refactor: 将子模块转为普通目录，移除外部 git 依赖

- 移除 JEPA/lejepa-identifiability 子模块 gitlink
- 移除 research/multiply/MultiPLY 子模块 gitlink
- 删除 .gitmodules（不再有外部 URL 依赖）
- 两个目录内容作为普通文件纳入主仓库追踪
- 删除各自内部 .git 目录，消除嵌套 git 仓库

JEPA/lejepa-identifiability/experiments/analysis/plot_2d.py

@@ -0,0 +1,82 @@
+"""
+2D illustration figure: one row per mixing (z, g(z), h(z)).
+Picks best seed per mixing by final_loss.
+
+Usage:
+    python analysis/plot_2d.py --results_dir results/2d/ --out figures/
+"""
+
+import argparse, os, glob
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+import colorsys
+
+MIXING_ORDER = ["spiral", "banana", "sinusoid", "nvp"]
+
+
+def make_colors(z):
+    x, y = z[:, 0], z[:, 1]
+    angles = np.arctan2(y, x)
+    radii = np.sqrt(x**2 + y**2)
+    hue = (angles + np.pi) / (2 * np.pi)
+    lightness = 0.3 + 0.4 * (radii / (radii.max() + 1e-8))
+    saturation = np.full_like(hue, 0.85)
+    return [colorsys.hls_to_rgb(h, l, s) for h, l, s in zip(hue, lightness, saturation)]
+
+
+def load_best_per_mixing(results_dir):
+    """Load all results, pick best lejepa run per mixing by final_loss."""
+    files = sorted(glob.glob(os.path.join(results_dir, "*.pt")))
+    by_mix = {}
+    for path in files:
+        r = torch.load(path, map_location="cpu", weights_only=False)
+        if r.get("mode") != "lejepa":
+            continue
+        mix = r["mixing"]
+        if mix not in by_mix or r["final_loss"] < by_mix[mix]["final_loss"]:
+            by_mix[mix] = r
+    return by_mix
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--results_dir", default="results/2d/")
+    p.add_argument("--out", default="figures/")
+    args = p.parse_args()
+    os.makedirs(args.out, exist_ok=True)
+
+    best = load_best_per_mixing(args.results_dir)
+
+    s, lim = 5, 4
+    for mix_name in MIXING_ORDER:
+        if mix_name not in best:
+            print(f"Missing {mix_name}"); continue
+        res = best[mix_name]
+        z, x, h = res["z"], res["x"], res["h"]
+        colors = make_colors(z)
+
+        fig, axes = plt.subplots(1, 3, figsize=(9, 3))
+        for i, (ax, data, labels) in enumerate(zip(
+            axes, [z, x, h],
+            [("True Latent 0", "True Latent 1"),
+             ("Observation 0", "Observation 1"),
+             ("Learned Latent 0", "Learned Latent 1")],
+        )):
+            ax.scatter(data[:, 0], data[:, 1], c=colors, s=s, linewidths=0)
+            ax.set_xlabel(labels[0])
+            ax.set_ylabel(labels[1])
+            ax.grid(alpha=0.3)
+            if i == 0 or i == 2:
+                ax.set_xlim(-4, 4)
+                ax.set_ylim(-4, 4)
+
+        fig.tight_layout()
+        out_path = os.path.join(args.out, f"fig_2d_{mix_name}.jpg")
+        fig.savefig(out_path, bbox_inches="tight", dpi=500)
+        print(f"Saved {out_path}")
+        plt.close()
+
+
+if __name__ == "__main__":
+    main()