| """Per reviewed MP row: compare geometry with each candidate task and choose the energy source. |
| |
| Writes decisions.parquet (one row per reviewed LeMat row) and replacements.jsonl (the new |
| per-row payload for every row whose DFT record is replaced). |
| """ |
| import sys, json, glob, numpy as np, pandas as pd |
| from pathlib import Path |
| from scipy.optimize import linear_sum_assignment |
|
|
| W = Path(sys.argv[1]) |
| T_OK = 0.2 |
| REF_GAIN = 0.05 |
| SAME, NEAR = 1e-3, 0.05 |
|
|
| f = pd.read_parquet(W / "identify.parquet") |
| f = f[f.review != ""].set_index("immutable_id") |
| cand = pd.read_parquet(W / "candidates.parquet") |
| cand_by = {k: g for k, g in cand.groupby("immutable_id")} |
| ext = {} |
| for fn in glob.glob(str(W / "s3/*.jsonl")): |
| for line in open(fn): |
| d = json.loads(line) |
| ext[d["task_id"]] = d |
| L = pd.read_parquet(W / "lemat_review_rows.parquet", |
| columns=["immutable_id", "lattice_vectors", "cartesian_site_positions", |
| "species_at_sites"]).drop_duplicates("immutable_id").set_index("immutable_id") |
|
|
|
|
| def compare(lat, sp, xyz, d): |
| """Max lattice deviation, permutation (task sites -> LeMat order), max site deviation.""" |
| tl = np.asarray(d["lattice"], float); tsp = np.asarray(d["species"]); txyz = np.asarray(d["xyz"], float) |
| sp = np.asarray(sp) |
| if len(tsp) != len(sp) or sorted(tsp) != sorted(sp): |
| return np.inf, None, np.inf |
| dlat = float(np.abs(tl - lat).max()) |
| if dlat > NEAR: |
| return dlat, None, np.inf |
| inv = np.linalg.inv(lat); perm = np.empty(len(sp), int) |
| for el in np.unique(sp): |
| i = np.flatnonzero(sp == el); j = np.flatnonzero(tsp == el) |
| fr = (txyz[j][None, :, :] - xyz[i][:, None, :]) @ inv; fr -= np.round(fr) |
| rr, cc = linear_sum_assignment(np.linalg.norm(fr @ lat, axis=2)); perm[i[rr]] = j[cc] |
| fr = (txyz[perm] - xyz) @ inv; fr -= np.round(fr) |
| return dlat, perm, float(np.linalg.norm(fr @ lat, axis=1).max()) |
|
|
|
|
| records, payloads = [], [] |
| for mid, r in f.iterrows(): |
| lr = L.loc[mid] |
| lat = np.stack(lr.lattice_vectors); xyz = np.stack(lr.cartesian_site_positions); sp = list(lr.species_at_sites) |
| mlip = r.epa - r.d_med if pd.notna(r.d_med) else np.nan |
| a = abs(r.d_med) if pd.notna(r.d_med) else np.nan |
| cs = [] |
| for c in (cand_by.get(mid).itertuples() if mid in cand_by else []): |
| d = ext.get(c.task_id) |
| if d is None or "error" in d or d.get("energy") is None: |
| continue |
| dlat, perm, dpos = compare(lat, sp, xyz, d) |
| geo = "same" if max(dlat, dpos) <= SAME else "near" if max(dlat, dpos) <= NEAR else "different" |
| e = d["energy"] / len(d["species"]) |
| cs.append(dict(task_id=c.task_id, dep=c.task_deprecated, is_lemat=c.is_lemat_task, is_mp=c.is_mp_task, |
| calc_type=c.calc_type, geo=geo, dlat=dlat, dpos=dpos, epa=e, |
| b=abs(e - mlip) if geo != "different" else np.nan, perm=perm, doc=d)) |
| live = [c for c in cs if not c["dep"] and not (c["is_lemat"] and r.identified) |
| and (not isinstance(r.mp_run, str) or c["calc_type"].split(" ")[0] == r.mp_run)] |
| live_geo = [c for c in live if c["geo"] != "different"] |
| mp_c = next((c for c in live if c["is_mp"]), None) |
| choice, action = None, None |
| if r.review in ("control_same", "control_live"): |
| action = "control" |
| choice = next((c for c in cs if c["is_lemat"]), None) |
| elif r.review == "stale": |
| if mp_c is not None and mp_c["geo"] != "different" and (np.isnan(a) or mp_c["b"] <= max(T_OK, a + REF_GAIN)): |
| choice, action = mp_c, "replace_energy" |
| elif live_geo: |
| best = min(live_geo, key=lambda c: (c["b"], c["calc_type"].endswith("Optimization"))) |
| if np.isnan(a) or best["b"] <= max(T_OK, a + REF_GAIN): |
| choice, action = best, "replace_energy" |
| if choice is None and mp_c is not None and mp_c["geo"] == "different": |
| |
| |
| b_s = abs(mp_c["epa"] - mlip) if pd.notna(mlip) else np.nan |
| if np.isnan(a) or np.isnan(b_s) or b_s <= max(T_OK, a + REF_GAIN): |
| choice, action = mp_c, "replace_structure" |
| if choice is None: |
| action = "keep_stale_mlip_consistent" if (pd.notna(a) and a <= T_OK and r.material_deprecated is False) else "remove" |
| else: |
| if live_geo and pd.notna(a) and a > T_OK: |
| best = min(live_geo, key=lambda c: c["b"]) |
| if best["b"] < a - REF_GAIN and best["b"] <= T_OK: |
| choice, action = best, "replace_energy" |
| if action is None and mp_c is not None and mp_c["geo"] == "different" and pd.notna(a) and a > T_OK: |
| b_s = abs(mp_c["epa"] - mlip) |
| if b_s < a - REF_GAIN and b_s <= T_OK: |
| choice, action = mp_c, "replace_structure" |
| if action is None: |
| action = "keep" |
| rec = dict(immutable_id=mid, review=r.review, grp=r.grp, action=action, n_cand=len(cs), n_live_geo=len(live_geo), |
| old_epa=r.epa, mlip_epa=mlip, old_vs_mlip=r.d_med, lemat_task=r.lemat_task, lemat_calc_type=r.lemat_calc_type, |
| lemat_batch=r.lemat_batch, lemat_task_deprecated=r.lemat_task_deprecated, mp_task=r.mp_task, |
| mp_calc_type=r.mp_calc_type, material_deprecated=r.material_deprecated) |
| if choice is not None: |
| d = choice["doc"] |
| rec.update(new_task=choice["task_id"], new_calc_type=choice["calc_type"], new_epa=choice["epa"], |
| new_vs_mlip=choice["epa"] - mlip, geo=choice["geo"], dlat=choice["dlat"], dpos=choice["dpos"], |
| new_is_mp_task=choice["is_mp"]) |
| if action in ("replace_energy", "replace_structure", "control"): |
| perm = choice["perm"] if choice["perm"] is not None else np.arange(len(d["species"])) |
| take = lambda v: None if v is None else [v[i] for i in perm] |
| payloads.append(dict(immutable_id=mid, action=action, task_id=choice["task_id"], energy=d["energy"], |
| perm=[int(i) for i in perm], |
| lattice=d["lattice"], species=take(d["species"]), xyz=take(d["xyz"]), |
| forces=take(d["forces"]) if d.get("forces") else None, stress=d.get("stress"), |
| magmom=take(d["magmom"]), total_magnetization=d.get("total_magnetization"), |
| efermi=d.get("efermi"), last_updated=d.get("last_updated"), |
| potcars=d.get("potcars"), hubbards=d.get("hubbards"))) |
| records.append(rec) |
|
|
| out = pd.DataFrame(records) |
| out.to_parquet(W / "decisions.parquet") |
| with open(W / "replacements.jsonl", "w") as fh: |
| for p in payloads: |
| fh.write(json.dumps(p) + "\n") |
| pd.set_option("display.width", 220) |
| print(pd.crosstab([out.review, out.grp], out.action, margins=True)) |
| print(pd.crosstab(out.action, out.geo.fillna("-"))) |
|
|
