| """Example: Visualize dataset distributions. |
| |
| Requires: matplotlib, numpy |
| """ |
| import json, numpy as np |
| from collections import Counter |
|
|
| with open("dataset/entries_final_v3.json") as f: |
| entries = json.load(f) |
|
|
| |
| fe_vals = np.array([e.get("formation_energy_per_atom", 0) |
| for e in entries if e.get("formation_energy_per_atom") is not None]) |
|
|
| print("FE Distribution (eV/atom):") |
| fe_range = (-5, 3) |
| bins = np.linspace(fe_range[0], fe_range[1], 40) |
| hist, edges = np.histogram(fe_vals, bins=bins) |
| max_bar = max(hist) |
| for i in range(len(hist)): |
| if hist[i] < max_bar * 0.01: |
| continue |
| bar_len = int(60 * hist[i] / max_bar) |
| print(f" {edges[i]:+5.2f}: {'█' * bar_len} ({hist[i]:,})") |
|
|
| |
| bg_vals = np.array([e.get("band_gap", 0) |
| for e in entries if e.get("band_gap") is not None]) |
| bg_nonzero = bg_vals[bg_vals > 0.01] |
| print(f"\nBand Gap Distribution:") |
| print(f" Zero gap (metals): {np.sum(bg_vals <= 0.01):,} " |
| f"({100*np.sum(bg_vals <= 0.01)/len(bg_vals):.0f}%)") |
| print(f" Non-zero mean: {np.mean(bg_nonzero):.3f} eV") |
| print(f" Non-zero median: {np.median(bg_nonzero):.3f} eV") |
| print(f" Max: {np.max(bg_vals):.2f} eV") |
|
|
| |
| tiers = Counter(e.get("tier", "unknown") for e in entries) |
| print(f"\nTier Distribution:") |
| for tier, count in tiers.most_common(): |
| print(f" {tier:12s}: {count:>7,} ({100*count/len(entries):.1f}%)") |
|
|
