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Calling All Chocolatiers! Whipping Up Windows Automation with Chocolatey Central Management

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Downloads:

18,175

Downloads of v 1.17.1:

411

Last Update:

27 Aug 2019

Package Maintainer(s):

Software Author(s):

  • SciPy developers

Tags:

numpy python scientific computing

NumPy

This is not the latest version of NumPy available.

  • 1
  • 2
  • 3

1.17.1 | Updated: 27 Aug 2019

Downloads:

18,175

Downloads of v 1.17.1:

411

Software Author(s):

  • SciPy developers

NumPy 1.17.1

This is not the latest version of NumPy available.

  • 1
  • 2
  • 3

All Checks are Passing

3 Passing Tests


Validation Testing Passed


Verification Testing Passed

Details

Scan Testing Successful:

No detections found in any package files

Details
Learn More

Deployment Method: Individual Install, Upgrade, & Uninstall

To install NumPy, run the following command from the command line or from PowerShell:

>

To upgrade NumPy, run the following command from the command line or from PowerShell:

>

To uninstall NumPy, run the following command from the command line or from PowerShell:

>

Deployment Method:

NOTE

This applies to both open source and commercial editions of Chocolatey.

1. Enter Your Internal Repository Url

(this should look similar to https://community.chocolatey.org/api/v2/)


2. Setup Your Environment

1. Ensure you are set for organizational deployment

Please see the organizational deployment guide

2. Get the package into your environment

  • Open Source or Commercial:
    • Proxy Repository - Create a proxy nuget repository on Nexus, Artifactory Pro, or a proxy Chocolatey repository on ProGet. Point your upstream to https://community.chocolatey.org/api/v2/. Packages cache on first access automatically. Make sure your choco clients are using your proxy repository as a source and NOT the default community repository. See source command for more information.
    • You can also just download the package and push it to a repository Download

3. Copy Your Script

choco upgrade numpy -y --source="'INTERNAL REPO URL'" --version="'1.17.1'" [other options]

See options you can pass to upgrade.

See best practices for scripting.

Add this to a PowerShell script or use a Batch script with tools and in places where you are calling directly to Chocolatey. If you are integrating, keep in mind enhanced exit codes.

If you do use a PowerShell script, use the following to ensure bad exit codes are shown as failures:


choco upgrade numpy -y --source="'INTERNAL REPO URL'" --version="'1.17.1'" 
$exitCode = $LASTEXITCODE

Write-Verbose "Exit code was $exitCode"
$validExitCodes = @(0, 1605, 1614, 1641, 3010)
if ($validExitCodes -contains $exitCode) {
  Exit 0
}

Exit $exitCode

- name: Install numpy
  win_chocolatey:
    name: numpy
    version: '1.17.1'
    source: INTERNAL REPO URL
    state: present

See docs at https://docs.ansible.com/ansible/latest/modules/win_chocolatey_module.html.


chocolatey_package 'numpy' do
  action    :install
  source   'INTERNAL REPO URL'
  version  '1.17.1'
end

See docs at https://docs.chef.io/resource_chocolatey_package.html.


cChocoPackageInstaller numpy
{
    Name     = "numpy"
    Version  = "1.17.1"
    Source   = "INTERNAL REPO URL"
}

Requires cChoco DSC Resource. See docs at https://github.com/chocolatey/cChoco.


package { 'numpy':
  ensure   => '1.17.1',
  provider => 'chocolatey',
  source   => 'INTERNAL REPO URL',
}

Requires Puppet Chocolatey Provider module. See docs at https://forge.puppet.com/puppetlabs/chocolatey.


4. If applicable - Chocolatey configuration/installation

See infrastructure management matrix for Chocolatey configuration elements and examples.

Package Approved

This package was approved as a trusted package on 27 Aug 2019.

Description

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

  • a powerful N-dimensional array object
  • sophisticated (broadcasting) functions
  • tools for integrating C/C++ and Fortran code
  • useful linear algebra, Fourier transform, and random number capabilities
    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.

NumPy is licensed under the BSD license, enabling reuse with few restrictions.

Getting Started

To install NumPy, we strongly recommend using a scientific Python distribution. See Installing the SciPy Stack for details.

Many high quality online tutorials, courses, and books are available to get started with NumPy. For a quick introduction to NumPy we provide the NumPy Tutorial. We also recommend the SciPy Lecture Notes for a broader introduction to the scientific Python ecosystem.

For more information on the SciPy Stack (for which NumPy provides the fundamental array data structure), see scipy.org.

Documentation

The most up-to-date NumPy documentation can be found at Latest (development) version. It includes a user guide, full reference documentation, a developer guide, meta information, and “NumPy Enhancement Proposals” (which include the NumPy Roadmap and detailed plans for major new features).

A complete archive of documentation for all NumPy releases (minor versions; bug fix releases don’t contain significant documentation changes) since 2009 can be found at https://docs.scipy.org.


tools\chocolateyinstall.ps1
Update-SessionEnvironment
$version = '1.17.1'
 
$proxy = Get-EffectiveProxy
if ($proxy) {
  Write-Host "Setting CLI proxy: $proxy"
  $env:http_proxy = $env:https_proxy = $proxy
}
python -m pip install numpy==$version
tools\chocolateyuninstall.ps1
python -m pip uninstall numpy -y

Log in or click on link to see number of positives.

In cases where actual malware is found, the packages are subject to removal. Software sometimes has false positives. Moderators do not necessarily validate the safety of the underlying software, only that a package retrieves software from the official distribution point and/or validate embedded software against official distribution point (where distribution rights allow redistribution).

Chocolatey Pro provides runtime protection from possible malware.

Add to Builder Version Downloads Last Updated Status
NumPy 1.26.3 157 Wednesday, January 3, 2024 Approved
NumPy 1.26.1 425 Sunday, October 15, 2023 Approved
NumPy 1.26.0 158 Sunday, September 17, 2023 Approved
NumPy 1.25.2 287 Monday, July 31, 2023 Approved
NumPy 1.25.1 157 Sunday, July 9, 2023 Approved
NumPy 1.25.0 144 Saturday, June 17, 2023 Approved
NumPy 1.24.3 300 Sunday, April 23, 2023 Approved
NumPy 1.24.2 562 Monday, February 6, 2023 Approved
NumPy 1.24.1 218 Monday, December 26, 2022 Approved
NumPy 1.24.0 117 Monday, December 19, 2022 Approved
NumPy 1.23.5 190 Sunday, November 20, 2022 Approved
NumPy 1.23.4 238 Wednesday, October 12, 2022 Approved
NumPy 1.23.3 274 Saturday, September 10, 2022 Approved
NumPy 1.23.2 291 Monday, August 15, 2022 Approved
NumPy 1.23.1 260 Saturday, July 9, 2022 Approved
NumPy 1.23.0 146 Thursday, June 23, 2022 Approved
NumPy 1.22.4 182 Saturday, May 21, 2022 Approved
NumPy 1.22.3 373 Tuesday, March 8, 2022 Approved
NumPy 1.22.2 242 Friday, February 4, 2022 Approved
NumPy 1.22.1 215 Saturday, January 15, 2022 Approved
NumPy 1.22.0 165 Saturday, January 1, 2022 Approved
NumPy 1.21.5 190 Monday, December 20, 2021 Approved
NumPy 1.21.4 322 Friday, November 5, 2021 Approved
NumPy 1.21.3 150 Thursday, October 21, 2021 Approved
NumPy 1.21.2 572 Monday, August 16, 2021 Approved
NumPy 1.21.1 169 Monday, July 19, 2021 Approved
NumPy 1.21.0 195 Tuesday, June 22, 2021 Approved
NumPy 1.20.3 228 Monday, May 10, 2021 Approved
NumPy 1.20.2 237 Sunday, March 28, 2021 Approved
NumPy 1.20.1 282 Monday, February 8, 2021 Approved
NumPy 1.20.0 170 Sunday, January 31, 2021 Approved
NumPy 1.19.5 233 Wednesday, January 6, 2021 Approved
NumPy 1.19.4 482 Monday, November 2, 2020 Approved
NumPy 1.19.3 190 Thursday, October 29, 2020 Approved
NumPy 1.19.2 528 Friday, September 11, 2020 Approved
NumPy 1.19.1 345 Wednesday, July 22, 2020 Approved
NumPy 1.18.5 358 Friday, June 5, 2020 Approved
NumPy 1.18.4 211 Sunday, May 3, 2020 Approved
NumPy 1.18.3 295 Monday, April 20, 2020 Approved
NumPy 1.18.2 336 Tuesday, March 17, 2020 Approved
NumPy 1.18.1 579 Tuesday, January 7, 2020 Approved
NumPy 1.18.0 263 Monday, December 23, 2019 Approved
NumPy 1.17.4 629 Monday, November 11, 2019 Approved
NumPy 1.17.3 434 Thursday, October 17, 2019 Approved
NumPy 1.17.2 361 Saturday, September 7, 2019 Approved
NumPy 1.17.1 411 Tuesday, August 27, 2019 Approved
NumPy 1.17.0 340 Saturday, July 27, 2019 Approved
NumPy 1.16.4 406 Wednesday, May 29, 2019 Approved
NumPy 1.16.3 239 Tuesday, May 14, 2019 Approved
numpy 1.8.1 3389 Tuesday, July 15, 2014 Approved

Discussion for the NumPy Package

Ground Rules:

  • This discussion is only about NumPy and the NumPy package. If you have feedback for Chocolatey, please contact the Google Group.
  • This discussion will carry over multiple versions. If you have a comment about a particular version, please note that in your comments.
  • The maintainers of this Chocolatey Package will be notified about new comments that are posted to this Disqus thread, however, it is NOT a guarantee that you will get a response. If you do not hear back from the maintainers after posting a message below, please follow up by using the link on the left side of this page or follow this link to contact maintainers. If you still hear nothing back, please follow the package triage process.
  • Tell us what you love about the package or NumPy, or tell us what needs improvement.
  • Share your experiences with the package, or extra configuration or gotchas that you've found.
  • If you use a url, the comment will be flagged for moderation until you've been whitelisted. Disqus moderated comments are approved on a weekly schedule if not sooner. It could take between 1-5 days for your comment to show up.
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