Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261015T100456Z-figure1-diffusion-readable-corpus/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/figure1_overview_20261015.py

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+"""Diffusion-explicit overview; schematic gene values, not recorded trajectories."""
+from pathlib import Path
+import hashlib
+import json
+import os
+import sys
+import numpy as np
+if os.environ.get('CUDA_VISIBLE_DEVICES') != '':
+    raise SystemExit('CPU-only: CUDA_VISIBLE_DEVICES must be empty')
+D=Path(__file__).resolve().parents[1]
+M=D.parents[1]
+A=D/'data/figure1_focus_20261015/overview'
+O=D/'figures/figure1_focus_20261015/overview'
+sys.path.insert(0,str(D/'tools'))
+import style
+import matplotlib.pyplot as plt
+plt.rcParams['svg.fonttype']='none'
+from matplotlib.patches import PathPatch, Circle, FancyArrowPatch, Rectangle
+from matplotlib.path import Path as MPath
+INK='#222222'; BLUE='#168FE5'; PALE='#D7ECF9'; GOLD='#D9B23F'
+CELL=['#4C7195','#9ABACF','#C98A4B','#6B977B','#A3BE97','#9576A2']
+
+
+def sha(p):
+    with Path(p).open('rb') as f:return hashlib.file_digest(f,'sha256').hexdigest()
+
+
+def organic(ax,x,y,rx,ry,colour,edge='none',lw=.8,phase=0,alpha=1):
+    t=np.linspace(0,2*np.pi,145)
+    r=1+.045*np.sin(3*t+phase)+.025*np.cos(5*t-phase)
+    xy=np.column_stack([x+rx*r*np.cos(t),y+ry*r*np.sin(t)])
+    ax.add_patch(PathPatch(MPath(xy,closed=True),facecolor=colour,edgecolor=edge,lw=lw,alpha=alpha,zorder=2))
+
+
+def cell(ax,x,y,r,c,phase=0,nucleus=True):
+    organic(ax,x,y,r,r*.91,c,phase=phase)
+    if nucleus:organic(ax,x-r*.12,y+r*.02,r*.40,r*.38,'#FFFFFF',phase=phase+1,alpha=.53)
+
+
+def cloud(ax,x,y,scale=1,noise=False):
+    coords=[(-.45,.20),(-.19,.36),(.10,.30),(.41,.20),(-.53,-.11),(-.24,-.02),(.05,.02),(.35,-.08),(-.31,-.32),(.02,-.30),(.37,-.34)]
+    for j,(dx,dy) in enumerate(coords):
+        cell(ax,x+dx*scale,y+dy*scale,.115*scale, '#C8CDD1' if noise else CELL[j%len(CELL)],phase=j*.7,nucleus=not noise)
+
+
+def model(ax,x,y,adapted=False,scale=1):
+    organic(ax,x,y,.63*scale,.46*scale,PALE,edge='#93BAD4',lw=1.0,phase=.8)
+    ax.text(x,y+.09*scale,'PBISC',ha='center',va='center',fontsize=11*scale,fontweight='bold',color='#0D6EA8',zorder=4)
+    ax.text(x,y-.16*scale,'denoiser',ha='center',va='center',fontsize=10.5*scale,color='#28495E',zorder=4)
+    if adapted:
+        organic(ax,x+.47*scale,y+.37*scale,.27*scale,.16*scale,'#F3E6B7',edge='#BDA155',phase=.2)
+        ax.text(x+.47*scale,y+.37*scale,'LoRA',ha='center',va='center',fontsize=8.5*scale,color='#665315',zorder=5)
+
+
+def gene_profile(ax,x,y,values,w=1.2,h=.47,noise=False):
+    """Fixed illustrative gene-space values; no measured expression or score."""
+    for j,v in enumerate(values):
+        xx=x-w/2+(j+.5)*w/len(values)
+        ax.add_patch(Rectangle((xx-w/len(values)*.32,y+min(v,0)*h),w/len(values)*.64,abs(v)*h,
+                              facecolor='#89969E' if noise else BLUE,edgecolor='none',zorder=3))
+        if not noise and v==0:ax.scatter([xx],[y],s=9,c='#B6BFC5',zorder=4)
+    ax.plot([x-w/2-.02,x+w/2+.02],[y,y],color='#67747D',lw=.7)
+
+
+def arrow(ax,start,end,via=None,label=None,label_xy=None):
+    if via:
+        verts=[start,*via,end]
+        path=MPath(verts,[MPath.MOVETO]+[MPath.LINETO]*(len(verts)-1))
+        patch=FancyArrowPatch(path=path,arrowstyle='-|>',mutation_scale=12,lw=1.05,color=INK,zorder=1)
+    else:patch=FancyArrowPatch(start,end,arrowstyle='-|>',mutation_scale=12,lw=1.05,color=INK,zorder=1)
+    ax.add_patch(patch)
+    if label:
+        lx,ly=label_xy or ((start[0]+end[0])/2,(start[1]+end[1])/2+.18)
+        ax.text(lx,ly,label,ha='center',va='center',fontsize=10.5,color=INK,
+                bbox=dict(facecolor='white',edgecolor='none',pad=1.5))
+
+
+def bars(ax,x,y,w=.7,h=.42):
+    vals=[.16,.55,.9,.36,.71,.18,.43,.78,.29]
+    for j,v in enumerate(vals):ax.add_patch(Rectangle((x-w/2+j*w/len(vals),y),w/len(vals)*.67,h*v,facecolor='#B89D4D',edgecolor='none',zorder=3))
+    ax.plot([x-w/2,x+w/2],[y,y],color='#888888',lw=.6)
+
+
+def main():
+    A.mkdir(parents=True,exist_ok=True);O.mkdir(parents=True,exist_ok=True)
+    fig,ax=plt.subplots(figsize=(12.3,5.7));ax.axis('off');ax.set_xlim(-.1,12.3);ax.set_ylim(-.6,5.1);ax.set_aspect('equal')
+    for x,num,title in [(.12,'1','Pretrain on single cells'),(4.45,'2','Adapt on measured pairs'),(8.5,'3','Generate from new bulk')]:
+        ax.text(x,4.94,num,fontsize=16,fontweight='bold',va='top')
+        ax.text(x+.30,4.94,title,fontsize=14,va='top',fontweight='bold')
+    # Training: same-sample member counts define the conditioning pseudobulk.
+    cloud(ax,.90,3.72,.92)
+    ax.text(.90,2.94,'33.14M\ntraining cells',ha='center',va='top',fontsize=12.5,linespacing=1.35)
+    arrow(ax,(1.48,3.73),(2.12,3.73))
+    organic(ax,2.87,3.65,.58,.83,'#F3F3F0',edge='#B4B6B2',phase=2)
+    bars(ax,2.87,3.87,.71,.40)
+    ax.text(2.87,4.46,'Same sample',ha='center',fontsize=11)
+    ax.text(2.87,3.68,'Pseudobulk',ha='center',fontsize=10.5)
+    cell(ax,2.87,3.31,.19,CELL[3],phase=1.4)
+    ax.text(2.87,2.99,'Member cell',ha='center',fontsize=10.5)
+    arrow(ax,(2.66,2.78),(2.22,2.16),label='condition + target',label_xy=(3.10,2.35))
+    model(ax,2.00,1.60)
+    ax.text(2.00,.86,'Pretrained PBISC',ha='center',fontsize=13,fontweight='bold')
+    ax.text(2.00,.49,'Learn gene value and detection',ha='center',fontsize=10.5,color='#555555')
+    # Adaptation: pairing is represented by a light common envelope, not a Venn overlap.
+    organic(ax,6.22,3.67,1.20,.64,'#F2F5F3',phase=1)
+    bars(ax,5.63,3.55,.73,.52)
+    cloud(ax,6.83,3.75,.66)
+    ax.text(6.22,4.46,'Paired samples · same donor',ha='center',fontsize=11)
+    ax.text(5.63,3.04,'Measured bulk',ha='center',fontsize=10.5)
+    ax.text(6.83,3.04,'scRNA-seq',ha='center',fontsize=10.5)
+    arrow(ax,(6.22,2.81),(6.22,2.19),label='LoRA adaptation',label_xy=(7.28,2.53))
+    arrow(ax,(2.66,1.60),(5.54,1.60),label='pretrained weights',label_xy=(4.09,1.83))
+    model(ax,6.22,1.60,adapted=True)
+    ax.text(6.22,.86,'Adapted PBISC',ha='center',fontsize=13,fontweight='bold')
+    ax.text(6.22,.49,'Update small trainable adapters',ha='center',fontsize=10.5,color='#555555')
+    # Inference: new measured bulk is shared; N_gen noise draws produce cells.
+    bars(ax,9.11,3.64,.80,.52)
+    ax.text(9.11,4.46,'New measured bulk',ha='center',fontsize=10.5)
+    gene_profile(ax,11.32,3.79,[-.60,.92,-.35,.28,-.84,.62,.14,-.43],w=1.18,h=.37,noise=True)
+    ax.text(11.32,4.46,'Noisy cell state',ha='center',fontsize=11.2,fontweight='bold')
+    ax.text(11.32,3.20,'Starts from Gaussian noise',ha='center',fontsize=9.3,color='#555555')
+    arrow(ax,(9.31,3.37),(9.91,2.94))
+    arrow(ax,(11.02,3.12),(10.76,2.94))
+    model(ax,10.35,2.47,adapted=False,scale=.95)
+    arrow(ax,(6.96,1.60),(9.65,2.47),via=[(8.50,1.60),(8.50,2.47)],label='reuse model',label_xy=(7.78,1.83))
+    # The loop denotes repeated updates in gene space, not cell-to-cell movement.
+    ax.add_patch(FancyArrowPatch((10.94,2.13),(10.94,2.73),connectionstyle='arc3,rad=1.1',
+                                arrowstyle='-|>',mutation_scale=11,color=INK,lw=1.0,zorder=1))
+    ax.text(11.78,2.39,'Iterative\ndenoising',ha='center',va='center',fontsize=10.5,linespacing=1.25)
+    arrow(ax,(10.35,1.98),(10.35,1.40))
+    ax.text(10.35,1.76,'Value + detection readout',ha='center',fontsize=9.8,
+            bbox=dict(facecolor='white',edgecolor='none',pad=1))
+    gene_profile(ax,10.00,.91,[.28,0,.86,0,0,.50,0,.68],w=1.48,h=.44)
+    arrow(ax,(10.83,1.03),(11.14,1.03))
+    cell(ax,11.43,1.05,.23,CELL[3],phase=1.4)
+    ax.text(10.35,.58,'Sparse gene expression',ha='center',fontsize=11.7,fontweight='bold')
+    ax.text(10.35,.20,'Nonzero values + exact zeros',ha='center',fontsize=10.4,color='#555555')
+    ax.plot([.12,12.08],[-.13,-.13],color='#D7D7D7',lw=.7)
+    ax.text(6.10,-.43,'Held-out paired evaluation: cell composition · disease signals · rare populations',
+            ha='center',va='center',fontsize=11.6,color='#444444')
+    fig.subplots_adjust(left=.01,right=.99,top=.99,bottom=.01)
+    for ext in ['png','pdf','svg']:fig.savefig(O/f'experimental_overview.{ext}',dpi=260)
+    plt.close(fig)
+    detail=('Diffusion-explicit schematic with editable organic vector circles, muted fills, thin dark arrows and open white space, '
+            'inspired by the supplied reference panel d. No scientific objects or numeric results were copied from that paper. '
+            '33,141,380 is the verified unique training-cell universe. A same-sample K-cell recipe supplies pseudobulk conditioning '
+            'to each member target cell. Pretrained weights initialize LoRA adaptation on measured bulk–scRNA pairs. '
+            'New measured bulk and independent Gaussian-noise draws generate cells, without supplied cell-type quotas. '
+            'Generic network nodes are replaced by explicit PBISC denoiser labels. The inference path shows an initial noisy '
+            'gene-space state, iterative denoising, and a value/detection readout producing sparse expression with exact zeros. '
+            'Plotted profile values are fixed illustrative glyphs, not recorded states, data or quantitative predictions. '
+            'Intermediate noisy state is not nonnegative expression; the displayed final zeros are schematic measured-gene '
+            'readouts, not an encoding of unmeasured genes. Cell colours are schematic, not cell-type quotas. '
+            'The main model uses no cell-type/disease input labels; Figure2 oracle/prediction-hint ablations are separate. '
+            'Generated annotations are post hoc. Conditioning recipe K and generated-cell number N_gen are distinct. '
+            'Held-out scRNA is evaluation truth, never shown as an inference input. Rare-population endpoints are evaluation '
+            'objectives, not a claim of completed source-cluster recovery; Figure6 bulk-only published-finding reproduction remains TODO.')
+    src=[Path(__file__),M/'runs/f01_6k_1x_seed20260627/ALLCELL_DATA_CONTRACT.json',
+         D/'data/figure_assignment_20261013/AUTHOR_SCOPE.md',D/'PBISC-Diffusion_color_palette_20261013.md',
+         M/'src/pbisc_sdd/diffusion.py',
+         D/'tools/figure1_overview_20261014.py',
+         Path('/home/sj_server_1/PBISC/paper-release-20260928/manuscript/scripts/build_figure1_architecture_nature.py')]
+    result={'schema':'pbisc.figure1-overview.20261015.v1','slides':[{'kind':'content','title':'Experimental overview',
+        'section':'Figure 1','deck_group':'Figure 1','section_label':'Fig. 1a','layout':'full','keywords':[],
+        'figures':[str((O/'experimental_overview.png').relative_to(D))],
+        'footnote':'Schematic gene profiles. Each independent noise draw yields one cell; N_gen is separate from recipe K. Held-out scRNA is evaluation only.',
+        'footnote_full':detail,'sources':'; '.join(str(p) for p in src)}],
+        'reference_style':{'path':'/home/sj_server_1/.codex/attachments/15a605fb-2cca-4162-9f87-354b7c57cac6/codex-clipboard-98e511c6-7c97-416f-9054-2e6db758e44e.png','interpretation':'panel d: organic flat circles, minimal black arrows, white background; existing method palette retained'},
+        'assets':[str(p) for p in O.glob('*')], 'sources':[{'path':str(p),'sha256':sha(p)} for p in src],
+        'new_training':False,'new_inference':False}
+    (A/'result.json').write_text(json.dumps(result,indent=2)+'\n')
+
+
+if __name__=='__main__':main()

paper-releases/pbisc-diffusion/updates/20261015T100456Z-figure1-diffusion-readable-corpus/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/verify_figure1_focused_master_20261015.py

@@ -0,0 +1,78 @@
+"""Verify source hashes, page preservation, embedded pictures and rendered bounds."""
+from pathlib import Path
+from datetime import datetime,timezone
+import hashlib
+import json
+import sys
+import fitz
+from pptx import Presentation
+D=Path(__file__).resolve().parents[1]
+A=D/'data/figure1_focus_20261015';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'];pp=D/spec['deck']['output_pptx'];pf=D/spec['deck']['output_pdf']
+    prs=Presentation(pp);pdf=fitz.open(pf);fail=[]
+    counts={'spec':len(records),'pptx':len(prs.slides),'pdf':len(pdf)}
+    if set(counts.values())!={m['total_slides']}:fail.append(['page_counts',counts])
+    if sorted(s['parent_155_page'] for s in records if 'parent_155_page' in s)!=list(range(1,156)):fail.append(['parent_coverage'])
+    if sorted(s['original_slide'] for s in records if 'original_slide' in s)!=list(range(1,77)):fail.append(['original_coverage'])
+    for p,b in m['sources'].items():
+        if sha(p)!=b['sha256']:fail.append(['source_hash',p])
+    for p,h in m['protected_sources'].items():
+        if sha(p)!=h:fail.append(['protected_hash',p])
+    original=json.loads(Path(m['parent_spec']).read_text())['slides']
+    for s in records:
+        if 'parent_155_page' in s and s['parent_155_page']!=7 and s.get('figures',[])!=original[s['parent_155_page']-1].get('figures',[]):
+            fail.append(['parent_picture_replaced',s['parent_155_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']])
+    if records[6].get('revision_key')!='figure1a:explicit_diffusion':fail.append(['overview_placement'])
+    if records[7].get('addition_key')!='figure1b:focused_training_corpus':fail.append(['corpus_main_placement'])
+    if records[8].get('parent_155_page')!=8:fail.append(['training_detail_preserved'])
+    review=fitz.open();review.insert_pdf(pdf,from_page=6,to_page=7)
+    review_path=D/'PBISC-Diffusion_Figure1_review_20261015.pdf'
+    before=[p.get_pixmap().samples for p in review]
+    for p in review:p.clean_contents()
+    review.save(review_path,garbage=4,deflate=True);review.close()
+    with fitz.open(review_path) as compact:
+        if [p.get_pixmap().samples for p in compact]!=before:fail.append(['review_pdf_pixels_changed'])
+    m['output']['review_pdf']={'path':str(review_path),'sha256':sha(review_path)}
+    renders=H._render(pdf,records)
+    m['output']['pdf']={'path':str(pf),'sha256':sha(pf)}
+    (A/'manifest.json').write_text(json.dumps(m,indent=2)+'\n')
+    report=dict(schema='pbisc.figure1-focused.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,parent155_represented=True,modified_parent_pages=[7],original76_preserved=True,
+                newpages=m['newpages'],groups=m['groups'],image_checks=images,renders=renders,fonts=sorted(fonts),
+                artifacts=m['output'],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}))
+    if fail:raise SystemExit(1)
+
+
+if __name__=='__main__':main()

paper-releases/pbisc-diffusion/updates/20261015T100456Z-figure1-diffusion-readable-corpus/diffusion/metadata/inference_locations.json

@@ -0,0 +1,5 @@
+{
+  "locations": [],
+  "project_id": "pbisc-diffusion",
+  "update_id": "20261015T100456Z-figure1-diffusion-readable-corpus"
+}

paper-releases/pbisc-diffusion/updates/20261015T100456Z-figure1-diffusion-readable-corpus/diffusion/metadata/request.json

@@ -0,0 +1,259 @@
+{
+  "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/ara/trace/exploration_tree.yaml",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/trace/pm_reasoning_log.yaml",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/trace/sessions/2026-10-15_001.yaml",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "report",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/ara/trace/sessions/session_index.yaml",
+      "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_Figure1_review_20261015.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_20261015_figure1_focused.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_20261015_figure1_focused.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/figure1_focus_20261015/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/figure1_focus_20261015/corpus/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/figure1_focus_20261015/corpus/VISUAL_QA.json",
+      "state": "completed"
+    },
+    {
+      "host": "server_1",
+      "kind": "table",
+      "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/figure1_focus_20261015/corpus/display_aggregations.csv",
+      "state": "completed"
+    },
+    {
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+}