from __future__ import annotations

import argparse
from pathlib import Path
from typing import TYPE_CHECKING
import warnings

import numpy as np

from rgram import KernelSmoother, Regressogram, SupportWarning, quantile

if TYPE_CHECKING:
    from matplotlib.axes import Axes
    from matplotlib.figure import Figure

# A consistent palette makes comparisons readable across the gallery.
COLORS = ["#1764ab", "#de682b", "#21856b", "#8855a2"]


def _create_axes(
    title: str, xlabel: str = "Feature", ylabel: str = "Response"
) -> tuple[Figure, Axes]:
    """Create a readable, single-panel figure with consistent gallery styling."""
    import matplotlib.pyplot as plt

    with plt.rc_context({"font.size": 14, "axes.labelsize": 16, "axes.titlesize": 20}):
        figure, ax = plt.subplots(figsize=(10, 6.6), layout="constrained")
        ax.set(title=title, xlabel=xlabel, ylabel=ylabel)
    ax.set_facecolor("#f8fafc")
    ax.grid(color="#dce3ec", linewidth=0.8)
    ax.set_axisbelow(True)
    ax.spines[["top", "right"]].set_visible(False)
    ax.tick_params(labelsize=13)
    return figure, ax


def _plot_observations(ax: Axes, X: np.ndarray, y: np.ndarray) -> None:
    """Show original rows without connecting them or hiding response spread."""
    ax.scatter(
        X,
        y,
        color="#64748b",
        alpha=0.4,
        s=28,
        edgecolors="none",
        label="Observed responses",
    )


def _add_legend(ax: Axes) -> None:
    """Identify every plotted comparison using the same readable legend style."""
    ax.legend(fontsize=12, framealpha=0.96, edgecolor="#dce3ec", loc="best")


def _gaussian_smoother(bandwidth: float, **kwargs) -> KernelSmoother:
    """Keep kernel and bandwidth units explicit; kwargs select other model options."""
    return KernelSmoother(
        kernel="gaussian", bandwidth="manual", bandwidth_value=bandwidth, **kwargs
    )


def intervals() -> Figure:
    """Display supported pointwise bootstrap bounds for a regression curve."""
    rng = np.random.default_rng(17)
    # Sample a random design with independent noise, appropriate to paired IID draws.
    # Sorting is explicit here for the display; the estimator never sorts rows.
    X = np.sort(rng.uniform(0, 6, 100))
    y = np.sin(X) + rng.normal(0, 0.3, len(X))
    # Keep original queries in the observed range; resampled ranges can differ.
    query = np.linspace(X.min(), X.max(), 100)
    model = _gaussian_smoother(0.4).fit(X, y)
    # Fix the seed for reproducibility. More draws reduce Monte Carlo variation,
    # but do not correct smoothing bias or include bandwidth-selection uncertainty.
    interval = model.predict_interval(query, n_resamples=100, random_state=17)
    figure, ax = _create_axes("Pointwise bootstrap uncertainty for the curve")
    _plot_observations(ax, X, y)
    # Connecting pointwise bounds is a display choice, not a simultaneous band.
    # NaN bounds would remain gaps rather than being silently filled.
    ax.fill_between(
        query,
        interval["lower"].to_numpy(),
        interval["upper"].to_numpy(),
        color=COLORS[0],
        alpha=0.2,
        label="95% pointwise bootstrap bounds",
    )
    ax.plot(
        query,
        interval["prediction"].to_numpy(),
        color=COLORS[0],
        lw=3,
        label="Fitted local mean",
    )
    ax.plot(query, np.sin(query), "--", color="#334155", lw=2, label="Generating mean")
    _add_legend(ax)
    return figure

if __name__ == "__main__":
    import matplotlib.pyplot as plt

    figure = intervals()
    plt.show()
    plt.close(figure)