from pathlib import Path
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt

try:
    from umap import UMAP
except Exception:
    from umap.umap_ import UMAP


def umap_dir_shared_coords(
    dir_path: str,
    *,
    metric: str = "cosine",
    n_neighbors: int = 15,
    min_dist: float = 0.0,
    spread: float = 1.2,
    standardize: bool = False,
    context: bool = True,
    make_joint: bool = True,
    init: str = "random",     # 关键：禁用谱初始化，避免告警；也可用 "pca"
    jitter: float = 0.0,      # 可选：拟合前加微弱噪声，如 1e-6
    random_state: int = 42
) -> None:
    """
    在同一 UMAP 坐标系中为目录内每个 .npy 文件生成 2D 图。
    - 每个 .npy 形状为 (n_samples, 30) 或 (30, n_samples)
    - 统一坐标轴范围；各自输出 *_umap_shared.png，另可输出总览图
    """
    p = Path(dir_path)
    if not p.is_dir():
        raise ValueError(f"{dir_path!r} 不是有效文件夹")

    files = sorted(p.glob("*.npy"))
    if not files:
        print(f"目录 {p} 中未找到 .npy 文件")
        return

    X_list, paths, counts = [], [], []
    for f in files:
        try:
            data = np.load(f)
            if data.ndim != 2:
                print(f"[跳过] {f.name}: 期望二维数组，实际 shape={data.shape}")
                continue

            if data.shape[1] == 30:
                X = data
            elif data.shape[0] == 30:
                X = data.T
            else:
                print(f"[跳过] {f.name}: shape={data.shape}, 未检测到 30 维特征")
                continue

            mask = np.isfinite(X).all(axis=1)
            if not np.all(mask):
                X = X[mask]
                print(f"[提示] {f.name}: 移除了含 NaN/Inf 的样本行")

            if X.shape[0] < 3:
                print(f"[跳过] {f.name}: 样本数过少(n={X.shape[0]})")
                continue

            X_list.append(X)
            paths.append(f)
            counts.append(X.shape[0])
        except Exception as e:
            print(f"[错误] 读取 {f.name} 失败: {e}")

    if not X_list:
        print("未找到可用的数据文件")
        return

    X_all = np.vstack(X_list)

    if standardize:
        mean = X_all.mean(axis=0)
        std = X_all.std(axis=0)
        std[std == 0] = 1.0
        X_all = (X_all - mean) / std

    if jitter and jitter > 0:
        rng = np.random.default_rng(random_state)
        X_all = X_all + rng.normal(scale=jitter, size=X_all.shape)

    reducer = UMAP(
        n_components=2,
        n_neighbors=int(max(2, n_neighbors)),
        min_dist=float(min_dist),
        spread=float(spread),
        metric=metric,
        init=init,                 # 关键改动：避免谱初始化告警
        random_state=random_state,
    )
    Z_all = reducer.fit_transform(X_all)

    x_min, x_max = float(Z_all[:, 0].min()), float(Z_all[:, 0].max())
    y_min, y_max = float(Z_all[:, 1].min()), float(Z_all[:, 1].max())
    pad_x = 0.05 * (x_max - x_min) if x_max > x_min else 1.0
    pad_y = 0.05 * (y_max - y_min) if y_max > y_min else 1.0

    base_colors = [
        "#1f77b4","#ff7f0e","#2ca02c","#d62728","#9467bd",
        "#8c564b","#e377c2","#7f7f7f","#bcbd22","#17becf"
    ]

    start = 0
    for i, (f, n) in enumerate(zip(paths, counts)):
        Zi = Z_all[start:start + n]
        start += n

        fig, ax = plt.subplots(figsize=(6, 5), dpi=150)
        if context:
            ax.scatter(Z_all[:, 0], Z_all[:, 1], s=5, c="#cccccc",
                       alpha=0.35, edgecolors="none", label="All")
        ax.scatter(Zi[:, 0], Zi[:, 1], s=10,
                   c=base_colors[i % len(base_colors)],
                   alpha=0.9, edgecolors="none", label=f.name)

        ax.set_title(
            f"{f.name} • UMAP(shared) (nn={n_neighbors}, min={min_dist}, metric={metric}, init={init})",
            fontsize=9
        )
        ax.set_xlabel("UMAP-1")
        ax.set_ylabel("UMAP-2")
        ax.set_xlim(x_min - pad_x, x_max + pad_x)
        ax.set_ylim(y_min - pad_y, y_max + pad_y)
        ax.grid(True, linestyle="--", linewidth=0.3, alpha=0.5)
        if context:
            ax.legend(loc="best", fontsize=8, frameon=False)
        fig.tight_layout()

        out_png = f.with_suffix("").as_posix() + "_umap_shared.png"
        fig.savefig(out_png)
        plt.close(fig)
        print(f"[完成] {f.name} -> {out_png}")

    if make_joint:
        start = 0
        fig, ax = plt.subplots(figsize=(7, 6), dpi=150)
        for i, (f, n) in enumerate(zip(paths, counts)):
            Zi = Z_all[start:start + n]; start += n
            ax.scatter(Zi[:, 0], Zi[:, 1], s=8,
                       c=base_colors[i % len(base_colors)],
                       alpha=0.85, edgecolors="none", label=f.name)
        ax.set_title(f"UMAP(shared) overview (metric={metric}, nn={n_neighbors}, min={min_dist}, init={init})",
                     fontsize=10)
        ax.set_xlabel("UMAP-1"); ax.set_ylabel("UMAP-2")
        ax.set_xlim(x_min - pad_x, x_max + pad_x)
        ax.set_ylim(y_min - pad_y, y_max + pad_y)
        ax.grid(True, linestyle="--", linewidth=0.3, alpha=0.5)
        ax.legend(loc="best", fontsize=8, frameon=False, ncol=1)
        fig.tight_layout()
        out_png = Path(dir_path) / "umap_shared_overview.png"
        fig.savefig(out_png.as_posix())
        plt.close(fig)
        print(f"[完成] 总览 -> {out_png}")
if __name__=="__main__":
    umap_dir_shared_coords("data")