[DRAFT] Benchmark platform and runtime artifact foundation#60
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[DRAFT] Benchmark platform and runtime artifact foundation#60
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- plan_01: Benchmark infrastructure with orchestration, storage, comparison - plan_02: Dataset acquisition with fail-loud validation and caching - plan_03: Tool adapters for OpenHCS, CellProfiler, ImageJ, Python - plan_04: Metric collectors (Time, Memory, GPU, Correctness) - plan_05: Pipeline equivalence system for fair comparison All plans include: - UML class diagrams - Flow diagrams - Sequence diagrams - Complete implementation code - Integration examples Ready for implementation following smell-loop approval.
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Research findings from publications using BBBC datasets: - Complete BBBC021/022/038 dataset specifications with real URLs, sizes, formats - Real CellProfiler pipeline parameters from actual analysis.cppipe files - Evaluation metrics from NuSeT (2020), Cimini et al. (2023), and other benchmarking papers - Illumination correction parameters from Singh et al. (2014) - Ground truth availability and usage strategies - Preprocessing pipelines and subsetting approaches Files added: - plan_02_ADDENDUM_real_dataset_specs.md: Complete BBBC dataset specs, download strategies, validation without checksums - plan_03_ADDENDUM_real_pipelines.md: Real CellProfiler pipeline from BBBC021 analysis.cppipe with all 27 modules - plan_04_ADDENDUM_correctness_metrics.md: Pixel-level and object-level evaluation metrics from publications - RESEARCH_SUMMARY.md: Complete investigation report with all sources cited All findings sourced from publications, GitHub repos, and BBBC downloads. No handwaving. Remaining gaps (require downloads to fill): - BBBC022 filename pattern (need to download 1 plate to reverse-engineer) - Dataset checksums (not provided by Broad, will compute or skip) - File manifests (impractical to list 39,600 files, will use count validation) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implement proper ABC-compliant handlers for BBBC datasets:
BBBC021Handler (ImageXpress format):
- Pattern: {Well}_{Site}_{Channel}{UUID}.tif (e.g., G10_s1_w1BEDC2073...tif)
- Channels: w1=DAPI, w2=Tubulin, w4=Actin
- FilenameParser with regex for Well/Site/Channel extraction
- MetadataHandler for CSV metadata (BBBC021_v1_image.csv)
- No virtual mapping needed (already flat structure)
BBBC038Handler (Kaggle nuclei, PNG format):
- Folder-based organization: stage1_train/{ImageId}/images/{ImageId}.png
- No structured filename pattern (uses ImageId as identifier)
- FilenameParser accepts .png files, extracts ImageId from path
- MetadataHandler for metadata.xlsx and CSV labels
- Handles segmentation masks in separate masks/ folders
Both handlers:
- Implement all abstract methods from MicroscopeHandler ABC
- Define compatible_backends (DISK only)
- Auto-register via _microscope_type class attribute
- Support FileManager abstraction throughout
No handwaving - ready for benchmark platform integration.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implement ABC-compliant handlers with PASSING TESTS for BBBC datasets:
BBBC021Handler (ImageXpress-like with UUID):
- Parses: G10_s1_w1{UUID}.tif (original files in Week#/Week#_##### subdirectories)
- Constructs: G10_s1_w1_z001_t001.tif (virtual workspace with all components)
- Pattern handles BOTH original (with UUID) and virtual (with z/t) filenames
- Flattens Week#/Week#_##### folder structure to plate root
- Adds default z_index=1, timepoint=1 for pattern discovery consistency
- Channels: w1=DAPI, w2=Tubulin, w4=Actin (w3 not used)
BBBC038Handler (Kaggle nuclei, PNG):
- Parses: {hex_id}.png from stage1_train/{ImageId}/images/ subdirectories
- ImageId treated as unique "well" identifier
- Single channel, single site, no Z or timepoint
- Flattens folder structure to stage1_train/ directory
Both handlers:
- Follow virtual workspace architecture: ALL components in constructed filenames
- Implement all MicroscopeHandler ABC methods
- Auto-register via _microscope_type
- Compatible backends: [DISK]
- Ready for benchmark platform integration
Tests included:
- BBBC021: 6 real filenames from BBBC021_v1_image.csv (ALL PASS)
- BBBC038: 3 hex ID filenames (ALL PASS)
- Roundtrip: parse → construct → parse (ALL PASS)
No handwaving - tested with actual BBBC filenames.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
MICROSCOPE DETECTION & REGISTRATION: - Add MetadataDetectMixin: reusable detect() implementation delegating to metadata handler - Add TiffPixelSizeMixin: extract pixel size and channel names from TIFF tags - BBBC021Handler: implement detect() via filename pattern matching - BBBC038Handler: implement detect() via stage1_train folder detection - ImageXpressHandler, OperaPhenixHandler, OpenHCSMicroscopeHandler: use MetadataDetectMixin - Remove hardcoded handler registration at end of bbbc.py (now automatic via metaclass) METADATA CACHING: - Simplify MetadataCache: remove per-file mtime tracking and validation checks - Cache is now explicit-clear-only (no automatic invalidation) - Reduces complexity while maintaining correctness for single-plate workflows REGISTRY DISCOVERY: - LazyDiscoveryDict: skip cache when secondary registries present - After discovery, populate secondary registries via _register_secondary hook - Prevents stale cache from blocking secondary registry population SIGNAL BATCHING (ImageBrowser performance): - ColumnFilterWidget.select_all/select_none: always block signals during batch updates - Emit single filter_changed signal at end instead of N signals - Fixes signal storm when clicking 'None' button on 96-well filter (96 -> 1 signal) DEPENDENCIES: - Add tqdm>=4.66.5 for progress indication This refactor improves: - Microscope detection: deterministic, side-effect-free, testable - Code reuse: mixins eliminate duplication across handlers - Performance: signal batching prevents UI thrashing - Maintainability: explicit registration removed, automatic via metaclass
ARCHITECTURE:
- Contracts: ToolAdapter, MetricCollector, DatasetSpec (immutable specs)
- Datasets: Registry of BBBC021, BBBC022, BBBC038 with download/extract/validate
- Pipelines: Registry of benchmark pipelines (nuclei_segmentation)
- Metrics: TimeMetric (perf_counter), MemoryMetric (RSS sampling)
- Adapters: OpenHCSAdapter implementing ToolAdapter contract
- Runner: Orchestrates tool validation, dataset acquisition, execution
DATASET ACQUISITION:
- Download with progress bars (tqdm)
- Extract zip archives atomically
- Validate by image count (±5% tolerance) or manifest
- Cache to ~/.cache/openhcs/benchmark_datasets/{id}/
- Fast path: skip re-download if cached and valid
OPENHCS ADAPTER:
- Validates OpenHCS installation
- Creates FileManager and microscope handler
- Runs minimal segmentation pipeline: blur → threshold → label
- Supports parameter validation (threshold_method, declump_method, diameter_range)
- Collects metrics via context managers
- Returns normalized BenchmarkResult with provenance
METRICS:
- TimeMetric: wall-clock execution time (perf_counter)
- MemoryMetric: peak RSS memory in background thread (psutil)
- Both implement MetricCollector ABC (context manager pattern)
PIPELINES:
- NUCLEI_SEGMENTATION: Otsu threshold + morphological operations
- Parameters: opening_radius, diameter_range, fill_holes
- Extensible: easy to add CELL_PAINTING, etc.
DATASETS:
- BBBC021_SINGLE_PLATE: 720 images, 839 MB
- BBBC022_SINGLE_PLATE_DNA: 3,456 images, 7.8 GB
- BBBC038_FULL: 33,215 images, 382 MB
- All with validation rules and microscope type
This enables:
- Reproducible benchmarking across tools
- Standardized metrics collection
- Dataset caching and validation
- Easy tool adapter implementation
- Extensible pipeline registry
…tion SUMMARY ======= Add complete CellProfiler conversion infrastructure for benchmarking OpenHCS against CellProfiler. Uses a two-phase approach: one-time library absorption (LLM converts entire CellProfiler library), then instant .cppipe conversion (registry lookup, no LLM needed at conversion time). CONVERTER INFRASTRUCTURE (benchmark/converter/) =============================================== - absorb.py: CLI for one-time library absorption python -m benchmark.converter.absorb --model google/gemini-3-flash-preview - library_absorber.py: Core absorption logic - Scans cellprofiler_source/library/modules/_*.py - LLM converts each to OpenHCS format - Validates: syntax, @numpy decorator, 'image' first param, no relative imports - Writes to cellprofiler_library/functions/ - llm_converter.py: Dual-backend LLM converter - Ollama (local): model names like 'qwen2.5-coder:7b' - OpenRouter (cloud): model names like 'google/gemini-3-flash-preview' - Auto-detects backend from model name format (org/model = OpenRouter) - system_prompt.py: Comprehensive first-principles OpenHCS explanation (~470 lines) - Dimensional dataflow architecture - ProcessingContract semantics (PURE_2D, PURE_3D, FLEXIBLE, VOLUMETRIC_TO_SLICE) - Multi-input operations (stack along dim 0, unstack inside function) - special_outputs/special_inputs for labels and measurements - Conversion rules and template - contract_inference.py: Runtime contract inference - source_locator.py: CellProfiler source code locator - parser.py: .cppipe file parser - pipeline_generator.py: Generate OpenHCS pipelines - settings_binder.py: Bind .cppipe settings to function kwargs - convert.py: CLI for .cppipe conversion ABSORBED LIBRARY (benchmark/cellprofiler_library/) ================================================= 26 CellProfiler modules converted to OpenHCS functions: closing, colortogray, combineobjects, convertimagetoobjects, convertobjectstoimage, correctilluminationapply, crop, dilateimage, enhanceedges, enhanceorsuppressfeatures, erodeimage, erodeobjects, expandorshrinkobjects, fillobjects, gaussianfilter, measureimageoverlap, measureobjectsizeshape, medialaxis, medianfilter, morphologicalskeleton, opening, overlayobjects, reducenoise, savecroppedobjects, threshold, watershed CELLPROFILER SOURCE (benchmark/cellprofiler_source/) ==================================================== Extracted CellProfiler source code for LLM reference: - modules/: 90 module class files - library/modules/: 27 pure algorithm implementations - library/functions/: Core utility functions - library/opts/: Enums and options EXAMPLE PIPELINES ================= - benchmark/cellprofiler_pipelines/: Original .cppipe files + converted - benchmark/pipelines/: OpenHCS benchmark pipelines (numpy, cupy, gpu variants)
EXPERIMENTAL - may be reverted. - flash_config.py: Remove max_fps cap (None instead of 60) - geometry_tracking.py: New orthogonal geometry tracking - WidgetSizeMonitor: Detects size changes in watched widgets - AutoGeometryTracker: Discovers geometry-affecting widgets - FlashGeometryTracker: Queues flashes during layout changes - Eliminates timing race conditions by state transitions, not arbitrary delays
CHANGES: - system_prompt.py: Request structured JSON output with contract, category, confidence, reasoning - llm_converter.py: Parse JSON response, populate ConversionResult with LLM-inferred metadata - library_absorber.py: Use LLM-inferred values instead of hardcoded pure_2d/0.5 defaults - pipeline_generator.py: Map category → variable_components (z_projection→Z_INDEX, channel_operation→CHANNEL) - Removed LLM fallback mode - purely deterministic conversion from absorbed library - Deleted broken ExampleHuman_openhcs.py (garbage from early LLM run) CONTRACTS.JSON NOW INCLUDES: - contract: PURE_2D | PURE_3D | FLEXIBLE | VOLUMETRIC_TO_SLICE - category: image_operation | z_projection | channel_operation - confidence: 0.0-1.0 (LLM's confidence in inference) - reasoning: Why this contract/category was chosen PIPELINE GENERATION: - Fail-loud if modules missing from absorbed library (no fallback) - variable_components derived from LLM-inferred category
…bsorbed modules Implemented LLM-powered converter system that transpiles CellProfiler pipelines (.cppipe) into native OpenHCS pipelines. Successfully absorbed all 88 CellProfiler modules using Claude Opus 4.5 and converted both benchmark pipelines (ExampleHuman and ExampleFly) to runnable OpenHCS code. Three-phase system: (1) Absorption - LLM extracts pure algorithms from CellProfiler source, infers contracts and categories; (2) Parsing - deterministic .cppipe parsing; (3) Generation - maps modules to OpenHCS functions with proper variable_components. Key features: ROI+CSV materialization for segmentation, infrastructure module handling (LoadData/ExportToSpreadsheet), retry logic, registry system with contracts.json. Results: 88 absorbed modules (segmentation, measurements, image processing, morphology, projections, transformations), 2 converted pipelines (ExampleHuman 4 modules, ExampleFly 9 modules). Technical highlights: CamelCase registry fix, dual-axis resolution integration, special I/O handling, fail-loud error handling.
- Fixed parameter name normalization to exactly match SettingsBinder logic
- Remove parenthetical content before normalization (e.g., '(Min,Max)')
- This fixes mapping of tuple parameters like 'Typical diameter (Min,Max)' -> [min_diameter, max_diameter]
- Fixed FunctionStep API usage to use tuple pattern: func=(function, {kwargs})
- Previously was incorrectly passing kwargs directly to FunctionStep
- Now correctly passes kwargs dict as second element of tuple
- Backfilled parameter mappings for 83/88 absorbed CellProfiler functions
- Used Gemini Flash 3.0 to generate mappings from original source + absorbed function
- Mappings stored in function docstrings as single source of truth
- Added backfill_parameter_mappings.py script
- Generated pipelines now have proper kwargs instead of comments
- ExampleFly: min_diameter=10, max_diameter=40 correctly mapped from tuple
- ExampleHuman: min_diameter=8, max_diameter=80 correctly mapped from tuple
- All other parameters properly translated using docstring mappings
…semantics
Used LLM (Gemini 3.0 Flash Preview) to analyze all 88 absorbed functions and determine
correct categories based on input shape expectations and iteration semantics.
Changes:
- Created recategorize_functions.py script for LLM-based recategorization
- Updated contracts.json with 7 category changes (81 unchanged)
Category changes:
z_projection (3 functions):
- MakeProjection: Processes z-stacks (D, H, W) → (H, W) projections
- Morphologicalskeleton: Has volumetric parameter for 3D processing
- TrackObjects: Processes temporal sequences (frames over time)
channel_operation (4 functions):
- CorrectIlluminationCalculate: Per-channel illumination correction
- IdentifyPrimaryObjects: Segment same marker across all sites per channel
- RescaleIntensity: Per-channel intensity normalization
- Tile: Assembles sites into montage per channel
Impact:
- IdentifyPrimaryObjects now uses VariableComponents.CHANNEL instead of SITE
- MakeProjection now uses VariableComponents.Z_INDEX instead of SITE
- Generated pipelines have semantically correct iteration order
- Functions receive correct input shapes based on their processing semantics
All changes verified against OpenHCS PURE_2D contract behavior:
- PURE_2D unstacks dim 0 and calls function on each (H, W) slice
- variable_components controls what dim 0 represents (sites, channels, or z-slices)
- Total function calls remain the same, only iteration order changes
…ents semantics Updated LLM recategorization prompt with correct dimensional dataflow semantics: - image_operation (SITE): Single-channel operations across all sites (default) - z_projection (Z_INDEX): Functions that NEED z-stacks (projections, 3D ops) - channel_operation (CHANNEL): Functions that NEED multiple channels simultaneously Results: - channel_operation (4): ColorToGray, GrayToColorRgb, MeasureColocalization, UnmixColors - z_projection (2): MakeProjection, Morphologicalskeleton - image_operation (82): Everything else (single-channel operations) Fixed incorrect categorizations from previous run: - IdentifyPrimaryObjects: channel_operation → image_operation ✓ - CorrectIlluminationCalculate: channel_operation → image_operation ✓ - RescaleIntensity: channel_operation → image_operation ✓ - Tile: channel_operation → image_operation ✓ - TrackObjects: z_projection → image_operation ✓ (time-lapse uses sequential_components) Added correct categorizations: - ColorToGray: image_operation → channel_operation ✓ - MeasureColocalization: image_operation → channel_operation ✓ - UnmixColors: image_operation → channel_operation ✓ - GrayToColorRgb: image_operation → channel_operation ✓ (manual fix) Regenerated pipelines with correct variable_components.
UnmixColors has PURE_2D contract, which means it receives (H, W) and processes each site independently. PURE_2D with channel_operation would unstack dimension 0 and process each channel independently, which defeats the purpose. Dimensional dataflow rule: - PURE_2D contract → ALWAYS image_operation (processes each site independently) - FLEXIBLE/PURE_3D contract → can be channel_operation or z_projection (processes dim 0 together) Final categorizations: - channel_operation (3): ColorToGray, GrayToColorRgb, MeasureColocalization All have FLEXIBLE contract and process multiple channels together - z_projection (2): MakeProjection, Morphologicalskeleton Process z-stacks (volumetric data) - image_operation (83): Everything else, including all PURE_2D functions
Key changes: 1. measure_colocalization: Added channel_1/channel_2 params for arbitrary N-channel input 2. gray_to_color_rgb: Added red/green/blue_channel params for arbitrary N-channel input 3. gray_to_color_cmyk: Added channel selection params for arbitrary N-channel input 4. Fixed @numpy decorator: Removed invalid contract=ProcessingContract.X usage 5. Removed unused ProcessingContract imports from all 88 functions 6. Rewrote __init__.py with dynamic function loading from contracts.json 7. Regenerated pipelines with correct variable_components The dimensional dataflow compiler perspective: - Dimension 0 can be of ARBITRARY size (1, 2, 3, 4, 5, ... N) - Functions should parameterize channel selection, not hardcode indices - ProcessingContract is orthogonal to variable_components
1. Removed unused ProcessingContract import from header template 2. Removed duplicate imports in header template 3. Changed to dynamic function loading with get_function() 4. Fixed measurecolocalization parameter mapping: - 'Select images to measure' -> (pipeline-handled) (requires pipeline context) - 'Run all metrics?' -> (pipeline-handled) (multi-param not auto-mappable) 5. Regenerated ExampleFly and ExampleHuman pipelines with clean parameters
1. Removed duplicate parameter mapping from _outline helper function
2. Added correct mapping to identify_tertiary_objects docstring
3. Object selection settings ('Select the larger/smaller identified objects')
are now (pipeline-handled) since they're @special_inputs
4. Only shrink_primary is an actual function parameter
In OpenHCS, @special_inputs are wired at compile time by name matching,
not passed as string parameters. CellProfiler's object naming convention
doesn't map directly to function kwargs.
- Categorized all 88 absorbed functions into FLEXIBLE vs PURE_3D contracts - Identified critical architectural issues: * Contract mismatch (PURE_2D vs PURE_3D) * Tuple handling bug in _execute_pure_2d * Inconsistent special outputs format - Created phased refactoring plan with timeline and risk mitigation - Documented 14 FLEXIBLE functions (support true 3D + slice-by-slice) - Documented 74 PURE_3D functions (always internal slicing) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Comprehensive design document covering: - Architecture comparison (CellProfiler vs OpenHCS) - Identified abstraction leaks (A1-A3, B1-B4, C1-C4) - What we're certain about (contract system, aggregation orthogonality) - Design proposal: AggregationSpec and compile-time symbol resolution - Implementation phases - Open questions for further discussion
Detailed mapping of: - Core concept mapping (pipeline, data containers, object model) - Semantic gaps requiring new concepts (ObjectRegistry, etc.) - Adapter layer design for CellProfiler modules - ProcessingContract mapping - Measurement naming conventions - Settings system mapping - Abstraction leak analysis
…sign doc Includes: - Essential files to read (OpenHCS core + CellProfiler integration) - Detailed execution flow diagram - ProcessingContract implementation with code snippets - Special outputs system explanation - CellProfiler workspace structure - Absorbed function patterns (current buggy vs required) - Key terms glossary - Quick reference: what to read when
Introduce typed artifact contracts and invocation plans, split FunctionStep execution into focused runtime/output/materialization modules, and move artifact declaration extraction out of path planning. Rename in-tree special input/output decorators to artifact inputs/outputs, clean stale contract naming, update external submodule pointers, and keep the ZMQ integration test harness isolated from conftest startup.
Replace the runtime plan facade over compiled dicts with typed input-conversion and materialized-output sections, so FunctionStep execution no longer depends on plan.raw access. Fix artifact materialization filenames to use the typed pipeline position and add unit coverage for the snapshot boundary.
Update materialized analysis result paths and stored materialization config when path collision resolution rewrites a step subdirectory. Rename the stale artifact bridge variable and add unit coverage for collision path rewrites.
Move disabled-function stripping and kwargs/artifact-input injection out of path planning and into the function-pattern module that owns callable pattern semantics. This removes path planner responsibility for mutating callable shapes without adding a wrapper around compiled plan dictionaries.
# Conflicts: # openhcs/core/orchestrator/orchestrator.py # openhcs/core/pipeline/compiler.py # openhcs/processing/backends/analysis/consolidate_analysis_results.py
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Benchmark Platform and CellProfiler Runtime Refactor
Main goal
Upgrade the OpenHCS compiler/runtime so converted CellProfiler pipelines execute against native OpenHCS semantics instead of workspace dicts, wrapper state, or fallback-heavy compatibility glue. The target model is:
Current branch state
Landed so far:
CellProfilerModuleExecutorandCellProfilerRuntimeAdapter..cppipesetup modules compile into typed selector-bearing source bindings and core-owned pipeline image schemas instead of raw external-image string tables.NamesAndTypesmetadata/order image-set matching now compile into typed source-binding state.STEP_INPUTandPIPELINE_STARTbindings, including metadata-only selectors and sidecar source files.imageio..cppipeshape and validates materialized CSV measurement headers..cppipecorpus prepares at21/21, including ClassifyObjects, DefineGrid, IdentifyObjectsInGrid, AreaOccupied binary/object inputs, binary-mask images, mask/worm output object names, exact metadata source filters, and object/location/shape setting disambiguation.A01, covering real PNG inputs, grouped colocalization measurements, relationship-heavy object flows, area-occupied measurements, and downstream CalculateMath.Children_<child>_Countmeasurement rows so downstreamFilterObjectscan consume CellProfiler relationship semantics.DisplayDataOnImageresolves selected object measurements from typed measurement tables instead of receiving positional image payloads by accident.CalculateMathnow bindsCount_<object>operands from runtime object-label artifacts and uses nominal operation/rounding strategies.FilterObjectsand runtime display/math binding share one CellProfiler measurement lookup implementation.MeasureObjectIntensity-style modules measure each declared image/object pair.MeasureColocalization-style modules keep the composed multi-image payload as one semantic input and now carry its metric switches through a nominal options record internally.PURE_2Dexecution can broadcast a 2D image across stacked runtime kwargs, which matches OpenHCS label-stack semantics for object modules.MeasureImageAreaOccupiedreduces label stacks as 2D CellProfiler planes instead of asking skimage for unsupported 3D perimeters.IdentifyPrimaryObjectsno longer fills holes with an object-by-object full-image scan, avoiding a real ExampleColocalization runtime cliff.Recent commits on this branch include:
fd99cad0Broaden CellProfiler runtime compatibility1a48dce8Support CellProfiler relationship measurement consumers73ccf18fExpand CellProfiler example pipeline compatibility16385a34Support exact CellProfiler source filters42f5179aSupport CellProfiler object measurement executioncb9dc65bRun official ExampleHuman through source schema4ae97155Accept current official ExampleHuman cppipea178ef17Support overlapping CellProfiler names by kind486629ceExecute CellProfiler pipeline start source bindings906dd512Execute BBBC021 analysis through orchestrator767e933aInfer common CellProfiler module contracts1f66cc84Unblock module-level GrayToColor conversion09a46cf2Compile NamesAndTypes image-set match plans5a460673Promote metadata rules into source bindingseb28f245Resolve typed runtime source bindingsd07d7e2bMake compiled step edges explicit20330316Add first-class FunctionStep source bindingsDependent PolyStore commit:
f2c41d1Support standard raster image formats on branchcellprofiler-raster-image-formatsWhat is still missing
This branch is much closer to real CellProfiler support, but it should still not claim full parity yet.
Remaining blockers:
Validation
Latest validation for
fd99cad0:pytest -q tests/unit/test_cellprofiler_library_loading.py tests/unit/test_cellprofiler_module_execution.py tests/unit/test_function_artifact_materialization.py tests/integration/test_cellprofiler_generated_pipeline.py43 passed, 2 warnings/tmp/cellprofiler_examples/ExampleColocalization/ExampleColocalization.cppipeA01successes {'A01': True}benchmark/cellprofiler_compat/module_execution.py:No refactoring findings.openhcs/core/steps/function_artifact_materialization.py:No refactoring findings.benchmark/cellprofiler_library/functions/identifyprimaryobjects.py:No refactoring findings.benchmark/cellprofiler_library/functions/measureimageareaoccupied.py:No refactoring findings.benchmark/cellprofiler_library/functions/measurecolocalization.py: internal context/builder duplication reduced; remaining finding is the explicit public CellProfiler settings surface shared by the image and object absorbed variants.python -m py_compileon touched implementation files:Earlier validation for
1a48dce8:pytest -q tests/unit/test_cellprofiler_module_execution.py tests/unit/test_cellprofiler_runtime_adapter.py tests/unit/test_cellprofiler_symbol_table.py tests/unit/test_function_step_execution_plan.py --tb=short85 passedpytest -q tests/integration/test_cellprofiler_generated_pipeline.py --tb=short8 passed, 2 warningsOPENHCS_HEADLESS=true OPENHCS_DISABLE_NAPARI=true OPENHCS_DISABLE_FIJI=true pytest -q tests/integration/test_main.py --it-visualizers=none --it-zmq-mode=direct --it-exec-mode=multiprocessing --it-dims=3d --it-sequential=none --it-microscopes=ImageXpress,OperaPhenix --tb=short4 passed, 1 warning0 findingspython -m py_compileon touched implementation/test files:Earlier validation for
73ccf18f:CellProfiler/examples:21 OK / 0 failedNext step
Move from representative execution acceptance to broader official-corpus execution acceptance, then tighten export-heavy semantics through the same typed runtime path rather than benchmark-only shortcuts.