lars
a14a4f973a
Whitespace-only reflow (line wrapping, blank lines between defs); no logic changes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
"""One-off export of the energy-budget-violation plot for the
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energy-conservation PoC checkpoint (ALR simplex output space, retrained on
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the patched-Geant4 regenerated dataset). Not part of the package; run
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manually.
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Unlike `plot_total_energy` (per-step conservation only, no reference to the
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fixed primary/incident energy), this adds an explicit comparison against
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`primary_E` (== max(pre_E) per event, since this PoC dataset uses a single
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fixed incident energy) to show event-level conservation violations that the
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per-step ALR simplex constraint does not prevent.
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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 polars as pl
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from giant.analysis import _edep_pl, _hist_edges
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FILE = "/home/lars/Programming/giant/9879e806-5e88-4b06-b1fa-0e61de9cda6f.parquet"
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OUT = Path("/home/lars/knowledge-base/meta/attachments")
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PREFIX = "giant-energy-conservation-poc"
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print("=== per-event primary energy + total edep ===")
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per_event = (
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pl.scan_parquet(FILE)
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.group_by("event_id")
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.agg(
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pl.col("pre_E").max().alias("primary_E"),
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_edep_pl("true").sum().alias("real_total_edep"),
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_edep_pl("pred").sum().alias("gen_total_edep"),
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)
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.collect(engine="streaming")
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)
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print("n events:", per_event.height)
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primary_E = per_event["primary_E"].to_numpy()
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real_tot = per_event["real_total_edep"].to_numpy()
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gen_tot = per_event["gen_total_edep"].to_numpy()
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assert np.unique(primary_E).size == 1, "expected a single fixed incident energy"
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E0 = float(primary_E[0])
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print(f"fixed incident energy E0 = {E0} MeV")
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print()
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print(
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f"real: mean={real_tot.mean():.3f} std={real_tot.std():.3f} "
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f"sigma/mu={real_tot.std() / real_tot.mean():.4f} max={real_tot.max():.3f} "
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f"frac>E0={np.mean(real_tot > E0):.4f}"
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)
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print(
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f"gen: mean={gen_tot.mean():.3f} std={gen_tot.std():.3f} "
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f"sigma/mu={gen_tot.std() / gen_tot.mean():.4f} max={gen_tot.max():.3f} "
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f"frac>E0={np.mean(gen_tot > E0):.4f}"
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)
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print(
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f"gen/E0 ratio: mean={np.mean(gen_tot / E0):.4f} max={np.max(gen_tot / E0):.4f} "
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f"p99={np.quantile(gen_tot / E0, 0.99):.4f}"
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)
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print("=== plot: total edep per event, marked against incident energy ===")
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fig, ax = plt.subplots(figsize=(6, 4))
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edges = _hist_edges(real_tot, gen_tot, bins=50).tolist()
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ax.hist(
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real_tot,
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bins=edges,
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density=True,
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histtype="step",
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label=f"real (σ/μ={real_tot.std() / real_tot.mean():.3f})",
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)
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ax.hist(
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gen_tot,
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bins=edges,
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density=True,
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histtype="step",
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label=f"generated (σ/μ={gen_tot.std() / gen_tot.mean():.3f}, "
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f"{np.mean(gen_tot > E0):.1%} > E0)",
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)
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ax.axvline(
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E0, color="k", linestyle="--", linewidth=1, label=f"incident energy E0={E0:.0f} MeV"
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)
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ax.set_yscale("log")
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ax.set_xlabel("total deposited energy per event [MeV]")
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ax.legend(fontsize=8)
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fig.tight_layout()
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fig.savefig(
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OUT / f"{PREFIX}-event-total-energy-vs-E0.png", dpi=150, bbox_inches="tight"
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)
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print("=== plot: total edep / incident energy ratio ===")
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fig, ax = plt.subplots(figsize=(6, 4))
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ratio_real = real_tot / E0
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ratio_gen = gen_tot / E0
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edges = _hist_edges(ratio_real, ratio_gen, bins=60).tolist()
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ax.hist(ratio_real, bins=edges, density=True, histtype="step", label="real")
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ax.hist(ratio_gen, bins=edges, density=True, histtype="step", label="generated")
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ax.axvline(1.0, color="k", linestyle="--", linewidth=1, label="conservation limit (=1)")
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ax.set_yscale("log")
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ax.set_xlabel("total deposited energy / incident energy, per event")
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ax.legend(fontsize=8)
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fig.tight_layout()
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fig.savefig(OUT / f"{PREFIX}-event-energy-ratio.png", dpi=150, bbox_inches="tight")
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print("DONE")
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