CLI quickstart¶
This page provides a minimal, task-oriented introduction to the BrkRaw command-line interface. The examples are intended for first-time users who want to inspect and convert Paravision datasets from the terminal.
Inspect a dataset (brkraw info)¶
Print a structured overview of a Paravision dataset, including study-level and scan-level information.
brkraw info /path/to/study
This command works with dataset directories, zip archives, and
Paravision-exported .PvDatasets files.
To include detailed scan and reconstruction information:
brkraw info /path/to/study --scope full
Inspect scan parameters (brkraw params)¶
Search acquisition or reconstruction parameters across scans.
brkraw params /path/to/study PVM_RepetitionTime
Limit the search to a specific scan:
brkraw params /path/to/study PVM_RepetitionTime --scan-id 3
This is useful for verifying protocol settings before conversion.
Convert a scan to NIfTI (brkraw convert)¶
Convert a single scan using the default reconstruction.
brkraw convert /path/to/study --scan-id 3
Specify a reconstruction ID and output directory:
brkraw convert /path/to/study \
--scan-id 3 \
--reco-id 1 \
--out ./nifti_out
Output filenames and directory structure are controlled by the configured layout entries and templates.
Generate sidecar metadata¶
Write metadata sidecars alongside the converted NIfTI files.
brkraw convert /path/to/study \
--scan-id 3 \
--reco-id 1 \
--write-metadata
The content of sidecar metadata is determined by context maps, rules, and specs.
Convert multiple scans (batch-style usage)¶
Convert all available scans in a dataset using a shell loop.
for sid in 1 2 3 4; do
brkraw convert /path/to/study \
--scan-id $sid \
--reco-id 1 \
--out ./nifti_out
done
For large-scale automation, consider using the Python API.
Manage addons (brkraw addon)¶
List installed addons (rules, specs, hooks):
brkraw addon list
Add a new addon:
brkraw addon add /path/to/spec.yaml
Remove an installed addon:
brkraw addon remove spec.yaml
When to use the CLI¶
The CLI is best suited for:
- Interactive inspection of datasets
- One-off or small batch conversions
- Verifying metadata and scan IDs
- Running conversions in shell-based workflows
For complex logic, conditional processing, or large-scale automation, use the Python API instead.