#!/usr/bin/env python3
"""Standalone figures: t-SNE, 3-D PCA (PC1-PC3), and 2-D PCA (PC1/PC2).

One plot per file so each can be dropped into a slide on its own. Every title
carries the held-out classification accuracy reachable from exactly the axes
drawn, so a reader can tell apart "looks separated" from "is separated".
Held-out episodes only; splits are by episode because frames within an episode
are near-duplicates.
"""
import argparse
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D  # noqa: F401
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.manifold import TSNE
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler

DIA = {"cable 1.70 sq": 1.7, "blue cable": 2.6, "cable 3.30 sq": 4.5}
NAME = {1.7: "1.7 mm", 2.6: "2.6 mm", 4.5: "4.5 mm"}
COLOR = {1.7: "#C05621", 2.6: "#2B6CB0", 4.5: "#2F855A"}


def acc(A, y, tr, cols=None):
    Z = A if cols is None else A[:, cols]
    c = make_pipeline(StandardScaler(), LogisticRegression(max_iter=3000)).fit(Z[tr], y[tr])
    return c.score(Z[~tr], y[~tr])


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--cache", default="outputs/pca/cache_cable_feats.npz")
    ap.add_argument("--outdir", default="outputs/pca")
    ap.add_argument("--perplexity", type=float, default=30.0)
    args = ap.parse_args()

    d = np.load(args.cache, allow_pickle=True)
    X, y, ep = d["feats"], d["y"], d["eps"]
    rng = np.random.default_rng(0)
    tr = np.zeros(len(y), bool)
    for t in np.unique(y):
        e = np.unique(ep[y == t])
        tr |= np.isin(ep, rng.choice(e, size=int(len(e) * 0.7), replace=False))
    te = ~tr
    order = sorted(set(y), key=lambda s: DIA[s])

    Xc = X - X.mean(0)
    p = PCA(n_components=30).fit(Xc)
    Z, evr = p.transform(Xc), p.explained_variance_ratio_
    plt.rcParams.update({"font.family": ["DejaVu Sans"], "font.size": 12})

    # --- 1) t-SNE only
    emb = TSNE(n_components=2, perplexity=args.perplexity, init="pca",
               random_state=0).fit_transform(Z)
    fig, ax = plt.subplots(figsize=(7.2, 6.4))
    for t in order:
        m = te & (y == t)
        ax.scatter(emb[m, 0], emb[m, 1], s=18, alpha=0.65, edgecolors="none",
                   c=COLOR[DIA[t]], label=NAME[DIA[t]])
    ax.set_xlabel("t-SNE 1"); ax.set_ylabel("t-SNE 2")
    ax.set_title(f"t-SNE (perplexity {args.perplexity:g})", fontsize=13)
    ax.legend(frameon=False, title="cable diameter")
    ax.spines[["top", "right"]].set_visible(False)
    fig.tight_layout(); fig.savefig(f"{args.outdir}/cable_tsne.png", dpi=160, bbox_inches="tight")
    print(f"wrote {args.outdir}/cable_tsne.png")

    # --- 2) 3-D PCA, default first three components
    a3 = acc(Z, y, tr, [0, 1, 2])
    fig = plt.figure(figsize=(7.6, 6.6))
    ax = fig.add_subplot(111, projection="3d")
    for t in order:
        m = te & (y == t)
        ax.scatter(Z[m, 0], Z[m, 1], Z[m, 2], s=11, alpha=0.55, edgecolors="none",
                   c=COLOR[DIA[t]], label=NAME[DIA[t]])
    ax.set_xlabel("PC1"); ax.set_ylabel("PC2"); ax.set_zlabel("PC3")
    ax.set_title(f"3-D PCA (PC1-PC3)\n{a3:.1%} accuracy (chance 33.3%)", fontsize=13)
    ax.legend(frameon=False, title="cable diameter", loc="upper left")
    ax.view_init(elev=18, azim=-60)
    fig.tight_layout(); fig.savefig(f"{args.outdir}/cable_pca3d_pc123.png", dpi=160, bbox_inches="tight")
    print(f"wrote {args.outdir}/cable_pca3d_pc123.png   ({a3:.1%})")

    # --- 3) 2-D PCA, default components
    a2 = acc(Z, y, tr, [0, 1])
    fig, ax = plt.subplots(figsize=(7.2, 6.4))
    for t in order:
        m = te & (y == t)
        ax.scatter(Z[m, 0], Z[m, 1], s=18, alpha=0.65, edgecolors="none",
                   c=COLOR[DIA[t]], label=NAME[DIA[t]])
    ax.set_xlabel(f"PC1 ({evr[0]:.1%} of variance)")
    ax.set_ylabel(f"PC2 ({evr[1]:.1%} of variance)")
    ax.set_title(f"PCA (PC1 vs PC2)\n{a2:.1%} accuracy (chance 33.3%)", fontsize=13)
    ax.legend(frameon=False, title="cable diameter")
    ax.spines[["top", "right"]].set_visible(False)
    fig.tight_layout(); fig.savefig(f"{args.outdir}/cable_pca2d_pc12.png", dpi=160, bbox_inches="tight")
    print(f"wrote {args.outdir}/cable_pca2d_pc12.png   ({a2:.1%})")


if __name__ == "__main__":
    main()