"""Visualisation helpers for the deployment surface.

The Gradio demo overlays a reference 24-hour day on top of the generated
scenario ensemble — these helpers do the loading and the plotting. Both
are pure functions so the plot logic can be unit-tested without spinning
up a Gradio session.
"""

from __future__ import annotations

from pathlib import Path

import matplotlib.figure
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from pc_ddpm_alberta.config import DATA_DIR

DEMO_DIR = DATA_DIR / "demo"

REFERENCE_DAYS: dict[str, str] = {
    "typical_day": "typical_day.csv",
    "high_wind_day": "high_wind_day.csv",
    "low_wind_day": "low_wind_day.csv",
}

CHANNEL_LABELS: tuple[str, str, str] = ("Wind (MW)", "Solar (MW)", "Load (MW)")
CHANNEL_COLUMNS: tuple[str, str, str] = ("wind_mw", "solar_mw", "load_mw")


def load_reference_day(name: str, demo_dir: Path = DEMO_DIR) -> pd.DataFrame:
    """Load a 24-hour reference day by short name (`typical_day` etc.).

    Returns a DataFrame with a parsed `DatetimeIndex` of length 24 and the
    full upstream column schema (`wind_mw, solar_mw, load_mw, ...`).
    """
    if name not in REFERENCE_DAYS:
        raise ValueError(
            f"unknown reference day {name!r}; expected one of {list(REFERENCE_DAYS)}"
        )
    path = demo_dir / REFERENCE_DAYS[name]
    df = pd.read_csv(path, index_col=0, parse_dates=True)
    if len(df) != 24:
        raise ValueError(f"{path}: expected 24 rows, got {len(df)}")
    return df


def build_overlay_figure(
    scenarios: np.ndarray,
    reference: pd.DataFrame,
    title: str | None = None,
) -> matplotlib.figure.Figure:
    """Three-panel plot of generated scenarios overlaid on a reference day.

    `scenarios` is the `(n_scenarios, 3, 24)` array returned by `predict()`;
    channel order is (wind, solar, load) per the upstream training script.
    The reference day is drawn in red; scenarios are translucent grey so the
    ensemble spread reads at a glance.
    """
    if scenarios.ndim != 3 or scenarios.shape[1:] != (3, 24):
        raise ValueError(
            f"scenarios must be (n, 3, 24); got {scenarios.shape}"
        )

    hours = np.arange(24)
    fig, axes = plt.subplots(3, 1, figsize=(9, 7), sharex=True)
    n_scenarios = scenarios.shape[0]
    alpha = max(0.05, min(0.4, 4.0 / n_scenarios))

    for ch, (ax, label, col) in enumerate(zip(axes, CHANNEL_LABELS, CHANNEL_COLUMNS, strict=True)):
        ax.plot(hours, scenarios[:, ch, :].T, color="0.4", alpha=alpha, linewidth=0.8)
        ax.plot(
            hours,
            reference[col].to_numpy(),
            color="crimson",
            linewidth=2.0,
            label="reference day",
        )
        ax.set_ylabel(label)
        ax.grid(True, alpha=0.3)
        ax.set_xlim(0, 23)

    axes[0].legend(
        [f"{n_scenarios} generated scenarios", "reference day"],
        loc="upper right",
        fontsize=9,
    )
    axes[-1].set_xlabel("Hour of day")

    if title is None:
        ref_date = reference.index[0].date() if hasattr(reference.index[0], "date") else "—"
        title = f"PC-DDPM scenarios vs reference day ({ref_date})"
    fig.suptitle(title, fontsize=12)
    fig.tight_layout()
    return fig