EOS/tests/test_pvforecastakkudoktor.py

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import sys
from pathlib import Path
from unittest.mock import Mock, patch
import pytest
from akkudoktoreos.core.ems import get_ems
from akkudoktoreos.prediction.prediction import get_prediction
from akkudoktoreos.prediction.pvforecastakkudoktor import (
AkkudoktorForecastHorizon,
AkkudoktorForecastMeta,
AkkudoktorForecastValue,
PVForecastAkkudoktor,
PVForecastAkkudoktorDataRecord,
)
from akkudoktoreos.utils.datetimeutil import compare_datetimes, to_datetime, to_duration
DIR_TESTDATA = Path(__file__).absolute().parent.joinpath("testdata")
FILE_TESTDATA_PV_FORECAST_INPUT_1 = DIR_TESTDATA.joinpath("pv_forecast_input_1.json")
FILE_TESTDATA_PV_FORECAST_RESULT_1 = DIR_TESTDATA.joinpath("pv_forecast_result_1.txt")
@pytest.fixture
def sample_settings(config_eos):
"""Fixture that adds settings data to the global config."""
settings = {
"prediction_hours": 48,
"prediction_historic_hours": 24,
"latitude": 52.52,
"longitude": 13.405,
"pvforecast_provider": "PVForecastAkkudoktor",
"pvforecast0_peakpower": 5.0,
"pvforecast0_surface_azimuth": -10,
"pvforecast0_surface_tilt": 7,
"pvforecast0_userhorizon": [20, 27, 22, 20],
"pvforecast0_inverter_paco": 10000,
"pvforecast1_peakpower": 4.8,
"pvforecast1_surface_azimuth": -90,
"pvforecast1_surface_tilt": 7,
"pvforecast1_userhorizon": [30, 30, 30, 50],
"pvforecast1_inverter_paco": 10000,
"pvforecast2_peakpower": 1.4,
"pvforecast2_surface_azimuth": -40,
"pvforecast2_surface_tilt": 60,
"pvforecast2_userhorizon": [60, 30, 0, 30],
"pvforecast2_inverter_paco": 2000,
"pvforecast3_peakpower": 1.6,
"pvforecast3_surface_azimuth": 5,
"pvforecast3_surface_tilt": 45,
"pvforecast3_userhorizon": [45, 25, 30, 60],
"pvforecast3_inverter_paco": 1400,
"pvforecast4_peakpower": None,
}
# Merge settings to config
config_eos.merge_settings_from_dict(settings)
return config_eos
@pytest.fixture
def sample_forecast_data():
"""Fixture that returns sample forecast data converted to pydantic model."""
with open(FILE_TESTDATA_PV_FORECAST_INPUT_1, "r", encoding="utf8") as f_in:
input_data = f_in.read()
return PVForecastAkkudoktor._validate_data(input_data)
@pytest.fixture
def sample_forecast_data_raw():
"""Fixture that returns raw sample forecast data."""
with open(FILE_TESTDATA_PV_FORECAST_INPUT_1, "r", encoding="utf8") as f_in:
input_data = f_in.read()
return input_data
@pytest.fixture
def sample_forecast_report():
"""Fixture that returns sample forecast data report."""
with open(FILE_TESTDATA_PV_FORECAST_RESULT_1, "r", encoding="utf8") as f_res:
input_data = f_res.read()
return input_data
@pytest.fixture
def sample_forecast_start(sample_forecast_data):
"""Fixture that returns the start date of the sample forecast data."""
forecast_start = to_datetime(sample_forecast_data.values[0][0].datetime)
expected_datetime = to_datetime("2024-10-06T00:00:00.000+02:00")
assert compare_datetimes(to_datetime(forecast_start), expected_datetime).equal
timezone_name = sample_forecast_data.meta.timezone
assert timezone_name == "Europe/Berlin"
return forecast_start
@pytest.fixture
def provider():
"""Fixture that returns the PVForecastAkkudoktor instance from the prediction."""
prediction = get_prediction()
provider = prediction.provider_by_id("PVForecastAkkudoktor")
assert isinstance(provider, PVForecastAkkudoktor)
return provider
@pytest.fixture
def provider_empty_instance():
"""Fixture that returns an empty instance of PVForecast."""
empty_instance = PVForecastAkkudoktor()
empty_instance.clear()
assert len(empty_instance) == 0
return empty_instance
# Sample data for testing
sample_horizon = AkkudoktorForecastHorizon(altitude=30, azimuthFrom=90, azimuthTo=180)
sample_meta = AkkudoktorForecastMeta(
lat=52.52,
lon=13.405,
power=[5000],
azimuth=[180],
tilt=[30],
timezone="Europe/Berlin",
albedo=0.25,
past_days=5,
inverterEfficiency=0.8,
powerInverter=[10000],
cellCoEff=-0.36,
range=True,
horizont=[[sample_horizon]],
horizontString=["sample_horizon"],
)
sample_value = AkkudoktorForecastValue(
datetime="2024-11-09T12:00:00",
dcPower=500.0,
power=480.0,
sunTilt=30.0,
sunAzimuth=180.0,
temperature=15.0,
relativehumidity_2m=50.0,
windspeed_10m=10.0,
)
sample_config_data = {
"prediction_hours": 48,
"prediction_historic_hours": 24,
"latitude": 52.52,
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"longitude": 13.405,
"pvforecast_provider": "PVForecastAkkudoktor",
"pvforecast0_peakpower": 5.0,
"pvforecast0_surface_azimuth": 180,
"pvforecast0_surface_tilt": 30,
"pvforecast0_inverter_paco": 10000,
}
# Tests for AkkudoktorForecastHorizon
def test_akkudoktor_forecast_horizon():
horizon = AkkudoktorForecastHorizon(altitude=30, azimuthFrom=90, azimuthTo=180)
assert horizon.altitude == 30
assert horizon.azimuthFrom == 90
assert horizon.azimuthTo == 180
# Tests for AkkudoktorForecastMeta
def test_akkudoktor_forecast_meta():
meta = sample_meta
assert meta.lat == 52.52
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assert meta.lon == 13.405
assert meta.power == [5000]
assert meta.tilt == [30]
assert meta.timezone == "Europe/Berlin"
# Tests for AkkudoktorForecastValue
def test_akkudoktor_forecast_value():
value = sample_value
assert value.dcPower == 500.0
assert value.power == 480.0
assert value.temperature == 15.0
assert value.windspeed_10m == 10.0
# Tests for PVForecastAkkudoktorDataRecord
def test_pvforecast_akkudoktor_data_record():
record = PVForecastAkkudoktorDataRecord(
pvforecastakkudoktor_ac_power_measured=1000.0,
pvforecastakkudoktor_wind_speed_10m=10.0,
pvforecastakkudoktor_temp_air=15.0,
)
assert record.pvforecastakkudoktor_ac_power_measured == 1000.0
assert record.pvforecastakkudoktor_wind_speed_10m == 10.0
assert record.pvforecastakkudoktor_temp_air == 15.0
assert (
record.pvforecastakkudoktor_ac_power_any == 1000.0
) # Assuming AC power measured is preferred
def test_pvforecast_akkudoktor_validate_data(provider_empty_instance, sample_forecast_data_raw):
"""Test validation of PV forecast data on sample data."""
with pytest.raises(
ValueError,
match="Field: meta\nError: Field required\nType: missing\nField: values\nError: Field required\nType: missing\n",
):
ret = provider_empty_instance._validate_data("{}")
data = provider_empty_instance._validate_data(sample_forecast_data_raw)
# everything worked
@patch("requests.get")
def test_pvforecast_akkudoktor_update_with_sample_forecast(
mock_get, sample_settings, sample_forecast_data_raw, sample_forecast_start, provider
):
"""Test data processing using sample forecast data."""
# Mock response object
mock_response = Mock()
mock_response.status_code = 200
mock_response.content = sample_forecast_data_raw
mock_get.return_value = mock_response
# Test that update properly inserts data records
ems_eos = get_ems()
ems_eos.set_start_datetime(sample_forecast_start)
provider.update_data(force_enable=True, force_update=True)
assert compare_datetimes(provider.start_datetime, sample_forecast_start).equal
assert compare_datetimes(provider[0].date_time, to_datetime(sample_forecast_start)).equal
# Report Generation Test
def test_report_ac_power_and_measurement(provider, config_eos):
# Set the configuration
config_eos.merge_settings_from_dict(sample_config_data)
record = PVForecastAkkudoktorDataRecord(
pvforecastakkudoktor_ac_power_measured=900.0,
pvforecast_dc_power=450.0,
pvforecast_ac_power=400.0,
)
provider.append(record)
report = provider.report_ac_power_and_measurement()
assert "DC: 450.0" in report
assert "AC: 400.0" in report
assert "AC sampled: 900.0" in report
@pytest.mark.skipif(
sys.platform.startswith("win"), reason="'other_timezone' fixture not supported on Windows"
)
@patch("requests.get")
def test_timezone_behaviour(
mock_get,
sample_settings,
sample_forecast_data_raw,
sample_forecast_start,
provider,
set_other_timezone,
):
"""Test PVForecast in another timezone."""
mock_response = Mock()
mock_response.status_code = 200
mock_response.content = sample_forecast_data_raw
mock_get.return_value = mock_response
# sample forecast start in other timezone
other_timezone = set_other_timezone()
other_start_datetime = to_datetime(sample_forecast_start, in_timezone=other_timezone)
assert compare_datetimes(other_start_datetime, sample_forecast_start).equal
expected_datetime = to_datetime("2024-10-06T00:00:00+0200", in_timezone=other_timezone)
assert compare_datetimes(other_start_datetime, expected_datetime).equal
provider.clear()
assert len(provider) == 0
ems_eos = get_ems()
ems_eos.set_start_datetime(other_start_datetime)
provider.update_data(force_update=True)
assert compare_datetimes(provider.start_datetime, other_start_datetime).equal
# Check wether first record starts at requested sample start time
assert compare_datetimes(provider[0].date_time, sample_forecast_start).equal
# Test updating AC power measurement for a specific date.
provider.update_value(sample_forecast_start, "pvforecastakkudoktor_ac_power_measured", 1000)
# Check wether first record was filled with ac power measurement
assert provider[0].pvforecastakkudoktor_ac_power_measured == 1000
# Test fetching temperature forecast for a specific date.
other_end_datetime = other_start_datetime + to_duration("24 hours")
expected_end_datetime = to_datetime("2024-10-07T00:00:00+0200", in_timezone=other_timezone)
assert compare_datetimes(other_end_datetime, expected_end_datetime).equal
forecast_temps = provider.key_to_series(
"pvforecastakkudoktor_temp_air", other_start_datetime, other_end_datetime
)
assert len(forecast_temps) == 23 # 24-1, first temperature is null
assert forecast_temps.iloc[0] == 6.5
assert forecast_temps.iloc[1] == 6.0
# Test fetching AC power forecast
other_end_datetime = other_start_datetime + to_duration("48 hours")
forecast_measured = provider.key_to_series(
"pvforecastakkudoktor_ac_power_measured", other_start_datetime, other_end_datetime
)
Fix2 config and predictions revamp. (#281) measurement: - Add new measurement class to hold real world measurements. - Handles load meter readings, grid import and export meter readings. - Aggregates load meter readings aka. measurements to total load. - Can import measurements from files, pandas datetime series, pandas datetime dataframes, simple daetime arrays and programmatically. - Maybe expanded to other measurement values. - Should be used for load prediction adaptions by real world measurements. core/coreabc: - Add mixin class to access measurements core/pydantic: - Add pydantic models for pandas datetime series and dataframes. - Add pydantic models for simple datetime array core/dataabc: - Provide DataImport mixin class for generic import handling. Imports from JSON string and files. Imports from pandas datetime dataframes and simple datetime arrays. Signature of import method changed to allow import datetimes to be given programmatically and by data content. - Use pydantic models for datetime series, dataframes, arrays - Validate generic imports by pydantic models - Provide new attributes min_datetime and max_datetime for DataSequence. - Add parameter dropna to drop NAN/ None values when creating lists, pandas series or numpy array from DataSequence. config/config: - Add common settings for the measurement module. predictions/elecpriceakkudoktor: - Use mean values of last 7 days to fill prediction values not provided by akkudoktor.net (only provides 24 values). prediction/loadabc: - Extend the generic prediction keys by 'load_total_adjusted' for load predictions that adjust the predicted total load by measured load values. prediction/loadakkudoktor: - Extend the Akkudoktor load prediction by load adjustment using measured load values. prediction/load_aggregator: - Module removed. Load aggregation is now handled by the measurement module. prediction/load_corrector: - Module removed. Load correction (aka. adjustment of load prediction by measured load energy) is handled by the LoadAkkudoktor prediction and the generic 'load_mean_adjusted' prediction key. prediction/load_forecast: - Module removed. Functionality now completely handled by the LoadAkkudoktor prediction. utils/cacheutil: - Use pydantic. - Fix potential bug in ttl (time to live) duration handling. utils/datetimeutil: - Added missing handling of pendulum.DateTime and pendulum.Duration instances as input. Handled before as datetime.datetime and datetime.timedelta. utils/visualize: - Move main to generate_example_report() for better testing support. server/server: - Added new configuration option server_fastapi_startup_server_fasthtml to make startup of FastHTML server by FastAPI server conditional. server/fastapi_server: - Add APIs for measurements - Improve APIs to provide or take pandas datetime series and datetime dataframes controlled by pydantic model. - Improve APIs to provide or take simple datetime data arrays controlled by pydantic model. - Move fastAPI server API to v1 for new APIs. - Update pre v1 endpoints to use new prediction and measurement capabilities. - Only start FastHTML server if 'server_fastapi_startup_server_fasthtml' config option is set. tests: - Adapt import tests to changed import method signature - Adapt server test to use the v1 API - Extend the dataabc test to test for array generation from data with several data interval scenarios. - Extend the datetimeutil test to also test for correct handling of to_datetime() providing now(). - Adapt LoadAkkudoktor test for new adjustment calculation. - Adapt visualization test to use example report function instead of visualize.py run as process. - Removed test_load_aggregator. Functionality is now tested in test_measurement. - Added tests for measurement module docs: - Remove sphinxcontrib-openapi as it prevents build of documentation. "site-packages/sphinxcontrib/openapi/openapi31.py", line 305, in _get_type_from_schema for t in schema["anyOf"]: KeyError: 'anyOf'" Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2024-12-29 18:42:49 +01:00
assert len(forecast_measured) == 1
assert forecast_measured.iloc[0] == 1000.0 # changed before