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The database supports backend selection, compression, incremental data load, automatic data saving to storage, automatic vaccum and compaction. Make SQLite3 and LMDB database backends available. Update tests for new interface conventions regarding data sequences, data containers, data providers. This includes the measurements provider and the prediction providers. Add database documentation. The fix includes several bug fixes that are not directly related to the database implementation but are necessary to keep EOS running properly and to test and document the changes. * fix: config eos test setup Make the config_eos fixture generate a new instance of the config_eos singleton. Use correct env names to setup data folder path. * fix: startup with no config Make cache and measurements complain about missing data path configuration but do not bail out. * fix: soc data preparation and usage for genetic optimization. Search for soc measurments 48 hours around the optimization start time. Only clamp soc to maximum in battery device simulation. * fix: dashboard bailout on zero value solution display Do not use zero values to calculate the chart values adjustment for display. * fix: openapi generation script Make the script also replace data_folder_path and data_output_path to hide real (test) environment pathes. * feat: add make repeated task function make_repeated_task allows to wrap a function to be repeated cyclically. * chore: removed index based data sequence access Index based data sequence access does not make sense as the sequence can be backed by the database. The sequence is now purely time series data. * chore: refactor eos startup to avoid module import startup Avoid module import initialisation expecially of the EOS configuration. Config mutation, singleton initialization, logging setup, argparse parsing, background task definitions depending on config and environment-dependent behavior is now done at function startup. * chore: introduce retention manager A single long-running background task that owns the scheduling of all periodic server-maintenance jobs (cache cleanup, DB autosave, …) * chore: canonicalize timezone name for UTC Timezone names that are semantically identical to UTC are canonicalized to UTC. * chore: extend config file migration for default value handling Extend the config file migration handling values None or nonexisting values that will invoke a default value generation in the new config file. Also adapt test to handle this situation. * chore: extend datetime util test cases * chore: make version test check for untracked files Check for files that are not tracked by git. Version calculation will be wrong if these files will not be commited. * chore: bump pandas to 3.0.0 Pandas 3.0 now performs inference on the appropriate resolution (a.k.a. unit) for the output dtype which may become datetime64[us] (before it was ns). Also numeric dtype detection is now more strict which needs a different detection for numerics. * chore: bump pydantic-settings to 2.12.0 pydantic-settings 2.12.0 under pytest creates a different behaviour. The tests were adapted and a workaround was introduced. Also ConfigEOS was adapted to allow for fine grain initialization control to be able to switch off certain settings such as file settings during test. * chore: remove sci learn kit from dependencies The sci learn kit is not strictly necessary as long as we have scipy. * chore: add documentation mode guarding for sphinx autosummary Sphinx autosummary excecutes functions. Prevent exceptions in case of pure doc mode. * chore: adapt docker-build CI workflow to stricter GitHub handling Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
357 lines
12 KiB
Python
357 lines
12 KiB
Python
import sys
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from pathlib import Path
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from unittest.mock import Mock, patch
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import pytest
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from loguru import logger
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from akkudoktoreos.core.coreabc import get_ems, get_prediction
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from akkudoktoreos.prediction.pvforecastakkudoktor import (
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AkkudoktorForecastHorizon,
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AkkudoktorForecastMeta,
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AkkudoktorForecastValue,
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PVForecastAkkudoktor,
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PVForecastAkkudoktorDataRecord,
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)
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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_PV_FORECAST_INPUT_1 = DIR_TESTDATA.joinpath("pv_forecast_input_1.json")
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FILE_TESTDATA_PV_FORECAST_INPUT_SINGLE_PLANE = DIR_TESTDATA.joinpath(
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"pv_forecast_input_single_plane.json"
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)
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FILE_TESTDATA_PV_FORECAST_RESULT_1 = DIR_TESTDATA.joinpath("pv_forecast_result_1.txt")
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@pytest.fixture
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def sample_settings(config_eos):
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"""Fixture that adds settings data to the global config."""
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settings = {
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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"prediction": {
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"hours": 48,
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"historic_hours": 24,
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},
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"pvforecast": {
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"provider": "PVForecastAkkudoktor",
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"planes": [
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{
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"peakpower": 5.0,
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"surface_azimuth": 170,
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"surface_tilt": 7,
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"userhorizon": [20, 27, 22, 20],
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"inverter_paco": 10000,
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},
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{
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"peakpower": 4.8,
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"surface_azimuth": 90,
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"surface_tilt": 7,
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"userhorizon": [30, 30, 30, 50],
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"inverter_paco": 10000,
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},
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{
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"peakpower": 1.4,
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"surface_azimuth": 140,
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"surface_tilt": 60,
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"userhorizon": [60, 30, 0, 30],
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"inverter_paco": 2000,
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},
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{
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"peakpower": 1.6,
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"surface_azimuth": 185,
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"surface_tilt": 45,
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"userhorizon": [45, 25, 30, 60],
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"inverter_paco": 1400,
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},
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],
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},
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}
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# Merge settings to config
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config_eos.merge_settings_from_dict(settings)
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assert config_eos.pvforecast.provider == "PVForecastAkkudoktor"
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return config_eos
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@pytest.fixture
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def sample_forecast_data():
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"""Fixture that returns sample forecast data converted to pydantic model."""
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with FILE_TESTDATA_PV_FORECAST_INPUT_1.open("r", encoding="utf-8", newline=None) as f_in:
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input_data = f_in.read()
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return PVForecastAkkudoktor._validate_data(input_data)
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@pytest.fixture
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def sample_forecast_data_raw():
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"""Fixture that returns raw sample forecast data."""
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with FILE_TESTDATA_PV_FORECAST_INPUT_1.open("r", encoding="utf-8", newline=None) as f_in:
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input_data = f_in.read()
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return input_data
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@pytest.fixture
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def sample_forecast_data_single_plane_raw():
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"""Fixture that returns raw sample forecast data."""
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with FILE_TESTDATA_PV_FORECAST_INPUT_SINGLE_PLANE.open(
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"r", encoding="utf-8", newline=None
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) as f_in:
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input_data = f_in.read()
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return input_data
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@pytest.fixture
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def sample_forecast_report():
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"""Fixture that returns sample forecast data report."""
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with FILE_TESTDATA_PV_FORECAST_RESULT_1.open("r", encoding="utf-8", newline=None) as f_res:
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input_data = f_res.read()
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return input_data
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@pytest.fixture
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def sample_forecast_start(sample_forecast_data):
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"""Fixture that returns the start date of the sample forecast data."""
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forecast_start = to_datetime(sample_forecast_data.values[0][0].datetime)
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expected_datetime = to_datetime("2024-10-06T00:00:00.000+02:00")
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assert compare_datetimes(to_datetime(forecast_start), expected_datetime).equal
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timezone_name = sample_forecast_data.meta.timezone
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assert timezone_name == "Europe/Berlin"
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return forecast_start
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@pytest.fixture
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def provider():
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"""Fixture that returns the PVForecastAkkudoktor instance from the prediction."""
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prediction = get_prediction()
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provider = prediction.provider_by_id("PVForecastAkkudoktor")
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assert isinstance(provider, PVForecastAkkudoktor)
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return provider
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@pytest.fixture
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def provider_empty_instance():
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"""Fixture that returns an empty instance of PVForecast."""
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empty_instance = PVForecastAkkudoktor()
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empty_instance.delete_by_datetime(start_datetime=None, end_datetime=None)
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assert len(empty_instance) == 0
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return empty_instance
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# Sample data for testing
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sample_horizon = AkkudoktorForecastHorizon(altitude=30, azimuthFrom=90, azimuthTo=180)
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sample_meta = AkkudoktorForecastMeta(
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lat=52.52,
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lon=13.405,
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power=[5000],
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azimuth=[180],
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tilt=[30],
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timezone="Europe/Berlin",
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albedo=0.25,
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past_days=5,
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inverterEfficiency=0.8,
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powerInverter=[10000],
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cellCoEff=-0.36,
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range=True,
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horizont=[[sample_horizon]],
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horizontString=["sample_horizon"],
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)
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sample_value = AkkudoktorForecastValue(
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datetime="2024-11-09T12:00:00",
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dcPower=500.0,
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power=480.0,
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sunTilt=30.0,
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sunAzimuth=180.0,
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temperature=15.0,
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relativehumidity_2m=50.0,
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windspeed_10m=10.0,
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)
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sample_config_data = {
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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"prediction": {
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"hours": 48,
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"historic_hours": 24,
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},
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"pvforecast": {
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"provider": "PVForecastAkkudoktor",
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"planes": [
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{
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"peakpower": 5.0,
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"surface_azimuth": 180,
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"surface_tilt": 30,
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"inverter_paco": 10000,
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}
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],
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},
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}
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# Tests for AkkudoktorForecastHorizon
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def test_akkudoktor_forecast_horizon():
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horizon = AkkudoktorForecastHorizon(altitude=30, azimuthFrom=90, azimuthTo=180)
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assert horizon.altitude == 30
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assert horizon.azimuthFrom == 90
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assert horizon.azimuthTo == 180
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# Tests for AkkudoktorForecastMeta
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def test_akkudoktor_forecast_meta():
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meta = sample_meta
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assert meta.lat == 52.52
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assert meta.lon == 13.405
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assert meta.power == [5000]
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assert meta.tilt == [30]
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assert meta.timezone == "Europe/Berlin"
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# Tests for AkkudoktorForecastValue
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def test_akkudoktor_forecast_value():
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value = sample_value
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assert value.dcPower == 500.0
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assert value.power == 480.0
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assert value.temperature == 15.0
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assert value.windspeed_10m == 10.0
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# Tests for PVForecastAkkudoktorDataRecord
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def test_pvforecast_akkudoktor_data_record():
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record = PVForecastAkkudoktorDataRecord(
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pvforecastakkudoktor_ac_power_measured=1000.0,
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pvforecastakkudoktor_wind_speed_10m=10.0,
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pvforecastakkudoktor_temp_air=15.0,
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)
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assert record.pvforecastakkudoktor_ac_power_measured == 1000.0
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assert record.pvforecastakkudoktor_wind_speed_10m == 10.0
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assert record.pvforecastakkudoktor_temp_air == 15.0
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assert (
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record.pvforecastakkudoktor_ac_power_any == 1000.0
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) # Assuming AC power measured is preferred
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def test_pvforecast_akkudoktor_validate_data(provider_empty_instance, sample_forecast_data_raw):
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"""Test validation of PV forecast data on sample data."""
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logger.info("The following errors are intentional and part of the test.")
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with pytest.raises(
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ValueError,
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match="Field: meta\nError: Field required\nType: missing\nField: values\nError: Field required\nType: missing\n",
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):
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ret = provider_empty_instance._validate_data("{}")
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data = provider_empty_instance._validate_data(sample_forecast_data_raw)
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# everything worked
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def test_pvforecast_akkudoktor_validate_data_single_plane(
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provider_empty_instance, sample_forecast_data_single_plane_raw
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):
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"""Test validation of PV forecast data on sample data with a single plane."""
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logger.info("The following errors are intentional and part of the test.")
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with pytest.raises(
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ValueError,
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match="Field: meta\nError: Field required\nType: missing\nField: values\nError: Field required\nType: missing\n",
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):
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ret = provider_empty_instance._validate_data("{}")
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data = provider_empty_instance._validate_data(sample_forecast_data_single_plane_raw)
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# everything worked
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@patch("requests.get")
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def test_pvforecast_akkudoktor_update_with_sample_forecast(
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mock_get, sample_settings, sample_forecast_data_raw, sample_forecast_start, provider
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):
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"""Test data processing using sample forecast data."""
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# Mock response object
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mock_response = Mock()
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mock_response.status_code = 200
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mock_response.content = sample_forecast_data_raw
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mock_get.return_value = mock_response
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# Test that update properly inserts data records
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ems_eos = get_ems()
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ems_eos.set_start_datetime(sample_forecast_start)
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provider.update_data(force_enable=True, force_update=True)
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assert compare_datetimes(provider.ems_start_datetime, sample_forecast_start).equal
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assert compare_datetimes(provider.records[0].date_time, to_datetime(sample_forecast_start)).equal
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# Report Generation Test
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def test_report_ac_power_and_measurement(provider, config_eos):
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# Set the configuration
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config_eos.merge_settings_from_dict(sample_config_data)
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record = PVForecastAkkudoktorDataRecord(
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pvforecastakkudoktor_ac_power_measured=900.0,
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pvforecast_dc_power=450.0,
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pvforecast_ac_power=400.0,
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)
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provider.insert_by_datetime(record)
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report = provider.report_ac_power_and_measurement()
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assert "DC: 450.0" in report
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assert "AC: 400.0" in report
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assert "AC sampled: 900.0" in report
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@pytest.mark.skipif(
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sys.platform.startswith("win"), reason="'other_timezone' fixture not supported on Windows"
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)
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@patch("requests.get")
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def test_timezone_behaviour(
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mock_get,
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sample_settings,
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sample_forecast_data_raw,
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sample_forecast_start,
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provider,
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set_other_timezone,
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):
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"""Test PVForecast in another timezone."""
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mock_response = Mock()
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mock_response.status_code = 200
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mock_response.content = sample_forecast_data_raw
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mock_get.return_value = mock_response
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# sample forecast start in other timezone
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other_timezone = set_other_timezone()
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other_start_datetime = to_datetime(sample_forecast_start, in_timezone=other_timezone)
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assert compare_datetimes(other_start_datetime, sample_forecast_start).equal
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expected_datetime = to_datetime("2024-10-06T00:00:00+0200", in_timezone=other_timezone)
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assert compare_datetimes(other_start_datetime, expected_datetime).equal
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provider.delete_by_datetime(start_datetime=None, end_datetime=None)
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assert len(provider) == 0
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ems_eos = get_ems()
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ems_eos.set_start_datetime(other_start_datetime)
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provider.update_data(force_update=True)
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assert compare_datetimes(provider.ems_start_datetime, other_start_datetime).equal
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# Check wether first record starts at requested sample start time
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assert compare_datetimes(provider.records[0].date_time, sample_forecast_start).equal
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# Test updating AC power measurement for a specific date.
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provider.update_value(sample_forecast_start, "pvforecastakkudoktor_ac_power_measured", 1000)
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# Check wether first record was filled with ac power measurement
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assert provider.records[0].pvforecastakkudoktor_ac_power_measured == 1000
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# Test fetching temperature forecast for a specific date.
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other_end_datetime = other_start_datetime + to_duration("24 hours")
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expected_end_datetime = to_datetime("2024-10-07T00:00:00+0200", in_timezone=other_timezone)
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assert compare_datetimes(other_end_datetime, expected_end_datetime).equal
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forecast_temps = provider.key_to_series(
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"pvforecastakkudoktor_temp_air", other_start_datetime, other_end_datetime
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)
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assert len(forecast_temps) == 23 # 24-1, first temperature is null
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assert forecast_temps.iloc[0] == 6.5
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assert forecast_temps.iloc[1] == 6.0
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# Test fetching AC power forecast
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other_end_datetime = other_start_datetime + to_duration("48 hours")
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forecast_measured = provider.key_to_series(
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"pvforecastakkudoktor_ac_power_measured", other_start_datetime, other_end_datetime
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)
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assert len(forecast_measured) == 1
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assert forecast_measured.iloc[0] == 1000.0 # changed before
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