Archive 3 checkpoints (1-3)

paper-releases/pbisc-diffusion/updates/20261012T130738Z-strictpairs-implementation/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T130738Z-strictpairs-implementation/snapshot/legacy_adapters/plans/paired_finetune_intradataset_20260927/scsemi/tools/export_npz.py

@@ -0,0 +1,200 @@
+#!/usr/bin/env python
+"""Adapter: completed scSemiProfiler target shards -> the benchmark's generated NPZ + JSON receipt (CPU only).
+
+Per target (condition-NPZ order):
+  cells  upstream output has one generated cell per cell of the assigned representative (n_rep cells).
+         Keep 1,500: n_rep >= 1500 -> 1,500 drawn without replacement; n_rep < 1500 -> whole permutations of the
+         n_rep cells concatenated until 1,500 (each cell used floor/ceil(1500/n_rep) times).  Stream
+         default_rng([20260927, uint64(sha256(target)[:8])]) (the scorer's donor-keyed stream form); kept rows sorted.
+  depth  upstream values are normalized expression (library-normalised ZINB means, values <= 10 zeroed).  Each kept
+         cell is rescaled to sum to D = the median per-cell UMI on the modeled genes of the representatives'
+         observed cells (inputs_manifest depth.value; label-free, from the training pairs only).  All-zero cells stay
+         zero (counted).  Result = expected counts (Poisson rates), what score_ft.py reads out.
+  genes  modeled genes placed at their 6k-panel columns; every other panel column is 0.
+metadata_json.input_condition_path = the split's test_raw.npz, so the scorer's bulk-broadcast baseline works.
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import math
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+if os.environ.get("CUDA_VISIBLE_DEVICES") != "":
+    raise SystemExit("run with CUDA_VISIBLE_DEVICES=''")
+
+import numpy as np
+import pandas as pd
+import scipy.sparse as sp
+
+M8 = Path("/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion")
+PANEL = M8 / "artifacts/panel_ladder_floor01_1m_20260923/tiers/6k/selected_genes.csv"
+SEED = 20260927
+N_CELLS = 1500
+
+
+def sha(path: Path) -> str:
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for block in iter(lambda: f.read(1 << 24), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+def target_rng(target: str) -> np.random.Generator:
+    key = int.from_bytes(hashlib.sha256(str(target).encode()).digest()[:8], "little")
+    return np.random.default_rng([SEED, key])
+
+
+def pick_rows(n_src: int, target: str, n_out: int = N_CELLS) -> np.ndarray:
+    if n_src < 1:
+        raise ValueError(f"{target}: no generated cells")
+    rng = target_rng(target)
+    if n_src >= n_out:
+        return np.sort(rng.choice(n_src, size=n_out, replace=False))
+    reps = math.ceil(n_out / n_src)
+    return np.sort(np.concatenate([rng.permutation(n_src) for _ in range(reps)])[:n_out])
+
+
+def to_expected_counts(X: np.ndarray, depth: float) -> tuple[np.ndarray, int]:
+    lib = X.sum(1, dtype=np.float64)
+    scale = np.divide(depth, lib, out=np.zeros_like(lib), where=lib > 0)
+    return (X * scale[:, None]).astype(np.float32), int((lib <= 0).sum())
+
+
+def strict_export_arguments(man: dict, work: Path, out: Path) -> list[str] | None:
+    """Opt-in dispatch only; existing fewshot/CV/budget export semantics stay unchanged."""
+    if man.get("mode") != "strict":
+        return None
+    plan = M8 / "plans/paper_figures_strictpairs_20261012"
+    condition = Path(man["condition_npz"]).resolve()
+    if not condition.is_relative_to(plan / "data"):
+        raise ValueError("strict export condition is outside the versioned strict-pairs plan")
+    relative = condition.relative_to(plan / "data").parent
+    support = plan / "protocol/splits" / relative / "support.json"
+    return ["--work", str(work), "--out", str(out), "--support-manifest", str(support)]
+
+
+def main(argv=None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--work", type=Path, required=True)
+    ap.add_argument("--out", type=Path, required=True, help="generated NPZ path (receipt = same stem .log)")
+    a = ap.parse_args(argv)
+    work, out = a.work.resolve(), a.out.resolve()
+    man = json.loads((work / "inputs/inputs_manifest.json").read_text())
+    strict_args = strict_export_arguments(man, work, out)
+    if strict_args is not None:
+        import importlib.util
+        plan_tools = M8 / "plans/paper_figures_strictpairs_20261012/tools"
+        sys.path.insert(0, str(plan_tools))
+        spec = importlib.util.spec_from_file_location("strict_support_exporter", plan_tools / "export_scsemi_strict.py")
+        module = importlib.util.module_from_spec(spec)
+        spec.loader.exec_module(module)
+        return module.main(strict_args)
+    run = json.loads((work / "run_manifest.json").read_text())
+    smoke = bool(man.get("smoke"))
+    if smoke and "/scsemi/smoke/" not in str(out):
+        raise SystemExit("smoke outputs never go to the benchmark's generated/ tree")
+    if out.exists() or out.with_suffix(".log").exists():
+        raise SystemExit(f"{out} or its receipt exists; never overwrite (preserve and inspect)")
+    signature = run["run_signature"]
+    genes = man["modeled_genes"]["gene_ids"]
+    pidx = np.asarray(man["modeled_genes"]["panel_index"], dtype=np.int64)
+    panel = pd.read_csv(PANEL)
+    panel_ids = np.asarray(panel.gene_id.astype(str).tolist(), dtype=str)   # fixed-width unicode: loads with allow_pickle=False
+    panel_symbols = np.asarray(panel.gene_symbol.astype(str).tolist(), dtype=str)
+    if not np.array_equal(panel_ids[pidx], np.asarray(genes)):
+        raise SystemExit("modeled genes do not sit at the recorded panel columns")
+    depth = float(man["depth"]["value"])
+    if not (np.isfinite(depth) and depth > 0):
+        raise SystemExit("invalid depth")
+    targets = man["condition_sample_order"] if not smoke else [t for t in man["condition_sample_order"]
+                                                                  if t in set(man["target_order_test_samples"])]
+    if sorted(targets) != sorted(man["target_order_test_samples"]):
+        raise SystemExit("targets differ from the condition NPZ order")
+    per, src_rows = [], []
+    G = np.zeros((len(targets) * N_CELLS, panel_ids.size), dtype=np.float32)
+    for i, t in enumerate(targets):
+        rec = json.loads((work / "shards" / f"{t}.json").read_text())
+        shard = work / "shards" / f"{t}.npz"
+        if not rec.get("complete") or rec["run_signature"] != signature or rec["shard_sha256"] != sha(shard):
+            raise SystemExit(f"{t}: shard incomplete or receipt mismatch")
+        C = sp.load_npz(shard).tocsr()
+        if C.shape[1] != len(genes):
+            raise SystemExit(f"{t}: shard has {C.shape[1]} genes")
+        rows = pick_rows(C.shape[0], t)
+        X = C[rows].toarray().astype(np.float64)
+        if not np.isfinite(X).all() or (X < 0).any():
+            raise SystemExit(f"{t}: non-finite or negative values")
+        E, n_zero = to_expected_counts(X, depth)
+        G[i * N_CELLS:(i + 1) * N_CELLS, pidx] = E
+        src_rows.append(rows.astype(np.int32))
+        mult = np.bincount(rows, minlength=C.shape[0])
+        per.append({"sample_id": t, "representative": rec["representative"], "n_upstream_cells": int(C.shape[0]),
+                    "with_replacement": bool(C.shape[0] < N_CELLS), "max_multiplicity": int(mult.max()),
+                    "zero_library_cells": n_zero, "elapsed_seconds": rec["elapsed_seconds"],
+                    "median_nonzero_genes": float(np.median((E > 0).sum(1)))})
+    lib = G.sum(1, dtype=np.float64)
+    nz = lib > 0
+    checks = {"finite": bool(np.isfinite(G).all()), "nonnegative": bool((G >= 0).all()),
+              "zero_outside_modeled_genes": bool(not G[:, np.setdiff1d(np.arange(panel_ids.size), pidx)].any()),
+              "nonzero_cells_sum_to_depth": bool(np.allclose(lib[nz], depth, rtol=1e-4)),
+              "gene_order_equals_panel": True, "cells_per_target": N_CELLS}
+    if not all(v for k, v in checks.items() if isinstance(v, bool)):
+        raise SystemExit(f"output checks failed: {checks}")
+    n_t = len(targets)
+    avail = np.zeros((n_t, panel_ids.size), dtype=bool)
+    avail[:, pidx] = True
+    arm = {"fewshot": "scsemi5", "cv": "scsemi"}.get(man["mode"])
+    if arm is None:            # budget split (added 2026-10-02): the equal-pair-budget arm of LoRA lora_n<n>
+        arm = f"scsemi_n{int(man['budget']['n'])}"
+    meta = {"method": "scSemiProfiler", "arm": arm,
+            "setting": run["protocol"]["setting"], "dataset": man["dataset"], "mode": man["mode"], "split": man["split"],
+            "input_condition_path": man["condition_npz"], "input_condition_sha256": man["condition_sha256"],
+            "seed": SEED, "run_signature": signature, "upstream_commit": run["protocol"]["upstream_commit"],
+            "protocol": run["protocol"], "representatives": [r["sample_id"] for r in man["representatives"]],
+            "train_donors": man["train_donors"], "n_modeled_genes": len(genes),
+            "unit": "expected_counts (Poisson rates)", "depth_rule": man["depth"]["rule"], "depth": depth,
+            "cell_rule": "1500 per target from the n_rep upstream cells: without replacement if n_rep >= 1500, else "
+                         "whole permutations concatenated to 1500; default_rng([20260927, uint64 LE sha256(target)[:8]]); "
+                         "kept rows sorted (source_row)",
+            "support_readout": "upstream (values <= 10 normalized units zeroed by fast_semi)", "smoke": smoke,
+            "code_migrations": [{"at": m["at"], "transitions": m["transitions"], "to_code_signature": m["to_code_signature"]}
+                                for m in run.get("migrations", [])],
+            "work": str(work)}
+    if "budget" in man:
+        meta["budget"] = man["budget"]
+    if "reuse" in run:        # pretraining / hvset reused from another run (run_scsemi_paired.py --reuse-from)
+        meta["reuse"] = run["reuse"]
+    out.parent.mkdir(parents=True, exist_ok=True)
+    tmp = out.with_name(out.stem + ".tmp.npz")
+    np.savez_compressed(tmp, generated_counts=G, gene_ids=panel_ids, gene_symbols=panel_symbols,
+                        sample_ids=np.asarray(targets), bulk_index=np.repeat(np.arange(n_t, dtype=np.int64), N_CELLS),
+                        cells_per_bulk=np.full(n_t, N_CELLS, dtype=np.int64), output_availability_mask=avail,
+                        source_row=np.concatenate(src_rows), metadata_json=np.asarray(json.dumps(meta)))
+    with np.load(tmp, allow_pickle=False) as chk:   # the scorer opens NPZs with allow_pickle=False
+        for key in chk.files:
+            chk[key]
+    os.replace(tmp, out)
+    receipt = {"output": str(out), "output_sha256": sha(out), "written_utc": datetime.now(timezone.utc).isoformat(),
+               "condition_npz": man["condition_npz"], "condition_sha256": man["condition_sha256"], "seed": SEED,
+               "bulks": n_t, "cells": int(G.shape[0]), "genes": int(panel_ids.size), "modeled_genes": len(genes),
+               "depth": depth, "checks": checks, "per_target": per,
+               "total_target_seconds": float(sum(p["elapsed_seconds"] for p in per)),
+               "median_target_seconds": float(np.median([p["elapsed_seconds"] for p in per])),
+               "run_signature": signature, "code_migrations": meta["code_migrations"],
+               "exporter_sha256": sha(Path(__file__)), "smoke": smoke}
+    rtmp = out.with_name(out.stem + ".log.tmp")
+    rtmp.write_text(json.dumps(receipt, indent=1) + "\n")
+    os.replace(rtmp, out.with_suffix(".log"))
+    print(json.dumps({k: receipt[k] for k in ("output", "bulks", "cells", "modeled_genes", "depth",
+                                              "median_target_seconds")}))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

paper-releases/pbisc-diffusion/updates/20261012T130738Z-strictpairs-implementation/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T130738Z-strictpairs-implementation/snapshot/legacy_adapters/plans/paired_finetune_intradataset_20260927/scsemi/tools/prepare_draw_inputs.py

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+#!/usr/bin/env python
+"""Build scSemiProfiler inputs for one paired split (CPU only).
+
+Representatives = every usable row of the split's training donors (paired sc + real bulk).
+Targets        = the split's test rows (real bulk only; their cells are never read here).
+
+  --mode fewshot --draw j : training donors = data/<ds>/fewshot_n5/draw<j>/train_donors.txt,
+                            targets = draw<j>/test_samples.txt, condition = draw<j>/test_raw.npz
+  --mode cv --fold k      : training donors = every usable donor not in fold k (inner-val donors included),
+                            targets = fold k's rows, condition = data/<ds>/fold<k>/test_raw.npz
+  --mode budget --fold k --n N   (added 2026-10-02): representatives = exactly the rows LoRA lora_n<N> of fold k saw,
+                            runs/<ds>/fold<k>/lora_n<N>/RUN.json train_rows + inner_val_rows (checked against
+                            data/<ds>/CAL_BUDGET.json fit_sample_ids when present); targets = fold k's rows (checked
+                            against the run's test_rows_never_loaded_for_training); condition = fold<k>/test_raw.npz.
+                            The manifest also records target_donors (used by run_scsemi_paired.py --reuse-from).
+  --check-only            (added 2026-10-02): build and check everything (split, genes, cells, depth, condition), write
+                            nothing under --work; print the summary (and the representative / target lists to
+                            --check-out if given).  Without it the builder behaves exactly as before.
+
+Modeled genes = final 6k panel genes measured in the bulk of every representative and target row AND in the
+single cells of every representative row (features.npz bulk_valid / sc_valid).  No target single-cell mask,
+depth or cell count enters.
+
+Writes <work>/inputs/:
+  bulk.h5ad        raw bulk counts (float64) of reps + targets on the modeled genes, obs['sample_ids'],
+                   rows in features.npz order; upstream initsetup(normed=False) library-normalises + log1p's it
+  rep_sc.h5ad      raw UMI counts (float32 CSR) of every representative cell on the modeled genes,
+                   obs['sample_ids'] = the representative row's sample id; upstream scprocess(normed=False)
+  inputs_manifest.json   rows, genes, depth rule, source hashes
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import sys
+from datetime import datetime, timezone
+from pathlib import Path
+
+if os.environ.get("CUDA_VISIBLE_DEVICES") != "":
+    raise SystemExit("run with CUDA_VISIBLE_DEVICES=''")
+
+import numpy as np
+import pandas as pd
+import scipy.sparse as sp
+
+TASK = Path("/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927")
+HERE = TASK / "scsemi"
+sys.path.insert(0, str(TASK / "tools"))
+import prepare_dataset as PD  # noqa: E402  (the benchmark's own loader: bulk_raw with unmeasured NaN -> 0, masks, use)
+
+SEED = 20260927
+
+
+def sha(path: Path) -> str:
+    h = hashlib.sha256()
+    with open(path, "rb") as f:
+        for block in iter(lambda: f.read(1 << 24), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+def split_rows(ds: str, mode: str, j: int, sample_ids: np.ndarray, folds: pd.DataFrame,
+               n: int | None = None, support_manifest: Path | None = None) -> dict:
+    data = TASK / "data" / ds
+    usable = folds["fold"].to_numpy() >= 0
+    donors = folds["donor_id"].astype(str).to_numpy()
+    extra = {}
+    if support_manifest is not None:
+        strict_tools = TASK.parents[1] / "plans/paper_figures_strictpairs_20261012/tools"
+        sys.path.insert(0, str(strict_tools))
+        from support_manifest import load_manifest, select_rows
+        m = load_manifest(support_manifest, folds)
+        if m["cohort"] != ds:
+            raise SystemExit("support cohort differs from dataset")
+        rep, tgt = select_rows(m, folds)
+        rel = support_manifest.resolve().parent.relative_to(
+            TASK.parents[1] / "plans/paper_figures_strictpairs_20261012/protocol/splits")
+        cond = TASK.parents[1] / "plans/paper_figures_strictpairs_20261012/data" / rel / "test_raw.npz"
+        return {"rep": rep, "tgt": tgt, "train_donors": m["train_donor_ids"],
+                "test": m["test_sample_ids"], "condition": cond,
+                "sources": {"support.json": support_manifest},
+                "support_sha256": m["sha256"], "target_donors": m["test_donor_ids"]}
+    if mode == "fewshot":
+        dd = data / "fewshot_n5" / f"draw{j}"
+        train_donors = [x.strip() for x in (dd / "train_donors.txt").read_text().splitlines() if x.strip()]
+        test = [x.strip() for x in (dd / "test_samples.txt").read_text().splitlines() if x.strip()]
+        cond = dd / "test_raw.npz"
+        sources = {"train_donors.txt": dd / "train_donors.txt", "test_samples.txt": dd / "test_samples.txt"}
+        rep = np.flatnonzero(np.isin(donors, train_donors) & usable)
+    elif mode == "cv":
+        fold = folds["fold"].to_numpy()
+        test = sample_ids[fold == j].tolist()
+        rep = np.flatnonzero(usable & (fold != j))
+        train_donors = sorted(set(donors[rep]))
+        cond = data / f"fold{j}" / "test_raw.npz"
+        sources = {}
+    elif mode == "budget":
+        fold = folds["fold"].to_numpy()
+        rj = TASK / "runs" / ds / f"fold{j}" / f"lora_n{n}" / "RUN.json"
+        run = json.loads(rj.read_text())
+        if run["fold"] != j or (run.get("train_subset") or {}).get("train_n") != n:
+            raise SystemExit(f"{rj}: not the fold {j} n={n} learning-curve run")
+        fit = list(run["train_rows"]) + list(run["inner_val_rows"])
+        test = sample_ids[fold == j].tolist()
+        if sorted(run["test_rows_never_loaded_for_training"]) != sorted(test):
+            raise SystemExit(f"{rj}: its test rows differ from fold {j}'s rows")
+        pos = {s: i for i, s in enumerate(sample_ids)}
+        if len(set(fit)) != len(fit) or any(s not in pos for s in fit):
+            raise SystemExit(f"{rj}: duplicate or unknown fit rows")
+        rep = np.asarray(sorted(pos[s] for s in fit), dtype=np.int64)
+        if not (usable[rep] & (fold[rep] != j)).all():
+            raise SystemExit(f"{rj}: a fit row is unusable or in fold {j}")
+        cb = data / "CAL_BUDGET.json"
+        if cb.exists():
+            e = json.loads(cb.read_text())["folds"][str(j)]["n"][str(n)]
+            if sorted(e["fit_sample_ids"]) != sorted(fit) or e["lora_checkpoint_sha256"] != run["checkpoint_sha256"]:
+                raise SystemExit(f"{cb}: fold {j} n={n} fit rows / LoRA checkpoint differ from {rj}")
+        train_donors = sorted(set(donors[rep]))
+        cond = data / f"fold{j}" / "test_raw.npz"
+        sources = {"lora_RUN.json": rj, **({"CAL_BUDGET.json": cb} if cb.exists() else {})}
+        extra = {"budget": {"n": int(n), "fold": int(j), "lora_run": str(rj), "lora_checkpoint_sha256": run["checkpoint_sha256"],
+                            "n_train_rows": len(run["train_rows"]), "n_inner_val_rows": len(run["inner_val_rows"]),
+                            "train_rows": list(run["train_rows"]), "inner_val_rows": list(run["inner_val_rows"])}}
+    else:
+        raise SystemExit(f"unknown mode {mode}")
+    pos = {s: i for i, s in enumerate(sample_ids)}
+    miss = [s for s in test if s not in pos]
+    if miss:
+        raise SystemExit(f"test samples not in features.npz: {miss[:5]}")
+    tgt = np.asarray([pos[s] for s in test], dtype=np.int64)
+    if not usable[tgt].all():
+        raise SystemExit("a test row is not usable (fold -1)")
+    if set(donors[rep]) & set(donors[tgt]):
+        raise SystemExit("a donor is both representative and target")
+    if len(set(test)) != len(test):
+        raise SystemExit("duplicate test samples")
+    return {"rep": rep, "tgt": tgt, "train_donors": train_donors, "test": test, "condition": cond, "sources": sources,
+            **extra}
+
+
+def main(argv=None) -> int:
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--dataset", required=True)
+    ap.add_argument("--mode", choices=["fewshot", "cv", "budget"], default="fewshot")
+    ap.add_argument("--draw", type=int, help="fewshot draw index")
+    ap.add_argument("--fold", type=int, help="cv / budget fold index")
+    ap.add_argument("--n", type=int, help="budget: the LoRA learning-curve n (runs/<ds>/fold<k>/lora_n<n>)")
+    ap.add_argument("--work", type=Path, required=True)
+    ap.add_argument("--smoke-reps", type=int, default=0, help="SMOKE ONLY: keep the first N representative rows")
+    ap.add_argument("--smoke-cells", type=int, default=0, help="SMOKE ONLY: at most N cells per representative")
+    ap.add_argument("--smoke-targets", default="", help="SMOKE ONLY: comma list of target sample ids")
+    ap.add_argument("--smoke-rep-ids", default="", help="SMOKE ONLY: comma list of representative sample ids to keep")
+    ap.add_argument("--check-only", action="store_true", help="dry run: check everything, write nothing under --work")
+    ap.add_argument("--check-out", type=Path, default=None, help="--check-only: write the split summary JSON here")
+    ap.add_argument("--support-manifest", type=Path, default=None,
+                    help="strict support.json; representatives are its exact n sample IDs")
+    a = ap.parse_args(argv)
+    if a.support_manifest is not None:
+        if a.draw is not None or a.fold is not None or a.n is not None:
+            raise SystemExit("--support-manifest cannot be combined with --draw/--fold/--n")
+        a.mode = "strict"
+        j = 0
+    else:
+        j = a.draw if a.mode == "fewshot" else a.fold
+    if j is None:
+        raise SystemExit("--draw (fewshot) or --fold (cv, budget) required")
+    if (a.mode == "budget") != (a.n is not None):
+        raise SystemExit("--n is required with --mode budget and only valid there")
+    smoke = bool(a.smoke_reps or a.smoke_cells or a.smoke_targets or a.smoke_rep_ids)
+    if a.support_manifest is not None and smoke:
+        raise SystemExit("strict support forbids smoke subsetting")
+    work = a.work.resolve()
+    if smoke and "/scsemi/smoke/" not in str(work) + "/":
+        raise SystemExit("smoke subsetting only under scsemi/smoke/")
+    out = work / "inputs"
+    if not a.check_only:
+        if (out / "inputs_manifest.json").exists():
+            raise SystemExit(f"{out}/inputs_manifest.json exists; inputs are immutable (preserve and inspect)")
+        out.mkdir(parents=True, exist_ok=True)
+    data = TASK / "data" / a.dataset
+    d = PD.load(data)
+    folds = pd.read_csv(data / "folds.csv")
+    if not np.array_equal(folds["sample_id"].astype(str).to_numpy(), d["sample_ids"]):
+        raise SystemExit("folds.csv rows differ from features.npz")
+    s = split_rows(a.dataset, a.mode, j, d["sample_ids"], folds, n=a.n,
+                   support_manifest=a.support_manifest)
+    rep, tgt = s["rep"], s["tgt"]
+    if a.smoke_rep_ids:
+        keep_r = set(a.smoke_rep_ids.split(","))
+        rep = np.asarray([r for r in rep if d["sample_ids"][r] in keep_r], dtype=np.int64)
+    if a.smoke_reps:
+        rep = rep[: a.smoke_reps]
+    if a.smoke_targets:
+        keep = set(a.smoke_targets.split(","))
+        tgt = np.asarray([t for t in tgt if d["sample_ids"][t] in keep], dtype=np.int64)
+        s["test"] = [x for x in s["test"] if x in keep]
+    if a.mode == "fewshot" and not smoke:
+        usable = np.flatnonzero(folds["fold"].to_numpy() >= 0)
+        if set(rep.tolist()) | set(tgt.tolist()) != set(usable.tolist()):
+            raise SystemExit("representatives + targets do not cover the usable rows")
+    # modeled genes
+    z = np.load(data / "cells_panel6k.npz", allow_pickle=False)
+    panel = z["panel_gene_ids"].astype(str)
+    gpos = {g: i for i, g in enumerate(d["gene_ids"])}
+    pcanon = np.asarray([gpos[g] for g in panel], dtype=np.int64)
+    rows = np.sort(np.concatenate([rep, tgt]))
+    bulk_ok = d["bulk_valid"][rows][:, pcanon].all(0)
+    sc_ok = d["sc_valid"][rep][:, pcanon].all(0)
+    model_pidx = np.flatnonzero(bulk_ok & sc_ok)
+    genes = panel[model_pidx]
+    if genes.size < 1000:
+        raise SystemExit(f"only {genes.size} modeled genes")
+    # scorer's scoring genes (all rows of the dataset) for the record
+    score_mask = d["bulk_valid"][:, pcanon].all(0) & d["sc_valid"][:, pcanon].all(0)
+    # bulk
+    import anndata as ad
+    B = d["bulk"][rows][:, pcanon[model_pidx]].astype(np.float64)
+    if not np.isfinite(B).all() or (B < 0).any():
+        raise SystemExit("non-finite or negative bulk on the modeled genes")
+    if (B.sum(1) <= 0).any():
+        raise SystemExit("a bulk row has no counts on the modeled genes")
+    sym = pd.read_csv(TASK.parents[1] / "artifacts/panel_ladder_floor01_1m_20260923/tiers/6k/selected_genes.csv")
+    sym = sym.set_index("gene_id").gene_symbol.astype(str)
+    var = pd.DataFrame({"gene_symbol": sym.reindex(genes).to_numpy()}, index=pd.Index(genes, name="gene_id"))
+    obs_b = pd.DataFrame({"sample_ids": d["sample_ids"][rows], "donor_id": d["donor_ids"][rows],
+                          "group": d["groups"][rows], "role": np.where(np.isin(rows, rep), "representative", "target")},
+                         index=pd.Index(d["sample_ids"][rows], name="obs"))
+    if not a.check_only:
+        ad.AnnData(X=B, obs=obs_b, var=var).write_h5ad(out / "bulk.h5ad")
+    # representative cells
+    X = sp.csr_matrix((z["data"], z["indices"], z["indptr"]), shape=tuple(int(x) for x in z["shape"]))
+    rs = np.asarray(z["row_sample"], dtype=np.int64)
+    blocks, obs_rows, per_rep = [], [], []
+    rng = np.random.default_rng(SEED)
+    for r in rep:
+        cells = np.flatnonzero(rs == r)
+        if a.smoke_cells and cells.size > a.smoke_cells:
+            cells = np.sort(rng.choice(cells, a.smoke_cells, replace=False))
+        Xr = X[cells][:, model_pidx].tocsr().astype(np.float32)
+        lib = np.asarray(Xr.sum(1)).ravel()
+        if (lib <= 0).any():
+            raise SystemExit(f"{d['sample_ids'][r]}: {int((lib <= 0).sum())} cells without counts on the modeled genes")
+        blocks.append(Xr)
+        obs_rows.append(pd.DataFrame({"sample_ids": d["sample_ids"][r], "donor_id": d["donor_ids"][r],
+                                      "source_cell_row": cells}))
+        per_rep.append({"sample_id": str(d["sample_ids"][r]), "donor_id": str(d["donor_ids"][r]),
+                        "group": str(d["groups"][r]), "n_cells": int(cells.size),
+                        "median_umi_modeled_genes": float(np.median(lib))})
+    Xs = sp.vstack(blocks, format="csr")
+    obs_s = pd.concat(obs_rows, ignore_index=True)
+    obs_s.index = pd.Index([f"{a_}|{b_}" for a_, b_ in zip(obs_s.sample_ids, obs_s.source_cell_row)], name="cell")
+    if not a.check_only:
+        ad.AnnData(X=Xs, obs=obs_s, var=var.copy()).write_h5ad(out / "rep_sc.h5ad")
+    lib_all = np.asarray(Xs.sum(1)).ravel()
+    depth = float(np.median(lib_all))
+    cond = s["condition"]
+    man = {
+        "schema": "scsemi_paired.inputs.v1", "written_utc": datetime.now(timezone.utc).isoformat(),
+        "dataset": a.dataset, "mode": a.mode, "split": j, "smoke": smoke,
+        "smoke_settings": {"reps": a.smoke_reps, "cells": a.smoke_cells, "targets": a.smoke_targets,
+                           "rep_ids": a.smoke_rep_ids} if smoke else None,
+        "train_donors": s["train_donors"],
+        "representatives": per_rep, "targets": [str(x) for x in d["sample_ids"][tgt]],
+        "target_order_test_samples": s["test"],
+        "bulk_row_order": [str(x) for x in d["sample_ids"][rows]],
+        "n_representatives": int(rep.size), "n_targets": int(tgt.size), "n_rep_cells": int(Xs.shape[0]),
+        "modeled_genes": {"n": int(genes.size), "gene_ids": genes.tolist(), "panel_index": model_pidx.tolist(),
+                          "rule": "6k panel genes with bulk_valid in every rep+target row and sc_valid in every rep row",
+                          "equals_scorer_scoring_genes": bool(np.array_equal(model_pidx, np.flatnonzero(score_mask))),
+                          "n_scorer_scoring_genes": int(score_mask.sum())},
+        "depth": {"value": depth, "rule": "median over all representative cells (all rows of the training donors) of the "
+                  "per-cell UMI sum on the modeled genes; every generated cell is scaled to this expected library",
+                  "n_cells": int(lib_all.size)},
+        "condition_npz": str(cond), "condition_sha256": sha(cond),
+        "sources": {"features.npz": sha(data / "features.npz"), "folds.csv": sha(data / "folds.csv"),
+                    "cells_panel6k.npz": sha(data / "cells_panel6k.npz"),
+                    **{k: sha(v) for k, v in s["sources"].items()}},
+        "files": ({"bulk.h5ad": sha(out / "bulk.h5ad"), "rep_sc.h5ad": sha(out / "rep_sc.h5ad")}
+                  if not a.check_only else None),
+        "builder_sha256": sha(Path(__file__)),
+    }
+    if a.mode == "strict":
+        man["support_sha256"] = s["support_sha256"]
+        man["target_donors"] = s["target_donors"]
+    if a.mode == "budget":
+        man["target_donors"] = sorted({str(x) for x in d["donor_ids"][tgt]})
+        man["budget"] = s["budget"]
+    with np.load(cond, allow_pickle=False) as cz:
+        cond_ids = cz["sample_ids"].astype(str).tolist()
+    if not smoke and sorted(cond_ids) != sorted(man["targets"]):
+        raise SystemExit("condition NPZ sample_ids differ from the targets")
+    man["condition_sample_order"] = cond_ids
+    if a.check_only:
+        summary = {"dataset": a.dataset, "mode": a.mode, "split": j, "n": a.n, "work": str(work),
+                   "reps": [r["sample_id"] for r in per_rep], "targets": man["targets"],
+                   "rep_cells": {r["sample_id"]: r["n_cells"] for r in per_rep}, "n_rep_cells": int(Xs.shape[0]),
+                   "modeled_genes": int(genes.size), "modeled_panel_index": model_pidx.tolist(),
+                   "equals_scorer_genes": man["modeled_genes"]["equals_scorer_scoring_genes"],
+                   "depth": depth, "condition_npz": str(cond), "condition_matches_targets": True,
+                   "train_donors": s["train_donors"], **({"budget": s["budget"]} if a.mode == "budget" else {})}
+        if a.check_out is not None:
+            a.check_out.parent.mkdir(parents=True, exist_ok=True)
+            a.check_out.write_text(json.dumps(summary, indent=1) + "\n")
+        print(json.dumps({k: summary[k] for k in ("dataset", "mode", "split", "n", "n_rep_cells", "modeled_genes",
+                                                  "equals_scorer_genes", "depth")} | {"reps": len(summary["reps"]),
+                                                                                       "targets": len(summary["targets"])}))
+        return 0
+    tmp = out / "inputs_manifest.json.tmp"
+    tmp.write_text(json.dumps(man, indent=1) + "\n")
+    tmp.replace(out / "inputs_manifest.json")
+    print(json.dumps({"dataset": a.dataset, "mode": a.mode, "split": j, "reps": int(rep.size), "targets": int(tgt.size),
+                      "rep_cells": int(Xs.shape[0]), "modeled_genes": int(genes.size),
+                      "equals_scorer_genes": man["modeled_genes"]["equals_scorer_scoring_genes"], "depth": depth}))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

paper-releases/pbisc-diffusion/updates/20261012T130738Z-strictpairs-implementation/diffusion/dataset/server_1/model8/plans/paper_figures_strictpairs_20261012/archive/20261012T130738Z-strictpairs-implementation/snapshot/runs/immicro/healthy/fold0/n5/lora/RUN.json

@@ -0,0 +1,105 @@
+{
+ "schema": "pbisc_sdd.ft_intradataset.run.v1",
+ "source_checkpoint": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/runs/f01_6k_1x_seed20260627/checkpoint_epoch_016.pt",
+ "source_checkpoint_sha256": "8c7cd0e83e2bc19fe03dfcbc084919a1e2f61be8cc8bc12f4caf543dfda91633",
+ "dataset_dir": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro",
+ "condition_npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/conditions_raw_fixed.npz",
+ "condition_npz_sha256": "2be5717d19e86bdf38e4838d4bd2886a211ba78bfbfe749c458305a3e0ac2dcc",
+ "fold": 0,
+ "mode": "lora",
+ "trainable": {
+  "mode": "lora",
+  "lora_layers": [
+   "time_encoder.0",
+   "time_encoder.2",
+   "condition_encoder.0",
+   "condition_encoder.3",
+   "context_mixer.2",
+   "down_blocks.0.input_projection",
+   "down_blocks.0.context_projection.1",
+   "down_blocks.1.input_projection",
+   "down_blocks.1.context_projection.1",
+   "down_blocks.2.input_projection",
+   "down_blocks.2.context_projection.1",
+   "down_blocks.3.input_projection",
+   "down_blocks.3.context_projection.1",
+   "up_blocks.0.input_projection",
+   "up_blocks.0.context_projection.1",
+   "up_blocks.1.input_projection",
+   "up_blocks.1.context_projection.1",
+   "up_blocks.2.input_projection",
+   "up_blocks.2.context_projection.1",
+   "output_trunk.0",
+   "value_head",
+   "active_head"
+  ],
+  "lora_rank": 16,
+  "lora_alpha": 16.0,
+  "n_trainable": 1296224,
+  "n_total_base": 54185184,
+  "trainable_tensors": 44
+ },
+ "lr": 0.0005,
+ "warmup": 200,
+ "batch_size": 256,
+ "ema_decay": 0.999,
+ "max_steps": 3000,
+ "steps_run": 3000,
+ "eval_every": 250,
+ "patience": 8,
+ "train_subset": null,
+ "train_donors_file": null,
+ "support_manifest": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paper_figures_strictpairs_20261012/protocol/splits/immicro/healthy/fold0/n5/support.json",
+ "support_sha256": "85c04462a9c768c205fc0adfcb0a40e5f94f504a93d6d822944387bc055b65d4",
+ "train_donor_ids": [
+  "HS40",
+  "HS30",
+  "HS53",
+  "HS39",
+  "HS109"
+ ],
+ "excluded_selected_donor_visits": [],
+ "fixed_steps": 3000,
+ "selected_step": 3000,
+ "selected_inner_val_loss": NaN,
+ "pretrained_inner_val_loss": NaN,
+ "train_rows": [
+  "HS30",
+  "HS39",
+  "HS40",
+  "HS53",
+  "HS109"
+ ],
+ "inner_val_rows": [],
+ "test_rows_never_loaded_for_training": [
+  "HS3",
+  "HS6",
+  "HS7",
+  "HS13",
+  "HS18",
+  "HS31",
+  "HS34",
+  "HS37",
+  "HS38",
+  "HS52",
+  "HS58",
+  "HS60",
+  "HS66",
+  "HS81",
+  "HS82",
+  "HS83",
+  "HS85",
+  "HS86",
+  "HS95",
+  "HS98",
+  "HS101",
+  "HS107"
+ ],
+ "max_abs_weight_change": 0.5038369297981262,
+ "n_tensors_changed": 22,
+ "seed": 20260927,
+ "wall_seconds": 113.0,
+ "written_utc": "2026-10-12T10:58:15.169906+00:00",
+ "checkpoint_sha256": "51eb269bf13e9ead76da3fdd3bb9ca6400c2de7014f85286ecc0e0c11ad9df11",
+ "reload_forward_max_abs_diff": 0.0
+}