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import sys
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sys.path.append("/home/jeandet/Documents/prog/build-SciQLop-Desktop-Debug/core")
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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 math
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from spwc.cache import _cache
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from spwc.common.datetime_range import DateTimeRange
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from functools import partial
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from datetime import datetime, timedelta, timezone
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someglobal = 1
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def make_scalar(x):
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y = np.cos(x/10.)
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return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=y)
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def make_vector(x):
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v=np.ones((len(x),3))
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for i in range(3):
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v.transpose()[:][i] = np.cos(x/10. + float(i)) + (100. * np.cos(x/10000. + float(i)))
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return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=v)
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def make_multicomponent(x):
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v=np.ones((len(x),4))
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for i in range(4):
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v.transpose()[:][i] = float(i+1) * np.cos(x/10. + float(i))
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return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=v)
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def _get_data(p_type, start, stop):
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if type(start) is datetime:
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start = start.timestamp()
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stop = stop.timestamp()
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x = np.arange(math.ceil(start), math.floor(stop))
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if p_type == 'scalar':
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return make_scalar(x)
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if p_type == 'vector':
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return make_vector(x)
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if p_type == 'multicomponent':
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return make_multicomponent(x)
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return None
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def get_data(metadata,start,stop):
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ts_type = pysciqlopcore.ScalarTimeSerie
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default_ctor_args = 1
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use_cache = False
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p_type = 'scalar'
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try:
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for key,value in metadata:
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if key == 'type':
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p_type = value
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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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if key == 'cache' and value == 'true':
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use_cache = True
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if use_cache:
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cache_product = f"tests/{p_type}"
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df = _cache.get_data(cache_product, DateTimeRange(datetime.fromtimestamp(start, tz=timezone.utc), datetime.fromtimestamp(stop, tz=timezone.utc)),
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partial(_get_data, p_type),
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fragment_hours=24)
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else:
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print("No Cache")
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df = _get_data(p_type, start, stop)
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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 test.py ",str(e))
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return ts_type(default_ctor_args)
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products = [
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("/tests/without_cache/scalar",[],[("type","scalar")]),
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("/tests/without_cache/vector",[],[("type","vector")]),
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("/tests/without_cache/multicomponent",[],[("type","multicomponent"),('size','4')]),
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("/tests/with_cache/scalar",[],[("type","scalar"), ("cache","true")]),
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("/tests/with_cache/vector",[],[("type","vector"), ("cache","true")]),
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("/tests/with_cache/multicomponent",[],[("type","multicomponent"),('size','4'), ("cache","true")])
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]
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PythonProviders.register_product(products ,get_data)
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