from pathlib import Path
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA

def tsne_dir_shared_coords(
    dir_path: str,
    *,
    metric: str = "euclidean",     # 可试 "cosine"；想保留尺度差异用 "euclidean"
    perplexity: float = 50.0,      # 30k~50k 样本建议 30~50
    n_iter: int = 1000,
    early_exaggeration: float = 12.0,
    learning_rate = "auto",
    standardize: bool = False,
    pca_dim: int | None = None,    # 先用 PCA 降到 pca_dim(如 20) 再跑 t-SNE，可提速
    context: bool = True,
    make_joint: bool = True,
    init: str = "pca",
    random_state: int = 42
) -> None:
    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

            # 统一到 (n_samples, 30)
            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 pca_dim is not None and pca_dim > 2:
        X_all = PCA(n_components=pca_dim, random_state=random_state).fit_transform(X_all)

    tsne = TSNE(
        n_components=2,
        metric=metric,
        perplexity=float(perplexity),
        early_exaggeration=float(early_exaggeration),
        learning_rate=learning_rate,
        init=init,
        random_state=random_state,
        method="barnes_hut",   # 适合大样本
        angle=0.5,
        verbose=0,
    )
    Z_all = tsne.fit_transform(X_all)

    # 统一坐标轴范围
    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

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

    # 分文件出图
    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=8, c=colors[i % len(colors)], alpha=0.9, edgecolors="none", label=f.name)
        ax.set_title(f"{f.name} • t-SNE(shared) (perp={perplexity}, metric={metric})", fontsize=9)
        ax.set_xlabel("t-SNE-1"); ax.set_ylabel("t-SNE-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() + "_tsne_shared.png"
        fig.savefig(out_png); plt.close(fig)
        print(f"[完成] {f.name} -> {out_png}")

    # 总览图
    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=colors[i % len(colors)], alpha=0.85, edgecolors="none", label=f.name)
        ax.set_title(f"t-SNE(shared) overview (perp={perplexity}, metric={metric})", fontsize=10)
        ax.set_xlabel("t-SNE-1"); ax.set_ylabel("t-SNE-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)
        fig.tight_layout()
        out_png = Path(dir_path) / "tsne_shared_overview.png"
        fig.savefig(out_png.as_posix()); plt.close(fig)
        print(f"[完成] 总览 -> {out_png}")

if __name__ == "__main__":
    tsne_dir_shared_coords("data")