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fix(eosdash): render predictions without provider-specific load statistics (#1479)

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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.
This commit is contained in:
Normann
2026-10-30 16:58:59 +01:00
committed by GitHub
parent e8dcfecf73
commit a7e98b5b82
2 changed files with 223 additions and 32 deletions
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src/akkudoktoreos/server/dash/prediction.py
+53 -32
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@@ -168,14 +168,6 @@ def LoadForecast(predictions: pd.DataFrame, config: dict, date_time_tz: str, dar
sizing_mode="stretch_width",
height=400,
)
# Add secondary y-axis for stddev
stddev_min = predictions["loadakkudoktor_std_power_w"].min()
stddev_max = predictions["loadakkudoktor_std_power_w"].max()
plot.extra_y_ranges["stddev"] = Range1d(start=stddev_min - 5, end=stddev_max + 5)
y2_axis = LinearAxis(y_range_name="stddev", axis_label="Load Standard Deviation [W]")
y2_axis.axis_label_text_color = "green"
plot.add_layout(y2_axis, "left")
plot.line(
"date_time",
"loadforecast_power_w",
@@ -183,21 +175,31 @@ def LoadForecast(predictions: pd.DataFrame, config: dict, date_time_tz: str, dar
legend_label="Load forecast value (adjusted by measurement)",
color="red",
)
plot.line(
"date_time",
"loadakkudoktor_mean_power_w",
source=source,
legend_label="Load mean value",
color="blue",
)
plot.line(
"date_time",
"loadakkudoktor_std_power_w",
source=source,
legend_label="Load standard deviation",
color="green",
y_range_name="stddev",
)
if "loadakkudoktor_mean_power_w" in predictions:
plot.line(
"date_time",
"loadakkudoktor_mean_power_w",
source=source,
legend_label="Load mean value",
color="blue",
)
if "loadakkudoktor_std_power_w" in predictions:
# Add secondary y-axis for stddev
stddev_min = predictions["loadakkudoktor_std_power_w"].min()
stddev_max = predictions["loadakkudoktor_std_power_w"].max()
plot.extra_y_ranges["stddev"] = Range1d(start=stddev_min - 5, end=stddev_max + 5)
y2_axis = LinearAxis(y_range_name="stddev", axis_label="Load Standard Deviation [W]")
y2_axis.axis_label_text_color = "green"
plot.add_layout(y2_axis, "left")
plot.line(
"date_time",
"loadakkudoktor_std_power_w",
source=source,
legend_label="Load standard deviation",
color="green",
y_range_name="stddev",
)
plot.toolbar.autohide = True
bokey_apply_theme_to_plot(plot, dark)
@@ -247,8 +249,14 @@ def Prediction(eos_host: str, eos_port: Union[str, int], data: Optional[dict] =
series_list = []
try:
result = requests.get(f"{server}/v1/prediction/keys", timeout=10)
result.raise_for_status()
available_keys = set(result.json())
for options in prediction_requests:
key = options[0]
if key not in available_keys:
continue
resample_method = options[1]
fill_method = options[2]
@@ -265,10 +273,14 @@ def Prediction(eos_host: str, eos_port: Union[str, int], data: Optional[dict] =
params=params,
timeout=10,
)
# The provider may have changed since the keys were retrieved.
if result.status_code == 404:
continue
result.raise_for_status()
series = PydanticDateTimeSeries(**result.json()).to_series().rename(key)
series_list.append(series)
if not series.dropna().empty:
series_list.append(series)
except requests.exceptions.HTTPError as err:
detail = result.json()["detail"]
@@ -279,6 +291,9 @@ def Prediction(eos_host: str, eos_port: Union[str, int], data: Optional[dict] =
# ---------------------------------------------------------------------
# Merge into dataframe
# ---------------------------------------------------------------------
if not series_list:
return P("No prediction data available.", cls="text-center")
predictions = pd.concat(series_list, axis=1).reset_index()
predictions = predictions.rename(columns={"index": "date_time"})
@@ -286,11 +301,17 @@ def Prediction(eos_host: str, eos_port: Union[str, int], data: Optional[dict] =
date_time_tz = predictions["date_time"].dt.tz
predictions["date_time"] = pd.to_datetime(predictions["date_time"]).dt.tz_localize(None)
return Grid(
PVForecast(predictions, config, date_time_tz, dark),
ElectricityPriceForecast(predictions, config, date_time_tz, dark),
WeatherTempAirHumidityForecast(predictions, config, date_time_tz, dark),
WeatherIrradianceForecast(predictions, config, date_time_tz, dark),
LoadForecast(predictions, config, date_time_tz, dark),
cols_max=2,
)
plots = []
for forecast, required_keys in (
(PVForecast, {"pvforecast_ac_power"}),
(ElectricityPriceForecast, {"elecprice_marketprice_kwh"}),
(WeatherTempAirHumidityForecast, {"weather_temp_air", "weather_relative_humidity"}),
(WeatherIrradianceForecast, {"weather_ghi", "weather_dni", "weather_dhi"}),
(LoadForecast, {"loadforecast_power_w"}),
):
if required_keys.issubset(predictions.columns):
plots.append(forecast(predictions, config, date_time_tz, dark))
if not plots:
return P("No prediction data available.", cls="text-center")
return Grid(*plots, cols_max=2)