Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261013T094815Z-all-celltypes-final-palette-142/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/build_celltypes_palette_master_20261013.py

@@ -0,0 +1,150 @@
+"""Integrate all-cell-type pair budgets and the approved method palette."""
+from pathlib import Path
+from collections import defaultdict
+from datetime import datetime, timezone
+import copy
+import csv
+import hashlib
+import json
+import os
+import re
+import sys
+if os.environ.get('CUDA_VISIBLE_DEVICES')!='':
+    raise SystemExit('CPU-only deck build requires empty CUDA_VISIBLE_DEVICES')
+D=Path(__file__).resolve().parents[1]
+A=D/'data/celltypes_palette_master_20261013'
+PARENT=D/'data/figure_enrichment_20261013/spec.json'
+PALETTE_SPEC=D/'PBISC-Diffusion_color_palette_20261013.md'
+PALETTE_RESULT=D/'data/final_palette_20261013/result.json'
+CELL_RESULT=D/'data/pair_budget_celltypes_20261013/result.json'
+OUT='PBISC-Diffusion_expanded_master_20261013_celltypes_palette'
+sys.path.insert(0,str(D));sys.path.insert(0,str(D/'tools'))
+import generate_deck as G
+from reorder_master_figures_20261013 import draw_card,draw_note
+from pptx import Presentation
+from pptx.util import Inches
+
+
+def sha(path):
+    with Path(path).open('rb') as f:return hashlib.file_digest(f,'sha256').hexdigest()
+
+
+def rel(path):
+    p=Path(path)
+    if p.is_absolute():
+        try:return str(p.relative_to(D))
+        except ValueError:return str(p)
+    return str(p)
+
+
+def new_slide(record,kind):
+    s=copy.deepcopy(record)
+    s.update(deck_group='Figure 3',section='Figure 3',section_label='Fig. 3d' if kind=='correlation' else 'Fig. 3d detail',
+             kind='content',layout='full',addition_key='all_celltypes:'+kind)
+    s['figures']=[rel(p) for p in s['figures']]
+    s.setdefault('keywords',[])
+    if isinstance(s.get('sources'),list):
+        s['source_bindings']=s['sources']
+        s['sources']='; '.join(x['path'] if isinstance(x,dict) else x for x in s['sources'])
+    return s
+
+
+def main():
+    A.mkdir(parents=True,exist_ok=True)
+    parent=json.loads(PARENT.read_text())['slides']
+    cells=json.loads(CELL_RESULT.read_text())
+    palette=json.loads(PALETTE_RESULT.read_text())
+    mapping=palette['mapping']
+    # Require a complete explicit mapping, including assets whose biological
+    # colours remain unchanged; an absent rule must not silently pass through.
+    original_assets={str((D/f).resolve()) for s in parent for f in s.get('figures',[])}
+    assert original_assets <= set(mapping),sorted(original_assets-set(mapping))
+    old_manifest=json.loads((PARENT.parent/'manifest.json').read_text())
+    for asset in original_assets:
+        assert sha(asset)==old_manifest['sources'][asset]['sha256'],('Parent scientific asset changed',asset)
+    slides=[];held=None
+    for n,s0 in enumerate(parent,1):
+        s=copy.deepcopy(s0);s['parent_enriched_page']=n
+        s['figures_before_final_palette']=s.get('figures',[])
+        if s.get('figures'):
+            s['figures']=[rel(mapping[str((D/f).resolve())]) for f in s['figures']]
+        s['method_palette']='PBISC-Diffusion_color_palette_20261013.md'
+        if n==31:
+            held=s
+            held.update(deck_group='Supp S06',section='Supp S06',section_label='Supp. S06',
+                title='Pair-budget summary: composition, coverage and steps')
+            held['footnote']='Historical n training + 7 validation. All six cell types are shown in the main pair-budget panels; this is the original summary.'
+            held['footnote_full']=held['footnote']+'\n'+held.get('footnote_full','')
+            held['keywords']=[];held.pop('say',None);held.pop('refs',None)
+            slides.extend([new_slide(cells['slides'][0],'correlation'),new_slide(cells['slides'][1],'mae')])
+            continue
+        if s['deck_group']=='Supp S06' and held is not None:
+            slides.append(held);held=None
+        slides.append(s)
+    assert held is None
+    assert len(slides)==142
+    assert sorted(s['parent_enriched_page'] for s in slides if 'parent_enriched_page' in s)==list(range(1,141))
+    assert sorted(s['original_slide'] for s in slides if 'original_slide' in s)==list(range(1,77))
+    page_map={s['parent_enriched_page']:i for i,s in enumerate(slides,1) if 'parent_enriched_page' in s}
+    oldid={s['source_page_131']:s for s in slides if s.get('source_page_131')}
+    groups=defaultdict(list)
+    for i,s in enumerate(slides,1):groups[s['deck_group']].append(i)
+    # Existing plan cards refer to enriched-parent page numbers. Relocate only
+    # explicit page-location cells, never numerical results or panel IDs.
+    for s in slides:
+        if s.get('kind')=='plan_card':
+            newrows=[]
+            for head,body,location in s['rows']:
+                if re.match(r'^pp?\.',location):
+                    location=re.sub(r'\d+',lambda m:str(page_map[int(m.group())]),location)
+                newrows.append((head,body,location))
+            s['rows']=newrows
+    plan=oldid[53]
+    plan['rows']=[(a,'All six cell types: donor correlation and fraction error','pp. 31, 32') if a=='3d' else (a,b,c)
+                  for a,b,c in plan['rows']]
+    plan['footnote']='All performance panels use healthy measured pairs. Pair-budget curves use n training + 7 validation; genuine total-five results are separate.'
+    plan['footnote_full']=plan['footnote']
+    oldid[1]['subtitle']='142 slides | All-cell-type pair budgets | Approved blue / mint / yellow method palette'
+    # A compact style note does not change biological/cohort category encodings.
+    oldid[3]['rows'][-1]=('Figure 3: all cell types','T, B, Monocyte, NK, DC and Other are compared using both donor correlation and absolute fraction error.')
+    oldid[3]['footnote']='Method palette: PBISC blue, scSemiProfiler mint, external comparators yellow. Biological category and quantitative colour scales retain their meaning.'
+    oldid[3]['footnote_full']=oldid[3]['footnote']
+    supp=oldid[109]
+    for i,(a,b,c) in enumerate(supp['rows']):
+        if a=='S03-S06':supp['rows'][i]=(a,b,f'pp. {min(groups["Supp S03"])}-{max(groups["Supp S06"])}')
+    prs=Presentation();prs.slide_width=Inches(G.SLIDE_W);prs.slide_height=Inches(G.SLIDE_H)
+    funcs={'title':G.build_title,'agenda':G.build_agenda,'divider':G.build_divider,'content':G.build_content,
+           'plan_card':draw_card,'evidence_note':draw_note}
+    for i,s in enumerate(slides,1):
+        fn=funcs[s['kind']]
+        fn(prs,s) if s['kind']=='title' else fn(prs,s,i)
+    prs.save(D/(OUT+'.pptx'))
+    spec={'deck':{'output_pptx':OUT+'.pptx','output_pdf':OUT+'.pdf'},'slides':slides}
+    (A/'spec.json').write_text(json.dumps(spec,indent=2)+'\n')
+    rows=[{'current_page':i,'parent_140_page':s.get('parent_enriched_page',''),
+        'original_131_page':s.get('source_page_131',''),'original_76_page':s.get('original_slide',''),
+        'group':s['deck_group'],'panel':s.get('section_label',''),'title':s['title']} for i,s in enumerate(slides,1)]
+    with (A/'page_map.csv').open('w',encoding='utf-8-sig',newline='') as f:
+        w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows)
+    inputs={str(p):{'sha256':sha(p)} for p in [PARENT,PALETTE_SPEC,PALETTE_RESULT,CELL_RESULT,
+        Path(__file__),D/'generate_deck.py',D/'tools/reorder_master_figures_20261013.py']}
+    for s in slides:
+        for f in s.get('figures',[]):inputs[str(D/f)]={'sha256':sha(D/f)}
+    for binding in cells['sources']:
+        p=Path(binding['path']);assert sha(p)==binding['sha256'];inputs[str(p)]={'sha256':sha(p)}
+    protected={str(p):sha(p) for p in [D/'PBISC-Diffusion_expanded_master_20261013_enriched.pptx',
+                D/'PBISC-Diffusion_expanded_master_20261013_enriched.pdf',PARENT]}
+    protected.update({p:sha(p) for p in original_assets})
+    manifest={'schema':'pbisc.celltypes-palette-master.v1','created_at':datetime.now(timezone.utc).isoformat(),
+        'parent_spec':str(PARENT),'slides':slides,'groups':dict(groups),'total_slides':142,'parent_pages':140,
+        'sources':inputs,'protected_sources':protected,'palette_spec':str(PALETTE_SPEC),
+        'palette_spec_sha256':sha(PALETTE_SPEC),'palette_result':str(PALETTE_RESULT),
+        'new_training':False,'new_inference':False,'celltype_summary_rows':cells['summary_rows'],
+        'celltype_donor_rows':cells['long_rows'],'new_statistics':'Descriptive per-celltype correlations and absolute errors from saved donor fractions.',
+        'output':{'pptx':{'path':str(D/(OUT+'.pptx')),'sha256':sha(D/(OUT+'.pptx'))}}}
+    (A/'manifest.json').write_text(json.dumps(manifest,indent=2)+'\n')
+    print(json.dumps({'pages':142,'celltype_pages':[31,32],'historical_summary_page':page_map[31],
+                      'palette_assets':len(original_assets),'groups':dict(groups)}))
+
+
+if __name__=='__main__':main()

paper-releases/pbisc-diffusion/updates/20261013T094815Z-all-celltypes-final-palette-142/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/expand_pair_budget_celltypes_20261013.py

@@ -0,0 +1,244 @@
+"""Expand the historical healthy pair-budget plot to every saved cell type.
+
+CPU-only aggregation of existing per-donor CSVs. No generated arrays, checkpoints,
+model execution, or fitting are loaded. This is the historical five-fold n+7
+validation design, not the separate strict-five-pair experiment.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+import math
+import os
+from pathlib import Path
+
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+import numpy as np
+import pandas as pd
+import style as ST
+import matplotlib.pyplot as plt
+from matplotlib.lines import Line2D
+from matplotlib.ticker import MaxNLocator
+
+D = Path(__file__).resolve().parents[1]
+P = D.parents[1] / "plans/paired_finetune_intradataset_20260927"
+DATA = D / "data/pair_budget_celltypes_20261013"
+FIG = D / "figures/pair_budget_celltypes_20261013"
+TYPES = ["T cell", "B cell", "Monocyte", "NK cell", "DC", "Other"]
+BUDGETS = ["0", "5", "10", "20", "40", "all"]
+COLORS = {"raw": "#C3E2F4", "calibration": "#59A8DF", "lora": "#168FE5"}
+BASE = P / "scores/immicro/pooled"
+CAL = P / "scores/immicro/cal_budget_20261001/pooled"
+
+
+def sha(path: Path) -> str:
+    h = hashlib.sha256()
+    with path.open("rb") as handle:
+        for block in iter(lambda: handle.read(1 << 20), b""):
+            h.update(block)
+    return h.hexdigest()
+
+
+def pearson(x: np.ndarray, y: np.ndarray) -> tuple[float, str]:
+    """Do not discard donors or represent an undefined coefficient as zero."""
+    if not np.isfinite(x).all() or not np.isfinite(y).all():
+        return float("nan"), "undefined: nonfinite fraction"
+    if len(x) < 2 or np.ptp(x) == 0 or np.ptp(y) == 0:
+        return float("nan"), "undefined: zero variance or fewer than two donors"
+    return float(np.corrcoef(x, y)[0, 1]), "defined"
+
+
+def build() -> dict:
+    DATA.mkdir(parents=True, exist_ok=True)
+    FIG.mkdir(parents=True, exist_ok=True)
+    bindings: dict[str, dict] = {}
+
+    def source(path: Path, role: str) -> Path:
+        path = path.resolve()
+        bindings[str(path)] = dict(path=str(path), role=role, sha256=sha(path), bytes=path.stat().st_size)
+        return path
+
+    def read_json(path: Path, role: str) -> dict:
+        return json.loads(source(path, role).read_text())
+
+    source(Path(__file__), "reproducible CPU-only plotting and metric definitions")
+    source(D / "tools/style.py", "Arial slide dimensions and typography; method palette overridden locally")
+    palette = source(D / "PBISC-Diffusion_color_palette_20261013.md", "approved 2026-10-13 method palette")
+    for color in COLORS.values():
+        assert color in palette.read_text(), ("Approved palette changed", color)
+    source(D / "tools/fig23_healthy.py", "original fig2_healthy_pairs method and scope definition")
+    source(D / "tools/sec08_pairs_needed.py", "original pooled Pearson definition")
+    source(P / "tools/prepare_cal_budget.py", "same-support calibration construction")
+    old_signal = pd.read_csv(source(D / "data/sec08_pairs_signal.csv", "original NK values for reproduction check"))
+    summaries = {root: read_json(root / "summary.json", "saved scoring metadata and recorded generation receipts")
+                 for root in [BASE, CAL]}
+    tables = {root: pd.read_csv(source(root / "per_donor.csv", "saved observed and generated donor fractions"))
+              for root in [BASE, CAL]}
+    prep = read_json(P / "data/immicro/PREPARE.json", "historical five-fold support and held-out counts")
+    cal_budget = read_json(P / "data/immicro/CAL_BUDGET.json", "recorded calibration fit donor IDs")
+    folds = pd.read_csv(source(P / "data/immicro/folds.csv", "donor-to-test-fold assignment"))
+    assert summaries[BASE]["n_donors"] == summaries[CAL]["n_donors"] == 107
+    assert summaries[BASE]["reference"]["reference_sha256"] == summaries[CAL]["reference"]["reference_sha256"]
+    donors = set(summaries[BASE]["donors"])
+    assert len(donors) == 107 and donors == set(summaries[CAL]["donors"])
+    fold_map = dict(zip(folds.sample_id, folds.fold.astype(int)))
+    assert set(fold_map) == donors and len(fold_map) == len(folds) == 107
+    support = []
+    for fold in range(5):
+        test_ids = {d for d, f in fold_map.items() if f == fold}
+        assert len(test_ids) == prep["fold_sizes"][str(fold)]["test"]
+        for budget in BUDGETS[1:]:
+            name = "real_lora" if budget == "all" else f"lora_n{budget}"
+            run = read_json(P / f"runs/immicro/fold{fold}/{name}/RUN.json", "historical LoRA training/validation support")
+            train, val = set(run["train_rows"]), set(run["inner_val_rows"])
+            assert run["mode"] == "lora" and len(val) == 7 and train.isdisjoint(val)
+            assert (train | val).isdisjoint(test_ids) and (train | val) <= donors
+            assert len(train) == (prep["fold_sizes"][str(fold)]["train"] if budget == "all" else int(budget))
+            if budget != "all":
+                c = cal_budget["folds"][str(fold)]["n"][budget]
+                assert set(c["fit_sample_ids"]) == train | val
+                assert c["n_fit_rows"] == len(train) + len(val)
+                assert c["n_train_rows"] == len(train) and c["n_inner_val_rows"] == len(val)
+            else:
+                assert train | val | test_ids == donors
+            support.append(dict(fold=fold, budget=budget, n_train=len(train), n_validation=len(val),
+                                n_total_paired_access=len(train | val), n_test=len(test_ids)))
+
+    configs = [(BASE, "zs__raw", "raw", "0")]
+    configs += [(BASE, "real_lora__raw" if b == "all" else f"lora_n{b}__raw", "lora", b) for b in BUDGETS[1:]]
+    configs += [(BASE if b == "all" else CAL, "zs__cal" if b == "all" else f"zs__cal_n{b}", "calibration", b)
+                for b in BUDGETS[1:]]
+    observed = tables[BASE].query("arm == 'zs__raw'").set_index("donor").sort_index()
+    long_rows, summary_rows, generation_bindings = [], [], []
+    for root, arm, method, budget in configs:
+        meta = summaries[root]["per_arm"][arm]
+        assert meta["receipt_verified"] is True and meta["n_donors"] == 107
+        q = tables[root].loc[tables[root].arm == arm].set_index("donor").sort_index()
+        assert len(q) == 107 and q.index.is_unique and set(q.index) == donors
+        for prefix in ["gen_", "obs_"]:
+            fractions = q[[prefix + ct for ct in TYPES]].to_numpy(float)
+            assert np.isfinite(fractions).all() and ((fractions >= 0) & (fractions <= 1)).all()
+            assert np.allclose(fractions.sum(axis=1), 1, atol=1e-12, rtol=0)
+        assert np.allclose(q.comp_l1.mean(), summaries[root]["headline"][arm]["comp_l1"], atol=1e-9, rtol=0)
+        generation_bindings.append(dict(arm=arm, saved_summary=str(root / "summary.json"),
+                                       receipt_verified_by_saved_scoring=meta["receipt_verified"],
+                                       generation_npz=meta["npz"], recorded_generation_sha256=meta["npz_sha256"]))
+        for ct in TYPES:
+            x, y = q[f"gen_{ct}"].to_numpy(float), q[f"obs_{ct}"].to_numpy(float)
+            assert np.array_equal(y, observed[f"obs_{ct}"].to_numpy(float)), (arm, ct, "reference mismatch")
+            correlation, status = pearson(x, y)
+            error = np.abs(x - y) * 100
+            summary_rows.append(dict(method=method, arm=arm, budget=budget, x_position=BUDGETS.index(budget),
+                                     cell_type=ct, n_donors=len(q), pearson_r=correlation, pearson_status=status,
+                                     mae_percentage_points=float(error.mean()), source=str(root / "per_donor.csv")))
+            for donor, a, b, e in zip(q.index, x, y, error):
+                long_rows.append(dict(method=method, arm=arm, budget=budget, donor=donor, fold=fold_map[donor],
+                                      cell_type=ct, observed_fraction=b, generated_fraction=a,
+                                      absolute_error_percentage_points=e, source=str(root / "per_donor.csv")))
+    summary, long = pd.DataFrame(summary_rows), pd.DataFrame(long_rows)
+    assert len(summary) == 66 and len(long) == 7062
+    gaps = []
+    for _, row in summary.loc[summary.cell_type == "NK cell"].iterrows():
+        series = {"raw": "zero_shot", "calibration": "zs_cal_budget", "lora": "lora"}[row.method]
+        saved = old_signal.loc[(old_signal.dataset == "immicro") & (old_signal.panel == "nk_fraction_r") &
+                               (old_signal.series == series) & (old_signal.pairs == row.budget), "value"]
+        assert len(saved) == 1
+        gaps.append(abs(row.pearson_r - float(saved.iloc[0])))
+    assert max(gaps) < 1e-12
+    summary.to_csv(DATA / "summary.csv", index=False)
+    long.to_csv(DATA / "donor_fractions_long.csv", index=False)
+    pd.DataFrame(support).to_csv(DATA / "support_by_fold.csv", index=False)
+
+    asset_paths = []
+    metric_config = [("pearson_r", "fraction_correlation", "Observed vs generated fraction: Pearson r",
+                      "Healthy pair budget: correlation in all cell types"),
+                     ("mae_percentage_points", "fraction_mae", "Mean absolute fraction error (percentage points)",
+                      "Healthy pair budget: error in all cell types")]
+    slides = []
+    for metric, stem, ylabel, title in metric_config:
+        fig, axes = plt.subplots(2, 3, figsize=ST.FULL)
+        fig.subplots_adjust(left=0.075, right=0.985, bottom=0.19, top=0.84, hspace=0.50, wspace=0.28)
+        for ax, ct in zip(axes.flat, TYPES):
+            ax.axvspan(0.78, 1.22, color="#F1F4F7", zorder=0)
+            for method in ["raw", "calibration", "lora"]:
+                q = summary.loc[(summary.cell_type == ct) & (summary.method == method)].sort_values("x_position")
+                common = dict(color=COLORS[method], zorder=4 if method == "lora" else 3)
+                if method == "raw":
+                    ax.plot(q.x_position, q[metric], marker="D", ms=6.8, mec="#4D4D4D", mew=0.8, ls="none", **common)
+                elif method == "calibration":
+                    ax.plot(q.x_position, q[metric], marker="s", ms=6.1, mfc="white", mec=COLORS[method], mew=1.7,
+                            ls=(0, (4, 2)), lw=1.8, **common)
+                else:
+                    ax.plot(q.x_position, q[metric], marker="o", ms=6.2, mec="#23516F", mew=0.5, lw=2.3, **common)
+                missing = q.loc[q[metric].isna()]
+                for _, row in missing.iterrows():
+                    ax.text(row.x_position, 0.03, "NA", transform=ax.get_xaxis_transform(), ha="center", fontsize=10)
+            ax.set_title(ct, loc="left", fontsize=14, pad=5)
+            ax.set_xlim(-0.3, 5.3)
+            ax.set_xticks(range(6), BUDGETS, fontsize=11.5)
+            ax.tick_params(axis="y", labelsize=11.5)
+            ax.set_axisbelow(True)
+            ax.grid(axis="y", color="#E4E4E4", lw=0.7)
+            if metric == "pearson_r":
+                finite = summary[metric].dropna()
+                lo = min(0.0, math.floor(float(finite.min()) * 10 - 1) / 10) if len(finite) else -1
+                ax.set_ylim(max(-1, lo), 1)
+                ax.set_yticks([v for v in [-1, -.75, -.5, -.25, 0, .25, .5, .75, 1] if v >= max(-1, lo)])
+            else:
+                maximum = float(summary.loc[summary.cell_type == ct, metric].max())
+                ax.set_ylim(0, maximum * 1.12)
+                ax.yaxis.set_major_locator(MaxNLocator(4, min_n_ticks=3))
+        handles = [Line2D([], [], marker="D", ls="none", color=COLORS["raw"], mec="#4D4D4D", ms=7),
+                   Line2D([], [], marker="s", ls=(0, (4, 2)), color=COLORS["calibration"], mfc="white", mew=1.5, lw=1.8),
+                   Line2D([], [], marker="o", color=COLORS["lora"], mec="#23516F", mew=.5, lw=2.3)]
+        fig.legend(handles, ["Zero-shot, raw bulk", "Zero-shot + calibration", "PBISC LoRA"],
+                   ncol=3, loc="upper center", bbox_to_anchor=(.52, 1.025), fontsize=12, columnspacing=2)
+        context = "107 donors · pooled predictions from 5-fold CV"
+        if metric != "pearson_r":
+            context += " · y-axis range varies by cell type"
+        fig.text(.53, .906, context, ha="center", fontsize=11.5, color="#4D4D4D")
+        fig.text(.012, .52, ylabel, va="center", rotation=90, fontsize=13)
+        fig.text(.53, .085, "Training pairs per fold (all = 78–79); each nonzero point also uses 7 validation pairs",
+                 ha="center", fontsize=11.5)
+        fig.text(.53, .024, "Calibration fits the same n + 7 pairs; raw zero-shot uses none. Shading marks n = 5.",
+                 ha="center", fontsize=11, color="#4D4D4D")
+        for ext in ["png", "pdf", "svg"]:
+            target = FIG / f"{stem}.{ext}"
+            fig.savefig(target, dpi=300, bbox_inches="tight", pad_inches=.04)
+            asset_paths.append(str(target))
+        plt.close(fig)
+        caveat = ("Historical ImMicro healthy five-fold CV: 107 unique donors, one held-out prediction per donor per arm. "
+                  "All six saved cell types are included, with the identical observed fractions and scoring reference across arms. "
+                  "The x-axis is training pairs: 0=raw zero-shot with no pairs; 5/10/20/40/all LoRA training pairs plus 7 "
+                  "inner-validation pairs per fold for early stopping. All means 78–79 training pairs. Calibration fits alpha "
+                  "on exactly the same training plus validation pairs, including at all. This is not the strict five-total-pair "
+                  "experiment. Correlation is Pearson between generated and observed donor fractions over pooled 107 donors, "
+                  "not an average of fold correlations. MAE is mean(abs(generated_fraction-observed_fraction))*100 in percentage "
+                  "points, with equal donor weights. No statistical significance or universal improvement is claimed. "
+                  "Missing correlation remains NA; no undefined value is replaced by zero. No donor/type/arm selection by outcome, "
+                  "no new generation, and no scSemiProfiler arm added to the original fig2_healthy_pairs comparison. "
+                  "MAE panels have explicitly labelled per-celltype axis ranges; correlation axes are shared.")
+        slides.append(dict(kind="content", key=f"pair_budget_{stem}", role=f"f3_pair_budget_{stem}",
+                           title=title, section_label="Fig. 3 · Pair budget", layout="full", keywords=[],
+                           figures=[str((FIG / f"{stem}.png").relative_to(D))], palette="native",
+                           after_original=31, replaces_figure="fig2_healthy_pairs",
+                           footnote="107 donors, pooled 5-fold CV. Training n + 7 validation pairs; calibration uses the same n + 7. All = 78–79 training pairs.",
+                           footnote_full=caveat, notes=caveat,
+                           sources="; ".join(str(p / "per_donor.csv") for p in [BASE, CAL])))
+    asset_paths += [str(DATA / n) for n in ["summary.csv", "donor_fractions_long.csv", "support_by_fold.csv"]]
+    result = dict(schema="pbisc.pair_budget_celltypes.v1", slides=slides, assets=asset_paths,
+                  sources=list(bindings.values()), n_donors=107, n_cell_types=6, n_arm_budget_settings=11,
+                  long_rows=len(long), summary_rows=len(summary), nk_reproduction_max_abs_gap=max(gaps),
+                  undefined_correlations=summary.loc[summary.pearson_status != "defined"].replace({np.nan: None}).to_dict("records"),
+                  palette=COLORS, generation_bindings=generation_bindings,
+                  proof_scope="Saved score-table/metadata consistency and input file hashes; generated arrays/checkpoints are not re-read or re-hashed.")
+    for entry in bindings.values():
+        assert sha(Path(entry["path"])) == entry["sha256"], ("Source changed during build", entry["path"])
+    (DATA / "result.json").write_text(json.dumps(result, indent=2, ensure_ascii=False, allow_nan=False) + "\n")
+    return result
+
+
+if __name__ == "__main__":
+    result = build()
+    print(json.dumps({k: result[k] for k in ["n_donors", "n_cell_types", "long_rows", "summary_rows", "nk_reproduction_max_abs_gap"]}))

paper-releases/pbisc-diffusion/updates/20261013T094815Z-all-celltypes-final-palette-142/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/verify_celltypes_palette_master_20261013.py

@@ -0,0 +1,108 @@
+"""Check all-cell-type data, palette mapping and the final presentation."""
+from pathlib import Path
+from datetime import datetime,timezone
+import hashlib
+import json
+import sys
+import fitz
+import numpy as np
+import pandas as pd
+from pptx import Presentation
+D=Path(__file__).resolve().parents[1]
+A=D/'data/celltypes_palette_master_20261013';Q=A/'qa'
+sys.path.insert(0,str(D/'tools'))
+import verify_expanded_master_20261013 as H
+H.QA=Q
+
+
+def sha(path):
+    with Path(path).open('rb') as f:return hashlib.file_digest(f,'sha256').hexdigest()
+
+
+def main():
+    Q.mkdir(exist_ok=True)
+    spec=json.loads((A/'spec.json').read_text());m=json.loads((A/'manifest.json').read_text())
+    records=spec['slides'];pptx=D/spec['deck']['output_pptx'];pdfpath=D/spec['deck']['output_pdf']
+    prs=Presentation(pptx);pdf=fitz.open(pdfpath);fail=[]
+    counts={'spec':len(records),'pptx':len(prs.slides),'pdf':len(pdf)}
+    if set(counts.values())!={142}:fail.append(['page_counts',counts])
+    if sorted(s['parent_enriched_page'] for s in records if 'parent_enriched_page' in s)!=list(range(1,141)):
+        fail.append(['parent140_coverage'])
+    if sorted(s['original_slide'] for s in records if 'original_slide' in s)!=list(range(1,77)):
+        fail.append(['original76_coverage'])
+    if records[30].get('addition_key')!='all_celltypes:correlation' or records[31].get('addition_key')!='all_celltypes:mae':
+        fail.append(['celltype_placement'])
+    if records[83].get('parent_enriched_page')!=31 or records[83]['deck_group']!='Supp S06':
+        fail.append(['historical_summary_preserved'])
+    for path,binding in m['sources'].items():
+        if sha(path)!=binding['sha256']:fail.append(['source_hash',path])
+    for path,digest in m['protected_sources'].items():
+        if sha(path)!=digest:fail.append(['parent_source_changed',path])
+    C=D/'data/pair_budget_celltypes_20261013'
+    values=pd.read_csv(C/'donor_fractions_long.csv',dtype={'budget':str,'donor':str})
+    summary=pd.read_csv(C/'summary.csv',dtype={'budget':str})
+    types={'T cell','B cell','Monocyte','NK cell','DC','Other'}
+    if set(values.cell_type)!=types or len(values)!=7062 or len(summary)!=66:fail.append(['celltype_coverage'])
+    deviations=[]
+    for row in summary.itertuples():
+        v=values[(values.arm==row.arm)&(values.cell_type==row.cell_type)]
+        if len(v)!=107 or v.donor.nunique()!=107:fail.append(['donor_units',row.arm,row.cell_type])
+        r=float(np.corrcoef(v.observed_fraction,v.generated_fraction)[0,1])
+        mae=float(np.abs(v.observed_fraction-v.generated_fraction).mean()*100)
+        deviations.append(max(abs(r-row.pearson_r),abs(mae-row.mae_percentage_points)))
+    if max(deviations)>1e-10:fail.append(['summary_reproduction',max(deviations)])
+    palette=json.loads(Path(m['palette_result']).read_text())
+    if palette['unresolved'] or not palette['coverage']['preservation_pass'] or not palette['originals_unchanged']:
+        fail.append(['palette_preservation'])
+    if palette['decision']['sha256']!=sha(Path(m['palette_spec'])):
+        fail.append(['approved_palette_revision'])
+    for record in palette['records']:
+        if not record['preservation_pass'] or sha(record['output'])!=record['output_sha256']:
+            fail.append(['palette_output_proof',record['original']])
+        if sha(record['original'])!=record['source_png_sha256']:
+            fail.append(['palette_original_preservation',record['original']])
+    original=json.loads(Path(m['parent_spec']).read_text())['slides']
+    required={str((D/p).resolve()) for s in original for p in s.get('figures',[])}
+    if not required<=set(palette['mapping']):fail.append(['palette_coverage'])
+    mapped_checks=[]
+    for s in records:
+        if 'parent_enriched_page' not in s:continue
+        p=original[s['parent_enriched_page']-1]
+        expected=[str(Path(palette['mapping'][str((D/f).resolve())]).resolve()) for f in p.get('figures',[])]
+        actual=[str((D/f).resolve()) for f in s.get('figures',[])]
+        if expected!=actual:fail.append(['palette_embed_mapping',s['parent_enriched_page']])
+        mapped_checks.append(s['parent_enriched_page'])
+    images=[];fonts=set()
+    for n,(slide,s) in enumerate(zip(prs.slides,records),1):
+        images.append(H._image_proofs(slide,s,n,fail))
+        for shape in slide.shapes:
+            if shape.left < -2 or shape.top < -2 or shape.left+shape.width > prs.slide_width+2 or shape.top+shape.height > prs.slide_height+2:
+                fail.append(['shape_bounds',n,shape.name])
+            for tf in H._text_frames(shape):
+                for paragraph in tf.paragraphs:
+                    for run in paragraph.runs:fonts.update(H._font_names(run,paragraph))
+    if fonts!={'Arial'}:fail.append(['font',sorted(fonts)])
+    for n,page in enumerate(pdf,1):
+        for block in page.get_text('dict')['blocks']:
+            for line in block.get('lines',[]):
+                for span in line.get('spans',[]):
+                    x0,y0,x1,y1=span['bbox']
+                    if x0<-.75 or y0<-.75 or x1>page.rect.width+.75 or y1>page.rect.height+.75:
+                        fail.append(['pdf_text_bounds',n,span['text']])
+    renders=H._render(pdf,records)
+    m['output']['pdf']={'path':str(pdfpath),'sha256':sha(pdfpath)}
+    (A/'manifest.json').write_text(json.dumps(m,indent=2)+'\n')
+    report={'schema':'pbisc.celltypes-palette-master.qa.v1','checked_at':datetime.now(timezone.utc).isoformat(),
+        'status':'automated_pass_visual_pending' if not fail else 'failed','automated_checks_pass':not fail,
+        'failures':fail,'counts':counts,'celltype_summary_max_abs_difference':max(deviations),
+        'cell_types':sorted(types),'celltype_pages':[31,32],'all_original140_pages_retained':True,
+        'palette_assets_mapped':len(required),'palette_result':m['palette_result'],'palette_spec_sha256':m['palette_spec_sha256'],
+        'groups':m['groups'],'image_checks':images,'renders':renders,'fonts':sorted(fonts),
+        'artifacts':{'pptx':{'path':str(pptx),'sha256':sha(pptx)},'pdf':{'path':str(pdfpath),'sha256':sha(pdfpath)}},
+        'visual_review':{'status':'pending'}}
+    (Q/'VERIFICATION.json').write_text(json.dumps(report,indent=2)+'\n')
+    print(json.dumps({'pass':not fail,'counts':counts,'failures':fail,'celltype_max_abs_error':max(deviations)}))
+    if fail:raise SystemExit(1)
+
+
+if __name__=='__main__':main()

paper-releases/pbisc-diffusion/updates/20261013T094815Z-all-celltypes-final-palette-142/diffusion/metadata/request.json

@@ -0,0 +1,1879 @@
+{
+  "assets": [
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/AGENTS.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_color_palette_20261013.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_expanded_master_20261013_celltypes_palette.pdf",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_expanded_master_20261013_celltypes_palette.pptx",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/README.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/README.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/manifest.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/page_map.csv",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/VERIFICATION.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact01.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact02.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact03.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact04.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact05.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact06.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact07.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact08.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact09.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact10.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact11.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/qa/contact12.png",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/render.log",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "code",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/render.py",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/celltypes_palette_master_20261013/spec.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/figure_assignment_20261013/ASSIGNMENT.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/figure_assignment_20261013/STRENGTHENING_PROPOSAL.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/figure_enrichment_20261013/README.md",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/ASSET_MAP.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/ORIGINALS_BEFORE.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/SEMANTIC_MAP.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/arch_all_arm_compute.proof.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/arch_context_vs_trunk.proof.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/arch_hint_injection.proof.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/arch_panel_arm_register.proof.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/arch_panel_loss_progression.proof.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/arch_width_arm_register.proof.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/arch_width_loss_progression.proof.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/behcet_k4000.proof.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/final_palette_20261013/deconv_expression_coverage.proof.json",
+      "state": "completed"
+    },
+    {
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+  "project_id": "pbisc-diffusion",
+  "summary": "142-slide master: page31all6healthycelltypePearsoncurves,page32matchedMAE;107donors,11arms/budgets,66summaries7062rows fromsavedpredictions. OriginalNKvaluesexact;historicalntraining+7validationexplicit;oldglobalbudgetsummarypreserved84. Approvedmethodpaletteappliedacross121assets:PBISCblue,scSemimint,othercomparatorsyellow;biological/quantitativeencodingspreserved. Includesvector/native/scopedrasterpreservationproofs andfullQA. No training,inference,orcalibrationfit."
+}