DiamondLightSource /
Savu
| @@ 354-376 (lines=23) @@ | ||
| 351 | cor = ncol - overlap / 2.0 - 1.0 |
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| 352 | return [np.array([cor]), np.array([cor])] |
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| 353 | ||
| 354 | def post_process(self): |
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| 355 | in_datasets, out_datasets = self.get_datasets() |
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| 356 | cor_prev = out_datasets[0].data[...] |
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| 357 | cor_broad = out_datasets[1].data[...] |
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| 358 | cor_broad[:] = np.median(np.squeeze(cor_prev)) |
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| 359 | self.cor_for_executive_summary = np.median(cor_broad[:]) |
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| 360 | if self.broadcast_method == 'mean': |
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| 361 | cor_broad[:] = np.mean(np.squeeze(cor_prev)) |
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| 362 | self.cor_for_executive_summary = np.mean(cor_broad[:]) |
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| 363 | if (self.broadcast_method == 'linear_fit') and (len(cor_prev) > 1): |
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| 364 | afact, bfact = np.polyfit(self.plugin_prev, cor_prev[:, 0], 1) |
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| 365 | list_cor = self.origin_prev * afact + bfact |
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| 366 | cor_broad[:, 0] = list_cor |
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| 367 | self.cor_for_executive_summary = cor_broad[:] |
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| 368 | if (self.broadcast_method == 'nearest') and (len(cor_prev) > 1): |
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| 369 | for i, pos in enumerate(self.origin_prev): |
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| 370 | minpos = np.argmin(np.abs(pos - self.plugin_prev)) |
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| 371 | cor_broad[i, 0] = cor_prev[minpos, 0] |
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| 372 | self.cor_for_executive_summary = cor_broad[:] |
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| 373 | out_datasets[1].data[:] = cor_broad[:] |
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| 374 | self.populate_meta_data('cor_preview', np.squeeze(cor_prev)) |
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| 375 | self.populate_meta_data('centre_of_rotation', |
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| 376 | out_datasets[1].data[:].squeeze(axis=1)) |
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| 377 | ||
| 378 | def populate_meta_data(self, key, value): |
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| 379 | datasets = self.parameters['datasets_to_populate'] |
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| @@ 234-256 (lines=23) @@ | ||
| 231 | self.search_step, self.ratio, self.drop) |
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| 232 | return [np.array([cor]), np.array([cor])] |
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| 233 | ||
| 234 | def post_process(self): |
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| 235 | in_datasets, out_datasets = self.get_datasets() |
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| 236 | cor_prev = out_datasets[0].data[...] |
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| 237 | cor_broad = out_datasets[1].data[...] |
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| 238 | cor_broad[:] = np.median(np.squeeze(cor_prev)) |
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| 239 | self.cor_for_executive_summary = np.median(cor_broad[:]) |
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| 240 | if self.broadcast_method == 'mean': |
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| 241 | cor_broad[:] = np.mean(np.squeeze(cor_prev)) |
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| 242 | self.cor_for_executive_summary = np.mean(cor_broad[:]) |
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| 243 | if (self.broadcast_method == 'linear_fit') and (len(cor_prev) > 1): |
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| 244 | afact, bfact = np.polyfit(self.plugin_prev, cor_prev[:, 0], 1) |
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| 245 | list_cor = self.origin_prev * afact + bfact |
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| 246 | cor_broad[:, 0] = list_cor |
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| 247 | self.cor_for_executive_summary = cor_broad[:] |
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| 248 | if (self.broadcast_method == 'nearest') and (len(cor_prev) > 1): |
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| 249 | for i, pos in enumerate(self.origin_prev): |
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| 250 | minpos = np.argmin(np.abs(pos - self.plugin_prev)) |
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| 251 | cor_broad[i, 0] = cor_prev[minpos, 0] |
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| 252 | self.cor_for_executive_summary = cor_broad[:] |
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| 253 | out_datasets[1].data[:] = cor_broad[:] |
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| 254 | self.populate_meta_data('cor_preview', np.squeeze(cor_prev)) |
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| 255 | self.populate_meta_data('centre_of_rotation', |
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| 256 | out_datasets[1].data[:].squeeze(axis=1)) |
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| 257 | ||
| 258 | def populate_meta_data(self, key, value): |
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| 259 | datasets = self.parameters['datasets_to_populate'] |
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