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import os
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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 pandas as pds
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import requests
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from datetime import datetime, timedelta, timezone
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from spwc.cdaweb import cdaweb
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cd = cdaweb()
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def cda_get_sample(metadata, start,stop):
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ts_type = pysciqlopcore.ScalarTimeSerie
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default_ctor_args = 1
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try:
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variable_id = None
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dataset_id = None
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drop_cols = []
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for key,value in metadata:
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if key == 'VAR_ID':
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variable_id = value
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elif key == 'DATASET_ID':
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dataset_id = value
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elif key == 'drop_col':
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drop_cols.append(value)
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elif key == 'type':
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if value == 'vector':
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ts_type = pysciqlopcore.VectorTimeSerie
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elif value == 'multicomponent':
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ts_type = pysciqlopcore.MultiComponentTimeSerie
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default_ctor_args = (0,2)
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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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df = cd.get_variable(dataset=dataset_id,variable=variable_id,tstart=tstart,tend=tend)
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if len(df):
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df = df.drop(columns = drop_cols)
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t = np.array([d.timestamp() for d in df.index])
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values = df.values
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return ts_type(t,values)
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except Exception as e:
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print(traceback.format_exc())
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print("Error in cdaweb.py ",str(e))
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return ts_type(default_ctor_args)
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products = [
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("/CDA/Themis/ThA/tha_fgl_gsm", [], [("type","vector"), ('drop_col','UT__sec'), ("DATASET_ID","THA_L2_FGM"), ("VAR_ID","tha_fgl_gsm")]),
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("/CDA/Themis/ThB/thb_fgl_gsm", [], [("type","vector"), ('drop_col','UT__sec'), ("DATASET_ID","THB_L2_FGM"), ("VAR_ID","thb_fgl_gsm")]),
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]
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PythonProviders.register_product(products, cda_get_sample)
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