pyxarr 0.2.0

a minimal and light-weight equivalent of xarray.DataArray

pyxarr

This is a package which provides a minimal and light-weight class to work with multi-dimensional labeled arrays.

Description

The software of pyxarr is written from scratch. I have tried to mimic the classes DataArray, Dataset and Coords from xarray. My main goal is to keep the code light and fast.

Installation

Pyxarr is available as pyxarr on PyPI. To install it use pip:

$ pip install pyxarr

The module pyxarr requires Python3.10+ and Python modules: h5py, numpy.

Usage

Working with a pyxarr DataArray:

import numpy as np
from pyxarr import DataArray

xds = DataArray()
bool(xds) # will return False, thus if xda: ... will work
len(xds)  # will return 0

rng = np.random.default_rng()
xda = DataArray(
   rng.random((120, 11, 17)),
   dims=("time", "y", "x"),
   coords=(
      np.arange("2025-02-24T14:32:00", "2025-02-24T15:32:00", 30, dtype="datetime64[s]"),
      list(range(11)),
      np.arange(17),
   ),
   attrs={
      "long_name": "noisy signal",
      "units": "1",
   },
)
bool(xds)  # returns True
len(xds)   # returns 120
xda.shape  # returns (120, 11, 17)
xda.size   # returns 22440
"x" in xda.coords  # returns True
xda[4, :, :]  # slicing works
xda.coords += ("orbit", list(range(500, 620))  # will add an auxiliary coordinate
xda.swap_dims("orbit", "time")  # will make the auxiliary coordinate the dimension coordinate and visa versa
xda.mean("time")  # will return a new DataArray averaged over the time axis
xda + xda2  # will return a new DataArray with the sum of the data of both arrays, other supported operator are sub, div and mul.

Authors and acknowledgment

The code is developed by R.M. van Hees (SRON)

License

• Copyright: SRON (https://www.sron.nl)
• License: Apache-2.0