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numpy/numpy.org
d8153d9862967559ce9dcc7483da8f8059750fb4
Streamline formatting in references
diff --git a/content/en/learn.md b/content/en/learn.md index 2f79a8d..723d29e 100644 --- a/content/en/learn.md +++ b/content/en/learn.md @@ -1,96 +1,97 @@ --- title: Learn sidebar: false --- For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). *** Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. ## Beginners There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: <i class="fas fa-chalkboard"></i> **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). -* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [NumPy Illustrated: The Visual Guide to NumPy](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +*by Lev Maximov* * [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. * [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) -* [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) -* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy tutorial](https://github.com/rougier/numpy-tutorial) *by Nicolas Rougier* +* [Stanford CS231](http://cs231n.github.io/python-numpy-tutorial/) *by Justin Johnson* * [NumPy User Guide](https://numpy.org/devdocs) <i class="fas fa-book"></i> **Books** -* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) +* [Guide to NumPy](https://web.mit.edu/dvp/Public/numpybook.pdf) *by Travis E. Oliphant* This is the first and *free* edition of the book. To purchase the latest edition, [click here](https://www.amazon.com/exec/obidos/ASIN/151730007X/acmorg-20). * [From Python to NumPy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) *by Nicolas P. Rougier* *(free)* * [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. <i class="far fa-file-video"></i> **Videos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** ## Advanced Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. <i class="fas fa-chalkboard"></i> **Tutorials** * [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* * [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). <i class="fas fa-book"></i> **Books** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* * [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* * [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* <i class="far fa-file-video"></i> **Videos** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** ## NumPy Talks * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) * [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) * [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stéfan van der Walt, Charles Harris* (2019) * [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** ## Citing NumPy If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy).
numpy/numpy.org
e8d99cbfe510d66a0e63ed87758d66e58d1d1b69
Minor fix in formatting
diff --git a/content/en/learn.md b/content/en/learn.md index 322df18..2f79a8d 100644 --- a/content/en/learn.md +++ b/content/en/learn.md @@ -1,76 +1,96 @@ --- title: Learn sidebar: false --- For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). *** -Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. +Below is a curated collection of educational resources, both for self-learning and +teaching others, developed by NumPy contributors and vetted by the community. ## Beginners -There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: +There's a ton of information about NumPy out there. If you are just starting, we'd +strongly recommend the following: <i class="fas fa-chalkboard"></i> **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and +educational materials in the format of Jupyter Notebooks developed and maintained by +the NumPy Documentation team. To submit your own content, visit the +[numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -* [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +* [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering +NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. * [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) * [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) <i class="fas fa-book"></i> **Books** -* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is the first and *free* edition of the book. To purchase the latest edition, [click here](https://www.amazon.com/exec/obidos/ASIN/151730007X/acmorg-20). -* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) *(free)* -* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* +* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) +This is the first and *free* edition of the book. To purchase the latest edition, +[click here](https://www.amazon.com/exec/obidos/ASIN/151730007X/acmorg-20). +* [From Python to NumPy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +*by Nicolas P. Rougier* *(free)* +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) +*by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* -You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. +You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) +on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," +which has NumPy at its core. <i class="far fa-file-video"></i> **Videos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** ## Advanced Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. <i class="fas fa-chalkboard"></i> **Tutorials** -* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* -* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* -* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* -* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +* [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) +*by Nicolas P. Rougier* +* [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) +*by M. Scott Shell* +* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) +*by Stéfan van der Walt* +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational +materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. +To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). <i class="fas fa-book"></i> **Books** -* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* -* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) +*by Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) +*by Wes McKinney* * [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* <i class="far fa-file-video"></i> **Videos** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) +*by Juan Nunez-Iglesias* *** ## NumPy Talks * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) * [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) * [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stéfan van der Walt, Charles Harris* (2019) * [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** ## Citing NumPy If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy).
numpy/numpy.org
1e8c78a7b485f358664d991b7aea5177698d23d0
tweak front page for new release (#866)
diff --git a/content/en/config.yaml b/content/en/config.yaml index 6ee1230..120e133 100644 --- a/content/en/config.yaml +++ b/content/en/config.yaml @@ -1,117 +1,117 @@ languageName: English params: description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. navbarlogo: image: logo.svg text: NumPy link: / hero: # Main hero title title: NumPy # Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python # Button text - buttontext: "Latest release: NumPy 2.2. View all releases" + buttontext: "Latest release: NumPy 2.3. View all releases" # Where the main hero button links to buttonlink: "/news/#releases" # Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - title: Try NumPy text: Use the interactive shell to try NumPy in the browser docslink: Don't forget to check out the <a href="https://numpy.org/doc/stable" target="_blank">docs</a>. casestudies: title: CASE STUDIES features: - title: First Image of a Black Hole text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole img: /images/content_images/case_studies/blackhole.png alttext: First image of a black hole. It is an orange circle in a black background. url: /case-studies/blackhole-image - title: Detection of Gravitational Waves text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. img: /images/content_images/case_studies/gravitional.png alttext: Two orbs orbiting each other. They are displacing gravity around them. url: /case-studies/gw-discov - title: Sports Analytics text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. img: /images/content_images/case_studies/sports.jpg alttext: Cricket ball on green field. url: /case-studies/cricket-analytics - title: Pose Estimation using deep learning text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. img: /images/content_images/case_studies/deeplabcut.png alttext: Cheetah pose analysis url: /case-studies/deeplabcut-dnn tabs: title: ECOSYSTEM section5: false navbar: - title: Install url: /install - title: Documentation url: https://numpy.org/doc/stable - title: Learn url: /learn - title: Community url: /community - title: About Us url: /about - title: News url: /news - title: Contribute url: /contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - link: https://github.com/numpy/numpy icon: github - link: https://www.youtube.com/@NumPy_team icon: youtube quicklinks: column1: title: "" links: - text: Install link: /install - text: Documentation link: https://numpy.org/doc/stable - text: Learn link: /learn - text: Citing NumPy link: /citing-numpy - text: Roadmap link: https://numpy.org/neps/roadmap.html column2: links: - text: About us link: /about - text: Community link: /community - text: User surveys link: /user-surveys - text: Contribute link: /contribute - text: Code of conduct link: /code-of-conduct column3: links: - text: Get help link: /gethelp - text: Terms of use link: /terms - text: Privacy link: /privacy - text: Press kit link: /press-kit diff --git a/content/en/news.md b/content/en/news.md index 42795e1..d4cc1ee 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,516 +1,516 @@ --- title: "News" sidebar: false -newsHeader: "NumPy 2.2.0 released!" -date: 2024-12-08 +newsHeader: "NumPy 2.3.0 released!" +date: 2025-06-07 --- ### NumPy 2.3.0 released _7 Jun, 2025_ -- The NumPy 2.3.0 release improves free threaded Python support and annotations together with the usual set of bug fixes. It is unusual in the number of expired deprecations, code modernizations, and style cleanups. The latter may not be visible to users, but is important for code maintenance over the long term. Note that we have also upgraded from manylinux2014 to manylinux_2_28. Highlights are: - Interactive examples in the NumPy documentation. - Building NumPy with OpenMP Parallelization. - Preliminary support for Windows on ARM. - Improved support for free threaded Python. - Improved annotations. This release supports Python versions 3.11-3.13, Python 3.14 will be supported when it is released. ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.3.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.3.0)) -- _7 Jun 2025_. - NumPy 2.2.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.6)) -- _17 May 2025_. - NumPy 2.2.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.5)) -- _19 Apr 2025_. - NumPy 2.2.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.4)) -- _16 Mar 2025_. - NumPy 2.2.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.3)) -- _13 Feb 2025_. - NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2025_. - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
0b30bb533a277408bd975b0181cc5096760a272e
Include the feedback from the review
diff --git a/content/en/learn.md b/content/en/learn.md index 25fc428..f354688 100644 --- a/content/en/learn.md +++ b/content/en/learn.md @@ -1,76 +1,76 @@ --- title: Learn sidebar: false --- For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). *** Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. ## Beginners There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: <i class="fas fa-chalkboard"></i> **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) * [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. * [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) * [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) <i class="fas fa-book"></i> **Books** -* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is the first edition of the book published in 2006. For the latest edition released in 2015, [click here](https://dl.acm.org/doi/10.5555/2886196). -* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is the first and *free* edition of the book. To purchase the latest edition, [click here](https://www.amazon.com/exec/obidos/ASIN/151730007X/acmorg-20). +* [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) *(free)* * [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. <i class="far fa-file-video"></i> **Videos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** ## Advanced Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. <i class="fas fa-chalkboard"></i> **Tutorials** * [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* * [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). <i class="fas fa-book"></i> **Books** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* * [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* * [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* <i class="far fa-file-video"></i> **Videos** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** ## NumPy Talks * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) * [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) * [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) * [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** ## Citing NumPy If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy).
numpy/numpy.org
554b3afedf3d13709c975e04a9af4aacba015fe2
announce the NumPy 2.3.0 release (#865)
diff --git a/content/en/news.md b/content/en/news.md index 3dcc92e..42795e1 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,496 +1,516 @@ --- title: "News" sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-08 --- +### NumPy 2.3.0 released + +_7 Jun, 2025_ -- The NumPy 2.3.0 release improves free threaded Python support +and annotations together with the usual set of bug fixes. It is unusual in the +number of expired deprecations, code modernizations, and style cleanups. The +latter may not be visible to users, but is important for code maintenance over +the long term. Note that we have also upgraded from manylinux2014 to +manylinux_2_28. Highlights are: + +- Interactive examples in the NumPy documentation. +- Building NumPy with OpenMP Parallelization. +- Preliminary support for Windows on ARM. +- Improved support for free threaded Python. +- Improved annotations. + +This release supports Python versions 3.11-3.13, Python 3.14 will be +supported when it is released. + + ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.3.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.3.0)) -- _7 Jun 2025_. - NumPy 2.2.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.6)) -- _17 May 2025_. - NumPy 2.2.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.5)) -- _19 Apr 2025_. - NumPy 2.2.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.4)) -- _16 Mar 2025_. - NumPy 2.2.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.3)) -- _13 Feb 2025_. - NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2025_. - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
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ef3cd04eebdad2fbfe8e07e9d5a7cfd54be06744
Use current quansight logo (#864)
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numpy/numpy.org
5edac4332b7d9919fcdaa9a32551f2f5ea02cbd0
DOC: Add how to contribute translations in crowdin (#681)
diff --git a/content/en/contribute.md b/content/en/contribute.md index acad323..8a5d9e5 100644 --- a/content/en/contribute.md +++ b/content/en/contribute.md @@ -1,115 +1,118 @@ --- title: Contribute to NumPy sidebar: false --- The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at <[email protected]> or on [Slack](https://numpy-team.slack.com) (write <[email protected]> for an invite). We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. For a visual guide on how to contribute to NumPy, check out this [comic](https://heyzine.com/flip-book/3e66a13901.html). {{< comic >}} ### Writing code Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase. <br>Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: * check if older bugs are still present * find duplicate issues and link related ones * add good self-contained reproducers to issues * label issues correctly (this requires triage rights -- just ask) Please just dive in. ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. ### Translating website content -We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy -accessible to users in their native language. Volunteer translators are at the heart -of this effort. See -[here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) -for background; comment on [this GitHub -issue](https://github.com/numpy/numpy.org/issues/55) to sign up. +We are working on translating [numpy.org](https://numpy.org) into multiple languages to make +its content more accessible to NumPy users all over the globe. (See +[NEP 28](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) +for background.) Volunteer translators are at the heart of this effort. If you'd like to help, join the +*translation* channel on the +[Scientific Python Discord server](https://discord.com/channels/786703927705862175/1131695137370669158). + +To get familiar with our translation process, read the guide +[How to translate content using Crowdin](https://scientific-python-translations.github.io/translate/). ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. ### Donate If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate).
numpy/numpy.org
2e38577379acc09eb5a5ee90181c7e4b12558736
announce NumPy 2.2.6
diff --git a/content/en/news.md b/content/en/news.md index 3133b6f..3dcc92e 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,495 +1,496 @@ --- title: "News" sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-08 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.2.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.6)) -- _17 May 2025_. - NumPy 2.2.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.5)) -- _19 Apr 2025_. - NumPy 2.2.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.4)) -- _16 Mar 2025_. - NumPy 2.2.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.3)) -- _13 Feb 2025_. - NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2025_. - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
5d3476bc8fb019d559551b4ec74319058da2062e
Make the name accents consistent
diff --git a/content/en/learn.md b/content/en/learn.md index 373d8a0..a56f90f 100644 --- a/content/en/learn.md +++ b/content/en/learn.md @@ -1,76 +1,76 @@ --- title: Learn sidebar: false --- For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). *** Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. ## Beginners There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: <i class="fas fa-chalkboard"></i> **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) * [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. * [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) * [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) <i class="fas fa-book"></i> **Books** * [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://dl.acm.org/doi/10.5555/2886196). * [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) -* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* +* [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stéfan van der Walt, and Harriet Dashnow* You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. <i class="far fa-file-video"></i> **Videos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** ## Advanced Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. <i class="fas fa-chalkboard"></i> **Tutorials** * [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* * [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). <i class="fas fa-book"></i> **Books** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* * [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* * [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* <i class="far fa-file-video"></i> **Videos** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** ## NumPy Talks * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) * [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) -* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stéfan van der Walt, Charles Harris* (2019) * [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** ## Citing NumPy If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy).
numpy/numpy.org
471d70b9670f4de52acca1ca34cd7e284fc1a608
Announce the NumPy 2.2.5 release (#849)
diff --git a/content/en/news.md b/content/en/news.md index c940dac..3133b6f 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,494 +1,495 @@ --- title: "News" sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-08 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.2.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.5)) -- _19 Apr 2025_. - NumPy 2.2.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.4)) -- _16 Mar 2025_. - NumPy 2.2.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.3)) -- _13 Feb 2025_. - NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2025_. - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
384a0ddf34a2975ce1d5906309413213047a8eeb
Correct capitalization of NumPy in config.yaml (#848)
diff --git a/content/en/config.yaml b/content/en/config.yaml index 63ac094..6ee1230 100644 --- a/content/en/config.yaml +++ b/content/en/config.yaml @@ -1,117 +1,117 @@ languageName: English params: description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. navbarlogo: image: logo.svg text: NumPy link: / hero: # Main hero title title: NumPy # Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python # Button text buttontext: "Latest release: NumPy 2.2. View all releases" # Where the main hero button links to buttonlink: "/news/#releases" # Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - title: Try NumPy text: Use the interactive shell to try NumPy in the browser docslink: Don't forget to check out the <a href="https://numpy.org/doc/stable" target="_blank">docs</a>. casestudies: title: CASE STUDIES features: - title: First Image of a Black Hole text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole img: /images/content_images/case_studies/blackhole.png alttext: First image of a black hole. It is an orange circle in a black background. url: /case-studies/blackhole-image - title: Detection of Gravitational Waves text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. img: /images/content_images/case_studies/gravitional.png alttext: Two orbs orbiting each other. They are displacing gravity around them. url: /case-studies/gw-discov - title: Sports Analytics text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. img: /images/content_images/case_studies/sports.jpg alttext: Cricket ball on green field. url: /case-studies/cricket-analytics - title: Pose Estimation using deep learning text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. img: /images/content_images/case_studies/deeplabcut.png alttext: Cheetah pose analysis url: /case-studies/deeplabcut-dnn tabs: title: ECOSYSTEM section5: false navbar: - title: Install url: /install - title: Documentation url: https://numpy.org/doc/stable - title: Learn url: /learn - title: Community url: /community - title: About Us url: /about - title: News url: /news - title: Contribute url: /contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - link: https://github.com/numpy/numpy icon: github - link: https://www.youtube.com/@NumPy_team icon: youtube quicklinks: column1: title: "" links: - text: Install link: /install - text: Documentation link: https://numpy.org/doc/stable - text: Learn link: /learn - - text: Citing Numpy + - text: Citing NumPy link: /citing-numpy - text: Roadmap link: https://numpy.org/neps/roadmap.html column2: links: - text: About us link: /about - text: Community link: /community - text: User surveys link: /user-surveys - text: Contribute link: /contribute - text: Code of conduct link: /code-of-conduct column3: links: - text: Get help link: /gethelp - text: Terms of use link: /terms - text: Privacy link: /privacy - text: Press kit link: /press-kit
numpy/numpy.org
3dae42be21c5c1af23f9661e92ad0b6c80d3e31a
Remove a broken link in cricket-analytics.md
diff --git a/content/en/case-studies/cricket-analytics.md b/content/en/case-studies/cricket-analytics.md index 926a562..6abd3b0 100644 --- a/content/en/case-studies/cricket-analytics.md +++ b/content/en/case-studies/cricket-analytics.md @@ -1,164 +1,164 @@ --- title: "Case Study: Cricket Analytics, the game changer!" sidebar: false --- {{< figure >}} src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest Cricket Festival in India' alt = 'Indian Premier League Cricket cup and stadium' attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' attributionlink = 'https://unsplash.com/@aksh1802' {{< /figure >}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" >}} You don't play for the crowd, you play for the country. {{< /blockquote >}} ## About Cricket It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: * batting performance moving average, * score forecasting, * gaining insights into fitness and performance of a player against different opposition, * player contribution to wins and losses for making strategic decisions on team composition {{< figure >}} src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the focal point in the field' alt = 'A cricket pitch with bowler and batsmen' align = 'center' attribution = '(Image credit: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< /figure >}} ### Key Data Analytics Objectives * Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. * Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. * Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. {{< figure >}} src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### The Challenges * **Data Cleaning and preprocessing** IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. * **Dynamic Modeling** In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. * **Predictive Analytics Complexity** Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. ## NumPy’s Role in Cricket Analytics Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: * **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. -* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. +* **Data Visualization:** Data graphing and visualization provide useful insights into relationship between various datasets. ## Summary Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. {{< figure >}} src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' {{< /figure >}}
numpy/numpy.org
d2f40688ee11f9d4be724c033d7eb424c1c42614
Change PyMC3 to PyMC due to the name change of the project
diff --git a/content/en/tabcontents.yaml b/content/en/tabcontents.yaml index 69ddf6e..1ffe038 100644 --- a/content/en/tabcontents.yaml +++ b/content/en/tabcontents.yaml @@ -1,295 +1,295 @@ params: machinelearning: paras: - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications &mdash; among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) &mdash; one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. arraylibraries: intro: - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. headers: - text: Array Library - text: Capabilities & Application areas libraries: - title: Dask text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: NumPy-compatible array library for GPU-accelerated computing with Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.dev - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://jax.readthedocs.io/ - title: Xarray text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: Deep learning framework that accelerates the path from research prototyping to production deployment. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: Arrow text: A cross-language development platform for columnar in-memory data and analytics. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://arrow.apache.org/ - title: xtensor text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array text: Manipulate JSON-like data with NumPy-like idioms. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Nearly every scientist working in Python draws on the power of NumPy. - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." libraries: - title: Quantum Computing alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - url: https://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - url: https://qiskit.org label: Qiskit - url: https://pennylane.ai label: PennyLane - title: Statistical Computing alttext: A line graph with the line moving up. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - url: https://www.statsmodels.org/ label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - url: https://seaborn.pydata.org/ label: Seaborn - title: Signal Processing alttext: A bar chart with positive and negative values. img: /images/content_images/sc_dom_img/signal_processing.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets - url: https://python-control.org/ label: python-control - url: https://hyperspy.org/ label: HyperSpy - title: Image Processing alttext: An photograph of the mountains. img: /images/content_images/sc_dom_img/image_processing.svg links: - url: https://scikit-image.org/ label: Scikit-image - url: https://opencv.org/ label: OpenCV - url: https://mahotas.rtfd.io/ label: Mahotas - title: Graphs and Networks alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg links: - url: https://networkx.org/ label: NetworkX - url: https://graph-tool.skewed.de/ label: graph-tool - url: https://igraph.org/python/ label: igraph - url: https://pygsp.rtfd.io/ label: PyGSP - title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - url: https://sunpy.org/ label: SunPy - url: https://spacepy.github.io/ label: SpacePy - title: Cognitive Psychology alttext: A human head with gears. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - title: Bioinformatics alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - url: https://biopython.org/ label: BioPython - url: http://scikit-bio.org/ label: Scikit-Bio - url: https://github.com/openvax/pyensembl label: PyEnsembl - url: http://etetoolkit.org/ label: ETE - title: Bayesian Inference alttext: A graph with a bell-shaped curve. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ - label: PyMC3 + label: PyMC - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ label: emcee - title: Mathematical Analysis alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://www.sympy.org/ label: SymPy - url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: Chemistry alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg links: - url: https://cantera.org/ label: Cantera - url: https://www.mdanalysis.org/ label: MDAnalysis - url: https://github.com/rdkit/rdkit label: RDKit - url: https://www.pybamm.org/ label: PyBaMM - title: Geoscience alttext: The Earth. img: /images/content_images/sc_dom_img/geoscience.svg links: - url: https://pangeo.io/ label: Pangeo - url: https://simpeg.xyz/ label: Simpeg - url: https://github.com/obspy/obspy/wiki label: ObsPy - url: https://www.fatiando.org/ label: Fatiando a Terra - title: Geographic Processing alttext: A map. img: /images/content_images/sc_dom_img/GIS.svg links: - url: https://shapely.readthedocs.io/ label: Shapely - url: https://geopandas.org/ label: GeoPandas - url: https://python-visualization.github.io/folium label: Folium - title: Architecture & Engineering alttext: A microprocessor development board. img: /images/content_images/sc_dom_img/robotics.svg links: - url: https://compas.dev/ label: COMPAS - url: https://cityenergyanalyst.com/ label: City Energy Analyst - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" image1: - img: /images/content_images/ds-landscape.png alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. image2: - img: /images/content_images/data-science.png alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" content: - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: A streamplot made in matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: A scatter-plot graph made in ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: A box-plot made in plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: A streamgraph made in altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - url: https://napari.org img: /images/content_images/v_napari.png alttext: A multi-dimensionan image made in napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: A Voronoi diagram made in vispy. content: - text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 1b4e97a..81b705c 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -1,275 +1,275 @@ params: machinelearning: paras: - para1: NumPy constituye la base de potentes librerías de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de librerías basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones&mdash; entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra librería de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. para2: Las técnicas estadísticas denominadas métodos [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) &mdash; uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático. arraylibraries: intro: - text: La API de NumPy es el punto de partida cuando se escriben librerías para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona. headers: - text: Librería de arreglos - text: Capacidades y áreas de aplicación libraries: - title: Dask text: Arreglos distribuidos y paralelismo avanzado para análisis, que permiten un rendimiento a escala. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: Librería de arreglos compatible con NumPy para cálculo acelerado en la GPU con Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.dev - title: JAX text: "Transformaciones componibles de programas NumPy: diferenciar, vectorizar, compilación justo-a-tiempo a GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://jax.readthedocs.io/ - title: Xarray text: Arreglos multidimensionales indexados y etiquetados para análisis y visualización avanzados. img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: Librería de arreglos dispersos compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: Marco de aprendizaje profundo que acelera el camino desde la creación de prototipos de investigación hasta la implantación en producción. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: Una plataforma integral de aprendizaje automático para crear y desplegar fácilmente aplicaciones basadas en ML. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: Arrow text: Plataforma de desarrollo multilingüe para datos y análisis columnares en memoria. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://arrow.apache.org/ - title: xtensor text: Arreglos multidimensionales con difusión y computación perezosa para análisis numérico. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array text: Manipular datos similares a JSON con expresiones similares a NumPy. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray text: Sistema de backend de Python que desacopla la API de la implementación; unumpy proporciona una API de NumPy. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly text: Aprendizaje tensorial, álgebra y backends para usar de manera fluida NumPy, PyTorch, TensorFlow o CuPy. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Casi todos los científicos que trabajan en Python recurren a la potencia de NumPy. - text: "NumPy aporta la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante." libraries: - title: Computación Cuántica alttext: Un chip para computador. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - url: https://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - url: https://qiskit.org label: Qiskit - url: https://pennylane.ai label: PennyLane - title: Computación Estadística alttext: Un gráfico lineal con la línea moviéndose hacia arriba. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - url: https://www.statsmodels.org/ label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - url: https://seaborn.pydata.org/ label: Seaborn - title: Procesamiento de Señales alttext: Un gráfico de barras con valores positivos y negativos. img: /images/content_images/sc_dom_img/signal_processing.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets - url: https://python-control.org/ label: python-control - url: https://hyperspy.org/ label: HiperSpy - title: Procesamiento de Imágenes alttext: Una fotografía de las montañas. img: /images/content_images/sc_dom_img/image_processing.svg links: - url: https://scikit-image.org/ label: Scikit-image - url: https://opencv.org/ label: OpenCV - url: https://mahotas.rtfd.io/ label: Mahotas - title: Grafos y Redes alttext: Un grafo simple. img: /images/content_images/sc_dom_img/sd6.svg links: - url: https://networkx.org/ label: NetworkX - url: https://graph-tool.skewed.de/ label: graph-tool - url: https://igraph.org/python/ label: igraph - url: https://pygsp.rtfd.io/ label: PyGSP - title: Astronomía alttext: Un telescopio. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - url: https://sunpy.org/ label: SunPy - url: https://spacepy.github.io/ label: SpacePy - title: Psicología Cognitiva alttext: Una cabeza humana con engranajes. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - title: Bioinformática alttext: Una hebra de ADN. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - url: https://biopython.org/ label: BioPython - url: http://scikit-bio.org/ label: Scikit-Bio - url: https://github.com/openvax/pyensembl label: PyEnsembl - url: http://etetoolkit.org/ label: ETE - title: Inferencia Bayesiana alttext: Un gráfico con una curva en forma de campanas. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ - label: PyMC3 + label: PyMC - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ label: emcee - title: Análisis Matemático alttext: Cuatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://www.sympy.org/ label: SymPy - url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: Química alttext: Un tubo de ensayo. img: /images/content_images/sc_dom_img/chemistry.svg links: - url: https://cantera.org/ label: Cantera - url: https://www.mdanalysis.org/ label: MDAnalysis - url: https://github.com/rdkit/rdkit label: RDKit - url: https://www.pybamm.org/ label: PyBaMM - title: Geociencia alttext: La Tierra. img: /images/content_images/sc_dom_img/geoscience.svg links: - url: https://pangeo.io/ label: Pangeo - url: https://simpeg.xyz/ label: Simpeg - url: https://github.com/obspy/obspy/wiki label: ObsPy - url: https://www.fatiando.org/ label: Fatiando a Terra - title: Procesamiento Geográfico alttext: Un mapa. img: /images/content_images/sc_dom_img/GIS.svg links: - url: https://shapely.readthedocs.io/ label: Shapely - url: https://geopandas.org/ label: GeoPandas - url: https://python-visualization.github.io/folium label: Folium - title: Arquitectura e Ingeniería alttext: Una placa de desarrollo de microprocesadores. img: /images/content_images/sc_dom_img/robotics.svg links: - url: https://compas.dev/ label: COMPAS - url: https://cityenergyanalyst.com/ label: City Energy Analyst - Analista de Energía de Ciudad - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "NumPy es el núcleo de un rico ecosistema de librerías de ciencia de datos. Un flujo de trabajo exploratorio típico de ciencia de datos podría verse así:" image1: - img: /images/content_images/ds-landscape.png alttext: Diagrama de las librerías de Python. Las cinco categorías son "Extraer, Transformar, Cargar", "Exploración de Datos", "Modelado de Datos", "Evaluación de Datos" y "Presentación de Datos". image2: - img: /images/content_images/data-science.png alttext: Diagrama de tres círculos superpuestos. Los círculos se denominan "Matemáticas", "Ciencias de la Computación" y "Conocimientos Especializados". En el centro del diagrama, con los tres círculos superpuestos, hay un área denominada "Ciencia de datos". examples: - text: "<b>Extraer, Transformar, Cargar: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - text: "<b>Análisis Exploratorio: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "<b>Modelado y evaluación: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - text: "<b>Modelado y evaluación: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "<b>Informes en un panel de control: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - text: Para grandes volúmenes de datos, [Dask](https://dask.org) y [Ray](https://ray.io/) están diseñados para escalarse. Las implementaciones estables se basan en el versionado de datos ([DVC](https://dvc.org)), rastreo de experimentos ([MLFlow](https://mlflow.org)), y automatización del flujo de trabajo ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) y [Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: Un diagrama de flujo hecho en matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: Un diagrama de dispersión hecho en ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: Un diagrama de caja hecho en plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: Un diagrama de flujo hecho en altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: Un gráfico de pares de dos tipos de gráficos, un gráfico de trazado y un gráfico de frecuencias hecho en seaborn - url: https://docs.pyvista.org/examples/index.html img: /images/content_images/v_pyvista.png alttext: Un renderizado de volumen 3D realizado en PyVista. - url: https://napari.org img: /images/content_images/v_napari.png alttext: Una imagen multidimensional hecha en napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: Un diagrama de Voronoi hecho en vispy. content: - text: NumPy es un componente esencial en el floreciente [panorama de visualización de Python](https://pyviz.org/overviews/index.html), que incluye [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), y [PyVista](https://github.com/pyvista/pyvista), por nombrar algunos. - text: El procesamiento acelerado de arreglos de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar. diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 26c3241..c038cdc 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -1,373 +1,373 @@ params: machinelearning: paras: - para1: NumPyは、[scikit-learn](https://scikit-learn.org)や[SciPy](https://www.scipy.org)のような強力な機械学習ライブラリの基礎を形成しています。機械学習の技術分野が成長するにつれ、NumPyをベースにしたライブラリの数も増えています。[TensorFlow](https://www.tensorflow.org)の深層学習機能は、音声認識や画像認識、テキストベースのアプリケーション、時系列分析、動画検出など、幅広い応用用途があります。[PyTorch](https://pytorch.org)も、コンピュータビジョンや自然言語処理の研究者に人気のある深層学習ライブラリです。[MXNet](https://github.com/apache/incubator-mxnet)もAIパッケージの一つで、深層学習の設計図やテンプレート機能を提供しています。 para2: '[ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205)法と呼ばれる統計的手法であるビンニング、バギング、スタッキングや、[XGBoost](https://github.com/dmlc/xgboost)、[LightGBM](https://lightgbm.readthedocs.io/en/latest/)、[CatBoost](https://catboost.ai)などのツールで実装されているブースティングなどは、機械学習アルゴリズムの一つであり、最速の推論エンジンの一つです。[Yellowbrick](https://www.scikit-yb.org/en/latest/)や[Eli5](https://eli5.readthedocs.io/en/latest/)は機械学習の可視化機能を提供しています。' arraylibraries: intro: - text: NumPyのAPIは、革新的なハードウェアを利用したり、特殊な配列タイプを作成したり、NumPyが提供する以上の機能を追加するためにライブラリを作成する際の基礎となります。 headers: - text: 配列ライブラリ - text: 機能と応用分野 libraries: - title: Dask text: 分析用の分散配列と高度な並列処理により、大規模な処理を可能にします。 img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: Python を使用した GPUによる高速計算用のNumPy互換配列ライブラリ img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - title: JAX text: "NumPyコードの合成可能な変換ライブラリ: 微分、ベクトル化、GPU/TPUへのジャストインタイムコンパイル" img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax - title: Xarray text: 高度な分析と視覚化のためのラベルとインデックス付き多次元配列 img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: Dask と SciPy の疎行列の線形代数ライブラリを統合した、Numpy 互換の疎行列ライブラリ img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: 研究用のプロトタイピングから本番運用への展開を加速させる、深層学習フレームワーク img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: 機械学習を利用したアプリケーションを簡単に構築・展開するための、エンド・ツー・エンドの機械学習プラットフォーム img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: MXNet text: 柔軟や研究用のプロトタイピングから、実際の運用まで利用可能な深層学習フレームワーク img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - title: Arrow text: 列型のインメモリーデータやその分析のための、複数の言語に対応した開発プラットフォーム img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow - title: xtensor text: 数値解析のためのブロードキャスティングと遅延計算を備えた多次元配列 img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward text: Numpy のような イディオムを使って JSON のようなデータを操作するライブラリ img: /images/content_images/arlib/xnd.png alttext: awkward url: https://awkward-array.org/ - title: uarray text: APIを実装から切り離すPythonバックエンドシステム (unumpyはNumPy APIを提供しています) img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ scientificdomains: intro: - text: Pythonを使って働くほとんどの科学者はNumPyの力を利用しています。 - text: "Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。" libraries: - title: 量子コンピューティング alttext: コンピューターチップ img: /images/content_images/sc_dom_img/quantum_computing.svg links: - url: https://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - url: https://qiskit.org label: Qiskit - url: https://pennylane.ai label: PennyLane - title: 統計コンピューティング alttext: 線グラフが上に移動します。 img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - url: https://github.com/statsmodels/statsmodels label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - url: https://github.com/mwaskom/seaborn label: Seaborn - title: 信号処理 alttext: 正と負の値を持つ棒グラフ。 img: /images/content_images/sc_dom_img/signal_processing.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets - url: https://python-control.org/ label: python-control - url: https://hyperspy.org/ label: HyperSpy - title: 画像処理 alttext: 山々の写真 img: /images/content_images/sc_dom_img/image_processing.svg links: - url: https://scikit-image.org/ label: Scikit-image - url: https://opencv.org/ label: OpenCV - url: https://mahotas.rtfd.io/ label: Mahotas - title: グラフとネットワーク alttext: シンプルなグラフ img: /images/content_images/sc_dom_img/sd6.svg links: - url: https://networkx.org/ label: NetworkX - url: https://graph-tool.skewed.de/ label: graph-tool - url: https://igraph.org/python/ label: igraph - url: https://pygsp.rtfd.io/ label: PyGSP - title: 天文学 alttext: 望遠鏡 img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - url: https://github.com/sunpy/sunpy label: SunPy - url: https://github.com/spacepy/spacepy label: SpacePy - title: 認知心理学 alttext: ギアをつけた人間の頭部 img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - title: 生命情報科学 alttext: DNAの鎖 img: /images/content_images/sc_dom_img/bioinformatics.svg links: - url: https://biopython.org/ label: BioPython - url: http://scikit-bio.org/ label: Scikit-Bio - url: https://github.com/openvax/pyensembl label: PyEnsembl - url: http://etetoolkit.org/ label: ETE - title: ベイズ推論 alttext: 鐘形の曲線のグラフ img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ - label: PyMC3 + label: PyMC - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ label: emcee - title: 数学的分析 alttext: ４つの数学記号 img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://www.sympy.org/ label: SymPy - url: https://github.com/cvxgrp/cvxpy label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: 化学 alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg links: - url: https://cantera.org/ label: Cantera - url: https://www.mdanalysis.org/ label: MDAnalysis - url: https://github.com/rdkit/rdkit label: RDKit - url: https://www.pybamm.org/ label: PyBaMM - title: 地球科学 alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg links: - url: https://pangeo.io/ label: Pangeo - url: https://simpeg.xyz/ label: Simpeg - url: https://github.com/obspy/obspy/wiki label: ObsPy - url: https://www.fatiando.org/ label: Fatiando a Terra - title: 地理情報処理 alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg links: - url: https://shapely.readthedocs.io/ label: Shapely - url: https://geopandas.org/ label: GeoPandas - url: https://python-visualization.github.io/folium label: Folium - title: アーキテクチャとエンジニアリング alttext: マイクロプロセッサ開発ボード img: /images/content_images/sc_dom_img/robotics.svg links: - url: https://compas.dev/ label: COMPAS - url: https://cityenergyanalyst.com/ label: 都市エネルギー分析 - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "Numpy は豊富なデータサイエンスライブラリのエコシステムの中核にあります。一般的なデータサイエンスのワークフローは次のようになります。" image1: - img: /images/content_images/ds-landscape.png alttext: Python ライブラリの図 。5 つのカテゴリに分類され、「抽出、変換、読み込み」、「データ探索」、「モデリング」、「評価」、「可視化」です。 image2: - img: /images/content_images/data-science.png alttext: 三つの円が重なり合う図。円はそれぞれ「数学」、「コンピューターサイエンス」、「専門知識」でラベル付けされています。図の中心部には、三つの円が重なり合って形成されるエリアがあり、「データサイエンス」とラベル付けされています。 examples: - text: "<b>抽出, 変換, 読み込み: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "<b>探索的解析: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "<b>モデリングと評価: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + text: "<b>モデリングと評価: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "<b>ダッシュボードでのレポート: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - text: 大規模データに対して、[Dask](https://dask.org)と[Ray](https://ray.io/)はスケールすることを目指して設計されています。安定したデプロイメントに関しては、データのバージョニング（[DVC](https://dvc.org)）、実験の追跡（[MLFlow](https://mlflow.org)）、ワークフローの自動化（[Airflow](https://airflow.apache.org)および[Prefect](https://www.prefect.io)が重要ですが様々なNumPyベースのツールが提供されています。 visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: matplotlibで作られたストリームプロット - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: ggpyで作られた散布図グラフ - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: plotyで作られた箱ひげ図 - url: https://alta-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: altairで作られたストリームグラフ - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: 2種類のグラフによるペアプロット。seabornで作られたプロットと周波数グラフ" - url: https://docs.pyvista.org/examples/index.html img: /images/content_images/v_pyvista.png alttext: PyVista製の3Dボリュームレンダリング - url: https://napari.org img: /images/content_images/v_napari.png alttext: napariで作られた多次元画像 - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: vispyで作られたボロノイ図 content: - text: NumPyは、[Matplotlib](https://matplotlib.org)、[Seaborn](https://seaborn.pydata.org)、[Plotly](https://plot.ly)、[Altair](https://altair-viz.github.io)、[Bokeh](https://docs.bokeh.org/en/latest/)、[Holoviz](https://holoviz.org)、[Vispy](http://vispy.org)、[Napari](https://github.com/napari/napari)、[PyVista](https://github.com/pyvista/pyvista)などの、急成長している[Python visualization landscape](https://pyviz.org/overviews/index.html)に欠かせないコンポーネントです。 - text: NumPy の大規模配列の高速処理により、研究者は、ネイティブの Python が扱うことができるよりもはるかに大きなデータセットを可視化することができます。 diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index e34f7cc..6a2e194 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -1,280 +1,280 @@ params: machinelearning: paras: - para1: O NumPy forma a base de bibliotecas de aprendizagem de máquina poderosas como [scikit-learn](https://scikit-learn.org) e [SciPy](https://www.scipy.org). À medida que a disciplina de aprendizagem de máquina cresce, a lista de bibliotecas construidas a partir do NumPy também cresce. As funcionalidades de deep learning do [TensorFlow](https://www.tensorflow.org) tem diversas aplicações &mdash; entre elas, reconhecimento de imagem e de fala, aplicações baseadas em texto, análise de séries temporais, e detecção de vídeo. O [PyTorch](https://pytorch.org), outra biblioteca de deep learning, é popular entre pesquisadores em visão computacional e processamento de linguagem natural. O [MXNet](https://github.com/apache/incubator-mxnet) é outro pacote de IA, que fornece templates e protótipos para deep learning. para2: Técnicas estatísticas chamadas métodos de [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) tais como binning, bagging, stacking, e boosting estão entre os algoritmos de ML implementados por ferramentas tais como [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), e [CatBoost](https://catboost.ai) &mdash; um dos motores de inferência mais rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) e [Eli5](https://eli5.readthedocs.io/en/latest/) oferecem visualizações para aprendizagem de máquina. arraylibraries: intro: - text: A API do NumPy é o ponto de partida quando bibliotecas são escritas para explorar hardware inovador, criar tipos de arrays especializados, ou adicionar capacidades além do que o NumPy fornece. headers: - text: Biblioteca de Arrays - text: Recursos e áreas de aplicação libraries: - title: Dask text: Arrays distribuídas e paralelismo avançado para análise, permitindo desempenho em escala. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: Biblioteca de matriz compatível com NumPy para computação acelerada pela GPU com Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - title: JAX text: "Transformações combináveis de programas NumPy: vetorização, compilação just-in-time para GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax - title: Xarray text: Arrays multidimensionais rotuladas e indexadas para análise e visualização avançadas img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: Biblioteca de arrays compatíveis com o NumPy que pode ser integrada com Dask e álgebra linear esparsa da SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: Framework de deep learning que acelera o caminho entre prototipação de pesquisa e colocação em produção. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: Uma plataforma completa para aprendizagem de máquina que permite construir e colocar em produção aplicações usando ML facilmente. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: MXNet text: Framework de deep learning voltado para flexibilizar prototipação em pesquisa e produção. img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - title: Arrow text: Uma plataforma de desenvolvimento multi-linguagens para dados e análise para dados armazenados em colunas na memória. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow - title: xtensor text: Arrays multidimensionais com broadcasting e avaliação preguiçosa (lazy computing) para análise numérica. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array text: Manipulação de dados JSON-like com sintaxe NumPy-like. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray text: Sistema de backend Python que dissocia a API da implementação; unumpy fornece uma API NumPy. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly text: Ferramentas para aprendizagem com tensores, algebra e backends para usar NumPy, MXNet, PyTorch, TensorFlow ou CuPy sem esforço. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy. - text: "NumPy traz o poder computacional de linguagens como C e Fortran para Python, uma linguagem muito mais fácil de aprender e usar. Com esse poder vem a simplicidade: uma solução no NumPy é frequentemente clara e elegante." libraries: - title: Computação quântica alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - url: https://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - url: https://qiskit.org label: Qiskit - url: https://pennylane.ai label: PennyLane - title: Computação estatística alttext: Um gráfico com uma linha em movimento para cima. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - url: https://www.statsmodels.org/ label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - url: https://seaborn.pydata.org/ label: Seaborn - title: Processamento de sinais alttext: Um gráfico de barras com valores positivos e negativos. img: /images/content_images/sc_dom_img/signal_processing.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets - url: https://python-control.org/ label: python-control - url: https://hyperspy.org/ label: HiperSpy - title: Processamento de imagens alttext: Uma fotografia das montanhas. links: - url: https://scikit-image.org/ label: Scikit-image - url: https://opencv.org/ label: OpenCV - url: https://mahotas.rtfd.io/ label: Mahotas img: /images/content_images/sc_dom_img/image_processing.svg - title: Gráficos e Redes alttext: Um grafo simples. img: /images/content_images/sc_dom_img/sd6.svg links: - url: https://networkx.org/ label: NetworkX - url: https://graph-tool.skewed.de/ label: graph-tool - url: https://igraph.org/python/ label: igraph - url: https://pygsp.rtfd.io/ label: PyGSP - title: Processos de Astronomia alttext: Um telescópio. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - url: https://sunpy.org/ label: SunPy - url: https://spacepy.github.io/ label: SpacePy - title: Psicologia Cognitiva alttext: Uma cabeça humana com engrenagens. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - title: Bioinformática alttext: Um pedaço de DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - url: https://biopython.org/ label: BioPython - url: http://scikit-bio.org/ label: Scikit-Bio - url: https://github.com/openvax/pyensembl label: PyEnsembl - url: http://etetoolkit.org/ label: ETE - title: Inferência Bayesiana alttext: Um gráfico com uma curva em forma de sino. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ - label: PyMC3 + label: PyMC - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ label: emcee - title: Análise Matemática alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://www.sympy.org/ label: SymPy - url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: Química alttext: Um tubo de ensaio. img: /images/content_images/sc_dom_img/chemistry.svg links: - url: https://cantera.org/ label: Cantera - url: https://www.mdanalysis.org/ label: MDAnalysis - url: https://github.com/rdkit/rdkit label: RDKit - url: https://www.pybamm.org/ label: PyBaMM - title: Geociências alttext: A Terra. img: /images/content_images/sc_dom_img/geoscience.svg links: - url: https://pangeo.io/ label: Pangeo - url: https://simpeg.xyz/ label: Simpeg - url: https://github.com/obspy/obspy/wiki label: ObsPy - url: https://www.fatiando.org/ label: Fatiando a Terra - title: Processamento Geográfico alttext: Um mapa. img: /images/content_images/sc_dom_img/GIS.svg links: - url: https://shapely.readthedocs.io/ label: Shapely - url: https://geopandas.org/ label: GeoPandas - url: https://python-visualization.github.io/folium label: Folium - title: Arquitetura e Engenharia alttext: Uma placa de desenvolvimento de microprocessador. img: /images/content_images/sc_dom_img/robotics.svg links: - url: https://compas.dev/ label: COMPAS - url: https://cityenergyanalyst.com/ label: City Energy Analyst - Analista de Energía de Ciudad - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "NumPy está no centro de um rico ecossistema de bibliotecas de ciência de dados. Um fluxo de trabalho típico de ciência de dados exploratório pode parecer assim:" image1: - img: /images/content_images/ds-landscape.png alttext: Diagrama de bibliotecas Python. As cinco categorias são 'Extrair, Transformar, Carregar', 'Exploração de Dados', 'Modelo de Dados', 'Avaliação de Dados' e 'Apresentação de Dados'. image2: - img: /images/content_images/data-science.png alttext: Diagram of three overlapping circle. The circles labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" + - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC](https://docs.pymc.io),[ spaCy](https://spacy.io)" - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" content: - text: For high data volumes, [Dask](https://dask.org) and[Ray](https://ray.io/) are designed to scale. Stabledeployments rely on data versioning ([DVC](https://dvc.org)),experiment tracking ([MLFlow](https://mlflow.org)), andworkflow automation ([Airflow](https://airflow.apache.org) and[Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: Um streamplot feito em matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: Um gráfico scatter-plot feito em ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: Um box-plot feito no plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: Um gráfico streamgraph feito em altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: A plot duplo com dois tipos de gráficos, um plot-graph e um gráfico de frequência feitos no seaborn - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: Uma renderização de volume 3D feita no PyVista. - url: https://napari.org img: /images/content_images/v_napari.png alttext: Uma imagem multidimensional, feita em napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: Diagrama de Voronoi feito com vispy. content: - text: NumPy é um componente essencial no crescente [campo de visualização em Python](https://pyviz.org/overviews/index.html), que inclui [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), e [PyVista](https://github.com/pyvista/pyvista), para citar alguns. - text: O processamento de grandes arrays acelerado pela NumPy permite que os pesquisadores visualizem conjuntos de dados muito maiores do que o Python nativo poderia permitir.
numpy/numpy.org
bb0098c13321db3c6340246dfa0a944bfaf17b9a
announce the NumPy 2.2.4 release (#841)
diff --git a/content/en/news.md b/content/en/news.md index 7225722..c940dac 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,493 +1,494 @@ --- title: "News" sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-08 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.2.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.4)) -- _16 Mar 2025_. - NumPy 2.2.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.3)) -- _13 Feb 2025_. - NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2025_. - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
340f7a992b795f01f166c0129748c7e8a39a1db9
Remove hyperlinks to the NumPy-related conferences
diff --git a/content/en/community.md b/content/en/community.md index 73338a5..50f850e 100644 --- a/content/en/community.md +++ b/content/en/community.md @@ -1,77 +1,77 @@ --- title: Community sidebar: false --- NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. ## Participate online The following are ways to engage directly with the NumPy project and community. _Please note that we encourage users and community members to support each other for usage questions - see [Get Help](/gethelp)._ ### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. A searchable archive of this list is available [here](https://mail.python.org/archives/list/[email protected]/). *** ### [GitHub issue tracker](https://github.com/numpy/numpy/issues) - For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); - documentation issues (e.g. "I found this section unclear"); - and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). _Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ *** ### [Slack](https://numpy-team.slack.com) A real-time chat room to ask questions about _contributing_ to NumPy. This is a private space, specifically meant for people who are hesitant to bring up their questions or ideas on a large public mailing list or GitHub. Please see [here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more details and how to get an invite. ## Study Groups and Meetups If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). ## Conferences The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: -- [SciPy US](https://conference.scipy.org) -- [EuroSciPy](https://www.euroscipy.org) -- [SciPy Latin America](https://pythoncientifico.ar/) -- [SciPy India](https://scipy.in) -- [SciPy Japan](https://www.scipyjapan.scipy.org/) -- [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) +- SciPy US +- EuroSciPy +- SciPy Latin America +- SciPy India +- SciPyData Japan +- PyData conferences (15-20 events a year spread over many countries) Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. ## Join the NumPy community To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/).
numpy/numpy.org
24b21f7e7a2ffb5ca70321de38b841505629f6d0
Adjust comic dimension
diff --git a/layouts/shortcodes/comic.html b/layouts/shortcodes/comic.html index 80e1f4b..3c6d2aa 100644 --- a/layouts/shortcodes/comic.html +++ b/layouts/shortcodes/comic.html @@ -1,12 +1,12 @@ <div class="comic"></div> <a href="https://heyzine.com/flip-book/3e66a13901.html"> <img src="/images/content_images/numpy-comic.png" alt="NumPy Contributor comic book cover"> </a> </div> <style> .comic { - width: 30%; - height: auto; + max-width: 20%; + max-height: 20%; } </style>
numpy/numpy.org
a487d5ac034d695177760c0ca0d27392db94db86
[skip actions][skip azp][skip cirrus] removed installation guide lines
diff --git a/content/en/install.md b/content/en/install.md index 569bbdd..e2a5596 100644 --- a/content/en/install.md +++ b/content/en/install.md @@ -1,137 +1,131 @@ --- title: Installing NumPy sidebar: false --- {{< admonition tip >}} This page assumes you are comfortable using a terminal and are familiar with package managers. The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. {{< /admonition >}} The recommended method of installing NumPy depends on your preferred workflow. Below, we break down the installation methods into the following categories: - **Project-based** (e.g., uv, pixi) *(recommended for new users)* - **Environment-based** (e.g., pip, conda) *(the traditional workflow)* - **System package managers** *(not recommended for most users)* - **Building from source** *(for advanced users and development purposes)* Choose the method that best suits your needs. If you're unsure, start with the **Environment-based** method using `conda` or `pip`. - -NumPy can be installed with a `package manager` on -macOS and Linux, or [from source](https://numpy.org/devdocs/building). -For more detailed instructions, consult our **Python and NumPy -installation** guide below. - Below are the different methods for **installing NumPy**. Click on the tabs to explore each method: {{< tabs >}} [[tab]] name = 'Project Based' content = ''' Recommended for new users who want a streamlined workflow. - **uv:** A modern Python package manager designed for speed and simplicity. ```bash uv pip install numpy ``` - **pixi:** A cross-platform package manager for Python and other languages. ```bash pixi add numpy ``` ''' [[tab]] name = 'Environment Based' content = ''' The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We’ll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can’t. The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically “defaults” or “conda-forge”). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. - **Conda:** If you use conda, you can install NumPy from the defaults or conda-forge channels: ```bash conda create -n my-env conda activate my-env conda install numpy ``` - **Pip:** ```bash pip install numpy ``` {{< admonition tip >}} **Tip:** Use a virtual environment for better dependency management {{< /admonition >}} ```bash python -m venv my-env source my-env/bin/activate # macOS/Linux my-env\Scripts\activate # Windows pip install numpy ``` ''' [[tab]] name = 'System Package Managers' content = ''' Not recommended for most users, but available for convenience. **macOS (Homebrew):** ```bash brew install numpy ``` **Linux (APT):** ```bash sudo apt install python3-numpy ``` **Windows (Chocolatey):** ```bash choco install numpy ``` ''' [[tab]] name = 'Building from Source' content = ''' For advanced users and developers who want to customize or debug **NumPy**. A word of warning: building Numpy from source can be a nontrivial exercise. We recommend using binaries instead if those are available for your platform via one of the above methods. For details on how to build from source, see [the building from source guide in the Numpy docs](https://numpy.org/devdocs/building/). ''' {{< /tabs >}} ## Verifying the Installation After installing NumPy, verify the installation by running the following in a Python shell or script: ```python import numpy as np print(np.__version__) ``` This should print the installed version of NumPy without errors. ## Troubleshooting If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ```
numpy/numpy.org
d061d378a28a92b5b7656ea36bac773cee7808ac
DOC: Update the link to Guide to NumPy (2015)
diff --git a/content/en/learn.md b/content/en/learn.md index 4f9fa53..373d8a0 100644 --- a/content/en/learn.md +++ b/content/en/learn.md @@ -1,76 +1,76 @@ --- title: Learn sidebar: false --- For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). *** Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. ## Beginners There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: <i class="fas fa-chalkboard"></i> **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) * [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. * [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) * [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) <i class="fas fa-book"></i> **Books** -* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1144670472). +* [Guide to NumPy *by Travis E. Oliphant*](https://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://dl.acm.org/doi/10.5555/2886196). * [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) * [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. <i class="far fa-file-video"></i> **Videos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** ## Advanced Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. <i class="fas fa-chalkboard"></i> **Tutorials** * [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* * [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). <i class="fas fa-book"></i> **Books** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1098121228) *by Jake Vanderplas* * [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* * [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* <i class="far fa-file-video"></i> **Videos** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** ## NumPy Talks * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) * [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) * [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) * [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** ## Citing NumPy If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy).
numpy/numpy.org
8eb1d8f4c052494a6fcc6c1439e77fbb4536ed53
fix URL redirects
diff --git a/content/en/case-studies/blackhole-image.md b/content/en/case-studies/blackhole-image.md index 969af15..f7fd5ec 100644 --- a/content/en/case-studies/blackhole-image.md +++ b/content/en/case-studies/blackhole-image.md @@ -1,143 +1,143 @@ --- title: "Case Study: First Image of a Black Hole" sidebar: false --- {{< figure >}} src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" >}} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. {{< /blockquote >}} ## A telescope the size of the earth The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, studing the universe with unprecedented sensitivity and resolution. The huge virtual telescope, which uses a technique called very-long-baseline interferometry (VLBI), has an angular resolution of [20 micro-arcseconds][resolution] — enough to read a newspaper in New York from a sidewalk café in Paris! [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole ### Key Goals and Results * **A New View of the Universe:** The groundwork for the EHT's groundbreaking image had been laid 100 years earlier when [Sir Arthur Eddington][eddington] yielded the first observational support of Einstein's theory of general relativity. * **The Black Hole:** EHT was trained on a supermassive black hole approximately 55 million light-years from Earth, lying at the center of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is 6.5 billion times the Sun's. It had been studied for [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before had a black hole been visually observed. * **Comparing Observations to Theory:** From Einstein’s general theory of relativity, scientists expected to find a shadow-like region caused by gravitational bending and capture of light. Scientists could use it to measure the black hole's enormous mass. [eddington]: https://en.wikipedia.org/wiki/Eddington_experiment ### The Challenges * **Computational scale** EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. * **Too much information** Each day EHT generates over 350 terabytes of observations, stored on helium-filled hard drives. Reducing the volume and complexity of this much data is enormously difficult. * **Into the unknown** When the goal is to see something never before seen, how can scientists be confident the image is correct? {{< figure >}} src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< /figure >}} ## NumPy’s Role What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed? The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole. Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. {{< figure >}} src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'The role of NumPy in Black Hole imaging' {{< /figure >}} For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below. {{< figure >}} src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' {{< /figure >}} [ehtim]: https://github.com/achael/eht-imaging Besides NumPy, many other packages, such as -[SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the +[SciPy](https://scipy.org) and [Pandas](https://pandas.pydata.org), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole. [astropy]: https://www.astropy.org/ [mpl]: https://matplotlib.org/ ## Summary The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories. Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe. {{< figure >}} src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' {{< /figure >}} diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index ba02e5f..57a3be4 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -1,95 +1,95 @@ --- title: "Caso de estudio: La primera imagen de un Agujero Negro" sidebar: false --- {{< figure >}} src = '/images/content_images/cs/blackhole.jpg' title = 'Agujero Negro M87' alt = 'Imagen de agujero negro' attribution = '(Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Profesora Asistente, Ciencias de la Computación & Matemáticas, Caltech*" >}} Capturar imágenes del Agujero Negro M87 es como intentar ver algo que por definición es imposible de ver. {{< /blockquote >}} ## Un telescopio del tamaño de la Tierra El [ Telescopio Event Horizon (EHT) ](https://eventhorizontelescope.org), es un conjunto de ocho radiotelescopios terrestres que forman un telescopio computacional del tamaño de la Tierra, estudiando al universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada Interferometría de línea de base muy larga (VLBI), tiene una resolución angular de [20 microsegundos de arco][resolution] — ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París! ### Objetivos clave y resultados * **Una nueva vista del universo:** El trabajo preliminar de la innovadora imagen de EHT se había establecido 100 años antes, cuando [Sir Arthur Eddington][eddington] dio el primer apoyo observacional a la teoría de la relatividad general de Einstein. * **El agujero negro:** EHT se entrenó en un enorme agujero negro supermasivo aproximadamente a 55 millones de años luz de la Tierra, situado en el centro de la galaxia Messier 87 (M87) en el cúmulo de galaxias Virgo. Su masa es 6.5 mil millones de veces la del sol. Se había estudiado por [más de 100 años](https://www.jpl.nasa.gov/news/news.php?feature=7385), pero nunca antes se había observado un agujero negro. * **Comparando las observaciones con la teoría:** A partir de la teoría general de la relatividad de Einstein, los científicos esperaban encontrar una región similar a una sombra causada por la flexión gravitacional y la captura de la luz. Los científicos pudieron utilizarla para medir la enorme masa del agujero negro. ### Los desafíos * **Escala computacional** EHT plantea enormes desafíos de procesamiento de datos, incluyendo las rápidas fluctuaciones de fase atmosféricas, amplio ancho de banda de grabación, y telescopios que son ampliamente disímiles y geográficamente dispersos. * **Demasiada información** Cada día, el EHT genera más de 350 terabytes de observaciones, almacenados en discos duros llenos de helio. Reducir el volumen y complejidad de estos datos es enormemente difícil. * **Hacia lo desconocido** Cuando el objetivo es ver algo nunca antes visto, ¿cómo pueden los científicos estar seguros de que la imagen es correcta? {{< figure >}} src = '/images/content_images/cs/dataprocessbh.png' title = 'Flujo de Trabajo de Procesamiento de Datos EHT' alt = 'flujo de datos' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< /figure >}} ## El Rol de NumPy ¿Qué pasa si hay un problema con los datos? O tal vez un algoritmo depende demasiado de una suposición en particular. ¿Cambiará drásticamente la imagen si se cambia un solo parámetro? La colaboración del EHT respondió a estos desafíos haciendo que los equipos independientes evaluaran los datos, utilizando técnicas de reconstrucción de imágenes ya establecidas y de vanguardia. Cuando los resultados se mostraron consistentes, se combinaron para producir la primera imagen de su tipo de un agujero negro. Su trabajo ilustra el rol que desempeña el ecosistema científico de Python en el avance de la ciencia a través del análisis de datos colaborativos. {{< figure >}} src = '/images/content_images/cs/bh_numpy_role.png' alt = 'rol de numpy' title = 'El rol de NumPy en la imagen del agujero negro' {{< /figure >}} Por ejemplo, el paquete de Python [`eht-imaging`][ehtim] proporciona herramientas para simular y realizar reconstrucción de imágenes en datos VLBI. NumPy está en el núcleo del procesamiento de datos de matrices utilizados en este paquete, como se muestra a continuación en el gráfico parcial de dependencias de software. {{< figure >}} src = '/images/content_images/cs/ehtim_numpy.png' alt = 'mapa de dependencias de ehtim resaltando a numpy' title = 'Gráfico de dependencias de software del paquete ehtim resaltando a NumPy' {{< /figure >}} -Además de NumPy, muchos otros paquetes, como [SciPy](https://www.scipy.org) y [Pandas](https://pandas.io), son parte del flujo de procesamiento de datos para fotografiar el agujero negro. Los formatos estándar de archivos astronómicos y transformaciones de tiempo/coordenadas fueron manejados por [Astropy][astropy], mientras que [Matplotlib][mpl] fue utilizado en la visualización de datos a través del flujo de análisis, incluyendo la generación de la imagen final del agujero negro. +Además de NumPy, muchos otros paquetes, como [SciPy](https://scipy.org) y [Pandas](https://pandas.pydata.org), son parte del flujo de procesamiento de datos para fotografiar el agujero negro. Los formatos estándar de archivos astronómicos y transformaciones de tiempo/coordenadas fueron manejados por [Astropy][astropy], mientras que [Matplotlib][mpl] fue utilizado en la visualización de datos a través del flujo de análisis, incluyendo la generación de la imagen final del agujero negro. ## Resumen El eficiente y adaptable arreglo n-dimensional que es la característica central de NumPy, permitió a los investigadores manipular grandes conjuntos de datos numéricos, proporcionando una base para la primera imagen de un agujero negro. Un momento histórico en la ciencia ofrece una impresionante evidencia visual de la teoría de Einstein. Este logro abarca no solo los avances tecnológicos sino también la colaboración internacional de más de 200 científicos y algunos de los mejores radio observatorios del mundo. Algoritmos innovadores y técnicas de procesamiento de datos, mejorando los modelos astronómicos existentes, ayudaron a desvelar un misterio del universo. {{< figure >}} src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'beneficios de numpy' title = 'Capacidades clave de NumPy utilizadas' {{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole [eddington]: https://en.wikipedia.org/wiki/Eddington_experiment [ehtim]: https://github.com/achael/eht-imaging [astropy]: https://www.astropy.org/ [mpl]: https://matplotlib.org/ diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index f3e11a7..896d46d 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -1,97 +1,97 @@ --- title: "ケーススタディ：世界初のブラックホール画像" sidebar: false --- {{< figure >}} src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="*カリフォルニア工科大学 計算・数理学部*のKatie Bouman助教授" >}} M87ブラックホールを画像化することは、見ることのできないものを、あえて見ようとするようなものです。 {{< /blockquote >}} ## 地球大の望遠鏡 [Event Horizon telescope(EHT)](https:/eventhorizontelescope.org)は、地球サイズの解析望遠鏡を形成する8台の地上型電波望遠鏡から成るシステムで、これまでに前例のない感度と解像度で宇宙を研究することができます。 超長基線干渉法(VLBI) と呼ばれる手法を用いた巨大な仮想望遠鏡の角度分解能は、[20マイクロ秒][resolution]で、ニューヨークにある新聞をパリの歩道のカフェから読むのに十分な解像度です! ### 主な目標と結果 * **宇宙の新しい見方:** EHTの画期的な考え方の基礎が築かれたのは、100年前に [Sir Arthur Eddington][eddington]がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 * **ブラックホール:** EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 [100年以上](https://www.jpl.nasa.gov/news/news.php?feature=7385)に渡る研究が行われてもなお、これまでに視覚的にブラックホールを観測できたことはありませんでした。 * **観測と理論の比較:** 科学者たちの間で、アインシュタインの一般相対性理論から、重力による光の曲げや光の捕獲による影のような領域が観測できるのではないかと期待されていました。 これはブラックホールの巨大な質量を測定するために利用することができます。 ### 課題 * **大規模な計算** EHTは膨大なデータ処理の課題を抱えていました。 大気の位相変動は急速で、記録帯域の幅は大きく、望遠鏡はそれぞれ異なっていて地理的にも分散しています。 * **大量のデータ** EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 * **よくわからないものを観測する** 今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? {{< figure >}} src = '/images/content_images/cs/dataprocessbh.png' title = 'EHTのデータ処理パイプライン' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< /figure >}} ## NumPyが果たした役割 データに問題がある場合はどうなるでしょう？ あるいは、アルゴリズムが特定の仮定に あまりにも大きく依存しているかもしれません。 もしあるパラメータを変更した場合、画像は大きく変化するのでしょうか？ EHTの共同研究では、最先端の画像再構成技術を使用して、それぞれのチームがデータを評価することによって、これらの課題に対処しました。 それぞれのチームの解析結果が同じであることが証明されると、それらの結果を組み合わせることで、ブラックホール画像を得ることができました。 彼らの研究は、共同のデータ解析を通じて科学を進歩させる、科学的なPythonエコシステムが果たす役割を如実に表しています。 {{< figure >}} src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'ブラックホール画像化でNumPyが果たした役割' {{< /figure >}} 例えば、 [`eht-imaging`][ehtim] というPython パッケージは VLBI データで画像の再構築をシミュレートし、実行するためのツールです。 NumPyは、以下のソフトウェア依存関係チャートで示されているように、このパッケージで使用される配列データ処理の中核を担っています。 {{< figure >}} src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'NumPyの中心としたehtimのソフトウェア依存図' {{< /figure >}} -NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas.io)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 +NumPyだけでなく、[SciPy](https://scipy.org)や[Pandas](https://pandas.pydata.org)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 ## まとめ NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 {{< figure >}} src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = '利用されたNumPyの主要機能' {{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole [eddington]: https://en.wikipedia.org/wiki/Eddington_experiment [ehtim]: https://github.com/achael/eht-imaging [astropy]: https://www.astropy.org/ [mpl]: https://matplotlib.org/ diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index d8429b3..ec740bf 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -1,97 +1,97 @@ --- title: "Estudo de Caso: A Primeira Imagem de um Buraco Negro" sidebar: false --- {{< figure >}} src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Créditos: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Professora Assistente, Ciências da Computação e Matemática, Caltech*" >}} Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver. {{< /blockquote >}} ## Um telescópio do tamanho da Terra O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um conjunto de oito telescópios em solo formando um telescópio computacional do tamanho da Terra, projetado para estudar o universo com sensibilidade e resolução sem precedentes. O enorme telescópio virtual, que usa uma técnica chamada interferometria de longa linha de base (VLBI), tem uma resolução angular de [20 micro-arcossegundos][resolution] — o suficiente para ler um jornal em Nova Iorque a partir de um café em uma calçada de Paris! ### Principais Objetivos e Resultados * **Uma nova visão do universo:** A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. * **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. * **Comparando observações com a teoria:** Pela teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir a enorme massa do mesmo. ### Desafios * **Escala computacional** O EHT representa um desafio imenso em processamento de dados, incluindo rápidas flutuações de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. * **Muitas informações** A cada dia, o EHT gera mais de 350 terabytes de observações, armazenadas em discos rígidos cheios de hélio. Reduzir o volume e a complexidade desse volume de dados é extremamente difícil. * **Em direção ao desconhecido** Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta? {{< figure >}} src = '/images/content_images/cs/dataprocessbh.png' title = 'Etapas de Processamento de Dados do EHT' alt = 'data pipeline' align = 'center' attribution = '(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< /figure >}} ## O papel do NumPy E se houver um problema com os dados? Ou talvez um algoritmo seja muito dependente de uma hipótese em particular. A imagem será alterada drasticamente se um único parâmetro for alterado? A colaboração do EHT venceu esses desafios ao estabelecer equipes independentes que avaliaram os dados usando técnicas de reconstrução de imagem estabelecidas e de ponta para verificar se as imagens resultantes eram consistentes. Quando os resultados se provaram consistentes, eles foram combinados para produzir a imagem inédita do buraco negro. O trabalho desse grupo ilustra o papel do ecossistema científico do Python no avanço da ciência através da análise de dados colaborativa. {{< figure >}} src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'O papel do NumPy na criação da primeira imagem de um Buraco Negro' {{< /figure >}} Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para simular e realizar reconstrução de imagem nos dados do VLBI. O NumPy está no coração do processamento de dados vetoriais usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo. {{< figure >}} src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Diagrama de dependência de software do pacote ehtim evidenciando o NumPy' {{< /figure >}} -Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. +Além do NumPy, muitos outros pacotes como [SciPy](https://scipy.org) e [Pandas](https://pandas.pydata.org) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. ## Resumo A estrutura de dados n-dimensional que é a funcionalidade central do NumPy permitiu aos pesquisadores manipular grandes conjuntos de dados, fornecendo a base para a primeira imagem de um buraco negro. Esse momento marcante na ciência fornece evidências visuais impressionantes para a teoria de Einstein. Esta conquista abrange não apenas avanços tecnológicos, mas colaboração científica em escala internacional entre mais de 200 cientistas e alguns dos melhores observatórios de rádio do mundo. Eles usaram algoritmos e técnicas de processamento de dados inovadores, que aperfeiçoaram os modelos astronômicos existentes, para ajudar a descobrir um dos mistérios do universo. {{< figure >}} src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Funcionalidades-chave do NumPy utilizadas' {{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole [eddington]: https://en.wikipedia.org/wiki/Eddington_experiment [ehtim]: https://github.com/achael/eht-imaging [astropy]: https://www.astropy.org/ [mpl]: https://matplotlib.org/
numpy/numpy.org
014343e05b93a5ebe208fe5923e1a52947e91d97
announce the NumPy 2.2.3 release (#836)
diff --git a/content/en/news.md b/content/en/news.md index fa3d14c..7225722 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,492 +1,493 @@ --- title: "News" sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-08 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. -- NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2024_. +- NumPy 2.2.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.3)) -- _13 Feb 2025_. +- NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2025_. - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
54dc5a79550b2838827111521f5115f2756cb8ca
Remove header
diff --git a/content/en/contribute.md b/content/en/contribute.md index f23d40d..60d4b95 100644 --- a/content/en/contribute.md +++ b/content/en/contribute.md @@ -1,117 +1,116 @@ --- title: Contribute to NumPy sidebar: false --- The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at <[email protected]> or on [Slack](https://numpy-team.slack.com) (write <[email protected]> for an invite). We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. -### 'How to Contribute to NumPy' comic -For a visual guide, check out this [comic](https://heyzine.com/flip-book/3e66a13901.html). +For a visual guide on how to contribute to NumPy, check out this [comic](https://heyzine.com/flip-book/3e66a13901.html). {{< comic >}} ### Writing code Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase. <br>Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: * check if older bugs are still present * find duplicate issues and link related ones * add good self-contained reproducers to issues * label issues correctly (this requires triage rights -- just ask) Please just dive in. ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. ### Donate If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate).
numpy/numpy.org
9b75ac87033b5deb4b44bd63f36ca9fd1561bcda
Fix invalid link for LIGO in Japanease doc (#833)
diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 8ba1f92..e92252d 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -1,91 +1,91 @@ --- title: "ケーススタディ: 重力波の発見" sidebar: false --- {{< figure >}} src = '/images/content_images/cs/gw_sxs_image.png' title = '重力波' alt = 'binary coalesce black hole generating gravitational waves' attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' attributionlink = 'https://youtu.be/Zt8Z_uzG71o' {{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGOサイエンティフィック・コラボレーション*" >}} 科学計算のためのPythonエコシステムはLIGOで行われている研究のための重要なインフラです。 {{< /blockquote >}} ## [重力波](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) と [LIGO](https://www.ligo.caltech.edu) について 重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。 -\[レーザー干渉計重力波天文台(LIGO)\](https://www. ligo. caltech. edu)は、アインシュタインの一般相対性理論によって予測された重力波の直接検出を通して、重力波天体物理学の分野を切り開くために設計されました。 このシステムは、アメリカのワシントン州ハンフォードとルイジアナ州リビングストンにある2つの干渉計が一体となって構成され、重力波を検出します。 それぞれのシステムには、レーザー干渉法を用いた数キロ規模の重力波検出器が設置されています。 LIGO Scientific Collaboration（LSC）は、米国をはじめとする14カ国の大学から1000人以上の科学者が集まり、90以上の大学・研究機関によって支援されています。 また、約250人の学生も参加しています。 今回のLIGOの発見は、重力波が地球を通過する際に生じる空間と時間の微小な乱れの測定により、重力波そのものを初めて観測しました。 これにより、新しい天体物理学のフロンティアが開かれました。 これは、宇宙の歪んだ側面、つまり歪んだ時空から作られた物体とそれに現象を切り拓くものです。 +[レーザー干渉計重力波天文台(LIGO)](https://www.ligo.caltech.edu)は、アインシュタインの一般相対性理論によって予測された重力波の直接検出を通して、重力波天体物理学の分野を切り開くために設計されました。 このシステムは、アメリカのワシントン州ハンフォードとルイジアナ州リビングストンにある2つの干渉計が一体となって構成され、重力波を検出します。 それぞれのシステムには、レーザー干渉法を用いた数キロ規模の重力波検出器が設置されています。 LIGO Scientific Collaboration（LSC）は、米国をはじめとする14カ国の大学から1000人以上の科学者が集まり、90以上の大学・研究機関によって支援されています。 また、約250人の学生も参加しています。 今回のLIGOの発見は、重力波が地球を通過する際に生じる空間と時間の微小な乱れの測定により、重力波そのものを初めて観測しました。 これにより、新しい天体物理学のフロンティアが開かれました。 これは、宇宙の歪んだ側面、つまり歪んだ時空から作られた物体とそれに現象を切り拓くものです。 ### 主な目的 * LIGOの[ミッション](https://www.ligo.caltech.edu/page/what-is-ligo)は、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 * 複雑な数学を含む相対性理論の数値計算によって観測データを解析し、信号とノイズを識別し、関連性のある信号をフィルタリングし、観測データの有意性を統計的に推定することで、宇宙の始まりのクランチを観測できるようになります。 * バイナリや数値の結果を理解しやすいようにデータを可視化することも必要です。 ### 課題 * **計算** 合成により放出される重力波は、スーパーコンピュータを用いて数値相対性を手あたり次第に試すような方法では計算できません。 LIGOが収集するデータ量は、重力波の信号が少ないのと同じくらい不可解です。 * **データの氾濫** 観測装置がより高感度で信頼性を持つようになると、データの大洪水によって、干し草の中から針を探すような問題が、多重に発生することがわかります。 LIGOは毎日テラバイトのデータを生成しているのです！ この大量のデータを解釈するには、各検出ごとに多大な労力が必要です。 例えば、LIGOによって収集される信号は、数十万個の重力波シグネチャのテンプレートで構成されており、スーパーコンピュータでしか解析できません。 * **可視化** アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。 画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。 {{< figure >}} src = '/images/content_images/cs/gw_strain_amplitude.png' alt = 'gravitational waves strain amplitude' title = 'GW150914から推定される重力波の歪みの振幅' attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' {{< /figure >}} ## 重力波の検出におけるNumPyの役割 ブラックホール合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。 Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波検出プロジェクトで実行される様々なタスクに使用されるソフトウェアで利用されています。 NumPyは、複雑な数学処理や高速なデータ操作に役立ちました。 次にいくつかの例を示します。 * [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出、[ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm)。 * データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 * 統計解析: 観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 * データ可視化 - 時系列データ - スペクトログラム * 相関計算 * 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。 {{< figure >}} src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'GwPyのNumPy依存グラフ' {{< /figure >}} ---- {{< figure >}} src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'PyCBCのNumPy依存グラフ' {{< /figure >}} ## まとめ 一方で、これまで知られてきた深遠な天体物理学の現象に、多くに新たな洞察を提供しました。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な質問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。 {{< figure >}} src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = '利用されたNumPyの主要機能' {{< /figure >}}
numpy/numpy.org
e7508479d27ee17b3ae4dfe40d2527d2a317318b
[skip actions][skip azp][skip cirrus] updated the numpy installation page with tab based navigation
diff --git a/content/en/install.md b/content/en/install.md index b493f60..3b50b5d 100644 --- a/content/en/install.md +++ b/content/en/install.md @@ -1,206 +1,217 @@ --- title: Installing NumPy sidebar: false --- The only prerequisite for installing NumPy is Python itself. If you don't have Python yet and want the simplest way to get started, we recommend you use the [Anaconda Distribution](https://www.anaconda.com/download) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/building). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. **CONDA** If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: ```bash # Best practice, use an environment rather than install in the base env conda create -n my-env conda activate my-env # If you want to install from conda-forge conda config --env --add channels conda-forge # The actual install command conda install numpy ``` **PIP** If you use `pip`, you can install NumPy with: ```bash pip install numpy ``` Also when using pip, it's good practice to use a virtual environment - see [Reproducible Installs](#reproducible-installs) below for why, and [this guide](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) for details on using virtual environments. <a name="python-numpy-install-guide"></a> # Python and NumPy installation guide Installing and managing packages in Python is complicated, there are a number of alternative solutions for most tasks. This guide tries to give the reader a sense of the best (or most popular) solutions, and give clear recommendations. It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. -## Recommendations +{{< tabs >}} + +[[tab]] +name = 'Recommended Method' +content = ''' We'll start with recommendations based on the user's experience level and operating system of interest. If you're in between "beginning" and "advanced", please go with "beginning" if you want to keep things simple, and with "advanced" if you want to work according to best practices that go a longer way in the future. ### Beginning users On all of Windows, macOS, and Linux: - Install [Anaconda](https://www.anaconda.com/download) (it installs all packages you need and all other tools mentioned below). - For writing and executing code, use notebooks in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) for exploratory and interactive computing, and [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/) for writing scripts and packages. - Use [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) to manage your packages and start JupyterLab, Spyder, or Visual Studio Code. ### Advanced users #### Conda - Install [Miniforge](https://github.com/conda-forge/miniforge). - Keep the `base` conda environment minimal, and use one or more [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) to install the package you need for the task or project you're working on. #### Alternative if you prefer pip/PyPI For users who know, from personal preference or reading about the main differences between conda and pip below, they prefer a pip/PyPI-based solution, we recommend: - Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. - Use [Poetry](https://python-poetry.org/) as the most well-maintained tool that provides a dependency resolver and environment management capabilities in a similar fashion as conda does. +''' -## Python package management +[[tab]] +name = 'Python Package Management' +content = ''' Managing packages is a challenging problem, and, as a result, there are lots of tools. For web and general purpose Python development there's a whole [host of tools](https://packaging.python.org/guides/tool-recommendations/) complementary with pip. For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. For most NumPy users though, [conda](https://conda.io/en/latest/) and [pip](https://pip.pypa.io/en/stable/) are the two most popular tools. ### Pip & conda The two main tools that install Python packages are `pip` and `conda`. Their functionality partially overlaps (e.g. both can install `numpy`), however, they can also work together. We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can't. The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically "defaults" or "conda-forge"). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. <a name="reproducible-installs"></a> ### Reproducible installs As libraries get updated, results from running your code can change, or your code can break completely. It's important to be able to reconstruct the set of packages and versions you're using. Best practice is to: 1. use a different environment per project you're working on, 2. record package names and versions using your package installer; each has its own metadata format for this: - Conda: [conda environments and environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - Pip: [virtual environments](https://docs.python.org/3/tutorial/venv.html) and [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - Poetry: [virtual environments and pyproject.toml](https://python-poetry.org/docs/basic-usage/) +''' -## NumPy packages & accelerated linear algebra libraries +[[tab]] +name = 'NumPy packages & Libraries' +content = ''' NumPy doesn't depend on any other Python packages, however, it does depend on an accelerated linear algebra library - typically [Intel MKL](https://software.intel.com/en-us/mkl) or [OpenBLAS](https://www.openblas.net/). Users don't have to worry about installing those (they're automatically included in all NumPy install methods). Power users may still want to know the details, because the used BLAS can affect performance, behavior and size on disk: - The NumPy wheels on PyPI, which is what pip installs, are built with OpenBLAS. The OpenBLAS libraries are included in the wheel. This makes the wheel larger, and if a user installs (for example) SciPy as well, they will now have two copies of OpenBLAS on disk. - In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy. - In the conda-forge channel, NumPy is built against a dummy "BLAS" package. When a user installs NumPy from conda-forge, that BLAS package then gets installed together with the actual library - this defaults to OpenBLAS, but it can also be MKL (from the defaults channel), or even [BLIS](https://github.com/flame/blis) or reference BLAS. - The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. - MKL is typically a little faster and more robust than OpenBLAS. Besides install sizes, performance and robustness, there are two more things to consider: - Intel MKL is not open source. For normal use this is not a problem, but if a user needs to redistribute an application built with NumPy, this could be an issue. - Both MKL and OpenBLAS will use multi-threading for function calls like `np.dot`, with the number of threads being determined by both a build-time option and an environment variable. Often all CPU cores will be used. This is sometimes unexpected for users; NumPy itself doesn't auto-parallelize any function calls. It typically yields better performance, but can also be harmful - for example when using another level of parallelization with Dask, scikit-learn or multiprocessing. - +''' +{{< /tabs >}} ## Troubleshooting If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ```
numpy/numpy.org
81d6282ef470c94f0e15ea909fe087de5db6cdc2
Shortened NumPy YouTube link in config.yaml (#828)
diff --git a/content/en/config.yaml b/content/en/config.yaml index a2277de..63ac094 100644 --- a/content/en/config.yaml +++ b/content/en/config.yaml @@ -1,117 +1,117 @@ languageName: English params: description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. navbarlogo: image: logo.svg text: NumPy link: / hero: # Main hero title title: NumPy # Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python # Button text buttontext: "Latest release: NumPy 2.2. View all releases" # Where the main hero button links to buttonlink: "/news/#releases" # Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - title: Try NumPy text: Use the interactive shell to try NumPy in the browser docslink: Don't forget to check out the <a href="https://numpy.org/doc/stable" target="_blank">docs</a>. casestudies: title: CASE STUDIES features: - title: First Image of a Black Hole text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole img: /images/content_images/case_studies/blackhole.png alttext: First image of a black hole. It is an orange circle in a black background. url: /case-studies/blackhole-image - title: Detection of Gravitational Waves text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. img: /images/content_images/case_studies/gravitional.png alttext: Two orbs orbiting each other. They are displacing gravity around them. url: /case-studies/gw-discov - title: Sports Analytics text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. img: /images/content_images/case_studies/sports.jpg alttext: Cricket ball on green field. url: /case-studies/cricket-analytics - title: Pose Estimation using deep learning text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. img: /images/content_images/case_studies/deeplabcut.png alttext: Cheetah pose analysis url: /case-studies/deeplabcut-dnn tabs: title: ECOSYSTEM section5: false navbar: - title: Install url: /install - title: Documentation url: https://numpy.org/doc/stable - title: Learn url: /learn - title: Community url: /community - title: About Us url: /about - title: News url: /news - title: Contribute url: /contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - link: https://github.com/numpy/numpy icon: github - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + - link: https://www.youtube.com/@NumPy_team icon: youtube quicklinks: column1: title: "" links: - text: Install link: /install - text: Documentation link: https://numpy.org/doc/stable - text: Learn link: /learn - text: Citing Numpy link: /citing-numpy - text: Roadmap link: https://numpy.org/neps/roadmap.html column2: links: - text: About us link: /about - text: Community link: /community - text: User surveys link: /user-surveys - text: Contribute link: /contribute - text: Code of conduct link: /code-of-conduct column3: links: - text: Get help link: /gethelp - text: Terms of use link: /terms - text: Privacy link: /privacy - text: Press kit link: /press-kit
numpy/numpy.org
3bfce2f11da9fac2605ad7a1b42b123615868dd0
Disable code execution for NumPy REPL (#824)
diff --git a/layouts/partials/shell.html b/layouts/partials/shell.html index a37d60d..7a0db79 100644 --- a/layouts/partials/shell.html +++ b/layouts/partials/shell.html @@ -1,25 +1,25 @@ {{- $shell := .Site.Params.shell }} {{- $intro := index $shell "intro" }} <div class="hero-right"> <div class="flex-column shell-title-container"> {{- range $intro }} <div class="shell-title">{{ .title }}</div> <div class="shell-content-message"> <p>{{ .text }}</p> </div> {{- end }} </div> <div class="numpy-shell-canvas"> <div class="numpy-shell-container"> <div class="shell-lesson shell-content"> {{partial "shell-lesson.html" | print | markdownify}} </div> <!-- Interactive Shell --> <iframe class="numpy-shell" - src="https://jupyterlite.github.io/demo/repl/?toolbar=1&kernel=python&code=import%20numpy%20as%20np" + src="https://jupyterlite.github.io/demo/repl/?toolbar=1&kernel=python&execute=0&code=import%20numpy%20as%20np" > </iframe> </div> </div> </div>
numpy/numpy.org
b2a88231243f0bfd003825e2ec841df69f4dbff3
Use plain ascii anchors to avoid problems with link checker (#825)
diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md index 70ca4d8..304f138 100644 --- a/content/ja/code-of-conduct.md +++ b/content/ja/code-of-conduct.md @@ -1,83 +1,83 @@ --- title: NumPy行動規範 sidebar: false aliases: - /ja/conduct/ --- ### はじめに この行動規範は、NumPy プロジェクトによって管理されるすべての場所で適用されます。 この場所とは、すべてのパブリックおよびプライベートのメーリングリスト、イシュートラッカー、Wiki、ブログ、Twitter、コミュニティで使用されているその他の通信チャンネルなどを含みます。 NumPy プロジェクトでは対面でのイベントは開催していません。 しかし、我々のコミュニティに関連するものであれば、対面のイベントでも同様の行動規範を持つ必要があります。 この行動規範は、NumPy コミュニティに正式または非公式に参加するすべての人が順守する必要があります。 その他にも、NumPyとの提携・関連するプロジェクト活動においては、特にそれらのプロジェクトを代表する場合、同様の行動規範に従う必要があります。 この行動規範は完全ではありません。 しかし、行動規範は我々が理解すべき、互いの協力の仕方や、共通の場所のあるべき姿、我々のゴールなどをまとめるのに重要な役目を果たします。 フレンドリーで生産的な環境を生み出し、周囲のコミュニティにより良い影響を与えるため、ぜひこの行動規範に従ってください。 ### ガイドラインの概要 私たちは下記の内容に真摯に取り組みます。 1. 開けたコミュニティにしましょう。 私たちは、誰でもコミュニティに参加できるようにします。 私たちは、公にすべきではない内容を議論する場合以外、プロジェクトに関連するメッセージを公の場で告知することを選びます。 これは、NumPyに関するヘルプやプロジェクトサポートにも適用されます。公式なサポートだけでなく、NumPyに関する質問に答える場合もです。 これにより、質問に答えた際の意図しない間違いを、より簡単に検出し、訂正できるようになります。 2. 共感し、歓迎し、友好的で、そして我慢強くありましょう。 私たちは互いに争いを解決し合い、互いの善意を信じ合います。 私たちは時折り不満を感じるかもしれません。 しかしそのような場合も、不満を個人的な攻撃に変えることは許容されません。 人々が不快や脅威を感じるコミュニティは、生産的ではないからです。 3. 互いに協力し合おう。 私たちの開発成果は他の人々によって利用され、一方で、たちは他の人々の開発成果に依存しているのです。 私たちがプロジェクトために何かを作るとき、私たちはそれがどのように動作するかを他の人に説明する必要があります。 しかし、この作業により、より良いものを作り上げることができるのです。 私たちが下す全ての決断は、ユーザと開発コミュニティに影響を与えうるし、その決断がもたらす結果を私たちは真摯に受け止めます。 4. 好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。 5. 使う言葉に注意しましょう。 私たちは、コミュニティにおけるコミュニケーションに注意と敬意を払います。 そして、私たちは自分の言葉に責任を持ちます。 他人に優しくしましょう。 他のコミュニティの参加者を侮辱しないでください。 私たちは、以下のようなハラスメントやその他の排斥行為を許しません。 : * 他の人に向けられた暴力的な行為や言葉。 * 性差別や人種差別、その他の差別的なジョークや言動。 * 性的または暴力的な内容の投稿。 * 他のユーザーの個人情報を投稿すること。 (または投稿すると脅すこと)。 * 公開目的のない電子メールや、ICRチャットのようなログの残らないフォーラムの履歴など、プライベートなコンテンツを送信者の同意なしに共有すること。 * 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。 * 不快な思いをさせる性的な言動。 * 過度に粗暴に振る舞うこと。 ひどいな言葉を使うのを避けてください。 人々は怒りを覚える感度が、それぞれ大きく異なります。 * 他人に対するハラスメントの繰り返し。 一般的に、誰かがあなたにある言動を止めるように要求した場合、その言動をやめて下さい。 * 上記のいずれかの行動を擁護すること、または奨励すること。 ### 多様性に関する声明 NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。 あなたの自己認識や、他者のあなたへの認識は関係ありません。 私たちはあなたを歓迎します。 民族、遺伝、性同一性あるいは関連する表現、言語、国籍、神経学的な差異、生物学的な差異、 政治的信条、職業、人種、宗教、性的指向、社会経済的地位、文化的な差異、技術的な能力。 私たちはすべての種類の言語言語話者の参加を歓迎しますが、NumPy 開発は英語で行われます。 NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 NumPyコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです (次のセクションを参照)。 ### 報告ガイドライン 私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 意図的な行動規範違反については、行動規範委員会に報告してください (下記参照)。 もし、違反が意図的でない可能性がある場合、その人にこの行動規範の存在を知らせることも可能です (パブリックでもプライベートでも、適切な方法で)。 もし直接指摘したくない場合は、ぜひ、行動規範委員会に直接連絡するか、違反の確度について助言を求めて下さい。 NumPy行動規範委員会に問題を報告する場合は、こちらにご連絡下さい: [email protected]。 現在、行動規範委員会は以下のメンバーで構成されています: * Stefan van der Walt * Melissa Weber Mendonça * Rohit Goswami もしあなたの違反報告に委員会のメンバーが含まれている場合, または彼らがそれを処理する上で利益相反をしていると感じる場合、そのメンバーはあなたの報告を評価する立場からは辞退してもらいます。 もしくは、行動規範委員会に報告するのが躊躇われる場合は、こちらからNumFOCUSのシニアスタッフに連絡することも可能です:[[email protected]](https://numfocus.org/code-of-conduct#persons-responsible) 。 ### インシデント報告の解決 & 行動規範の実施 本節では、_最も重要な点のみをまとめます。 _詳細については、[NumPy Code of Conduct - How to follow up on a report](report-handling-manual) をご覧ください。 私たちはすべての訴えを調査し、対応するようにします。 NumPy行動規範委員会およびNumPy運営委員会(もし関係する場合) は、報告者の身元を保護します。 また(報告者が同意しない限り) 苦情の内容を機密として扱うこととします。 もし深刻で明らかな違反の場合、例えば、 個人的な脅し、または暴力的、性差別的または人種差別的な発言などの場合、我々は直ちにNumPyのコミュニケーションの場から発言者を退場させます。詳細についてはマニュアルを参照してください。 もし、行動規範に対して明白な違反がみられない場合、受領された行動規範違反報告に対するプロセスは以下の通りです。 1. 報告書の受領を確認 2. 建設的な議論/フィードバック 3. 調停（報告者と報告を受けたものの両方がフィードバックが役に立たなかったと同意した場合に限る） -4. 行動規範委員会による透明性のある決定と執行( [決議](report-handling-manual/#解決方法)を参照) +4. 行動規範委員会による透明性のある決定と執行( [決議](report-handling-manual/#resolutions)を参照) 行動規範委員会は、可能な限り速やかに対応し、最大で72時間以内に対応する様にします。 ### 文末脚注: 私たちは下記のドキュメントを作成したグループに感謝します。 内容・発想ともに大いに影響されています。 - [SciPy行動規範](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 10ec4d6..831fee0 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -1,95 +1,95 @@ --- title: NumPy行動規範 - 報告書のフォローアップ方法 sidebar: false --- NumPyの行動規範委員会はこのマニュアルに従います。 このマニュアルは様々な問題に対応する際に使用され、一貫性と公平性を確保します。 [行動規範](/ja/code-of-conduct) を施行することは、私たちのコミュニティの現在のため、未来のために重要です。 この施行は、軽いものではありません。 施行の基準を見直す際、行動規範委員会は以下の考え方とガイドラインに留意するようにします。 * 機械的ではなく、人間的に行動します。 委員会は、当事者のプライバシーと報告者の必要なだけの機密性を尊重しながら、状況を理解するように働きかけることができます. ただし、1人以上の個人と直接連絡を取る必要がある場合もあります。 委員会の目標は正しい決定を下すのではなく、コミュニティの健康を改善することなのです。 * 行動を判断するのではなく、個人への共感を強調し、「良い」と「悪い」の二値評価を避けます。 明確な攻撃性とハラスメントが存在した場合、私たちはそれらに対処します。 しかし、解決が困難なシナリオの多くは、通常の意見の相違が、複数の当事者による無益または有害な行動に発展した場合です。 完全に文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、コミュニティにとって最終的に最も有益な方法です。 * 私たちは、電子メールが判断に困難な媒体であり、独立して利用できることを理解しています。 個人の情報なしに電子メール上で批判を受けることは、特に苦痛である場合もあります。 そこで、他者の見解に対して、開放的で、敬意を持った雰囲気を保つことが重要になります。 それはまた、私たちの行動が透明でなければならないことを意味します。 全てのメンバーが公平かつ同情をもって扱われるようにするため、私たちは全力を尽くします。 * 差別の境界は時に曖昧で、また無意識に行われている場合もあります。 これにより、普通の人との関わりの中で、不公平感や敵意として現れてくるのです。 私達は、このようなことが起こることはわかっているので、気をつけて見ていきたいと思います。 不当な扱いを受けたと思われる方は、ぜひご連絡ください。 * 良い議論を実践することで、エンゲージメントの向上に取り組みます。例えば議論がどこで止まっているのかを特定したり、 実践的な情報、指針、資源を提供することで、これらの問題を前向きな方向に変えていきます。 * 新しいメンバーが何を必要としているかに留意します。 特に社会的地位の低いグループからの参加を増やすことを目的に、明確なサポートと配慮を提供していきます。 * 一人一人の文化的背景や母国語は異なります。 ネイティブでない人が起こした悪気のない誤解を確認し、問題を理解してもらい、不快感を与えないために何を変えればよいかを教えてあげてください。 外国語での複雑な議論はとても難しいものであり、国籍や文化を超えて多様性を育てていきたいと考えています。 ## 仲介 自主的な非公式の調停は、私たちの重要な役割です。 2つのグループ以上の当事者が不適切な行動をエスカレートした場合(人類の紛争では悲しいことに一般的なものですが)、調停プロセスを促進するは非常に重要です。 ちなみに、これは一例に過ぎません。委員会は、どのようなケースでも調停を検討することができますが、このプロセスはあくまでも自発的なものであり、当事者に参加を迫ることはできないことを念頭に置いて下さい。 委員会が調停を提案する場合は、次のようにすべきです。 * 調停者として役立つ候補者を見つけます。 * 報告者の合意を取得します。 報告者は、調停のアイデアを拒否したり、代替の調停者を提案する権利を持ちます。 * 報告者の同意を取得します。 * 調停人を決定します。当事者は、提案された候補者とは別の調停人を提案することができます。すべての条件で共通の合意に達した場合のみ、プロセスを進めることができます。 * 調停が完了するまでのタイムラインを設定し、理想的には2週間以内に完了させます。 調停者は、すべての当事者と関わり、すべての人に満足のいく決議を求めていきます。 終了後、調停人は（プロセスの全当事者によって吟味された）報告書を委員会に提出し、今後のステップに関する推奨事項を提示します。 委員会は、これらの結果(満足のいく決議が達成されたか否か) を評価し、必要と判断される追加的な措置を決定します。 ## 報告に対する委員会の対応 委員会(または委員) が行動規範違反報告を受けた時、その報告が明確で深刻な違反であるかどうかは判断されます(以下に違反項目を定義します)。 違反判定された場合は、通常のレポート処理プロセスに加えて、即時の対応が必要になります。 ## 明確かつ深刻な違反行為の解決 私たちは、インターネットでの会話が簡単にひどい誹謗中傷になってしまうことを、痛いほど知っています。 個人的な脅迫、暴力的、性差別的、人種差別的な言葉など、明らかで深刻な違反に対しては、迅速に対処します。 行動規範委員会のメンバーは、明確かつ深刻な違反に気づいた場合、以下のように行動します。 * 直ちにすべてのNumPyのオンラインコミュニティから違反者を排除します。 * 報告が受信され、違反者が排除されたことを報告者に連絡します。 * どのような場合でも、モデレーターは違反者に連絡するための合理的な努力を行い、違反者の言葉や行動がどのように「明確かつ重大な違反」に該当するのかを具体的に伝えるべきです。 モデレーターは、違反者がこれは不当だと思う場合、あるいはNumPyチャンネルとの再接続を望む場合には、行動規範委員会による以下のような審査を求める権利があることも述べるべきです。 モデレータは、この説明を行動規範委員会に転送する必要があります。 * 行動規範委員会は、このプロセスが適用されたすべてのケースを正式にレビューし署名することで、よくある盛り上がりすぎた論争を諫めるためこのプロセスが使用されたのでないことを確認します。 ## 報告の処理 報告が委員会に送られると、直ちに報告者に返信して報告を受領したことを確認します。 この返信は72時間以内に送信される必要があり、委員会はそれよりもはるかに迅速に対応するよう努める必要があります。 レポートに十分な情報が含まれていない場合、委員会は行動する前に、関連するすべてのデータを取得するようにします。 委員会は、事件の状況を全て知るために関係する個人に連絡する際に、運営協議会に代わって行動する権限を与えられています。 その後、委員会は今回の問題を見直し、効果を最大限に発揮する対策を決定します。 * 問題の種類 * 今回の事情が行動規範違反であるかどうか。 * 責任者が誰であるか * これが進行中の状況であるか、誰の物理的安全に脅威があるかどうか。 これらの情報は書面で収集され、可能な限りグループの審議が記録され、保持されます (例えば、チャットの記録、Eメールのディスカッション、会議通話の記録、音声会話の概要など)。 行動の一貫性を確保し、プロジェクトのために記録を残すために、委員会のすべての活動のアーカイブを保持することが重要です。 この活動支援するために、委員会のデフォルトの議論チャネルは、正当化された要求に応じて、委員会の現在および将来のメンバー、および運営委員会のメンバーがアクセスできるプライベートメーリングリストにします。 委員会がリストにはない連絡方法を使用する必要がある場合(例: 早期/迅速な対応を求める電話など)、そのプロセスの良い記録となるように、これらをリストにまとめて戻すべきです。 行動規範委員会は、2週間以内に決議の合意を目指すべきです。 その期間内に決議が確定できない場合。 委員会は、レポーターに対して現状の更新と今後のタイムラインを連絡します。 -## 解決方法 +## 解決方法 {#resolutions} 委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。 ありうる返答は次のとおりです: * これ以上アクションを取らない。 - 違反が起きていないと判断された - 検討中に問題が明らかに解決された * 調停の調整。すべての関係者が合意した場合、委員会は上記のように調停プロセスを促進することができます。 * 公の場における説明。 どの行動・言動・言語が不適切で、現在の状況がなぜか引き起こされ、人々を傷つけたのかを説明し、コミュニティに自省を要求します。 * 委員会から関係者(複数可) への非公開処分の実施。 この場合、委員会は、電子メールを介して、グループにccを入れながら、対象者に問題の指摘を連絡します。 * 公の場での指摘。 この場合、委員会の議長は、違反が発生したのと同じ場所で、実用性の範囲内で叱責を行います。 例えば、メールルールの違反の元のメーリングリストなどです。 しかし、人や状況がかわるかもしれないチャットルームなどの場合、他の手段を利用する可能性もあります。 文書化のため、この問題のメッセージを他の場所で公開することを対策グループが選択する場合もあります。 * 報告者がこの考えに同意することを前提とした、公的または私的な謝罪の要求。 報告者は自分の裁量で、違反者とのさらなる接触を拒否することもできます。 委員会がこの要求をお届けします。 委員会は、必要に応じてこの要求に「条件」を付けることができます。例えば、メーリングリストの会員資格を維持するために、違反者に謝罪を求めることができます。 * 「相互に合意した休止」の要求。 これは、委員会から個人への、コミュニティへの参加を一時的に控えるような要請です。 対象者が自発的に一時的な休みを取らないことを選択した場合、委員会は「冷却期限」を準備することがあります。 * これは、一部またはすべてのNumPyオンラインコミュニティ (メーリングリスト、gitter.im など) からの永続的または一時的な出入り禁止。 将来的に禁止が見直されるのか、維持されるか決定できるよう、対策グループは出入り禁止の記録を全て保持します。 決議が合意されると制定される前に、委員会は、元の報告者およびその他の影響を受けた当事者に連絡し、提案された決議を説明します。 委員会は、この決議が受け入れられるかどうかを尋ねます。 そして、記録のためのフィードバックに注意を払います。 最後に 委員会は、NumPy Steering Councilに報告を行います(NumPy Coreチームにも、出入り禁止など進行中の出来事については報告します)。 委員会はこの問題について公に議論することはありません。 すべての公開声明は、行動規範委員会またはNumPy Steering Councilの議長によって行われます。 ## 利益相反 利益相反が発生した場合、委員会メンバーは直ちに他のメンバーに通知し、必要に応じて対応を辞退しなければなりません。 diff --git a/content/pt/code-of-conduct.md b/content/pt/code-of-conduct.md index 1e2c9e5..52f6057 100644 --- a/content/pt/code-of-conduct.md +++ b/content/pt/code-of-conduct.md @@ -1,83 +1,83 @@ --- title: Código de Conduta NumPy sidebar: false aliases: - /pt/conduct/ --- ### Introdução Este código de conduta aplica-se a todos os espaços gerenciados pelo projeto NumPy, incluindo todas as listas de discussão públicas e privadas, *issue tracker*, wikis, blogs, Twitter e qualquer outro canal de comunicação usado pela nossa comunidade. O projeto NumPy não organiza eventos presenciais. No entanto, os eventos relacionados à nossa comunidade devem ter um código de conduta semelhante ao atual. Este Código de Conduta deve ser honrado por todas as pessoas que participam da comunidade NumPy formal ou informalmente, ou que reivindicam qualquer afiliação com o projeto, em qualquer atividade relacionada ao projeto, especialmente ao representar o projeto, em qualquer função. Este código não é exaustivo ou completo. Serve para disseminar a nossa compreensão comum de um ambiente colaborativo e de objetivos compartilhados entre nós. Por favor, tente seguir este código tanto na essência quanto ao pé da letra, para criar um ambiente amigável e produtivo que enriqueça a comunidade em geral. ### Diretrizes específicas Nós nos esforçamos para: 1. Sermos abertos. Convidamos qualquer pessoa a participar da nossa comunidade. Preferimos usar métodos públicos de comunicação para mensagens relacionadas aos projetos, a menos que estejamos discutindo algo sensível. Isso se aplica a mensagens em busca de ajuda ou suporte relacionado ao projeto também; não só é muito mais provável que um pedido de ajuda público resulte em uma resposta, mas isso também garante que qualquer erro involuntário na resposta seja mais facilmente detectado e corrigido. 2. Sermos empáticos, acolhedores, amigáveis e pacientes. Trabalhamos juntos para resolver conflitos e acreditamos em boas intenções. Todos nós podemos sentir alguma frustração de vez em quando, mas não permitimos que a frustração se transforme num ataque pessoal. Uma comunidade onde as pessoas se sentem desconfortáveis ou ameaçadas não é uma comunidade produtiva. 3. Sermos colaborativos. O nosso trabalho será utilizado por outras pessoas e, por sua vez, dependeremos do trabalho dos outros. Quando fazemos algo em benefício do projeto, estamos dispostos a explicar aos outros como esse algo funciona, para que outros possam desenvolver o trabalho e torná-lo ainda melhor. Qualquer decisão que tomemos afetará nossos usuários e os colegas, e levamos essas consequências a sério quando tomamos decisões. 4. Sermos inquisitivos. Ninguém sabe tudo! Fazer perguntas antecipadamente evita muitos problemas mais tarde, por isso encorajamos as perguntas, embora possamos encaminhá-las para um fórum adequado. Vamos nos esforçar para sermos sensíveis e úteis. 5. Termos cuidado com as palavras que escolhemos. Somos cuidadosos e respeitosos na nossa comunicação e assumimos a responsabilidade pelo nosso próprio discurso. Seja gentil com os outros. Não insulte ou deprecie outros participantes. Nós não aceitaremos assédio ou outros comportamentos exclusivos, como: * Ameaças ou linguagem violenta direcionadas contra outra pessoa. * Piadas e linguagem sexista, racista ou discriminatória. * Postagem de material sexualmente explícito ou violento. * Postar (ou ameaçar postar) informações pessoais de outras pessoas (“doxing”). * Compartilhar conteúdo privado, como e-mails enviados de maneira privada ou não-pública, ou fóruns não registrados, como histórico de canais IRC, sem o consentimento do remetente. * Insultos pessoais, especialmente aqueles que utilizam termos racistas ou sexistas. * Atenção sexual não consentida. * Profanidade excessiva. Por favor, evite palavrões; as pessoas diferem muito na sua sensibilidade à linguagem. * Assédio reiterado. Em geral, se alguém pedir que você pare, então pare. * Advogar em favor ou encorajar qualquer um dos comportamentos acima. ### Declaração de diversidade O projeto NumPy convida e incentiva a participação de todas as pessoas. Estamos empenhados em ser uma comunidade da qual todas as pessoas gostem de fazer parte. Embora nem sempre sejamos capazes de acomodar as preferências de cada indivíduo, nós tentamos o nosso melhor para tratar todos gentilmente. Não importa como você se identifica ou como os outros percebem você: nós lhe damos as boas-vindas. Embora nenhuma lista possa esperar ser totalmente abrangente, honramos explicitamente a diversidade em: idade, cultura, etnia, genótipo, identidade ou expressão de gênero, língua, origem, neurotipo, fenotipo, crenças políticas, profissão, raça, religião, orientação sexual, estado socioeconômico, subcultura e capacidade técnica, na medida em que não entrem em conflito com este código de conduta. Embora sejamos receptivos às pessoas fluentes em todas as línguas, o desenvolvimento do NumPy é conduzido em inglês. Padrões de comportamento na comunidade NumPy estão detalhados no Código de Conduta acima. Os participantes da nossa comunidade devem se comportar de acordo com esses padrões em todas as suas interações e ajudar os outros a fazê-lo também (veja a próxima seção). ### Diretrizes de resposta a incidentes Sabemos que é mais comum do que o desejado que a comunicação na Internet comece ou se transforme em abusos óbvios e flagrantes. Reconhecemos também que, por vezes, as pessoas podem ter um dia ruim, ou não conhecer algumas das orientações deste Código de Conduta. Tenha isto em mente ao decidir como responder a uma violação deste Código. Em caso de violações claramente intencionais, o Comitê do Código de Conduta (veja abaixo) deve ser informado. Para violações possivelmente não intencionais, você pode responder à pessoa e apontar este código de conduta (seja em público ou em privado, o que for mais apropriado). Se preferir não o fazer, sinta-se à vontade para informar diretamente o Comitê do Código de Conduta, ou peça ao Comitê um conselho, sigilosamente. Você pode relatar problemas ao Comitê do Código de Conduta NumPy em [email protected]. Atualmente, o comitê é formato por: * Stefan van der Walt * Melissa Weber Mendonça * Rohit Goswami Se o seu relatório envolve algum membro da comissão, ou se você sentir que existe um conflito de interesses em tratá-lo, então os membros abster-se-ão de considerar o seu relatório. Como alternativa, se por qualquer razão você se sentir desconfortável em fazer um relatório à comissão, então você também pode entrar em contato com a equipe sênior da NumFOCUS em [[email protected]](https://numfocus.org/code-of-conduct#persons-responsible). ### Resolução de Incidentes & Aplicação do Código de Conduta _Esta seção resume os pontos mais importantes, mais detalhes podem ser encontrados em_ [Código de Conduta do NumPy - Como dar seguimento a um relatório](report-handling-manual). Vamos investigar e responder a todas as queixas. O Comitê do Código de Conduta do NumPy e o Comitê Diretor do NumPy (se envolvido) protegerão a identidade do relatante, e tratarão o conteúdo das reclamações como confidencial (a menos que o relatante aceite o contrário). Em caso de violações graves e óbvias, por exemplo, ameaça pessoal ou linguagem violenta, sexista ou racista, vamos imediatamente desconectar a pessoa relatada dos canais de comunicação do NumPy; por favor, consulte o manual para mais detalhes. Em casos que não envolvam claras violações graves e óbvias deste Código de Conduta, o processo de ação referente a qualquer relato de violação do Código de Conduta recebido será: 1. acusar o recebimento do relato, 2. discussão/feedback razoável, 3. mediação (se o feedback não ajudar e somente se ambos o relatante e relatado concordarem com isso), -4. aplicação de solução via decisão transparente (veja as [Resoluções](report-handling-manual/#resoluções)) do Comitê do Código de Conduta. +4. aplicação de solução via decisão transparente (veja as [Resoluções](report-handling-manual/#resolutions)) do Comitê do Código de Conduta. O comitê responderá a qualquer relatório o mais rapidamente possível e, no máximo, no prazo de 72 horas. ### Notas Somos gratos aos grupos responsáveis pelos documentos abaixo, dos quais retiramos conteúdo e inspiração: - [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) diff --git a/content/pt/report-handling-manual.md b/content/pt/report-handling-manual.md index 14418d0..e33dd0d 100644 --- a/content/pt/report-handling-manual.md +++ b/content/pt/report-handling-manual.md @@ -1,95 +1,95 @@ --- title: Código de Conduta NumPy - Como dar seguimento a um relatório sidebar: false --- Este é o manual seguido pelo Comitê do Código de Conduta do NumPy. É usado quando respondemos a um incidente para nos certificarmos de que somos pessoas consistentes e justas. Garantir que o [Código de Conduta](/code-of-conduct) seja respeitado afeta nossa comunidade hoje e no futuro. É uma ação que levamos muito a sério. Ao analisar medidas de aplicação do Código de Conduta, o Comitê terá em mente os seguintes valores e orientações: * Agir de forma pessoal e não impessoal. O Comitê pode levar as partes a compreender a situação, respeitando simultaneamente a privacidade e a necessária confidencialidade das pessoas relatantes. No entanto, por vezes, é necessário comunicar diretamente com um ou mais indivíduos: o objetivo do Comitê é melhorar a saúde da nossa comunidade, em vez de produzir apenas uma decisão formal. * Enfatizar empatia pelos indivíduos ao invés de julgar o comportamento, evitando rótulos binários de "bom" e "mau". Existem atos de agressão e assédio claros e visíveis, e vamos abordá-los com firmeza. Mas muitos cenários que podem ser desafiadores são aqueles em que as discordâncias normais se transformam em comportamento desnecessário ou prejudicial de várias partes. Compreender o contexto completo e encontrar um caminho que traga um entendimento entre as partes é difícil, mas, em última análise, é o resultado mais produtivo para a nossa comunidade. * Compreendemos que o e-mail é um meio difícil e que pode causar uma sensação de isolamento. Receber críticas por e-mail, sem contato pessoal, pode ser particularmente doloroso. Isto faz com que seja especialmente importante manter um clima de respeito aberto pelas opiniões dos outros. Significa também que temos de ser transparentes nas nossas ações, e que faremos tudo o que estiver ao nosso alcance para garantir que todos os nossos membros sejam tratados de forma justa e com simpatia. * A discriminação pode ser sutil e pode ser inconsciente. Pode revelar-se em tratamentos injustos e hostis em interações que normalmente seriam ordinárias. Sabemos que isso acontece, e teremos o cuidado de ter isso em mente. Gostaríamos muito de ouvir se você acha que foi tratado injustamente, e usaremos esses procedimentos para garantir que a sua reclamação seja ouvida e abordada. * Ajudar a aumentar o envolvimento em uma boa prática de discussão: tentar identificar onde a discussão pode ter falhado, e fornecer informações úteis, indicadores e recursos que podem levar a mudanças positivas nestes pontos. * Estar ciente das necessidades de novos membros: fornecer-lhes apoio e consideração explícitos, com o objetivo de aumentar a participação de grupos sub-representados, em particular. * As pessoas vêm de meios culturais e linguísticos diferentes. Tentar identificar quaisquer mal-entendidos honestos causados por falantes não-nativos e ajudá-los a entender a questão e o que pode ser modificado para evitar causar ofensa. Uma discussão complexa numa língua estrangeira pode ser muito intimidante, e queremos aumentar a nossa diversidade também entre nacionalidades e culturas. ## Mediação A mediação informal voluntária é um instrumento à nossa disposição. Em contextos em que duas ou mais partes escalaram ao ponto de demonstrarem comportamento inapropriado (algo tristemente comum no conflito humano), poderá ser útil facilitar um processo de mediação. Isto é apenas um exemplo: em todo caso, o Comitê pode considerar a mediação, tendo em conta que o processo se destina a ser estritamente voluntário e que nenhuma das partes pode ser pressionada a participar. Se o Comitê sugerir mediação, deve: * Encontrar uma pessoa candidata que possa servir de mediadora. * Obter o acordo da(s) pessoa(s) relatante(s). A(s) pessoa(s) relatante(s) têm total liberdade para recusar a ideia de mediação ou propor um mediador alternativo. * Obter o acordo da(s) pessoa(s) relatada(s). * Estabelecer uma pessoa mediadora: enquanto as partes podem propor um mediador diferente da pessoa sugerida, o processo só poderá avançar se for alcançado um acordo comum em todos os termos. * Estabelecer um cronograma para a mediação ser concluida, idealmente dentro de duas semanas. A pessoa mediadora entrará em contato com todas as partes e procurará uma resolução satisfatória para todos. Após a sua conclusão, a pessoa mediadora apresentará ao Comitê um relatório (examinado por todas as partes envolvidas no processo) com recomendações sobre outras medidas. O Comitê avaliará então esses resultados (em caso de resolução satisfatória ou não) e decidirá sobre quaisquer medidas adicionais consideradas necessárias. ## Como o Comitê responderá aos relatórios Quando o Comitê (ou um membro do Comitê) recebe um relatório, será inicialmente determinado se o relatório é sobre uma violação clara e severa (como definido abaixo). Em caso afirmativo, medidas imediatas serão tomadas para além do processo regular de tratamento dos relatórios. ## Ações claras e severas de violação Sabemos que é mais comum do que o desejado que a comunicação na Internet comece ou se transforme em abusos óbvios e flagrantes. Trataremos rapidamente de violações claras e severas como ameaças pessoais, linguagem violenta, sexista ou racista. Quando um membro do Comitê do Código de Conduta tomar conhecimento de uma violação clara e grave, fará o seguinte: * Desligará imediatamente a pessoa originadora de todos os canais de comunicação do NumPy. * Responderá à pessoa relatante para informá-la que seu relatório foi recebido e que a pessoa originadora foi desligada. * Em todos os casos, a pessoa moderadora deve fazer um esforço razoável para entrar em contato com a pessoa originadora, e dizer-lhes especificamente como sua linguagem ou ações se qualificam como uma "violação clara e severa". A pessoa moderadora deve também dizer que, se a pessoa originadora considerar que isso é injusto ou quiser ser reconectada ao NumPy, tem o direito de solicitar uma revisão, de acordo com as disposições do Comitê do Código de Conduta. A pessoa moderadora deve copiar esta explicação para o Comitê do Código de Conduta. * O Comitê do Código de Conduta procederá formalmente à análise e decisão em todos os casos em que este mecanismo tenha sido aplicado para garantir que não seja utilizado para controlar desentendimentos acalorados comuns. ## Tratamento de relatórios Quando um relatório é enviado ao Comitê, ele responderá imediatamente à pessoa relatante para confirmar a sua recepção. Esta resposta deve ser enviada no prazo de 72 horas, e o grupo deve esforçar-se por responder muito mais rapidamente. Se um relatório não contiver informações suficientes, o Comitê obterá todos os dados relevantes antes de agir. O Comitê tem poderes para agir em nome do Conselho Diretor ao contactar quaisquer pessoas envolvidas para obter um relato mais completo dos acontecimentos. O Comitê analisará então o incidente e determinará, do melhor jeito possível: * O que aconteceu. * Se este evento constitui ou não uma violação do Código de Conduta. * Quem são as pessoas responsáveis. * Se se trata de uma situação contínua, e existe uma ameaça para a segurança física de alguém. Estas informações serão recolhidas por escrito e, sempre que possível, as deliberações do grupo serão gravadas e armazenadas (por exemplo, transcrições de conversas, discussões por e-mail, chamadas gravadas de videoconferência, resumos de conversas por voz, etc). É importante manter um arquivo de todas as atividades deste Comitê para garantir a consistência no comportamento e fornecer memória institucional ao projeto. Para ajudar com isto, o canal de discussão padrão para este Comitê será uma lista de e-mail privada, acessível a atuais e futuros membros do Comitê, bem como aos membros do Conselho Diretor a pedido justificado. Se o Comitê sentir a necessidade de usar comunicações fora da lista (por exemplo, chamadas por telefone para resposta precoce/rápida), deve em todos os casos resumi-las de volta para a lista, para que haja um bom registro do processo. O Comitê do Código de Conduta deve ter por objetivo chegar a um acordo sobre uma resolução no prazo de duas semanas. Caso uma resolução não possa ser determinada nesse período, o Comitê responderá à(s) pessoa(s) relatante(s) com uma atualização e cronograma previsto para a resolução. -## Resoluções +## Resoluções {#resolutions} O Comitê tem de chegar a um acordo sobre uma resolução por consenso. Se o grupo não conseguir chegar a um consenso e permanece bloqueado durante mais de uma semana, o grupo encaminhará o assunto para o Conselho Diretor para resolução. Possíveis respostas podem incluir: * Não tomar nenhuma outra ação: - se determinarmos que não ocorreram violações; - se a questão tiver sido resolvida publicamente enquanto o Comitê estava considerando uma resposta. * Coordenação de mediação voluntária: se todas as partes envolvidas concordarem, o Comitê poderá facilitar um processo de mediação, conforme detalhado acima. * Salientar publicamente que alguns comportamentos, ações ou linguagem foram julgados inapropriados ou podem ser considerados danosos para algumas pessoas, explicando por que no contexto atual e solicitando que a comunidade se auto-ajuste. * Uma advertência privada do Comitê para a(s) pessoa(s) envolvida(s). Neste caso, a pessoa presidente do Comitê irá entregar essa advertência à(s) pessoa(s) por e-mail, em cópia (CC) ao grupo. * Uma advertência pública. Neste caso, a pessoa presidente do Comitê vai apresentar essa advertência no mesmo fórum em que ocorreu a violação, dentro dos limites da viabilidade. Exemplo: a lista original para uma violação de e-mail, mas para uma discussão em sala de bate-papo onde a pessoa/contexto pode sumir, isto pode ser feito por outros meios. O grupo pode optar por publicar esta mensagem em outro local para fins de documentação. * Um pedido de desculpas públicas ou privadas, supondo que a(s) pessoa(s) relatante(s) concorde(m) com esta ideia: a(s) pessoa(s) pode(m), a seu critério, recusar contatos adicionais com a pessoa relatada. A Presidência dará seguimento a este pedido. O Comitê, se escolher, pode anexar condições adicionais a este pedido inicial: por exemplo, o grupo pode pedir à pessoa relatada que se desculpe para que tenha o direito de manter a sua adesão a uma lista de e-mails. * Um “acordo mútuo de trégua” onde o Comitê solicita à pessoa que se abstenha temporariamente da participação na comunidade. Se a pessoa optar por não fazer uma pausa temporária voluntariamente, o Comitê pode aplicar um “período de afastamento obrigatório”. * Um banimento permanente ou temporário de alguns ou todos os espaços do NumPy (listas de e-mails, gitter.im, etc.). O grupo manterá registro de todas essas proibições, para que elas possam ser revistas no futuro ou mantidas. Uma vez aprovada uma resolução, mas antes de ser efetivamente aplicada, o Comitê entrará em contato com a pessoa relatante original e quaisquer outras partes afetadas e explicará a resolução proposta. O Comitê perguntará se esta resolução é aceitável e terá de tomar nota da sua resposta para registro futuro. Finalmente, o Comitê apresentará um relatório ao Conselho Diretor do NumPy (bem como ao time *core* do NumPy no caso de uma resolução em curso, como um banimento). O Comitê nunca discutirá publicamente a questão; todas as declarações públicas serão feitas pela pessoa presidente do Comitê do Código de Conduta ou pelo Conselho Diretor do NumPy. ## Conflitos de Interesse Em caso de conflito de interesses, um membro do Comitê deve notificar imediatamente os outros membros e abdicar de sua participação no processo caso seja necessário.
numpy/numpy.org
3954e4c0169523e68c04d3248a6dae03a0859d4b
Announce NumPy 2.2.2 (#822)
diff --git a/content/en/news.md b/content/en/news.md index 930e685..fa3d14c 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,491 +1,492 @@ --- title: "News" sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-08 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.2.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.2)) -- _18 Jan 2024_. - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
813e91bdb14e9d9730039c6fe364246b7b2e7532
Remove dash from main page title (#819)
diff --git a/content/en/_index.md b/content/en/_index.md index 9e9534d..43b25e3 100644 --- a/content/en/_index.md +++ b/content/en/_index.md @@ -1,49 +1,49 @@ --- -title: +title: NumPy --- {{< grid columns="1 2 2 3" >}} [[item]] type = 'card' title = 'Powerful N-dimensional arrays' body = ''' Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. ''' [[item]] type = 'card' title = 'Numerical computing tools' body = ''' NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. ''' [[item]] type = 'card' title = 'Open source' body = ''' Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). ''' [[item]] type = 'card' title = 'Interoperable' body = ''' NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. ''' [[item]] type = 'card' title = 'Performant' body = ''' The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. ''' [[item]] type = 'card' title = 'Easy to use' body = ''' NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. ''' {{< /grid>}} diff --git a/content/es/_index.md b/content/es/_index.md index 67652c5..1d2edb1 100644 --- a/content/es/_index.md +++ b/content/es/_index.md @@ -1,49 +1,49 @@ --- -title: null +title: NumPy --- {{< grid columns="1 2 2 3" >}} [[item]] type = 'card' title = 'Matrices N-dimensionales potentes' body = ''' Rápida y versátil, la vectorización, indexación y conceptos de broadcasting de NumPy son los estándares de facto en el cálculo de matrices hoy en día. ''' [[item]] type = 'card' title = 'Herramientas de cálculo numérico' body = ''' NumPy ofrece funciones matemáticas completas, generadores de números aleatorios, rutinas de álgebra lineal, transformadas de Fourier, y más. ''' [[item]] type = 'card' title = 'Código abierto' body = ''' Distribuido bajo una [licencia BSD] liberal (https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy es desarrollado y mantenido [públicamente en GitHub](https://github.com/numpy/numpy) por una vibrante, receptiva y diversa [comunidad](/es/community). ''' [[item]] type = 'card' title = 'Interoperable' body = ''' NumPy soporta una amplia gama de hardware y plataformas de computación, y funciona bien con librerías distribuidas, de GPU y de matrices dispersas. ''' [[item]] type = 'card' title = 'Óptimo' body = ''' El núcleo de NumPy está optimizado adecuadamente con código en C. Disfrute de la flexibilidad de Python con la velocidad del código compilado. ''' [[item]] type = 'card' title = 'Fácil de usar' body = ''' La sintaxis de alto nivel de NumPy lo hace accesible y productivo para programadores de cualquier formación o nivel de experiencia. ''' {{< /grid>}} diff --git a/content/ja/_index.md b/content/ja/_index.md index 7e6a38c..18ee86b 100644 --- a/content/ja/_index.md +++ b/content/ja/_index.md @@ -1,52 +1,52 @@ --- -title: null +title: NumPy --- {{< grid columns="1 2 2 3" >}} [[item]] type = 'card' title = '強力な多次元配列' body = ''' NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャストの考え方は、現在の配列計算におけるデファクト・スタ>ンダードです。 ''' [[item]] type = 'card' title = '数値計算ツール群' body = ''' NumPyは、様々な数学関数、乱数生成器、線形代数ルーチン、フーリエ変換などを提供しています。 ''' [[item]] type = 'card' title = 'オープンソース' body = ''' NumPyは、寛容な[BSDライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt)で公開されています。NumPyは活発で、互いを尊重し、多様性を認め合う[コミュニティ](/ja/community)によって、 [GitHub](https://github.com/numpy/numpy)上でオープンに開発されていま す. ''' [[item]] type = 'card' title = '相互運用性' body = ''' NumPyは、幅広いハードウェアとコンピューティング・プラットフォームをサポートしており、分散処理、GPU、疎行列ライブラリにも対 応しています。 ''' [[item]] type = 'card' title = '高パフォーマンス' body = ''' NumPyの大部分は最適化されたC言語のコードで構成されています。これによりPythonの柔軟性とコンパイルされたコードの高速性の両方 を享受できます。 ''' [[item]] type = 'card' title = '使いやすさ' body = ''' NumPyの高水準なシンタックスは、どんなバックグラウンドや経験を持つのプログラマーでも簡単に利用することができ、生産性を高め>ることができます。 ''' {{< /grid >}} diff --git a/content/pt/_index.md b/content/pt/_index.md index 0a39687..0b598dd 100644 --- a/content/pt/_index.md +++ b/content/pt/_index.md @@ -1,49 +1,49 @@ --- -title: +title: NumPy --- {{< grid columns="1 2 2 3" >}} [[item]] type = 'card' title = 'Arrays n-dimensionais poderosas' body = ''' Rápidos e versáteis, os conceitos de vetorização, indexação e broadcasting do NumPy são, na prática, o padrão em computação com arrays. ''' [[item]] type = 'card' title = 'Ferramentas de computação numérica' body = ''' O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais. ''' [[item]] type = 'card' title = 'Interoperabilidade' body = ''' O NumPy suporta um grande número de plataformas de hardware e computação, e pode ser combinado com bibliotecas de computação com arrays esparsas, distribuidas ou em GPUs. ''' [[item]] type = 'card' title = 'Alto desempenho' body = ''' O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. ''' [[item]] type = 'card' title = 'Fácil de usar' body = ''' A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação. ''' [[item]] type = 'card' title = 'Código aberto' body = ''' Distribuido com uma [licença BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt) liberal, o NumPy é desenvolvido e mantido [publicamente no GitHub](https://github.com/numpy/numpy) por uma [comunidade](/pt/community) vibrante, responsiva, e diversa. ''' {{< /grid >}}
numpy/numpy.org
10b2f51c308c8a101700272cf58c96fee106a290
Fix date format in news.md (#815)
diff --git a/content/en/news.md b/content/en/news.md index a7af251..930e685 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,491 +1,491 @@ --- -title: News +title: "News" sidebar: false newsHeader: "NumPy 2.2.0 released!" -date: 2024-12-8 +date: 2024-12-08 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
db2a056925b8854d2520f6afebcca62501096b19
announce the NumPy 2.2.1 release (#810)
diff --git a/content/en/news.md b/content/en/news.md index 396c9a0..a7af251 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,490 +1,491 @@ --- title: News sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-8 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions ``matvec`` and ``vecmat``, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.2.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.1)) -- _21 Dec 2024_. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
a074f0c6e8cf2c5072072078706f9f021babde19
Removed Twitter link in community.md
diff --git a/content/en/community.md b/content/en/community.md index c7e0357..48208ae 100644 --- a/content/en/community.md +++ b/content/en/community.md @@ -1,77 +1,77 @@ --- title: Community sidebar: false --- NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](/code-of-conduct) for guidance on how to interact with others in a way that makes the community thrive. We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. ## Participate online The following are ways to engage directly with the NumPy project and community. _Please note that we encourage users and community members to support each other for usage questions - see [Get Help](/gethelp)._ ### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) This list is the main forum for longer-form discussions, like adding new features to NumPy, making changes to the NumPy Roadmap, and all kinds of project-wide decision making. Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. A searchable archive of this list is available [here](https://mail.python.org/archives/list/[email protected]/). *** ### [GitHub issue tracker](https://github.com/numpy/numpy/issues) - For bug reports (e.g. "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); - documentation issues (e.g. "I found this section unclear"); - and feature requests (e.g. "I would like to have a new interpolation method in `np.percentile`"). _Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it [here](https://tidelift.com/docs/security)._ *** ### [Slack](https://numpy-team.slack.com) A real-time chat room to ask questions about _contributing_ to NumPy. This is a private space, specifically meant for people who are hesitant to bring up their questions or ideas on a large public mailing list or GitHub. Please see [here](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) for more details and how to get an invite. ## Study Groups and Meetups If you would like to find a local meetup or study group to learn more about NumPy and the wider ecosystem of Python packages for data science and scientific computing, we recommend exploring the [PyData meetups](https://www.meetup.com/pro/pydata/) (150+ meetups, 100,000+ members). -NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) and [X](https://x.com/numpy_team). +NumPy also organizes in-person sprints for its team and interested contributors occasionally. These are typically planned several months in advance and will be announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). ## Conferences The NumPy project doesn't organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series: - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) - [SciPy Latin America](https://www.scipyla.org) - [SciPy India](https://scipy.in) - [SciPy Japan](https://conference.scipy.org) - [PyData conferences](https://pydata.org/event-schedule/) (15-20 events a year spread over many countries) Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects. ## Join the NumPy community To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar [here](https://scientific-python.org/calendars/).
numpy/numpy.org
76678795ed53ff24e1ed23e2378bcbb0a3f80657
Removed Twitter link in contribute.md
diff --git a/content/en/contribute.md b/content/en/contribute.md index 1006251..f5ea653 100644 --- a/content/en/contribute.md +++ b/content/en/contribute.md @@ -1,112 +1,111 @@ --- title: Contribute to NumPy sidebar: false --- The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at <[email protected]> or on [Slack](https://numpy-team.slack.com) (write <[email protected]> for an invite). We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. ### Writing code Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase. <br>Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: * check if older bugs are still present * find duplicate issues and link related ones * add good self-contained reproducers to issues * label issues correctly (this requires triage rights -- just ask) Please just dive in. ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. ### Community coordination and outreach Through community contact we share our work more widely and learn where we're -falling short. We're eager to get more people involved in efforts like our -[X](https://x.com/numpy_team) account, organizing NumPy [code +falling short. We're eager to get more people involved in efforts like organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. ### Donate If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate).
numpy/numpy.org
7c9c18f963d396c5797d1be41f0efbec14aee537
Revert "Update contribute.md"
diff --git a/content/pt/contribute.md b/content/pt/contribute.md index 1f1861e..65b8263 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -1,66 +1,66 @@ --- title: Contribua com o NumPy sidebar: false --- O projeto NumPy precisa de sua experiência e entusiasmo! Suas opções de não são limitadas à programação -- além de Se você não sabe por onde começar ou como suas habilidades podem ajudar, _fale conosco!_ Você pode perguntar na nossa [lista de emails](https://mail.python.org/mailman/listinfo/numpy-discussion) ou [GitHub](http://github.com/numpy/numpy) (abrindo uma [issue](https://github.com/numpy/numpy/issues) ou comentando em uma issue relevante). Estes são os nossos canais de comunicação preferidos (projetos de código aberto são abertos por natureza!). No entanto, se você preferir discutir em privado, entre em contato com os coordenadores da comunidade em <[email protected]> ou no [Slack](https://numpy-team.slack.com) (envie um e-mail para <[email protected]> para obter um convite antes de entrar). Nós também temos uma _reunião aberta da comunidade_ a cada duas semanas. Os detalhes são anunciados na nossa [lista de emails](https://mail.python.org/mailman/listinfo/numpy-discussion). Convidamos você a participar. Se você nunca contribuiu para projetos de código aberto, recomendamos fortemente que você leita [esse guia](https://opensource.guide/how-to-contribute/). Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contribuições. Temos um [Código de Conduta](/pt/code-of-conduct) para promover um ambiente aberto e acolhedor. ### Escrevendo código Para pessoas programadoras, este [guia](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica como contribuir para a base de código. <br>Confira também nosso [canal do YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) para obter informações adicionais. ### Revisar pull requests O projeto tem mais de 250 pull requests abertos -- o que significa que muitas potenciais melhorias e muitos contribuidores de código aberto estão aguardando feedback. Se você é uma pessoa programadora que conhece o NumPy, você pode ajudar, mesmo que não tenha familiaridade com o código. Você pode: * resumir uma discussão longa * fazer triagem de PRs de documentação * testar alterações propostas ### Desenvolvimento de materiais educacionais O [Guia do Usuário](https://numpy.org/devdocs) do Numpy está sendo reformado. Precisamos de novos tutoriais, how-to's e de explicações de conceitos, e o site precisa de reestruturação. Oportunidades não se limitam a pessoas com experiência em escrita técnica. Também procuramos exemplos práticos, notebooks e vídeos. A [NEP 44](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) explica nossas ideias para reestruturar a documentação do NumPy — talvez você também tenha outras ideias. ### Triagem de Issues O [*issue tracker* do NumPy](https://github.com/numpy/numpy/issues) tem _um monte_ de issues abertas. Algumas não são mais válidas, algumas deveriam ser priorizadas, e algumas poderiam ser boas para pessoas que estão procurando sua primeira contribuição. Você pode: * verificar se erros mais antigos ainda estão presentes * encontrar issues duplicadas e criar links entre issues relacionadas * adicionar bons exemplos autocontidos que reproduzam issues * rotular issues corretamente (isso requer direitos de triagem -- basta perguntar) Sinta-se à vontade! ### Desenvolvimento do site Acabamos de renovar o nosso site, mas estamos longe de terminar. Se você adora o desenvolvimento web, estas [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) listam algumas de nossas necessidades não atendidas -- e sinta-se livre para compartilhar suas próprias ideias. ### Design gráfico Nós mal podemos começar a listar as contribuições que uma pessoa com conhecimento em design gráfico pode fazer aqui. Nossa documentação precisa de ilustrações; nosso site crescente precisa de imagens -- há muitas oportunidades. ### Traduzir conteúdo do site Planejamos várias traduções do [numpy.org](https://numpy.org) para tornar o NumPy acessível aos usuários em seu idioma nativo. Tradutores voluntários estão no coração deste esforço. Veja [aqui](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para informações; comente [nesta issue do GitHub](https://github.com/numpy/numpy.org/issues/55) para se envolver. ### Coordenação e promoção na comunidade -Através do contato com a comunidade podemos compartilhar nosso trabalho para mais pessoas e descobrir onde precisamos trabalhar mais. Estamos ansiosos para que mais pessoas se envolvam em esforços como nossa conta no [X](https://x.com/numpy_team), na organização de [sprints](https://scisprints.github.io/) sobre o NumPy, uma newsletter, e talvez um blog. +Através do contato com a comunidade podemos compartilhar nosso trabalho para mais pessoas e descobrir onde precisamos trabalhar mais. Estamos ansiosos para que mais pessoas se envolvam em esforços como nossa conta no [Twitter](https://twitter.com/numpy_team), na organização de [sprints](https://scisprints.github.io/) sobre o NumPy, uma newsletter, e talvez um blog. ### Financiamento O NumPy foi um projeto totalmente voluntário por muitos anos, mas conforme sua importância cresceu, tornou-se clara a necessidade de apoio financeiro para garantir estabilidade e crescimento. [Esta palestra na SciPy'19](https://www.youtube.com/watch?v=dBTJD_FDVjU) explica quanta diferença esse suporte fez. Como todo o mundo das organizações sem fins lucrativos, nós estamos constantemente procurando bolsas, patrocinadores e outros tipos de apoio. Nós temos uma série de ideias e é claro que nós damos as boas-vindas a mais. Habilidade de buscar financiamento é uma habilidade rara aqui -- apreciaríamos a sua ajuda.
numpy/numpy.org
c5a68f3d3e39d77f1f46fb30e6d27ca58f48ea44
Revert "Update community.md"
diff --git a/content/pt/community.md b/content/pt/community.md index 5a6046f..7992ff2 100644 --- a/content/pt/community.md +++ b/content/pt/community.md @@ -1,66 +1,66 @@ --- title: Comunidade sidebar: false --- NumPy é um projeto de código aberto impulsionado pela comunidade desenvolvido por um grupo muito diversificado de [contribuidores](/pt/teams/). A liderança do NumPy assumiu um forte compromisso de criar uma comunidade aberta, inclusiva e positiva. Por favor, leia [o Código de Conduta NumPy](/pt/code-of-conduct) para orientações sobre como interagir com os outros de uma forma que faça a comunidade prosperar. Oferecemos vários canais de comunicação para aprender, compartilhar seu conhecimento e se conectar com outros dentro da comunidade NumPy. ## Participar online Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a comunidade do NumPy. _Por favor, note que encorajamos os usuários e membros da comunidade a apoiarem-se uns aos outros para perguntas sobre utilização - veja [Obter Ajuda](/gethelp)._ ### [Lista de discussões NumPy](https://mail.python.org/mailman/listinfo/numpy-discussion) Esta lista é o principal fórum para discussões mais longas, como adicionar novos recursos ao NumPy, fazer alterações no roadmap do NumPy e em todos os tipos de tomada de decisão para todo o projeto. Anúncios sobre o NumPy, como novas versões, reuniões de desenvolvedores, sprints ou palestras de conferência também são feitas nesta lista. Nesta lista, por favor, use *bottom posting*, responda à lista (em vez de a outro remetente), e não responda aos *digests*. Um arquivo pesquisável desta lista está disponível [aqui](https://mail.python.org/archives/list/[email protected]/). *** ### [Página de issues do GitHub](https://github.com/numpy/numpy/issues) - Para relatórios de bugs (por exemplo, "`np.arange(3).shape` retorna `(5,)`, quando deveria retornar `(3,)`"); - problemas de documentação (ex. "Eu achei esta seção confusa"); - e pedidos de recursos (por exemplo, "Eu gostaria de ter um novo método de interpolação em `np.percentile`"). _Por favor, note que o GitHub não é o lugar certo para relatar uma vulnerabilidade de segurança. Se você acha que encontrou uma vulnerabilidade de segurança no NumPy, relate-a [aqui](https://tidelift.com/docs/security)._ *** ### [Slack](https://numpy-team.slack.com) Uma sala de bate-papo em tempo real para fazer perguntas sobre _contribuir_ para o NumPy. Este é um fórum privado, especificamente para pessoas hesitantes em levantar suas perguntas ou idéias em uma grande lista de e-mails públicos ou no GitHub. Por favor, clique [aqui](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para mais detalhes e como obter um convite. ## Grupos de Estudo e Meetups Se você gostaria de encontrar um encontro ou grupo de estudo local para aprender mais sobre o NumPy e o ecossistema mais amplo de pacotes Python para ciência de dados e computação científica, recomendamos explorar os [meetups PyData](https://www.meetup.com/pro/pydata/) (mais de 150 encontros, mais de 100.000 membros). -O NumPy também organiza sprints presenciais para sua equipe e colaboradores interessados ocasionalmente. Estes eventos são normalmente planejados com vários meses de antecedência e serão anunciados na [lista de discussão](https://mail.python.org/mailman/listinfo/numpy-discussion) e no [X](https://x.com/numpy_team). +O NumPy também organiza sprints presenciais para sua equipe e colaboradores interessados ocasionalmente. Estes eventos são normalmente planejados com vários meses de antecedência e serão anunciados na [lista de discussão](https://mail.python.org/mailman/listinfo/numpy-discussion) e no [Twitter](https://twitter.com/numpy_team). ## Conferências O projeto NumPy não organiza suas próprias conferências. As conferências que tradicionalmente têm sido mais populares com mantenedores, colaboradores e usuários são as conferências SciPy e PyData: - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) - [SciPy Latin America](https://www.scipyla.org) - [SciPy India](https://scipy.in) - [SciPy Japan](https://conference.scipy.org) - [conferências PyData](https://pydata.org/event-schedule/) (15-20 eventos por ano espalhados por muitos países) Muitas dessas conferências incluem dias de tutorial sobre o NumPy e/ou sprints onde você pode aprender como contribuir com o NumPy ou projetos de código aberto relacionados. ## Junte-se à comunidade NumPy Para prosperar, o projeto NumPy precisa de sua experiência e entusiasmo. Não é uma pessoa programadora? Sem problemas! Existem muitas maneiras de contribuir com o NumPy. Se você está interessado em se tornar um contribuidor do NumPy (oba!) recomendamos que você confira nossa página sobre [Contribuições](/pt/contribute). Além disso, sinta-se à vontade para passar por aqui e dizer oi em uma de nossas reuniões da comunidade. Para acompanhá-las, confira nosso calendário de eventos [aqui](https://scientific-python.org/calendars/).
numpy/numpy.org
07213fb30dc4153b1d4d0513dccad6c97320bfbe
Revert "Update config.yaml"
diff --git a/content/pt/config.yaml b/content/pt/config.yaml index c1e8156..14ab9ee 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -1,111 +1,111 @@ languageName: Português params: description: Por que NumPy? Arrays n-dimensionais poderosas. Ferramentas para computação numérica. Interoperabilidade. Alto desempenho. Código aberto. navbarlogo: image: logo.svg text: NumPy link: /pt/ hero: #Main hero title title: NumPy #Hero subtitle (optional) subtitle: A biblioteca fundamental para computação científica com Python #Button text buttontext: "Última versão: NumPy 1.26. Veja todas as versões" #Where the main hero button links to buttonlink: "/pt/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - title: Experimentar o NumPy text: Use o shell interativo para testar o NumPy no navegador docslink: Não se esqueça de conferir a <a href="https://numpy.org/doc/stable" target="_blank">documentação</a>. casestudies: title: ESTUDOS DE CASO features: - title: A Primeira Imagem de um Buraco Negro text: Como o NumPy, junto com outras bibliotecas como SciPy e Matplotlib que dependem do NumPy, permitiram ao Event Horizon Telescope gerar a primeira imagem de um buraco negro da história. img: /images/content_images/case_studies/blackhole.png alttext: Primeira imagem de um buraco negro. É um círculo laranja em um fundo preto. url: /pt/case-studies/blackhole-image - title: Descoberta de Ondas Gravitacionais text: Em 1916, Albert Einstein previu ondas gravitacionais; 100 anos depois, sua existência foi confirmada pelos cientistas do LIGO usando NumPy. img: /images/content_images/case_studies/gravitional.png alttext: Duas esferas orbitando a si mesmas. Elas deslocam a gravidade em seu entorno. url: /pt/case-studies/gw-discov - title: Análise Esportiva text: A análise de críquete está mudando o jogo ao melhorar o desempenho de jogadores e times através de modelagem estatística e análise preditiva. O NumPy possibilita muitas dessas análises. img: /images/content_images/case_studies/sports.jpg alttext: Bola de críquete em um campo verde url: /pt/case-studies/cricket-analytics - title: Estimação de poses usando deep learning text: DeepLabCut usa o NumPy para acelerar estudos científicos que envolvem comportamento animal para entender melhor o controle motor em várias espécies e escalas de tempo. img: /images/content_images/case_studies/deeplabcut.png alttext: Análise de pose de um guepardo url: /pt/case-studies/deeplabcut-dnn tabs: title: ECOSSISTEMA section5: false navbar: - title: Instalação url: /pt/install - title: Documentação url: https://numpy.org/doc/stable - title: Aprenda url: /pt/learn - title: Comunidade url: /pt/community - title: Sobre url: /pt/about - title: Notícias url: /pt/news - title: Contribuir url: /pt/contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - link: https://github.com/numpy/numpy icon: github - - link: https://www.youtube.com/@NumPy_team + - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - link: https://x.com/numpy_team - icon: x + - link: https://twitter.com/numpy_team + icon: twitter quicklinks: column1: title: "" links: - text: Instalação link: /pt/install - text: Documentação link: https://numpy.org/doc/stable - text: Aprenda link: /pt/learn - text: Citando o Numpy link: /pt/citing-numpy - text: Roadmap link: https://numpy.org/neps/roadmap.html column2: links: - text: Sobre link: /pt/about - text: Comunidade link: /pt/community - text: Pesquisas de usuário link: /pt/user-surveys - text: Contribuir link: /pt/contribute - text: Código de Conduta link: /pt/code-of-conduct column3: links: - text: Ajuda link: /pt/gethelp - text: Termos de uso (EN) link: /pt/terms - text: Privacidade link: /pt/privacy - text: Kit de imprensa link: /pt/press-kit
numpy/numpy.org
8eb58bb01f541b10655eb1e9a0f5312b0cae4cdf
Revert "Update contribute.md"
diff --git a/content/ja/contribute.md b/content/ja/contribute.md index 39d7f08..d2720fd 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -1,65 +1,65 @@ --- title: NumPy に貢献する sidebar: false --- NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 貢献方法はプログラミングに限定されません。 このページには**あなたができる** 様々な種類の貢献方法が示されています。 もしどこから始めればいいか、あなたのスキルをどう生かせばいいかがわからない場合は、 _是非ご連絡下さい。 _ 連絡の方法としては、 [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) 、 [GitHub](http://github.com/numpy/numpy)、 [イシューの作成](https://github.com/numpy/numpy/issues) 、関連するイシューへのコメントがあります。 連絡先としては、 <[email protected]> または、[Slack](https://numpy-team.slack.com) (グループに招待するためにこちらに連絡お願いします: <[email protected]>)があります。 また、隔週の _コミュニティミーティング_もあり、詳細は [メーリングリスト](https://mail.python.org/mailman/listinfo/numpy-discussion) で発表されています。 あなたの参加を大いに歓迎します。 オープンソースプロジェクトに貢献するのが初めての方は、是非、 [このガイド](https://opensource.guide/how-to-contribute/) を読んでみて下さい。 私たちのコミュニティは、誰もが平等に扱われ、すべての貢献を平等に評価することを目指しています。 私たちはオープンで居心地の良いコミュニティを作るために [行動基準](/ja/code-of-conduct) を制定しています。 ### コードを書く プログラマーの方には、こちらの [ガイド](https://numpy.org/devdocs/dev/index.html#development-process-summary)でNumPyのコードに貢献する方法を説明しています。 <br>追加情報に関しては、 こちらの[YouTube チャンネル](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) もご覧ください。 ### プルリクエストのレビュー NumPyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumPyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、 * 長期にわたる議論をまとめる * ドキュメントのPRをトリアージする * 提案された変更をテストする ### 教育用の資料を作成する NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模な再設計中です。 新しいNumPyのWebページは、新しいチュートリアルや、NumPyの使い方、NumPy内部の深い説明など必要としており、サイト全体にも再設計と再構築が必要です。 このウェブサイトの再構築の作業は、ドキュメントを書くだけではありません。 コード例や、ノートブック、ビデオなどの作成も歓迎しています。 [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)に、ウェブサイトの再構築についての詳細が説明されています。 ### イシューのトリアージ [NumPyのイシュートラッカー](https://github.com/numpy/numpy/issues) には、 _沢山の_Open状態のイシューがあります。 すでに解決されたもの、優先順位付けされるべきもの、 初心者が取り組むのに適したものがあります。 あなたができることは、いくつもあります: * 古いバグがまだ残っているか確認する * 重複したイシューを見つけ、お互いに関連づける * 問題を再現するコードを作成する * イシューに正しいラベル付けをする (トリアージ権が必要なので、連絡下さい) ぜひ、やってみて下さい。 ### ウェブサイトの開発 私たちはちょうどウェブサイトを作り直し始めたところですが、それらはまだ完了していません。 Web開発が好きなら、この[イシュー](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) に未完成な要求が列挙されています。 ぜひ、あなたのアイデアを共有してください。 ### グラフィックデザイン グラフィックデザイナーの方が可能な貢献は、枚挙にいとまがありません。 しかし、私たちのドキュメントは説明のために可視化が重要であり、私たちの拡大しているウェブサイトは良い画像を求めていることから、 貢献する機会が沢山あると言えます。 ### ウェブサイトの翻訳 私たちは、[numpy.org](https://numpy.org) を複数言語に翻訳し、NumPyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 ### コミュニティとの連携とアウトリーチ -コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、[X](https://x.com/numpy_team) アカウントや、NumPy[コードスプリント](https://scisprints.github.io/)の開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。 +コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy[コードスプリント](https://scisprints.github.io/)の開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。 ### 資金調達 NumPyは何年にも渡ってボランティアだけ活動していましたが、その重要性が高まるにつれ、安定性と成長のためには資金面での支援が必要であることがわかってきました。 こちらの[SciPy'19のプレゼン](https://www.youtube.com/watch?v=dBTJD_FDVjU) では、資金的なサポートを受けたことで、どれだけ違いが出たかを説明しています。 他の非営利団体のように、私たちは助成金や、スポンサーシップ、その他の資金支援を常に探しています。 私たちはすでにいくつかの資金調達のアイデアを持っていますが、他にもより多くを資金調達を受けたいと思っています。 資金調達に関する知識は、我々には不足しているスキルです。 是非、あなたのサポートをお待ちしています。 ### 寄付 寄付をすることでNumpyに貢献したい場合は、 [https://numpy.org/about/#donate](https://numpy.org/about/#donate) をご覧ください。
numpy/numpy.org
b6df19aec264ede127bfd18524726063d22aa6d5
Revert "Update config.yaml"
diff --git a/content/ja/config.yaml b/content/ja/config.yaml index d074c8a..f662552 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -1,137 +1,137 @@ languageName: 日本語 (Japanese) params: description: NumPyが広く利用される理由 強力な多次元配列、数値計算ツール群、相互運用性、高いパフォーマンス、オープンソース navbarlogo: image: logo.svg text: NumPy link: /ja/ hero: #Main hero title title: NumPy #Hero subtitle (optional) subtitle: Pythonによる科学技術計算の基礎パッケージ #Button text buttontext: "最新リリース: Numpy 1.26. すべてのリリースを表示する" #Where the main hero button links to buttonlink: "/ja/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - title: NumPy を試す text: インタラクティブシェルを使用して、ブラウザ上で Numpy を試してみてください。 docslink: <a href="https://numpy.org/doc/stable" target="_blank">ドキュメント</a> を確認することを忘れないでください。 casestudies: title: ケーススタディ features: - title: 世界初のブラックホール画像 text: NumPyはどのように、SciPyやMatplotlibなどのNumPyに依存するライブラリとともに、イベントホライズンテレスコープによる世界初のブラックホール画像の作成を可能にしたのでしょうか。 img: /images/content_images/case_studies/blackhole.png alttext: 世界初のブラックホール画像。黒い背景にオレンジ色の円で描かれています。 url: /ja/case-studies/blackhole-image - title: 重力波の検知 text: 1916年、アルバート・アインシュタインは重力波を予言しました。100年後、LIGOの研究者たちはNumPyを使ってその存在を確認しました。 img: /images/content_images/case_studies/gravitional.png alttext: 2つのオーブがお互いに周回し、周りの重力を変位させています。 url: /ja/case-studies/gw-discov - title: スポーツ分析 text: クリケット分析は、統計的モデリングと予測分析によって選手やチームのパフォーマンスを向上させることで、クリケットの試合を変えようとしています。多くの分析が、NumPyにより可能になりました。 img: /images/content_images/case_studies/sports.jpg alttext: 緑のフィールド上にあるクリケットボール。 url: /ja/case-studies/cricket-analytics - title: 深層学習による姿勢推定 text: DeepLabCutはNumPyを利用し、動物の種類や時間スケールによらない運動制御の理解へ向け、動物の行動観察を含む科学技術研究を加速させています。 img: /images/content_images/case_studies/deeplabcut.png alttext: チータの姿勢推定 url: /ja/case-studies/deeplabcut-dnn tabs: title: NumPyのエコシステム section5: false navbar: - title: インストール url: /ja/install - title: ドキュメント url: https://numpy.org/doc/stable - title: 学び方 url: /ja/learn - title: コミュニティ url: /ja/community - title: 私達について url: /ja/about - title: ニュース url: /ja/news - title: NumPyに貢献する url: /ja/contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - link: https://github.com/numpy/numpy icon: github - - link: https://www.youtube.com/@NumPy_team + link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube quicklinks: column1: title: "" links: - text: インストール link: /ja/install - text: ドキュメント link: https://numpy.org/doc/stable - text: 学び方 link: /ja/learn - text: 引用する link: /ja/citing-numpy - text: ロードマップ link: https://numpy.org/neps/roadmap.html column2: links: - text: 私達について link: /ja/about - text: コミュニティ link: /ja/community - text: ユーザーの調査 link: /ja/user-surveys - text: NumPyに貢献する link: /ja/contribute - text: 行動規範 link: /ja/code-of-conduct column3: links: - text: サポートを得る方法 link: /ja/gethelp - text: 利用規約 link: /ja/terms - text: プライバシーポリシー link: /ja/privacy - text: プレス用資料 link: /ja/press-kit
numpy/numpy.org
cf58e68b77ff28e6cdbfd0d2bb8eba31ded0134e
Revert "Update code-of-conduct.md"
diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md index 0d7581e..70ca4d8 100644 --- a/content/ja/code-of-conduct.md +++ b/content/ja/code-of-conduct.md @@ -1,83 +1,83 @@ --- title: NumPy行動規範 sidebar: false aliases: - /ja/conduct/ --- ### はじめに -この行動規範は、NumPy プロジェクトによって管理されるすべての場所で適用されます。 この場所とは、すべてのパブリックおよびプライベートのメーリングリスト、イシュートラッカー、Wiki、ブログ、X、コミュニティで使用されているその他の通信チャンネルなどを含みます。 NumPy プロジェクトでは対面でのイベントは開催していません。 しかし、我々のコミュニティに関連するものであれば、対面のイベントでも同様の行動規範を持つ必要があります。 +この行動規範は、NumPy プロジェクトによって管理されるすべての場所で適用されます。 この場所とは、すべてのパブリックおよびプライベートのメーリングリスト、イシュートラッカー、Wiki、ブログ、Twitter、コミュニティで使用されているその他の通信チャンネルなどを含みます。 NumPy プロジェクトでは対面でのイベントは開催していません。 しかし、我々のコミュニティに関連するものであれば、対面のイベントでも同様の行動規範を持つ必要があります。 この行動規範は、NumPy コミュニティに正式または非公式に参加するすべての人が順守する必要があります。 その他にも、NumPyとの提携・関連するプロジェクト活動においては、特にそれらのプロジェクトを代表する場合、同様の行動規範に従う必要があります。 この行動規範は完全ではありません。 しかし、行動規範は我々が理解すべき、互いの協力の仕方や、共通の場所のあるべき姿、我々のゴールなどをまとめるのに重要な役目を果たします。 フレンドリーで生産的な環境を生み出し、周囲のコミュニティにより良い影響を与えるため、ぜひこの行動規範に従ってください。 ### ガイドラインの概要 私たちは下記の内容に真摯に取り組みます。 1. 開けたコミュニティにしましょう。 私たちは、誰でもコミュニティに参加できるようにします。 私たちは、公にすべきではない内容を議論する場合以外、プロジェクトに関連するメッセージを公の場で告知することを選びます。 これは、NumPyに関するヘルプやプロジェクトサポートにも適用されます。公式なサポートだけでなく、NumPyに関する質問に答える場合もです。 これにより、質問に答えた際の意図しない間違いを、より簡単に検出し、訂正できるようになります。 2. 共感し、歓迎し、友好的で、そして我慢強くありましょう。 私たちは互いに争いを解決し合い、互いの善意を信じ合います。 私たちは時折り不満を感じるかもしれません。 しかしそのような場合も、不満を個人的な攻撃に変えることは許容されません。 人々が不快や脅威を感じるコミュニティは、生産的ではないからです。 3. 互いに協力し合おう。 私たちの開発成果は他の人々によって利用され、一方で、たちは他の人々の開発成果に依存しているのです。 私たちがプロジェクトために何かを作るとき、私たちはそれがどのように動作するかを他の人に説明する必要があります。 しかし、この作業により、より良いものを作り上げることができるのです。 私たちが下す全ての決断は、ユーザと開発コミュニティに影響を与えうるし、その決断がもたらす結果を私たちは真摯に受け止めます。 4. 好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。 5. 使う言葉に注意しましょう。 私たちは、コミュニティにおけるコミュニケーションに注意と敬意を払います。 そして、私たちは自分の言葉に責任を持ちます。 他人に優しくしましょう。 他のコミュニティの参加者を侮辱しないでください。 私たちは、以下のようなハラスメントやその他の排斥行為を許しません。 : * 他の人に向けられた暴力的な行為や言葉。 * 性差別や人種差別、その他の差別的なジョークや言動。 * 性的または暴力的な内容の投稿。 * 他のユーザーの個人情報を投稿すること。 (または投稿すると脅すこと)。 * 公開目的のない電子メールや、ICRチャットのようなログの残らないフォーラムの履歴など、プライベートなコンテンツを送信者の同意なしに共有すること。 * 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。 * 不快な思いをさせる性的な言動。 * 過度に粗暴に振る舞うこと。 ひどいな言葉を使うのを避けてください。 人々は怒りを覚える感度が、それぞれ大きく異なります。 * 他人に対するハラスメントの繰り返し。 一般的に、誰かがあなたにある言動を止めるように要求した場合、その言動をやめて下さい。 * 上記のいずれかの行動を擁護すること、または奨励すること。 ### 多様性に関する声明 NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。 あなたの自己認識や、他者のあなたへの認識は関係ありません。 私たちはあなたを歓迎します。 民族、遺伝、性同一性あるいは関連する表現、言語、国籍、神経学的な差異、生物学的な差異、 政治的信条、職業、人種、宗教、性的指向、社会経済的地位、文化的な差異、技術的な能力。 私たちはすべての種類の言語言語話者の参加を歓迎しますが、NumPy 開発は英語で行われます。 NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 NumPyコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです (次のセクションを参照)。 ### 報告ガイドライン 私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 意図的な行動規範違反については、行動規範委員会に報告してください (下記参照)。 もし、違反が意図的でない可能性がある場合、その人にこの行動規範の存在を知らせることも可能です (パブリックでもプライベートでも、適切な方法で)。 もし直接指摘したくない場合は、ぜひ、行動規範委員会に直接連絡するか、違反の確度について助言を求めて下さい。 NumPy行動規範委員会に問題を報告する場合は、こちらにご連絡下さい: [email protected]。 現在、行動規範委員会は以下のメンバーで構成されています: * Stefan van der Walt * Melissa Weber Mendonça * Rohit Goswami もしあなたの違反報告に委員会のメンバーが含まれている場合, または彼らがそれを処理する上で利益相反をしていると感じる場合、そのメンバーはあなたの報告を評価する立場からは辞退してもらいます。 もしくは、行動規範委員会に報告するのが躊躇われる場合は、こちらからNumFOCUSのシニアスタッフに連絡することも可能です:[[email protected]](https://numfocus.org/code-of-conduct#persons-responsible) 。 ### インシデント報告の解決 & 行動規範の実施 本節では、_最も重要な点のみをまとめます。 _詳細については、[NumPy Code of Conduct - How to follow up on a report](report-handling-manual) をご覧ください。 私たちはすべての訴えを調査し、対応するようにします。 NumPy行動規範委員会およびNumPy運営委員会(もし関係する場合) は、報告者の身元を保護します。 また(報告者が同意しない限り) 苦情の内容を機密として扱うこととします。 もし深刻で明らかな違反の場合、例えば、 個人的な脅し、または暴力的、性差別的または人種差別的な発言などの場合、我々は直ちにNumPyのコミュニケーションの場から発言者を退場させます。詳細についてはマニュアルを参照してください。 もし、行動規範に対して明白な違反がみられない場合、受領された行動規範違反報告に対するプロセスは以下の通りです。 1. 報告書の受領を確認 2. 建設的な議論/フィードバック 3. 調停（報告者と報告を受けたものの両方がフィードバックが役に立たなかったと同意した場合に限る） 4. 行動規範委員会による透明性のある決定と執行( [決議](report-handling-manual/#解決方法)を参照) 行動規範委員会は、可能な限り速やかに対応し、最大で72時間以内に対応する様にします。 ### 文末脚注: 私たちは下記のドキュメントを作成したグループに感謝します。 内容・発想ともに大いに影響されています。 - [SciPy行動規範](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html)
numpy/numpy.org
4c15ad2501803033d80301a764a3f83355579232
Revert "Update config.yaml"
diff --git a/content/es/config.yaml b/content/es/config.yaml index a13b0a0..0357ec7 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -1,109 +1,109 @@ languageName: Español params: description: '¿Por qué NumPy? Potentes arreglos n-dimensionales. Herramientas de cálculo numérico. Interoperabilidad. Rendimiento. Código abierto.' navbarlogo: image: logo.svg text: NumPy link: /es/ hero: #Main hero title title: NumPy #Hero subtitle (optional) subtitle: El paquete fundamental para la computación científica con Python #Button text buttontext: "Última versión: NumPy 2.0. Ver todas las versiones" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: title: marcador intro: - title: Prueba NumPy text: Utilice el terminal interactivo para probar NumPy en el navegador docslink: No olvides echarle un ojo a la <a href="https://numpy.org/doc/stable" target="_blank">documentación</a>. casestudies: title: CASOS DE ESTUDIO features: - title: Primera imagen de un Agujero Negro text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Telescopio del Horizonte de Sucesos producir la primera imagen de un agujero negro img: /images/content_images/case_studies/blackhole.png alttext: Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro. url: /es/case-studies/blackhole-image - title: Detección de Ondas Gravitacionales text: En 1916 Albert Einstein predijo las ondas gravitacionales; 100 años después se confirmó su existencia por científicos del LIGO, utilizando NumPy. img: /images/content_images/case_studies/gravitional.png alttext: Dos cuerpos orbitándose mutuamente. Estos desplazan la gravedad a su alrededor. url: /es/case-studies/gw-discov - title: Analíticas Deportivas text: El Análisis de Críquet está cambiando el juego, mejorando el rendimiento de los jugadores y equipos mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. img: /images/content_images/case_studies/sports.jpg alttext: Bola de Cricket sobre un campo verde. url: /es/case-studies/cricket-analytics - title: Estimación de la pose mediante aprendizaje profundo text: DeepLabCut utiliza NumPy para acelerar estudios científicos que implican la observación del comportamiento animal para una mejor comprensión del control motriz, a través de especies y escalas de tiempo. img: /images/content_images/case_studies/deeplabcut.png alttext: Análisis de la pose de un Guepardo url: /es/case-studies/deeplabcut-dnn tabs: title: ECOSISTEMA section5: false navbar: - title: Instalar url: /es/install - title: Documentación url: https://numpy.org/doc/stable - title: Aprende url: /es/learn - title: Comunidad url: /es/community - title: Quiénes somos url: /es/about - title: Noticias url: /es/news - title: Contribuye url: /es/contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - link: https://github.com/numpy/numpy icon: github - - link: https://www.youtube.com/@NumPy_team + - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube quicklinks: column1: title: "" links: - text: Instalar link: /es/install - text: Documentación link: https://numpy.org/doc/stable - text: Aprende link: /es/learn - text: Citando a NumPy link: /es/citing-numpy - text: Mapa de ruta link: https://numpy.org/neps/roadmap.html column2: links: - text: Acerca de nosotros link: /es/about - text: Comunidad link: /es/community - text: Encuestas a usuarios link: /es/user-surveys - text: Contribuye link: /es/contribute - text: Código de Conducta link: /es/code-of-conduct column3: links: - text: Buscar ayuda link: /es/gethelp - text: Términos de uso link: /es/terms - text: Confidencialidad link: /es/privacy - text: Kit de prensa link: /es/press-kit
numpy/numpy.org
f657d137fe2d2339bf64f5e97d07ed05b38a93a4
Revert "Update community.md"
diff --git a/content/es/community.md b/content/es/community.md index 153ebd2..f96474c 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -1,66 +1,66 @@ --- title: Comunidad sidebar: false --- NumPy es un proyecto de código abierto impulsado por la comunidad y desarrollado por un grupo diverso de [colaboradores](/teams/). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el [Código de Conducta de NumPy](/code-of-conduct) para obtener orientación sobre cómo interactuar con los demás de una manera que haga que la comunidad prospere. Ofrecemos varios canales de comunicación para aprender, compartir conocimientos y conectarse con otros dentro de la comunidad de NumPy. ## Participa en línea Las siguientes son formas de relacionarse directamente con el proyecto y la comunidad de NumPy. _Ten en cuenta que animamos a los usuarios y a los miembros de la comunidad a apoyarse mutuamente por preguntas de uso - ver [Obtener ayuda](/gethelp)._ ### [Lista de correo de NumPy](https://mail.python.org/mailman/listinfo/numpy-discussion) Este es el foro principal para discusiones más extensas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones sobre el proyecto. Aquí también se realizan los anuncios sobre NumPy, tales como lanzamientos, reuniones de desarrolladores, sprints o charlas en conferencias. En esta lista, por favor, utiliza el botón de envío inferior, responde a la lista (en lugar de a otro remitente) y no respondas a los resúmenes. El archivo de consulta de esta lista está disponible [aquí](https://mail.python.org/archives/list/[email protected]/). *** ### [Seguimiento de incidencias en GitHub](https://github.com/numpy/numpy/issues) - Para informes de error (por ejemplo, "`np.arange(3).shape` devuelve `(5,)`, cuando debería devolver `(3,)`"); - problemas en la documentación (por ejemplo, "Esta sección me pareció poco clara"); - y solicitudes de funcionalidades (por ejemplo, "Me gustaría tener un nuevo método de interpolación en `np.percentile`"). _Ten en cuenta que GitHub no es el lugar adecuado para reportar una vulnerabilidad de seguridad. Si crees que has encontrado una vulnerabilidad de seguridad en NumPy, por favor repórtalo [aquí](https://tidelift.com/docs/security)._ *** ### [Slack](https://numpy-team.slack.com) Una sala de chat en tiempo real para hacer preguntas sobre las _contribuciones_ a NumPy. Este es un espacio privado, destinado específicamente a las personas que no se atreven a plantear sus preguntas o ideas en la lista de correo pública o en GitHub. Por favor, visita [aquí](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para más detalles, y sobre cómo obtener una invitación. ## Grupos de Estudio y Reuniones Si desea encontrar un grupo de estudio o reunión local para aprender más sobre NumPy y el ecosistema más amplio de paquetes de Python para ciencia de datos y computación científica, te recomendamos que explores los [PyData meetups](https://www.meetup.com/pro/pydata/) (más de 150 reuniones, más de 100,000 miembros). -NumPy también organiza ocasionalmente sprints presenciales para su equipo y colaboradores interesados. Estos normalmente se planifican con varios meses de anticipación y se anunciarán en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion) y en [X (antes conocido como Twitter)](https://x.com/numpy_team). +NumPy también organiza ocasionalmente sprints presenciales para su equipo y colaboradores interesados. Estos normalmente se planifican con varios meses de anticipación y se anunciarán en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion) y en [X (antes conocido como Twitter)](https://twitter.com/numpy_team). ## Conferencias El proyecto NumPy no organiza sus propias conferencias. Las conferencias que tradicionalmente han sido más populares entre los responsables, colaboradores y usuarios de NumPy son la serie de conferencias de SciPy y PyData: - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) - [SciPy Latinoamérica](https://www.scipyla.org) - [SciPy India](https://scipy.in) - [SciPy Japan](https://conference.scipy.org) - [Conferencias PyData](https://pydata.org/event-schedule/) (de 15 a 20 eventos al año, repartidos entre muchos países) Muchas de estas conferencias incluyen tutoriales y/o sprints que cubren NumPy, en donde puedes aprender cómo contribuir a Numpy o proyectos de código abierto relacionados. ## Únete a la comunidad NumPy Para prosperar, el proyecto NumPy necesita tu experiencia y entusiasmo. ¿No sabes programar? ¡No es un problema! Hay muchas maneras de contribuir a NumPy. Si te interesa colaborar en NumPy (¡hurra!) te recomendamos que visites nuestra página [Contribuir](/contribute). No dudes en pasar a saludarnos en uno de nuestros encuentros de la comunidad. Para enterarte del próximo, consulta nuestro calendario de eventos [aquí](https://scientific-python.org/calendars/).
numpy/numpy.org
0b5bdfbdbe5ad30870eeb20494fdb19c263f2101
Add translations team (#806)
diff --git a/content/en/teams/index.md b/content/en/teams/index.md index 1baaaa3..f5be9df 100644 --- a/content/en/teams/index.md +++ b/content/en/teams/index.md @@ -1,36 +1,40 @@ --- title: NumPy Teams sidebar: false --- We are an international team on a mission to support scientific and research communities worldwide by building quality, open-source software. [Join us](/contribute)! ### Maintainers {{< grid file="maintainers.toml" columns="2 3 4 5" />}} ### Docs team {{< grid file="docs-team.toml" columns="2 3 4 5" />}} ### Web team {{< grid file="web-team.toml" columns="2 3 4 5" />}} ### Triage team {{< grid file="triage-team.toml" columns="2 3 4 5" />}} ### Survey team {{< grid file="survey-team.toml" columns="2 3 4 5" />}} +### Translations team + +{{< grid file="translations-team.toml" columns="2 3 4 5" />}} + ### Emeritus maintainers {{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}} # Governance For the list of the Steering Council members, please see [here](https://numpy.org/about/). diff --git a/content/en/teams/translations-team.toml b/content/en/teams/translations-team.toml new file mode 100644 index 0000000..d0b025a --- /dev/null +++ b/content/en/teams/translations-team.toml @@ -0,0 +1,99 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4"' +alt = 'Avatar of Melissa Weber Mendonça' +{{< /image >}} +Melissa Weber Mendonça''' +link = 'https://github.com/melissawm' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Avatar of Juan Pablo Duque' +{{< /image >}} +Juan Pablo Duque (@juanpabloduqueo)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Avatar of Yeimi' +{{< /image >}} +Yeimi (@yeimiyaz)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Avatar of Atsushi Sakai' +{{< /image >}} +Atsushi Sakai (@AtsushiSakai)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Avatar of Getúlio Silva' +{{< /image >}} +Getúlio Silva (@getuliosilva)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Oriol Abril-Pla' +{{< /image >}} +Oriol Abril-Pla (@OriolAbril)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Avatar of @julio' +{{< /image >}} +@julio''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Avatar of Ali Faraji' +{{< /image >}} +Ali Faraji (@ali)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Avatar of Saeed Foroutan' +{{< /image >}} +Saeed Foroutan (@SaeedForoutan)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' +alt = 'Avatar of @pyjavo' +{{< /image >}} +@pyjavo''' +link = 'https://scientific-python.crowdin.com'
numpy/numpy.org
7303a797b92d1c4784e7448b30aa1b785276d232
Use Python 3.13 on netlify
diff --git a/netlify.toml b/netlify.toml index cb211b3..18d4ef1 100644 --- a/netlify.toml +++ b/netlify.toml @@ -1,31 +1,31 @@ # Settings in the [build] context are global and are applied to all contexts # unless otherwise overridden by more specific contexts. [build.environment] - PYTHON_VERSION = "3.8" # netlify currently only support 2.7 and 3.8 + PYTHON_VERSION = "3.13" HUGO_VERSION = "0.134.3" DART_SASS_VERSION = "1.77.5" DART_SASS_URL = "https://github.com/sass/dart-sass/releases/download/" [build] base = "/" publish = "public" command = """\ export DART_SASS_TARBALL="dart-sass-${DART_SASS_VERSION}-linux-x64.tar.gz" && \ curl -LJO ${DART_SASS_URL}/${DART_SASS_VERSION}/${DART_SASS_TARBALL} && \ tar -xf ${DART_SASS_TARBALL} && \ rm ${DART_SASS_TARBALL} && \ export PATH=/opt/build/repo/dart-sass:$PATH && \ pip install pyyaml && \ make html \ """ # Here is another way to define context specific environment variables. [context.deploy-preview.environment] NUMPYORG_WITH_TRANSLATIONS = "1" [[plugins]] package = "netlify-plugin-checklinks" [plugins.inputs] todoPatterns = [" public/pt/user-surveys", " public/ja/user-surveys"] skipPatterns = ["https://fonts.gstatic.com", "https://fonts.googleapis.com"]
numpy/numpy.org
e4acc20092f2d6350dedcb461bcb29bb1ce9d3c6
New translations news.md (Portuguese, Brazilian)
diff --git a/content/pt/news.md b/content/pt/news.md index cfb217b..3195ef5 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,322 +1,322 @@ --- title: Notícias sidebar: false newsHeader: "NumPy versão 1.26.0" date: 2023-09-16 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions `matvec` and `vecmat`, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### Lançado o NumPy versão 1.26.0 -_18 de agosto, 2024_ -- NumPy 2.1.0 fornece suporte para Python 3.13 e remove suporte para Python 3.9. Além das habituais correções de erros e suporte a Python atualizado, esta versão ajuda a trazer o NumPy de volta ao ciclo habitual de lançamento após o longo desenvolvimento da versão 2.0. Os destaques desta versão são: +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. Os destaques desta versão são: - Suporte ao Python 3.12.0. -- Suporte preliminar para Python 3.13 free threaded. -- Suporte para array-api 2023.12 standard. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. -As versões 3.10-3.13 do Python são suportadas por esta versão. +Python versions 3.10-3.13 are supported by this release. -### NumPy 2.0.0 lançada +### NumPy 2.0.0 released -_16 de junho, 2024_ -- NumPy 2.0.0 é a primeira grande versão desde 2006. É o resultado de 11 meses de desenvolvimento desde a última feature release e é o trabalho de 212 contribuidores espalhado por 1078 pull requests. Esta versão contém um grande número de novas funcionalidades interessantes, bem como mudanças nas APIs Python e C. As mudanças incluem quebras de compatibilidade que não puderam acontecer em uma versão regular menor - incluindo uma quebra na ABI, mudanças nas regras de promoção de tipo e mudanças na API que poderiam não estar emitindo alertas de fim de suporte nas versões 1.26.x. Documentos-chave, relacionados a como se adaptar às mudanças em NumPy 2.0, incluem: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- O [guia de migração NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- As [notas de lançamento da versão 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Issue de anúncio para atualizações de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -A postagem de blog ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) conta um pouco da história sobre como esta versão foi construída. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. -### Data de lançamento da NumPy 2.0: 16 de junho +### NumPy 2.0 release date: June 16 -_23 de maio, 2024_ -- Estamos animados em anunciar que planejamos lançar a NumPy 2.0 em 16 de junho de 2024. Este lançamento está em desenvolvimento há mais de um ano, e é o primeiro grande lançamento desde 2006. Importante, além de muitas funcionalidades novas e melhoria de desempenho, esta versão contém **quebras de compatibilidade** com a ABI e com as APIs Python e C. É provável que os pacotes downstream e o código de usuário final precisem ser adaptados - se você puder, por favor, verifique se o seu código funciona com NumPy `2.0.0rc2`. **Por favor, veja o seguinte para mais detalhes:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** -- O [guia de migração NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- As [notas de lançamento da versão 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Issue de anúncio para atualizações de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### Lançado o NumPy versão 1.26.0 _19 de dez, 2023_ -- O NumFOCUS se juntou ao PyCharm durante sua campanha de final de ano para oferecer 30% de desconto em licenças de PyCharm para novos usuários. Todas as receitas do primeiro ano das compras do PyCharm a partir de agora até 23 de dezembro, 2023 irão diretamente para os programas NumFOCUS. Use a URL única que permitirá rastrear as compras https://lp.jetbrains.com/support-data-science/ ou um código de cupom ISUPPORTDATASCIENCE  ### NumPy versão 1.24.0 -_17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. Os destaques desta versão são: +_16 de setembro de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) está disponível. Os destaques desta versão são: * Suport ao Python 3.12.0. * Compatibilidade com Cython 3.0.0. * Utilização do sistema Meson para compilação * Suport a SIMD atualizado * Melhorias para f2py, suporte a meson e bind(x) * Suporte à versão mais recente da biblioteca Accelerate BLAS/LAPACK A versão 1.26.0 é uma continuação da série de versões 1.25.x que marcam a transição para o sistema de compilação Meson e oferecem suporte preliminar para o Cython 3.0.0. Um total de 20 pessoas contribuíram para este lançamento e 59 pull requests foram incorporadas. As versões do Python suportadas por esta versão são 3.9-3.12. ### numpy.org agora está disponível em japonês e português _2 de agosto de 2023_ -- numpy.org agora está disponível em 2 idiomas adicionais: japonês e português. Isto não seria possível sem nossos voluntários dedicados: _Português:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japonês:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) O trabalho na infraestrutura de traduções é financiado pela CZI. No futuro, adoraríamos traduzir o site para mais línguas. Se você quiser ajudar, por favor entre em contato com o time de traduções do NumPy no Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Procure pelo canal de #translations.) (Procure pelo canal #translations) Também estamos organizando um time de tradutores que serão responsáveis por trabalhar na localização da documentação e conteúdo educacional para o ecossistema Scientific Python. Se esse trabalho te interessa, junte-se a nós no Discord do projeto Scientific Python: https://discord.gg/khWtqY6RKr. (Procure pelo canal #translation) ### NumPy versão 1.22.0 -_18 de dezembro de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) está agora disponível. Os destaques desta versão são: +_17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. Os destaques desta versão são: * Suporte para MUSL, agora existem rodas MUSL. * Suporte para o compilador Fujitsu C/C++. * Arrays de objetos agora são suportados em einsum. * Suporte para a multiplicação da matriz inplace (`@=`). A versão 1.25.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Também tem havido trabalho preparatório para a futura versão 2.0.0, resultando em um grande número de depreciações novas e expiradas. Um total de 148 pessoas contribuíram para este lançamento e 530 pull requests foram incorporadas. As versões do Python suportadas por esta versão são 3.9-3.11. ### Promovendo uma cultura inclusiva: Chamada de participação _10 de maio de 2023_ -- Promovendo uma Cultura Inclusiva: Chamada de Participação Como podemos ser melhores quando se trata de diversidade e de inclusão? Leia o relatório e descubra como colaborar [aqui](https://contributor-experience.org/docs/posts/dei-report/). ### Transição de liderança do time de documentação do NumPy _6 de janeiro de 2023_ –- Mukulika Pahari e Ross Barnowski são nomeados como lideres do time de documentação do NumPy, substituindo Melissa Mendonça. Agradecemos a Melissa por todas suas contribuições para a documentação oficial do NumPy e materiais educacionais, e Mukulika e Ross por aceitarem o desafio. ### NumPy versão 1.23.0 -_31 de dezembro de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) está agora disponível. Os destaques desta versão são: +_18 de dezembro de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) está agora disponível. Os destaques desta versão são: * Novas palavras-chave "dtype" e "casting" para funções que atuam com stacking. * Novas funcionalidades e correções do F2PY. * Muitas depreciações novas, confira. * Muitas depreciações expiradas. A versão 1.24.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Há um grande número de depreciações novas e expiradas devido a mudanças na promoção de dtypes e limpezas no código. É o trabalho de 177 contribuidores espalhados em 444 pull requests. As versões suportadas do Python são 3.8-3.11. ### NumPy versão 1.19.0 -_22 de junho de 2022_ -- O [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) está disponível. Os destaques desta versão são: +_31 de dezembro de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) está agora disponível. Os destaques desta versão são: * Implementação de `loadtxt` em C, melhorando muito seu desempenho. * Exposição do DLPack ao nível de Python para facilitar a troca de dados. * Mudanças na promoção e comparações de dtypes estruturados. * Melhorias no f2py. A versão 1.23.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade de execução, na documentação e na expiração de depreciações. É o trabalho de 151 contribuidores espalhados em 494 pull requests. As versões do Python suportadas por esta versão 3.8-3.10. Python 3.11 será suportado quando chegar na etapa rc. ### Pesquisa NumFOCUS DEI: chamada para participação _13 de abril de 2022_ -- O NumPy está trabalhando com a [NumFOCUS](http://numfocus.org/) em um [projeto de pesquisa](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado pela [Gordon & Betty Moore Foundation](https://www.moore.org/) para entender as barreiras à participação que contribuidores, especialmente aqueles de grupos historicamente subrepresentados, enfrentam na comunidade open source. A equipe da pesquisa gostaria de falar com novos colaboradores, desenvolvedores e mantenedores, e aqueles que contribuíram no passado sobre suas experiências contribuindo para o NumPy. **Quer compartilhar suas experiências?** Por favor, preencha este breve formulário: ["Participant Interest form"](https://numfocus.typeform.com/to/WBWVJSqe) que contém informações adicionais sobre os objetivos da pesquisa, privacidade e considerações de confidencialidade. Sua participação será valiosa para o crescimento e sustentabilidade de comunidades de software open source diversas e inclusivas. Os participantes aceitos participarão de uma entrevista de 30 minutos com um membro da equipe de pesquisa. ### NumPy versão 1.20.0 _23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) está disponível. Os destaques desta versão são: * Anotações de tipo do namespace principal estão praticamente completas. Ainda há trabalho a se fazer no upstream, mas a maior parte do trabalho está feita. Esta é provavelmente a melhoria mais visível para os usuários nesta versão. * Uma versão preliminar da proposta do [array API Standard](https://data-apis.org/array-api/latest/) está disponível (veja [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este é um passo na criação de uma coleção padrão de funções que podem ser compartilhadas entre bibliotecas como CuPy e JAX. * NumPy agora tem um backend de DLPack. DLPack fornece um formato comum de compartilhamento para dados de arrays (tensores). * Novos métodos para `quantile`, `percentile`, e funções relacionadas. Os novos métodos fornecem um conjunto completo dos métodos comumente encontrados na literatura. * As funções universais foram refatoradas para implementar a maior parte da [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Isso também desbloqueia a capacidade de experimentar a futura API DType. * Um novo alocador de memória configurável para uso pelos projetos downstream. NumPy 1.22.0 é uma versão importante com o trabalho de 153 contribuidores espalhados por mais de 609 pull requests. As versões do Python suportadas por esta versão são 3.8-3.10. ### Promovendo uma cultura inclusiva no ecossistema científico de Python _31 de agosto de 2021_ -- Estamos felizes em anunciar que a Chan Zuckerberg Initiative [vai financiar](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) um projeto para apoiar a integração, inclusão, e retenção de pessoas de grupos marginalizados historicamente em projetos científicos em Python, e para estruturalmente melhorar a dinâmica das comunidades para o NumPy, SciPy, Matplotlib, e Pandas. Como parte do programa [CZI's Essential Open Source Software for Science](https://chanzuckerberg.com/eoss/), esse [financiamento adicional para diversidade e inclusão](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) vai apoiar a criação de posições de Contributor Experience Lead para identificar, documentar e implementar práticas para fomentar comunidades open source inclusivas. Este projeto será liderado por Melissa Mendonça (NumPy), com apoio adicional de Ralf Gommers (NumPy, SciPy), Hannah Aizenman e Thomas Caswell (Matplotlib), Matt Haberland (SciPy), e Joris Van den Bossche (Pandas). Esse é um projeto ambicioso que visa descobrir e implementar atividades que devem estruturalmente melhorar a dinâmica da comunidade de nossos projetos. Ao criar essas novas funções entre projetos, esperamos introduzir um novo modelo de colaboração às comunidades de Python científico, permitir que o trabalho de construção da comunidade no ecossistema seja feito de forma mais eficiente e com maiores resultados. Também esperamos desenvolver uma imagem mais clara do que funciona e o que não funciona em nossos projetos para engajar e reter novos colaboradores, especialmente de grupos historicamente sub-representados. Finalmente, planejamos produzir relatórios detalhados sobre as ações executadas, explicando como eles afetaram nossos projetos em termos de representação e interação com nossas comunidades. O projeto de dois anos deverá começar em novembro de 2021 e estamos animados para ver os resultados deste trabalho! [Você pode ler a proposta completa aqui](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### Pesquisa NumPy 2021 _12 de julho de 2021_ -- Nós do NumPy acreditamos no poder da nossa comunidade. 1,236 usuários do NumPy de 75 países participaram da nossa primeira pesquisa ano passado. Os resultados da pesquisa nos ajudaram a compreender muito bem o que devemos fazer pelos 12 meses seguintes. Chegou a hora de fazer outra pesquisa e estamos contando com você novamente. Vai levar cerca de 15 minutos do seu tempo. Além de Inglês, o questionário de pesquisa está disponível em 8 idiomas adicionais: Bangla, Francês, Hindi, Japonês, Mandarim, Português, Russo e Espanhol. Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### NumPy versão 1.19.0 -_16 de setembro de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) está disponível. Os destaques desta versão são: +_22 de junho de 2022_ -- O [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) está disponível. Os destaques desta versão são: - a continuação do trabalho com SIMD para suportar mais funções e plataformas, - trabalho inicial na infraestrutura e conversão de novos dtypes, - wheels universal2 para Python 3.8 e Python 3.9 no Mac, - melhorias na documentação, - melhorias nas anotações de tipos, - novo bitgenerator `PCG64DXSM` para números aleatórios. Esta versão do NumPy é o resultado de 581 pull requests aceitos, a partir das contribuições de 175 pessoas. As versões do Python suportadas por esta versão são 3.7-3.9; o suporte para o Python 3.10 será adicionado após o lançamento do Python 3.10. ### Resultados da pesquisa NumPy 2020 _22 de junho de 2021_ -- Em 2020, o time de pesquisas NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, realizou a primeira pesquisa oficial sobre a comunidade NumPy. Encontre os resultados da pesquisa aqui: https://numpy.org/user-survey-2020/. ### NumPy versão 1.20.0 _30 de janeiro de 2021_ -- O [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) está disponível. Este é o maior lançamento do NumPy até hoje, graças a mais de 180 colaboradores. As duas novidades mais emocionantes são: - Anotações de tipos para grandes partes do NumPy, e um novo submódulo `numpy.typing` contendo aliases `ArrayLike` e `DtypeLike` que usuários e bibliotecas downstream podem usar quando quiserem adicionar anotações de tipos em seu próprio código. - Otimizações de compilação SIMD multi-plataforma, com suporte para instruções x86 (SSE, AVX), ARM64 (Neon) e PowerPC (VSX). Isso rendeu melhorias significativas de desempenho para muitas funções (exemplos: [sen/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversidade no projeto NumPy _20 de setembro de 2020_ -- Escrevemos uma [declaração sobre o estado da diversidade e inclusão no projeto NumPy e discussões em redes sociais sobre isso.](/diversity_sep2020). ### Primeiro artigo oficial do NumPy publicado na Nature! _16 de setembro de 2020_ -- Temos o prazer de anunciar a publicação do [primeiro artigo oficial do NumPy](https://www.nature.com/articles/s41586-020-2649-2) como um artigo de revisão na Nature. Isso ocorre 14 anos após o lançamento do NumPy 1.0. O artigo abrange aplicações e conceitos fundamentais da programação de matrizes, o rico ecossistema científico de Python construído em cima do NumPy, e os protocolos de array recentemente adicionados para facilitar a interoperabilidade com bibliotecas externas para computação com matrizes e tensores, como CuPy, Dask e JAX. ### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? _14 de setembro de 2020_ -- Python 3.9 será lançado em algumas semanas. Se você for quiser usar imediatamente a nova versão do Python, você pode ficar desapontado ao descobrir que o NumPy (e outros pacotes binários como SciPy) não terão wheels no dia do lançamento. É um grande esforço adaptar a infraestrutura de compilação a uma nova versão de Python e normalmente leva algumas semanas para que os pacotes apareçam no PyPI e no conda-forge. Em preparação para este evento, por favor, certifique-se de - atualizar seu `pip` para a versão 20.1 pelo menos para suportar `manylinux2010` e `manylinux2014` - usar [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) ou `--only-binary=:all:` para impedir `pip` de tentar compilar a partir do código fonte. ### NumPy versão 1.18.0 _10 de setembro de 2020_ -- O [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) está disponível. Essa última versão da série 1.19 corrige vários bugs, inclui preparações para o lançamento [do Cython 3](http://docs.cython.org/en/latest/src/changes.html) e fixa o setuptools para que o distutils continue funcionando enquanto modificações upstream estão sendo feitas. As wheels para aarch64 são compiladas com manylinux2014 mais recente que conserta um problema com distribuições linux diferentes. ### A primeira pesquisa NumPy está aqui! _2 de julho de 2020_ -- Esta pesquisa tem como objetivo guiar e definir prioridades para tomada de decisões sobre o desenvolvimento do NumPy como software e como comunidade. A pesquisa está disponível em mais 8 idiomas além do inglês: Bangla, Hindi, Japonês, Mandarim, Português, Russo, Espanhol e Francês. Ajude-nos a melhorar o NumPy respondendo à pesquisa [aqui](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### O NumPy tem um novo logo! _24 de junho de 2020_ -- NumPy agora tem um novo logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> O logotipo é uma versão moderna do antigo, com um design mais limpo. Obrigado à Isabela Presedo-Floyd por projetar o novo logotipo, bem como ao Travis Vaught pelo o logotipo antigo que nos serviu bem durante mais de 15 anos. ### NumPy versão 1.19.0 _20 de junho de 2020_ -- O NumPy 1.19.0 está disponível. Esta é a primeira versão sem suporte ao Python 2, portanto foi uma "versão de limpeza". A versão mínima de Python suportada agora é Python 3.6. Uma característica nova importante é que a infraestrutura de geração de números aleatórios que foi introduzida na NumPy 1.17.0 agora está acessível a partir do Cython. ### Aceitação no programa Season of Docs _11 de maio de 2020_ -- O NumPy foi aceito como uma das organizações mentoras do programa Google Season of Docs. Estamos animados com a oportunidade de trabalhar com um *technical writer* para melhorar a documentação do NumPy mais uma vez! Para mais detalhes, consulte [o site oficial do programa Season of Docs](https://developers.google.com/season-of-docs/) e nossa [página de ideias](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy versão 1.19.2 _22 de dezembro de 2019_ -- O NumPy 1.18.0 está disponível. Após as principais mudanças em 1.17.0, esta é uma versão de consolidação. É a última versão menor que suportará Python 3.5. Destaques dessa versão incluem a adição de uma infraestrutura básica para permitir o link com as bibliotecas BLAS e LAPACK em 64 bits durante a compilação, e uma nova C-API para `numpy.random`. Por favor, veja as [notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0) para mais detalhes. ### O NumPy recebe financiamento da Chan Zuckerberg Initiative _15 de novembro de 2019_ -- Estamos felizes em anunciar que o NumPy e a OpenBLAS, uma das dependências-chave do NumPy, receberam um auxílio conjunto de $195,000 da Chan Zuckerberg Initiative através do seu programa [Essential Open Source Software for Science](https://chanzuckerberg.com/eoss/) que apoia a manutenção, crescimento, desenvolvimento e envolvimento da comunidade em ferramentas de código aberto fundamentais para a ciência. Este auxílio será usado para aumentar os esforços de melhoria da documentação do NumPy, reformulação do site, desenvolvimento comunitário para melhor servir a nossa grande, e rapidamente crescente, base de usuários, assim como para garantir a sustentabilidade do projeto a longo prazo. Enquanto a equipe OpenBLAS se concentrará em tratar de um conjunto de questões técnicas fundamentais, em particular relacionadas a *thread-safety*, AVX-512, e *thread-local storage* (TLS), bem como melhorias algorítmicas na ReLAPACK (Recursive LAPACK) da qual a OpenBLAS depende. Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrados na [proposta completa de concessão de auxílio](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). O trabalho está agendado para começar no dia 1 de dezembro de 2019 e continuar pelos próximos 12 meses. <a name="releases"></a> ## Lançamentos Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. -- NumPy 2.1.2 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 de outubro de 2024_. -- NumPy 2.1.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 de setembro de 2024_. -- NumPy 2.0.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto de 2024_. -- NumPy 2.1.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. -- NumPy 2.0.1 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julho de 2024_. -- NumPy 2.0.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junho de 2024_. -- NumPy 1.26.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de fevereiro de 2024_. -- NumPy 1.26.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de janeiro de 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 de novembro de 2023_. - NumPy 1.26.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de outubro de 2023_. - NumPy 1.26.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de setembro de 2023_. - NumPy 1.25.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julho de 2023_. - NumPy 1.25.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julho de 2023_. - NumPy 1.24.4 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junho de 2023_. - NumPy 1.25.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junho de 2023_. - NumPy 1.24.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. - NumPy 1.24.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de fevereiro de 2023_. - NumPy 1.24.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de dezembro de 2022_. - NumPy 1.24.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de dezembro de 2022_. - NumPy 1.23.5 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 de novembro de 2022_. - NumPy 1.23.4 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de outubro de 2022_. - NumPy 1.23.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de setembro de 2022_. - NumPy 1.23.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. - NumPy 1.23.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julho de 2022_. - NumPy 1.23.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junho de 2022_. - NumPy 1.22.4 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de maio de 2022_. - NumPy 1.21.6 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. - NumPy 1.22.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de março de 2022_. - NumPy 1.22.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de fevereiro de 2022_. - NumPy 1.22.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de janeiro de 2022_. - NumPy 1.22.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de dezembro de 2021_. - NumPy 1.21.5 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de dezembro de 2021_. - NumPy 1.21.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junho de 2021_. - NumPy 1.20.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de maio de 2021_. - NumPy 1.20.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de janeiro de 2021_. - NumPy 1.19.5 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de janeiro de 2021_. - NumPy 1.19.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junho de 2020_. - NumPy 1.18.4 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de maio de 2020_. - NumPy 1.17.5 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de janeiro de 2020_. - NumPy 1.18.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de dezembro de 2019_. - NumPy 1.17.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julho de 2019_. - NumPy 1.16.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 de janeiro de 2019_. - NumPy 1.15.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julho de 2018_. - NumPy 1.14.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de janeiro de 2018_.
numpy/numpy.org
065094504da17eaa92b62daa6a99d693fcffe924
New translations news.md (Chinese Simplified)
diff --git a/content/zh/news.md b/content/zh/news.md index 8f47d8b..7a7aba2 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,322 +1,322 @@ --- -title: 社区快讯 +title: News sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-8 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions `matvec` and `vecmat`, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. 此次发布的重点是： +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. 此次发布的重点是： +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (`@=`). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released -_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. 此次发布的重点是： +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released -_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. 此次发布的重点是： +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of `loadtxt` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. -### NumPy 1.22.0 发布 +### Numpy 1.22.0 release -_2021年6月31日_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) 正式发布。 此次发布的重点是： +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: -* 主命名空间的注释类型基本上已完成。 上游是个移动目标，所以很可能会有进一步的改进，但是主要的 项工作已经完成。 这可能是本次发布中用户最可见的增强功能。 -* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). 这是创建一个可以在 CuPy 和 JAX 等库中使用的函数的标准收藏的一个步骤。 -* NumPy 现在有一个 DLPack 后端。 DLPack提供了一个数组 (tensor) 数据的通用交换格式。 -* `量化`, `百分比`以及相关函数的新方法。 新的 方法提供了一整套常见于 文献中的方法。 -* 通用函数已被重新考虑，以实现大多数的 [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html) 这也会解锁实验未来DType API的能力。 -* 一个新的可配置内存分配器，供下游项目使用。 +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. -NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. 此版本支持的 Python 版本是 3.8-3.10。 +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. -### 促进Python科学生态系统中的包容性文化 +### Advancing an inclusive culture in the scientific Python ecosystem -_8月31日， 2021_ — 我们很高兴宣布Chan Zuckerberg倡议 [授予赠款](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) 以支持历史上被边缘化群体的人在科学Python项目中的融入、包容和留存，并为NumPy、SciPy、Matplotlib和Pandas的社区动态进行结构性改善。 +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. -作为 [CZI 基本开放源码科学程序](https://chanzuckerberg.com/eoss/)的一部分， 这 个[多样性 & 包容性补充赠款](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) 将支持创建专门的负责人职位，以确定、记录和实施促进包容性开源社区的实践。 这个项目将由Melissa Mendoncstima (NumPy) 领导，由Ralf Gommers (NumPy, SciPy) Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas)， 提供 额外的辅导和指导 +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). -这是一个雄心勃勃的项目，旨在发现和执行 应该从结构上改善我们项目的社区动态的活动。 通过 建立这些新的跨项目角色，我们希望在Scientific Python社区引进一个新的 协作模型。 使生态系统中的 社区建设工作能够更有效地开展， 取得更大的成果。 我们还希望在项目中了解什么有效，什么无效，以吸引和留住来自历史上未被代表的群体的新贡献者，建立更清晰的认知。 最后，我们计划制作详细的报告，说明我们采取的措施如何在代表性和与社区互动方面对我们的项目产生影响。 +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. -这个为期两年的项目预计将于2021年11月开始，我们很期待看到这项工作的成果! [您可以在这里阅读完整的提案](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063)。 +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### 2021 Numpy调查 +### 2021 NumPy survey -_2021年7月12日_ -- 我们相信NumPy社区的力量。 来自75个国家的1236 名用户参加了我们去年的首次调查。 调查结果使我们对今后12个月应该集中注意的问题有了很好的了解。 +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -现在是时候进行另一次调查了，我们将再度尋求您的合作。 这份调查将耗费您大约15分钟的时间。 除英文外，调查问卷还提供另外8种语文：孟加拉语、法语、印地语、日语、普通话、葡萄牙语、俄语和西班牙语。 +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. -点击链接开始：https://berkeley.qualtrics.com/jfe/form/SV_aOONjgcBXDSl4q。 +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### NumPy 1.21.0 发布 +### Numpy 1.21.0 release -_2021年6月23日_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 此次发布的重点是： +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: -- 继续开展SIMD工作，涵盖更多的功能和平台 -- 新dtype的基础和型态转换初步工作 -- 适用于Mac平台的Python 3.8和Python 3.9的universal2 wheels -- 改进文档 -- 改进注释 -- 新的 `PCG64DXSM` 位元生成器，用于生成随机数字 +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. -这个NumPy版本包含175人所贡献的581个合并请求。 此发布版本支持Python 3.7-3.9，将在Python 3.10发布后添加Python 3.10支持。 +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. -### 2020 Numpy调研结果结果 +### 2020 NumPy survey results -_2021年6月22日_ -- 在2020年, NumPy调研小组与密歇根大学和马里兰大学的学生和教职员工合作，进行了第一次官方NumPy社区调查。 在这里可以查看调研结果：https://numpy.org/user-survey-2020/。 +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### NumPy 1.20.0 发布 +### Numpy 1.20.0 release -_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢180+位贡献者。 最令人振奋的两个特点是： -- 为大部分Numpy代码做了类型注解，並添加了一个全新的`numpy.typing` 子模块，其中包含 `ArrayLike` 和 `DtypeLike`别名 ，使得用户和下游依赖库可以为自己的代码添加类型注解。 -- 为多个架构进行SIMD编译优化，其支持X86(SSE、AVX)、ARM64(Neon) 和PowerPC(VSX) 指令集。 大幅提高许多函数的性能(例如： [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194))。 +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### NumPy项目的多样性 +### Diversity in the NumPy project -_2020年9月20日_ -- 我们就NumPy项目的社交媒体、多样性和包容性的现状以及相关的讨论撰写了一份[声明](/diversity_sep2020)。 +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). -### NumPy官方第一次在Nature发表论文！ +### First official NumPy paper published in Nature! -_2020年9月16日_ - 我们高兴地宣布 [Numpy的第一篇官方论文](https://www.nature.com/articles/s41586-020-2649-2)刊登在Nature的评论文章。 这离NumPy 1.0发布已经过去了整整14年。 这篇论文涵盖了数组编程的应用和基本概念，基于NumPy构建的丰富科学Python生态系统，以及最近添加的数组协议，以促进与外部数组和张量库（如CuPy、Dask和JAX）的互操作性。 +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. -### Python 3.9 即将来临，新版本的NumPy 将在何时发布？ +### Python 3.9 is coming, when will NumPy release binary wheels? -_2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Python版本的早期采用者， 您可能会失望的发现NumPy(以及其他二进制软件包，如SciPy) 在Python新版发布当天还不会发布相应的版本。 构建兼容新的 Python 版本的基础设施需要付出重大努力，通常需要几周时间才能让新版本出现在 PyPI 和 conda-forge 上。 为了这次版本升级得以顺利进行，请确保： -- 将您的 `pip` 升级到 20.1 版本，至少要支持`manylinux2010` 和 `manylinux2014` -- 使用 [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) 或 `--only-binary=:all:` 选项来防止 `pip` 尝试从源码构建。 +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -### NumPy 1.19.2 发布 +### Numpy 1.19.2 release -_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 做准备，並固定设置工具以在上游修改正在进行时保持 distutils 工作。 Aarch64架构的安装包是用最新的 manylinux2014 版本构建的，它修复了 linux 发行版之间使用不同大小内存页的问题。 +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. -### 首次NumPy调研即将开始！ +### The inaugural NumPy survey is live! -_Jul 2, 2020_ - 本次调查旨在指导并确定 关于开发NumPy 作为软件和社区的决策重点。 除英文外，调查还提供了另外8种语言的版本：孟加拉语、印地语、日语、普通话、葡萄牙语、俄语、西班牙语和法语。 +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. -请帮助我们让 NumPy 变得更好，在[这里](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)参与调查。 +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy 有新标志了! +### NumPy has a new logo! -_2020年7月24日_ -- NumPy 现在有一个新的标志： +_Jun 24, 2020_ -- NumPy now has a new logo: -<img src="/images/logos/numpy_logo.svg" alt="NumPy 标志" title="新的 NumPy 标志" width=300> +<img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> -这个标志是对旧标志的现代化演绎，采用更加简洁的设计。 感谢Isabela Presedo-Floryd的设计方案， 同时感谢Travis Vaugh设计的旧图标为我们服务了15年以上。 +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -### NumPy 1.19.0 发布 +### NumPy 1.19.0 release -_2020年6月20日_ -- NumPy 1.19.0 正式发布。 这是第一个不支持Python 2的版本，因此它是一个“清理版本”。 目前支持的最低Python 版本是 Python 3.6。 本版本拥有一个重要的新特性，NumPy 1.17.0引进的随机数字生成基础模块现在可以通过Cython访问。 +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. -### 文档整改时段 +### Season of Docs acceptance -_2020年5月11日_ -- NumPy 已成为Google Season 文档项目之一。 我们很高兴看到有机会和技术写作者一起再次改进NumPy的技术文档！ 更多详情，请参考 [文档整改时段官方网站](https://developers.google.com/season-of-docs/) 和我们的 [意见页面](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)。 +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### NumPy 1.18.0 发布 +### NumPy 1.18.0 release -_2019年12月22日_ -- NumPy 1.18.0 正式发布。 在1.17.0发生重大变化后，这是一个合并版本。 这是最后一个支持 Python 3.5的小版本。 该版本的重要更新包括两个，添加了与64位 BLAS 和 LAPACK 库有关的底层更新， 添加 一个用于`numpy.random`的新C-API更新。 +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. -详情请看 [版本说明](https://github.com/numpy/numpy/releases/tag/v1.18.0)。 +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. -### NumPy 从Chan Zuckerberg Initiative获得了一笔捐款 +### NumPy receives a grant from the Chan Zuckerberg Initiative -_2019年11月15日_ -- 我们高兴地宣布NumPy和 OpenBLAS (Numpy的一个核心依赖库)已经收到一笔19,5000美元的联合赠款。 捐款来自于Chan Zuckerberg Initiative通过的[基础开源科学计算软件项目](https://chanzuckerberg.com/eoss/)，用来支持对科学发展起到关键作用的开源软件的维护、增长、开发和社区参与。 +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. -这笔赠款将用来加速改进NumPy文档、网站重构和社区开发，进而更好地为我们庞大和迅速增长的用户基础服务，并确保项目的长期可持续性。 OpenBLAS 团队将侧重于处理几个关键技术问题，特别是线程安全问题、AVX-512和 thread-local 存储(TLS) 问题，以及OpenBLAS 依赖的 ReLAPACK (递归的LAPACK) 算法改进。 +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. -若想查看更多关于捐款的倡议和交付件的详情，可在 [全额赠款提案](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) 中找到。 项目开始于2019年12月1日，今后12个月将持续运作下去。 +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> -## 版本发布 +## Releases -这是NumPy 版本列表，包含了对应版本发布说明的链接。 所有的 bug修复版本(即在 `x.y.z`格式版本号中只有 `z`改变)没有新功能；小版本更新(`y` 改变)有新功能。 +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. -- NumPy1.22.0 (<a>发行说明</a>) -- _2021年12月31日_. -- NumPy1.21.5 (<a>发行说明</a>) -- _2021年12月19日_. -- NumPy1.21.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年6月22日_. -- NumPy1.23.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年5月10日_. -- NumPy1.20.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021年1月30日_. -- NumPy1.19.5 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021年1月5日_. -- NumPy1.19.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020年6月20日_. -- NumPy1.18.4 (<a>发行说明</a>) -- _2020年5月3日_. -- NumPy1.17.5 (<a>发行说明</a>) -- _2020年1月1日_. -- NumPy1.18.0 (<a>发行说明</a>) -- _2019年12月22日_. -- NumPy1.17.0 (<a>发行说明</a>) -- _2019年7月26日_. -- NumPy1.16.0 (<a>发行说明</a>) -- _2019年1月14日_. -- NumPy1.15.0 (<a>发行说明</a>) -- _2018年7月23日_. -- NumPy1.14.0 (<a>发行说明</a>) -- _2018年1月7日_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
28379a9d888fbb71aaca918294f8fa500d3f9a9b
New translations news.md (Korean)
diff --git a/content/ko/news.md b/content/ko/news.md index 4fb6664..0808edd 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,322 +1,322 @@ --- title: 소식 sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2023-09-16 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions `matvec` and `vecmat`, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0 출시일: 6월 16일 _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- [NumPy 2.0 이주 가이드](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- [2.0.0 릴리즈 노트](https://numpy.org/devdocs/release/2.0.0-notes.html) -- 상태 업데이트 공지용 이슈: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 출시일: 6월 16일 _2024년 5월 23일_ -- NumPy 2.0이 2024년 6월 16일에 출시할 예정이라는 소식을 발표하게 되어 기쁩니다. 이 릴리즈를 제작하는 데 1년이 넘게 걸렸고, 2006년 이후 첫 번째 메인 릴리즈입니다. 중요한 건 많은 기능과 성능 개선 외에도, ABI와 Python, C API에 대한 **획기적인 변화**를 이뤄냈다는 것입니다. 아마 의존하는 패키지와 최종 사용자의 코드를 수정해야 할 겁니다. 가능하다면 코드가 `2.0.0rc2`에서 잘 작동하는지 검증해 주세요. **자세한 내용은 아래 항목들을 확인해 주세요.** - [NumPy 2.0 이주 가이드](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 릴리즈 노트](https://numpy.org/devdocs/release/2.0.0-notes.html) - 상태 업데이트 공지용 이슈: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumPy 1.26.0 출시 _2023년 12월 19_ -- NumFOCUS에서 연말 캠페인 기간 동안 PyCharm과 협력해 최초 PyCharm 이용자의 라이선스를 30% 할인된 가격에 제공했습니다. 지금부터 2023년 12월 23일까지 PyCharm 구매로 발생한 모든 수익은 NumFOCUS 프로그램으로 직접 전달됩니다. 구매를 추적할 수 있는 고유 URL을 이용하거나: https://lp.jetbrains.com/support-data-science/ 쿠폰 코드를 사용하세요: ISUPPORTDATASCIENCE  ### NumPy 1.26.0 출시 _2023년 12월 16일_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: * 파이썬 3.12.0 지원 * Cython 3.0.0 호환 * Meson 빌드 시스템 사용 * 업데이트된 SIMD 지원 * f2py 수정, meson 및 bind(x) 지원 * 업데이트된 Accelerate BLAS/LAPACK 라이브러리 지원 NumPy 1.26.0 릴리스는 Meson 빌드 시스템으로의 전환과 Cython 3.0.0 지원을 표시하는 1.25.x 시리즈의 연장입니다. 총 20명의 사람들이 이 릴리스에 기여하였으며 59개의 풀 리퀘스트가 병합되었습니다. 본 릴리즈에서 지원하는 Python 버전은 3.3.9-3.12입니다. ### numpy.org은 이제 일본어와 포르투갈어로도 이용 가능합니다. _2023년 8월 2일_ - numpy.org은 이제 추가로 일본어와 포르투갈어로 이용 가능합니다. 이는 다음의 헌신적인 자원봉사자들의 노력 없이는 가능하지 않았을 것입니다: _포르투갈어_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _일본어_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) 번역 인프라에 대한 작업은 CZI로부터의 자금 지원을 받아 진행되었습니다. 나아가서 NumPy 웹사이트가 더 많은 언어로 번역되기를 바랍니다. 도움을 주시려면 다음 Slack 링크를 통해 NumPy Translations Team 에 연락을 주십시오: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (#translations 채널을 add 해주세요) 또한 과학적 파이썬 생태계 전반에서 문서 및 교육 콘텐츠를 지역화하는데 참여할 Translations Team을 구축하고 있습니다. 이에 흥미를 느낀다면 Scientific Python Discord에서 함께해 주세요: https://discord.gg/khWtqY6RKr. (#translation 채널을 찾아보세요) ### NumPy 1.25.0 출시 _2023년 6월 17일_ -- 이제 [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html)을 이용할 수 있습니다. 주요 기능들은 다음과 같습니다: * MUSL 지원, 이제 MUSL Wheel도 배포됩니다. * Fujitsu C/C++ 컴파일러 지원 * Einsum에서 객체 배열 지원 * Inplace 행렬 곱셈 (`@=`) 지원 NumPy 1.25.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업을 계속하고 있습니다. 미래의 NumPy 2.0.0을 위한 준비 작업도 있었는데, 이로 인해 수많은 기능들이 지원 종료 예정에 새로 포함되거나 완전히 만료되었습니다. 총 148명의 사람들이 이 릴리스에 기여하였으며 530개의 풀 리퀘스트가 병합되었습니다. 본 릴리즈에서 지원하는 Python 버전은 3.9-3.11입니다. ### 포용적인 문화 조성: 참여 요청 _2023년 5월 10일_ -- 포용적인 문화 조성: 참여 요청 다양성과 포용성의 측면에서 우리는 어떻게 더 나아질 수 있을까요? [여기](https://contributor-experience.org/docs/posts/dei-report/)에서 보고서를 읽고 함께 참여하는 방법을 알아보세요. ### NumPy 문서 팀 리더 변경 _2023년 1월 6일_ –- Mukulika Pahari, Ross Barnowski가 Melissa Mendonça를 대신해 새 NumPy 문서 팀 리더로 임명되었습니다. NumPy 공식 문서와 교육 자료에 기여한 Melissa와 한 걸음 더 나아간 Mukulika, Ross에게 감사를 표합니다. ### NumPy 1.24.0 출시 _2022년 12월 18일_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: * 스태킹 함수를 위한 새 "dtype" 및 "casting" 키워드. * 새 F2PY 기능 및 수정. * 수많은 지원 종료 예정 기능, 확인하세요. * 수많은 만료된 기능, NumPy 1.24.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업을 계속하고 있습니다. dtype의 형변환 및 정리를 변경하는 과정에서 수많은 기능들이 지원 종료 예정에 새로 포함되거나 완전히 만료되었습니다. 177명의 기여자가 생성한 444개의 풀 요청을 바탕으로 한 성과입니다. 지원하는 Python 버전은 3.8-3.11입니다. ### NumPy 1.23.0 출시 _2022년 6월 22일_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: * `loadtxt`를 C로 구현하여 성능이 크게 향상되었습니다. * 데이터 교환을 쉽게 하기 위해 Python 수준에서 DLPack을 개방합니다. * 구조화된 dtype의 형변환 및 비교 방법을 변경했습니다. * f2py를 개선했습니다. NumPy 1.23.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업, 오래된 지원 종료 예정 기능을 완전히 만료시키는 작업을 계속하고 있습니다. 151명의 기여자가 생성한 494개의 풀 요청을 바탕으로 한 성과입니다. 본 릴리즈에서 지원하는 Python 버전은 3.8-3.10입니다. Python 3.11은 rc 단계에 다다르면 지원할 예정입니다. ### NumFOCUS DEI 연구: 참여 요청 _2022년 4월 13일_ -- NumPy는 [NumFOCUS](http://numfocus.org/)와 협력하여 [Gordon & Betty Moore 재단](https://www.moore.org/)에서 기금을 제공하는 [연구 프로젝트](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)를 진행합니다. 본 연구는 오픈 소스 소프트웨어 커뮤니티에 기여자, 특히 역사적으로 과소평가된 집단의 기여자가 참여할 때 직면하는 장벽을 이해하는 것을 목표로 합니다. 연구팀은 새 기여자, 프로젝트 개발자 및 유지관리자, 과거에 기여한 사람들과 NumPy에 참여하고 기여한 경험에 대해 이야기하고자 합니다. **경험을 공유하고 싶으신가요?** 간단한 ["참여 희망" 양식](https://numfocus.typeform.com/to/WBWVJSqe)을 작성해주세요. 양식에서 연구 목표, 개인정보 보호, 기밀 유지 사항에 대한 추가 정보를 확인할 수 있습니다. 당신의 참여가 다양성과 포용성을 갖춘 오픈 소스 소프트웨어 커뮤니티의 성장과 지속 가능성에 도움이 될 것입니다. 승인된 참가자는 연구팀과 30분 면담을 진행하게 됩니다. ### Numpy 1.22.0 출시 _2021년 12월 31일_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: * 기본 네임스페이스에 대해 유형 주석의 지원을 거의 완료했습니다. 업스트림 코드는 항상 변하므로 추가 개선이 있을 수 있지만 주요 작업은 완료되었습니다. 아마도 이 릴리스에서 가장 체감되는 개선 사항일 것입니다. * 제안된 [배열 API 표준의 예비 버전](https://data-apis.org/array-api/latest/)이 제공됩니다([NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) 참조). 이는 CuPy 및 JAX와 같은 라이브러리에서 사용할 수 있는 표준 함수 모음을 만드는 단계입니다. * NumPy가 DLPack 백엔드로 구동됩니다. DLPack은 배열(텐서) 데이터에 대한 공통 교환 형식을 제공합니다. * `quantile`, `percentile` 관련 함수를 위한 새 메서드를 추가했습니다. 새 메서드를 이용해 문헌에서 일반적으로 쓰이는 처리를 진행할 수 있습니다. * 범용 함수가 대부분의 [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html)을 구현하도록 리팩터링되었습니다. 이를 통해 미래의 DType API를 실험할 수 있는 능력도 갖췄습니다. * 새 구성 가능한 메모리 할당자를 다운스트림 프로젝트에서 사용할 수 있습니다. NumPy 1.22.0은 153명의 기여자가 생성한 609개의 풀 요청을 바탕으로 만들어진 대형 릴리즈입니다. 본 릴리즈에서 지원하는 Python 버전은 3.8-3.10입니다. ### 과학 Python 생태계에서 포용적 문화 발전 _2021년 8월 31일_ -- Chan Zuckerberg Initiative가 과학적 Python 프로젝트에서 역사적으로 소외된 그룹의 사람들을 온보딩, 포함 및 유지하고 NumPy, SciPy, Matplotlib 그리고 Pandas 의 커뮤니티 역학을 구조적으로 개선하기 위한 [보조금을 수여](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/)했음을 발표하게 되어 기쁩니다. [CZI의 Essential Open Source Software for Science 프로그램](https://chanzuckerberg.com/eoss/)의 일환으로 이 [Diversity & 포함 추가 보조금](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)은 포괄적인 오픈 소스 커뮤니티를 육성하기 위한 관행을 식별, 문서화 및 구현하기 위한 전담 기여자 경험 리드 직책 생성을 지원합니다. 이 프로젝트는 Melissa Mendonça(NumPy) 님이 이끌고 Ralf Gommers(NumPy, SciPy), Hannah Aizenman, Thomas Caswell(Matplotlib), Matt Haberland(SciPy), Joris Van den Bossche(Pandas) 님이 추가 멘토링 및 지침을 제공합니다. 이것은 프로젝트의 커뮤니티 역학을 구조적으로 개선해야 하는 활동을 발견하고 구현하는 것을 목표로 하는 야심 찬 프로젝트입니다. 새로운 교차 프로젝트 역할을 설정함으로써 과학적 Python 커뮤니티에 새로운 협업 모델을 도입하여 생태계 내에서 커뮤니티 구축 작업을 보다 효율적으로 수행하고 더 큰 결과를 얻을 수 있을 것으로 기대됩니다. 또한 특히 역사적으로 과소대표된 집단의 새로운 기여자를 참여시키고 유지하기 위해, 프로젝트에서 효과적인 것과 그렇지 않은 것에 대한 명확한 그림을 구축할 것으로 기대합니다. 마지막으로, 시행된 조치에 대해 자세한 보고서를 작성하여 커뮤니티와의 대표 및 상호 작용 측면에서 프로젝트에 어떤 영향을 미쳤는지 설명할 계획입니다. 2개년 프로젝트가 2021년 11월부터 시작될 예정입니다. 프로젝트의 결과를 볼 날이 기대되네요! [여기에서 전체 제안서를 열람할 수 있습니다](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021년도 NumPy 설문조사 _2021년 7월 12일_ -- NumPy에서, 우리는 커뮤니티의 힘을 믿습니다. 작년에 75개국에서 1,236명의 NumPy 사용자가 첫 번째 설문조사에 참여했습니다. 설문 조사 결과를 통해 다음 12개월 동안 우리가 어떤 것에 집중해야 할지 아주 잘 이해할 수 있었습니다. 이제 또다른 설문 조사를 진행할 시간이고, 여러분의 도움이 다시 한 번 필요합니다. 완료하는 데 약 15분 정도 소요될 겁니다. 설문지는 영어 외에도 8개 국어로 제공됩니다: 벵골어, 프랑스어, 힌디어, 일본어, 중국 관화, 포르투갈어, 러시아어, 스페인어. 시작하려면 아래 링크를 눌러 주세요: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 출시 _2021년 9월 23일_ -- [NumPy 1.1.21](https://numpy.org/doc/stable/release/1.21.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: - 더 많은 기능과 플랫폼을 다루는 지속적인 SIMD 작업, - 새로운 dtype 인프라 및 캐스팅에 대한 초기 작업, - Mac의 Python 3.8 및 Python 3.9용 universal2 휠, - 문서화 향상, - 주석 향상, - 난수 생성에 이용되는 새 `PCG64DXSM` 비트 생성기. 이번 NumPy 릴리즈는 175명이 기여해주신 581개의 풀 리퀘스트가 합쳐진 결과입니다. 본 릴리즈에서 지원하는 Python 버전은 3.7-3.9입니다. Python 3.10은 Python 3.10 릴리즈 이후 지원할 예정입니다. ### 2020년도 NumPy 설문조사 결과 _2021년 6월 22일_ -- 2020년에, NumPy 조사 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. 여기서 조사 결과를 확인하세요: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 출시 _2021년 9월 30일_ -- [NumPy 1.1.20](https://numpy.org/doc/stable/release/1.20.0-notes.html)이 출시되었습니다. 역대 최대의 NumPy 릴리즈입니다. 180명이 넘는 기여자분들께 감사드립니다. 다음은 이번 출시에서 가장 흥미로운 두가지 기능들 입니다. - NumPy의 많은 부분에 대한 유형 주석 및 사용자와 다운스트림 라이브러리가 추가할 때 사용할 수 있는 `ArrayLike` 및 `DtypeLike` 별칭을 포함하는 새로운 `numpy.typing` 하위 모듈 자체 코드에 주석을 입력합니다. - x86(SSE, AVX), ARM64(Neon) 및 PowerPC(VSX) 명령을 지원하는 다중 플랫폼 SIMD 컴파일러 최적화 입니다. 이는 많은 함수들의 상당한 성능향상을 가져왔습니다 (예: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### NumPy 프로젝트 내 다양성 _2020년 9월 20일_ -- 우리는 [NumPy 프로젝트 안에서의 다양성과 포용성에 관한 소셜 미디어의 상태 및 토론에 대한 성명서를 작성했습니다](/diversity_sep2020). ### Nature에 첫 공식 NumPy 논문 발표! _2020년 9월 16일_ -- [NumPy에 대한 첫 번째 공식 논문](https://www.nature.com/articles/s41586-020-2649-2)이 Nature에 리뷰 기사로 게재되었음을 발표하게 되어 기쁩니다. NumPy 1.0이 나온 지 14년 만입니다. 이 백서에서는 배열 프로그래밍의 응용 프로그램 및 기본 개념, NumPy 위에 구축된 풍부한 과학적 Python 생태계, CuPy, Dask 및 JAX와 같은 외부 배열 및 텐서 라이브러리와의 상호 운용성을 촉진하기 위해 최근에 추가된 배열 프로토콜을 다룹니다. ### Python 3.9가 곧 출시하는데, NumPy는 바이너리 Wheel을 언제 출시합니까? _2020년 9월 14일_ -- Python 3.9가 몇 주 내로 출시될 것입니다. 만약 Python 얼리어답터라면, NumPy (그리고 SciPy 등 다른 바이너리 패키지) 가 릴리즈 시일에 바이너리 Wheel을 준비하지 못한다는 것을 알고 실망했을 수 있습니다. 새로운 Python 버전에 빌드 환경을 맞추는 것은 많은 노력을 요하고, 패키지가 PyPI 및 conda-forge에 배포되는 데에는 일반적으로 몇 주가 걸립니다. 출시를 대비하려면 아래 요건을 충족하도록 하십시오. - `pip` 버전을 최소 20.1로 업데이트하여 `manylinux2010` 및 `manylinux2014`를 지원하도록 합니다 - [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary)를 사용하거나 또는 `--only-binary=:all:`을 사용하여 `pip`가 소스에서 빌드하는 것을 막아주세요. ### NumPy 1.19.2 출시 _2020년 9월 10일_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html)이 출시되었습니다. 1.19 시리즈의 이 최신 릴리스는 몇 가지 버그를 수정하고 [다가오는 Cython 3.x 릴리스](http://docs.cython.org/en/latest/src/changes.html)를 준비하며 setuptools를 고정하여 업스트림 수정이 진행되는 동안 distutils가 계속 작동하도록 합니다. aarch64 휠은 다양한 Linux 배포판에서 사용되는 다양한 페이지 크기 문제를 해결하는 최신 manylinux2014 릴리스로 제작되었습니다. ### 최초의 NumPy 설문조사가 진행 중입니다! _2020년 7월 2일_ -- 본 설문조사는 소프트웨어 및 커뮤니티로서의 NumPy 개발에 대하여, 의사결정의 우선 순위를 안내하고 설정하기 위해 실시됩니다. 설문지는 영어 외에도 8개 국어로 제공됩니다: 벵골어, 프랑스어, 힌디어, 일본어, 중국 관화, 포르투갈어, 러시아어, 스페인어. NumPy를 개선하게 도와주시고 이를위해 설문조사에 참여해 주세요. [여기](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy에 새로운 로고가 생겼습니다! _2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. <img src="/images/logos/numpy_logo.svg" alt="NumPy 로고" title="새 NumPy 로고" width=300> 이전 로고를 깔끔하고 현대적으로 다시 디자인했습니다. 새 로고를 만들어 주신 Isabela Presedo-Floyd님께 감사드립니다. 또 15년이 넘는 기간 동안 저희가 사용했던 로고를 만들어 주신 Travis Vaught님께도 감사의 말씀을 드립니다. ### NumPy 1.19.0 출시 _2020년 6월 20일_ -- NumPy 1.19.0이 출시되었습니다. Python 2의 지원을 중단한 첫 릴리즈라서 "정리 릴리즈"라고도 불립니다. 이제 지원하는 Python 최소 버전은 3.6입니다. 중요한 새 기능을 꼽자면, NumPy 1.17.0에 도입된 난수 생성 인프라를 Cython에서 접근할 수 있게 되었다는 것입니다. ### Season of Docs 승인 _2020년 5월 11일_ -- NumPy가 Google Season of Docs 프로그램의 선도 조직으로 승인되었습니다. 테크니컬 라이터와 협력해서 NumPy 문서를 다시 한 번 개선할 수 있는 기회를 갖게 되어 좋습니다! 이상 자세한 내용은 [공식 문서 시즌 사이트](https://developers.google.com/season-of-docs/) 및 [아이디어 페이지](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) 를 참조하세요. ### NumPy 1.18.0 출시 _2019년 12월 22일_ -- NumPy 1.18.0이 출시되었습니다. 1.17.0에서의 주요 변경점을 통합하는 릴리즈입니다. 본 릴리즈는 Python 3.5를 지원하는 마지막 마이너 릴리즈입니다. 릴리즈의 주요 내용으로는, 64비트 BLAS 및 LAPACK 라이브러리와 연결하기 위한 환경 조성, `numpy.random`을 위한 새로운 C-API 등이 있습니다. 자세한 내용은 [출시 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)를 참조하세요. ### NumPy가 Chan Zuckerberg Initiative에서 보조금을 받았습니다 _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 OpenBLAS가 챈 저커버그 이니셔티브의 [과학 프로그램용 중요 오픈소스 소프트웨어](https://chanzuckerberg.com/eoss/) 지원을 통해 19만 5천 달러에 달하는 공동 보조금을 받았다는 소식을 전할 수 있어 기쁩니다. 이곳에서는 과학에 중요한 오픈소스 도구에 대해 유지 관리, 성장, 개발 및 커뮤니티 참여를 지원합니다. 이 보조금은 NumPy 문서, 웹사이트 재설계 및 커뮤니티 개발을 개선하여 빠르게 성장하는 대규모 사용자 기반에 더 나은 서비스를 제공하고 프로젝트의 장기적인 지속 가능성을 보장하는 데 사용될 것입니다. OpenBLAS 팀은 OpenBLAS가 의존하는 ReLAPACK(Recursive LAPACK) 의 알고리즘 개선뿐만 아니라 특히 스레드 안전성, AVX-512 및 스레드 로컬 스토리지(TLS) 문제와 같은 일련의 핵심 기술 문제를 해결하는 데 집중할 것입니다. 제안된 계획 및 결과물에 대한 자세한 내용은 [전체 보조금 제안](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167)에서 확인할 수 있습니다. 2019년 12월 1일부터 작업을 시작하여 다음 12개월 동안 진행할 예정입니다. <a name="releases"></a> ## 릴리즈 NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _ 2024년 2월 5일_. - NumPy 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2024년 1월 2일_. - NumPy 1.26.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023년 1월 2일_. - NumPy 1.26.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023년 10월 14일_. - NumPy 1.26.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023년 16월 9일_. - NumPy 1.25.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023년 7월 31일_. - NumPy 1.25.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _2023년 7월 8일_. - NumPy 1.24.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _2023년 6월 26일_. - NumPy 1.25.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _2023년 6월 17일_. - NumPy 1.24.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _2023년 4월 22일_. - NumPy 1.24.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _2023년 2월 5일_. - NumPy 1.24.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _2022년 12월 26일_. - NumPy 1.24.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _2022년 12월 18일_. - NumPy 1.23.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _2022년 11월 19일_. - NumPy 1.23.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _2022년 10월 12일_. - NumPy 1.23.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _2022년 9월 9일_. - NumPy 1.23.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _2022년 8월 14일_. - NumPy 1.23.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _2022년 7월 8일_. - NumPy 1.23.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _2022년 6월 22일_. - NumPy 1.22.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022년 5월 20일_. - NumPy 1.21.6 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022년 4월 12일_. - NumPy 1.22.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _2022년 3월 7일_. - NumPy 1.22.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _2022년 2월 3일_. - NumPy 1.22.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _2022년 1월 14일_. - NumPy 1.22.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _2021년 12월 31일_. - NumPy 1.21.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _2021년 12월 19일_. - NumPy 1.21.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021년 6월 22일_. - NumPy 1.20.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021년 5월 10일_. - NumPy 1.20.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021년 1월 30일_. - NumPy 1.19.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021년 1월 5일_. - NumPy 1.19.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020년 6월 20일_. - NumPy 1.18.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020년 5월 3일_. - NumPy 1.17.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020년 1월 1일_. - NumPy 1.18.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _2019년 12월 22일_. - NumPy 1.17.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _2019년 7월 26일_. - NumPy 1.16.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019년 1월 14일_. - NumPy 1.15.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018년 7월 23일_. - NumPy 1.14.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018년 1월 7일_.
numpy/numpy.org
06b683d66de11bfbf5a4c5c0c13493a8df007d9f
New translations news.md (Japanese)
diff --git a/content/ja/news.md b/content/ja/news.md index acd02d6..673f502 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,322 +1,322 @@ --- title: ニュース sidebar: false newsHeader: "NumPy 1.26.0 がリリースされました。" date: 2023-09-16 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions `matvec` and `vecmat`, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 1.26.0 がリリースされました。 -_2024 Aug, 2024_ -- Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。 +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Python 3.12.0 のサポート - 多くの期限切れの非推奨(Deprecation)の削除 -- Array-api 2023.12 標準のサポート +- Support for the array-api 2023.12 standard. -Python バージョン 3.10-3.13 か、このリリースでサポートされています。 +Python versions 3.10-3.13 are supported by this release. ### 多くの新しい非推奨(Deprecation)の追加 -_2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) -- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 1.25.0 リリース _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) - ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 _2023年12月19日_ -- NumFOCUSは、年末キャンペーンでPyCharmチームと協力し、PyCharmライセンスの初回購入に30%の割引を提供しています。 2023年12月23日までのPyCharm購入による1年目の収益は全てNumFOCUSのプログラムに直接寄付されます。 購入される方はこちらのURLか: https://lp.jetbrains.com/support-data-science/ こちらのクーポンコードを利用してください: ISUPPORTDATASCIENCE  ### NumPy 1.20.0 リリース -_2022年12月18日_ -- [Numpy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 +_2023年1月17日_ -- [Numpy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 * Python 3.12.0 のサポート * Cython 3.0.0 との互換性 * Mesonビルドシステムの利用 * SIMD サポートの改善 * f2py のバグ修正, meson と bind(x) のサポート * 更新された BLAS/LAPACK の高速化ライブラリのサポート Numpy 1.26.0 は 1.25 からの互換性を保持しています。Mesonビルドシステムへの移行とCython 3.0.0へのサポートが目的のリリースです。 合計20人がこのリリースに貢献し、59個のプルリクエストがマージされました。 このリリースでサポートされている Python のバージョンは3.9から 3.12 です。 ### numpy.orgが日本語とポルトガル語で利用可能になりました _2023年4月2日_ -- numpy.orgが2つの言語で利用可能になりました： 日本語とポルトガル語。 熱心なボランティアがいなければ、このプロジェクトは不可能でした： _ポルトガル語_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _日本語：_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) 翻訳インフラストラクチャに関するプロジェクトは、CZIからの資金援助でサポートされています。 今後も、NumPyのウェブサイトをより多くの言語に翻訳したいと思っています。 もし手伝える場合は、Slack上のNumPy翻訳チームに連絡をお願います: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (#translation チャンネルを探してください) (#translation チャンネルを探してください) また、Scientific Pythonエコシステム全体のドキュメントや教育コンテンツのローカライズに取り組む翻訳チームも 立ち上げています。 このプロジェクトにも興味がある場合は、是非Scientific Python Discordに参加してください: https://discord.gg/khWtqY6RKr. (#translation チャンネルを探してください) ### Numpy 1.23.0 リリース -_2022年1月22日_ -- [Numpy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 +_2022年12月18日_ -- [Numpy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 * MUSLのサポート。 MUSLのWheelが準備されました。 * 富士通のC/C++コンパイラサポート * einsum でオブジェクト配列がサポートされるようになりました. * 行列の置き換え(inplace)掛け算のサポート (`@=`). Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 将来の NumPy 2.0.0 に向けた準備作業も行われており、 多数の新規および期限切れの機能廃止が可能となってきています。 合計148人がこのリリースに貢献し、530個のプルリクエストが マージされました。 このリリースでサポートされている Python のバージョンは3.3.9 - 3.11 です。 ### インクルーシブな文化の育成: 参加の募集 _2023年5月10日_ -- インクルーシブ・カルチャーの育成: 参加募集 NumPyプロジェクトの多様性とインクルージョンに関して、我々はどのようなことを実施すればいいでしょうか？ 興味がある方はこちらの [レポート](https://contributor-experience.org/docs/posts/dei-report/) を読んで参加する方法を確認してください。 ### NumPy ドキュメンテーションチームのリーダーの変更 _2023年1月6日_ –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudioに代わるNumPyドキュメンテーションチームの新しいリーダーとして任命されました。 私たちは、MelissaにNumPyの公式ドキュメントと教育資料に対するすべての貢献に感謝し、MukulikaとRossに新しい役割にステップアップしてもらったことに感謝します。 ### NumPy 1.24.0 リリース -_2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) がリリースされました。 今回のリリースのハイライトは下記の通りです。 +_2022年1月22日_ -- [Numpy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 * スタッキング関数のための新しい"dtype"と"casting"キーワードの追加 * F2PYの新機能追加とバグ修正 * 多くの新しい非推奨(Deprecation)の追加 * 多くの期限切れの非推奨(Deprecation)の削除 Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 dtype のプロモーションとクリーンアップの変更により、多数の新規と期限切れの非推奨が存在しています。 今回のリリースは、444個のプルリクエストと177人のコントリビューターによるものです。 サポートされている Python のバージョンは 3.8-3.11 です。 ### Numpy 1.26.0 は 1.25 からの互換性を保持しています。 -_2021年12月31日_ -- [Numpy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 +_2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) がリリースされました。 今回のリリースのハイライトは下記の通りです。 * `loadtxt` がCで実装されたことによる、大幅なパフォーマンス向上 * より簡単なデータ交換のためのPythonレベルでのDLPackの公開 * 構造化されたdtypesのプロモーションと比較方法の変更 * f2pyの改善 Numpy 1.23. リリースでは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 今回のリリースは、494個のプルリクエストと151人のコントリビューターによるものです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 Python 3.11がrc ステージに到達すると Python 3.11 もサポートされます。 ### NumFOCUS DEI研究への参加募集 _2022年4月13日_ -- NumPyは、[NumFOCUS](http://numfocus.org/)と協力して、[ある研究プロジェクト](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)を進めており、これは[Gordon & Betty Moore Foundation](https://www.moore.org/)によって資金提供されています。 この研究チームは、新しい貢献者、プロジェクトの開発者およびメンテナー、そして過去に貢献した方々に、NumPyに参加し貢献した経験について話を聞きたいと考えています。 **あなたの経験を共有することに興味がありますか?** もし興味がある場合は、研究目標、プライバシー、および 守秘義務に関する追加情報が記載されている、この簡単な[参加者の興味](https://numfocus.typeform.com/to/WBWVJSqe)フォームに記入をお願いします。 多様で包括的なオープンソースソフトウェアコミュニティの 成長と持続可能性のために、このプロジェクトへのあなたの参加は非常に大きな価値があります。 参加を受け入れられた人は、研究チームメンバーと30分間のインタビューに参加することになります。 ### NumPy 1.19.2 リリース -_2023年9月16日_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)がリリースされました。 今回のリリースの目玉機能は次のとおりです。 +_2021年12月31日_ -- [Numpy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 * メインの名前空間の型アノテーションは基本的に完了しました。 上流のコードは常に変化するものなので、さらなる改良が必要でしょうが、大きな作業は終わったと考えています。 これはおそらく、今回のリリースで最も目に見える改良でしょう。 * 以前から提案されていた [array API 標準](https://data-apis.org/array-api/latest/) のベータ版が提供されています ( [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) を参照) 。 これは、CuPy や JAX などのライブラリで使用できる 関数の標準的なコレクションを作成するために必要なステップです。 * NumPy に DLPack バックエンドが追加されました。 DLPack は、配列(テンソル) データ用の共通のデータ変換フォーマットを提供します。 * `quantile`, `percentile`, および関連する関数に新しいメソッドが追加されました。 これらの新しいメソッドは、論文で一般的に見られる一通りの処理を提供します。 * ユニバーサル関数は、[NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html) の多くを実装するためにリファクタリングされました。 これにより将来の DType API の処理も可能にします。 * ダウンストリームのプロジェクトで使用するための新しい設定可能なメモリー・アロケーターが追加されました。 NumPy 1.22.0は、153人の貢献者が609のプルリクエストを作成した 非常に大きなリリースです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 ### 科学的なPythonエコシステムにおける包括的な文化の前進 _ 2021年8月31日_ -- この度、Chan Zuckerberg Initiativeより、科学的なPythonプロジェクトにおいて、歴史的に疎外されてきたグループの人々のオンボーディング、インクルージョン、リテンションを支援し、NumPy、SciPy、Matplotlib、Pandasのコミュニティダイナミクスを構造的に改善するための [ 助成金を授与されました ](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) ことをお知らせします。 [ CZIのEssential Open Source Software for Scienceプログラム ](https://chanzuckerberg.com/eoss/)の一環として、この[ Diversity & Inclusion補助金 ](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)は、開けたなオープンソースコミュニティを育成するためにやるべきことを特定したり、文書化したり、実施したりするためのコントリビュータ体験のリーダー専任職の創設を支援することになります。 このプロジェクトは、Melissa Mendonça (NumPy) が中心となって、下記の方々の追加のメンタリングとサポートにより実施されます。 Ralf Gommers (NumPy、SciPy)、Hannah AizenmanとThomas Caswell (Matplotlib)、Matt Haberland (SciPy)、そして Joris Van den Bossche (Pandas)。 このプロジェクトは私たちのOSSプロジェクトのコミュニティダイナミクスを構造的に改善する方法を発見し、実施することを目指す野心的なプロジェクトです。 このような複数のプロジェクトの横断的な役割を確立することで、Scientific Pythonコミュニティに新しいコラボレーションモデルを導入し、エコシステム内のコミュニティ構築作業をより効率的に、より大きな成果を生めるようにしたいと考えています。 特にこのプロジェクトにより、歴史的にこれまで代表的ではなかったグループからの新しいコントリビュータを引き付け、貢献を維持するために、何がうまくいき、何がうまくいかないかを、より明確に把握できるようになると期待しています。 最後に、実施したアクションについて詳細な報告書を作成し、プロジェクトの代表者やコミュニティとの交流の面で、プロジェクトにどのような影響を与えたかを説明する予定です。 2021年11月から2年間のプロジェクトが始まると予想されており、このプロジェクトの成果を楽しみにしています! このプロジェクトの提案書に関しては、[こちら](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063) から全文を読むことができます. ### 2021年度NumPyアンケート _2021年7月12日_ -- NumPy ではコミュニティの力を信じています。 昨年の第1回アンケートには、75カ国から1,236名のNumPyユーザーが参加してくれました。 この調査結果により、今後12ヶ月間、私たちがどのようなことに集中すべきかを、非常に良く理解することができました。 今年もアンケートの時間が来ました。もう一度アンケートへの回答をお願いいたします。 アンケートへの回答は15分ほどで終了します。 アンケートは英語以外にも、ベンガル語、フランス語、ヒンディー語、日本語、マンダリン、ポルトガル語、ロシア語、スペイン語の8ヶ国語に対応しています。 こちらのリンク先から、アンケートを始めることができます: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSL4q. ### Numpy 1.18.0 リリース -_2023年1月17日_ -- [Numpy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 +_2023年9月16日_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)がリリースされました。 今回のリリースの目玉機能は次のとおりです。 - より多くの機能やプラットフォームをカバーするためのSIMD関連の改善が実施されました。 - dtypeのための新しいインフラとキャストの準備 - Mac 版の Python 3.8 と Python 3.9 用 universal2 wheel - ドキュメントの改善 - アノテーションの改善 - 乱数生成用の新しい `PCG64DXSM` ビット生成機 今回のNumpy リリースは、175人による581件のプルリクエストのマージの結果です。 このリリースでサポートされている Python のバージョンは 3.7-3.9 です。 Python 3.10 がリリースされた後、Python 3.10 のサポートが追加されます。 ### 2020年度 NumPy アンケート結果 _2021年6月22日_ -- NumPyの調査チームは、2020年に ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらから確認できます。 https://numpy.org/user-survey-2020/ ### NumPy 1.19.2 リリース _2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) がリリースされました。 今回のリリースは180 人以上のコントリビューターのおかげで、これまでで最大の NumPyのリリースとなりました。 最も重要な2つの新機能は次のとおりです。 - NumPyの大部分のコードに型注釈が追加されました。 そして新しいサブモジュールである`numpy.typing`が追加されました。 このサブモジュールは`ArrayLike` や`DtypeLike`という型注釈のエイリアスが定義されており、これによりユーザーやダウンストリームのライブラリはこの型注釈を使うことができます。 - X86(SSE、AVX)、ARM64(Neon)、およびPowerPC (VSX) 命令をサポートするマルチプラットフォームSIMDコンパイラの最適化が実施されました。 これにより、多くの関数で大きく パフォーマンスが向上しました (例: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### NumPyプロジェクトの多様性 _2020年9月20日に_ 、私たちは[ NumPyプロジェクトにおけるダイバーシティやインクルージョンの状況や、ソーシャルメディア上での議論についての宣言 ](/diversity_sep2020)について書きました。 ### Natureに初の公式NumPy論文が掲載されました! _2020年9月16日_ -- NumPyに関する [ 最初の公式の論文 ](https://www.nature.com/articles/s41586-020-2649-2)がNatureに査読付き論文として掲載されました。 これはNumPy 1.0のリリースから14年後のことになりました。 この論文では、配列プログラミングのアプリケーションと基本的なコンセプト、NumPyの上に構築された様々な科学的Pythonエコシステム、そしてCuPy、Dask、JAXのような外部の配列およびテンソルライブラリとの相互運用を容易にするために最近追加された配列プロトコルについて説明しています。 ### Python 3.9のリリースに伴い、いつNumPyのバイナリwheelがリリースされるのですか？ _2020年9月14日_ -- Python 3.9 は数週間後にリリースされる予定です。 もしあなたが新しいPythonのバージョンをいち早く利用している場合、NumPy（およびSciPyのような他のパッケージ）がリリース当日にバイナリwheelを用意していないことを知ってがっかりしたかもしれませんね。 ビルド用のインフラを新しいPythonのバージョンに適応させるのは非常に大変な作業で、PyPIやconda-forgeにパッケージが掲載されるまでには通常数週間かかります。 今後のwheelのリリースに備えて、以下を確認してください。 - `pip` が`manylinux2010` と `manylinux2014` をサポートするためにpipを少なくともバージョン 20.1 に更新する。 - [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) または `--only-binary=:all:` を`pip`がソースからビルドしようとするのを防ぐために使用します。 ### NumPy 1.19.2 リリース _2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) がリリースされました。 この 1.19 シリーズの最新リリースでは、いくつかのバグが修正され、[ 来るべき Cython 3.xリリース ](http:/docs.cython.orgenlatestsrcchanges.html)への準備が行われ、アップストリームの修正が進行中の間も distutils の動作を維持するためのsetuptoolsのバージョンの固定が実施されています。 aarch64 wheelは最新のmanylinux2014リリースでビルドされており、異なるLinuxディストリビューションで使用される異なるページサイズの問題が修正されています。 ### 初めてのNumPyの調査が公開されました!! _2020年7月2日_ -- このアンケート調査は、NumPyにおける、ソフトウェアとしてとコミュニティの両方における意思決定の指針となり、優先順位を決定する役に立ちました。 この調査結果は英語以外のこれらの8つの言語で利用可能です: バングラ, ヒンディー語, 日本語, マンダリン, ポルトガル語, ロシア語, スペイン語とフランス語. NumPy をより良くするために、こちらの \[アンケート\](https://umdsurvey. umd. edu/jfe/form/SV_8bJrXjbhXf7saAl) に協力してもらえると助かります。 ### NumPy に新しいロゴができました! _2020年6月24日_ -- NumPyのロゴが新しくなりました: <img src="/images/logos/numpy_logo.svg" alt="NumPyのロゴ" title="新しいNumPyロゴ" width=300> 新しいロゴは、古いロゴに比べて、モダンでよりクリーンなデザインになりました。 新しいロゴをデザインしてくれたIsabela Presedo-Floydと、15年以上にわたって使用してきた旧ロゴをデザインしてくれたTravis Vaughtに感謝します。 ### NumPy 1.19.0 リリース _2020年6月20日_ -- NumPy 1.19.0 がリリースされました。 このバージョンは Python 2系のサポートがない最初のリリースであり、"クリーンアップ用のリリース" です。 サポートされている一番古いPython のバージョンは Python 3.6 になりました。 また、今回の重要な新機能はNumPy 1.17.0で導入された乱数生成用のインフラにCythonからアクセスできるようになったことです。 ### ドキュメント受諾期間 _2020年5月11日_ -- NumPyは、 Googleのシーズンオブドキュメントプログラムのメンター団体の1つとして選ばれました。 NumPy のドキュメントを改善するために、テクニカルライターと協力するこの機会を楽しみにしています! 詳細については、 [シーズンオブドキュメント公式サイト](https://developers.google.com/season-of-docs/) と [アイデアページ](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) をご覧ください。 ### NumPy 1.18.0 リリース _2019年12月22日_ -- NumPy 1.18.0 がリリースされました。 このリリースは、1.17.0での主要な変更の後の、まとめのようなリリースです。 Python 3.5 をサポートする最後のマイナーリリースになります。 今回のリリースでは、64ビットのBLASおよびLAPACKライブラリとリンクするためのインフラの追加や、`numpy.random`のための新しいC-APIの追加などが行われました。 詳細については、 [リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.0) を参照してください。 ### NumPyはChan Zuckerberg財団から助成金を受けました。 _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つであるOpenBLASが、Chan Zuckerberg財団の[Essential Open Source Software for Scienceプログラム](https:/chanzuckerberg.comeoss)を通じて、科学に不可欠なオープンソースツールのソフトウェアのメンテナンス、成長、開発、コミュニティへの参加などを支援する195,000ドルの共同助成金を獲得したことを発表しました。 この助成金は、Numpy ドキュメントやウェブサイトの再設計などの改善に向けた取り組みを促進するために使用されます。 大規模かつ急速に拡大するユーザーの体験をより良くし、プロジェクトの長期的な持続可能性を確保するためのコミュニティ開発を行っていきます。 OpenBLASチームは、技術的に非常に重要な問題である、スレッド安全性、AVX-512に対処することに注力します。 また、スレッドローカルストレージ(TLS) の問題や、OpenBLASが依存するReLAPACK(再帰的なLAPACK) のアルゴリズムの改善も実施します。 提案されたイニシアチブとその成果の詳細については、 [フルグラントプロポーザル](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) を参照してください。 この取り組みは2019年12月1日から始まり、今後12ヶ月間継続実施される予定です。 <a name="releases"></a> ## 過去のリリース こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. -- NumPy 2.1.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _2024年10月5日_. -- NumPy 2.1.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _2024年9月3日_. -- NumPy 2.0.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _2024年8月26日_. -- NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 1.22.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022年5月20日_. -- NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. -- NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. - NumPy 1.25.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023年7月31日_. - NumPy 1.25.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _2023年7月8日_. - NumPy 1.24.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _2023年6月26日_. - NumPy 1.25.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _2023年6月17日_. - NumPy 1.24.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _2023年4月22日_. - NumPy 1.24.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _2023年2月5日_. - NumPy 1.24.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _2022年12月26日_. - NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年4月19日_. - NumPy 1.23.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _2022年11月19日_. - NumPy 1.23.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _2022年10月12日_. - NumPy 1.23.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _2022年9月9日_. - NumPy 1.23.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _2022年8月14日_. - NumPy 1.23.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _2022年7月8日_. - NumPy 1.23.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _2022年6月22日_. - NumPy 1.22.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022年5月20日_. - NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022年4月12日_. - NumPy 1.22.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2022年3月7日_. - NumPy 1.22.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _2022年2月3日_. - NumPy 1.22.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _2022年1月14日_. - NumPy 1.22.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _2021年12月31日_. - NumPy 1.21.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _2021年12月19日_. - NumPy 1.21.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年6月22日_. - NumPy 1.20.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年5月10日_. - NumPy 1.20.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021年1月30日_. - NumPy 1.19.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021年1月5日_. - NumPy 1.19.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020年6月20日_. - NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年5月3日_. - NumPy 1.17.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020年1月1日_. - NumPy 1.18.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _2019年12月22日_. - NumPy 1.17.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _2019年7月26日_. - NumPy 1.16.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019年1月14日_. - NumPy 1.15.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018年7月23日_. - NumPy 1.14.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018年1月7日_.
numpy/numpy.org
645139f378e6bf0106b2bbbf394128fe1dabce9a
New translations news.md (Arabic)
diff --git a/content/ar/news.md b/content/ar/news.md index 08bd83d..7a7aba2 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,322 +1,322 @@ --- -title: الأخبار +title: News sidebar: false newsHeader: "NumPy 2.2.0 released!" date: 2024-12-8 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions `matvec` and `vecmat`, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (`@=`). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of `loadtxt` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new `PCG64DXSM` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> -## الإصدارات +## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
43f25c914d01f7ab96d1fff92177915fb36108a0
New translations news.md (Spanish)
diff --git a/content/es/news.md b/content/es/news.md index 209ea5b..dc73b05 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,322 +1,322 @@ --- -title: Noticias +title: News sidebar: false -newsHeader: "¡NumPy 2.0 ha sido lanzado!" -date: 2024-06-17 +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 --- ### NumPy 2.2.0 released _8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: * New functions `matvec` and `vecmat`, * Many improved annotations, * Improved support for the new StringDType, * Improved support for free threaded Python, * Fixes for f2py. This release supports Python versions 3.10-3.13. -### Lanzamiento de NumPy 2.1.0 +### NumPy 2.1.0 released -_18 de agosto 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. Además de las habituales correcciones de errores y soporte actualizado de Python, ayuda a que NumPy vuelva a su ciclo de publicación habitual después del extenso desarrollo de 2.0. Los aspectos más destacados son: +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: -- Soporte para Python 3.13. -- Soporte preliminar para Python 3.13 de hilos libres. -- Compatibilidad con la norma array-api 2023.12. +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. -Esta versión es compatible con las versiones 3.10-3.13 de Python. +Python versions 3.10-3.13 are supported by this release. -### Lanzamiento de NumPy 2.0.0 +### NumPy 2.0.0 released -_16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características interesantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían producirse en un lanzamiento menor regular, como una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que podrían no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Emisión de anuncios para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -La publicación ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. -### Fecha de lanzamiento de NumPy 2.0: 16 de junio +### NumPy 2.0 release date: June 16 -_23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puedes, por favor verifica que tu código funciona con NumPy `2.0.0rc2`. **Por favor, revisa lo siguiente para más detalles:** +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** -- La [Guía de migración de NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [notas de lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) -- Emisión de anuncios para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) -### Recaudación de fondos de fin de año de NumFOCUS -_19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Utiliza una URL única que te permitirá rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  -### NumPy 1.26.0 ha sido lanzado +### NumPy 1.26.0 released -_16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. The highlights of the release are: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. * Utilización del sistema de compilación Meson * Actualización del soporte de SIMD * Correcciones de f2py, meson y soporte de bind(x) * Soporte para la librería actualizada Accelerate BLAS/LAPACK La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la transición al sistema de compilación Meson y que provee soporte para Cython 3.0.0. Un total de 20 personas contribuyeron a esta versión y 59 solicitudes de cambios fueron fusionadas. Las versiones de Python compatibles con esta versión son 3.9-3.12. -### numpy.org ya está disponible en japonés y portugués +### numpy.org is now available in Japanese and Portuguese -_ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: -_Portugués:_ -* Melissa Weber Mendonça (melissawm) -* Precios Ricardo (ricardoprins) -* Getúlio Silva (getuliosilva) +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japonés:_ +_Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) -El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. +The work on the translation infrastructure is supported with funding from CZI. -De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) -### NumPy 1.25.0 ha sido lanzado +### NumPy 1.25.0 released -_17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: -* Soporte para MUSL, ahora hay ruedas MUSL. -* Soporte para el compilador de Fujitsu C/C++. -* Los arreglos de objetos ahora están soportadas en einsum. -* Soporte para la multiplicación de matrices in situ (`@=`). +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). -NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. -Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. +A total of 148 people contributed to this release and 530 pull requests were merged. -Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. +The Python versions supported by this release are 3.9-3.11. -### Fomentar una Cultura Inclusiva: Convocatoria de Participación +### Fostering an Inclusive Culture: Call for Participation -_10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). -### Transición en el liderazgo del equipo de documentación de NumPy +### NumPy documentation team leadership transition -_6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. -### Lanzamiento de NumPy 1.24.0 +### NumPy 1.24.0 released -_18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: -* Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. -* Nuevas características y correcciones de F2PY. -* Muchas nuevas obsolescencias, revísalas. -* Muchas obsolescencias caducadas, +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, -El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. -### NumPy 1.23.0 ha sido lanzado +### Numpy 1.23.0 released -_22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: -* Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. -* Exposición de DLPack a nivel Python para facilitar el intercambio de datos. -* Cambios a la promoción y comparación de dtypes estructurados. -* Mejoras a f2py. +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. -El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. -### Estudio de investigación NumFOCUS DEI: llamado a participar +### NumFOCUS DEI research study: call for participation -_13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. -**¿Estás interesado en compartir tus experiencias?** +**Interested in sharing your experiences?** -Por favor, completa este breve [formulario de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Tu participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. -### Lanzamiento de NumPy 1.22.0 +### Numpy 1.22.0 release -_31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: -* Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. Esta es probablemente la mejora más visible para el usuario en esta versión. -* Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. -* NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). -* Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. -* Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. -* Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. -NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. -### Promoviendo una cultura inclusiva en el ecosistema científico de Python +### Advancing an inclusive culture in the scientific Python ecosystem -_31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. -Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). -Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una idea más clara tanto de lo que funciona y lo que no en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. -Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puedes leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). -### Encuesta de NumPy de 2021 +### 2021 NumPy survey -_12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. -Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. -Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Lanzamiento de NumPy 1.21.0 +### Numpy 1.21.0 release -_23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. Los aspectos más destacados de esta versión son: -- trabajo SIMD continuo que cubre más funciones y plataformas, -- trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, -- universal2 wheels para Python 3.8 y Python 3.9 en Mac, -- documentación mejorada, -- anotaciones mejoradas, -- nuevo `PCG64DXSM` generador de bits para números aleatorios. +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. -Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. -### Resultados de la encuesta de NumPy de 2020 +### 2020 NumPy survey results -_22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### Lanzamiento de NumPy 1.20.0 +### Numpy 1.20.0 release -_30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más importantes son: -- Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. -- Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). -### Diversidad en el proyecto NumPy +### Diversity in the NumPy project -_20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). -### Primer artículo oficial de NumPy publicado en Nature! +### First official NumPy paper published in Nature! -_16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. -### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? +### Python 3.9 is coming, when will NumPy release binary wheels? -_14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de -- actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` -- utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. -### Lanzamiento de NumPy 1.19.2 +### Numpy 1.19.2 release -_10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las wheels para aarch64 están construidas con la última versión de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. -### La encuesta inaugural de NumPy ya está disponible! +### The inaugural NumPy survey is live! -_2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. -Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### ¡NumPy tiene un nuevo logo! +### NumPy has a new logo! -_24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: +_Jun 24, 2020_ -- NumPy now has a new logo: -<img src="/images/logos/numpy_logo.svg" alt="Logo de NumPy" title="El nuevo logo de NumPy" width=300> +<img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> -El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -### Lanzamiento de NumPy 1.19.0 +### NumPy 1.19.0 release -_20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. -### Aceptación a Season of Docs +### Season of Docs acceptance -_11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### Lanzamiento de NumPy 1.18.0 +### NumPy 1.18.0 release -_22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. -Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. -### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg +### NumPy receives a grant from the Chan Zuckerberg Initiative -_15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. -Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. -Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> -## Lanzamientos +## Releases -Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. -- NumPy 2.1.1 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 de septiembre 2024_. -- NumPy 2.0.2 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto 2024_. -- NumPy 2.1.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. -- NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. -- NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. -- NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. -- NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. -- NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. -- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. -- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. -- NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. -- NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. -- NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. -- NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. -- NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. -- NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. -- NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. -- NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. -- NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. -- NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. -- NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. -- NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. -- NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. -- NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. -- NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. -- NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. -- NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. -- NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. -- NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. -- NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. -- NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. -- NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. -- NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. -- NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. -- NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. -- NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. -- NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. -- NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. -- NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. -- NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. -- NumPy 1.16.0 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. -- NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. -- NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
ec9f5730972f435fd2898b3d4fb3a703ef74fc6c
New translations news.md (Hindi)
diff --git a/content/hi/news.md b/content/hi/news.md new file mode 100644 index 0000000..7a7aba2 --- /dev/null +++ b/content/hi/news.md @@ -0,0 +1,322 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 +--- + +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. + + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + +<img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. + + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + +<a name="releases"></a> + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
f64ad45d9c4800e5d75d551765e5a15d71d4c7fa
New translations news.md (Persian)
diff --git a/content/fa/news.md b/content/fa/news.md new file mode 100644 index 0000000..7a7aba2 --- /dev/null +++ b/content/fa/news.md @@ -0,0 +1,322 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 +--- + +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. + + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + +<img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. + + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + +<a name="releases"></a> + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
26434ad9e7e95032a24e6b683af1b0a67a233dc9
New translations news.md (Portuguese, Brazilian)
diff --git a/content/pt/news.md b/content/pt/news.md index e972a74..cfb217b 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,263 +1,322 @@ --- title: Notícias sidebar: false newsHeader: "NumPy versão 1.26.0" date: 2023-09-16 --- +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + ### Lançado o NumPy versão 1.26.0 -_16 de setembro de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) está disponível. Os destaques desta versão são: +_18 de agosto, 2024_ -- NumPy 2.1.0 fornece suporte para Python 3.13 e remove suporte para Python 3.9. Além das habituais correções de erros e suporte a Python atualizado, esta versão ajuda a trazer o NumPy de volta ao ciclo habitual de lançamento após o longo desenvolvimento da versão 2.0. Os destaques desta versão são: + +- Suporte ao Python 3.12.0. +- Suporte preliminar para Python 3.13 free threaded. +- Suporte para array-api 2023.12 standard. + +As versões 3.10-3.13 do Python são suportadas por esta versão. + + +### NumPy 2.0.0 lançada + +_16 de junho, 2024_ -- NumPy 2.0.0 é a primeira grande versão desde 2006. É o resultado de 11 meses de desenvolvimento desde a última feature release e é o trabalho de 212 contribuidores espalhado por 1078 pull requests. Esta versão contém um grande número de novas funcionalidades interessantes, bem como mudanças nas APIs Python e C. As mudanças incluem quebras de compatibilidade que não puderam acontecer em uma versão regular menor - incluindo uma quebra na ABI, mudanças nas regras de promoção de tipo e mudanças na API que poderiam não estar emitindo alertas de fim de suporte nas versões 1.26.x. Documentos-chave, relacionados a como se adaptar às mudanças em NumPy 2.0, incluem: + +- O [guia de migração NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- As [notas de lançamento da versão 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Issue de anúncio para atualizações de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +A postagem de blog ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) conta um pouco da história sobre como esta versão foi construída. + + +### Data de lançamento da NumPy 2.0: 16 de junho + +_23 de maio, 2024_ -- Estamos animados em anunciar que planejamos lançar a NumPy 2.0 em 16 de junho de 2024. Este lançamento está em desenvolvimento há mais de um ano, e é o primeiro grande lançamento desde 2006. Importante, além de muitas funcionalidades novas e melhoria de desempenho, esta versão contém **quebras de compatibilidade** com a ABI e com as APIs Python e C. É provável que os pacotes downstream e o código de usuário final precisem ser adaptados - se você puder, por favor, verifique se o seu código funciona com NumPy `2.0.0rc2`. **Por favor, veja o seguinte para mais detalhes:** + +- O [guia de migração NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- As [notas de lançamento da versão 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Issue de anúncio para atualizações de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### Lançado o NumPy versão 1.26.0 +_19 de dez, 2023_ -- O NumFOCUS se juntou ao PyCharm durante sua campanha de final de ano para oferecer 30% de desconto em licenças de PyCharm para novos usuários. Todas as receitas do primeiro ano das compras do PyCharm a partir de agora até 23 de dezembro, 2023 irão diretamente para os programas NumFOCUS. + +Use a URL única que permitirá rastrear as compras https://lp.jetbrains.com/support-data-science/ ou um código de cupom ISUPPORTDATASCIENCE  + +### NumPy versão 1.24.0 -* Suporte ao Python 3.12.0. +_17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. Os destaques desta versão são: + +* Suport ao Python 3.12.0. * Compatibilidade com Cython 3.0.0. * Utilização do sistema Meson para compilação * Suport a SIMD atualizado * Melhorias para f2py, suporte a meson e bind(x) * Suporte à versão mais recente da biblioteca Accelerate BLAS/LAPACK A versão 1.26.0 é uma continuação da série de versões 1.25.x que marcam a transição para o sistema de compilação Meson e oferecem suporte preliminar para o Cython 3.0.0. Um total de 20 pessoas contribuíram para este lançamento e 59 pull requests foram incorporadas. As versões do Python suportadas por esta versão são 3.9-3.12. ### numpy.org agora está disponível em japonês e português _2 de agosto de 2023_ -- numpy.org agora está disponível em 2 idiomas adicionais: japonês e português. Isto não seria possível sem nossos voluntários dedicados: _Português:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -Japonês: +_Japonês:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) O trabalho na infraestrutura de traduções é financiado pela CZI. -No futuro, adoraríamos traduzir o site para mais línguas. Se você quiser ajudar, por favor entre em contato com o time de traduções do NumPy no Slack: -https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Procure pelo canal #translations) -Também estamos organizando um time de tradutores que serão responsáveis por trabalhar na localização da documentação e conteúdo educacional para o ecossistema Scientific Python. Se esse trabalho te interessa, junte-se a nós no Discord do projeto Scientific Python: https://discord.gg/khWtqY6RKr. (Procure pelo canal #translation) +No futuro, adoraríamos traduzir o site para mais línguas. Se você quiser ajudar, por favor entre em contato com o time de traduções do NumPy no Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Procure pelo canal de #translations.) (Procure pelo canal #translations) Também estamos organizando um time de tradutores que serão responsáveis por trabalhar na localização da documentação e conteúdo educacional para o ecossistema Scientific Python. Se esse trabalho te interessa, junte-se a nós no Discord do projeto Scientific Python: https://discord.gg/khWtqY6RKr. (Procure pelo canal #translation) -_17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. Os destaques desta versão são: +### NumPy versão 1.22.0 + +_18 de dezembro de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) está agora disponível. Os destaques desta versão são: * Suporte para MUSL, agora existem rodas MUSL. * Suporte para o compilador Fujitsu C/C++. * Arrays de objetos agora são suportados em einsum. * Suporte para a multiplicação da matriz inplace (`@=`). A versão 1.25.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Também tem havido trabalho preparatório para a futura versão 2.0.0, resultando em um grande número de depreciações novas e expiradas. Um total de 148 pessoas contribuíram para este lançamento e 530 pull requests foram incorporadas. As versões do Python suportadas por esta versão são 3.9-3.11. ### Promovendo uma cultura inclusiva: Chamada de participação _10 de maio de 2023_ -- Promovendo uma Cultura Inclusiva: Chamada de Participação Como podemos ser melhores quando se trata de diversidade e de inclusão? Leia o relatório e descubra como colaborar [aqui](https://contributor-experience.org/docs/posts/dei-report/). ### Transição de liderança do time de documentação do NumPy _6 de janeiro de 2023_ –- Mukulika Pahari e Ross Barnowski são nomeados como lideres do time de documentação do NumPy, substituindo Melissa Mendonça. Agradecemos a Melissa por todas suas contribuições para a documentação oficial do NumPy e materiais educacionais, e Mukulika e Ross por aceitarem o desafio. -### NumPy versão 1.24.0 +### NumPy versão 1.23.0 -_18 de dezembro de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) está agora disponível. Os destaques desta versão são: +_31 de dezembro de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) está agora disponível. Os destaques desta versão são: * Novas palavras-chave "dtype" e "casting" para funções que atuam com stacking. * Novas funcionalidades e correções do F2PY. * Muitas depreciações novas, confira. * Muitas depreciações expiradas. A versão 1.24.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Há um grande número de depreciações novas e expiradas devido a mudanças na promoção de dtypes e limpezas no código. É o trabalho de 177 contribuidores espalhados em 444 pull requests. As versões suportadas do Python são 3.8-3.11. -### NumPy versão 1.23.0 +### NumPy versão 1.19.0 _22 de junho de 2022_ -- O [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) está disponível. Os destaques desta versão são: * Implementação de `loadtxt` em C, melhorando muito seu desempenho. * Exposição do DLPack ao nível de Python para facilitar a troca de dados. * Mudanças na promoção e comparações de dtypes estruturados. * Melhorias no f2py. A versão 1.23.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade de execução, na documentação e na expiração de depreciações. É o trabalho de 151 contribuidores espalhados em 494 pull requests. As versões do Python suportadas por esta versão 3.8-3.10. Python 3.11 será suportado quando chegar na etapa rc. ### Pesquisa NumFOCUS DEI: chamada para participação _13 de abril de 2022_ -- O NumPy está trabalhando com a [NumFOCUS](http://numfocus.org/) em um [projeto de pesquisa](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado pela [Gordon & Betty Moore Foundation](https://www.moore.org/) para entender as barreiras à participação que contribuidores, especialmente aqueles de grupos historicamente subrepresentados, enfrentam na comunidade open source. A equipe da pesquisa gostaria de falar com novos colaboradores, desenvolvedores e mantenedores, e aqueles que contribuíram no passado sobre suas experiências contribuindo para o NumPy. **Quer compartilhar suas experiências?** Por favor, preencha este breve formulário: ["Participant Interest form"](https://numfocus.typeform.com/to/WBWVJSqe) que contém informações adicionais sobre os objetivos da pesquisa, privacidade e considerações de confidencialidade. Sua participação será valiosa para o crescimento e sustentabilidade de comunidades de software open source diversas e inclusivas. Os participantes aceitos participarão de uma entrevista de 30 minutos com um membro da equipe de pesquisa. -### NumPy versão 1.22.0 +### NumPy versão 1.20.0 -_31 de dezembro de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) está agora disponível. Os destaques desta versão são: +_23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) está disponível. Os destaques desta versão são: * Anotações de tipo do namespace principal estão praticamente completas. Ainda há trabalho a se fazer no upstream, mas a maior parte do trabalho está feita. Esta é provavelmente a melhoria mais visível para os usuários nesta versão. * Uma versão preliminar da proposta do [array API Standard](https://data-apis.org/array-api/latest/) está disponível (veja [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este é um passo na criação de uma coleção padrão de funções que podem ser compartilhadas entre bibliotecas como CuPy e JAX. * NumPy agora tem um backend de DLPack. DLPack fornece um formato comum de compartilhamento para dados de arrays (tensores). * Novos métodos para `quantile`, `percentile`, e funções relacionadas. Os novos métodos fornecem um conjunto completo dos métodos comumente encontrados na literatura. * As funções universais foram refatoradas para implementar a maior parte da [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Isso também desbloqueia a capacidade de experimentar a futura API DType. * Um novo alocador de memória configurável para uso pelos projetos downstream. NumPy 1.22.0 é uma versão importante com o trabalho de 153 contribuidores espalhados por mais de 609 pull requests. As versões do Python suportadas por esta versão são 3.8-3.10. ### Promovendo uma cultura inclusiva no ecossistema científico de Python _31 de agosto de 2021_ -- Estamos felizes em anunciar que a Chan Zuckerberg Initiative [vai financiar](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) um projeto para apoiar a integração, inclusão, e retenção de pessoas de grupos marginalizados historicamente em projetos científicos em Python, e para estruturalmente melhorar a dinâmica das comunidades para o NumPy, SciPy, Matplotlib, e Pandas. Como parte do programa [CZI's Essential Open Source Software for Science](https://chanzuckerberg.com/eoss/), esse [financiamento adicional para diversidade e inclusão](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) vai apoiar a criação de posições de Contributor Experience Lead para identificar, documentar e implementar práticas para fomentar comunidades open source inclusivas. Este projeto será liderado por Melissa Mendonça (NumPy), com apoio adicional de Ralf Gommers (NumPy, SciPy), Hannah Aizenman e Thomas Caswell (Matplotlib), Matt Haberland (SciPy), e Joris Van den Bossche (Pandas). Esse é um projeto ambicioso que visa descobrir e implementar atividades que devem estruturalmente melhorar a dinâmica da comunidade de nossos projetos. Ao criar essas novas funções entre projetos, esperamos introduzir um novo modelo de colaboração às comunidades de Python científico, permitir que o trabalho de construção da comunidade no ecossistema seja feito de forma mais eficiente e com maiores resultados. Também esperamos desenvolver uma imagem mais clara do que funciona e o que não funciona em nossos projetos para engajar e reter novos colaboradores, especialmente de grupos historicamente sub-representados. Finalmente, planejamos produzir relatórios detalhados sobre as ações executadas, explicando como eles afetaram nossos projetos em termos de representação e interação com nossas comunidades. O projeto de dois anos deverá começar em novembro de 2021 e estamos animados para ver os resultados deste trabalho! [Você pode ler a proposta completa aqui](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### Pesquisa NumPy 2021 _12 de julho de 2021_ -- Nós do NumPy acreditamos no poder da nossa comunidade. 1,236 usuários do NumPy de 75 países participaram da nossa primeira pesquisa ano passado. Os resultados da pesquisa nos ajudaram a compreender muito bem o que devemos fazer pelos 12 meses seguintes. Chegou a hora de fazer outra pesquisa e estamos contando com você novamente. Vai levar cerca de 15 minutos do seu tempo. Além de Inglês, o questionário de pesquisa está disponível em 8 idiomas adicionais: Bangla, Francês, Hindi, Japonês, Mandarim, Português, Russo e Espanhol. Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### NumPy versão 1.19.0 -_23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) está disponível. Os destaques desta versão são: +_16 de setembro de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) está disponível. Os destaques desta versão são: - a continuação do trabalho com SIMD para suportar mais funções e plataformas, - trabalho inicial na infraestrutura e conversão de novos dtypes, - wheels universal2 para Python 3.8 e Python 3.9 no Mac, - melhorias na documentação, - melhorias nas anotações de tipos, - novo bitgenerator `PCG64DXSM` para números aleatórios. Esta versão do NumPy é o resultado de 581 pull requests aceitos, a partir das contribuições de 175 pessoas. As versões do Python suportadas por esta versão são 3.7-3.9; o suporte para o Python 3.10 será adicionado após o lançamento do Python 3.10. ### Resultados da pesquisa NumPy 2020 _22 de junho de 2021_ -- Em 2020, o time de pesquisas NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, realizou a primeira pesquisa oficial sobre a comunidade NumPy. Encontre os resultados da pesquisa aqui: https://numpy.org/user-survey-2020/. ### NumPy versão 1.20.0 _30 de janeiro de 2021_ -- O [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) está disponível. Este é o maior lançamento do NumPy até hoje, graças a mais de 180 colaboradores. As duas novidades mais emocionantes são: - Anotações de tipos para grandes partes do NumPy, e um novo submódulo `numpy.typing` contendo aliases `ArrayLike` e `DtypeLike` que usuários e bibliotecas downstream podem usar quando quiserem adicionar anotações de tipos em seu próprio código. - Otimizações de compilação SIMD multi-plataforma, com suporte para instruções x86 (SSE, AVX), ARM64 (Neon) e PowerPC (VSX). Isso rendeu melhorias significativas de desempenho para muitas funções (exemplos: [sen/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversidade no projeto NumPy _20 de setembro de 2020_ -- Escrevemos uma [declaração sobre o estado da diversidade e inclusão no projeto NumPy e discussões em redes sociais sobre isso.](/diversity_sep2020). ### Primeiro artigo oficial do NumPy publicado na Nature! _16 de setembro de 2020_ -- Temos o prazer de anunciar a publicação do [primeiro artigo oficial do NumPy](https://www.nature.com/articles/s41586-020-2649-2) como um artigo de revisão na Nature. Isso ocorre 14 anos após o lançamento do NumPy 1.0. O artigo abrange aplicações e conceitos fundamentais da programação de matrizes, o rico ecossistema científico de Python construído em cima do NumPy, e os protocolos de array recentemente adicionados para facilitar a interoperabilidade com bibliotecas externas para computação com matrizes e tensores, como CuPy, Dask e JAX. ### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? _14 de setembro de 2020_ -- Python 3.9 será lançado em algumas semanas. Se você for quiser usar imediatamente a nova versão do Python, você pode ficar desapontado ao descobrir que o NumPy (e outros pacotes binários como SciPy) não terão wheels no dia do lançamento. É um grande esforço adaptar a infraestrutura de compilação a uma nova versão de Python e normalmente leva algumas semanas para que os pacotes apareçam no PyPI e no conda-forge. Em preparação para este evento, por favor, certifique-se de - atualizar seu `pip` para a versão 20.1 pelo menos para suportar `manylinux2010` e `manylinux2014` - usar [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) ou `--only-binary=:all:` para impedir `pip` de tentar compilar a partir do código fonte. -### NumPy versão 1.19.2 +### NumPy versão 1.18.0 _10 de setembro de 2020_ -- O [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) está disponível. Essa última versão da série 1.19 corrige vários bugs, inclui preparações para o lançamento [do Cython 3](http://docs.cython.org/en/latest/src/changes.html) e fixa o setuptools para que o distutils continue funcionando enquanto modificações upstream estão sendo feitas. As wheels para aarch64 são compiladas com manylinux2014 mais recente que conserta um problema com distribuições linux diferentes. ### A primeira pesquisa NumPy está aqui! _2 de julho de 2020_ -- Esta pesquisa tem como objetivo guiar e definir prioridades para tomada de decisões sobre o desenvolvimento do NumPy como software e como comunidade. A pesquisa está disponível em mais 8 idiomas além do inglês: Bangla, Hindi, Japonês, Mandarim, Português, Russo, Espanhol e Francês. Ajude-nos a melhorar o NumPy respondendo à pesquisa [aqui](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### O NumPy tem um novo logo! _24 de junho de 2020_ -- NumPy agora tem um novo logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> O logotipo é uma versão moderna do antigo, com um design mais limpo. Obrigado à Isabela Presedo-Floyd por projetar o novo logotipo, bem como ao Travis Vaught pelo o logotipo antigo que nos serviu bem durante mais de 15 anos. ### NumPy versão 1.19.0 _20 de junho de 2020_ -- O NumPy 1.19.0 está disponível. Esta é a primeira versão sem suporte ao Python 2, portanto foi uma "versão de limpeza". A versão mínima de Python suportada agora é Python 3.6. Uma característica nova importante é que a infraestrutura de geração de números aleatórios que foi introduzida na NumPy 1.17.0 agora está acessível a partir do Cython. ### Aceitação no programa Season of Docs _11 de maio de 2020_ -- O NumPy foi aceito como uma das organizações mentoras do programa Google Season of Docs. Estamos animados com a oportunidade de trabalhar com um *technical writer* para melhorar a documentação do NumPy mais uma vez! Para mais detalhes, consulte [o site oficial do programa Season of Docs](https://developers.google.com/season-of-docs/) e nossa [página de ideias](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### NumPy versão 1.18.0 +### NumPy versão 1.19.2 _22 de dezembro de 2019_ -- O NumPy 1.18.0 está disponível. Após as principais mudanças em 1.17.0, esta é uma versão de consolidação. É a última versão menor que suportará Python 3.5. Destaques dessa versão incluem a adição de uma infraestrutura básica para permitir o link com as bibliotecas BLAS e LAPACK em 64 bits durante a compilação, e uma nova C-API para `numpy.random`. Por favor, veja as [notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0) para mais detalhes. ### O NumPy recebe financiamento da Chan Zuckerberg Initiative _15 de novembro de 2019_ -- Estamos felizes em anunciar que o NumPy e a OpenBLAS, uma das dependências-chave do NumPy, receberam um auxílio conjunto de $195,000 da Chan Zuckerberg Initiative através do seu programa [Essential Open Source Software for Science](https://chanzuckerberg.com/eoss/) que apoia a manutenção, crescimento, desenvolvimento e envolvimento da comunidade em ferramentas de código aberto fundamentais para a ciência. Este auxílio será usado para aumentar os esforços de melhoria da documentação do NumPy, reformulação do site, desenvolvimento comunitário para melhor servir a nossa grande, e rapidamente crescente, base de usuários, assim como para garantir a sustentabilidade do projeto a longo prazo. Enquanto a equipe OpenBLAS se concentrará em tratar de um conjunto de questões técnicas fundamentais, em particular relacionadas a *thread-safety*, AVX-512, e *thread-local storage* (TLS), bem como melhorias algorítmicas na ReLAPACK (Recursive LAPACK) da qual a OpenBLAS depende. Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrados na [proposta completa de concessão de auxílio](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). O trabalho está agendado para começar no dia 1 de dezembro de 2019 e continuar pelos próximos 12 meses. <a name="releases"></a> ## Lançamentos Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix lança (apenas o `z` muda no `x.y.` número da versão) não tem novos recursos; versões menores (o `y` aumenta) sim. +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 de outubro de 2024_. +- NumPy 2.1.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 de setembro de 2024_. +- NumPy 2.0.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto de 2024_. +- NumPy 2.1.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. +- NumPy 2.0.1 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julho de 2024_. +- NumPy 2.0.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junho de 2024_. +- NumPy 1.26.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de fevereiro de 2024_. +- NumPy 1.26.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de janeiro de 2024_. - NumPy 1.26.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 de novembro de 2023_. - NumPy 1.26.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de outubro de 2023_. - NumPy 1.26.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de setembro de 2023_. - NumPy 1.25.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julho de 2023_. - NumPy 1.25.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julho de 2023_. - NumPy 1.24.4 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junho de 2023_. - NumPy 1.25.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junho de 2023_. - NumPy 1.24.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. - NumPy 1.24.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de fevereiro de 2023_. - NumPy 1.24.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de dezembro de 2022_. - NumPy 1.24.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de dezembro de 2022_. - NumPy 1.23.5 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 de novembro de 2022_. - NumPy 1.23.4 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de outubro de 2022_. - NumPy 1.23.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de setembro de 2022_. - NumPy 1.23.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. - NumPy 1.23.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julho de 2022_. - NumPy 1.23.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junho de 2022_. - NumPy 1.22.4 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de maio de 2022_. - NumPy 1.21.6 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. - NumPy 1.22.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de março de 2022_. - NumPy 1.22.2 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de fevereiro de 2022_. - NumPy 1.22.1 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de janeiro de 2022_. - NumPy 1.22.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de dezembro de 2021_. - NumPy 1.21.5 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de dezembro de 2021_. - NumPy 1.21.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junho de 2021_. - NumPy 1.20.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de maio de 2021_. - NumPy 1.20.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de janeiro de 2021_. - NumPy 1.19.5 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de janeiro de 2021_. - NumPy 1.19.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junho de 2020_. - NumPy 1.18.4 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de maio de 2020_. - NumPy 1.17.5 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de janeiro de 2020_. - NumPy 1.18.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de dezembro de 2019_. - NumPy 1.17.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julho de 2019_. - NumPy 1.16.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 de janeiro de 2019_. - NumPy 1.15.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julho de 2018_. - NumPy 1.14.0 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de janeiro de 2018_.
numpy/numpy.org
361cf0880ef57d02c0aab9e94e2acdd5a7840145
New translations news.md (Chinese Simplified)
diff --git a/content/zh/news.md b/content/zh/news.md new file mode 100644 index 0000000..8f47d8b --- /dev/null +++ b/content/zh/news.md @@ -0,0 +1,322 @@ +--- +title: 社区快讯 +sidebar: false +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 +--- + +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. 此次发布的重点是： + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. 此次发布的重点是： + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. 此次发布的重点是： + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. 此次发布的重点是： + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### NumPy 1.22.0 发布 + +_2021年6月31日_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) 正式发布。 此次发布的重点是： + +* 主命名空间的注释类型基本上已完成。 上游是个移动目标，所以很可能会有进一步的改进，但是主要的 项工作已经完成。 这可能是本次发布中用户最可见的增强功能。 +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). 这是创建一个可以在 CuPy 和 JAX 等库中使用的函数的标准收藏的一个步骤。 +* NumPy 现在有一个 DLPack 后端。 DLPack提供了一个数组 (tensor) 数据的通用交换格式。 +* `量化`, `百分比`以及相关函数的新方法。 新的 方法提供了一整套常见于 文献中的方法。 +* 通用函数已被重新考虑，以实现大多数的 [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html) 这也会解锁实验未来DType API的能力。 +* 一个新的可配置内存分配器，供下游项目使用。 + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. 此版本支持的 Python 版本是 3.8-3.10。 + +### 促进Python科学生态系统中的包容性文化 + +_8月31日， 2021_ — 我们很高兴宣布Chan Zuckerberg倡议 [授予赠款](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) 以支持历史上被边缘化群体的人在科学Python项目中的融入、包容和留存，并为NumPy、SciPy、Matplotlib和Pandas的社区动态进行结构性改善。 + +作为 [CZI 基本开放源码科学程序](https://chanzuckerberg.com/eoss/)的一部分， 这 个[多样性 & 包容性补充赠款](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) 将支持创建专门的负责人职位，以确定、记录和实施促进包容性开源社区的实践。 这个项目将由Melissa Mendoncstima (NumPy) 领导，由Ralf Gommers (NumPy, SciPy) Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas)， 提供 额外的辅导和指导 + +这是一个雄心勃勃的项目，旨在发现和执行 应该从结构上改善我们项目的社区动态的活动。 通过 建立这些新的跨项目角色，我们希望在Scientific Python社区引进一个新的 协作模型。 使生态系统中的 社区建设工作能够更有效地开展， 取得更大的成果。 我们还希望在项目中了解什么有效，什么无效，以吸引和留住来自历史上未被代表的群体的新贡献者，建立更清晰的认知。 最后，我们计划制作详细的报告，说明我们采取的措施如何在代表性和与社区互动方面对我们的项目产生影响。 + +这个为期两年的项目预计将于2021年11月开始，我们很期待看到这项工作的成果! [您可以在这里阅读完整的提案](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063)。 + +### 2021 Numpy调查 + +_2021年7月12日_ -- 我们相信NumPy社区的力量。 来自75个国家的1236 名用户参加了我们去年的首次调查。 调查结果使我们对今后12个月应该集中注意的问题有了很好的了解。 + +现在是时候进行另一次调查了，我们将再度尋求您的合作。 这份调查将耗费您大约15分钟的时间。 除英文外，调查问卷还提供另外8种语文：孟加拉语、法语、印地语、日语、普通话、葡萄牙语、俄语和西班牙语。 + +点击链接开始：https://berkeley.qualtrics.com/jfe/form/SV_aOONjgcBXDSl4q。 + + +### NumPy 1.21.0 发布 + +_2021年6月23日_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 此次发布的重点是： + +- 继续开展SIMD工作，涵盖更多的功能和平台 +- 新dtype的基础和型态转换初步工作 +- 适用于Mac平台的Python 3.8和Python 3.9的universal2 wheels +- 改进文档 +- 改进注释 +- 新的 `PCG64DXSM` 位元生成器，用于生成随机数字 + +这个NumPy版本包含175人所贡献的581个合并请求。 此发布版本支持Python 3.7-3.9，将在Python 3.10发布后添加Python 3.10支持。 + + +### 2020 Numpy调研结果结果 + +_2021年6月22日_ -- 在2020年, NumPy调研小组与密歇根大学和马里兰大学的学生和教职员工合作，进行了第一次官方NumPy社区调查。 在这里可以查看调研结果：https://numpy.org/user-survey-2020/。 + + +### NumPy 1.20.0 发布 + +_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢180+位贡献者。 最令人振奋的两个特点是： +- 为大部分Numpy代码做了类型注解，並添加了一个全新的`numpy.typing` 子模块，其中包含 `ArrayLike` 和 `DtypeLike`别名 ，使得用户和下游依赖库可以为自己的代码添加类型注解。 +- 为多个架构进行SIMD编译优化，其支持X86(SSE、AVX)、ARM64(Neon) 和PowerPC(VSX) 指令集。 大幅提高许多函数的性能(例如： [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194))。 + +### NumPy项目的多样性 + +_2020年9月20日_ -- 我们就NumPy项目的社交媒体、多样性和包容性的现状以及相关的讨论撰写了一份[声明](/diversity_sep2020)。 + + +### NumPy官方第一次在Nature发表论文！ + +_2020年9月16日_ - 我们高兴地宣布 [Numpy的第一篇官方论文](https://www.nature.com/articles/s41586-020-2649-2)刊登在Nature的评论文章。 这离NumPy 1.0发布已经过去了整整14年。 这篇论文涵盖了数组编程的应用和基本概念，基于NumPy构建的丰富科学Python生态系统，以及最近添加的数组协议，以促进与外部数组和张量库（如CuPy、Dask和JAX）的互操作性。 + + +### Python 3.9 即将来临，新版本的NumPy 将在何时发布？ + +_2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Python版本的早期采用者， 您可能会失望的发现NumPy(以及其他二进制软件包，如SciPy) 在Python新版发布当天还不会发布相应的版本。 构建兼容新的 Python 版本的基础设施需要付出重大努力，通常需要几周时间才能让新版本出现在 PyPI 和 conda-forge 上。 为了这次版本升级得以顺利进行，请确保： +- 将您的 `pip` 升级到 20.1 版本，至少要支持`manylinux2010` 和 `manylinux2014` +- 使用 [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) 或 `--only-binary=:all:` 选项来防止 `pip` 尝试从源码构建。 + + +### NumPy 1.19.2 发布 + +_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 做准备，並固定设置工具以在上游修改正在进行时保持 distutils 工作。 Aarch64架构的安装包是用最新的 manylinux2014 版本构建的，它修复了 linux 发行版之间使用不同大小内存页的问题。 + +### 首次NumPy调研即将开始！ + +_Jul 2, 2020_ - 本次调查旨在指导并确定 关于开发NumPy 作为软件和社区的决策重点。 除英文外，调查还提供了另外8种语言的版本：孟加拉语、印地语、日语、普通话、葡萄牙语、俄语、西班牙语和法语。 + +请帮助我们让 NumPy 变得更好，在[这里](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)参与调查。 + + +### NumPy 有新标志了! + +_2020年7月24日_ -- NumPy 现在有一个新的标志： + +<img src="/images/logos/numpy_logo.svg" alt="NumPy 标志" title="新的 NumPy 标志" width=300> + +这个标志是对旧标志的现代化演绎，采用更加简洁的设计。 感谢Isabela Presedo-Floryd的设计方案， 同时感谢Travis Vaugh设计的旧图标为我们服务了15年以上。 + + +### NumPy 1.19.0 发布 + +_2020年6月20日_ -- NumPy 1.19.0 正式发布。 这是第一个不支持Python 2的版本，因此它是一个“清理版本”。 目前支持的最低Python 版本是 Python 3.6。 本版本拥有一个重要的新特性，NumPy 1.17.0引进的随机数字生成基础模块现在可以通过Cython访问。 + + +### 文档整改时段 + +_2020年5月11日_ -- NumPy 已成为Google Season 文档项目之一。 我们很高兴看到有机会和技术写作者一起再次改进NumPy的技术文档！ 更多详情，请参考 [文档整改时段官方网站](https://developers.google.com/season-of-docs/) 和我们的 [意见页面](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)。 + + +### NumPy 1.18.0 发布 + +_2019年12月22日_ -- NumPy 1.18.0 正式发布。 在1.17.0发生重大变化后，这是一个合并版本。 这是最后一个支持 Python 3.5的小版本。 该版本的重要更新包括两个，添加了与64位 BLAS 和 LAPACK 库有关的底层更新， 添加 一个用于`numpy.random`的新C-API更新。 + +详情请看 [版本说明](https://github.com/numpy/numpy/releases/tag/v1.18.0)。 + + +### NumPy 从Chan Zuckerberg Initiative获得了一笔捐款 + +_2019年11月15日_ -- 我们高兴地宣布NumPy和 OpenBLAS (Numpy的一个核心依赖库)已经收到一笔19,5000美元的联合赠款。 捐款来自于Chan Zuckerberg Initiative通过的[基础开源科学计算软件项目](https://chanzuckerberg.com/eoss/)，用来支持对科学发展起到关键作用的开源软件的维护、增长、开发和社区参与。 + +这笔赠款将用来加速改进NumPy文档、网站重构和社区开发，进而更好地为我们庞大和迅速增长的用户基础服务，并确保项目的长期可持续性。 OpenBLAS 团队将侧重于处理几个关键技术问题，特别是线程安全问题、AVX-512和 thread-local 存储(TLS) 问题，以及OpenBLAS 依赖的 ReLAPACK (递归的LAPACK) 算法改进。 + +若想查看更多关于捐款的倡议和交付件的详情，可在 [全额赠款提案](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) 中找到。 项目开始于2019年12月1日，今后12个月将持续运作下去。 + + +<a name="releases"></a> + +## 版本发布 + +这是NumPy 版本列表，包含了对应版本发布说明的链接。 所有的 bug修复版本(即在 `x.y.z`格式版本号中只有 `z`改变)没有新功能；小版本更新(`y` 改变)有新功能。 + +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy1.22.0 (<a>发行说明</a>) -- _2021年12月31日_. +- NumPy1.21.5 (<a>发行说明</a>) -- _2021年12月19日_. +- NumPy1.21.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年6月22日_. +- NumPy1.23.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年5月10日_. +- NumPy1.20.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021年1月30日_. +- NumPy1.19.5 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021年1月5日_. +- NumPy1.19.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020年6月20日_. +- NumPy1.18.4 (<a>发行说明</a>) -- _2020年5月3日_. +- NumPy1.17.5 (<a>发行说明</a>) -- _2020年1月1日_. +- NumPy1.18.0 (<a>发行说明</a>) -- _2019年12月22日_. +- NumPy1.17.0 (<a>发行说明</a>) -- _2019年7月26日_. +- NumPy1.16.0 (<a>发行说明</a>) -- _2019年1月14日_. +- NumPy1.15.0 (<a>发行说明</a>) -- _2018年7月23日_. +- NumPy1.14.0 (<a>发行说明</a>) -- _2018年1月7日_.
numpy/numpy.org
b23d296444e8500eab0982f75716a457695d3045
New translations news.md (Russian)
diff --git a/content/ru/news.md b/content/ru/news.md new file mode 100644 index 0000000..7a7aba2 --- /dev/null +++ b/content/ru/news.md @@ -0,0 +1,322 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 +--- + +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. + + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + +<img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. + + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + +<a name="releases"></a> + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
3d11a5d0e474a90afd105ef181c181cf1775bf4c
New translations news.md (Korean)
diff --git a/content/ko/news.md b/content/ko/news.md new file mode 100644 index 0000000..4fb6664 --- /dev/null +++ b/content/ko/news.md @@ -0,0 +1,322 @@ +--- +title: 소식 +sidebar: false +newsHeader: "NumPy 2.2.0 released!" +date: 2023-09-16 +--- + +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + +### NumPy 2.0 출시일: 6월 16일 + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- [NumPy 2.0 이주 가이드](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 릴리즈 노트](https://numpy.org/devdocs/release/2.0.0-notes.html) +- 상태 업데이트 공지용 이슈: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 출시일: 6월 16일 + +_2024년 5월 23일_ -- NumPy 2.0이 2024년 6월 16일에 출시할 예정이라는 소식을 발표하게 되어 기쁩니다. 이 릴리즈를 제작하는 데 1년이 넘게 걸렸고, 2006년 이후 첫 번째 메인 릴리즈입니다. 중요한 건 많은 기능과 성능 개선 외에도, ABI와 Python, C API에 대한 **획기적인 변화**를 이뤄냈다는 것입니다. 아마 의존하는 패키지와 최종 사용자의 코드를 수정해야 할 겁니다. 가능하다면 코드가 `2.0.0rc2`에서 잘 작동하는지 검증해 주세요. **자세한 내용은 아래 항목들을 확인해 주세요.** + +- [NumPy 2.0 이주 가이드](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- [2.0.0 릴리즈 노트](https://numpy.org/devdocs/release/2.0.0-notes.html) +- 상태 업데이트 공지용 이슈: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumPy 1.26.0 출시 +_2023년 12월 19_ -- NumFOCUS에서 연말 캠페인 기간 동안 PyCharm과 협력해 최초 PyCharm 이용자의 라이선스를 30% 할인된 가격에 제공했습니다. 지금부터 2023년 12월 23일까지 PyCharm 구매로 발생한 모든 수익은 NumFOCUS 프로그램으로 직접 전달됩니다. + +구매를 추적할 수 있는 고유 URL을 이용하거나: https://lp.jetbrains.com/support-data-science/ 쿠폰 코드를 사용하세요: ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 출시 + +_2023년 12월 16일_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +* 파이썬 3.12.0 지원 +* Cython 3.0.0 호환 +* Meson 빌드 시스템 사용 +* 업데이트된 SIMD 지원 +* f2py 수정, meson 및 bind(x) 지원 +* 업데이트된 Accelerate BLAS/LAPACK 라이브러리 지원 + +NumPy 1.26.0 릴리스는 Meson 빌드 시스템으로의 전환과 Cython 3.0.0 지원을 표시하는 1.25.x 시리즈의 연장입니다. 총 20명의 사람들이 이 릴리스에 기여하였으며 59개의 풀 리퀘스트가 병합되었습니다. + +본 릴리즈에서 지원하는 Python 버전은 3.3.9-3.12입니다. + +### numpy.org은 이제 일본어와 포르투갈어로도 이용 가능합니다. + +_2023년 8월 2일_ - numpy.org은 이제 추가로 일본어와 포르투갈어로 이용 가능합니다. 이는 다음의 헌신적인 자원봉사자들의 노력 없이는 가능하지 않았을 것입니다: + +_포르투갈어_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_일본어_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +번역 인프라에 대한 작업은 CZI로부터의 자금 지원을 받아 진행되었습니다. + +나아가서 NumPy 웹사이트가 더 많은 언어로 번역되기를 바랍니다. 도움을 주시려면 다음 Slack 링크를 통해 NumPy Translations Team 에 연락을 주십시오: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (#translations 채널을 add 해주세요) 또한 과학적 파이썬 생태계 전반에서 문서 및 교육 콘텐츠를 지역화하는데 참여할 Translations Team을 구축하고 있습니다. 이에 흥미를 느낀다면 Scientific Python Discord에서 함께해 주세요: https://discord.gg/khWtqY6RKr. (#translation 채널을 찾아보세요) + +### NumPy 1.25.0 출시 + +_2023년 6월 17일_ -- 이제 [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html)을 이용할 수 있습니다. 주요 기능들은 다음과 같습니다: + +* MUSL 지원, 이제 MUSL Wheel도 배포됩니다. +* Fujitsu C/C++ 컴파일러 지원 +* Einsum에서 객체 배열 지원 +* Inplace 행렬 곱셈 (`@=`) 지원 + +NumPy 1.25.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업을 계속하고 있습니다. 미래의 NumPy 2.0.0을 위한 준비 작업도 있었는데, 이로 인해 수많은 기능들이 지원 종료 예정에 새로 포함되거나 완전히 만료되었습니다. + +총 148명의 사람들이 이 릴리스에 기여하였으며 530개의 풀 리퀘스트가 병합되었습니다. + +본 릴리즈에서 지원하는 Python 버전은 3.9-3.11입니다. + +### 포용적인 문화 조성: 참여 요청 + +_2023년 5월 10일_ -- 포용적인 문화 조성: 참여 요청 + +다양성과 포용성의 측면에서 우리는 어떻게 더 나아질 수 있을까요? [여기](https://contributor-experience.org/docs/posts/dei-report/)에서 보고서를 읽고 함께 참여하는 방법을 알아보세요. + +### NumPy 문서 팀 리더 변경 + +_2023년 1월 6일_ –- Mukulika Pahari, Ross Barnowski가 Melissa Mendonça를 대신해 새 NumPy 문서 팀 리더로 임명되었습니다. NumPy 공식 문서와 교육 자료에 기여한 Melissa와 한 걸음 더 나아간 Mukulika, Ross에게 감사를 표합니다. + +### NumPy 1.24.0 출시 + +_2022년 12월 18일_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +* 스태킹 함수를 위한 새 "dtype" 및 "casting" 키워드. +* 새 F2PY 기능 및 수정. +* 수많은 지원 종료 예정 기능, 확인하세요. +* 수많은 만료된 기능, + +NumPy 1.24.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업을 계속하고 있습니다. dtype의 형변환 및 정리를 변경하는 과정에서 수많은 기능들이 지원 종료 예정에 새로 포함되거나 완전히 만료되었습니다. 177명의 기여자가 생성한 444개의 풀 요청을 바탕으로 한 성과입니다. 지원하는 Python 버전은 3.8-3.11입니다. + +### NumPy 1.23.0 출시 + +_2022년 6월 22일_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +* `loadtxt`를 C로 구현하여 성능이 크게 향상되었습니다. +* 데이터 교환을 쉽게 하기 위해 Python 수준에서 DLPack을 개방합니다. +* 구조화된 dtype의 형변환 및 비교 방법을 변경했습니다. +* f2py를 개선했습니다. + +NumPy 1.23.0 릴리스에서는 dtype의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업, 오래된 지원 종료 예정 기능을 완전히 만료시키는 작업을 계속하고 있습니다. 151명의 기여자가 생성한 494개의 풀 요청을 바탕으로 한 성과입니다. 본 릴리즈에서 지원하는 Python 버전은 3.8-3.10입니다. Python 3.11은 rc 단계에 다다르면 지원할 예정입니다. + +### NumFOCUS DEI 연구: 참여 요청 + +_2022년 4월 13일_ -- NumPy는 [NumFOCUS](http://numfocus.org/)와 협력하여 [Gordon & Betty Moore 재단](https://www.moore.org/)에서 기금을 제공하는 [연구 프로젝트](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)를 진행합니다. 본 연구는 오픈 소스 소프트웨어 커뮤니티에 기여자, 특히 역사적으로 과소평가된 집단의 기여자가 참여할 때 직면하는 장벽을 이해하는 것을 목표로 합니다. 연구팀은 새 기여자, 프로젝트 개발자 및 유지관리자, 과거에 기여한 사람들과 NumPy에 참여하고 기여한 경험에 대해 이야기하고자 합니다. + +**경험을 공유하고 싶으신가요?** + +간단한 ["참여 희망" 양식](https://numfocus.typeform.com/to/WBWVJSqe)을 작성해주세요. 양식에서 연구 목표, 개인정보 보호, 기밀 유지 사항에 대한 추가 정보를 확인할 수 있습니다. 당신의 참여가 다양성과 포용성을 갖춘 오픈 소스 소프트웨어 커뮤니티의 성장과 지속 가능성에 도움이 될 것입니다. 승인된 참가자는 연구팀과 30분 면담을 진행하게 됩니다. + +### Numpy 1.22.0 출시 + +_2021년 12월 31일_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +* 기본 네임스페이스에 대해 유형 주석의 지원을 거의 완료했습니다. 업스트림 코드는 항상 변하므로 추가 개선이 있을 수 있지만 주요 작업은 완료되었습니다. 아마도 이 릴리스에서 가장 체감되는 개선 사항일 것입니다. +* 제안된 [배열 API 표준의 예비 버전](https://data-apis.org/array-api/latest/)이 제공됩니다([NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) 참조). 이는 CuPy 및 JAX와 같은 라이브러리에서 사용할 수 있는 표준 함수 모음을 만드는 단계입니다. +* NumPy가 DLPack 백엔드로 구동됩니다. DLPack은 배열(텐서) 데이터에 대한 공통 교환 형식을 제공합니다. +* `quantile`, `percentile` 관련 함수를 위한 새 메서드를 추가했습니다. 새 메서드를 이용해 문헌에서 일반적으로 쓰이는 처리를 진행할 수 있습니다. +* 범용 함수가 대부분의 [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html)을 구현하도록 리팩터링되었습니다. 이를 통해 미래의 DType API를 실험할 수 있는 능력도 갖췄습니다. +* 새 구성 가능한 메모리 할당자를 다운스트림 프로젝트에서 사용할 수 있습니다. + +NumPy 1.22.0은 153명의 기여자가 생성한 609개의 풀 요청을 바탕으로 만들어진 대형 릴리즈입니다. 본 릴리즈에서 지원하는 Python 버전은 3.8-3.10입니다. + +### 과학 Python 생태계에서 포용적 문화 발전 + +_2021년 8월 31일_ -- Chan Zuckerberg Initiative가 과학적 Python 프로젝트에서 역사적으로 소외된 그룹의 사람들을 온보딩, 포함 및 유지하고 NumPy, SciPy, Matplotlib 그리고 Pandas 의 커뮤니티 역학을 구조적으로 개선하기 위한 [보조금을 수여](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/)했음을 발표하게 되어 기쁩니다. + +[CZI의 Essential Open Source Software for Science 프로그램](https://chanzuckerberg.com/eoss/)의 일환으로 이 [Diversity & 포함 추가 보조금](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)은 포괄적인 오픈 소스 커뮤니티를 육성하기 위한 관행을 식별, 문서화 및 구현하기 위한 전담 기여자 경험 리드 직책 생성을 지원합니다. 이 프로젝트는 Melissa Mendonça(NumPy) 님이 이끌고 Ralf Gommers(NumPy, SciPy), Hannah Aizenman, Thomas Caswell(Matplotlib), Matt Haberland(SciPy), Joris Van den Bossche(Pandas) 님이 추가 멘토링 및 지침을 제공합니다. + +이것은 프로젝트의 커뮤니티 역학을 구조적으로 개선해야 하는 활동을 발견하고 구현하는 것을 목표로 하는 야심 찬 프로젝트입니다. 새로운 교차 프로젝트 역할을 설정함으로써 과학적 Python 커뮤니티에 새로운 협업 모델을 도입하여 생태계 내에서 커뮤니티 구축 작업을 보다 효율적으로 수행하고 더 큰 결과를 얻을 수 있을 것으로 기대됩니다. 또한 특히 역사적으로 과소대표된 집단의 새로운 기여자를 참여시키고 유지하기 위해, 프로젝트에서 효과적인 것과 그렇지 않은 것에 대한 명확한 그림을 구축할 것으로 기대합니다. 마지막으로, 시행된 조치에 대해 자세한 보고서를 작성하여 커뮤니티와의 대표 및 상호 작용 측면에서 프로젝트에 어떤 영향을 미쳤는지 설명할 계획입니다. + +2개년 프로젝트가 2021년 11월부터 시작될 예정입니다. 프로젝트의 결과를 볼 날이 기대되네요! [여기에서 전체 제안서를 열람할 수 있습니다](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021년도 NumPy 설문조사 + +_2021년 7월 12일_ -- NumPy에서, 우리는 커뮤니티의 힘을 믿습니다. 작년에 75개국에서 1,236명의 NumPy 사용자가 첫 번째 설문조사에 참여했습니다. 설문 조사 결과를 통해 다음 12개월 동안 우리가 어떤 것에 집중해야 할지 아주 잘 이해할 수 있었습니다. + +이제 또다른 설문 조사를 진행할 시간이고, 여러분의 도움이 다시 한 번 필요합니다. 완료하는 데 약 15분 정도 소요될 겁니다. 설문지는 영어 외에도 8개 국어로 제공됩니다: 벵골어, 프랑스어, 힌디어, 일본어, 중국 관화, 포르투갈어, 러시아어, 스페인어. + +시작하려면 아래 링크를 눌러 주세요: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 출시 + +_2021년 9월 23일_ -- [NumPy 1.1.21](https://numpy.org/doc/stable/release/1.21.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: + +- 더 많은 기능과 플랫폼을 다루는 지속적인 SIMD 작업, +- 새로운 dtype 인프라 및 캐스팅에 대한 초기 작업, +- Mac의 Python 3.8 및 Python 3.9용 universal2 휠, +- 문서화 향상, +- 주석 향상, +- 난수 생성에 이용되는 새 `PCG64DXSM` 비트 생성기. + +이번 NumPy 릴리즈는 175명이 기여해주신 581개의 풀 리퀘스트가 합쳐진 결과입니다. 본 릴리즈에서 지원하는 Python 버전은 3.7-3.9입니다. Python 3.10은 Python 3.10 릴리즈 이후 지원할 예정입니다. + + +### 2020년도 NumPy 설문조사 결과 + +_2021년 6월 22일_ -- 2020년에, NumPy 조사 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. 여기서 조사 결과를 확인하세요: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 출시 + +_2021년 9월 30일_ -- [NumPy 1.1.20](https://numpy.org/doc/stable/release/1.20.0-notes.html)이 출시되었습니다. 역대 최대의 NumPy 릴리즈입니다. 180명이 넘는 기여자분들께 감사드립니다. 다음은 이번 출시에서 가장 흥미로운 두가지 기능들 입니다. +- NumPy의 많은 부분에 대한 유형 주석 및 사용자와 다운스트림 라이브러리가 추가할 때 사용할 수 있는 `ArrayLike` 및 `DtypeLike` 별칭을 포함하는 새로운 `numpy.typing` 하위 모듈 자체 코드에 주석을 입력합니다. +- x86(SSE, AVX), ARM64(Neon) 및 PowerPC(VSX) 명령을 지원하는 다중 플랫폼 SIMD 컴파일러 최적화 입니다. 이는 많은 함수들의 상당한 성능향상을 가져왔습니다 (예: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### NumPy 프로젝트 내 다양성 + +_2020년 9월 20일_ -- 우리는 [NumPy 프로젝트 안에서의 다양성과 포용성에 관한 소셜 미디어의 상태 및 토론에 대한 성명서를 작성했습니다](/diversity_sep2020). + + +### Nature에 첫 공식 NumPy 논문 발표! + +_2020년 9월 16일_ -- [NumPy에 대한 첫 번째 공식 논문](https://www.nature.com/articles/s41586-020-2649-2)이 Nature에 리뷰 기사로 게재되었음을 발표하게 되어 기쁩니다. NumPy 1.0이 나온 지 14년 만입니다. 이 백서에서는 배열 프로그래밍의 응용 프로그램 및 기본 개념, NumPy 위에 구축된 풍부한 과학적 Python 생태계, CuPy, Dask 및 JAX와 같은 외부 배열 및 텐서 라이브러리와의 상호 운용성을 촉진하기 위해 최근에 추가된 배열 프로토콜을 다룹니다. + + +### Python 3.9가 곧 출시하는데, NumPy는 바이너리 Wheel을 언제 출시합니까? + +_2020년 9월 14일_ -- Python 3.9가 몇 주 내로 출시될 것입니다. 만약 Python 얼리어답터라면, NumPy (그리고 SciPy 등 다른 바이너리 패키지) 가 릴리즈 시일에 바이너리 Wheel을 준비하지 못한다는 것을 알고 실망했을 수 있습니다. 새로운 Python 버전에 빌드 환경을 맞추는 것은 많은 노력을 요하고, 패키지가 PyPI 및 conda-forge에 배포되는 데에는 일반적으로 몇 주가 걸립니다. 출시를 대비하려면 아래 요건을 충족하도록 하십시오. +- `pip` 버전을 최소 20.1로 업데이트하여 `manylinux2010` 및 `manylinux2014`를 지원하도록 합니다 +- [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary)를 사용하거나 또는 `--only-binary=:all:`을 사용하여 `pip`가 소스에서 빌드하는 것을 막아주세요. + + +### NumPy 1.19.2 출시 + +_2020년 9월 10일_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html)이 출시되었습니다. 1.19 시리즈의 이 최신 릴리스는 몇 가지 버그를 수정하고 [다가오는 Cython 3.x 릴리스](http://docs.cython.org/en/latest/src/changes.html)를 준비하며 setuptools를 고정하여 업스트림 수정이 진행되는 동안 distutils가 계속 작동하도록 합니다. aarch64 휠은 다양한 Linux 배포판에서 사용되는 다양한 페이지 크기 문제를 해결하는 최신 manylinux2014 릴리스로 제작되었습니다. + +### 최초의 NumPy 설문조사가 진행 중입니다! + +_2020년 7월 2일_ -- 본 설문조사는 소프트웨어 및 커뮤니티로서의 NumPy 개발에 대하여, 의사결정의 우선 순위를 안내하고 설정하기 위해 실시됩니다. 설문지는 영어 외에도 8개 국어로 제공됩니다: 벵골어, 프랑스어, 힌디어, 일본어, 중국 관화, 포르투갈어, 러시아어, 스페인어. + +NumPy를 개선하게 도와주시고 이를위해 설문조사에 참여해 주세요. [여기](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy에 새로운 로고가 생겼습니다! + +_2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. + +<img src="/images/logos/numpy_logo.svg" alt="NumPy 로고" title="새 NumPy 로고" width=300> + +이전 로고를 깔끔하고 현대적으로 다시 디자인했습니다. 새 로고를 만들어 주신 Isabela Presedo-Floyd님께 감사드립니다. 또 15년이 넘는 기간 동안 저희가 사용했던 로고를 만들어 주신 Travis Vaught님께도 감사의 말씀을 드립니다. + + +### NumPy 1.19.0 출시 + +_2020년 6월 20일_ -- NumPy 1.19.0이 출시되었습니다. Python 2의 지원을 중단한 첫 릴리즈라서 "정리 릴리즈"라고도 불립니다. 이제 지원하는 Python 최소 버전은 3.6입니다. 중요한 새 기능을 꼽자면, NumPy 1.17.0에 도입된 난수 생성 인프라를 Cython에서 접근할 수 있게 되었다는 것입니다. + + +### Season of Docs 승인 + +_2020년 5월 11일_ -- NumPy가 Google Season of Docs 프로그램의 선도 조직으로 승인되었습니다. 테크니컬 라이터와 협력해서 NumPy 문서를 다시 한 번 개선할 수 있는 기회를 갖게 되어 좋습니다! 이상 자세한 내용은 [공식 문서 시즌 사이트](https://developers.google.com/season-of-docs/) 및 [아이디어 페이지](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) 를 참조하세요. + + +### NumPy 1.18.0 출시 + +_2019년 12월 22일_ -- NumPy 1.18.0이 출시되었습니다. 1.17.0에서의 주요 변경점을 통합하는 릴리즈입니다. 본 릴리즈는 Python 3.5를 지원하는 마지막 마이너 릴리즈입니다. 릴리즈의 주요 내용으로는, 64비트 BLAS 및 LAPACK 라이브러리와 연결하기 위한 환경 조성, `numpy.random`을 위한 새로운 C-API 등이 있습니다. + +자세한 내용은 [출시 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)를 참조하세요. + + +### NumPy가 Chan Zuckerberg Initiative에서 보조금을 받았습니다 + +_2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 OpenBLAS가 챈 저커버그 이니셔티브의 [과학 프로그램용 중요 오픈소스 소프트웨어](https://chanzuckerberg.com/eoss/) 지원을 통해 19만 5천 달러에 달하는 공동 보조금을 받았다는 소식을 전할 수 있어 기쁩니다. 이곳에서는 과학에 중요한 오픈소스 도구에 대해 유지 관리, 성장, 개발 및 커뮤니티 참여를 지원합니다. + +이 보조금은 NumPy 문서, 웹사이트 재설계 및 커뮤니티 개발을 개선하여 빠르게 성장하는 대규모 사용자 기반에 더 나은 서비스를 제공하고 프로젝트의 장기적인 지속 가능성을 보장하는 데 사용될 것입니다. OpenBLAS 팀은 OpenBLAS가 의존하는 ReLAPACK(Recursive LAPACK) 의 알고리즘 개선뿐만 아니라 특히 스레드 안전성, AVX-512 및 스레드 로컬 스토리지(TLS) 문제와 같은 일련의 핵심 기술 문제를 해결하는 데 집중할 것입니다. + +제안된 계획 및 결과물에 대한 자세한 내용은 [전체 보조금 제안](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167)에서 확인할 수 있습니다. 2019년 12월 1일부터 작업을 시작하여 다음 12개월 동안 진행할 예정입니다. + + +<a name="releases"></a> + +## 릴리즈 + +NumPy 릴리즈의 목록입니다. 릴리즈 노트로 링크도 걸려 있습니다. 버그 수정 릴리즈(`x.y.z`에서 `z`만 바뀐 경우)에는 새로운 기능이 없습니다. 마이너 릴리즈(`y`가 증가한 경우)에는 새로운 기능이 있습니다. + +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _ 2024년 2월 5일_. +- NumPy 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2024년 1월 2일_. +- NumPy 1.26.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023년 1월 2일_. +- NumPy 1.26.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023년 10월 14일_. +- NumPy 1.26.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023년 16월 9일_. +- NumPy 1.25.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023년 7월 31일_. +- NumPy 1.25.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _2023년 7월 8일_. +- NumPy 1.24.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _2023년 6월 26일_. +- NumPy 1.25.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _2023년 6월 17일_. +- NumPy 1.24.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _2023년 4월 22일_. +- NumPy 1.24.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _2023년 2월 5일_. +- NumPy 1.24.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _2022년 12월 26일_. +- NumPy 1.24.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _2022년 12월 18일_. +- NumPy 1.23.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _2022년 11월 19일_. +- NumPy 1.23.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _2022년 10월 12일_. +- NumPy 1.23.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _2022년 9월 9일_. +- NumPy 1.23.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _2022년 8월 14일_. +- NumPy 1.23.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _2022년 7월 8일_. +- NumPy 1.23.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _2022년 6월 22일_. +- NumPy 1.22.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022년 5월 20일_. +- NumPy 1.21.6 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022년 4월 12일_. +- NumPy 1.22.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _2022년 3월 7일_. +- NumPy 1.22.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _2022년 2월 3일_. +- NumPy 1.22.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _2022년 1월 14일_. +- NumPy 1.22.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _2021년 12월 31일_. +- NumPy 1.21.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _2021년 12월 19일_. +- NumPy 1.21.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021년 6월 22일_. +- NumPy 1.20.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021년 5월 10일_. +- NumPy 1.20.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021년 1월 30일_. +- NumPy 1.19.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021년 1월 5일_. +- NumPy 1.19.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020년 6월 20일_. +- NumPy 1.18.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020년 5월 3일_. +- NumPy 1.17.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020년 1월 1일_. +- NumPy 1.18.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _2019년 12월 22일_. +- NumPy 1.17.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _2019년 7월 26일_. +- NumPy 1.16.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019년 1월 14일_. +- NumPy 1.15.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018년 7월 23일_. +- NumPy 1.14.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018년 1월 7일_.
numpy/numpy.org
c2bb0e7b394a3ba40c92cdee46d2c2ec65607449
New translations news.md (Japanese)
diff --git a/content/ja/news.md b/content/ja/news.md index 33c10a9..acd02d6 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,308 +1,322 @@ --- title: ニュース sidebar: false newsHeader: "NumPy 1.26.0 がリリースされました。" -date: 2024-08-18 +date: 2023-09-16 --- +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + ### NumPy 1.26.0 がリリースされました。 _2024 Aug, 2024_ -- Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。 - Python 3.12.0 のサポート - 多くの期限切れの非推奨(Deprecation)の削除 - Array-api 2023.12 標準のサポート -このリリースでは、Pythonのバージョン 3.10-3.13 がサポートされています。 +Python バージョン 3.10-3.13 か、このリリースでサポートされています。 ### 多くの新しい非推奨(Deprecation)の追加 _2024年6月16日_ -- Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。 - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) -- ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) +- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ブログ記事 ["NumPy 2.0: 進化のマイルストーン"](https://blog.scientific-python.org/numpy/numpy2/) は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。 ### NumPy 1.25.0 リリース _ 2024年5月23日_ -- NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 **破壊的な変更** である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy `2.0.0rc2`で動作するかどうか確認をお願いします。 **詳細は下記をご覧ください:** - [NumPy 2.0移行ガイド](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- [2.0.0 リリースノート](https://numpy.org/devdocs/release/2.0.0-notes.html) -- ステータス更新のお知らせイシューチケット: [numpy#24300](https://github.com/numpy/numpy/issues/24300) +- [2.0.0 リリース ノート](https://numpy.org/devdocs/release/2.0.0-notes.html) +- ステータスアップデートお知らせに関する問題: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUSの年末の資金調達 _2023年12月19日_ -- NumFOCUSは、年末キャンペーンでPyCharmチームと協力し、PyCharmライセンスの初回購入に30%の割引を提供しています。 2023年12月23日までのPyCharm購入による1年目の収益は全てNumFOCUSのプログラムに直接寄付されます。 購入される方はこちらのURLか: https://lp.jetbrains.com/support-data-science/ こちらのクーポンコードを利用してください: ISUPPORTDATASCIENCE  ### NumPy 1.20.0 リリース _2022年12月18日_ -- [Numpy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 * Python 3.12.0 のサポート * Cython 3.0.0 との互換性 * Mesonビルドシステムの利用 * SIMD サポートの改善 * f2py のバグ修正, meson と bind(x) のサポート * 更新された BLAS/LAPACK の高速化ライブラリのサポート Numpy 1.26.0 は 1.25 からの互換性を保持しています。Mesonビルドシステムへの移行とCython 3.0.0へのサポートが目的のリリースです。 合計20人がこのリリースに貢献し、59個のプルリクエストがマージされました。 このリリースでサポートされている Python のバージョンは3.9から 3.12 です。 ### numpy.orgが日本語とポルトガル語で利用可能になりました _2023年4月2日_ -- numpy.orgが2つの言語で利用可能になりました： 日本語とポルトガル語。 熱心なボランティアがいなければ、このプロジェクトは不可能でした： _ポルトガル語_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _日本語：_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) 翻訳インフラストラクチャに関するプロジェクトは、CZIからの資金援助でサポートされています。 今後も、NumPyのウェブサイトをより多くの言語に翻訳したいと思っています。 もし手伝える場合は、Slack上のNumPy翻訳チームに連絡をお願います: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (#translation チャンネルを探してください) (#translation チャンネルを探してください) また、Scientific Pythonエコシステム全体のドキュメントや教育コンテンツのローカライズに取り組む翻訳チームも 立ち上げています。 このプロジェクトにも興味がある場合は、是非Scientific Python Discordに参加してください: https://discord.gg/khWtqY6RKr. (#translation チャンネルを探してください) ### Numpy 1.23.0 リリース _2022年1月22日_ -- [Numpy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) がリリースされました。 今回のリリースのハイライトは次のとおりです。 * MUSLのサポート。 MUSLのWheelが準備されました。 * 富士通のC/C++コンパイラサポート * einsum でオブジェクト配列がサポートされるようになりました. * 行列の置き換え(inplace)掛け算のサポート (`@=`). Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 将来の NumPy 2.0.0 に向けた準備作業も行われており、 多数の新規および期限切れの機能廃止が可能となってきています。 合計148人がこのリリースに貢献し、530個のプルリクエストが マージされました。 このリリースでサポートされている Python のバージョンは3.3.9 - 3.11 です。 ### インクルーシブな文化の育成: 参加の募集 _2023年5月10日_ -- インクルーシブ・カルチャーの育成: 参加募集 NumPyプロジェクトの多様性とインクルージョンに関して、我々はどのようなことを実施すればいいでしょうか？ 興味がある方はこちらの [レポート](https://contributor-experience.org/docs/posts/dei-report/) を読んで参加する方法を確認してください。 ### NumPy ドキュメンテーションチームのリーダーの変更 _2023年1月6日_ –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudioに代わるNumPyドキュメンテーションチームの新しいリーダーとして任命されました。 私たちは、MelissaにNumPyの公式ドキュメントと教育資料に対するすべての貢献に感謝し、MukulikaとRossに新しい役割にステップアップしてもらったことに感謝します。 ### NumPy 1.24.0 リリース _2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) がリリースされました。 今回のリリースのハイライトは下記の通りです。 * スタッキング関数のための新しい"dtype"と"casting"キーワードの追加 * F2PYの新機能追加とバグ修正 * 多くの新しい非推奨(Deprecation)の追加 * 多くの期限切れの非推奨(Deprecation)の削除 Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 dtype のプロモーションとクリーンアップの変更により、多数の新規と期限切れの非推奨が存在しています。 今回のリリースは、444個のプルリクエストと177人のコントリビューターによるものです。 サポートされている Python のバージョンは 3.8-3.11 です。 ### Numpy 1.26.0 は 1.25 からの互換性を保持しています。 _2021年12月31日_ -- [Numpy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 * `loadtxt` がCで実装されたことによる、大幅なパフォーマンス向上 * より簡単なデータ交換のためのPythonレベルでのDLPackの公開 * 構造化されたdtypesのプロモーションと比較方法の変更 * f2pyの改善 Numpy 1.23. リリースでは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 今回のリリースは、494個のプルリクエストと151人のコントリビューターによるものです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 Python 3.11がrc ステージに到達すると Python 3.11 もサポートされます。 ### NumFOCUS DEI研究への参加募集 _2022年4月13日_ -- NumPyは、[NumFOCUS](http://numfocus.org/)と協力して、[ある研究プロジェクト](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c)を進めており、これは[Gordon & Betty Moore Foundation](https://www.moore.org/)によって資金提供されています。 この研究チームは、新しい貢献者、プロジェクトの開発者およびメンテナー、そして過去に貢献した方々に、NumPyに参加し貢献した経験について話を聞きたいと考えています。 **あなたの経験を共有することに興味がありますか?** もし興味がある場合は、研究目標、プライバシー、および 守秘義務に関する追加情報が記載されている、この簡単な[参加者の興味](https://numfocus.typeform.com/to/WBWVJSqe)フォームに記入をお願いします。 多様で包括的なオープンソースソフトウェアコミュニティの 成長と持続可能性のために、このプロジェクトへのあなたの参加は非常に大きな価値があります。 参加を受け入れられた人は、研究チームメンバーと30分間のインタビューに参加することになります。 ### NumPy 1.19.2 リリース _2023年9月16日_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)がリリースされました。 今回のリリースの目玉機能は次のとおりです。 * メインの名前空間の型アノテーションは基本的に完了しました。 上流のコードは常に変化するものなので、さらなる改良が必要でしょうが、大きな作業は終わったと考えています。 これはおそらく、今回のリリースで最も目に見える改良でしょう。 * 以前から提案されていた [array API 標準](https://data-apis.org/array-api/latest/) のベータ版が提供されています ( [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html) を参照) 。 これは、CuPy や JAX などのライブラリで使用できる 関数の標準的なコレクションを作成するために必要なステップです。 * NumPy に DLPack バックエンドが追加されました。 DLPack は、配列(テンソル) データ用の共通のデータ変換フォーマットを提供します。 * `quantile`, `percentile`, および関連する関数に新しいメソッドが追加されました。 これらの新しいメソッドは、論文で一般的に見られる一通りの処理を提供します。 * ユニバーサル関数は、[NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html) の多くを実装するためにリファクタリングされました。 これにより将来の DType API の処理も可能にします。 * ダウンストリームのプロジェクトで使用するための新しい設定可能なメモリー・アロケーターが追加されました。 NumPy 1.22.0は、153人の貢献者が609のプルリクエストを作成した 非常に大きなリリースです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 ### 科学的なPythonエコシステムにおける包括的な文化の前進 _ 2021年8月31日_ -- この度、Chan Zuckerberg Initiativeより、科学的なPythonプロジェクトにおいて、歴史的に疎外されてきたグループの人々のオンボーディング、インクルージョン、リテンションを支援し、NumPy、SciPy、Matplotlib、Pandasのコミュニティダイナミクスを構造的に改善するための [ 助成金を授与されました ](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) ことをお知らせします。 [ CZIのEssential Open Source Software for Scienceプログラム ](https://chanzuckerberg.com/eoss/)の一環として、この[ Diversity & Inclusion補助金 ](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b)は、開けたなオープンソースコミュニティを育成するためにやるべきことを特定したり、文書化したり、実施したりするためのコントリビュータ体験のリーダー専任職の創設を支援することになります。 このプロジェクトは、Melissa Mendonça (NumPy) が中心となって、下記の方々の追加のメンタリングとサポートにより実施されます。 Ralf Gommers (NumPy、SciPy)、Hannah AizenmanとThomas Caswell (Matplotlib)、Matt Haberland (SciPy)、そして Joris Van den Bossche (Pandas)。 このプロジェクトは私たちのOSSプロジェクトのコミュニティダイナミクスを構造的に改善する方法を発見し、実施することを目指す野心的なプロジェクトです。 このような複数のプロジェクトの横断的な役割を確立することで、Scientific Pythonコミュニティに新しいコラボレーションモデルを導入し、エコシステム内のコミュニティ構築作業をより効率的に、より大きな成果を生めるようにしたいと考えています。 特にこのプロジェクトにより、歴史的にこれまで代表的ではなかったグループからの新しいコントリビュータを引き付け、貢献を維持するために、何がうまくいき、何がうまくいかないかを、より明確に把握できるようになると期待しています。 最後に、実施したアクションについて詳細な報告書を作成し、プロジェクトの代表者やコミュニティとの交流の面で、プロジェクトにどのような影響を与えたかを説明する予定です。 2021年11月から2年間のプロジェクトが始まると予想されており、このプロジェクトの成果を楽しみにしています! このプロジェクトの提案書に関しては、[こちら](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063) から全文を読むことができます. ### 2021年度NumPyアンケート _2021年7月12日_ -- NumPy ではコミュニティの力を信じています。 昨年の第1回アンケートには、75カ国から1,236名のNumPyユーザーが参加してくれました。 この調査結果により、今後12ヶ月間、私たちがどのようなことに集中すべきかを、非常に良く理解することができました。 今年もアンケートの時間が来ました。もう一度アンケートへの回答をお願いいたします。 アンケートへの回答は15分ほどで終了します。 アンケートは英語以外にも、ベンガル語、フランス語、ヒンディー語、日本語、マンダリン、ポルトガル語、ロシア語、スペイン語の8ヶ国語に対応しています。 こちらのリンク先から、アンケートを始めることができます: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSL4q. ### Numpy 1.18.0 リリース _2023年1月17日_ -- [Numpy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) がリリースされました。 今回のリリースの目玉機能は次のとおりです。 - より多くの機能やプラットフォームをカバーするためのSIMD関連の改善が実施されました。 - dtypeのための新しいインフラとキャストの準備 - Mac 版の Python 3.8 と Python 3.9 用 universal2 wheel - ドキュメントの改善 - アノテーションの改善 - 乱数生成用の新しい `PCG64DXSM` ビット生成機 今回のNumpy リリースは、175人による581件のプルリクエストのマージの結果です。 このリリースでサポートされている Python のバージョンは 3.7-3.9 です。 Python 3.10 がリリースされた後、Python 3.10 のサポートが追加されます。 ### 2020年度 NumPy アンケート結果 _2021年6月22日_ -- NumPyの調査チームは、2020年に ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらから確認できます。 https://numpy.org/user-survey-2020/ ### NumPy 1.19.2 リリース _2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) がリリースされました。 今回のリリースは180 人以上のコントリビューターのおかげで、これまでで最大の NumPyのリリースとなりました。 最も重要な2つの新機能は次のとおりです。 - NumPyの大部分のコードに型注釈が追加されました。 そして新しいサブモジュールである`numpy.typing`が追加されました。 このサブモジュールは`ArrayLike` や`DtypeLike`という型注釈のエイリアスが定義されており、これによりユーザーやダウンストリームのライブラリはこの型注釈を使うことができます。 - X86(SSE、AVX)、ARM64(Neon)、およびPowerPC (VSX) 命令をサポートするマルチプラットフォームSIMDコンパイラの最適化が実施されました。 これにより、多くの関数で大きく パフォーマンスが向上しました (例: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### NumPyプロジェクトの多様性 _2020年9月20日に_ 、私たちは[ NumPyプロジェクトにおけるダイバーシティやインクルージョンの状況や、ソーシャルメディア上での議論についての宣言 ](/diversity_sep2020)について書きました。 ### Natureに初の公式NumPy論文が掲載されました! _2020年9月16日_ -- NumPyに関する [ 最初の公式の論文 ](https://www.nature.com/articles/s41586-020-2649-2)がNatureに査読付き論文として掲載されました。 これはNumPy 1.0のリリースから14年後のことになりました。 この論文では、配列プログラミングのアプリケーションと基本的なコンセプト、NumPyの上に構築された様々な科学的Pythonエコシステム、そしてCuPy、Dask、JAXのような外部の配列およびテンソルライブラリとの相互運用を容易にするために最近追加された配列プロトコルについて説明しています。 ### Python 3.9のリリースに伴い、いつNumPyのバイナリwheelがリリースされるのですか？ _2020年9月14日_ -- Python 3.9 は数週間後にリリースされる予定です。 もしあなたが新しいPythonのバージョンをいち早く利用している場合、NumPy（およびSciPyのような他のパッケージ）がリリース当日にバイナリwheelを用意していないことを知ってがっかりしたかもしれませんね。 ビルド用のインフラを新しいPythonのバージョンに適応させるのは非常に大変な作業で、PyPIやconda-forgeにパッケージが掲載されるまでには通常数週間かかります。 今後のwheelのリリースに備えて、以下を確認してください。 - `pip` が`manylinux2010` と `manylinux2014` をサポートするためにpipを少なくともバージョン 20.1 に更新する。 - [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) または `--only-binary=:all:` を`pip`がソースからビルドしようとするのを防ぐために使用します。 ### NumPy 1.19.2 リリース _2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) がリリースされました。 この 1.19 シリーズの最新リリースでは、いくつかのバグが修正され、[ 来るべき Cython 3.xリリース ](http:/docs.cython.orgenlatestsrcchanges.html)への準備が行われ、アップストリームの修正が進行中の間も distutils の動作を維持するためのsetuptoolsのバージョンの固定が実施されています。 aarch64 wheelは最新のmanylinux2014リリースでビルドされており、異なるLinuxディストリビューションで使用される異なるページサイズの問題が修正されています。 ### 初めてのNumPyの調査が公開されました!! _2020年7月2日_ -- このアンケート調査は、NumPyにおける、ソフトウェアとしてとコミュニティの両方における意思決定の指針となり、優先順位を決定する役に立ちました。 この調査結果は英語以外のこれらの8つの言語で利用可能です: バングラ, ヒンディー語, 日本語, マンダリン, ポルトガル語, ロシア語, スペイン語とフランス語. NumPy をより良くするために、こちらの \[アンケート\](https://umdsurvey. umd. edu/jfe/form/SV_8bJrXjbhXf7saAl) に協力してもらえると助かります。 ### NumPy に新しいロゴができました! _2020年6月24日_ -- NumPyのロゴが新しくなりました: <img src="/images/logos/numpy_logo.svg" alt="NumPyのロゴ" title="新しいNumPyロゴ" width=300> 新しいロゴは、古いロゴに比べて、モダンでよりクリーンなデザインになりました。 新しいロゴをデザインしてくれたIsabela Presedo-Floydと、15年以上にわたって使用してきた旧ロゴをデザインしてくれたTravis Vaughtに感謝します。 ### NumPy 1.19.0 リリース _2020年6月20日_ -- NumPy 1.19.0 がリリースされました。 このバージョンは Python 2系のサポートがない最初のリリースであり、"クリーンアップ用のリリース" です。 サポートされている一番古いPython のバージョンは Python 3.6 になりました。 また、今回の重要な新機能はNumPy 1.17.0で導入された乱数生成用のインフラにCythonからアクセスできるようになったことです。 ### ドキュメント受諾期間 _2020年5月11日_ -- NumPyは、 Googleのシーズンオブドキュメントプログラムのメンター団体の1つとして選ばれました。 NumPy のドキュメントを改善するために、テクニカルライターと協力するこの機会を楽しみにしています! 詳細については、 [シーズンオブドキュメント公式サイト](https://developers.google.com/season-of-docs/) と [アイデアページ](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas) をご覧ください。 ### NumPy 1.18.0 リリース _2019年12月22日_ -- NumPy 1.18.0 がリリースされました。 このリリースは、1.17.0での主要な変更の後の、まとめのようなリリースです。 Python 3.5 をサポートする最後のマイナーリリースになります。 今回のリリースでは、64ビットのBLASおよびLAPACKライブラリとリンクするためのインフラの追加や、`numpy.random`のための新しいC-APIの追加などが行われました。 詳細については、 [リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.0) を参照してください。 ### NumPyはChan Zuckerberg財団から助成金を受けました。 _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つであるOpenBLASが、Chan Zuckerberg財団の[Essential Open Source Software for Scienceプログラム](https:/chanzuckerberg.comeoss)を通じて、科学に不可欠なオープンソースツールのソフトウェアのメンテナンス、成長、開発、コミュニティへの参加などを支援する195,000ドルの共同助成金を獲得したことを発表しました。 この助成金は、Numpy ドキュメントやウェブサイトの再設計などの改善に向けた取り組みを促進するために使用されます。 大規模かつ急速に拡大するユーザーの体験をより良くし、プロジェクトの長期的な持続可能性を確保するためのコミュニティ開発を行っていきます。 OpenBLASチームは、技術的に非常に重要な問題である、スレッド安全性、AVX-512に対処することに注力します。 また、スレッドローカルストレージ(TLS) の問題や、OpenBLASが依存するReLAPACK(再帰的なLAPACK) のアルゴリズムの改善も実施します。 提案されたイニシアチブとその成果の詳細については、 [フルグラントプロポーザル](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) を参照してください。 この取り組みは2019年12月1日から始まり、今後12ヶ月間継続実施される予定です。 <a name="releases"></a> ## 過去のリリース こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号`x.y.z` の`z`だけが変更されたもの)は新しい機能追加はされず、マイナーリリース (`y` が増えたもの)は、新しい機能追加されています。 -- NumPy 2.1.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2024年11月2日_. +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _2024年10月5日_. - NumPy 2.1.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _2024年9月3日_. - NumPy 2.0.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _2024年8月26日_. - NumPy 2.1.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _2024年8月18日_. - NumPy 1.22.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022年5月20日_. - NumPy 2.0.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _2024年6月16日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _ 2024年1月2日_. - NumPy 1.26.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _ 2024年1月2日_. - NumPy 1.26.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023年11月12日_. - NumPy 1.26.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023年10月14日_. - NumPy 1.26.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _2023年9月16日_. - NumPy 1.25.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023年7月31日_. - NumPy 1.25.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _2023年7月8日_. - NumPy 1.24.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _2023年6月26日_. - NumPy 1.25.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _2023年6月17日_. - NumPy 1.24.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _2023年4月22日_. - NumPy 1.24.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _2023年2月5日_. - NumPy 1.24.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _2022年12月26日_. - NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年4月19日_. - NumPy 1.23.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _2022年11月19日_. - NumPy 1.23.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _2022年10月12日_. - NumPy 1.23.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _2022年9月9日_. - NumPy 1.23.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _2022年8月14日_. - NumPy 1.23.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _2022年7月8日_. - NumPy 1.23.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _2022年6月22日_. - NumPy 1.22.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _2022年5月20日_. - NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _2022年4月12日_. - NumPy 1.22.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2022年3月7日_. - NumPy 1.22.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _2022年2月3日_. - NumPy 1.22.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _2022年1月14日_. - NumPy 1.22.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _2021年12月31日_. - NumPy 1.21.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _2021年12月19日_. - NumPy 1.21.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年6月22日_. - NumPy 1.20.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年5月10日_. - NumPy 1.20.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021年1月30日_. - NumPy 1.19.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021年1月5日_. - NumPy 1.19.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020年6月20日_. - NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年5月3日_. - NumPy 1.17.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020年1月1日_. - NumPy 1.18.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _2019年12月22日_. - NumPy 1.17.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _2019年7月26日_. - NumPy 1.16.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019年1月14日_. - NumPy 1.15.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018年7月23日_. - NumPy 1.14.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018年1月7日_.
numpy/numpy.org
5d193cdd52d6fb6cf42ec11830573be78db77b06
New translations news.md (Catalan)
diff --git a/content/ca/news.md b/content/ca/news.md new file mode 100644 index 0000000..7a7aba2 --- /dev/null +++ b/content/ca/news.md @@ -0,0 +1,322 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 +--- + +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. + + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + +<img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. + + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + +<a name="releases"></a> + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
d78fbfb66a3a50e81accefecc83db2abe3018363
New translations news.md (Arabic)
diff --git a/content/ar/news.md b/content/ar/news.md new file mode 100644 index 0000000..08bd83d --- /dev/null +++ b/content/ar/news.md @@ -0,0 +1,322 @@ +--- +title: الأخبار +sidebar: false +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 +--- + +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. + + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + +<img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. + + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + +<a name="releases"></a> + +## الإصدارات + +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
09a27a3d3e2748b729a4d43dc1d259a69311494f
New translations news.md (Spanish)
diff --git a/content/es/news.md b/content/es/news.md index 1e34cd9..209ea5b 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,306 +1,322 @@ --- title: Noticias sidebar: false newsHeader: "¡NumPy 2.0 ha sido lanzado!" date: 2024-06-17 --- +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + ### Lanzamiento de NumPy 2.1.0 _18 de agosto 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. Además de las habituales correcciones de errores y soporte actualizado de Python, ayuda a que NumPy vuelva a su ciclo de publicación habitual después del extenso desarrollo de 2.0. Los aspectos más destacados son: - Soporte para Python 3.13. - Soporte preliminar para Python 3.13 de hilos libres. - Compatibilidad con la norma array-api 2023.12. Esta versión es compatible con las versiones 3.10-3.13 de Python. ### Lanzamiento de NumPy 2.0.0 _16 de junio de 2024_ -- NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características interesantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían producirse en un lanzamiento menor regular, como una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que podrían no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen: -- La [Guía de migración de NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [notas de lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) - Emisión de anuncios para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) La publicación ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) cuenta un poco de la historia sobre cómo se llegó a este lanzamiento. ### Fecha de lanzamiento de NumPy 2.0: 16 de junio _23 de mayo de 2024_ -- Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene **cambios disruptivos** frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puedes, por favor verifica que tu código funciona con NumPy `2.0.0rc2`. **Por favor, revisa lo siguiente para más detalles:** -- La [guía de migración a NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) -- Las [ notas del lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) +- La [Guía de migración de NumPy 2.0](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- Las [notas de lanzamiento 2.0.0](https://numpy.org/devdocs/release/2.0.0-notes.html) - Emisión de anuncios para actualizaciones de estado: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### Recaudación de fondos de fin de año de NumFOCUS _19 de diciembre de 2023_ -- NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS. Utiliza una URL única que te permitirá rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE  ### NumPy 1.26.0 ha sido lanzado _16 de septiembre de 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) ahora está disponible. Los aspectos más destacados del lanzamiento son: * Soporte de Python 3.12.0. * Compatibilidad con Cython 3.0.0. * Utilización del sistema de compilación Meson * Actualización del soporte de SIMD * Correcciones de f2py, meson y soporte de bind(x) * Soporte para la librería actualizada Accelerate BLAS/LAPACK La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la transición al sistema de compilación Meson y que provee soporte para Cython 3.0.0. Un total de 20 personas contribuyeron a esta versión y 59 solicitudes de cambios fueron fusionadas. Las versiones de Python compatibles con esta versión son 3.9-3.12. ### numpy.org ya está disponible en japonés y portugués _ 2 de agosto de 2023_ -- numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios: _Portugués:_ * Melissa Weber Mendonça (melissawm) * Precios Ricardo (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japonés:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI. De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations) ### NumPy 1.25.0 ha sido lanzado _17 de junio de 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: * Soporte para MUSL, ahora hay ruedas MUSL. * Soporte para el compilador de Fujitsu C/C++. * Los arreglos de objetos ahora están soportadas en einsum. * Soporte para la multiplicación de matrices in situ (`@=`). NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias. Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas. Las versiones de Python soportadas por este lanzamiento son 3.9-3.11. ### Fomentar una Cultura Inclusiva: Convocatoria de Participación _10 de mayo de 2023_ -- Fomentar una Cultura Inclusiva: Convocatoria de Participación ¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte [aquí](https://contributor-experience.org/docs/posts/dei-report/). ### Transición en el liderazgo del equipo de documentación de NumPy _6 de enero de 2023_ –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol. ### Lanzamiento de NumPy 1.24.0 _18 de diciembre de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: * Nuevas palabras clave "dtype" y "casting" para las funciones de apilamiento. * Nuevas características y correcciones de F2PY. * Muchas nuevas obsolescencias, revísalas. * Muchas obsolescencias caducadas, El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11. ### NumPy 1.23.0 ha sido lanzado _22 de junio de 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: * Implementación de `loadtxt` en C, mejorando enormemente su rendimiento. * Exposición de DLPack a nivel Python para facilitar el intercambio de datos. * Cambios a la promoción y comparación de dtypes estructurados. * Mejoras a f2py. El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc. ### Estudio de investigación NumFOCUS DEI: llamado a participar _13 de abril de 2022_ -- NumPy está trabajando con [NumFOCUS](http://numfocus.org/) en un [proyecto de investigación](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) financiado por la [Fundación Gordon & Betty Moore](https://www.moore.org/) para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy. **¿Estás interesado en compartir tus experiencias?** Por favor, completa este breve [formulario de "Interés del Participante"](https://numfocus.typeform.com/to/WBWVJSqe), que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Tu participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación. ### Lanzamiento de NumPy 1.22.0 _31 de diciembre de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) ya está disponible. Los aspectos más destacados del lanzamiento son: * Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. Esta es probablemente la mejora más visible para el usuario en esta versión. * Una versión preliminar del propuesto [Estándar API de Arreglos](https://data-apis.org/array-api/latest/) es suministrada (véase [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX. * NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor). * Nuevos métodos para `cuantil`, `percentil` y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura. * Las funciones universales se han refactorizado para implementar la mayor parte de [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). Esto también desbloquea la capacidad de experimentar con la futura API DType. * Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream. NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. ### Promoviendo una cultura inclusiva en el ecosistema científico de Python _31 de agosto de 2021_ -- Nos complace anunciar que la Iniciativa Chan Zuckerberg ha [otorgado una subvención](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas. Como parte del [Programa de Software Esencial de Código Abierto para la Ciencia de CZI](https://chanzuckerberg.com/eoss/), esta subvención suplementaria de [Diversidad &e Inclusión](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas). Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una idea más clara tanto de lo que funciona y lo que no en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades. Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! [Puedes leer la propuesta completa aquí](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### Encuesta de NumPy de 2021 _12 de julio de 2021_ -- En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses. Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español. Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Lanzamiento de NumPy 1.21.0 _23 de junio de 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) ya está disponible. Los aspectos más destacados de esta versión son: - trabajo SIMD continuo que cubre más funciones y plataformas, - trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo, - universal2 wheels para Python 3.8 y Python 3.9 en Mac, - documentación mejorada, - anotaciones mejoradas, - nuevo `PCG64DXSM` generador de bits para números aleatorios. Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10. ### Resultados de la encuesta de NumPy de 2020 _22 de junio de 2021_ -- En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/. ### Lanzamiento de NumPy 1.20.0 _30 de enero de 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más importantes son: - Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo `numpy.typing` que contiene los alias `ArralyLike` y `DtypeLike` que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código. - Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversidad en el proyecto NumPy _20 de septiembre de 2020_ -- Escribimos una [declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy](/diversity_sep2020). ### Primer artículo oficial de NumPy publicado en Nature! _16 de septiembre de 2020_ -- Nos complace anunciar la publicación del [primer artículo oficial sobre NumPy](https://www.nature.com/articles/s41586-020-2649-2) como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX. ### Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias? _14 de septiembre de 2020_ -- Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de - actualizar su versión de `pip` al menos a la 20.1 para soportar `manylinux2010` y `manylinux2014` - utiliza [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) o `--only-binary=:all:` para evitar que `pip` intente compilar desde la fuente. ### Lanzamiento de NumPy 1.19.2 _10 de septiembre de 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el [lanzamiento próximo de Cython 3.x](http://docs.cython.org/en/latest/src/changes.html) y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las wheels para aarch64 están construidas con la última versión de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux. ### La encuesta inaugural de NumPy ya está disponible! _2 de julio de 2020_ -- Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés. Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: [aquí](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### ¡NumPy tiene un nuevo logo! _24 de junio de 2020_ -- NumPy tiene ahora un nuevo logo: <img src="/images/logos/numpy_logo.svg" alt="Logo de NumPy" title="El nuevo logo de NumPy" width=300> El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años. ### Lanzamiento de NumPy 1.19.0 _20 de junio de 2020_ -- NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una "versión de limpieza". La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython. ### Aceptación a Season of Docs _11 de mayo de 2020_ -- NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte [el sitio oficial de Season of Docs](https://developers.google.com/season-of-docs/) y nuestra [página de ideas](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### Lanzamiento de NumPy 1.18.0 _22 de diciembre de 2019_ -- NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para `numpy.random`. Por favor revise las [notas del lanzamiento](https://github.com/npm/npm/releases/tag/v2.11.0) para conocer más detalles. ### NumPy recibe una subvención de la Iniciativa Chan Zuckerberg _15 de noviembre de 2019_ -- Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su [programa Esencial de Software Abierto para la Ciencia](https://chanzuckerberg.com/eoss/) que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia. Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS. Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la [propuesta completa de subvención](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses. <a name="releases"></a> ## Lanzamientos Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la `z` en el número de versión `x.y.z`) no tienen nuevas características; las versiones menores (aumenta la `y`) sí las tienen. +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 de septiembre 2024_. - NumPy 2.0.2 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 de agosto 2024_. - NumPy 2.1.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 de agosto de 2024_. - NumPy 2.0.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 de julio de 2024_. - NumPy 2.0.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 de junio de 2024_. - NumPy 1.26.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 de febrero de 2024_. - NumPy 1.26.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 de enero de 2024_. - NumPy 1.26.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _12 de noviembre de 2023_. - NumPy 1.26.1 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 de octubre de 2023_. - NumPy 1.26.0 ([notas de publicación](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 de septiembre de 2023_. - NumPy 1.25.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 de julio de 2023_. - NumPy 1.25.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 de julio de 2023_. - NumPy 1.24.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junio de 2023_. - NumPy 1.25.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 de junio de 2023_. - NumPy 1.24.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. - NumPy 1.24.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 de febrero de 2023_. - NumPy 1.24.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 de diciembre de 2022_. - NumPy 1.24.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 de diciembre de 2022_. - NumPy 1.23.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _19 de noviembre de 2022_. - NumPy 1.23.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 de octubre de 2022_. - NumPy 1.23.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 de septiembre de 2022_. - NumPy 1.23.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 de agosto de 2022_. - NumPy 1.23.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 de julio de 2022_. - NumPy 1.23.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 de junio de 2022_. - NumPy 1.22.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 de mayo de 2022_. - NumPy 1.21.6 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 de abril de 2022_. - NumPy 1.22.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 de marzo de 2022_. - NumPy 1.22.2 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 de febrero de 2022_. - NumPy 1.22.1 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 de enero de 2022_. - NumPy 1.22.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 de diciembre de 2021_. - NumPy 1.21.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 de diciembre de 2021_. - NumPy 1.21.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junio de 2021_. - NumPy 1.20.3 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de mayo de 2021_. - NumPy 1.20.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de enero de 2021_. - NumPy 1.19.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de enero de 2021_. - NumPy 1.19.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junio de 2020_. - NumPy 1.18.4 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de mayo de 2020_. - NumPy 1.17.5 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de enero de 2020_. - NumPy 1.18.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de diciembre de 2019_. - NumPy 1.17.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julio de 2019_. - NumPy 1.16.0 ([notas de lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julio de 2018_. - NumPy 1.14.0 ([notas del lanzamiento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de enero de 2018_.
numpy/numpy.org
e0c7183748aa4c9591faa4787d1e0b84aa6f1782
New translations news.md (French)
diff --git a/content/fr/news.md b/content/fr/news.md new file mode 100644 index 0000000..7a7aba2 --- /dev/null +++ b/content/fr/news.md @@ -0,0 +1,322 @@ +--- +title: News +sidebar: false +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 +--- + +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, improvements to the StringDType, and better support for free threaded Python. Highlights are: + +* New functions `matvec` and `vecmat`, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. + + +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + +### NumFOCUS end of the year fundraiser +_Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. + +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  + +### NumPy 1.26.0 released + +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: + +* Python 3.12.0 support. +* Cython 3.0.0 compatibility. +* Use of the Meson build system +* Updated SIMD support +* f2py fixes, meson and bind(x) support +* Support for the updated Accelerate BLAS/LAPACK library + +The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. + +The Python versions supported by this release are 3.9-3.12. + +### numpy.org is now available in Japanese and Portuguese + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: + +_Portuguese:_ +* Melissa Weber Mendonça (melissawm) +* Ricardo Prins (ricardoprins) +* Getúlio Silva (getuliosilva) +* Julio Batista Silva (jbsilva) +* Alexandre de Siqueira (alexdesiqueira) +* Alexandre B A Villares (villares) +* Vini Salazar (vinisalazar) + +_Japanese:_ +* Atsushi Sakai (AtsushiSakai) +* KKunai +* Tom Kelly (TomKellyGenetics) +* Yuji Kanagawa (kngwyu) +* Tetsuo Koyama (tkoyama010) + +The work on the translation infrastructure is supported with funding from CZI. + +Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) + +### NumPy 1.25.0 released + +_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: + +* Support for MUSL, there are now MUSL wheels. +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). + +The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. + +A total of 148 people contributed to this release and 530 pull requests were merged. + +The Python versions supported by this release are 3.9-3.11. + +### Fostering an Inclusive Culture: Call for Participation + +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation + +How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). + +### NumPy documentation team leadership transition + +_Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. + +### NumPy 1.24.0 released + +_Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: + +* New "dtype" and "casting" keywords for stacking functions. +* New F2PY features and fixes. +* Many new deprecations, check them out. +* Many expired deprecations, + +The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. + +### Numpy 1.23.0 released + +_Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: + +* Implementation of `loadtxt` in C, greatly improving its performance. +* Exposure of DLPack at the Python level for easy data exchange. +* Changes to the promotion and comparisons of structured dtypes. +* Improvements to f2py. + +The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. + +### NumFOCUS DEI research study: call for participation + +_Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. + +**Interested in sharing your experiences?** + +Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. + +### Numpy 1.22.0 release + +_Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: + +* Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. +* A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. +* NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. +* New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. +* The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. +* A new configurable memory allocator for use by downstream projects. + +NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. + +### Advancing an inclusive culture in the scientific Python ecosystem + +_August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. + +As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). + +This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. + +The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). + +### 2021 NumPy survey + +_July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. + +It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. + +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. + + +### Numpy 1.21.0 release + +_Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: + +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. + +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. + + +### 2020 NumPy survey results + +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. + + +### Numpy 1.20.0 release + +_Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: +- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. +- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). + +### Diversity in the NumPy project + +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). + + +### First official NumPy paper published in Nature! + +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. + + +### Python 3.9 is coming, when will NumPy release binary wheels? + +_Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to +- update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` +- use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. + + +### Numpy 1.19.2 release + +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. + +### The inaugural NumPy survey is live! + +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. + +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). + + +### NumPy has a new logo! + +_Jun 24, 2020_ -- NumPy now has a new logo: + +<img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> + +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. + + +### NumPy 1.19.0 release + +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. + + +### Season of Docs acceptance + +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). + + +### NumPy 1.18.0 release + +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. + +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. + + +### NumPy receives a grant from the Chan Zuckerberg Initiative + +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. + +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. + +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. + + +<a name="releases"></a> + +## Releases + +Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. +- NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. +- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. +- NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. +- NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. +- NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. +- NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. +- NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. +- NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. +- NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. +- NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. +- NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
05a541cc198a898e2f7eea93d73659a8b95ffb4b
announce the NumPy 2.2.0 release
diff --git a/content/en/news.md b/content/en/news.md index 04f94e6..396c9a0 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,473 +1,490 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.1 released!" -date: 2024-08-18 +newsHeader: "NumPy 2.2.0 released!" +date: 2024-12-8 --- +### NumPy 2.2.0 released + +_8 Dec, 2024_ -- The NumPy 2.2.0 release is a quick release that brings us back +into sync with the usual twice yearly release cycle. There have been a number +of small cleanups, improvements to the StringDType, and better support for free +threaded Python. Highlights are: + +* New functions ``matvec`` and ``vecmat``, +* Many improved annotations, +* Improved support for the new StringDType, +* Improved support for free threaded Python, +* Fixes for f2py. + +This release supports Python versions 3.10-3.13. + + ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.2.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.2.0)) -- _8 Dec 2024_. - NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
ab458d1feed23346f061e119c9a9e38e298f80c6
audit workflows via zizmor and correct use of env variables
diff --git a/.github/workflows/create-translations-pr.yml b/.github/workflows/create-translations-pr.yml index 3804511..c692fe9 100644 --- a/.github/workflows/create-translations-pr.yml +++ b/.github/workflows/create-translations-pr.yml @@ -1,56 +1,61 @@ name: Create Translations PR on: workflow_dispatch: inputs: language_code: description: 'Crowdin language code for the language of interest' required: true jobs: create-translations-pr: runs-on: ubuntu-latest # Run only on main branch in upstream repo. if: ${{ github.repository == 'numpy/numpy.org' && github.ref == 'refs/heads/main' }} steps: - name: Checkout numpy.org uses: actions/checkout@v4 with: repository: 'numpy/numpy.org' path: 'numpy.org' ref: 'main' fetch-depth: 0 # Gets full github history. # Full history is needed for the scripted interactive rebase # which takes place in create_branch_for_language.sh below. + persist-credentials: false - name: Checkout scientific-python-translations automations uses: actions/checkout@v4 with: repository: 'scientific-python-translations/automations' path: 'automations' ref: 'main' + persist-credentials: false - name: Create translations branch for language of interest env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + LANG: ${{ github.event.inputs.language_code }} run: | git config --global user.email "[email protected]" git config --global user.name "GitHub Actions" - ../automations/scripts/create_branch_for_language.sh origin main l10n_main ${{ github.event.inputs.language_code }} + ../automations/scripts/create_branch_for_language.sh origin main l10n_main "$LANG" branch_name=$(git rev-parse --abbrev-ref HEAD) - git push -u origin $branch_name + git push -u origin "$branch_name" echo "BRANCH_NAME=$branch_name" >> $GITHUB_ENV working-directory: ./numpy.org - name: Create Pull Request env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + BRANCH_NAME: ${{ env.BRANCH_NAME }} + LANG: ${{ github.event.inputs.language_code }} run: | - language_name=$(../automations/scripts/get_language_name.sh ${{ github.event.inputs.language_code }}) - gh pr create --base main --head ${{ env.BRANCH_NAME }} --title "Update translations for $language_name" \ + language_name=$(../automations/scripts/get_language_name.sh "$LANG") + gh pr create --base main --head "$BRANCH_NAME" --title "Update translations for $language_name" \ --body "This PR to update translations for $language_name was generated by the GitHub workflow, \ auto-translations-pr.yml and includes all commits from this repo's Crowdin branch for the language \ of interest. A final check of the rendered docs is needed to identify if there are any formatting \ errors due to incorrect string segmentation by Crowdin. If there are such formatting errors, they \ should be fixed directly on this branch, not through Crowdin." working-directory: ./numpy.org diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 822b8e1..8d29fe6 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -1,68 +1,69 @@ # https://gohugo.io/hosting-and-deployment/hosting-on-github/ name: github pages on: push: branches: - main # Allows you to run this workflow manually from the Actions tab workflow_dispatch: # Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. # However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. concurrency: group: pages cancel-in-progress: false jobs: build-and-deploy: runs-on: ubuntu-latest steps: - name: Checkout - uses: actions/checkout@v3 + uses: actions/checkout@v4 with: + persist-credentials: false submodules: recursive fetch-depth: 0 - name: Read Hugo version id: hugo-version run: cat netlify.toml | grep HUGO_VERSION | tr -d ' "' >> "$GITHUB_OUTPUT" - name: Read DART SASS version id: dart-sass-version run: cat netlify.toml | grep --max-count=1 DART_SASS_VERSION | tr -d ' "' >> "$GITHUB_OUTPUT" - name: Install Hugo CLI env: HUGO_VERSION: ${{ steps.hugo-version.outputs.HUGO_VERSION }} run: | - wget -O ${{ runner.temp }}/hugo.deb https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_extended_${HUGO_VERSION}_linux-amd64.deb \ - && sudo dpkg -i ${{ runner.temp }}/hugo.deb + wget -O /tmp/hugo.deb https://github.com/gohugoio/hugo/releases/download/v${HUGO_VERSION}/hugo_extended_${HUGO_VERSION}_linux-amd64.deb \ + && sudo dpkg -i /tmp/hugo.deb - name: Install Dart Sass env: DART_SASS_VERSION: ${{ steps.dart-sass-version.outputs.DART_SASS_VERSION }} DART_SASS_URL: "https://github.com/sass/dart-sass/releases/download/" run: | export DART_SASS_TARBALL="dart-sass-${DART_SASS_VERSION}-linux-x64.tar.gz" && \ curl -LJO ${DART_SASS_URL}/${DART_SASS_VERSION}/${DART_SASS_TARBALL} && \ tar -xf ${DART_SASS_TARBALL} && \ rm ${DART_SASS_TARBALL} - name: Generate config run: python gen_config.py - name: Build with Hugo run: | export PATH=$(pwd)/dart-sass:$PATH hugo --gc --minify - name: Deploy pages uses: JamesIves/github-pages-deploy-action@releases/v4 with: folder: ./public repository-name: numpy/numpy.github.com branch: main token: ${{ secrets.PERSONAL_TOKEN }}
numpy/numpy.org
15b83f5c82ffc1aa576bea5cf2065c7190d323d9
Add shortcode to contribute.md
diff --git a/content/en/contribute.md b/content/en/contribute.md index 32f5ea4..f23d40d 100644 --- a/content/en/contribute.md +++ b/content/en/contribute.md @@ -1,119 +1,117 @@ --- title: Contribute to NumPy sidebar: false --- The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at <[email protected]> or on [Slack](https://numpy-team.slack.com) (write <[email protected]> for an invite). We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. ### 'How to Contribute to NumPy' comic For a visual guide, check out this [comic](https://heyzine.com/flip-book/3e66a13901.html). -<a href="https://heyzine.com/flip-book/3e66a13901.html"> - <img src="static/images/content_images/numpy-comic.png" alt="NumPy Contributor comic book cover"> -</a> +{{< comic >}} ### Writing code Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase. <br>Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: * check if older bugs are still present * find duplicate issues and link related ones * add good self-contained reproducers to issues * label issues correctly (this requires triage rights -- just ask) Please just dive in. ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. ### Donate If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate).
numpy/numpy.org
7b8b64e909ee9e8b7e04f779c382c585f69c2deb
Add shortcode
diff --git a/layouts/shortcodes/comic.html b/layouts/shortcodes/comic.html new file mode 100644 index 0000000..e69de29
numpy/numpy.org
59262ff6bd8f64cd49747b0edd3c4a8f492651e6
Add resized comic cover
diff --git a/static/images/content_images/numpy-comic.png b/static/images/content_images/numpy-comic.png new file mode 100644 index 0000000..b2cc417 Binary files /dev/null and b/static/images/content_images/numpy-comic.png differ
numpy/numpy.org
5f038b48ddb15e6edebce3e1b47790125f036d35
Convert makefile to original state
diff --git a/Makefile b/Makefile index a53582b..cc76605 100644 --- a/Makefile +++ b/Makefile @@ -1,49 +1,49 @@ # type `make help` to see all options BASEURL ?= ifdef BASEURL BASEURLARG=-b $(BASEURL) endif .PHONY: help prepare teams-clean teams serve clean # Add help text after each target name starting with '\#\#' help: ## show this help @echo "\nHelp for this makefile" @echo "Possible commands are:" @grep -h "##" $(MAKEFILE_LIST) | grep -v grep | sed -e 's/\(.*\):.*##\(.*\)/ \1: \2/' prepare: git submodule update --init - python3 gen_config.py + python gen_config.py # All translations share the <team>.toml files in the en translation TEAMS_DIR = content/en/teams TEAMS = emeritus-maintainers maintainers docs-team triage-team survey-team web-team TEAMS_QUERY = python themes/scientific-python-hugo-theme/tools/team_query.py $(TEAMS_DIR)/%.toml: $(TEAMS_QUERY) --org numpy --team "$*" > $(TEAMS_DIR)/$*.toml teams-clean: prepare for team in $(TEAMS); do \ rm -f $(TEAMS_DIR)/$${team}.toml ;\ done teams: | teams-clean $(patsubst %,$(TEAMS_DIR)/%.toml,$(TEAMS)) ## generates numpy.org team gallery pages serve: prepare ## serve the website hugo $(BASEURLARG) --printI18nWarnings server -D # Serve the site for development purposes (leaving submodules as-is, etc). serve-dev: python gen_config.py hugo $(BASEURLARG) --printI18nWarnings server --buildDrafts --disableFastRender --poll 1000ms html: prepare ## build the website in ./public hugo $(BASEURLARG) clean: ## remove the build artifacts, mainly the "public" directory rm -rf public
numpy/numpy.org
d41b9205537885dd06db91aa5c780d42720e9880
Add link and image of NumPy comcis
diff --git a/Makefile b/Makefile index cc76605..a53582b 100644 --- a/Makefile +++ b/Makefile @@ -1,49 +1,49 @@ # type `make help` to see all options BASEURL ?= ifdef BASEURL BASEURLARG=-b $(BASEURL) endif .PHONY: help prepare teams-clean teams serve clean # Add help text after each target name starting with '\#\#' help: ## show this help @echo "\nHelp for this makefile" @echo "Possible commands are:" @grep -h "##" $(MAKEFILE_LIST) | grep -v grep | sed -e 's/\(.*\):.*##\(.*\)/ \1: \2/' prepare: git submodule update --init - python gen_config.py + python3 gen_config.py # All translations share the <team>.toml files in the en translation TEAMS_DIR = content/en/teams TEAMS = emeritus-maintainers maintainers docs-team triage-team survey-team web-team TEAMS_QUERY = python themes/scientific-python-hugo-theme/tools/team_query.py $(TEAMS_DIR)/%.toml: $(TEAMS_QUERY) --org numpy --team "$*" > $(TEAMS_DIR)/$*.toml teams-clean: prepare for team in $(TEAMS); do \ rm -f $(TEAMS_DIR)/$${team}.toml ;\ done teams: | teams-clean $(patsubst %,$(TEAMS_DIR)/%.toml,$(TEAMS)) ## generates numpy.org team gallery pages serve: prepare ## serve the website hugo $(BASEURLARG) --printI18nWarnings server -D # Serve the site for development purposes (leaving submodules as-is, etc). serve-dev: python gen_config.py hugo $(BASEURLARG) --printI18nWarnings server --buildDrafts --disableFastRender --poll 1000ms html: prepare ## build the website in ./public hugo $(BASEURLARG) clean: ## remove the build artifacts, mainly the "public" directory rm -rf public diff --git a/content/en/contribute.md b/content/en/contribute.md index a0968d2..9f8cd62 100644 --- a/content/en/contribute.md +++ b/content/en/contribute.md @@ -1,112 +1,119 @@ --- title: Contribute to NumPy sidebar: false --- The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at <[email protected]> or on [Slack](https://numpy-team.slack.com) (write <[email protected]> for an invite). We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. +### 'How to Contribute to NumPy' comic +For a visual guide, check out this [comic](https://heyzine.com/flip-book/3e66a13901.html). + +<a href="https://heyzine.com/flip-book/3e66a13901.html"> + <img src="/images/numpy-comic.png"/> +</a> + ### Writing code Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase. <br>Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: * check if older bugs are still present * find duplicate issues and link related ones * add good self-contained reproducers to issues * label issues correctly (this requires triage rights -- just ask) Please just dive in. ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it became clear that to ensure stability and growth we would need financial support. [This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, and other kinds of funding. We have a number of ideas and of course we welcome more. Fundraising is a scarce skill here -- we'd appreciate your help. ### Donate If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). diff --git a/images/numpy-comic.png b/images/numpy-comic.png new file mode 100644 index 0000000..24abdab Binary files /dev/null and b/images/numpy-comic.png differ
numpy/numpy.org
1f824621497cec390cc1db3e1b37dc7a893ae998
announce the NumPy 2.1.3 release
diff --git a/content/en/news.md b/content/en/news.md index 3e479cf..04f94e6 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,472 +1,473 @@ --- title: News sidebar: false newsHeader: "NumPy 2.1 released!" date: 2024-08-18 --- ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.3)) -- _2 Nov 2024_. - NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
e266b4ac39ecb903b388fb7acfd9206c60e5927a
announce the NumPy 2.1.2 release
diff --git a/content/en/news.md b/content/en/news.md index 78dd3fa..3e479cf 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,471 +1,472 @@ --- title: News sidebar: false newsHeader: "NumPy 2.1 released!" date: 2024-08-18 --- ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.2)) -- _5 Oct 2024_. - NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
90365677482a7d7ddf9258e46740863fbc6b8dd2
Fix display of ecosystem tab for Ja and Pt (#784)
diff --git a/assets/css/tabs.scss b/assets/css/tabs.scss index c1b84be..560da2b 100644 --- a/assets/css/tabs.scss +++ b/assets/css/tabs.scss @@ -1,136 +1,137 @@ [role="tablist"] { justify-content: center; } table td:not([align]), table th:not([align]) { text-align: inherit; } table td, table th { vertical-align: top; } .tabs-section { display: flex; flex-direction: column; } .tabs-section .container { display: flex; flex-direction: column; } .tabs-title { display: flex; justify-content: center; letter-spacing: 1.5px; font-size: 27px; margin: 30px 0; } .visualization, .data-science, .machine-learning, .array-libraries { max-width: 900px; margin: 15px auto; } @media only screen and (max-width: 1200px) { .tabs-section { margin: 30px 10px; } .tabs-title { margin: 30px; } } .grid-container { display: grid; grid-template-columns: auto auto; grid-gap: 20px; } .grid-container > div { background-color: var(--pst-color-background); text-align: middle; } @media only screen and (max-width: 600px) { .grid-container { display: block; } } /* Visualization */ .visualization-images > img { border-radius: 10px; } .image-grid { display: grid; grid-template-columns: auto auto auto auto; grid-gap: 10px; } .image-grid > div { background-color: var(--pst-color-surface); border: 2px solid var(--pst-color-surface); border-radius: 10px; padding: 10px; } /* Scientific Domains */ section.scientific-domains { max-width: 900px !important; & ul { display: flex; flex-wrap: wrap; list-style: none; margin: 15px auto; padding-inline-start: 0; & li { align-content: center; font-size: 0.8rem; line-height: 130%; margin: 0.2em 0.4em; flex-basis: 13%; + align-self: baseline; & header { // FIXME: Use appropriate PST color for this header text. color: var(--pst-color-text-base); font-weight: 700; // Ensure headers are the same minimum height (some wrap // to two lines). min-height: 3.3em; text-align: left; } & img { width: 50px; height: 50px; margin-bottom: 0.5em; } & ul { align-content: left; display: flex; flex-direction: column; padding-inline-start: 0; & li { margin-left: 0em; } } } } } /* Array Libraries */ img.first-column-layout { max-width: 100px; max-height: 30px; margin: 0px 20px 0px 10px; } td.left-text { vertical-align: middle; } diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index fa57763..b22cc54 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -1,373 +1,275 @@ params: machinelearning: paras: - - - para1: NumPy constituye la base de potentes librerías de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de librerías basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones&mdash; entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra librería de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. + - para1: NumPy constituye la base de potentes librerías de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://www.scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de librerías basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones&mdash; entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra librería de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. para2: Las técnicas estadísticas denominadas métodos [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) &mdash; uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático. arraylibraries: intro: - - - text: La API de NumPy es el punto de partida cuando se escriben librerías para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona. + - text: La API de NumPy es el punto de partida cuando se escriben librerías para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona. headers: - - - text: Librería de arreglos - - - text: Capacidades y áreas de aplicación + - text: Librería de arreglos + - text: Capacidades y áreas de aplicación libraries: - - - title: Dask + - title: Dask text: Arreglos distribuidos y paralelismo avanzado para análisis, que permiten un rendimiento a escala. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - - - title: CuPy + - title: CuPy text: Librería de arreglos compatible con NumPy para cálculo acelerado en la GPU con Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.dev - - - title: JAX + - title: JAX text: "Transformaciones componibles de programas NumPy: diferenciar, vectorizar, compilación justo-a-tiempo a GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://jax.readthedocs.io/ - - - title: Xarray + - title: Xarray text: Arreglos multidimensionales indexados y etiquetados para análisis y visualización avanzados. img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - - - title: Sparse + - title: Sparse text: Librería de arreglos dispersos compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - - - title: PyTorch + - title: PyTorch text: Marco de aprendizaje profundo que acelera el camino desde la creación de prototipos de investigación hasta la implantación en producción. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - - - title: TensorFlow + - title: TensorFlow text: Una plataforma integral de aprendizaje automático para crear y desplegar fácilmente aplicaciones basadas en ML. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - - - title: Arrow + - title: Arrow text: Plataforma de desarrollo multilingüe para datos y análisis columnares en memoria. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://arrow.apache.org/ - - - title: xtensor + - title: xtensor text: Arreglos multidimensionales con difusión y computación perezosa para análisis numérico. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - - - title: Awkward Array + - title: Awkward Array text: Manipular datos similares a JSON con expresiones similares a NumPy. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - - - title: uarray + - title: uarray text: Sistema de backend de Python que desacopla la API de la implementación; unumpy proporciona una API de NumPy. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly + - title: tensorly text: Aprendizaje tensorial, álgebra y backends para usar de manera fluida NumPy, PyTorch, TensorFlow o CuPy. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - - - text: Casi todos los científicos que trabajan en Python recurren a la potencia de NumPy. - - - text: "NumPy aporta la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante." + - text: Casi todos los científicos que trabajan en Python recurren a la potencia de NumPy. + - text: "NumPy aporta la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante." libraries: - - - title: Computación Cuántica + - title: Computación Cuántica alttext: Un chip para computador. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - - - url: http://qutip.org + - url: http://qutip.org label: QuTiP - - - url: https://pyquil-docs.rigetti.com/en/stable + - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - - - url: https://qiskit.org + - url: https://qiskit.org label: Qiskit - - - url: https://pennylane.ai + - url: https://pennylane.ai label: PennyLane - - - title: Computación Estadística + - title: Computación Estadística alttext: Un gráfico lineal con la línea moviéndose hacia arriba. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - - - url: https://pandas.pydata.org/ + - url: https://pandas.pydata.org/ label: Pandas - - - url: https://www.statsmodels.org/ + - url: https://www.statsmodels.org/ label: statsmodels - - - url: https://xarray.pydata.org/en/stable/ + - url: https://xarray.pydata.org/en/stable/ label: Xarray - - - url: https://seaborn.pydata.org/ + - url: https://seaborn.pydata.org/ label: Seaborn - - - title: Procesamiento de Señales + - title: Procesamiento de Señales alttext: Un gráfico de barras con valores positivos y negativos. img: /images/content_images/sc_dom_img/signal_processing.svg links: - - - url: https://www.scipy.org/ + - url: https://www.scipy.org/ label: SciPy - - - url: https://pywavelets.readthedocs.io/ + - url: https://pywavelets.readthedocs.io/ label: PyWavelets - - - url: https://python-control.org/ + - url: https://python-control.org/ label: python-control - - - url: https://hyperspy.org/ + - url: https://hyperspy.org/ label: HiperSpy - - - title: Procesamiento de Imágenes + - title: Procesamiento de Imágenes alttext: Una fotografía de las montañas. img: /images/content_images/sc_dom_img/image_processing.svg links: - - - url: https://scikit-image.org/ + - url: https://scikit-image.org/ label: Scikit-image - - - url: https://opencv.org/ + - url: https://opencv.org/ label: OpenCV - - - url: https://mahotas.rtfd.io/ + - url: https://mahotas.rtfd.io/ label: Mahotas - - - title: Grafos y Redes + - title: Grafos y Redes alttext: Un grafo simple. img: /images/content_images/sc_dom_img/sd6.svg links: - - - url: https://networkx.org/ + - url: https://networkx.org/ label: NetworkX - - - url: https://graph-tool.skewed.de/ + - url: https://graph-tool.skewed.de/ label: graph-tool - - - url: https://igraph.org/python/ + - url: https://igraph.org/python/ label: igraph - - - url: https://pygsp.rtfd.io/ + - url: https://pygsp.rtfd.io/ label: PyGSP - - - title: Astronomía + - title: Astronomía alttext: Un telescopio. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - - - url: https://www.astropy.org/ + - url: https://www.astropy.org/ label: AstroPy - - - url: https://sunpy.org/ + - url: https://sunpy.org/ label: SunPy - - - url: https://spacepy.github.io/ + - url: https://spacepy.github.io/ label: SpacePy - - - title: Psicología Cognitiva + - title: Psicología Cognitiva alttext: Una cabeza humana con engranajes. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - - - url: https://www.psychopy.org/ + - url: https://www.psychopy.org/ label: PsychoPy - - - title: Bioinformática + - title: Bioinformática alttext: Una hebra de ADN. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - - - url: https://biopython.org/ + - url: https://biopython.org/ label: BioPython - - - url: http://scikit-bio.org/ + - url: http://scikit-bio.org/ label: Scikit-Bio - - - url: https://github.com/openvax/pyensembl + - url: https://github.com/openvax/pyensembl label: PyEnsembl - - - url: http://etetoolkit.org/ + - url: http://etetoolkit.org/ label: ETE - - - title: Inferencia Bayesiana + - title: Inferencia Bayesiana alttext: Un gráfico con una curva en forma de campanas. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - - - url: https://pystan.readthedocs.io/en/latest/ + - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - - - url: https://docs.pymc.io/ + - url: https://docs.pymc.io/ label: PyMC3 - - - url: https://arviz-devs.github.io/arviz/ + - url: https://arviz-devs.github.io/arviz/ label: ArviZ - - - url: https://emcee.readthedocs.io/ + - url: https://emcee.readthedocs.io/ label: emcee - - - title: Análisis Matemático + - title: Análisis Matemático alttext: Cuatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - - - url: https://www.scipy.org/ + - url: https://www.scipy.org/ label: SciPy - - - url: https://www.sympy.org/ + - url: https://www.sympy.org/ label: SymPy - - - url: https://www.cvxpy.org/ + - url: https://www.cvxpy.org/ label: cvxpy - - - url: https://fenicsproject.org/ + - url: https://fenicsproject.org/ label: FEniCS - - - title: Química + - title: Química alttext: Un tubo de ensayo. img: /images/content_images/sc_dom_img/chemistry.svg links: - - - url: https://cantera.org/ + - url: https://cantera.org/ label: Cantera - - - url: https://www.mdanalysis.org/ + - url: https://www.mdanalysis.org/ label: MDAnalysis - - - url: https://github.com/rdkit/rdkit + - url: https://github.com/rdkit/rdkit label: RDKit - - - url: https://www.pybamm.org/ + - url: https://www.pybamm.org/ label: PyBaMM - - - title: Geociencia + - title: Geociencia alttext: La Tierra. img: /images/content_images/sc_dom_img/geoscience.svg links: - - - url: https://pangeo.io/ + - url: https://pangeo.io/ label: Pangeo - - - url: https://simpeg.xyz/ + - url: https://simpeg.xyz/ label: Simpeg - - - url: https://github.com/obspy/obspy/wiki + - url: https://github.com/obspy/obspy/wiki label: ObsPy - - - url: https://www.fatiando.org/ + - url: https://www.fatiando.org/ label: Fatiando a Terra - - - title: Procesamiento Geográfico + - title: Procesamiento Geográfico alttext: Un mapa. img: /images/content_images/sc_dom_img/GIS.svg links: - - - url: https://shapely.readthedocs.io/ + - url: https://shapely.readthedocs.io/ label: Shapely - - - url: https://geopandas.org/ + - url: https://geopandas.org/ label: GeoPandas - - - url: https://python-visualization.github.io/folium + - url: https://python-visualization.github.io/folium label: Folium - - - title: Arquitectura e Ingeniería + - title: Arquitectura e Ingeniería alttext: Una placa de desarrollo de microprocesadores. img: /images/content_images/sc_dom_img/robotics.svg links: - - - url: https://compas.dev/ + - url: https://compas.dev/ label: COMPAS - - - url: https://cityenergyanalyst.com/ + - url: https://cityenergyanalyst.com/ label: City Energy Analyst - Analista de Energía de Ciudad - - - url: https://nortikin.github.io/sverchok/ + - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "NumPy es el núcleo de un rico ecosistema de librerías de ciencia de datos. Un flujo de trabajo exploratorio típico de ciencia de datos podría verse así:" image1: - - - img: /images/content_images/ds-landscape.png + - img: /images/content_images/ds-landscape.png alttext: Diagrama de las librerías de Python. Las cinco categorías son "Extraer, Transformar, Cargar", "Exploración de Datos", "Modelado de Datos", "Evaluación de Datos" y "Presentación de Datos". image2: - - - img: /images/content_images/data-science.png + - img: /images/content_images/data-science.png alttext: Diagrama de tres círculos superpuestos. Los círculos se denominan "Matemáticas", "Ciencias de la Computación" y "Conocimientos Especializados". En el centro del diagrama, con los tres círculos superpuestos, hay un área denominada "Ciencia de datos". examples: - - - text: "<b>Extraer, Transformar, Cargar: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "<b>Análisis Exploratorio: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - - text: "<b>Modelado y evaluación: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - - text: "<b>Informes en un panel de control: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + - text: "<b>Extraer, Transformar, Cargar: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - text: "<b>Análisis Exploratorio: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - text: "<b>Modelado y evaluación: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - text: "<b>Informes en un panel de control: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - - - text: Para grandes volúmenes de datos, [Dask](https://dask.org) y [Ray](https://ray.io/) están diseñados para escalarse. Las implementaciones estables se basan en el versionado de datos ([DVC](https://dvc.org)), rastreo de experimentos ([MLFlow](https://mlflow.org)), y automatización del flujo de trabajo ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) y [Prefect](https://www.prefect.io)). + - text: Para grandes volúmenes de datos, [Dask](https://dask.org) y [Ray](https://ray.io/) están diseñados para escalarse. Las implementaciones estables se basan en el versionado de datos ([DVC](https://dvc.org)), rastreo de experimentos ([MLFlow](https://mlflow.org)), y automatización del flujo de trabajo ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) y [Prefect](https://www.prefect.io)). visualization: images: - - - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: Un diagrama de flujo hecho en matplotlib - - - url: https://github.com/yhat/ggpy + - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: Un diagrama de dispersión hecho en ggpy - - - url: https://www.journaldev.com/19692/python-plotly-tutorial + - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: Un diagrama de caja hecho en plotly - - - url: https://altair-viz.github.io/gallery/streamgraph.html + - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: Un diagrama de flujo hecho en altair - - - url: https://seaborn.pydata.org + - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: Un gráfico de pares de dos tipos de gráficos, un gráfico de trazado y un gráfico de frecuencias hecho en seaborn - - - url: https://docs.pyvista.org/examples/index.html + - url: https://docs.pyvista.org/examples/index.html img: /images/content_images/v_pyvista.png alttext: Un renderizado de volumen 3D realizado en PyVista. - - - url: https://napari.org + - url: https://napari.org img: /images/content_images/v_napari.png alttext: Una imagen multidimensional hecha en napari. - - - url: https://vispy.org/gallery/index.html + - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: Un diagrama de Voronoi hecho en vispy. content: - - - text: NumPy es un componente esencial en el floreciente [panorama de visualización de Python](https://pyviz.org/overviews/index.html), que incluye [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), y [PyVista](https://github.com/pyvista/pyvista), por nombrar algunos. - - - text: El procesamiento acelerado de arreglos de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar. + - text: NumPy es un componente esencial en el floreciente [panorama de visualización de Python](https://pyviz.org/overviews/index.html), que incluye [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), y [PyVista](https://github.com/pyvista/pyvista), por nombrar algunos. + - text: El procesamiento acelerado de arreglos de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar. diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index e3dc2ba..e6089c6 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -1,219 +1,297 @@ params: machinelearning: paras: - - - para1: NumPyは、[scikit-learn](https://scikit-learn.org)や[SciPy](https://www.scipy.org)のような強力な機械学習ライブラリの基礎を形成しています。機械学習の技術分野が成長するにつれ、NumPyをベースにしたライブラリの数も増えています。[TensorFlow](https://www.tensorflow.org)の深層学習機能は、音声認識や画像認識、テキストベースのアプリケーション、時系列分析、動画検出など、幅広い応用用途があります。[PyTorch](https://pytorch.org)も、コンピュータビジョンや自然言語処理の研究者に人気のある深層学習ライブラリです。[MXNet](https://github.com/apache/incubator-mxnet)もAIパッケージの一つで、深層学習の設計図やテンプレート機能を提供しています。 + - para1: NumPyは、[scikit-learn](https://scikit-learn.org)や[SciPy](https://www.scipy.org)のような強力な機械学習ライブラリの基礎を形成しています。機械学習の技術分野が成長するにつれ、NumPyをベースにしたライブラリの数も増えています。[TensorFlow](https://www.tensorflow.org)の深層学習機能は、音声認識や画像認識、テキストベースのアプリケーション、時系列分析、動画検出など、幅広い応用用途があります。[PyTorch](https://pytorch.org)も、コンピュータビジョンや自然言語処理の研究者に人気のある深層学習ライブラリです。[MXNet](https://github.com/apache/incubator-mxnet)もAIパッケージの一つで、深層学習の設計図やテンプレート機能を提供しています。 para2: '[ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205)法と呼ばれる統計的手法であるビンニング、バギング、スタッキングや、[XGBoost](https://github.com/dmlc/xgboost)、[LightGBM](https://lightgbm.readthedocs.io/en/latest/)、[CatBoost](https://catboost.ai)などのツールで実装されているブースティングなどは、機械学習アルゴリズムの一つであり、最速の推論エンジンの一つです。[Yellowbrick](https://www.scikit-yb.org/en/latest/)や[Eli5](https://eli5.readthedocs.io/en/latest/)は機械学習の可視化機能を提供しています。' arraylibraries: intro: - - - text: NumPyのAPIは、革新的なハードウェアを利用したり、特殊な配列タイプを作成したり、NumPyが提供する以上の機能を追加するためにライブラリを作成する際の基礎となります。 + - text: NumPyのAPIは、革新的なハードウェアを利用したり、特殊な配列タイプを作成したり、NumPyが提供する以上の機能を追加するためにライブラリを作成する際の基礎となります。 headers: - - - text: 配列ライブラリ - - - text: 機能と応用分野 + - text: 配列ライブラリ + - text: 機能と応用分野 libraries: - - - title: Dask + - title: Dask text: 分析用の分散配列と高度な並列処理により、大規模な処理を可能にします。 img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - - - title: CuPy + - title: CuPy text: Python を使用した GPUによる高速計算用のNumPy互換配列ライブラリ img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - - - title: JAX + - title: JAX text: "NumPyコードの合成可能な変換ライブラリ: 微分、ベクトル化、GPU/TPUへのジャストインタイムコンパイル" img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax - - - title: Xarray + - title: Xarray text: 高度な分析と視覚化のためのラベルとインデックス付き多次元配列 img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - - - title: Sparse + - title: Sparse text: Dask と SciPy の疎行列の線形代数ライブラリを統合した、Numpy 互換の疎行列ライブラリ img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - - - title: PyTorch + - title: PyTorch text: 研究用のプロトタイピングから本番運用への展開を加速させる、深層学習フレームワーク img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - - - title: TensorFlow + - title: TensorFlow text: 機械学習を利用したアプリケーションを簡単に構築・展開するための、エンド・ツー・エンドの機械学習プラットフォーム img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - - - title: MXNet + - title: MXNet text: 柔軟や研究用のプロトタイピングから、実際の運用まで利用可能な深層学習フレームワーク img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - - - title: Arrow + - title: Arrow text: 列型のインメモリーデータやその分析のための、複数の言語に対応した開発プラットフォーム img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow - - - title: xtensor + - title: xtensor text: 数値解析のためのブロードキャスティングと遅延計算を備えた多次元配列 img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - - - title: Awkward + - title: Awkward text: Numpy のような イディオムを使って JSON のようなデータを操作するライブラリ img: /images/content_images/arlib/xnd.png alttext: awkward url: https://awkward-array.org/ - - - title: uarray + - title: uarray text: APIを実装から切り離すPythonバックエンドシステム (unumpyはNumPy APIを提供しています) img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly + - title: tensorly text: Numpy、MXNet、PyTorch、TensorFlowまたはCupyをシームレスに使用するための、テンソル学習、テンソル代数、およびそれらのテンソル計算のためのバックエンド img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - - - text: Pythonを使って働くほとんどの科学者はNumPyの力を利用しています。 - - - text: "Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。" - librariesrow1: - - - title: 量子コンピューティング + - text: Pythonを使って働くほとんどの科学者はNumPyの力を利用しています。 + - text: "Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。" + libraries: + - title: 量子コンピューティング alttext: コンピューターチップ img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: 統計コンピューティング + links: + - url: http://qutip.org + label: QuTiP + - url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - url: https://qiskit.org + label: Qiskit + - url: https://pennylane.ai + label: PennyLane + - title: 統計コンピューティング alttext: 線グラフで、グラフが上に移動します。 img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: 信号処理 + links: + - url: https://pandas.pydata.org/ + label: Pandas + - url: https://www.statsmodels.org/ + label: statsmodels + - url: https://xarray.pydata.org/en/stable/ + label: Xarray + - url: https://seaborn.pydata.org/ + label: Seaborn + - title: 信号処理 alttext: 正と負の値を持つ棒グラフ。 img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: 画像処理 + links: + - url: https://www.scipy.org/ + label: SciPy + - url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - url: https://python-control.org/ + label: python-control + - url: https://hyperspy.org/ + label: HiperSpy + - title: 画像処理 alttext: 山々の写真 img: /images/content_images/sc_dom_img/image_processing.svg - - - title: グラフとネットワーク + links: + - url: https://scikit-image.org/ + label: Scikit-image + - url: https://opencv.org/ + label: OpenCV + - url: https://mahotas.rtfd.io/ + label: Mahotas + - title: グラフとネットワーク alttext: シンプルなグラフ img: /images/content_images/sc_dom_img/sd6.svg - - - title: 天文学 + links: + - url: https://networkx.org/ + label: NetworkX + - url: https://graph-tool.skewed.de/ + label: graph-tool + - url: https://igraph.org/python/ + label: igraph + - url: https://pygsp.rtfd.io/ + label: PyGSP + - title: 天文学 alttext: 望遠鏡 img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: 認知心理学 + links: + - url: https://www.astropy.org/ + label: AstroPy + - url: https://sunpy.org/ + label: SunPy + - url: https://spacepy.github.io/ + label: SpacePy + - title: 認知心理学 alttext: ギアをつけた人間の頭部 img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: - - - title: 生命情報科学 + links: + - url: https://www.psychopy.org/ + label: PsychoPy + - title: 生命情報科学 alttext: DNAの鎖 img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: ベイズ推論 + links: + - url: https://biopython.org/ + label: BioPython + - url: http://scikit-bio.org/ + label: Scikit-Bio + - url: https://github.com/openvax/pyensembl + label: PyEnsembl + - url: http://etetoolkit.org/ + label: ETE + - title: ベイズ推論 alttext: 鐘形の曲線のグラフ img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: 数学的分析 + links: + - url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - url: https://docs.pymc.io/ + label: PyMC3 + - url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - url: https://emcee.readthedocs.io/ + label: emcee + - title: 数学的分析 alttext: ４つの数学記号 img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: 化学 + links: + - url: https://www.scipy.org/ + label: SciPy + - url: https://www.sympy.org/ + label: SymPy + - url: https://www.cvxpy.org/ + label: cvxpy + - url: https://fenicsproject.org/ + label: FEniCS + - title: 化学 alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg - - - title: 地球科学 + links: + - url: https://cantera.org/ + label: Cantera + - url: https://www.mdanalysis.org/ + label: MDAnalysis + - url: https://github.com/rdkit/rdkit + label: RDKit + - url: https://www.pybamm.org/ + label: PyBaMM + - title: 地球科学 alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg - - - title: 地理情報処理 + links: + - url: https://pangeo.io/ + label: Pangeo + - url: https://simpeg.xyz/ + label: Simpeg + - url: https://github.com/obspy/obspy/wiki + label: ObsPy + - url: https://www.fatiando.org/ + label: Fatiando a Terra + - title: 地理情報処理 alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg - - - title: アーキテクチャとエンジニアリング + links: + - url: https://shapely.readthedocs.io/ + label: Shapely + - url: https://geopandas.org/ + label: GeoPandas + - url: https://python-visualization.github.io/folium + label: Folium + - title: アーキテクチャとエンジニアリング alttext: マイクロプロセッサ開発ボード img: /images/content_images/sc_dom_img/robotics.svg + links: + - url: https://compas.dev/ + label: COMPAS + - url: https://cityenergyanalyst.com/ + label: City Energy Analyst - Analista de Energía de Ciudad + - url: https://nortikin.github.io/sverchok/ + label: Sverchok datascience: intro: "Numpy は豊富なデータサイエンスライブラリのエコシステムの中核にあります。一般的なデータサイエンスのワークフローは次のようになります。" image1: - img: /images/content_images/ds-landscape.png alttext: Python ライブラリの図 。5 つのカテゴリに分類され、「抽出、変換、読み込み」、「データ探索」、「モデリング」、「評価」、「可視化」です。 image2: - img: /images/content_images/data-science.png alttext: 三つの円が重なり合う図。円はそれぞれ「数学」、「コンピューターサイエンス」、「専門知識」でラベル付けされています。図の中心部には、三つの円が重なり合って形成されるエリアがあり、「データサイエンス」とラベル付けされています。 examples: - text: "<b>抽出, 変換, 読み込み: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "<b>探索的解析: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - text: "<b>モデリングと評価: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "<b>ダッシュボードでのレポート: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - text: 大規模データに対して、[Dask](https://dask.org)と[Ray](https://ray.io/)はスケールすることを目指して設計されています。安定したデプロイメントに関しては、データのバージョニング（[DVC](https://dvc.org)）、実験の追跡（[MLFlow](https://mlflow.org)）、ワークフローの自動化（[Airflow](https://airflow.apache.org)および[Prefect](https://www.prefect.io)が重要ですが様々なNumPyベースのツールが提供されています。 visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: matplotlibで作られたストリームプロット - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: ggpyで作られた散布図グラフ - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: plotyで作られた箱ひげ図 - url: https://alta-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: altairで作られたストリームグラフ - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: 2種類のグラフによるペアプロット。seabornで作られたプロットと周波数グラフ" - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: PyVista製の3Dボリュームレンダリング - url: https://napari.org img: /images/content_images/v_napari.png alttext: napariで作られた多次元画像 - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: vispyで作られたボロノイ図 content: - text: NumPyは、[Matplotlib](https://matplotlib.org)、[Seaborn](https://seaborn.pydata.org)、[Plotly](https://plot.ly)、[Altair](https://altair-viz.github.io)、[Bokeh](https://docs.bokeh.org/en/latest/)、[Holoviz](https://holoviz.org)、[Vispy](http://vispy.org)、[Napari](https://github.com/napari/napari)、[PyVista](https://github.com/pyvista/pyvista)などの、急成長している[Python visualization landscape](https://pyviz.org/overviews/index.html)に欠かせないコンポーネントです。 - text: NumPy の大規模配列の高速処理により、研究者は、ネイティブの Python が扱うことができるよりもはるかに大きなデータセットを可視化することができます。 diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index a2d2b0d..5f0b4ca 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -1,219 +1,280 @@ params: machinelearning: paras: - - - para1: O NumPy forma a base de bibliotecas de aprendizagem de máquina poderosas como [scikit-learn](https://scikit-learn.org) e [SciPy](https://www.scipy.org). À medida que a disciplina de aprendizagem de máquina cresce, a lista de bibliotecas construidas a partir do NumPy também cresce. As funcionalidades de deep learning do [TensorFlow](https://www.tensorflow.org) tem diversas aplicações &mdash; entre elas, reconhecimento de imagem e de fala, aplicações baseadas em texto, análise de séries temporais, e detecção de vídeo. O [PyTorch](https://pytorch.org), outra biblioteca de deep learning, é popular entre pesquisadores em visão computacional e processamento de linguagem natural. O [MXNet](https://github.com/apache/incubator-mxnet) é outro pacote de IA, que fornece templates e protótipos para deep learning. + - para1: O NumPy forma a base de bibliotecas de aprendizagem de máquina poderosas como [scikit-learn](https://scikit-learn.org) e [SciPy](https://www.scipy.org). À medida que a disciplina de aprendizagem de máquina cresce, a lista de bibliotecas construidas a partir do NumPy também cresce. As funcionalidades de deep learning do [TensorFlow](https://www.tensorflow.org) tem diversas aplicações &mdash; entre elas, reconhecimento de imagem e de fala, aplicações baseadas em texto, análise de séries temporais, e detecção de vídeo. O [PyTorch](https://pytorch.org), outra biblioteca de deep learning, é popular entre pesquisadores em visão computacional e processamento de linguagem natural. O [MXNet](https://github.com/apache/incubator-mxnet) é outro pacote de IA, que fornece templates e protótipos para deep learning. para2: Técnicas estatísticas chamadas métodos de [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) tais como binning, bagging, stacking, e boosting estão entre os algoritmos de ML implementados por ferramentas tais como [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), e [CatBoost](https://catboost.ai) &mdash; um dos motores de inferência mais rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) e [Eli5](https://eli5.readthedocs.io/en/latest/) oferecem visualizações para aprendizagem de máquina. arraylibraries: intro: - - - text: A API do NumPy é o ponto de partida quando bibliotecas são escritas para explorar hardware inovador, criar tipos de arrays especializados, ou adicionar capacidades além do que o NumPy fornece. + - text: A API do NumPy é o ponto de partida quando bibliotecas são escritas para explorar hardware inovador, criar tipos de arrays especializados, ou adicionar capacidades além do que o NumPy fornece. headers: - - - text: Biblioteca de Arrays - - - text: Recursos e áreas de aplicação + - text: Biblioteca de Arrays + - text: Recursos e áreas de aplicação libraries: - - - title: Dask + - title: Dask text: Arrays distribuídas e paralelismo avançado para análise, permitindo desempenho em escala. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - - - title: CuPy + - title: CuPy text: Biblioteca de matriz compatível com NumPy para computação acelerada pela GPU com Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - - - title: JAX + - title: JAX text: "Transformações combináveis de programas NumPy: vetorização, compilação just-in-time para GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax - - - title: Xarray + - title: Xarray text: Arrays multidimensionais rotuladas e indexadas para análise e visualização avançadas img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - - - title: Sparse + - title: Sparse text: Biblioteca de arrays compatíveis com o NumPy que pode ser integrada com Dask e álgebra linear esparsa da SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - - - title: PyTorch + - title: PyTorch text: Framework de deep learning que acelera o caminho entre prototipação de pesquisa e colocação em produção. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - - - title: TensorFlow + - title: TensorFlow text: Uma plataforma completa para aprendizagem de máquina que permite construir e colocar em produção aplicações usando ML facilmente. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - - - title: MXNet + - title: MXNet text: Framework de deep learning voltado para flexibilizar prototipação em pesquisa e produção. img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - - - title: Arrow + - title: Arrow text: Uma plataforma de desenvolvimento multi-linguagens para dados e análise para dados armazenados em colunas na memória. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow - - - title: xtensor + - title: xtensor text: Arrays multidimensionais com broadcasting e avaliação preguiçosa (lazy computing) para análise numérica. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - - - title: Awkward Array + - title: Awkward Array text: Manipulação de dados JSON-like com sintaxe NumPy-like. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - - - title: uarray + - title: uarray text: Sistema de backend Python que dissocia a API da implementação; unumpy fornece uma API NumPy. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly + - title: tensorly text: Ferramentas para aprendizagem com tensores, algebra e backends para usar NumPy, MXNet, PyTorch, TensorFlow ou CuPy sem esforço. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - - - text: Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy. - - - text: "NumPy traz o poder computacional de linguagens como C e Fortran para Python, uma linguagem muito mais fácil de aprender e usar. Com esse poder vem a simplicidade: uma solução no NumPy é frequentemente clara e elegante." - librariesrow1: - - - title: Computação quântica + - text: Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy. + - text: "NumPy traz o poder computacional de linguagens como C e Fortran para Python, uma linguagem muito mais fácil de aprender e usar. Com esse poder vem a simplicidade: uma solução no NumPy é frequentemente clara e elegante." + libraries: + - title: Computação quântica alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Computação estatística + links: + - url: http://qutip.org + label: QuTiP + - url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - url: https://qiskit.org + label: Qiskit + - url: https://pennylane.ai + label: PennyLane + - title: Computação estatística alttext: Um gráfico com uma linha em movimento para cima. img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: Processamento de sinais + links: + - url: https://pandas.pydata.org/ + label: Pandas + - url: https://www.statsmodels.org/ + label: statsmodels + - url: https://xarray.pydata.org/en/stable/ + label: Xarray + - url: https://seaborn.pydata.org/ + label: Seaborn + - title: Processamento de sinais alttext: Um gráfico de barras com valores positivos e negativos. img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: Processamento de imagens + links: + - url: https://www.scipy.org/ + label: SciPy + - url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - url: https://python-control.org/ + label: python-control + - url: https://hyperspy.org/ + label: HiperSpy + - title: Processamento de imagens alttext: Uma fotografia das montanhas. + links: + - url: https://scikit-image.org/ + label: Scikit-image + - url: https://opencv.org/ + label: OpenCV + - url: https://mahotas.rtfd.io/ + label: Mahotas img: /images/content_images/sc_dom_img/image_processing.svg - - - title: Gráficos e Redes + - title: Gráficos e Redes alttext: Um grafo simples. img: /images/content_images/sc_dom_img/sd6.svg - - - title: Processos de Astronomia + links: + - url: https://networkx.org/ + label: NetworkX + - url: https://graph-tool.skewed.de/ + label: graph-tool + - url: https://igraph.org/python/ + label: igraph + - url: https://pygsp.rtfd.io/ + label: PyGSP + - title: Processos de Astronomia alttext: Um telescópio. img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: Psicologia Cognitiva + links: + - url: https://www.astropy.org/ + label: AstroPy + - url: https://sunpy.org/ + label: SunPy + - url: https://spacepy.github.io/ + label: SpacePy + - title: Psicologia Cognitiva alttext: Uma cabeça humana com engrenagens. img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: - - - title: Bioinformática + links: + - url: https://www.psychopy.org/ + label: PsychoPy + - title: Bioinformática alttext: Um pedaço de DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Inferência Bayesiana + links: + - url: https://biopython.org/ + label: BioPython + - url: http://scikit-bio.org/ + label: Scikit-Bio + - url: https://github.com/openvax/pyensembl + label: PyEnsembl + - url: http://etetoolkit.org/ + label: ETE + - title: Inferência Bayesiana alttext: Um gráfico com uma curva em forma de sino. img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: Análise Matemática + links: + - url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - url: https://docs.pymc.io/ + label: PyMC3 + - url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - url: https://emcee.readthedocs.io/ + label: emcee + - title: Análise Matemática alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: Química + links: + - url: https://www.scipy.org/ + label: SciPy + - url: https://www.sympy.org/ + label: SymPy + - url: https://www.cvxpy.org/ + label: cvxpy + - url: https://fenicsproject.org/ + label: FEniCS + - title: Química alttext: Um tubo de ensaio. img: /images/content_images/sc_dom_img/chemistry.svg - - - title: Geociências + links: + - url: https://cantera.org/ + label: Cantera + - url: https://www.mdanalysis.org/ + label: MDAnalysis + - url: https://github.com/rdkit/rdkit + label: RDKit + - url: https://www.pybamm.org/ + label: PyBaMM + - title: Geociências alttext: A Terra. img: /images/content_images/sc_dom_img/geoscience.svg - - - title: Processamento Geográfico + links: + - url: https://pangeo.io/ + label: Pangeo + - url: https://simpeg.xyz/ + label: Simpeg + - url: https://github.com/obspy/obspy/wiki + label: ObsPy + - url: https://www.fatiando.org/ + label: Fatiando a Terra + - title: Processamento Geográfico alttext: Um mapa. img: /images/content_images/sc_dom_img/GIS.svg - - - title: Arquitetura e Engenharia + links: + - url: https://shapely.readthedocs.io/ + label: Shapely + - url: https://geopandas.org/ + label: GeoPandas + - url: https://python-visualization.github.io/folium + label: Folium + - title: Arquitetura e Engenharia alttext: Uma placa de desenvolvimento de microprocessador. img: /images/content_images/sc_dom_img/robotics.svg + links: + - url: https://compas.dev/ + label: COMPAS + - url: https://cityenergyanalyst.com/ + label: City Energy Analyst - Analista de Energía de Ciudad + - url: https://nortikin.github.io/sverchok/ + label: Sverchok datascience: intro: "NumPy está no centro de um rico ecossistema de bibliotecas de ciência de dados. Um fluxo de trabalho típico de ciência de dados exploratório pode parecer assim:" image1: - - - img: /images/content_images/ds-landscape.png + - img: /images/content_images/ds-landscape.png alttext: Diagrama de bibliotecas Python. As cinco categorias são 'Extrair, Transformar, Carregar', 'Exploração de Dados', 'Modelo de Dados', 'Avaliação de Dados' e 'Apresentação de Dados'. image2: - - - img: /images/content_images/data-science.png + - img: /images/content_images/data-science.png alttext: Diagram of three overlapping circle. The circles labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - - - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - - - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - - - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" - - - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" + - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" + - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" + - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" content: - - - text: For high data volumes, [Dask](https://dask.org) and[Ray](https://ray.io/) are designed to scale. Stabledeployments rely on data versioning ([DVC](https://dvc.org)),experiment tracking ([MLFlow](https://mlflow.org)), andworkflow automation ([Airflow](https://airflow.apache.org) and[Prefect](https://www.prefect.io)). + - text: For high data volumes, [Dask](https://dask.org) and[Ray](https://ray.io/) are designed to scale. Stabledeployments rely on data versioning ([DVC](https://dvc.org)),experiment tracking ([MLFlow](https://mlflow.org)), andworkflow automation ([Airflow](https://airflow.apache.org) and[Prefect](https://www.prefect.io)). visualization: images: - - - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: Um streamplot feito em matplotlib - - - url: https://github.com/yhat/ggpy + - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: Um gráfico scatter-plot feito em ggpy - - - url: https://www.journaldev.com/19692/python-plotly-tutorial + - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: Um box-plot feito no plotly - - - url: https://altair-viz.github.io/gallery/streamgraph.html + - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: Um gráfico streamgraph feito em altair - - - url: https://seaborn.pydata.org + - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: A plot duplo com dois tipos de gráficos, um plot-graph e um gráfico de frequência feitos no seaborn - - - url: https://docs.pyvista.org/ + - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: Uma renderização de volume 3D feita no PyVista. - - - url: https://napari.org + - url: https://napari.org img: /images/content_images/v_napari.png alttext: Uma imagem multidimensional, feita em napari. - - - url: https://vispy.org/gallery/index.html + - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: Diagrama de Voronoi feito com vispy. content: - - - text: NumPy é um componente essencial no crescente [campo de visualização em Python](https://pyviz.org/overviews/index.html), que inclui [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), e [PyVista](https://github.com/pyvista/pyvista), para citar alguns. - - - text: O processamento de grandes arrays acelerado pela NumPy permite que os pesquisadores visualizem conjuntos de dados muito maiores do que o Python nativo poderia permitir. + - text: NumPy é um componente essencial no crescente [campo de visualização em Python](https://pyviz.org/overviews/index.html), que inclui [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), e [PyVista](https://github.com/pyvista/pyvista), para citar alguns. + - text: O processamento de grandes arrays acelerado pela NumPy permite que os pesquisadores visualizem conjuntos de dados muito maiores do que o Python nativo poderia permitir.
numpy/numpy.org
bafcf218164ac51e7b7ecf5226e811ed18ada00c
Fix display of ecosystem tab for Ja and Pt (#783)
diff --git a/content/ja/config.yaml b/content/ja/config.yaml index bb63338..395f1ad 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -1,141 +1,112 @@ languageName: 日本語 (Japanese) params: description: NumPyが広く利用される理由 強力な多次元配列、数値計算ツール群、相互運用性、高いパフォーマンス、オープンソース navbarlogo: image: logo.svg text: NumPy link: /ja/ hero: #Main hero title title: NumPy #Hero subtitle (optional) subtitle: Pythonによる科学技術計算の基礎パッケージ #Button text buttontext: "最新リリース: Numpy 1.26. すべてのリリースを表示する" #Where the main hero button links to buttonlink: "/ja/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - - - title: NumPy を試す + - title: NumPy を試す text: インタラクティブシェルを使用して、ブラウザ上で Numpy を試してみてください。 docslink: <a href="https://numpy.org/doc/stable" target="_blank">ドキュメント</a> を確認することを忘れないでください。 casestudies: title: ケーススタディ features: - - - title: 世界初のブラックホール画像 + - title: 世界初のブラックホール画像 text: NumPyはどのように、SciPyやMatplotlibなどのNumPyに依存するライブラリとともに、イベントホライズンテレスコープによる世界初のブラックホール画像の作成を可能にしたのでしょうか。 img: /images/content_images/case_studies/blackhole.png alttext: 世界初のブラックホール画像。黒い背景にオレンジ色の円で描かれています。 url: /ja/case-studies/blackhole-image - - - title: 重力波の検知 + - title: 重力波の検知 text: 1916年、アルバート・アインシュタインは重力波を予言しました。100年後、LIGOの研究者たちはNumPyを使ってその存在を確認しました。 img: /images/content_images/case_studies/gravitional.png alttext: 2つのオーブがお互いに周回し、周りの重力を変位させています。 url: /ja/case-studies/gw-discov - - - title: スポーツ分析 + - title: スポーツ分析 text: クリケット分析は、統計的モデリングと予測分析によって選手やチームのパフォーマンスを向上させることで、クリケットの試合を変えようとしています。多くの分析が、NumPyにより可能になりました。 img: /images/content_images/case_studies/sports.jpg alttext: 緑のフィールド上にあるクリケットボール。 url: /ja/case-studies/cricket-analytics - - - title: 深層学習による姿勢推定 + - title: 深層学習による姿勢推定 text: DeepLabCutはNumPyを利用し、動物の種類や時間スケールによらない運動制御の理解へ向け、動物の行動観察を含む科学技術研究を加速させています。 img: /images/content_images/case_studies/deeplabcut.png alttext: チータの姿勢推定 url: /ja/case-studies/deeplabcut-dnn tabs: title: NumPyのエコシステム section5: false navbar: - - - title: インストール + - title: インストール url: /ja/install - - - title: ドキュメント + - title: ドキュメント url: https://numpy.org/doc/stable - - - title: 学び方 + - title: 学び方 url: /ja/learn - - - title: コミュニティ + - title: コミュニティ url: /ja/community - - - title: 私達について + - title: 私達について url: /ja/about - - - title: ニュース + - title: ニュース url: /ja/news - - - title: NumPyに貢献する + - title: NumPyに貢献する url: /ja/contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - - - link: https://github.com/numpy/numpy + - link: https://github.com/numpy/numpy icon: github - - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team + - link: https://twitter.com/numpy_team icon: twitter quicklinks: column1: title: "" links: - - - text: インストール + - text: インストール link: /ja/install - - - text: ドキュメント + - text: ドキュメント link: https://numpy.org/doc/stable - - - text: 学び方 + - text: 学び方 link: /ja/learn - - - text: 引用する + - text: 引用する link: /ja/citing-numpy - - - text: ロードマップ + - text: ロードマップ link: https://numpy.org/neps/roadmap.html column2: links: - - - text: 私達について + - text: 私達について link: /ja/about - - - text: コミュニティ + - text: コミュニティ link: /ja/community - - - text: ユーザーの調査 + - text: ユーザーの調査 link: /ja/user-surveys - - - text: NumPyに貢献する + - text: NumPyに貢献する link: /ja/contribute - - - text: 行動規範 + - text: 行動規範 link: /ja/code-of-conduct column3: links: - - - text: サポートを得る方法 + - text: サポートを得る方法 link: /ja/gethelp - - - text: 利用規約 + - text: 利用規約 link: /ja/terms - - - text: プライバシーポリシー + - text: プライバシーポリシー link: /ja/privacy - - - text: プレス用資料 + - text: プレス用資料 link: /ja/press-kit diff --git a/content/pt/config.yaml b/content/pt/config.yaml index fadb422..14ab9ee 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -1,140 +1,111 @@ languageName: Português params: description: Por que NumPy? Arrays n-dimensionais poderosas. Ferramentas para computação numérica. Interoperabilidade. Alto desempenho. Código aberto. navbarlogo: image: logo.svg text: NumPy link: /pt/ hero: #Main hero title title: NumPy #Hero subtitle (optional) subtitle: A biblioteca fundamental para computação científica com Python #Button text buttontext: "Última versão: NumPy 1.26. Veja todas as versões" #Where the main hero button links to buttonlink: "/pt/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - - - title: Experimentar o NumPy + - title: Experimentar o NumPy text: Use o shell interativo para testar o NumPy no navegador docslink: Não se esqueça de conferir a <a href="https://numpy.org/doc/stable" target="_blank">documentação</a>. casestudies: title: ESTUDOS DE CASO features: - - - title: A Primeira Imagem de um Buraco Negro + - title: A Primeira Imagem de um Buraco Negro text: Como o NumPy, junto com outras bibliotecas como SciPy e Matplotlib que dependem do NumPy, permitiram ao Event Horizon Telescope gerar a primeira imagem de um buraco negro da história. img: /images/content_images/case_studies/blackhole.png alttext: Primeira imagem de um buraco negro. É um círculo laranja em um fundo preto. url: /pt/case-studies/blackhole-image - - - title: Descoberta de Ondas Gravitacionais + - title: Descoberta de Ondas Gravitacionais text: Em 1916, Albert Einstein previu ondas gravitacionais; 100 anos depois, sua existência foi confirmada pelos cientistas do LIGO usando NumPy. img: /images/content_images/case_studies/gravitional.png alttext: Duas esferas orbitando a si mesmas. Elas deslocam a gravidade em seu entorno. url: /pt/case-studies/gw-discov - - - title: Análise Esportiva + - title: Análise Esportiva text: A análise de críquete está mudando o jogo ao melhorar o desempenho de jogadores e times através de modelagem estatística e análise preditiva. O NumPy possibilita muitas dessas análises. img: /images/content_images/case_studies/sports.jpg alttext: Bola de críquete em um campo verde url: /pt/case-studies/cricket-analytics - - - title: Estimação de poses usando deep learning + - title: Estimação de poses usando deep learning text: DeepLabCut usa o NumPy para acelerar estudos científicos que envolvem comportamento animal para entender melhor o controle motor em várias espécies e escalas de tempo. img: /images/content_images/case_studies/deeplabcut.png alttext: Análise de pose de um guepardo url: /pt/case-studies/deeplabcut-dnn tabs: title: ECOSSISTEMA section5: false navbar: - - - title: Instalação + - title: Instalação url: /pt/install - - - title: Documentação + - title: Documentação url: https://numpy.org/doc/stable - - - title: Aprenda + - title: Aprenda url: /pt/learn - - - title: Comunidade + - title: Comunidade url: /pt/community - - - title: Sobre + - title: Sobre url: /pt/about - - - title: Notícias + - title: Notícias url: /pt/news - - - title: Contribuir + - title: Contribuir url: /pt/contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - - - link: https://github.com/numpy/numpy + - link: https://github.com/numpy/numpy icon: github - - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team + - link: https://twitter.com/numpy_team icon: twitter quicklinks: column1: title: "" links: - - - text: Instalação + - text: Instalação link: /pt/install - - - text: Documentação + - text: Documentação link: https://numpy.org/doc/stable - - - text: Aprenda + - text: Aprenda link: /pt/learn - - - text: Citando o Numpy + - text: Citando o Numpy link: /pt/citing-numpy - - - text: Roadmap + - text: Roadmap link: https://numpy.org/neps/roadmap.html column2: links: - - - text: Sobre + - text: Sobre link: /pt/about - - - text: Comunidade + - text: Comunidade link: /pt/community - - - text: Pesquisas de usuário + - text: Pesquisas de usuário link: /pt/user-surveys - - - text: Contribuir + - text: Contribuir link: /pt/contribute - - - text: Código de Conduta + - text: Código de Conduta link: /pt/code-of-conduct column3: links: - - - text: Ajuda + - text: Ajuda link: /pt/gethelp - - - text: Termos de uso (EN) + - text: Termos de uso (EN) link: /pt/terms - - - text: Privacidade + - text: Privacidade link: /pt/privacy - - - text: Kit de imprensa + - text: Kit de imprensa link: /pt/press-kit
numpy/numpy.org
8160743313fdbbab0ab84671aebd2ce936411942
Redirect to spanish home page (#780)
diff --git a/content/es/config.yaml b/content/es/config.yaml index 2f35773..0357ec7 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -1,109 +1,109 @@ languageName: Español params: description: '¿Por qué NumPy? Potentes arreglos n-dimensionales. Herramientas de cálculo numérico. Interoperabilidad. Rendimiento. Código abierto.' navbarlogo: image: logo.svg text: NumPy - link: / + link: /es/ hero: #Main hero title title: NumPy #Hero subtitle (optional) subtitle: El paquete fundamental para la computación científica con Python #Button text buttontext: "Última versión: NumPy 2.0. Ver todas las versiones" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: title: marcador intro: - title: Prueba NumPy text: Utilice el terminal interactivo para probar NumPy en el navegador docslink: No olvides echarle un ojo a la <a href="https://numpy.org/doc/stable" target="_blank">documentación</a>. casestudies: title: CASOS DE ESTUDIO features: - title: Primera imagen de un Agujero Negro text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Telescopio del Horizonte de Sucesos producir la primera imagen de un agujero negro img: /images/content_images/case_studies/blackhole.png alttext: Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro. url: /es/case-studies/blackhole-image - title: Detección de Ondas Gravitacionales text: En 1916 Albert Einstein predijo las ondas gravitacionales; 100 años después se confirmó su existencia por científicos del LIGO, utilizando NumPy. img: /images/content_images/case_studies/gravitional.png alttext: Dos cuerpos orbitándose mutuamente. Estos desplazan la gravedad a su alrededor. url: /es/case-studies/gw-discov - title: Analíticas Deportivas text: El Análisis de Críquet está cambiando el juego, mejorando el rendimiento de los jugadores y equipos mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. img: /images/content_images/case_studies/sports.jpg alttext: Bola de Cricket sobre un campo verde. url: /es/case-studies/cricket-analytics - title: Estimación de la pose mediante aprendizaje profundo text: DeepLabCut utiliza NumPy para acelerar estudios científicos que implican la observación del comportamiento animal para una mejor comprensión del control motriz, a través de especies y escalas de tiempo. img: /images/content_images/case_studies/deeplabcut.png alttext: Análisis de la pose de un Guepardo url: /es/case-studies/deeplabcut-dnn tabs: title: ECOSISTEMA section5: false navbar: - title: Instalar url: /es/install - title: Documentación url: https://numpy.org/doc/stable - title: Aprende url: /es/learn - title: Comunidad url: /es/community - title: Quiénes somos url: /es/about - title: Noticias url: /es/news - title: Contribuye url: /es/contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - link: https://github.com/numpy/numpy icon: github - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube quicklinks: column1: title: "" links: - text: Instalar link: /es/install - text: Documentación link: https://numpy.org/doc/stable - text: Aprende link: /es/learn - text: Citando a NumPy link: /es/citing-numpy - text: Mapa de ruta link: https://numpy.org/neps/roadmap.html column2: links: - text: Acerca de nosotros link: /es/about - text: Comunidad link: /es/community - text: Encuestas a usuarios link: /es/user-surveys - text: Contribuye link: /es/contribute - text: Código de Conducta link: /es/code-of-conduct column3: links: - text: Buscar ayuda link: /es/gethelp - text: Términos de uso link: /es/terms - text: Confidencialidad link: /es/privacy - text: Kit de prensa link: /es/press-kit
numpy/numpy.org
7f4fb6eb7878d782e34bb07aac8d72084d1ebd72
Add teams subfolder to crowdin.yml (#777)
diff --git a/crowdin.yml b/crowdin.yml index d307b43..e81f80a 100644 --- a/crowdin.yml +++ b/crowdin.yml @@ -1,17 +1,20 @@ project_identifier: PROJECT_IDENTIFIER_CROWDIN api_key: API_KEY_CROWDIN base_path: ./ preserve_hierarchy: true files: - source: /content/en/*.yaml translation: /content/%two_letters_code%/%original_file_name% update_option: "update_as_unapproved" - source: /content/en/*.md translation: /content/%two_letters_code%/%original_file_name% update_option: "update_as_unapproved" ignore: - /content/en/terms.md - /content/en/diversity_sep2020.md - source: /content/en/case-studies/*.md translation: /content/%two_letters_code%/case-studies/%original_file_name% update_option: "update_as_unapproved" + - source: /content/en/teams/index.md + translation: /content/%two_letters_code%/teams/%original_file_name% + update_option: "update_as_unapproved"
numpy/numpy.org
b210a3f4afdeb9415bf74c0dc5e5c4af867a85e0
Add gh workflow for creating translations PR (#772)
diff --git a/.github/workflows/create-translations-pr.yml b/.github/workflows/create-translations-pr.yml new file mode 100644 index 0000000..3804511 --- /dev/null +++ b/.github/workflows/create-translations-pr.yml @@ -0,0 +1,56 @@ +name: Create Translations PR + +on: + workflow_dispatch: + inputs: + language_code: + description: 'Crowdin language code for the language of interest' + required: true + +jobs: + create-translations-pr: + runs-on: ubuntu-latest + # Run only on main branch in upstream repo. + if: ${{ github.repository == 'numpy/numpy.org' && github.ref == 'refs/heads/main' }} + steps: + - name: Checkout numpy.org + uses: actions/checkout@v4 + with: + repository: 'numpy/numpy.org' + path: 'numpy.org' + ref: 'main' + fetch-depth: 0 # Gets full github history. + # Full history is needed for the scripted interactive rebase + # which takes place in create_branch_for_language.sh below. + + - name: Checkout scientific-python-translations automations + uses: actions/checkout@v4 + with: + repository: 'scientific-python-translations/automations' + path: 'automations' + ref: 'main' + + - name: Create translations branch for language of interest + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + git config --global user.email "[email protected]" + git config --global user.name "GitHub Actions" + ../automations/scripts/create_branch_for_language.sh origin main l10n_main ${{ github.event.inputs.language_code }} + branch_name=$(git rev-parse --abbrev-ref HEAD) + git push -u origin $branch_name + echo "BRANCH_NAME=$branch_name" >> $GITHUB_ENV + working-directory: ./numpy.org + + - name: Create Pull Request + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + run: | + language_name=$(../automations/scripts/get_language_name.sh ${{ github.event.inputs.language_code }}) + gh pr create --base main --head ${{ env.BRANCH_NAME }} --title "Update translations for $language_name" \ + --body "This PR to update translations for $language_name was generated by the GitHub workflow, \ + auto-translations-pr.yml and includes all commits from this repo's Crowdin branch for the language \ + of interest. A final check of the rendered docs is needed to identify if there are any formatting \ + errors due to incorrect string segmentation by Crowdin. If there are such formatting errors, they \ + should be fixed directly on this branch, not through Crowdin." + working-directory: ./numpy.org
numpy/numpy.org
3544e872ff4703f998cb478dfe6a6ced392d8c06
announce the NumPy 2.1.1 release (#773)
diff --git a/content/en/news.md b/content/en/news.md index a73ebc4..78dd3fa 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,470 +1,471 @@ --- title: News sidebar: false newsHeader: "NumPy 2.1 released!" date: 2024-08-18 --- ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.1)) -- _3 Sep 2024_. - NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
f3d23b1895eca38a95797ead35c788647edc9b28
announce the NumPy 2.0.2 release
diff --git a/content/en/news.md b/content/en/news.md index 174a52b..a73ebc4 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,469 +1,470 @@ --- title: News sidebar: false newsHeader: "NumPy 2.1 released!" date: 2024-08-18 --- ### NumPy 2.1.0 released _18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get NumPy back to its usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. - Support for the array-api 2023.12 standard. Python versions 3.10-3.13 are supported by this release. ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.2)) -- _26 Aug 2024_. - NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
c24cf8bc8fe2c87f246eccca2d8354d65fbc513b
announce the NumPy 2.1.0 release
diff --git a/content/en/news.md b/content/en/news.md index 22901b0..174a52b 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,453 +1,469 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 released!" -date: 2024-06-17 +newsHeader: "NumPy 2.1 released!" +date: 2024-08-18 --- +### NumPy 2.1.0 released + +_18 Aug, 2024_ -- NumPy 2.1.0 provides support for Python 3.13 and +drops support for Python 3.9. In addition to the usual bug fixes and +updated Python support, it helps get NumPy back to its usual release +cycle after the extended development of 2.0. The highlights for this +release are: + +- Support for Python 3.13. +- Preliminary support for free threaded Python 3.13. +- Support for the array-api 2023.12 standard. + +Python versions 3.10-3.13 are supported by this release. + + ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.1.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.1.0)) -- _18 Aug 2024_. - NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
d590e012ffad6318f8fd61c2e4661f92bdf75335
Add info about donating to NumPy to contribute.md (#768)
diff --git a/content/en/contribute.md b/content/en/contribute.md index 77c77cb..a0968d2 100644 --- a/content/en/contribute.md +++ b/content/en/contribute.md @@ -1,114 +1,112 @@ --- title: Contribute to NumPy sidebar: false --- The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. If you're unsure where to start or how your skills fit in, _reach out!_ You can ask on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) or [GitHub](http://github.com/numpy/numpy) (open an [issue](https://github.com/numpy/numpy/issues) or comment on a relevant issue). Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at <[email protected]> or on [Slack](https://numpy-team.slack.com) (write <[email protected]> for an invite). We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). You are very welcome to join. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). Our community aspires to treat everyone equally and to value all contributions. We have a [Code of Conduct](/code-of-conduct) to foster an open and welcoming environment. ### Writing code Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase. <br>Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. - ### Reviewing pull requests The project has more than 250 open pull requests -- meaning many potential improvements and many open-source contributors waiting for feedback. If you're a developer who knows NumPy, you can help even if you're not familiar with the codebase. You can: * summarize a long-running discussion * triage documentation PRs * test proposed changes - ### Developing educational materials NumPy's [User Guide](https://numpy.org/devdocs) is undergoing rehabilitation. We're in need of new tutorials, how-to's, and deep-dive explanations, and the site needs restructuring. Opportunities aren't limited to writers. We'd also welcome worked examples, notebooks, and videos. [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) lays out our ideas -- and you may have others. - ### Issue triaging The [NumPy issue tracker](https://github.com/numpy/numpy/issues) has a _lot_ of open issues. Some are no longer valid, some should be prioritized, and some would make good issues for new contributors. You can: * check if older bugs are still present * find duplicate issues and link related ones * add good self-contained reproducers to issues * label issues correctly (this requires triage rights -- just ask) Please just dive in. - ### Website development We've just revamped our website, but we're far from done. If you love web development, these [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) list some of our unmet needs -- and feel free to share your own ideas. - ### Graphic design We can barely begin to list the contributions a graphic designer can make here. Our docs are parched for illustration; our growing website craves images -- opportunities abound. - ### Translating website content We plan multiple translations of [numpy.org](https://numpy.org) to make NumPy accessible to users in their native language. Volunteer translators are at the heart of this effort. See [here](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) for background; comment on [this GitHub issue](https://github.com/numpy/numpy.org/issues/55) to sign up. - ### Community coordination and outreach Through community contact we share our work more widely and learn where we're falling short. We're eager to get more people involved in efforts like our [Twitter](https://twitter.com/numpy_team) account, organizing NumPy [code sprints](https://scisprints.github.io/), a newsletter, and perhaps a blog. ### Fundraising -NumPy was all-volunteer for many years, but as its importance grew it became -clear that to ensure stability and growth we'd need financial support. [This -SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much -difference that support has made. Like all the nonprofit world, we're -constantly searching for grants, sponsorships, and other kinds of support. We -have a number of ideas and of course we welcome more. Fundraising is a scarce -skill here -- we'd appreciate your help. +For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it +became clear that to ensure stability and growth we would need financial support. +[This SciPy'19 talk](https://www.youtube.com/watch?v=dBTJD_FDVjU) explains how much difference +that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, +and other kinds of funding. We have a number of ideas and of course we welcome more. +Fundraising is a scarce skill here -- we'd appreciate your help. + +### Donate + +If you'd like to contribute to NumPy by making a donation, visit [https://numpy.org/about/#donate](https://numpy.org/about/#donate). + +
numpy/numpy.org
772dec2080e5d5774c513472a594eb178387a106
Remove the link to the NumPy Twitter/X account (#767)
diff --git a/content/en/config.yaml b/content/en/config.yaml index be76851..5546551 100644 --- a/content/en/config.yaml +++ b/content/en/config.yaml @@ -1,119 +1,117 @@ languageName: English params: description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. navbarlogo: image: logo.svg text: NumPy link: / hero: # Main hero title title: NumPy # Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python # Button text buttontext: "Latest release: NumPy 2.0. View all releases" # Where the main hero button links to buttonlink: "/news/#releases" # Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - title: Try NumPy text: Use the interactive shell to try NumPy in the browser docslink: Don't forget to check out the <a href="https://numpy.org/doc/stable" target="_blank">docs</a>. casestudies: title: CASE STUDIES features: - title: First Image of a Black Hole text: How NumPy, together with libraries like SciPy and Matplotlib that depend on NumPy, enabled the Event Horizon Telescope to produce the first ever image of a black hole img: /images/content_images/case_studies/blackhole.png alttext: First image of a black hole. It is an orange circle in a black background. url: /case-studies/blackhole-image - title: Detection of Gravitational Waves text: In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy. img: /images/content_images/case_studies/gravitional.png alttext: Two orbs orbiting each other. They are displacing gravity around them. url: /case-studies/gw-discov - title: Sports Analytics text: Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses. img: /images/content_images/case_studies/sports.jpg alttext: Cricket ball on green field. url: /case-studies/cricket-analytics - title: Pose Estimation using deep learning text: DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales. img: /images/content_images/case_studies/deeplabcut.png alttext: Cheetah pose analysis url: /case-studies/deeplabcut-dnn tabs: title: ECOSYSTEM section5: false navbar: - title: Install url: /install - title: Documentation url: https://numpy.org/doc/stable - title: Learn url: /learn - title: Community url: /community - title: About Us url: /about - title: News url: /news - title: Contribute url: /contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - link: https://github.com/numpy/numpy icon: github - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" links: - text: Install link: /install - text: Documentation link: https://numpy.org/doc/stable - text: Learn link: /learn - text: Citing Numpy link: /citing-numpy - text: Roadmap link: https://numpy.org/neps/roadmap.html column2: links: - text: About us link: /about - text: Community link: /community - text: User surveys link: /user-surveys - text: Contribute link: /contribute - text: Code of conduct link: /code-of-conduct column3: links: - text: Get help link: /gethelp - text: Terms of use link: /terms - text: Privacy link: /privacy - text: Press kit link: /press-kit
numpy/numpy.org
8d8a14f7bd5fcb42b77a47c0204b6256c14be9c4
announce the NumPy 2.0.1 release
diff --git a/content/en/news.md b/content/en/news.md index 50a0872..22901b0 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,452 +1,453 @@ --- title: News sidebar: false newsHeader: "NumPy 2.0 released!" date: 2024-06-17 --- ### NumPy 2.0.0 released _16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 212 contributors spread over 1078 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. It includes breaking changes that could not happen in a regular minor release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) tells a bit of the story about how this release came together. ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.1)) -- _21 Jul 2024_. - NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
2552f07232af56426dc507d23d601cb5cf7f330a
Specify hero/navbar font
diff --git a/assets/css/custom.css b/assets/css/custom.css new file mode 100644 index 0000000..1b54986 --- /dev/null +++ b/assets/css/custom.css @@ -0,0 +1,4 @@ +.hero-title, +.navbar-logo-text { + font-family: "Lato"; +}
numpy/numpy.org
3d57ba4cb2f4033e0f6a1999f27df04eb9505a58
Fix mailchimp footer
diff --git a/layouts/partials/footer_actions.html b/layouts/partials/footer_actions.html index 4c583f8..2b823f2 100644 --- a/layouts/partials/footer_actions.html +++ b/layouts/partials/footer_actions.html @@ -1,21 +1,21 @@ -<p>Sign up for the latest NumPy news, resources, <br> and more</p> +Sign up for the latest NumPy news, resources, and more <!-- Begin Mailchimp --> <form action="https://numpy.us4.list-manage.com/subscribe/post?u=5ddd0d1d6e807900a8212481a&amp;id=287fa4253c" method="post" id="mc-embedded-subscribe-form" name="mc-embedded-subscribe-form" class="validate sign-up-container" target="_blank" novalidate> <div class="sign-up-image"> {{ partial "svg-icon" "mail" }} </div> <input type="email" value="" name="EMAIL" class="required email sign-up-input" id="mce-EMAIL" aria-label="Input for email, press enter to submit" onkeypress="if (event.which === 13 || event.keyCode === 13 || event.key === 'Enter') sendThankYou()" /> <div class="submission-instructions">Press Enter</div> <button class="signup-button" onclick="sendThankYou()" aria-label="Submit"> {{ partial "svg-icon" "sent" }} </button> <div id="mce-responses" class="clear"> <div class="response" id="mce-error-response" style="display:none"></div> <div class="response" id="mce-success-response" style="display:none"></div> </div> <!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups--> <div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_5ddd0d1d6e807900a8212481a_287fa4253c" tabindex="-1" value=""></div> <div class="clear"><input type="submit" value="Subscribe" name="subscribe" id="mc-embedded-subscribe" class="button" style="display: none;"></div> </form> <div class="thank-you">Thank you! &#127881;</div> <!--End MailChimp -->
numpy/numpy.org
a0450edc33d61a77f24888a72622caa32755f4e2
Use new color names
diff --git a/assets/css/mailchimp.css b/assets/css/mailchimp.css index 7441684..d9fa5e0 100644 --- a/assets/css/mailchimp.css +++ b/assets/css/mailchimp.css @@ -1,95 +1,95 @@ :root { --numpySlateGray: rgb(108, 122, 137); } .sign-up-container { display: flex; flex-direction: row; align-items: center; position: relative; height: 75px; } .sign-up-image { height: 35px; padding: 5px 10px 6px 10px; - border-right: 1px solid var(--colorPrimaryDark); - background-color: var(--colorSecondary); + border-right: 1px solid var(--spht-color-dark); + background-color: var(--spht-color-light); border-radius: 5px 0 0 5px; } #footer .sign-up-image svg.icon { - fill: var(--colorPrimaryDark); + fill: var(--spht-color-dark); width: 1.25rem; height: 1.25rem; padding-bottom: 2px; } .sign-up-input { - background-color: var(--colorSecondary); + background-color: var(--spht-color-light); border-radius: 0 5px 5px 0; border: none; width: 75%; height: 35px; padding-left: 5px; font-size: 14px; - color: var(--colorPrimaryDark); + color: var(--spht-color-dark); } .submission-instructions { position: absolute; right: 18%; font-size: 10px; color: var(--numpySlateGray); } .signup-button { display: none; } #footer .signup-button svg.icon { - fill: var(--colorPrimaryDark); + fill: var(--spht-color-dark); width: 1.6rem; height: 1.6rem; padding-bottom: 2px; padding-left: 8px; } .thank-you { display: none; height: 75px; - color: var(--colorSecondary); + color: var(--spht-color-light); align-items: center; - color: var(--colorSecondary); + color: var(--spht-color-light); } @media only screen and (max-width: 1150px) { .sign-up-input { font-size: 12px; } } @media only screen and (max-width: 850px) { .sign-up-input { width: 100%; } .thank-you { justify-content: center; } .submission-instructions { display: none; } .signup-button { display: block; height: 35px; border-radius: 5px; margin-left: 5px; width: 60px; color: black; border:none; outline:none; } }
numpy/numpy.org
252c35ab032b920f00fc76749afa4f73ceed2c80
Announce NumPy 2.0.0 (#760)
diff --git a/content/en/news.md b/content/en/news.md index 7412b03..50a0872 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,432 +1,452 @@ --- title: News sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" -date: 2024-05-23 +newsHeader: "NumPy 2.0 released!" +date: 2024-06-17 --- +### NumPy 2.0.0 released + +_16 Jun, 2024_ -- NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include: + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + +The blog post ["NumPy 2.0: an evolutionary milestone"](https://blog.scientific-python.org/numpy/numpy2/) +tells a bit of the story about how this release came together. + + ### NumPy 2.0 release date: June 16 _23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be released on June 16, 2024. This release has been over a year in the making, and is the first major release since 2006. Importantly, in addition to many new features and performance improvement, it contains **breaking changes** to the ABI as well as the Python and C APIs. It is likely that downstream packages and end user code needs to be adapted - if you can, please verify whether your code works with NumPy `2.0.0rc2`. **Please see the following for more details:** - The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) - The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) - Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 2.0.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v2.0.0)) -- _16 Jun 2024_. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
6611459759ae20e0ae952efc41ec87cb240f91b5
remove unnecesarry single bullet point (#752)
diff --git a/layouts/partials/array-libraries.html b/layouts/partials/array-libraries.html index 2ea7960..46601c2 100644 --- a/layouts/partials/array-libraries.html +++ b/layouts/partials/array-libraries.html @@ -1,28 +1,28 @@ <!-- Array libraries Tab Content --> {{- $arraylibraries := .Site.Params.arraylibraries }} {{- $intro := index $arraylibraries "intro" }} {{- $headers := index $arraylibraries "headers" }} {{- $libraries := index $arraylibraries "libraries" }} -<li class="array-libraries"> +<section class="array-libraries"> {{- range $intro }} <p> {{ .text }} </p> {{- end }} <table> <tr class="highlight-th"> <td class="bold-text"></td> {{- range $headers }} <td class="bold-text">{{ .text }}</td> {{- end }} </td> </tr> {{- range $libraries }} <tr> <td style="text-align: center"><img class="first-column-layout" src="{{ .img }}" alt="{{ .alttext }}"></td> <td class="full-center-text"><a href="{{ .url }}">{{ .title }}</a></td> <td class="left-text">{{ .text }}</td> </tr> {{- end }} </table> -</li> +</section> diff --git a/layouts/partials/data-science.html b/layouts/partials/data-science.html index 2825218..87c89d1 100644 --- a/layouts/partials/data-science.html +++ b/layouts/partials/data-science.html @@ -1,46 +1,46 @@ <!-- Data Science Tab Content --> {{- $datascience := .Site.Params.datascience }} {{- $intro := index $datascience "intro" }} {{- $content := index $datascience "content" }} {{- $examples := index $datascience "examples" }} {{- $image1 := index $datascience "image1" }} {{- $image2 := index $datascience "image2" }} -<li class="data-science"> +<section class="data-science"> <div class="grid-container"> <div> {{- range $image1 }} <a href="{{ .img }}" target="_blank"> <img src="{{ .img }}" alt="{{ .alttext }}" align="left"> </a> {{- end }} </div> <div> <p> {{ $intro }} <ul class="content-tab"> {{- range $examples }} <li>{{ .text | markdownify }}</li> {{- end }} </ul> </p> </div> </div> <div class="grid-container"> <div> <p></p> {{- range $content }} <p> {{ .text | markdownify }} </p> {{- end }} </p> </div> <div> {{- range $image2 }} <img src="{{ .img }}" alt="{{ .alttext }}" align="centre" width="75%"> </a> {{- end }} </div> </div> -</li> +</section> diff --git a/layouts/partials/machine-learning.html b/layouts/partials/machine-learning.html index e7d9e45..ccfdf30 100644 --- a/layouts/partials/machine-learning.html +++ b/layouts/partials/machine-learning.html @@ -1,31 +1,31 @@ <!-- Machine Learning Tab Content --> {{- $machinelearning := .Site.Params.machinelearning }} {{- $paras := index $machinelearning "paras" }} {{- range $paras }} -<li class="machine-learning"> +<section class="machine-learning"> <div class="grid-container"> <div class="animation-holder"> <a href="https://ai.googleblog.com/2016/12/open-sourcing-embedding-projector-tool.html"> <img src="/images/content_images/ml_img/tensorflow-ml-anim.gif" alt="An animated gif showing a three-dimensional graph of embeddings made in Tensorflow." align="middle" class="animation-img"> </a> <p> <i style="font-size:80%;"> <a href="https://ai.googleblog.com/2016/12/open-sourcing-embedding-projector-tool.html">Source: Google AI Blog</a> </i> </p> </div> <div> <p> {{ .para1 | markdownify }} </p> </div> </div> <div> <div> <p> {{ .para2 | markdownify }} </p> </div> </div> -</li> +</section> {{- end }} diff --git a/layouts/partials/visualization.html b/layouts/partials/visualization.html index e3aff21..5cd2350 100644 --- a/layouts/partials/visualization.html +++ b/layouts/partials/visualization.html @@ -1,28 +1,28 @@ <!-- Visualization Tab Content --> {{- $visualization := .Site.Params.visualization }} {{- $content := index $visualization "content" }} {{- $images := index $visualization "images" }} -<li class="visualization"> +<section class="visualization"> <div class="grid-container"> <div class="visualization-images"> <div class="image-grid"> {{- range $images }} <div> <a href="{{ .url }}"> <img src="{{ .img }}" alt="{{ .alttext }}" align="middle"/> </a> </div> {{- end }} </div> </div> <div> {{- range $content }} <p> {{ .text | markdownify }} </p> {{- end }} </div> <div> </div> </div> -</li> +</section>
numpy/numpy.org
817f01d8fb2372eb5c7d8bff79c870f71585d201
replace the book with the latest 3rd edition link (#754)
diff --git a/content/en/learn.md b/content/en/learn.md index a10aa18..ca45c14 100644 --- a/content/en/learn.md +++ b/content/en/learn.md @@ -1,76 +1,76 @@ --- title: Learn sidebar: false --- For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). *** Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. ## Beginners There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: <i class="fas fa-chalkboard"></i> **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) * [Scientific Python Lectures](https://lectures.scientific-python.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. * [NumPy: the absolute basics for beginners](https://numpy.org/devdocs/user/absolute_beginners.html) * [NumPy tutorial *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) <i class="fas fa-book"></i> **Books** * [Guide to NumPy *by Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) This is a free version 1 from 2006. For the latest copy (2015) see [here](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). * [From Python to NumPy *by Nicolas P. Rougier*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) * [Elegant SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* You may also want to check out the [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy) on the subject of "Python+SciPy." Most books there are about the "SciPy ecosystem," which has NumPy at its core. <i class="far fa-file-video"></i> **Videos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** ## Advanced Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. <i class="fas fa-chalkboard"></i> **Tutorials** * [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *by Nicolas P. Rougier* * [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* * [NumPy Tutorials](https://numpy.org/numpy-tutorials) A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team. To submit your own content, visit the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). <i class="fas fa-book"></i> **Books** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *by Jake Vanderplas* -* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-Jupyter/dp/109810403X) *by Wes McKinney* * [Numerical Python: Scientific Computing and Data Science Applications with Numpy, SciPy, and Matplotlib](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *by Robert Johansson* <i class="far fa-file-video"></i> **Videos** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** ## NumPy Talks * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) * [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) * [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) * [Brief Review of Array Computing in Python](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** ## Citing NumPy If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, please see [this citation information](/citing-numpy).
numpy/numpy.org
c7f100cba9206dc51e1eabd33dd71ba62df941c0
Add a news entry about the 2.0 release date (#750)
diff --git a/content/en/news.md b/content/en/news.md index 1a38417..7412b03 100644 --- a/content/en/news.md +++ b/content/en/news.md @@ -1,417 +1,432 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.26.0 released" -date: 2023-09-16 +newsHeader: "NumPy 2.0 release date: June 16" +date: 2024-05-23 --- +### NumPy 2.0 release date: June 16 + +_23 May, 2024_ -- We are excited to announce that NumPy 2.0 is planned to be +released on June 16, 2024. This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains **breaking changes** to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy `2.0.0rc2`. **Please see the following for more details:** + +- The [NumPy 2.0 migration guide](https://numpy.org/devdocs/numpy_2_0_migration_guide.html) +- The [2.0.0 release notes](https://numpy.org/devdocs/release/2.0.0-notes.html) +- Announcement issue for status updates: [numpy#24300](https://github.com/numpy/numpy/issues/24300) + + ### NumFOCUS end of the year fundraiser _Dec 19, 2023_ -- NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy 1.26.0 released _Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. The highlights of the release are: * Python 3.12.0 support. * Cython 3.0.0 compatibility. * Use of the Meson build system * Updated SIMD support * f2py fixes, meson and bind(x) support * Support for the updated Accelerate BLAS/LAPACK library The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the transition to the Meson build system and provision of support for Cython 3.0.0. A total of 20 people contributed to this release and 59 pull requests were merged. The Python versions supported by this release are 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese _Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) _Japanese:_ * Atsushi Sakai (AtsushiSakai) * KKunai * Tom Kelly (TomKellyGenetics) * Yuji Kanagawa (kngwyu) * Tetsuo Koyama (tkoyama010) The work on the translation infrastructure is supported with funding from CZI. Looking ahead, we’d love to translate the website into more languages. If you’d like to help, please connect with the NumPy Translations Team on Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Look for the #translations channel.) We are also building a Translations Team who will be working on localizing documentation and educational content across the Scientific Python ecosystem. If this piqued your interest, join us on the Scientific Python Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.) ### NumPy 1.25.0 released _Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (``@=``). The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported by this release are 3.9-3.11. ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation How can we be better when it comes to diversity and inclusion? Read the report and find out how to get involved [here](https://contributor-experience.org/docs/posts/dei-report/). ### NumPy documentation team leadership transition _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy documentation team leads replacing Melissa Mendonça. We thank Melissa for all her contributions to the NumPy official documentation and educational materials, and Mukulika and Ross for stepping up. ### NumPy 1.24.0 released _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) is now available. The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. * Many new deprecations, check them out. * Many expired deprecations, The NumPy 1.24.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase execution speed, and clarify the documentation. There are a large number of new and expired deprecations due to changes in dtype promotion and cleanups. It is the work of 177 contributors spread over 444 pull requests. The supported Python versions are 3.8-3.11. ### Numpy 1.23.0 released _Jun 22, 2022_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html) is now available. The highlights of the release are: * Implementation of ``loadtxt`` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. * Changes to the promotion and comparisons of structured dtypes. * Improvements to f2py. The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage. ### NumFOCUS DEI research study: call for participation _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [research project](https://numfocus.org/diversity-inclusion-disc/a-pivotal-time-in-numfocuss-project-aimed-dei-efforts?eType=EmailBlastContent&eId=f41a86c3-60d4-4cf9-86cf-58eb49dc968c) funded by the [Gordon & Betty Moore Foundation](https://www.moore.org/) to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software community. The research team would like to talk to new contributors, project developers and maintainers, and those who have contributed in the past about their experiences joining and contributing to NumPy. **Interested in sharing your experiences?** Please complete this brief [“Participant Interest” form](https://numfocus.typeform.com/to/WBWVJSqe) which contains additional information on the research goals, privacy, and confidentiality considerations. Your participation will be valuable to the growth and sustainability of diverse and inclusive open-source software communities. Accepted participants will participate in a 30-minute interview with a research team member. ### Numpy 1.22.0 release _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html) is now available. The highlights of the release are: * Type annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. * A preliminary version of the proposed [array API Standard](https://data-apis.org/array-api/latest/) is provided (see [NEP 47](https://numpy.org/neps/nep-0047-array-api-standard.html)). This is a step in creating a standard collection of functions that can be used across libraries such as CuPy and JAX. * NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. * New methods for ``quantile``, ``percentile``, and related functions. The new methods provide a complete set of the methods commonly found in the literature. * The universal functions have been refactored to implement most of [NEP 43](https://numpy.org/neps/nep-0043-extensible-ufuncs.html). This also unlocks the ability to experiment with the future DType API. * A new configurable memory allocator for use by downstream projects. NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. The Python versions supported by this release are 3.8-3.10. ### Advancing an inclusive culture in the scientific Python ecosystem _August 31, 2021_ -- We are happy to announce the Chan Zuckerberg Initiative has [awarded a grant](https://chanzuckerberg.com/newsroom/czi-awards-16-million-for-foundational-open-source-software-tools-essential-to-biomedicine/) to support the onboarding, inclusion, and retention of people from historically marginalized groups on scientific Python projects, and to structurally improve the community dynamics for NumPy, SciPy, Matplotlib, and Pandas. As a part of [CZI's Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/), this [Diversity & Inclusion supplemental grant](https://cziscience.medium.com/advancing-diversity-and-inclusion-in-scientific-open-source-eaabe6a5488b) will support the creation of dedicated Contributor Experience Lead positions to identify, document, and implement practices to foster inclusive open-source communities. This project will be led by Melissa Mendonça (NumPy), with additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy), Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and Joris Van den Bossche (Pandas). This is an ambitious project aiming to discover and implement activities that should structurally improve the community dynamics of our projects. By establishing these new cross-project roles, we hope to introduce a new collaboration model to the Scientific Python communities, allowing community-building work within the ecosystem to be done more efficiently and with greater outcomes. We also expect to develop a clearer picture of what works and what doesn't in our projects to engage and retain new contributors, especially from historically underrepresented groups. Finally, we plan on producing detailed reports on the actions executed, explaining how they have impacted our projects in terms of representation and interaction with our communities. The two-year project is expected to start by November 2021, and we are excited to see the results from this work! [You can read the full proposal here](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021 NumPy survey _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. The survey findings gave us a very good understanding of what we should focus on for the next 12 months. It’s time for another survey, and we are counting on you once again. It will take about 15 minutes of your time. Besides English, the survey questionnaire is available in 8 additional languages: Bangla, French, Hindi, Japanese, Mandarin, Portuguese, Russian, and Spanish. Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 release _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. ### 2020 NumPy survey results _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. ### Numpy 1.20.0 release _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are: - Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. - Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). ### Diversity in the NumPy project _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). ### First official NumPy paper published in Nature! _Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. ### Python 3.9 is coming, when will NumPy release binary wheels? _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an early adopter of Python versions, you may be dissapointed to find that NumPy (and other binary packages like SciPy) will not have binary wheels ready on the day of the release. It is a major effort to adapt the build infrastructure to a new Python version and it typically takes a few weeks for the packages to appear on PyPI and conda-forge. In preparation for this event, please make sure to - update your `pip` to version 20.1 at least to support `manylinux2010` and `manylinux2014` - use [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) or `--only-binary=:all:` to prevent `pip` from trying to build from source. ### Numpy 1.19.2 release _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. ### The inaugural NumPy survey is live! _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy has a new logo! _Jun 24, 2020_ -- NumPy now has a new logo: <img src="/images/logos/numpy_logo.svg" alt="NumPy logo" title="The new NumPy logo" width=300> The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. ### NumPy 1.19.0 release _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. ### Season of Docs acceptance _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). ### NumPy 1.18.0 release _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for ``numpy.random``. Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### NumPy receives a grant from the Chan Zuckerberg Initiative _Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. <a name="releases"></a> ## Releases Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 1.26.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.4)) -- _5 Feb 2024_. - NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - NumPy 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - NumPy 1.24.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.0)) -- _18 Dec 2022_. - NumPy 1.23.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.23.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.4)) -- _12 Oct 2022_. - NumPy 1.23.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. - NumPy 1.23.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. - NumPy 1.23.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. - NumPy 1.23.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. - NumPy 1.22.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. - NumPy 1.21.6 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.22.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.3)) -- _7 Mar 2022_. - NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. - NumPy 1.22.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. - NumPy 1.22.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. - NumPy 1.21.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. - NumPy 1.21.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. - NumPy 1.20.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. - NumPy 1.20.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. - NumPy 1.19.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.19.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 Jun 2020_. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. - NumPy 1.16.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 Jan 2019_. - NumPy 1.15.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 Jul 2018_. - NumPy 1.14.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 Jan 2018_.
numpy/numpy.org
b5da85720ae1a5c4cca514dce9d901422851a507
pyjanitor link corrected (#749)
diff --git a/content/en/tabcontents.yaml b/content/en/tabcontents.yaml index 6dbdc89..41fedc8 100644 --- a/content/en/tabcontents.yaml +++ b/content/en/tabcontents.yaml @@ -1,295 +1,295 @@ params: machinelearning: paras: - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications &mdash; among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) &mdash; one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. arraylibraries: intro: - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. headers: - text: Array Library - text: Capabilities & Application areas libraries: - title: Dask text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: NumPy-compatible array library for GPU-accelerated computing with Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://jax.readthedocs.io/ - title: Xarray text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: Deep learning framework that accelerates the path from research prototyping to production deployment. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: Arrow text: A cross-language development platform for columnar in-memory data and analytics. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://arrow.apache.org/ - title: xtensor text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array text: Manipulate JSON-like data with NumPy-like idioms. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Nearly every scientist working in Python draws on the power of NumPy. - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." libraries: - title: Quantum Computing alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - url: http://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - url: https://qiskit.org label: Qiskit - url: https://pennylane.ai label: PennyLane - title: Statistical Computing alttext: A line graph with the line moving up. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - url: https://www.statsmodels.org/ label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - url: https://seaborn.pydata.org/ label: Seaborn - title: Signal Processing alttext: A bar chart with positive and negative values. img: /images/content_images/sc_dom_img/signal_processing.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets - url: https://python-control.org/ label: python-control - url: https://hyperspy.org/ label: HyperSpy - title: Image Processing alttext: An photograph of the mountains. img: /images/content_images/sc_dom_img/image_processing.svg links: - url: https://scikit-image.org/ label: Scikit-image - url: https://opencv.org/ label: OpenCV - url: https://mahotas.rtfd.io/ label: Mahotas - title: Graphs and Networks alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg links: - url: https://networkx.org/ label: NetworkX - url: https://graph-tool.skewed.de/ label: graph-tool - url: https://igraph.org/python/ label: igraph - url: https://pygsp.rtfd.io/ label: PyGSP - title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - url: https://sunpy.org/ label: SunPy - url: https://spacepy.github.io/ label: SpacePy - title: Cognitive Psychology alttext: A human head with gears. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - title: Bioinformatics alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - url: https://biopython.org/ label: BioPython - url: http://scikit-bio.org/ label: Scikit-Bio - url: https://github.com/openvax/pyensembl label: PyEnsembl - url: http://etetoolkit.org/ label: ETE - title: Bayesian Inference alttext: A graph with a bell-shaped curve. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ label: PyMC3 - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ label: emcee - title: Mathematical Analysis alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://www.sympy.org/ label: SymPy - url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: Chemistry alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg links: - url: https://cantera.org/ label: Cantera - url: https://www.mdanalysis.org/ label: MDAnalysis - url: https://github.com/rdkit/rdkit label: RDKit - url: https://www.pybamm.org/ label: PyBaMM - title: Geoscience alttext: The Earth. img: /images/content_images/sc_dom_img/geoscience.svg links: - url: https://pangeo.io/ label: Pangeo - url: https://simpeg.xyz/ label: Simpeg - url: https://github.com/obspy/obspy/wiki label: ObsPy - url: https://www.fatiando.org/ label: Fatiando a Terra - title: Geographic Processing alttext: A map. img: /images/content_images/sc_dom_img/GIS.svg links: - url: https://shapely.readthedocs.io/ label: Shapely - url: https://geopandas.org/ label: GeoPandas - url: https://python-visualization.github.io/folium label: Folium - title: Architecture & Engineering alttext: A microprocessor development board. img: /images/content_images/sc_dom_img/robotics.svg links: - url: https://compas.dev/ label: COMPAS - url: https://cityenergyanalyst.com/ label: City Energy Analyst - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" image1: - img: /images/content_images/ds-landscape.png alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. image2: - img: /images/content_images/data-science.png alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" content: - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: A streamplot made in matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: A scatter-plot graph made in ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: A box-plot made in plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: A streamgraph made in altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - url: https://napari.org img: /images/content_images/v_napari.png alttext: A multi-dimensionan image made in napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: A Voronoi diagram made in vispy. content: - text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 78f299b..e3dc2ba 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -1,219 +1,219 @@ params: machinelearning: paras: - para1: NumPyは、[scikit-learn](https://scikit-learn.org)や[SciPy](https://www.scipy.org)のような強力な機械学習ライブラリの基礎を形成しています。機械学習の技術分野が成長するにつれ、NumPyをベースにしたライブラリの数も増えています。[TensorFlow](https://www.tensorflow.org)の深層学習機能は、音声認識や画像認識、テキストベースのアプリケーション、時系列分析、動画検出など、幅広い応用用途があります。[PyTorch](https://pytorch.org)も、コンピュータビジョンや自然言語処理の研究者に人気のある深層学習ライブラリです。[MXNet](https://github.com/apache/incubator-mxnet)もAIパッケージの一つで、深層学習の設計図やテンプレート機能を提供しています。 para2: '[ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205)法と呼ばれる統計的手法であるビンニング、バギング、スタッキングや、[XGBoost](https://github.com/dmlc/xgboost)、[LightGBM](https://lightgbm.readthedocs.io/en/latest/)、[CatBoost](https://catboost.ai)などのツールで実装されているブースティングなどは、機械学習アルゴリズムの一つであり、最速の推論エンジンの一つです。[Yellowbrick](https://www.scikit-yb.org/en/latest/)や[Eli5](https://eli5.readthedocs.io/en/latest/)は機械学習の可視化機能を提供しています。' arraylibraries: intro: - text: NumPyのAPIは、革新的なハードウェアを利用したり、特殊な配列タイプを作成したり、NumPyが提供する以上の機能を追加するためにライブラリを作成する際の基礎となります。 headers: - text: 配列ライブラリ - text: 機能と応用分野 libraries: - title: Dask text: 分析用の分散配列と高度な並列処理により、大規模な処理を可能にします。 img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: Python を使用した GPUによる高速計算用のNumPy互換配列ライブラリ img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - title: JAX text: "NumPyコードの合成可能な変換ライブラリ: 微分、ベクトル化、GPU/TPUへのジャストインタイムコンパイル" img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax - title: Xarray text: 高度な分析と視覚化のためのラベルとインデックス付き多次元配列 img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: Dask と SciPy の疎行列の線形代数ライブラリを統合した、Numpy 互換の疎行列ライブラリ img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: 研究用のプロトタイピングから本番運用への展開を加速させる、深層学習フレームワーク img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: 機械学習を利用したアプリケーションを簡単に構築・展開するための、エンド・ツー・エンドの機械学習プラットフォーム img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: MXNet text: 柔軟や研究用のプロトタイピングから、実際の運用まで利用可能な深層学習フレームワーク img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - title: Arrow text: 列型のインメモリーデータやその分析のための、複数の言語に対応した開発プラットフォーム img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow - title: xtensor text: 数値解析のためのブロードキャスティングと遅延計算を備えた多次元配列 img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward text: Numpy のような イディオムを使って JSON のようなデータを操作するライブラリ img: /images/content_images/arlib/xnd.png alttext: awkward url: https://awkward-array.org/ - title: uarray text: APIを実装から切り離すPythonバックエンドシステム (unumpyはNumPy APIを提供しています) img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly text: Numpy、MXNet、PyTorch、TensorFlowまたはCupyをシームレスに使用するための、テンソル学習、テンソル代数、およびそれらのテンソル計算のためのバックエンド img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Pythonを使って働くほとんどの科学者はNumPyの力を利用しています。 - text: "Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。" librariesrow1: - title: 量子コンピューティング alttext: コンピューターチップ img: /images/content_images/sc_dom_img/quantum_computing.svg - title: 統計コンピューティング alttext: 線グラフで、グラフが上に移動します。 img: /images/content_images/sc_dom_img/statistical_computing.svg - title: 信号処理 alttext: 正と負の値を持つ棒グラフ。 img: /images/content_images/sc_dom_img/signal_processing.svg - title: 画像処理 alttext: 山々の写真 img: /images/content_images/sc_dom_img/image_processing.svg - title: グラフとネットワーク alttext: シンプルなグラフ img: /images/content_images/sc_dom_img/sd6.svg - title: 天文学 alttext: 望遠鏡 img: /images/content_images/sc_dom_img/astronomy_processes.svg - title: 認知心理学 alttext: ギアをつけた人間の頭部 img: /images/content_images/sc_dom_img/cognitive_psychology.svg librariesrow2: - title: 生命情報科学 alttext: DNAの鎖 img: /images/content_images/sc_dom_img/bioinformatics.svg - title: ベイズ推論 alttext: 鐘形の曲線のグラフ img: /images/content_images/sc_dom_img/bayesian_inference.svg - title: 数学的分析 alttext: ４つの数学記号 img: /images/content_images/sc_dom_img/mathematical_analysis.svg - title: 化学 alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg - title: 地球科学 alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg - title: 地理情報処理 alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg - title: アーキテクチャとエンジニアリング alttext: マイクロプロセッサ開発ボード img: /images/content_images/sc_dom_img/robotics.svg datascience: intro: "Numpy は豊富なデータサイエンスライブラリのエコシステムの中核にあります。一般的なデータサイエンスのワークフローは次のようになります。" image1: - img: /images/content_images/ds-landscape.png alttext: Python ライブラリの図 。5 つのカテゴリに分類され、「抽出、変換、読み込み」、「データ探索」、「モデリング」、「評価」、「可視化」です。 image2: - img: /images/content_images/data-science.png alttext: 三つの円が重なり合う図。円はそれぞれ「数学」、「コンピューターサイエンス」、「専門知識」でラベル付けされています。図の中心部には、三つの円が重なり合って形成されるエリアがあり、「データサイエンス」とラベル付けされています。 examples: - - text: "<b>抽出, 変換, 読み込み: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "<b>抽出, 変換, 読み込み: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "<b>探索的解析: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - text: "<b>モデリングと評価: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "<b>ダッシュボードでのレポート: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - text: 大規模データに対して、[Dask](https://dask.org)と[Ray](https://ray.io/)はスケールすることを目指して設計されています。安定したデプロイメントに関しては、データのバージョニング（[DVC](https://dvc.org)）、実験の追跡（[MLFlow](https://mlflow.org)）、ワークフローの自動化（[Airflow](https://airflow.apache.org)および[Prefect](https://www.prefect.io)が重要ですが様々なNumPyベースのツールが提供されています。 visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: matplotlibで作られたストリームプロット - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: ggpyで作られた散布図グラフ - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: plotyで作られた箱ひげ図 - url: https://alta-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: altairで作られたストリームグラフ - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: 2種類のグラフによるペアプロット。seabornで作られたプロットと周波数グラフ" - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: PyVista製の3Dボリュームレンダリング - url: https://napari.org img: /images/content_images/v_napari.png alttext: napariで作られた多次元画像 - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: vispyで作られたボロノイ図 content: - text: NumPyは、[Matplotlib](https://matplotlib.org)、[Seaborn](https://seaborn.pydata.org)、[Plotly](https://plot.ly)、[Altair](https://altair-viz.github.io)、[Bokeh](https://docs.bokeh.org/en/latest/)、[Holoviz](https://holoviz.org)、[Vispy](http://vispy.org)、[Napari](https://github.com/napari/napari)、[PyVista](https://github.com/pyvista/pyvista)などの、急成長している[Python visualization landscape](https://pyviz.org/overviews/index.html)に欠かせないコンポーネントです。 - text: NumPy の大規模配列の高速処理により、研究者は、ネイティブの Python が扱うことができるよりもはるかに大きなデータセットを可視化することができます。 diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index fffddb2..a2d2b0d 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -1,219 +1,219 @@ params: machinelearning: paras: - para1: O NumPy forma a base de bibliotecas de aprendizagem de máquina poderosas como [scikit-learn](https://scikit-learn.org) e [SciPy](https://www.scipy.org). À medida que a disciplina de aprendizagem de máquina cresce, a lista de bibliotecas construidas a partir do NumPy também cresce. As funcionalidades de deep learning do [TensorFlow](https://www.tensorflow.org) tem diversas aplicações &mdash; entre elas, reconhecimento de imagem e de fala, aplicações baseadas em texto, análise de séries temporais, e detecção de vídeo. O [PyTorch](https://pytorch.org), outra biblioteca de deep learning, é popular entre pesquisadores em visão computacional e processamento de linguagem natural. O [MXNet](https://github.com/apache/incubator-mxnet) é outro pacote de IA, que fornece templates e protótipos para deep learning. para2: Técnicas estatísticas chamadas métodos de [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) tais como binning, bagging, stacking, e boosting estão entre os algoritmos de ML implementados por ferramentas tais como [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), e [CatBoost](https://catboost.ai) &mdash; um dos motores de inferência mais rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) e [Eli5](https://eli5.readthedocs.io/en/latest/) oferecem visualizações para aprendizagem de máquina. arraylibraries: intro: - text: A API do NumPy é o ponto de partida quando bibliotecas são escritas para explorar hardware inovador, criar tipos de arrays especializados, ou adicionar capacidades além do que o NumPy fornece. headers: - text: Biblioteca de Arrays - text: Recursos e áreas de aplicação libraries: - title: Dask text: Arrays distribuídas e paralelismo avançado para análise, permitindo desempenho em escala. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: Biblioteca de matriz compatível com NumPy para computação acelerada pela GPU com Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - title: JAX text: "Transformações combináveis de programas NumPy: vetorização, compilação just-in-time para GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax - title: Xarray text: Arrays multidimensionais rotuladas e indexadas para análise e visualização avançadas img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: Biblioteca de arrays compatíveis com o NumPy que pode ser integrada com Dask e álgebra linear esparsa da SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: Framework de deep learning que acelera o caminho entre prototipação de pesquisa e colocação em produção. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: Uma plataforma completa para aprendizagem de máquina que permite construir e colocar em produção aplicações usando ML facilmente. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: MXNet text: Framework de deep learning voltado para flexibilizar prototipação em pesquisa e produção. img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - title: Arrow text: Uma plataforma de desenvolvimento multi-linguagens para dados e análise para dados armazenados em colunas na memória. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow - title: xtensor text: Arrays multidimensionais com broadcasting e avaliação preguiçosa (lazy computing) para análise numérica. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array text: Manipulação de dados JSON-like com sintaxe NumPy-like. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray text: Sistema de backend Python que dissocia a API da implementação; unumpy fornece uma API NumPy. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly text: Ferramentas para aprendizagem com tensores, algebra e backends para usar NumPy, MXNet, PyTorch, TensorFlow ou CuPy sem esforço. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy. - text: "NumPy traz o poder computacional de linguagens como C e Fortran para Python, uma linguagem muito mais fácil de aprender e usar. Com esse poder vem a simplicidade: uma solução no NumPy é frequentemente clara e elegante." librariesrow1: - title: Computação quântica alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg - title: Computação estatística alttext: Um gráfico com uma linha em movimento para cima. img: /images/content_images/sc_dom_img/statistical_computing.svg - title: Processamento de sinais alttext: Um gráfico de barras com valores positivos e negativos. img: /images/content_images/sc_dom_img/signal_processing.svg - title: Processamento de imagens alttext: Uma fotografia das montanhas. img: /images/content_images/sc_dom_img/image_processing.svg - title: Gráficos e Redes alttext: Um grafo simples. img: /images/content_images/sc_dom_img/sd6.svg - title: Processos de Astronomia alttext: Um telescópio. img: /images/content_images/sc_dom_img/astronomy_processes.svg - title: Psicologia Cognitiva alttext: Uma cabeça humana com engrenagens. img: /images/content_images/sc_dom_img/cognitive_psychology.svg librariesrow2: - title: Bioinformática alttext: Um pedaço de DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - title: Inferência Bayesiana alttext: Um gráfico com uma curva em forma de sino. img: /images/content_images/sc_dom_img/bayesian_inference.svg - title: Análise Matemática alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - title: Química alttext: Um tubo de ensaio. img: /images/content_images/sc_dom_img/chemistry.svg - title: Geociências alttext: A Terra. img: /images/content_images/sc_dom_img/geoscience.svg - title: Processamento Geográfico alttext: Um mapa. img: /images/content_images/sc_dom_img/GIS.svg - title: Arquitetura e Engenharia alttext: Uma placa de desenvolvimento de microprocessador. img: /images/content_images/sc_dom_img/robotics.svg datascience: intro: "NumPy está no centro de um rico ecossistema de bibliotecas de ciência de dados. Um fluxo de trabalho típico de ciência de dados exploratório pode parecer assim:" image1: - img: /images/content_images/ds-landscape.png alttext: Diagrama de bibliotecas Python. As cinco categorias são 'Extrair, Transformar, Carregar', 'Exploração de Dados', 'Modelo de Dados', 'Avaliação de Dados' e 'Apresentação de Dados'. image2: - img: /images/content_images/data-science.png alttext: Diagram of three overlapping circle. The circles labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" content: - text: For high data volumes, [Dask](https://dask.org) and[Ray](https://ray.io/) are designed to scale. Stabledeployments rely on data versioning ([DVC](https://dvc.org)),experiment tracking ([MLFlow](https://mlflow.org)), andworkflow automation ([Airflow](https://airflow.apache.org) and[Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: Um streamplot feito em matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: Um gráfico scatter-plot feito em ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: Um box-plot feito no plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: Um gráfico streamgraph feito em altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: A plot duplo com dois tipos de gráficos, um plot-graph e um gráfico de frequência feitos no seaborn - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: Uma renderização de volume 3D feita no PyVista. - url: https://napari.org img: /images/content_images/v_napari.png alttext: Uma imagem multidimensional, feita em napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: Diagrama de Voronoi feito com vispy. content: - text: NumPy é um componente essencial no crescente [campo de visualização em Python](https://pyviz.org/overviews/index.html), que inclui [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), e [PyVista](https://github.com/pyvista/pyvista), para citar alguns. - text: O processamento de grandes arrays acelerado pela NumPy permite que os pesquisadores visualizem conjuntos de dados muito maiores do que o Python nativo poderia permitir.
numpy/numpy.org
b3320dd27c5f367ecca4b77e6765d8efd6cffd82
remove references to mxnet which has been discontinued (#747)
diff --git a/content/en/tabcontents.yaml b/content/en/tabcontents.yaml index 2bf49d5..6dbdc89 100644 --- a/content/en/tabcontents.yaml +++ b/content/en/tabcontents.yaml @@ -1,300 +1,295 @@ params: machinelearning: paras: - - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications &mdash; among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. [MXNet](https://mxnet.apache.org/) is another AI package, providing blueprints and templates for deep learning. + - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications &mdash; among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) &mdash; one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. arraylibraries: intro: - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. headers: - text: Array Library - text: Capabilities & Application areas libraries: - title: Dask text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: NumPy-compatible array library for GPU-accelerated computing with Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://jax.readthedocs.io/ - title: Xarray text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: Deep learning framework that accelerates the path from research prototyping to production deployment. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - - title: MXNet - text: Deep learning framework suited for flexible research prototyping and production. - img: /images/content_images/arlib/mxnet_logo.png - alttext: MXNet - url: https://mxnet.apache.org/ - title: Arrow text: A cross-language development platform for columnar in-memory data and analytics. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://arrow.apache.org/ - title: xtensor text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array text: Manipulate JSON-like data with NumPy-like idioms. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly - text: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Nearly every scientist working in Python draws on the power of NumPy. - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." libraries: - title: Quantum Computing alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - url: http://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - url: https://qiskit.org label: Qiskit - url: https://pennylane.ai label: PennyLane - title: Statistical Computing alttext: A line graph with the line moving up. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - url: https://www.statsmodels.org/ label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - url: https://seaborn.pydata.org/ label: Seaborn - title: Signal Processing alttext: A bar chart with positive and negative values. img: /images/content_images/sc_dom_img/signal_processing.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets - url: https://python-control.org/ label: python-control - url: https://hyperspy.org/ label: HyperSpy - title: Image Processing alttext: An photograph of the mountains. img: /images/content_images/sc_dom_img/image_processing.svg links: - url: https://scikit-image.org/ label: Scikit-image - url: https://opencv.org/ label: OpenCV - url: https://mahotas.rtfd.io/ label: Mahotas - title: Graphs and Networks alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg links: - url: https://networkx.org/ label: NetworkX - url: https://graph-tool.skewed.de/ label: graph-tool - url: https://igraph.org/python/ label: igraph - url: https://pygsp.rtfd.io/ label: PyGSP - title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - url: https://sunpy.org/ label: SunPy - url: https://spacepy.github.io/ label: SpacePy - title: Cognitive Psychology alttext: A human head with gears. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - title: Bioinformatics alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - url: https://biopython.org/ label: BioPython - url: http://scikit-bio.org/ label: Scikit-Bio - url: https://github.com/openvax/pyensembl label: PyEnsembl - url: http://etetoolkit.org/ label: ETE - title: Bayesian Inference alttext: A graph with a bell-shaped curve. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ label: PyMC3 - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ label: emcee - title: Mathematical Analysis alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://www.sympy.org/ label: SymPy - url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: Chemistry alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg links: - url: https://cantera.org/ label: Cantera - url: https://www.mdanalysis.org/ label: MDAnalysis - url: https://github.com/rdkit/rdkit label: RDKit - url: https://www.pybamm.org/ label: PyBaMM - title: Geoscience alttext: The Earth. img: /images/content_images/sc_dom_img/geoscience.svg links: - url: https://pangeo.io/ label: Pangeo - url: https://simpeg.xyz/ label: Simpeg - url: https://github.com/obspy/obspy/wiki label: ObsPy - url: https://www.fatiando.org/ label: Fatiando a Terra - title: Geographic Processing alttext: A map. img: /images/content_images/sc_dom_img/GIS.svg links: - url: https://shapely.readthedocs.io/ label: Shapely - url: https://geopandas.org/ label: GeoPandas - url: https://python-visualization.github.io/folium label: Folium - title: Architecture & Engineering alttext: A microprocessor development board. img: /images/content_images/sc_dom_img/robotics.svg links: - url: https://compas.dev/ label: COMPAS - url: https://cityenergyanalyst.com/ label: City Energy Analyst - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" image1: - img: /images/content_images/ds-landscape.png alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. image2: - img: /images/content_images/data-science.png alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" content: - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: A streamplot made in matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: A scatter-plot graph made in ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: A box-plot made in plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: A streamgraph made in altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - url: https://napari.org img: /images/content_images/v_napari.png alttext: A multi-dimensionan image made in napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: A Voronoi diagram made in vispy. content: - text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), and [PyVista](https://docs.pyvista.org/), to name a few. - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.
numpy/numpy.org
87abc3538bec6022be3518cbeb71bb05e58f8ef3
Use markdown links (#743)
diff --git a/content/en/about.md b/content/en/about.md index 8bd7af1..2b77f7d 100644 --- a/content/en/about.md +++ b/content/en/about.md @@ -1,91 +1,91 @@ --- title: About Us sidebar: false --- NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html). ## Steering Council The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name): - Sebastian Berg - Ralf Gommers - Charles Harris - Stephan Hoyer - Inessa Pawson - Matti Picus - Stéfan van der Walt - Melissa Weber Mendonça - Eric Wieser Emeritus: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) - Marten van Kerkwijk (2017-2019) - Travis Oliphant (project founder, 2005-2012) - Nathaniel Smith (2012-2021) - Julian Taylor (2013-2021) - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) To contact the NumPy Steering Council, please email [email protected]. ## Teams The NumPy project leadership is actively working on diversifying contribution pathways to the project.<br> NumPy currently has the following teams: - development - documentation - triage - website - survey - translations - sprint mentors - optimization - funding and grants -See the [Team]({{< relref "/teams" >}}) page for more info. +See the [Team](/teams) page for more info. ## NumFOCUS Subcommittee - Charles Harris - Ralf Gommers - Inessa Pawson - Sebastian Berg - External member: Thomas Caswell ## Sponsors NumPy receives direct funding from the following sources: {{< sponsors >}} ## Institutional Partners Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: - UC Berkeley (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) - NVIDIA (Sebastian Berg) {{< partners >}} ## Donate If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information. Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation. NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). {{<opencollective>}} diff --git a/content/en/teams/index.md b/content/en/teams/index.md index 2c5a4c5..1baaaa3 100644 --- a/content/en/teams/index.md +++ b/content/en/teams/index.md @@ -1,36 +1,36 @@ --- title: NumPy Teams sidebar: false --- We are an international team on a mission to support scientific and research communities worldwide by building quality, open-source software. -[Join us]({{< relref "/contribute" >}})! +[Join us](/contribute)! ### Maintainers {{< grid file="maintainers.toml" columns="2 3 4 5" />}} ### Docs team {{< grid file="docs-team.toml" columns="2 3 4 5" />}} ### Web team {{< grid file="web-team.toml" columns="2 3 4 5" />}} ### Triage team {{< grid file="triage-team.toml" columns="2 3 4 5" />}} ### Survey team {{< grid file="survey-team.toml" columns="2 3 4 5" />}} ### Emeritus maintainers {{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}} # Governance For the list of the Steering Council members, please see [here](https://numpy.org/about/). diff --git a/content/ja/teams/index.md b/content/ja/teams/index.md index cb6acb2..f7bd323 100644 --- a/content/ja/teams/index.md +++ b/content/ja/teams/index.md @@ -1,34 +1,34 @@ --- title: NumPy開発チーム sidebar: false --- -私たちは、高品質のオープンソースソフトウェアを構築することで、世界中の科学・研究コミュニティをサポートすることを使命とする国際的なチームです。 是非[参加してください]({{< relref "/contribute" >}})! +私たちは、高品質のオープンソースソフトウェアを構築することで、世界中の科学・研究コミュニティをサポートすることを使命とする国際的なチームです。 是非[参加してください](/contribute)! ### Maintainers {{< grid file="maintainers.toml" columns="2 3 4 5" />}} ### Docs team {{< grid file="docs-team.toml" columns="2 3 4 5" />}} ### Web team {{< grid file="web-team.toml" columns="2 3 4 5" />}} ### Triage team {{< grid file="triage-team.toml" columns="2 3 4 5" />}} ### Survey team {{< grid file="survey-team.toml" columns="2 3 4 5" />}} ### Emeritus maintainers {{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}} # ガバナンス For the list of people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). diff --git a/content/pt/about.md b/content/pt/about.md index 9482ec1..8461f68 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -1,90 +1,90 @@ --- title: Quem Somos sidebar: false --- NumPy é um projeto de código aberto que visa possibilitar a computação numérica com Python. Foi criado em 2005, com base no trabalho inicial das bibliotecas Numeric e Numarray. O NumPy sempre será 100% software de código aberto, livre para que todos usem. É lançado sob os termos liberais da [licença BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). O NumPy é desenvolvido no GitHub, através do consenso da comunidade NumPy e de uma comunidade mais ampla de Python científico. Para obter mais informações sobre nossa abordagem de governança, por favor, consulte nosso [Documento de Governança](https://www.numpy.org/devdocs/dev/governance/index.html). ## Conselho Diretor (Steering Council) O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo prazo do projeto, tanto nos aspectos técnicos quanto na comunidade. Isso é feito através do trabalho com e para a comunidade NumPy em geral. O Conselho Diretor do NumPy atualmente consiste dos seguintes membros (em ordem alfabética, pelo sobrenome): - Sebastian Berg - Ralf Gommers - Charles Harris - Stephan Hoyer - Inessa Pawson - Matti Picus - Stéfan van der Walt - Melissa Weber Mendonça - Eric Wieser Membros Eméritos: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) - Marten van Kerkwijk (2017-2019) - Travis Oliphant (project founder, 2005-2012) - Nathaniel Smith (2012-2021) - Julian Taylor (2013-2021) - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) Para entrar em contato com o conselho diretor do NumPy, por favor envie um email para [email protected]. ## Times A liderança do projeto NumPy trabalha ativamente na diversificação dos caminhos possíveis para contribuições.<br> Atualmente, o NumPy conta com os seguintes times: - desenvolvimento - documentação - triagem - website - pesquisa - traduções - mentores para sprints de desenvolvimento - otimização - financiamento e bolsas -Veja a página sobre os [Times]({{< relref "/teams" >}}) para mais informações. +Veja a página sobre os [Times](/teams) para mais informações. ## Subcomitê NumFOCUS - Charles Harris - Ralf Gommers - Inessa Pawson - Sebastian Berg - Membro externo: Thomas Caswell ## Patrocinadores O NumPy recebe financiamento direto das seguintes fontes: {{< sponsors >}} ## Parceiros Institucionais Os Parceiros Institucionais são organizações que apoiam o projeto, empregando pessoas que contribuem para a NumPy como parte de seu trabalho. Os parceiros institucionais atuais incluem: - UC Berkeley (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) - NVIDIA (Sebastian Berg) {{< partners >}} ## Doações Se você achou o NumPy útil no seu trabalho, pesquisa ou empresa, por favor considere fazer uma doação para o projeto que seja compatível com seus recursos. Qualquer quantidade ajuda! Todas as doações serão utilizadas estritamente para financiar o desenvolvimento do software de código aberto da NumPy, documentação e comunidade. NumPy é um Projeto Patrocinado da NumFOCUS, uma instituição de caridade sem fins lucrativos nos Estados Unidos. A NumFOCUS fornece ao NumPy apoio fiscal, legal e administrativo para ajudar a garantir a saúde e a sustentabilidade do projeto. Visite [numfocus.org](https://numfocus.org) para obter mais informações. Doações para o NumPy são gerenciadas pela [NumFOCUS](https://numfocus.org). Para doadores nos Estados Unidos, sua doação é dedutível para fins fiscais na medida oferecida pela lei. Como em qualquer doação, você deve consultar seu conselheiro fiscal sobre sua situação fiscal em particular. O Conselho Diretor da NumPy tomará as decisões sobre a melhor forma de utilizar os fundos recebidos. Prioridades técnicas e de infraestrutura estão documentadas no [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). {{<opencollective>}} diff --git a/content/pt/teams/index.md b/content/pt/teams/index.md index bdd16ff..b84f16a 100644 --- a/content/pt/teams/index.md +++ b/content/pt/teams/index.md @@ -1,34 +1,34 @@ --- title: Times NumPy sidebar: false --- -Somos uma equipe internacional com a missão de apoiar comunidades científicas e de pesquisa em todo o mundo construindo software de código aberto de qualidade. [Junte-se a nós]({{< relref "/contribute" >}})! +Somos uma equipe internacional com a missão de apoiar comunidades científicas e de pesquisa em todo o mundo construindo software de código aberto de qualidade. [Junte-se a nós](/contribute)! ### Maintainers {{< grid file="maintainers.toml" columns="2 3 4 5" />}} ### Docs team {{< grid file="docs-team.toml" columns="2 3 4 5" />}} ### Web team {{< grid file="web-team.toml" columns="2 3 4 5" />}} ### Triage team {{< grid file="triage-team.toml" columns="2 3 4 5" />}} ### Survey team {{< grid file="survey-team.toml" columns="2 3 4 5" />}} ### Emeritus maintainers {{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}} # Governança Para a lista de pessoas no Conselho Diretor, veja [aqui](https://numpy.org/devdocs/dev/governance/people.html).
numpy/numpy.org
2f2dbe74b80458890e4026ca0891862dfffba55d
Fix: (.grid-container > div) background-color for dark-mode
diff --git a/assets/css/tabs.scss b/assets/css/tabs.scss index e95eda6..c1b84be 100644 --- a/assets/css/tabs.scss +++ b/assets/css/tabs.scss @@ -1,137 +1,136 @@ [role="tablist"] { justify-content: center; } table td:not([align]), table th:not([align]) { text-align: inherit; } table td, table th { vertical-align: top; } .tabs-section { display: flex; flex-direction: column; } .tabs-section .container { display: flex; flex-direction: column; } .tabs-title { display: flex; justify-content: center; letter-spacing: 1.5px; font-size: 27px; margin: 30px 0; } .visualization, .data-science, .machine-learning, .array-libraries { max-width: 900px; margin: 15px auto; } @media only screen and (max-width: 1200px) { .tabs-section { margin: 30px 10px; } .tabs-title { margin: 30px; } } .grid-container { display: grid; grid-template-columns: auto auto; grid-gap: 20px; } .grid-container > div { - /* White, with 80% opacity */ - background-color: rgba(255, 255, 255, 0.8); + background-color: var(--pst-color-background); text-align: middle; } @media only screen and (max-width: 600px) { .grid-container { display: block; } } /* Visualization */ .visualization-images > img { border-radius: 10px; } .image-grid { display: grid; grid-template-columns: auto auto auto auto; grid-gap: 10px; } .image-grid > div { background-color: var(--pst-color-surface); border: 2px solid var(--pst-color-surface); border-radius: 10px; padding: 10px; } /* Scientific Domains */ section.scientific-domains { max-width: 900px !important; & ul { display: flex; flex-wrap: wrap; list-style: none; margin: 15px auto; padding-inline-start: 0; & li { align-content: center; font-size: 0.8rem; line-height: 130%; margin: 0.2em 0.4em; flex-basis: 13%; & header { // FIXME: Use appropriate PST color for this header text. color: var(--pst-color-text-base); font-weight: 700; // Ensure headers are the same minimum height (some wrap // to two lines). min-height: 3.3em; text-align: left; } & img { width: 50px; height: 50px; margin-bottom: 0.5em; } & ul { align-content: left; display: flex; flex-direction: column; padding-inline-start: 0; & li { margin-left: 0em; } } } } } /* Array Libraries */ img.first-column-layout { max-width: 100px; max-height: 30px; margin: 0px 20px 0px 10px; } td.left-text { vertical-align: middle; }
numpy/numpy.org
abfb6120b491e0707d79de92bc6e347094274533
Fix: (.image-grid > div) Image background and border color
diff --git a/assets/css/tabs.scss b/assets/css/tabs.scss index 420ec1d..e95eda6 100644 --- a/assets/css/tabs.scss +++ b/assets/css/tabs.scss @@ -1,137 +1,137 @@ [role="tablist"] { justify-content: center; } table td:not([align]), table th:not([align]) { text-align: inherit; } table td, table th { vertical-align: top; } .tabs-section { display: flex; flex-direction: column; } .tabs-section .container { display: flex; flex-direction: column; } .tabs-title { display: flex; justify-content: center; letter-spacing: 1.5px; font-size: 27px; margin: 30px 0; } .visualization, .data-science, .machine-learning, .array-libraries { max-width: 900px; margin: 15px auto; } @media only screen and (max-width: 1200px) { .tabs-section { margin: 30px 10px; } .tabs-title { margin: 30px; } } .grid-container { display: grid; grid-template-columns: auto auto; grid-gap: 20px; } .grid-container > div { /* White, with 80% opacity */ background-color: rgba(255, 255, 255, 0.8); text-align: middle; } @media only screen and (max-width: 600px) { .grid-container { display: block; } } /* Visualization */ .visualization-images > img { border-radius: 10px; } .image-grid { display: grid; grid-template-columns: auto auto auto auto; grid-gap: 10px; } .image-grid > div { - background-color: rgb(238, 238, 238); - border: 2px solid rgb(255, 255, 255); + background-color: var(--pst-color-surface); + border: 2px solid var(--pst-color-surface); border-radius: 10px; padding: 10px; } /* Scientific Domains */ section.scientific-domains { max-width: 900px !important; & ul { display: flex; flex-wrap: wrap; list-style: none; margin: 15px auto; padding-inline-start: 0; & li { align-content: center; font-size: 0.8rem; line-height: 130%; margin: 0.2em 0.4em; flex-basis: 13%; & header { // FIXME: Use appropriate PST color for this header text. color: var(--pst-color-text-base); font-weight: 700; // Ensure headers are the same minimum height (some wrap // to two lines). min-height: 3.3em; text-align: left; } & img { width: 50px; height: 50px; margin-bottom: 0.5em; } & ul { align-content: left; display: flex; flex-direction: column; padding-inline-start: 0; & li { margin-left: 0em; } } } } } /* Array Libraries */ img.first-column-layout { max-width: 100px; max-height: 30px; margin: 0px 20px 0px 10px; } td.left-text { vertical-align: middle; }
numpy/numpy.org
c3724eeeba4b2ae5ae0d5335ef98ed4c779d7eb9
Fix: (sd6.svg) Make background transparent for dark mode inversion
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numpy/numpy.org
3e8f22a4825940e53e18b7364526f4fbaaa60117
Fix: .scientific-domains header color
diff --git a/assets/css/tabs.scss b/assets/css/tabs.scss index 489341f..420ec1d 100644 --- a/assets/css/tabs.scss +++ b/assets/css/tabs.scss @@ -1,137 +1,137 @@ [role="tablist"] { justify-content: center; } table td:not([align]), table th:not([align]) { text-align: inherit; } table td, table th { vertical-align: top; } .tabs-section { display: flex; flex-direction: column; } .tabs-section .container { display: flex; flex-direction: column; } .tabs-title { display: flex; justify-content: center; letter-spacing: 1.5px; font-size: 27px; margin: 30px 0; } .visualization, .data-science, .machine-learning, .array-libraries { max-width: 900px; margin: 15px auto; } @media only screen and (max-width: 1200px) { .tabs-section { margin: 30px 10px; } .tabs-title { margin: 30px; } } .grid-container { display: grid; grid-template-columns: auto auto; grid-gap: 20px; } .grid-container > div { /* White, with 80% opacity */ background-color: rgba(255, 255, 255, 0.8); text-align: middle; } @media only screen and (max-width: 600px) { .grid-container { display: block; } } /* Visualization */ .visualization-images > img { border-radius: 10px; } .image-grid { display: grid; grid-template-columns: auto auto auto auto; grid-gap: 10px; } .image-grid > div { background-color: rgb(238, 238, 238); border: 2px solid rgb(255, 255, 255); border-radius: 10px; padding: 10px; } /* Scientific Domains */ section.scientific-domains { max-width: 900px !important; & ul { display: flex; flex-wrap: wrap; list-style: none; margin: 15px auto; padding-inline-start: 0; & li { align-content: center; font-size: 0.8rem; line-height: 130%; margin: 0.2em 0.4em; flex-basis: 13%; & header { // FIXME: Use appropriate PST color for this header text. - color: var(--colorPrimaryDark); + color: var(--pst-color-text-base); font-weight: 700; // Ensure headers are the same minimum height (some wrap // to two lines). min-height: 3.3em; text-align: left; } & img { width: 50px; height: 50px; margin-bottom: 0.5em; } & ul { align-content: left; display: flex; flex-direction: column; padding-inline-start: 0; & li { margin-left: 0em; } } } } } /* Array Libraries */ img.first-column-layout { max-width: 100px; max-height: 30px; margin: 0px 20px 0px 10px; } td.left-text { vertical-align: middle; }
numpy/numpy.org
957a264fdb8dce4069ad7a1c6040a258b6f5edac
Replace grid1 shotcode with grid
diff --git a/content/en/_index.md b/content/en/_index.md index 8f69427..9e9534d 100644 --- a/content/en/_index.md +++ b/content/en/_index.md @@ -1,49 +1,49 @@ --- title: --- -{{< grid1 columns="1 2 2 3" >}} +{{< grid columns="1 2 2 3" >}} [[item]] type = 'card' title = 'Powerful N-dimensional arrays' body = ''' Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. ''' [[item]] type = 'card' title = 'Numerical computing tools' body = ''' NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. ''' [[item]] type = 'card' title = 'Open source' body = ''' Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). ''' [[item]] type = 'card' title = 'Interoperable' body = ''' NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. ''' [[item]] type = 'card' title = 'Performant' body = ''' The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. ''' [[item]] type = 'card' title = 'Easy to use' body = ''' NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. ''' -{{< /grid1>}} +{{< /grid>}} diff --git a/content/en/teams/index.md b/content/en/teams/index.md index 824152b..2c5a4c5 100644 --- a/content/en/teams/index.md +++ b/content/en/teams/index.md @@ -1,36 +1,36 @@ --- title: NumPy Teams sidebar: false --- We are an international team on a mission to support scientific and research communities worldwide by building quality, open-source software. [Join us]({{< relref "/contribute" >}})! ### Maintainers -{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}} +{{< grid file="maintainers.toml" columns="2 3 4 5" />}} ### Docs team -{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}} +{{< grid file="docs-team.toml" columns="2 3 4 5" />}} ### Web team -{{< grid1 file="web-team.toml" columns="2 3 4 5" />}} +{{< grid file="web-team.toml" columns="2 3 4 5" />}} ### Triage team -{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}} +{{< grid file="triage-team.toml" columns="2 3 4 5" />}} ### Survey team -{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}} +{{< grid file="survey-team.toml" columns="2 3 4 5" />}} ### Emeritus maintainers -{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}} +{{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}} # Governance For the list of the Steering Council members, please see [here](https://numpy.org/about/). diff --git a/content/ja/_index.md b/content/ja/_index.md index 192989e..1109f91 100644 --- a/content/ja/_index.md +++ b/content/ja/_index.md @@ -1,52 +1,52 @@ --- title: --- -{{< grid1 columns="1 2 2 3" >}} +{{< grid columns="1 2 2 3" >}} [[item]] type = 'card' title = '強力な多次元配列' body = ''' NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャストの考え方は、現在の配列計算におけるデファクト・スタ>ンダードです。 ''' [[item]] type = 'card' title = '数値計算ツール群' body = ''' NumPyは、様々な数学関数、乱数生成器、線形代数ルーチン、フーリエ変換などを提供しています。 ''' [[item]] type = 'card' title = '相互運用性' body = ''' NumPyは、幅広いハードウェアとコンピューティング・プラットフォームをサポートしており、分散処理、GPU、疎行列ライブラリにも対 応しています。 ''' [[item]] type = 'card' title = '高パフォーマンス' body = ''' NumPyの大部分は最適化されたC言語のコードで構成されています。これによりPythonの柔軟性とコンパイルされたコードの高速性の両方 を享受できます。 ''' [[item]] type = 'card' title = '使いやすさ' body = ''' NumPyの高水準なシンタックスは、どんなバックグラウンドや経験を持つのプログラマーでも簡単に利用することができ、生産性を高め>ることができます。 ''' [[item]] type = 'card' title = 'オープンソース' body = ''' NumPyは、寛容な[BSDライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt)で公開されています。NumPyは活発で、互>いを尊重し、多様性を認め合う[コミュニティ](/ja/community)によって、 [GitHub](https://github.com/numpy/numpy)上でオープンに開発されていま す. ''' -{{< /grid1 >}} +{{< /grid >}} diff --git a/content/ja/teams/index.md b/content/ja/teams/index.md index bb60e53..cb6acb2 100644 --- a/content/ja/teams/index.md +++ b/content/ja/teams/index.md @@ -1,34 +1,34 @@ --- title: NumPy開発チーム sidebar: false --- 私たちは、高品質のオープンソースソフトウェアを構築することで、世界中の科学・研究コミュニティをサポートすることを使命とする国際的なチームです。 是非[参加してください]({{< relref "/contribute" >}})! ### Maintainers -{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}} +{{< grid file="maintainers.toml" columns="2 3 4 5" />}} ### Docs team -{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}} +{{< grid file="docs-team.toml" columns="2 3 4 5" />}} ### Web team -{{< grid1 file="web-team.toml" columns="2 3 4 5" />}} +{{< grid file="web-team.toml" columns="2 3 4 5" />}} ### Triage team -{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}} +{{< grid file="triage-team.toml" columns="2 3 4 5" />}} ### Survey team -{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}} +{{< grid file="survey-team.toml" columns="2 3 4 5" />}} ### Emeritus maintainers -{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}} +{{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}} # ガバナンス For the list of people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). diff --git a/content/pt/_index.md b/content/pt/_index.md index db817f5..0a39687 100644 --- a/content/pt/_index.md +++ b/content/pt/_index.md @@ -1,49 +1,49 @@ --- title: --- -{{< grid1 columns="1 2 2 3" >}} +{{< grid columns="1 2 2 3" >}} [[item]] type = 'card' title = 'Arrays n-dimensionais poderosas' body = ''' Rápidos e versáteis, os conceitos de vetorização, indexação e broadcasting do NumPy são, na prática, o padrão em computação com arrays. ''' [[item]] type = 'card' title = 'Ferramentas de computação numérica' body = ''' O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais. ''' [[item]] type = 'card' title = 'Interoperabilidade' body = ''' O NumPy suporta um grande número de plataformas de hardware e computação, e pode ser combinado com bibliotecas de computação com arrays esparsas, distribuidas ou em GPUs. ''' [[item]] type = 'card' title = 'Alto desempenho' body = ''' O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. ''' [[item]] type = 'card' title = 'Fácil de usar' body = ''' A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação. ''' [[item]] type = 'card' title = 'Código aberto' body = ''' Distribuido com uma [licença BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt) liberal, o NumPy é desenvolvido e mantido [publicamente no GitHub](https://github.com/numpy/numpy) por uma [comunidade](/pt/community) vibrante, responsiva, e diversa. ''' -{{< /grid1 >}} +{{< /grid >}} diff --git a/content/pt/teams/index.md b/content/pt/teams/index.md index cc50b7b..bdd16ff 100644 --- a/content/pt/teams/index.md +++ b/content/pt/teams/index.md @@ -1,34 +1,34 @@ --- title: Times NumPy sidebar: false --- Somos uma equipe internacional com a missão de apoiar comunidades científicas e de pesquisa em todo o mundo construindo software de código aberto de qualidade. [Junte-se a nós]({{< relref "/contribute" >}})! ### Maintainers -{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}} +{{< grid file="maintainers.toml" columns="2 3 4 5" />}} ### Docs team -{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}} +{{< grid file="docs-team.toml" columns="2 3 4 5" />}} ### Web team -{{< grid1 file="web-team.toml" columns="2 3 4 5" />}} +{{< grid file="web-team.toml" columns="2 3 4 5" />}} ### Triage team -{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}} +{{< grid file="triage-team.toml" columns="2 3 4 5" />}} ### Survey team -{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}} +{{< grid file="survey-team.toml" columns="2 3 4 5" />}} ### Emeritus maintainers -{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}} +{{< grid file="emeritus-maintainers.toml" columns="2 3 4 5" />}} # Governança Para a lista de pessoas no Conselho Diretor, veja [aqui](https://numpy.org/devdocs/dev/governance/people.html).
numpy/numpy.org
9538ad94353ace4887ce7a44dd740333fb090936
Use theme's figure shortcode
diff --git a/content/en/case-studies/blackhole-image.md b/content/en/case-studies/blackhole-image.md index b7c0035..969af15 100644 --- a/content/en/case-studies/blackhole-image.md +++ b/content/en/case-studies/blackhole-image.md @@ -1,118 +1,143 @@ --- title: "Case Study: First Image of a Black Hole" sidebar: false --- -{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Image Credits: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' +title = 'Black Hole M87' +alt = 'black hole image' +attribution = '(Image Credits: Event Horizon Telescope Collaboration)' +attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*" >}} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. {{< /blockquote >}} ## A telescope the size of the earth The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, studing the universe with unprecedented sensitivity and resolution. The huge virtual telescope, which uses a technique called very-long-baseline interferometry (VLBI), has an angular resolution of [20 micro-arcseconds][resolution] — enough to read a newspaper in New York from a sidewalk café in Paris! [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole ### Key Goals and Results * **A New View of the Universe:** The groundwork for the EHT's groundbreaking image had been laid 100 years earlier when [Sir Arthur Eddington][eddington] yielded the first observational support of Einstein's theory of general relativity. * **The Black Hole:** EHT was trained on a supermassive black hole approximately 55 million light-years from Earth, lying at the center of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is 6.5 billion times the Sun's. It had been studied for [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before had a black hole been visually observed. * **Comparing Observations to Theory:** From Einstein’s general theory of relativity, scientists expected to find a shadow-like region caused by gravitational bending and capture of light. Scientists could use it to measure the black hole's enormous mass. [eddington]: https://en.wikipedia.org/wiki/Eddington_experiment ### The Challenges * **Computational scale** EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. * **Too much information** Each day EHT generates over 350 terabytes of observations, stored on helium-filled hard drives. Reducing the volume and complexity of this much data is enormously difficult. * **Into the unknown** When the goal is to see something never before seen, how can scientists be confident the image is correct? -{{< figure src="/images/content_images/cs/dataprocessbh.png" class="csfigcaption" caption="**EHT Data Processing Pipeline**" alt="data pipeline" align="middle" attr="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}} +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' +title = 'EHT Data Processing Pipeline' +alt = 'data pipeline' +align = 'center' +attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' +attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} ## NumPy’s Role What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed? The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole. Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. -{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**The role of NumPy in Black Hole imaging**" >}} +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' +alt = 'role of numpy' +title = 'The role of NumPy in Black Hole imaging' +{{< /figure >}} For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below. -{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="ehtim dependency map highlighting numpy" caption="**Software dependency chart of ehtim package highlighting NumPy**" >}} +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' +alt = 'ehtim dependency map highlighting numpy' +title = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /figure >}} [ehtim]: https://github.com/achael/eht-imaging Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole. [astropy]: https://www.astropy.org/ [mpl]: https://matplotlib.org/ ## Summary The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories. Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe. -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} diff --git a/content/en/case-studies/cricket-analytics.md b/content/en/case-studies/cricket-analytics.md index 77aef51..926a562 100644 --- a/content/en/case-studies/cricket-analytics.md +++ b/content/en/case-studies/cricket-analytics.md @@ -1,141 +1,164 @@ --- title: "Case Study: Cricket Analytics, the game changer!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, the biggest Cricket Festival in India**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' +title = 'IPLT20, the biggest Cricket Festival in India' +alt = 'Indian Premier League Cricket cup and stadium' +attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' +attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" >}} You don't play for the crowd, you play for the country. {{< /blockquote >}} ## About Cricket It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: * batting performance moving average, * score forecasting, * gaining insights into fitness and performance of a player against different opposition, * player contribution to wins and losses for making strategic decisions on team composition -{{< figure src="/images/content_images/cs/cricket-pitch.png" class="csfigcaption" caption="**Cricket Pitch, the focal point in the field**" alt="A cricket pitch with bowler and batsmen" align="middle" attr="*(Image credit: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}} +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' +title = 'Cricket Pitch, the focal point in the field' +alt = 'A cricket pitch with bowler and batsmen' +align = 'center' +attribution = '(Image credit: Debarghya Das)' +attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} ### Key Data Analytics Objectives * Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. * Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. * Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. -{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="pose estimator" caption="**Cricket Pose Estimator**" attr="*(Image credit: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' +alt = 'pose estimator' +title = 'Cricket Pose Estimator' +attribution = '(Image credit: connect.vin)' +attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} ### The Challenges * **Data Cleaning and preprocessing** IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. * **Dynamic Modeling** In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. * **Predictive Analytics Complexity** Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. ## NumPy’s Role in Cricket Analytics Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: * **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. * **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. ## Summary Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. -{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="Diagram showing benefits of using NumPy for cricket analytics" caption="**Key NumPy Capabilities utilized**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' +alt = 'Diagram showing benefits of using NumPy for cricket analytics' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} diff --git a/content/en/case-studies/deeplabcut-dnn.md b/content/en/case-studies/deeplabcut-dnn.md index 2d9e428..9124368 100644 --- a/content/en/case-studies/deeplabcut-dnn.md +++ b/content/en/case-studies/deeplabcut-dnn.md @@ -1,149 +1,184 @@ --- title: "Case Study: DeepLabCut 3D Pose Estimation" sidebar: false --- -{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Analyzing mice hand-movement using DeepLapCut**" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}} +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' +title = 'Analyzing mice hand-movement using DeepLapCut' +alt = 'micehandanim' +attribution = '(Source: www.deeplabcut.org )' +attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" >}} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. {{< /blockquote >}} ## About DeepLabCut [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. -{{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**Colored dots track the positions of a racehorse’s body part**" alt="horserideranim" attr="*(Source: Mackenzie Mathis)*">}} +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' +title = 'Colored dots track the positions of a racehorse’s body part' +alt = 'horserideranim' +attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. ### Key Goals and Results * **Automation of animal pose analysis for scientific studies:** The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). * **Creation of an easy-to-use Python toolkit for pose estimation:** DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. Their [toolkit][DLCToolkit] is now available as open source. A typical DeepLabCut Workflow includes: - creation and refining of training sets via active learning - creation of tailored neural networks for specific animals and scenarios - code for large-scale inference on videos - draw inferences using integrated visualization tools -{{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**Pose estimation steps with DeepLabCut**" alt="dlcsteps" align="middle" attr="(Source: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' +title = 'Pose estimation steps with DeepLabCut' +alt = 'dlcsteps' +align = 'center' +attribution = '(Source: DeepLabCut)' +attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} [DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut ### The Challenges * **Speed** Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. * **Combinatorics** Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. * **Data Processing** Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. -{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Pose estimation variety and complexity**" alt="challengesfig" align="middle" attr="(Source: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' +title = 'Pose estimation variety and complexity' +alt = 'challengesfig' +align = 'center' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} ## NumPy's Role in meeting Pose Estimation Challenges NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: * Vectorization * Masked Array Operations * Linear Algebra * Random Sampling * Reshaping of large arrays DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. -{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCut Workflow**" alt="workflow" attr="*(Source: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' +title = 'DeepLabCut Workflow' +alt = 'workflow' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} ## Summary Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. -{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} diff --git a/content/en/case-studies/gw-discov.md b/content/en/case-studies/gw-discov.md index 0139915..ead650f 100644 --- a/content/en/case-studies/gw-discov.md +++ b/content/en/case-studies/gw-discov.md @@ -1,143 +1,167 @@ --- title: "Case Study: Discovery of Gravitational Waves" sidebar: false --- -{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**Gravitational Waves**" alt="binary coalesce black hole generating gravitational waves" attr="*(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' +title = 'Gravitational Waves' +alt = 'binary coalesce black hole generating gravitational waves' +attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' +attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGO Scientific Collaboration*" >}} The scientific Python ecosystem is critical infrastructure for the research done at LIGO. {{< /blockquote >}} ## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) Gravitational waves are ripples in the fabric of space and time, generated by cataclysmic events in the universe such as collision and merging of two black holes or coalescing binary stars or supernovae. Observing GW can not only help in studying gravity but also in understanding some of the obscure phenomena in the distant universe and its impact. The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.ligo.caltech.edu) was designed to open the field of gravitational-wave astrophysics through the direct detection of gravitational waves predicted by Einstein’s General Theory of Relativity. It comprises two widely separated interferometers within the United States — one in Hanford, Washington and the other in Livingston, Louisiana — operated in unison to detect gravitational waves. Each of them has multi-kilometer-scale gravitational wave detectors that use laser interferometry. The LIGO Scientific Collaboration (LSC), is a group of more than 1000 scientists from universities around the United States and in 14 other countries supported by more than 90 universities and research institutes; approximately 250 students actively contributing to the collaboration. The new LIGO discovery is the first observation of gravitational waves themselves, made by measuring the tiny disturbances the waves make to space and time as they pass through the earth. It has opened up new astrophysical frontiers that explore the warped side of the universe—objects and phenomena that are made from warped spacetime. ### Key Objectives * Though its [mission](https://www.ligo.caltech.edu/page/what-is-ligo) is to detect gravitational waves from some of the most violent and energetic processes in the Universe, the data LIGO collects may have far-reaching effects on many areas of physics including gravitation, relativity, astrophysics, cosmology, particle physics, and nuclear physics. * Crunch observed data via numerical relativity computations that involves complex maths in order to discern signal from noise, filter out relevant signal and statistically estimate significance of observed data * Data visualization so that the binary / numerical results can be comprehended. ### The Challenges * **Computation** Gravitational Waves are hard to detect as they produce a very small effect and have tiny interaction with matter. Processing and analyzing all of LIGO's data requires a vast computing infrastructure.After taking care of noise, which is billions of times of the signal, there is still very complex relativity equations and huge amounts of data which present a computational challenge: [O(10^7) CPU hrs needed for binary merger analyses](https://youtu.be/7mcHknWWzNI) spread on 6 dedicated LIGO clusters * **Data Deluge** As observational devices become more sensitive and reliable, the challenges posed by data deluge and finding a needle in a haystack rise multi-fold. LIGO generates terabytes of data every day! Making sense of this data requires an enormous effort for each and every detection. For example, the signals being collected by LIGO must be matched by supercomputers against hundreds of thousands of templates of possible gravitational-wave signatures. * **Visualization** Once the obstacles related to understanding Einstein’s equations well enough to solve them using supercomputers are taken care of, the next big challenge was making data comprehensible to the human brain. Simulation modeling as well as signal detection requires effective visualization techniques. Visualization also plays a role in lending more credibility to numerical relativity in the eyes of pure science aficionados, who did not give enough importance to numerical relativity until imaging and simulations made it easier to comprehend results for a larger audience. Speed of complex computations and rendering, re-rendering images and simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. -{{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="gravitational waves strain amplitude" caption="**Estimated gravitational-wave strain amplitude from GW150914**" attr="(**Graph Credits:** Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}} +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' +alt = 'gravitational waves strain amplitude' +title = 'Estimated gravitational-wave strain amplitude from GW150914' +attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' +attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} ## NumPy’s Role in the Detection of Gravitational Waves Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. The amount of data LIGO collects is as incomprehensibly large as gravitational wave signals are small. NumPy, the standard numerical analysis package for Python, was utilized by the software used for various tasks performed during the GW detection project at LIGO. NumPy helped in solving complex maths and data manipulation at high speed. Here are some examples: * [Signal Processing](https://www.uv.es/virgogroup/Denoising_ROF.html): Glitch detection, [Noise identification and Data Characterization](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) * Data retrieval: Deciding which data can be analyzed, figuring out whether it contains a signal - needle in a haystack * Statistical analysis: estimate the statistical significance of observational data, estimating the signal parameters (e.g. masses of stars, spin velocity, and distance) by comparison with a model. * Visualization of data - Time series - Spectrograms * Compute Correlations * Key [Software](https://github.com/lscsoft) developed in GW data analysis such as [GwPy](https://gwpy.github.io/docs/stable/overview.html) and [PyCBC](https://pycbc.org) uses NumPy and AstroPy under the hood for providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. -{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**Dependency graph showing how GwPy package depends on NumPy**" >}} +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' +alt = 'gwpy-numpy depgraph' +title = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} ---- -{{< figure src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Dependency graph showing how PyCBC package depends on NumPy**" >}} +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' +alt = 'PyCBC-numpy depgraph' +title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} ## Summary GW detection has enabled researchers to discover entirely unexpected phenomena while providing new insight into many of the most profound astrophysical phenomena known. Number crunching and data visualization is a crucial step that helps scientists gain insights into data gathered from the scientific observations and understand the results. The computations are complex and cannot be comprehended by humans unless it is visualized using computer simulations that are fed with the real observed data and analysis. NumPy along with other Python packages such as matplotlib, pandas, and scikit-learn is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. -{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' +alt = 'numpy benefits' +title = 'Key NumPy Capabilities utilized' +{{< /figure >}} diff --git a/content/en/user-survey-2020.md b/content/en/user-survey-2020.md index 73b49a4..d82c322 100644 --- a/content/en/user-survey-2020.md +++ b/content/en/user-survey-2020.md @@ -1,23 +1,27 @@ --- title: 2020 NUMPY COMMUNITY SURVEY sidebar: false --- In 2020, the NumPy survey team in partnership with students and faculty from a Master’s course in Survey Methodology jointly hosted by the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. -{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'" width="250" >}} +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' +alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' +width = '250' +{{< /figure >}} **[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 816687d..7d7dfb2 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -1,72 +1,97 @@ --- title: "ケーススタディ：世界初のブラックホール画像" sidebar: false --- -{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Image Credits: Event Horizon Telescope Collaboration)*" attrk="https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg" >}} +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' +title = 'Black Hole M87' +alt = 'black hole image' +attribution = '(Image Credits: Event Horizon Telescope Collaboration)' +attrk = 'https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg' +{{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="*カリフォルニア工科大学 計算・数理学部*のKatie Bouman助教授" >}} M87ブラックホールを画像化することは、見ることのできないものを、あえて見ようとするようなものです。 {{< /blockquote >}} ## 地球大の望遠鏡 [Event Horizon telescope(EHT)](https:/eventhorizontelescope.org)は、地球サイズの解析望遠鏡を形成する8台の地上型電波望遠鏡から成るシステムで、これまでに前例のない感度と解像度で宇宙を研究することができます。 超長基線干渉法(VLBI) と呼ばれる手法を用いた巨大な仮想望遠鏡の角度分解能は、[20マイクロ秒][resolution]で、ニューヨークにある新聞をパリの歩道のカフェから読むのに十分な解像度です! ### 主な目標と結果 * **宇宙の新しい見方:** EHTの画期的な考え方の基礎が築かれたのは、100年前に [Sir Arthur Eddington][eddington]がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 * **ブラックホール:** EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 [100年以上](https://www.jpl.nasa.gov/news/news.php?feature=7385)に渡る研究が行われてもなお、これまでに視覚的にブラックホールを観測できたことはありませんでした。 * **観測と理論の比較:** 科学者たちの間で、アインシュタインの一般相対性理論から、重力による光の曲げや光の捕獲による影のような領域が観測できるのではないかと期待されていました。 これはブラックホールの巨大な質量を測定するために利用することができます。 ### 課題 * **大規模な計算** EHTは膨大なデータ処理の課題を抱えていました。 大気の位相変動は急速で、記録帯域の幅は大きく、望遠鏡はそれぞれ異なっていて地理的にも分散しています。 * **大量のデータ** EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 * **よくわからないものを観測する** 今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? -{{< figure src="/images/content_images/cs/dataprocessbh.png" class="csfigcaption" caption="**EHTのデータ処理パイプライン**" alt="data pipeline" align="middle" attr="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}} +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' +title = 'EHTのデータ処理パイプライン' +alt = 'data pipeline' +align = 'center' +attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' +attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} ## NumPyが果たした役割 データに問題がある場合はどうなるでしょう？ あるいは、アルゴリズムが特定の仮定に あまりにも大きく依存しているかもしれません。 もしあるパラメータを変更した場合、画像は大きく変化するのでしょうか？ EHTの共同研究では、最先端の画像再構成技術を使用して、それぞれのチームがデータを評価することによって、これらの課題に対処しました。 それぞれのチームの解析結果が同じであることが証明されると、それらの結果を組み合わせることで、ブラックホール画像を得ることができました。 彼らの研究は、共同のデータ解析を通じて科学を進歩させる、科学的なPythonエコシステムが果たす役割を如実に表しています。 -{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**ブラックホール画像化でNumPyが果たした役割**" >}} +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' +alt = 'role of numpy' +title = 'ブラックホール画像化でNumPyが果たした役割' +{{< /figure >}} 例えば、 [`eht-imaging`][ehtim] というPython パッケージは VLBI データで画像の再構築をシミュレートし、実行するためのツールです。 NumPyは、以下のソフトウェア依存関係チャートで示されているように、このパッケージで使用される配列データ処理の中核を担っています。 -{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="ehtim dependency map highlighting numpy" caption="**NumPyの中心としたehtimのソフトウェア依存図**" >}} +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' +alt = 'ehtim dependency map highlighting numpy' +title = 'NumPyの中心としたehtimのソフトウェア依存図' +{{< /figure >}} NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas.io)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 ## まとめ NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = '利用されたNumPyの主要機能' +{{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole [eddington]: https://en.wikipedia.org/wiki/Eddington_experiment [ehtim]: https://github.com/achael/eht-imaging [astropy]: https://www.astropy.org/ [mpl]: https://matplotlib.org/ diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index baf355a..b762498 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -1,66 +1,89 @@ --- title: "ケーススタディ: クリケット分析、ゲームチェンジャー!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="** IPLT20、インド最大のクリケットフェスティバル**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' +title = ' IPLT20、インド最大のクリケットフェスティバル' +alt = 'Indian Premier League Cricket cup and stadium' +attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' +attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni、 *インディアンチームの元キャプテン、インターナショナル・クリケットプレイヤー、チェンナイ・スーパー・キングスのためにIPLでプレイ*" >}} 観客のために競技をするのではなく、国のために競技するのです。 {{< /blockquote >}} ## クリケットについて インド人はクリケットが大好きだと言っても過言ではないでしょう。 この競技は、他のスポーツと異なり、インドの農村部や都市部を問わず、あらゆる場所でプレイされており、若者から年配の方まで広く人気があり、インドでは何十億人もの人々を結びつける役割を担っています。 クリケットは多くのメディアの注目を集めています。 クリケットは多くのメディアの注目を集め、非常に[多額のお金](https://www.statista.com/topics/4543/indian-premier-league-ipl/)と名声がかかっています。 過去数年間、テクノロジーは文字通りクリケットの試合を変えてきました。 視聴者はストリーミングメディア、トーナメント、モバイルベースの手頃なアクセスによるライブクリケット視聴などを享受しています。 インドプレミアリーグ (IPL) は、2008年に設立された20チームから成るプロクリケットリーグです。 これは世界で最も参加者が多いクリケットイベントの1つで、2019年の市場規模は[67億ドル](https://en.wikipedia.org/wiki/Indian_Premier_League)だと評価されています。 クリケットは数のゲームです。 バッツマンによってスコアされたランの数、ボウラーによって取られたウィケットの数、クリケットチームによって獲得した試合の数、バッツマンがボウリング攻撃に特定の方法で応答する回数。 クリケットの数字を掘り下げてパフォーマンスを向上させるとともに、NumPyなどの数値計算ソフトウェアを利用した強力な分析ツールを介して、クリケットのビジネスチャンス、市場全体、経済性を研究することは、大きな意味を持ちます。 クリケット分析は、試合に関する興味深い洞察と、ゲームの結果に関する予測AIを提供します。 現在では、クリケットゲームの記録と 利用可能な統計データは豊富で、ほぼ無限の宝の山だと言えます。 : [ESPN cricinfo や](https://stats.espncricinfo.com/ci/engine/stats/index.html) [cricsheet](https://cricsheet.org). これらのクリケットデータベースは、最新の機械学習と予測モデリングアルゴリズムを使用して、 [クリケット 分析](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) に使用されています。 メディアやプロスポーツ団体のエンターテインメントプラットフォームは、技術や分析を利用し、試合勝率を向上させるために、下記のような要素が主要なメトリックだと考え始めています。 * バッティング成績の移動平均 * スコア予測 * プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察 * チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 -{{< figure src="/images/content_images/cs/cricket-pitch.png" class="csfigcaption" caption="** フィールドのフォーカルポイントとなるクリケットピッチ**" alt="A cricket pitch with bowler and batsmen" align="middle" attr="*(Image credit: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}} +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' +title = ' フィールドのフォーカルポイントとなるクリケットピッチ' +alt = 'A cricket pitch with bowler and batsmen' +align = 'center' +attribution = '(Image credit: Debarghya Das)' +attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} ### データ分析の主要な目標 * スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 * リアルタイムデータ分析は、ゲーム中の洞察を得ることができ、チームや関連ビジネスが経済的利益と成長のために戦術を変更するためも役立ちます。 * 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。 -{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="pose estimator" caption="**クリケットの姿勢推定**" attr="*(Image credit: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' +alt = 'pose estimator' +title = 'クリケットの姿勢推定' +attribution = '(Image credit: connect.vin)' +attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} ### 課題 * **データのクリーニングと前処理** IPLは、クリケットを古典的なテストマッチ形式から、はるかに大規模に拡大させました。 毎シーズン、様々なフォーマットで行われる試合の数は増加しており、データ、アルゴリズム、最新のスポーツデータ分析技術、シミュレーションモデルも増加しています。 クリケットのデータ分析には、フィールドマッピング、プレイヤートラッキング、ボールトラッキング、プレイヤーショット分析、およびボールがどのように動くのか、その角度、スピン、速度、軌道など、他の沢山の種類のデータを必要とします。 これらの要因により、データクリーニングと前処理の複雑さが増してしまいました。 * **動的モデリング** クリケットでは、他のスポーツと同様、フィールド上の選手の様々な数字を追跡するために、関連する変数の数が多くなってしまいがちです。 たとえば、ボールやその属性情報、およびいくつかの行動をとるアクションのいくつかの可能性などの変数です。 データ分析とモデリングの複雑さは、分析中に必要となる予測のための質問の種類に正比例しており、データ表現とモデルにも大きく依存しています。 バッツマンが異なる角度や速度でボールを打った場合に何が起こるのかのような、動的なクリケットのプレーの予測が必要な場合、計算量やデータ比較が更に困難になります。 * **予測分析の複雑さ** クリケットにおいて、意思決定の多くは「ボウラーがある特定のタイプの場合、打者はどのくらいの頻度で特定の種類のショットを打つのか」「バッツマンが特定の方法であるボウラーに反応した場合、ボウラーはどのようにラインと長さを変更するのか 」などの質問に基づいています。 この種の予測分析クエリでは、精度の良いデータセットが利用できることと、データを合成して高精度な生成モデルを作成できることが必要とされます。 ## クリケット解析におけるNumPyの役割 スポーツ分析は現在、非常に盛んな分野です。 多くの研究者や企業は、最新の機械学習やAI技術以外にも、NumPyや、Scikit-learn, SciPy, Matplotlib, Jupyterなどの他のPyDataパッケージを[使っています](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)。 NumPyは以下のように、クリケット関連の様々なスポーツ分析に使用されています。 * **統計分析:** NumPyの数値計算機能は、様々なプレイヤーやゲーム戦術のコンテキストでの観測データで、試合中のイベントの統計的有意性を推定し、生成モデルや静的モデルと比較して試合結果を推定するのに役立ちます。 [因果分析](https://amplitude.com/blog/2017/01/19/causation-correlation) と [ビッグデータアプローチ](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/)が戦術的分析に使用されています。 * **データ可視化:** データのグラフ化・[可視化](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) は、さまざまなデータセット間の関係について、有益な洞察を与えてくれます。 ## まとめ スポーツアナリティクスは、プロの試合についてはまさにゲームチェンジャーです。 特に戦略的な意思決定については、最近まで主に「直感」や過去の伝統的な考え方に基づいて行われていたため、大きな影響があります。 NumPyは、データ分析・機械学習・人工知能のアルゴリズムに関連する高レベル関数を提供する沢山のPythonパッケージ群の、堅固な基盤となっています。 これらのパッケージは、ゲームの結果を変えるような意思決定を支援するリアルタイムのインサイトを得るため、クリケットの試合だけでなく関連する推論やビジネスの推進にも広く使用されています。 クリケットの試合結果につながる隠れたパラメータや、パターン、属性を見つけることは、ステークホルダーが数字や統計に隠されているゲームの洞察方法を見つけるのにも役に立つのです。 -{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="クリケット分析にNumPyを使用するメリットを示す図" caption="** 利用されている主なNumPy機能 **" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' +alt = 'クリケット分析にNumPyを使用するメリットを示す図' +title = ' 利用されている主なNumPy機能 ' +{{< /figure >}} diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index f115522..006daca 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -1,92 +1,127 @@ --- title: "ケーススタディ: DeepLabCut 三次元姿勢推定" sidebar: false --- -{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCutを用いたマウスの手の動きの解析**" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}} +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' +title = 'DeepLapCutを用いたマウスの手の動きの解析' +alt = 'micehandanim' +attribution = '(Source: www.deeplabcut.org )' +attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis、 *准教授、École polytechnology fe’rale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" >}} オープンソースソフトウェアは生体臨床医学を加速させています。 DeepLabCut を使用すると、深層学習を使用して動物の行動を自動的にビデオ解析することができます。 {{< /blockquote >}} ## DeepLabCut について [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut)は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。 神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 -{{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**色のついた点は競走馬の体の位置を追跡**" alt="horserideranim" attr="*(Source: Mackenzie Mathis)*">}} +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' +title = '色のついた点は競走馬の体の位置を追跡' +alt = 'horserideranim' +attribution = '(Source: Mackenzie Mathis)' +{{< /figure >}} DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から [チーター][cheetah-movement]のようなより珍しい動物まで、動物の種類を問わず利用できます。 DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術を使用しています。 これにより必要な学習データの量を大幅に削減し、学習の収束を加速させることができます。 必要に応じて、より高速な推論を提供するさまざまなネットワークアーキテクチャ(MobileNetV2など)を選択することができ、リアルタイムの実験データフィードバックと組み合わせることもできます。 DeepLabCutはもともと[DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれるパフォーマンスのよい人用のポーズ推定アーキテクチャの特徴検出器を使用しており、これが名前の由来になりました。 今ではこのパッケージは大幅に変更され、追加のアーキテクチャ・データの水増し・一通りのユーザー用フロントエンドを含んでいます。 さらに、 大規模な生物学的実験をサポートするため、DeepLabCutはオンライン学習の機能を提供しています。 これにより、動画の時間をこえて学習データを増やすことができ、エッジケースをカバーしたり、特定のコンテキスト内でポーズ推定アルゴリズムを堅牢にしたりできます。 最近、[DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo)が発表されました。 これは、霊長類の顔分析から犬の姿勢まで、様々な種や実験条件に対応した事前訓練済みモデルを提供しています。 これにより、例えば、新しいデータのラベルを付けることなくクラウドで予測を実行することができたり、ニューラルネットワークの学習を実行することができます。 プログラミング経験は必要ありません。 ### 主な目標と結果 * **科学研究のための動物姿勢解析の自動化:** DeepLabCutという技術の主な目的は、多様な環境で動物の姿勢を測定し追跡することです。 このデータは例えば神経科学の研究において、脳がどのように運動を制御しているかを理解するためのや、動物がどのように社会的に交流しているかを明らかにするために利用することができます。 研究者はDeepLabCutで [10倍のパフォーマンス向上](https://www.biorxiv.org/content/10.1101/457242v1) が可能であると発表しています。 オフラインでは最大1200フレーム/秒(FPS) で姿勢を推定することができます。 * **姿勢推定のための使いやすいPythonツールキットの作成:** DeepLabCutは、動物の姿勢推定技術を研究者が簡単に利用できるツールとして共有したいという考えから開発されています。 そこで開発者らはプロジェクト管理機能を備えた、単独で機能し、使いやすいPythonツールボックスとしてこのツールを作成しました。 これにより、姿勢推定を自動化するだけでなく、DeepLabCutツールキットユーザーをデータセット収集段階から共有可能・再利用可能な分析パイプラインを作成する段階まで補助し、プロジェクトをエンドツーエンドで管理することも可能になりました。 この[ツールキット][DLCToolkit] はオープンソースとして利用できます。 典型的なDeepLabCutワークフローは以下のようになります。 - オンライン学習によるトレーニングセットの作成と調整 - 特定の動物やシナリオに合わせたニューラルネットワークの構築 - 動画における大規模推論のためのコード作成 - 統合された可視化ツールを使用した推論の描画 -{{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**DeepLabCutによる姿勢推定のステップ**" alt="dlcsteps" align="middle" attr="(Source: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' +title = 'DeepLabCutによる姿勢推定のステップ' +alt = 'dlcsteps' +align = 'center' +attribution = '(Source: DeepLabCut)' +attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} ### 課題 * **速度** 動物行動動画の高速な処理は、動物の行動を測定し、科学実験をより効率的で正確にするために重要です。 動的に変化する背景の中で、マーカーを使用せずに、実験室での実験のために動物の詳細な姿勢を抽出することは、技術的にも、必要なリソース的にも、必要なトレーニングデータの面でも、困難な場合があります。 科学者が、より現実的な状況で研究を行うために、コンピュータビジョンなどの専門知識のスキルを必要とせずに使うことができるツールを開発することは、解決すべき重要な問題です。 * **組み合わせ問題** 組合せ問題とは、複数の四肢の動きを個々の動物行動に統合することを指します。 キーポイントと、その個々の動物行動との関連性を組み合わせ、時間的に結びつけることは、複雑なプロセスであり、非常に膨大な数値解析が必要となります。 特に、実験映像の中で複数の動物の動きを追跡する場合は大変です。 * **データ処理** 最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。 -{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**姿勢推定の多様性と難しさ**" alt="challengesfig" align="middle" attr="(Source: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' +title = '姿勢推定の多様性と難しさ' +alt = 'challengesfig' +align = 'center' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} ## 姿勢推定の課題に対応するためのNumPyの役割 NumPy は DeepLabCutにおける、行動分析の高速化のための数値計算の核となっています。 NumPyだけでなく、DeepLabCutは様々なNumPyをベースとしているPythonライブラリを利用しています。 [SciPy](https://www.scipy.org)、[Pandas](https://pandas.pydata.org)、[matplotlib](https://matplotlib.org)、[Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug)、[scikit-learn](https://scikit-learn.org/stable/)、[scikit-image](https://scikit-image.org)、[Tensorflow](https://www.tensorflow.org)などです。 以下に挙げるNumPyの特徴が、DeepLabCutの姿勢推定アルゴリズムでの画像処理・組み合わせ処理・高速計算において、重要な役割を果たしました。 * ベクトル化 * マスクされた配列操作 * 線形代数 * ランダムサンプリング * 大きな配列の再構成 DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。 TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。 -{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCutのワークフロー**" alt="workflow" attr="*(Source: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' +title = 'DeepLabCutのワークフロー' +alt = 'workflow' +attribution = '(Source: Mackenzie Mathis)' +attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} ## まとめ 行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。 -{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**NumPyの主要機能**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' +alt = 'numpy benefits' +title = 'NumPyの主要機能' +{{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 [DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index e0bdc84..c5275fd 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -1,70 +1,94 @@ --- title: "ケーススタディ: 重力波の発見" sidebar: false --- -{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**重力波**" alt="binary coalesce black hole generating gravitational waves" attr="*(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' +title = '重力波' +alt = 'binary coalesce black hole generating gravitational waves' +attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' +attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGOサイエンティフィック・コラボレーション*" >}} 科学計算のためのPythonエコシステムはLIGOで行われている研究のための重要なインフラです。 {{< /blockquote >}} ## [重力波](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) と [LIGO](https://www.ligo.caltech.edu) について 重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。 \[レーザー干渉計重力波天文台(LIGO)\](https://www. ligo. caltech. edu)は、アインシュタインの一般相対性理論によって予測された重力波の直接検出を通して、重力波天体物理学の分野を切り開くために設計されました。 このシステムは、アメリカのワシントン州ハンフォードとルイジアナ州リビングストンにある2つの干渉計が一体となって構成され、重力波を検出します。 それぞれのシステムには、レーザー干渉法を用いた数キロ規模の重力波検出器が設置されています。 LIGO Scientific Collaboration（LSC）は、米国をはじめとする14カ国の大学から1000人以上の科学者が集まり、90以上の大学・研究機関によって支援されています。 また、約250人の学生も参加しています。 今回のLIGOの発見は、重力波が地球を通過する際に生じる空間と時間の微小な乱れの測定により、重力波そのものを初めて観測しました。 これにより、新しい天体物理学のフロンティアが開かれました。 これは、宇宙の歪んだ側面、つまり歪んだ時空から作られた物体とそれに現象を切り拓くものです。 ### 主な目的 * LIGOの[ミッション](https://www.ligo.caltech.edu/page/what-is-ligo)は、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 * 複雑な数学を含む相対性理論の数値計算によって観測データを解析し、信号とノイズを識別し、関連性のある信号をフィルタリングし、観測データの有意性を統計的に推定することで、宇宙の始まりのクランチを観測できるようになります。 * バイナリや数値の結果を理解しやすいようにデータを可視化することも必要です。 ### 課題 * **計算** 合成により放出される重力波は、スーパーコンピュータを用いて数値相対性を手あたり次第に試すような方法では計算できません。 LIGOが収集するデータ量は、重力波の信号が少ないのと同じくらい不可解です。 * **データの氾濫** 観測装置がより高感度で信頼性を持つようになると、データの大洪水によって、干し草の中から針を探すような問題が、多重に発生することがわかります。 LIGOは毎日テラバイトのデータを生成しているのです！ この大量のデータを解釈するには、各検出ごとに多大な労力が必要です。 例えば、LIGOによって収集される信号は、数十万個の重力波シグネチャのテンプレートで構成されており、スーパーコンピュータでしか解析できません。 * **可視化** アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。 画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。 -{{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="gravitational waves strain amplitude" caption="**GW150914から推定される重力波の歪みの振幅**" attr="(**Graph Credits:** Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}} +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' +alt = 'gravitational waves strain amplitude' +title = 'GW150914から推定される重力波の歪みの振幅' +attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' +attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} ## 重力波の検出におけるNumPyの役割 合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。 Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波検出プロジェクトで実行される様々なタスクに使用されるソフトウェアで利用されています。 NumPyは、複雑な数学処理や高速なデータ操作に役立ちました。 次にいくつかの例を示します。 * [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出、[ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm)。 * データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 * 統計解析: 観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 * データ可視化 - 時系列データ - スペクトログラム * 相関計算 * 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。 -{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**GwPyのNumPy依存グラフ**" >}} +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' +alt = 'gwpy-numpy depgraph' +title = 'GwPyのNumPy依存グラフ' +{{< /figure >}} ---- -{{< figure src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**PyCBCのNumPy依存グラフ**" >}} +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' +alt = 'PyCBC-numpy depgraph' +title = 'PyCBCのNumPy依存グラフ' +{{< /figure >}} ## まとめ 一方で、これまで知られてきた深遠な天体物理学の現象に、多くに新たな洞察を提供しました。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な質問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。 -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = '利用されたNumPyの主要機能' +{{< /figure >}} diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md index ce32100..b79cc13 100644 --- a/content/ja/user-survey-2020.md +++ b/content/ja/user-survey-2020.md @@ -1,16 +1,20 @@ --- title: 2020年 NumPyコミュニティ調査 sidebar: false --- 2020年に、NumPyの調査チームは、ミシガン大学とメリーランド大学が共同で開催した、調査方法学の修士コースの学生と教員と共同で、初めて公式のNumPyコミュニティ調査を実施しました。 75カ国から1,200人以上のNumPyユーザーが参加してくれました。NumPyコミュニティの全体像を描き、プロジェクトの未来像についての意見を述べてもらいました。 -{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 Numpy User survey report, titled 'Numpyコミュニティ調査2020 - 結果'" width="250" >}} +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' +alt = 'Cover page of the 2020 Numpy User survey report, titled "Numpyコミュニティ調査2020 - 結果"' +width = '250' +{{< /figure >}} 調査結果を詳細を知りたい場合は、**[こちらのレポート](/surveys/NumPy_usersurvey_2020_report.pdf)** をダウンロードしてください。 結果の概要については、 **[こちらの図](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)** をチェックしてください。 より詳細が知りたくなりましたか？ **https://numpy.org/user-survey-2020-details/** をご覧ください。 diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index b4832fd..d8429b3 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -1,72 +1,97 @@ --- title: "Estudo de Caso: A Primeira Imagem de um Buraco Negro" sidebar: false --- -{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Créditos: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +{{< figure >}} +src = '/images/content_images/cs/blackhole.jpg' +title = 'Black Hole M87' +alt = 'black hole image' +attribution = '(Créditos: Event Horizon Telescope Collaboration)' +attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' +{{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Professora Assistente, Ciências da Computação e Matemática, Caltech*" >}} Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver. {{< /blockquote >}} ## Um telescópio do tamanho da Terra O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um conjunto de oito telescópios em solo formando um telescópio computacional do tamanho da Terra, projetado para estudar o universo com sensibilidade e resolução sem precedentes. O enorme telescópio virtual, que usa uma técnica chamada interferometria de longa linha de base (VLBI), tem uma resolução angular de [20 micro-arcossegundos][resolution] — o suficiente para ler um jornal em Nova Iorque a partir de um café em uma calçada de Paris! ### Principais Objetivos e Resultados * **Uma nova visão do universo:** A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. * **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. * **Comparando observações com a teoria:** Pela teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir a enorme massa do mesmo. ### Desafios * **Escala computacional** O EHT representa um desafio imenso em processamento de dados, incluindo rápidas flutuações de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. * **Muitas informações** A cada dia, o EHT gera mais de 350 terabytes de observações, armazenadas em discos rígidos cheios de hélio. Reduzir o volume e a complexidade desse volume de dados é extremamente difícil. * **Em direção ao desconhecido** Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta? -{{< figure src="/images/content_images/cs/dataprocessbh.png" class="csfigcaption" caption="**Etapas de Processamento de Dados do EHT**" alt="data pipeline" align="middle" attr="(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}} +{{< figure >}} +src = '/images/content_images/cs/dataprocessbh.png' +title = 'Etapas de Processamento de Dados do EHT' +alt = 'data pipeline' +align = 'center' +attribution = '(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)' +attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' +{{< /figure >}} ## O papel do NumPy E se houver um problema com os dados? Ou talvez um algoritmo seja muito dependente de uma hipótese em particular. A imagem será alterada drasticamente se um único parâmetro for alterado? A colaboração do EHT venceu esses desafios ao estabelecer equipes independentes que avaliaram os dados usando técnicas de reconstrução de imagem estabelecidas e de ponta para verificar se as imagens resultantes eram consistentes. Quando os resultados se provaram consistentes, eles foram combinados para produzir a imagem inédita do buraco negro. O trabalho desse grupo ilustra o papel do ecossistema científico do Python no avanço da ciência através da análise de dados colaborativa. -{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**O papel do NumPy na criação da primeira imagem de um Buraco Negro**" >}} +{{< figure >}} +src = '/images/content_images/cs/bh_numpy_role.png' +alt = 'role of numpy' +title = 'O papel do NumPy na criação da primeira imagem de um Buraco Negro' +{{< /figure >}} Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para simular e realizar reconstrução de imagem nos dados do VLBI. O NumPy está no coração do processamento de dados vetoriais usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo. -{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="ehtim dependency map highlighting numpy" caption="**Diagrama de dependência de software do pacote ehtim evidenciando o NumPy**" >}} +{{< figure >}} +src = '/images/content_images/cs/ehtim_numpy.png' +alt = 'ehtim dependency map highlighting numpy' +title = 'Diagrama de dependência de software do pacote ehtim evidenciando o NumPy' +{{< /figure >}} Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. ## Resumo A estrutura de dados n-dimensional que é a funcionalidade central do NumPy permitiu aos pesquisadores manipular grandes conjuntos de dados, fornecendo a base para a primeira imagem de um buraco negro. Esse momento marcante na ciência fornece evidências visuais impressionantes para a teoria de Einstein. Esta conquista abrange não apenas avanços tecnológicos, mas colaboração científica em escala internacional entre mais de 200 cientistas e alguns dos melhores observatórios de rádio do mundo. Eles usaram algoritmos e técnicas de processamento de dados inovadores, que aperfeiçoaram os modelos astronômicos existentes, para ajudar a descobrir um dos mistérios do universo. -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Funcionalidades-chave do NumPy utilizadas**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' +alt = 'numpy benefits' +title = 'Funcionalidades-chave do NumPy utilizadas' +{{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole [eddington]: https://en.wikipedia.org/wiki/Eddington_experiment [ehtim]: https://github.com/achael/eht-imaging [astropy]: https://www.astropy.org/ [mpl]: https://matplotlib.org/ diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 54da45b..8d70c77 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -1,66 +1,89 @@ --- title: "Estudo de Caso: Análise de Críquete, a revolução!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure >}} +src = '/images/content_images/cs/ipl-stadium.png' +title = 'IPLT20, o maior festival de Críquete da Índia' +alt = 'Copa e estádio da Indian Premier League Cricket' +attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' +attributionlink = 'https://unsplash.com/@aksh1802' +{{< /figure >}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *Jogador Internacional de Críquete, ex-capitão, Time Indiano, joga pelo Chennai Super Kings na IPL*" >}} Você não joga para a torcida, joga para o país. {{< /blockquote >}} ## Sobre Críquete Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. O cricket também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. A Primeira Liga Indiana (*Indian Premier League* - IPL) é uma liga profissional de críquete [Twenty20](https://pt.wikipedia.org/wiki/Twenty20), fundada em 2008. É um dos eventos de críquete mais assistidos no mundo, avaliado em [$6,7 bilhões de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) em 2019. perdidos por um boleador, as partidas ganhas por uma equipe de críquete, o número de vezes que um batsman responde de certa maneira a um tipo de arremesso do boleador, etc. A capacidade de investigar números de críquete para melhorar o desempenho e estudar as oportunidades de negócio, mercado e economia de críquete através de poderosas ferramentas de análise, alimentadas por softwares numéricos de computação, como o NumPy, é um grande negócio. A capacidade de investigar estatísticas do críquete para melhorar a performance dos times e estudar oportunidades de negócios, o mercado em si, e a economia do críquete através de ferramentas de análise poderosas alimentadas por softwares de computação numérica como o NumPy é um grande negócio. As análises de críquete fornecem informações interessantes sobre o jogo e informações preditivas sobre os resultados do jogo. Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações sobre jogos de críquete, por exemplo, [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) e [cricsheet](https://cricsheet.org). Estes e muitos outros bancos de dados de críquete foram usados para [análise de críquete](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) usando os mais modernos algoritmos de aprendizagem de máquina e modelagem preditiva. Plataformas de mídia e entretenimento, juntamente com entidades de esporte profissionais associadas ao jogo usam tecnologia e análise para determinar métricas chave para melhorar as chances de vitória: * média móvel do desempenho em rebatidas, * previsão de pontuação, * ganho de informações sobre desempenho e condição física de um determinado jogador contra determinado adversário, * contribuições dos jogadores para vitórias e derrotas para a tomada de decisões estratégicas na composição do time -{{< figure src="/images/content_images/cs/cricket-pitch.png" class="csfigcaption" caption="**Pitch de críquete, o ponto focal do campo**" alt="Um pitch de críquete com um boleador e batsmen" align="middle" attr="*(Créditos de imagem: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}} +{{< figure >}} +src = '/images/content_images/cs/cricket-pitch.png' +title = 'Pitch de críquete, o ponto focal do campo' +alt = 'Um pitch de críquete com um boleador e batsmen' +align = 'center' +attribution = '(Créditos de imagem: Debarghya Das)' +attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' +{{< /figure >}} ### Objetivos Principais da Análise de Dados * A análise de dados esportivos é usada não somente em críquete, mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. * A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. * Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise. -{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="estimador de postura" caption="**Estimador de Postura de Críquete**" attr="*(Créditos de imagem: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} +{{< figure >}} +src = '/images/content_images/cs/player-pose-estimator.png' +alt = 'estimador de postura' +title = 'Estimador de Postura de Críquete' +attribution = '(Créditos de imagem: connect.vin)' +attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} ### Desafios * **Limpeza e pré-processamento de dados** A IPL expandiu o formato de jogo clássico de cricket para uma escala muito maior. O número de partidas jogadas a cada temporada em vários formatos tem aumentado, assim como os dados, os algoritmos, as tecnologias de análise de dados mais recentes e modelos de simulação. A análise de dados de críquete requer mapeamento de campo, rastreamento do jogador, rastreamento de bola e análise de tiros do jogador, análise de lances do jogador e vários outros aspectos envolvidos em como a bola é lançada, seu ângulo, giro, velocidade e trajetória. Todos esses fatores em conjunto aumentaram a complexidade da limpeza e pré-processamento de dados. * **Modelagem Dinâmica** No críquete, como em qualquer outro esporte, pode haver um grande número de variáveis relacionadas ao rastreamento de vários jogadores no campo, seus atributos, a bola e várias possibilidades de ações em potencial. A complexidade da análise e modelagem de dados é diretamente proporcional ao tipo de questões preditivas que são consideradas durante a análise e são altamente dependentes da representação de dados e do modelo. As coisas são ainda mais desafiadoras em termos de computação e comparações de dados quando previsões dinâmicas de jogo de críquete são desejadas, como o que teria acontecido se o batsman tivesse atingido a bola com um ângulo ou velocidade diferentes. * **Complexidade da análise preditiva** Muito da tomada de decisões em críquete se baseia em questões como "com que frequência um batsman joga um certo tipo de lance se a recepção da bola for de um determinado tipo", ou "como um boleador muda a direção e alcance da sua jogada se o batsman responder de uma certa maneira". Esse tipo de consulta de análise preditiva requer a disponibilidade de conjuntos de dados altamente granulares e a capacidade de sintetizar dados e criar modelos generativos que sejam altamente precisos. ## O papel do NumPy na análise de críquete A análise de dados esportivos é um campo próspero. Muitos pesquisadores e empresas [usam NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) e outros pacotes PyData como Scikit-learn, SciPy, Matplotlib, e Jupyter, além de usar as últimas técnicas de aprendizagem de máquina e IA. O NumPy foi usado para vários tipos de análise esportiva relacionada a críquete, como: * **Análise Estatística:** Os recursos numéricos do NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em *big data*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. * **Visualização de dados:** Gráficos e [visualizações](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) fornecem informações úteis sobre as relações entre vários conjuntos de dados. ## Resumo A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo era principalmente feita com base na "intuição" ou adesão a tradições passadas. O NumPy forma uma fundação sólida para um grande conjunto de pacotes Python que fornecem funções de alto nível relacionadas à análise de dados, aprendizagem de máquina e algoritmos de IA. Estes pacotes são amplamente implantados para se obter informações em tempo real que ajudam na tomada de decisão para resultados decisivos, tanto em campo como para se derivar inferências e orientar negócios em torno do jogo de críquete. Encontrar os parâmetros ocultos, padrões, e atributos que levam ao resultado de uma partida de críquete ajuda os envolvidos a tomar nota das percepções do jogo que estariam de outra forma ocultas nos números e estatísticas. -{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="Diagrama mostrando os benefícios de usar a NumPy para análise de críquete" caption="**Recursos principais da NumPy utilizados**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_ca_benefits.png' +alt = 'Diagrama mostrando os benefícios de usar a NumPy para análise de críquete' +title = 'Recursos principais da NumPy utilizados' +{{< /figure >}} diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index a0c37db..557b336 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -1,92 +1,127 @@ --- title: "Estudo de Caso: Estimativa de Pose 3D com DeepLabCut" sidebar: false --- -{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Análise de movimentos de mãos de camundongos usando DeepLapCut**" alt="micehandanim" attr="*(Fonte: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}} +{{< figure >}} +src = '/images/content_images/cs/mice-hand.gif' +title = 'Análise de movimentos de mãos de camundongos usando DeepLapCut' +alt = 'micehandanim' +attribution = '(Fonte: www.deeplabcut.org )' +attributionlink = 'http://www.mousemotorlab.org/deeplabcut' +{{< /figure >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Professor Assistente, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" >}} Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning. {{< /blockquote >}} ## Sobre o DeepLabCut [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) é uma toolbox de código aberto que permite que pesquisadores de centenas de instituições em todo o mundo rastreiem o comportamento de animais de laboratório, com muito poucos dados de treinamento, mas com precisão no nível humano. Com a tecnologia DeepLabCut, cientistas podem aprofundar a compreensão científica do controle motor e do comportamento em diversas espécies animais e escalas temporais. Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas trabalhosas de monitoramento e marcação, junto com análise de dados baseada em redes neurais profundas, a DeepLabCut garante que estudos científicos envolvendo a observação de animais como primatas, camundongos, peixes, moscas etc. sejam mais rápidos e precisos. -{{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida**" alt="horserideranim" attr="*(Fonte: Mackenzie Mathis)*">}} +{{< figure >}} +src = '/images/content_images/cs/race-horse.gif' +title = 'Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida' +alt = 'horserideranim' +attribution = '(Fonte: Mackenzie Mathis)' +{{< /figure >}} O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (*frames*) distintos em vídeos e rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI especializada. A partir disso, a arquitetura de estimação de poses baseada em deep learning da DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios, como moscas e camundongos, até os mais incomuns, como [guepardos][cheetah-movement]. A DeepLabCut usa um princípio chamado [aprendizado por transferência (*transfer learning*)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), e que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de alto desempenho para estimativa de poses humanas, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experimentos biológicos em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo, para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab.org/dlc-modelzoo), que proporciona modelos pré-treinados para várias espécies e condições experimentais, desde a análise facial em primatas até à posição de cães. Isso pode ser executado na nuvem, por exemplo, sem qualquer rotulagem de novos dados ou treinamento em rede neural, e não é necessária nenhuma experiência em programação. ### Principais Objetivos e Resultados * **Automação da análise de poses animais para estudos científicos:** O objetivo principal da tecnologia DeepLabCut é medir e rastrear a postura dos animais em várias configurações. Esses dados podem ser usados, por exemplo, em estudos de neurociência para entender como o cérebro controla o movimento, ou para elucidar como os animais interagem socialmente. Pesquisadores observaram que [desempenho é 10 vezes melhor](https://www.biorxiv.org/content/10.1101/457242v1) com o DeepLabCut. Poses podem ser inferidas off-line em até 1200 quadros por segundo (FPS). * **Criação de um kit de ferramentas Python fácil de usar para estimativa de poses:** DeepLabCut queria compartilhar sua tecnologia de estimativa de poses animal na forma de uma ferramenta simples de usar que pudesse ser adotada pelos pesquisadores facilmente. Assim, criaram um conjunto de ferramentas em Python completo e fácil de usar, também com recursos de gerenciamento de projeto. Isso permite não apenas a automação de estimação de poses, mas também o gerenciamento do projeto de ponta a ponta, ajudando o usuário do DeepLabCut Toolkit desde a fase de coleta para criar fluxos de dados compartilháveis e reutilizáveis. Seu [conjunto de ferramentas][DLCToolkit] agora está disponível como software de código aberto. Um fluxo de trabalho típico na DeepLabCut inclui: - criação e refinamento de conjuntos de treinamento por meio de aprendizagem ativa - criação de redes neurais personalizadas para animais e cenários específicos - código para inferência em larga escala em vídeos - inferências de desenho usando ferramentas integradas de visualização -{{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**Passos na estimação de poses com DeepLabCut**" alt="dlcsteps" align="middle" attr="(Fonte: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' +title = 'Passos na estimação de poses com DeepLabCut' +alt = 'dlcsteps' +align = 'center' +attribution = '(Fonte: DeepLabCut)' +attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' +{{< /figure >}} ### Desafios * **Velocidade** Processamento rápido de vídeos de animais para medir seu comportamento e, ao mesmo tempo, tornar os experimentos científicos mais eficientes e precisos. Extrair poses animais detalhadas para experimentos em laboratório, sem marcadores, sobre fundos dinâmicos, pode ser desafiador tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. Criar uma ferramenta que seja fácil de usar sem necessidade de habilidades como expertise em visão computacional que permita aos cientistas fazerem pesquisa em contextos mais próximos do mundo real é um problema não-trivial a ser solucionado. * **Combinatória** Combinatória envolve a junção e integração de movimentos de múltiplos membros em um comportamento animal único. Reunir pontos-chave e suas conexões em movimentos animais individuais e encadeá-los em função do tempo é um processo complexo que exige análise numérica intensa, especialmente nos casos de rastreio de múltiplos animais em vídeos experimentais. * **Processamento de dados** Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador. -{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Estimação de poses e complexidade**" alt="challengesfig" align="middle" attr="(Fonte: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} +{{< figure >}} +src = '/images/content_images/cs/pose-estimation.png' +title = 'Estimação de poses e complexidade' +alt = 'challengesfig' +align = 'center' +attribution = '(Fonte: Mackenzie Mathis)' +attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' +{{< /figure >}} ## O papel da NumPy nos desafios da estimação de poses NumPy supre a principal necessidade da tecnologia DeepLabCut de cálculos numéricos de alta velocidade para análises comportamentais. Além da NumPy, DeepLabCut emprega várias bibliotecas Python que usam a NumPy como sua base, tais como [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) e [Tensorflow](https://www.tensorflow.org). As seguintes características da NumPy desempenharam um papel fundamental para atender às necessidades de processamento de imagens, combinatória e cálculos rápidos nos algoritmos de estimação de pose na DeepLabCut: * Vetorização * Operações em arrays com máscaras * Álgebra linear * Amostragem aleatória * Reordenamento de matrizes grandes A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação. Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. Para este propósito, densidades de alvo (*scoremaps*) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente. -{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**Fluxo de dados DeepLabCut**" alt="workflow" attr="*(Fonte: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} +{{< figure >}} +src = '/images/content_images/cs/deeplabcut-workflow.png' +title = 'Fluxo de dados DeepLabCut' +alt = 'workflow' +attribution = '(Fonte: Mackenzie Mathis)' +attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' +{{< /figure >}} ## Resumo Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy. -{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave do NumPy utilizados**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' +alt = 'numpy benefits' +title = 'Recursos chave do NumPy utilizados' +{{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 [DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index 4e8bcda..cb37191 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -1,70 +1,94 @@ --- title: "Estudo de Caso: Descoberta de Ondas Gravitacionais" sidebar: false --- -{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**Ondas gravitacionais**" alt="binary coalesce black hole generating gravitational waves" attr="*(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} +{{< figure >}} +src = '/images/content_images/cs/gw_sxs_image.png' +title = 'Ondas gravitacionais' +alt = 'binary coalesce black hole generating gravitational waves' +attribution = '(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)' +attributionlink = 'https://youtu.be/Zt8Z_uzG71o' +{{< /figure >}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *Colaborador Científico no LIGO*" >}} O ecossistema científico Python é uma infraestrutura crítica para a pesquisa feita no LIGO. {{< /blockquote >}} ## Sobre [Ondas Gravitacionais](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) e o [LIGO](https://www.ligo.caltech.edu) Ondas gravitacionais são ondulações no tecido espaço-tempo, gerado por eventos cataclísmicos no universo, como colisão e fusão de dois buracos negros ou a coalescência de estrelas binárias ou supernovas. A observação de ondas gravitacionais pode ajudar não só no estudo da gravidade, mas também no entendimento de alguns dos fenômenos obscuros existentes no universo distante e seu impacto. O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://www.ligo.caltech.edu) foi projetado para abrir o campo da astrofísica das ondas gravitacionais através da detecção direta de ondas gravitacionais previstas pela Teoria Geral da Relatividade de Einstein. O observatório consiste de dois interferômetros amplamente separados dentro dos Estados Unidos - um em Hanford, Washington e o outro em Livingston, Louisiana — operando em uníssono para detectar ondas gravitacionais. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a Terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. ### Objetivos * Embora sua [missão](https://www.ligo.caltech.edu/page/what-is-ligo) seja detectar ondas gravitacionais de alguns dos processos mais violentos e enérgicos no Universo, os dados que o LIGO coleta podem ter efeitos de grande alcance em muitas áreas da física, incluindo gravitação, relatividade, astrofísica, cosmologia, física de partículas e física nuclear. * Processar dados observados através de cálculos numéricos de relatividade que envolvem matemática complexa para identificar o sinal e o ruído, filtrar o sinal relevante e estimar estatisticamente o significado dos dados observados. * Visualização de dados para que os resultados binários/numéricos possam ser compreendidos. ### Desafios * **Computação** As ondas gravitacionais são difíceis de detectar pois produzem um efeito muito pequeno e têm uma pequena interação com a matéria. Processar e analisar todos os dados do LIGO requer uma vasta infraestrutura de computação. Depois de cuidar do ruído, que é bilhões de vezes maior que o sinal, ainda há equações de relatividade complexas e enormes quantidades de dados que apresentam um desafio computacional: [O(10^7) horas de CPU necessárias para análises de fusão binária](https://youtu.be/7mcHknWWzNI) espalhado em 6 clusters LIGO dedicados. * **Sobrecarga de dados** À medida que os dispositivos observacionais se tornam mais sensíveis e confiáveis, os desafios criados pela sobrecarga de dados e a procura por uma agulha em um palheiro se tornam muito maiores. O LIGO gera terabytes de dados todos os dias! Entender esses dados requer um enorme esforço para cada detecção. Por exemplo, os sinais sendo coletados pelo LIGO devem ser combinados por supercomputadores e comparados a centenas de milhares de modelos de possíveis assinaturas de ondas gravitacionais. * **Visualização** Uma vez que os obstáculos relacionados a compreender as equações de Einstein bem o suficiente para resolvê-las usando supercomputadores foram ultrapassados, o próximo grande desafio era tornar os dados compreensíveis para o cérebro humano. A modelagem de simulações, assim como a detecção de sinais, exigem técnicas de visualização efetiva. A visualização também desempenha um papel de fornecer mais credibilidade à relatividade numérica aos olhos dos aficionados pela ciência pura, que não dão importância suficiente à relatividade numérica até que a imagem e as simulações tornem mais fácil a compreensão dos resultados para um público maior. A velocidade da computação complexa, e da renderização, re-renderização de imagens e simulações usando as últimas entradas e informações experimentais pode ser uma atividade demorada que desafia pesquisadores neste domínio. -{{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="gravitational waves strain amplitude" caption="**Amplitude estimada da deformação das ondas gravitacionais do evento GW150914**" attr="(**Créditos do gráfico:** Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}} +{{< figure >}} +src = '/images/content_images/cs/gw_strain_amplitude.png' +alt = 'gravitational waves strain amplitude' +title = 'Amplitude estimada da deformação das ondas gravitacionais do evento GW150914' +attribution = '(Créditos do gráfico: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' +attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' +{{< /figure >}} ## O papel da NumPy na detecção de ondas gravitacionais Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. NumPy, o pacote padrão de análise numérica para Python, foi parte do software utilizado para várias tarefas executadas durante o projeto de detecção de ondas gravitacionais no LIGO. A NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: * [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) * Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro * Análise estatística: estimar o significado estatístico dos dados observados, estimando os parâmetros do sinal (por exemplo, massa de estrelas, velocidade de giro e distância) em comparação com um modelo. * Visualização de dados - Séries temporais - Espectrogramas * Cálculo de correlações * [Software](https://github.com/lscsoft) fundamental desenvolvido na análise de ondas gravitacionais, como [GwPy](https://gwpy.github.io/docs/stable/overview.html) e [PyCBC](https://pycbc.org) usam NumPy e AstroPy internamente para fornecer interfaces baseadas em objetos para utilidades, ferramentas e métodos para o estudo de dados de detectores de ondas gravitacionais. -{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**Grafo de dependências mostrando como o pacote GwPy depended da NumPy**" >}} +{{< figure >}} +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' +alt = 'gwpy-numpy depgraph' +title = 'Grafo de dependências mostrando como o pacote GwPy depended da NumPy' +{{< /figure >}} ---- -{{< figure src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Grafo de dependências mostrando como o pacote PyCBC depended da NumPy**" >}} +{{< figure >}} +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' +alt = 'PyCBC-numpy depgraph' +title = 'Grafo de dependências mostrando como o pacote PyCBC depended da NumPy' +{{< /figure >}} ## Resumo A detecção de ondas gravitacionais permitiu que pesquisadores descobrissem fenômenos totalmente inesperados ao mesmo tempo em que proporcionaram novas idéias sobre muitos dos fenômenos mais profundos conhecidos na astrofísica. O processamento e a visualização de dados é um passo crucial que ajuda cientistas a obter informações coletadas de observações científicas e a entender os resultados. Os cálculos são complexos e não podem ser compreendidos por humanos a não ser que sejam visualizados usando simulações de computador que são alimentadas com dados e análises reais observados. A NumPy, junto com outras bibliotecas Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo. -{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' +alt = 'numpy benefits' +title = 'Recursos chave da NumPy utilizados' +{{< /figure >}} diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md index 45ade42..8747efc 100644 --- a/content/pt/user-survey-2020.md +++ b/content/pt/user-survey-2020.md @@ -1,16 +1,20 @@ --- title: PESQUISA SOBRE A COMUNIDADE NUMPY 2020 sidebar: false --- Em 2020, o time de pesquisas do NumPy realizou a primeira pesquisa oficial sobre a comunidade NumPy, em parceria com alunos e docentes de um Mestrado em metodologia de pesquisa realizado conjuntamente pela Universidade de Michigan e pela Universidade da Maryland. Mais de 1200 usuários de 75 países participaram para nos ajudar a mapear uma paisagem da comunidade NumPy e expressaram seus pensamentos sobre o futuro do projeto. -{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado 'NumPy Community Survey 2020 - results'" width="250" >}} +{{< figure >}} +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' +alt = 'Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado "NumPy Community Survey 2020 - results"' +width = '250' +{{< /figure >}} **[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados. Para os destaques, confira **[este infográfico](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. Quer saber mais? Visite **https://numpy.org/user-survey-2020-details/**.
numpy/numpy.org
aacbe93f8e6478e38102684e5201108c34784037
Add HyperSpy (#736)
diff --git a/content/en/tabcontents.yaml b/content/en/tabcontents.yaml index b47c24a..1534f22 100644 --- a/content/en/tabcontents.yaml +++ b/content/en/tabcontents.yaml @@ -1,298 +1,300 @@ params: machinelearning: paras: - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications &mdash; among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. [MXNet](https://github.com/apache/incubator-mxnet) is another AI package, providing blueprints and templates for deep learning. para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) &mdash; one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. arraylibraries: intro: - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. headers: - text: Array Library - text: Capabilities & Application areas libraries: - title: Dask text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: NumPy-compatible array library for GPU-accelerated computing with Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax - title: Xarray - text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization + text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: Deep learning framework that accelerates the path from research prototyping to production deployment. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: MXNet text: Deep learning framework suited for flexible research prototyping and production. img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - title: Arrow text: A cross-language development platform for columnar in-memory data and analytics. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow - title: xtensor text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array text: Manipulate JSON-like data with NumPy-like idioms. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly text: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Nearly every scientist working in Python draws on the power of NumPy. - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." libraries: - title: Quantum Computing alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - url: http://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - url: https://qiskit.org label: Qiskit - url: https://pennylane.ai label: PennyLane - title: Statistical Computing alttext: A line graph with the line moving up. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - url: https://github.com/statsmodels/statsmodels label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - url: https://github.com/mwaskom/seaborn label: Seaborn - title: Signal Processing alttext: A bar chart with positive and negative values. img: /images/content_images/sc_dom_img/signal_processing.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets - url: https://python-control.org/ label: python-control + - url: https://hyperspy.org/ + label: HyperSpy - title: Image Processing alttext: An photograph of the mountains. img: /images/content_images/sc_dom_img/image_processing.svg links: - url: https://scikit-image.org/ label: Scikit-image - url: https://opencv.org/ label: OpenCV - url: https://mahotas.rtfd.io/ label: Mahotas - title: Graphs and Networks alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg links: - url: https://networkx.org/ label: NetworkX - url: https://graph-tool.skewed.de/ label: graph-tool - url: https://igraph.org/python/ label: igraph - url: https://pygsp.rtfd.io/ label: PyGSP - title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - url: https://github.com/sunpy/sunpy label: SunPy - url: https://github.com/spacepy/spacepy label: SpacePy - title: Cognitive Psychology alttext: A human head with gears. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - title: Bioinformatics alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - url: https://biopython.org/ label: BioPython - url: http://scikit-bio.org/ label: Scikit-Bio - url: https://github.com/openvax/pyensembl label: PyEnsembl - url: http://etetoolkit.org/ label: ETE - title: Bayesian Inference alttext: A graph with a bell-shaped curve. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ label: PyMC3 - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ label: emcee - title: Mathematical Analysis alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://www.sympy.org/ label: SymPy - url: https://github.com/cvxgrp/cvxpy label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: Chemistry alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg links: - url: https://cantera.org/ label: Cantera - url: https://www.mdanalysis.org/ label: MDAnalysis - url: https://github.com/rdkit/rdkit label: RDKit - url: https://www.pybamm.org/ label: PyBaMM - title: Geoscience alttext: The Earth. img: /images/content_images/sc_dom_img/geoscience.svg links: - url: https://pangeo.io/ label: Pangeo - url: https://simpeg.xyz/ label: Simpeg - url: https://github.com/obspy/obspy/wiki label: ObsPy - url: https://www.fatiando.org/ label: Fatiando a Terra - title: Geographic Processing alttext: A map. img: /images/content_images/sc_dom_img/GIS.svg links: - url: https://shapely.readthedocs.io/ label: Shapely - url: https://geopandas.org/ label: GeoPandas - url: https://python-visualization.github.io/folium label: Folium - title: Architecture & Engineering alttext: A microprocessor development board. img: /images/content_images/sc_dom_img/robotics.svg links: - url: https://compas.dev/ label: COMPAS - url: https://cityenergyanalyst.com/ label: City Energy Analyst - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" image1: - img: /images/content_images/ds-landscape.png alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. image2: - img: /images/content_images/data-science.png alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: A streamplot made in matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: A scatter-plot graph made in ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: A box-plot made in plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: A streamgraph made in altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - url: https://napari.org img: /images/content_images/v_napari.png alttext: A multi-dimensionan image made in napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: A Voronoi diagram made in vispy. content: - text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), and [PyVista](https://github.com/pyvista/pyvista), to name a few. - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. diff --git a/layouts/partials/tabs.html b/layouts/partials/tabs.html index 7b53dfc..11d9419 100644 --- a/layouts/partials/tabs.html +++ b/layouts/partials/tabs.html @@ -1,42 +1,42 @@ {{- $tabs := .Site.Params.tabs }} {{- $title := index $tabs "title" }} <section class="tabs-section"> <div class="container"> <h1 class="tabs-title">{{ $title }}</h1> <div class="tabs"> <div role="tablist" class="automatic"> <button id="0-tab-0" type="button" role="tab" aria-selected="true" aria-controls="0-tabpanel-0"> Scientific Domains </button> <button id="0-tab-1" type="button" role="tab" aria-selected="false" aria-controls="0-tabpanel-1"> - Array libraries + Array Libraries </button> <button id="0-tab-2" type="button" role="tab" aria-selected="false" aria-controls="0-tabpanel-2"> Data Science </button> <button id="0-tab-3" type="button" role="tab" aria-selected="false" aria-controls="0-tabpanel-3"> Machine Learning </button> <button id="0-tab-4" type="button" role="tab" aria-selected="false" aria-controls="0-tabpanel-4"> Visualization </button> </div> <div id="0-tabpanel-0" role="tabpanel" tabindex="0" aria-labelledby="$0-tab-0"> {{ partial "scientific-domains.html" . }} </div> <div id="0-tabpanel-1" role="tabpanel" tabindex="0" aria-labelledby="$0-tab-1"> {{ partial "array-libraries.html" . }} </div> <div id="0-tabpanel-2" role="tabpanel" tabindex="0" aria-labelledby="$0-tab-2"> {{ partial "data-science.html" . }} </div> <div id="0-tabpanel-3" role="tabpanel" tabindex="0" aria-labelledby="$0-tab-3"> {{ partial "machine-learning.html" . }} </div> <div id="0-tabpanel-4" role="tabpanel" tabindex="0" aria-labelledby="$0-tab-4"> {{ partial "visualization.html" . }} </div> </div> </div> </section>
numpy/numpy.org
47a8b420296afb465147b3f61d8ee95c53663c6b
replace github by doc links
diff --git a/content/en/tabcontents.yaml b/content/en/tabcontents.yaml index b47c24a..7c00aad 100644 --- a/content/en/tabcontents.yaml +++ b/content/en/tabcontents.yaml @@ -1,298 +1,298 @@ params: machinelearning: paras: - - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications &mdash; among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. [MXNet](https://github.com/apache/incubator-mxnet) is another AI package, providing blueprints and templates for deep learning. - para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) &mdash; one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://www.scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications &mdash; among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. [MXNet](https://mxnet.apache.org/) is another AI package, providing blueprints and templates for deep learning. + para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) &mdash; one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. arraylibraries: intro: - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides. headers: - text: Array Library - text: Capabilities & Application areas libraries: - title: Dask text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - title: CuPy text: NumPy-compatible array library for GPU-accelerated computing with Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX - url: https://github.com/google/jax + url: https://jax.readthedocs.io/ - title: Xarray text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - title: Sparse text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - title: PyTorch text: Deep learning framework that accelerates the path from research prototyping to production deployment. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - title: TensorFlow text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - title: MXNet text: Deep learning framework suited for flexible research prototyping and production. img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - title: Arrow text: A cross-language development platform for columnar in-memory data and analytics. img: /images/content_images/arlib/arrow.png alttext: arrow - url: https://github.com/apache/arrow + url: https://arrow.apache.org/ - title: xtensor text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array text: Manipulate JSON-like data with NumPy-like idioms. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - title: uarray text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly text: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Nearly every scientist working in Python draws on the power of NumPy. - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant." libraries: - title: Quantum Computing alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - url: http://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil - url: https://qiskit.org label: Qiskit - url: https://pennylane.ai label: PennyLane - title: Statistical Computing alttext: A line graph with the line moving up. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - url: https://pandas.pydata.org/ label: Pandas - - url: https://github.com/statsmodels/statsmodels + - url: https://www.statsmodels.org/ label: statsmodels - url: https://xarray.pydata.org/en/stable/ label: Xarray - - url: https://github.com/mwaskom/seaborn + - url: https://seaborn.pydata.org/ label: Seaborn - title: Signal Processing alttext: A bar chart with positive and negative values. img: /images/content_images/sc_dom_img/signal_processing.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets - url: https://python-control.org/ label: python-control - title: Image Processing alttext: An photograph of the mountains. img: /images/content_images/sc_dom_img/image_processing.svg links: - url: https://scikit-image.org/ label: Scikit-image - url: https://opencv.org/ label: OpenCV - url: https://mahotas.rtfd.io/ label: Mahotas - title: Graphs and Networks alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg links: - url: https://networkx.org/ label: NetworkX - url: https://graph-tool.skewed.de/ label: graph-tool - url: https://igraph.org/python/ label: igraph - url: https://pygsp.rtfd.io/ label: PyGSP - title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - url: https://www.astropy.org/ label: AstroPy - - url: https://github.com/sunpy/sunpy + - url: https://sunpy.org/ label: SunPy - - url: https://github.com/spacepy/spacepy + - url: https://spacepy.github.io/ label: SpacePy - title: Cognitive Psychology alttext: A human head with gears. img: /images/content_images/sc_dom_img/cognitive_psychology.svg links: - url: https://www.psychopy.org/ label: PsychoPy - title: Bioinformatics alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - url: https://biopython.org/ label: BioPython - url: http://scikit-bio.org/ label: Scikit-Bio - url: https://github.com/openvax/pyensembl label: PyEnsembl - url: http://etetoolkit.org/ label: ETE - title: Bayesian Inference alttext: A graph with a bell-shaped curve. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ label: PyMC3 - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ label: emcee - title: Mathematical Analysis alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - url: https://www.scipy.org/ label: SciPy - url: https://www.sympy.org/ label: SymPy - - url: https://github.com/cvxgrp/cvxpy + - url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ label: FEniCS - title: Chemistry alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg links: - url: https://cantera.org/ label: Cantera - url: https://www.mdanalysis.org/ label: MDAnalysis - url: https://github.com/rdkit/rdkit label: RDKit - url: https://www.pybamm.org/ label: PyBaMM - title: Geoscience alttext: The Earth. img: /images/content_images/sc_dom_img/geoscience.svg links: - url: https://pangeo.io/ label: Pangeo - url: https://simpeg.xyz/ label: Simpeg - url: https://github.com/obspy/obspy/wiki label: ObsPy - url: https://www.fatiando.org/ label: Fatiando a Terra - title: Geographic Processing alttext: A map. img: /images/content_images/sc_dom_img/GIS.svg links: - url: https://shapely.readthedocs.io/ label: Shapely - url: https://geopandas.org/ label: GeoPandas - url: https://python-visualization.github.io/folium label: Folium - title: Architecture & Engineering alttext: A microprocessor development board. img: /images/content_images/sc_dom_img/robotics.svg links: - url: https://compas.dev/ label: COMPAS - url: https://cityenergyanalyst.com/ label: City Energy Analyst - url: https://nortikin.github.io/sverchok/ label: Sverchok datascience: intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" image1: - img: /images/content_images/ds-landscape.png alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. image2: - img: /images/content_images/data-science.png alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - text: "<b>Extract, Transform, Load: </b>[Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - text: "<b>Exploratory analysis: </b>[Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - text: "<b>Model and evaluate: </b>[scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + - text: "<b>Report in a dashboard: </b>[Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://voila.readthedocs.io/)" content: - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)). visualization: images: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png alttext: A streamplot made in matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png alttext: A scatter-plot graph made in ggpy - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png alttext: A box-plot made in plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png alttext: A streamgraph made in altair - url: https://seaborn.pydata.org img: /images/content_images/v_seaborn.png alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - url: https://napari.org img: /images/content_images/v_napari.png alttext: A multi-dimensionan image made in napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: A Voronoi diagram made in vispy. content: - text: NumPy is an essential component in the burgeoning [Python visualization landscape](https://pyviz.org/overviews/index.html), which includes [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), - [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), - and [PyVista](https://github.com/pyvista/pyvista), to name a few. + [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), + and [PyVista](https://docs.pyvista.org/), to name a few. - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.
numpy/numpy.org
7dede0307a0d992598a2b59036b2a2a2c9b04cb7
Unify figure shortcode invocation (#738)
diff --git a/content/en/case-studies/deeplabcut-dnn.md b/content/en/case-studies/deeplabcut-dnn.md index 3ac3487..2d9e428 100644 --- a/content/en/case-studies/deeplabcut-dnn.md +++ b/content/en/case-studies/deeplabcut-dnn.md @@ -1,149 +1,149 @@ --- title: "Case Study: DeepLabCut 3D Pose Estimation" sidebar: false --- -{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Analyzing mice hand-movement using DeepLapCut**" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} +{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Analyzing mice hand-movement using DeepLapCut**" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" >}} Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. {{< /blockquote >}} ## About DeepLabCut [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) is an open source toolbox that empowers researchers at hundreds of institutions worldwide to track behaviour of laboratory animals, with very little training data, at human-level accuracy. With DeepLabCut technology, scientists can delve deeper into the scientific understanding of motor control and behavior across animal species and timescales. Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. {{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**Colored dots track the positions of a racehorse’s body part**" alt="horserideranim" attr="*(Source: Mackenzie Mathis)*">}} DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior through a Python based software toolkit. With DeepLabCut, researchers can identify distinct frames from videos, digitally label specific body parts in a few dozen frames with a tailored GUI, and then the deep learning based pose estimation architectures in DeepLabCut learn how to pick out those same features in the rest of the video and in other similar videos of animals. It works across species of animals, from common laboratory animals such as flies and mice to more unusual animals like [cheetahs][cheetah-movement]. [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 DeepLabCut uses a principle called [transfer learning](https://arxiv.org/pdf/1909.11229), which greatly reduces the amount of training data required and speeds up the convergence of the training period. Depending on the needs, users can pick different network architectures that provide faster inference (e.g. MobileNetV2), which can also be combined with real-time experimental feedback. DeepLabCut originally used the feature detectors from a top-performing human pose estimation architecture, called [DeeperCut](https://arxiv.org/abs/1605.03170), which inspired the name. The package now has been significantly changed to include additional architectures, augmentation methods, and a full front-end user experience. Furthermore, to support large-scale biological experiments DeepLabCut provides active learning capabilities so that users can increase the training set over time to cover edge cases and make their pose estimation algorithm robust within the specific context. Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) was introduced, which provides pre-trained models for various species and experimental conditions from facial analysis in primates to dog posture. This can be run for instance in the cloud without any labeling of new data, or neural network training, and no programming experience is necessary. ### Key Goals and Results * **Automation of animal pose analysis for scientific studies:** The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a [tenfold performance boost](https://www.biorxiv.org/content/10.1101/457242v1) with DeepLabCut. Poses can be inferred offline at up to 1200 frames per second (FPS). * **Creation of an easy-to-use Python toolkit for pose estimation:** DeepLabCut wanted to share their animal pose-estimation technology in the form of an easy to use tool that can be adopted by researchers easily. So they have created a complete, easy-to-use Python toolbox with project management features as well. These enable not only automation of pose-estimation but also managing the project end-to-end by helping the DeepLabCut Toolkit user right from the dataset collection stage to creating shareable and reusable analysis pipelines. Their [toolkit][DLCToolkit] is now available as open source. A typical DeepLabCut Workflow includes: - creation and refining of training sets via active learning - creation of tailored neural networks for specific animals and scenarios - code for large-scale inference on videos - draw inferences using integrated visualization tools {{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**Pose estimation steps with DeepLabCut**" alt="dlcsteps" align="middle" attr="(Source: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} [DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut ### The Challenges * **Speed** Fast processing of animal behavior videos in order to measure their behavior and at the same time make scientific experiments more efficient, accurate. Extracting detailed animal poses for laboratory experiments, without markers, in dynamically changing backgrounds, can be challenging, both technically as well as in terms of resource needs and training data required. Coming up with a tool that is easy to use without the need for skills such as computer vision expertise that enables scientists to do research in more real-world contexts, is a non-trivial problem to solve. * **Combinatorics** Combinatorics involves assembly and integration of movement of multiple limbs into individual animal behavior. Assembling keypoints and their connections into individual animal movements and linking them across time is a complex process that requires heavy-duty numerical analysis, especially in case of multi-animal movement tracking in experiment videos. * **Data Processing** Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. {{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Pose estimation variety and complexity**" alt="challengesfig" align="middle" attr="(Source: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} ## NumPy's Role in meeting Pose Estimation Challenges NumPy addresses DeepLabCut technology's core need of numerical computations at high speed for behavioural analytics. Besides NumPy, DeepLabCut employs various Python software that utilize NumPy at their core, such as [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) and [Tensorflow](https://www.tensorflow.org). The following features of NumPy played a key role in addressing the image processing, combinatorics requirements and need for fast computation in DeepLabCut pose estimation algorithms: * Vectorization * Masked Array Operations * Linear Algebra * Random Sampling * Reshaping of large arrays DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered by the toolkit. In particular, NumPy is used for sampling distinct frames for human annotation labeling, and for writing, editing and processing annotation data. Within TensorFlow the neural network is trained by DeepLabCut technology over thousands of iterations to predict the ground truth annotations from frames. For this purpose, target densities (scoremaps) are created to cast pose estimation as a image-to-image translation problem. To make the neural networks robust, data augmentation is employed, which requires the calculation of target scoremaps subject to various geometric and image processing steps. To make training fast, NumPy’s vectorization capabilities are leveraged. For inference, the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. {{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCut Workflow**" alt="workflow" attr="*(Source: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} ## Summary Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human level labeling accuracy, thus expanding its application to not only behavior analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. {{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} diff --git a/content/en/user-survey-2020.md b/content/en/user-survey-2020.md index 8bd9a6b..73b49a4 100644 --- a/content/en/user-survey-2020.md +++ b/content/en/user-survey-2020.md @@ -1,23 +1,23 @@ --- title: 2020 NUMPY COMMUNITY SURVEY sidebar: false --- In 2020, the NumPy survey team in partnership with students and faculty from a Master’s course in Survey Methodology jointly hosted by the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. -{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'" width="250">}} +{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'" width="250" >}} **[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index 9061ebd..f115522 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -1,92 +1,92 @@ --- title: "ケーススタディ: DeepLabCut 三次元姿勢推定" sidebar: false --- -{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCutを用いたマウスの手の動きの解析**" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} +{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCutを用いたマウスの手の動きの解析**" alt="micehandanim" attr="*(Source: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis、 *准教授、École polytechnology fe’rale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" >}} オープンソースソフトウェアは生体臨床医学を加速させています。 DeepLabCut を使用すると、深層学習を使用して動物の行動を自動的にビデオ解析することができます。 {{< /blockquote >}} ## DeepLabCut について [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut)は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。 神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 {{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**色のついた点は競走馬の体の位置を追跡**" alt="horserideranim" attr="*(Source: Mackenzie Mathis)*">}} DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から [チーター][cheetah-movement]のようなより珍しい動物まで、動物の種類を問わず利用できます。 DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術を使用しています。 これにより必要な学習データの量を大幅に削減し、学習の収束を加速させることができます。 必要に応じて、より高速な推論を提供するさまざまなネットワークアーキテクチャ(MobileNetV2など)を選択することができ、リアルタイムの実験データフィードバックと組み合わせることもできます。 DeepLabCutはもともと[DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれるパフォーマンスのよい人用のポーズ推定アーキテクチャの特徴検出器を使用しており、これが名前の由来になりました。 今ではこのパッケージは大幅に変更され、追加のアーキテクチャ・データの水増し・一通りのユーザー用フロントエンドを含んでいます。 さらに、 大規模な生物学的実験をサポートするため、DeepLabCutはオンライン学習の機能を提供しています。 これにより、動画の時間をこえて学習データを増やすことができ、エッジケースをカバーしたり、特定のコンテキスト内でポーズ推定アルゴリズムを堅牢にしたりできます。 最近、[DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo)が発表されました。 これは、霊長類の顔分析から犬の姿勢まで、様々な種や実験条件に対応した事前訓練済みモデルを提供しています。 これにより、例えば、新しいデータのラベルを付けることなくクラウドで予測を実行することができたり、ニューラルネットワークの学習を実行することができます。 プログラミング経験は必要ありません。 ### 主な目標と結果 * **科学研究のための動物姿勢解析の自動化:** DeepLabCutという技術の主な目的は、多様な環境で動物の姿勢を測定し追跡することです。 このデータは例えば神経科学の研究において、脳がどのように運動を制御しているかを理解するためのや、動物がどのように社会的に交流しているかを明らかにするために利用することができます。 研究者はDeepLabCutで [10倍のパフォーマンス向上](https://www.biorxiv.org/content/10.1101/457242v1) が可能であると発表しています。 オフラインでは最大1200フレーム/秒(FPS) で姿勢を推定することができます。 * **姿勢推定のための使いやすいPythonツールキットの作成:** DeepLabCutは、動物の姿勢推定技術を研究者が簡単に利用できるツールとして共有したいという考えから開発されています。 そこで開発者らはプロジェクト管理機能を備えた、単独で機能し、使いやすいPythonツールボックスとしてこのツールを作成しました。 これにより、姿勢推定を自動化するだけでなく、DeepLabCutツールキットユーザーをデータセット収集段階から共有可能・再利用可能な分析パイプラインを作成する段階まで補助し、プロジェクトをエンドツーエンドで管理することも可能になりました。 この[ツールキット][DLCToolkit] はオープンソースとして利用できます。 典型的なDeepLabCutワークフローは以下のようになります。 - オンライン学習によるトレーニングセットの作成と調整 - 特定の動物やシナリオに合わせたニューラルネットワークの構築 - 動画における大規模推論のためのコード作成 - 統合された可視化ツールを使用した推論の描画 {{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**DeepLabCutによる姿勢推定のステップ**" alt="dlcsteps" align="middle" attr="(Source: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} ### 課題 * **速度** 動物行動動画の高速な処理は、動物の行動を測定し、科学実験をより効率的で正確にするために重要です。 動的に変化する背景の中で、マーカーを使用せずに、実験室での実験のために動物の詳細な姿勢を抽出することは、技術的にも、必要なリソース的にも、必要なトレーニングデータの面でも、困難な場合があります。 科学者が、より現実的な状況で研究を行うために、コンピュータビジョンなどの専門知識のスキルを必要とせずに使うことができるツールを開発することは、解決すべき重要な問題です。 * **組み合わせ問題** 組合せ問題とは、複数の四肢の動きを個々の動物行動に統合することを指します。 キーポイントと、その個々の動物行動との関連性を組み合わせ、時間的に結びつけることは、複雑なプロセスであり、非常に膨大な数値解析が必要となります。 特に、実験映像の中で複数の動物の動きを追跡する場合は大変です。 * **データ処理** 最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。 {{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**姿勢推定の多様性と難しさ**" alt="challengesfig" align="middle" attr="(Source: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} ## 姿勢推定の課題に対応するためのNumPyの役割 NumPy は DeepLabCutにおける、行動分析の高速化のための数値計算の核となっています。 NumPyだけでなく、DeepLabCutは様々なNumPyをベースとしているPythonライブラリを利用しています。 [SciPy](https://www.scipy.org)、[Pandas](https://pandas.pydata.org)、[matplotlib](https://matplotlib.org)、[Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug)、[scikit-learn](https://scikit-learn.org/stable/)、[scikit-image](https://scikit-image.org)、[Tensorflow](https://www.tensorflow.org)などです。 以下に挙げるNumPyの特徴が、DeepLabCutの姿勢推定アルゴリズムでの画像処理・組み合わせ処理・高速計算において、重要な役割を果たしました。 * ベクトル化 * マスクされた配列操作 * 線形代数 * ランダムサンプリング * 大きな配列の再構成 DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。 TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。 {{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCutのワークフロー**" alt="workflow" attr="*(Source: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} ## まとめ 行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。 {{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**NumPyの主要機能**" >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 [DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md index 370138d..ce32100 100644 --- a/content/ja/user-survey-2020.md +++ b/content/ja/user-survey-2020.md @@ -1,16 +1,16 @@ --- title: 2020年 NumPyコミュニティ調査 sidebar: false --- 2020年に、NumPyの調査チームは、ミシガン大学とメリーランド大学が共同で開催した、調査方法学の修士コースの学生と教員と共同で、初めて公式のNumPyコミュニティ調査を実施しました。 75カ国から1,200人以上のNumPyユーザーが参加してくれました。NumPyコミュニティの全体像を描き、プロジェクトの未来像についての意見を述べてもらいました。 -{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 Numpy User survey report, titled 'Numpyコミュニティ調査2020 - 結果'" width="250">}} +{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Cover page of the 2020 Numpy User survey report, titled 'Numpyコミュニティ調査2020 - 結果'" width="250" >}} 調査結果を詳細を知りたい場合は、**[こちらのレポート](/surveys/NumPy_usersurvey_2020_report.pdf)** をダウンロードしてください。 結果の概要については、 **[こちらの図](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)** をチェックしてください。 より詳細が知りたくなりましたか？ **https://numpy.org/user-survey-2020-details/** をご覧ください。 diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index c3fd0b9..a0c37db 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -1,92 +1,92 @@ --- title: "Estudo de Caso: Estimativa de Pose 3D com DeepLabCut" sidebar: false --- -{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Análise de movimentos de mãos de camundongos usando DeepLapCut**" alt="micehandanim" attr="*(Fonte: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} +{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Análise de movimentos de mãos de camundongos usando DeepLapCut**" alt="micehandanim" attr="*(Fonte: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut" >}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Professor Assistente, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" >}} Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning. {{< /blockquote >}} ## Sobre o DeepLabCut [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) é uma toolbox de código aberto que permite que pesquisadores de centenas de instituições em todo o mundo rastreiem o comportamento de animais de laboratório, com muito poucos dados de treinamento, mas com precisão no nível humano. Com a tecnologia DeepLabCut, cientistas podem aprofundar a compreensão científica do controle motor e do comportamento em diversas espécies animais e escalas temporais. Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas trabalhosas de monitoramento e marcação, junto com análise de dados baseada em redes neurais profundas, a DeepLabCut garante que estudos científicos envolvendo a observação de animais como primatas, camundongos, peixes, moscas etc. sejam mais rápidos e precisos. {{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida**" alt="horserideranim" attr="*(Fonte: Mackenzie Mathis)*">}} O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (*frames*) distintos em vídeos e rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI especializada. A partir disso, a arquitetura de estimação de poses baseada em deep learning da DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios, como moscas e camundongos, até os mais incomuns, como [guepardos][cheetah-movement]. A DeepLabCut usa um princípio chamado [aprendizado por transferência (*transfer learning*)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), e que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de alto desempenho para estimativa de poses humanas, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experimentos biológicos em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo, para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab.org/dlc-modelzoo), que proporciona modelos pré-treinados para várias espécies e condições experimentais, desde a análise facial em primatas até à posição de cães. Isso pode ser executado na nuvem, por exemplo, sem qualquer rotulagem de novos dados ou treinamento em rede neural, e não é necessária nenhuma experiência em programação. ### Principais Objetivos e Resultados * **Automação da análise de poses animais para estudos científicos:** O objetivo principal da tecnologia DeepLabCut é medir e rastrear a postura dos animais em várias configurações. Esses dados podem ser usados, por exemplo, em estudos de neurociência para entender como o cérebro controla o movimento, ou para elucidar como os animais interagem socialmente. Pesquisadores observaram que [desempenho é 10 vezes melhor](https://www.biorxiv.org/content/10.1101/457242v1) com o DeepLabCut. Poses podem ser inferidas off-line em até 1200 quadros por segundo (FPS). * **Criação de um kit de ferramentas Python fácil de usar para estimativa de poses:** DeepLabCut queria compartilhar sua tecnologia de estimativa de poses animal na forma de uma ferramenta simples de usar que pudesse ser adotada pelos pesquisadores facilmente. Assim, criaram um conjunto de ferramentas em Python completo e fácil de usar, também com recursos de gerenciamento de projeto. Isso permite não apenas a automação de estimação de poses, mas também o gerenciamento do projeto de ponta a ponta, ajudando o usuário do DeepLabCut Toolkit desde a fase de coleta para criar fluxos de dados compartilháveis e reutilizáveis. Seu [conjunto de ferramentas][DLCToolkit] agora está disponível como software de código aberto. Um fluxo de trabalho típico na DeepLabCut inclui: - criação e refinamento de conjuntos de treinamento por meio de aprendizagem ativa - criação de redes neurais personalizadas para animais e cenários específicos - código para inferência em larga escala em vídeos - inferências de desenho usando ferramentas integradas de visualização {{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**Passos na estimação de poses com DeepLabCut**" alt="dlcsteps" align="middle" attr="(Fonte: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} ### Desafios * **Velocidade** Processamento rápido de vídeos de animais para medir seu comportamento e, ao mesmo tempo, tornar os experimentos científicos mais eficientes e precisos. Extrair poses animais detalhadas para experimentos em laboratório, sem marcadores, sobre fundos dinâmicos, pode ser desafiador tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. Criar uma ferramenta que seja fácil de usar sem necessidade de habilidades como expertise em visão computacional que permita aos cientistas fazerem pesquisa em contextos mais próximos do mundo real é um problema não-trivial a ser solucionado. * **Combinatória** Combinatória envolve a junção e integração de movimentos de múltiplos membros em um comportamento animal único. Reunir pontos-chave e suas conexões em movimentos animais individuais e encadeá-los em função do tempo é um processo complexo que exige análise numérica intensa, especialmente nos casos de rastreio de múltiplos animais em vídeos experimentais. * **Processamento de dados** Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador. {{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Estimação de poses e complexidade**" alt="challengesfig" align="middle" attr="(Fonte: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} ## O papel da NumPy nos desafios da estimação de poses NumPy supre a principal necessidade da tecnologia DeepLabCut de cálculos numéricos de alta velocidade para análises comportamentais. Além da NumPy, DeepLabCut emprega várias bibliotecas Python que usam a NumPy como sua base, tais como [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) e [Tensorflow](https://www.tensorflow.org). As seguintes características da NumPy desempenharam um papel fundamental para atender às necessidades de processamento de imagens, combinatória e cálculos rápidos nos algoritmos de estimação de pose na DeepLabCut: * Vetorização * Operações em arrays com máscaras * Álgebra linear * Amostragem aleatória * Reordenamento de matrizes grandes A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação. Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. Para este propósito, densidades de alvo (*scoremaps*) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente. {{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**Fluxo de dados DeepLabCut**" alt="workflow" attr="*(Fonte: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} ## Resumo Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy. {{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave do NumPy utilizados**" >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 [DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md index 0cb175d..45ade42 100644 --- a/content/pt/user-survey-2020.md +++ b/content/pt/user-survey-2020.md @@ -1,16 +1,16 @@ --- title: PESQUISA SOBRE A COMUNIDADE NUMPY 2020 sidebar: false --- Em 2020, o time de pesquisas do NumPy realizou a primeira pesquisa oficial sobre a comunidade NumPy, em parceria com alunos e docentes de um Mestrado em metodologia de pesquisa realizado conjuntamente pela Universidade de Michigan e pela Universidade da Maryland. Mais de 1200 usuários de 75 países participaram para nos ajudar a mapear uma paisagem da comunidade NumPy e expressaram seus pensamentos sobre o futuro do projeto. -{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado 'NumPy Community Survey 2020 - results'" width="250">}} +{{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado 'NumPy Community Survey 2020 - results'" width="250" >}} **[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados. Para os destaques, confira **[este infográfico](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. Quer saber mais? Visite **https://numpy.org/user-survey-2020-details/**.
numpy/numpy.org
48ee92f37e4fccfed4ca26168eacbc8104e5f44b
Unify figure shortcode invocation (#737)
diff --git a/content/en/case-studies/cricket-analytics.md b/content/en/case-studies/cricket-analytics.md index 3ac6612..77aef51 100644 --- a/content/en/case-studies/cricket-analytics.md +++ b/content/en/case-studies/cricket-analytics.md @@ -1,159 +1,141 @@ --- title: "Case Study: Cricket Analytics, the game changer!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" - caption="**IPLT20, the biggest Cricket Festival in India**" - alt="Indian Premier League Cricket cup and stadium" - attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" - attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, the biggest Cricket Festival in India**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL*" >}} You don't play for the crowd, you play for the country. {{< /blockquote >}} ## About Cricket It would be an understatement to state that Indians love cricket. The game is played in just about every nook and cranny of India, rural or urban, popular with the young and the old alike, connecting billions in India unlike any other sport. Cricket enjoys lots of media attention. There is a significant amount of [money](https://www.statista.com/topics/4543/indian-premier-league-ipl/) and fame at stake. Over the last several years, technology has literally been a game changer. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. The Indian Premier League (IPL) is a professional Twenty20 cricket league, founded in 2008. It is one of the most attended cricketing events in the world, valued at [$6.7 billion](https://en.wikipedia.org/wiki/Indian_Premier_League) in 2019. Cricket is a game of numbers - the runs scored by a batsman, the wickets taken by a bowler, the matches won by a cricket team, the number of times a batsman responds in a certain way to a kind of bowling attack, etc. The capability to dig into cricketing numbers for both improving performance and studying the business opportunities, overall market, and economics of cricket via powerful analytics tools, powered by numerical computing software such as NumPy, is a big deal. Cricket analytics provides interesting insights into the game and predictive intelligence regarding game outcomes. Today, there are rich and almost infinite troves of cricket game records and statistics available, e.g., [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) and [cricsheet](https://cricsheet.org). These and several such cricket databases have been used for [cricket analysis](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) using the latest machine learning and predictive modelling algorithms. Media and entertainment platforms along with professional sports bodies associated with the game use technology and analytics for determining key metrics for improving match winning chances: * batting performance moving average, * score forecasting, * gaining insights into fitness and performance of a player against different opposition, * player contribution to wins and losses for making strategic decisions on team composition -{{< figure src="/images/content_images/cs/cricket-pitch.png" - class="csfigcaption" - caption="**Cricket Pitch, the focal point in the field**" - alt="A cricket pitch with bowler and batsmen" - align="middle" - attr="*(Image credit: Debarghya Das)*" - attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}} +{{< figure src="/images/content_images/cs/cricket-pitch.png" class="csfigcaption" caption="**Cricket Pitch, the focal point in the field**" alt="A cricket pitch with bowler and batsmen" align="middle" attr="*(Image credit: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}} ### Key Data Analytics Objectives * Sports data analytics are used not only in cricket but many [other sports](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) for improving the overall team performance and maximizing winning chances. * Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. * Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. -{{< figure src="/images/content_images/cs/player-pose-estimator.png" - class="fig-center" - alt="pose estimator" - caption="**Cricket Pose Estimator**" - attr="*(Image credit: connect.vin)*" - attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} +{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="pose estimator" caption="**Cricket Pose Estimator**" attr="*(Image credit: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} ### The Challenges * **Data Cleaning and preprocessing** IPL has expanded cricket beyond the classic test match format to a much larger scale. The number of matches played every season across various formats has increased and so has the data, the algorithms, newer sports data analysis technologies and simulation models. Cricket data analysis requires field mapping, player tracking, ball tracking, player shot analysis, and several other aspects involved in how the ball is delivered, its angle, spin, velocity, and trajectory. All these factors together have increased the complexity of data cleaning and preprocessing. * **Dynamic Modeling** In cricket, just like any other sport, there can be a large number of variables related to tracking various numbers of players on the field, their attributes, the ball, and several possibilities of potential actions. The complexity of data analytics and modeling is directly proportional to the kind of predictive questions that are put forth during analysis and are highly dependent on data representation and the model. Things get even more challenging in terms of computation, data comparisons when dynamic cricket play predictions are sought such as what would have happened if the batsman had hit the ball at a different angle or velocity. * **Predictive Analytics Complexity** Much of the decision making in cricket is based on questions such as "how often does a batsman play a certain kind of shot if the ball delivery is of a particular type", or "how does a bowler change his line and length if the batsman responds to his delivery in a certain way". This kind of predictive analytics query requires highly granular dataset availability and the capability to synthesize data and create generative models that are highly accurate. ## NumPy’s Role in Cricket Analytics Sports Analytics is a thriving field. Many researchers and companies [use NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter, besides using the latest machine learning and AI techniques. NumPy has been used for various kinds of cricket related sporting analytics such as: * **Statistical Analysis:** NumPy's numerical capabilities help estimate the statistical significance of observational data or match events in the context of various player and game tactics, estimating the game outcome by comparison with a generative or static model. [Causal analysis](https://amplitude.com/blog/2017/01/19/causation-correlation) and [big data approaches](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) are used for tactical analysis. * **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provide useful insights into relationship between various datasets. ## Summary Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. -{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" - class="fig-center" - alt="Diagram showing benefits of using NumPy for cricket analytics" - caption="**Key NumPy Capabilities utilized**" >}} +{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="Diagram showing benefits of using NumPy for cricket analytics" caption="**Key NumPy Capabilities utilized**" >}}
numpy/numpy.org
f6680ba2e0df70af90d0dc43ede8683b3841c094
Use grid of cards for team gallery (#734)
diff --git a/Makefile b/Makefile index b6d5f30..cc76605 100644 --- a/Makefile +++ b/Makefile @@ -1,51 +1,49 @@ # type `make help` to see all options BASEURL ?= ifdef BASEURL BASEURLARG=-b $(BASEURL) endif .PHONY: help prepare teams-clean teams serve clean # Add help text after each target name starting with '\#\#' help: ## show this help @echo "\nHelp for this makefile" @echo "Possible commands are:" @grep -h "##" $(MAKEFILE_LIST) | grep -v grep | sed -e 's/\(.*\):.*##\(.*\)/ \1: \2/' prepare: git submodule update --init python gen_config.py -TEAMS_DIR = static/gallery +# All translations share the <team>.toml files in the en translation +TEAMS_DIR = content/en/teams TEAMS = emeritus-maintainers maintainers docs-team triage-team survey-team web-team TEAMS_QUERY = python themes/scientific-python-hugo-theme/tools/team_query.py -$(TEAMS_DIR): - mkdir -p $(TEAMS_DIR) - -$(TEAMS_DIR)/%.md: $(TEAMS_DIR) - $(TEAMS_QUERY) --org numpy --team "$*" > $(TEAMS_DIR)/$*.html +$(TEAMS_DIR)/%.toml: + $(TEAMS_QUERY) --org numpy --team "$*" > $(TEAMS_DIR)/$*.toml teams-clean: prepare for team in $(TEAMS); do \ - rm -f $(TEAMS_DIR)/$${team}.html ;\ + rm -f $(TEAMS_DIR)/$${team}.toml ;\ done -teams: | teams-clean $(patsubst %,$(TEAMS_DIR)/%.md,$(TEAMS)) ## generates numpy.org team gallery pages +teams: | teams-clean $(patsubst %,$(TEAMS_DIR)/%.toml,$(TEAMS)) ## generates numpy.org team gallery pages serve: prepare ## serve the website hugo $(BASEURLARG) --printI18nWarnings server -D # Serve the site for development purposes (leaving submodules as-is, etc). serve-dev: python gen_config.py hugo $(BASEURLARG) --printI18nWarnings server --buildDrafts --disableFastRender --poll 1000ms html: prepare ## build the website in ./public hugo $(BASEURLARG) clean: ## remove the build artifacts, mainly the "public" directory rm -rf public diff --git a/content/en/teams.md b/content/en/teams.md deleted file mode 100644 index 2e8116d..0000000 --- a/content/en/teams.md +++ /dev/null @@ -1,24 +0,0 @@ ---- -title: NumPy Teams -sidebar: false ---- - -We are an international team on a mission to support scientific and research -communities worldwide by building quality, open-source software. -[Join us]({{< relref "/contribute" >}})! - -{{< include-html "static/gallery/maintainers.html" >}} - -{{< include-html "static/gallery/docs-team.html" >}} - -{{< include-html "static/gallery/web-team.html" >}} - -{{< include-html "static/gallery/triage-team.html" >}} - -{{< include-html "static/gallery/survey-team.html" >}} - -{{< include-html "static/gallery/emeritus-maintainers.html" >}} - -# Governance - -For the list of the Steering Council members, please see [here](https://numpy.org/about/). diff --git a/content/en/teams/docs-team.toml b/content/en/teams/docs-team.toml new file mode 100644 index 0000000..09abff8 --- /dev/null +++ b/content/en/teams/docs-team.toml @@ -0,0 +1,69 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4"' +alt = 'Avatar of Rohit Goswami' +{{< /image >}} +Rohit Goswami''' +link = 'https://github.com/HaoZeke' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4"' +alt = 'Avatar of Inessa Pawson' +{{< /image >}} +Inessa Pawson''' +link = 'https://github.com/InessaPawson' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4"' +alt = 'Avatar of Mars Lee' +{{< /image >}} +Mars Lee''' +link = 'https://github.com/MarsBarLee' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4"' +alt = 'Avatar of Matti Picus' +{{< /image >}} +Matti Picus''' +link = 'https://github.com/mattip' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4"' +alt = 'Avatar of Melissa Weber Mendonça' +{{< /image >}} +Melissa Weber Mendonça''' +link = 'https://github.com/melissawm' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4"' +alt = 'Avatar of Mukulika' +{{< /image >}} +Mukulika''' +link = 'https://github.com/Mukulikaa' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4"' +alt = 'Avatar of Ross Barnowski' +{{< /image >}} +Ross Barnowski''' +link = 'https://github.com/rossbar' diff --git a/content/en/teams/emeritus-maintainers.toml b/content/en/teams/emeritus-maintainers.toml new file mode 100644 index 0000000..b61eef1 --- /dev/null +++ b/content/en/teams/emeritus-maintainers.toml @@ -0,0 +1,89 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/9040124?v=4"' +alt = 'Avatar of Allan Haldane' +{{< /image >}} +Allan Haldane''' +link = 'https://github.com/ahaldane' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/20568?v=4"' +alt = 'Avatar of Ondřej Čertík' +{{< /image >}} +Ondřej Čertík''' +link = 'https://github.com/certik' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/25111?v=4"' +alt = 'Avatar of David Cournapeau' +{{< /image >}} +David Cournapeau''' +link = 'https://github.com/cournape' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/3343990?v=4"' +alt = 'Avatar of Jaime' +{{< /image >}} +Jaime''' +link = 'https://github.com/jaimefrio' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/123428?v=4"' +alt = 'Avatar of Jarrod Millman' +{{< /image >}} +Jarrod Millman''' +link = 'https://github.com/jarrodmillman' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/542663?v=4"' +alt = 'Avatar of Julian Taylor' +{{< /image >}} +Julian Taylor''' +link = 'https://github.com/juliantaylor' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&v=4"' +alt = 'Avatar of Mark Wiebe' +{{< /image >}} +Mark Wiebe''' +link = 'https://github.com/mwiebe' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&v=4"' +alt = 'Avatar of Nathaniel J. Smith' +{{< /image >}} +Nathaniel J. Smith''' +link = 'https://github.com/njsmith' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/254880?v=4"' +alt = 'Avatar of Travis E. Oliphant' +{{< /image >}} +Travis E. Oliphant''' +link = 'https://github.com/teoliphant' diff --git a/content/en/teams/index.md b/content/en/teams/index.md new file mode 100644 index 0000000..824152b --- /dev/null +++ b/content/en/teams/index.md @@ -0,0 +1,36 @@ +--- +title: NumPy Teams +sidebar: false +--- + +We are an international team on a mission to support scientific and research +communities worldwide by building quality, open-source software. +[Join us]({{< relref "/contribute" >}})! + +### Maintainers + +{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}} + +### Docs team + +{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}} + +### Web team + +{{< grid1 file="web-team.toml" columns="2 3 4 5" />}} + +### Triage team + +{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}} + +### Survey team + +{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}} + +### Emeritus maintainers + +{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}} + +# Governance + +For the list of the Steering Council members, please see [here](https://numpy.org/about/). diff --git a/content/en/teams/maintainers.toml b/content/en/teams/maintainers.toml new file mode 100644 index 0000000..03120e8 --- /dev/null +++ b/content/en/teams/maintainers.toml @@ -0,0 +1,289 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4"' +alt = 'Avatar of Andrew Nelson' +{{< /image >}} +Andrew Nelson''' +link = 'https://github.com/andyfaff' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/43369155?u=1f1fcabf979a2f00f403c60b816ba9f573026181&v=4"' +alt = 'Avatar of Bas van Beek' +{{< /image >}} +Bas van Beek''' +link = 'https://github.com/BvB93' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/77272?v=4"' +alt = 'Avatar of Charles Harris' +{{< /image >}} +Charles Harris''' +link = 'https://github.com/charris' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/425260?v=4"' +alt = 'Avatar of Eric Wieser' +{{< /image >}} +Eric Wieser''' +link = 'https://github.com/eric-wieser' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/20969920?u=ec0e4d9dd70227549776ba8209f0e55a35d1fe84&v=4"' +alt = 'Avatar of Ganesh Kathiresan' +{{< /image >}} +Ganesh Kathiresan''' +link = 'https://github.com/ganesh-k13' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4"' +alt = 'Avatar of Rohit Goswami' +{{< /image >}} +Rohit Goswami''' +link = 'https://github.com/HaoZeke' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/67612?v=4"' +alt = 'Avatar of Matthew Brett' +{{< /image >}} +Matthew Brett''' +link = 'https://github.com/matthew-brett' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4"' +alt = 'Avatar of Matti Picus' +{{< /image >}} +Matti Picus''' +link = 'https://github.com/mattip' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/6570539?u=cfb3e218754e85c4fac18064d7cfdce0b67ddaa6&v=4"' +alt = 'Avatar of Matt Haberland' +{{< /image >}} +Matt Haberland''' +link = 'https://github.com/mdhaber' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4"' +alt = 'Avatar of Melissa Weber Mendonça' +{{< /image >}} +Melissa Weber Mendonça''' +link = 'https://github.com/melissawm' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/2789820?v=4"' +alt = 'Avatar of Marten van Kerkwijk' +{{< /image >}} +Marten van Kerkwijk''' +link = 'https://github.com/mhvk' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&v=4"' +alt = 'Avatar of Christopher Sidebottom' +{{< /image >}} +Christopher Sidebottom''' +link = 'https://github.com/Mousius' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&v=4"' +alt = 'Avatar of Mateusz Sokół' +{{< /image >}} +Mateusz Sokół''' +link = 'https://github.com/mtsokol' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4"' +alt = 'Avatar of Mukulika' +{{< /image >}} +Mukulika''' +link = 'https://github.com/Mukulikaa' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/3126246?u=a3c7cd970c0e4cbc4498febe0de777a263c522c5&v=4"' +alt = 'Avatar of Nathan Goldbaum' +{{< /image >}} +Nathan Goldbaum''' +link = 'https://github.com/ngoldbaum' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/402156?u=288a1f206a151f9e2b69f3c0ce11848d3381943e&v=4"' +alt = 'Avatar of Pearu Peterson' +{{< /image >}} +Pearu Peterson''' +link = 'https://github.com/pearu' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/15134881?v=4"' +alt = 'Avatar of Josh Wilson' +{{< /image >}} +Josh Wilson''' +link = 'https://github.com/person142' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/35046?v=4"' +alt = 'Avatar of Pauli Virtanen' +{{< /image >}} +Pauli Virtanen''' +link = 'https://github.com/pv' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/15245051?u=54810990f0fdb11ecaade02762c09d5549d72a11&v=4"' +alt = 'Avatar of Chunlin' +{{< /image >}} +Chunlin''' +link = 'https://github.com/Qiyu8' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&v=4"' +alt = 'Avatar of Raghuveer Devulapalli' +{{< /image >}} +Raghuveer Devulapalli''' +link = 'https://github.com/r-devulap' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4"' +alt = 'Avatar of Ralf Gommers' +{{< /image >}} +Ralf Gommers''' +link = 'https://github.com/rgommers' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/46135?u=305a96a4778daecacbc8ec97ac25a48099a239cc&v=4"' +alt = 'Avatar of Robert Kern' +{{< /image >}} +Robert Kern''' +link = 'https://github.com/rkern' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4"' +alt = 'Avatar of Ross Barnowski' +{{< /image >}} +Ross Barnowski''' +link = 'https://github.com/rossbar' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/61977?v=4"' +alt = 'Avatar of Sebastian Berg' +{{< /image >}} +Sebastian Berg''' +link = 'https://github.com/seberg' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/12713707?u=5a3f6a8de4801d7878750cbd0bb2e0427bf0af0b&v=4"' +alt = 'Avatar of Sayed Adel' +{{< /image >}} +Sayed Adel''' +link = 'https://github.com/seiko2plus' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/1217238?u=b61e7e0085405ce6d7d53f8f39a1360ef9723f72&v=4"' +alt = 'Avatar of Stephan Hoyer' +{{< /image >}} +Stephan Hoyer''' +link = 'https://github.com/shoyer' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&v=4"' +alt = 'Avatar of Stefan van der Walt' +{{< /image >}} +Stefan van der Walt''' +link = 'https://github.com/stefanv' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/7903078?u=2762d9ff13b992dc635f8f190a17f9a90cddfae1&v=4"' +alt = 'Avatar of Tyler Reddy' +{{< /image >}} +Tyler Reddy''' +link = 'https://github.com/tylerjereddy' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/321463?v=4"' +alt = 'Avatar of Warren Weckesser' +{{< /image >}} +Warren Weckesser''' +link = 'https://github.com/WarrenWeckesser' diff --git a/content/en/teams/survey-team.toml b/content/en/teams/survey-team.toml new file mode 100644 index 0000000..d1eb22b --- /dev/null +++ b/content/en/teams/survey-team.toml @@ -0,0 +1,29 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4"' +alt = 'Avatar of Inessa Pawson' +{{< /image >}} +Inessa Pawson''' +link = 'https://github.com/InessaPawson' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4"' +alt = 'Avatar of Ralf Gommers' +{{< /image >}} +Ralf Gommers''' +link = 'https://github.com/rgommers' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4"' +alt = 'Avatar of Ross Barnowski' +{{< /image >}} +Ross Barnowski''' +link = 'https://github.com/rossbar' diff --git a/content/en/teams/triage-team.toml b/content/en/teams/triage-team.toml new file mode 100644 index 0000000..8bfddc6 --- /dev/null +++ b/content/en/teams/triage-team.toml @@ -0,0 +1,279 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4"' +alt = 'Avatar of Andrew Nelson' +{{< /image >}} +Andrew Nelson''' +link = 'https://github.com/andyfaff' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/1522319?v=4"' +alt = 'Avatar of Anirudh Subramanian' +{{< /image >}} +Anirudh Subramanian''' +link = 'https://github.com/anirudh2290' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/71486?u=cc88e2a4e4c6c496dcb9dd88cead5c0dab496c89&v=4"' +alt = 'Avatar of Aaron Meurer' +{{< /image >}} +Aaron Meurer''' +link = 'https://github.com/asmeurer' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/3813847?v=4"' +alt = 'Avatar of Atsushi Sakai' +{{< /image >}} +Atsushi Sakai''' +link = 'https://github.com/AtsushiSakai' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/6691888?v=4"' +alt = 'Avatar of Ben Nathanson' +{{< /image >}} +Ben Nathanson''' +link = 'https://github.com/bjnath' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/35413198?u=e67bd9ebc361fb207f914979d935fd1956eb626c&v=4"' +alt = 'Avatar of Anne Bonner' +{{< /image >}} +Anne Bonner''' +link = 'https://github.com/bonn0062' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/6788290?u=d9a388224b87d55106cb3e6199d02ebc1d8e0553&v=4"' +alt = 'Avatar of Brigitta Sipőcz' +{{< /image >}} +Brigitta Sipőcz''' +link = 'https://github.com/bsipocz' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/5476002?u=5352f057ef8cb5de29e4d2a9fa8b0d0f49580dc8&v=4"' +alt = 'Avatar of carlkl' +{{< /image >}} +carlkl''' +link = 'https://github.com/carlkl' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/11371428?u=9b425a337d076ec86b75ebc759724283f0970d9a&v=4"' +alt = 'Avatar of Ryan C Cooper' +{{< /image >}} +Ryan C Cooper''' +link = 'https://github.com/cooperrc' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/36567889?u=cbc76d558d375ebafd4a05a505f500eb94e00611&v=4"' +alt = 'Avatar of ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)' +{{< /image >}} +ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)''' +link = 'https://github.com/czgdp1807' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/2190658?u=b85e13f985d0bf87eeb3a7a146b61dcc9586019b&v=4"' +alt = 'Avatar of Hameer Abbasi' +{{< /image >}} +Hameer Abbasi''' +link = 'https://github.com/hameerabbasi' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4"' +alt = 'Avatar of Inessa Pawson' +{{< /image >}} +Inessa Pawson''' +link = 'https://github.com/InessaPawson' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/8078968?v=4"' +alt = 'Avatar of jbrockmendel' +{{< /image >}} +jbrockmendel''' +link = 'https://github.com/jbrockmendel' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&v=4"' +alt = 'Avatar of Kai' +{{< /image >}} +Kai''' +link = 'https://github.com/Kai-Striega' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/16046705?u=1bf01e87adb556503c1fe07789c194cc04d38490&v=4"' +alt = 'Avatar of Yuji Kanagawa' +{{< /image >}} +Yuji Kanagawa''' +link = 'https://github.com/kngwyu' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/22004158?u=2ebb3919ebaa3d7e0865ea5583032bc08bd0f526&v=4"' +alt = 'Avatar of Kriti Singh' +{{< /image >}} +Kriti Singh''' +link = 'https://github.com/kritisingh1' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/149655?u=249f7995c486de232c34e7970fbea505f518a1be&v=4"' +alt = 'Avatar of Christopher Albert' +{{< /image >}} +Christopher Albert''' +link = 'https://github.com/krystophny' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/20306270?u=235cdf82e88f76ba2f5f4c2d33fa392319c60ad1&v=4"' +alt = 'Avatar of Lysandros Nikolaou' +{{< /image >}} +Lysandros Nikolaou''' +link = 'https://github.com/lysnikolaou' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/34613774?u=61535ebfff07c68ea672cd8cd68c46187a38d3c1&v=4"' +alt = 'Avatar of Meekail Zain' +{{< /image >}} +Meekail Zain''' +link = 'https://github.com/Micky774' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&v=4"' +alt = 'Avatar of Christopher Sidebottom' +{{< /image >}} +Christopher Sidebottom''' +link = 'https://github.com/Mousius' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&v=4"' +alt = 'Avatar of Mateusz Sokół' +{{< /image >}} +Mateusz Sokół''' +link = 'https://github.com/mtsokol' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4"' +alt = 'Avatar of Mukulika' +{{< /image >}} +Mukulika''' +link = 'https://github.com/Mukulikaa' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/6564007?u=e5fb962de792bbce925c0c94fb7a748803c8bfa0&v=4"' +alt = 'Avatar of Noa Tamir' +{{< /image >}} +Noa Tamir''' +link = 'https://github.com/noatamir' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&v=4"' +alt = 'Avatar of Raghuveer Devulapalli' +{{< /image >}} +Raghuveer Devulapalli''' +link = 'https://github.com/r-devulap' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&v=4"' +alt = 'Avatar of shalz' +{{< /image >}} +shalz''' +link = 'https://github.com/shaloo' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/55803680?u=bb727a0da1f33ed5f2feb58dc0333943430d2318&v=4"' +alt = 'Avatar of Tina Oberoi' +{{< /image >}} +Tina Oberoi''' +link = 'https://github.com/tinaoberoi' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/13260794?u=5421923c831b67c4ef290bbdeb31ebfbdd906abc&v=4"' +alt = 'Avatar of Rakesh Vasudevan' +{{< /image >}} +Rakesh Vasudevan''' +link = 'https://github.com/vrakesh' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/8103276?v=4"' +alt = 'Avatar of Zijie (ZJ) Poh' +{{< /image >}} +Zijie (ZJ) Poh''' +link = 'https://github.com/zjpoh' diff --git a/content/en/teams/web-team.toml b/content/en/teams/web-team.toml new file mode 100644 index 0000000..ef317b2 --- /dev/null +++ b/content/en/teams/web-team.toml @@ -0,0 +1,89 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4"' +alt = 'Avatar of Inessa Pawson' +{{< /image >}} +Inessa Pawson''' +link = 'https://github.com/InessaPawson' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/123428?v=4"' +alt = 'Avatar of Jarrod Millman' +{{< /image >}} +Jarrod Millman''' +link = 'https://github.com/jarrodmillman' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/3891660?u=5de0ba1f1adad6f041f6dde1affef5d05bbed80a&v=4"' +alt = 'Avatar of Joe LaChance' +{{< /image >}} +Joe LaChance''' +link = 'https://github.com/joelachance' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4"' +alt = 'Avatar of Mars Lee' +{{< /image >}} +Mars Lee''' +link = 'https://github.com/MarsBarLee' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4"' +alt = 'Avatar of Ralf Gommers' +{{< /image >}} +Ralf Gommers''' +link = 'https://github.com/rgommers' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&v=4"' +alt = 'Avatar of shalz' +{{< /image >}} +shalz''' +link = 'https://github.com/shaloo' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&v=4"' +alt = 'Avatar of Shekhar Prasad Rajak' +{{< /image >}} +Shekhar Prasad Rajak''' +link = 'https://github.com/Shekharrajak' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&v=4"' +alt = 'Avatar of Stefan van der Walt' +{{< /image >}} +Stefan van der Walt''' +link = 'https://github.com/stefanv' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image >}} +src = 'https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&v=4"' +alt = 'Avatar of Albert Steppi' +{{< /image >}} +Albert Steppi''' +link = 'https://github.com/steppi' diff --git a/content/ja/about.md b/content/ja/about.md index a66076c..d153a89 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -1,90 +1,90 @@ --- title: 私たちについて sidebar: false --- NumPy は、Python で数値計算を可能にするためのオープンソースプロジェクトです。 NumPyは、NumericやNumarrayといった初期のライブラリのコードをもとに、2005年から開発が開始されました。 NumPyは完全にオープンソースなソフトウェアです。 そして、NumPyは[修正BSD ライセンス](https://github.com/numpy/numpy/blob/main/LICENSE.txt) の条項の下で、すべての人が利用可能です。 NumPy は 、NumPyコミュニティやより広範な科学計算用Python コミュニティとの合意のもと、GitHub 上でオープンに開発されています。 NumPyのガバナンス方法の詳細については、 [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html) をご覧ください。 ## 運営委員会 Numpy運営委員会はこのプロジェクトの管理組織です。 その役割は、Numpy コミュニティと協力し、Numpyのソフトウェアサービスを確実にユーザに提供することです。 ソフトウェアパッケージとコミュニティの両方において、プロジェクトの長期的な持続可能性を保っていきます。 NumPy運営委員会は現在以下のメンバーで構成されています (姓のアルファベット順): - Sebastian Berg - Ralf Gommers - Charles Harris - Stephan Hoyer - Inessa Pawson - Matti Picus - Stéfan van der Walt - Melissa Weber Mendonça - Eric Wieser 過去のメンバー - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) - Marten van Kerkwijk (2017-2019) - Travis Oliphant (プロジェクト創設者, 2005-2012) - Nathaniel Smith (2012-2021) - Julian Taylor (2013-2021) - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) Numpy運営委員会に連絡するには、[email protected]までメールしてください。 ## チーム Numpy プロジェクトのコアメンバーは、プロジェクトへの貢献の方法の多様化に積極的に取り組んでいます。<br> Numpyには現在以下のチームがあります: - 開発 - ドキュメント - トリアージ - ウェブサイト - 調査 - 翻訳 - スプリントのメンター - 最適化 - 資金と助成金 -個々のチームメンバーについては、 [チーム](/teams/) のページを参照してください。 +個々のチームメンバーについては、 [チーム](teams/) のページを参照してください。 ## NumFOCUSサブ委員会 - Charles Harris - Ralf Gommers - Inessa Pawson - Sebastian Berg - 外部メンバー: Thomas Caswell ## スポンサー情報 NumPyは以下の団体から直接資金援助を受けています。 {{< sponsors >}} ## パートナー団体 パートナー団体は、NumPyへの開発を仕事の一つとして、社員を雇っている団体です。 現在のパートナー団体としては、下記の通りです。 - カルフォルニア大学 バークレー校 (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) - NVIDIA (Sebastian Berg) {{< partners >}} ## 寄付 NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 NumPy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、NumPyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 NumPy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 米国の寄付提供者の場合、その人の寄付は法律によって定められる範囲で免税されます。 但し、他の寄付と同様に、あなたはあなたの税務状況について、あなたの税務担当と相談する必要があることを忘れないで下さい。 NumPyの運営委員会は、受け取った資金をどのように使えば良いかを検討し、使用する方法について決定します. NumPyに関する技術とインフラの投資の優先順位に関しては、[NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap) に記載されています。 {{<opencollective>}} diff --git a/content/ja/teams.md b/content/ja/teams/index.md similarity index 51% rename from content/ja/teams.md rename to content/ja/teams/index.md index c91e538..bb60e53 100644 --- a/content/ja/teams.md +++ b/content/ja/teams/index.md @@ -1,22 +1,34 @@ --- title: NumPy開発チーム sidebar: false --- 私たちは、高品質のオープンソースソフトウェアを構築することで、世界中の科学・研究コミュニティをサポートすることを使命とする国際的なチームです。 是非[参加してください]({{< relref "/contribute" >}})! -{{< include-html "static/gallery/maintainers.html" >}} +### Maintainers -{{< include-html "static/gallery/docs-team.html" >}} +{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}} -{{< include-html "static/gallery/web-team.html" >}} +### Docs team -{{< include-html "static/gallery/triage-team.html" >}} +{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}} -{{< include-html "static/gallery/survey-team.html" >}} +### Web team -{{< include-html "static/gallery/emeritus-maintainers.html" >}} +{{< grid1 file="web-team.toml" columns="2 3 4 5" />}} + +### Triage team + +{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}} + +### Survey team + +{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}} + +### Emeritus maintainers + +{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}} # ガバナンス For the list of people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). diff --git a/content/pt/teams.md b/content/pt/teams.md deleted file mode 100644 index fcbe93b..0000000 --- a/content/pt/teams.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -title: Times NumPy -sidebar: false ---- - -Somos uma equipe internacional com a missão de apoiar comunidades científicas e de pesquisa em todo o mundo construindo software de código aberto de qualidade. [Junte-se a nós]({{< relref "/contribute" >}})! - -{{< include-html "static/gallery/maintainers.html" >}} - -{{< include-html "static/gallery/docs-team.html" >}} - -{{< include-html "static/gallery/web-team.html" >}} - -{{< include-html "static/gallery/triage-team.html" >}} - -{{< include-html "static/gallery/survey-team.html" >}} - -{{< include-html "static/gallery/emeritus-maintainers.html" >}} - -# Governança - -Para a lista de pessoas no Conselho Diretor, veja [aqui](https://numpy.org/devdocs/dev/governance/people.html). diff --git a/content/pt/teams/index.md b/content/pt/teams/index.md new file mode 100644 index 0000000..cc50b7b --- /dev/null +++ b/content/pt/teams/index.md @@ -0,0 +1,34 @@ +--- +title: Times NumPy +sidebar: false +--- + +Somos uma equipe internacional com a missão de apoiar comunidades científicas e de pesquisa em todo o mundo construindo software de código aberto de qualidade. [Junte-se a nós]({{< relref "/contribute" >}})! + +### Maintainers + +{{< grid1 file="maintainers.toml" columns="2 3 4 5" />}} + +### Docs team + +{{< grid1 file="docs-team.toml" columns="2 3 4 5" />}} + +### Web team + +{{< grid1 file="web-team.toml" columns="2 3 4 5" />}} + +### Triage team + +{{< grid1 file="triage-team.toml" columns="2 3 4 5" />}} + +### Survey team + +{{< grid1 file="survey-team.toml" columns="2 3 4 5" />}} + +### Emeritus maintainers + +{{< grid1 file="emeritus-maintainers.toml" columns="2 3 4 5" />}} + +# Governança + +Para a lista de pessoas no Conselho Diretor, veja [aqui](https://numpy.org/devdocs/dev/governance/people.html). diff --git a/static/gallery/docs-team.html b/static/gallery/docs-team.html deleted file mode 100644 index 313a002..0000000 --- a/static/gallery/docs-team.html +++ /dev/null @@ -1,83 +0,0 @@ -<div class="team"> - <h3 id="docs-team" class="team-name">Docs team</h3> - <div class="team-members"> - <div class="team-member"> - <a href="https://github.com/HaoZeke" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4" - loading="lazy" - alt="Avatar of Rohit Goswami" - /> - </div> - Rohit Goswami - </a> - </div> <div class="team-member"> - <a href="https://github.com/InessaPawson" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4" - loading="lazy" - alt="Avatar of Inessa Pawson" - /> - </div> - Inessa Pawson - </a> - </div> <div class="team-member"> - <a href="https://github.com/MarsBarLee" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4" - loading="lazy" - alt="Avatar of Mars Lee" - /> - </div> - Mars Lee - </a> - </div> <div class="team-member"> - <a href="https://github.com/mattip" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4" - loading="lazy" - alt="Avatar of Matti Picus" - /> - </div> - Matti Picus - </a> - </div> <div class="team-member"> - <a href="https://github.com/melissawm" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4" - loading="lazy" - alt="Avatar of Melissa Weber Mendonça" - /> - </div> - Melissa Weber Mendonça - </a> - </div> <div class="team-member"> - <a href="https://github.com/Mukulikaa" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4" - loading="lazy" - alt="Avatar of Mukulika" - /> - </div> - Mukulika - </a> - </div> <div class="team-member"> - <a href="https://github.com/rossbar" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4" - loading="lazy" - alt="Avatar of Ross Barnowski" - /> - </div> - Ross Barnowski - </a> - </div> - </div> -</div> diff --git a/static/gallery/emeritus-maintainers.html b/static/gallery/emeritus-maintainers.html deleted file mode 100644 index 12619aa..0000000 --- a/static/gallery/emeritus-maintainers.html +++ /dev/null @@ -1,105 +0,0 @@ -<div class="team"> - <h3 id="emeritus-maintainers" class="team-name">Emeritus maintainers</h3> - <div class="team-members"> - <div class="team-member"> - <a href="https://github.com/ahaldane" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/9040124?v=4" - loading="lazy" - alt="Avatar of Allan Haldane" - /> - </div> - Allan Haldane - </a> - </div> <div class="team-member"> - <a href="https://github.com/certik" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/20568?v=4" - loading="lazy" - alt="Avatar of Ondřej Čertík" - /> - </div> - Ondřej Čertík - </a> - </div> <div class="team-member"> - <a href="https://github.com/cournape" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/25111?v=4" - loading="lazy" - alt="Avatar of David Cournapeau" - /> - </div> - David Cournapeau - </a> - </div> <div class="team-member"> - <a href="https://github.com/jaimefrio" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/3343990?v=4" - loading="lazy" - alt="Avatar of Jaime" - /> - </div> - Jaime - </a> - </div> <div class="team-member"> - <a href="https://github.com/jarrodmillman" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/123428?v=4" - loading="lazy" - alt="Avatar of Jarrod Millman" - /> - </div> - Jarrod Millman - </a> - </div> <div class="team-member"> - <a href="https://github.com/juliantaylor" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/542663?v=4" - loading="lazy" - alt="Avatar of Julian Taylor" - /> - </div> - Julian Taylor - </a> - </div> <div class="team-member"> - <a href="https://github.com/mwiebe" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&v=4" - loading="lazy" - alt="Avatar of Mark Wiebe" - /> - </div> - Mark Wiebe - </a> - </div> <div class="team-member"> - <a href="https://github.com/njsmith" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&v=4" - loading="lazy" - alt="Avatar of Nathaniel J. Smith" - /> - </div> - Nathaniel J. Smith - </a> - </div> <div class="team-member"> - <a href="https://github.com/teoliphant" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/254880?v=4" - loading="lazy" - alt="Avatar of Travis E. Oliphant" - /> - </div> - Travis E. Oliphant - </a> - </div> - </div> -</div> diff --git a/static/gallery/maintainers.html b/static/gallery/maintainers.html deleted file mode 100644 index bfe0f19..0000000 --- a/static/gallery/maintainers.html +++ /dev/null @@ -1,292 +0,0 @@ -<div class="team"> - <h3 id="maintainers" class="team-name">Maintainers</h3> - <div class="team-members"> - <div class="team-member"> - <a href="https://github.com/andyfaff" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4" - loading="lazy" - alt="Avatar of Andrew Nelson" - /> - </div> - Andrew Nelson - </a> - </div> <div class="team-member"> - <a href="https://github.com/BvB93" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/43369155?u=1f1fcabf979a2f00f403c60b816ba9f573026181&v=4" - loading="lazy" - alt="Avatar of Bas van Beek" - /> - </div> - Bas van Beek - </a> - </div> <div class="team-member"> - 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<img - src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4" - loading="lazy" - alt="Avatar of Rohit Goswami" - /> - </div> - Rohit Goswami - </a> - </div> <div class="team-member"> - <a href="https://github.com/matthew-brett" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/67612?v=4" - loading="lazy" - alt="Avatar of Matthew Brett" - /> - </div> - Matthew Brett - </a> - </div> <div class="team-member"> - <a href="https://github.com/mattip" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4" - loading="lazy" - alt="Avatar of Matti Picus" - /> - </div> - Matti Picus - </a> - </div> <div class="team-member"> - <a href="https://github.com/mdhaber" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/6570539?u=cfb3e218754e85c4fac18064d7cfdce0b67ddaa6&v=4" - loading="lazy" - alt="Avatar of Matt Haberland" - /> - </div> - Matt Haberland - </a> - </div> <div class="team-member"> - <a href="https://github.com/melissawm" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4" - loading="lazy" - alt="Avatar of Melissa Weber Mendonça" - /> - </div> - Melissa Weber Mendonça - </a> - </div> <div class="team-member"> - <a href="https://github.com/mhvk" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/2789820?v=4" - loading="lazy" - alt="Avatar of Marten van Kerkwijk" - /> - </div> - Marten van Kerkwijk - </a> - </div> <div class="team-member"> - <a href="https://github.com/Mukulikaa" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4" - loading="lazy" - alt="Avatar of Mukulika" - /> - </div> - Mukulika - </a> - </div> <div class="team-member"> - <a href="https://github.com/ngoldbaum" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/3126246?u=a3c7cd970c0e4cbc4498febe0de777a263c522c5&v=4" - loading="lazy" - alt="Avatar of Nathan Goldbaum" - /> - </div> - Nathan Goldbaum - </a> - </div> <div class="team-member"> - <a href="https://github.com/pearu" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/402156?u=288a1f206a151f9e2b69f3c0ce11848d3381943e&v=4" - loading="lazy" - alt="Avatar of Pearu Peterson" - /> - </div> - Pearu Peterson - </a> - </div> <div class="team-member"> - <a href="https://github.com/person142" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/15134881?v=4" - loading="lazy" - alt="Avatar of Josh Wilson" - /> - </div> - Josh Wilson - </a> - </div> <div class="team-member"> - 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<img - src="https://avatars.githubusercontent.com/u/46135?u=305a96a4778daecacbc8ec97ac25a48099a239cc&v=4" - loading="lazy" - alt="Avatar of Robert Kern" - /> - </div> - Robert Kern - </a> - </div> <div class="team-member"> - <a href="https://github.com/rossbar" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4" - loading="lazy" - alt="Avatar of Ross Barnowski" - /> - </div> - Ross Barnowski - </a> - </div> <div class="team-member"> - <a href="https://github.com/seberg" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/61977?v=4" - loading="lazy" - alt="Avatar of Sebastian Berg" - /> - </div> - Sebastian Berg - </a> - </div> <div class="team-member"> - <a href="https://github.com/seiko2plus" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/12713707?u=5a3f6a8de4801d7878750cbd0bb2e0427bf0af0b&v=4" - loading="lazy" - alt="Avatar of Sayed Adel" - /> - </div> - Sayed Adel - </a> - </div> <div class="team-member"> - <a href="https://github.com/shoyer" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/1217238?u=b61e7e0085405ce6d7d53f8f39a1360ef9723f72&v=4" - loading="lazy" - alt="Avatar of Stephan Hoyer" - /> - </div> - Stephan Hoyer - </a> - </div> <div class="team-member"> - <a href="https://github.com/stefanv" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&v=4" - loading="lazy" - alt="Avatar of Stefan van der Walt" - /> - </div> - Stefan van der Walt - </a> - </div> <div class="team-member"> - <a href="https://github.com/tylerjereddy" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/7903078?u=2762d9ff13b992dc635f8f190a17f9a90cddfae1&v=4" - loading="lazy" - alt="Avatar of Tyler Reddy" - /> - </div> - Tyler Reddy - </a> - </div> <div class="team-member"> - <a href="https://github.com/WarrenWeckesser" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/321463?v=4" - loading="lazy" - alt="Avatar of Warren Weckesser" - /> - </div> - Warren Weckesser - </a> - </div> - </div> -</div> diff --git a/static/gallery/survey-team.html b/static/gallery/survey-team.html deleted file mode 100644 index 95b6591..0000000 --- a/static/gallery/survey-team.html +++ /dev/null @@ -1,39 +0,0 @@ -<div class="team"> - <h3 id="survey-team" class="team-name">Survey team</h3> - <div class="team-members"> - <div class="team-member"> - <a href="https://github.com/InessaPawson" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4" - loading="lazy" - alt="Avatar of Inessa Pawson" - /> - </div> - Inessa Pawson - </a> - </div> <div class="team-member"> - <a href="https://github.com/rgommers" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4" - loading="lazy" - alt="Avatar of Ralf Gommers" - /> - </div> - Ralf Gommers - </a> - </div> <div class="team-member"> - <a href="https://github.com/rossbar" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4" - loading="lazy" - alt="Avatar of Ross Barnowski" - /> - </div> - Ross Barnowski - </a> - </div> - </div> -</div> diff --git a/static/gallery/triage-team.html b/static/gallery/triage-team.html deleted file mode 100644 index 84450ba..0000000 --- a/static/gallery/triage-team.html +++ /dev/null @@ -1,314 +0,0 @@ -<div class="team"> - <h3 id="triage-team" class="team-name">Triage team</h3> - <div class="team-members"> - 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<div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/3813847?v=4" - loading="lazy" - alt="Avatar of Atsushi Sakai" - /> - </div> - Atsushi Sakai - </a> - </div> <div class="team-member"> - <a href="https://github.com/bjnath" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/6691888?v=4" - loading="lazy" - alt="Avatar of Ben Nathanson" - /> - </div> - Ben Nathanson - </a> - </div> <div class="team-member"> - <a href="https://github.com/bonn0062" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/35413198?u=e67bd9ebc361fb207f914979d935fd1956eb626c&v=4" - loading="lazy" - alt="Avatar of Anne Bonner" - /> - </div> - Anne Bonner - </a> - </div> <div class="team-member"> - <a href="https://github.com/bsipocz" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/6788290?u=d9a388224b87d55106cb3e6199d02ebc1d8e0553&v=4" - loading="lazy" - alt="Avatar of Brigitta Sipőcz" - /> - </div> - Brigitta Sipőcz - </a> - </div> <div class="team-member"> - <a href="https://github.com/carlkl" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/5476002?u=5352f057ef8cb5de29e4d2a9fa8b0d0f49580dc8&v=4" - loading="lazy" - alt="Avatar of carlkl" - /> - </div> - carlkl - </a> - </div> <div class="team-member"> - <a href="https://github.com/cooperrc" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/11371428?u=9b425a337d076ec86b75ebc759724283f0970d9a&v=4" - loading="lazy" - alt="Avatar of Ryan C Cooper" - /> - </div> - Ryan C Cooper - </a> - </div> <div class="team-member"> - <a href="https://github.com/czgdp1807" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/36567889?u=cbc76d558d375ebafd4a05a505f500eb94e00611&v=4" - loading="lazy" - alt="Avatar of ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)" - /> - </div> - ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) - </a> - </div> <div class="team-member"> - <a href="https://github.com/hameerabbasi" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/2190658?u=b85e13f985d0bf87eeb3a7a146b61dcc9586019b&v=4" - loading="lazy" - alt="Avatar of Hameer Abbasi" - /> - </div> - Hameer Abbasi - </a> - </div> <div class="team-member"> - <a href="https://github.com/InessaPawson" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4" - loading="lazy" - alt="Avatar of Inessa Pawson" - /> - </div> - Inessa Pawson - </a> - </div> <div class="team-member"> - <a href="https://github.com/jbrockmendel" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/8078968?v=4" - loading="lazy" - alt="Avatar of jbrockmendel" - /> - </div> - jbrockmendel - </a> - </div> <div class="team-member"> - <a href="https://github.com/Kai-Striega" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&v=4" - loading="lazy" - alt="Avatar of Kai" - /> - </div> - Kai - </a> - </div> <div class="team-member"> - <a href="https://github.com/kngwyu" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/16046705?u=1bf01e87adb556503c1fe07789c194cc04d38490&v=4" - loading="lazy" - alt="Avatar of Yuji Kanagawa" - /> - </div> - Yuji Kanagawa - </a> - </div> <div class="team-member"> - <a href="https://github.com/kritisingh1" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/22004158?u=2ebb3919ebaa3d7e0865ea5583032bc08bd0f526&v=4" - loading="lazy" - alt="Avatar of Kriti Singh" - /> - </div> - Kriti Singh - </a> - </div> <div class="team-member"> - <a href="https://github.com/krystophny" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/149655?u=249f7995c486de232c34e7970fbea505f518a1be&v=4" - loading="lazy" - alt="Avatar of Christopher Albert" - /> - </div> - Christopher Albert - </a> - </div> <div class="team-member"> - <a href="https://github.com/lysnikolaou" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/20306270?u=235cdf82e88f76ba2f5f4c2d33fa392319c60ad1&v=4" - loading="lazy" - alt="Avatar of Lysandros Nikolaou" - /> - </div> - Lysandros Nikolaou - </a> - </div> <div class="team-member"> - <a href="https://github.com/Micky774" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/34613774?u=61535ebfff07c68ea672cd8cd68c46187a38d3c1&v=4" - loading="lazy" - alt="Avatar of Meekail Zain" - /> - </div> - 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Mukulika - </a> - </div> <div class="team-member"> - <a href="https://github.com/noatamir" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/6564007?u=e5fb962de792bbce925c0c94fb7a748803c8bfa0&v=4" - loading="lazy" - alt="Avatar of Noa Tamir" - /> - </div> - Noa Tamir - </a> - </div> <div class="team-member"> - <a href="https://github.com/r-devulap" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&v=4" - loading="lazy" - alt="Avatar of Raghuveer Devulapalli" - /> - </div> - Raghuveer Devulapalli - </a> - </div> <div class="team-member"> - <a href="https://github.com/shaloo" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&v=4" - loading="lazy" - alt="Avatar of shalz" - /> - </div> - shalz - </a> - </div> <div class="team-member"> - <a href="https://github.com/tinaoberoi" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/55803680?u=bb727a0da1f33ed5f2feb58dc0333943430d2318&v=4" - loading="lazy" - alt="Avatar of Tina Oberoi" - /> - </div> - Tina Oberoi - </a> - </div> <div class="team-member"> - <a href="https://github.com/vrakesh" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/13260794?u=5421923c831b67c4ef290bbdeb31ebfbdd906abc&v=4" - loading="lazy" - alt="Avatar of Rakesh Vasudevan" - /> - </div> - Rakesh Vasudevan - </a> - </div> <div class="team-member"> - <a href="https://github.com/zjpoh" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/8103276?v=4" - loading="lazy" - alt="Avatar of Zijie (ZJ) Poh" - /> - </div> - Zijie (ZJ) Poh - </a> - </div> - </div> -</div> diff --git a/static/gallery/web-team.html b/static/gallery/web-team.html deleted file mode 100644 index 8cae92e..0000000 --- a/static/gallery/web-team.html +++ /dev/null @@ -1,105 +0,0 @@ -<div class="team"> - <h3 id="web-team" class="team-name">Web team</h3> - <div class="team-members"> - <div class="team-member"> - <a href="https://github.com/InessaPawson" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4" - loading="lazy" - alt="Avatar of Inessa Pawson" - /> - </div> - Inessa Pawson - </a> - </div> <div class="team-member"> - <a href="https://github.com/jarrodmillman" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/123428?v=4" - loading="lazy" - alt="Avatar of Jarrod Millman" - /> - </div> - Jarrod Millman - </a> - </div> <div class="team-member"> - <a href="https://github.com/joelachance" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/3891660?u=5de0ba1f1adad6f041f6dde1affef5d05bbed80a&v=4" - loading="lazy" - alt="Avatar of Joe LaChance" - /> - </div> - Joe LaChance - </a> - </div> <div class="team-member"> - <a href="https://github.com/MarsBarLee" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4" - loading="lazy" - alt="Avatar of Mars Lee" - /> - </div> - Mars Lee - </a> - </div> <div class="team-member"> - <a href="https://github.com/rgommers" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4" - loading="lazy" - alt="Avatar of Ralf Gommers" - /> - </div> - Ralf Gommers - </a> - </div> <div class="team-member"> - <a href="https://github.com/shaloo" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&v=4" - loading="lazy" - alt="Avatar of shalz" - /> - </div> - shalz - </a> - </div> <div class="team-member"> - <a href="https://github.com/Shekharrajak" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&v=4" - loading="lazy" - alt="Avatar of Shekhar Prasad Rajak" - /> - </div> - Shekhar Prasad Rajak - </a> - </div> <div class="team-member"> - <a href="https://github.com/stefanv" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&v=4" - loading="lazy" - alt="Avatar of Stefan van der Walt" - /> - </div> - Stefan van der Walt - </a> - </div> <div class="team-member"> - <a href="https://github.com/steppi" class="team-member-name"> - <div class="team-member-photo"> - <img - src="https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&v=4" - loading="lazy" - alt="Avatar of Albert Steppi" - /> - </div> - Albert Steppi - </a> - </div> - </div> -</div>