xarray-schema's Introduction

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:

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()

roadmap

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 SchemaErrors when the are found.