xarray-schema

Schema validation for Xarray

installation

Install xarray-schema from PyPI:

pip install xarray-schema

Conda:

conda install -c conda-forge xarray-schema

Or install it from source:

pip install git+https://github.com/xarray-contrib/xarray-schema

usage

Xarray-schema's API is modeled after Pandera. The DataArraySchema and DatasetSchema objects both have .validate() methods.

The basic usage is as follows:

importnumpyasnpimportxarrayasxrfromxarray_schemaimportDataArraySchema, DatasetSchema, CoordsSchemada=xr.DataArray(np.ones(4, dtype='i4'), dims=['x'], name='foo')
schema=DataArraySchema(dtype=np.integer, name='foo', shape=(4, ), dims=['x'])
schema.validate(da)

You can also use it to validate a Dataset like so:

schema_ds = DatasetSchema({'foo': schema})
schema_ds.validate(da.to_dataset())

Each component of the Xarray data model is implemented as a stand alone class:

fromxarray_schema.componentsimport (
DTypeSchema,
DimsSchema,
ShapeSchema,
NameSchema,
ChunksSchema,
ArrayTypeSchema,
AttrSchema,
AttrsSchema
)
# example constructionsdtype_schema=DTypeSchema('i4')
dims_schema=DimsSchema(('x', 'y', None)) # None is used as a wildcardshape_schema=ShapeSchema((5, 10, None)) # None is used as a wildcardname_schema=NameSchema('foo')
chunk_schema=ChunksSchema({'x': None, 'y': -1}) # None is used as a wildcard, -1 is used asArrayTypeSchema=ArrayTypeSchema(np.ndarray)
# Example usagedtype_schema.validate(da.dtype)
# Each object schema can be exported to JSON formatdtype_json=dtype_schema.to_json()

roadmap

This is a very early prototype of a library. Some key things are missing:

1. Exceptions: Pandera accumulates schema exceptions and reports them all at once. Currently, we are a eagerly raising SchemaErrors when the are found.

license

All the code in this repository is MIT licensed.

history

This project was originally developed at CarbonPlan. It was transferred to the xarray-contrib organization in August 2022.