dynamically update params used in filter to build classification path

[github-actions]

whale-speech/src/stages/classify.py

Line 58 in 27dd22e

# TODO dynamically update params used in filter to build classification path

        self.plot_path_template     = config.classify.plot_path_template
        self.show_plots             = config.general.show_plots

        # store parameters
        self.store = config.classify.store_classifications
        self.output_array_path_template = config.classify.output_array_path_template
        self.output_table_path_template = config.classify.output_table_path_template
        
        self.project = config.general.project
        self.dataset_id = config.general.dataset_id
        self.table_id = config.classify.classification_table_id
        self.schema = self._schema_to_dict(config.classify.classification_table_schema)
        self.temp_location = config.general.temp_location
        self.workbucket = config.general.workbucket
        self.write_params = config.bigquery.__dict__
        
        # TODO dynamically update params used in filter to build classification path
        self.params_path_template = config.sift.butterworth.params_path_template
        self.path_params = {
            "name": "butterworth",
            "lowcut": config.sift.butterworth.lowcut,
            "highcut": config.sift.butterworth.highcut,
            "order": config.sift.butterworth.order,
            "threshold": config.sift.butterworth.sift_threshold,
        } 

    @staticmethod
    def _schema_to_dict(schema):
        return {
            "fields": [
                {
                    "name": name, 
                    "type": getattr(schema, name).type, 
                    "mode": getattr(schema, name).mode
                } 
                for name in vars(schema)
            ]
        }

    def _preprocess(self, pcoll):
        signal, start, end, encounter_ids, _, _ = pcoll
        key = self._build_key(start, end, encounter_ids)
        logging.info(f"Classifying {key} with signal shape {signal.shape}")

        # Resample
        signal = self._resample(signal)