Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/enrich_real_control_20261013.py

@@ -0,0 +1,127 @@
+"""Explain real-cell controls and relabel saved scientific plots; CPU only."""
+from pathlib import Path
+import hashlib
+import json
+import os
+import sys
+if os.environ.get('CUDA_VISIBLE_DEVICES') != '':
+    raise SystemExit('CPU only: CUDA_VISIBLE_DEVICES must be empty')
+HERE=Path(__file__).resolve().parent
+D=HERE.parent
+A=D/'data/figure_enrichment_20261013'
+F=D/'figures/figure_enrichment_20261013'
+A.mkdir(exist_ok=True);F.mkdir(exist_ok=True)
+sys.path.insert(0,str(HERE))
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+from matplotlib.patches import FancyBboxPatch, FancyArrowPatch
+from matplotlib.text import Text
+import results_review_style_panels as R
+import expanded_healthy_materials as H
+import sec04_metrics_overview as M
+
+
+def binding(path):
+    p=Path(path)
+    with p.open('rb') as f:h=hashlib.file_digest(f,'sha256').hexdigest()
+    return {'path':str(p),'sha256':h,'bytes':p.stat().st_size}
+
+
+def wording(text):
+    return (text.replace('Real R2','Real-cell repeat').replace('real-R2','real-cell repeat')
+            .replace('real R2','real-cell repeat').replace('Real R1','Observed reference')
+            .replace('real R1','observed reference').replace('real-R1','observed-reference'))
+
+
+def revise_text(fig):
+    for ax in fig.axes:
+        for get,setter in ((ax.get_xticklabels,ax.set_xticklabels),(ax.get_yticklabels,ax.set_yticklabels)):
+            labels=get()
+            if any(wording(t.get_text())!=t.get_text() for t in labels):
+                setter([wording(t.get_text()) for t in labels])
+    for t in fig.findobj(match=Text):
+        t.set_text(wording(t.get_text()))
+
+
+def save(fig,name,sources,details):
+    revise_text(fig)
+    outputs={}
+    for ext in ('png','pdf','svg'):
+        p=F/(name+'.'+ext)
+        fig.savefig(p,dpi=220,facecolor='white',bbox_inches='tight',pad_inches=.08)
+        outputs[ext]=str(p)
+    plt.close(fig)
+    proof=A/(name+'.json')
+    proof.write_text(json.dumps({'schema':'pbisc.real-control-wording.v1',
+        'sources':[binding(p) for p in sources],'outputs':{k:binding(v) for k,v in outputs.items()},
+        'operation':'saved-value plotting and label clarification; no scoring or model inference',
+        'new_training':False,'new_inference':False,'new_scoring':False,**details},indent=2)+'\n')
+    return {**outputs,'provenance':str(proof)}
+
+
+def main():
+    out={}
+    colors=['#222222','#C5AA4B','#B79A3F','#967B2B','#DEC975']
+    for mapping in (R.ARCH,R.PANELS):
+        for (key,(label,_,mark)),color in zip(list(mapping.items()),colors):
+            mapping[key]=(wording(label),color,mark)
+    R._save=lambda fig,key,inputs,details:save(fig,'labels_'+key,inputs,details)
+    out['ablation']=R._ablation()
+
+    H.FIG=F;H.DATA=A
+    H.LABEL['R2']='Real-cell repeat'
+    class Labels(H.MaterialBuilder):
+        def finish(self,fig,key,title,after,data=None,note='',native=True,footnote=None,palette='native'):
+            revise_text(fig)
+            if key=='within_type_centroids':
+                fig.axes[0].set_ylabel('Shift / observed RMS spread')
+                fig.axes[0].set_title('Relative to observed reference',fontsize=14)
+            super().finish(fig,'labels_'+key,title,after,data,wording(note),native,footnote,palette)
+    b=Labels()
+    H.centroids(b);H.sparsity(b)
+    out['supplement_replacements']=b.slides
+    M.ROWS=[tuple(c.replace('held-out real cells (R2)','separate observed-cell sample') for c in row) for row in M.ROWS]
+    M.FIGDIR=F;M.OUTDATA=A
+    def table_save(fig,name,folder):
+        out['metrics_overview']=save(fig,'labels_metrics_overview',[HERE/'sec04_metrics_overview.py'],
+            {'values':'same metric table; held-out real cells renamed without R1/R2 shorthand'})
+    M.style.save=table_save
+    M.main()
+    (A/'sec04_metrics_overview.csv').replace(A/'labels_metrics_overview.csv')
+
+    fig,ax=plt.subplots(figsize=(12.3,5.7))
+    ax.set(xlim=(0,12.3),ylim=(0,5.7));ax.axis('off')
+    def box(x,y,w,h,head,body,fill='#F3F5F6'):
+        ax.add_patch(FancyBboxPatch((x,y),w,h,boxstyle='round,pad=.04,rounding_size=.12',
+                                  facecolor=fill,edgecolor='#CFD6DA',lw=1))
+        ax.text(x+.22,y+h-.38,head,fontsize=17,weight='bold',va='top',color='#1A1A1A')
+        ax.text(x+.22,y+h-.95,body,fontsize=14,va='top',linespacing=1.7,color='#555555')
+    box(.15,3.76,3.35,1.65,'Observed single cells','Same 42 donors\nNo generated cells', '#F6F6F6')
+    box(4.05,3.76,3.63,1.65,'Reference cells','100 cells per donor\nFit the common PCA space')
+    box(8.27,3.76,3.83,1.65,'Real-cell repeat','Another 100 cells per donor\nNo cell overlap with reference')
+    ax.add_patch(FancyArrowPatch((3.56,4.52),(3.97,4.52),arrowstyle='-|>',mutation_scale=17,color='#7A858B',lw=1.2))
+    # Both groups are drawn from observed cells; the repeat is not generated
+    # from the reference group. Route the second branch below the boxes.
+    ax.plot([1.82,1.82,10.19],[3.7,3.39,3.39],color='#7A858B',lw=1.2)
+    ax.add_patch(FancyArrowPatch((10.19,3.39),(10.19,3.7),arrowstyle='-|>',mutation_scale=17,color='#7A858B',lw=1.2))
+    ax.text(.3,2.96,'How similar are two samples of REAL cells?',fontsize=23,weight='bold',color='#1A1A1A')
+    ax.text(.3,2.26,'Score the second observed-cell sample against the same reference used for generated cells.',
+            fontsize=15,color='#555555')
+    ax.text(.3,1.5,'Precision / coverage and cell-type composition include finite-cell sampling variation.',
+            fontsize=15,color='#555555')
+    ax.text(.3,.77,'This is a real-versus-real sampling reference, not a model and not a theoretical performance ceiling.',
+            fontsize=14,color='#555555')
+    ax.text(.3,.16,'100 + 100 disjoint cells per donor  |  4,200 cells per group  |  Reference-fit PCA, fixed for all methods',
+            fontsize=12,color='#777777')
+    P=D.parents[1]/'plans/paper_figures_strictpairs_20261012'
+    out['control_explanation']=save(fig,'labels_real_cell_repeat',
+        [P/'source_data/ablation_pretrain_pc_receipt.json',P/'tools/rescore_pretrain_ablations.py'],
+        {'definition':'Independent non-overlapping observed-cell draws from the same donors; not independent donors.',
+         'n_donors':42,'per_donor_per_set':100,'cell_sets':2})
+    out['assets']=list({p for result in out.values() if isinstance(result,dict) for p in result.values() if isinstance(p,str) and Path(p).is_file()} | set(b.assets))
+    (A/'labels_result.json').write_text(json.dumps(out,indent=2)+'\n')
+    print(json.dumps({'figures':list(out),'new_inference':False}))
+
+
+if __name__=='__main__':main()

paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/enrich_trajectory_20261013.py

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+"""Four exact recorded PCA frames for the paper overview, with progress colours.
+
+No sampling, interpolation, projection fitting or annotation is performed.
+All external website inputs are reduced to small owned snapshots for archival.
+"""
+from __future__ import annotations
+
+import csv
+import hashlib
+import importlib.util
+import json
+from datetime import datetime, timezone
+from pathlib import Path
+
+import numpy as np
+
+D = Path(__file__).resolve().parents[1]
+FIG = D / "figures/figure_enrichment_20261013"
+DATA = D / "data/figure_enrichment_20261013"
+WEB = Path("/home/sj_server_1/virtual-human-lab/research/pbisc-diffusion")
+URL = "https://www.virtualhumanlab.com/research/pbisc-diffusion/"
+STEPS = (0, 17, 33, 50)
+SCOPE = "Existing ctx2048 E12 EMA illustration; one GSE135779 donor pseudobulk; not final-base or paired bulk-sc validation."
+
+_spec = importlib.util.spec_from_file_location("_trajectory_deck_style", D / "tools/style.py")
+assert _spec is not None and _spec.loader is not None
+ST = importlib.util.module_from_spec(_spec)
+_spec.loader.exec_module(ST)
+plt = ST.plt
+
+
+def sha(path: Path) -> str:
+    with path.open("rb") as stream:
+        return hashlib.file_digest(stream, "sha256").hexdigest()
+
+
+def binding(path: Path, role: str) -> dict:
+    return {"path": str(path), "sha256": sha(path), "bytes": path.stat().st_size, "role": role}
+
+
+def save_json(path: Path, obj) -> None:
+    path.write_text(json.dumps(obj, indent=2, ensure_ascii=False, allow_nan=False) + "\n")
+
+
+def _sources() -> dict:
+    manifest_path = WEB / "assets/interactive-v1/manifest.json"
+    binary_path = WEB / "assets/interactive-v1/cells.bin"
+    methods_path = WEB / "assets/v3/methods.json"
+    coordinates_path = WEB / "assets/v3/coordinates.npz"
+    manifest = json.loads(manifest_path.read_text())
+    methods = json.loads(methods_path.read_text())
+    binary = binary_path.read_bytes()
+    if hashlib.sha256(binary).hexdigest() != manifest["assets"]["cells.bin"]["sha256"]:
+        raise ValueError("Website coordinate binary differs from its manifest")
+    if sha(methods_path) != manifest["provenance"]["source_cell_methods_sha256"]:
+        raise ValueError("Website methods differ from the interactive export provenance")
+    if (methods["status"] != "completed" or methods["observed_frames"] != 51
+            or methods["tracked_cells"] != 4096 or methods["reference_cells"] != 4362
+            or "final generated profiles only" not in methods["annotation"]["method"]):
+        raise ValueError("Unexpected recorded trajectory or annotation semantics")
+    arrays = {}
+    for key in ("pca", "real_pca"):
+        descriptor = manifest["cells"]["arrays"][key]
+        if descriptor["file"] != "cells.bin" or descriptor["dtype"] != "float32":
+            raise ValueError("Unexpected web coordinate descriptor")
+        arrays[key] = np.frombuffer(binary, dtype="<f4", count=descriptor["length"],
+                                    offset=descriptor["byte_offset"]).reshape(descriptor["shape"])
+    labels = np.asarray(manifest["cells"]["labels"], dtype=np.int8)
+    with np.load(coordinates_path, allow_pickle=False) as archive:
+        for key, stored in (("pca", "raw_pca"), ("real_pca", "reference_raw_pca")):
+            if not np.array_equal(arrays[key], archive[stored]):
+                raise ValueError("Web and stored trajectory coordinates differ")
+        if not np.array_equal(labels, archive["final_labels"]):
+            raise ValueError("Endpoint labels no longer align with tracked row IDs")
+        times = archive["state_times"].copy()
+        if not np.array_equal(np.asarray(manifest["cells"]["state_times"], dtype=np.float32), times):
+            raise ValueError("Recorded state times differ")
+        if archive["final_confidence"].shape != (4096,):
+            raise ValueError("Unexpected endpoint-confidence shape")
+    if arrays["pca"].shape != (51, 4096, 2) or arrays["real_pca"].shape != (4362, 2):
+        raise ValueError("Trajectory point count/coordinate dimensions changed")
+    if labels.shape != (4096,) or set(labels.tolist()) != set(range(6)):
+        raise ValueError("Missing endpoint cell-type labels")
+    if not all(np.isfinite(value).all() for value in arrays.values()):
+        raise ValueError("Nonfinite recorded coordinates")
+    bounds = manifest["cells"]["bounds"]["pca"]
+    for coordinates in arrays.values():
+        for dimension, key in enumerate(("x", "y")):
+            if coordinates[..., dimension].min() < bounds[key][0] or coordinates[..., dimension].max() > bounds[key][1]:
+                raise ValueError("Published fixed PCA bounds do not contain all recorded points")
+    selected = arrays["pca"][list(STEPS)].copy()
+    projected_reference = arrays["real_pca"].copy()
+    if not np.array_equal(selected[0], arrays["pca"][0]):
+        raise ValueError("Initial panel must contain actual recorded Gaussian x_t, not a clean prediction")
+    return {"selected": selected, "reference": projected_reference, "labels": labels,
+            "times": times[list(STEPS)], "bounds": bounds, "manifest": manifest, "methods": methods,
+            "external_sources": [binding(path, role) for path, role in (
+                (manifest_path, "website interactive export manifest"), (binary_path, "website exact recorded coordinates"),
+                (methods_path, "trajectory/annotation definitions"), (coordinates_path, "bit-identical stored coordinate cross-check"))]}
+
+
+def _snapshots(source: dict) -> list[Path]:
+    DATA.mkdir(parents=True, exist_ok=True)
+    points = DATA / "trajectory_recorded_points.npz"
+    np.savez_compressed(points, steps=np.asarray(STEPS), state_times=source["times"],
+                        raw_pca=source["selected"], reference_raw_pca=source["reference"],
+                        tracked_row_ids=np.arange(4096, dtype=np.int32),
+                        final_labels=source["labels"],
+                        label_names=np.asarray(source["manifest"]["labels"]),
+                        label_colors=np.asarray(source["manifest"]["colors"]),
+                        x_limits=np.asarray(source["bounds"]["x"]), y_limits=np.asarray(source["bounds"]["y"]))
+    methods = DATA / "trajectory_source_methods.json"
+    save_json(methods, source["methods"])
+    provenance = DATA / "trajectory_source_provenance.json"
+    save_json(provenance, {"public_page": URL, "external_source_bindings": source["external_sources"],
+                          "interactive_coordinate_descriptors": source["manifest"]["cells"]["arrays"],
+                          "interactive_provenance": source["manifest"]["provenance"],
+                          "live_page_reference_checked": True,
+                          "live_asset_bytes_verified": False,
+                          "live_asset_limitation": "Direct manifest fetch returned HTTP 403; web open could not retrieve the JSON. Local binary/manifest/stored-coordinate equivalence verified.",
+                          "archive_scope": "Archive these small Model8 snapshots; no broad website dependency is required."})
+    coordinate_csv = DATA / "trajectory_generated_coordinates.csv"
+    with coordinate_csv.open("w", newline="") as stream:
+        writer = csv.writer(stream)
+        writer.writerow(["step", "state_time", "tracked_row_id", "PC1", "PC2", "final_label_code", "endpoint_assigned_type", "colour_reveal_fraction"])
+        for panel, step in enumerate(STEPS):
+            for row, xy in enumerate(source["selected"][panel]):
+                code = int(source["labels"][row])
+                writer.writerow([step, format(float(source["times"][panel]), ".9g"), row,
+                                 format(float(xy[0]), ".9g"), format(float(xy[1]), ".9g"), code,
+                                 source["manifest"]["labels"][code], step / 50])
+    reference_csv = DATA / "trajectory_reference_coordinates.csv"
+    with reference_csv.open("w", newline="") as stream:
+        writer = csv.writer(stream); writer.writerow(["reference_row_id", "PC1", "PC2", "role"])
+        for row, xy in enumerate(source["reference"]):
+            writer.writerow([row, format(float(xy[0]), ".9g"), format(float(xy[1]), ".9g"), "observed_conditioning_donor"])
+    return [points, methods, provenance, coordinate_csv, reference_csv]
+
+
+def _panel(ax, source: dict, index: int, *, show_y: bool = True) -> None:
+    from matplotlib.colors import to_rgb
+    step = STEPS[index]
+    xyz = source["selected"][index]
+    observed = source["reference"]
+    endpoint_colors = np.asarray([to_rgb(value) for value in source["manifest"]["colors"]])
+    neutral = np.array([.56, .56, .56])
+    fraction = step / 50
+    colours = neutral + fraction * (endpoint_colors[source["labels"]] - neutral)
+    ax.scatter(observed[:, 0], observed[:, 1], s=3.2, c="#B8B8B8", alpha=.32,
+               edgecolors="none", zorder=1, rasterized=False)
+    ax.scatter(xyz[:, 0], xyz[:, 1], s=2.6, c=colours, alpha=.70,
+               edgecolors="none", zorder=2, rasterized=False)
+    ax.set_xlim(source["bounds"]["x"]); ax.set_ylim(source["bounds"]["y"])
+    ax.set_aspect("equal", adjustable="box")
+    ax.set_xticks([-10, 0, 10, 20]); ax.set_yticks([-5, 0, 5, 10, 15])
+    ax.tick_params(labelsize=11, length=3, width=.8)
+    ax.spines[["top", "right"]].set_visible(False)
+    ax.spines[["left", "bottom"]].set_color("#7A7A7A")
+    ax.grid(color="#EDEDED", linewidth=.55, zorder=0); ax.set_axisbelow(True)
+    variance = source["methods"]["raw_pca_explained_variance_ratio"]
+    ax.set_xlabel(f"PC1 ({100 * variance[0]:.1f}%)", fontsize=13)
+    if show_y:
+        ax.set_ylabel(f"PC2 ({100 * variance[1]:.1f}%)", fontsize=13)
+    else:
+        ax.set_yticklabels([])
+    stage = "Initial Gaussian state" if step == 0 else "Final generated state" if step == 50 else "Recorded denoising state"
+    ax.set_title(f"Step {step} / 50\n{stage}", fontsize=13, loc="left", pad=12, linespacing=1.55)
+    ax.text(.98, .97, f"t = {float(source['times'][index]):.2f}", transform=ax.transAxes,
+            fontsize=10.5, color="#4D4D4D", ha="right", va="top")
+
+
+def _save(fig, stem: str) -> dict[str, str]:
+    FIG.mkdir(parents=True, exist_ok=True)
+    paths = {}
+    for ext in ("png", "pdf", "svg"):
+        path = FIG / f"{stem}.{ext}"
+        fig.savefig(path, dpi=240, bbox_inches="tight", pad_inches=.07, facecolor="white")
+        paths[ext] = str(path)
+    plt.close(fig)
+    return paths
+
+
+def build() -> dict:
+    source = _sources()
+    snapshots = _snapshots(source)
+    from matplotlib.lines import Line2D
+    with plt.rc_context({"font.family": "sans-serif", "font.sans-serif": ["Arial", "Liberation Sans", "DejaVu Sans"],
+                         "font.size": 14, "svg.fonttype": "none", "pdf.fonttype": 42, "ps.fonttype": 42}):
+        fig, axes = plt.subplots(1, 4, figsize=(12.3, 4.15))
+        fig.subplots_adjust(left=.065, right=.99, top=.85, bottom=.31, wspace=.15)
+        for index, ax in enumerate(axes):
+            _panel(ax, source, index, show_y=index == 0)
+        handles = [Line2D([], [], marker="o", linestyle="", color=color, markersize=5, label=label)
+                   for label, color in zip(source["manifest"]["labels"], source["manifest"]["colors"])]
+        handles.append(Line2D([], [], marker="o", linestyle="", color="#B8B8B8", markersize=5, label="Observed reference"))
+        fig.legend(handles=handles, loc="lower center", bbox_to_anchor=(.52, .125), ncol=7,
+                   fontsize=10.5, handletextpad=.25, columnspacing=1.25)
+        fig.text(.065, .073, "Same 4,096 tracked cells · fixed real-reference PCA axes · 4,362 observed cells in grey", fontsize=11, color="#4D4D4D")
+        fig.text(.065, .024, "Hue reveal shows step progress using endpoint labels; it is not timestep identity confidence.", fontsize=11, color="#4D4D4D")
+        main = _save(fig, "trajectory_four_pca_frames")
+        individuals = []
+        for index, step in enumerate(STEPS):
+            fig, ax = plt.subplots(figsize=(4.0, 3.6))
+            fig.subplots_adjust(left=.16, right=.98, top=.80, bottom=.16)
+            _panel(ax, source, index)
+            individuals.append({"step": step, **_save(fig, f"trajectory_step_{step:02d}")})
+
+    caption = ("Four recorded noisy-state PCA frames (steps 0, 17, 33 and 50) from an existing ctx2048 E12 EMA run. "
+               "All 4,096 tracked cells retain their row identities; axes are fitted once on 4,362 observed cells from the conditioning donor. "
+               "Colours use frozen final predicted PBMC5+Other labels, revealed according to step/50; early states were not classified. "
+               "This is computational denoising, not biological time, and the same-donor reference is not independent validation.")
+    proof_path = DATA / "trajectory_provenance.json"
+    assets = [*snapshots, *[Path(value) for value in main.values()],
+              *[Path(value) for item in individuals for key, value in item.items() if key != "step"]]
+    proof = {"schema": "pbisc.figure1.recorded-pca-trajectory.v1", "created_at_utc": datetime.now(timezone.utc).isoformat(),
+             "scope": SCOPE, "public_source": URL, "caption": caption, "selected_steps": list(STEPS),
+             "selection_reason": "Start, approximately one-third, two-thirds and endpoint of the 50-step recorded sequence; no outcome-based frame selection.",
+             "state_times": [float(value) for value in source["times"]], "projection": source["methods"]["raw_projection"],
+             "axis_limits": source["bounds"], "coordinate_fit_repeated": False,
+             "new_inference": False, "new_annotation": False, "coordinate_interpolation": False,
+             "point_subsampling": False, "tracked_rows": 4096, "observed_rows": 4362,
+             "point_identity": "Stable row index from the existing recorded coordinate tensor, 0 through 4095",
+             "color_semantics": {"label_source": "final endpoint annotation only", "reveal_fraction": [step / 50 for step in STEPS],
+                                 "rule": "RGB = neutral_grey + (step/50) * (endpoint_type_RGB - neutral_grey)",
+                                 "per_timestep_confidence_available": False, "endpoint_confidence_used_for_color": False},
+             "verification": {"web_binary_hash_matches_local_manifest": True, "website_and_npz_pca_bit_identical": True,
+                              "website_and_npz_reference_bit_identical": True, "tracked_endpoint_labels_equal": True,
+                              "all_recorded_frames_inside_fixed_bounds": True},
+             "sources": [binding(path, "owned recorded-coordinate snapshot") for path in snapshots],
+             "external_source_bindings": source["external_sources"],
+             "outputs": [binding(path, "trajectory figure") for path in assets if path not in snapshots],
+             "renderer": binding(Path(__file__), "CPU-only saved-coordinate renderer")}
+    save_json(proof_path, proof); assets.append(proof_path)
+    notes = caption + "\n" + SCOPE + "\nPCA1/2 explained variance: 9.47% / 3.37% of real-reference encoded value/support states."
+    slide = {"kind": "content", "layout": "full", "title": "From Gaussian noise to generated cell states",
+             "section_label": "Figure 1 · recorded illustration", "figures": [main["png"]], "keywords": [],
+             "footnote": "Recorded ctx2048 E12 EMA; one same-donor pseudobulk. Colour reveal = step progress, not confidence. Illustrative, not paired validation.",
+             "footnote_full": notes, "notes": notes, "sources": str(proof_path),
+             "addition_key": "trajectory:four_recorded_pca_frames", "palette": "biological"}
+    return {"slides": [slide], "sources": [binding(path, "owned trajectory source/proof") for path in [*snapshots, proof_path]],
+            "assets": [str(path) for path in assets], "figure": main, "individual_panels": individuals,
+            "caption": caption, "notes": notes, "steps": list(STEPS), "provenance": str(proof_path)}
+
+
+if __name__ == "__main__":
+    result = build()
+    print(json.dumps({"figure": result["figure"], "steps": result["steps"], "provenance": result["provenance"]}, indent=2))

paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/verify_enriched_master_20261013.py

@@ -0,0 +1,98 @@
+"""Validate the expanded Figure1-3 deck and render its review contact sheets."""
+from pathlib import Path
+from datetime import datetime,timezone
+import hashlib
+import json
+import re
+import sys
+import fitz
+from pptx import Presentation
+D=Path(__file__).resolve().parents[1]
+A=D/'data/figure_enrichment_20261013';Q=A/'qa'
+sys.path.insert(0,str(D/'tools'))
+import verify_expanded_master_20261013 as H
+H.QA=Q
+
+
+def sha(p):
+    with Path(p).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());rows=spec['slides']
+    m=json.loads((A/'manifest.json').read_text())
+    parent=json.loads(Path(m['parent_spec']).read_text())['slides']
+    pptx=D/spec['deck']['output_pptx'];pdfpath=D/spec['deck']['output_pdf']
+    prs=Presentation(pptx);pdf=fitz.open(pdfpath);fail=[]
+    counts={'spec':len(rows),'pptx':len(prs.slides),'pdf':len(pdf)}
+    if set(counts.values())!={140}:fail.append(['page_counts',counts])
+    if sorted(r['parent_page'] for r in rows if 'parent_page' in r)!=list(range(1,132)):fail.append(['parent_coverage'])
+    if sorted(r['original_slide'] for r in rows if 'original_slide' in r)!=list(range(1,77)):fail.append(['original76_coverage'])
+    allowed_changed_sources={12,15,21,31,41,47}
+    for r in rows:
+        if 'parent_page' not in r:continue
+        p=parent[r['parent_page']-1]
+        if r.get('source_page_131') not in allowed_changed_sources and p.get('figures',[])!=r.get('figures',[]):
+            fail.append(['unexpected_figure_change',r['parent_page']])
+    for path,digest in m['protected_sources'].items():
+        if sha(path)!=digest:fail.append(['original_source_changed',path])
+    for path,binding in m['sources'].items():
+        if sha(path)!=binding['sha256']:fail.append(['source_binding',path])
+    f2=[r for r in rows if r['deck_group']=='Figure 2']
+    if len(f2)!=13 or any(r.get('role','').startswith('f3_') for r in f2):fail.append(['figure2_scope'])
+    if next(r for r in rows if r.get('source_page_131')==12)['deck_group']!='Figure 2':fail.append(['loss_not_promoted'])
+    arch=json.loads((A/'arch_result.json').read_text())
+    if not all(arch['source_validation'].values()):fail.append(['architecture_source_validation'])
+    mismatch=json.loads((A/'mismatch_result.json').read_text())['summary']
+    if (mismatch['n_donors'],mismatch['n_shared_genes'])!=(107,5508) or mismatch['new_calibration_fit']:
+        fail.append(['mismatch_scope'])
+    t=json.loads((A/'trajectory_provenance.json').read_text())
+    if t['selected_steps']!=[0,17,33,50] or not all(t['verification'].values()):fail.append(['trajectory_coordinates'])
+    if t['color_semantics']['per_timestep_confidence_available'] is not False:fail.append(['trajectory_color_claim'])
+    images=[];fonts=set();seen_vectors=set();wording_checks=[]
+    for i,(slide,r) in enumerate(zip(prs.slides,rows),1):
+        images.append(H._image_proofs(slide,r,i,fail))
+        if r.get('source_page_131') in (12,21):
+            old_say=parent[r['parent_page']-1].get('say')
+            if old_say and old_say in slide.notes_slide.notes_text_frame.text:
+                fail.append(['stale_replaced_analysis_notes',i])
+        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(['pptx_bounds',i,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))
+        for image_path in r.get('figures',[]):
+            vector=(D/image_path).with_suffix('.pdf')
+            if vector in seen_vectors or not vector.is_file():continue
+            seen_vectors.add(vector)
+            with fitz.open(vector) as doc:text='\n'.join(p.get_text() for p in doc)
+            hits=re.findall(r'\b(?:[Rr]eal[- ]?R[12]|real[_-]R[12])\b',text)
+            wording_checks.append({'source':str(vector),'shorthand_hits':hits})
+            if hits:fail.append(['unexplained_real_R_shorthand',i,str(vector),hits])
+    if fonts!={'Arial'}:fail.append(['fonts',sorted(fonts)])
+    for i,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',i,span['text']])
+    rendered=H._render(pdf,rows)
+    m['output']['pdf']={'path':str(pdfpath),'sha256':sha(pdfpath)}
+    (A/'manifest.json').write_text(json.dumps(m,indent=2)+'\n')
+    report={'schema':'pbisc.enriched-master.qa.v1','checked_at':datetime.now(timezone.utc).isoformat(),
+        'automated_checks_pass':not fail,'status':'automated_pass_visual_pending' if not fail else 'failed',
+        'counts':counts,'failures':fail,'parent_131_coverage':True,'original76_coverage':True,
+        'groups':m['groups'],'fonts':sorted(fonts),'image_checks':images,'wording_checks':wording_checks,
+        'protected_sources_unchanged':len(m['protected_sources']),'arch_validation':arch['source_validation'],
+        'mismatch_summary':mismatch,'trajectory_checks':t['verification'],'renders':rendered,
+        '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,'contacts':len(rendered['contacts'])}))
+    if fail:raise SystemExit(1)
+
+
+if __name__=='__main__':main()

paper-releases/pbisc-diffusion/updates/20261013T072655Z-enriched-140-slide-master/diffusion/metadata/request.json

@@ -0,0 +1,697 @@
+{
+  "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_expanded_master_20261013_enriched.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_enriched.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/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/figure_enrichment_20261013/arch_additions.json",
+      "state": "completed"
+    },
+    {
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+      "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/arch_hashes.json",
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+    },
+    {
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+      "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/arch_inference_variant_register.csv",
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+      "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/arch_training_arms.csv",
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+    {
+      "host": "server_1",
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+      "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/labels_ablation.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_enrichment_20261013/labels_detection_and_library.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/figure_enrichment_20261013/labels_detection_and_library.provenance.json",
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+    },
+    {
+      "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/labels_metrics_overview.csv",
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+    },
+    {
+      "host": "server_1",
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+    },
+    {
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+    {
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+    },
+    {
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+      "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/labels_within_type_centroids.provenance.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_enrichment_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/figure_enrichment_20261013/mismatch_analysis.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_enrichment_20261013/mismatch_density_bins.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/figure_enrichment_20261013/mismatch_gene_axis.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/figure_enrichment_20261013/mismatch_per_donor_errors.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/figure_enrichment_20261013/mismatch_result.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_enrichment_20261013/page_map.csv",
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+    },
+    {
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+      "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/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/figure_enrichment_20261013/qa/contact01.png",
+      "state": "completed"
+    },
+    {
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+      "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/qa/contact02.png",
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+      "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/qa/contact05.png",
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+      "state": "completed"
+    },
+    {
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+      "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/qa/contact09.png",
+      "state": "completed"
+    },
+    {
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+      "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/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/figure_enrichment_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/figure_enrichment_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/figure_enrichment_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/figure_enrichment_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/figure_enrichment_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_enrichment_20261013/trajectory_generated_coordinates.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/figure_enrichment_20261013/trajectory_provenance.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_enrichment_20261013/trajectory_recorded_points.npz",
+      "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/trajectory_reference_coordinates.csv",
+      "state": "completed"
+    },
+    {
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