https://github.com/jcmgray/xyzpy

Efficiently generate and analyse high dimensional data.
https://github.com/jcmgray/xyzpy

distributed multidimensional-arrays pandas parallel plot xarray

Last synced: about 1 month ago
JSON representation

Efficiently generate and analyse high dimensional data.

• Host: GitHub
• URL: https://github.com/jcmgray/xyzpy
• Owner: jcmgray
• License: mit
• Created: 2016-05-24T18:41:21.000Z (about 9 years ago)
• Default Branch: main
• Last Pushed: 2024-09-05T17:58:17.000Z (9 months ago)
• Last Synced: 2025-04-30T03:49:40.508Z (about 1 month ago)
• Topics: distributed, multidimensional-arrays, pandas, parallel, plot, xarray
• Language: Python
• Homepage: http://xyzpy.readthedocs.io
• Size: 23.6 MB
• Stars: 69
• Watchers: 3
• Forks: 10
• Open Issues: 8
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
![xyzpy logo](https://github.com/jcmgray/xyzpy/blob/main/docs/_static/xyzpy-logo-title.png)

[![tests](https://github.com/jcmgray/xyzpy/actions/workflows/tests.yml/badge.svg)](https://github.com/jcmgray/xyzpy/actions/workflows/tests.yml)

[![codecov](https://codecov.io/gh/jcmgray/xyzpy/branch/main/graph/badge.svg?token=Q5evNiuT9S)](https://codecov.io/gh/jcmgray/xyzpy)

[![Codacy Badge](https://app.codacy.com/project/badge/Grade/ba896d74c4954dd58da01df30c7bf326)](https://app.codacy.com/gh/jcmgray/xyzpy/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)

[![Docs](https://readthedocs.org/projects/xyzpy/badge/?version=latest)](https://xyzpy.readthedocs.io)

[![PyPI](https://img.shields.io/pypi/v/xyzpy?color=teal)](https://pypi.org/project/xyzpy/)

[![Anaconda-Server Badge](https://anaconda.org/conda-forge/xyzpy/badges/version.svg)](https://anaconda.org/conda-forge/xyzpy)

-------------------------------------------------------------------------------

[`xyzpy`](https://github.com/jcmgray/xyzpy) is python library for efficiently generating, manipulating and plotting data with a lot of dimensions, of the type that often occurs in numerical simulations. It stands wholly atop the labelled N-dimensional array library [`xarray`](http://xarray.pydata.org). The project's documentation is hosted on [readthedocs](http://xyzpy.readthedocs.io).

The aim is to take the pain and errors out of generating and exploring data with a high number of possible parameters. This means:

- you don't have to write super nested for loops

- you don't have to remember which arrays/dimensions belong to which variables/parameters

- you don't have to parallelize over or distribute runs yourself

- you don't have to worry about loading, saving and merging disjoint data

- you don't have to guess when a set of runs is going to finish

- you don't have to write batch submission scripts or leave the notebook to use SGE, PBS or SLURM

As well as the ability to automatically parallelize over runs, ``xyzpy``

provides the ``Crop`` object that allows runs and results to be written to disk,

these can then be run by any process with access to the files - e.g. a batch system

such as SGE, PBS or SLURM - or just serve as a convenient persistent progress mechanism.

Once your data has been aggregated into a ``xarray.Dataset`` or ``pandas.DataFrame``

there exists many powerful visualization tools such as

[`seaborn`](https://seaborn.pydata.org), [`altair`](https://altair-viz.github.io) and

[`holoviews`](https://holoviews.org) / [`hvplot`](https://hvplot.holoviz.org).

To these ``xyzpy`` adds also a simple 'oneliner' interface for interactively plotting the data

using [`bokeh`](https://bokeh.pydata.org), or for static, publication ready figures

using [`matplotlib`](https://matplotlib.org), whilst being able to see the dependence on

up to 4 dimensions at once.

![example](https://github.com/jcmgray/xyzpy/blob/main/docs/ex_simple.png)

Please see the [docs](http://xyzpy.readthedocs.io) for more information.