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EOS/tests/test_elecpriceenergycharts.py

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fix: move data management to async (#1015) FAstAPI is an async framework. Data may be imported and exported, load and save, set and get asynchronously. Prevent interleaving data operations to corrupt the data. In the previous design sync and async data access was intermixed leading to data corruption. The basic data classes DataSequence and DataContainer and the derived classes like Provider and Measurement now are async. Data access is protected by several async locks. To support the async design of the data classes the database interface became async. The energy management is also adapted to the new async design. Optimization is still off-loaded to another thread, but the prepration for the optimization and the post optimization actions now follow the async design. Adapter operations are now also protected by async locks. Tests were adapted to the async design and new tests were created. Besides this major fix several other improvements and fixes are included in this PR. * fix: key_to_dict/list/array only regard data records with key value set. Before the exclusion of no value data records was only done if the dropna flag was set. * fix: test for visual result pdf generation Due to updates in the library the generated charts text was a little bit different. Adapt the test to create the comaprison pdf in the test data durectory and update the reference pdf. * chore: Remove MutableMapping from DataSequence and DataContainer. Mutable Mapping does not fit to the now async design. * chore: Add NoDB database backend This backend implements the full database backend interface but performs no actual persistence. It is intended for configurations where database persistence is disabled (`provider=None`). * chore: Improve measurement data import testing with real world scenarios. Added two new endpoints to support testing. * chore: Add mermaid to supported documentation tools * chore: Add documentation about async design * chore: Add documentation about generic data handling Covers the basics of measurement and prediction time series data handling. * chore: Add empty lines around markdown lists. * chore: sync pre-commit config to updated package versions Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2026-07-15 16:38:53 +02:00
import asyncio
import json
from pathlib import Path
from unittest.mock import Mock, patch
import numpy as np
import pytest
import requests
from loguru import logger
from akkudoktoreos.core.cache import CacheFileStore
Add database support for measurements and historic prediction data. (#848) 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>
2026-02-22 14:12:42 +01:00
from akkudoktoreos.core.coreabc import get_ems
from akkudoktoreos.prediction.elecpriceakkudoktor import (
AkkudoktorElecPrice,
AkkudoktorElecPriceValue,
ElecPriceAkkudoktor,
)
from akkudoktoreos.prediction.elecpriceenergycharts import (
ElecPriceEnergyCharts,
EnergyChartsElecPrice,
)
from akkudoktoreos.utils.datetimeutil import to_datetime
DIR_TESTDATA = Path(__file__).absolute().parent.joinpath("testdata")
FILE_TESTDATA_ELECPRICE_ENERGYCHARTS_JSON = DIR_TESTDATA.joinpath(
"elecpriceforecast_energycharts.json"
)
@pytest.fixture
def provider(monkeypatch, config_eos):
"""Fixture to create a ElecPriceProvider instance."""
monkeypatch.setenv("EOS_ELECPRICE__ELECPRICE_PROVIDER", "ElecPriceEnergyCharts")
config_eos.reset_settings()
return ElecPriceEnergyCharts()
@pytest.fixture
def sample_energycharts_json():
"""Fixture that returns sample forecast data report."""
with FILE_TESTDATA_ELECPRICE_ENERGYCHARTS_JSON.open(
"r", encoding="utf-8", newline=None
) as f_res:
input_data = json.load(f_res)
return input_data
@pytest.fixture
def cache_store():
"""A pytest fixture that creates a new CacheFileStore instance for testing."""
return CacheFileStore()
fix: move data management to async (#1015) FAstAPI is an async framework. Data may be imported and exported, load and save, set and get asynchronously. Prevent interleaving data operations to corrupt the data. In the previous design sync and async data access was intermixed leading to data corruption. The basic data classes DataSequence and DataContainer and the derived classes like Provider and Measurement now are async. Data access is protected by several async locks. To support the async design of the data classes the database interface became async. The energy management is also adapted to the new async design. Optimization is still off-loaded to another thread, but the prepration for the optimization and the post optimization actions now follow the async design. Adapter operations are now also protected by async locks. Tests were adapted to the async design and new tests were created. Besides this major fix several other improvements and fixes are included in this PR. * fix: key_to_dict/list/array only regard data records with key value set. Before the exclusion of no value data records was only done if the dropna flag was set. * fix: test for visual result pdf generation Due to updates in the library the generated charts text was a little bit different. Adapt the test to create the comaprison pdf in the test data durectory and update the reference pdf. * chore: Remove MutableMapping from DataSequence and DataContainer. Mutable Mapping does not fit to the now async design. * chore: Add NoDB database backend This backend implements the full database backend interface but performs no actual persistence. It is intended for configurations where database persistence is disabled (`provider=None`). * chore: Improve measurement data import testing with real world scenarios. Added two new endpoints to support testing. * chore: Add mermaid to supported documentation tools * chore: Add documentation about async design * chore: Add documentation about generic data handling Covers the basics of measurement and prediction time series data handling. * chore: Add empty lines around markdown lists. * chore: sync pre-commit config to updated package versions Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2026-07-15 16:38:53 +02:00
@pytest.mark.asyncio
class TestElecPriceEnergyCharts:
# ------------------------------------------------
# General forecast
# ------------------------------------------------
def test_singleton_instance(self, provider):
"""Test that ElecPriceForecast behaves as a singleton."""
another_instance = ElecPriceEnergyCharts()
assert provider is another_instance
def test_invalid_provider(self, provider, monkeypatch):
"""Test requesting an unsupported provider."""
monkeypatch.setenv("EOS_ELECPRICE__ELECPRICE_PROVIDER", "<invalid>")
provider.config.reset_settings()
assert not provider.enabled()
# ------------------------------------------------
# Akkudoktor
# ------------------------------------------------
@patch("akkudoktoreos.prediction.elecpriceenergycharts.logger.error")
def test_validate_data_invalid_format(self, mock_logger, provider):
"""Test validation for invalid Energy-Charts data."""
invalid_data = '{"invalid": "data"}'
with pytest.raises(ValueError):
provider._validate_data(invalid_data)
mock_logger.assert_called_once_with(mock_logger.call_args[0][0])
@patch("requests.get")
def test_request_forecast(self, mock_get, provider, sample_energycharts_json):
"""Test requesting forecast from Energy-Charts."""
# Mock response object
mock_response = Mock()
mock_response.status_code = 200
mock_response.content = json.dumps(sample_energycharts_json)
mock_get.return_value = mock_response
# Test function
energy_charts_data = provider._request_forecast()
assert isinstance(energy_charts_data, EnergyChartsElecPrice)
assert energy_charts_data.unix_seconds[0] == 1733785200
assert energy_charts_data.price[0] == 92.85
@patch("requests.get")
async def test_update_data(self, mock_get, provider, sample_energycharts_json, cache_store):
"""Test fetching forecast from Energy-Charts."""
# Mock response object
mock_response = Mock()
mock_response.status_code = 200
mock_response.content = json.dumps(sample_energycharts_json)
mock_get.return_value = mock_response
cache_store.clear(clear_all=True)
# Call the method
ems_eos = get_ems()
ems_eos.set_start_datetime(to_datetime("2024-12-11 00:00:00", in_timezone="Europe/Berlin"))
await provider.update_data(force_enable=True, force_update=True)
# Assert: Verify the result is as expected
mock_get.assert_called_once()
assert (
len(provider) == 73
) # we have 48 datasets in the api response, we want to know 48h into the future. The data we get has already 23h into the future so we need only 25h more. 48+25=73
# Assert we get hours prioce values by resampling
np_price_array = await provider.key_to_array(
key="elecprice_marketprice_wh",
start_datetime=provider.ems_start_datetime,
end_datetime=provider.end_datetime,
)
assert len(np_price_array) == provider.total_hours
@patch("requests.get")
async def test_update_data_with_incomplete_forecast(self, mock_get, provider):
"""Test `_update_data` with incomplete or missing forecast data."""
incomplete_data: dict = {"license_info": "", "unix_seconds": [], "price": [], "unit": "", "deprecated": False}
mock_response = Mock()
mock_response.status_code = 200
mock_response.content = json.dumps(incomplete_data)
mock_get.return_value = mock_response
logger.info("The following errors are intentional and part of the test.")
with pytest.raises(ValueError):
await provider._update_data(force_update=True)
@pytest.mark.parametrize(
"status_code, exception",
[(400, requests.exceptions.HTTPError), (500, requests.exceptions.HTTPError), (200, None)],
)
fix: move data management to async (#1015) FAstAPI is an async framework. Data may be imported and exported, load and save, set and get asynchronously. Prevent interleaving data operations to corrupt the data. In the previous design sync and async data access was intermixed leading to data corruption. The basic data classes DataSequence and DataContainer and the derived classes like Provider and Measurement now are async. Data access is protected by several async locks. To support the async design of the data classes the database interface became async. The energy management is also adapted to the new async design. Optimization is still off-loaded to another thread, but the prepration for the optimization and the post optimization actions now follow the async design. Adapter operations are now also protected by async locks. Tests were adapted to the async design and new tests were created. Besides this major fix several other improvements and fixes are included in this PR. * fix: key_to_dict/list/array only regard data records with key value set. Before the exclusion of no value data records was only done if the dropna flag was set. * fix: test for visual result pdf generation Due to updates in the library the generated charts text was a little bit different. Adapt the test to create the comaprison pdf in the test data durectory and update the reference pdf. * chore: Remove MutableMapping from DataSequence and DataContainer. Mutable Mapping does not fit to the now async design. * chore: Add NoDB database backend This backend implements the full database backend interface but performs no actual persistence. It is intended for configurations where database persistence is disabled (`provider=None`). * chore: Improve measurement data import testing with real world scenarios. Added two new endpoints to support testing. * chore: Add mermaid to supported documentation tools * chore: Add documentation about async design * chore: Add documentation about generic data handling Covers the basics of measurement and prediction time series data handling. * chore: Add empty lines around markdown lists. * chore: sync pre-commit config to updated package versions Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2026-07-15 16:38:53 +02:00
@patch("requests.get")
def test_request_forecast_status_codes(
self, mock_get, provider, sample_energycharts_json, status_code, exception
):
"""Test handling of various API status codes."""
mock_response = Mock()
mock_response.status_code = status_code
mock_response.content = json.dumps(sample_energycharts_json)
mock_response.raise_for_status.side_effect = (
requests.exceptions.HTTPError if exception else None
)
mock_get.return_value = mock_response
if exception:
with pytest.raises(exception):
provider._request_forecast()
else:
provider._request_forecast()
fix: move data management to async (#1015) FAstAPI is an async framework. Data may be imported and exported, load and save, set and get asynchronously. Prevent interleaving data operations to corrupt the data. In the previous design sync and async data access was intermixed leading to data corruption. The basic data classes DataSequence and DataContainer and the derived classes like Provider and Measurement now are async. Data access is protected by several async locks. To support the async design of the data classes the database interface became async. The energy management is also adapted to the new async design. Optimization is still off-loaded to another thread, but the prepration for the optimization and the post optimization actions now follow the async design. Adapter operations are now also protected by async locks. Tests were adapted to the async design and new tests were created. Besides this major fix several other improvements and fixes are included in this PR. * fix: key_to_dict/list/array only regard data records with key value set. Before the exclusion of no value data records was only done if the dropna flag was set. * fix: test for visual result pdf generation Due to updates in the library the generated charts text was a little bit different. Adapt the test to create the comaprison pdf in the test data durectory and update the reference pdf. * chore: Remove MutableMapping from DataSequence and DataContainer. Mutable Mapping does not fit to the now async design. * chore: Add NoDB database backend This backend implements the full database backend interface but performs no actual persistence. It is intended for configurations where database persistence is disabled (`provider=None`). * chore: Improve measurement data import testing with real world scenarios. Added two new endpoints to support testing. * chore: Add mermaid to supported documentation tools * chore: Add documentation about async design * chore: Add documentation about generic data handling Covers the basics of measurement and prediction time series data handling. * chore: Add empty lines around markdown lists. * chore: sync pre-commit config to updated package versions Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
2026-07-15 16:38:53 +02:00
@patch("requests.get")
@patch("akkudoktoreos.core.cache.CacheFileStore")
async def test_cache_integration(self, mock_cache, mock_get, provider, sample_energycharts_json):
"""Test caching of 8-day electricity price data."""
# Mock response object
mock_response = Mock()
mock_response.status_code = 200
mock_response.content = json.dumps(sample_energycharts_json)
mock_get.return_value = mock_response
# Mock cache object
mock_cache_instance = mock_cache.return_value
mock_cache_instance.get.return_value = None # Simulate no cache
await provider._update_data(force_update=True)
mock_cache_instance.create.assert_called_once()
mock_cache_instance.get.assert_called_once()
async def test_key_to_array_resampling(self, provider):
"""Test resampling of forecast data to NumPy array."""
await provider.update_data(force_update=True)
array = await provider.key_to_array(
key="elecprice_marketprice_wh",
start_datetime=provider.ems_start_datetime,
end_datetime=provider.end_datetime,
)
assert isinstance(array, np.ndarray)
assert len(array) == provider.total_hours
@patch("requests.get")
def test_request_forecast_url_bidding_zone_is_value(self, mock_get, provider, sample_energycharts_json):
"""Test that the bidding zone in the API URL uses the enum *value* (e.g. 'DE-LU'),
not the enum repr (e.g. 'EnergyChartsBiddingZones.DE_LU').
Regression test for: bzn=EnergyChartsBiddingZones.DE_LU appearing in the URL
instead of bzn=DE-LU, which caused a 400 Bad Request from the Energy-Charts API.
"""
mock_response = Mock()
mock_response.status_code = 200
mock_response.content = json.dumps(sample_energycharts_json)
mock_get.return_value = mock_response
provider._request_forecast(force_update=True)
assert mock_get.called, "requests.get was never called"
actual_url: str = mock_get.call_args[0][0]
# Extract the bzn= query parameter value from the URL
from urllib.parse import parse_qs, urlparse
parsed = urlparse(actual_url)
query_params = parse_qs(parsed.query)
assert "bzn" in query_params, f"'bzn' parameter missing from URL: {actual_url}"
bzn_value = query_params["bzn"][0]
# Must be the raw enum value, never contain a class name or dot notation
assert "." not in bzn_value, (
f"Bidding zone in URL looks like an enum repr: '{bzn_value}'. "
f"Use .value when building the URL, not str(enum)."
)
assert bzn_value == provider.config.elecprice.energycharts.bidding_zone.value, (
f"Expected bzn='{provider.config.elecprice.energycharts.bidding_zone.value}' "
f"but got bzn='{bzn_value}' in URL: {actual_url}"
)
# ------------------------------------------------
# Development Energy Charts
# ------------------------------------------------
@pytest.mark.skip(reason="For development only")
def test_energycharts_development_forecast_data(self, provider):
"""Fetch data from real Energy-Charts server."""
# Preset, as this is usually done by update_data()
provider.ems_start_datetime = to_datetime("2024-10-26 00:00:00")
energy_charts_data = provider._request_forecast()
with FILE_TESTDATA_ELECPRICE_ENERGYCHARTS_JSON.open(
"w", encoding="utf-8", newline="\n"
) as f_out:
json.dump(energy_charts_data, f_out, indent=4)