from __future__ import annotations

from pathlib import Path

import matplotlib
import numpy as np

matplotlib.use("Agg")
import matplotlib.pyplot as plt

DEFAULT_WEATHER_COLUMNS = [
    "dry_bulb",
    "dew_point",
    "relative_humidity",
    "global_horizontal_radiation",
    "direct_normal_radiation",
    "diffuse_horizontal_radiation",
    "wind_speed",
]

WEATHER_UNITS = {
    "dry_bulb": "degC",
    "dew_point": "degC",
    "relative_humidity": "%",
    "global_horizontal_radiation": "W/m2",
    "direct_normal_radiation": "W/m2",
    "diffuse_horizontal_radiation": "W/m2",
    "wind_direction": "deg",
}

FIG_SIZE_IN = 4.2
AXIS_LABEL_FONT_SIZE = 8
TICK_LABEL_FONT_SIZE = 8
INPLOT_LABEL_FONT_SIZE = 10


def _to_hourly_feature(values: np.ndarray) -> np.ndarray:
    arr = np.asarray(values)
    if arr.ndim != 2:
        raise ValueError(f"Expected 2D weather matrix, got shape={arr.shape}")

    if arr.shape[0] == 8760:
        return np.asarray(arr, dtype=float)
    if arr.shape[1] == 8760:
        return np.asarray(arr.T, dtype=float)

    raise ValueError(f"Neither axis is 8760 for weather matrix: shape={arr.shape}")


def _decode_columns(columns_arr: np.ndarray | None, width: int) -> list[str]:
    if columns_arr is None:
        return [f"feature_{i}" for i in range(width)]

    cols = np.asarray(columns_arr).reshape(-1)
    names = [str(c, "utf-8") if isinstance(c, (bytes, np.bytes_)) else str(c) for c in cols]
    if len(names) < width:
        names.extend([f"feature_{i}" for i in range(len(names), width)])
    return names[:width]


def _pick_weather_indices(column_names: list[str]) -> list[int]:
    lower_to_idx = {name.lower(): idx for idx, name in enumerate(column_names)}

    selected: list[int] = []
    for name in DEFAULT_WEATHER_COLUMNS:
        idx = lower_to_idx.get(name.lower())
        if idx is not None:
            selected.append(idx)

    if len(selected) < 7:
        for idx in range(len(column_names)):
            if idx not in selected:
                selected.append(idx)
            if len(selected) == 7:
                break

    return selected


def _time_window(hourly: np.ndarray, start_hour: int, window_hours: int) -> tuple[np.ndarray, int, int]:
    total = int(hourly.shape[0])
    if total < 1:
        raise ValueError("No hourly weather records found")

    start_idx = max(0, min(total - 1, int(start_hour) - 1))
    window = max(1, int(window_hours))
    end_idx = min(total, start_idx + window)
    if end_idx <= start_idx:
        raise ValueError(f"Invalid weather window: start={start_hour}, hours={window_hours}")

    return hourly[start_idx:end_idx, :], start_idx + 1, end_idx


def _major_ticks(length: int) -> list[int]:
    if length <= 8:
        return list(range(1, length + 1))

    tick_count = 6
    ticks = np.linspace(1, length, num=tick_count, dtype=int)
    uniq = sorted(set(int(t) for t in ticks))
    if uniq[-1] != length:
        uniq.append(length)
    return uniq


def _label_with_unit(name: str) -> str:
    key = name.strip().lower().replace(" ", "_").replace("-", "_")
    unit = WEATHER_UNITS.get(key)
    if unit is None:
        return name
    return f"{name} ({unit})"


def visualize_weather(
    weather_npz: str | Path,
    output_png: str | Path,
    *,
    start_hour: int = 1,
    window_hours: int = 24,
    dpi: int = 220,
) -> Path:
    """Plot weather subplots from PACK weather npz (values + columns) in a selected time window."""
    weather_npz = Path(weather_npz)
    output_png = Path(output_png)

    with np.load(weather_npz, allow_pickle=True) as data:
        if "values" not in data:
            keys = ", ".join(sorted(data.files))
            raise KeyError(f"Missing key 'values' in {weather_npz}; keys=[{keys}]")
        values = np.asarray(data["values"], dtype=float)
        columns = np.asarray(data["columns"], dtype=object) if "columns" in data else None

    hourly = _to_hourly_feature(values)
    window, window_start, window_end = _time_window(hourly, start_hour=start_hour, window_hours=window_hours)
    names = _decode_columns(columns, window.shape[1])
    idx_list = _pick_weather_indices(names)
    if len(idx_list) == 0:
        raise ValueError(f"No weather series available in {weather_npz}")

    fig, axes = plt.subplots(
        len(idx_list),
        1,
        figsize=(FIG_SIZE_IN, FIG_SIZE_IN),
        sharex=True,
        gridspec_kw={"hspace": 0.0},
    )
    if len(idx_list) == 1:
        axes = [axes]

    x = np.arange(1, window.shape[0] + 1, dtype=int)
    major_ticks = _major_ticks(window.shape[0])
    for row_idx, feat_idx in enumerate(idx_list):
        ax = axes[row_idx]
        y = window[:, feat_idx]
        ax.plot(x, y, linewidth=0.9, color="#4C72B0")
        ax.set_ylabel("")
        ax.text(
            0.02,
            0.86,
            _label_with_unit(names[feat_idx]),
            transform=ax.transAxes,
            ha="left",
            va="top",
            rotation=0,
            fontsize=INPLOT_LABEL_FONT_SIZE,
            bbox={"facecolor": "white", "alpha": 0.65, "edgecolor": "none", "pad": 1.5},
        )
        ax.set_xticks(major_ticks)
        ax.grid(axis="y", alpha=0.3, linewidth=0.5)
        ax.grid(axis="x", alpha=0.22, linewidth=0.45)
        ax.tick_params(axis="both", which="both", labelsize=TICK_LABEL_FONT_SIZE)
        if row_idx < len(idx_list) - 1:
            ax.tick_params(axis="x", which="both", labelbottom=False)

    axes[-1].set_xlabel(f"hour index in window ({window_start}-{window_end})", fontsize=AXIS_LABEL_FONT_SIZE)
    axes[-1].set_xlim(1, window.shape[0] + 0.5)
    axes[-1].set_xticks(major_ticks)
    axes[-1].set_xticklabels([str(t) for t in major_ticks], fontsize=TICK_LABEL_FONT_SIZE)
    fig.subplots_adjust(left=0.18, right=0.94, bottom=0.14, top=0.98, hspace=0.0)

    output_png.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output_png, dpi=dpi)
    plt.close(fig)
    return output_png