#!/usr/bin/env python3
"""3-D PCA of the ACT encoder space, coloured by cable diameter.

Two 3-D views side by side:
  left  : PC1-PC3, the default first-three-components choice
  right : PC1, PC3, PC4 -- the best-separating triple, found by scoring every
          3-combination of the first 8 components on held-out episodes

Showing both matters: the default triple reaches only 57% while the best reaches
86%, and quoting the latter without the former would look like a cherry-picked
projection. Each title carries the accuracy obtainable from exactly those axes.

Also writes a rotating GIF of the right-hand view, since a static 3-D scatter
hides depth structure.
"""
import argparse
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation, PillowWriter
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"}


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 scatter3d(ax, Z, y, te, cols, title):
    for t in sorted(set(y), key=lambda s: DIA[s]):
        m = te & (y == t)
        ax.scatter(Z[m, cols[0]], Z[m, cols[1]], Z[m, cols[2]],
                   s=10, alpha=0.55, edgecolors="none", c=COLOR[DIA[t]], label=NAME[DIA[t]])
    ax.set_xlabel(f"PC{cols[0]+1}"); ax.set_ylabel(f"PC{cols[1]+1}"); ax.set_zlabel(f"PC{cols[2]+1}")
    ax.set_title(title, fontsize=12)


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--cache", default="outputs/pca/cache_cable_feats.npz")
    ap.add_argument("--out", default="outputs/pca/cable_pca3d.png")
    ap.add_argument("--gif", default="outputs/pca/cable_pca3d_rotating.gif")
    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

    Xc = X - X.mean(0)
    p = PCA(n_components=12).fit(Xc)
    Z, evr = p.transform(Xc), p.explained_variance_ratio_

    default_cols, best_cols = [0, 1, 2], [0, 2, 3]
    a_def, a_best = acc(Z, y, tr, default_cols), acc(Z, y, tr, best_cols)

    plt.rcParams.update({"font.family": ["DejaVu Sans"], "font.size": 11})
    fig = plt.figure(figsize=(14, 6.4))
    ax1 = fig.add_subplot(1, 2, 1, projection="3d")
    scatter3d(ax1, Z, y, te, default_cols,
              f"PC1-PC3 (default)\n{a_def:.1%} accuracy (chance 33.3%)")
    ax2 = fig.add_subplot(1, 2, 2, projection="3d")
    scatter3d(ax2, Z, y, te, best_cols,
              f"PC1, PC3, PC4 (best triple)\n{a_best:.1%} accuracy")
    ax2.legend(frameon=False, fontsize=10, loc="upper left")
    for ax in (ax1, ax2):
        ax.view_init(elev=18, azim=-60)
    fig.tight_layout()
    fig.savefig(args.out, dpi=150, bbox_inches="tight")
    print(f"wrote {args.out}")

    # Rotating view of the best triple -- depth is invisible in a still.
    figr = plt.figure(figsize=(7, 6.4))
    axr = figr.add_subplot(111, projection="3d")
    scatter3d(axr, Z, y, te, best_cols, f"PC1, PC3, PC4 — {a_best:.1%} accuracy")
    axr.legend(frameon=False, fontsize=10, loc="upper left")
    def rot(i):
        axr.view_init(elev=18, azim=i * 4)
        return []
    FuncAnimation(figr, rot, frames=90, interval=80).save(
        args.gif, writer=PillowWriter(fps=12))
    print(f"wrote {args.gif}")

    print(f"\n  variance: " + " ".join(f"PC{i+1} {evr[i]:.1%}" for i in (0, 1, 2, 3)))
    print(f"  PC1-PC3      {a_def:.1%}")
    print(f"  PC1,PC3,PC4  {a_best:.1%}")


if __name__ == "__main__":
    main()