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Change PDF visualization to be created on demand and per optimization algorithm. The PDF for the GENETIC0 optimization is provided by the /visualization_results.pdf endpoint. There is no change in the interface. By this the optimization algorithm is offloaded from the PDF generation which spares some time. To cope with several users may call the /visualization_results.pdf endpoint at the same time the PDF is generated on the fly without any intermediate file taking the stored GENETIC0 solution as an input. SVG picture generation is removed as this would again create intermediate files. Chart pictures can easily be taken from the PDF. To allow on demand creation of the optimization results visualization the optimisation solution stored is extended by several new attributes. To keep the deprecated /optimize endpoint compatible the optimization solution is stripped to the legacy content before returned. Due to the extension of the solution the optimization tests were adapted to cover the extended content. The optimization tests are adapted to test the generated visualization report by the pypdf reader. Pypdf is added to the development dependencies. Besides the adaptation several fixes and improvements are added: * feat: extend /v1/prediction/series endpoint by resampling and filling Add parameters for resampling and filling. Add the processing parameter to control wether raw data or resampled data shall be returned. * feat: extend /v1/measurement/series endpoint by resampling and filling Add parameters for resampling and filling: Add the processing parameter to control wether raw data or resampled data shall be returned. * feat: standardize and improve API error response Use FASTApi exception handlers to provide a standardized API exception handling. All exceptions are logged. Exception traces are only returned if the new logging configuration parameter logging.api_logging_level is set to "DEBUG" or "TRACE". Avoids unwanted leackage of server internals on exceptions. * fix: align to intervall when resampling Ensure resampling is aligned to interval also when the buckets are shifted due to the align_to_intervall parameter is set. * chore: make dropna mandatory and default to True * chore: refactor key_to_xxx data management methods Make key_to_series the central method for data resampling and fill. Add a new key_to_raw_series to retrieve the data as it is stored (without resampling and filling). Users of key_to_series were mostly moved to key_to_raw_series as this resembles the former interface. Especially in predictions and tests this was done. * chore: create test data sub-directory for each optimization algorithm To prevent cluttering the test data directory and ease test data management for optimization algorithms each algorithm got it's own sub-directory. The current test data was moved to these sub-directories. * chore: update version Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
363 lines
12 KiB
Python
363 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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import pytest_asyncio
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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_asyncio.fixture
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async 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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await 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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@pytest.mark.asyncio
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async 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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@pytest.mark.asyncio
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async 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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@pytest.mark.asyncio
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@patch("requests.get")
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async 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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await 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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@pytest.mark.asyncio
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async 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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await 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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@pytest.mark.asyncio
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@patch("requests.get")
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async 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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await 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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await 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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await 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 = await provider.key_to_raw_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 = await provider.key_to_raw_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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