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>
64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
"""Recreate of the photon (pdg=22) edep histogram in transformed (local-frame)
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coordinates, with the deposited-energy axis in electron volts.
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Same data path as export_photon_edep.py, but edep is converted from the raw
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MeV units to eV (x1e6) so the small-deposition photon spike is readable.
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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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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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MEV_TO_EV = 1e6
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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] * MEV_TO_EV # MeV -> eV
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gen = samples.gen_raw[mask, edep_idx] * MEV_TO_EV
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print("n photon rows:", mask.sum())
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print("real [eV]: mean", real.mean(), "max", real.max(), "frac==0", (real == 0).mean())
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print("gen [eV]: mean", gen.mean(), "max", gen.max(), "frac==0", (gen == 0).mean())
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fig, ax = plt.subplots(figsize=(6, 4))
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bins = np.linspace(0, np.quantile(real, 0.999), 80)
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ax.hist(real, bins=bins, alpha=0.6, label="real", density=True)
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ax.hist(gen, bins=bins, alpha=0.6, label="gen", density=True)
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ax.set_yscale("log")
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ax.set_xlabel("deposited energy [eV] (photons, pdg=22)")
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ax.set_ylabel("density")
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ax.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-ev.png", dpi=150, bbox_inches="tight"
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)
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print("saved photon-edep-ev")
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# Second version: log-spaced energy axis to expose the low-deposition structure.
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fig, ax = plt.subplots(figsize=(6, 4))
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lo = max(min(real[real > 0].min(), gen[gen > 0].min()), 1.0)
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hi = max(real.max(), gen.max())
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log_bins = np.geomspace(lo, hi, 80)
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ax.hist(real, bins=log_bins, alpha=0.6, label="real", density=True)
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ax.hist(gen, bins=log_bins, alpha=0.6, label="gen", density=True)
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ax.set_xscale("log")
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ax.set_yscale("log")
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ax.set_xlabel("deposited energy [eV] (photons, pdg=22)")
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ax.set_ylabel("density")
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ax.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-ev-logx.png",
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dpi=150,
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bbox_inches="tight",
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)
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print("saved photon-edep-ev-logx")
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