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https://github.com/Akkudoktor-EOS/EOS.git
synced 2026-11-09 23:33:20 +01:00
fix(eosdash): render predictions without provider-specific load statistics (#1479)
With load.provider = "LoadImport", EOSdash requests Akkudoktor-only statistics and returns an error instead of rendering any prediction charts when the API correctly responds with 404. Fetch only keys advertised by /v1/prediction/keys, tolerate individual 404s if availability changes, and render the load mean/stddev lines only when their series exist. Charts without their required series are skipped; empty/all-null responses produce a no-data message instead of invalid axes or concatenation errors. Other HTTP failures remain visible. Regression coverage uses real Bokeh plotting for LoadImport, LoadAkkudoktor/Adjusted, independently available statistics, missing/empty series, and non-404 errors. The existing timezone/DST regression remains covered.
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Normann
@@ -168,6 +168,22 @@ def LoadForecast(predictions: pd.DataFrame, config: dict, date_time_tz: str, dar
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sizing_mode="stretch_width",
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height=400,
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)
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plot.line(
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"date_time",
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"loadforecast_power_w",
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source=source,
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legend_label="Load forecast value (adjusted by measurement)",
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color="red",
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)
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if "loadakkudoktor_mean_power_w" in predictions:
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plot.line(
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"date_time",
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"loadakkudoktor_mean_power_w",
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source=source,
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legend_label="Load mean value",
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color="blue",
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)
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if "loadakkudoktor_std_power_w" in predictions:
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# Add secondary y-axis for stddev
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stddev_min = predictions["loadakkudoktor_std_power_w"].min()
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stddev_max = predictions["loadakkudoktor_std_power_w"].max()
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@@ -176,20 +192,6 @@ def LoadForecast(predictions: pd.DataFrame, config: dict, date_time_tz: str, dar
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y2_axis.axis_label_text_color = "green"
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plot.add_layout(y2_axis, "left")
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plot.line(
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"date_time",
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"loadforecast_power_w",
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source=source,
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legend_label="Load forecast value (adjusted by measurement)",
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color="red",
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)
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plot.line(
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"date_time",
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"loadakkudoktor_mean_power_w",
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source=source,
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legend_label="Load mean value",
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color="blue",
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)
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plot.line(
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"date_time",
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"loadakkudoktor_std_power_w",
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@@ -247,8 +249,14 @@ def Prediction(eos_host: str, eos_port: Union[str, int], data: Optional[dict] =
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series_list = []
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try:
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result = requests.get(f"{server}/v1/prediction/keys", timeout=10)
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result.raise_for_status()
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available_keys = set(result.json())
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for options in prediction_requests:
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key = options[0]
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if key not in available_keys:
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continue
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resample_method = options[1]
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fill_method = options[2]
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@@ -265,9 +273,13 @@ def Prediction(eos_host: str, eos_port: Union[str, int], data: Optional[dict] =
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params=params,
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timeout=10,
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)
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# The provider may have changed since the keys were retrieved.
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if result.status_code == 404:
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continue
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result.raise_for_status()
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series = PydanticDateTimeSeries(**result.json()).to_series().rename(key)
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if not series.dropna().empty:
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series_list.append(series)
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except requests.exceptions.HTTPError as err:
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@@ -279,6 +291,9 @@ def Prediction(eos_host: str, eos_port: Union[str, int], data: Optional[dict] =
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# ---------------------------------------------------------------------
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# Merge into dataframe
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# ---------------------------------------------------------------------
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if not series_list:
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return P("No prediction data available.", cls="text-center")
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predictions = pd.concat(series_list, axis=1).reset_index()
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predictions = predictions.rename(columns={"index": "date_time"})
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@@ -286,11 +301,17 @@ def Prediction(eos_host: str, eos_port: Union[str, int], data: Optional[dict] =
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date_time_tz = predictions["date_time"].dt.tz
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predictions["date_time"] = pd.to_datetime(predictions["date_time"]).dt.tz_localize(None)
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return Grid(
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PVForecast(predictions, config, date_time_tz, dark),
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ElectricityPriceForecast(predictions, config, date_time_tz, dark),
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WeatherTempAirHumidityForecast(predictions, config, date_time_tz, dark),
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WeatherIrradianceForecast(predictions, config, date_time_tz, dark),
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LoadForecast(predictions, config, date_time_tz, dark),
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cols_max=2,
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)
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plots = []
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for forecast, required_keys in (
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(PVForecast, {"pvforecast_ac_power"}),
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(ElectricityPriceForecast, {"elecprice_marketprice_kwh"}),
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(WeatherTempAirHumidityForecast, {"weather_temp_air", "weather_relative_humidity"}),
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(WeatherIrradianceForecast, {"weather_ghi", "weather_dni", "weather_dhi"}),
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(LoadForecast, {"loadforecast_power_w"}),
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):
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if required_keys.issubset(predictions.columns):
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plots.append(forecast(predictions, config, date_time_tz, dark))
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if not plots:
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return P("No prediction data available.", cls="text-center")
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return Grid(*plots, cols_max=2)
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@@ -2,9 +2,23 @@ from unittest.mock import Mock
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import pandas as pd
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import pytest
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import requests
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from akkudoktoreos.server.dash import prediction
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PREDICTION_KEYS = [
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"pvforecast_ac_power",
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"elecprice_marketprice_kwh",
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"weather_relative_humidity",
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"weather_temp_air",
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"weather_ghi",
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"weather_dni",
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"weather_dhi",
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"loadforecast_power_w",
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"loadakkudoktor_std_power_w",
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"loadakkudoktor_mean_power_w",
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]
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@pytest.mark.parametrize("dark", [False, True])
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def test_prediction_preserves_series_and_local_times_without_pandas_deprecations(monkeypatch, dark):
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@@ -17,6 +31,8 @@ def test_prediction_preserves_series_and_local_times_without_pandas_deprecations
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def get(url, **kwargs):
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if url.endswith("/v1/config"):
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return Mock(json=lambda: config)
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if url.endswith("/v1/prediction/keys"):
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return Mock(json=lambda: PREDICTION_KEYS)
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assert url.endswith("/v1/prediction/series")
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keys.append(kwargs["params"]["key"])
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return Mock(json=lambda: {
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@@ -55,3 +71,157 @@ def test_prediction_preserves_series_and_local_times_without_pandas_deprecations
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assert str(timezone) == "Europe/Berlin"
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assert actual_dark is dark
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grid.assert_called_once_with(*(plot.return_value for plot in plots), cols_max=2)
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class TestAvailablePredictionSeries:
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@pytest.fixture(autouse=True)
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def dashboard(self, monkeypatch):
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self.config: dict = {
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"load": {
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"provider": "LoadImport",
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"loadakkudoktor": {"loadakkudoktor_year_energy_kwh": 4000},
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},
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"pvforecast": {"provider": "PVForecastImport"},
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"elecprice": {"provider": "ElecPriceImport"},
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"weather": {"provider": "WeatherImport"},
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}
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self.available = PREDICTION_KEYS[:-2]
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self.requested: list[str] = []
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self.failures: dict[str, int] = {}
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self.empty_keys: set[str] = set()
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self.null_keys: set[str] = set()
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self.keys_status = 200
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def response(payload, status=200):
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result = Mock(status_code=status, json=lambda: payload)
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if status >= 400:
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result.raise_for_status.side_effect = requests.HTTPError(
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str(status), response=result
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)
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return result
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def get(url, **kwargs):
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if url.endswith("/v1/config"):
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return response(self.config)
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if url.endswith("/v1/prediction/keys"):
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if self.keys_status != 200:
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return response({"detail": "Cannot list keys"}, self.keys_status)
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return response(self.available)
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assert url.endswith("/v1/prediction/series")
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key = kwargs["params"]["key"]
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self.requested.append(key)
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assert key in self.available
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if key in self.failures:
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return response({"detail": f"Cannot retrieve {key}"}, self.failures[key])
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values = {} if key in self.empty_keys else {
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"2026-10-30T00:00:00Z": None if key in self.null_keys else 100.0,
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"2026-10-30T00:15:00Z": None if key in self.null_keys else 200.0,
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}
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return response({"data": values, "dtype": "float64", "tz": "Europe/Berlin"})
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monkeypatch.setattr(prediction.requests, "get", get)
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# Keep real Bokeh plotting: missing columns must not be hidden by plot mocks.
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monkeypatch.setattr(prediction, "Bokeh", lambda plot: plot)
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self.grid = Mock(return_value="prediction grid")
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self.error = Mock(return_value="prediction error")
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self.message = Mock(return_value="prediction message")
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monkeypatch.setattr(prediction, "Grid", self.grid)
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monkeypatch.setattr(prediction, "Error", self.error)
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monkeypatch.setattr(prediction, "P", self.message)
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def test_loadimport_preserves_all_five_charts_without_optional_statistics(self):
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assert prediction.Prediction("localhost", 8503) == "prediction grid"
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assert self.requested == PREDICTION_KEYS[:-2]
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plots = self.grid.call_args.args
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assert len(plots) == 5
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load = plots[-1]
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assert load.title.text == "Load Prediction (LoadImport)"
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assert [renderer.glyph.y for renderer in load.renderers] == ["loadforecast_power_w"]
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assert "stddev" not in load.extra_y_ranges
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self.error.assert_not_called()
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@pytest.mark.parametrize("provider", ["LoadAkkudoktor", "LoadAkkudoktorAdjusted"])
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def test_akkudoktor_statistics_are_preserved(self, provider):
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self.config["load"]["provider"] = provider
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self.available = PREDICTION_KEYS
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assert prediction.Prediction("localhost", 8503) == "prediction grid"
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load = self.grid.call_args.args[-1]
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assert [renderer.glyph.y for renderer in load.renderers] == [
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"loadforecast_power_w",
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"loadakkudoktor_mean_power_w",
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"loadakkudoktor_std_power_w",
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]
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assert "stddev" in load.extra_y_ranges
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if provider == "LoadAkkudoktorAdjusted":
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assert "4000 kWh" in load.title.text
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@pytest.mark.parametrize(
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"optional_key",
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["loadakkudoktor_mean_power_w", "loadakkudoktor_std_power_w"],
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)
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def test_optional_statistics_can_be_available_independently(self, optional_key):
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self.available = [*PREDICTION_KEYS[:-2], optional_key]
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assert prediction.Prediction("localhost", 8503) == "prediction grid"
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load = self.grid.call_args.args[-1]
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assert [renderer.glyph.y for renderer in load.renderers] == [
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"loadforecast_power_w", optional_key
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]
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assert ("stddev" in load.extra_y_ranges) == optional_key.endswith("std_power_w")
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def test_series_disappearing_after_key_discovery_does_not_abort_other_charts(self):
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self.available = PREDICTION_KEYS
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self.failures = {
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"elecprice_marketprice_kwh": 404,
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"loadakkudoktor_std_power_w": 404,
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}
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assert prediction.Prediction("localhost", 8503) == "prediction grid"
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assert self.requested == PREDICTION_KEYS
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plots = self.grid.call_args.args
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assert len(plots) == 4
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assert "stddev" not in plots[-1].extra_y_ranges
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self.error.assert_not_called()
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@pytest.mark.parametrize("status", [400, 500])
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def test_other_http_errors_remain_visible(self, status):
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self.failures = {"pvforecast_ac_power": status}
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assert prediction.Prediction("localhost", 8503) == "prediction error"
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assert "Cannot retrieve pvforecast_ac_power" in self.error.call_args.args[0]
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self.grid.assert_not_called()
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def test_key_discovery_errors_remain_visible(self):
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self.keys_status = 500
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assert prediction.Prediction("localhost", 8503) == "prediction error"
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assert "Cannot list keys" in self.error.call_args.args[0]
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assert self.requested == []
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def test_unavailable_charts_are_skipped(self):
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self.available = ["loadforecast_power_w"]
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assert prediction.Prediction("localhost", 8503) == "prediction grid"
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assert len(self.grid.call_args.args) == 1
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assert self.grid.call_args.args[0].title.text == "Load Prediction (LoadImport)"
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@pytest.mark.parametrize("scenario", ["no_keys", "empty", "null", "disappeared"])
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def test_no_usable_series_shows_a_message(self, scenario):
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self.available = ["loadforecast_power_w"]
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if scenario == "no_keys":
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self.available = []
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elif scenario == "empty":
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self.empty_keys = {"loadforecast_power_w"}
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elif scenario == "null":
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self.null_keys = {"loadforecast_power_w"}
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else:
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self.failures = {"loadforecast_power_w": 404}
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assert prediction.Prediction("localhost", 8503) == "prediction message"
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self.message.assert_called_once_with(
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"No prediction data available.", cls="text-center"
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)
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self.grid.assert_not_called()
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self.error.assert_not_called()
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def test_empty_statistics_do_not_create_an_invalid_axis(self):
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self.available = PREDICTION_KEYS
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self.null_keys = set(PREDICTION_KEYS[-2:])
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assert prediction.Prediction("localhost", 8503) == "prediction grid"
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load = self.grid.call_args.args[-1]
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assert [renderer.glyph.y for renderer in load.renderers] == ["loadforecast_power_w"]
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assert "stddev" not in load.extra_y_ranges
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