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import traceback
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import os
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from datetime import datetime, timedelta, timezone
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import PythonProviders
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import pysciqlopcore
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import numpy as np
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import requests
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import copy
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from spwc.amda import AMDA
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amda = AMDA()
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def amda_make_scalar(var=None):
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if var is None:
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return pysciqlopcore.ScalarTimeSerie(1)
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else:
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return pysciqlopcore.ScalarTimeSerie(var.time,var.data)
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def amda_make_vector(var=None):
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if var is None:
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return pysciqlopcore.VectorTimeSerie(1)
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else:
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return pysciqlopcore.VectorTimeSerie(var.time,var.data)
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def amda_make_multi_comp(var=None):
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if var is None:
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return pysciqlopcore.MultiComponentTimeSerie((0,2))
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else:
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return pysciqlopcore.MultiComponentTimeSerie(var.time,var.data)
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def amda_make_spectro(var=None):
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if var is None:
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return pysciqlopcore.SpectrogramTimeSerie((0,2))
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else:
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min_sampling = float(var.meta.get("DATASET_MIN_SAMPLING","nan"))
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max_sampling = float(var.meta.get("DATASET_MAX_SAMPLING","nan"))
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if "PARAMETER_TABLE_MIN_VALUES[1]" in var.meta:
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min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[1]"].split(',') ])
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max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[1]"].split(',') ])
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y = (max_v + min_v)/2.
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elif "PARAMETER_TABLE_MIN_VALUES[0]" in var.meta:
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min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[0]"].split(',') ])
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max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[0]"].split(',') ])
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y = (max_v + min_v)/2.
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else:
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y = np.logspace(1,3,var.data.shape[1])[::-1]
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return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data,min_sampling,max_sampling)
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def amda_get_sample(metadata,start,stop):
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ts_type = amda_make_scalar
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try:
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param_id = None
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for key,value in metadata:
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if key == 'xml:id':
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param_id = value
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elif key == 'type':
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if value == 'vector':
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ts_type = amda_make_vector
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elif value == 'multicomponent':
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ts_type = amda_make_multi_comp
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elif value == 'spectrogram':
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ts_type = amda_make_spectro
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tstart=datetime.fromtimestamp(start, tz=timezone.utc)
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tend=datetime.fromtimestamp(stop, tz=timezone.utc)
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var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST")
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return ts_type(var)
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except Exception as e:
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print(traceback.format_exc())
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print("Error in amda.py ",str(e))
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return ts_type()
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if len(amda.component) is 0:
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amda.update_inventory()
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parameters = copy.deepcopy(amda.parameter)
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for name,component in amda.component.items():
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if 'components' in parameters[component['parameter']]:
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parameters[component['parameter']]['components'].append(component)
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else:
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parameters[component['parameter']]['components']=[component]
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products = []
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for key,parameter in parameters.items():
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path = f"/AMDA/{parameter['mission']}/{parameter.get('observatory','')}/{parameter['instrument']}/{parameter['dataset']}/{parameter['name']}"
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components = [component['name'] for component in parameter.get('components',[])]
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metadata = [ (key,item) for key,item in parameter.items() if key is not 'components' ]
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n_components = parameter.get('size',0)
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if n_components == '3':
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metadata.append(("type","vector"))
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elif parameter.get('display_type','')=="spectrogram":
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metadata.append(("type","spectrogram"))
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elif n_components !=0:
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metadata.append(("type","multicomponent"))
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else:
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metadata.append(("type","scalar"))
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products.append( (path, components, metadata))
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PythonProviders.register_product(products, amda_get_sample)
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