lars
25718f175e
Removes unused imports and an ambiguous variable name, narrows Optional types before use so ty's flow analysis is satisfied, swaps sum() over polars expressions for pl.sum_horizontal to avoid the Literal[0] fallback type, and converts numpy bin edges to plain lists before passing to matplotlib's hist (whose stub only accepts Sequence[float]). Also applies ruff format across the repo, which had drifted out of sync with the formatter. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
77 lines
1.8 KiB
Python
77 lines
1.8 KiB
Python
"""Zoomed-in real-vs-generated edep histogram for photons only (pdg=22),
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to characterize the KL spike flagged by plot_kl_bars_pl(group_by='pdg')."""
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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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FILE = "/home/lars/Programming/giant/pbwo4_10k_9_predicted_local.parquet"
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OUT = Path("/home/lars/knowledge-base/meta/attachments")
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samples = a.load_predicted_local(FILE, sample_frac=0.15)
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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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print("n photon rows:", mask.sum())
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print(
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"real: mean",
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real.mean(),
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"std",
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real.std(),
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"max",
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real.max(),
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"frac==0",
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(real == 0).mean(),
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)
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print(
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"gen: mean",
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gen.mean(),
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"std",
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gen.std(),
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"max",
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gen.max(),
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"frac==0",
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(gen == 0).mean(),
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)
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for q in [0.5, 0.9, 0.99, 0.999]:
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print(f"q={q}: real={np.quantile(real, q):.4f} gen={np.quantile(gen, q):.4f}")
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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,
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bins=bins,
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alpha=0.6,
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label="real",
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density=True,
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cumulative=True,
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histtype="step",
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)
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axes[1].hist(
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gen,
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bins=bins,
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alpha=0.6,
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label="gen",
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density=True,
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cumulative=True,
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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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fig.savefig(
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OUT / "giant-h1024n8d0.1lr3e-4-photon-edep-zoom.png", dpi=150, bbox_inches="tight"
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)
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print("saved photon-edep-zoom")
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