Add export script for ETP group-update presentation plots

One-off script (like export_validation_plots.py / export_event_observables.py)
that writes a 3x3 marginals grid, post_dir/travel_dir norm histograms, and
vector-PDF copies of the KL-bars/photon-edep/event-level plots directly into
the thesis-presentations repo's images/ folder.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

analysis/export_presentation_plots.py

@@ -0,0 +1,127 @@
+"""One-off export of presentation-specific plots for the 2026-06-24 ETP group
+update, written directly into the presentation's images/ folder. Not part of
+the package; run manually.
+
+All plots are saved as PDF (vector) except the pairwise scatter plot, which
+stays PNG/raster in the .tex (scatter plots with thousands of points blow up
+as vector files and gain nothing from being scalable).
+
+1. Aggregate marginals, split into a 3x3 grid (instead of plot_marginals'
+   single wide row of 9 columns) for a more manageable slide aspect ratio.
+2. post_dir/travel_dir unit-norm histograms only (subset of
+   plot_constraint_violations).
+3. KL bar plot stratified by pdg.
+4. Zoomed-in photon edep histogram + CDF.
+5. Event-level profiles/histograms/pdg-share plots.
+"""
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+import giant.analysis as a
+from giant.analysis import RAW_TARGET_NAMES, _hist_edges
+
+FILE = "/home/lars/Programming/giant/pbwo4_10k_9_predicted_local.parquet"
+OUT = Path(
+    "/home/lars/Programming/thesis-presentations/presentations/2026-06-24-group-update/images"
+)
+PREFIX = "giant-h1024n8d0.1lr3e-4"
+
+
+def savefig(fig, name: str) -> None:
+    fig.savefig(OUT / f"{PREFIX}-{name}.pdf", bbox_inches="tight")
+    print("saved", name)
+
+
+print("=== loading sampled SampleCollection ===")
+samples = a.load_predicted_local(FILE, sample_frac=0.15)
+print("n rows:", len(samples.gen_raw))
+
+print("=== marginals, split 3x3 grid ===")
+n_rows, n_cols = 3, 3
+fig, axes = plt.subplots(n_rows, n_cols, figsize=(3.6 * n_cols, 2.8 * n_rows))
+for idx, name in enumerate(RAW_TARGET_NAMES):
+    row, col = divmod(idx, n_cols)
+    ax = axes[row][col]
+    real, gen = samples.real_raw[:, idx], samples.gen_raw[:, idx]
+    edges = _hist_edges(real, gen, bins=50)
+    ax.hist(real, bins=edges, density=True, histtype="step", label="real")
+    ax.hist(gen, bins=edges, density=True, histtype="step", label="generated")
+    ax.set_yscale("log")
+    ax.set_title(name, fontsize=9)
+    if idx == 0:
+        ax.legend(fontsize=7)
+fig.tight_layout()
+savefig(fig, "marginals-grid")
+
+print("=== post_dir / travel_dir unit-norm histograms ===")
+gen = samples.gen_raw
+post_norm = np.linalg.norm(gen[:, 3:6], axis=1)
+travel_norm = np.linalg.norm(gen[:, 6:9], axis=1)
+
+fig, axes = plt.subplots(1, 2, figsize=(8, 3.5))
+for ax, norm, title in [
+    (axes[0], post_norm, r"$\|\mathrm{post\_dir}\|$"),
+    (axes[1], travel_norm, r"$\|\mathrm{travel\_dir}\|$"),
+]:
+    ax.hist(norm, bins=_hist_edges(norm, bins=50), histtype="step")
+    ax.set_yscale("log")
+    ax.axvline(1.0, color="k", linestyle="--", linewidth=1)
+    ax.set_title(title)
+fig.tight_layout()
+savefig(fig, "direction-norms")
+
+print("=== KL bars by pdg ===")
+fig = a.plot_kl_bars_pl(FILE, group_by="pdg")
+savefig(fig, "kl-bars-pdg")
+
+print("=== photon edep zoom ===")
+mask = samples.pdg == 22
+edep_idx = a.RAW_TARGET_NAMES.index("edep")
+real = samples.real_raw[mask, edep_idx]
+gen = samples.gen_raw[mask, edep_idx]
+
+fig, axes = plt.subplots(1, 2, figsize=(10, 4))
+bins = np.linspace(0, np.quantile(real, 0.999), 80)
+axes[0].hist(real, bins=bins, alpha=0.6, label="real", density=True)
+axes[0].hist(gen, bins=bins, alpha=0.6, label="gen", density=True)
+axes[0].set_yscale("log")
+axes[0].set_xlabel("edep (photons, pdg=22)")
+axes[0].legend()
+axes[1].hist(
+    real, bins=bins, alpha=0.6, label="real", density=True, cumulative=True, histtype="step"
+)
+axes[1].hist(
+    gen, bins=bins, alpha=0.6, label="gen", density=True, cumulative=True, histtype="step"
+)
+axes[1].set_xlabel("edep (photons, pdg=22) - CDF")
+axes[1].legend()
+fig.tight_layout()
+savefig(fig, "photon-edep-zoom")
+
+print("=== event observables ===")
+obs = a.compute_event_observables_pl(FILE)
+
+fig = a.plot_total_energy(obs)
+savefig(fig, "event-total-energy")
+
+fig = a.plot_total_length(obs)
+savefig(fig, "event-total-length")
+
+fig = a.plot_longitudinal_profile(obs)
+savefig(fig, "event-longitudinal-profile")
+
+fig = a.plot_transverse_profile(obs)
+savefig(fig, "event-transverse-profile")
+
+fig = a.plot_shower_max_depth(obs)
+savefig(fig, "event-shower-max-depth")
+
+print("=== pdg length share ===")
+pdg_table = a.pdg_contribution_table_pl(FILE)
+fig = a.plot_pdg_length_share(pdg_table)
+savefig(fig, "pdg-length-share")
+
+print("DONE")