mask

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mask#

mask(xds: str | CloudPath | Path | tuple[ndarray | MaskedArray, dict] | DataArray | Dataset | DatasetReader | DatasetWriter, shapes: GeoDataFrame | Polygon | list, nodata: int | None = None, **kwargs) → DataArray | Dataset[source]#

Masking a dataset: setting nodata outside the given shapes, but without cropping the raster to the shapes extent.

The original nodata is kept and completed with the nodata provided by the shapes.

Overload of rasterio mask function in order to create a xarray.

The mask function docs can be seen here. It basically masks a raster with a vector mask, with the possibility to crop the raster to the vector’s extent.

Parameters:

  • xds (AnyRasterType) – Path to the raster or a rasterio dataset or a xarray

  • shapes (Union[gpd.GeoDataFrame, Polygon, list]) – Shapes with the same CRS as the dataset (except if a GeoDataFrame is passed, in which case it will automatically be converted)

  • nodata (int) – Nodata value. If not set, uses the ds.nodata. If doesn’t exist, set to 0.

  • **kwargs – Other rasterio.mask options

Returns:

Masked array as a xarray

Return type:

AnyXrDataStructure

Example

>>> raster_path = "path/to/raster.tif"
>>> shape_path = "path/to/shapes.geojson"  # Any vector that geopandas can read
>>> shapes = gpd.read_file(shape_path)
>>> mask1 = mask(raster_path, shapes)
>>>
>>> # or
>>> with rasterio.open(raster_path) as ds:
>>>     mask2 = mask(ds, shapes)
>>>
>>> # Assert those two approaches give the same result
>>> mask1 == mask2
True
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