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Fundamental package for array computing in Python
NumPy is the fundamental package for scientific computing with Python.
It provides:
Testing:
NumPy requires pytest
and hypothesis
. Tests can then be run after installation with:
python -c "import numpy, sys; sys.exit(numpy.test() is False)"
NumPy is a community-driven open source project developed by a diverse group of contributors. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the NumPy Code of Conduct for guidance on how to interact with others in a way that makes our community thrive.
The NumPy project welcomes your expertise and enthusiasm!
Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact us through the mailing list first.
Writing code isn’t the only way to contribute to NumPy. You can also: - review pull requests - help us stay on top of new and old issues - develop tutorials, presentations, and other educational materials - maintain and improve our website - develop graphic design for our brand assets and promotional materials - translate website content - help with outreach and onboard new contributors - write grant proposals and help with other fundraising efforts
For more information about the ways you can contribute to NumPy, visit our website. If you’re unsure where to start or how your skills fit in, reach out! You can ask on the mailing list or here, on GitHub, by opening a new issue or leaving a comment on a relevant issue that is already open.
Our preferred channels of communication are all public, but if you’d like to speak to us in private first, contact our community coordinators at numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for an invitation).
We also have a biweekly community call, details of which are announced on the mailing list. You are very welcome to join.
If you are new to contributing to open source, this guide helps explain why, what, and how to successfully get involved.
This section shows a list of objects the current object is dependent upon in order to be used.
For dependencies that are only relevant as part of the build or runtime of the object, it is best to describe those local to those sections.
This section tracks information useful to describing how to build this object.
numpy-1.26.2.tar.gz
application/gzip
Python Source for numpy 1.26.2
https://files.pythonhosted.org/packages/dd/2b/205ddff2314d4eea852e31d53b8e55eb3f32b292efc3dd86bd827ab9019d/numpy-1.26.2.tar.gz
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downloadargparse-1.4.0.tar.gz
application/gzip
Python Source for argparse 1.4.0
https://files.pythonhosted.org/packages/18/dd/e617cfc3f6210ae183374cd9f6a26b20514bbb5a792af97949c5aacddf0f/argparse-1.4.0.tar.gz
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download/bin/bash
{{ paths.mount }}/build.sh
numpy-1.26.2/
3
numpy
argparse-1.4.0.tar.gz
linux
x86-64
Objects have a set of files that comprise that object, as seen in the Files tab. When an object is built, they have a set of files that are the result of that build. In each case, these sets of files are the ones that are accessible within a virtual machine when running the object.
This section describes how those files are accessible and visible to that machine. When a virtual machine is created, the data is always accessible read-only within a particular mounted directory. However, applications may expect to be located at particular directories. This section allows one to place files in different directories when the virtual machine is launched.
There are two types of access. One is a link where the file is not copied from its original location. Instead, it creates what is called a symbolic link which is a "shortcut" that acts like a normal file but allows the actual data to be in a different directory. The other type is a traditional copy which copies the data explicitly to the provided destination. This takes more time, but the copy can be modified, unlike the read-only link type.
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