SPWC-Amda.py
128 lines
| 5.3 KiB
| text/x-python
|
PythonLexer
| r1496 | import traceback | |||
| import os | ||||
| from datetime import datetime, timedelta, timezone | ||||
| from SciQLopBindings import PyDataProvider, Product, VectorTimeSerie, ScalarTimeSerie, DataSeriesType | ||||
| import numpy as np | ||||
| import requests | ||||
| import copy | ||||
| from spwc.amda import AMDA | ||||
| amda = AMDA() | ||||
| r1498 | ||||
| r1496 | def amda_make_scalar(var=None): | |||
| if var is None: | ||||
| r1498 | return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SCALAR) | |||
| r1496 | else: | |||
| r1498 | return (((var.time, np.array([])), var.data), DataSeriesType.SCALAR) | |||
| r1496 | ||||
| def amda_make_vector(var=None): | ||||
| if var is None: | ||||
| r1498 | return (((np.array([]), np.array([])), np.array([])), DataSeriesType.VECTOR) | |||
| r1496 | else: | |||
| r1498 | return (((var.time, np.array([])), var.data), DataSeriesType.VECTOR) | |||
| r1496 | ||||
| def amda_make_multi_comp(var=None): | ||||
| if var is None: | ||||
| r1498 | return (((np.array([]), np.array([])), np.array([])), DataSeriesType.MULTICOMPONENT) | |||
| r1496 | else: | |||
| r1498 | return (((var.time, np.array([])), var.data), DataSeriesType.MULTICOMPONENT) | |||
| r1496 | ||||
| def amda_make_spectro(var=None): | ||||
| if var is None: | ||||
| r1498 | return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SPECTROGRAM) | |||
| r1496 | else: | |||
| r1498 | min_sampling = float(var.meta.get("DATASET_MIN_SAMPLING", "nan")) | |||
| max_sampling = float(var.meta.get("DATASET_MAX_SAMPLING", "nan")) | ||||
| if var.y is None and len(var.data): | ||||
| var.y = np.logspace(1, 3, var.data.shape[1])[::-1] | ||||
| return (((var.time, var.y), var.data), DataSeriesType.SPECTROGRAM) | ||||
| r1496 | #return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data,min_sampling,max_sampling,True) | |||
| r1498 | ||||
| def amda_get_sample(metadata, start, stop): | ||||
| r1496 | ts_type = amda_make_scalar | |||
| try: | ||||
| param_id = None | ||||
| r1498 | for key, value in metadata: | |||
| r1496 | if key == 'xml:id': | |||
| param_id = value | ||||
| elif key == 'type': | ||||
| if value == 'vector': | ||||
| ts_type = amda_make_vector | ||||
| elif value == 'multicomponent': | ||||
| ts_type = amda_make_multi_comp | ||||
| elif value == 'spectrogram': | ||||
| ts_type = amda_make_spectro | ||||
| r1498 | tstart = datetime.fromtimestamp(start, tz=timezone.utc) | |||
| tend = datetime.fromtimestamp(stop, tz=timezone.utc) | ||||
| r1496 | var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST") | |||
| return ts_type(var) | ||||
| except Exception as e: | ||||
| print(traceback.format_exc()) | ||||
| r1498 | print("Error in amda.py ", str(e)) | |||
| r1496 | return ts_type() | |||
| class AmdaProvider(PyDataProvider): | ||||
| def __init__(self): | ||||
| r1498 | super(AmdaProvider, self).__init__() | |||
| r1496 | if len(amda.component) is 0: | |||
| amda.update_inventory() | ||||
| parameters = copy.deepcopy(amda.parameter) | ||||
| r1498 | for name, component in amda.component.items(): | |||
| r1496 | if 'components' in parameters[component['parameter']]: | |||
| parameters[component['parameter']]['components'].append(component) | ||||
| else: | ||||
| parameters[component['parameter']]['components']=[component] | ||||
| products = [] | ||||
| r1498 | for key, parameter in parameters.items(): | |||
| r1502 | mission_name = amda.mission[parameter['mission']]['name'] | |||
| observatory_name = parameter.get('observatory','') | ||||
| if observatory_name != '': | ||||
| observatory_name = amda.observatory[observatory_name]['name'] | ||||
| instrument_name = amda.instrument[parameter['instrument']]['name'] | ||||
| dataset_name = amda.dataset[parameter['dataset']]['name'] | ||||
| path = f"/AMDA/{mission_name}/{observatory_name}/{instrument_name}/{dataset_name}/{parameter['name']}" | ||||
| r1496 | components = [component['name'] for component in parameter.get('components',[])] | |||
| r1498 | metadata = {key: item for key, item in parameter.items() if key is not 'components'} | |||
| n_components = parameter.get('size', 0) | ||||
| r1496 | if n_components == '3': | |||
| r1498 | metadata["type"] = "vector" | |||
| elif parameter.get('display_type', '')=="spectrogram": | ||||
| metadata["type"] = "spectrogram" | ||||
| elif n_components != 0: | ||||
| metadata["type"] = "multicomponent" | ||||
| r1496 | else: | |||
| r1498 | metadata["type"] = "scalar" | |||
| products.append(Product(path, components, metadata)) | ||||
| r1496 | self.register_products(products) | |||
| r1502 | for _,mission in amda.mission.items(): | |||
| if ('target' in mission) and (mission['xml:id'] != 'Ephemerides') and (mission['target'] != 'Earth'): | ||||
| self.set_icon(f'/AMDA/{mission["name"]}','satellite') | ||||
| r1496 | ||||
| r1498 | def get_data(self, metadata, start, stop): | |||
| r1496 | ts_type = amda_make_scalar | |||
| try: | ||||
| param_id = metadata['xml:id'] | ||||
| ts_type_str = metadata['type'] | ||||
| if ts_type_str == 'vector': | ||||
| ts_type = amda_make_vector | ||||
| elif ts_type_str == 'multicomponent': | ||||
| ts_type = amda_make_multi_comp | ||||
| elif ts_type_str == 'spectrogram': | ||||
| ts_type = amda_make_spectro | ||||
| r1498 | tstart = datetime.fromtimestamp(start, tz=timezone.utc) | |||
| tend = datetime.fromtimestamp(stop, tz=timezone.utc) | ||||
| r1496 | var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST") | |||
| return ts_type(var) | ||||
| except Exception as e: | ||||
| print(traceback.format_exc()) | ||||
| r1498 | print("Error in amda.py ", str(e)) | |||
| r1496 | return ts_type() | |||
| _amda = AmdaProvider() | ||||
