41 lines
1.7 KiB
Python
41 lines
1.7 KiB
Python
"""Zoomed-in real-vs-generated edep histogram for photons only (pdg=22),
|
|
to characterize the KL spike flagged by plot_kl_bars_pl(group_by='pdg')."""
|
|
|
|
from pathlib import Path
|
|
|
|
import matplotlib.pyplot as plt
|
|
import numpy as np
|
|
|
|
import giant.analysis as a
|
|
|
|
FILE = "/home/lars/Programming/giant/pbwo4_10k_9_predicted_local.parquet"
|
|
OUT = Path("/home/lars/knowledge-base/meta/attachments")
|
|
|
|
samples = a.load_predicted_local(FILE, sample_frac=0.15)
|
|
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]
|
|
print("n photon rows:", mask.sum())
|
|
print("real: mean", real.mean(), "std", real.std(), "max", real.max(), "frac==0", (real == 0).mean())
|
|
print("gen: mean", gen.mean(), "std", gen.std(), "max", gen.max(), "frac==0", (gen == 0).mean())
|
|
for q in [0.5, 0.9, 0.99, 0.999]:
|
|
print(f"q={q}: real={np.quantile(real, q):.4f} gen={np.quantile(gen, q):.4f}")
|
|
|
|
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()
|
|
fig.savefig(OUT / "giant-h1024n8d0.1lr3e-4-photon-edep-zoom.png", dpi=150, bbox_inches="tight")
|
|
print("saved photon-edep-zoom")
|