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https://github.com/Akkudoktor-EOS/EOS.git
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b01bb1c61c
Automatic optimization used to take the adjusted load data even if there were no measurements leading to 0 load values. Split LoadAkkudoktor into LoadAkkudoktor and LoadAkkudoktorAdjusted. This allows to select load data either purely from the load data database or load data additionally adjusted by load measurements. Some value names have been adapted to denote also the unit of a value. For better load bug squashing the optimization solution data availability was improved. For better data visbility prediction data can now be distinguished from solution data in the generic optimization solution. Some predictions that may be of interest to understand the solution were added. Documentation was updated to resemble the addition load prediction provider and the value name changes. Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
119 lines
3.6 KiB
Python
119 lines
3.6 KiB
Python
import json
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from unittest.mock import call, patch
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import pendulum
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import pytest
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import requests
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from akkudoktoreos.prediction.loadvrm import (
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LoadVrm,
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VrmForecastRecords,
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VrmForecastResponse,
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)
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@pytest.fixture
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def load_vrm_instance(config_eos):
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# Settings für LoadVrm
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settings = {
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"load": {
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"provider": "LoadVrm",
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"provider_settings": {
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"LoadVrm": {
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"load_vrm_token": "dummy-token",
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"load_vrm_idsite": 12345,
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},
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}
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}
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}
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config_eos.merge_settings_from_dict(settings)
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# start_datetime initialize
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start_dt = pendulum.datetime(2025, 1, 1, tz='Europe/Berlin')
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# create LoadVrm-instance with config and start_datetime
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lv = LoadVrm(config=config_eos.load, start_datetime=start_dt)
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return lv
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def mock_forecast_response():
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"""Return a fake VrmForecastResponse with sample data."""
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return VrmForecastResponse(
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success=True,
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records=VrmForecastRecords(
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vrm_consumption_fc=[
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(pendulum.datetime(2025, 1, 1, 0, 0, tz='Europe/Berlin').int_timestamp * 1000, 100.5),
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(pendulum.datetime(2025, 1, 1, 1, 0, tz='Europe/Berlin').int_timestamp * 1000, 101.2)
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],
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solar_yield_forecast=[]
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),
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totals={}
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)
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def test_update_data_calls_update_value(load_vrm_instance):
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with patch.object(load_vrm_instance, "_request_forecast", return_value=mock_forecast_response()), \
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patch.object(LoadVrm, "update_value") as mock_update:
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load_vrm_instance._update_data()
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assert mock_update.call_count == 2
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expected_calls = [
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call(
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pendulum.datetime(2025, 1, 1, 0, 0, 0, tz='Europe/Berlin'),
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{"loadforecast_power_w": 100.5,}
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),
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call(
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pendulum.datetime(2025, 1, 1, 1, 0, 0, tz='Europe/Berlin'),
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{"loadforecast_power_w": 101.2,}
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),
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]
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mock_update.assert_has_calls(expected_calls, any_order=False)
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def test_validate_data_accepts_valid_json():
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"""Test that _validate_data doesn't raise with valid input."""
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response = mock_forecast_response()
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json_data = response.model_dump_json()
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validated = LoadVrm._validate_data(json_data)
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assert validated.success
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assert len(validated.records.vrm_consumption_fc) == 2
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def test_validate_data_raises_on_invalid_json():
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"""_validate_data should raise ValueError on schema mismatch."""
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invalid_json = json.dumps({"success": True}) # missing 'records'
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with pytest.raises(ValueError) as exc_info:
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LoadVrm._validate_data(invalid_json)
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assert "Field:" in str(exc_info.value)
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assert "records" in str(exc_info.value)
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def test_request_forecast_raises_on_http_error(load_vrm_instance):
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with patch("requests.get", side_effect=requests.Timeout("Request timed out")) as mock_get:
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with pytest.raises(RuntimeError) as exc_info:
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load_vrm_instance._request_forecast(0, 1)
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assert "Failed to fetch load forecast" in str(exc_info.value)
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mock_get.assert_called_once()
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def test_update_data_does_nothing_on_empty_forecast(load_vrm_instance):
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empty_response = VrmForecastResponse(
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success=True,
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records=VrmForecastRecords(vrm_consumption_fc=[], solar_yield_forecast=[]),
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totals={}
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)
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with patch.object(load_vrm_instance, "_request_forecast", return_value=empty_response), \
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patch.object(LoadVrm, "update_value") as mock_update:
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load_vrm_instance._update_data()
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mock_update.assert_not_called()
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