test.py
87 lines
| 3.1 KiB
| text/x-python
|
PythonLexer
| r1440 | import traceback | |||
| import pandas as pds | ||||
| r1431 | import PythonProviders | |||
| import pysciqlopcore | ||||
| import numpy as np | ||||
| r1433 | import math | |||
| r1439 | from spwc.cache import _cache | |||
| from spwc.common.datetime_range import DateTimeRange | ||||
| from functools import partial | ||||
| from datetime import datetime, timedelta, timezone | ||||
| r1431 | ||||
| someglobal = 1 | ||||
| r1432 | def make_scalar(x): | |||
| r1431 | y = np.cos(x/10.) | |||
| r1439 | return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=y) | |||
| r1431 | ||||
| r1432 | def make_vector(x): | |||
| r1436 | v=np.ones((len(x),3)) | |||
| r1432 | for i in range(3): | |||
| r1439 | v.transpose()[:][i] = np.cos(x/10. + float(i)) + (100. * np.cos(x/10000. + float(i))) | |||
| return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=v) | ||||
| r1432 | ||||
| def make_multicomponent(x): | ||||
| r1436 | v=np.ones((len(x),4)) | |||
| r1432 | for i in range(4): | |||
| r1436 | v.transpose()[:][i] = float(i+1) * np.cos(x/10. + float(i)) | |||
| r1439 | return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=v) | |||
| r1432 | ||||
| r1439 | def _get_data(p_type, start, stop): | |||
| if type(start) is datetime: | ||||
| start = start.timestamp() | ||||
| stop = stop.timestamp() | ||||
| r1433 | x = np.arange(math.ceil(start), math.floor(stop)) | |||
| r1439 | if p_type == 'scalar': | |||
| return make_scalar(x) | ||||
| if p_type == 'vector': | ||||
| return make_vector(x) | ||||
| if p_type == 'multicomponent': | ||||
| return make_multicomponent(x) | ||||
| return None | ||||
| r1432 | ||||
| r1439 | def get_data(metadata,start,stop): | |||
| ts_type = pysciqlopcore.ScalarTimeSerie | ||||
| default_ctor_args = 1 | ||||
| use_cache = False | ||||
| p_type = 'scalar' | ||||
| try: | ||||
| for key,value in metadata: | ||||
| if key == 'type': | ||||
| p_type = value | ||||
| if value == 'vector': | ||||
| ts_type = pysciqlopcore.VectorTimeSerie | ||||
| elif value == 'multicomponent': | ||||
| ts_type = pysciqlopcore.MultiComponentTimeSerie | ||||
| default_ctor_args = (0,2) | ||||
| if key == 'cache' and value == 'true': | ||||
| use_cache = True | ||||
| if use_cache: | ||||
| cache_product = f"tests/{p_type}" | ||||
| df = _cache.get_data(cache_product, DateTimeRange(datetime.fromtimestamp(start, tz=timezone.utc), datetime.fromtimestamp(stop, tz=timezone.utc)), | ||||
| partial(_get_data, p_type), | ||||
| fragment_hours=24) | ||||
| else: | ||||
| print("No Cache") | ||||
| df = _get_data(p_type, start, stop) | ||||
| t = np.array([d.timestamp() for d in df.index]) | ||||
| values = df.values | ||||
| return ts_type(t,values) | ||||
| except Exception as e: | ||||
| print(traceback.format_exc()) | ||||
| print("Error in test.py ",str(e)) | ||||
| return ts_type(default_ctor_args) | ||||
| r1431 | ||||
| r1439 | products = [ | |||
| ("/tests/without_cache/scalar",[],[("type","scalar")]), | ||||
| ("/tests/without_cache/vector",[],[("type","vector")]), | ||||
| ("/tests/without_cache/multicomponent",[],[("type","multicomponent"),('size','4')]), | ||||
| ("/tests/with_cache/scalar",[],[("type","scalar"), ("cache","true")]), | ||||
| ("/tests/with_cache/vector",[],[("type","vector"), ("cache","true")]), | ||||
| ("/tests/with_cache/multicomponent",[],[("type","multicomponent"),('size','4'), ("cache","true")]) | ||||
| ] | ||||
| r1431 | ||||
| r1439 | PythonProviders.register_product(products ,get_data) | |||
