Schema validation for Xarray
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
Xarray-schema's API is modeled after Pandera. The DataArraySchema and DatasetSchema objects both have .validate() methods.
The basic usage is as follows:
import numpy as np import xarray as xr from xarray_schema import DataArraySchema, DatasetSchema, CoordsSchema da = 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:
from xarray_schema.components import ( DTypeSchema, DimsSchema, ShapeSchema, NameSchema, ChunksSchema, ArrayTypeSchema, AttrSchema, AttrsSchema ) # example constructions dtype_schema = DTypeSchema('i4') dims_schema = DimsSchema(('x', 'y', None)) # None is used as a wildcard shape_schema = ShapeSchema((5, 10, None)) # None is used as a wildcard name_schema = NameSchema('foo') chunk_schema = ChunksSchema({'x': None, 'y': -1}) # None is used as a wildcard, -1 is used as ArrayTypeSchema = ArrayTypeSchema(np.ndarray) # Example usage dtype_schema.validate(da.dtype) # Each object schema can be exported to JSON format dtype_json = dtype_schema.to_json()
This is a very early prototype of a library. Some key things are missing:
- Exceptions: Pandera accumulates schema exceptions and reports them all at once. Currently, we are a eagerly raising
SchemaErrorswhen the are found.
All the code in this repository is MIT licensed.
This project was originally developed at CarbonPlan. It was transferred to the xarray-contrib organization in August 2022.