EOS/src/akkudoktoreos/server/dash/demo.py

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,
    )