 'NDF_penalty': self.parameters['regularisation_NDF_penalty'],
                                 'methodTV': self.parameters['regularisation_methodTV']}

    def process_frames(self, data):
        cor, angles, self.vol_shape, init = self.get_frame_params()
        self.anglesRAD = np.deg2rad(angles.astype(np.float32))
        projdata3D = data[0].astype(np.float32)
        dim_tuple = np.shape(projdata3D)
        self.Horiz_det = dim_tuple[self.det_dimX_ind]
        half_det_width = 0.5 * self.Horiz_det
        projdata3D[projdata3D > 10 ** 15] = 0.0
        projdata3D = np.require(np.swapaxes(projdata3D, 0, 1), requirements='CA')
        self._data_.update({'projection_norm_data': projdata3D})

        # setup the CoR and offset
        cor_astra = half_det_width - np.mean(cor)
        CenterOffset_scalar = cor_astra.item() - 0.5
        CenterOffset = np.zeros(np.shape(self.projection_shifts))
        CenterOffset[:, 0] = CenterOffset_scalar
        CenterOffset[:, 1] = -0.5 # TODO: maybe needs to be tweaked?

        # check if Projection2dAlignment is in the process list, and if so,
        # fetch the value of the "registration" parameter (in order to decide
        # whether projection shifts need to be taken into account or not)
        registration = False
        for plugin_dict in self.exp.meta_data.plugin_list.plugin_list:
            if plugin_dict['name'] == 'Projection2dAlignment':
                registration = plugin_dict['data']['registration']
                break

        if np.sum(self.projection_shifts) != 0.0 and not registration:
            # modify the offset to take into account the shifts
            CenterOffset[:, 0] -= self.projection_shifts[:, 0]
            CenterOffset[:, 1] -= self.projection_shifts[:, 1]

        # set parameters and initiate a TomoBar class object for iterative reconstruction
        RectoolsIter = RecToolsIR(DetectorsDimH=self.Horiz_det,  # DetectorsDimH # detector dimension (horizontal)
                                   DetectorsDimV=self.Vert_det,   # DetectorsDimV # detector dimension (vertical) for 3D case only
                                   CenterRotOffset=CenterOffset,  # The center of rotation combined with  the shift offsets
                                   AnglesVec=-self.anglesRAD,  # the vector of angles in radians
                                   ObjSize=self.vol_shape[0],  # a scalar to define the reconstructed object dimensions
                                   datafidelity=self.parameters['data_fidelity'], # data fidelity, choose LS, PWLS, SWLS
                                   device_projector=self.parameters['GPU_index'])

        # set parameters and initiate a TomoBar class object for direct reconstruction
        RectoolsDIR = RecToolsDIR(DetectorsDimH=self.Horiz_det,  # DetectorsDimH # detector dimension (horizontal)
                                   DetectorsDimV=self.Vert_det,  # DetectorsDimV # detector dimension (vertical) for 3D case only
                                   CenterRotOffset=CenterOffset,  # The center of rotation combined with the shift offsets
                                   AnglesVec=-self.anglesRAD,  # the vector of angles in radians
                                   ObjSize=self.vol_shape[0],  # a scalar to define the reconstructed object dimensions
                                   device_projector=self.parameters['GPU_index'])

        if self.parameters['reconstruction_method'] == 'FBP3D':
            recon = RectoolsDIR.FBP(projdata3D) #perform FBP3D

        if self.parameters['reconstruction_method'] == 'CGLS3D':
            # Run CGLS 3D reconstruction algorithm here
            self._algorithm_.update({'lipschitz_const': None})
            recon = RectoolsIter.CGLS(self._data_, self._algorithm_)

        if self.parameters['reconstruction_method'] == 'SIRT3D':
            # Run SIRT 3D reconstruction algorithm here
            self._algorithm_.update({'lipschitz_const': None})
            recon = RectoolsIter.SIRT(self._data_, self._algorithm_)

        if self.parameters['reconstruction_method'] == 'FISTA3D':
            if self.parameters['regularisation_method'] == 'PD_TV':
                self._regularisation_.update({'device_regulariser': self.parameters['GPU_index']})
            # if one selects PWLS or SWLS models then raw data is also required (2 inputs)
            if (self.parameters['data_fidelity'] == 'PWLS') or (self.parameters['data_fidelity'] == 'SWLS'):
                rawdata3D = data[1].astype(np.float32)
                rawdata3D[rawdata3D > 10 ** 15] = 0.0
                rawdata3D = np.swapaxes(rawdata3D, 0, 1) / np.max(np.float32(rawdata3D))
                self._data_.update({'projection_raw_data': rawdata3D})
                self._data_.update({'beta_SWLS': self.parameters['data_beta_SWLS'] * np.ones(self.Horiz_det)})
            # Run FISTA reconstruction algorithm here
            recon = RectoolsIter.FISTA(self._data_, self._algorithm_, self._regularisation_)
        return np.require(np.swapaxes(recon, 0, 1), requirements='CA')


    def nInput_datasets(self):
        return max(len(self.parameters['in_datasets']), 1)

    # total number of output datasets
    def nOutput_datasets(self):
        if check_if_end_plugin_in_iterate_group(self.exp):
            return 2
        else:
            return 1

    # total number of output datasets that are clones
    def nClone_datasets(self):
        if check_if_end_plugin_in_iterate_group(self.exp):
            return 1
        else:
            return 0

    def _set_max_frames(self, frames):
        self._max_frames = frames


1		# Copyright 2019 Diamond Light Source Ltd.
2		#
3		# Licensed under the Apache License, Version 2.0 (the "License");
4		# you may not use this file except in compliance with the License.
5		# You may obtain a copy of the License at
6		#
7		# http://www.apache.org/licenses/LICENSE-2.0
8		#
9		# Unless required by applicable law or agreed to in writing, software
10		# distributed under the License is distributed on an "AS IS" BASIS,
11		# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12		# See the License for the specific language governing permissions and
13		# limitations under the License.
14
15		"""
16		.. module:: tomobar_recon_3D
17		:platform: Unix
18		:synopsis: A wrapper around TOmographic MOdel-BAsed Reconstruction (ToMoBAR) software \
19		for direct and advanced iterative image reconstruction using _3D_ capabilities of regularisation. \
20		This plugin will divide 3D projection data into overlapping subsets using padding.
21
22		.. moduleauthor:: Daniil Kazantsev <[email protected]>
23		"""
24
25		from savu.plugins.reconstructions.base_recon import BaseRecon
26		from savu.plugins.driver.gpu_plugin import GpuPlugin
27
28		import numpy as np
29		from tomobar.methodsIR import RecToolsIR
30		from tomobar.methodsDIR import RecToolsDIR
31		from savu.plugins.utils import register_plugin
32		from savu.core.iterate_plugin_group_utils import enable_iterative_loop, \
33		check_if_end_plugin_in_iterate_group, setup_extra_plugin_data_padding
34
35
36		@register_plugin
37		class TomobarRecon3d(BaseRecon, GpuPlugin):
38
39		def __init__(self):
40		super(TomobarRecon3d, self).__init__("TomobarRecon3d")
41		self.Vert_det = None
42		self.pad = None
43
44	View Code Duplication	@setup_extra_plugin_data_padding
		0 ignored issues – show Duplication introduced 2022-03-02 13:24 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Loading history...
45		def set_filter_padding(self, in_pData, out_pData):
46		self.pad = self.parameters['padding']
47		in_data = self.get_in_datasets()[0]
48		det_y = in_data.get_data_dimension_by_axis_label('detector_y')
49		pad_det_y = '%s.%s' % (det_y, self.pad)
50		pad_dict = {'pad_directions': [pad_det_y], 'pad_mode': 'edge'}
51		in_pData[0].padding = pad_dict
52		out_pData[0].padding = pad_dict
53		if len(self.get_in_datasets()) > 1:
54		in_pData[1].padding = pad_dict
55
56		@enable_iterative_loop
57		def setup(self):
58		in_dataset = self.get_in_datasets()[0]
59		procs = self.exp.meta_data.get("processes")
60		procs = len([i for i in procs if 'GPU' in i]) # calculates the total number of GPU processes
61		dim = in_dataset.get_data_dimension_by_axis_label('detector_y')
62		nSlices = int(np.ceil(in_dataset.get_shape()[dim] / float(procs)))
63		# calculate the amount of slices than would fit the GPU memory
64		gpu_available_mb = self.get_gpu_memory()[0]/procs # get the free GPU memory of a first device if many
65		det_x_dim = in_dataset.get_shape()[in_dataset.get_data_dimension_by_axis_label('detector_x')]
66		rot_angles_dim = in_dataset.get_shape()[in_dataset.get_data_dimension_by_axis_label('rotation_angle')]
67		slice_size_mbbytes = int(np.ceil(((det_x_dim * det_x_dim) * 1024 * 4) / (1024 ** 3)))
68
69		if self.parameters['reconstruction_method'] == 'FISTA3D':
70		# calculate the GPU memory required based on 3D regularisation restrictions (avoiding CUDA-error)
71		if 'ROF_TV' in self.parameters['regularisation_method']:
72		slice_size_mbbytes *= 8
73		if 'FGP_TV' in self.parameters['regularisation_method']:
74		slice_size_mbbytes *= 12
75		if 'SB_TV' in self.parameters['regularisation_method']:
76		slice_size_mbbytes *= 10
77		if 'PD_TV' in self.parameters['regularisation_method']:
78		slice_size_mbbytes *= 8
79		if 'LLT_ROF' in self.parameters['regularisation_method']:
80		slice_size_mbbytes *= 12
81		if 'TGV' in self.parameters['regularisation_method']:
82		slice_size_mbbytes *= 15
83		if 'NDF' in self.parameters['regularisation_method']:
84		slice_size_mbbytes *= 5
85		if 'Diff4th' in self.parameters['regularisation_method']:
86		slice_size_mbbytes *= 5
87		if 'NLTV' in self.parameters['regularisation_method']:
88		slice_size_mbbytes *= 8
89		if self.parameters['reconstruction_method'] == 'SIRT3D' or self.parameters['reconstruction_method'] == 'CGLS3D':
90		slice_size_mbbytes *= 3
91
92		slices_fit_total = int(gpu_available_mb / slice_size_mbbytes) - 2*self.parameters['padding']
93		if nSlices > slices_fit_total:
94		nSlices = slices_fit_total
95		if nSlices < self.parameters['padding']:
96		print("The padding value is larger than the number of slices in the 3D slab")
97		self._set_max_frames(nSlices)
98		# get experimental metadata of projection_shifts
99		if 'projection_shifts' in list(self.exp.meta_data.dict.keys()):
100		self.projection_shifts = self.exp.meta_data.dict['projection_shifts']
101		super(TomobarRecon3d, self).setup()
102
103		def pre_process(self):
104		in_pData = self.get_plugin_in_datasets()[0]
105		self.det_dimX_ind = in_pData.get_data_dimension_by_axis_label('detector_x')
106		try:
107		self.det_dimY_ind = in_pData.get_data_dimension_by_axis_label('detector_y')
108		except ValueError:
109		raise ValueError('<<<The dimension of the given projection data is 2D, while 3D is required! >>>')
110		# getting the value for padded vertical detector
111		self.Vert_det = in_pData.get_shape()[self.det_dimY_ind] + 2 * self.pad
112
113		# extract given parameters into dictionaries suitable for ToMoBAR input
114		self._data_ = {'OS_number': self.parameters['algorithm_ordersubsets'],
115		'huber_threshold': self.parameters['data_Huber_thresh'],
116		'ringGH_lambda': self.parameters['data_full_ring_GH'],
117		'ringGH_accelerate': self.parameters['data_full_ring_accelerator_GH']}
118
119		self._algorithm_ = {'iterations': self.parameters['algorithm_iterations'],
120		'nonnegativity': self.parameters['algorithm_nonnegativity'],
121		'mask_diameter': self.parameters['algorithm_mask'],
122		'verbose': self.parameters['algorithm_verbose']}
123
124		self._regularisation_ = {'method': self.parameters['regularisation_method'],
125		'regul_param': self.parameters['regularisation_parameter'],
126		'iterations': self.parameters['regularisation_iterations'],
127		'device_regulariser': self.parameters['regularisation_device'],
128		'edge_threhsold': self.parameters['regularisation_edge_thresh'],
129		'time_marching_step': self.parameters['regularisation_timestep'],
130		'regul_param2': self.parameters['regularisation_parameter2'],
131		'PD_LipschitzConstant': self.parameters['regularisation_PD_lip'],
132		'NDF_penalty': self.parameters['regularisation_NDF_penalty'],
133		'methodTV': self.parameters['regularisation_methodTV']}
134
135		def process_frames(self, data):
136		cor, angles, self.vol_shape, init = self.get_frame_params()
137		self.anglesRAD = np.deg2rad(angles.astype(np.float32))
138		projdata3D = data[0].astype(np.float32)
139		dim_tuple = np.shape(projdata3D)
140		self.Horiz_det = dim_tuple[self.det_dimX_ind]
141		half_det_width = 0.5 * self.Horiz_det
142		projdata3D[projdata3D > 10 ** 15] = 0.0
143		projdata3D = np.require(np.swapaxes(projdata3D, 0, 1), requirements='CA')
144		self._data_.update({'projection_norm_data': projdata3D})
145
146		# setup the CoR and offset
147		cor_astra = half_det_width - np.mean(cor)
148		CenterOffset_scalar = cor_astra.item() - 0.5
149		CenterOffset = np.zeros(np.shape(self.projection_shifts))
150		CenterOffset[:, 0] = CenterOffset_scalar
151		CenterOffset[:, 1] = -0.5 # TODO: maybe needs to be tweaked?
152
153		# check if Projection2dAlignment is in the process list, and if so,
154		# fetch the value of the "registration" parameter (in order to decide
155		# whether projection shifts need to be taken into account or not)
156		registration = False
157		for plugin_dict in self.exp.meta_data.plugin_list.plugin_list:
158		if plugin_dict['name'] == 'Projection2dAlignment':
159		registration = plugin_dict['data']['registration']
160		break
161
162		if np.sum(self.projection_shifts) != 0.0 and not registration:
163		# modify the offset to take into account the shifts
164		CenterOffset[:, 0] -= self.projection_shifts[:, 0]
165		CenterOffset[:, 1] -= self.projection_shifts[:, 1]
166
167		# set parameters and initiate a TomoBar class object for iterative reconstruction
168		RectoolsIter = RecToolsIR(DetectorsDimH=self.Horiz_det, # DetectorsDimH # detector dimension (horizontal)
169		DetectorsDimV=self.Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
170		CenterRotOffset=CenterOffset, # The center of rotation combined with the shift offsets
171		AnglesVec=-self.anglesRAD, # the vector of angles in radians
172		ObjSize=self.vol_shape[0], # a scalar to define the reconstructed object dimensions
173		datafidelity=self.parameters['data_fidelity'], # data fidelity, choose LS, PWLS, SWLS
174		device_projector=self.parameters['GPU_index'])
175
176		# set parameters and initiate a TomoBar class object for direct reconstruction
177		RectoolsDIR = RecToolsDIR(DetectorsDimH=self.Horiz_det, # DetectorsDimH # detector dimension (horizontal)
178		DetectorsDimV=self.Vert_det, # DetectorsDimV # detector dimension (vertical) for 3D case only
179		CenterRotOffset=CenterOffset, # The center of rotation combined with the shift offsets
180		AnglesVec=-self.anglesRAD, # the vector of angles in radians
181		ObjSize=self.vol_shape[0], # a scalar to define the reconstructed object dimensions
182		device_projector=self.parameters['GPU_index'])
183
184		if self.parameters['reconstruction_method'] == 'FBP3D':
185		recon = RectoolsDIR.FBP(projdata3D) #perform FBP3D
186
187		if self.parameters['reconstruction_method'] == 'CGLS3D':
188		# Run CGLS 3D reconstruction algorithm here
189		self._algorithm_.update({'lipschitz_const': None})
190		recon = RectoolsIter.CGLS(self._data_, self._algorithm_)
191
192		if self.parameters['reconstruction_method'] == 'SIRT3D':
193		# Run SIRT 3D reconstruction algorithm here
194		self._algorithm_.update({'lipschitz_const': None})
195		recon = RectoolsIter.SIRT(self._data_, self._algorithm_)
196
197		if self.parameters['reconstruction_method'] == 'FISTA3D':
198		if self.parameters['regularisation_method'] == 'PD_TV':
199		self._regularisation_.update({'device_regulariser': self.parameters['GPU_index']})
200		# if one selects PWLS or SWLS models then raw data is also required (2 inputs)
201		if (self.parameters['data_fidelity'] == 'PWLS') or (self.parameters['data_fidelity'] == 'SWLS'):
202		rawdata3D = data[1].astype(np.float32)
203		rawdata3D[rawdata3D > 10 ** 15] = 0.0
204		rawdata3D = np.swapaxes(rawdata3D, 0, 1) / np.max(np.float32(rawdata3D))
205		self._data_.update({'projection_raw_data': rawdata3D})
206		self._data_.update({'beta_SWLS': self.parameters['data_beta_SWLS'] * np.ones(self.Horiz_det)})
207		# Run FISTA reconstruction algorithm here
208		recon = RectoolsIter.FISTA(self._data_, self._algorithm_, self._regularisation_)
209		return np.require(np.swapaxes(recon, 0, 1), requirements='CA')
		0 ignored issues – show introduced 2022-06-15 14:24 UTC by Report Bug Copy Issue Report The variable `recon` does not seem to be defined in case `SubscriptNode == 'FBP3D'` on line `184` is `False`. Are you sure this can never be the case? Loading history...
210
211		def nInput_datasets(self):
212		return max(len(self.parameters['in_datasets']), 1)
213
214		# total number of output datasets
215		def nOutput_datasets(self):
216		if check_if_end_plugin_in_iterate_group(self.exp):
217		return 2
218		else:
219		return 1
220
221		# total number of output datasets that are clones
222		def nClone_datasets(self):
223		if check_if_end_plugin_in_iterate_group(self.exp):
224		return 1
225		else:
226		return 0
227
228		def _set_max_frames(self, frames):
229		self._max_frames = frames
230

DiamondLightSource / Savu

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TomobarRecon3d.process_frames() D

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