ndcube is an open-source SunPy affiliated package for manipulating, inspecting and visualizing multi-dimensional contiguous and non-contiguous coordinate-aware data arrays. It combines data, uncertainties, units, metadata, masking, and coordinate transformations into classes with unified slicing and generic coordinate transformations and plotting/animation capabilities. It is designed to handle data of any number of dimensions and axis types (e.g. spatial, temporal, spectral, etc.) whose relationship between the array elements and the real world can be described by World Coordinate System (WCS) translations. See the ndcube docs for more details.
ndcube requires Python 3.5+, SunPy 0.8+, astropy and matplotlib.
There are two options for installing the stable version of ndcube. The first is via the anaconda distribution using the conda-forge channel:
$ conda install --channel conda-forge ndcube
For more information on installing the anaconda distribution, see the anaconda website.
To update ndcube do:
$ conda update ndcube
The second option for installing the stable verison of ndcube is via pip:
$ pip install ndcube
Then to update ndcube do:
$ pip install ndcube --upgrade
The stable version of ndcube will be relatively reliable. However, if you value getting the latest updates immediately over reliablility, or want to contribute to the development of ndcube, you will need to install the bleeding edge version via github. The recommended way to set up your system is to first fork the ndcube github repository to your github account and then clone your forked repo to your local machine. This setup has the advantage of being able to push any changes you make in local version to your github account. From there you can issue pull requests to have your changes merged into the main repo and thus shared with other users. You can also set up a remote between your local version and the main repo so that you can stay updated with the latest changes to ndcube. Let's step through how to do this.
Once you've forked the main ndcube github repository to your github account, create a conda environment on your local machine to hold the ndcube bleeding edge version and activate that environment. Type the following into a terminal:
$ conda config --append channels conda-forge $ conda create -n ndcube-dev python sunpy hypothesis pytest-mock $ source activate ndcube-dev
Next clone the ndcube repo from your github account to a new
directory. Let's call it ndcude-git:
$ git clone https://github.com/your-github-name/ndcube.git ndcube-git
To install, change into the new directory and run the install script:
$ cd ndcube-git $ pip install -e .
Finally add a remote to the main repo so you can pull the latest version:
$ git remote add upstream https://github.com/sunpy/ndcube.git
Then to ensure you stay up-to-date with the latest version of ndcube, regularly do:
$ git pull upstream master
To push any changes you make to your github account by doing:
$ git push origin branch-name
where branch-name is the name of the branch you're working on. Then
the main repo. For more information on on git version control, github, and issuing pull requests, see SunPy's version control guide.
As a SunPy-affiliated package, ndcube relies on the SunPy support infrastructure. To pose questions to ndcube and SunPy developers and to get annoucements regarding ndcube and SunPy in general, sign up to the
To get quicker feedback and chat directly to ndcube and SunPy developers check out the
If you would like to get involved, start by joining the SunPy mailing list and check out the Developer’s Guide section of the SunPy docs. Stop by our chat room #sunpy:matrix.org if you have any questions. Help is always welcome so let us know what you like to work on, or check out the issues page for the list of known outstanding items.
For more information on contributing to ncdube or the SunPy organization, please read the SunPy contributing guide.
Imposter syndrome disclaimer: We want your help. No, really.
There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?
We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.
Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.
Note: This disclaimer was originally written by Adrienne Lowe for a PyCon talk, and was adapted by ndcube based on its use in the README file for the MetPy project.
When you are interacting with the SunPy community you are asked to follow our Code of Conduct.
This project is Copyright (c) SunPy Developers and licensed under the terms of the BSD 2-Clause license. See the licenses folder for more information.