Vecorel CLI

A command-line interface (CLI) for working with Vecorel files.

• Getting Started
• Documentation / Commands
• Development

Getting Started

In order to make working with Vecorel easier we have developed command-line interface (CLI) tools such as inspection, validation and file format conversions.

Installation

Using Pixi (Recommended)

This project uses Pixi for dependency management. Install Pixi first, then:

# Clone the repository and navigate to it
git clone https://github.com/vecorel/cli.git
cd cli

# Install all dependencies
pixi install

# Run the CLI
pixi run vec

Using pip or uv

Alternatively, you can install from PyPI with Python 3.11 or any later version:

pip install vecorel-cli
# or
uv pip install vecorel-cli

Execute a command

After the installation you should be able to run the following command: vec (or pixi run vec if using Pixi)

You should see usage instructions and available commands for the CLI.

Vecorel CLI supports various commands to work with the files:

• Vecorel CLI
  • Getting Started
    • Installation
      • Using Pixi (Recommended)
      • Using pip
    • Execute a command
  • Commands
    • Validation
    • Create Vecorel GeoParquet from GeoJSON
    • Create Vecorel GeoJSON from GeoParquet
    • Inspect Vecorel GeoParquet file
    • Merge Vecorel GeoParquet files
    • Create JSON Schema from Vecorel Schema
    • Validate a Vecorel Schema
    • Improve a Vecorel Parquet file
    • Update an extension template with new names
    • Converter for existing datasets
  • Development
    • Implement a converter

Commands

Validation

To validate a Vecorel GeoParquet or GeoJSON file, you can for example run:

• GeoJSON: vec validate example.json --collection collection.json
• GeoParquet: vec validate example.parquet --data

Check vec validate --help for more details.

The validator also supports remote files.

• http:// or https://: no further configuration is needed.
• s3://: With Pixi, run pixi install -e s3 or with pip/uv, run pip install vecorel-cli[s3] and you may need to set environment variables. Refer to the s3fs credentials documentation for how to define credentials.
• gs://: With Pixi, run pixi install -e gcs or with pip/uv, run pip install vecorel-cli[gcs]. By default, gcsfs will attempt to use your default gcloud credentials or, attempt to get credentials from the google metadata service, or fall back to anonymous access.

Create Vecorel GeoParquet from GeoJSON

To create a Vecorel-compliant GeoParquet for a Vecorel-compliant set of GeoJSON files containing Features or FeatureCollections, you can for example run:

• vec create-geoparquet geojson/example.json -o example.parquet -c geojson/collection.json

Check vec create-geoparquet --help for more details.

Create Vecorel GeoJSON from GeoParquet

To create one or multiple Vecorel-compliant GeoJSON file(s) for a Vecorel-compliant GeoParquet file, you can for example run:

• GeoJSON FeatureCollection: vec create-geojson example.parquet -o dest-folder

• GeoJSON Features (with indentation and max. 100 features): vec create-geojson example.parquet -o dest-folder -n 100 -i 2 -f

  vec create-geojson example.parquet -o dest-folder -n 100 -i 2 -f

Check vec create-geojson --help for more details.

Inspect Vecorel GeoParquet file

To look into a Vecorel GeoParquet file to get a rough understanding of the content, the following can be executed:

• vec describe example.parquet

Check vec describe --help for more details.

Merge Vecorel GeoParquet files

Merges multiple Vecorel datasets to a combined Vecorel dataset:

• vec merge ec_ee.parquet ec_lv.parquet -o merged.parquet
• Only the core properties and some additional properties: vec merge ec_ee.parquet ec_lv.parquet -o merged.parquet -i ec:hcat_name -i ec:hcat_code
• All properties except for some: vec merge ec_ee.parquet ec_lv.parquet -o merged.parquet -e ec:translated_name

The merged dataset is in EPSG:4326 by default. Use --crs to choose another CRS, or --crs first to keep the CRS of the first dataset.

Local GeoParquet files that are all in the target CRS are merged with DuckDB, so they don't need to fit into memory. All other datasets (e.g. GeoJSON or datasets that need to be reprojected) are merged in memory. Use --engine to choose the engine explicitly.

-i and -e apply to all properties, including collection-level metadata. The geometry is required, so it can't be excluded. Constants that differ between the datasets are moved from the collection metadata to the features. The collection of the features is stored in a column if the datasets have multiple collections or if a dataset has a collection column already, otherwise only in the collection metadata.

Geometries are merged as they are, except for the reprojection to the target CRS: they are neither made valid nor converted to other geometry types (use vec improve -g for that), and features with an empty or missing geometry are not dropped (see below). The bounding boxes (bbox) are computed again for the merged GeoParquet file.

Strict and non-strict mode

By default, vec merge is strict: problems that make the merged dataset invalid are errors and no dataset is written. With --no-strict, vec merge is fail-safe: these problems are reported as warnings and the dataset is written anyway. Check it with vec validate afterwards.

Datasets with different versions of the Vecorel specification or of an extension can't be merged, as the CLI can't upgrade them automatically yet.

The strict mode only checks what a merge can break or check with little effort. It doesn't validate the values against the schemas, e.g. patterns or value ranges, so a merged dataset is only valid if the values of the source datasets are valid.

Check vec merge --help for more details.

Create JSON Schema from Vecorel Schema

To create a JSON Schema for a Vecorel Schema YAML file, you can for example run:

• vec jsonschema example.json --id=https://vecorel.org/specification/v0.1.0/geojson/schema.json -o schema.json

Check vec jsonschema --help for more details.

Validate a Vecorel Schema

To validate a Vecorel Schema YAML file, you can for example run:

• vec validate-schema schema/schema.yaml

Check vec validate-schema --help for more details.

Improve a Vecorel Parquet file

Various "improvements" can be applied to a Vecorel GeoParquet file. The commands allows to

• change the CRS (--crs)
• change the GeoParquet version (-gp1) and compression (-pc)
• add/fill missing perimeter/area values (-sz)
• fix invalid geometries (-g)
• rename columns (-r)

Example:

• vec improve file.parquet -o file2.parquet -g -sz -r old=new -pc zstd

Check vec improve --help for more details.

Update an extension template with new names

Once you've created and git cloned a new extension, you can use the CLI to update all template placeholders with proper names.

For example, if your extension is meant to have

• the title "Administrative Division Extension",
• the prefix admin (e.g. field admin:country_code or admin:subdivision_code),
• is hosted at https://github.io/vecorel/administrative-division-extension (organization: vecorel, repository /administrative-division-extension),
• and you run Vecorel in the folder of the extension.

Then the following command could be used:

• vec rename-extension . -t "Administrative Division" -p admin -s administrative-division-extension -o vecorel

Check vec rename-extension --help for more details.

Converter for existing datasets

The CLI ships various converters for existing datasets.

To get a list of available converters/datasets with title, license, etc. run:

• vec converters

Use any of the IDs from the list to convert an existing dataset to Vecorel:

• vec convert de_nrw

See Implement a converter for details about how to

Development

This project uses Pixi for dependency management and development workflows.

# Install all dependencies including development tools
pixi install -e dev

# Install the package in editable mode
pixi run install-dev

# Run tests
pixi run -e dev test

# Format and lint code
pixi run format
pixi run lint

# Run all checks (lint, format, test)
pixi run -e dev check

# Install and run pre-commit
pixi run pre-commit-install
pixi run pre-commit-run

Implement a converter

The following high-level description gives an idea how to implement a converter in Vecorel CLI:

1. Create a new file in vecorel_cli/datasets based on the template.py
2. Fill in the required variables / test it / run it
3. Add missing dependencies into pyproject.toml (and the appropriate pixi feature group if needed)
4. Add the converter to the list above
5. Create a PR to submit your converter for review