File size: 1,243 Bytes
c4cfcd3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | """Example: Compute per-family and per-source statistics."""
import json
import numpy as np
from collections import Counter
with open("dataset/entries_final_v3.json") as f:
entries = json.load(f)
# Per-family statistics
families = Counter(f for e in entries for f in e.get("families", ["unknown"]))
print("Family Distribution:")
for fam, count in families.most_common():
pct = 100 * count / len(entries)
print(f" {fam:25s}: {count:>7,} ({pct:.1f}%)")
# Per-source FE distribution
print("\nFE Distribution by Source:")
for src in ["mp", "oqmd", "jarvis"]:
subset = [e for e in entries if e.get("source") == src]
fe_vals = [e.get("formation_energy_per_atom", 0) for e in subset
if e.get("formation_energy_per_atom") is not None]
print(f" {src:8s}: mean={np.mean(fe_vals):.3f} "
f"median={np.median(fe_vals):.3f} "
f"std={np.std(fe_vals):.3f} "
f"N={len(fe_vals):,}")
# Coverage analysis
print("\nProperty Coverage:")
for prop in ["formation_energy_per_atom", "energy_above_hull", "band_gap"]:
present = sum(1 for e in entries if e.get(prop) is not None)
print(f" {prop:35s}: {present:>7,} / {len(entries):,} "
f"({100*present/len(entries):.1f}%)")
|