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