Rescale and LOT correction by Soorya19Pradeep · Pull Request #424 · Biohub/VisCy

Soorya19Pradeep · 2026-05-19T03:39:06Z

The following have been implemented in this branch:

TripletDataModule that rescales the patches to a reference pixel and a uses the Z-MIP projection transform
computation and use of the computed linear optical transfer function for batch correction between datasets.
a simple case of computation of Maximum Mean Discrepancy (MMD) with sample config.

…staging Scale-aware patch extraction (packages/viscy-data): - Add _read_pixel_size() helper to read X pixel size from OME-Zarr metadata - Add reference_pixel_size parameter to TripletDataModule: when set, computes initial_yx_patch_size from the pixel-size ratio so the same physical area is covered at inference time - Replace BatchedRescaleYXd (removed per review) with existing BatchedZoomd using scale_factor=(1.0, final_y/initial_y, final_x/initial_x) and mode="bilinear" with antialias; import is lazy to avoid a hard dep LOT batch correction (applications/dynaclr): - Add dynaclr.evaluation.lot_correction submodule with core logic (fit, apply, save, load), Pydantic configs, and Click CLI entry points - Register fit-lot-correction and apply-lot-correction in cli.py via LazyCommand - Add pot and joblib to optional-dependencies.eval in pyproject.toml Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Plots n cell patches side-by-side: - Left: raw patch at initial_yx_patch_size (larger physical area) - Right: same patch bilinearly downscaled to final_yx_patch_size (model input) Both columns share the same percentile contrast window so differences are spatial. Physical scale (µm × µm) is shown in each panel title. Usage: python visualize_triplet_rescaling.py --data-path /path/to/data.zarr --tracks-path /path/to/tracks --source-channel Phase3D --z-range 0 5 --final-yx-patch-size 224 224 --reference-pixel-size 0.325 --output rescaling_comparison.png Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

…meta _collate_norm_meta iterated every normalization level and called torch.stack on each stat, but timepoint_statistics is nested {timepoint: {stat: tensor}} rather than flat {stat: tensor}. Any zarr carrying timepoint_statistics (alongside fov/dataset stats) crashed batch collation in TripletDataModule with "expected Tensor as element 0 ... but got dict". Stack within each timepoint sub-dict instead. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

…etDataModule Lets a 3D OME-Zarr feed a 2D model without materializing a separate MIP dataset, and centers the extracted Z window on each FOV's focus plane. - z_reduction ("mip"/"center"): collapse the extracted z_range to one slice via BatchedChannelWiseZReductiond. Label-free channels (resolved by parse_channel_name) take the center slice; others are max-projected. on_after_batch_transfer expects Z=1 when reduction is on. - z_extraction_window/z_focus_offset/focus_channel: resolve a per-FOV focus-centered window from each position's focus_slice[ch].fov_statistics.z_focus_mean (fallback z_total//2), all windows the same width. z_range stays as an explicit override; exactly one of z_range / z_extraction_window must be given. Per-FOV windows are resolved at setup() and looked up per patch in the dataset. Tests cover both reduction strategies (discriminating center vs MIP), the normalize-then-reduce order, per-FOV focus resolution, and the z_range/z_extraction_window XOR guard. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Documents the TripletDataModule predict path (zarr + tracking, not parquet) and adds a runnable sample config demonstrating z_reduction + reference_pixel_size to feed a 3D dataset to a 2D model. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Notebook-style script (no CLI) that loads a 3D OME-Zarr + tracking, extracts a per-FOV focus-centered Z window, collapses it via z_reduction, applies a random affine so anchor/positive diverge, and visualizes a couple of batches to a PNG. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Copilot

Pull request overview

This PR expands the Triplet-based data loading path to better support cross-dataset inference (pixel-size aware patch extraction + on-the-fly Z reduction + focus-centered per-FOV Z windows), and adds a LOT (Linear Optimal Transport) batch-correction pipeline with CLI entrypoints for embedding-space correction.

Changes:

Extend TripletDataModule/TripletDataset to support per-FOV z_range resolution via z_extraction_window + focus_slice metadata, plus optional z_reduction and pixel-size aware rescaling.
Add LOT correction core functions + Pydantic config models + dynaclr CLI commands for fitting/applying correction pipelines.
Add tests for the new Triplet Z-window/Z-reduction behavior and update docs/config examples for inference.

Reviewed changes

Copilot reviewed 14 out of 14 changed files in this pull request and generated 7 comments.

Show a summary per file

File	Description
packages/viscy-data/src/viscy_data/triplet.py	Adds pixel-size rescaling, per-FOV focus-centered Z windows, and optional Z-reduction in the Triplet datamodule/dataset.
packages/viscy-data/tests/test_triplet.py	Adds tests for `z_extraction_window` XOR validation, per-FOV focus-centered windows, and Z-reduction behavior/order.
packages/viscy-data/src/viscy_data/_utils.py	Extends norm-meta collation to correctly stack nested `timepoint_statistics`.
applications/dynaclr/src/dynaclr/evaluation/lot_correction/lot_correction.py	Implements LOT correction fit/apply + pipeline save/load helpers.
applications/dynaclr/src/dynaclr/evaluation/lot_correction/config.py	Adds Pydantic models for fit/apply configs and filter specs.
applications/dynaclr/src/dynaclr/evaluation/lot_correction/fit_lot_correction.py	CLI wrapper for fitting and saving a LOT pipeline from YAML config.
applications/dynaclr/src/dynaclr/evaluation/lot_correction/apply_lot_correction.py	CLI wrapper for applying a saved LOT pipeline to an embedding zarr.
applications/dynaclr/src/dynaclr/evaluation/lot_correction/init.py	Exposes LOT correction functions at the package level.
applications/dynaclr/src/dynaclr/cli.py	Registers new `fit-lot-correction` and `apply-lot-correction` CLI subcommands.
applications/dynaclr/pyproject.toml	Adds `joblib` + `pot` to `eval` extras for LOT correction dependencies.
applications/dynaclr/scripts/dataloader_inspection/visualize_triplet_rescaling.py	Adds a visualization utility for the new pixel-size rescaling behavior.
applications/dynaclr/scripts/dataloader_inspection/triplet_dataloader_zprojection.py	Adds an inspection notebook/script for focus-centered Z windows + Z-reduction.
applications/dynaclr/docs/DAGs/inference_triplet.md	Documents Triplet inference path and new options (needs a small wording update for z-window semantics).
applications/dynaclr/configs/prediction/predict_triplet_2d_from_3d.yml	Provides a sample prediction config demonstrating `reference_pixel_size` + `z_reduction`.

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- Declare viscy-transforms in viscy-data's triplet extra and hoist the BatchedZoomd/BatchedChannelWiseZReductiond imports to module top; the inline imports were masking a missing runtime dependency that would ImportError for anyone using z_reduction or reference_pixel_size. - Raise in _resolve_per_fov_z_ranges when a FOV's Z is smaller than z_extraction_window, instead of silently emitting a narrower window that breaks cross-FOV batch stacking. - Remove a dead duplicate break in test_focus_centered_z_range. - Correct the inference DAG doc: embeddings live in .X (the embedding_key array), mirrored to obsm["X_backbone"]/["X_projections"]. uv.lock intentionally omitted: the workspace glob currently entangles the untracked applications/eet package, so a clean regen of the single viscy-transforms edge is not possible until eet is committed or removed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Replace the NumPy/scipy.cdist backend with PyTorch so the pooled RBF kernel matrix is built once on the available device (CUDA if usable, else CPU) and reused across all permutations. Device selection probes a trivial kernel launch and falls back to CPU, so a present-but-unusable GPU (e.g. compute capability older than the torch build) does not crash. Public API is unchanged: median_heuristic/compute_mmd_unbiased return the same bandwidth-convention values, and mmd_permutation_test still returns (mmd2, p_value, null_distribution) so dynaclr's effect-size and activity z-score computations keep working. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Add witness_function to the MMD module: the per-point empirical witness w(z) = mean_i k(z, x_i) - mean_j k(z, y_j), the RKHS direction along which distributions P (X) and Q (Y) differ. Positive scores lean toward X, negative toward Y. Reuses the GPU RBF-kernel helpers and bandwidth (median-heuristic) convention already in the module, and chunks over query points to bound the intermediate kernel matrices. Tests cover distribution separation, X<->Y antisymmetry, and chunk-size invariance. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Adds `label_source: witness` to the linear-classifier eval as a second option to annotation-CSV labels. Per marker, cells are pooled across experiments, control/perturbed references are built from per-experiment wells, the MMD witness is fit and scored per cell, and scores are gated (sign + dead-zone) into control/perturbed pseudo-labels. Everything downstream (train_linear_classifier, publish, append-predictions, plots) is unchanged — only the label source differs. - evaluate_config.py: WitnessLabelSource, WitnessSettings, label_source switch on LinearClassifiersStepConfig (annotations stays the default). - witness_labels.py: well->reference mask (path-component match so C/1 != C/10), witness fit/score, dead-zone gating. - orchestrated.py: branch label assembly into _annotation_run_specs / _witness_run_specs / _build_labeled_adata; training loop unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Five integration tests exercising the real code path: - well-prefix match rejects C/1 vs C/10 confusion - witness scores separate control/perturbed clusters - dead-zone drops ambiguous near-origin cells - empty AnnData when a reference group has no cells - end-to-end run_linear_classifiers witness mode → metrics + summary PDF Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Mirrors linear_classifiers_infectomics.yml but with label_source: witness. Per-experiment control/perturbed wells replace annotation CSVs; wells are templates to edit per plate layout. One classifier per marker (G3BP1, SEC61B, Phase3D, viral_sensor), dead_zone 0.1, track-level split. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

- witness_score_classifiers.md: full DAG (why, step-by-step, gating rule, config, notes) plus embedded visuals. - visuals/: three Graphviz flow diagrams (data flow, gating, pipeline) as .dot + png + pdf, and four mock example-output plots (witness-score histogram, per-marker metrics bar, ROC, F1-over-time) with a reproducible generator (mock_witness_plots.py). Mock plots are watermarked synthetic. - Cross-links from evaluation.md and linear_classifiers/README.md so the new DAG is discoverable from the annotation path. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

The witness label is a deterministic function of the embedding, so evaluating a witness-trained classifier against that same label is circular — it reports a trivial ~1.0 (verified on real infectomics embeddings: 1.000 vs witness label, 0.712 vs true infection_state). WitnessSettings gains eval_against (default "infection_state") and eval_class_map ({control: uninfected, perturbed: infected}). In witness mode, val metrics are recomputed on the val split against the ground-truth obs column via the class map; the summary records eval_source. When the column is absent, metrics fall back to the witness label flagged as eval_source="witness_label". Adds two tests (annotation eval + fallback). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Score the witness-trained classifier against infection_state (not the self-referential witness label) with the control→uninfected / perturbed→infected class map, matching the leakage fix. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

- witness_score_classifiers.md: add "Evaluation: avoid the circularity trap" section with the real per-marker metrics table (scored vs infection_state), eval_against/eval_class_map in the config block, and step 7 in the flow; refresh the Notes caveat (metrics are honest now, check eval_source; note class imbalance). - witness_dataflow.dot/png/pdf: add the EVALUATE-vs-annotations node. - mock_witness_plots.py + bar/ROC png/pdf: use real run values (viral_sensor 0.87, SEC61B 0.76, Phase3D 0.58, G3BP1 0.49) instead of invented all-high numbers; watermark clarifies bar/ROC are real, hist/F1 synthetic. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Segment cells from the projected virtual-stain membrane with Cellpose CP-DINO, assign tracked nuclei to cells, derive cytoplasm masks, and measure per-cell fluorescence for track eligibility. Includes the 0.58 um recipes (April pilot and organelle_remodeling rollout), the per-FOV Slurm array scripts, the review tools behind the pilot diagnostics, and the recipe doc. The reader accepts OME-Zarr identity transforms used by legacy tracking stores. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Fit the pooled pAL17 teacher used for pAL-anchored alignment: per-dataset control median/MAD normalization, pooled PCA80, an RBF witness against controls, and an all-cell joint control+ZIKV GMM with tied covariance and 0.8/0.2 posterior gates. Leave-one-dataset-out pAL->pAL and pAL->Phase classifiers measure agreement with the teacher. The threshold sweep writes predictions_by_threshold.parquet, the per-cell posterior table that the onset alignment reads. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Align ZIKV organelle remodeling to each lineage's pAL response. Track segments are grouped into parent-linked families within dataset and FOV, descendants are collapsed by median pAL posterior, and tau=0 is the first of three frames with posterior >=0.8 after three frames <=0.2. SEC61B, G3BP1, and TOMM20 embeddings are centered on the smoothed exact-HPI control median, scaled by the control MAD, and reduced to PCA80. Null-standardized MMD then compares HPI and tau on the same onset-bearing cohort in 30-minute bins. quantify_pal_alignment_gain.py computes the spline-fit alignment metrics, and bootstrap_pal_alignment_gain.py resamples whole families within dataset, well, and condition. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Plot pAL posterior curves on the HPI and tau axes, onset support, and five-bin organelle-divergence calls. The DENV module holds the shared plotting functions, and the ZIKV script reuses them. The per-dataset audit freezes normalization, PCA, bandwidth, and onsets, reconstructs the pooled curves, and then compares each marker's datasets individually. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Render the HPI-versus-tau MMD figure, the per-marker lineage montages sampled every 3 hours from tau=-9 to +9 h, and the shared organelle UMAP. The assembler combines the ZIKV and DENV panels into the pseudotime paper figure under paper_figure_pseudotime/. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

- evaluation/temporal/forecast.py, config_forecast.py: multi-event, multi-horizon onset forecasting from causal embedding histories (TCN and RoPE transformer heads, FOV-grouped folds). - evaluation/temporal/dynaformer.py, dynaformer_data.py, dynaformer_model.py, config_dynaformer.py: self-supervised RoPE temporal head over lineage paths on frozen embeddings, plus the baseline ladder and probes. - evaluation/temporal/model.py: RotaryEmbedding, ContinuousRotaryEmbedding, RoPESelfAttention, RoPETransformerLayer, BidirectionalRoPETransformer (exported from temporal/__init__.py). - evaluation/geometry.py: embedding geometry and probe diagnostics. - cli.py: register `temporal-forecast` and `dynaformer` commands. - Phase3D 2026 annotation recipes, tools/dynaformer.sbatch, and tests (test_dynaformer, test_temporal_forecast, test_geometry). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Legacy tracking label images hold nuclei with no track row, and a concave nucleus can have its rounded centroid on background. Both rejected whole frames as track_label_encoding_unverified, skipping almost every 2024/2025 frame. An encoding is now valid when at least one centroid hits its own label and none lands on a different label. Untracked nuclei stay in the label image and are still removed from the cytoplasm. Adds centroid_in_footprint and nuclear_footprints_in_cell (all nuclei, tracked or not, per cell). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…attempts Renders each completed frame of a pilot attempt next to a reference attempt (VS membrane and Phase3D with cell, shared-mask and nucleus outlines) and writes per-frame and pooled mask metrics. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…s infection Feeds cached TOMM20 X_normalized_pca80 embeddings as per-class dumps to the pinned OPS set classifier (czbiohub-sf/ops_monorepo@ab11b56, run unchanged from an external copy). Model I classifies onset-aligned pseudotime bins against bystander and control references with phase-matched pools; Model C learns G1/S/G2/M from uninfected cells. Includes bag-size sweep, held-out evaluation, per-cell scoring, baselines and montage rendering, plus the attempt-13 crop helpers it loads (style-only lint fixes; AST unchanged apart from import order, one unused assignment and one variable rename). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…ssifier Onset lineages now need an onset from both the Phase->pAL TCN and the pAL17 sensor, and bystanders must be not-infected by both. Renames the two models to the infection classifier and the cell-cycle classifier, adds the sensor-verified config (attempt 26) and an attempt 25 vs 26 comparison table, and extends the tests to the new lineage rule. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

TripletDataset can mark predict-time patches whose QC channels are all zero or NaN with an is_blank index column, which EmbeddingWriter carries into AnnData obs. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…ict-triplet predict-triplet can now run Katamari foundation checkpoints, restrict predictions to cells passing the phenotyping eligibility filter, and pass every experiment channel to the blank-patch QC. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…valuation Representation export first writes obsm["X_control_mad"], then fits and writes the pooled marker-specific obsm["X_normalized_pca80"]. Linear classifiers forward the requested embedding key. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

phenotyping-batch plans one CP-DINO phenotyping job per dataset and submits only explicitly selected experiments. audit-ai-readiness reports each collection's storage location and flags stores older than their latest source assembly. predict_batch gains the stale-source check, explicit normalization channels, eligibility, and Katamari options. The CLI also registers filter-embedding-observations and phenotype-cells. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…nto the model matrix The matrix preflight now fails on stale AI-ready stores, predict skips datasets whose normalized representation is already written, and an optional phenotyping stage runs once per dataset ahead of predict. matrix-eval prepares the pooled representation as a dependent job before evaluations, and gains a temporal_forecast evaluation type. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…emodeling matrix The organelle_remodeling matrix gains DINOv3 ConvNeXt-Tiny, DINOv3 ViT-B, and Katamari G10 ViT-L rows with their eval configs. The collection adds the PCNA DENV/ZIKV plates, and the infectomics classifier recipe adds uninfected pseudo-controls for the 2025 mock wells. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Planning docs live in the separate ed-planning repo, and .ed_planning is already git-ignored; this one protocol was committed before the move. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…t.sbatch run-matrix passes DYNACLR_ELIGIBILITY and positional args 19-24 (YX patch size, Katamari input size, readout, teacher flag, EvolutionaryScale root, Pixi executable). predict.sbatch now forwards them to predict-batch and launches Katamari through Pixi from the EvolutionaryScale checkout. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

Soorya19Pradeep requested a review from edyoshikun May 19, 2026 03:39

edyoshikun force-pushed the rescale-lot-correction branch from a93d071 to 37b81ff Compare June 29, 2026 17:59

edyoshikun changed the base branch from modular-viscy-staging to main June 29, 2026 20:22

Soorya19Pradeep and others added 6 commits June 29, 2026 13:28

edyoshikun force-pushed the rescale-lot-correction branch from 901d5b7 to dc39073 Compare June 29, 2026 20:35

edyoshikun requested a review from Copilot June 29, 2026 20:44

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edyoshikun and others added 17 commits June 29, 2026 14:07

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CLI to split ann data by a column to group by

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feat(dynaclr): expand organelle remodeling matrix

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edyoshikun pushed a commit that referenced this pull request Aug 7, 2026

Merge PR #424 into dynaclr_models

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edyoshikun and others added 28 commits August 12, 2026 15:15

feat(dynaclr): normalize biological MMD representations

2abcc52

feat(dynaclr): persist normalized PCA representations

2150116

docs(dynaclr): clarify pooled PCA balancing

c7c889d

feat(dynaclr): promote pooled witness TCN stack

d719a66

feat(dynaclr): normalize embeddings in model matrix

3c17cb9

feat(dynaclr): expand organelle matrix evaluation

57b9616

feat(dynaclr): add systematic infection controls

b8beff7

research(protocol): test CELL-DINO RoPE history shortcuts

242466d

research(eval): add RoPE history shortcut interventions

7f77932

fix(eval): tolerate FP32 RoPE replay drift

5a62aaa

feat(viscy-data): flag blank patches during triplet prediction

9c05825

TripletDataset can mark predict-time patches whose QC channels are all zero or NaN with an is_blank index column, which EmbeddingWriter carries into AnnData obs. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

chore: stop tracking .ed_planning protocol

9dd7013

Planning docs live in the separate ed-planning repo, and .ed_planning is already git-ignored; this one protocol was committed before the move. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

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Rescale and LOT correction - #424

Rescale and LOT correction#424
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Soorya19Pradeep commented May 19, 2026 •

edited by edyoshikun

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Soorya19Pradeep commented May 19, 2026 •

edited by edyoshikun

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