aind-ibl-ephys-alignment-preprocessing

AIND IBL Ephys Alignment Preprocessing

Preprocessing pipeline that prepares SmartSPIM histology and Neuropixels electrophysiology data for the IBL ephys alignment GUI. It registers histology volumes to the Allen Common Coordinate Framework (CCF), converts Neuroglancer probe-track annotations into atlas coordinates, and optionally extracts spike-sorted ephys data into IBL ALF format.

Installation

From PyPI:

pip install aind-ibl-ephys-alignment-preprocessing

From source:

git clone https://github.com/AllenNeuralDynamics/aind-ibl-ephys-alignment-preprocessing.git
cd aind-ibl-ephys-alignment-preprocessing
pip install .

Quick start

The package provides a CLI entry point, aind-ibl-preprocess:

aind-ibl-preprocess \
    --data-root /path/to/data \
    --results-root /path/to/results \
    --neuroglancer neuroglancer.json \
    --manifest manifest.csv

CLI options

Validation

Before running the full pipeline, you can check that all required inputs are present and correctly structured:

aind-ibl-preprocess \
    --data-root /path/to/data \
    --results-root /path/to/results \
    --neuroglancer neuroglancer.json \
    --manifest manifest.csv \
    --validate-only

This checks file existence, manifest structure, reference data availability, disk space (warns below 50 GB, errors below 10 GB), and available RAM (minimum 8 GB).

Regenerating only datapackage.json

If preprocessing has already produced the histology/ephys outputs, you can rewrite just the datapackage metadata:

aind-ibl-preprocess \
    --data-root /path/to/data \
    --results-root /path/to/results \
    --neuroglancer neuroglancer.json \
    --manifest manifest.csv \
    --datapackage-only

This path rediscovers asset metadata, infers successful manifest rows from existing xyz_picks*.json outputs, validates referenced files, and writes a fresh datapackage.json. It does not rerun ANTs transforms, zarr reads, probe track conversion, or ephys extraction.

On Code Ocean, previous /results assets are immutable when attached back to a new capsule. Attach the old preprocessed results asset under /data, then copy it into the new run's /results before rewriting the datapackage:

aind-ibl-preprocess \
    --data-root /data \
    --results-root /results \
    --neuroglancer neuroglancer.json \
    --manifest manifest.csv \
    --datapackage-only \
    --source-results /data/<old_preprocessed_results_asset>

--source-results may point either at the prior results asset root, containing <mouseid>/datapackage.json, or directly at the <mouseid> output directory.

Python API

The package exposes a small public API:

from aind_ibl_ephys_alignment_preprocessing import (
    PipelineConfig,
    run_pipeline,
    run_pipeline_async,
)

config = PipelineConfig(
    data_root="/path/to/data",
    results_root="/path/to/results",
    neuroglancer_file="neuroglancer.json",
    manifest_csv="manifest.csv",
    skip_ephys=False,            # set True to skip ephys extraction
    desired_voxel_size_um=25.0,  # multiscale zarr level selection
    num_parallel_jobs=4,         # parallel workers for ephys
)

# Synchronous
results = run_pipeline(config)

# Asynchronous (structured concurrency with TaskGroups)
import asyncio
results = asyncio.run(run_pipeline_async(config))

Each element of results is a ProcessResult containing the probe ID, recording ID, list of files written, and an optional skip reason.

Input data requirements

Data root layout

All input paths can be absolute or relative to data_root. The expected directory structure is:

data_root/
|
|-- neuroglancer.json
|-- manifest.csv
|
|-- smartspim_lca_template/
|   +-- smartspim_lca_template_25.nii.gz
|
|-- allen_mouse_ccf/
|   +-- average_template/
|       +-- average_template_25.nii.gz
|
|-- allen_mouse_ccf_annotations_lateralized_compact/
|   |-- ccf_2017_annotation_25_lateralized_compact.nrrd
|   +-- ccf_2017_annotation_25_lateralized_unique_vals.npz
|
|-- iblatlas_allenatlas/
|   +-- ...
|
|-- spim_template_to_ccf/
|   |-- syn_0GenericAffine.mat
|   +-- syn_1InverseWarp.nii.gz
|
|-- <smartspim_asset>/
|   |-- image_tile_fusing/
|   |   +-- OMEZarr/
|   |       |-- <registration_channel>.zarr/
|   |       +-- <additional_channels>.zarr/
|   +-- image_atlas_alignment/
|       +-- <registration_channel_stem>/
|           |-- ls_to_template_SyN_0GenericAffine.mat
|           +-- ls_to_template_SyN_1InverseWarp.nii.gz
|
|-- <sorted_recording>/
|   +-- ...  (spike sorting output)
|
+-- **/<probe_file>.json

Each of these is described in detail below.

Manifest CSV

A CSV file describing how histology tracks map onto ephys collections. Each row represents one histology track (or one histology shank) mapped to one ephys collection and ephys shank.

The contract is declared once in types.MANIFEST_COLUMNS and drives both the parser and pre-flight validation, so what a run accepts cannot drift from what validation demands. Optional columns are never required: a manifest written before a column existed stays valid.

Required columns:

Optional columns:

Constraints:

• All rows must have the same mouseid.
• The tuple (mouseid, histology_track_id, histology_shank) must be unique.
• The tuple (recording_id, ephys_collection, histology_shank) must be unique for GUI filename generation.
• The tuple (recording_id, ephys_collection, ephys_shank) must be unique for ephys shank mapping.
• For single-stream multi-shank probes, multiple rows share one ephys_collection and differ in ephys_shank.
• For split quadbase probes, multiple rows can share one logical_probe but use different ephys_collection values; each collection can have ephys_shank=0.

Example:

mouseid,sorted_recording,probe_file,histology_track_id,logical_probe,ephys_collection,histology_shank,ephys_shank
mouse001,2024-06-01_rec_sorted,track_annotations_probeA,A0001,probeA,ProbeA,,
mouse001,2024-06-01_rec_sorted,track_annotations_probeB_shank0,B0001,probeB,ProbeB,0,0
mouse001,2024-06-01_rec_sorted,track_annotations_probeB_shank1,B0001,probeB,ProbeB,1,1
mouse001,2024-06-01_rec_sorted,track_annotations_quad_shank3,Q0001,quad0,ProbeD,3,0

Neuroglancer JSON

A Neuroglancer state JSON file that contains image source URIs pointing to the SmartSPIM OME-Zarr volumes. The pipeline extracts the first image source URI to locate the SmartSPIM asset directory and discover the registration channel and any additional channels.

The asset directory must contain:

• image_tile_fusing/OMEZarr/<channel>.zarr/ -- fused OME-Zarr volumes
• image_atlas_alignment/<registration_channel_stem>/ -- ANTs registration outputs: the light-sheet to template affine and inverse warp

Reference volumes

These files are required for atlas registration. Default paths are relative to data_root and can be overridden in PipelineConfig.

Probe annotation files

Per-probe Neuroglancer point annotation files. For each row in the manifest, the pipeline searches data_root for a file matching the glob pattern */<probe_file>.<annotation_format> (e.g., */track_annotations_probeA.json).

These files contain 3D point annotations marking the probe track in the SmartSPIM image space. They are converted through the transform chain:

Neuroglancer pixels --> SPIM (LPS) --> Template --> CCF (LPS/um) --> Bregma (IBL)

Electrophysiology data

Unless --skip-ephys is passed, the pipeline expects spike-sorted data at data_root/<sorted_recording>/ for each unique recording in the manifest. The ephys extraction (delegated to aind-ephys-ibl-gui-conversion) converts this into IBL ALF format (e.g., spikes.times.npy, spikes.clusters.npy, channels.localCoordinates.npy, etc.).

Ephys extraction runs once per unique recording ID (deduplicated across manifest rows).

Output structure

All outputs are written under results_root/<mouseid>/:

results_root/
|-- manifest.csv                              # Copy of input manifest
+-- <mouseid>/
    |-- ccf_space_histology/                  # QC only (emit_qc); GUI never reads it
    |   +-- histology_<channel>.nrrd          # Additional channels in CCF space
    |
    |-- image_space_histology/
    |   |-- histology_registration.nrrd       # Registration channel in image space
    |   |-- histology_registration_pipeline.nrrd
    |   |-- ccf_in_mouse.nrrd                 # CCF template warped to image space
    |   +-- labels_in_mouse.nrrd              # CCF labels warped to image space
    |
    |-- track_data/
    |   |-- spim/<probe_id>.*                 # Track coordinates in SPIM space
    |   |-- template/<probe_id>.*             # Track coordinates in template space
    |   |-- ccf/<probe_id>.*                  # Track coordinates in CCF space
    |   |-- bregma_xyz/<probe_id>.*           # Track coordinates in IBL bregma space
    |   +-- datapackage.json                  # Machine-readable output manifest
    |
    +-- <recording_id>/
        +-- <ephys_collection>/
            |-- xyz_picks.json                # Probe track picks (CCF coordinates)
            |-- xyz_picks_image_space.json    # Probe track picks (image space)
            |-- xyz_picks_shank<N>.json       # Per-shank picks (multi-shank only)
            |-- channels.localCoordinates.npy  # Ephys channel table
            |-- channels.rawInd.npy
            |-- channels.contactId.npy          # Optional; new ephys conversion outputs
            |-- channels.shankInd.npy
            +-- band_corr/                    # Full matrices + row_channels.json

The datapackage.json file contains a structured manifest of all outputs, including transform chain paths, histology volume paths, and per-probe metadata. It can be loaded back with:

from aind_ibl_ephys_alignment_preprocessing.datapackage import load_datapackage

dp = load_datapackage("/path/to/datapackage.json")

Pipeline overview

The pipeline performs the following steps:

1. Asset discovery -- Parse the Neuroglancer JSON to locate the SmartSPIM OME-Zarr volumes and ANTs registration outputs.
2. Validation -- Check that all required inputs exist and are correctly structured.
3. Histology processing -- Reorient the registration channel to CCF space, export additional channels, and compute inverse transforms (CCF template and labels warped back to image space).
4. Probe processing -- For each manifest row, load the Neuroglancer point annotations and convert them through the transform chain (SPIM -> template -> CCF -> bregma), writing coordinate files at each stage and producing xyz_picks.json files for the alignment GUI.
5. Ephys extraction (optional) -- Convert spike-sorted data into IBL ALF format.
6. Manifest generation -- Write datapackage.json summarizing all outputs.

Adapting to your own data

If you are not using the AIND SmartSPIM pipeline but want to use this package with your own histology and ephys data, the key requirements are:

1. OME-Zarr volumes -- Your fused histology images must be in OME-Zarr format, discoverable via a Neuroglancer JSON state file.
2. ANTs registration outputs -- You need ANTs SyN registration transforms mapping your histology images to a common template and from that template to the Allen CCF. The pipeline expects standard ANTs output files (*_0GenericAffine.mat, *_1InverseWarp.nii.gz).
3. Reference volumes -- The Allen CCF template, lateralized annotation labels, and the SmartSPIM LCA template at 25 um resolution. These can be overridden in PipelineConfig if your paths differ from the defaults.
4. Probe annotations -- Neuroglancer point annotation JSON files marking probe tracks in histology image space.
5. Spike-sorted data (optional) -- If you want ephys extraction, provide spike-sorted output compatible with aind-ephys-ibl-gui-conversion.

The coordinate convention used throughout is the IBL convention: x = ML (right positive), y = AP (anterior positive), z = DV (dorsal positive), with origin at bregma, in meters.

Development

To develop the code, run:

uv sync

Please test your changes using the full linting and testing suite:

./scripts/run_linters_and_checks.sh -c

Or run individual commands:

uv run --frozen ruff format          # Code formatting
uv run --frozen ruff check           # Linting
uv run --frozen mypy                 # Type checking
uv run --frozen interrogate -v src   # Documentation coverage
uv run --frozen codespell --check-filenames  # Spell checking
uv run --frozen pytest --cov aind_ibl_ephys_alignment_preprocessing  # Tests with coverage

Documentation

sphinx-build -b html docs/source/ docs/build/html

License

This project is licensed under the MIT License - see the LICENSE file for details.