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https://github.com/Akkudoktor-EOS/EOS.git
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3bb0e02aed
Configuration for some providers was given in the sub-field `provider_settings` combining the settings of several providers. Pydantic does not understand this very well and the configuration became cumbersome, especially in EOSdash. All provider settings from the `provider-settings` sub-field are now lifted to the top level field of the configuration. The changes are automatically migrated in the configuration. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
115 lines
4.1 KiB
Python
115 lines
4.1 KiB
Python
import json
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from pathlib import Path
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import numpy.testing as npt
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import pytest
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from akkudoktoreos.core.coreabc import get_ems
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from akkudoktoreos.prediction.pvforecastimport import PVForecastImport
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from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
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DIR_TESTDATA = Path(__file__).absolute().parent.joinpath("testdata")
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FILE_TESTDATA_PVFORECASTIMPORT_1_JSON = DIR_TESTDATA.joinpath("import_input_1.json")
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@pytest.fixture
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def provider(sample_import_1_json, config_eos):
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"""Fixture to create a PVForecastProvider instance."""
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settings = {
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"pvforecast": {
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"provider": "PVForecastImport",
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"pvforecastimport": {
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"import_file_path": str(FILE_TESTDATA_PVFORECASTIMPORT_1_JSON),
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"import_json": json.dumps(sample_import_1_json),
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},
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}
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}
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config_eos.merge_settings_from_dict(settings)
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provider = PVForecastImport()
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assert provider.enabled()
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return provider
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@pytest.fixture
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def sample_import_1_json():
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"""Fixture that returns sample forecast data report."""
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with FILE_TESTDATA_PVFORECASTIMPORT_1_JSON.open("r", encoding="utf-8", newline=None) as f_res:
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input_data = json.load(f_res)
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return input_data
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# ------------------------------------------------
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# General forecast
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# ------------------------------------------------
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def test_singleton_instance(provider):
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"""Test that PVForecastForecast behaves as a singleton."""
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another_instance = PVForecastImport()
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assert provider is another_instance
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def test_invalid_provider(provider, config_eos):
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"""Test requesting an unsupported provider."""
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settings = {
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"pvforecast": {
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"provider": "<invalid>",
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"pvforecastimport": {
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"import_file_path": str(FILE_TESTDATA_PVFORECASTIMPORT_1_JSON),
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},
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}
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}
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with pytest.raises(ValueError, match="not a valid PV forecast provider"):
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config_eos.merge_settings_from_dict(settings)
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# ------------------------------------------------
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# Import
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# ------------------------------------------------
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"start_datetime, from_file",
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[
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("2024-11-10 00:00:00", True), # No DST in Germany
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("2024-08-10 00:00:00", True), # DST in Germany
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("2024-03-31 00:00:00", True), # DST change in Germany (23 hours/ day)
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("2024-10-27 00:00:00", True), # DST change in Germany (25 hours/ day)
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("2024-11-10 00:00:00", False), # No DST in Germany
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("2024-08-10 00:00:00", False), # DST in Germany
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("2024-03-31 00:00:00", False), # DST change in Germany (23 hours/ day)
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("2024-10-27 00:00:00", False), # DST change in Germany (25 hours/ day)
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],
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)
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async def test_import(provider, sample_import_1_json, start_datetime, from_file, config_eos):
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"""Test fetching forecast from import."""
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key = "pvforecast_ac_power"
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ems_eos = get_ems()
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ems_eos.set_start_datetime(to_datetime(start_datetime, in_timezone="Europe/Berlin"))
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if from_file:
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config_eos.pvforecast.pvforecastimport.import_json = None
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assert config_eos.pvforecast.pvforecastimport.import_json is None
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else:
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config_eos.pvforecast.pvforecastimport.import_file_path = None
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assert config_eos.pvforecast.pvforecastimport.import_file_path is None
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await provider.delete_by_datetime(start_datetime=None, end_datetime=None)
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# Call the method
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await provider.update_data()
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# Assert: Verify the result is as expected
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assert provider.ems_start_datetime is not None
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assert provider.total_hours is not None
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assert compare_datetimes(provider.ems_start_datetime, ems_eos.start_datetime).equal
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expected_values = sample_import_1_json[key]
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result_values = await provider.key_to_array(
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key=key,
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start_datetime=provider.ems_start_datetime,
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end_datetime=provider.ems_start_datetime + to_duration(f"{len(expected_values)} hours"),
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interval=to_duration("1 hour"),
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)
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# Allow for some difference due to value calculation on DST change
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npt.assert_allclose(result_values, expected_values, rtol=0.001)
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