DiamondLightSource /
Savu
| Conditions | 10 |
| Total Lines | 77 |
| Code Lines | 53 |
| Lines | 0 |
| Ratio | 0 % |
| Changes | 0 | ||
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:
Complex classes like savu.plugins.simulation.tomo_phantom.TomoPhantom.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.
Once you have determined the fields that belong together, you can apply the Extract Class refactoring. If the component makes sense as a sub-class, Extract Subclass is also a candidate, and is often faster.
| 1 | # Copyright 2014 Diamond Light Source Ltd. |
||
| 133 | def process_frames(self, data): |
||
| 134 | # print "The input data shape is", data[0].shape |
||
| 135 | if (self.out_shape_sino[1] == 1): |
||
| 136 | # create a 2D phantom |
||
| 137 | model = TomoP2D.Model(self.model, self.dims, self.path_library2D) |
||
| 138 | # create a 2D sinogram |
||
| 139 | projdata_clean = TomoP2D.ModelSino(self.model, self.dims, self.detectors_num, self.angles, self.path_library2D) |
||
| 140 | # forming dictionaries with different artifact types and noise |
||
| 141 | _noise_ = {'noise_type' : self.parameters['artifacts_noise_type'], |
||
| 142 | 'noise_sigma' : self.parameters['artifacts_noise_sigma'], |
||
| 143 | 'noise_seed' : 0} |
||
| 144 | # misalignment dictionary |
||
| 145 | _sinoshifts_ = {} |
||
| 146 | if self.parameters['artifacts_misalignment_maxamplitude'] is not None: |
||
| 147 | _sinoshifts_ = {'sinoshifts_maxamplitude' : self.parameters['artifacts_misalignment_maxamplitude']} |
||
| 148 | |||
| 149 | # adding zingers and stripes |
||
| 150 | _zingers_ = {} |
||
| 151 | if self.parameters['artifacts_zingers_percentage'] is not None: |
||
| 152 | _zingers_ = {'zingers_percentage' : self.parameters['artifacts_zingers_percentage'], |
||
| 153 | 'zingers_modulus' : 10} |
||
| 154 | _stripes_ = {} |
||
| 155 | if self.parameters['artifacts_stripes_percentage'] is not None: |
||
| 156 | _stripes_ = {'stripes_percentage' : self.parameters['artifacts_stripes_percentage'], |
||
| 157 | 'stripes_maxthickness' : self.parameters['artifacts_stripes_maxthickness'], |
||
| 158 | 'stripes_intensity' : self.parameters['artifacts_stripes_intensity'], |
||
| 159 | 'stripes_type' : self.parameters['artifacts_stripes_type'], |
||
| 160 | 'stripes_variability' : self.parameters['artifacts_stripes_variability']} |
||
| 161 | |||
| 162 | if self.parameters['artifacts_misalignment_maxamplitude'] is not None: |
||
| 163 | [projdata, shifts] = _Artifacts_(projdata_clean, **_noise_, **_zingers_, **_stripes_, **_sinoshifts_) |
||
| 164 | else: |
||
| 165 | projdata = _Artifacts_(projdata_clean, **_noise_, **_zingers_, **_stripes_, **_sinoshifts_) |
||
| 166 | else: |
||
| 167 | # create a 3D phantom |
||
| 168 | frame_idx = self.out_pData[0].get_current_frame_idx()[0] |
||
| 169 | model = TomoP3D.ModelSub(self.model, self.dims, (frame_idx, frame_idx+1), self.path_library3D) |
||
| 170 | model = np.swapaxes(model,0,1) |
||
| 171 | model = np.flipud(model[:,0,:]) |
||
| 172 | # create a 3D projection data |
||
| 173 | projdata_clean = TomoP3D.ModelSinoSub(self.model, self.dims, self.detectors_num, self.dims, (frame_idx, frame_idx+1), self.angles, self.path_library3D) |
||
| 174 | # forming dictionaries with different artifact types and noise |
||
| 175 | _noise_ = {'noise_type' : self.parameters['artifacts_noise_type'], |
||
| 176 | 'noise_sigma' : self.parameters['artifacts_noise_sigma'], |
||
| 177 | 'noise_seed' : 0} |
||
| 178 | # misalignment dictionary |
||
| 179 | _sinoshifts_ = {} |
||
| 180 | if self.parameters['artifacts_misalignment_maxamplitude'] is not None: |
||
| 181 | _sinoshifts_ = {'sinoshifts_maxamplitude' : self.parameters['artifacts_misalignment_maxamplitude']} |
||
| 182 | |||
| 183 | # adding zingers and stripes |
||
| 184 | _zingers_ = {} |
||
| 185 | if self.parameters['artifacts_zingers_percentage'] is not None: |
||
| 186 | _zingers_ = {'zingers_percentage' : self.parameters['artifacts_zingers_percentage', |
||
| 187 | 'zingers_modulus' : 10} |
||
|
introduced
by
|
|||
| 188 | _stripes_ = {} |
||
| 189 | if self.parameters['artifacts_stripes_percentage'] is not None: |
||
| 190 | _stripes_ = {'stripes_percentage' : self.parameters['artifacts_stripes_percentage'], |
||
| 191 | 'stripes_maxthickness' : self.parameters['artifacts_stripes_maxthickness'], |
||
| 192 | 'stripes_intensity' : self.parameters['artifacts_stripes_intensity'], |
||
| 193 | 'stripes_type' : self.parameters['artifacts_stripes_type'], |
||
| 194 | 'stripes_variability' : self.parameters['artifacts_stripes_variability']} |
||
| 195 | |||
| 196 | if self.parameters['artifacts_misalignment_maxamplitude'] is not None: |
||
| 197 | [projdata, shifts] = _Artifacts_(projdata_clean, **_noise_, **_zingers_, **_stripes_, **_sinoshifts_) |
||
| 198 | else: |
||
| 199 | projdata = _Artifacts_(projdata_clean, **_noise_, **_zingers_, **_stripes_, **_sinoshifts_) |
||
| 200 | projdata = np.swapaxes(projdata,0,1) |
||
| 201 | return [projdata, model] |
||
| 202 | |||
| 203 | def get_citation_information(self): |
||
| 204 | cite_info1 = CitationInformation() |
||
| 205 | cite_info1.name = 'citation1' |
||
| 206 | cite_info1.description = \ |
||
| 207 | ("TomoPhantom is a software package to generate 2D-4D analytical phantoms and their Radon transforms for various testing purposes.") |
||
| 208 | cite_info1.bibtex = \ |
||
| 209 | ("@article{kazantsevTP2018,\n" + |
||
| 210 | "title={TomoPhantom, a software package to generate 2D-4D analytical phantoms for CT image reconstruction algorithm benchmarks},\n" + |
||
| 236 |
Loading history...