#!/usr/bin/env python3
"""PCA of the ACT encoder space, computed separately before and after contact.

Each phase gets its own PCA: the dominant directions differ between reaching and
manipulation, so a shared basis would misrepresent both. Contact onset is
detected per episode from finger torque, so "pre" is genuinely before the
GelSight touches anything.

Top row is the default PC1/PC2 view, bottom row the first three components in
3-D. Accuracies are from a probe on exactly the axes drawn, held-out episodes.
"""
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.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"}
TITLE = {"pre": "pre-contact  (GelSight untouched)", "post": "post-contact  (cable in hand)"}


def acc(Z, y, tr, cols):
    c = make_pipeline(StandardScaler(), LogisticRegression(max_iter=3000))
    c.fit(Z[tr][:, cols], y[tr])
    return c.score(Z[~tr][:, cols], y[~tr])


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--cache", default="outputs/pca/cache_cable_contact.npz")
    ap.add_argument("--out", default="outputs/pca/cable_contact_pca.png")
    args = ap.parse_args()

    d = np.load(args.cache, allow_pickle=True)
    X, y, ep, ph = d["feats"], d["y"], d["eps"], d["phase"]
    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))
    order = sorted(set(y), key=lambda s: DIA[s])

    plt.rcParams.update({"font.family": ["DejaVu Sans"], "font.size": 11})
    fig = plt.figure(figsize=(13, 11))

    for col, pname in enumerate(["pre", "post"]):
        w = ph == pname
        Xp, yp, trp = X[w], y[w], tr[w]
        Xc = Xp - Xp.mean(0)
        p = PCA(n_components=10).fit(Xc)
        Z, evr = p.transform(Xc), p.explained_variance_ratio_
        tep = ~trp

        a2 = acc(Z, yp, trp, [0, 1])
        ax = fig.add_subplot(2, 2, col + 1)
        for t in order:
            m = tep & (yp == t)
            ax.scatter(Z[m, 0], Z[m, 1], s=16, alpha=0.65, edgecolors="none",
                       c=COLOR[DIA[t]], label=NAME[DIA[t]])
        ax.set_xlabel(f"PC1 ({evr[0]:.1%})"); ax.set_ylabel(f"PC2 ({evr[1]:.1%})")
        ax.set_title(f"{TITLE[pname]}\nPCA PC1 vs PC2  —  {a2:.1%} (chance 33.3%)", fontsize=12)
        ax.spines[["top", "right"]].set_visible(False)
        if col == 0:
            ax.legend(frameon=False, title="cable diameter")

        a3 = acc(Z, yp, trp, [0, 1, 2])
        ax = fig.add_subplot(2, 2, col + 3, projection="3d")
        for t in order:
            m = tep & (yp == 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)  —  {a3:.1%}", fontsize=12)
        ax.view_init(elev=18, azim=-60)

        print(f"  {pname:4s}  PC1/PC2 {a2:.1%}   PC1-PC3 {a3:.1%}   "
              f"variance PC1 {evr[0]:.1%} PC2 {evr[1]:.1%} PC3 {evr[2]:.1%}", flush=True)

    fig.tight_layout(); fig.savefig(args.out, dpi=150, bbox_inches="tight")
    print(f"\nwrote {args.out}")


if __name__ == "__main__":
    main()