Set default low and high values based on normalization for each image.
(self)
| 458 | self.set_low_high_value() |
| 459 | |
| 460 | def set_low_high_value(self): |
| 461 | """Set default low and high values based on normalization for each image.""" |
| 462 | # do not apply scaler norm on not scalable data |
| 463 | self.range_dict.clear() |
| 464 | |
| 465 | for data_name in self.dict_to_plot.keys(): |
| 466 | if self.quantitative_normalization: |
| 467 | # Quantitative normalization |
| 468 | data_arr, _ = self.param_quant_analysis.apply_quantitative_normalization( |
| 469 | data_in=self.dict_to_plot[data_name], |
| 470 | scaler_dict=self.scaler_norm_dict, |
| 471 | scaler_name_default=self.get_selected_scaler_name(), |
| 472 | data_name=data_name, |
| 473 | ref_name=self.quantitative_ref_eline, |
| 474 | name_not_scalable=self.name_not_scalable, |
| 475 | ) |
| 476 | else: |
| 477 | # Normalize by the selected scaler in a regular way |
| 478 | data_arr = normalize_data_by_scaler( |
| 479 | data_in=self.dict_to_plot[data_name], |
| 480 | scaler=self.scaler_data, |
| 481 | data_name=data_name, |
| 482 | name_not_scalable=self.name_not_scalable, |
| 483 | ) |
| 484 | |
| 485 | lowv, highv = np.min(data_arr), np.max(data_arr) |
| 486 | # Create some 'artificially' small range in case the array is constant |
| 487 | if lowv == highv: |
| 488 | lowv -= 0.005 |
| 489 | highv += 0.005 |
| 490 | self.range_dict[data_name] = {"low": lowv, "low_default": lowv, "high": highv, "high_default": highv} |
| 491 | |
| 492 | def reset_low_high(self, name): |
| 493 | """Reset low and high value to default based on normalization.""" |
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