How to contribute new layers
It’s recommended to use the CLI to create a dataset and/or layer template to
help you along. In the below commands, replace filenames, paths, and ids with
real ones. NOTE: When generating templates, but not when fetching, you can
./scripts/experimental/local_cli.sh config-template <dataset|layer> in
place of the
Add a dataset
If the layer does not use an existing dataset, start with a new dataset.
./scripts/cli.sh config-template dataset > \ qgreenland/config/datasets/new_dataset.py
After running the command above, a new_dataset.py file will be populated with dataset information that is needed to create a layer. Make sure that the abstract and title are filled out.
Fetch the data
Once you are finished filling in the dataset information, you can try testing the dataset by fetching the data:
./scripts/cli.sh fetch new_dataset_id
If your fetch command results in an error, there may be issues with the entry of your dataset. Go back to your dataset.py file and make sure that all fields are filled in (abstract, title, etc.) to avoid linting errors.
Create new layer
The next step is to create the new data layer. To do this, create new layer directories as needed, and then define your new layer in a Python file with a descriptive name within the appropriate layer group.
You can do this by running this command:
./scripts/cli.sh config-template layer > \ qgreenland/config/layers/Group/Subgroup/new_layer.py
The above command generates a new_layer.py file. Once you see this file, follow the documentation within the file to fill out your layer configuration.
If the group directory where you have created your layer file has a settings.py file, you must add your new group to the ‘order’ list of this file. Make sure it is spelled exactly the same as in your file structure.
In order for a new dataset to be added to QGreenland, we strongly encourage public archival with OGC-compliant metadata. If data is not publicly archived or stored in a non-standard format, maintenance of that layer takes an order of magnitude more effort and therefore we are unable to promise permanent inclusion of such data. File formats that are particularly challenging include: raw binary grids, Excel files, Word documents. We prefer GeoTIFFs or NetCDFs for raster data, and GeoPackages or shapefiles for vector data.
A correct QGreenland data pipeline will output data that:
Is in EPSG:3413. This is to reduce load on QGIS caused by on-the-fly reprojection. Some exceptions may exist in the current code as a workaround, but they are bugs.
Is subset to one of the defined layer boundaries in
config/project.py. Existing layer tasks can do this for vector or raster data.
For raster data:
In GeoTIFF (
Includes overviews, for raster data. This improves QGIS performance.
Is losslessly compressed using the DEFLATE algorithm.
For vector data:
In GeoPackage (
labelattribute name for pre-calculated labels when using generic styles with labels, for example