Validated NWB conversion

Supported streams

The executable workflow covers two explicit input streams in one session:

Input	NWB representation	Tested constraints
Two-photon grayscale multi-page TIFF + frame timestamps CSV	Acquisition TwoPhotonSeries named Imaging through NeuroConv	One channel, one plane, one 2D image per page, fixed shape and dtype
Calibrated position CSV (time_s,x,y)	Behavior Position / SpatialSeries through PyNWB	Coordinates in m, cm or mm; converted to meters without temporal resampling

Other acquisition readers require their own format-specific tests. In particular, this helper does not decode SpikeGLX, Open Ephys, multichannel TIFF, volumetric TIFF, compressed video, or pixel-to-world calibration. Do not rename an arbitrary numeric table to a supported stream.

Install the tested environment

uv venv --python 3.12 nwb-env
uv pip install --python nwb-env/bin/python 'neuroconv[tiff]==0.10.2' pynwb==4.2.0 \
  nwbinspector==0.7.2 roiextractors==0.10.0 tifffile==2026.9.20 \
  zarr==2.18.7 hdmf-zarr==0.11.3

Keep both Zarr pins even for an HDF5-only conversion: NeuroConv 0.10.2 imports its backend configuration modules at startup, and the tested unconstrained Zarr 3.4.0 installation failed on zarr.codec_registry. The pinned environment ran the real conversion, PyNWB validation and Inspector successfully on macOS ARM64. The dependency resolver supplies NumPy and HDF5 support. These are compatibility pins, not claims that Zarr 2 and hdmf-zarr 0.11.3 are the latest releases. Current interface checks and the tested dependency exception are recorded in references/upstream-review.md.

Workflow

1. Inventory the actual inputs and acquisition metadata. Identify image plane/channel, optical settings, subject/session identifiers, timezone, behavior coordinate system, units and the timestamp clock for every stream. Preserve originals. Do not replace missing metadata with plausible defaults from a sample config.
2. Copy assets/session-template.json beside the raw data and replace the explicitly synthetic values. Paths resolve from that JSON file. Read references/input-contract.md for the exact CSV and metadata contract and the pulse-pair variant. TIFF pixels are retained as acquired; a raw arbitrary-unit intensity does not become a photon count merely by changing its unit label.
3. Establish the common timebase from acquisition evidence. Frame timestamps must already be reference-clock seconds since the timezone-aware session start. For position, provide either a documented shared clock or matched synchronization pulses. The helper fits one affine clock transform, checks its residual against a specified tolerance, and refuses extrapolation beyond the pulse range. It never estimates synchronization from coincident-looking neural/behavioral signals. Clock resets or nonlinear drift require an explicitly validated piecewise mapping.
4. Execute the converter. Inputs must have finite, strictly increasing timestamps and matching image/timestamp counts. Explicitly declare one channel and one plane; known TIFF channel/plane metadata must agree. Grayscale pages alone cannot exclude undocumented interleaving. The acquisition samples stay intact; only coordinate units and, when evidenced, behavior timestamps are transformed.
5. Read the .validation.json alongside the NWB file. Schema compliance, Inspector findings and data equality answer different questions. The script exits with an error for schema failures and flags critical Inspector findings for review in the report. Review all findings in context; successful validation cannot establish that anatomical labels, pulse pairing or calibration supplied by the user are correct.
6. Deliver the NWB, validation JSON, original conversion config and an explanation of remaining metadata gaps or Inspector findings. No upload or archive submission is part of this workflow.

Execute

Run the following from the skill directory, with paths to the actual analysis files:

nwb-env/bin/python scripts/convert_session.py /path/to/session.json --output /path/to/session.nwb

nwb-env must point to the environment created above; the absolute example input paths are illustrative. The command requires a .nwb output and refuses to overwrite an existing NWB or validation report. Output contains source and converter checksums, package versions, full supplied metadata, units and clock-fit provenance in both a scratch record and the validation report. When adapting this command for large data, TIFF writes are iterative and equality checking loads one frame at a time; position CSV currently loads into memory. Round-trip checks also verify dtype, unit scaling, optical-channel links, subject metadata, position reference frame, common time origin and embedded provenance. Inspector findings requiring review appear in the CLI summary; exit zero alone does not mean the file is scientifically correct. An exception during writing or round-trip checks can leave an incomplete NWB without a report; retain the error and use a fresh output path after correcting the cause.

The real-library test converts eight non-square uint16 images with irregular frame timing plus four position samples, asserts exact pixel and timestamp round trips, and checks centimeter-to-meter conversion. A second integration test recovers a known 1000-ppm clock drift and 50-ms offset from three matched pulses. Duplicate timestamps, mismatched frame counts, absent clock evidence, nonlinear pulse disagreement and missing timezone are rejection cases. The mapping is TIFF (time,y,x) to NWB (time,x,y), explicitly checked against every transposed source page. NWB Inspector flags the short fixture with a critical orientation heuristic because width exceeds frame count; the report retains that finding and adds the exact frame/timestamp equality evidence. No transpose is performed merely to satisfy a longest-axis heuristic.

Primary references

• NeuroConv TIFF conversion
• NeuroConv temporal alignment
• PyNWB
• NWB Inspector and its relation to schema validation