Improve EOSdash.

Make EOSdash use UI components from MonsterUI to ease further development.

- Add a first menu with some dummy pages and the configuration page.
- Make the configuration scrollable.
- Add markdown component that uses markdown-it-py (same as used by
  the myth-parser for documentation generation).
- Add bokeh (https://docs.bokeh.org/) component for charts
- Added several prediction charts to demo
- Add a footer that displays connection status with EOS server
- Add logo and favicon

Update EOS server:

- Move error message generation to extra module
- Use redirect instead of proxy

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

src/akkudoktoreos/server/dash/demo.py

@@ -0,0 +1,217 @@
+import json
+from pathlib import Path
+from typing import Union
+
+import pandas as pd
+import requests
+from bokeh.models import ColumnDataSource, Range1d
+from bokeh.plotting import figure
+from monsterui.franken import FT, Grid, P
+
+from akkudoktoreos.core.logging import get_logger
+from akkudoktoreos.core.pydantic import PydanticDateTimeDataFrame
+from akkudoktoreos.server.dash.bokeh import Bokeh
+
+DIR_DEMODATA = Path(__file__).absolute().parent.joinpath("data")
+FILE_DEMOCONFIG = DIR_DEMODATA.joinpath("democonfig.json")
+if not FILE_DEMOCONFIG.exists():
+    raise ValueError(f"File does not exist: {FILE_DEMOCONFIG}")
+
+logger = get_logger(__name__)
+
+# bar width for 1 hour bars (time given in millseconds)
+BAR_WIDTH_1HOUR = 1000 * 60 * 60
+
+
+def DemoPVForecast(predictions: pd.DataFrame, config: dict) -> FT:
+    source = ColumnDataSource(predictions)
+    provider = config["pvforecast"]["provider"]
+
+    plot = figure(
+        x_axis_type="datetime",
+        title=f"PV Power Prediction ({provider})",
+        x_axis_label="Datetime",
+        y_axis_label="Power [W]",
+        sizing_mode="stretch_width",
+        height=400,
+    )
+
+    plot.vbar(
+        x="date_time",
+        top="pvforecast_ac_power",
+        source=source,
+        width=BAR_WIDTH_1HOUR * 0.8,
+        legend_label="AC Power",
+        color="lightblue",
+    )
+
+    return Bokeh(plot)
+
+
+def DemoElectricityPriceForecast(predictions: pd.DataFrame, config: dict) -> FT:
+    source = ColumnDataSource(predictions)
+    provider = config["elecprice"]["provider"]
+
+    plot = figure(
+        x_axis_type="datetime",
+        y_range=Range1d(
+            predictions["elecprice_marketprice_kwh"].min() - 0.1,
+            predictions["elecprice_marketprice_kwh"].max() + 0.1,
+        ),
+        title=f"Electricity Price Prediction ({provider})",
+        x_axis_label="Datetime",
+        y_axis_label="Price [€/kWh]",
+        sizing_mode="stretch_width",
+        height=400,
+    )
+    plot.vbar(
+        x="date_time",
+        top="elecprice_marketprice_kwh",
+        source=source,
+        width=BAR_WIDTH_1HOUR * 0.8,
+        legend_label="Market Price",
+        color="lightblue",
+    )
+
+    return Bokeh(plot)
+
+
+def DemoWeatherTempAir(predictions: pd.DataFrame, config: dict) -> FT:
+    source = ColumnDataSource(predictions)
+    provider = config["weather"]["provider"]
+
+    plot = figure(
+        x_axis_type="datetime",
+        y_range=Range1d(
+            predictions["weather_temp_air"].min() - 1.0, predictions["weather_temp_air"].max() + 1.0
+        ),
+        title=f"Air Temperature Prediction ({provider})",
+        x_axis_label="Datetime",
+        y_axis_label="Temperature [°C]",
+        sizing_mode="stretch_width",
+        height=400,
+    )
+    plot.line(
+        "date_time", "weather_temp_air", source=source, legend_label="Air Temperature", color="blue"
+    )
+
+    return Bokeh(plot)
+
+
+def DemoWeatherIrradiance(predictions: pd.DataFrame, config: dict) -> FT:
+    source = ColumnDataSource(predictions)
+    provider = config["weather"]["provider"]
+
+    plot = figure(
+        x_axis_type="datetime",
+        title=f"Irradiance Prediction ({provider})",
+        x_axis_label="Datetime",
+        y_axis_label="Irradiance [W/m2]",
+        sizing_mode="stretch_width",
+        height=400,
+    )
+    plot.line(
+        "date_time",
+        "weather_ghi",
+        source=source,
+        legend_label="Global Horizontal Irradiance",
+        color="red",
+    )
+    plot.line(
+        "date_time",
+        "weather_dni",
+        source=source,
+        legend_label="Direct Normal Irradiance",
+        color="green",
+    )
+    plot.line(
+        "date_time",
+        "weather_dhi",
+        source=source,
+        legend_label="Diffuse Horizontal Irradiance",
+        color="blue",
+    )
+
+    return Bokeh(plot)
+
+
+def Demo(eos_host: str, eos_port: Union[str, int]) -> str:
+    server = f"http://{eos_host}:{eos_port}"
+
+    # Get current configuration from server
+    try:
+        result = requests.get(f"{server}/v1/config")
+        result.raise_for_status()
+    except requests.exceptions.HTTPError as err:
+        detail = result.json()["detail"]
+        return P(
+            f"Can not retrieve configuration from {server}: {err}, {detail}",
+            cls="text-center",
+        )
+    config = result.json()
+
+    # Set demo configuration
+    with FILE_DEMOCONFIG.open("r", encoding="utf-8") as fd:
+        democonfig = json.load(fd)
+    try:
+        result = requests.put(f"{server}/v1/config", json=democonfig)
+        result.raise_for_status()
+    except requests.exceptions.HTTPError as err:
+        detail = result.json()["detail"]
+        # Try to reset to original config
+        requests.put(f"{server}/v1/config", json=config)
+        return P(
+            f"Can not set demo configuration on {server}: {err}, {detail}",
+            cls="text-center",
+        )
+
+    # Update all predictions
+    try:
+        result = requests.post(f"{server}/v1/prediction/update")
+        result.raise_for_status()
+    except requests.exceptions.HTTPError as err:
+        detail = result.json()["detail"]
+        # Try to reset to original config
+        requests.put(f"{server}/v1/config", json=config)
+        return P(
+            f"Can not update predictions on {server}: {err}, {detail}",
+            cls="text-center",
+        )
+
+    # Get Forecasts
+    try:
+        params = {
+            "keys": [
+                "pvforecast_ac_power",
+                "elecprice_marketprice_kwh",
+                "weather_temp_air",
+                "weather_ghi",
+                "weather_dni",
+                "weather_dhi",
+            ],
+        }
+        result = requests.get(f"{server}/v1/prediction/dataframe", params=params)
+        result.raise_for_status()
+        predictions = PydanticDateTimeDataFrame(**result.json()).to_dataframe()
+    except requests.exceptions.HTTPError as err:
+        detail = result.json()["detail"]
+        return P(
+            f"Can not retrieve predictions from {server}: {err}, {detail}",
+            cls="text-center",
+        )
+    except Exception as err:
+        return P(
+            f"Can not retrieve predictions from {server}: {err}",
+            cls="text-center",
+        )
+
+    # Reset to original config
+    requests.put(f"{server}/v1/config", json=config)
+
+    return Grid(
+        DemoPVForecast(predictions, democonfig),
+        DemoElectricityPriceForecast(predictions, democonfig),
+        DemoWeatherTempAir(predictions, democonfig),
+        DemoWeatherIrradiance(predictions, democonfig),
+        cols_max=2,
+    )