xarray-dataclasses 0.3.1

xarray extension for DataArray and Dataset classes

xarray-dataclasses

xarray extension for DataArray and Dataset classes

TL;DR

xarray-dataclasses is a Python package for creating DataArray and Dataset classes in the same manner as the Python's native dataclass. Here is an example code of what the package provides:

from xarray_dataclasses import Coord, Data, dataarrayclass


@dataarrayclass
class Image:
    """DataArray that represents an image."""

    data: Data[tuple['x', 'y'], float]
    x: Coord['x', int] = 0
    y: Coord['y', int] = 0


# create a DataArray instance
image = Image.new([[0, 1], [2, 3]], x=[0, 1], y=[0, 1])


# create a DataArray instance filled with ones
ones = Image.ones((2, 2), x=[0, 1], y=[0, 1])

Features

• DataArray or Dataset instances with fixed dimensions, data type, and coordinates can easily be created.
• NumPy-like special functions such as ones() are provided as class methods.
• 100% compatible with the Python's native dataclass.
• 100% compatible with static type check by Pyright.

Installation

$ pip install xarray-dataclasses

Introduction

xarray is useful for handling labeled multi-dimensional data, but it is a bit troublesome to create a DataArray or Dataset instance with fixed dimensions, data type, or coordinates. For example, let us think about the following specifications of DataArray instances:

• Dimensions of data must be ('x', 'y').
• Data type of data must be float.
• Data type of dimensions must be int.
• Default value of dimensions must be 0.

Then a function to create a spec-compliant DataArray instance is something like this:

import numpy as np
import xarray as xr


def spec_dataarray(data, x=None, y=None):
    """Create a spec-comliant DataArray instance."""
    data = np.array(data)

    if x is None:
        x = np.zeros(data.shape[0])
    else:
        x = np.array(x)

    if y is None:
        y = np.zeros(data.shape[1])
    else:
        y = np.array(y)

    return xr.DataArray(
        data=data.astype(float),
        dims=('x', 'y'),
        coords={
            'x': ('x', x.astype(int)),
            'y': ('y', y.astype(int)),
        },
    )


dataarray = spec_dataarray([[0, 1], [2, 3]])

The issues are (1) it is hard to figure out the specs from the code and (2) it is hard to reuse the code, for example, to add a new coordinate to the original specs.

xarray-dataclasses resolves them by defining the specs as a dataclass with dedicated type hints:

from xarray_dataclasses import Coord, Data, dataarrayclass


@dataarrayclass
class Specs:
    data: Data[tuple['x', 'y'], float]
    x: Coord['x', int] = 0
    y: Coord['y', int] = 0


dataarray = Specs.new([[0, 1], [2, 3]])

The specs are now much easier to read: The type hints, Data[<dims>, <dtype>] and Coord[<dims>, <dtype>], have complete information of DataArray creation. The default values are given as class variables.

The class decorator, @dataarrayclass, converts a class to the Python's native dataclass and add class methods such as new() to it. The extension of the specs is then easy by class inheritance.

Basic usage

xarray-dataclasses uses the Python's native dataclass (please learn how to use it before proceeding). Data (or data variables), coordinates, attribute members, and name of a DataArray or Dataset instance are defined as dataclass fields with the following dedicated type hints.

Data

Data[<dims>, <dtype>] specifies the field whose value will become the data of a DataArray instance or a member of the data variables of a Dataset instance. It accepts two type variables, <dims> and <dtype>, for fixing dimensions and data type, respectively. For example:

Type hint	Inferred dims	Inferred dtype
Data['x', typing.Any]	('x',)	None
Data['x', int]	('x',)	numpy.dtype('int64')
Data['x', float]	('x',)	numpy.dtype('float64')
Data[tuple['x', 'y'], float]	('x', 'y')	numpy.dtype('float64')

Note: for Python 3.7 and 3.8, use typing.Tuple[...] instead of tuple[...].

Coord

Coord[<dims>, <dtypes>] specifies the field whose value will become a coordinate of a DataArray or Dataset instance. Similar to Data, it accepts two type variables, <dims> and <dtype>, for fixing dimensions and data type, respectively.

Attr

Attr[<type>] specifies the field whose value will become a member of the attributes (attrs) of a DataArray or Dataset instance. It accepts a type variable, <type>, for specifying the type of the value. For example:

@dataarrayclass
class Specs:
    units: Attr[str] = 'm/s'  # equivalent to str

Name

Name[<type>] specifies the field whose value will become the name of a DataArray. It accepts a type variable, <type>, for specifying the type of the value. For example:

@dataarrayclass
class Specs:
    name: Name[str] = 'default'  # equivalent to str

DataArray class

DataArray class is a dataclass that defines DataArray creation. For example:

from xarray_dataclasses import Attr, Coord, Data, Name, dataarrayclass


@dataarrayclass
class Image:
    """DataArray that represents an image."""

    data: Data[tuple['x', 'y'], float]
    x: Coord['x', int] = 0
    y: Coord['y', int] = 0
    dpi: Attr[int] = 300
    name: Name[str] = 'default'

where exactly one Data-typed field is allowed. ValueError is raised if more than two Data-type fields exist. A spec-compliant DataArray instance is created by a shorthand method, new():

Image.new([[0, 1], [2, 3]], x=[0, 1], y=[0, 1])

<xarray.DataArray 'default' (x: 2, y: 2)>
array([[0., 1.],
       [2., 3.]])
Coordinates:
  * x        (x) int64 0 1
  * y        (y) int64 0 1
Attributes:
    dpi:      300

DataArray class has NumPy-like empty(), zeros(), ones(), full() methods:

Image.ones((3, 3), dpi=200, name='flat')

<xarray.DataArray 'flat' (x: 3, y: 3)>
array([[1., 1., 1.],
       [1., 1., 1.],
       [1., 1., 1.]])
Coordinates:
  * x        (x) int64 0 0 0
  * y        (y) int64 0 0 0
Attributes:
    dpi:      200

Dataset class

Dataset class is a dataclass that defines Dataset creation. For example:

from xarray_dataclasses import Attr, Coord, Data, datasetclass


@datasetclass
class RGBImage:
    """Dataset that represents a three-color image."""

    red: Data[tuple['x', 'y'], float]
    green: Data[tuple['x', 'y'], float]
    blue: Data[tuple['x', 'y'], float]
    x: Coord['x', int] = 0
    y: Coord['y', int] = 0
    dpi: Attr[int] = 300

where multiple Data-typed fields are allowed. A spec-compliant Dataset instance is created by a shorthand method, new():

RGBImage.new(
    [[0, 0], [0, 0]],  # red
    [[1, 1], [1, 1]],  # green
    [[2, 2], [2, 2]],  # blue
)

<xarray.Dataset>
Dimensions:  (x: 2, y: 2)
Coordinates:
  * x        (x) int64 0 0
  * y        (y) int64 0 0
Data variables:
    red      (x, y) float64 0.0 0.0 0.0 0.0
    green    (x, y) float64 1.0 1.0 1.0 1.0
    blue     (x, y) float64 2.0 2.0 2.0 2.0
Attributes:
    dpi:      300