"""Visualisations d'indicateurs de corrélation (heatmaps et séries décalées)."""

from __future__ import annotations

from pathlib import Path
from typing import Sequence

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

from .base import export_plot_dataset
from meteo.variables import Variable

__all__ = ['plot_lagged_correlation', 'plot_correlation_heatmap', 'plot_rolling_correlation_heatmap']


def plot_lagged_correlation(
    lag_df: pd.DataFrame,
    var_x: Variable,
    var_y: Variable,
    output_path: str | Path,
) -> Path:
    """
    Trace la corrélation en fonction du lag (en minutes) entre deux variables.
    """
    output_path = Path(output_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    export_plot_dataset(lag_df, output_path)

    plt.figure()
    plt.plot(lag_df.index, lag_df["correlation"])
    plt.axvline(0, linestyle="--")  # lag = 0
    plt.xlabel("Décalage (minutes)\n(lag > 0 : X précède Y)")
    plt.ylabel("Corrélation")
    plt.title(f"Corrélation décalée : {var_x.label} → {var_y.label}")
    plt.grid(True)
    plt.tight_layout()
    plt.savefig(output_path, dpi=150)
    plt.close()

    return output_path.resolve()

def plot_correlation_heatmap(
    corr: pd.DataFrame,
    variables: Sequence[Variable],
    output_path: str | Path,
    *,
    annotate: bool = True,
) -> Path:
    """
    Trace une heatmap de la matrice de corrélation.

    Paramètres
    ----------
    corr :
        Matrice de corrélation (index et colonnes doivent correspondre
        aux noms de colonnes des variables).
    variables :
        Liste de Variable, dans l'ordre où elles doivent apparaître.
    output_path :
        Chemin du fichier image à écrire.
    annotate :
        Si True, affiche la valeur numérique dans chaque case.
    """
    output_path = Path(output_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    columns = [v.column for v in variables]
    labels = [v.label for v in variables]

    # On aligne la matrice sur l'ordre désiré
    corr = corr.loc[columns, columns]
    export_plot_dataset(corr, output_path)

    data = corr.to_numpy()

    fig, ax = plt.subplots()
    im = ax.imshow(data, vmin=-1.0, vmax=1.0)

    # Ticks et labels
    ax.set_xticks(np.arange(len(labels)))
    ax.set_yticks(np.arange(len(labels)))
    ax.set_xticklabels(labels, rotation=45, ha="right")
    ax.set_yticklabels(labels)

    # Axe en haut/bas selon préférence (ici on laisse en bas)
    ax.set_title("Matrice de corrélation (coef. de Pearson)")

    # Barre de couleur
    cbar = plt.colorbar(im, ax=ax)
    cbar.set_label("Corrélation")

    # Annotation des cases
    if annotate:
        n = data.shape[0]
        for i in range(n):
            for j in range(n):
                if i == j:
                    text = "—"
                else:
                    val = data[i, j]
                    if np.isnan(val):
                        text = ""
                    else:
                        text = f"{val:.2f}"
                ax.text(
                    j,
                    i,
                    text,
                    ha="center",
                    va="center",
                )

    plt.tight_layout()
    plt.savefig(output_path, dpi=150)
    plt.close(fig)

    return output_path.resolve()

def plot_rolling_correlation_heatmap(
    rolling_corr: pd.DataFrame,
    output_path: str | Path,
    *,
    cmap: str = "coolwarm",
    vmin: float = -1.0,
    vmax: float = 1.0,
    time_tick_count: int = 6,
) -> Path:
    """
    Visualise l'évolution de corrélations glissantes pour plusieurs paires.
    """
    output_path = Path(output_path)
    output_path.parent.mkdir(parents=True, exist_ok=True)

    export_plot_dataset(rolling_corr, output_path)

    if rolling_corr.empty:
        fig, ax = plt.subplots()
        ax.text(0.5, 0.5, "Aucune donnée de corrélation glissante.", ha="center", va="center")
        ax.set_axis_off()
        fig.savefig(output_path, dpi=150, bbox_inches="tight")
        plt.close(fig)
        return output_path.resolve()

    labels = list(rolling_corr.columns)
    data = rolling_corr.to_numpy().T

    height = max(3.0, 0.6 * len(labels))
    fig, ax = plt.subplots(figsize=(10, height))
    im = ax.imshow(data, aspect="auto", cmap=cmap, vmin=vmin, vmax=vmax)

    ax.set_yticks(np.arange(len(labels)))
    ax.set_yticklabels(labels)

    if isinstance(rolling_corr.index, pd.DatetimeIndex):
        times = rolling_corr.index
        if len(times) > 1:
            tick_idx = np.linspace(0, len(times) - 1, num=min(time_tick_count, len(times)), dtype=int)
        else:
            tick_idx = np.array([0])
        tick_labels = [times[i].strftime("%Y-%m-%d\n%H:%M") for i in tick_idx]
    else:
        tick_idx = np.linspace(0, len(rolling_corr.index) - 1, num=min(time_tick_count, len(rolling_corr.index)), dtype=int)
        tick_labels = [str(rolling_corr.index[i]) for i in tick_idx]

    ax.set_xticks(tick_idx)
    ax.set_xticklabels(tick_labels, rotation=30, ha="right")

    ax.set_xlabel("Temps (fin de fenêtre)")
    ax.set_ylabel("Paire de variables")
    ax.set_title("Corrélations glissantes")

    cbar = fig.colorbar(im, ax=ax)
    cbar.set_label("Coefficient de corrélation")

    fig.tight_layout()
    fig.savefig(output_path, dpi=150)
    plt.close(fig)

    return output_path.resolve()