Skip to main content

Numpy arrays with labeled axes, similar to xarray but with support for uncertainties

Project description

named_arrays

tests codecov Documentation Status PyPI version

named_arrays is an implementation of a named tensor, which assigns names to each axis of an n-dimensional array such as a numpy array.

When using a numpy array, we often have to insert singleton dimensions to align axes before using binary operators etc. This is not necessary when using a named tensor implementation such as xarray or named_arrays, axes are aligned automatically using their names.

Installation

named_arrays is available on PyPi and can be installed using pip

pip install named-arrays

Examples

ScalarArray

The fundamental type of named_arrays is the ScalarArray, which is a composition of a numpy ndarray-like object and a tuple of axis names which must have the same length as the number of dimensions in the array.

import numpy as np
import named_arrays as na

a = na.ScalarArray(np.array([1, 2, 3]), axes=('x',))

If we create another array with a different axis name, it will be broadcasted automatically against the first array if we add them together

b = na.ScalarArray(np.array([4, 5]), axes=('y',))
c = a + b
c
ScalarArray(
    ndarray=[[5, 6],
             [6, 7],
             [7, 8]],
    axes=('x', 'y'),
)

All the usual numpy reduction operations use the axis name instead of the axis index

c.mean('x')
ScalarArray(
    ndarray=[6., 7.],
    axes=('y',),
)

To index the array we can use a dictionary with the axis names as the keys

c[dict(x=0)]
ScalarArray(
    ndarray=[5, 6],
    axes=('y',),
)

ScalarLinearSpace

We recommend that you rarely directly create instances of ScalarArray directly. Instead, you can use the implicit array classes: ScalarLinearSpace, ScalarLogarithmicSpace, and ScalarGeometricSpace to create arrays in a similar fashion to numpy.linspace(), numpy.logspace(), and numpy.geomspace() with the advantage of being able to access the inputs to these functions at a later point.

d = na.ScalarLinearSpace(0, 1, axis='z', num=4)
d
ScalarLinearSpace(start=0, stop=1, axis='z', num=4, endpoint=True)

Thses implicit array classes work just like ScalarArray and can be used with any of the usual array operations.

a + d
ScalarArray(
    ndarray=[[1.        , 1.33333333, 1.66666667, 2.        ],
             [2.        , 2.33333333, 2.66666667, 3.        ],
             [3.        , 3.33333333, 3.66666667, 4.        ]],
    axes=('x', 'z'),
)


          

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

named_arrays-0.2.0.tar.gz (145.5 kB view hashes)

Uploaded Source

Built Distribution

named_arrays-0.2.0-py3-none-any.whl (127.9 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page