DiamondLightSource /
Savu
| Conditions | 13 |
| Total Lines | 58 |
| Code Lines | 41 |
| 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_artifacts.TomoPhantomArtifacts.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. |
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| 43 | def process_frames(self, data): |
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| 44 | proj_data = data[0] |
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| 45 | |||
| 46 | if self.parameters['pattern'] == 'PROJECTION': |
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| 47 | proj_data = np.expand_dims(proj_data, axis=1) |
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| 48 | |||
| 49 | # apply a variety of artifacts to the generated data: |
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| 50 | _noise_ = {} |
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| 51 | if self.parameters['artifacts_noise_type'] is not None: |
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| 52 | _noise_ = {'noise_type': self.parameters['artifacts_noise_type'], |
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| 53 | 'noise_amplitude': self.parameters['artifacts_noise_amplitude'], |
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| 54 | 'noise_seed': 0, |
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| 55 | 'verbose': False} |
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| 56 | |||
| 57 | # misalignment dictionary |
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| 58 | _datashifts_ = {} |
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| 59 | if self.parameters['datashifts_maxamplitude_pixel'] is not None: |
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| 60 | _datashifts_ = {'datashifts_maxamplitude_pixel': self.parameters['datashifts_maxamplitude_pixel']} |
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| 61 | if self.parameters['datashifts_maxamplitude_subpixel'] is not None: |
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| 62 | _datashifts_ = {'datashifts_maxamplitude_subpixel': self.parameters['datashifts_maxamplitude_subpixel']} |
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| 63 | |||
| 64 | # adding zingers |
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| 65 | _zingers_ = {} |
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| 66 | if self.parameters['artifacts_zingers_percentage'] is not None: |
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| 67 | _zingers_ = {'zingers_percentage': self.parameters['artifacts_zingers_percentage'], |
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| 68 | 'zingers_modulus': self.parameters['artifacts_zingers_modulus']} |
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| 69 | _stripes_ = {} |
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| 70 | |||
| 71 | # adding stripes |
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| 72 | if self.parameters['pattern'] == 'SINOGRAM': |
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| 73 | if self.parameters['artifacts_stripes_percentage'] is not None: |
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| 74 | _stripes_ = {'stripes_percentage': self.parameters['artifacts_stripes_percentage'], |
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| 75 | 'stripes_maxthickness': self.parameters['artifacts_stripes_maxthickness'], |
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| 76 | 'stripes_intensity': self.parameters['artifacts_stripes_intensity'], |
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| 77 | 'stripes_type': self.parameters['artifacts_stripes_type'], |
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| 78 | 'stripes_variability': self.parameters['artifacts_stripes_variability']} |
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| 79 | |||
| 80 | # partial volume effect dictionary |
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| 81 | _pve_ = {} |
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| 82 | if self.parameters['artifacts_pve'] is not None: |
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| 83 | _pve_ = {'pve_strength': self.parameters['artifacts_pve']} |
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| 84 | |||
| 85 | # fresnel propagator |
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| 86 | _fresnel_propagator_ = {} |
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| 87 | if self.parameters['artifacts_fresnel_distance'] is not None: |
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| 88 | _fresnel_propagator_ = {'fresnel_dist_observation': self.parameters['artifacts_fresnel_distance'], |
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| 89 | 'fresnel_scale_factor': self.parameters['artifacts_fresnel_scale_factor'], |
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| 90 | 'fresnel_wavelenght': self.parameters['artifacts_fresnel_wavelenght']} |
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| 91 | |||
| 92 | if (self.parameters['datashifts_maxamplitude_pixel']) or (self.parameters['datashifts_maxamplitude_subpixel']) is not None: |
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| 93 | [data_artifacts, shifts] = _Artifacts_(proj_data.copy(), **_noise_, **_zingers_, **_stripes_, **_datashifts_, **_pve_, **_fresnel_propagator_) |
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| 94 | else: |
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| 95 | data_artifacts = _Artifacts_(proj_data.copy(), **_noise_, **_zingers_, **_stripes_, **_datashifts_, **_pve_, **_fresnel_propagator_) |
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| 96 | |||
| 97 | if self.parameters['pattern'] == 'PROJECTION': |
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| 98 | data_artifacts = data_artifacts[:, 0, :] |
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introduced
by
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| 99 | |||
| 100 | return data_artifacts |
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| 101 | |||
| 110 |
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