From 0404332235a3b5fce6c2ea48f6ca821ab3fbca79 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:47 +0200 Subject: [PATCH 001/711] New translations config.yaml (Spanish) --- content/es/config.yaml | 160 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 160 insertions(+) create mode 100644 content/es/config.yaml diff --git a/content/es/config.yaml b/content/es/config.yaml new file mode 100644 index 0000000000..b6f50c9934 --- /dev/null +++ b/content/es/config.yaml @@ -0,0 +1,160 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + 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 1.24.2. 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 docs. + 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 + keyfeatures: + features: + - + title: Powerful N-dimensional arrays + text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. + - + title: Numerical computing tools + text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + - + title: Open source + text: 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). + - + title: Interoperable + text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + - + title: Performant + text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + - + title: Easy to use + text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + 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 + From 13cb9e00df1eed48e73585abb2308c9ccdf0584a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:48 +0200 Subject: [PATCH 002/711] New translations config.yaml (Arabic) --- content/ar/config.yaml | 160 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 160 insertions(+) create mode 100644 content/ar/config.yaml diff --git a/content/ar/config.yaml b/content/ar/config.yaml new file mode 100644 index 0000000000..b6f50c9934 --- /dev/null +++ b/content/ar/config.yaml @@ -0,0 +1,160 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + 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 1.24.2. 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 docs. + 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 + keyfeatures: + features: + - + title: Powerful N-dimensional arrays + text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. + - + title: Numerical computing tools + text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + - + title: Open source + text: 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). + - + title: Interoperable + text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + - + title: Performant + text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + - + title: Easy to use + text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + 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 + From b06a93bbc3110783d79b3fd9f9d1380a55b8603f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:49 +0200 Subject: [PATCH 003/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 189 +++++++++++++++++++++++------------------ 1 file changed, 105 insertions(+), 84 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index f031c22750..e5cf19c5ab 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -1,139 +1,160 @@ -# FOR TRANSLATORS: this is a YAML file, with lines being of the form: -# -# key: value -# -# Please translate the `value`s, not the `key`s! -# Comments (starting with `#`) and `url:` or `link:` lines also do not -# need to be translated. `title:` and `text:` lines do need translation. -# 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 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 - buttontext: 使い始める + #Button text + buttontext: "使い始める" + #Where the main hero button links to buttonlink: "/ja/install" + #Hero image (from static/images/___) image: logo.svg - - news: - title: NumPy v1.20.0 - content: 型アノテーションサポート - 複数のプラットフォームにおけるSIMDを利用したパフォーマンス改善 - url: /ja/news - shell: - title: placeholder # do not translate - + title: placeholder intro: - - title: Try NumPy - text: Use the interactive shell to try NumPy in the browser - + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser docslink: Don't forget to check out the docs. - 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、種族・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しています。 - img: /images/content_images/case_studies/deeplabcut.png - alttext: チータの姿勢推定 - url: /ja/case-studies/deeplabcut-dnn - + - + 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、種族・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しています。 + img: /images/content_images/case_studies/deeplabcut.png + alttext: チータの姿勢推定 + url: /ja/case-studies/deeplabcut-dnn keyfeatures: features: - - title: 強力な多次元配列 - text: NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャスティングのコンセプトは、今日の配列計算のデファクト・スタンダードです。 - - title: 数値計算ツール群 - text: 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。 - - title: 相互運用性 - text: 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。 - - title: 高パフォーマンス - text: NumPyの中核は最適化されたC言語のコードです。Pythonの柔軟性を、コンパイルされたコードの高速さとともに享受できます。 - - title: 使いやすさ - text: 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値のプログラマーでも利用でき、生産性を高めることができます。 - - title: オープンソース - text: 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. - + - + title: 強力な多次元配列 + text: NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャスティングのコンセプトは、今日の配列計算のデファクト・スタンダードです。 + - + title: 数値計算ツール群 + text: 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。 + - + title: 相互運用性 + text: 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。 + - + title: 高パフォーマンス + text: NumPyの中核は最適化されたC言語のコードです。Pythonの柔軟性を、コンパイルされたコードの高速さとともに享受できます。 + - + title: 使いやすさ + text: 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値のプログラマーでも利用でき、生産性を高めることができます。 + - + title: オープンソース + text: 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. tabs: title: 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: NumPyに貢献する + - + title: NumPyに貢献する url: /ja/contribute - + - + 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 + - + 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: 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: User surveys + - + text: User surveys 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 + From 6d8e31a11571119734add74713d7083b90cf8fa6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:49 +0200 Subject: [PATCH 004/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 160 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 160 insertions(+) create mode 100644 content/ko/config.yaml diff --git a/content/ko/config.yaml b/content/ko/config.yaml new file mode 100644 index 0000000000..b6f50c9934 --- /dev/null +++ b/content/ko/config.yaml @@ -0,0 +1,160 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + 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 1.24.2. 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 docs. + 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 + keyfeatures: + features: + - + title: Powerful N-dimensional arrays + text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. + - + title: Numerical computing tools + text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + - + title: Open source + text: 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). + - + title: Interoperable + text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + - + title: Performant + text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + - + title: Easy to use + text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + 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 + From 698ff3526bbaa73acf555a9b3bfee9891967f69e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:50 +0200 Subject: [PATCH 005/711] New translations config.yaml (Russian) --- content/ru/config.yaml | 160 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 160 insertions(+) create mode 100644 content/ru/config.yaml diff --git a/content/ru/config.yaml b/content/ru/config.yaml new file mode 100644 index 0000000000..b6f50c9934 --- /dev/null +++ b/content/ru/config.yaml @@ -0,0 +1,160 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + 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 1.24.2. 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 docs. + 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 + keyfeatures: + features: + - + title: Powerful N-dimensional arrays + text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. + - + title: Numerical computing tools + text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + - + title: Open source + text: 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). + - + title: Interoperable + text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + - + title: Performant + text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + - + title: Easy to use + text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + 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 + From 1b2d1b39d2ca646289ff6758ee4aac3270abdf98 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:51 +0200 Subject: [PATCH 006/711] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 160 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 160 insertions(+) create mode 100644 content/zh/config.yaml diff --git a/content/zh/config.yaml b/content/zh/config.yaml new file mode 100644 index 0000000000..b6f50c9934 --- /dev/null +++ b/content/zh/config.yaml @@ -0,0 +1,160 @@ +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logo.svg + 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 1.24.2. 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 docs. + 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 + keyfeatures: + features: + - + title: Powerful N-dimensional arrays + text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. + - + title: Numerical computing tools + text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + - + title: Open source + text: 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). + - + title: Interoperable + text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + - + title: Performant + text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + - + title: Easy to use + text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + 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 + From ca788b4d7e226f69260fb7a1c396468bf4180ffa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:52 +0200 Subject: [PATCH 007/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 188 +++++++++++++++++++++++------------------ 1 file changed, 104 insertions(+), 84 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index df96976a9e..fdfc29fb3b 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -1,140 +1,160 @@ -# FOR TRANSLATORS: this is a YAML file, with lines being of the form: -# -# key: value -# -# Please translate the `value`s, not the `key`s! -# Comments (starting with `#`) and `url:` or `link:` lines also do not -# need to be translated. `title:` and `text:` lines do need translation. -# languageName: Português params: description: Por que NumPy? Arrays n-dimensionais poderosas. Ferramentas para computação numérica. Interoperabilidade. Alto desempenho. Código aberto. navbarlogo: image: logo.svg link: /pt/ - hero: + #Main hero title title: NumPy + #Hero subtitle (optional) subtitle: A biblioteca fundamental para computação científica com Python - buttontext: Comece aqui + #Button text + buttontext: "Comece aqui" + #Where the main hero button links to buttonlink: "/pt/install" + #Hero image (from static/images/___) image: logo.svg - - news: - title: NumPy v1.20.0 - content: Suporte a anotações de tipos - Melhorias no desempenho através de SIMD multi-plataformas - url: /pt/news - shell: - title: placeholder # do not translate - + title: placeholder intro: - - title: Try NumPy - text: Use the interactive shell to try NumPy in the browser - + - + title: Try NumPy + text: Use the interactive shell to try NumPy in the browser docslink: Don't forget to check out the docs. - 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 - + - + 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 keyfeatures: features: - - title: Arrays n-dimensionais poderosas - text: Rápidos e versáteis, os conceitos de vetorização, indexação e broadcasting do NumPy são, na prática, o padrão em computação com arrays. - - title: Ferramentas de computação numérica - text: O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais. - - title: Interoperabilidade - text: O NumPy suporta um grande número de plataformas de hardware e computação, e pode ser combinada com bibliotecas de computação com arrays esparsas, distribuidas ou em GPUs. - - title: Alto desempenho - text: O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. - - title: Fácil de usar - text: A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação. - - title: Código aberto - text: 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. - + - + title: Arrays n-dimensionais poderosas + text: Rápidos e versáteis, os conceitos de vetorização, indexação e broadcasting do NumPy são, na prática, o padrão em computação com arrays. + - + title: Ferramentas de computação numérica + text: O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais. + - + title: Interoperabilidade + text: O NumPy suporta um grande número de plataformas de hardware e computação, e pode ser combinada com bibliotecas de computação com arrays esparsas, distribuidas ou em GPUs. + - + title: Alto desempenho + text: O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. + - + title: Fácil de usar + text: A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação. + - + title: Código aberto + text: 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. 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: Contribuir + - + title: Contribuir url: /pt/contribute - + - + 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 + - + 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: 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: User surveys + - + text: User surveys 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 From 1c162235acb3925e66fcb6254572cc69be3c6bff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:53 +0200 Subject: [PATCH 008/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 218 ++++++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 content/es/tabcontents.yaml diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml new file mode 100644 index 0000000000..1ba5a7ce1d --- /dev/null +++ b/content/es/tabcontents.yaml @@ -0,0 +1,218 @@ +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg +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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From f3803eadc79f134818c6803ca791c7734dd4f5a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:54 +0200 Subject: [PATCH 009/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 218 ++++++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 content/ar/tabcontents.yaml diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml new file mode 100644 index 0000000000..1ba5a7ce1d --- /dev/null +++ b/content/ar/tabcontents.yaml @@ -0,0 +1,218 @@ +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg +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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From f8edd26a470fdbe4c5aebf69f40a30b830e45e82 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:54 +0200 Subject: [PATCH 010/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 369 +++++++++++++++++++----------------- 1 file changed, 199 insertions(+), 170 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 9f4deb6121..5c96301713 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -1,189 +1,218 @@ 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 — 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) — 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 — 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) — 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. - + - + 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 - + - + 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 - 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: XND - text: Develop libraries for array computing, recreating NumPy's foundational concepts. - img: /images/content_images/arlib/xnd.png - alttext: xnd - url: https://xnd.io - - 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 - + - + 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 + 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: XND + text: Develop libraries for array computing, recreating NumPy's foundational concepts. + img: /images/content_images/arlib/xnd.png + alttext: xnd + url: https://xnd.io + - + 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." - + - + 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." librariesrow1: - - title: Quantum Computing - alttext: A computer chip. - img: /images/content_images/sc_dom_img/quantum_computing.svg - - title: Statistical Computing - alttext: A line graph with the line moving up. - img: /images/content_images/sc_dom_img/statistical_computing.svg - - title: Signal Processing - alttext: A bar chart with positive and negative values. - img: /images/content_images/sc_dom_img/signal_processing.svg - - title: Image Processing - alttext: An photograph of the mountains. - img: /images/content_images/sc_dom_img/image_processing.svg - - title: Graphs and Networks - alttext: A simple graph. - img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes - alttext: A telescope. - img: /images/content_images/sc_dom_img/astronomy_processes.svg - - title: Cognitive Psychology - alttext: A human head with gears. - img: /images/content_images/sc_dom_img/cognitive_psychology.svg - + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg librariesrow2: - - title: Bioinformatics - alttext: A strand of DNA. - img: /images/content_images/sc_dom_img/bioinformatics.svg - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. - img: /images/content_images/sc_dom_img/bayesian_inference.svg - - title: Mathematical Analysis - alttext: Four mathematical symbols. - img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - title: Chemistry - alttext: A test tube. - img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience - alttext: The Earth. - img: /images/content_images/sc_dom_img/geoscience.svg - - title: Geographic Processing - alttext: A map. - img: /images/content_images/sc_dom_img/GIS.svg - - title: Architecture & Engineering - alttext: A microprocessor development board. - img: /images/content_images/sc_dom_img/robotics.svg - + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg 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'. - + - + 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 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'. - + - + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" - + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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 - 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/examples/index.html - 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. - + - + 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/examples/index.html + 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. \ No newline at end of file + - + 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. From 28205d05daef4c951454c96f557deb6127ca79d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:55 +0200 Subject: [PATCH 011/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 218 ++++++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 content/ko/tabcontents.yaml diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml new file mode 100644 index 0000000000..1ba5a7ce1d --- /dev/null +++ b/content/ko/tabcontents.yaml @@ -0,0 +1,218 @@ +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg +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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From 158585c947545ead7b03e535158ac572b47f1da8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:56 +0200 Subject: [PATCH 012/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 218 ++++++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 content/ru/tabcontents.yaml diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml new file mode 100644 index 0000000000..1ba5a7ce1d --- /dev/null +++ b/content/ru/tabcontents.yaml @@ -0,0 +1,218 @@ +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg +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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From 8a753c2f0d56ad77c87e6474fe221afd9b0269a4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:57 +0200 Subject: [PATCH 013/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 218 ++++++++++++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 content/zh/tabcontents.yaml diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml new file mode 100644 index 0000000000..1ba5a7ce1d --- /dev/null +++ b/content/zh/tabcontents.yaml @@ -0,0 +1,218 @@ +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg +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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From 6054282c02379898593e11f0a9bb7d82e79683c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:58 +0200 Subject: [PATCH 014/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 369 +++++++++++++++++++----------------- 1 file changed, 199 insertions(+), 170 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 9f4deb6121..5c96301713 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -1,189 +1,218 @@ 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 — 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) — 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 — 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) — 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. - + - + 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 - + - + 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 - 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: XND - text: Develop libraries for array computing, recreating NumPy's foundational concepts. - img: /images/content_images/arlib/xnd.png - alttext: xnd - url: https://xnd.io - - 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 - + - + 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 + 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: XND + text: Develop libraries for array computing, recreating NumPy's foundational concepts. + img: /images/content_images/arlib/xnd.png + alttext: xnd + url: https://xnd.io + - + 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." - + - + 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." librariesrow1: - - title: Quantum Computing - alttext: A computer chip. - img: /images/content_images/sc_dom_img/quantum_computing.svg - - title: Statistical Computing - alttext: A line graph with the line moving up. - img: /images/content_images/sc_dom_img/statistical_computing.svg - - title: Signal Processing - alttext: A bar chart with positive and negative values. - img: /images/content_images/sc_dom_img/signal_processing.svg - - title: Image Processing - alttext: An photograph of the mountains. - img: /images/content_images/sc_dom_img/image_processing.svg - - title: Graphs and Networks - alttext: A simple graph. - img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes - alttext: A telescope. - img: /images/content_images/sc_dom_img/astronomy_processes.svg - - title: Cognitive Psychology - alttext: A human head with gears. - img: /images/content_images/sc_dom_img/cognitive_psychology.svg - + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg librariesrow2: - - title: Bioinformatics - alttext: A strand of DNA. - img: /images/content_images/sc_dom_img/bioinformatics.svg - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. - img: /images/content_images/sc_dom_img/bayesian_inference.svg - - title: Mathematical Analysis - alttext: Four mathematical symbols. - img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - title: Chemistry - alttext: A test tube. - img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience - alttext: The Earth. - img: /images/content_images/sc_dom_img/geoscience.svg - - title: Geographic Processing - alttext: A map. - img: /images/content_images/sc_dom_img/GIS.svg - - title: Architecture & Engineering - alttext: A microprocessor development board. - img: /images/content_images/sc_dom_img/robotics.svg - + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg 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'. - + - + 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 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'. - + - + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" - + - + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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 - 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/examples/index.html - 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. - + - + 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/examples/index.html + 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. \ No newline at end of file + - + 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. From de3f2d92a08b40784272fc968af816779b22b2b4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:25:59 +0200 Subject: [PATCH 015/711] New translations 404.md (Spanish) --- content/es/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/es/404.md diff --git a/content/es/404.md b/content/es/404.md new file mode 100644 index 0000000000..da192c53c0 --- /dev/null +++ b/content/es/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From 599141c77e00b408859a0450a84c8220416c2e11 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:00 +0200 Subject: [PATCH 016/711] New translations 404.md (Arabic) --- content/ar/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ar/404.md diff --git a/content/ar/404.md b/content/ar/404.md new file mode 100644 index 0000000000..da192c53c0 --- /dev/null +++ b/content/ar/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From 96cf4b79b3847a3274041f9363351da1436de3c4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:01 +0200 Subject: [PATCH 017/711] New translations 404.md (Japanese) --- content/ja/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/404.md b/content/ja/404.md index 855d17f885..bec1ba1cf2 100644 --- a/content/ja/404.md +++ b/content/ja/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -おっとっと！ 間違った所にアクセスしているようです。 +おっとっと！ You've reached a dead end. -何かがここにページがあるべきだと思ったら、GitHub で [issue](https://github.com/numpy/numpy.org/issues) を作成してください。 +何かがここにページがあるべきだと思ったら、GitHub で [issue](https://github.com/numpy/numpy.org/issues) を作成してください。 From 901035b3e093b396178c88e094b880c77b6b2580 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:02 +0200 Subject: [PATCH 018/711] New translations 404.md (Korean) --- content/ko/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ko/404.md diff --git a/content/ko/404.md b/content/ko/404.md new file mode 100644 index 0000000000..da192c53c0 --- /dev/null +++ b/content/ko/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From 62c617ad9f7ce8f025a9a3f49a9a98a8d28823ee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:03 +0200 Subject: [PATCH 019/711] New translations 404.md (Russian) --- content/ru/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ru/404.md diff --git a/content/ru/404.md b/content/ru/404.md new file mode 100644 index 0000000000..da192c53c0 --- /dev/null +++ b/content/ru/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From ac5ad46367a35a3ddf26061d2260d9ae55a4608a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:04 +0200 Subject: [PATCH 020/711] New translations 404.md (Chinese Simplified) --- content/zh/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/zh/404.md diff --git a/content/zh/404.md b/content/zh/404.md new file mode 100644 index 0000000000..da192c53c0 --- /dev/null +++ b/content/zh/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! You've reached a dead end. + +If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. From 69f80f021f77a5dc6152210c023d2ffe0dc07d61 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:05 +0200 Subject: [PATCH 021/711] New translations about.md (Spanish) --- content/es/about.md | 89 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/es/about.md diff --git a/content/es/about.md b/content/es/about.md new file mode 100644 index 0000000000..8c769cfc9d --- /dev/null +++ b/content/es/about.md @@ -0,0 +1,89 @@ +--- +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 numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- funding and grants + +See the [Team]({{< relref "/teams" >}}) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- 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). + +{{}} + From 878b577b1d32b93058b78fe9680e3e7df8814420 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:06 +0200 Subject: [PATCH 022/711] New translations about.md (Arabic) --- content/ar/about.md | 89 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/ar/about.md diff --git a/content/ar/about.md b/content/ar/about.md new file mode 100644 index 0000000000..8c769cfc9d --- /dev/null +++ b/content/ar/about.md @@ -0,0 +1,89 @@ +--- +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 numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- funding and grants + +See the [Team]({{< relref "/teams" >}}) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- 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). + +{{}} + From 788eba5908f5bc1d808395bdf049f16cd2f9892c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:07 +0200 Subject: [PATCH 023/711] New translations about.md (Japanese) --- content/ja/about.md | 36 +++++++++++++++++++++++++++--------- 1 file changed, 27 insertions(+), 9 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 58909a1333..e7106ae1e6 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -3,16 +3,14 @@ title: 私たちについて sidebar: false --- -_このページでは、NumPyのプロジェクトとそれを支えるコミュニティについて説明します。_ - -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あり、[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、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。 It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPyは完全にオープンソースなソフトウェアであり、[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。 It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). 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運営委員会の役割は、NumPyのコミュニティと協力しサポートすることを通じて、技術的にもコミュニティ的にも長期的にNumPyプロジェクトを良い状態に保つことです。 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 (アルファベット順、姓で): +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. 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 (アルファベット順、姓で): - Sebastian Berg - Ralf Gommers @@ -24,7 +22,7 @@ NumPy運営委員会の役割は、NumPyのコミュニティと協力しサポ - Melissa Weber Mendonça - Eric Wieser -終身名誉委員 +Emeritus: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -35,18 +33,31 @@ NumPy運営委員会の役割は、NumPyのコミュニティと協力しサポ - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + ## チーム -NumPy プロジェクトは拡大しているため、いくつかのチームが設置されています。 +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: -- コード +- development - ドキュメント +- triage - ウェブサイト -- トリアージ +- survey +- translations +- sprint mentors - 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。 +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- Sebastian Berg +- External member: 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。 @@ -56,6 +67,11 @@ NumPyは以下の団体から直接資金援助を受けています。 ## 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。 + +- UC Berkeley (Stéfan van der Walt) +- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- NVIDIA (Sebastian Berg) + {{< partners >}} @@ -68,4 +84,6 @@ NumPy は NumFOCUS にスポンサーされたプロジェクトであり、米 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。 -{{< numfocus >}} + +{{}} + From c38b95ed5d3379b6dbe316e93bff92c9290bea51 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:08 +0200 Subject: [PATCH 024/711] New translations about.md (Korean) --- content/ko/about.md | 89 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/ko/about.md diff --git a/content/ko/about.md b/content/ko/about.md new file mode 100644 index 0000000000..8c769cfc9d --- /dev/null +++ b/content/ko/about.md @@ -0,0 +1,89 @@ +--- +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 numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- funding and grants + +See the [Team]({{< relref "/teams" >}}) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- 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). + +{{}} + From cb2bf702f41347e845efa81fc867836b81d62d72 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:09 +0200 Subject: [PATCH 025/711] New translations about.md (Russian) --- content/ru/about.md | 89 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/ru/about.md diff --git a/content/ru/about.md b/content/ru/about.md new file mode 100644 index 0000000000..8c769cfc9d --- /dev/null +++ b/content/ru/about.md @@ -0,0 +1,89 @@ +--- +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 numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- funding and grants + +See the [Team]({{< relref "/teams" >}}) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- 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). + +{{}} + From ba509b50ece5b7fdaa5e66835dcf6861084c49b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:10 +0200 Subject: [PATCH 026/711] New translations about.md (Chinese Simplified) --- content/zh/about.md | 89 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) create mode 100644 content/zh/about.md diff --git a/content/zh/about.md b/content/zh/about.md new file mode 100644 index 0000000000..8c769cfc9d --- /dev/null +++ b/content/zh/about.md @@ -0,0 +1,89 @@ +--- +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 numpy-team@googlegroups.com. + +## Teams + +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: + +- development +- documentation +- triage +- website +- survey +- translations +- sprint mentors +- funding and grants + +See the [Team]({{< relref "/teams" >}}) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- 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). + +{{}} + From 33e926e46d68f8016f46fdc73b1abb3e7ed29c63 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:11 +0200 Subject: [PATCH 027/711] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 38 ++++++++++++++++++++++++++++---------- 1 file changed, 28 insertions(+), 10 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index fd3fb1217a..8a46582fc8 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -3,9 +3,7 @@ title: Quem Somos sidebar: false --- -_Algumas informações sobre o projeto NumPy e a comunidade_ - -NumPy é um projeto de código aberto visando habilitar a computação numérica com Python. Foi criado em 2005, com base no trabalho inicial das bibliotecas Numeric e Numarray. O NumPy sempre será um software 100% de código aberto, livre para que todos usem e disponibilizados sob os termos liberais da [licença BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +NumPy é um projeto de código aberto visando habilitar a computação numérica com Python. Foi criado em 2005, com base no trabalho inicial das bibliotecas Numeric e Numarray. O NumPy sempre será um software 100% de código aberto, livre para que todos usem e disponibilizados sob os termos liberais da [licença BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). It is released under the liberal terms of the [modified BSD license](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 científica em Python mais ampla. 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). @@ -24,7 +22,7 @@ O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo p - Melissa Weber Mendonça - Eric Wieser -Membros Eméritos: +Emeritus: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -35,17 +33,30 @@ Membros Eméritos: - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) -## Times +To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. + +## Teams -O projeto NumPy está crescendo; temos equipes para +The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: -- código +- development - documentação -- website - triagem +- website +- survey +- translations +- sprint mentors - financiamento e bolsas -Veja a página de [Times](/teams/) para membros individuais de cada time. +See the [Team]({{< relref "/teams" >}}) page for more info. + +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- Sebastian Berg +- External member: Thomas Caswell ## Patrocinadores @@ -56,6 +67,11 @@ O NumPy recebe financiamento direto das seguintes fontes: ## 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 >}} @@ -68,4 +84,6 @@ NumPy é um Projeto Patrocinado da NumFOCUS, uma instituição de caridade sem f 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). -{{< numfocus >}} + +{{}} + From 959f9236f49cbb94b8bba0596c8ce52f80f69ec5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:12 +0200 Subject: [PATCH 028/711] New translations arraycomputing.md (Spanish) --- content/es/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/es/arraycomputing.md diff --git a/content/es/arraycomputing.md b/content/es/arraycomputing.md new file mode 100644 index 0000000000..abd29d11c1 --- /dev/null +++ b/content/es/arraycomputing.md @@ -0,0 +1,21 @@ +--- +title: Array Computing +sidebar: false +--- + +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* + +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + +arraycl + +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From 15eee65dfa8d12b246dcf8de0d4fc36ca418795a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:13 +0200 Subject: [PATCH 029/711] New translations arraycomputing.md (Arabic) --- content/ar/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ar/arraycomputing.md diff --git a/content/ar/arraycomputing.md b/content/ar/arraycomputing.md new file mode 100644 index 0000000000..abd29d11c1 --- /dev/null +++ b/content/ar/arraycomputing.md @@ -0,0 +1,21 @@ +--- +title: Array Computing +sidebar: false +--- + +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* + +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + +arraycl + +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From 782c61c86dd71827075ce0325bed9f4e820959f4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:14 +0200 Subject: [PATCH 030/711] New translations arraycomputing.md (Japanese) --- content/ja/arraycomputing.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/arraycomputing.md b/content/ja/arraycomputing.md index fd7a74d380..867f806e4c 100644 --- a/content/ja/arraycomputing.md +++ b/content/ja/arraycomputing.md @@ -3,13 +3,13 @@ title: 配列演算 sidebar: false --- -*配列演算は統計、数学、科学計算の基礎です。可視化、信号処理、画像処理、生命情報学、機械学習、人工知能など、現代のデータサイエンスやデータ分析の様々な分野で配列演算は中核を担っています。* +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* 大規模なデータ処理やデータ変換には、効率的な配列演算が重要です。 データ分析や、機械学習、効率的な数値計算に最適な言語のひとつは **Python** です。 **Num**erical **Py**thon: NumPyは、Pythonにおけるデファクトスタンダードなライブラリであり、大規模な多次元配列や行列、そして、それらの配列を処理する様々な分野の数学ルーチンをサポートしています。 -2006年にNumPyが発表されてから、2008年にPandasが登場し、その後、数年間にいくつかの配列演算関連のライブラリが次々と現れるようになりました。そこから配列演算界隈は盛り上がり始めました。 これらの新しい配列演算ライブラリの多くは、NumPyの機能や能力を模倣しており、機械学習や人工知能向けの新しいアルゴリズムや機能を持っています。 +2006年にNumPyが発表されてから、2008年にPandasが登場し、その後、数年間にいくつかの配列演算関連のライブラリが次々と現れるようになりました。 これらの新しい配列演算ライブラリの多くは、NumPyの機能や能力を模倣しており、機械学習や人工知能向けの新しいアルゴリズムや機能を持っています。 Date: Wed, 3 May 2023 10:26:15 +0200 Subject: [PATCH 031/711] New translations arraycomputing.md (Korean) --- content/ko/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ko/arraycomputing.md diff --git a/content/ko/arraycomputing.md b/content/ko/arraycomputing.md new file mode 100644 index 0000000000..abd29d11c1 --- /dev/null +++ b/content/ko/arraycomputing.md @@ -0,0 +1,21 @@ +--- +title: Array Computing +sidebar: false +--- + +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* + +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + +arraycl + +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From 67e5901a66a6a827ce052b9367c5dea843b0e7d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:15 +0200 Subject: [PATCH 032/711] New translations arraycomputing.md (Russian) --- content/ru/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ru/arraycomputing.md diff --git a/content/ru/arraycomputing.md b/content/ru/arraycomputing.md new file mode 100644 index 0000000000..abd29d11c1 --- /dev/null +++ b/content/ru/arraycomputing.md @@ -0,0 +1,21 @@ +--- +title: Array Computing +sidebar: false +--- + +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* + +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + +arraycl + +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From 70c898f5353c5386ea919ac9172490ecb848ad67 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:16 +0200 Subject: [PATCH 033/711] New translations arraycomputing.md (Chinese Simplified) --- content/zh/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/zh/arraycomputing.md diff --git a/content/zh/arraycomputing.md b/content/zh/arraycomputing.md new file mode 100644 index 0000000000..abd29d11c1 --- /dev/null +++ b/content/zh/arraycomputing.md @@ -0,0 +1,21 @@ +--- +title: Array Computing +sidebar: false +--- + +*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* + +Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** + +**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. + +Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. + +arraycl + +**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. + +Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. From 67b691cfedb5a4848b4e19c7c0de0ca945418e06 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:18 +0200 Subject: [PATCH 034/711] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/es/citing-numpy.md diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md new file mode 100644 index 0000000000..5bb5d791b4 --- /dev/null +++ b/content/es/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + + ``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 91ec8010b2bbd4b11aed55b5ef8b06ba31e9f809 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:19 +0200 Subject: [PATCH 035/711] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/ar/citing-numpy.md diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md new file mode 100644 index 0000000000..5bb5d791b4 --- /dev/null +++ b/content/ar/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + + ``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 3b3c60c05fdaad4c2e755bca2d3eaa6ed5ef9f6a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:19 +0200 Subject: [PATCH 036/711] New translations citing-numpy.md (Japanese) --- content/ja/citing-numpy.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/content/ja/citing-numpy.md b/content/ja/citing-numpy.md index 9696c6e4d1..55c4487c1c 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -1,5 +1,5 @@ --- -title: NumPy を引用する場合 +title: Citing NumPy sidebar: false --- @@ -12,8 +12,7 @@ _BibTeX形式:_ ``` @Article{ harris2020array, title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. - van der Walt and Ralf Gommers and Pauli Virtanen and David + author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. van der Walt and Ralf Gommers and Pauli Virtanen and David Cournapeau and Eric Wieser and Julian Taylor and Sebastian Berg and Nathaniel J. Smith and Robert Kern and Matti Picus and Stephan Hoyer and Marten H. van Kerkwijk and Matthew From 3c1cf744b9998a1f3de35e22af5ebba3ff3ae634 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:20 +0200 Subject: [PATCH 037/711] New translations citing-numpy.md (Korean) --- content/ko/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/ko/citing-numpy.md diff --git a/content/ko/citing-numpy.md b/content/ko/citing-numpy.md new file mode 100644 index 0000000000..5bb5d791b4 --- /dev/null +++ b/content/ko/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + + ``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 29717f96b9931f241b7578d774953243f62ddd51 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:21 +0200 Subject: [PATCH 038/711] New translations citing-numpy.md (Russian) --- content/ru/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/ru/citing-numpy.md diff --git a/content/ru/citing-numpy.md b/content/ru/citing-numpy.md new file mode 100644 index 0000000000..5bb5d791b4 --- /dev/null +++ b/content/ru/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + + ``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 589692496ee0bd7c2aafee2b7232fc25c8183e87 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:22 +0200 Subject: [PATCH 039/711] New translations citing-numpy.md (Chinese Simplified) --- content/zh/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/zh/citing-numpy.md diff --git a/content/zh/citing-numpy.md b/content/zh/citing-numpy.md new file mode 100644 index 0000000000..5bb5d791b4 --- /dev/null +++ b/content/zh/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citing NumPy +sidebar: false +--- + +If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: + +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). + +_In BibTeX format:_ + + ``` +@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + van der Walt and Ralf Gommers and Pauli Virtanen and David + Cournapeau and Eric Wieser and Julian Taylor and Sebastian + Berg and Nathaniel J. Smith and Robert Kern and Matti Picus + and Stephan Hoyer and Marten H. van Kerkwijk and Matthew + Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del + R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and + Warren Weckesser and Hameer Abbasi and Christoph Gohlke and + Travis E. Oliphant}, + year = {2020}, + month = sep, + journal = {Nature}, + volume = {585}, + number = {7825}, + pages = {357--362}, + doi = {10.1038/s41586-020-2649-2}, + publisher = {Springer Science and Business Media {LLC}}, + url = {https://doi.org/10.1038/s41586-020-2649-2} +} +``` From 89ff16b923a0b293db276d0947b5e3fb03dab366 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:23 +0200 Subject: [PATCH 040/711] New translations citing-numpy.md (Portuguese, Brazilian) --- content/pt/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/citing-numpy.md b/content/pt/citing-numpy.md index f6f9567450..f947689548 100644 --- a/content/pt/citing-numpy.md +++ b/content/pt/citing-numpy.md @@ -5,7 +5,7 @@ sidebar: false Se a NumPy é importante na sua pesquisa, e você gostaria de dar reconhecimento ao projeto na sua publicação acadêmica, sugerimos citar os seguintes documentos: -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Link da editora](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Link da editora](https://www.nature.com/articles/s41586-020-2649-2)). _Em formato BibTeX:_ From f5671c74f2444e1f060fbb7fed5b5d8d55229b34 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:24 +0200 Subject: [PATCH 041/711] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/es/code-of-conduct.md diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md new file mode 100644 index 0000000000..28dc308ac0 --- /dev/null +++ b/content/es/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 7c9badc7de8dd5808ff82fba264247237f549ed9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:24 +0200 Subject: [PATCH 042/711] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ar/code-of-conduct.md diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md new file mode 100644 index 0000000000..28dc308ac0 --- /dev/null +++ b/content/ar/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From ff7e3ee8b0cb50ac5e29667814cdef1188aa42eb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:25 +0200 Subject: [PATCH 043/711] New translations code-of-conduct.md (Japanese) --- content/ja/code-of-conduct.md | 40 +++++++++++++++++------------------ 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md index 9ae59b74b2..45999314c5 100644 --- a/content/ja/code-of-conduct.md +++ b/content/ja/code-of-conduct.md @@ -5,31 +5,31 @@ aliases: - /ja/conduct/ --- -### はじめに +### Introduction -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 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、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。 +We strive to: -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。もっとも、その質問に対して、適切なフォーラムを紹介する場合もありますが。 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。: +1. Be open. 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。 We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. 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。 Our work will be used by other people, and in turn we will depend on the work of others. 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。 We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. 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)。 + * 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。 + * Unwelcome sexual attention. + * Excessive profanity. 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。 @@ -37,7 +37,7 @@ aliases: 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。 完璧なリストは望むべくもありませんが、私たちは行動規範に反しない限り、下記の多様性を尊重すると明言します: 年齢、文化。 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。 +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。 @@ -61,13 +61,13 @@ 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 ### 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。 +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。 +私たちはすべての訴えを調査し、対応するようにします。 The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). -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。 +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. -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。 +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: 1. 報告書の受領を確認 2. 建設的な議論/フィードバック @@ -76,8 +76,8 @@ 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 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: +### Endnotes -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。 +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/reference/dev/conduct/code_of_conduct.html) +- [SciPy行動規範](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 3770f240bd796c378307b338950fefb5bf4d94f5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:26 +0200 Subject: [PATCH 044/711] New translations code-of-conduct.md (Korean) --- content/ko/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ko/code-of-conduct.md diff --git a/content/ko/code-of-conduct.md b/content/ko/code-of-conduct.md new file mode 100644 index 0000000000..28dc308ac0 --- /dev/null +++ b/content/ko/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From f0ccb8f8d96d1a12a74be721ff4af19dc939927a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:27 +0200 Subject: [PATCH 045/711] New translations code-of-conduct.md (Russian) --- content/ru/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ru/code-of-conduct.md diff --git a/content/ru/code-of-conduct.md b/content/ru/code-of-conduct.md new file mode 100644 index 0000000000..28dc308ac0 --- /dev/null +++ b/content/ru/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From a555be83b74f4740f17ded511aaa1d7700bd0885 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:28 +0200 Subject: [PATCH 046/711] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/zh/code-of-conduct.md diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md new file mode 100644 index 0000000000..28dc308ac0 --- /dev/null +++ b/content/zh/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: NumPy Code of Conduct +sidebar: false +aliases: + - /conduct.html +--- + +### Introduction + +This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. + +This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. + +This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. + +### Specific Guidelines + +We strive to: + +1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. +2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. +3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. +4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. +5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: + * Violent threats or language directed against another person. + * Sexist, racist, or otherwise discriminatory jokes and language. + * Posting sexually explicit or violent material. + * Posting (or threatening to post) other people’s personally identifying information (“doxing”). + * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. + * Personal insults, especially those using racist or sexist terms. + * Unwelcome sexual attention. + * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. + * Repeated harassment of others. In general, if someone asks you to stop, then stop. + * Advocating for, or encouraging, any of the above behaviour. + +### Diversity Statement + +The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. + +No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. + +Though we welcome people fluent in all languages, NumPy development is conducted in English. + +Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). + +### Reporting Guidelines + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. + +For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. + +You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. + +Currently, the Committee consists of: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Rohit Goswami + +If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### Incident reporting resolution & Code of Conduct enforcement + +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). + +We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). + +In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. + +In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: + +1. acknowledge report is received, +2. reasonable discussion/feedback, +3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), +4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. + +The Committee will respond to any report as soon as possible, and at most within 72 hours. + +### Endnotes + +We are thankful to the groups behind the following documents, from which we drew content and inspiration: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 0bd0245414044c82e583fee552c8dcac887a9171 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:29 +0200 Subject: [PATCH 047/711] New translations code-of-conduct.md (Portuguese, Brazilian) --- content/pt/code-of-conduct.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/code-of-conduct.md b/content/pt/code-of-conduct.md index 13ce6cb018..e8fbe57696 100644 --- a/content/pt/code-of-conduct.md +++ b/content/pt/code-of-conduct.md @@ -43,7 +43,7 @@ Embora sejamos receptivos às pessoas fluentes em todas as línguas, o desenvolv 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 +### Reporting Guidelines 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. @@ -80,4 +80,4 @@ O comitê responderá a qualquer relatório o mais rapidamente possível e, no m 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/reference/dev/conduct/code_of_conduct.html) +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 062112427cb3ba72b7b3f0bcf19284cf0aa2adcc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:30 +0200 Subject: [PATCH 048/711] New translations community.md (Spanish) --- content/es/community.md | 66 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/es/community.md diff --git a/content/es/community.md b/content/es/community.md new file mode 100644 index 0000000000..5034fba239 --- /dev/null +++ b/content/es/community.md @@ -0,0 +1,66 @@ +--- +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/numpy-discussion@python.org/). + +*** + +### [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 [Twitter](https://twitter.com/numpy_team). + + +## 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/). From b08ee20e05f9615b6d3d4f2d5d8f1aa995980bcc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:31 +0200 Subject: [PATCH 049/711] New translations community.md (Arabic) --- content/ar/community.md | 66 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/ar/community.md diff --git a/content/ar/community.md b/content/ar/community.md new file mode 100644 index 0000000000..5034fba239 --- /dev/null +++ b/content/ar/community.md @@ -0,0 +1,66 @@ +--- +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/numpy-discussion@python.org/). + +*** + +### [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 [Twitter](https://twitter.com/numpy_team). + + +## 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/). From 074fd31b27635867b1b3d84b96e8aaa598729293 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:32 +0200 Subject: [PATCH 050/711] New translations community.md (Japanese) --- content/ja/community.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/community.md b/content/ja/community.md index 35a58f4a7f..5d09c81760 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -17,7 +17,7 @@ NumPy プロジェクトやコミュニティと直接交流する方法は次 このメーリングリストは、NumPy に新しい機能を追加するなど、より長い期間の議論のための主なコミュニケーションの場です。 NumPyのRoadmapに変更を加えたり、プロジェクト全体での意思決定を行います。 このメーリングリストでは、リリース、開発者会議、スプリント、カンファレンストークなど、NumPy についてのアナウンスなどにも利用されます。 -このメーリングリストでは、一番下のメールを使用し、メーリングリストに返信して下さい( 他の送信者ではなく)。 また、自動送信のメールには返信しないでください。 このメーリングリストの検索可能なアーカイブは [こちら](https://mail.python.org/archives/list/numpy-discussion@python.org/) にあります。 +このメーリングリストでは、一番下のメールを使用し、メーリングリストに返信して下さい( 他の送信者ではなく)。 このメーリングリストの検索可能なアーカイブは [こちら](https://mail.python.org/archives/list/numpy-discussion@python.org/) にあります。 *** @@ -33,7 +33,7 @@ _ちなみに、セキュリティの脆弱性を報告するには、GitHubの ### [Slack](https://numpy-team.slack.com) -SlackはNumPyに_ 貢献するための質問をする_、リアルタイムのチャットルームです。 Slackはプライベートな空間です。具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合は[こちら](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)を確認下さい。 +A real-time chat room to ask questions about _contributing_ to NumPy. 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合は[こちら](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)を確認下さい。 ## 勉強会とミートアップ @@ -52,7 +52,7 @@ NumPy プロジェクトは独自のカンファレンスは開催していま - [SciPy Latin America](https://www.scipyla.org) - [SciPy India](https://scipy.in) - [SciPy Japan](https://conference.scipy.org) -- [PyData conference](https://pydata.org/event-schedule/) (年に15~20のイベントが様々な国で開催されています。) +- [PyData conference](https://pydata.org/event-schedule/) (年に15~20のイベントが様々な国で開催されています。 ) これらのカンファレンスの多くは、NumPyの使い方や関連するオープンソースプロジェクトに貢献する方法を学ぶことができるチュートリアルを開催しています。 @@ -63,4 +63,4 @@ 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 もし、NumPyに貢献したい場合は、 [コントリビュート](/ja/contribute) ページをご覧いただくことをお勧めします。 - +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/). From d881c4225a0b6fa2b44ad3d468819436ec5f209d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:33 +0200 Subject: [PATCH 051/711] New translations community.md (Korean) --- content/ko/community.md | 66 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/ko/community.md diff --git a/content/ko/community.md b/content/ko/community.md new file mode 100644 index 0000000000..5034fba239 --- /dev/null +++ b/content/ko/community.md @@ -0,0 +1,66 @@ +--- +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/numpy-discussion@python.org/). + +*** + +### [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 [Twitter](https://twitter.com/numpy_team). + + +## 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/). From 8293d9e4e4cdb033d0ef5cdcdc9c2a58786c13cc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:34 +0200 Subject: [PATCH 052/711] New translations community.md (Russian) --- content/ru/community.md | 66 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/ru/community.md diff --git a/content/ru/community.md b/content/ru/community.md new file mode 100644 index 0000000000..5034fba239 --- /dev/null +++ b/content/ru/community.md @@ -0,0 +1,66 @@ +--- +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/numpy-discussion@python.org/). + +*** + +### [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 [Twitter](https://twitter.com/numpy_team). + + +## 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/). From 2bfa4e49c33f63eace6b9640e982c4e480527611 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:35 +0200 Subject: [PATCH 053/711] New translations community.md (Chinese Simplified) --- content/zh/community.md | 66 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/zh/community.md diff --git a/content/zh/community.md b/content/zh/community.md new file mode 100644 index 0000000000..5034fba239 --- /dev/null +++ b/content/zh/community.md @@ -0,0 +1,66 @@ +--- +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/numpy-discussion@python.org/). + +*** + +### [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 [Twitter](https://twitter.com/numpy_team). + + +## 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/). From c2b714ec8f7cd8f49062c03889a4bae66baef1eb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:36 +0200 Subject: [PATCH 054/711] New translations community.md (Portuguese, Brazilian) --- content/pt/community.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/community.md b/content/pt/community.md index c6b3115d76..bd7d7cf7e3 100644 --- a/content/pt/community.md +++ b/content/pt/community.md @@ -63,3 +63,4 @@ Para prosperar, o projeto NumPy precisa de sua experiência e entusiasmo. Não Se você está interessado em se tornar um contribuidor do NumPy (oba!) recomendamos que você confira nossa página sobre [Contribuições](/pt/contribute). +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/). From 6f723d954b78b98a94456a2bba4d71fcfcb507c6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:37 +0200 Subject: [PATCH 055/711] New translations contribute.md (Spanish) --- content/es/contribute.md | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/es/contribute.md diff --git a/content/es/contribute.md b/content/es/contribute.md new file mode 100644 index 0000000000..6efff53624 --- /dev/null +++ b/content/es/contribute.md @@ -0,0 +1,66 @@ +--- +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 or on [Slack](https://numpy-team.slack.com) (write 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.
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. From 2a50a251ebc7ae1f54ed8c7f5f0362780a99e8fb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:38 +0200 Subject: [PATCH 056/711] New translations contribute.md (Arabic) --- content/ar/contribute.md | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/ar/contribute.md diff --git a/content/ar/contribute.md b/content/ar/contribute.md new file mode 100644 index 0000000000..6efff53624 --- /dev/null +++ b/content/ar/contribute.md @@ -0,0 +1,66 @@ +--- +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 or on [Slack](https://numpy-team.slack.com) (write 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.
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. From 1a2b3dc8c7db4f890b7c3df1a28b8051920b627a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:39 +0200 Subject: [PATCH 057/711] New translations contribute.md (Japanese) --- content/ja/contribute.md | 43 ++++++++++++++-------------------------- 1 file changed, 15 insertions(+), 28 deletions(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index 1963693a98..b628681551 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -3,32 +3,20 @@ 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。 NumPyに貢献する方法はコーディングだけではありません。 +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。 Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. -- [コードを書く]({{< relref "contribute.md#writing-code" >}}) +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。 -以外にも、下記の貢献の方法があります: +連絡先としては、 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: )があります。 -- [プルリクエストをレビューする]({{< relref "contribute.md#reviewing-pull-requests" >}}) -- [チュートリアル・プレゼンテーションなど教育的資料を作成する]({{< relref "contribute.md#developing-educational-materials" >}}) -- [イシューをトリアージする]({{< relref "contribute.md#issue-triaging" >}}) -- [ウェブサイトをメンテナンスをする]({{< relref "contribute.md#website-development" >}}) -- [グラフィックデザインに貢献する]({{< relref "contribute.md#graphic-design" >}}) -- [ウェブサイトを翻訳する]({{< relref "contribute.md#translating-website-content" >}}) -- [コミュニティのコーディネーターをつとめる]({{< relref "contribute.md#community-coordination-and-outreach" >}}) -- [助成金のプロポーザルを書くなど、資金調達をサポートする]({{< relref "contribute.md#fundraising" >}}) - -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。 - -これらが私たちにとって好ましい連絡手段ですが(元来、オープンソースプロジェクトはオープンな方法を好みます)、もしどうしても非公開の方法で連絡を取りたい場合は、コミュニティコーディネーターに連絡して下さい。連絡先としては、 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: )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。 +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。 +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。
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + ### 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、 @@ -42,26 +30,26 @@ 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 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 +### Issue triaging -[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: +[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) +* find duplicate issues and link related ones +* add good self-contained reproducers to issues +* label issues correctly (this requires triage rights -- just ask) -ぜひ、やってみて下さい。 +Please just dive in. ### 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。 +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。私たちのドキュメントには可視化が必要で、私たちの拡大しているウェブサイトには良い画像が必要です。貢献する機会は沢山あります。 +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。 Our docs are parched for illustration; our growing website craves images -- opportunities abound. ### ウェブサイトの翻訳 @@ -75,5 +63,4 @@ NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模 ### 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。 - +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。 We have a number of ideas and of course we welcome more. 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。 From 6a5a3223f635810f6d9c036e5ba5c3b93e838e5c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:40 +0200 Subject: [PATCH 058/711] New translations contribute.md (Korean) --- content/ko/contribute.md | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/ko/contribute.md diff --git a/content/ko/contribute.md b/content/ko/contribute.md new file mode 100644 index 0000000000..6efff53624 --- /dev/null +++ b/content/ko/contribute.md @@ -0,0 +1,66 @@ +--- +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 or on [Slack](https://numpy-team.slack.com) (write 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.
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. From afa1e78747f48cf99ba610d79d2026872b6890d5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:41 +0200 Subject: [PATCH 059/711] New translations contribute.md (Russian) --- content/ru/contribute.md | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/ru/contribute.md diff --git a/content/ru/contribute.md b/content/ru/contribute.md new file mode 100644 index 0000000000..6efff53624 --- /dev/null +++ b/content/ru/contribute.md @@ -0,0 +1,66 @@ +--- +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 or on [Slack](https://numpy-team.slack.com) (write 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.
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. From 5a54fe1d4fc6bc9f8a625037941b4bc4a2533948 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:41 +0200 Subject: [PATCH 060/711] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 66 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 66 insertions(+) create mode 100644 content/zh/contribute.md diff --git a/content/zh/contribute.md b/content/zh/contribute.md new file mode 100644 index 0000000000..6efff53624 --- /dev/null +++ b/content/zh/contribute.md @@ -0,0 +1,66 @@ +--- +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 or on [Slack](https://numpy-team.slack.com) (write 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.
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. From 6963a79e1a393981fdb5d8c5e01c88c44de83a63 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:42 +0200 Subject: [PATCH 061/711] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 21 ++++----------------- 1 file changed, 4 insertions(+), 17 deletions(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index 5bf71db883..863533490e 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -5,30 +5,18 @@ sidebar: false O projeto NumPy precisa de sua experiência e entusiasmo! Suas opções de não são limitadas à programação -- além de -- [Escrever código]({{< relref "contribute.md#writing-code" >}}) - -você pode: - -- [Revisar pull requests]({{< relref "contribute.md#reviewing-pull-requests" >}}) -- [Desenvolver tutoriais, apresentações e outros materiais educacionais]({{< relref "contribute.md#developing-educational-materials" >}}) -- [Fazer triagem em issues]({{< relref "contribute.md#issue-triaging" >}}) -- [Trabalhar no nosso site]({{< relref "contribute.md#website-development" >}}) -- [Contribuir com design gráfico]({{< relref "contribute.md#graphic-design" >}}) -- [Traduzir conteúdo do site]({{< relref "contribute.md#translating-website-content" >}}) -- [Trabalhar coordenando a comunidade]({{< relref "contribute.md#community-coordination-and-outreach" >}}) -- [Escrever propostas e ajudar com outras atividades para financiamento]({{< relref "contribute.md#fundraising" >}}) - 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 ou no [Slack](https://numpy-team.slack.com) (envie um e-mail para 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 desta chamada se quiser. Se você nunca contribuiu para projetos de código aberto, recomendamos fortemente que você leita [esse guia](https://opensource.guide/how-to-contribute/). +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). You are very welcome to join. 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. +Para pessoas programadoras, este [guia](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica como contribuir para a base de código.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. + ### 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: @@ -51,7 +39,7 @@ O [*issue tracker* do NumPy](https://github.com/numpy/numpy/issues) tem _um mont * adicionar bons exemplos autocontidos que reproduzam issues * rotular issues corretamente (isso requer direitos de triagem -- basta perguntar) -Sinta-se à vontade! +Please just dive in. ### Desenvolvimento do site @@ -76,4 +64,3 @@ Através do contato com a comunidade podemos compartilhar nosso trabalho para ma ### 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. - From 54909ac2a87b44b3e89b06ca0f2ea4a35d5fe6b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:43 +0200 Subject: [PATCH 062/711] New translations gethelp.md (Spanish) --- content/es/gethelp.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 content/es/gethelp.md diff --git a/content/es/gethelp.md b/content/es/gethelp.md new file mode 100644 index 0000000000..a427b5b1f5 --- /dev/null +++ b/content/es/gethelp.md @@ -0,0 +1,34 @@ +--- +title: Get Help +sidebar: false +--- + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! + +**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). + + + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** + +### [Gitter](https://gitter.im/numpy/numpy) + +A real-time chat room where users and community members help each other. + +*** + +### [IRC](https://webchat.freenode.net/?channels=%23numpy) + +Another real-time chat room where users and community members help each other. + +*** From b22ff56185c2da6400d580c735431618ebeed995 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:44 +0200 Subject: [PATCH 063/711] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 content/ar/gethelp.md diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md new file mode 100644 index 0000000000..a427b5b1f5 --- /dev/null +++ b/content/ar/gethelp.md @@ -0,0 +1,34 @@ +--- +title: Get Help +sidebar: false +--- + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! + +**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). + + + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** + +### [Gitter](https://gitter.im/numpy/numpy) + +A real-time chat room where users and community members help each other. + +*** + +### [IRC](https://webchat.freenode.net/?channels=%23numpy) + +Another real-time chat room where users and community members help each other. + +*** From 447ab75dc7cb9a955cde1e949ded785c6a0dfa84 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:45 +0200 Subject: [PATCH 064/711] New translations gethelp.md (Japanese) --- content/ja/gethelp.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/gethelp.md b/content/ja/gethelp.md index 8ac9f7b9b4..1979cadc30 100644 --- a/content/ja/gethelp.md +++ b/content/ja/gethelp.md @@ -3,7 +3,7 @@ title: サポートを得る方法 sidebar: false --- -**ユーザーからの質問:** ユーザーからの質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)に質問を投稿することです。すでに数千ものユーザーからの回答を見ることができます。 規模は小さいですが、下記のような質問をする場所もあります: [IRC](https://webchat.freenode.net/?channels=%23numpy)、 [Gitter](https://gitter.im/numpy/numpy)、 [Reddit](https://www.reddit.com/r/Numpy/)。 私たちはこれらのサイトを定期的に確認して、直接質問に答えるようにしていますが、質問の数は膨大です。 +**ユーザーからの質問:** ユーザーからの質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)に質問を投稿することです。 規模は小さいですが、下記のような質問をする場所もあります: [IRC](https://webchat.freenode.net/?channels=%23numpy)、 [Gitter](https://gitter.im/numpy/numpy)、 [Reddit](https://www.reddit.com/r/Numpy/)。 We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! **開発関連の問題:** NumPyの開発関連の問題 (例: バグレポート) については、[コミュニティ](/community) のページを参照してください。 @@ -11,13 +11,13 @@ sidebar: false ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) -NumPyの使用方法に関する質問をするためのフォーラムです。例えば、「NumPyでXをするにはどうすればいいですか？」というような質問です。 質問をする時は、[ `#numpy` タグ](https://stackoverflow.com/help/tagging) を使用してください。 +NumPyの使用方法に関する質問をするためのフォーラムです。 例えば、「NumPyでXをするにはどうすればいいですか？ 質問をする時は、[ `#numpy` タグ](https://stackoverflow.com/help/tagging) を使用してください。 *** ### [Reddit](https://www.reddit.com/r/Numpy/) -もう一つの使い方に関する質問の場です。 +Another forum for usage questions. *** From 627567a8d1a7afef2698443412d5415a644c37e6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:46 +0200 Subject: [PATCH 065/711] New translations gethelp.md (Korean) --- content/ko/gethelp.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 content/ko/gethelp.md diff --git a/content/ko/gethelp.md b/content/ko/gethelp.md new file mode 100644 index 0000000000..a427b5b1f5 --- /dev/null +++ b/content/ko/gethelp.md @@ -0,0 +1,34 @@ +--- +title: Get Help +sidebar: false +--- + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! + +**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). + + + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** + +### [Gitter](https://gitter.im/numpy/numpy) + +A real-time chat room where users and community members help each other. + +*** + +### [IRC](https://webchat.freenode.net/?channels=%23numpy) + +Another real-time chat room where users and community members help each other. + +*** From 6e8fc570c3fe09352410707d08a2f057f6d25cce Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:47 +0200 Subject: [PATCH 066/711] New translations gethelp.md (Russian) --- content/ru/gethelp.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 content/ru/gethelp.md diff --git a/content/ru/gethelp.md b/content/ru/gethelp.md new file mode 100644 index 0000000000..a427b5b1f5 --- /dev/null +++ b/content/ru/gethelp.md @@ -0,0 +1,34 @@ +--- +title: Get Help +sidebar: false +--- + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! + +**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). + + + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** + +### [Gitter](https://gitter.im/numpy/numpy) + +A real-time chat room where users and community members help each other. + +*** + +### [IRC](https://webchat.freenode.net/?channels=%23numpy) + +Another real-time chat room where users and community members help each other. + +*** From dfcd939b67fdf73b7895619f5be8bae39b948de6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:48 +0200 Subject: [PATCH 067/711] New translations gethelp.md (Chinese Simplified) --- content/zh/gethelp.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 content/zh/gethelp.md diff --git a/content/zh/gethelp.md b/content/zh/gethelp.md new file mode 100644 index 0000000000..a427b5b1f5 --- /dev/null +++ b/content/zh/gethelp.md @@ -0,0 +1,34 @@ +--- +title: Get Help +sidebar: false +--- + +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! + +**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). + + + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +Another forum for usage questions. + +*** + +### [Gitter](https://gitter.im/numpy/numpy) + +A real-time chat room where users and community members help each other. + +*** + +### [IRC](https://webchat.freenode.net/?channels=%23numpy) + +Another real-time chat room where users and community members help each other. + +*** From d5f7cd6cbc8f5cb301e37a43aaca54bce4a01095 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:49 +0200 Subject: [PATCH 068/711] New translations history.md (Spanish) --- content/es/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/es/history.md diff --git a/content/es/history.md b/content/es/history.md new file mode 100644 index 0000000000..aa669d375b --- /dev/null +++ b/content/es/history.md @@ -0,0 +1,21 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* + +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* + +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From 5b89530835cc6d643cf83a17073bec67aa0a678b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:50 +0200 Subject: [PATCH 069/711] New translations history.md (Arabic) --- content/ar/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ar/history.md diff --git a/content/ar/history.md b/content/ar/history.md new file mode 100644 index 0000000000..aa669d375b --- /dev/null +++ b/content/ar/history.md @@ -0,0 +1,21 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* + +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* + +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From 49112bf3b33b7c47e5316f8d1df586be5a8c6fac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:51 +0200 Subject: [PATCH 070/711] New translations history.md (Japanese) --- content/ja/history.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/content/ja/history.md b/content/ja/history.md index 75ae93fbed..9b011e2db8 100644 --- a/content/ja/history.md +++ b/content/ja/history.md @@ -3,20 +3,23 @@ title: NumPyの歴史 sidebar: false --- -NumPy は配列データ構造と配列に関連する高速な数値ルーチンを提供する Python 基礎的なライブラリです。 開始当初は資金も少なく、主に大学院生により開発されていました。その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。少数の "野良"学生プログラマーのグループが、すでに確立されていた商用研究ソフトウェアのエコシステムをひっくり返すなんて、想像することすら馬鹿げていました。 -商用ソフトは、何百万もの資金と何百人もの優秀なエンジニアに支えられていましたから。それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。現在では、NumPyは科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 +NumPy は配列データ構造と配列に関連する高速な数値ルーチンを提供する Python 基礎的なライブラリです。 When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。 Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 NumPy および関連ライブラリの開発におけるマイルストーンの詳細については、 [arxiv.org](arxiv.org/abs/1907.10121) を参照してください。 NumPyのベースとなったNumericとNumarrayライブラリのコピーを入手したい場合は、以下のリンクを参照してください。 -[ *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/) のダウンロード* +[ *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/) のダウンロード** -[*Numarray *](https://sourceforge.net/projects/numpy/files/Old%20Numarray/) のダウンロード* +[*Numarray *](https://sourceforge.net/projects/numpy/files/Old%20Numarray/) のダウンロード** -*これらの古いパッケージはもはや保守されていないことに注意してください。配列関連の処理をしたい場合は、NumPyを使用するか、NumPyライブラリを利用するために既存のコードをリファクタリングすることを強くお勧めします。 +*これらの古いパッケージはもはや保守されていないことに注意してください。 配列関連の処理をしたい場合は、NumPyを使用するか、NumPyライブラリを利用するために既存のコードをリファクタリングすることを強くお勧めします。

-### 過去の資料 +

+ 過去の資料 +

-[*Numeric*マニュアルのダウンロード](static/numeric-manual.pdf) +

+ Numericマニュアルのダウンロード +

From 972cf69d939a3cd8b8f791a60c8bc77293a27010 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:52 +0200 Subject: [PATCH 071/711] New translations history.md (Korean) --- content/ko/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ko/history.md diff --git a/content/ko/history.md b/content/ko/history.md new file mode 100644 index 0000000000..aa669d375b --- /dev/null +++ b/content/ko/history.md @@ -0,0 +1,21 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* + +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* + +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From a88d331343bb00a79ae4abe5319a41b6a71d989d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:53 +0200 Subject: [PATCH 072/711] New translations history.md (Russian) --- content/ru/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ru/history.md diff --git a/content/ru/history.md b/content/ru/history.md new file mode 100644 index 0000000000..aa669d375b --- /dev/null +++ b/content/ru/history.md @@ -0,0 +1,21 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* + +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* + +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From ffd12b30c3bec59cfec33ee7db159d3184db5c05 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:54 +0200 Subject: [PATCH 073/711] New translations history.md (Chinese Simplified) --- content/zh/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/zh/history.md diff --git a/content/zh/history.md b/content/zh/history.md new file mode 100644 index 0000000000..aa669d375b --- /dev/null +++ b/content/zh/history.md @@ -0,0 +1,21 @@ +--- +title: History of NumPy +sidebar: false +--- + +NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. + +For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). + +If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: + +[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* + +[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* + +*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. + +### Historic Documentation + +[Download *`Numeric'* Manual](static/numeric-manual.pdf) + From f1a7beb1813435816c55611f1abe56c1e5f4df94 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:55 +0200 Subject: [PATCH 074/711] New translations install.md (Spanish) --- content/es/install.md | 125 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 content/es/install.md diff --git a/content/es/install.md b/content/es/install.md new file mode 100644 index 0000000000..6706c24ac1 --- /dev/null +++ b/content/es/install.md @@ -0,0 +1,125 @@ +--- +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/distribution) - 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/user/building.html). 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. + + + + +# 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 + +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/distribution/) (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 + +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. + + +### 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 + +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. + + +## 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. +``` + From c1cfaee802a4080dae970442150489d59a4e67b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:56 +0200 Subject: [PATCH 075/711] New translations install.md (Arabic) --- content/ar/install.md | 125 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 content/ar/install.md diff --git a/content/ar/install.md b/content/ar/install.md new file mode 100644 index 0000000000..6706c24ac1 --- /dev/null +++ b/content/ar/install.md @@ -0,0 +1,125 @@ +--- +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/distribution) - 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/user/building.html). 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. + + + + +# 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 + +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/distribution/) (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 + +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. + + +### 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 + +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. + + +## 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. +``` + From c426449046f57bfc6052e4bb4b0f5f731d39ffff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:57 +0200 Subject: [PATCH 076/711] New translations install.md (Japanese) --- content/ja/install.md | 41 ++++++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 19 deletions(-) diff --git a/content/ja/install.md b/content/ja/install.md index 8a4e049d09..8b6deb5964 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -3,10 +3,10 @@ title: NumPyのインストール sidebar: false --- -NumPy をインストールするために必ず必要なものはPython本体です。もしまだPythonをインストールしていないのであれば、最もシルプルな始め方として、[Anaconda Distribution](https://www.anaconda.com/distribution)を推奨します。AnacondaはPython・NumPyの他に、科学技術計算やデータサイエンスのために一般的に使用される沢山のパッケージが含まれています。 - NumPyは`conda`、`pip` 、macOSやLinuxのパッケージマネージャー、または [ソースコード](https://numpy.org/devdocs/user/building.html)からインストールすることが出来ます。 詳細な手順について、以下の [Python と NumPyの インストールガイド](#python-numpy-install-guide) を参照してください。 +NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/user/building.html). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. + **CONDA** `conda`を使用する場合、 `defaults` または `conda-forge` のチャンネルから NumPy をインストールできます。 @@ -28,7 +28,8 @@ conda install numpy ```bash pip install numpy ``` -またpipを使う場合、仮想環境を使うことをおすすめします。[再現可能なインストール](#reproducible-installs)を参照ください。[こちらの記事](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)では仮想環境を使う詳細について説明されています。 +またpipを使う場合、仮想環境を使うことをおすすめします。 [再現可能なインストール](#reproducible-installs)を参照ください。 [こちらの記事](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)では仮想環境を使う詳細について説明されています。 + @@ -45,7 +46,7 @@ Pythonパッケージのインストールと管理は複雑なので、ほと Windows、macOS、Linuxのすべてのユーザー向けには: - [Anaconda](https://www.anaconda.com/distribution/) をインストールします（必要な パッケージと以下に挙げるすべてのツールがインストールされます）。 -- コードを書いたり、実行してみましょう。探索的・対話的コンピューティングには[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html)のノートブックが便利です。スクリプトやパッケージの作成には[Spyder](https://www.spyder-ide.org/)や[Visual Studio Code](https://code.visualstudio.com/)を利用できます。 +- コードを書いたり、実行してみましょう。 探索的・対話的コンピューティングには[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html)のノートブックが便利です。 スクリプトやパッケージの作成には[Spyder](https://www.spyder-ide.org/)や[Visual Studio Code](https://code.visualstudio.com/)を利用できます。 - [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) を使ってパッケージを管理し、JupyterLab、Spyder、Visual Studio Codeを使い始められます。 @@ -70,36 +71,36 @@ Windows、macOS、Linuxのすべてのユーザー向けには: ### Pipとconda -`pip` と `conda` がPythonパッケージをインストールするための2つの主要なツールです。 これら二つのツールの機能は部分的に重複しますが(例えば、両方とも `numpy`をインストールできます)、一緒に動作することもできます。ここでは、pip とcond の主要な違いについて説明します。これは、パッケージをどのように効果的に管理するかを理解したい場合、重要な知識です。 - -最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。そのため、condaはPython 以外のライブラリや必要なツール (コンパイラ、CUDA、HDF5など) をインストールできますが、pip はできません。 +`pip` と `conda` がPythonパッケージをインストールするための2つの主要なツールです。 これら二つのツールの機能は部分的に重複しますが(例えば、両方とも `numpy`をインストールできます)、一緒に動作することもできます。 ここでは、pip とcond の主要な違いについて説明します。 これは、パッケージをどのように効果的に管理するかを理解したい場合、重要な知識です。 2つ目の違いは、pipはPython Packaging Index(PyPI) からパッケージをインストールするのに対し、condaは独自のチャンネル(一般的には "defaults "や "conda-forge "など) からインストールすることです。 PyPIは最大のパッケージ管理システムですが、人気のある全てのパッケージがcondaでも利用可能です。 -3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！) が必要になるかもしれないということです。 +最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。 pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。 PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. + +3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！ - ### 再現可能なインストール -ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に -壊れたりする可能性があります。なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです: +ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に 壊れたりする可能性があります。 なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです: 1. プロジェクトごとに異なる仮想環境を使用して下さい。 -2. パッケージインストーラを使用してパッケージ名とバージョンを記録するようにして下さい。それぞれ、独自のメタデータフォーマットがあります: +2. パッケージインストーラを使用してパッケージ名とバージョンを記録するようにして下さい。 それぞれ、独自のメタデータフォーマットがあります: - condaの場合: [conda environmentsとenvironment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) - pipの場合: [仮想環境](https://docs.python.org/3/tutorial/venv.html) と [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - poetryの場合: [仮想環境とpyproject.toml](https://python-poetry.org/docs/basic-usage/) + + ## NumPyパッケージと高速線形代数ライブラリ -NumPy は他の Python パッケージに依存していませんが、高速な線形代数ライブラリに依存しています。典型的には、[インテル® MKL](https://software.intel.com/en-us/mkl)や[OpenBLAS](https://www.openblas.net/)がこれにあたります。ユーザーは、これらの線形代数ライブラリのインストールを心配する必要はありません (NumPyのインストール方法に、あらかじめ含まれているためです)。 高度なユーザーは、使用されているBLASがパフォーマンスや、動作、ディスク上のサイズに影響を与えるため、より詳細を知りたがるかもしれません。 +NumPy は他の Python パッケージに依存していませんが、高速な線形代数ライブラリに依存しています。 典型的には、[インテル® MKL](https://software.intel.com/en-us/mkl)や[OpenBLAS](https://www.openblas.net/)がこれにあたります。 ユーザーは、これらの線形代数ライブラリのインストールを心配する必要はありません (NumPyのインストール方法に、あらかじめ含まれているためです)。 高度なユーザーは、使用されているBLASがパフォーマンスや、動作、ディスク上のサイズに影響を与えるため、より詳細を知りたがるかもしれません。 -- pipでインストールされるPyPI上の NumPy wheelは、OpenBLASを使ってビルドされます。つまりwheelにはOpenBLASライブラリが含まれています。そのため、ユーザが（例えば）SciPyも同じようにインストールした場合、ディスク上にOpenBLASのコピーをNumPyのものと2つ持つことになります +- pipでインストールされるPyPI上の NumPy wheelは、OpenBLASを使ってビルドされます。 つまりwheelにはOpenBLASライブラリが含まれています。 そのため、ユーザが（例えば）SciPyも同じようにインストールした場合、ディスク上にOpenBLASのコピーをNumPyのものと2つ持つことになります -- condaのデフォルトチャンネルでは、NumPy はインテル® MKLを使ってビルドされます。MKLはNumPyのインストール時に、独立したパッケージとしてユーザー環境にインストールされます。 +- condaのデフォルトチャンネルでは、NumPy はインテル® MKLを使ってビルドされます。 MKLはNumPyのインストール時に、独立したパッケージとしてユーザー環境にインストールされます。 -- conda-forgeのチャンネルでは、NumPyはダミーの「BLAS」パッケージを使ってビルドされています。 ユーザーがconda-forgeからNumPyをインストールすると、BLASパッケージが実際のライブラリと一緒にインストールされます。デフォルトはOpenBLASですが、MKL(default チャンネルの場合)や [BLIS](https://github.com/flame/blis)、またはBLASを利用することもできます。 +- conda-forgeのチャンネルでは、NumPyはダミーの「BLAS」パッケージを使ってビルドされています。 ユーザーがconda-forgeからNumPyをインストールすると、BLASパッケージが実際のライブラリと一緒にインストールされます。 デフォルトはOpenBLASですが、MKL(default チャンネルの場合)や [BLIS](https://github.com/flame/blis)、またはBLASを利用することもできます。 - OpenBLASは約30MBですが、MKLパッケージはOpenBLASよりもはるかに大きく、ディスク上の約700MBです。 @@ -108,14 +109,16 @@ NumPy は他の Python パッケージに依存していませんが、高速な インストールサイズ、パフォーマンスとロバスト性に加えて、考慮すべき2つの点があります: - インテル® MKL はオープンソースではありません。 通常の使用では問題ではありませんが、 ユーザーが NumPy で構築されたアプリケーションを再配布する必要がある場合、これは 問題が発生する可能性があります。 +- MKLとOpenBLASの両方とも、 np.dotのような関数呼び出しにマルチスレッドを使用し、スレッド数はビルド時オプションと環境変数の両方で決定されます。 多くの場合、すべての CPU コアが使用されます。 これにユーザーにとっては予想外のことかもしれません。 NumPy 自体は、関数呼び出しを自動的に並列化しないからです。 自動並列化により、一般にはパフォーマンスが向上しますが、逆にパフォーマンスが悪化する場合もあります。 例えば、Daskやscikit-learn、multiprocessingなど別のレベルの並列化を使用している場合です。 -- MKLとOpenBLASの両方とも、 np.dotのような関数呼び出しにマルチスレッドを使用し、スレッド数はビルド時オプションと環境変数の両方で決定されます。 多くの場合、すべての CPU コアが使用されます。 これにユーザーにとっては予想外のことかもしれません。NumPy 自体は、関数呼び出しを自動的に並列化しないからです。 自動並列化により、一般にはパフォーマンスが向上しますが、逆にパフォーマンスが悪化する場合もあります。例えば、Daskやscikit-learn、multiprocessingなど別のレベルの並列化を使用している場合です。 ## トラブルシューティング インストールに失敗した場合に、下記のエラーメッセージが表示される場合は、 トラブルシューティング ImportError を参照してください。 -
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
+```
+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.
-
+``` + From 4567907f0e82632eb8359f9853625c4d4cc5c1d2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:58 +0200 Subject: [PATCH 077/711] New translations install.md (Korean) --- content/ko/install.md | 125 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 content/ko/install.md diff --git a/content/ko/install.md b/content/ko/install.md new file mode 100644 index 0000000000..6706c24ac1 --- /dev/null +++ b/content/ko/install.md @@ -0,0 +1,125 @@ +--- +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/distribution) - 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/user/building.html). 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. + + + + +# 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 + +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/distribution/) (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 + +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. + + +### 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 + +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. + + +## 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. +``` + From f19418ffe208cdc064bc4e26e1002c289c2ff0c2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:26:59 +0200 Subject: [PATCH 078/711] New translations install.md (Russian) --- content/ru/install.md | 125 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 content/ru/install.md diff --git a/content/ru/install.md b/content/ru/install.md new file mode 100644 index 0000000000..6706c24ac1 --- /dev/null +++ b/content/ru/install.md @@ -0,0 +1,125 @@ +--- +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/distribution) - 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/user/building.html). 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. + + + + +# 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 + +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/distribution/) (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 + +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. + + +### 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 + +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. + + +## 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. +``` + From 78417a746902c04987506788e2df7ac1044da757 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:00 +0200 Subject: [PATCH 079/711] New translations install.md (Chinese Simplified) --- content/zh/install.md | 125 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 125 insertions(+) create mode 100644 content/zh/install.md diff --git a/content/zh/install.md b/content/zh/install.md new file mode 100644 index 0000000000..6706c24ac1 --- /dev/null +++ b/content/zh/install.md @@ -0,0 +1,125 @@ +--- +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/distribution) - 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/user/building.html). 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. + + + + +# 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 + +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/distribution/) (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 + +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. + + +### 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 + +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. + + +## 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. +``` + From addbb3552526e64fe9536067761bb1bac7b21ce2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:01 +0200 Subject: [PATCH 080/711] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/install.md b/content/pt/install.md index 0701460ee9..de364a9578 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -30,6 +30,7 @@ pip install numpy ``` Também ao usar o pip, é uma boa prática usar um ambiente virtual - veja em [Instalações Reprodutíveis](#reproducible-installs) abaixo por quê, e [esse guia](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) para detalhes sobre o uso de ambientes virtuais. + # Guia de instalação do Python e do NumPy @@ -65,7 +66,7 @@ Para usuários que preferem uma solução baseada em pip/PyPI, por preferência ## Gerenciamento de pacotes Python -Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma [série de ferramentas](https://packaging.python.org/guides/tool-recommendations/) complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para a maioria dos usuários NumPy, porém, o [conda](https://conda.io/en/latest/) e o [pip](https://pip.pypa.io/en/stable/) são as duas ferramentas mais populares. +Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma [série de ferramentas](https://packaging.python.org/guides/tool-recommendations/) complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. Para a maioria dos usuários NumPy, porém, o [conda](https://conda.io/en/latest/) e o [pip](https://pip.pypa.io/en/stable/) são as duas ferramentas mais populares. ### Pip & conda @@ -78,7 +79,6 @@ A segunda diferença é que o pip instala do Índice de Pacotes Python (Python P A terceira diferença é que o conda é uma solução integrada para gerenciar pacotes, dependências e ambientes, enquanto com o pip você pode precisar de outra ferramenta (há muitas!) para lidar com ambientes ou dependências complexas. - ### Instalações reprodutíveis From 3243a3285089348a5bd8f1fcafefa08ddf708f05 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:02 +0200 Subject: [PATCH 081/711] New translations learn.md (Spanish) --- content/es/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/es/learn.md diff --git a/content/es/learn.md b/content/es/learn.md new file mode 100644 index 0000000000..60e91719d6 --- /dev/null +++ b/content/es/learn.md @@ -0,0 +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: + + **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) +* [SciPy Lectures](https://scipy-lectures.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) + + **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. + + **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. + + **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). + + **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* +* [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* + + **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). From 3af4421bb11abfdb5c08b1c5a29fa0295c6b6ba6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:03 +0200 Subject: [PATCH 082/711] New translations learn.md (Arabic) --- content/ar/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/ar/learn.md diff --git a/content/ar/learn.md b/content/ar/learn.md new file mode 100644 index 0000000000..60e91719d6 --- /dev/null +++ b/content/ar/learn.md @@ -0,0 +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: + + **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) +* [SciPy Lectures](https://scipy-lectures.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) + + **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. + + **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. + + **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). + + **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* +* [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* + + **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). From 2d0dc2a3dcf7e6638f4fae90c8f30a2fe6d03231 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:04 +0200 Subject: [PATCH 083/711] New translations learn.md (Japanese) --- content/ja/learn.md | 43 +++++++++++++++---------------------------- 1 file changed, 15 insertions(+), 28 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index e8c3b29503..8bd2838dc7 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -1,38 +1,33 @@ --- -title: NumPyの学び方 +title: Learn sidebar: false --- **公式の NumPy ドキュメント** については [numpy.org/doc/stable](https://numpy.org/doc/stable)を参照してください。 -## NumPyのチュートリアル - -[NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 - *** -以下は、キュレーションされた外部リソースのリストです。こちらのリストに貢献するには、 [このページの末尾](#add-to-this-list) を参照してください。 +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. ## 初心者向け NumPyについての資料は多数存在しています。 初心者の方にはこちらの資料を強くお勧めします： - **チュートリアル** + **動画** * [NumPy Quickstart チュートリアル](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) * [SciPyレクチャー](https://scipy-lectures.org/) NumPyだけでなく、科学的なPythonソフトウェアエコシステムを広く紹介しています。 * [NumPy: 初心者のための基本](https://numpy.org/devdocs/user/absolute_beginners.html) -* [機械学習プラス - ndarray入門](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) -* [Edureka - NumPy配列を例題で学ぶ ](https://www.edureka.co/blog/python-numpy-tutorial/) -* [Dataquest - NumPyチュートリアル: Python を使ったデータ解析](https://www.dataquest.io/blog/numpy-tutorial-python/) * [NumPy チュートリアル *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPyユーザーガイド](https://numpy.org/devdocs) - **書籍** + **チュートリアル** * [NumPガイド *Travelis E. Oliphant著*](http://web.mit.edu/dvp/Public/numpybook.pdf) これは2006年の無料版の初版です 最新版(2015年)については、こちら [を参照ください](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). -* [PythonからNumPyまで *Nicolas P. Rougier著*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [PythonにおけるNumPy (発展編)](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) * [エレガントなSciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *Juan Nunez-Iglesias・Stefan van der Walt・Harriet Dashnow 著* また、「Python+SciPy」を題材にした[推薦本リスト](https://www.goodreads.com/shelf/show/python-scipy) もチェックしてみてください。 ほとんどの本にはNumPyを核とした「SciPyエコシステム」が説明されています。 @@ -47,43 +42,35 @@ NumPyについての資料は多数存在しています。 初心者の方に 高度なインデックス指定、分割、スタッキング、線形代数など、NumPyの概念をより深く理解するためには、これらの上級者向け資料を試してみてください。 - **チュートリアル** + **書籍** -* [NumPyエクササイズ100](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *Nicolas P. Rougier* +* https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm * [NumPyとSciPyへのイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *M. Scott Shell著* * [NumPy救急キット](http://mentat.za.net/numpy/numpy_advanced_slides/) *Stéfan van der Walt著* -* [PythonにおけるNumPy (発展編)](https://www.geeksforgeeks.org/numpy-python-set-2-advanced/) -* [高度なインデックス指定](https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm) -* [NumPyによる機械学習とデータ分析](https://www.machinelearningplus.com/python/numpy-tutorial-python-part2/) +* [NumPyチュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 - **書籍** + **チュートリアル** * [Pythonデータサイエンスハンドブック](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *Jake Vanderplas著* * [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *Wes McKinney著* * [数値解析Python: NumPy, SciPy, Matplotlibによる数値計算とデータサイエンスアプリケーション](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) *Robert Johansson著* - **動画** + **書籍** * [アドバンスドNumPy - ブロードキャストルール・ストライド・高度なインデックス指定](https://www.youtube.com/watch?v=cYugp9IN1-Q) *Fan Nunuz-Iglesias著* -* [NumPy配列における高度なインデクシング処理](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* *** -## NumPyに関するトーク +## NumPy Talks * [NumPyにおけるインデックス指定の未来](https://www.youtube.com/watch?v=o0EacbIbf58) *Jaime Fernadezによる* (2016) * [Pythonにおける配列計算の進化](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *Ralf Gommersによる* (2019) -* [NumPy: 今までどう変わってきて、今後どう変わっていくのか？](https://www.youtube.com/watch?v=YFLVQFjRmPY) *Matti Picusによる* (2019) +* [NumPy: 今までどう変わってきて、今後どう変わっていくのか？ ](https://www.youtube.com/watch?v=YFLVQFjRmPY) *Matti Picusによる* (2019) * [NumPyの内部](https://www.youtube.com/watch?v=dBTJD_FDVjU) *Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harrisによる* (2019) * [Pythonにおける配列計算の概要](https://www.youtube.com/watch?v=f176j2g2eNc) *Travis Oliphantによる* (2019) *** -## NumPy を引用する場合 +## NumPyに関するトーク もし、あなたの研究においてNumPyが重要な役割を果たし、論文でこのプロジェクトについて言及したい場合は、こちらの[ページ](/ja/citing-numpy)を参照して下さい。 - -## このページへの貢献 - - -このページのリストに新しいリンクを追加するには、[プルリクエスト](https://github.com/numpy/numpy.org/blob/main/content/en/learn.md)を使って提案してみて下さい。 あなたが推薦するものがこのページで紹介するに値する理由と、その情報によりどのような人が最も恩恵を受けるかを説明して下さい。 From e1b79835e703129bd2337dc3f25272c998a77f99 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:06 +0200 Subject: [PATCH 084/711] New translations learn.md (Korean) --- content/ko/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/ko/learn.md diff --git a/content/ko/learn.md b/content/ko/learn.md new file mode 100644 index 0000000000..60e91719d6 --- /dev/null +++ b/content/ko/learn.md @@ -0,0 +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: + + **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) +* [SciPy Lectures](https://scipy-lectures.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) + + **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. + + **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. + + **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). + + **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* +* [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* + + **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). From 49a9de287186dca0fb3bcdbf14fc147d0666f223 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:07 +0200 Subject: [PATCH 085/711] New translations learn.md (Russian) --- content/ru/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/ru/learn.md diff --git a/content/ru/learn.md b/content/ru/learn.md new file mode 100644 index 0000000000..60e91719d6 --- /dev/null +++ b/content/ru/learn.md @@ -0,0 +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: + + **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) +* [SciPy Lectures](https://scipy-lectures.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) + + **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. + + **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. + + **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). + + **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* +* [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* + + **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). From e3065514567f4f2a21ae2982b510d0e5d95f92a7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:08 +0200 Subject: [PATCH 086/711] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 76 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 content/zh/learn.md diff --git a/content/zh/learn.md b/content/zh/learn.md new file mode 100644 index 0000000000..60e91719d6 --- /dev/null +++ b/content/zh/learn.md @@ -0,0 +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: + + **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) +* [SciPy Lectures](https://scipy-lectures.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) + + **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. + + **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. + + **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). + + **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* +* [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* + + **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). From dfd7f6dc731b1f3bd1857cccb51238d91053bf3f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:09 +0200 Subject: [PATCH 087/711] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 18 +++++------------- 1 file changed, 5 insertions(+), 13 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index 9738385ffd..318130dc11 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -5,9 +5,10 @@ sidebar: false Para a **documentação oficial do NumPy** visite [numpy.org/doc/stable](https://numpy.org/doc/stable). -Abaixo está uma coleção de recursos externos selecionados. Para contribuir, veja o [fim desta página](#add-to-this-list). *** +Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. + ## Iniciantes Há uma tonelada de informações sobre o NumPy lá fora. Se você está começando, recomendamos fortemente estes: @@ -15,11 +16,10 @@ Há uma tonelada de informações sobre o NumPy lá fora. Se você está começa **Tutoriais** * [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](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) * [SciPy Lectures](https://scipy-lectures.org/) Além de incluir conteúdo sobre a NumPy, estas aulas oferecem uma introdução mais ampla ao ecossistema científico do Python. * [NumPy: the absolute basics for beginners ("o básico absoluto para inciantes")](https://numpy.org/devdocs/user/absolute_beginners.html) -* [Machine Learning Plus - Introduction to ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) -* [Edureka - Learn NumPy Arrays with Examples ](https://www.edureka.co/blog/python-numpy-tutorial/) -* [Dataquest - NumPy Tutorial: Data Analysis with Python](https://www.dataquest.io/blog/numpy-tutorial-python/) * [NumPy tutorial *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide (Guia de Usuário NumPy)](https://numpy.org/devdocs) @@ -47,9 +47,7 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos * [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *por Nicolas P. Rougier* * [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *por M. Scott Shell* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *por Stéfan van der Walt* -* [NumPy in Python (Advanced)](https://www.geeksforgeeks.org/numpy-python-set-2-advanced/) -* [Advanced Indexing](https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm) -* [Machine Learning and Data Analytics with NumPy](https://www.machinelearningplus.com/python/numpy-tutorial-python-part2/) +* [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). **Livros** @@ -60,7 +58,6 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos **Vídeos** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *por Juan Nunuz-Iglesias* -* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *por Amuls Academy* *** @@ -77,8 +74,3 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos ## Citando a NumPy Se a NumPy é importante na sua pesquisa, e você gostaria de dar reconhecimento ao projeto na sua publicação acadêmica, por favor veja [estas informações sobre citações](/pt/citing-numpy). - -## Contribua para esta lista - - -Para adicionar a essa coleção, envie uma recomendação [através de um pull request](https://github.com/numpy/numpy.org/blob/main/content/en/learn.md). Diga por que sua recomendação merece ser mencionada nesta página e também qual o público que mais se beneficiaria. From 8cff5cec39b1cbfb73ca31fb0c1634d98c8a9d1e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:10 +0200 Subject: [PATCH 088/711] New translations news.md (Spanish) --- content/es/news.md | 194 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 content/es/news.md diff --git a/content/es/news.md b/content/es/news.md new file mode 100644 index 0000000000..0ccb21d181 --- /dev/null +++ b/content/es/news.md @@ -0,0 +1,194 @@ +--- +title: News +sidebar: false +newsHeader: Meet the new NumPy docs team leads +date: +--- + +### 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: + +NumPy logo + +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. + + +## 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 1.24.3 ([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_. From c0dae79eeee05e3bfccf162f685111831076c188 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:11 +0200 Subject: [PATCH 089/711] New translations news.md (Arabic) --- content/ar/news.md | 194 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 content/ar/news.md diff --git a/content/ar/news.md b/content/ar/news.md new file mode 100644 index 0000000000..0ccb21d181 --- /dev/null +++ b/content/ar/news.md @@ -0,0 +1,194 @@ +--- +title: News +sidebar: false +newsHeader: Meet the new NumPy docs team leads +date: +--- + +### 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: + +NumPy logo + +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. + + +## 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 1.24.3 ([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_. From 7cfb74e51b7a17d1d72dc8b67f334d512f8bc7cc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:12 +0200 Subject: [PATCH 090/711] New translations news.md (Japanese) --- content/ja/news.md | 127 +++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 116 insertions(+), 11 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 8152792994..2fc1958f48 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,12 +1,99 @@ --- title: ニュース sidebar: false +newsHeader: Meet the new NumPy docs team leads +date: --- -### NumPy 1.20.0 リリース +### 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 1.19.2 リリース + +_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 1.19.0 リリース + +_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 1.18.0 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) が利用可能になりました。 今回のリリースは180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。 +- 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 @@ -16,17 +103,17 @@ _2020年9月20日に_ 、私たちは[ NumPyプロジェクトにおけるダイ ### Natureに初の公式NumPy論文が掲載されました! -_2020年9月16日_ -- [NumPyに関する初の公式論文] (https://www.nature.com/articles/s41586-020-2649-2) が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す。 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。 +_2020年9月16日_ -- \[NumPyに関する初の公式論文\] (https://www.nature.com/articles/s41586-020-2649-2) が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す。 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取り入れているのであれば、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。 ビルドインフラを新しいPythonのバージョンに適応させるのは大変な作業で、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。wheelのリリースに備えて、以下を確認してください。 +_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取り入れているのであれば、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。 ビルドインフラを新しいPythonのバージョンに適応させるのは大変な作業で、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。 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 +### Numpy 1.19.2 release _2020年1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の固定がされています。 aarch64 wheelは最新のmanylinux2014リリースで構築されており、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を修正しています。 @@ -34,7 +121,7 @@ _2020年1月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-no _2020年7月2日_ -- このサーベイは、ソフトウェアとして、またコミュニティとしてのNumPyの開発に関する意思決定の指針となり、優先順位を設定するためのものになりました。 この調査結果は英語以外の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嬉しいです。 +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嬉しいです。 ### NumPy に新しいロゴができました! @@ -46,7 +133,7 @@ _2020年6月24日_ -- NumPy に新しいロゴが作成されました: 新しいロゴは、古いもの比べてモダンで、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 +### NumPy 1.20.0 リリース _2020年6月20日_ -- NumPy 1.19.0 が利用可能になりました。 これのリリースは 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。 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。 @@ -56,7 +143,7 @@ _2020年6月20日_ -- NumPy 1.19.0 が利用可能になりました。 これ _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機会を楽しみにしています! 詳細については、 [公式ドキュメントサイト](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 +### NumPy 1.18.0 release _2019年12月22日_ -- NumPy 1.18.0 が利用可能になりました。 このリリースは、1.17.0の主要な変更の後の、統合的なリリースです。 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。 @@ -67,7 +154,7 @@ _2019年12月22日_ -- NumPy 1.18.0 が利用可能になりました。 この _2019年11月15日_ -- NumPyと、NumPyの重要な依存関係の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、ウェブサイトの再設計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基盤をより良くし、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、技術的に重要な問題、特にスレッド安全性、AVX-512に対処することに焦点を当てます。 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行っています。 +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. OpenBLASチームは、技術的に重要な問題、特にスレッド安全性、AVX-512に対処することに焦点を当てます。 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行っています。 提案されたイニシアチブと成果物の詳細については、 [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ヶ月間継続される予定です。 @@ -76,13 +163,31 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存関係の1つであるO こちらがより過去の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 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 1.24.3 ([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 1.18.4 ([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 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.17.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _2019年10月11日_. +- 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 1.18.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020年3月17日_. +- 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 1.18.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _2020年1月6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 1.17.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _2019年10月11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_. From 90cd1c727d91047d1468402038f2b253640c183d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:13 +0200 Subject: [PATCH 091/711] New translations news.md (Korean) --- content/ko/news.md | 194 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 content/ko/news.md diff --git a/content/ko/news.md b/content/ko/news.md new file mode 100644 index 0000000000..0ccb21d181 --- /dev/null +++ b/content/ko/news.md @@ -0,0 +1,194 @@ +--- +title: News +sidebar: false +newsHeader: Meet the new NumPy docs team leads +date: +--- + +### 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: + +NumPy logo + +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. + + +## 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 1.24.3 ([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_. From 818563fc9d97b6feda99855b3634723b59412a5c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:14 +0200 Subject: [PATCH 092/711] New translations news.md (Russian) --- content/ru/news.md | 194 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 content/ru/news.md diff --git a/content/ru/news.md b/content/ru/news.md new file mode 100644 index 0000000000..0ccb21d181 --- /dev/null +++ b/content/ru/news.md @@ -0,0 +1,194 @@ +--- +title: News +sidebar: false +newsHeader: Meet the new NumPy docs team leads +date: +--- + +### 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: + +NumPy logo + +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. + + +## 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 1.24.3 ([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_. From e7260316d87e77dd50fc2f2db4ce3d2a55e0fd39 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:15 +0200 Subject: [PATCH 093/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 194 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 content/zh/news.md diff --git a/content/zh/news.md b/content/zh/news.md new file mode 100644 index 0000000000..0ccb21d181 --- /dev/null +++ b/content/zh/news.md @@ -0,0 +1,194 @@ +--- +title: News +sidebar: false +newsHeader: Meet the new NumPy docs team leads +date: +--- + +### 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: + +NumPy logo + +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. + + +## 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 1.24.3 ([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_. From 5ba9c0055405faf27d114473b5b1e1030f8b2993 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:16 +0200 Subject: [PATCH 094/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 119 ++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 112 insertions(+), 7 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index dd7a55e3c8..ed98563096 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,9 +1,96 @@ --- title: Notícias sidebar: false +newsHeader: Meet the new NumPy docs team leads +date: --- -### NumPy versão 1.20.0 +### 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 versão 1.19.2 + +_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 versão 1.19.0 + +_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 versão 1.18.0 _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 release do NumPy até agora, graças a mais de 180 contribuidores. 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. @@ -26,7 +113,7 @@ _14 de setembro de 2020_ -- Python 3.9 será lançado em algumas semanas. Se voc - 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 1.19.2 release _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. @@ -46,7 +133,7 @@ _24 de junho de 2020_ -- NumPy agora tem um novo logo: O logo é uma versão moderna do antigo, com um design mais limpo. Obrigado a Isabela Presedo-Floyd por projetar o novo logo, bem como o Travis Vaught pelo o logo antigo que nos serviu bem durante mais de 15 anos. -### NumPy versão 1.19.0 +### NumPy versão 1.20.0 _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. @@ -56,7 +143,7 @@ _20 de junho de 2020_ -- O NumPy 1.19.0 está disponível. Esta é a primeira ve _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 1.18.0 release _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. Esta é a última versão menor que irá suportar 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`. @@ -76,14 +163,32 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Todos os lançamentos de bugfix (apenas o `z` muda no formato `x.y.z` do número da versão) não tem novos recursos; versões menores (o `y` aumenta) contém novos recursos. -- NumPy 1.18.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de maio de 2020_. +- 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 1.18.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 de abril de 2020_. +- 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 1.18.2 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 de março de 2020_. +- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.16.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 de janeiro de 2019_. +- 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 1.18.1 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 de janeiro de 2020_. +- NumPy 1.18.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de maio de 2020_. - NumPy 1.17.5 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de janeiro de 2020_. - NumPy 1.18.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de dezembro de 2019_. -- NumPy 1.17.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 de novembro de 2019_. - NumPy 1.17.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julho de 2019_. -- NumPy 1.16.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 de janeiro de 2019_. +- NumPy 1.17.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 de novembro de 2019_. - NumPy 1.15.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julho de 2018_. - NumPy 1.14.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de janeiro de 2018_. From 70b6f1181d25681298f4b6f3fd73314bb8b4d617 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:17 +0200 Subject: [PATCH 095/711] New translations press-kit.md (Spanish) --- content/es/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/es/press-kit.md diff --git a/content/es/press-kit.md b/content/es/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/es/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 5ca15e810435645914e724e6263a40ed9edeb5b3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:19 +0200 Subject: [PATCH 096/711] New translations press-kit.md (Arabic) --- content/ar/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ar/press-kit.md diff --git a/content/ar/press-kit.md b/content/ar/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/ar/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 09f4b74d167ca86415afecbb71cba5616e1987b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:19 +0200 Subject: [PATCH 097/711] New translations press-kit.md (Japanese) --- content/ja/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/press-kit.md b/content/ja/press-kit.md index a0fb78219d..6d28214989 100644 --- a/content/ja/press-kit.md +++ b/content/ja/press-kit.md @@ -5,4 +5,4 @@ sidebar: false 私たちはユーザーの皆さんが次に書く学術論文や、コース教材、プレゼンテーションなどに、NumPyプロジェクトのロゴを簡単に盛り込めるようにしたいと考えています。 -こちらから、様々な解像度のNumPyロゴのファイルをダウンロードできます: [ロゴリンク](https://github.com/numpy/numpy/tree/main/branding/logo)。numpy.orgのリソースを使用することで、[NumPy行動規範](/code-of-conduct) を受け入れたことになることに注意してください。 +こちらから、様々な解像度のNumPyロゴのファイルをダウンロードできます: [ロゴリンク](https://github.com/numpy/numpy/tree/main/branding/logo)。 numpy.orgのリソースを使用することで、[NumPy行動規範](/code-of-conduct) を受け入れたことになることに注意してください。 From e3ba4469f9410e379ff582477d76e5dddbd4f9cd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:20 +0200 Subject: [PATCH 098/711] New translations press-kit.md (Korean) --- content/ko/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ko/press-kit.md diff --git a/content/ko/press-kit.md b/content/ko/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/ko/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 68fe5cf69c0a938aa0b10d91c2c3fce7306d7b01 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:21 +0200 Subject: [PATCH 099/711] New translations press-kit.md (Russian) --- content/ru/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ru/press-kit.md diff --git a/content/ru/press-kit.md b/content/ru/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/ru/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 8fff04756b94d6bcfe92e08e288d2c69bbe926c4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:22 +0200 Subject: [PATCH 100/711] New translations press-kit.md (Chinese Simplified) --- content/zh/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/zh/press-kit.md diff --git a/content/zh/press-kit.md b/content/zh/press-kit.md new file mode 100644 index 0000000000..2c8970bb29 --- /dev/null +++ b/content/zh/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Press kit +sidebar: false +--- + +We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. + +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From b748323ed3f145999f7f32785e80c7a1ce08d5ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:24 +0200 Subject: [PATCH 101/711] New translations privacy.md (Spanish) --- content/es/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/es/privacy.md diff --git a/content/es/privacy.md b/content/es/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/es/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From 7c455dd18a5eb43e429775ca44fcf225c1cd89a2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:24 +0200 Subject: [PATCH 102/711] New translations privacy.md (Arabic) --- content/ar/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ar/privacy.md diff --git a/content/ar/privacy.md b/content/ar/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/ar/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From 4cf3b4e1ad16e9048414049505402f52e944aa9d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:26 +0200 Subject: [PATCH 103/711] New translations privacy.md (Korean) --- content/ko/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ko/privacy.md diff --git a/content/ko/privacy.md b/content/ko/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/ko/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From 66ed70b08a71b03ba56aef0ef4100134a55a238c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:27 +0200 Subject: [PATCH 104/711] New translations privacy.md (Russian) --- content/ru/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ru/privacy.md diff --git a/content/ru/privacy.md b/content/ru/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/ru/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From 8b56895a1eadde8ae633e66e184c48eff13a3c79 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:28 +0200 Subject: [PATCH 105/711] New translations privacy.md (Chinese Simplified) --- content/zh/privacy.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/zh/privacy.md diff --git a/content/zh/privacy.md b/content/zh/privacy.md new file mode 100644 index 0000000000..6064e4c4f1 --- /dev/null +++ b/content/zh/privacy.md @@ -0,0 +1,8 @@ +--- +title: Privacy Policy +sidebar: false +--- + +**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. + +If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. From b04127964c196cf6c4f244ea7ce66dca80f8a837 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:28 +0200 Subject: [PATCH 106/711] New translations privacy.md (Portuguese, Brazilian) --- content/pt/privacy.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/content/pt/privacy.md b/content/pt/privacy.md index be4b6613da..c95f1d5ec1 100644 --- a/content/pt/privacy.md +++ b/content/pt/privacy.md @@ -6,13 +6,3 @@ sidebar: false **numpy.org** é operado por [NumFOCUS, Inc.](https://numfocus.org), o patrocinador fiscal do projeto NumPy. Para a Política de Privacidade deste site, consulte https://numfocus.org/privacy-policy. Se você tiver alguma dúvida sobre a política ou as práticas de coleta de dados do NumFOCUS, uso e divulgação, entre em contato com a equipe do NumFOCUS em privacy@numfocus.org. - - - - - - - - - - From 4c4720feb68676e763c35625ba153f6b446524f3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:29 +0200 Subject: [PATCH 107/711] New translations report-handling-manual.md (Spanish) --- content/es/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/es/report-handling-manual.md diff --git a/content/es/report-handling-manual.md b/content/es/report-handling-manual.md new file mode 100644 index 0000000000..5586668cba --- /dev/null +++ b/content/es/report-handling-manual.md @@ -0,0 +1,95 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From 20cec23342c7931d70726f58bf153ba5ecd49e42 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:30 +0200 Subject: [PATCH 108/711] New translations report-handling-manual.md (Arabic) --- content/ar/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/ar/report-handling-manual.md diff --git a/content/ar/report-handling-manual.md b/content/ar/report-handling-manual.md new file mode 100644 index 0000000000..5586668cba --- /dev/null +++ b/content/ar/report-handling-manual.md @@ -0,0 +1,95 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From d0cb34552de023c7064973cab19f4bbcf025a738 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:31 +0200 Subject: [PATCH 109/711] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 72 ++++++++++++++-------------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index f048688b4b..613b84b8db 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -5,26 +5,26 @@ 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。 +[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。これは、いたって普通のコミュニケーションの中で、不公平感や敵意として現れてきます。こうしたことが起きうることはわかっているので、注意深く見ていきます。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。 +* Act in a personal manner rather than impersonal. 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。 It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* 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 +## Mediation -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のは非常に重要です。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。 +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: -* 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。 +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. 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。 @@ -36,34 +36,34 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この ## 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。 +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。モデレーターはまた、違反者の権利についても述べるべきです。違反者は、排除を不当だと思う場合やNumPyコミュニティへの復帰を望む場合、以下に述べる行動規範委員会による審査を求める権利があります。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。 +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* 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。 The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. 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。 +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. ## 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。 +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。 +The Committee will then review the incident and determine, to the best of their ability: -* 問題の種類 +* What happened. * 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。この活動を支援するため、委員会はデフォルトでプライベートメーリングリストを議論に使用します。このメーリングリストには、要求が正当なものなら、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。 To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. 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。 +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 @@ -73,23 +73,23 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この 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。 + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* 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。 -* 公の場における処分の実施。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、可能な範囲内で叱責を行います。例えば、メール規約違反の発生した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通達します。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。 +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. 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。 The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* 「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。 +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。 +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。 +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From 0eafadce76449053b9ef0c2af4a268084cf12e30 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:32 +0200 Subject: [PATCH 110/711] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/ko/report-handling-manual.md diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md new file mode 100644 index 0000000000..5586668cba --- /dev/null +++ b/content/ko/report-handling-manual.md @@ -0,0 +1,95 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From c955696242ef053ae0945bcaea9dfb628378b905 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:33 +0200 Subject: [PATCH 111/711] New translations report-handling-manual.md (Russian) --- content/ru/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/ru/report-handling-manual.md diff --git a/content/ru/report-handling-manual.md b/content/ru/report-handling-manual.md new file mode 100644 index 0000000000..5586668cba --- /dev/null +++ b/content/ru/report-handling-manual.md @@ -0,0 +1,95 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From bf634dd16695695535c158e92a4c9057df7dd7fc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:34 +0200 Subject: [PATCH 112/711] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/zh/report-handling-manual.md diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md new file mode 100644 index 0000000000..5586668cba --- /dev/null +++ b/content/zh/report-handling-manual.md @@ -0,0 +1,95 @@ +--- +title: NumPy Code of Conduct - How to follow up on a report +sidebar: false +--- + +This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. + +Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: + +* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. +* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. +* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. +* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. +* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. +* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. + + +## Mediation + +Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: + +* Find a candidate who can serve as a mediator. +* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. +* Obtain the agreement of the reported person(s). +* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. +* Establish a timeline for mediation to complete, ideally within two weeks. + +The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. + + +## How the Committee will respond to reports + +When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. + + +## Clear and severe breach actions + +We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. + +When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: + +* Immediately disconnect the originator from all NumPy communication channels. +* Reply to the reporter that their report has been received and that the originator has been disconnected. +* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. +* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. + + +## Report handling + +When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. + +If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. + +The Committee will then review the incident and determine, to the best of their ability: + +* What happened. +* Whether this event constitutes a Code of Conduct violation. +* Who are the responsible party(ies). +* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. + +This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). + +It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. + +The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. + + +## Resolutions + +The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. + +Possible responses may include: + +* Taking no further action: + - if we determine no violations have occurred; + - if the matter has been resolved publicly while the Committee was considering responses. +* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. +* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. +* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. +* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. +* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. +* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. + +Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. + +Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). + +The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. + + +## Conflicts of Interest + +In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. From 29cbe9c253c483f0055832b47745f99facddaebc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:36 +0200 Subject: [PATCH 113/711] New translations teams.md (Spanish) --- content/es/teams.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 content/es/teams.md diff --git a/content/es/teams.md b/content/es/teams.md new file mode 100644 index 0000000000..91cf5ca399 --- /dev/null +++ b/content/es/teams.md @@ -0,0 +1,22 @@ +--- +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/). From 5c1f9bcb4918ed3ef22a9d3032b54e88b6979e33 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:37 +0200 Subject: [PATCH 114/711] New translations teams.md (Arabic) --- content/ar/teams.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 content/ar/teams.md diff --git a/content/ar/teams.md b/content/ar/teams.md new file mode 100644 index 0000000000..91cf5ca399 --- /dev/null +++ b/content/ar/teams.md @@ -0,0 +1,22 @@ +--- +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/). From 682b484827ee0b94cc6a24ffa69572f149a831a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:38 +0200 Subject: [PATCH 115/711] New translations teams.md (Japanese) --- content/ja/teams.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/content/ja/teams.md b/content/ja/teams.md index 365aedcf19..cec31b9bc2 100644 --- a/content/ja/teams.md +++ b/content/ja/teams.md @@ -3,9 +3,7 @@ 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" >}})! +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" >}} From d0fbee1eaa560bee2416d2752b974aeadb79002a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:38 +0200 Subject: [PATCH 116/711] New translations teams.md (Korean) --- content/ko/teams.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 content/ko/teams.md diff --git a/content/ko/teams.md b/content/ko/teams.md new file mode 100644 index 0000000000..91cf5ca399 --- /dev/null +++ b/content/ko/teams.md @@ -0,0 +1,22 @@ +--- +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/). From 3f75bbc8caac092d71aa7c6f522926674735987c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:39 +0200 Subject: [PATCH 117/711] New translations teams.md (Russian) --- content/ru/teams.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 content/ru/teams.md diff --git a/content/ru/teams.md b/content/ru/teams.md new file mode 100644 index 0000000000..91cf5ca399 --- /dev/null +++ b/content/ru/teams.md @@ -0,0 +1,22 @@ +--- +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/). From b8a230ad9cc8067e67fea0dfa4ec58d2f2e30a25 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:40 +0200 Subject: [PATCH 118/711] New translations teams.md (Chinese Simplified) --- content/zh/teams.md | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 content/zh/teams.md diff --git a/content/zh/teams.md b/content/zh/teams.md new file mode 100644 index 0000000000..91cf5ca399 --- /dev/null +++ b/content/zh/teams.md @@ -0,0 +1,22 @@ +--- +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/). From 1b215a6be05a341e83e3fca67c2f355e2bf3f05d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:41 +0200 Subject: [PATCH 119/711] New translations teams.md (Portuguese, Brazilian) --- content/pt/teams.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/content/pt/teams.md b/content/pt/teams.md index 365aedcf19..cec31b9bc2 100644 --- a/content/pt/teams.md +++ b/content/pt/teams.md @@ -3,9 +3,7 @@ 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" >}})! +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" >}} From 29f92ae3d7808b8f4427be4d2cbde98ebcaeae13 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:42 +0200 Subject: [PATCH 120/711] New translations user-survey-2020.md (Spanish) --- content/es/user-survey-2020.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 content/es/user-survey-2020.md diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md new file mode 100644 index 0000000000..fe431e845c --- /dev/null +++ b/content/es/user-survey-2020.md @@ -0,0 +1,16 @@ +--- +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">}} + +**[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/**. + From d2dcc94dac74aa4750230c3d72343c33c2e8d43c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:43 +0200 Subject: [PATCH 121/711] New translations user-survey-2020.md (Arabic) --- content/ar/user-survey-2020.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 content/ar/user-survey-2020.md diff --git a/content/ar/user-survey-2020.md b/content/ar/user-survey-2020.md new file mode 100644 index 0000000000..fe431e845c --- /dev/null +++ b/content/ar/user-survey-2020.md @@ -0,0 +1,16 @@ +--- +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">}} + +**[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/**. + From ef84ee0bbc912a92342d1535233bd46a6582d5d2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:44 +0200 Subject: [PATCH 122/711] New translations user-survey-2020.md (Japanese) --- content/ja/user-survey-2020.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 content/ja/user-survey-2020.md diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md new file mode 100644 index 0000000000..fe431e845c --- /dev/null +++ b/content/ja/user-survey-2020.md @@ -0,0 +1,16 @@ +--- +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">}} + +**[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/**. + From 98a902d3d28715bc5d73d52d106d5e4b6864af0f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:45 +0200 Subject: [PATCH 123/711] New translations user-survey-2020.md (Korean) --- content/ko/user-survey-2020.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 content/ko/user-survey-2020.md diff --git a/content/ko/user-survey-2020.md b/content/ko/user-survey-2020.md new file mode 100644 index 0000000000..fe431e845c --- /dev/null +++ b/content/ko/user-survey-2020.md @@ -0,0 +1,16 @@ +--- +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">}} + +**[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/**. + From 4abb6591da4f1d559b50567e177bcf37aba22121 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:46 +0200 Subject: [PATCH 124/711] New translations user-survey-2020.md (Russian) --- content/ru/user-survey-2020.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 content/ru/user-survey-2020.md diff --git a/content/ru/user-survey-2020.md b/content/ru/user-survey-2020.md new file mode 100644 index 0000000000..fe431e845c --- /dev/null +++ b/content/ru/user-survey-2020.md @@ -0,0 +1,16 @@ +--- +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">}} + +**[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/**. + From 64e1e2aa067bac074f3ac60afbdaaf1c56ef3a0c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:47 +0200 Subject: [PATCH 125/711] New translations user-survey-2020.md (Chinese Simplified) --- content/zh/user-survey-2020.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 content/zh/user-survey-2020.md diff --git a/content/zh/user-survey-2020.md b/content/zh/user-survey-2020.md new file mode 100644 index 0000000000..fe431e845c --- /dev/null +++ b/content/zh/user-survey-2020.md @@ -0,0 +1,16 @@ +--- +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">}} + +**[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/**. + From 0694274ca78d5cc896d2affc2d5940e486dfcc5a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:48 +0200 Subject: [PATCH 126/711] New translations user-survey-2020.md (Portuguese, Brazilian) --- content/pt/user-survey-2020.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 content/pt/user-survey-2020.md diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md new file mode 100644 index 0000000000..fe431e845c --- /dev/null +++ b/content/pt/user-survey-2020.md @@ -0,0 +1,16 @@ +--- +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">}} + +**[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/**. + From b61c5b98b7954bc06524f0d6b6be0747d7253fa1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:49 +0200 Subject: [PATCH 127/711] New translations user-surveys.md (Spanish) --- content/es/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/es/user-surveys.md diff --git a/content/es/user-surveys.md b/content/es/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/es/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**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/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 3386450464e3b1a7a553224f94e886a10c6f7bc2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:50 +0200 Subject: [PATCH 128/711] New translations user-surveys.md (Arabic) --- content/ar/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/ar/user-surveys.md diff --git a/content/ar/user-surveys.md b/content/ar/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/ar/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**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/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 9ce2a71b63c95d6d14a90f6b6ca7c8d392c272d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:50 +0200 Subject: [PATCH 129/711] New translations user-surveys.md (Japanese) --- content/ja/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/ja/user-surveys.md diff --git a/content/ja/user-surveys.md b/content/ja/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/ja/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**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/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From c9dfb589616664d985003782fd5953b76dc78057 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:51 +0200 Subject: [PATCH 130/711] New translations user-surveys.md (Korean) --- content/ko/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/ko/user-surveys.md diff --git a/content/ko/user-surveys.md b/content/ko/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/ko/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**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/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From b7764457b05fc1cac00a24f5d44ff46f888260c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:52 +0200 Subject: [PATCH 131/711] New translations user-surveys.md (Russian) --- content/ru/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/ru/user-surveys.md diff --git a/content/ru/user-surveys.md b/content/ru/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/ru/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**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/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 5d9346fa377c2446c68cb8992dffc711eb360092 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:53 +0200 Subject: [PATCH 132/711] New translations user-surveys.md (Chinese Simplified) --- content/zh/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/zh/user-surveys.md diff --git a/content/zh/user-surveys.md b/content/zh/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/zh/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**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/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From d7acad2c4532c2a9131972a4a2019b7de779a50a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:54 +0200 Subject: [PATCH 133/711] New translations user-surveys.md (Portuguese, Brazilian) --- content/pt/user-surveys.md | 10 ++++++++++ 1 file changed, 10 insertions(+) create mode 100644 content/pt/user-surveys.md diff --git a/content/pt/user-surveys.md b/content/pt/user-surveys.md new file mode 100644 index 0000000000..89a2aa0460 --- /dev/null +++ b/content/pt/user-surveys.md @@ -0,0 +1,10 @@ +--- +title: NUMPY USER SURVEYS +sidebar: false +--- + +**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/). + +**2021** The collected data is currently being analyzed. + +If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). From 9fa3ef10bf5e92396493286542bffbcef07d1c29 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:55 +0200 Subject: [PATCH 134/711] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 70 ++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 content/es/case-studies/blackhole-image.md diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md new file mode 100644 index 0000000000..f2460d3d5b --- /dev/null +++ b/content/es/case-studies/blackhole-image.md @@ -0,0 +1,70 @@ +--- +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" >}} + +
+

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

+
Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
+
+ +## 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! + +### 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. + +### 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" >}} + +## 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**" >}} + +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**" >}} + +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. + +## 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**" >}} + +[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/ From 23befcbe2573efc4a7119d28a663216af320a7af Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:55 +0200 Subject: [PATCH 135/711] New translations blackhole-image.md (Arabic) --- content/ar/case-studies/blackhole-image.md | 70 ++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 content/ar/case-studies/blackhole-image.md diff --git a/content/ar/case-studies/blackhole-image.md b/content/ar/case-studies/blackhole-image.md new file mode 100644 index 0000000000..f2460d3d5b --- /dev/null +++ b/content/ar/case-studies/blackhole-image.md @@ -0,0 +1,70 @@ +--- +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" >}} + +
+

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

+
Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
+
+ +## 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! + +### 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. + +### 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" >}} + +## 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**" >}} + +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**" >}} + +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. + +## 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**" >}} + +[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/ From 70568b68eee14a76174604360ef9c39e15628b3f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:56 +0200 Subject: [PATCH 136/711] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 80eb5fd99a..a5f8ad3bbb 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -12,27 +12,27 @@ sidebar: false ## 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! +[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。 +* **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。 +* **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。 + 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** +* **Too much information** - 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。 + 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** +* **Into the unknown** 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? @@ -40,15 +40,15 @@ sidebar: false ## 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？ +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。 +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**" >}} -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。 +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**" >}} @@ -56,7 +56,7 @@ NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas. ## 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。 +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**" >}} From b9ebd42fa70b6ccde92f962a1e10571ba753e0cc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:57 +0200 Subject: [PATCH 137/711] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 70 ++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 content/ko/case-studies/blackhole-image.md diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md new file mode 100644 index 0000000000..f2460d3d5b --- /dev/null +++ b/content/ko/case-studies/blackhole-image.md @@ -0,0 +1,70 @@ +--- +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" >}} + +
+

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

+
Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
+
+ +## 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! + +### 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. + +### 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" >}} + +## 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**" >}} + +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**" >}} + +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. + +## 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**" >}} + +[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/ From 4a908147f3c29673e30594958826964f090da39f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:58 +0200 Subject: [PATCH 138/711] New translations blackhole-image.md (Russian) --- content/ru/case-studies/blackhole-image.md | 70 ++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 content/ru/case-studies/blackhole-image.md diff --git a/content/ru/case-studies/blackhole-image.md b/content/ru/case-studies/blackhole-image.md new file mode 100644 index 0000000000..f2460d3d5b --- /dev/null +++ b/content/ru/case-studies/blackhole-image.md @@ -0,0 +1,70 @@ +--- +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" >}} + +
+

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

+
Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
+
+ +## 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! + +### 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. + +### 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" >}} + +## 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**" >}} + +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**" >}} + +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. + +## 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**" >}} + +[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/ From 7d9d4922e5157142a82ff56eca607e81dfe831b3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:27:59 +0200 Subject: [PATCH 139/711] New translations blackhole-image.md (Chinese Simplified) --- content/zh/case-studies/blackhole-image.md | 70 ++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 content/zh/case-studies/blackhole-image.md diff --git a/content/zh/case-studies/blackhole-image.md b/content/zh/case-studies/blackhole-image.md new file mode 100644 index 0000000000..f2460d3d5b --- /dev/null +++ b/content/zh/case-studies/blackhole-image.md @@ -0,0 +1,70 @@ +--- +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" >}} + +
+

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

+
Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
+
+ +## 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! + +### 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. + +### 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" >}} + +## 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**" >}} + +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**" >}} + +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. + +## 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**" >}} + +[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/ From 3b90e5f8eb45f12eddb7276c591c04a2646ee52c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:01 +0200 Subject: [PATCH 140/711] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 64 ++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 content/es/case-studies/cricket-analytics.md diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..db140f858c --- /dev/null +++ b/content/es/case-studies/cricket-analytics.md @@ -0,0 +1,64 @@ +--- +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" >}} + +
+

You don't play for the crowd, you play for the country.

+
—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
+
+ +## 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" >}} + +### 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/" >}} + +### 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**" >}} From 4fb8371d7a49bc7f2de507b02c99eee589264422 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:02 +0200 Subject: [PATCH 141/711] New translations cricket-analytics.md (Arabic) --- content/ar/case-studies/cricket-analytics.md | 64 ++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 content/ar/case-studies/cricket-analytics.md diff --git a/content/ar/case-studies/cricket-analytics.md b/content/ar/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..db140f858c --- /dev/null +++ b/content/ar/case-studies/cricket-analytics.md @@ -0,0 +1,64 @@ +--- +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" >}} + +
+

You don't play for the crowd, you play for the country.

+
—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
+
+ +## 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" >}} + +### 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/" >}} + +### 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**" >}} From 8cbc07681196275b1294e990298fbe77fb3bb66a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:03 +0200 Subject: [PATCH 142/711] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index ec68246720..dd0850f521 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -12,13 +12,13 @@ sidebar: false ## 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、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。 Cricket enjoys lots of media attention. 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。 Over the last several years, technology has literally been a game changer. 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。パフォーマンスを向上させたり、クリケットのビジネスチャンス・市場・経済性などを研究するため、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によりクリケットの数字を掘り下げる能力は、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。 +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。 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. 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。 +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 @@ -39,26 +39,26 @@ sidebar: false * **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。 + 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。 + 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。 +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。 +* **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。 +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 **" >}} From 6aaf2fdbe68d5e64d0831846b843afc8e026e778 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:04 +0200 Subject: [PATCH 143/711] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 64 ++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 content/ko/case-studies/cricket-analytics.md diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..db140f858c --- /dev/null +++ b/content/ko/case-studies/cricket-analytics.md @@ -0,0 +1,64 @@ +--- +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" >}} + +
+

You don't play for the crowd, you play for the country.

+
—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
+
+ +## 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" >}} + +### 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/" >}} + +### 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**" >}} From 702837c909d82b5609f81c27fff5984a61a90add Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:05 +0200 Subject: [PATCH 144/711] New translations cricket-analytics.md (Russian) --- content/ru/case-studies/cricket-analytics.md | 64 ++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 content/ru/case-studies/cricket-analytics.md diff --git a/content/ru/case-studies/cricket-analytics.md b/content/ru/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..db140f858c --- /dev/null +++ b/content/ru/case-studies/cricket-analytics.md @@ -0,0 +1,64 @@ +--- +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" >}} + +
+

You don't play for the crowd, you play for the country.

+
—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
+
+ +## 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" >}} + +### 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/" >}} + +### 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**" >}} From e8c1f69e652ff485e74d02c70b1c0a9befcf0c30 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:05 +0200 Subject: [PATCH 145/711] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 64 ++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 content/zh/case-studies/cricket-analytics.md diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..db140f858c --- /dev/null +++ b/content/zh/case-studies/cricket-analytics.md @@ -0,0 +1,64 @@ +--- +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" >}} + +
+

You don't play for the crowd, you play for the country.

+
—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
+
+ +## 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" >}} + +### 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/" >}} + +### 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**" >}} From 0c3b6bde3e51571114dc75cb4bc573cb6a0f29da Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:06 +0200 Subject: [PATCH 146/711] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index c2fbcac9c8..837335ba6f 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -16,7 +16,7 @@ Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jog 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. -Críquete é um jogo dominado pelos números - as corridas executadas por um batsman, os wickets 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. As análises de críquete fornecem informações interessantes sobre o jogo e informações preditivas sobre os resultados do jogo. +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. 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. 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: @@ -49,7 +49,7 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações 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. -## Papel da NumPy na Análise de Críquete +## NumPy’s Role in Cricket Analytics 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: From fb8993be152dff2b9e66033b21e21a538904d933 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:07 +0200 Subject: [PATCH 147/711] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 90 +++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/es/case-studies/deeplabcut-dnn.md diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..b40ed2af50 --- /dev/null +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -0,0 +1,90 @@ +--- +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">}} + +
+

Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

+
—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
+
+ +## 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]. + +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" >}} + +### 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**" >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From bd8b24675fb35163c35bad2a3a321e123b314252 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:08 +0200 Subject: [PATCH 148/711] New translations deeplabcut-dnn.md (Arabic) --- content/ar/case-studies/deeplabcut-dnn.md | 90 +++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/ar/case-studies/deeplabcut-dnn.md diff --git a/content/ar/case-studies/deeplabcut-dnn.md b/content/ar/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..b40ed2af50 --- /dev/null +++ b/content/ar/case-studies/deeplabcut-dnn.md @@ -0,0 +1,90 @@ +--- +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">}} + +
+

Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

+
—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
+
+ +## 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]. + +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" >}} + +### 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**" >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 61cd929bdc6b4e37705ca65dc673c4cd0d66a2fc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:09 +0200 Subject: [PATCH 149/711] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 30 +++++++++++------------ 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index c597261ef4..ae2a74a400 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -12,29 +12,29 @@ sidebar: false ## 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。世界中の何百もの研究機関の研究者により使用されています。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。 +[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。 +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、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。 +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。 +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。 + 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。 + 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。 @@ -51,21 +51,21 @@ DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術 * **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。 + 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** +* **Combinatorics** - 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。 + 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。 + 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。 +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。 @@ -75,13 +75,13 @@ NumPy は DeepLabCutにおける、行動分析の高速化のための数値計 * 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。 +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。 +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**" >}} From 119041d4ba6ae84c1ef68df62489620fe0f6295b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:10 +0200 Subject: [PATCH 150/711] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 90 +++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/ko/case-studies/deeplabcut-dnn.md diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..b40ed2af50 --- /dev/null +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -0,0 +1,90 @@ +--- +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">}} + +
+

Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

+
—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
+
+ +## 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]. + +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" >}} + +### 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**" >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 6dc89c32c578d58d42ef6e71994640486a20b969 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:11 +0200 Subject: [PATCH 151/711] New translations deeplabcut-dnn.md (Russian) --- content/ru/case-studies/deeplabcut-dnn.md | 90 +++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/ru/case-studies/deeplabcut-dnn.md diff --git a/content/ru/case-studies/deeplabcut-dnn.md b/content/ru/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..b40ed2af50 --- /dev/null +++ b/content/ru/case-studies/deeplabcut-dnn.md @@ -0,0 +1,90 @@ +--- +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">}} + +
+

Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

+
—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
+
+ +## 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]. + +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" >}} + +### 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**" >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From f9d83d6ca4a3444a08b86e904f4603b592600b87 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:12 +0200 Subject: [PATCH 152/711] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 90 +++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/zh/case-studies/deeplabcut-dnn.md diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..b40ed2af50 --- /dev/null +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -0,0 +1,90 @@ +--- +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">}} + +
+

Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

+
—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
+
+ +## 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]. + +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" >}} + +### 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**" >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 060268b820178ac2ef6bcfe0b028c7e859c6d026 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:13 +0200 Subject: [PATCH 153/711] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index 1dd02b9f92..84aa10e350 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -72,7 +72,7 @@ As seguintes características da NumPy desempenharam um papel fundamental para a * Vetorização * Operações em arrays com máscaras * Álgebra linear -* Amostragem aleatória +* Random Sampling * 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. From f472310a86ea0c72d730c10850f6bea8ebf37847 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:14 +0200 Subject: [PATCH 154/711] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 69 ++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/es/case-studies/gw-discov.md diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md new file mode 100644 index 0000000000..b992584c87 --- /dev/null +++ b/content/es/case-studies/gw-discov.md @@ -0,0 +1,69 @@ +--- +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" >}} + +
+

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

+
David Shoemaker, LIGO Scientific Collaboration
+
+ +## 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" >}} + +## 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Dependency graph showing how PyCBC package depends on NumPy**" >}} + +## 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**" >}} From d8c0be44700dd84d463b84b50bf536a9d7d21657 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:15 +0200 Subject: [PATCH 155/711] New translations gw-discov.md (Arabic) --- content/ar/case-studies/gw-discov.md | 69 ++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/ar/case-studies/gw-discov.md diff --git a/content/ar/case-studies/gw-discov.md b/content/ar/case-studies/gw-discov.md new file mode 100644 index 0000000000..b992584c87 --- /dev/null +++ b/content/ar/case-studies/gw-discov.md @@ -0,0 +1,69 @@ +--- +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" >}} + +
+

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

+
David Shoemaker, LIGO Scientific Collaboration
+
+ +## 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" >}} + +## 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Dependency graph showing how PyCBC package depends on NumPy**" >}} + +## 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**" >}} From b7a226db34bc680c9fec04046d5e20bbccb6ac2e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:16 +0200 Subject: [PATCH 156/711] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 49d88c2845..c060650148 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -12,15 +12,15 @@ sidebar: false ## [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。 +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。 +* 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 * 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。 @@ -29,23 +29,23 @@ sidebar: false * **計算** - 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。例えば6つのLIGO専用クラスターに分散されたバイナリ結合解析には[10の7乗オーダーのCPU時間](https:/youtu.be7mcHknWWzNI)が必要です。 + 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** +* **Data Deluge** - 観測装置の感度と信頼性が高まると、様々な場所でデータの氾濫による困難が待ち受けています。それは、干し草の中から針を探すようなものです。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。 + 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、純粋な理論家に対し、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。理論家は、可視化とシミュレーションが結果の把握を容易にするまで、数値相対性を十分に重要視していませんでした。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。 + 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。 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. 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" >}} ## 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。 +Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. 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。 +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。 @@ -64,6 +64,6 @@ Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波 ## まとめ -重力波の検出により、研究者はこれまでに予期しなかった現象を発見することができました。 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/)。 +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**" >}} From 6c78f9fd8c78fab29975b1c30d226cf057e9e1c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:17 +0200 Subject: [PATCH 157/711] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 69 ++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/ko/case-studies/gw-discov.md diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md new file mode 100644 index 0000000000..b992584c87 --- /dev/null +++ b/content/ko/case-studies/gw-discov.md @@ -0,0 +1,69 @@ +--- +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" >}} + +
+

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

+
David Shoemaker, LIGO Scientific Collaboration
+
+ +## 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" >}} + +## 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Dependency graph showing how PyCBC package depends on NumPy**" >}} + +## 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**" >}} From faf29664e4e2e3a2ea9d857b98f3b44f3cbb8cb3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:18 +0200 Subject: [PATCH 158/711] New translations gw-discov.md (Russian) --- content/ru/case-studies/gw-discov.md | 69 ++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/ru/case-studies/gw-discov.md diff --git a/content/ru/case-studies/gw-discov.md b/content/ru/case-studies/gw-discov.md new file mode 100644 index 0000000000..b992584c87 --- /dev/null +++ b/content/ru/case-studies/gw-discov.md @@ -0,0 +1,69 @@ +--- +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" >}} + +
+

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

+
David Shoemaker, LIGO Scientific Collaboration
+
+ +## 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" >}} + +## 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Dependency graph showing how PyCBC package depends on NumPy**" >}} + +## 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**" >}} From 03224293819694e42055c8ef01f39cee63ef92af Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 May 2023 10:28:19 +0200 Subject: [PATCH 159/711] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 69 ++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/zh/case-studies/gw-discov.md diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md new file mode 100644 index 0000000000..b992584c87 --- /dev/null +++ b/content/zh/case-studies/gw-discov.md @@ -0,0 +1,69 @@ +--- +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" >}} + +
+

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

+
David Shoemaker, LIGO Scientific Collaboration
+
+ +## 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" >}} + +## 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Dependency graph showing how PyCBC package depends on NumPy**" >}} + +## 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**" >}} From 5fd2a0fcccde4ea5e8d059c70e7c9fe7609382a6 Mon Sep 17 00:00:00 2001 From: Stefan van der Walt Date: Wed, 3 May 2023 11:41:05 -0700 Subject: [PATCH 160/711] Fix reproducible-install links --- content/ja/install.md | 2 +- content/pt/install.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/install.md b/content/ja/install.md index 8b6deb5964..2b956a760b 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -80,7 +80,7 @@ Windows、macOS、Linuxのすべてのユーザー向けには: 3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！ -### 再現可能なインストール +### 再現可能なインストール {#reproducible-installs} ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に 壊れたりする可能性があります。 なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです: diff --git a/content/pt/install.md b/content/pt/install.md index de364a9578..5f60385839 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -80,7 +80,7 @@ A segunda diferença é que o pip instala do Índice de Pacotes Python (Python P A terceira diferença é que o conda é uma solução integrada para gerenciar pacotes, dependências e ambientes, enquanto com o pip você pode precisar de outra ferramenta (há muitas!) para lidar com ambientes ou dependências complexas. -### Instalações reprodutíveis +### Instalações reprodutíveis {#reproducible-installs} À medida que as bibliotecas são atualizadas, os resultados obtidos ao executar seu código podem mudar, ou o seu código pode parar de funcionar. É importante poder reconstruir o conjunto de pacotes e versões que você está usando. A recomendação é: From 137b8ffe7bbace3b95a9acf5f36c2264559b7a81 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 May 2023 12:18:10 +0200 Subject: [PATCH 161/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index b6f50c9934..146cce2660 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -1,14 +1,14 @@ -languageName: English +languageName: 한국어 params: - description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + description: 왜 NumPy인가? 강력한 n차원 배열. 수치 컴퓨팅 도구. 상호운용성. 고성능. 오픈소스. navbarlogo: image: logo.svg - link: / + link: /ko/ hero: #Main hero title title: NumPy #Hero subtitle (optional) - subtitle: The fundamental package for scientific computing with Python + subtitle: Python으로 과학적 컴퓨팅을 하기 위한 기초 패키지 #Button text buttontext: "Latest release: numpy 1.24.2. View all releases." #Where the main hero button links to @@ -16,10 +16,10 @@ params: #Hero image (from static/images/___) image: logo.svg shell: - title: placeholder + title: 자리 표시자 intro: - - title: Try NumPy + title: NumPy 써 보기 text: Use the interactive shell to try NumPy in the browser docslink: Don't forget to check out the docs. casestudies: @@ -70,7 +70,7 @@ params: title: Easy to use text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. tabs: - title: ECOSYSTEM + title: 생태계 section5: false navbar: - From 88225307f5223c113607dba4ac2386b657c0d4ba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 16 May 2023 09:14:33 +0200 Subject: [PATCH 162/711] New translations news.md (Spanish) --- content/es/news.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 0ccb21d181..64ba8e2ce2 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,10 +1,16 @@ --- title: News sidebar: false -newsHeader: Meet the new NumPy docs team leads -date: +newsHeader: "Fostering an Inclusive Culture: Call for Participation" +date: 2023-05-10 --- +### 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. From 9988a018cbe3728a7aaabfd1919840a376ac8507 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 16 May 2023 09:14:34 +0200 Subject: [PATCH 163/711] New translations news.md (Arabic) --- content/ar/news.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 0ccb21d181..64ba8e2ce2 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,16 @@ --- title: News sidebar: false -newsHeader: Meet the new NumPy docs team leads -date: +newsHeader: "Fostering an Inclusive Culture: Call for Participation" +date: 2023-05-10 --- +### 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. From 8c046f6495f44d94ab0361b96e5f0c105075e5a4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 16 May 2023 09:14:35 +0200 Subject: [PATCH 164/711] New translations news.md (Japanese) --- content/ja/news.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 2fc1958f48..ef13fc599d 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,10 +1,16 @@ --- title: ニュース sidebar: false -newsHeader: Meet the new NumPy docs team leads -date: +newsHeader: "Fostering an Inclusive Culture: Call for Participation" +date: 2023-05-10 --- +### 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. From 1c0040816b644618a237437d3e9eb9b736309bcc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 16 May 2023 09:14:36 +0200 Subject: [PATCH 165/711] New translations news.md (Korean) --- content/ko/news.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 0ccb21d181..64ba8e2ce2 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,16 @@ --- title: News sidebar: false -newsHeader: Meet the new NumPy docs team leads -date: +newsHeader: "Fostering an Inclusive Culture: Call for Participation" +date: 2023-05-10 --- +### 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. From 2f6bf86f54b7efc163bca20464246be3b4fad2c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 16 May 2023 09:14:37 +0200 Subject: [PATCH 166/711] New translations news.md (Russian) --- content/ru/news.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 0ccb21d181..64ba8e2ce2 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,16 @@ --- title: News sidebar: false -newsHeader: Meet the new NumPy docs team leads -date: +newsHeader: "Fostering an Inclusive Culture: Call for Participation" +date: 2023-05-10 --- +### 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. From ff32df77315454bc6e84856a671a2d00f8ca8def Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 16 May 2023 09:14:38 +0200 Subject: [PATCH 167/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 0ccb21d181..64ba8e2ce2 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,16 @@ --- title: News sidebar: false -newsHeader: Meet the new NumPy docs team leads -date: +newsHeader: "Fostering an Inclusive Culture: Call for Participation" +date: 2023-05-10 --- +### 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. From 28fa7e192632134cffd89e4878e6c5c74483f178 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 16 May 2023 09:14:40 +0200 Subject: [PATCH 168/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index ed98563096..4c59ca91ff 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,10 +1,16 @@ --- title: Notícias sidebar: false -newsHeader: Meet the new NumPy docs team leads -date: +newsHeader: "Fostering an Inclusive Culture: Call for Participation" +date: 2023-05-10 --- +### 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. From 89826210ffcaf15d1103c9d8cd2dcf64073d782a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 18 May 2023 17:26:17 +0200 Subject: [PATCH 169/711] New translations teams.md (Korean) --- content/ko/teams.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/teams.md b/content/ko/teams.md index 91cf5ca399..5654f4d6c2 100644 --- a/content/ko/teams.md +++ b/content/ko/teams.md @@ -1,9 +1,9 @@ --- -title: NumPy Teams +title: NumPy 팀 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" >}})! +저희는 양질의 오픈소스 소프트웨어를 구축하여 전 세계의 과학 및 연구 커뮤니티를 지원한다는 사명을 지닌 국제적 팀입니다. [팀에 참여하십시오]({{< relref "/contribute" >}})! {{< include-html "static/gallery/maintainers.html" >}} @@ -17,6 +17,6 @@ We are an international team on a mission to support scientific and research com {{< include-html "static/gallery/emeritus-maintainers.html" >}} -# Governance +# 운영 -For the list of the Steering Council members, please see [here](https://numpy.org/about/). +운영 위원회 구성원 목록은 [여기](https://numpy.org/about/)를 참고하십시오. From d9e1558dfe51c4a306a3daa7fa6a6e9bab48ec5e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 17:50:45 +0200 Subject: [PATCH 170/711] New translations user-survey-2020.md (Portuguese, Brazilian) --- content/pt/user-survey-2020.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md index fe431e845c..0cb175d668 100644 --- a/content/pt/user-survey-2020.md +++ b/content/pt/user-survey-2020.md @@ -1,16 +1,16 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: PESQUISA SOBRE A COMUNIDADE NUMPY 2020 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. +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="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="Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado '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. +**[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados. -For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. +Para os destaques, confira **[este infográfico](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/**. +Quer saber mais? Visite **https://numpy.org/user-survey-2020-details/**. From cd74ce04bc1abbba1b7a79ee1f6ced12e4c9c0c2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:01 +0200 Subject: [PATCH 171/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index e5cf19c5ab..1b2d2cbd23 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -19,9 +19,9 @@ params: title: placeholder intro: - - title: Try NumPy + title: NumPy を試す text: Use the interactive shell to try NumPy in the browser - docslink: Don't forget to check out the docs. + docslink: ドキュメント 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: @@ -92,8 +92,8 @@ navbar: title: NumPyに貢献する url: /ja/contribute - - title: Contribute - url: /contribute + title: NumPyに貢献する + url: /ja/contribute footer: logo: logo.svg socialmediatitle: "" @@ -135,7 +135,7 @@ footer: text: コミュニティ link: /ja/community - - text: User surveys + text: ユーザーの調査 link: /ja/user-surveys - text: NumPyに貢献する From 60a5d9e5bebe9984a2321d62794ecc1e437efe03 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:03 +0200 Subject: [PATCH 172/711] New translations 404.md (Japanese) --- content/ja/404.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/404.md b/content/ja/404.md index bec1ba1cf2..8e4db85255 100644 --- a/content/ja/404.md +++ b/content/ja/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -おっとっと！ You've reached a dead end. +おっとっと！ 間違った所にアクセスしているようです。 何かがここにページがあるべきだと思ったら、GitHub で [issue](https://github.com/numpy/numpy.org/issues) を作成してください。 From 8c301dc9507a39aa4f9a3e858674dfd03451fd2e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:04 +0200 Subject: [PATCH 173/711] New translations about.md (Japanese) --- content/ja/about.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index e7106ae1e6..26b398ad64 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -3,7 +3,7 @@ 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。 It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPyは完全にオープンソースなソフトウェアであり、[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。 It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +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あり、[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。 It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). 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。 @@ -41,9 +41,9 @@ The NumPy project leadership is actively working on diversifying contribution pa - development - ドキュメント -- triage +- トリアージ - ウェブサイト -- survey +- 調査 - translations - sprint mentors - 資金と助成金 From 853a49234b20cee5f869296e9488cc8c4b967a21 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:05 +0200 Subject: [PATCH 174/711] New translations arraycomputing.md (Japanese) --- content/ja/arraycomputing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/arraycomputing.md b/content/ja/arraycomputing.md index 867f806e4c..7713e7e0f2 100644 --- a/content/ja/arraycomputing.md +++ b/content/ja/arraycomputing.md @@ -3,7 +3,7 @@ title: 配列演算 sidebar: false --- -*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* +*配列演算は統計、数学、科学計算の基礎です。可視化、信号処理、画像処理、生命情報学、機械学習、人工知能など、現代のデータサイエンスやデータ分析の様々な分野で配列演算は中核を担っています。* 大規模なデータ処理やデータ変換には、効率的な配列演算が重要です。 データ分析や、機械学習、効率的な数値計算に最適な言語のひとつは **Python** です。 From 66c9a0e9b8dd27e41196c485a204fef200c23c80 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:06 +0200 Subject: [PATCH 175/711] New translations citing-numpy.md (Japanese) --- content/ja/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/citing-numpy.md b/content/ja/citing-numpy.md index 55c4487c1c..3ab952c040 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -1,5 +1,5 @@ --- -title: Citing NumPy +title: NumPyに関するトーク sidebar: false --- From dde102852fff617afab623d634eb7931532ff14f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:07 +0200 Subject: [PATCH 176/711] New translations code-of-conduct.md (Japanese) --- content/ja/code-of-conduct.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md index 45999314c5..044123f3d1 100644 --- a/content/ja/code-of-conduct.md +++ b/content/ja/code-of-conduct.md @@ -5,7 +5,7 @@ aliases: - /ja/conduct/ --- -### Introduction +### 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。 @@ -15,21 +15,21 @@ aliases: ### ガイドラインの概要 -We strive to: +私たちは下記の内容に真摯に取り組みます。 -1. Be open. 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。 We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. -2. Be empathetic, welcoming, friendly, and patient. 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。 Our work will be used by other people, and in turn we will depend on the work of others. 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。 We will try hard to be responsive and helpful. -5. Be careful in the words that we choose. 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。 : +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。 - * Unwelcome sexual attention. - * Excessive profanity. 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。 + * 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。 @@ -63,11 +63,11 @@ 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 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。 The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). +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。 -In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. +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。 -In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: +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 @@ -76,7 +76,7 @@ In cases not involving clear severe and obvious breaches of this Code of Conduct 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。 -### Endnotes +### 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。 From 75db4a6d82d30a8462cbd495af27b5f2e264ebc5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:08 +0200 Subject: [PATCH 177/711] New translations community.md (Japanese) --- content/ja/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/community.md b/content/ja/community.md index 5d09c81760..5884bd592e 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -33,7 +33,7 @@ _ちなみに、セキュリティの脆弱性を報告するには、GitHubの ### [Slack](https://numpy-team.slack.com) -A real-time chat room to ask questions about _contributing_ to NumPy. 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合は[こちら](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)を確認下さい。 +SlackはNumpyに_ 貢献するための質問をする_、リアルタイムのチャットルームです。 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合は[こちら](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)を確認下さい。 ## 勉強会とミートアップ From 2cf7df18acfbcea816da6ccf8dfb9b8d0c438650 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:09 +0200 Subject: [PATCH 178/711] New translations contribute.md (Japanese) --- content/ja/contribute.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index b628681551..9f270f91a9 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -30,16 +30,16 @@ 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 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。 -### Issue triaging +### 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 -* find duplicate issues and link related ones -* add good self-contained reproducers to issues -* label issues correctly (this requires triage rights -- just ask) +* 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) -Please just dive in. +ぜひ、やってみて下さい。 ### ウェブサイトの開発 @@ -49,7 +49,7 @@ Please just dive in. ### 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。 Our docs are parched for illustration; our growing website craves images -- opportunities abound. +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 @@ -63,4 +63,4 @@ Please just dive in. ### 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。 We have a number of ideas and of course we welcome more. 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。 +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。 From 5f65ebd582a4a592ab4d753078ff919298b6f5e2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:10 +0200 Subject: [PATCH 179/711] New translations gethelp.md (Japanese) --- content/ja/gethelp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/gethelp.md b/content/ja/gethelp.md index 1979cadc30..0a77e294c0 100644 --- a/content/ja/gethelp.md +++ b/content/ja/gethelp.md @@ -3,7 +3,7 @@ title: サポートを得る方法 sidebar: false --- -**ユーザーからの質問:** ユーザーからの質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)に質問を投稿することです。 規模は小さいですが、下記のような質問をする場所もあります: [IRC](https://webchat.freenode.net/?channels=%23numpy)、 [Gitter](https://gitter.im/numpy/numpy)、 [Reddit](https://www.reddit.com/r/Numpy/)。 We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! +**ユーザーからの質問:** ユーザーからの質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)に質問を投稿することです。 規模は小さいですが、下記のような質問をする場所もあります: [IRC](https://webchat.freenode.net/?channels=%23numpy)、 [Gitter](https://gitter.im/numpy/numpy)、 [Reddit](https://www.reddit.com/r/Numpy/)。 私たちはこれらのサイトを定期的に確認して、直接質問に答えるようにしていますが、質問の数は膨大です。 **開発関連の問題:** NumPyの開発関連の問題 (例: バグレポート) については、[コミュニティ](/community) のページを参照してください。 @@ -17,7 +17,7 @@ NumPyの使用方法に関する質問をするためのフォーラムです。 ### [Reddit](https://www.reddit.com/r/Numpy/) -Another forum for usage questions. +もう一つの使い方に関する質問の場です。 *** From 6e0b59c699cf3f55039883cc324a056c8bb0f45d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:11 +0200 Subject: [PATCH 180/711] New translations history.md (Japanese) --- content/ja/history.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/history.md b/content/ja/history.md index 9b011e2db8..04a5eb6432 100644 --- a/content/ja/history.md +++ b/content/ja/history.md @@ -3,7 +3,7 @@ title: NumPyの歴史 sidebar: false --- -NumPy は配列データ構造と配列に関連する高速な数値ルーチンを提供する Python 基礎的なライブラリです。 When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。 Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 +NumPy は配列データ構造と配列に関連する高速な数値ルーチンを提供する Python 基礎的なライブラリです。 開始当初は資金も少なく、主に大学院生により開発されていました。その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。少数の "野良"学生プログラマーのグループが、すでに確立されていた商用研究ソフトウェアのエコシステムをひっくり返すなんて、想像することすら馬鹿げていました。 商用ソフトは、何百万もの資金と何百人もの優秀なエンジニアに支えられていましたから。それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。現在では、NumPyは科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 このライブラリの開発開始当初は資金も少なく、主に大学院生が開発していましたが、その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。 何百万もの資金調達と何百人もの優秀なエンジニアに支えられている当時の商用研究ソフトウェアのエコシステムを、少数の "野良"学生プログラマーのグループがひっくり返すことができると想像することさえ、当時は馬鹿げていると考えられていました。 それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。 現在では、Numpy は科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 NumPy および関連ライブラリの開発におけるマイルストーンの詳細については、 [arxiv.org](arxiv.org/abs/1907.10121) を参照してください。 From d101ba52b9affb8518db97e6d19b0533bb4d8007 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:12 +0200 Subject: [PATCH 181/711] New translations install.md (Japanese) --- content/ja/install.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/install.md b/content/ja/install.md index 2b956a760b..1de57ef5e0 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -5,7 +5,7 @@ sidebar: false NumPyは`conda`、`pip` 、macOSやLinuxのパッケージマネージャー、または [ソースコード](https://numpy.org/devdocs/user/building.html)からインストールすることが出来ます。 詳細な手順について、以下の [Python と NumPyの インストールガイド](#python-numpy-install-guide) を参照してください。 -NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/user/building.html). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. +NumPyは`conda`、`pip` 、macOSやLinuxのパッケージマネージャー、または [ソースコード](https://numpy.org/devdocs/user/building.html)からインストールすることが出来ます。 詳細な手順については、以下の [Python と Numpyの インストールガイド](#python-numpy-install-guide) を参照してください。 **CONDA** @@ -75,12 +75,12 @@ Windows、macOS、Linuxのすべてのユーザー向けには: 2つ目の違いは、pipはPython Packaging Index(PyPI) からパッケージをインストールするのに対し、condaは独自のチャンネル(一般的には "defaults "や "conda-forge "など) からインストールすることです。 PyPIは最大のパッケージ管理システムですが、人気のある全てのパッケージがcondaでも利用可能です。 -最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。 pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。 PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well. +最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。 pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。 PyPIは、最大のパッケージ管理システムですが、すべての代表的なパッケージは、condaにも利用可能です。 3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！ -### 再現可能なインストール {#reproducible-installs} +### 再現可能なインストール ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に 壊れたりする可能性があります。 なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです: From 3180c2f5b1e81b4f97f83612b78d078706c2ee99 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:13 +0200 Subject: [PATCH 182/711] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index a5f8ad3bbb..4e03d6bea2 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -28,11 +28,11 @@ sidebar: false 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。 -* **Too much information** +* **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。 -* **Into the unknown** +* **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? From 58c9639599e12f014775a260e840d850f87a197d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:14 +0200 Subject: [PATCH 183/711] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index dd0850f521..8b57e07065 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -12,11 +12,11 @@ sidebar: false ## 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。 Cricket enjoys lots of media attention. 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。 Over the last several years, technology has literally been a game changer. 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。 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. 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。 +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。 From b8047f94f9e653441b9563a3b5553771f883023e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:15 +0200 Subject: [PATCH 184/711] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index ae2a74a400..2174db2e54 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -53,7 +53,7 @@ DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術 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。 -* **Combinatorics** +* **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。 From 2fa8fa02a6089ef12b64bfd77758e49123a9cfee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:16 +0200 Subject: [PATCH 185/711] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index c060650148..fe1e634e44 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -20,7 +20,7 @@ sidebar: false ### 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。 -* 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 +* 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。 @@ -31,19 +31,19 @@ sidebar: false 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。 -* **Data Deluge** +* **データの氾濫** - 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。 + 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。 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. 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。 + 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" >}} ## 重力波の検出におけるNumPyの役割 -Gravitational waves emitted from the merger cannot be computed using any technique except brute force numerical relativity using supercomputers. 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。 +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。 From cb5f69d425007284cfc5735aa9fe71be78a02276 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:17 +0200 Subject: [PATCH 186/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index fdfc29fb3b..9108eeed9a 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -19,9 +19,9 @@ params: 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 docs. + title: Experimentar o NumPy + text: Use o shell interativo para testar o NumPy no navegador + docslink: Não se esqueça de conferir a documentação. casestudies: title: ESTUDOS DE CASO features: @@ -92,7 +92,7 @@ navbar: title: Contribuir url: /pt/contribute - - title: Contribute + title: Contribuir url: /contribute footer: logo: logo.svg @@ -135,7 +135,7 @@ footer: text: Comunidade link: /pt/community - - text: User surveys + text: Pesquisas de usuário link: /pt/user-surveys - text: Contribuir From 6ab45159f1516b1481aa15047316784e20168108 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:18 +0200 Subject: [PATCH 187/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 116 ++++++++++++++++++------------------ 1 file changed, 58 insertions(+), 58 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 5c96301713..270fac1e56 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -1,27 +1,27 @@ 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 — 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) — 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: 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 — 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) — 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: 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. + 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: Array Library + text: Biblioteca de Arrays - - text: Capabilities & Application areas + text: Recursos e áreas de aplicação libraries: - title: Dask - text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + 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: NumPy-compatible array library for GPU-accelerated computing with Python. + 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 @@ -33,43 +33,43 @@ arraylibraries: url: https://github.com/google/jax - title: Xarray - text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization + 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: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + 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: Deep learning framework that accelerates the path from research prototyping to production deployment. + 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: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + 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: Deep learning framework suited for flexible research prototyping and production. + 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: A cross-language development platform for columnar in-memory data and analytics. + 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: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + 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 @@ -81,86 +81,86 @@ arraylibraries: url: https://xnd.io - title: uarray - text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + 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: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. + 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: Nearly every scientist working in Python draws on the power of NumPy. + text: Quase todos os cientistas que trabalham em Python se baseiam na potência do 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." + 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: Quantum Computing - alttext: A computer chip. + title: Computação quântica + alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg - - title: Statistical Computing - alttext: A line graph with the line moving up. + title: Computação estatística + alttext: Um gráfico com uma linha em movimento para cima. img: /images/content_images/sc_dom_img/statistical_computing.svg - - title: Signal Processing - alttext: A bar chart with positive and negative values. + 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: Image Processing - alttext: An photograph of the mountains. + title: Processamento de imagens + alttext: Uma fotografia das montanhas. img: /images/content_images/sc_dom_img/image_processing.svg - - title: Graphs and Networks - alttext: A simple graph. + title: Gráficos e Redes + alttext: Um grafo simples. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes - alttext: A telescope. + title: Processos de Astronomia + alttext: Um telescópio. img: /images/content_images/sc_dom_img/astronomy_processes.svg - - title: Cognitive Psychology - alttext: A human head with gears. + title: Psicologia Cognitiva + alttext: Uma cabeça humana com engrenagens. img: /images/content_images/sc_dom_img/cognitive_psychology.svg librariesrow2: - - title: Bioinformatics - alttext: A strand of DNA. + title: Bioinformática + alttext: Um pedaço de DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: Inferência Bayesiana + alttext: Um gráfico com uma curva em forma de sino. img: /images/content_images/sc_dom_img/bayesian_inference.svg - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: Análise Matemática + alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - title: Chemistry - alttext: A test tube. + title: Química + alttext: Um tubo de ensaio. img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience - alttext: The Earth. + title: Geociências + alttext: A Terra. img: /images/content_images/sc_dom_img/geoscience.svg - - title: Geographic Processing - alttext: A map. + title: Processamento Geográfico + alttext: Um mapa. img: /images/content_images/sc_dom_img/GIS.svg - - title: Architecture & Engineering - alttext: A microprocessor development board. + title: Arquitetura e Engenharia + alttext: Uma placa de desenvolvimento de microprocessador. img: /images/content_images/sc_dom_img/robotics.svg datascience: - intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + 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: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + 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 @@ -182,37 +182,37 @@ visualization: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png - alttext: A streamplot made in matplotlib + alttext: Um streamplot feito em matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png - alttext: A scatter-plot graph made in ggpy + 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: A box-plot made in plotly + alttext: Um box-plot feito no plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png - alttext: A streamgraph made in altair + alttext: Um gráfico streamgraph feito em 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" + 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/examples/index.html img: /images/content_images/v_pyvista.png - alttext: A 3D volume rendering made in PyVista. + alttext: Uma renderização de volume 3D feita no PyVista. - url: https://napari.org img: /images/content_images/v_napari.png - alttext: A multi-dimensionan image made in napari. + alttext: Uma imagem multidimensional, feita em napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png - alttext: A Voronoi diagram made in vispy. + alttext: Diagrama de Voronoi feito com 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 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: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. + 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. From 285140e5b6aafc44e884b23809ae9ba0aa1d511b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:20 +0200 Subject: [PATCH 188/711] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index 8a46582fc8..b5c14285a3 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -3,7 +3,7 @@ title: Quem Somos sidebar: false --- -NumPy é um projeto de código aberto visando habilitar a computação numérica com Python. Foi criado em 2005, com base no trabalho inicial das bibliotecas Numeric e Numarray. O NumPy sempre será um software 100% de código aberto, livre para que todos usem e disponibilizados sob os termos liberais da [licença BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +NumPy é um projeto de código aberto visando habilitar a computação numérica com Python. Foi criado em 2005, com base no trabalho inicial das bibliotecas Numeric e Numarray. O NumPy sempre será um software 100% de código aberto, livre para que todos usem e disponibilizados sob os termos liberais da [licença BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). 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 científica em Python mais ampla. 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). @@ -22,7 +22,7 @@ O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo p - Melissa Weber Mendonça - Eric Wieser -Emeritus: +Membros Eméritos: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -33,30 +33,30 @@ Emeritus: - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) -To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. +Para entrar em contato com o conselho diretor do NumPy, por favor envie um email para numpy-team@googlegroups.com. -## Teams +## Times -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: +A liderança do projeto NumPy trabalha ativamente na diversificação dos caminhos possíveis para contribuições.
Atualmente, o NumPy conta com os seguintes times: -- development +- desenvolvimento - documentação - triagem - website -- survey -- translations -- sprint mentors +- pesquisa +- traduções +- mentores para sprints de desenvolvimento - financiamento e bolsas -See the [Team]({{< relref "/teams" >}}) page for more info. +Veja a página sobre os [Times]({{< relref "/teams" >}}) para mais informações. -## NumFOCUS Subcommittee +## Subcomitê NumFOCUS - Charles Harris - Ralf Gommers - Melissa Weber Mendonça - Sebastian Berg -- External member: Thomas Caswell +- Membro externo: Thomas Caswell ## Patrocinadores From 144e53063ab791832182a8c26e6accfe04d7e491 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:22 +0200 Subject: [PATCH 189/711] New translations code-of-conduct.md (Portuguese, Brazilian) --- content/pt/code-of-conduct.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/code-of-conduct.md b/content/pt/code-of-conduct.md index e8fbe57696..8cf5a0fa4d 100644 --- a/content/pt/code-of-conduct.md +++ b/content/pt/code-of-conduct.md @@ -43,7 +43,7 @@ Embora sejamos receptivos às pessoas fluentes em todas as línguas, o desenvolv 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). -### Reporting Guidelines +### 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. From 943a4003d27ab23514a62b215918007c51699f7a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:23 +0200 Subject: [PATCH 190/711] New translations community.md (Portuguese, Brazilian) --- content/pt/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/community.md b/content/pt/community.md index bd7d7cf7e3..1bfe31d075 100644 --- a/content/pt/community.md +++ b/content/pt/community.md @@ -63,4 +63,4 @@ Para prosperar, o projeto NumPy precisa de sua experiência e entusiasmo. Não Se você está interessado em se tornar um contribuidor do NumPy (oba!) recomendamos que você confira nossa página sobre [Contribuições](/pt/contribute). -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/). +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/). From a3674ae0f3cc6090a5b944c0e5b00f03c9c4a1b4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:24 +0200 Subject: [PATCH 191/711] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index 863533490e..65b82636b8 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -9,13 +9,13 @@ Se você não sabe por onde começar ou como suas habilidades podem ajudar, _fal 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 ou no [Slack](https://numpy-team.slack.com) (envie um e-mail para 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). You are very welcome to join. Se você nunca contribuiu para projetos de código aberto, recomendamos fortemente que você leita [esse guia](https://opensource.guide/how-to-contribute/). +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.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +Para pessoas programadoras, este [guia](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica como contribuir para a base de código.
Confira também nosso [canal do YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) para obter informações adicionais. ### Revisar pull requests @@ -39,7 +39,7 @@ O [*issue tracker* do NumPy](https://github.com/numpy/numpy/issues) tem _um mont * adicionar bons exemplos autocontidos que reproduzam issues * rotular issues corretamente (isso requer direitos de triagem -- basta perguntar) -Please just dive in. +Sinta-se à vontade! ### Desenvolvimento do site From 1c722544f4167e252d00cb22638b9b6a13c108eb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:26 +0200 Subject: [PATCH 192/711] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/install.md b/content/pt/install.md index 5f60385839..d4349d8943 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -66,7 +66,7 @@ Para usuários que preferem uma solução baseada em pip/PyPI, por preferência ## Gerenciamento de pacotes Python -Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma [série de ferramentas](https://packaging.python.org/guides/tool-recommendations/) complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). For high-performance computing (HPC), [Spack](https://github.com/spack/spack) is worth considering. Para a maioria dos usuários NumPy, porém, o [conda](https://conda.io/en/latest/) e o [pip](https://pip.pypa.io/en/stable/) são as duas ferramentas mais populares. +Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma [série de ferramentas](https://packaging.python.org/guides/tool-recommendations/) complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para computação de alto desempenho (HPC), vale a pena considerar o [Spack](https://github.com/spack/spack). Para a maioria dos usuários NumPy, porém, o [conda](https://conda.io/en/latest/) e o [pip](https://pip.pypa.io/en/stable/) são as duas ferramentas mais populares. ### Pip & conda @@ -80,7 +80,7 @@ A segunda diferença é que o pip instala do Índice de Pacotes Python (Python P A terceira diferença é que o conda é uma solução integrada para gerenciar pacotes, dependências e ambientes, enquanto com o pip você pode precisar de outra ferramenta (há muitas!) para lidar com ambientes ou dependências complexas. -### Instalações reprodutíveis {#reproducible-installs} +### Instalações reprodutíveis À medida que as bibliotecas são atualizadas, os resultados obtidos ao executar seu código podem mudar, ou o seu código pode parar de funcionar. É importante poder reconstruir o conjunto de pacotes e versões que você está usando. A recomendação é: From 7ca8eabe44af8f74729a3d7f8b7d2828e9e7cf1f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:28 +0200 Subject: [PATCH 193/711] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index 318130dc11..e3a4b7bedc 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -7,7 +7,7 @@ Para a **documentação oficial do NumPy** visite [numpy.org/doc/stable](https:/ *** -Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. +Abaixo está uma coleção de recursos educacionais, tanto para autoaprendizado como para ensinar outros, desenvolvidos pelos colaboradores da NumPy e selecionados pela comunidade. ## Iniciantes @@ -16,12 +16,12 @@ Há uma tonelada de informações sobre o NumPy lá fora. Se você está começa **Tutoriais** * [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](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 Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). +* [NumPy Illustrated: The Visual Guide to NumPy *por Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) * [SciPy Lectures](https://scipy-lectures.org/) Além de incluir conteúdo sobre a NumPy, estas aulas oferecem uma introdução mais ampla ao ecossistema científico do Python. * [NumPy: the absolute basics for beginners ("o básico absoluto para inciantes")](https://numpy.org/devdocs/user/absolute_beginners.html) * [NumPy tutorial *por Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) -* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [Stanford CS231 *por Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide (Guia de Usuário NumPy)](https://numpy.org/devdocs) **Livros** @@ -47,7 +47,7 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos * [100 NumPy Exercises](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *por Nicolas P. Rougier* * [An Introduction to NumPy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *por M. Scott Shell* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *por 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). +* [NumPy Tutorials](https://numpy.org/numpy-tutorials) Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). **Livros** From c3526278eb53078f921a5e8c314771a603e277b5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:29 +0200 Subject: [PATCH 194/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 166 ++++++++++++++++++++++----------------------- 1 file changed, 83 insertions(+), 83 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 4c59ca91ff..fc4e6f8117 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,99 +1,99 @@ --- title: Notícias sidebar: false -newsHeader: "Fostering an Inclusive Culture: Call for Participation" +newsHeader: "Promovendo uma cultura inclusiva: Chamada de participação" date: 2023-05-10 --- -### Fostering an Inclusive Culture: Call for Participation +### Promovendo uma cultura inclusiva: Chamada de participação -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_10 de maio de 2023_ -- Promovendo uma Cultura Inclusiva: Chamada de Participação -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/). +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/). -### NumPy documentation team leadership transition +### Transição de liderança do time de documentação do NumPy -_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. +_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 1.24.0 released +### NumPy versão 1.24.0 -_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: +_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: -* New "dtype" and "casting" keywords for stacking functions. -* New F2PY features and fixes. -* Many new deprecations, check them out. -* Many expired deprecations, +* 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. -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. +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 1.23.0 released +### NumPy versão 1.23.0 -_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: +_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: -* 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. +* 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. -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. +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. -### NumFOCUS DEI research study: call for participation +### Pesquisa NumFOCUS DEI: chamada para participação -_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. +_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. -**Interested in sharing your experiences?** +**Quer compartilhar suas experiências?** -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. +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 1.19.2 +### NumPy versão 1.22.0 -_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: +_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: -* 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. +* 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 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. +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. -### Advancing an inclusive culture in the scientific Python ecosystem +### Avançando em uma cultura inclusiva no ecossistema científico de Python -_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. +_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. -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). +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). -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. +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. -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). +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). -### 2021 NumPy survey +### Pesquisa NumPy 2021 -_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. +_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. -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. +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. -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### NumPy versão 1.19.0 -_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: +_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: -- 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. +- 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. -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. +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. -### 2020 NumPy survey results +### Resultados da pesquisa NumPy 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/. +_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 1.18.0 @@ -119,7 +119,7 @@ _14 de setembro de 2020_ -- Python 3.9 será lançado em algumas semanas. Se voc - 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 1.19.2 release +### NumPy versão 1.19.2 _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. @@ -149,7 +149,7 @@ _20 de junho de 2020_ -- O NumPy 1.19.0 está disponível. Esta é a primeira ve _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 1.18.0 release +### NumPy versão 1.18.0 _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. Esta é a última versão menor que irá suportar 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`. @@ -169,32 +169,32 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Todos os lançamentos de bugfix (apenas o `z` muda no formato `x.y.z` do número da versão) não tem novos recursos; versões menores (o `y` aumenta) contém novos recursos. -- 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 1.18.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 de abril de 2020_. -- 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 1.18.2 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 de março de 2020_. -- NumPy 1.22.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. -- NumPy 1.16.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 de janeiro de 2019_. -- 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 1.18.1 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 de janeiro de 2020_. -- NumPy 1.18.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de maio de 2020_. -- NumPy 1.17.5 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 de janeiro de 2020_. -- NumPy 1.18.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 de dezembro de 2019_. -- NumPy 1.17.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julho de 2019_. -- NumPy 1.17.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 de novembro de 2019_. -- NumPy 1.15.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julho de 2018_. -- NumPy 1.14.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de janeiro de 2018_. +- 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_. From 9a0882b4f95ba0421219aa3de40dcd05a8e9b083 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:32 +0200 Subject: [PATCH 195/711] New translations teams.md (Portuguese, Brazilian) --- content/pt/teams.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/teams.md b/content/pt/teams.md index cec31b9bc2..fcbe93bcfb 100644 --- a/content/pt/teams.md +++ b/content/pt/teams.md @@ -1,9 +1,9 @@ --- -title: NumPy Teams +title: Times NumPy 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" >}})! +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" >}} @@ -17,6 +17,6 @@ We are an international team on a mission to support scientific and research com {{< include-html "static/gallery/emeritus-maintainers.html" >}} -# Governance +# Governança -For the list of people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). +Para a lista de pessoas no Conselho Diretor, veja [aqui](https://numpy.org/devdocs/dev/governance/people.html). From bad4dbe9823b79e52514090759766551a1bf89bc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:33 +0200 Subject: [PATCH 196/711] New translations user-surveys.md (Portuguese, Brazilian) --- content/pt/user-surveys.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/user-surveys.md b/content/pt/user-surveys.md index 89a2aa0460..4f60686926 100644 --- a/content/pt/user-surveys.md +++ b/content/pt/user-surveys.md @@ -1,10 +1,10 @@ --- -title: NUMPY USER SURVEYS +title: PESQUISA DE USUÁRIOS NUMPY sidebar: false --- -**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/). +**2020** O time de pesquisas da NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, conduziram a primeira pesquisa oficial sobre a comunidade NumPy. Você pode encontrar os resultados da pesquisa [aqui (em inglês)](https://numpy.org/user-survey-2020/). -**2021** The collected data is currently being analyzed. +**2021** Os dados coletados estão em análise. -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +Se você tem dúvidas ou sugestões sobre as pesquisas já realizadas ou futuras, por favor crie uma issue [aqui](https://github.com/numpy/numpy-surveys/issues). From 12405ad2fbbb7e9364b2b388294bbf86f185de14 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:35 +0200 Subject: [PATCH 197/711] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 837335ba6f..e3821f1aa7 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -16,7 +16,7 @@ Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jog 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. 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. As análises de críquete fornecem informações interessantes sobre o jogo e informações preditivas sobre os resultados do jogo. +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: @@ -49,7 +49,7 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações 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. -## NumPy’s Role in Cricket Analytics +## 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: From 2875b8361f40f705a2c0c0aa7b4175c7a788e8c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jun 2023 23:06:36 +0200 Subject: [PATCH 198/711] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index 84aa10e350..1dd02b9f92 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -72,7 +72,7 @@ As seguintes características da NumPy desempenharam um papel fundamental para a * Vetorização * Operações em arrays com máscaras * Álgebra linear -* Random Sampling +* 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. From efb3eb3af0beed9217f5c3f7ad98b3273d3cd8d8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jun 2023 04:16:19 +0200 Subject: [PATCH 199/711] New translations learn.md (Japanese) --- content/ja/learn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 8bd2838dc7..ef289fc9b0 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -1,5 +1,5 @@ --- -title: Learn +title: NumPyの学び方 sidebar: false --- @@ -16,12 +16,12 @@ NumPyについての資料は多数存在しています。 初心者の方に **動画** * [NumPy Quickstart チュートリアル](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チュートリアル](https://numpy.org/numpy-tutorials)で、いくつかのチュートリアルと教育的資料を見ることができます。 このページのゴールは、NumPyプロジェクトによる質のいい資料を提供することです。 自習と講義形式の両方を想定しており、Jupyterノートブック形式で提供されます。 もしあなた自身の資料を追加することに興味がある場合、[Github上のnumpy-tutorialsリポジトリ](https://github.com/numpy/numpy-tutorials)をチェックしてみて下さい。 +* [イラストで学ぶNumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) * [SciPyレクチャー](https://scipy-lectures.org/) NumPyだけでなく、科学的なPythonソフトウェアエコシステムを広く紹介しています。 * [NumPy: 初心者のための基本](https://numpy.org/devdocs/user/absolute_beginners.html) * [NumPy チュートリアル *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) -* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [スタンフォード大学 CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPyユーザーガイド](https://numpy.org/devdocs) **チュートリアル** From b91a2273fa765c9d525895615bf2556484fbc6ca Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jun 2023 04:16:20 +0200 Subject: [PATCH 200/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 64 ++++++++++++++++++------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 5c96301713..84828a4449 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -1,27 +1,27 @@ 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 — 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) — 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'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. + 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: Array Library + text: 配列ライブラリ - - text: Capabilities & Application areas + text: 機能と応用分野 libraries: - title: Dask - text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + 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: NumPy-compatible array library for GPU-accelerated computing with Python. + 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 @@ -33,37 +33,37 @@ arraylibraries: url: https://github.com/google/jax - title: Xarray - text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization + 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: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + 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: Deep learning framework that accelerates the path from research prototyping to production deployment. + 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: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + 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: Deep learning framework suited for flexible research prototyping and production. + 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: A cross-language development platform for columnar in-memory data and analytics. + 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 @@ -144,23 +144,23 @@ scientificdomains: alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience - alttext: The Earth. + title: 地球科学 + alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg - - title: Geographic Processing - alttext: A map. + title: 地理情報処理 + alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg - - title: Architecture & Engineering - alttext: A microprocessor development board. + 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 lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + 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: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + 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 @@ -182,37 +182,37 @@ visualization: - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries img: /images/content_images/v_matplotlib.png - alttext: A streamplot made in matplotlib + 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: A scatter-plot graph made in ggpy + 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: A box-plot made in plotly + alttext: plotyで作られた箱ひげ図 - - url: https://altair-viz.github.io/gallery/streamgraph.html + url: https://alta-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png - alttext: A streamgraph made in altair + 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: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + 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: A 3D volume rendering made in PyVista. + 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: A multi-dimensionan image made in napari. + alttext: ナパリ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: A Voronoi diagram made in vispy. + alttext: vispyで作られたボロノイ図 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's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. + 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。 From c4e5f57e732e4f419156f48929006c3d700d0927 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jun 2023 04:16:21 +0200 Subject: [PATCH 201/711] New translations news.md (Japanese) --- content/ja/news.md | 102 ++++++++++++++++++++++----------------------- 1 file changed, 51 insertions(+), 51 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index ef13fc599d..b8d94d6d13 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -15,9 +15,9 @@ How can we be better when it comes to diversity and inclusion? Read the report a _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 +### Numpy 1.24.0 リリース -_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: +_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。 * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. @@ -26,9 +26,9 @@ _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-not 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 +### Numpy 1.23.0 リリース -_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: +_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。 * Implementation of `loadtxt` in C, greatly improving its performance. * Exposure of DLPack at the Python level for easy data exchange. @@ -47,53 +47,53 @@ Please complete this brief [“Participant Interest” form](https://numfocus.ty ### NumPy 1.19.2 リリース -_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: +_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。 今回のリリースのハイライトは次のとおりです。 -* 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. +* 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 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. +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。 -### Advancing an inclusive culture in the scientific Python ecosystem +### 科学的なPythonエコシステムにおける包括的な文化の前進 -_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. +_ 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。 -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). +[ 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)。 -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. +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。 -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 NumPy survey +### 2021年度NumPyアンケート -_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. +_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。 -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. +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。 -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +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 1.19.0 リリース -_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: +_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次のとおりです。 -- 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. +- 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 -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. +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 NumPy survey results +### 2020年度 NumPy アンケート結果 -_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/. +_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 1.18.0 リリース @@ -149,7 +149,7 @@ _2020年6月20日_ -- NumPy 1.19.0 が利用可能になりました。 これ _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機会を楽しみにしています! 詳細については、 [公式ドキュメントサイト](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 release +### Numpy 1.18.0 リリース _2019年12月22日_ -- NumPy 1.18.0 が利用可能になりました。 このリリースは、1.17.0の主要な変更の後の、統合的なリリースです。 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。 @@ -160,7 +160,7 @@ _2019年12月22日_ -- NumPy 1.18.0 が利用可能になりました。 この _2019年11月15日_ -- NumPyと、NumPyの重要な依存関係の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。 -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. OpenBLASチームは、技術的に重要な問題、特にスレッド安全性、AVX-512に対処することに焦点を当てます。 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行っています。 +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、技術的に重要な問題、特にスレッド安全性、AVX-512に対処することに焦点を当てます。 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行っています。 提案されたイニシアチブと成果物の詳細については、 [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ヶ月間継続される予定です。 @@ -169,27 +169,27 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, こちらがより過去の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 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 1.24.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. +- NumPy 1.24.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. +- NumPy 1.24.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. - 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 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. +- NumPy 1.23.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.5)) -- _19 Nov 2022_. - NumPy 1.17.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _2019年10月11日_. -- 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 1.23.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. +- NumPy 1.23.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. +- NumPy 1.23.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. +- NumPy 1.23.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. +- NumPy 1.22.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. +- NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. - NumPy 1.18.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020年3月17日_. -- 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 1.22.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. +- NumPy 1.22.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. +- NumPy 1.22.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. +- NumPy 1.21.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. +- NumPy 1.21.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. +- NumPy 1.20.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. +- NumPy 1.20.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. +- NumPy 1.19.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. - NumPy 1.18.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _2020年1月6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_. From 248aace5fb01d1a232bd66238582ef0c406e3d72 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jun 2023 04:16:24 +0200 Subject: [PATCH 202/711] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 46 ++++++++++++++-------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 613b84b8db..b200124145 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -7,24 +7,24 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この [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。 -* Act in a personal manner rather than impersonal. 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。 +* 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。 It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. +* 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。 -## Mediation +## 仲介 -Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: +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。 -* Find a candidate who can serve as a mediator. -* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -* Obtain the agreement of the reported person(s). -* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -* Establish a timeline for mediation to complete, ideally within two weeks. +* 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。 @@ -40,10 +40,10 @@ Voluntary informal mediation is a tool at our disposal. In contexts such as when 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。 -* Immediately disconnect the originator from all NumPy communication channels. -* Reply to the reporter that their report has been received and that the originator has been disconnected. -* 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。 The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. 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。 -* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +* 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 @@ -52,16 +52,16 @@ Voluntary informal mediation is a tool at our disposal. In contexts such as when 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。 -The Committee will then review the incident and determine, to the best of their ability: +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。 -* What happened. +* 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。 To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. 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。 @@ -73,13 +73,13 @@ The Committee will then review the incident and determine, to the best of their 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。 - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. + - 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。 -* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. 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。 The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. +* 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。 @@ -92,4 +92,4 @@ The Committee will then review the incident and determine, to the best of their ## 利益相反 -In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. +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。 From 5fc014a3e5a53d41ec4bed9dd72141049f3be827 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jun 2023 04:16:25 +0200 Subject: [PATCH 203/711] New translations teams.md (Japanese) --- content/ja/teams.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/teams.md b/content/ja/teams.md index cec31b9bc2..c91e538a59 100644 --- a/content/ja/teams.md +++ b/content/ja/teams.md @@ -1,9 +1,9 @@ --- -title: NumPy Teams +title: NumPy開発チーム 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" >}})! +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" >}} @@ -17,6 +17,6 @@ We are an international team on a mission to support scientific and research com {{< include-html "static/gallery/emeritus-maintainers.html" >}} -# Governance +# 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). From cb2973ec433b1747d04a9e4c4b82a9f83a008cb5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jun 2023 04:16:26 +0200 Subject: [PATCH 204/711] New translations user-survey-2020.md (Japanese) --- content/ja/user-survey-2020.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md index fe431e845c..370138d6e7 100644 --- a/content/ja/user-survey-2020.md +++ b/content/ja/user-survey-2020.md @@ -1,16 +1,16 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: 2020年 NumPyコミュニティ調査 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. +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 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コミュニティ調査2020 - 結果'" width="250">}} -**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. +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。 -For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. +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。 -Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. +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。 From 7e90d8f76ace74bdd449b273f31c234a7020311e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jun 2023 04:16:27 +0200 Subject: [PATCH 205/711] New translations user-surveys.md (Japanese) --- content/ja/user-surveys.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/user-surveys.md b/content/ja/user-surveys.md index 89a2aa0460..7be9979c3a 100644 --- a/content/ja/user-surveys.md +++ b/content/ja/user-surveys.md @@ -1,10 +1,10 @@ --- -title: NUMPY USER SURVEYS +title: NumPyユーザアンケート sidebar: false --- -**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/). +**2020** NumPY調査チームは、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](https://numpy.org/user-survey-2020/)をご覧ください。 -**2021** The collected data is currently being analyzed. +**2021** 収集された調査データは現在解析中です。 -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +過去または今後のNumPyユーザ調査に関する質問や提案がある場合は、[こちら](https://github.com/numpy/numpy-surveys/issues)にイシューを作成してください。 From f67887d6494c8fa2eccbb4ad515f1c47d86ed39d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jun 2023 23:08:35 +0200 Subject: [PATCH 206/711] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index b5c14285a3..40407d3cdf 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -3,9 +3,9 @@ title: Quem Somos sidebar: false --- -NumPy é um projeto de código aberto visando habilitar a computação numérica com Python. Foi criado em 2005, com base no trabalho inicial das bibliotecas Numeric e Numarray. O NumPy sempre será um software 100% de código aberto, livre para que todos usem e disponibilizados sob os termos liberais da [licença BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). É lançado sob os termos liberais da [licença BSD modificada](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +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 científica em Python mais ampla. 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). +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) From 28d321533506bb93ce8c73ac4e1499701ebff5e1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jun 2023 00:07:17 +0200 Subject: [PATCH 207/711] New translations code-of-conduct.md (Portuguese, Brazilian) --- content/pt/code-of-conduct.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/code-of-conduct.md b/content/pt/code-of-conduct.md index 8cf5a0fa4d..fe68237a92 100644 --- a/content/pt/code-of-conduct.md +++ b/content/pt/code-of-conduct.md @@ -59,7 +59,7 @@ Atualmente, o comitê é formato por: 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 [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). -### Resolução de Incidentes & Execução do Código de Conduta +### 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). From 7f6f6286d34525abc19d39a0d15a74337de14185 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jun 2023 00:07:18 +0200 Subject: [PATCH 208/711] New translations community.md (Portuguese, Brazilian) --- content/pt/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/community.md b/content/pt/community.md index 1bfe31d075..7992ff2fd6 100644 --- a/content/pt/community.md +++ b/content/pt/community.md @@ -3,7 +3,7 @@ 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 da 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. +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. From 42376afc6175df77e77a7b30cf22c318ea7602f2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jun 2023 15:08:29 +0200 Subject: [PATCH 209/711] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index e3a4b7bedc..e20462b9c1 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -7,7 +7,7 @@ Para a **documentação oficial do NumPy** visite [numpy.org/doc/stable](https:/ *** -Abaixo está uma coleção de recursos educacionais, tanto para autoaprendizado como para ensinar outros, desenvolvidos pelos colaboradores da NumPy e selecionados pela comunidade. +Abaixo está uma coleção de recursos educacionais, tanto para autoaprendizado como para ensinar outras pessoas, desenvolvidos pelos colaboradores do NumPy e selecionados pela comunidade. ## Iniciantes From 8a4f15f5093f27b8d1c02f49bf8594db87e37abd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 17 Jun 2023 01:54:58 +0200 Subject: [PATCH 210/711] New translations about.md (Japanese) --- content/ja/about.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 26b398ad64..4b93c9f4d8 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -3,14 +3,14 @@ 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あり、[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。 It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). +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 -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. 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 (アルファベット順、姓で): +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 @@ -18,8 +18,8 @@ The NumPy Steering Council is the project's governing body. Its role is to ensur - Stephan Hoyer - Inessa Pawson - Matti Picus -- Stéfan van der Walt -- Melissa Weber Mendonça +- Stéfan van der Walt +- Melissa Weber Mendonça - Eric Wieser Emeritus: From 23a6f4f7c50506f6612f1098d0f62d4bac30d5ef Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 17 Jun 2023 03:24:38 +0200 Subject: [PATCH 211/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 1b2d2cbd23..1a3c039404 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -20,7 +20,7 @@ params: intro: - title: NumPy を試す - text: Use the interactive shell to try NumPy in the browser + 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: ドキュメント 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 @@ -103,7 +103,7 @@ footer: icon: github - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: youtube + icon: YouTube - link: https://twitter.com/numpy_team icon: twitter From c77259f1dff0226cfd17daa5aa2c281a3a9b379e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 17 Jun 2023 03:24:39 +0200 Subject: [PATCH 212/711] New translations about.md (Japanese) --- content/ja/about.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 4b93c9f4d8..3de210caec 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -22,41 +22,41 @@ Numpy運営委員会はこのプロジェクトの管理組織です。 その - Melissa Weber Mendonça - Eric Wieser -Emeritus: +過去のメンバー - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) - Marten van Kerkwijk (2017-2019) -- Travis Oliphant (project founder, 2005-2012) +- 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) +- Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) -To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. +Numpy運営委員会に連絡するには、numpy-team@googlegroups.comまでメールしてください。 ## チーム -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: +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。
Numpyには現在以下のチームがあります: -- development +- 開発 - ドキュメント - トリアージ - ウェブサイト - 調査 -- translations -- sprint mentors +- 翻訳 +- スプリントのメンター - 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。 -## NumFOCUS Subcommittee +## NumFOCUSサブ委員会 - Charles Harris - Ralf Gommers -- Melissa Weber Mendonça +- Melissa Weber Mendonça - Sebastian Berg -- External member: Thomas Caswell +- 外部メンバー: Thomas Caswell ## スポンサー情報 @@ -68,8 +68,8 @@ NumPyは以下の団体から直接資金援助を受けています。 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。 -- UC Berkeley (Stéfan van der Walt) -- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) +- 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 >}} From 57dc691471c616887508ae620d8015809e06641c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 17 Jun 2023 03:24:40 +0200 Subject: [PATCH 213/711] New translations community.md (Japanese) --- content/ja/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/community.md b/content/ja/community.md index 5884bd592e..d1d630d057 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -63,4 +63,4 @@ 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 もし、NumPyに貢献したい場合は、 [コントリビュート](/ja/contribute) ページをご覧いただくことをお勧めします。 -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/). +また、私たちのコミュニティミーティングにもぜひ参加してみてください。 コミュニティミーティングの活動を確認するには、[こちら](https://scientific-python.org/calendars/)のイベントカレンダーを確認ください。 From ee939e1ab94b578118e83255f158b7ca77943c98 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 17 Jun 2023 03:24:41 +0200 Subject: [PATCH 214/711] New translations contribute.md (Japanese) --- content/ja/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index 9f270f91a9..ec93def8e6 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -3,7 +3,7 @@ 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。 Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. +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。 **ここに** 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。 @@ -15,7 +15,7 @@ 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 ### 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。
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +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。
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 From 908f0ba1c174d7e3a967392bbe694bbd189c19a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 17 Jun 2023 03:24:42 +0200 Subject: [PATCH 215/711] New translations learn.md (Japanese) --- content/ja/learn.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index ef289fc9b0..7ab2dcde53 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -7,7 +7,7 @@ sidebar: false *** -Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. +以下は、Numpyへの貢献者とコミュニティによって開発された、NumPyの自己学習と他人への教育のための資料です。 ## 初心者向け @@ -61,7 +61,7 @@ NumPyについての資料は多数存在しています。 初心者の方に *** -## NumPy Talks +## NumPyに関する講演 * [NumPyにおけるインデックス指定の未来](https://www.youtube.com/watch?v=o0EacbIbf58) *Jaime Fernadezによる* (2016) * [Pythonにおける配列計算の進化](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *Ralf Gommersによる* (2019) @@ -71,6 +71,6 @@ NumPyについての資料は多数存在しています。 初心者の方に *** -## NumPyに関するトーク +## NumPyを引用する もし、あなたの研究においてNumPyが重要な役割を果たし、論文でこのプロジェクトについて言及したい場合は、こちらの[ページ](/ja/citing-numpy)を参照して下さい。 From a8d35077eb0795c32a3c1fb3c1fd9a7255885f2c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 17 Jun 2023 03:24:43 +0200 Subject: [PATCH 216/711] New translations news.md (Japanese) --- content/ja/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index b8d94d6d13..b27ef9843c 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -119,7 +119,7 @@ _2020年9月14日_ -- Python 3.9 は数週間後にリリースされる予定 - [`--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 release +### NumPy 1.19.2 リリース _2020年1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の固定がされています。 aarch64 wheelは最新のmanylinux2014リリースで構築されており、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を修正しています。 From cc3b347ea60d81481538d9decb2ede6759996d1d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 04:34:10 +0200 Subject: [PATCH 217/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 24 +++++++++++++++++++----- 1 file changed, 19 insertions(+), 5 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index fc4e6f8117..dfb6c6dae2 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,10 +1,23 @@ --- title: Notícias sidebar: false -newsHeader: "Promovendo uma cultura inclusiva: Chamada de participação" -date: 2023-05-10 +newsHeader: "NumPy 1.25.0 released" +date: 2023-06-17 --- +### 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. Some highlights are: + +* Support for MUSL, there are now MUSL wheels. +* Support the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum +* Support for 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 are 3.9-3.11. + ### Promovendo uma cultura inclusiva: Chamada de participação _10 de maio de 2023_ -- Promovendo uma Cultura Inclusiva: Chamada de Participação @@ -13,11 +26,11 @@ Como podemos ser melhores quando se trata de diversidade e de inclusão? Leia o ### 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. +_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 versão 1.24.0 +### NumPy 1.24.0 released -_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: +_18 de dezembro de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) está agora disponível. The highlights of the release are: * Novas palavras-chave "dtype" e "casting" para funções que atuam com stacking. * Novas funcionalidades e correções do F2PY. @@ -169,6 +182,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Todos os lançamentos de bugfix (apenas o `z` muda no formato `x.y.z` do número da versão) não tem novos recursos; versões menores (o `y` aumenta) contém novos recursos. +- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 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_. From d2affc39b40ae98358affd9a623a78ef9505607e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 04:34:12 +0200 Subject: [PATCH 218/711] New translations news.md (Japanese) --- content/ja/news.md | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index b27ef9843c..f6c1a7ec38 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,10 +1,23 @@ --- title: ニュース sidebar: false -newsHeader: "Fostering an Inclusive Culture: Call for Participation" -date: 2023-05-10 +newsHeader: "NumPy 1.25.0 released" +date: 2023-06-17 --- +### 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. Some highlights are: + +* Support for MUSL, there are now MUSL wheels. +* Support the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum +* Support for 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 are 3.9-3.11. + ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation @@ -15,9 +28,9 @@ How can we be better when it comes to diversity and inclusion? Read the report a _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 リリース +### NumPy 1.24.0 released -_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。 +_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。 The highlights of the release are: * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. @@ -169,6 +182,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存関係の1つであるO こちらがより過去の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 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. - NumPy 1.24.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. - NumPy 1.24.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. From 6a127972d3df7448d9687104329720cce1bb5747 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 04:34:13 +0200 Subject: [PATCH 219/711] New translations news.md (Spanish) --- content/es/news.md | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 64ba8e2ce2..5a42fc14c5 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,10 +1,23 @@ --- title: News sidebar: false -newsHeader: "Fostering an Inclusive Culture: Call for Participation" -date: 2023-05-10 +newsHeader: "NumPy 1.25.0 released" +date: 2023-06-17 --- +### 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. Some highlights are: + +* Support for MUSL, there are now MUSL wheels. +* Support the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum +* Support for 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 are 3.9-3.11. + ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation @@ -15,7 +28,7 @@ How can we be better when it comes to diversity and inclusion? Read the report a _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 +### 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: @@ -169,6 +182,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.0 ([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_. From de2f7abbb215ff809820881cc1d046b6222c1a22 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 04:34:14 +0200 Subject: [PATCH 220/711] New translations news.md (Arabic) --- content/ar/news.md | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 64ba8e2ce2..5a42fc14c5 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,23 @@ --- title: News sidebar: false -newsHeader: "Fostering an Inclusive Culture: Call for Participation" -date: 2023-05-10 +newsHeader: "NumPy 1.25.0 released" +date: 2023-06-17 --- +### 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. Some highlights are: + +* Support for MUSL, there are now MUSL wheels. +* Support the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum +* Support for 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 are 3.9-3.11. + ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation @@ -15,7 +28,7 @@ How can we be better when it comes to diversity and inclusion? Read the report a _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 +### 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: @@ -169,6 +182,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.0 ([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_. From 459e9ca95fa888c8ef89135a3002d4a448276c60 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 04:34:15 +0200 Subject: [PATCH 221/711] New translations news.md (Korean) --- content/ko/news.md | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 64ba8e2ce2..5a42fc14c5 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,23 @@ --- title: News sidebar: false -newsHeader: "Fostering an Inclusive Culture: Call for Participation" -date: 2023-05-10 +newsHeader: "NumPy 1.25.0 released" +date: 2023-06-17 --- +### 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. Some highlights are: + +* Support for MUSL, there are now MUSL wheels. +* Support the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum +* Support for 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 are 3.9-3.11. + ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation @@ -15,7 +28,7 @@ How can we be better when it comes to diversity and inclusion? Read the report a _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 +### 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: @@ -169,6 +182,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.0 ([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_. From 326e20bcb92d7f94a9d00bfa18b55f71b3d4d76c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 04:34:17 +0200 Subject: [PATCH 222/711] New translations news.md (Russian) --- content/ru/news.md | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 64ba8e2ce2..5a42fc14c5 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,23 @@ --- title: News sidebar: false -newsHeader: "Fostering an Inclusive Culture: Call for Participation" -date: 2023-05-10 +newsHeader: "NumPy 1.25.0 released" +date: 2023-06-17 --- +### 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. Some highlights are: + +* Support for MUSL, there are now MUSL wheels. +* Support the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum +* Support for 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 are 3.9-3.11. + ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation @@ -15,7 +28,7 @@ How can we be better when it comes to diversity and inclusion? Read the report a _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 +### 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: @@ -169,6 +182,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.0 ([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_. From c7f15d18b16b8e2796b24d660b9468741c4dffe6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 04:34:18 +0200 Subject: [PATCH 223/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 64ba8e2ce2..5a42fc14c5 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,23 @@ --- title: News sidebar: false -newsHeader: "Fostering an Inclusive Culture: Call for Participation" -date: 2023-05-10 +newsHeader: "NumPy 1.25.0 released" +date: 2023-06-17 --- +### 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. Some highlights are: + +* Support for MUSL, there are now MUSL wheels. +* Support the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum +* Support for 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 are 3.9-3.11. + ### Fostering an Inclusive Culture: Call for Participation _May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation @@ -15,7 +28,7 @@ How can we be better when it comes to diversity and inclusion? Read the report a _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 +### 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: @@ -169,6 +182,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.0 ([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_. From 08357e9d9903356acc0fc3ccaf7b6fceca9c120f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 21:46:23 +0200 Subject: [PATCH 224/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index dfb6c6dae2..9933046ff5 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,7 +1,7 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy 1.25.0 released" +newsHeader: "Lançado o NumPy 1.25.0" date: 2023-06-17 --- From 6b19b7ff6450c678b96802fff9e59e081b4afe76 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 18 Jun 2023 22:49:54 +0200 Subject: [PATCH 225/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 9933046ff5..0b2f20fea1 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -5,9 +5,9 @@ newsHeader: "Lançado o NumPy 1.25.0" date: 2023-06-17 --- -### NumPy 1.25.0 released +### Lançado o NumPy 1.25.0 -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. Some highlights are: +_17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. Alguns dos destaques são: * Support for MUSL, there are now MUSL wheels. * Support the Fujitsu C/C++ compiler. From 538ca31cff273940e17e9d7cce199ce60523a5e3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 02:16:32 +0200 Subject: [PATCH 226/711] New translations news.md (Japanese) --- content/ja/news.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index f6c1a7ec38..100af21065 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,16 +1,16 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 1.25.0 released" +newsHeader: "NumPy 1.25.0 リリース" date: 2023-06-17 --- -### NumPy 1.25.0 released +### NumPy 1.25.0 リリース -_Jun 17, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) is now available. Some highlights are: +_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。 このリリースの目玉機能は下記の通りです。 -* Support for MUSL, there are now MUSL wheels. -* Support the Fujitsu C/C++ compiler. +* 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 * Object arrays are now supported in einsum * Support for inplace matrix multiplication (`@=`). From 012b651172a4501bf7b4a77653fbd29a3988e822 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 03:24:00 +0200 Subject: [PATCH 227/711] New translations news.md (Japanese) --- content/ja/news.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 100af21065..9ea08f5a85 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -11,26 +11,26 @@ _2023年1月17日_ -- [Numpy 1.25.0](https://numpy.org/doc/stable/release/1.25.0 * 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 -* Object arrays are now supported in einsum -* Support for inplace matrix multiplication (`@=`). +* 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 (`@=`). -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. +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。 -A total of 148 people contributed to this release and 530 pull requests were merged. The Python versions supported are 3.9-3.11. +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。 サポートされている Python のバージョンは 3.9-3.11 です。 -### Fostering an Inclusive Culture: Call for Participation +### インクルーシブな文化の育成: 参加の募集 -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_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 -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 documentation team leadership transition +### NumPy ドキュメンテーションチームのリーダーの変更 -_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. +_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 released +### NumPy 1.24.0 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。 The highlights of the release are: +_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。 今回のリリースの目玉機能は下記の通りです。 * New "dtype" and "casting" keywords for stacking functions. * New F2PY features and fixes. From 4e687217b78499014459dc537d045a4663be4df2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 04:28:29 +0200 Subject: [PATCH 228/711] New translations news.md (Japanese) --- content/ja/news.md | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 9ea08f5a85..a8f6fbf7e0 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -32,31 +32,31 @@ _2023年1月6日_ –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudi _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。 今回のリリースの目玉機能は下記の通りです。 -* New "dtype" and "casting" keywords for stacking functions. -* New F2PY features and fixes. -* Many new deprecations, check them out. -* Many expired deprecations, +* 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 -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 1.25. 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 1.23.0 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。 今回のリリースの目玉機能は次のとおりです。 -* 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. +* `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 -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. +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 research study: call for participation +### NumFOCUS DEI研究への参加募集 -_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. +_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。このプロジェクトでは、オープンソースソフトウェアコミュニティにおいて、特に歴史的に代表されてこなかったグループからの貢献者が参加する際の障壁を理解することを目的としています。 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。 -**Interested in sharing your experiences?** +**あなたの経験を共有することに興味がありますか?** -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. +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 From 5fc5843cb51262fdbbaf8618fcb1f988e67b94d5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 07:29:37 +0200 Subject: [PATCH 229/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 84828a4449..0420198dc2 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -69,7 +69,7 @@ arraylibraries: url: https://github.com/apache/arrow - title: xtensor - text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + 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 @@ -81,7 +81,7 @@ arraylibraries: url: https://xnd.io - title: uarray - text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + 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/ From c5980099dced51e62515916ec06f1c2e824ee902 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 08:27:17 +0200 Subject: [PATCH 230/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 50 ++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 0420198dc2..cc476e6e26 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -87,61 +87,61 @@ arraylibraries: url: https://uarray.org/en/latest/ - title: tensorly - text: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. + 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: Nearly every scientist working in Python draws on the power of NumPy. + 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 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." + 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: Quantum Computing - alttext: A computer chip. + 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: Statistical Computing - alttext: A line graph with the line moving up. + 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: Signal Processing - alttext: A bar chart with positive and negative values. + 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: Image Processing - alttext: An photograph of the mountains. + title: 画像処理 + alttext: 山々の写真 img: /images/content_images/sc_dom_img/image_processing.svg - - title: Graphs and Networks - alttext: A simple graph. + 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: Astronomy Processes - alttext: A telescope. + title: 天文学における計算 + alttext: 望遠鏡 img: /images/content_images/sc_dom_img/astronomy_processes.svg - - title: Cognitive Psychology - alttext: A human head with gears. + 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: Bioinformatics - alttext: A strand of DNA. + title: 生命情報科学 + alttext: DNAの鎖 img: /images/content_images/sc_dom_img/bioinformatics.svg - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + 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: Mathematical Analysis - alttext: Four mathematical symbols. + 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: Chemistry - alttext: A test tube. + title: 化学 + alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg - title: 地球科学 From 5baa22c0e39101cb269330828c719369466ec92a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 08:27:18 +0200 Subject: [PATCH 231/711] New translations news.md (Japanese) --- content/ja/news.md | 44 ++++++++++++++++++++++---------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index a8f6fbf7e0..c00c9da22a 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -182,29 +182,29 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存関係の1つであるO こちらがより過去の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 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. -- NumPy 1.24.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 Apr 2023_. -- NumPy 1.24.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.2)) -- _5 Feb 2023_. -- NumPy 1.24.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.24.1)) -- _26 Dec 2022_. +- 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)) -- _19 Nov 2022_. -- NumPy 1.17.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _2019年10月11日_. -- NumPy 1.23.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.3)) -- _9 Sep 2022_. -- NumPy 1.23.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.2)) -- _14 Aug 2022_. -- NumPy 1.23.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.1)) -- _8 Jul 2022_. -- NumPy 1.23.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.23.0)) -- _22 Jun 2022_. -- NumPy 1.22.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.4)) -- _20 May 2022_. -- NumPy 1.21.6 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.6)) -- _12 Apr 2022_. -- NumPy 1.18.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020年3月17日_. -- NumPy 1.22.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.2)) -- _3 Feb 2022_. -- NumPy 1.22.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.1)) -- _14 Jan 2022_. -- NumPy 1.22.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.22.0)) -- _31 Dec 2021_. -- NumPy 1.21.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.5)) -- _19 Dec 2021_. -- NumPy 1.21.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 Jun 2021_. -- NumPy 1.20.3 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 May 2021_. -- NumPy 1.20.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 Jan 2021_. -- NumPy 1.19.5 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 Jan 2021_. -- NumPy 1.18.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _2020年1月6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_. From 11ab12d5d47176c67a50b797fb459423f7d485f8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:32 +0200 Subject: [PATCH 232/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 1a3c039404..412c36c4c2 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -10,7 +10,7 @@ params: #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: "使い始める" + buttontext: "Latest release: NumPy 1.25. View all releases" #Where the main hero button links to buttonlink: "/ja/install" #Hero image (from static/images/___) From 77d7a430cb52e39a9035ff03085d13ad5348eb80 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:34 +0200 Subject: [PATCH 233/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 9108eeed9a..621439b97b 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: A biblioteca fundamental para computação científica com Python #Button text - buttontext: "Comece aqui" + buttontext: "Latest release: NumPy 1.25. View all releases" #Where the main hero button links to buttonlink: "/pt/install" #Hero image (from static/images/___) From 05d80a434feffcf55d4f6ca600a109925ca985c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:35 +0200 Subject: [PATCH 234/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 88 ++++++++++++++++++++++++---------------------- 1 file changed, 45 insertions(+), 43 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 0b2f20fea1..62aa7d2ee6 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -7,22 +7,24 @@ date: 2023-06-17 ### Lançado o NumPy 1.25.0 -_17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. Alguns dos destaques são: +_17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. The highlights of the release are: * Support for MUSL, there are now MUSL wheels. -* Support the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum -* Support for inplace matrix multiplication (`@=`). +* 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 are 3.9-3.11. +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. ### Promovendo uma cultura inclusiva: Chamada de participação -_10 de maio de 2023_ -- Promovendo uma Cultura Inclusiva: Chamada de Participação +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -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/). +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/). ### Transição de liderança do time de documentação do NumPy @@ -30,37 +32,37 @@ _Jan 6, 2023_ –- Mukulika Pahari and Ross Barnowski are appointed as the new N ### NumPy 1.24.0 released -_18 de dezembro de 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html) está agora disponível. The highlights of the release are: +_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: * 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. +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 versão 1.23.0 -_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: +_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: * 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. +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. ### 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. +_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. -**Quer compartilhar suas experiências?** +**Interested in sharing your experiences?** -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. +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 versão 1.22.0 -_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: +_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: * 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. @@ -69,30 +71,30 @@ _31 de dezembro de 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/ * 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. +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. ### Avançando em 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. +_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 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). +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). -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. +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. -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). +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). ### 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. +_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. -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. +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. -Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Follow the link to get started: 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: +_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: - a continuação do trabalho com SIMD para suportar mais funções e plataformas, - trabalho inicial na infraestrutura e conversão de novos dtypes, @@ -101,86 +103,86 @@ _23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1 - 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. +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 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/. +_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 versão 1.18.0 -_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 release do NumPy até agora, graças a mais de 180 contribuidores. As duas novidades mais emocionantes são: +_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: - 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). +_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). ### 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. +_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. ### 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 +_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 - 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 -_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. +_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. ### 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. +_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. -Ajude-nos a melhorar o NumPy respondendo à pesquisa [aqui](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). ### O NumPy tem um novo logo! -_24 de junho de 2020_ -- NumPy agora tem um novo logo: +_Jun 24, 2020_ -- NumPy now has a new logo: NumPy logo -O logo é uma versão moderna do antigo, com um design mais limpo. Obrigado a Isabela Presedo-Floyd por projetar o novo logo, bem como o Travis Vaught pelo o logo antigo que nos serviu bem durante mais de 15 anos. +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 versão 1.20.0 -_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. +_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. ### 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). +_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 versão 1.18.0 -_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. Esta é a última versão menor que irá suportar 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`. +_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, veja as [notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0) para mais detalhes. +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### 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 da 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 com a comunidade de ferramentas de software open source fundamentais para a ciência. +_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. -Este auxílio será usado para aumentar os esforços de melhoria da documentação do NumPy, atualização do design do site, e desenvolvimento comunitário para servir melhor a nossa grande e rápida base de usuários, e 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. +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. -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. +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. ## Lançamentos -Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Todos os lançamentos de bugfix (apenas o `z` muda no formato `x.y.z` do número da versão) não tem novos recursos; versões menores (o `y` aumenta) contém novos recursos. +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 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. From 7f9e09160233646521525dbaefa9a276ecfb84f4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:36 +0200 Subject: [PATCH 235/711] New translations news.md (Japanese) --- content/ja/news.md | 92 +++++++++++++++++++++++----------------------- 1 file changed, 47 insertions(+), 45 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index c00c9da22a..57004edaa0 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -7,60 +7,62 @@ date: 2023-06-17 ### NumPy 1.25.0 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。 このリリースの目玉機能は下記の通りです。 +_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。 The highlights of the release are: * 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 (`@=`). +* Support for the Fujitsu C/C++ compiler. +* Object arrays are now supported in einsum. +* Support for the inplace matrix multiplication (`@=`). 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。 サポートされている Python のバージョンは 3.9-3.11 です。 +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. ### 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 +_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation -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。 +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 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。 +_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 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。 今回のリリースの目玉機能は下記の通りです。 +_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: * 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。 +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 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。 今回のリリースの目玉機能は次のとおりです。 +_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: * `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。 +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。このプロジェクトでは、オープンソースソフトウェアコミュニティにおいて、特に歴史的に代表されてこなかったグループからの貢献者が参加する際の障壁を理解することを目的としています。 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。 +_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. -**あなたの経験を共有することに興味がありますか?** +**Interested in sharing your experiences?** -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。 +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 1.19.2 リリース -_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。 今回のリリースのハイライトは次のとおりです。 +_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: * 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。 @@ -69,30 +71,30 @@ _2021年12月31日_ -- [Numpy 1.22.0](https://numpy.org/doc/stable/release/1.22. * 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。 +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. ### 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。 +_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. -[ 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)。 +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). -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。 +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. -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. +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。 +_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. -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。 +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. -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. +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### NumPy 1.19.0 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次のとおりです。 +_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: - 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 @@ -101,86 +103,86 @@ _2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0 - 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。 +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/ +_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 1.18.0 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) が利用可能になりました。 今回のリリースは180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。 +_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: - 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。 +_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). ### Natureに初の公式NumPy論文が掲載されました! -_2020年9月16日_ -- \[NumPyに関する初の公式論文\] (https://www.nature.com/articles/s41586-020-2649-2) が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す。 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。 +_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取り入れているのであれば、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。 ビルドインフラを新しいPythonのバージョンに適応させるのは大変な作業で、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。 wheelのリリースに備えて、以下を確認してください。 +_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 - `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1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の固定がされています。 aarch64 wheelは最新のmanylinux2014リリースで構築されており、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を修正しています。 +_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. ### 初めてのNumPyの調査が公開されました!! -_2020年7月2日_ -- このサーベイは、ソフトウェアとして、またコミュニティとしてのNumPyの開発に関する意思決定の指針となり、優先順位を設定するためのものになりました。 この調査結果は英語以外の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. +_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. -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嬉しいです。 +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy に新しいロゴができました! -_2020年6月24日_ -- NumPy に新しいロゴが作成されました: +_Jun 24, 2020_ -- NumPy now has a new logo: -NumPyのロゴ +NumPy logo -新しいロゴは、古いもの比べてモダンで、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。 +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 1.20.0 リリース -_2020年6月20日_ -- NumPy 1.19.0 が利用可能になりました。 これのリリースは 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。 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。 +_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. ### 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機会を楽しみにしています! 詳細については、 [公式ドキュメントサイト](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。 +_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 リリース -_2019年12月22日_ -- NumPy 1.18.0 が利用可能になりました。 このリリースは、1.17.0の主要な変更の後の、統合的なリリースです。 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。 +_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`. -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。 +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. ### 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の重要な依存関係の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。 +_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. -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、技術的に重要な問題、特にスレッド安全性、AVX-512に対処することに焦点を当てます。 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行っています。 +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. -提案されたイニシアチブと成果物の詳細については、 [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ヶ月間継続される予定です。 +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. ## 過去のリリース -こちらがより過去の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。 +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 1.25.0 ([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_. From 453f5667f193f0778b503ca14c11325d0a244827 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:37 +0200 Subject: [PATCH 236/711] New translations news.md (Spanish) --- content/es/news.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 5a42fc14c5..04f1e5d7ad 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -7,16 +7,18 @@ date: 2023-06-17 ### 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. Some highlights are: +_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 the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum -* Support for inplace matrix multiplication (`@=`). +* 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 are 3.9-3.11. +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 From 7d1d2acc4ea0931b571b44dd5dcf0bb3ec763384 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:38 +0200 Subject: [PATCH 237/711] New translations news.md (Arabic) --- content/ar/news.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 5a42fc14c5..04f1e5d7ad 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -7,16 +7,18 @@ date: 2023-06-17 ### 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. Some highlights are: +_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 the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum -* Support for inplace matrix multiplication (`@=`). +* 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 are 3.9-3.11. +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 From 7ac54f5d2d65f8c3277797bc0a928eb264de74bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:39 +0200 Subject: [PATCH 238/711] New translations news.md (Korean) --- content/ko/news.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 5a42fc14c5..04f1e5d7ad 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -7,16 +7,18 @@ date: 2023-06-17 ### 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. Some highlights are: +_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 the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum -* Support for inplace matrix multiplication (`@=`). +* 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 are 3.9-3.11. +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 From 75ffa28983d52ea3001438b801f23b139914a1cb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:40 +0200 Subject: [PATCH 239/711] New translations news.md (Russian) --- content/ru/news.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 5a42fc14c5..04f1e5d7ad 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -7,16 +7,18 @@ date: 2023-06-17 ### 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. Some highlights are: +_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 the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum -* Support for inplace matrix multiplication (`@=`). +* 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 are 3.9-3.11. +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 From 1fd8b6220aba0fdab8019ad1b8cf0719a52e87e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:41 +0200 Subject: [PATCH 240/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 5a42fc14c5..04f1e5d7ad 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -7,16 +7,18 @@ date: 2023-06-17 ### 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. Some highlights are: +_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 the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum -* Support for inplace matrix multiplication (`@=`). +* 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 are 3.9-3.11. +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 From 220b85d64d6ce548d88000f96f454462ce7a815d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:42 +0200 Subject: [PATCH 241/711] New translations config.yaml (Spanish) --- content/es/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index b6f50c9934..97c8a62798 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: numpy 1.24.2. View all releases." + buttontext: "Latest release: NumPy 1.25. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 559f4403f1671b41ede001c23cd7361c333edcad Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:43 +0200 Subject: [PATCH 242/711] New translations config.yaml (Arabic) --- content/ar/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index b6f50c9934..97c8a62798 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: numpy 1.24.2. View all releases." + buttontext: "Latest release: NumPy 1.25. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 891b389a3c18f1dda3e0187b50ccd29cafadee28 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:44 +0200 Subject: [PATCH 243/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 146cce2660..45d57761a2 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: Python으로 과학적 컴퓨팅을 하기 위한 기초 패키지 #Button text - buttontext: "Latest release: numpy 1.24.2. View all releases." + buttontext: "Latest release: NumPy 1.25. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 38054e8a4b590e70a1f6ec9fa70f154a71ef5d42 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:45 +0200 Subject: [PATCH 244/711] New translations config.yaml (Russian) --- content/ru/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index b6f50c9934..97c8a62798 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: numpy 1.24.2. View all releases." + buttontext: "Latest release: NumPy 1.25. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From c39b22af8425d1075c843c789c393c616438cab2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 10:12:46 +0200 Subject: [PATCH 245/711] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index b6f50c9934..97c8a62798 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: numpy 1.24.2. View all releases." + buttontext: "Latest release: NumPy 1.25. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 50cfb897600a58f796f206b99d77f3ce1d7ec7b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 17:28:42 +0200 Subject: [PATCH 246/711] New translations news.md (Korean) --- content/ko/news.md | 72 +++++++++++++++++++++++----------------------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 04f1e5d7ad..2a218bd547 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,15 +1,15 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.25.0 released" +newsHeader: "NumPy 1.25.0 출시" date: 2023-06-17 --- -### NumPy 1.25.0 released +### NumPy 1.25.0 출시 -_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: +_2023년 6월 17일_ -- 이제 [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html)을 이용할 수 있습니다. The highlights of the release are: -* Support for MUSL, there are now MUSL wheels. +* MUSL 지원, 이제 MUSL Wheel도 배포됩니다. * Support for the Fujitsu C/C++ compiler. * Object arrays are now supported in einsum. * Support for the inplace matrix multiplication (`@=`). @@ -30,7 +30,7 @@ How can we be better when it comes to diversity and inclusion? Read the report a _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 +### NumPy 1.24.0 출시 _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: @@ -149,7 +149,7 @@ Please help us make NumPy better and take the survey [here](https://umdsurvey.um _Jun 24, 2020_ -- NumPy now has a new logo: -NumPy logo +NumPy 로고 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. @@ -184,33 +184,33 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.0 ([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 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_. From c29222873d0a8a5b28a7cbd0b0836c3e62dcc5a7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:42 +0200 Subject: [PATCH 247/711] New translations 404.md (Korean) --- content/ko/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/404.md b/content/ko/404.md index da192c53c0..41504d0c8a 100644 --- a/content/ko/404.md +++ b/content/ko/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -Oops! You've reached a dead end. +앗! 잘못된 접근입니다. -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +만약 이곳에 어떤 페이지가 있어야 한다면 [Issue 열기](https://github.com/numpy/numpy.org/issues)에서 문제를 제기할 수 있습니다. From 024e59a456351ab1f05221decb352f8022c2ccb0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:43 +0200 Subject: [PATCH 248/711] New translations about.md (Korean) --- content/ko/about.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index 8c769cfc9d..ddc765e09f 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -1,14 +1,14 @@ --- -title: About Us +title: NumPy 정보 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). +NumPy는 Python을 통해 수치적 컴퓨팅을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. 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 +## 운영 위원회 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): @@ -22,20 +22,20 @@ The NumPy Steering Council is the project's governing body. Its role is to ensur - Melissa Weber Mendonça - Eric Wieser -Emeritus: +명예 회원 - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) - Marten van Kerkwijk (2017-2019) -- Travis Oliphant (project founder, 2005-2012) +- Travis Oliphant (프로젝트 설립자, 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 numpy-team@googlegroups.com. +NumPy 운영 위원회에 문의하려면, numpy-team@googlegroups.com 주소로 이메일을 보내세요. -## Teams +## 팀 The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: @@ -58,24 +58,24 @@ See the [Team]({{< relref "/teams" >}}) page for more info. - Sebastian Berg - External member: Thomas Caswell -## Sponsors +## 스폰서 -NumPy receives direct funding from the following sources: +NumPy는 다음과 같은 곳들에서 직접적으로 자금을 받습니다. {{< sponsors >}} -## Institutional Partners +## 기관 파트너 -Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: +기관 파트너는 그들의 업무의 일환으로 NumPy에 기여하는 직원을 고용하여 프로젝트를 지원하는 조직입니다. 현재 기관 파트너는 다음과 같습니다. -- UC Berkeley (Stéfan van der Walt) +- UC 버클리 (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) - NVIDIA (Sebastian Berg) {{< partners >}} -## Donate +## 후원 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. From edc17e5e67bfbdaae0ecffbae9cc32bd2382ef21 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:44 +0200 Subject: [PATCH 249/711] New translations gethelp.md (Korean) --- content/ko/gethelp.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/gethelp.md b/content/ko/gethelp.md index a427b5b1f5..7fcf03e099 100644 --- a/content/ko/gethelp.md +++ b/content/ko/gethelp.md @@ -1,34 +1,34 @@ --- -title: Get Help +title: 도움 구하기 sidebar: false --- -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! +**사용 시 질문:** 도움을 받는 가장 좋은 방법은 [StackOverflow](http://stackoverflow.com/questions/tagged/numpy)와 같이 수많은 사용자들이 답변할 수 있는 사이트에 질문을 게시하는 것입니다. 규모가 좀 더 작은 대체 사이트로는 [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), [Reddit](https://www.reddit.com/r/Numpy/)이 있습니다. 저희가 직접 이런 사이트들을 주시하거나 질문에 대해 답해드리고 싶지만, 그러기에는 질문의 양이 너무 많습니다! -**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). +**개발 이슈:** NumPy 개발 관련 문제(버그 제보 등)의 경우, [커뮤니티](/community)를 방문해주시기 바랍니다. ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) -A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) +"How do I do X in NumPy?”와 같이 사용 중 질문을 올리는 포럼입니다. [`#numpy` 태그를 사용](https://stackoverflow.com/help/tagging)해주세요. *** ### [Reddit](https://www.reddit.com/r/Numpy/) -Another forum for usage questions. +사용 중 질문을 올리는 또다른 포럼입니다. *** ### [Gitter](https://gitter.im/numpy/numpy) -A real-time chat room where users and community members help each other. +사용자와 커뮤니티 구성원이 서로를 돕는 실시간 채팅방입니다. *** ### [IRC](https://webchat.freenode.net/?channels=%23numpy) -Another real-time chat room where users and community members help each other. +사용자와 커뮤니티 구성원이 서로를 돕는 또다른 실시간 채팅방입니다. *** From 18b741a37f8431ee348e17dd0b46265d980db2c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:45 +0200 Subject: [PATCH 250/711] New translations press-kit.md (Korean) --- content/ko/press-kit.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/press-kit.md b/content/ko/press-kit.md index 2c8970bb29..ddce954013 100644 --- a/content/ko/press-kit.md +++ b/content/ko/press-kit.md @@ -1,8 +1,8 @@ --- -title: Press kit +title: 홍보 자료 sidebar: false --- -We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. +저희는 당신이 NumPy 프로젝트의 상징을 논문, 코스 자료, 발표 자료 등에 삽입하기 쉽도록 하고자 합니다. -You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). +[여기에서](https://github.com/numpy/numpy/tree/main/branding/logo) 여러 버전의 고화질 NumPy 로고를 찾을 수 있습니다. numpy.org 자료를 이용하는 경우, [NumPy 이용약관](/code-of-conduct)에 동의하게 됨을 명심하십시오. From 4fc36ccee92b1d90547d5df489ec8f7c23cf4bdd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:47 +0200 Subject: [PATCH 251/711] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md index 5586668cba..3836540453 100644 --- a/content/ko/report-handling-manual.md +++ b/content/ko/report-handling-manual.md @@ -1,22 +1,22 @@ --- -title: NumPy Code of Conduct - How to follow up on a report +title: NumPy 이용 약관 - 보고서의 후속 조치 방법 sidebar: false --- -This is the manual followed by NumPy’s Code of Conduct Committee. It’s used when we respond to an issue to make sure we’re consistent and fair. +NumPy 행동 강령 위원회는 본 설명을 따릅니다. 문제를 해결할 때 일관성과 공정성을 확보하기 위한 지침입니다. -Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today and for the future. It’s an action that we do not take lightly. When reviewing enforcement measures, the Code of Conduct Committee will keep the following values and guidelines in mind: +[행동 강령](/code-of-conduct)을 시행하면 현재와 미래의 커뮤니티에 영향을 미칩니다. 우리가 가볍게 받아들이지 않는 행동입니다. 집행 조치를 검토할 때 행동 강령 위원회는 다음 가치와 지침을 염두에 둘 것입니다. -* Act in a personal manner rather than impersonal. The Committee can engage the parties to understand the situation while respecting the privacy and any necessary confidentiality of reporters. However, sometimes it is necessary to communicate with one or more individuals directly: the Committee’s goal is to improve the health of our community rather than only produce a formal decision. -* Emphasize empathy for individuals rather than judging behavior, avoiding binary labels of “good” and “bad/evil”. Overt, clear-cut aggression and harassment exist, and we will address them firmly. But many scenarios that can prove challenging to resolve are those where normal disagreements devolve into unhelpful or harmful behavior from multiple parties. Understanding the full context and finding a path that re-engages all is hard, but ultimately the most productive for our community. -* We understand that email is a difficult medium and can be isolating. Receiving criticism over email, without personal contact, can be particularly painful. This makes it especially important to keep an atmosphere of open-minded respect for the views of others. It also means that we must be transparent in our actions, and that we will do everything in our power to make sure that all our members are treated fairly and with sympathy. -* Discrimination can be subtle and it can be unconscious. It can show itself as unfairness and hostility in otherwise ordinary interactions. We know that this does occur, and we will take care to look out for it. We would very much like to hear from you if you feel you have been treated unfairly, and we will use these procedures to make sure that your complaint is heard and addressed. -* Help increase engagement in good discussion practice: try to identify where discussion may have broken down, and provide actionable information, pointers, and resources that can lead to positive change on these points. -* Be mindful of the needs of new members: provide them with explicit support and consideration, with the aim of increasing participation from underrepresented groups in particular. -* Individuals come from different cultural backgrounds and native languages. Try to identify any honest misunderstandings caused by a non-native speaker and help them understand the issue and what they can change to avoid causing offence. Complex discussion in a foreign language can be very intimidating, and we want to grow our diversity also across nationalities and cultures. +* 비인간적이기보다는 개인적인 방식으로 행동하십시오. 위원회는 신고자의 사생활과 필요한 기밀을 존중하면서 상황을 이해하도록 당사자들을 참여시킬 수 있습니다. 그러나 때때로 한 명 이상의 개인과 직접 소통해야 할 필요가 있습니다. 위원회의 목표는 단지 공식적인 결정을 내리는 것이 아니라 우리 지역 사회의 건강을 개선하는 것입니다. +* 행동을 판단하기보다는 개인에 대한 공감을 강조하고 "좋음"과 "나쁨/악"이라는 이분법적인 레이블을 피하십시오. 노골적이고 분명한 공격성과 괴롭힘이 존재하며 우리는 단호하게 대처할 것입니다. 그러나 해결하기 어려운 것으로 입증될 수 있는 많은 시나리오는 정상적인 불일치가 여러 당사자의 도움이 되지 않거나 해로운 행동으로 귀결되는 시나리오입니다. 전체 맥락을 이해하고 모두를 다시 참여시키는 경로를 찾는 것은 어렵지만 궁극적으로 우리 커뮤니티에 가장 생산적입니다. +* 우리는 이메일이 어려운 매체이며 고립될 수 있음을 이해합니다. 개인적인 연락 없이 이메일을 통해 비판을 받는 것은 특히 고통스러울 수 있습니다. 따라서 다른 사람의 견해를 열린 마음으로 존중하는 분위기를 유지하는 것이 특히 중요합니다. 또한 투명하게 행동해야 하며 모든 구성원이 공정하고 공감하는 대우를 받을 수 있도록 최선을 다하겠다는 의미이기도 합니다. +* 차별은 미묘할 수도 있고 무의식적일 수도 있습니다. 일상적인 상호 작용에서 불공평과 적대감으로 나타날 수 있습니다. 저희는 이런 차별이 발생한다는 점을 인지하고 있으며 주의를 기울이고 조심할 것입니다. 저희는 귀하가 부당한 대우를 받았다고 느끼시는 경우 귀하의 의견을 듣고 싶습니다. 귀하의 불만 사항을 듣고 해결하기 위한 절차를 밟을 것입니다. +* 좋은 토론 관행에 참여를 늘리도록 도와주세요: 토론이 중단되었을 수 있는 부분을 파악하고 이러한 점에서 긍정적인 변화를 가져올 수 있는 실행 가능한 정보, 포인터 및 리소스를 제공하세요. +* 신입 회원의 필요를 염두에 두십시오. 특히 소외된 그룹의 참여를 늘리는 것을 목표로 명시적인 지원과 배려를 제공하십시오. +* 개인은 서로 다른 문화적 배경과 모국어를 가지고 있습니다. 원어민이 아닌 사람으로 인한 정직한 오해를 식별하고 문제를 이해하고 불쾌감을 주지 않도록 변경할 수 있는 사항을 이해하도록 돕습니다. 외국어로 복잡한 토론을 하는 것은 매우 위협적일 수 있으며 국적과 문화를 넘어 다양성을 키우고자 합니다. -## Mediation +## 중재 Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: From e7b233da24054003eab9718aa1c3cd94fb7f95c3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:48 +0200 Subject: [PATCH 252/711] New translations user-survey-2020.md (Korean) --- content/ko/user-survey-2020.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/user-survey-2020.md b/content/ko/user-survey-2020.md index fe431e845c..902100702b 100644 --- a/content/ko/user-survey-2020.md +++ b/content/ko/user-survey-2020.md @@ -1,16 +1,16 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: 2020 NUMPY 커뮤니티 설문조사 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. +2020년, NumPy 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. 75개국 내 1200명 이상의 사용자 여러분들께서 저희가 NumPy 커뮤니티의 가닥을 잡을 수 있도록 도와주기 위해 참여해주셨으며 프로젝트의 미래에 대한 생각을 표현해주셨습니다. -{{< 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="'NumPy Community Survey 2020 - results'라는 제목이 붙은 2020년 NumPy 사용자 설문조사 보고서 표지" width="250">}} -**[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. +**[보고서를 내려받아서](/surveys/NumPy_usersurvey_2020_report.pdf)** 설문조사 결과를 자세히 들여다 보세요. -For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. +요점만 보시려면, **[이 인포그래픽](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**을 참고하시기 바랍니다. -Ready for a deep dive? Visit **https://numpy.org/user-survey-2020-details/**. +더욱 자세한 정보가 궁금하신가요? **https://numpy.org/user-survey-2020-details/** 페이지를 방문하세요. From fadf0b66e78a6404b232588e7704bc7d1f5dba93 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:49 +0200 Subject: [PATCH 253/711] New translations user-surveys.md (Korean) --- content/ko/user-surveys.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/user-surveys.md b/content/ko/user-surveys.md index 89a2aa0460..9fec78190d 100644 --- a/content/ko/user-surveys.md +++ b/content/ko/user-surveys.md @@ -1,10 +1,10 @@ --- -title: NUMPY USER SURVEYS +title: NUMPY 사용자 설문조사 sidebar: false --- -**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/). +**2020년** NumPy 조사 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. [여기](https://numpy.org/user-survey-2020/)서 조사 결과를 확인하세요. -**2021** The collected data is currently being analyzed. +**2021년** 수집한 데이터가 현재 분석 중입니다. -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +과거나 미래 설문조사에 대해 질문이나 제안 사항이 있으시면, [여기](https://github.com/numpy/numpy-surveys/issues)서 이슈를 생성하세요. From 84634a55d0db05bd72cbea5e35bd4aea918207a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:50 +0200 Subject: [PATCH 254/711] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 60 +++++++++++----------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index f2460d3d5b..f16b121250 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -1,68 +1,68 @@ --- -title: "Case Study: First Image of a Black Hole" +title: "사례 연구: 최초의 블랙홀 사진" 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" caption="**블랙홀 M87**" alt="블랙홀 사진" attr="*(사진 크레딧: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}
-

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

-
Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
+

M87 블랙홀을 시각화하는 것은 정의상 볼 수 없는 것을 보려고 하는 것과도 같다.

+
Katie Bouman, 캘리포니아 공과대학 컴퓨터과학부 겸 수리과학부 연구 조교수
-## A telescope the size of the earth +## 지구 크기의 망원경 -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! +[사건의 지평선 망원경(EHT)](https://eventhorizontelescope.org)은 8개의 지상 전파 망원경으로 구성된 지구 크기의 전산 망원경으로, 전례없는 감도와 해상도로 우주를 연구하는 데 쓰입니다. 초장기선 간섭 관측법(VLBI)이라는 기술을 사용하는 거대한 가상 망원경의 각해상도는 [20 마이크로각초][resolution]에 달하며 파리의 길거리 카페에서 뉴욕의 신문을 읽기에 충분한 정도입니다! -### Key Goals and Results +### 주요 목표 및 결과 -* **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. +* **우주를 보는 새로운 방식:** EHT라는 획기적인 발상의 토대는 [아서 에딩턴 경][eddington]의 관측으로 아인슈타인의 일반 상대성이론이 최초로 관측적 지지를 받았던 시기인 100년 전에 마련되었습니다. -* **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. +* **블랙홀:** EHT는 처녀자리 은하단의 Messier 87(M87) 은하의 중심부에 있는 초대질량 블랙홀로 훈련되었으며 이는 지구에서 약 5500만 광년 떨어져 있습니다. 이 천체의 질량은 태양의 65억 배입니다. [100년 넘게](https://www.jpl.nasa.gov/news/news.php?feature=7385) 연구되었으나, 블랙홀을 시각적으로 볼 수 있게 구현한 바는 없었습니다. -* **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. +* **관찰과 이론의 비교:** 아인슈타인의 일반 상대성이론에 따라 과학자들은 중력의 시공간 왜곡이나 빛 흡수에 의해 어둡게 보이는 영역이 나타날 것으로 예측하였습니다. 과학자들은 이를 블랙홀의 엄청난 질량을 재는 데 이용할 수 있었죠. -### The Challenges +### 도전 -* **Computational scale** +* **계산의 규모** - EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. + EHT는 급격한 대기 위상의 변동, 큰 기록 대역폭, 완전히 다르고 지리적으로 분산된 망원경 등의 문제를 포함한 막대한 데이터를 처리해야 하는 문제를 낳습니다. -* **Too much information** +* **지나치게 많은 정보** - 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. + EHT는 매일 350 테라바이트의 관측 결과를 생성하며, 이 정보는 헬륨으로 채운 하드 드라이브에 저장됩니다. 이토록 많은 데이터의 양과 복잡성을 줄여나가는 것은 지극히 어려운 일입니다. -* **Into the unknown** +* **잘 알지 못함** - When the goal is to see something never before seen, how can scientists be confident the image is correct? + 만약 목표가 이전에 본 적이 없는 것을 보는 것이라면, 과학자들은 어떻게 이 사진이 옳다고 입증할 수 있을까요? -{{< 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" class="csfigcaption" caption="**EHT 데이터 처리 파이프라인**" alt="데이터 파이프라인" align="middle" attr="(다이어그램 크레딧: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}} -## NumPy’s Role +## NumPy의 역할 -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? +데이터에 만약 문제가 있다면 어떨까요? 아니면 알고리즘이 특정 가정에 지나치게 의존할 수도 있습니다. 매개변수 하나만 달라져도 사진이 크게 바뀔까요? -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. +EHT는 기존 및 최첨된 이미지 재구성 기술을 모두 사용한 뒤, 개개의 팀이 데이터를 평가하도록 하여 이런 문제를 해결했습니다. 결과가 일관적이라는 것을 검증한 뒤, 이들을 결합해 최초의 블랙홀 이미지를 만들어내었습니다. -Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. +그들의 연구는 협업 데이터 분석을 통해 과학을 발전시키는 과학적인 Python 생태계의 역할을 보여줍니다. -{{< 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" class="fig-center" alt="numpy의 역할" caption="**블랙홀 시각화에서 NumPy의 역할**" >}} -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. +예를 들어, [`eht-imaging`][ehtim] Python 패키지는 VLBI 데이터를 통해 실험이나 이미지 재구성을 수행할 때 필요한 도구를 제공합니다. NumPy는 아래 소프트웨어 종속성 차트에 나와 있는 것처럼 이 패키지에서 사용되는 배열 데이터 처리의 핵심 역할을 합니다. -{{< 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" class="fig-center" alt="numpy를 강조하는 ehtim의 종속성 맵" caption="**NumPy를 강조하는 ehtim 패키지의 소프트웨어 종속성 차트**" >}} -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. +NumPy 외에도 [SciPy](https://www.scipy.org)와 [Pandas](https://pandas.io) 등의 다른 많은 패키지가 블랙홀을 시각화하는 데이터 처리 파이프라인의 일부입니다. 표준 천문 파일 형식과 시간/좌표 변환에는 [Astropy][astropy]가 쓰였고 [Matplotlib][mpl]는 분석 과정 전체에서 블랙홀의 최종 사진을 생성하는 등 데이터를 시각화하는 데 쓰였습니다. -## Summary +## 요약 -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. +NumPy의 핵심 기능인 효율적이고 유용한 n차원 배열은 연구자들이 대규모 수치 데이터셋을 다룰 수 있도록 하여 최초의 블랙홀 사진을 만드는 데 토대를 제공했습니다. 이번 관측은 아인슈타인의 이론에 훌륭한 시각적 증거를 준 관측으로, 과학계에 한 획을 그은 순간이었습니다. 기술적 혁신뿐만 아니라 200명 이상의 과학자와 세계 최고의 전파 관측소 간의 국제 협력도 이루어 냈습니다. 기존의 천문학 모델을 개선한 혁신적인 알고리즘과 데이터 처리 기술이 우주의 비밀을 알아내는 데 도움을 주었습니다. -{{< 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" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole -[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment +[eddington]: https://ko.wikipedia.org/wiki/%EC%95%84%EC%84%9C_%EC%8A%A4%ED%83%A0%EB%A6%AC_%EC%97%90%EB%94%A9%ED%84%B4#%EC%9D%BC%EB%B0%98%EC%83%81%EB%8C%80%EC%84%B1%EC%9D%B4%EB%A1%A0%EC%9D%98_%EC%8B%A4%ED%97%98%EC%A0%81_%EA%B2%80%EC%A6%9D [ehtim]: https://github.com/achael/eht-imaging From 34c5a68e6a0ae7a42365402fbe531d60eeabadda Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:51 +0200 Subject: [PATCH 255/711] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 66 ++++++++++---------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index db140f858c..6e7e54aa2a 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -1,64 +1,64 @@ --- -title: "Case Study: Cricket Analytics, the game changer!" +title: "사례 연구: 판도를 뒤집은 크리켓 통계!" 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="**인도 최대의 크리켓 축제인 IPLT20**" alt="인도 프리미어 리그 크리켓 컵 및 경기장" attr="*(사진 출처: IPLT20 (컵 및 로고) & Akash Yadav (경기장))*" attrlink="https://unsplash.com/@aksh1802" >}}
-

You don't play for the crowd, you play for the country.

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—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
+

군중을 위해서가 아니라, 국가를 위해 뛰는 겁니다.

+
—M S Dhoni, 국제 크리켓 선수, 전 팀장, 인도 팀, IPL의 Chennai Super Kings에서 활약
-## About Cricket +## 크리켓이란 -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. +인도인들이 크리켓과 사랑에 빠졌다고 해도 과언이 아닙니다. 크리켓은 인도의 거의 모든 지역 구석구석에서 시골이든 도시든 상관없이 사랑받고 있습니다. 다른 스포츠와 달리 인도의 수십억 명을 연결하는 매개체 역할을 하는 데다 남녀노소 모두에게 인기가 있습니다. 크리켓은 많은 미디어의 관심을 받고 있기도 합니다. 엄청난 [돈](https://www.statista.com/topics/4543/indian-premier-league-ipl/)과 명성이 달려 있기도 하죠. 최근 몇 년 동안, 기술이 이 분야의 판도를 뒤집어 버렸습니다. 청중들은 스트리밍 미디어, 토너먼트, 모바일 기기를 통해 실시간 크리켓 경기를 저렴하게 볼 수 있습니다. -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. +인도 프리미어 리그(IPL)는 2008년 설립되어 20개 팀으로 구성된 프로 크리켓 리그입니다. 이는 세계에서 가장 참가자가 많은 크리켓 이벤트 중 하나로, 2019년에 [67억 달러](https://en.wikipedia.org/wiki/Indian_Premier_League)에 달하는 가치로 추산됩니다. -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. +크리켓은 숫자의 게임이다 - 타자가 득점하고, 위켓이 가져간다. 크리켓 팀이 이긴 경기, 타자가 이긴 횟수 일종의 볼링 공격 등에 특정한 방식으로 반응합니다. NumPy와 같은 수치 컴퓨팅 소프트웨어로 구동되는 강력한 분석 도구를 통해 크리켓의 성능 향상과 사업 기회, 전반적인 시장, 경제학을 연구하기 위한 크리켓 숫자를 파헤칠 수 있는 능력은 큰 일이다. 크리켓 분석은 게임에 대한 흥미로운 통찰력과 게임 결과에 대한 예측 지능을 제공한다. -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: +오늘날, 크리켓 경기 기록의 풍부하고 거의 무한한 트로브가 있습니다. 이용 가능한 통계, 예를 들어, [ ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) 및 [cricsheet](https://cricsheet.org). 이들 및 몇몇 그러한 크리켓 데이터베이스는 최신 머신 러닝 및 예측 모델링 알고리즘을 이용한 [크리켓 분석](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances)에 사용되어 왔습니다. 게임과 관련된 전문 스포츠 기관과 함께 미디어 및 엔터테인먼트 플랫폼은 경기 승리 기회를 향상시키기 위한 주요 측정 기준을 결정하기 위한 기술 및 분석을 사용합니다. -* 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 +* 타격 성적 이동 평균, +* 점수 예측, +* 다른 상대에 맞서 선수의 체력과 경기력에 대한 통찰력을 얻기, +* 팀 구성에 대한 전략적 결정을 내리는 플레이어의 승패 기여도 -{{< 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="**경기장의 중심이 되는 크리켓 피치**" alt="볼러와 배트맨으로 이루어진 크리켓 피치" align="middle" attr="*(사진 출처: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}} -### Key Data Analytics Objectives +### 데이터 분석의 주요 목표 -* 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. +* 스포츠 데이터는 크리켓에서뿐만 아니라 [다른 스포츠](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)에서도 팀의 전체 역량과 승리 확률을 높이는 데 쓰입니다. +* 실시간 데이터 분석은 경기 중에도 팀과 관련 사업의 변화하는 전략에 대한 통찰력을 확보하여 경제적 이익과 성장을 도모하는 데 도움이 될 수 있습니다. +* 과거 분석 외에도 예측 모델을 활용하여 상당한 수의 크런칭과 데이터 과학 노하우, 시각화 도구 및 분석에 더 새로운 관찰을 포함시킬 수 있는 기능이 필요한 가능한 일치 결과를 결정합니다. -{{< 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="자세 예측" caption="**크리켓 자세 예측**" attr="*(사진 출처: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} -### The Challenges +### 도전 -* **Data Cleaning and preprocessing** +* **데이터 정리 및 전처리** - 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. + IPL은 크리켓을 고전적인 테스트 매치 형식에서 훨씬 더 큰 규모로 확대시켰습니다. 매 시즌 다양한 형식으로 열리는 경기의 수가 증가하고 있으며, 데이터, 알고리즘, 최신 스포츠 데이터 분석 기술, 시뮬레이션 모델 또한 증가하고 있습니다. 크리켓 데이터 분석에는 필드 매핑, 플레이어 추적, 공 추적, 플레이어의 타격 분석 및 공이 어떻게 움직이는지에 대한 각도, 스핀, 속도, 궤도 등 다른 많은 종류의 데이터를 필요로 합니다. 이 수많은 인자들은 데이터 정리 및 전처리 과정의 복잡성을 증가시켰습니다. -* **Dynamic Modeling** +* **동적 모델링** - 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. + 크리켓에서는 다른 스포츠와 마찬가지로 다양한 선수의 수, 선수의 속성, 공이나 잠재적 행동의 가능성 등 여러 가능성을 추적할 때 많은 변수가 작용합니다. 데이터 분석 및 모델링의 복잡성은 분석 중 제시되는 예측 질문의 종류에 비례하며, 데이터 표현 및 모델에 크게 의존합니다. 타자가 다른 각도나 속도로 공을 쳤을 때 일어날 일과 같은 동적인 크리켓 경기를 예측할 때, 계산이나 데이터 비교 측면에서 상황이 훨씬 더 어려워집니다. -* **Predictive Analytics Complexity** +* **예측 분석의 복잡성** - 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. + 크리켓에서 의사결정의 상당 부분은 '볼 전달이 특정 유형일 경우 타자가 얼마나 자주 특정 종류의 샷을 하느냐', '배트맨이 특정 방식으로 전달에 반응하면 볼러가 라인과 길이를 어떻게 바꾸느냐' 등의 질문에 따른 것입니다. 이러한 예측 분석 쿼리는 매우 세분화된 데이터셋 가용성과 데이터를 합성하고 정확도가 높은 생성 모델을 만들 수 있는 기능이 필요합니다. -## NumPy’s Role in Cricket Analytics +## 크리켓 분석에서 NumPy의 역할 -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: +스포츠 분석은 현재 매우 활발한 분야입니다. 많은 연구자들과 기업체에서는 최신 머신러닝 및 AI 기법을 쓰는 대신 [NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)나 Scikit-learn, SciPy, Matplotlib, Jupyter같은 PyData 패키지를 이용합니다. NumPy는 크리켓과 관련된 여러 스포츠 통계에 다음과 같이 쓰였습니다. -* **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. +* **통계적 분석:** NumPy의 수치적 기능은 다양한 플레이어 및 게임 전술에서 관찰 데이터 또는 경기의 통계적 중요성을 추정하는 데 도움을 주거나, 생성적 또는 정적 모델과 비교하여 게임 결과를 추정합니다. 전술 분석에는 [인과 분석](https://amplitude.com/blog/2017/01/19/causation-correlation) 및 [빅데이터 접근법](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/)이 쓰입니다. -* **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. +* **데이터 시각화:** 그래프 그리기 및 [시각화](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b)는 다양한 데이터셋 사이의 관계를 볼 수 있는 유용한 관점을 제공해 줍니다. -## Summary +## 요약 -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. +스포츠 분석은 프로 게임의 판도를 바꿀 것입니다. 특히 최근까지는 주로 "직감"이나 과거부터 내려오던 것을 답습하는 식으로 이뤄진 전략적 의사 결정에 대해서 말입니다. NumPy는 데이터 분석, 기계 학습 및 AI 알고리즘과 관련하여 더욱 높은 수준의 기능을 제공하는 Python 패키지들의 견고한 기반을 제공합니다. 이들 패키지는 크리켓 경기뿐 아니라 크리켓 관련 추론이나 사업을 추진하면서, 판도를 바꿀만한 결정을 이끌어 내는 영감을 실시간으로 제공하는 데 널리 이용되고 있습니다. 크리켓 경기의 결과로 이어지는 숨겨진 매개변수, 패턴이나 속성을 찾는 것은 관계자가 숫자와 통계에 숨겨진 게임을 분석하는 방법을 파악하는 데 도움이 됩니다. -{{< 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="NumPy를 크리켓 분석에 사용했을 때의 이익을 보여주는 다이어그램" caption="**활용된 주요 NumPy 기능**" >}} From 0dc3f686fd3d3e90c16947719fea7b92d8a041e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:52 +0200 Subject: [PATCH 256/711] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 90 +++++++++++------------ 1 file changed, 45 insertions(+), 45 deletions(-) diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md index b40ed2af50..d8008a244c 100644 --- a/content/ko/case-studies/deeplabcut-dnn.md +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -1,89 +1,89 @@ --- -title: "Case Study: DeepLabCut 3D Pose Estimation" +title: "사례 연구: DeepLabCut 3D 포즈 추정" 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="**DeepLapCut을 활용한 쥐의 손 움직임 분석**" alt="쥐 손 애니메이션" attr="*(출처: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}}
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Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

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—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
+

오픈 소스 소프트웨어는 생물 의학을 가속화하고 있습니다. DeepLabCut은 딥 러닝을 사용하여 동물 행동에 대한 자동화된 비디오 분석을 가능하게 합니다.

+
—알렉산더 마티스, 조교수, 로잔 연방 공과대학교 (EPFL)
-## About DeepLabCut +## DeepLabCut 소개 -[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. +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) 은 전 세계 수백 개 기관의 연구원이 인간 수준의 정확도로 매우 적은 훈련 데이터로 실험실 동물의 행동을 추적할 수 있는 오픈 소스 도구 상자입니다. DeepLabCut 기술을 통해 과학자들은 동물 종과 시간 척도에 걸쳐 운동 제어 및 행동에 대한 과학적 이해를 더 깊이 탐구할 수 있습니다. -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. +신경 과학, 의학 및 생체 역학을 포함한 여러 연구 분야에서 동물의 움직임을 추적한 데이터를 사용합니다. DeepLabCut은 필름에 기록된 동작을 구문 분석하여 인간과 다른 동물이 하는 일을 이해하는 데 도움을 줍니다. 심층 신경망 기반 데이터 분석과 함께 태깅 및 모니터링의 힘든 작업에 자동화를 사용하는 DeepLabCut은 영장류, 생쥐, 물고기, 파리 등과 같은 동물 관찰과 관련된 과학적 연구를 훨씬 빠르고 정확하게 만듭니다. -{{< 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" class="fig-center" caption="**경주마 신체 부위의 위치를 트래킹하는 색 점**" alt="경주마 애니메이션" attr="*(출처: 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. +DeeDeepLabCut의 동물 자세 추출을 통한 동물의 비침습적 행동 추적은 생체 역학, 유전학, 행동학 & 신경 과학과 같은 영역에서 과학적 추구에 매우 중요합니다. 동적으로 변화하는 배경에서 마커 없이 비디오에서 동물 포즈를 비침습적으로 측정하는 것은 기술적으로 뿐만 아니라 필요한 리소스 요구 사항 및 필요한 훈련 데이터 측면에서 계산적으로 어려운 일입니다. -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]. +DeepLabCut을 사용하면 연구자가 피험자의 자세를 추정하고 Python 기반 소프트웨어 툴킷을 통해 행동을 정량화할 수 있습니다. DeepLabCut을 사용하여 연구원은 비디오에서 고유한 프레임을 식별하고 맞춤형 GUI를 사용하여 수십 개의 프레임에서 특정 신체 부위에 디지털 레이블을 지정한 다음 DeepLabCut의 딥 러닝 기반 포즈 추정 아키텍처가 나머지 프레임에서 동일한 기능을 선택하는 방법을 학습할 수 있습니다. 그것은 파리와 생쥐와 같은 일반적인 실험실 동물에서 [치타][cheetah-movement]와 같은 좀 더 특이한 동물에 이르기까지 다양한 동물 종에 걸쳐 작동합니다. -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. +DeepLabCut은 [전이 학습](https://arxiv.org/pdf/1909.11229), 이라는 원리를 사용하여 필요한 훈련 데이터의 양을 크게 줄이고 훈련 기간의 수렴 속도를 높입니다. 필요에 따라 사용자는 실시간 실험 피드백과 결합할 수 있는 더 빠른 추론(예: MobileNetV2)을 제공하는 다양한 네트워크 아키텍처를 선택할 수 있습니다. DeepLabCut은 원래 [DeeperCut](https://arxiv.org/abs/1605.03170),이라는 최고 성능의 인간 포즈 추정 아키텍처의 특징 검출기를 사용했습니다. 이 이름에 영감을 받았습니다. 이제 패키지가 추가 아키텍처, 증강 방법 및 전체 프런트 엔드 사용자 경험을 포함하도록 크게 변경되었습니다. 또한 대규모 생물학적 실험을 지원하기 위해 DeepLabCut은 능동적 학습 기능을 제공하므로 사용자는 시간이 지남에 따라 트레이닝 세트를 늘려 엣지 케이스를 다루고 특정 상황 내에서 포즈 추정 알고리즘을 강력하게 만들 수 있습니다. -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. +최근에는, 영장류의 안면 분석부터 개 자세까지 다양한 종과 실험 조건에 대해 사전 훈련된 모델을 제공하는 [DeepLabCut 모델 동물원](http://www.mousemotorlab.org/dlc-modelzoo) 이 도입되었습니다. 예를 들어 새 데이터의 레이블 지정이나 신경망 교육 없이 클라우드에서 실행할 수 있으며 프로그래밍 경험이 필요하지 않습니다. -### Key Goals and Results +### 주요 목표 및 결과 -* **Automation of animal pose analysis for scientific studies:** +* **과학적 연구를 위한 동물 자세 분석 자동화:** - 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). + DeepLabCut 기술의 주요 목표는 다양한 환경에서 동물의 자세를 측정하고 추적하는 것입니다. 예를 들어, 이 데이터는 뇌가 움직임을 제어하는 ​​방법을 이해하거나 동물이 사회적으로 상호 작용하는 방식을 설명하기 위해 신경 과학 연구에서 사용될 수 있습니다. 연구원들은 DeepLabCut으로 [성능이 10배 향상](https://www.biorxiv.org/content/10.1101/457242v1) 되는 것을 관찰했습니다. 포즈는 최대 1200 초당 프레임 수(FPS)로 오프라인에서 추론할 수 있습니다. -* **Creation of an easy-to-use Python toolkit for pose estimation:** +* **포즈 추정을 위해 사용하기 쉬운 Python 툴킷 생성:** - 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. + DeepLabCut은 연구자들이 쉽게 채택할 수 있는 사용하기 쉬운 도구 형태로 동물 자세 추정 기술을 공유하고 싶었습니다. 그래서 그들은 프로젝트 관리 기능도 포함된 완전하고 사용하기 쉬운 Python 툴킷을 만들었습니다. 이를 통해 포즈 추정의 자동화는 물론 DeepLabCut Toolkit 사용자가 데이터 세트 수집 단계부터 공유 가능하고 재사용 가능한 분석 파이프라인 생성에 이르기까지 프로젝트 전체를 관리할 수 있습니다. - Their [toolkit][DLCToolkit] is now available as open source. + [툴킷][DLCToolkit]은 현재 오픈 소스로 제공됩니다. - A typical DeepLabCut Workflow includes: + 일반적인 DeepLabCut 작업 흐름에는 다음이 포함됩니다. - - 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 + - 능동적 학습을 통한 훈련 세트 생성 및 정제 + - 특정 동물 및 시나리오를 위한 맞춤형 신경망 생성 + - 동영상에 대한 대규모 추론을 위한 코드 + - 통합 시각화 도구를 사용하여 추론 도출 -{{< 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" class="csfigcaption" caption="**포즈 추정 단계 - DeepLabCut**" alt="DLC 단계" align="middle" attr="(출처: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} -### The Challenges +### 도전 -* **Speed** +* **속도** - 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. + 동물의 행동을 측정하고 동시에 과학 실험을 보다 효율적이고 정확하게 하기 위해 동물 행동 비디오를 빠르게 처리합니다. 동적으로 변화하는 배경에서 마커 없이 실험실 실험을 위해 상세한 동물 포즈를 추출하는 것은 기술적으로 뿐만 아니라 필요한 리소스 요구 사항 및 필요한 교육 데이터 측면에서 어려울 수 있습니다. 컴퓨터 비전 전문 지식과 같은 기술 없이도 사용하기 쉬운 도구를 생각해내어 과학자들이 보다 실제 상황에서 연구를 수행할 수 있도록 하는 것은 해결해야 할 사소한 문제가 아닙니다. -* **Combinatorics** +* **조합론** - 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. + 조합론은 여러 사지의 움직임을 개별 동물 행동으로 조립하고 통합하는 것을 포함합니다. 키포인트와 연결을 개별 동물 움직임으로 조립하고 시간에 따라 연결하는 것은 특히 실험 비디오에서 여러 동물 움직임을 추적하는 경우 강력한 수치 분석이 필요한 복잡한 프로세스입니다. -* **Data Processing** +* **데이터 처리** - Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. + 마지막으로 배열 조작 - 다양한 이미지, 대상 텐서 및 키포인트에 해당하는 대규모 배열 스택을 처리하는 것은 상당히 어렵습니다. -{{< 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" class="csfigcaption" caption="**포즈 추정 변수 및 복잡도**" alt="난점 설명" align="middle" attr="(출처: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} -## NumPy's Role in meeting Pose Estimation Challenges +## 포즈 추정 문제를 해결하는 NumPy의 역할 -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). +NumPy는 행동 분석을 위한 고속 수치 계산에 대한 DeepLabCut 기술의 핵심 요구 사항을 해결합니다. NumPy 외에도 DeepLabCut은 [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) 그리고 [Tensorflow](https://www.tensorflow.org) 와 같이 핵심에서 NumPy를 활용하는 다양한 Python 소프트웨어를 사용합니다. -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: +NumPy의 다음 기능은 이미지 처리, 조합 요구 사항 및 DeepLabCut 포즈 추정 알고리즘의 빠른 계산 요구 사항을 해결하는 데 중요한 역할을 했습니다. -* Vectorization -* Masked Array Operations -* Linear Algebra -* Random Sampling -* Reshaping of large arrays +* 벡터화 +* 마스킹된 어레이 작업 +* 선형 대수학 +* 무작위 샘플링 +* 큰 배열의 재구성 -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”. +DeepLabCut은 툴킷에서 제공하는 워크플로 전체에서 NumPy의 어레이 기능을 활용합니다. 특히, NumPy는 사람의 주석 레이블 지정을 위한 개별 프레임 샘플링과 주석 데이터 작성, 편집 및 처리에 사용됩니다. TensorFlow 내에서 신경망은 DeepLabCut 기술로 수천 번의 반복을 통해 훈련되어 프레임에서 실측 주석을 예측합니다. 이를 위해 이미지 대 이미지 변환 문제로 포즈 추정을 캐스팅하기 위해 목표 밀도(점수 지도)가 생성됩니다. 신경망을 강력하게 만들기 위해 다양한 기하 및 이미지 처리 단계에 따라 대상 스코어맵을 계산해야 하는 데이터 확대가 사용됩니다. 학습을 빠르게 하기 위해 NumPy의 벡터화 기능이 활용됩니다. 추론을 위해 대상 스코어맵에서 가장 가능성이 높은 예측을 추출해야 하며 효율적으로 "개별 동물을 조립하기 위해 예측을 연결"해야 합니다. -{{< 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" class="fig-center" caption="**DeepLabCut 워크플로우**" alt="워크플로우" attr="*(출처: 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 +## 요약 -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. +행동을 관찰하고 효율적으로 설명하는 것은 현대 행동학, 신경과학, 의학 및 기술의 핵심 테넌트입니다. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf)을 사용하면 연구자가 피험자의 자세를 추정하여 행동을 정량화할 수 있습니다. DeepLabCut Python 툴킷은 작은 훈련 이미지 세트만으로 인간 수준의 라벨링 정확도 내에서 신경망을 훈련할 수 있습니다. 따라서 실험실에서의 행동 분석뿐만 아니라 잠재적으로 스포츠, 보행 분석, 의학 및 재활 연구에도 응용 분야를 확장합니다. DeepLabCut 알고리즘이 직면한 복잡한 조합, 데이터 처리 문제는 NumPy의 배열 조작 기능을 사용하여 해결됩니다. -{{< 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" class="fig-center" alt="numpy를 통한 이익" caption="**활용한 주요 NumPy 기능**" >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 9977a9c053178c523476e8e4a437e1f36177c4b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 18:53:53 +0200 Subject: [PATCH 257/711] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 64 ++++++++++++++-------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index b992584c87..7713993442 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -1,69 +1,69 @@ --- -title: "Case Study: Discovery of Gravitational Waves" +title: "사례 연구: 중력파의 발견" 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" class="fig-center" caption="**중력파**" alt="이항 결합하며 중력파를 생성하는 블랙홀" attr="*(사진 크레딧: LIGO의 Simulating eXtreme Spacetimes (SXS) 프로젝트)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}}
-

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

+

과학적 Python 생태계는 LIGO 연구에 있어서 중요한 인프라에 해당합니다.

David Shoemaker, LIGO Scientific Collaboration
-## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) +## [중력파](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) 그리고 [LIGO](https://www.ligo.caltech.edu)에 대해 -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. +중력파는 '시공간 천막'의 물결이라고 할 수 있으며, 두 블랙홀의 충돌이나 병합, 쌍성의 결합 혹은 초신성과 같이 우주가 대격변하는 사건으로부터 생성됩니다. 중력파를 관측하는 것은 비단 중력 연구에 도움을 줄 뿐만 아니라 먼 우주에서의 모호한 현상들과 이것이 미치는 영향에 대해서도 이해할 수 있게 해 줍니다. -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. +[레이저 간섭계 중력파 관측소(LIGO)](https://www.ligo.caltech.edu) 아인슈타인의 일반상대성이론으로 예측한 중력파를 직접 탐지해 중력파 천체물리학의 장을 열기 위해 고안되었습니다. 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 +### 주요 목표 -* 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. +* [임무](https://www.ligo.caltech.edu/page/what-is-ligo)는 우주에서 가장 격렬하고 에너지가 넘치는 일부 과정에서 발생하는 중력파를 감지하는 것이지만, LIGO가 수집하는 데이터는 중력, 상대성 이론, 천체 물리학, 우주론, 입자 물리학 및 핵 물리학을 포함한 많은 물리학 영역에 광범위한 영향을 미칠 수 있습니다. +* 노이즈에서 신호를 식별하고, 관련 신호를 필터링하고, 관찰된 데이터의 유의성을 통계적으로 추정하기 위해 복잡한 수학을 포함하는 수치 상대성 계산을 통해 관측된 데이터를 고속 처리합니다. +* 이진/수치 상의 결과를 이해할 수 있도록 데이터 시각화를 진행합니다. -### The Challenges +### 도전 -* **Computation** +* **계산** - 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 + 중력파는 매우 작은 효과를 생성하고 물질과의 상호 작용이 작기 때문에 감지하기 어렵습니다. LIGO의 모든 데이터를 처리하고 분석하려면 방대한 컴퓨팅 인프라가 필요합니다. 신호의 수십억 배에 해당하는 노이즈를 처리한 후에도 여전히 매우 복잡한 상대성 방정식과 엄청난 양의 데이터가 있어 계산상의 어려움이 있습니다. [바이너리 병합 분석에 필요한 O(10^7) CPU 시간](https://youtu.be/7mcHknWWzNI)은 6개의 전용 LIGO 클러스터에 퍼져 있습니다. -* **Data Deluge** +* **데이터 홍수** - 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. + 관찰 장치가 더 민감하고 신뢰할 수 있게 됨에 따라 데이터 폭증과 건초더미에서 바늘 찾기로 인해 제기되는 문제가 여러 배로 증가합니다. LIGO는 매일 테라바이트의 데이터를 생성합니다! 이 데이터를 이해하려면 탐지할 때마다 막대한 노력이 필요합니다. 예를 들어, LIGO가 수집하는 신호는 슈퍼컴퓨터에서 수십만 개의 가능한 중력파 서명 템플릿과 일치해야 합니다. -* **Visualization** +* **시각화** 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" >}} -## NumPy’s Role in the Detection of Gravitational Waves +## 중력파 검출에서 NumPy의 역할 -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. +병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. -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: +Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝트 동안 수행되는 다양한 작업에 사용되는 소프트웨어에서 활용되었습니다. NumPy는 복잡한 수학 및 데이터 조작을 고속으로 해결하는 데 도움이 되었습니다. 몇 가지 예시를 들자면: -* [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. +* [신호 처리](https://www.uv.es/virgogroup/Denoising_ROF.html): 글리치 검출, [잡음 식별 및 데이터 결정](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* 데이터 수집: 어떤 데이터를 분석할 수 있을지 결정하고, 모래 속 바늘과 같이 미미한 신호가 있는지 파악 +* 통계적 분석: 관측 데이터의 통계적 유의성 추정, 모델을 비교하여 신호 매개변수(예: 별의 질량, 회전 속도, 거리 등)를 추정 +* 데이터의 시각화 + - 시계열 데이터 + - 스펙트로그램 +* 상관 분석 연산 +* [GwPy](https://gwpy.github.io/docs/stable/overview.html) 및 [PyCBC](https://pycbc.org)와 같은 중력파 데이터 분석에서 개발된 주요 [소프트웨어](https://github.com/lscsoft)는 후드 아래에서 NumPy 및 AstroPy를 사용하여 중력파 검출기에서 데이터를 연구하기 위한 유틸리티, 도구 및 방법에 객체 기반 인터페이스를 제공합니다. -{{< 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" class="fig-center" alt="gwpy-numpy 종속성" caption="**GwPy 패키지가 어떻게 NumPy에 종속하는지를 나타내는 종속성 그래프**" >}} ---- -{{< 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" class="fig-center" alt="PyCBC-numpy 종속성" caption="**PyCBC 패키지가 어떻게 NumPy에 종속하는지를 나타내는 종속성 그래프**" >}} -## Summary +## 요약 -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. +중력파 검출을 통하여 연구자들은 완전히 예상치 못한 현상을 발견하게 됨으로써, 알려진 것 중 가장 난해한 천체물리학적 현상에 대하여 많은 사람들에게 새로운 통찰을 주었습니다. 숫자 계산 및 데이터 시각화는 과학자가 과학적 관찰에서 수집한 데이터에 대한 통찰력을 얻고 결과를 이해하는 데 도움이 되는 중요한 단계입니다. 계산은 복잡하며 실제 관찰 데이터 및 분석을 제공하는 컴퓨터 시뮬레이션을 사용하여 시각화하지 않는 한 사람이 이해할 수 없습니다. NumPy는 matplotlib, pandas 및 scikit-learn과 같은 다른 Python 패키지와 함께 [연구원](https://www.gw-openscience.org/events/GW150914/)이 복잡한 질문에 답하고 우주에 대한 이해의 새로운 지평을 발견할 수 있도록 합니다. -{{< 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" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} From 59b80bf1f0e85bd5935e7135075033534ae6bce5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jun 2023 21:54:55 +0200 Subject: [PATCH 258/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 62aa7d2ee6..704b21d39e 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -89,7 +89,7 @@ _July 12, 2021_ -- At NumPy, we believe in the power of our community. 1,236 Num 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. +Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### NumPy versão 1.19.0 @@ -168,21 +168,21 @@ _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for _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. +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 -_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. +_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. -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. +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. 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. ## Lançamentos -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. +Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. From c522059f5f0ae1b9baf5e8ac07d4fc9352dd870b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jun 2023 21:19:58 +0200 Subject: [PATCH 259/711] New translations install.md (Japanese) --- content/ja/install.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/install.md b/content/ja/install.md index 1de57ef5e0..2573d2a382 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -79,6 +79,7 @@ Windows、macOS、Linuxのすべてのユーザー向けには: 3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！ + ### 再現可能なインストール From 90998558a9fd4c28ad68aa6e0cb4955487da8564 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jun 2023 21:19:59 +0200 Subject: [PATCH 260/711] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/install.md b/content/pt/install.md index d4349d8943..ff2f33845a 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -79,6 +79,7 @@ A segunda diferença é que o pip instala do Índice de Pacotes Python (Python P A terceira diferença é que o conda é uma solução integrada para gerenciar pacotes, dependências e ambientes, enquanto com o pip você pode precisar de outra ferramenta (há muitas!) para lidar com ambientes ou dependências complexas. + ### Instalações reprodutíveis From 2ec86d3e2fa9a6141186d5fa6f43ee5e2b6aee97 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jun 2023 21:20:00 +0200 Subject: [PATCH 261/711] New translations install.md (Spanish) --- content/es/install.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/install.md b/content/es/install.md index 6706c24ac1..3500153a64 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -79,6 +79,7 @@ The second difference is that pip installs from the Python Packaging Index (PyPI 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. + ### Reproducible installs From fd4e59de1ddfd715f9e7cfc260ab3dd68f6fd17d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jun 2023 21:20:01 +0200 Subject: [PATCH 262/711] New translations install.md (Arabic) --- content/ar/install.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/install.md b/content/ar/install.md index 6706c24ac1..3500153a64 100644 --- a/content/ar/install.md +++ b/content/ar/install.md @@ -79,6 +79,7 @@ The second difference is that pip installs from the Python Packaging Index (PyPI 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. + ### Reproducible installs From b9870a7b2e89386b9989cc7118b98a4fcc18f175 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jun 2023 21:20:03 +0200 Subject: [PATCH 263/711] New translations install.md (Korean) --- content/ko/install.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/install.md b/content/ko/install.md index 6706c24ac1..3500153a64 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -79,6 +79,7 @@ The second difference is that pip installs from the Python Packaging Index (PyPI 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. + ### Reproducible installs From 549e07c313403fa16d9558193abd1c4d121523a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jun 2023 21:20:04 +0200 Subject: [PATCH 264/711] New translations install.md (Russian) --- content/ru/install.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/install.md b/content/ru/install.md index 6706c24ac1..3500153a64 100644 --- a/content/ru/install.md +++ b/content/ru/install.md @@ -79,6 +79,7 @@ The second difference is that pip installs from the Python Packaging Index (PyPI 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. + ### Reproducible installs From 0f6c7931aaf222ec2b6f879cd3955e4fbc2c9955 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jun 2023 21:20:05 +0200 Subject: [PATCH 265/711] New translations install.md (Chinese Simplified) --- content/zh/install.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/install.md b/content/zh/install.md index 6706c24ac1..3500153a64 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -79,6 +79,7 @@ The second difference is that pip installs from the Python Packaging Index (PyPI 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. + ### Reproducible installs From 053c44eb2371d30d89574d1c1ae843245912c23e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 25 Jun 2023 01:35:52 +0200 Subject: [PATCH 266/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 412c36c4c2..6fc5e3dd36 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -10,7 +10,7 @@ params: #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: "Latest release: NumPy 1.25. View all releases" + buttontext: "最新リリース: Numpy 1.25. すべてのリリースを表示する" #Where the main hero button links to buttonlink: "/ja/install" #Hero image (from static/images/___) From 3822591ae4e4bc2d6887098b0b1236ad14fc9d41 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 25 Jun 2023 01:35:53 +0200 Subject: [PATCH 267/711] New translations news.md (Japanese) --- content/ja/news.md | 92 +++++++++++++++++++++++----------------------- 1 file changed, 46 insertions(+), 46 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 57004edaa0..4082e432fd 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -7,62 +7,62 @@ date: 2023-06-17 ### NumPy 1.25.0 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。 The highlights of the release are: +_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。 * MUSLのサポート。MUSLのWheelが準備されました。 -* Support for the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum. -* Support for the inplace matrix multiplication (`@=`). +* 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。 -A total of 148 people contributed to this release and 530 pull requests were merged. +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。 -The Python versions supported by this release are 3.9-3.11. +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 -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_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 -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 -_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. +_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 -_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: +_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。 今回のリリースの目玉機能は次のとおりです。 * 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 -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 1.25. 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 1.23.0 リリース -_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: +_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。 * `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 -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. +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 -_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. +_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。このプロジェクトでは、オープンソースソフトウェアコミュニティにおいて、特に歴史的に代表されてこなかったグループからの貢献者が参加する際の障壁を理解することを目的としています。 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。 -**Interested in sharing your experiences?** +**あなたの経験を共有することに興味がありますか?** -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. +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 -_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: +_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。 @@ -71,30 +71,30 @@ _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-not * 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 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. +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 -_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. +_ 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。 -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). +[ 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)。 -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. +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。 -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 -_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. +_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。 -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. +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。 -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +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 1.19.0 リリース -_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: +_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 @@ -103,86 +103,86 @@ _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-not - 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 -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. +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 -_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/. +_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 1.18.0 リリース -_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: +_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 -_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). +_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! -_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. +_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？ -_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 +_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 -_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. +_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!! -_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. +_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. -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +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! -_Jun 24, 2020_ -- NumPy now has a new logo: +_2020年6月24日_ -- NumPyのロゴが新しくなりました: -NumPy logo +NumPyのロゴ -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. +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 1.20.0 リリース -_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. +_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 -_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). +_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 -_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`. +_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。 -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +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。 -_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. +_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。 -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. +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。 -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. +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。 ## 過去のリリース -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. +こちらがより過去の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 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_. From b7f2795ba26ac3d65bd6e1f2fab7307449cc8dab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jun 2023 17:27:16 +0200 Subject: [PATCH 268/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index 704b21d39e..586df8a16a 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 1.24.4 ([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 ([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_. From 8e68700e7c9a039e88f60ca3ad31f2be2c527cc5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jun 2023 17:27:18 +0200 Subject: [PATCH 269/711] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 4082e432fd..70049beb10 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -184,6 +184,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらがより過去の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 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - 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_. From e073a68f92fd45ddc9ece157393cc197469841cb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jun 2023 17:27:19 +0200 Subject: [PATCH 270/711] New translations news.md (Spanish) --- content/es/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/news.md b/content/es/news.md index 04f1e5d7ad..bc7159f39e 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.24.4 ([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_. From e01abf901fdad3c32da5b77fff6b12a0a9f30ce9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jun 2023 17:27:20 +0200 Subject: [PATCH 271/711] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index 04f1e5d7ad..bc7159f39e 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.24.4 ([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_. From 30dc176a557d9d2faf6acb7fa6c8358caa246942 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jun 2023 17:27:21 +0200 Subject: [PATCH 272/711] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index 2a218bd547..13d107d754 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. - 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_. From d18ecb5368758fbc1c96163a19de38b3e49cb22d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jun 2023 17:27:22 +0200 Subject: [PATCH 273/711] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index 04f1e5d7ad..bc7159f39e 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.24.4 ([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_. From 06f27ab00aee70326292345991b52a52d02928f0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jun 2023 17:27:23 +0200 Subject: [PATCH 274/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index 04f1e5d7ad..bc7159f39e 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.24.4 ([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_. From 94516afd3db71539acb4b45875e8def12b05c0c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Jun 2023 03:18:01 +0200 Subject: [PATCH 275/711] New translations news.md (Korean) --- content/ko/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index 13d107d754..4064fc91c6 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -184,7 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- 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_. From 8b650057e258fd52de7aaadf4715b195114f8e70 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Jun 2023 04:42:40 +0200 Subject: [PATCH 276/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 586df8a16a..dc0ace236c 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -184,7 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. -- NumPy 1.24.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 Jun 2023_. +- NumPy 1.24.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junho de 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 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_. From e602956bbb097a4105190501f500295ca87a5ed2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 30 Jun 2023 04:24:57 +0200 Subject: [PATCH 277/711] New translations teams.md (Chinese Simplified) --- content/zh/teams.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/teams.md b/content/zh/teams.md index 91cf5ca399..8a32f6e5ef 100644 --- a/content/zh/teams.md +++ b/content/zh/teams.md @@ -3,7 +3,7 @@ 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" >}})! +我们是一个国际团队，通过建造优质开放源码软件，支持世界各地的科学和研究 个社区。 [加入我们]({{< relref "/contribute" >}})！ {{< include-html "static/gallery/maintainers.html" >}} From 11bc44d80387a828f375b58ef29fb7f8d71130e8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 30 Jun 2023 05:40:46 +0200 Subject: [PATCH 278/711] New translations 404.md (Chinese Simplified) --- content/zh/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/404.md b/content/zh/404.md index da192c53c0..0f27a36ee9 100644 --- a/content/zh/404.md +++ b/content/zh/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -Oops! You've reached a dead end. +抱歉······ 目标网页并不存在。 -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +如果您认为这个页面应该展示些什么东西，请在 GitHub 上面 [发起一个 issue](https://github.com/numpy/numpy.org/issues). From 886706fe9b7cf76b559066d5eee57f313b95f0b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 Jul 2023 01:30:01 +0200 Subject: [PATCH 279/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index dc0ace236c..87fe0c8146 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +- NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junho de 2023_. - NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 2023_. - NumPy 1.24.3 ([notas de versão](https://github.com/numpy/numpy/releases/tag/v1.24.3)) -- _22 de abril de 2023_. From 6529aee8b25e28215ec7728731397eaf8409cc63 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 Jul 2023 01:30:03 +0200 Subject: [PATCH 280/711] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 70049beb10..2ce6c4b64f 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -184,6 +184,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらがより過去の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 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 ([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_. From c1d6fcb1829ce2ff580d821cd6f92693ee762940 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 Jul 2023 01:30:04 +0200 Subject: [PATCH 281/711] New translations news.md (Spanish) --- content/es/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/news.md b/content/es/news.md index bc7159f39e..8151b728b3 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.1 ([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_. From d083e9660e4bb7ef13b9284accf8a295c61c0d83 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 Jul 2023 01:30:05 +0200 Subject: [PATCH 282/711] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index bc7159f39e..8151b728b3 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.1 ([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_. From c55779eaa5b49a86695aa6fd25eab9d21b5093f1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 Jul 2023 01:30:07 +0200 Subject: [PATCH 283/711] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index 4064fc91c6..23f631b890 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - 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_. From 190bb435eb9174f53789e8c0ed9936f1044e3905 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 Jul 2023 01:30:08 +0200 Subject: [PATCH 284/711] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index bc7159f39e..8151b728b3 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.1 ([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_. From 333564b263f1957e402f75986e228ae7f3cfbbf9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 Jul 2023 01:30:09 +0200 Subject: [PATCH 285/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index bc7159f39e..8151b728b3 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -184,6 +184,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.1 ([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_. From d8456360cb56e1166e340cd411da4f871e3830d6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jul 2023 17:44:23 +0200 Subject: [PATCH 286/711] New translations news.md (Japanese) --- content/ja/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 2ce6c4b64f..bf1c5975ef 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -184,8 +184,8 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ こちらがより過去の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 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 1.25.1 ([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_. From 74edb25ae7b74356aa84e7e1064eba36339af887 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jul 2023 18:46:34 +0200 Subject: [PATCH 287/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/pt/news.md b/content/pt/news.md index 87fe0c8146..41d6499bc6 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -180,6 +180,8 @@ Este auxílio será usado para aumentar os esforços de melhoria da documentaç 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. + + ## Lançamentos Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. From ea9a7a82ebb7d652a469589b5f9e7cf6d0957a9e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jul 2023 18:46:35 +0200 Subject: [PATCH 288/711] New translations news.md (Japanese) --- content/ja/news.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/ja/news.md b/content/ja/news.md index bf1c5975ef..844216a946 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -180,6 +180,8 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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。 + + ## 過去のリリース こちらがより過去の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。 From bd26be126796016601aaaba7717b30f316ba623b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jul 2023 18:46:36 +0200 Subject: [PATCH 289/711] New translations news.md (Spanish) --- content/es/news.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/es/news.md b/content/es/news.md index 8151b728b3..a53a96b85d 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -180,6 +180,8 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, 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. + + ## 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. From 42f92f9965f8e0824acb872b7afcec878fb769bd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jul 2023 18:46:37 +0200 Subject: [PATCH 290/711] New translations news.md (Arabic) --- content/ar/news.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/ar/news.md b/content/ar/news.md index 8151b728b3..a53a96b85d 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -180,6 +180,8 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, 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. + + ## 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. From 561948210f38ff97cc9782b5144815d49e73966a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jul 2023 18:46:38 +0200 Subject: [PATCH 291/711] New translations news.md (Korean) --- content/ko/news.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/ko/news.md b/content/ko/news.md index 23f631b890..a0155928ee 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -180,6 +180,8 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, 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. + + ## 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. From 2e8b62a83cf60a32d8ab2c1611dd61840a082a92 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jul 2023 18:46:39 +0200 Subject: [PATCH 292/711] New translations news.md (Russian) --- content/ru/news.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/ru/news.md b/content/ru/news.md index 8151b728b3..a53a96b85d 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -180,6 +180,8 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, 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. + + ## 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. From db62638da008172d7050d63a69357ad78e4844b9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 Jul 2023 18:46:40 +0200 Subject: [PATCH 293/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/zh/news.md b/content/zh/news.md index 8151b728b3..a53a96b85d 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -180,6 +180,8 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, 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. + + ## 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. From 63f74bf48eb3b77da4ad558d156719c2671b199d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 19 Jul 2023 16:56:49 +0200 Subject: [PATCH 294/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 6fc5e3dd36..8aa64c35bc 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -12,7 +12,7 @@ params: #Button text buttontext: "最新リリース: Numpy 1.25. すべてのリリースを表示する" #Where the main hero button links to - buttonlink: "/ja/install" + buttonlink: "/ja/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: From a0c86a28d1300128158def5a76a04d02f21a0ee6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 19 Jul 2023 16:56:50 +0200 Subject: [PATCH 295/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 621439b97b..14170b32ca 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -12,7 +12,7 @@ params: #Button text buttontext: "Latest release: NumPy 1.25. View all releases" #Where the main hero button links to - buttonlink: "/pt/install" + buttonlink: "/pt/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: @@ -89,8 +89,8 @@ navbar: title: Sobre url: /pt/about - - title: Contribuir - url: /pt/contribute + title: Notícias + url: /pt/news - title: Contribuir url: /contribute From efff5a6bb4c39f4f6ae5490a72a8526193cba57c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 20 Jul 2023 23:11:10 +0200 Subject: [PATCH 296/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 8aa64c35bc..36b49a3337 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -89,8 +89,8 @@ navbar: title: 私達について url: /ja/about - - title: NumPyに貢献する - url: /ja/contribute + title: ニュース + url: /ja/news - title: NumPyに貢献する url: /ja/contribute From 23d011bf531a7a71d77741a94ef293e3a70cb4c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 01:48:07 +0200 Subject: [PATCH 297/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 36b49a3337..2844be7ae4 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -53,7 +53,7 @@ params: features: - title: 強力な多次元配列 - text: NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャスティングのコンセプトは、今日の配列計算のデファクト・スタンダードです。 + text: 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。 - title: 数値計算ツール群 text: 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。 From 2ca2821df154d5af1ae964eff943f53bd8dfd805 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 02:45:43 +0200 Subject: [PATCH 298/711] New translations news.md (Japanese) --- content/ja/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index 844216a946..1dfe039fa6 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -184,7 +184,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ ## 過去のリリース -こちらがより過去の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。 +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 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_. From 1db03ae7ce58dedd8dcff24f91022fb42bf49a29 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 02:45:44 +0200 Subject: [PATCH 299/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 2844be7ae4..1007d30234 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -45,7 +45,7 @@ params: 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、種族・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しています。 + 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 @@ -62,15 +62,15 @@ params: text: 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。 - title: 高パフォーマンス - text: NumPyの中核は最適化されたC言語のコードです。Pythonの柔軟性を、コンパイルされたコードの高速さとともに享受できます。 + text: 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。 - title: 使いやすさ - text: 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値のプログラマーでも利用でき、生産性を高めることができます。 + text: 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。 - title: オープンソース - text: 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. + text: 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. tabs: - title: エコシステム + title: NumPyのエコシステム section5: false navbar: - From a99c9d68e7b0202b305899974058c68a1898d168 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 02:45:45 +0200 Subject: [PATCH 300/711] New translations citing-numpy.md (Japanese) --- content/ja/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/citing-numpy.md b/content/ja/citing-numpy.md index 3ab952c040..397ca192ab 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -1,5 +1,5 @@ --- -title: NumPyに関するトーク +title: NumPyを引用する sidebar: false --- From 094da7e9908cc58e5ff35f2c95bd0cacc91399ee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 07:08:39 +0200 Subject: [PATCH 301/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index cc476e6e26..19d7fd4190 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -27,7 +27,7 @@ arraylibraries: url: https://cupy.chainer.org - title: JAX - text: "Composable transformations of NumPy programs differentiate: vectorize, just-in-time compilation to GPU/TPU." + 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 @@ -75,7 +75,7 @@ arraylibraries: url: https://github.com/xtensor-stack/xtensor-python - title: XND - text: Develop libraries for array computing, recreating NumPy's foundational concepts. + 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: xnd url: https://xnd.io @@ -87,7 +87,7 @@ arraylibraries: 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、およびバックエンド + 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 From 654d99ea2cd29415705b94b99c6cacda642df360 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 08:10:38 +0200 Subject: [PATCH 302/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 19d7fd4190..e77eefdb63 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -164,19 +164,19 @@ datascience: 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'. + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](Intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" + text: "探索的解析: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" + text: "モデリングと評価: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" + text: "ダッシュボードでのレポート: [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: 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: - From 7901123f9896108ac826c81d3c35b5fd6456a54a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 08:10:38 +0200 Subject: [PATCH 303/711] New translations community.md (Japanese) --- content/ja/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/community.md b/content/ja/community.md index d1d630d057..2629f72358 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -33,7 +33,7 @@ _ちなみに、セキュリティの脆弱性を報告するには、GitHubの ### [Slack](https://numpy-team.slack.com) -SlackはNumpyに_ 貢献するための質問をする_、リアルタイムのチャットルームです。 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合は[こちら](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)を確認下さい。 +SlackはNumpyに_ 貢献するための質問をするための_、リアルタイムのチャットルームです。 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合は[こちら](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)を確認下さい。 ## 勉強会とミートアップ From 969fcc336443333e02a18298044ff1aa85fc9a11 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 08:10:39 +0200 Subject: [PATCH 304/711] New translations contribute.md (Japanese) --- content/ja/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index ec93def8e6..90db608852 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -3,7 +3,7 @@ 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。 **ここに** 様々な種類の貢献方法が示されています。 +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。 From d41c4ee76b5b6dc2b0ca1b8a863a143171d92b09 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 Jul 2023 08:10:41 +0200 Subject: [PATCH 305/711] New translations install.md (Japanese) --- content/ja/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/install.md b/content/ja/install.md index 2573d2a382..fad90210ea 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -3,7 +3,7 @@ title: NumPyのインストール sidebar: false --- -NumPyは`conda`、`pip` 、macOSやLinuxのパッケージマネージャー、または [ソースコード](https://numpy.org/devdocs/user/building.html)からインストールすることが出来ます。 詳細な手順について、以下の [Python と NumPyの インストールガイド](#python-numpy-install-guide) を参照してください。 +NumPyをインストールするための唯一必要なものは、Pythonそのものだけです。 もしまだPythonをイントールしておらず、最もシンプルなインストール方法をお探しなら、[Anaconda Distribution](https://www.anaconda.com/distribution)の使用をおすすめします。これにはPython、NumPy、および科学計算やデータサイエンスでよく使われる様々な多くのパッケージが含まれています。 NumPyは`conda`、`pip` 、macOSやLinuxのパッケージマネージャー、または [ソースコード](https://numpy.org/devdocs/user/building.html)からインストールすることが出来ます。 詳細な手順については、以下の [Python と Numpyの インストールガイド](#python-numpy-install-guide) を参照してください。 From d013eaf363f02eb0e0167f63a04bbff62d78c9c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 23 Jul 2023 00:01:40 +0200 Subject: [PATCH 306/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index e77eefdb63..4c1b555bdd 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -167,7 +167,7 @@ datascience: 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: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](Intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - text: "探索的解析: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - From 2d7919f8a9644c7f6f13d9222ca71557dcc047d4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 24 Jul 2023 14:35:27 +0200 Subject: [PATCH 307/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 100 ++++++++++++++++++++++----------------------- 1 file changed, 50 insertions(+), 50 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 41d6499bc6..8931ac69c8 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -7,62 +7,62 @@ date: 2023-06-17 ### Lançado o NumPy 1.25.0 -_17 de junho, 2023_ -- [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html) está disponível agora. The highlights of the release are: +_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: -* 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 (`@=`). +* 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 (`@=`). -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 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. -A total of 148 people contributed to this release and 530 pull requests were merged. +Um total de 148 pessoas contribuíram para este lançamento e 530 pull requests foram incorporadas. -The Python versions supported by this release are 3.9-3.11. +As versões do Python suportadas por esta versão são 3.9-3.11. ### Promovendo uma cultura inclusiva: Chamada de participação -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_10 de maio de 2023_ -- Promovendo uma Cultura Inclusiva: Chamada de Participação -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/). +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 -_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. +_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 1.24.0 released +### NumPy versão 1.24.0 -_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: +_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. -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. +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 -_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: +_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. -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. +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 -_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. +_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. -**Interested in sharing your experiences?** +**Quer compartilhar suas experiências?** -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. +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 -_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: +_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: * 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. @@ -71,30 +71,30 @@ _Dec 31, 2021_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-not * 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 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. +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. -### Avançando em uma cultura inclusiva no ecossistema científico de Python +### Promovendo uma cultura inclusiva no ecossistema científico de Python -_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. +_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. -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). +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). -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. +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. -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). +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 -_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. +_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. -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. +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 -_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: +_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: - a continuação do trabalho com SIMD para suportar mais funções e plataformas, - trabalho inicial na infraestrutura e conversão de novos dtypes, @@ -103,70 +103,70 @@ _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-not - melhorias nas anotações de tipos, - novo bitgenerator `PCG64DXSM` para números aleatórios. -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. +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 -_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/. +_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 1.18.0 +### NumPy versão 1.20.0 -_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: +_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 -_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). +_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! -_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. +_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? -_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 +_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 -_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. +_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! -_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. +_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. -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +Ajude-nos a melhorar o NumPy respondendo à pesquisa [aqui](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### O NumPy tem um novo logo! -_Jun 24, 2020_ -- NumPy now has a new logo: +_24 de junho de 2020_ -- NumPy agora tem um novo logo: NumPy logo -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. +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 1.20.0 +### NumPy versão 1.19.0 -_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. +_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 -_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). +_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 -_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`. +_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. @@ -175,20 +175,20 @@ Por favor, veja as [notas de lançamento](https://github.com/numpy/numpy/release _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. 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. +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. -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. +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. ## Lançamentos -Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +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 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 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 lançamento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junho de 2023_. -- NumPy 1.25.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.0)) -- _17 Jun 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_. From 379baf783c46ac00395aa1acb01a0ace3bdc71df Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 24 Jul 2023 14:35:29 +0200 Subject: [PATCH 308/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 14170b32ca..a0b70147f7 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: A biblioteca fundamental para computação científica com Python #Button text - buttontext: "Latest release: NumPy 1.25. View all releases" + buttontext: "Última versão: NumPy 1.25. Veja todas as versões" #Where the main hero button links to buttonlink: "/pt/news/#releases" #Hero image (from static/images/___) From c2f0934ff68ff3e1e5d81c700aa5f5038fee93ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 18:06:54 +0200 Subject: [PATCH 309/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index 8931ac69c8..3621c7ad7e 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -186,6 +186,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 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 lançamento](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_. From f7dd6e936a3f390dda206683e04e5bf8b78a80be Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 18:06:55 +0200 Subject: [PATCH 310/711] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 1dfe039fa6..d96b4ec235 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -186,6 +186,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. - 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_. From d02f308989779bd51ca7946c114c157ccb40a1d4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 18:06:57 +0200 Subject: [PATCH 311/711] New translations news.md (Spanish) --- content/es/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/news.md b/content/es/news.md index a53a96b85d..13a2180bab 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -186,6 +186,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.2 ([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_. From ca4e7b8b47f6f5d5072f41f7c5797f68c8588268 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 18:06:58 +0200 Subject: [PATCH 312/711] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index a53a96b85d..13a2180bab 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -186,6 +186,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.2 ([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_. From f542abd79970e513cc41e33568b3c79e1469a776 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 18:06:59 +0200 Subject: [PATCH 313/711] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index a0155928ee..423f0f814f 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -186,6 +186,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.2 ([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 ([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_. From 7f0ead5057d9b7a44808314656c8dfd1396fd8fc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 18:07:00 +0200 Subject: [PATCH 314/711] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index a53a96b85d..13a2180bab 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -186,6 +186,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.2 ([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_. From f1c38fabb7877669f8a833e1783a8789b2a82104 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 18:07:01 +0200 Subject: [PATCH 315/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index a53a96b85d..13a2180bab 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -186,6 +186,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.2 ([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_. From 6e6d7757a71181a5f8b6219da1ae74efdcd88b26 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 19:13:35 +0200 Subject: [PATCH 316/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 3621c7ad7e..41233995d7 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -186,9 +186,9 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 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 lançamento](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _26 de junho 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_. From e8cfbd197f0030e77647de6f42c4c7bdc1bbfe2f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 31 Jul 2023 19:13:36 +0200 Subject: [PATCH 317/711] New translations news.md (Japanese) --- content/ja/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index d96b4ec235..f75c3419b1 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -186,7 +186,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- 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_. From 3c2e0fa4fbc9712de88dec7aa84f343fb4a0faf6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 06:08:45 +0200 Subject: [PATCH 318/711] New translations news.md (Korean) --- content/ko/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index 423f0f814f..f6675394f0 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -186,7 +186,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.25.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _31 Jul 2023_. +- NumPy 1.25.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023년 7월 31일_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - 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_. From b8d209df1057ff736b89ac7822eca74702ac71da Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:38 +0200 Subject: [PATCH 319/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 165 ++++++++++++++++++++--------------------- 1 file changed, 82 insertions(+), 83 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 1007d30234..b878d3aa95 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -72,89 +72,88 @@ params: 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: + navbar: - - link: https://github.com/numpy/numpy - icon: github + title: Install + url: /install - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: YouTube + title: Documentation + url: https://numpy.org/doc/stable - - link: https://twitter.com/numpy_team - icon: twitter - 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 - + 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 From 06fc32581ec6cef8b486525cc47d604218a6fdd7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:39 +0200 Subject: [PATCH 320/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 165 ++++++++++++++++++++--------------------- 1 file changed, 82 insertions(+), 83 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index a0b70147f7..6253a68212 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -72,89 +72,88 @@ params: 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: /contribute -footer: - logo: logo.svg - socialmediatitle: "" - socialmedia: + navbar: - - link: https://github.com/numpy/numpy - icon: github + title: Install + url: /install - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: youtube + title: Documentation + url: https://numpy.org/doc/stable - - 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 - + 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 From fb8554184ce7a8829ffe789042763f153456d46b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:40 +0200 Subject: [PATCH 321/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 437 ++++++++++++++++++------------------ 1 file changed, 219 insertions(+), 218 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 4c1b555bdd..5944f970b3 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -1,218 +1,219 @@ -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: XND - 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: xnd - url: https://xnd.io - - - 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: 天文学における計算 - 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: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "探索的解析: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - - text: "モデリングと評価: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - - text: "ダッシュボードでのレポート: [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: ナパリ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。 +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From c588301b9e12b48bdfdaf406b5640afe4d3a0d27 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:41 +0200 Subject: [PATCH 322/711] New translations config.yaml (Spanish) --- content/es/config.yaml | 165 ++++++++++++++++++++--------------------- 1 file changed, 82 insertions(+), 83 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 97c8a62798..37562686c0 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -72,89 +72,88 @@ params: 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: + navbar: - - link: https://github.com/numpy/numpy - icon: github + title: Install + url: /install - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: youtube + title: Documentation + url: https://numpy.org/doc/stable - - 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 - + 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 From 160542f799cda5dd9a0a58ce9b64ec004b403a39 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:42 +0200 Subject: [PATCH 323/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 437 ++++++++++++++++++------------------ 1 file changed, 219 insertions(+), 218 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 1ba5a7ce1d..5944f970b3 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -1,218 +1,219 @@ -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 — 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) — 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 - 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." - librariesrow1: - - - title: Quantum Computing - alttext: A computer chip. - img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Statistical Computing - alttext: A line graph with the line moving up. - img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: Signal Processing - alttext: A bar chart with positive and negative values. - img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: Image Processing - alttext: An photograph of the mountains. - img: /images/content_images/sc_dom_img/image_processing.svg - - - title: Graphs and Networks - alttext: A simple graph. - img: /images/content_images/sc_dom_img/sd6.svg - - - title: Astronomy Processes - alttext: A telescope. - img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: Cognitive Psychology - alttext: A human head with gears. - img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: - - - title: Bioinformatics - alttext: A strand of DNA. - img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. - img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: Mathematical Analysis - alttext: Four mathematical symbols. - img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: Chemistry - alttext: A test tube. - img: /images/content_images/sc_dom_img/chemistry.svg - - - title: Geoscience - alttext: The Earth. - img: /images/content_images/sc_dom_img/geoscience.svg - - - title: Geographic Processing - alttext: A map. - img: /images/content_images/sc_dom_img/GIS.svg - - - title: Architecture & Engineering - alttext: A microprocessor development board. - img: /images/content_images/sc_dom_img/robotics.svg -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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - - text: "Report in a dashboard: [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/examples/index.html - 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. +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From 6e86a8ccaf9fc518c6c55ff7c9558daccc285ffc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:43 +0200 Subject: [PATCH 324/711] New translations config.yaml (Arabic) --- content/ar/config.yaml | 165 ++++++++++++++++++++--------------------- 1 file changed, 82 insertions(+), 83 deletions(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 97c8a62798..37562686c0 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -72,89 +72,88 @@ params: 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: + navbar: - - link: https://github.com/numpy/numpy - icon: github + title: Install + url: /install - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: youtube + title: Documentation + url: https://numpy.org/doc/stable - - 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 - + 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 From 5a939b149dff921f41ea144db72b2265846da311 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:44 +0200 Subject: [PATCH 325/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 437 ++++++++++++++++++------------------ 1 file changed, 219 insertions(+), 218 deletions(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index 1ba5a7ce1d..5944f970b3 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -1,218 +1,219 @@ -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 — 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) — 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 - 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." - librariesrow1: - - - title: Quantum Computing - alttext: A computer chip. - img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Statistical Computing - alttext: A line graph with the line moving up. - img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: Signal Processing - alttext: A bar chart with positive and negative values. - img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: Image Processing - alttext: An photograph of the mountains. - img: /images/content_images/sc_dom_img/image_processing.svg - - - title: Graphs and Networks - alttext: A simple graph. - img: /images/content_images/sc_dom_img/sd6.svg - - - title: Astronomy Processes - alttext: A telescope. - img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: Cognitive Psychology - alttext: A human head with gears. - img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: - - - title: Bioinformatics - alttext: A strand of DNA. - img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. - img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: Mathematical Analysis - alttext: Four mathematical symbols. - img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: Chemistry - alttext: A test tube. - img: /images/content_images/sc_dom_img/chemistry.svg - - - title: Geoscience - alttext: The Earth. - img: /images/content_images/sc_dom_img/geoscience.svg - - - title: Geographic Processing - alttext: A map. - img: /images/content_images/sc_dom_img/GIS.svg - - - title: Architecture & Engineering - alttext: A microprocessor development board. - img: /images/content_images/sc_dom_img/robotics.svg -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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - - text: "Report in a dashboard: [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/examples/index.html - 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. +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From a8a5cb3d0e5fecb500995a453781c12f68a9128f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:45 +0200 Subject: [PATCH 326/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 165 ++++++++++++++++++++--------------------- 1 file changed, 82 insertions(+), 83 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 45d57761a2..66f7f6554c 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -72,89 +72,88 @@ params: tabs: title: 생태계 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: + navbar: - - link: https://github.com/numpy/numpy - icon: github + title: Install + url: /install - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: youtube + title: Documentation + url: https://numpy.org/doc/stable - - 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 - + 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 From 67ac21afa538de4cd78a31045b3242c07f2a55e5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:46 +0200 Subject: [PATCH 327/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 437 ++++++++++++++++++------------------ 1 file changed, 219 insertions(+), 218 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index 1ba5a7ce1d..5944f970b3 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -1,218 +1,219 @@ -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 — 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) — 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 - 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." - librariesrow1: - - - title: Quantum Computing - alttext: A computer chip. - img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Statistical Computing - alttext: A line graph with the line moving up. - img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: Signal Processing - alttext: A bar chart with positive and negative values. - img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: Image Processing - alttext: An photograph of the mountains. - img: /images/content_images/sc_dom_img/image_processing.svg - - - title: Graphs and Networks - alttext: A simple graph. - img: /images/content_images/sc_dom_img/sd6.svg - - - title: Astronomy Processes - alttext: A telescope. - img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: Cognitive Psychology - alttext: A human head with gears. - img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: - - - title: Bioinformatics - alttext: A strand of DNA. - img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. - img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: Mathematical Analysis - alttext: Four mathematical symbols. - img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: Chemistry - alttext: A test tube. - img: /images/content_images/sc_dom_img/chemistry.svg - - - title: Geoscience - alttext: The Earth. - img: /images/content_images/sc_dom_img/geoscience.svg - - - title: Geographic Processing - alttext: A map. - img: /images/content_images/sc_dom_img/GIS.svg - - - title: Architecture & Engineering - alttext: A microprocessor development board. - img: /images/content_images/sc_dom_img/robotics.svg -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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - - text: "Report in a dashboard: [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/examples/index.html - 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. +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From e3e0b437ef3484a79e9b70032cd45d19a2c01c27 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:47 +0200 Subject: [PATCH 328/711] New translations config.yaml (Russian) --- content/ru/config.yaml | 165 ++++++++++++++++++++--------------------- 1 file changed, 82 insertions(+), 83 deletions(-) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index 97c8a62798..37562686c0 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -72,89 +72,88 @@ params: 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: + navbar: - - link: https://github.com/numpy/numpy - icon: github + title: Install + url: /install - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: youtube + title: Documentation + url: https://numpy.org/doc/stable - - 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 - + 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 From 8f7129e9d426f8c2f84a949901cf518da19ee324 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:48 +0200 Subject: [PATCH 329/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 437 ++++++++++++++++++------------------ 1 file changed, 219 insertions(+), 218 deletions(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index 1ba5a7ce1d..5944f970b3 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -1,218 +1,219 @@ -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 — 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) — 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 - 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." - librariesrow1: - - - title: Quantum Computing - alttext: A computer chip. - img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Statistical Computing - alttext: A line graph with the line moving up. - img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: Signal Processing - alttext: A bar chart with positive and negative values. - img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: Image Processing - alttext: An photograph of the mountains. - img: /images/content_images/sc_dom_img/image_processing.svg - - - title: Graphs and Networks - alttext: A simple graph. - img: /images/content_images/sc_dom_img/sd6.svg - - - title: Astronomy Processes - alttext: A telescope. - img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: Cognitive Psychology - alttext: A human head with gears. - img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: - - - title: Bioinformatics - alttext: A strand of DNA. - img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. - img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: Mathematical Analysis - alttext: Four mathematical symbols. - img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: Chemistry - alttext: A test tube. - img: /images/content_images/sc_dom_img/chemistry.svg - - - title: Geoscience - alttext: The Earth. - img: /images/content_images/sc_dom_img/geoscience.svg - - - title: Geographic Processing - alttext: A map. - img: /images/content_images/sc_dom_img/GIS.svg - - - title: Architecture & Engineering - alttext: A microprocessor development board. - img: /images/content_images/sc_dom_img/robotics.svg -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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - - text: "Report in a dashboard: [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/examples/index.html - 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. +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From 53cd49581bddf7b6ca9b0b1b036fcbd6c31724cc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:49 +0200 Subject: [PATCH 330/711] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 165 ++++++++++++++++++++--------------------- 1 file changed, 82 insertions(+), 83 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 97c8a62798..37562686c0 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -72,89 +72,88 @@ params: 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: + navbar: - - link: https://github.com/numpy/numpy - icon: github + title: Install + url: /install - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: youtube + title: Documentation + url: https://numpy.org/doc/stable - - 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 - + 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 From 187ee84f5a82581f8a8aef91ef5f50adcbacff1b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:50 +0200 Subject: [PATCH 331/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 437 ++++++++++++++++++------------------ 1 file changed, 219 insertions(+), 218 deletions(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index 1ba5a7ce1d..5944f970b3 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -1,218 +1,219 @@ -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 — 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) — 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 - 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." - librariesrow1: - - - title: Quantum Computing - alttext: A computer chip. - img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Statistical Computing - alttext: A line graph with the line moving up. - img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: Signal Processing - alttext: A bar chart with positive and negative values. - img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: Image Processing - alttext: An photograph of the mountains. - img: /images/content_images/sc_dom_img/image_processing.svg - - - title: Graphs and Networks - alttext: A simple graph. - img: /images/content_images/sc_dom_img/sd6.svg - - - title: Astronomy Processes - alttext: A telescope. - img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: Cognitive Psychology - alttext: A human head with gears. - img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: - - - title: Bioinformatics - alttext: A strand of DNA. - img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. - img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: Mathematical Analysis - alttext: Four mathematical symbols. - img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: Chemistry - alttext: A test tube. - img: /images/content_images/sc_dom_img/chemistry.svg - - - title: Geoscience - alttext: The Earth. - img: /images/content_images/sc_dom_img/geoscience.svg - - - title: Geographic Processing - alttext: A map. - img: /images/content_images/sc_dom_img/GIS.svg - - - title: Architecture & Engineering - alttext: A microprocessor development board. - img: /images/content_images/sc_dom_img/robotics.svg -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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - - text: "Report in a dashboard: [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/examples/index.html - 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. +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From 589af06bc3332afab724dbc2acad202eb834cbb6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 20:07:51 +0200 Subject: [PATCH 332/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 437 ++++++++++++++++++------------------ 1 file changed, 219 insertions(+), 218 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 270fac1e56..5944f970b3 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -1,218 +1,219 @@ -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 — 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) — 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: "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: 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: XND - text: Develop libraries for array computing, recreating NumPy's foundational concepts. - img: /images/content_images/arlib/xnd.png - alttext: xnd - url: https://xnd.io - - - 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" - - - text: "Report in a dashboard: [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/examples/index.html - 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. +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 — 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) — 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 + 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." + librariesrow1: + - + title: Quantum Computing + alttext: A computer chip. + img: /images/content_images/sc_dom_img/quantum_computing.svg + - + title: Statistical Computing + alttext: A line graph with the line moving up. + img: /images/content_images/sc_dom_img/statistical_computing.svg + - + title: Signal Processing + alttext: A bar chart with positive and negative values. + img: /images/content_images/sc_dom_img/signal_processing.svg + - + title: Image Processing + alttext: An photograph of the mountains. + img: /images/content_images/sc_dom_img/image_processing.svg + - + title: Graphs and Networks + alttext: A simple graph. + img: /images/content_images/sc_dom_img/sd6.svg + - + title: Astronomy Processes + alttext: A telescope. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + - + title: Cognitive Psychology + alttext: A human head with gears. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + librariesrow2: + - + title: Bioinformatics + alttext: A strand of DNA. + img: /images/content_images/sc_dom_img/bioinformatics.svg + - + title: Bayesian Inference + alttext: A graph with a bell-shaped curve. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + - + title: Mathematical Analysis + alttext: Four mathematical symbols. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + - + title: Chemistry + alttext: A test tube. + img: /images/content_images/sc_dom_img/chemistry.svg + - + title: Geoscience + alttext: The Earth. + img: /images/content_images/sc_dom_img/geoscience.svg + - + title: Geographic Processing + alttext: A map. + img: /images/content_images/sc_dom_img/GIS.svg + - + title: Architecture & Engineering + alttext: A microprocessor development board. + img: /images/content_images/sc_dom_img/robotics.svg + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - + text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + - + text: "Report in a dashboard: [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/examples/index.html + 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. From 57e72bda1d604a046c5d00c4287f63bf025e8808 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 21:10:36 +0200 Subject: [PATCH 333/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 76 +++++++++++++++++++++--------------------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 6253a68212..77753c979c 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -74,26 +74,26 @@ params: section5: false navbar: - - title: Install - url: /install + title: Instalação + url: /pt/install - - title: Documentation + title: Documentação url: https://numpy.org/doc/stable - - title: Learn - url: /learn + title: Aprenda + url: /pt/learn - - title: Community - url: /community + title: Comunidade + url: /pt/community - - title: About Us - url: /about + title: Sobre + url: /pt/about - - title: News - url: /news + title: Notícias + url: /pt/news - - title: Contribute - url: /contribute + title: Contribuir + url: /pt/contribute footer: logo: logo.svg socialmediatitle: "" @@ -112,48 +112,48 @@ params: title: "" links: - - text: Install - link: /install + text: Instalação + link: /pt/install - - text: Documentation + text: Documentação link: https://numpy.org/doc/stable - - text: Learn - link: /learn + text: Aprenda + link: /pt/learn - - text: Citing Numpy - link: /citing-numpy + text: Citando o Numpy + link: /pt/citing-numpy - text: Roadmap link: https://numpy.org/neps/roadmap.html column2: links: - - text: About us - link: /about + text: Sobre + link: /pt/about - - text: Community - link: /community + text: Comunidade + link: /pt/community - - text: User surveys - link: /user-surveys + text: Pesquisas de usuário + link: /pt/user-surveys - - text: Contribute - link: /contribute + text: Contribuir + link: /pt/contribute - - text: Code of conduct - link: /code-of-conduct + text: Código de Conduta + link: /pt/code-of-conduct column3: links: - - text: Get help - link: /gethelp + text: Ajuda + link: /pt/gethelp - - text: Terms of use - link: /terms + text: Termos de uso (EN) + link: /pt/terms - - text: Privacy - link: /privacy + text: Privacidade + link: /pt/privacy - - text: Press kit - link: /press-kit + text: Kit de imprensa + link: /pt/press-kit From 5d4d15a275c69b6554ed9183a8c2dbbf84abb1f0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 1 Aug 2023 21:10:37 +0200 Subject: [PATCH 334/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 5944f970b3..f6c4473c86 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -2,51 +2,51 @@ 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 — 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) — 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: 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 — 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) — 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: 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. + 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: Array Library + text: Biblioteca de Arrays - - text: Capabilities & Application areas + text: Recursos e áreas de aplicação libraries: - title: Dask - text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + 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: NumPy-compatible array library for GPU-accelerated computing with Python. + 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: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + 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: 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: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + 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: Deep learning framework that accelerates the path from research prototyping to production deployment. + 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/ From 4161aec10c8b8675e3f1f5bf3c557158a3d60980 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 19:07:05 +0200 Subject: [PATCH 335/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 41233995d7..6a2086e42b 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,10 +1,34 @@ --- title: Notícias sidebar: false -newsHeader: "Lançado o NumPy 1.25.0" -date: 2023-06-17 +newsHeader: "numpy.org agora está disponível em japonês e português" +date: 2023-08-02 --- +### numpy.org agora está disponível em japonês e português + +_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. 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 tradução é apoiado com financiamento da CZI. + +Futuramente, nós adoraríamos traduzir o site para mais idiomas. Se você quiser ajudar, conecte-se com a Equipe de Traduções NumPy no Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Procure pelo canal de #translations.) Também estamos construindo uma Equipe de Traduções que vai trabalhar na localização da documentação e conteúdo educacional por todo o ecossistema de Python científico. Se isto despertou o seu interesse, junte-se a nós no servidor Discord Scientific Python: https://discord.gg/khWtqY6RKr. (Procure pelo canal #translation.) + ### Lançado o NumPy 1.25.0 _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: From c59df90eedde84cebf067fe900333b98b74b56d8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 19:07:06 +0200 Subject: [PATCH 336/711] New translations news.md (Japanese) --- content/ja/news.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index f75c3419b1..0a1d35d181 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,10 +1,34 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 1.25.0 リリース" -date: 2023-06-17 +newsHeader: "numpy.org is now available in Japanese and Portuguese" +date: 2023-08-02 --- +### 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 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。 From c3df2552f4ff3b171d899668723c7cafc142148f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 19:07:07 +0200 Subject: [PATCH 337/711] New translations news.md (Spanish) --- content/es/news.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 13a2180bab..5e98ec46af 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,10 +1,34 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.25.0 released" -date: 2023-06-17 +newsHeader: "numpy.org is now available in Japanese and Portuguese" +date: 2023-08-02 --- +### 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: From 2804b29694c2659e8f79f7611d77512062e7d823 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 19:07:08 +0200 Subject: [PATCH 338/711] New translations news.md (Arabic) --- content/ar/news.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 13a2180bab..5e98ec46af 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,34 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.25.0 released" -date: 2023-06-17 +newsHeader: "numpy.org is now available in Japanese and Portuguese" +date: 2023-08-02 --- +### 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: From d0ce39997aac9c14fc21a73f4851b05046014022 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 19:07:09 +0200 Subject: [PATCH 339/711] New translations news.md (Korean) --- content/ko/news.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index f6675394f0..cf6fc16634 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,34 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.25.0 출시" -date: 2023-06-17 +newsHeader: "numpy.org is now available in Japanese and Portuguese" +date: 2023-08-02 --- +### 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 출시 _2023년 6월 17일_ -- 이제 [NumPy 1.25.0](https://numpy.org/doc/stable/release/1.25.0-notes.html)을 이용할 수 있습니다. The highlights of the release are: From 04713d6934a54991a31d789a2a03a3d8a19db234 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 19:07:10 +0200 Subject: [PATCH 340/711] New translations news.md (Russian) --- content/ru/news.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 13a2180bab..5e98ec46af 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,34 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.25.0 released" -date: 2023-06-17 +newsHeader: "numpy.org is now available in Japanese and Portuguese" +date: 2023-08-02 --- +### 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: From 66d03ad89330bf488d0af04e60b86c2941c8218d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 19:07:11 +0200 Subject: [PATCH 341/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 13a2180bab..5e98ec46af 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,34 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.25.0 released" -date: 2023-06-17 +newsHeader: "numpy.org is now available in Japanese and Portuguese" +date: 2023-08-02 --- +### 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: From 6abaa1d788320802b02f16cda147dcd568d47861 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 20:06:45 +0200 Subject: [PATCH 342/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 6a2086e42b..ada03d87bf 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -7,7 +7,7 @@ date: 2023-08-02 ### numpy.org agora está disponível em japonês e português -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. Isto não seria possível sem nossos voluntários dedicados: +_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) From 79bf08a8e08686760088bd0f9c041887c037b894 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 20:06:46 +0200 Subject: [PATCH 343/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 5944f970b3..688dbe8431 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -120,7 +120,7 @@ params: alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes + title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - From f2ceaba6c47924addfef2ecec579c60b26c3bdce Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 20:06:46 +0200 Subject: [PATCH 344/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 5944f970b3..688dbe8431 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -120,7 +120,7 @@ params: alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes + title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - From ae69c9e208a167d00a9085965d7e7caed6b9e5c6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 20:06:47 +0200 Subject: [PATCH 345/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index 5944f970b3..688dbe8431 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -120,7 +120,7 @@ params: alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes + title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - From 4ba10dbf2b5debf81aed00671ee7ac2ec596c558 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 20:06:48 +0200 Subject: [PATCH 346/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index 5944f970b3..688dbe8431 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -120,7 +120,7 @@ params: alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes + title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - From 6d9e53fedf1be2c5c3af1982143113ebfa068224 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 20:06:49 +0200 Subject: [PATCH 347/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index 5944f970b3..688dbe8431 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -120,7 +120,7 @@ params: alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes + title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - From c2d98902ea34d51714cad22ba45f7d5cd64e5a72 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 20:06:50 +0200 Subject: [PATCH 348/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index 5944f970b3..688dbe8431 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -120,7 +120,7 @@ params: alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes + title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - From 1a228da7a46a7ce15498804b29ac6fb9a248effd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 20:06:51 +0200 Subject: [PATCH 349/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index f6c4473c86..4171573634 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -120,7 +120,7 @@ params: alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes + title: Astronomy alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - From 04d0393068db39e28bbf21acb7e88cdf7a94c210 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 2 Aug 2023 22:06:56 +0200 Subject: [PATCH 350/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 4171573634..53716aae26 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -120,7 +120,7 @@ params: alttext: A simple graph. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy + title: Astronomia alttext: A telescope. img: /images/content_images/sc_dom_img/astronomy_processes.svg - From 4444c3c8269c8101fb5bce875a19d63cc63f8ee1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 6 Aug 2023 17:28:37 +0200 Subject: [PATCH 351/711] New translations gethelp.md (Spanish) --- content/es/gethelp.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/gethelp.md b/content/es/gethelp.md index a427b5b1f5..80e2603df5 100644 --- a/content/es/gethelp.md +++ b/content/es/gethelp.md @@ -1,34 +1,34 @@ --- -title: Get Help +title: Buscar Ayuda sidebar: false --- -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! +**Preguntas de los usuarios:** La mejor manera de obtener ayuda es publicando tu pregunta en sitios como [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), con miles de usuarios disponibles para responder. Alternativas más pequeñas incluyen [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), y [Reddit](https://www.reddit.com/r/Numpy/). Nos gustaría poder estar al pendiente en estos sitios, o responder preguntas de manera directa, ¡pero el volumen es un poco abrumador! -**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). +**Cuestiones de desarrollo:** Para asuntos relacionados con el desarrollo de NumPy (por ejemplo, informes de errores), por favor visita la [Comunidad](/community). ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) -A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [use the `#numpy` tag](https://stackoverflow.com/help/tagging) +Un foro para hacer preguntas de uso, como por ejemplo: "¿Cómo hago X cosa en NumPy?". Por favor [usa la etiqueta `#numpy`](https://stackoverflow.com/help/tagging) *** ### [Reddit](https://www.reddit.com/r/Numpy/) -Another forum for usage questions. +Otro foro para hacer preguntas de uso. *** ### [Gitter](https://gitter.im/numpy/numpy) -A real-time chat room where users and community members help each other. +Una sala de chat en tiempo real en donde usuarios y miembros de la comunidad se ayudan mutuamente. *** ### [IRC](https://webchat.freenode.net/?channels=%23numpy) -Another real-time chat room where users and community members help each other. +Otra sala de chat en tiempo real en donde los usuarios y los miembros de la comunidad se ayudan mutuamente. *** From 089bfd093495667d97fe121824e693747f478ce5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 6 Aug 2023 17:28:38 +0200 Subject: [PATCH 352/711] New translations history.md (Spanish) --- content/es/history.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/history.md b/content/es/history.md index aa669d375b..b3124468ad 100644 --- a/content/es/history.md +++ b/content/es/history.md @@ -1,9 +1,9 @@ --- -title: History of NumPy +title: Historia de NumPy sidebar: false --- -NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. +NumPy es una biblioteca fundamental de Python que proporciona estructuras de datos de matrices y rutinas numéricas rápidas relacionadas. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). From fcb4cc778a3459833777653c67a1bb0d64a53f30 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 6 Aug 2023 18:37:03 +0200 Subject: [PATCH 353/711] New translations history.md (Spanish) --- content/es/history.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/es/history.md b/content/es/history.md index b3124468ad..e4e988a3df 100644 --- a/content/es/history.md +++ b/content/es/history.md @@ -3,19 +3,19 @@ title: Historia de NumPy sidebar: false --- -NumPy es una biblioteca fundamental de Python que proporciona estructuras de datos de matrices y rutinas numéricas rápidas relacionadas. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. +NumPy es una biblioteca fundamental de Python que proporciona estructuras de datos de matrices y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la biblioteca contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en informática y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores "rebeldes" pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amistosa con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados en el análisis de ondas gravitacionales utilizan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy. -For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). +Para conocer en profundidad los hitos en el desarrollo de NumPy y las bibliotecas relacionadas, consulta [arxiv.org](arxiv.org/abs/1907.10121). -If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: +Si deseas obtener una copia de las bibliotecas originales Numeric y Numarray, sigue los siguientes enlaces: -[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* +[Página de Descarga de *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* +[Página de Descarga de *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. +*Ten en cuenta que estos paquetes antiguos ya no se mantienen, y se recomienda encarecidamente a los usuarios que utilicen NumPy para cualquier propósito relacionado con matrices o que refactoricen cualquier código preexistente para utilizar la biblioteca NumPy. -### Historic Documentation +### Documentación Histórica -[Download *`Numeric'* Manual](static/numeric-manual.pdf) +[Descarga el Manual de *`Numeric'*](static/numeric-manual.pdf) From 2c5a55e33b4066485c9b9a4ecb133a027707baaf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 6 Aug 2023 20:10:39 +0200 Subject: [PATCH 354/711] New translations config.yaml (Spanish) --- content/es/config.yaml | 108 ++++++++++++++++++++--------------------- 1 file changed, 54 insertions(+), 54 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 37562686c0..87498cf6aa 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -1,6 +1,6 @@ -languageName: English +languageName: Inglés params: - description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + description: '¿Por qué NumPy? Potentes matrices n-dimensionales. Herramientas de cálculo numérico. Interoperable. Rendimiento. Código abierto.' navbarlogo: image: logo.svg link: / @@ -8,91 +8,91 @@ params: #Main hero title title: NumPy #Hero subtitle (optional) - subtitle: The fundamental package for scientific computing with Python + subtitle: El paquete fundamental para la computación científica con Python #Button text - buttontext: "Latest release: NumPy 1.25. View all releases" + buttontext: "Última versión: NumPy 1.25. Ver todas las versiones" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: - title: placeholder + title: marcador intro: - - title: Try NumPy - text: Use the interactive shell to try NumPy in the browser - docslink: Don't forget to check out the docs. + title: Prueba NumPy + text: Utilice el terminal interactivo para probar NumPy en el navegador + docslink: No olvides echarle un ojo a la documentación. casestudies: - title: CASE STUDIES + title: CASOS DE ESTUDIO 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 + title: Primera imagen de un Agujero Negro + text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Event Horizon Telescope producir la primera imagen de un agujero negro img: /images/content_images/case_studies/blackhole.png - alttext: First image of a black hole. It is an orange circle in a black background. + alttext: Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro. 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. + 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: Two orbs orbiting each other. They are displacing gravity around them. + alttext: Dos cuerpos orbitándose mutuamente. Estos desplazan la gravedad a su alrededor. 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. + title: Analíticas Deportivas + text: El uso de Analíticas está cambiando al Cricket, al mejorar 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: Cricket ball on green field. + alttext: Bola de Cricket sobre un campo verde. 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. + 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: Cheetah pose analysis + alttext: Análisis de la pose de un Guepardo url: /case-studies/deeplabcut-dnn keyfeatures: features: - - title: Powerful N-dimensional arrays - text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. + title: Matrices N-dimensionales Poderosas + text: Rápidos y versátiles, los conceptos de vectorización, indexación y retransmisión de NumPy son los estándares de facto de la computación de matrices en la actualidad. - - title: Numerical computing tools - text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + title: Herramientas de Cómputo Numérico + text: NumPy ofrece funciones matemáticas integrales, generadores de números aleatorios, rutinas de álgebra lineal, transformadas de Fourier y mucho más. - - title: Open source - text: 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). + title: Código abierto + text: Distribuida bajo la licencia liberal [BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy es desarrollado y mantenido [públicamente en GitHub](https://github.com/numpy/numpy) por una [comunidad](/community) vibrante, responsable y diversa. - title: Interoperable - text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + text: NumPy es compatible con una amplia gama de plataformas informáticas y de hardware, y funciona bien con bibliotecas distribuidas, de GPU y de matrices dispersas. - - title: Performant - text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + title: Rendimiento + text: El núcleo de NumPy es código C bien optimizado. Disfrute de la flexibilidad de Python con la velocidad del código compilado. - - title: Easy to use - text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + title: Fácil de usar + text: La sintaxis de alto nivel de NumPy lo hace accesible y productivo para programadores de cualquier formación o nivel de experiencia. tabs: - title: ECOSYSTEM + title: ECOSISTEMA section5: false navbar: - - title: Install + title: Instalar url: /install - - title: Documentation + title: Documentación url: https://numpy.org/doc/stable - - title: Learn + title: Aprende url: /learn - - title: Community + title: Comunidad url: /community - - title: About Us + title: Acerca de nosotros url: /about - - title: News + title: Noticias url: /news - - title: Contribute + title: Contribuye url: /contribute footer: logo: logo.svg @@ -112,48 +112,48 @@ params: title: "" links: - - text: Install + text: Instalar link: /install - - text: Documentation + text: Documentación link: https://numpy.org/doc/stable - - text: Learn + text: Aprende link: /learn - - text: Citing Numpy + text: Citando a NumPy link: /citing-numpy - - text: Roadmap + text: Mapa de ruta link: https://numpy.org/neps/roadmap.html column2: links: - - text: About us + text: Acerca de nosotros link: /about - - text: Community + text: Comunidad link: /community - - text: User surveys + text: Encuestas a usuarios link: /user-surveys - - text: Contribute + text: Contribuye link: /contribute - - text: Code of conduct + text: Código de Conducta link: /code-of-conduct column3: links: - - text: Get help + text: Buscar ayuda link: /gethelp - - text: Terms of use + text: Términos de uso link: /terms - - text: Privacy + text: Confidencialidad link: /privacy - - text: Press kit + text: Kit de prensa link: /press-kit From c0658da0ceaf866524c4783a5f4fbbc57394e102 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 6 Aug 2023 22:09:59 +0200 Subject: [PATCH 355/711] New translations 404.md (Spanish) --- content/es/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/404.md b/content/es/404.md index da192c53c0..b38d73b758 100644 --- a/content/es/404.md +++ b/content/es/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -Oops! You've reached a dead end. +¡Oh, oh! Has llegado a un callejón sin salida. -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +Si crees que algo debería estar aquí, puedes [reportar este problema](https://github.com/numpy/numpy.org/issues) en GitHub. From 1eb2cec26e9067076df6a5edb734f87a0c6be413 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 6 Aug 2023 23:06:07 +0200 Subject: [PATCH 356/711] New translations community.md (Spanish) --- content/es/community.md | 44 ++++++++++++++++++++--------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/es/community.md b/content/es/community.md index 5034fba239..1bd0f188a1 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -1,60 +1,60 @@ --- -title: Community +title: Comunidad 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. +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. -We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. +Ofrecemos varios canales de comunicación para aprender, compartir conocimientos y conectarse con otros dentro de la comunidad de NumPy. -## Participate online +## Participa en línea -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)._ +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)._ -### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) +### [Lista de correo de NumPy](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. +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 hacen los anuncios sobre NumPy, tales como lanzamientos, reuniones de desarrolladores, sprints o conferencias. -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/numpy-discussion@python.org/). +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/numpy-discussion@python.org/). *** -### [GitHub issue tracker](https://github.com/numpy/numpy/issues) +### [Seguimiento de incidencias en GitHub](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`"). +- 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`"). -_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)._ +_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) -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. +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. -## Study Groups and Meetups +## Grupos de Estudio y Reuniones -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). +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 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 [Twitter](https://twitter.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 [Twitter](https://twitter.com/numpy_team). -## Conferences +## Conferencias -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: +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 Latin America](https://www.scipyla.org) +- [SciPy Latinoamérica](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) +- [Conferencias PyData](https://pydata.org/event-schedule/) (de 15 a 20 eventos al año, repartidos entre muchos países) -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. +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. ## Join the NumPy community From 0371fc2ae7dcbb7286be985b43096b625ae929b5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 7 Aug 2023 00:14:57 +0200 Subject: [PATCH 357/711] New translations community.md (Spanish) --- content/es/community.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/community.md b/content/es/community.md index 1bd0f188a1..0e1e53211b 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -57,10 +57,10 @@ El proyecto NumPy no organiza sus propias conferencias. Las conferencias que tra 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. -## Join the NumPy community +## Únete a la comunidad Numpy -To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to 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. -If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. +Si te interesa colaborar en NumPy (¡yupi!) te recomendamos que visites nuestra página [Contribuir](/contribute). -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/). +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/). From a975174f35405a23b6d39a4ffbbaeff88cc646c6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 7 Aug 2023 03:44:00 +0200 Subject: [PATCH 358/711] New translations press-kit.md (Spanish) --- content/es/press-kit.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/press-kit.md b/content/es/press-kit.md index 2c8970bb29..9b66e3e683 100644 --- a/content/es/press-kit.md +++ b/content/es/press-kit.md @@ -1,8 +1,8 @@ --- -title: Press kit +title: Kit de prensa sidebar: false --- -We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation. +Nos gustaría facilitarte el trabajo para incluir la identidad del proyecto NumPy en tu próximo documento académico, material de curso o presentación. -You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). +[Aquí](https://github.com/numpy/numpy/tree/main/branding/logo) encontrarás varias versiones en alta resolución del logo de NumPy. Ten en cuenta que al utilizar los recursos de numpy.org, aceptas el [Código de Conducta de NumPy](/code-of-conduct). From 8767120902a1d66c08cfedcc65d2338efcdb57ac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 7 Aug 2023 03:44:01 +0200 Subject: [PATCH 359/711] New translations privacy.md (Spanish) --- content/es/privacy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/privacy.md b/content/es/privacy.md index 6064e4c4f1..4b62b24be2 100644 --- a/content/es/privacy.md +++ b/content/es/privacy.md @@ -1,8 +1,8 @@ --- -title: Privacy Policy +title: Política de Privacidad sidebar: false --- -**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. +**numpy.org** está operado por [NumFOCUS, Inc.](https://numfocus.org), el patrocinador fiscal del proyecto NumPy. Para ver la Política de Privacidad de este sitio web, por favor dirígete a https://numfocus.org/privacy-policy. -If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. +Si tienes alguna pregunta sobre la política o la recolección, uso y divulgación de datos de NumFOCUS, por favor ponte en contacto con el personal de NumFOCUS en privacy@numfocus.org. From 4c6fadf5d947bd00aa2ec84f7132e06732e13ddc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 8 Aug 2023 00:25:12 +0200 Subject: [PATCH 360/711] New translations contribute.md (Spanish) --- content/es/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index 6efff53624..280125dc05 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -1,9 +1,9 @@ --- -title: Contribute to NumPy +title: Contribuye a 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. +¡El proyecto NumPy agradece tu experiencia y entusiasmo! 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). From 6f63cd9bbb3607b2dfc2b43333a6d0a510263b5b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 8 Aug 2023 01:22:37 +0200 Subject: [PATCH 361/711] New translations contribute.md (Spanish) --- content/es/contribute.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index 280125dc05..07f433a5f7 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -3,26 +3,26 @@ title: Contribuye a NumPy sidebar: false --- -¡El proyecto NumPy agradece tu experiencia y entusiasmo! Your choices aren't limited to programming, as you can see below there are many areas where we need **your** help. +¡El proyecto NumPy agradece tu experiencia y entusiasmo! Tus opciones no se limitan a la programación. Como puedes ver más abajo, existen muchas áreas en las que necesitamos **tu** ayuda. -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). +Si no estás seguro por dónde empezar o cómo encajan tus habilidades, _¡acércate!_ Puedes preguntar en la [lista de correos](https://mail.python.org/mailman/listinfo/numpy-discussion) o [GitHub](http://github.com/numpy/numpy) (abre una [incidencia](https://github.com/numpy/numpy/issues) o comenta en una relevante). -Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at or on [Slack](https://numpy-team.slack.com) (write for an invite). +Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la communidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). -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/). +También hacemos _llamadas a la comunidad_ de manera quincenal, cuyos detalles se anuncian en la [lista de correo](https://mail.python.org/mailman/listinfo/numpy-discussion). Te invitamos a unirte. Si es la primera vez que contribuyes al código abierto, también te recomendamos encarecidamente que leas [esta guía](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. +Nuestra comunidad aspira a tratar a todos por igual y a valorar todas las contribuciones. Tenemos un [Código de Conducta](/code-of-conduct) para fomentar un entorno abierto y acogedor. -### Writing code +### Escribiendo código -Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +Programadores, esta [guía](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica cómo contribuir al código base.
También revisa nuestro [canal de YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) por consejos adicionales. -### 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 +### Revisando pull requests +El proyecto tiene más de 250 pull requests abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes: +* resumir un debate extenso +* categorizar PRs de documentación +* probar los cambios propuestos ### Developing educational materials From 7c005a1c81790ee33185ea277f09b14f7cfecd9f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 8 Aug 2023 02:17:39 +0200 Subject: [PATCH 362/711] New translations contribute.md (Spanish) --- content/es/contribute.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index 07f433a5f7..066ed3b815 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -25,12 +25,12 @@ El proyecto tiene más de 250 pull requests abiertos, lo que significa muchas me * probar los cambios propuestos -### Developing educational materials +### Creando material educativo -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. +La [Guía de usuario](https://numpy.org/devdocs) de NumPy está en proceso de rehabilitación. Necesitamos nuevos tutoriales, instrucciones y explicaciones detalladas, y la página necesita una reestructuración. Las oportunidades no se limitan a escritores. También ejemplos prácticos, notebooks y vídeos. La propuesta [NEP 44 - Reestructuración de la Documentación NumPy](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) expone nuestras ideas -- y tal vez tú puedas tener otras. -### Issue triaging +### Clasificación de incidencias 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: From d1a59e3fec718974f43075e0b07ffee4a845a72d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 8 Aug 2023 03:13:37 +0200 Subject: [PATCH 363/711] New translations contribute.md (Spanish) --- content/es/contribute.md | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index 066ed3b815..e71ae59af5 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -32,35 +32,35 @@ La [Guía de usuario](https://numpy.org/devdocs) de NumPy está en proceso de re ### Clasificación de incidencias -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: +El [rastreador de incidencias de NumPy](https://github.com/numpy/numpy/issues) tiene _muchos_ temas abiertos. Algunos ya no son válidos, otros deberían priorizarse y otros serían buenos temas para nuevos colaboradores. Puedes: -* 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) +* revisar si errores antiguos siguen presentes +* encontrar incidencias duplicadas, y enlazar las relacionadas +* añadir buenos reproductores autónomos de las incidencias +* etiquetar correctamente las incidencias (para ello es necesario tener derechos de categorización, solo necesitas preguntar) -Please just dive in. +Por favor, solo sumérgete. -### Website development +### Desarrollo de la Página Web -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. +Acabamos de renovar nuestro sitio web, pero aún no hemos terminado. Si te gusta el desarrollo web, estas [incidencias](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) enumeran algunas de nuestras necesidades insatisfechas -- y no dudes en compartir tus propias ideas. -### Graphic design +### Diseño gráfico -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. +Apenas podemos empezar a enumerar las aportaciones que puede hacer un diseñador gráfico. Nuestra documentación está sedienta de ilustraciones; nuestro sitio web, en pleno crecimiento, ansía imágenes... las oportunidades abundan. -### Translating website content +### Traduciendo el contenido de la página web -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. +Planeamos múltiples traducciones de [numpy.org](https://numpy.org) para hacer a NumPy accesible a los usuarios en su lengua materna. Los traductores voluntarios son el núcleo de este esfuerzo. Consulta [aquí](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para más información; comenta en [este tema de GitHub](https://github.com/numpy/numpy.org/issues/55) para inscribirte. -### Community coordination and outreach +### Coordinación y divulgación de la comunidad -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. +A través del contacto con la comunidad compartimos nuestro trabajo más ampliamente, y aprendemos en dónde nos estamos quedando cortos. Estamos ansiosos por conseguir más gente involucrada en esfuerzos como nuestra cuenta de [Twitter](https://twitter.com/numpy_team), organizando [code sprints](https://scisprints.github.io/) de NumPy, un boletín y quizás un blog. -### Fundraising +### Recaudación de fondos -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. +NumPy fue durante muchos años un proyecto voluntario, pero a medida que crecía su importancia se hizo evidente que necesitaríamos apoyo financiero para garantizar su estabilidad y crecimiento. [Esta charla en SciPy'19](https://www.youtube.com/watch?v=dBTJD_FDVjU) explica cuánta diferencia ha supuesto este apoyo. Como todo en el mundo sin ánimo de lucro, estamos constantemente en busca de subvenciones, patrocinios y otros tipos de ayuda. Tenemos varias ideas y, por supuesto, aceptamos más. La recaudación de fondos es una habilidad escasa aquí -- apreciaríamos tu ayuda. From dac937f8eb98c011e6f530757da21e358a84f945 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 9 Aug 2023 02:00:57 +0200 Subject: [PATCH 364/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 688dbe8431..4d0d0c8078 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -2,7 +2,7 @@ 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 — 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. + para1: NumPy constituye la base de potentes bibliotecas 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 bibliotecas basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series temporales y la detección de vídeo. [PyTorch](https://pytorch.org), otra biblioteca de aprendizaje profundo, es popular entre los investigadores de visión por ordenador y procesamiento del lenguaje natural. [MXNet](https://github.com/apache/incubator-mxnet) es otro paquete de IA que proporciona modelos y plantillas para el aprendizaje profundo. 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) — 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: From 5b11e8dc9197f470c62d696aa14cde548a1f44cc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 9 Aug 2023 02:00:58 +0200 Subject: [PATCH 365/711] New translations contribute.md (Spanish) --- content/es/contribute.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/contribute.md b/content/es/contribute.md index e71ae59af5..75e24296f6 100644 --- a/content/es/contribute.md +++ b/content/es/contribute.md @@ -5,7 +5,7 @@ sidebar: false ¡El proyecto NumPy agradece tu experiencia y entusiasmo! Tus opciones no se limitan a la programación. Como puedes ver más abajo, existen muchas áreas en las que necesitamos **tu** ayuda. -Si no estás seguro por dónde empezar o cómo encajan tus habilidades, _¡acércate!_ Puedes preguntar en la [lista de correos](https://mail.python.org/mailman/listinfo/numpy-discussion) o [GitHub](http://github.com/numpy/numpy) (abre una [incidencia](https://github.com/numpy/numpy/issues) o comenta en una relevante). +Si no estás seguro por dónde empezar o cómo encajan tus habilidades, _¡acércate!_ Puedes preguntar en la [lista de correos](https://mail.python.org/mailman/listinfo/numpy-discussion) o [GitHub](http://github.com/numpy/numpy) (abre una [propuesta](https://github.com/numpy/numpy/issues) o comenta en una relevante). Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la communidad en o en [Slack](https://numpy-team.slack.com) (escribe a para recibir una invitación). @@ -18,8 +18,8 @@ Nuestra comunidad aspira a tratar a todos por igual y a valorar todas las contri Programadores, esta [guía](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica cómo contribuir al código base.
También revisa nuestro [canal de YouTube](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) por consejos adicionales. -### Revisando pull requests -El proyecto tiene más de 250 pull requests abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes: +### Revisando solicitudes de cambios +El proyecto tiene más de 250 solicitudes de cambios abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes: * resumir un debate extenso * categorizar PRs de documentación * probar los cambios propuestos @@ -30,14 +30,14 @@ El proyecto tiene más de 250 pull requests abiertos, lo que significa muchas me La [Guía de usuario](https://numpy.org/devdocs) de NumPy está en proceso de rehabilitación. Necesitamos nuevos tutoriales, instrucciones y explicaciones detalladas, y la página necesita una reestructuración. Las oportunidades no se limitan a escritores. También ejemplos prácticos, notebooks y vídeos. La propuesta [NEP 44 - Reestructuración de la Documentación NumPy](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) expone nuestras ideas -- y tal vez tú puedas tener otras. -### Clasificación de incidencias +### Clasificación de propuestas -El [rastreador de incidencias de NumPy](https://github.com/numpy/numpy/issues) tiene _muchos_ temas abiertos. Algunos ya no son válidos, otros deberían priorizarse y otros serían buenos temas para nuevos colaboradores. Puedes: +El [rastreador de propuestas de NumPy](https://github.com/numpy/numpy/issues) tiene _muchos_ temas abiertos. Algunos ya no son válidos, otros deberían priorizarse y otros serían buenos temas para nuevos colaboradores. Puedes: * revisar si errores antiguos siguen presentes -* encontrar incidencias duplicadas, y enlazar las relacionadas +* encontrar propuestas duplicadas, y enlazar las relacionadas * añadir buenos reproductores autónomos de las incidencias -* etiquetar correctamente las incidencias (para ello es necesario tener derechos de categorización, solo necesitas preguntar) +* etiquetar correctamente las propuestas (para ello es necesario tener derechos de categorización, solo necesitas preguntar) Por favor, solo sumérgete. From 50051610b168be95aca4e8c163fd2063491802b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 9 Aug 2023 03:11:04 +0200 Subject: [PATCH 366/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 4d0d0c8078..ba14bb8238 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -3,68 +3,68 @@ params: paras: - para1: NumPy constituye la base de potentes bibliotecas 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 bibliotecas basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series temporales y la detección de vídeo. [PyTorch](https://pytorch.org), otra biblioteca de aprendizaje profundo, es popular entre los investigadores de visión por ordenador y procesamiento del lenguaje natural. [MXNet](https://github.com/apache/incubator-mxnet) es otro paquete de IA que proporciona modelos y plantillas para el aprendizaje profundo. - 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) — 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. + 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://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — 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: 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. + text: La API de NumPy es el punto de partida cuando se escriben bibliotecas para explotar hardware innovador, crear tipos de matrices especializadas o añadir capacidades más allá de lo que NumPy proporciona. headers: - - text: Array Library + text: Biblioteca de matrices - - text: Capabilities & Application areas + text: Capacidades y áreas de aplicación libraries: - title: Dask - text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + text: Matrices distribuidas 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: NumPy-compatible array library for GPU-accelerated computing with Python. + text: Biblioteca de matrices compatible con NumPy para cálculo acelerado en la GPU con 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." + 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://github.com/google/jax - title: Xarray - text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization + text: Matrices multidimensionales indexadas y etiquetadas 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: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + text: Biblioteca de matrices dispersas 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: Deep learning framework that accelerates the path from research prototyping to production deployment. + 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: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + 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: MXNet - text: Deep learning framework suited for flexible research prototyping and production. + text: Marco de aprendizaje profundo adecuado para la creación flexible de prototipos de investigación y la producción. 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. + text: Plataforma de desarrollo multilenguaje para datos y análisis columnares en memoria. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow From 7a895264bffa8d2e35d511b1e5ed1359018276af Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 9 Aug 2023 03:11:06 +0200 Subject: [PATCH 367/711] New translations about.md (Spanish) --- content/es/about.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 8c769cfc9d..f88e2e6576 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -1,16 +1,16 @@ --- -title: About Us +title: Quiénes Somos 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 es un proyecto de código abierto cuyo objetivo es permitir la computación numérica en Python. Se creó en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy siempre será software 100% código abierto, de uso libre para todos. Fue liberado bajo los términos liberales de la [licencia BSD modificada](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). +NumPy se desarrolla de forma abierta en GitHub, mediante el consenso de las comunidades NumPy y científica de Python en general. Para más información sobre nuestro enfoque de gobernanza, consulte nuestro [Documento de Gobernanza](https://www.numpy.org/devdocs/dev/governance/index.html). -## Steering Council +## Consejo Directivo -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): +El Consejo de Dirección de NumPy es el órgano de gobernanza del proyecto. Su papel es el garantizar, a través del trabajo con la comunidad NumPy en general y al servicio de la misma, el bienestar a largo plazo del proyecto, tanto desde el punto de vista técnico como de la comunidad. El Consejo Directivo de NumPy está formado actualmente por los siguientes miembros (en orden alfabético): - Sebastian Berg - Ralf Gommers @@ -22,7 +22,7 @@ The NumPy Steering Council is the project's governing body. Its role is to ensur - Melissa Weber Mendonça - Eric Wieser -Emeritus: +Eméritos: - Alex Griffing (2015-2017) - Allan Haldane (2015-2021) @@ -33,7 +33,7 @@ Emeritus: - Jaime Fernández del Río (2014-2021) - Pauli Virtanen (2008-2021) -To contact the NumPy Steering Council, please email numpy-team@googlegroups.com. +Para contactar con el Consejo Directivo de NumPy, por favor envía un correo electrónico a numpy-team@googlegroups.com. ## Teams From 700c4805eb229fdeeeecbf2216244413f716529c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 9 Aug 2023 03:11:07 +0200 Subject: [PATCH 368/711] New translations teams.md (Spanish) --- content/es/teams.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/teams.md b/content/es/teams.md index 91cf5ca399..ee292266a6 100644 --- a/content/es/teams.md +++ b/content/es/teams.md @@ -1,9 +1,9 @@ --- -title: NumPy Teams +title: Equipos de NumPy 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" >}})! +Somos un equipo internacional en una misión para apoyar a las comunidades científicas y de investigaciones alrededor del mundo, mediante la construcción de software de código abierto de calidad. ¡[Únete a nosotros]({{< relref "/contribute" >}})! {{< include-html "static/gallery/maintainers.html" >}} @@ -17,6 +17,6 @@ We are an international team on a mission to support scientific and research com {{< include-html "static/gallery/emeritus-maintainers.html" >}} -# Governance +# Gobernanza -For the list of the Steering Council members, please see [here](https://numpy.org/about/). +Para la lista de personas del Consejo Directivo, por favor ve [aquí](https://numpy.org/about/). From b398b9af909bde9770b3a6dc17a04bf23acda4a2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 9 Aug 2023 04:36:19 +0200 Subject: [PATCH 369/711] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md index 5bb5d791b4..50b9bb2b78 100644 --- a/content/es/citing-numpy.md +++ b/content/es/citing-numpy.md @@ -1,13 +1,13 @@ --- -title: Citing NumPy +title: Citando a NumPy sidebar: false --- -If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: +Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, te sugerimos que cites el siguiente documento: * Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). -_In BibTeX format:_ +_En formato BibTeX:_ ``` @Article{ harris2020array, From 2f8eed464b87a1c651c3e4291869140c2f6a87e6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 10 Aug 2023 02:06:28 +0200 Subject: [PATCH 370/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index ba14bb8238..77d79395c2 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -70,13 +70,13 @@ params: url: https://github.com/apache/arrow - title: xtensor - text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + text: Matrices multidimensionales con emisión y computación de llamada-bajo-demanda para el análisis numérico. 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. + text: Manipula datos tipo JSON con modismos similares a los de NumPy. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ From e0b51f4a32250cb5e1ba8a7f0956b2b589e67d92 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 10 Aug 2023 03:51:17 +0200 Subject: [PATCH 371/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 98 ++++++++++++++++++------------------- 1 file changed, 49 insertions(+), 49 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 77d79395c2..f0167cd571 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -82,138 +82,138 @@ params: url: https://awkward-array.org/ - title: uarray - text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + text: Sistema de backend de Python que desvincula 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: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. + text: Aprendizaje tensorial, álgebra y backends para utilizar NumPy, MXNet, PyTorch, TensorFlow o 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: Casi todos los científicos que trabajan en Python recurren a la potencia de 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." + text: "NumPy lleva 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." librariesrow1: - title: Quantum Computing - alttext: A computer chip. + alttext: Un chip de ordenador. img: /images/content_images/sc_dom_img/quantum_computing.svg - - title: Statistical Computing - alttext: A line graph with the line moving up. + title: Computación Estadística + alttext: Un gráfico lineal con la línea moviéndose hacia arriba. img: /images/content_images/sc_dom_img/statistical_computing.svg - - title: Signal Processing - alttext: A bar chart with positive and negative values. + title: Procesamiento de Señales + alttext: Un gráfico de barras con valores positivos y negativos. img: /images/content_images/sc_dom_img/signal_processing.svg - - title: Image Processing - alttext: An photograph of the mountains. + title: Procesamiento de Imágenes + alttext: Una fotografía de unas montañas. img: /images/content_images/sc_dom_img/image_processing.svg - - title: Graphs and Networks - alttext: A simple graph. + title: Grafos y Redes + alttext: Un simple grafo. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy - alttext: A telescope. + title: Astronomía + alttext: Un telescopio. img: /images/content_images/sc_dom_img/astronomy_processes.svg - - title: Cognitive Psychology - alttext: A human head with gears. + title: Psicología Cognitiva + alttext: Una cabeza humana con engranajes. img: /images/content_images/sc_dom_img/cognitive_psychology.svg librariesrow2: - - title: Bioinformatics - alttext: A strand of DNA. + title: Bioinformática + alttext: Una cadena de ADN. img: /images/content_images/sc_dom_img/bioinformatics.svg - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: Inferencia Bayesiana + alttext: El gráfico de una curva en forma de campana. img: /images/content_images/sc_dom_img/bayesian_inference.svg - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: Análisis Matemático + alttext: Cuatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - title: Chemistry - alttext: A test tube. + title: Química + alttext: Un tubo de ensayo. img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience - alttext: The Earth. + title: Geociencia + alttext: La Tierra. img: /images/content_images/sc_dom_img/geoscience.svg - - title: Geographic Processing - alttext: A map. + title: Procesamiento Geográfico + alttext: Un mapa. img: /images/content_images/sc_dom_img/GIS.svg - - title: Architecture & Engineering - alttext: A microprocessor development board. + title: Arquitectura e Ingeniería + alttext: Una placa de desarrollo de microprocesadores. img: /images/content_images/sc_dom_img/robotics.svg datascience: - intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + 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: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + 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: 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'. + alttext: Diagrama de tres círculos superpuestos. Los círculos se denominan "Matemáticas", "Informática" y "Conocimientos Especializados". En el centro del diagrama, con los tres círculos superpuestos, hay un área denominada "Ciencia de datos". examples: - - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "Extraer, Transformar, Cargar: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + text: "Análisis Exploratorio: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + text: "Modelado y evaluación: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "Informes en paneles: [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)). + 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: A streamplot made in matplotlib + alttext: Un diagrama de flujo hecho en matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png - alttext: A scatter-plot graph made in ggpy + 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: A box-plot made in plotly + 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: A streamgraph made in altair + alttext: Un diagrama de flujo hecho en 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" + 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: A 3D volume rendering made in PyVista. + alttext: Un renderizado de volumen 3D realizado en PyVista. - url: https://napari.org img: /images/content_images/v_napari.png - alttext: A multi-dimensionan image made in napari. + alttext: Una imagen multidimensional hecha en napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png - alttext: A Voronoi diagram made in vispy. + alttext: Un diagrama de Voronoi hecho en 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 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: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. + text: El procesamiento acelerado de matrices de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar. From a2ace6357dc969d437b786e86311f71b8d680d5c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 10 Aug 2023 03:51:18 +0200 Subject: [PATCH 372/711] New translations user-survey-2020.md (Spanish) --- content/es/user-survey-2020.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md index fe431e845c..aaf35e521a 100644 --- a/content/es/user-survey-2020.md +++ b/content/es/user-survey-2020.md @@ -1,16 +1,16 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: ENCUESTA DE LA COMUNIDAD NUMPY 2020 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. +En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto. {{< 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. +**[Descarga el informe](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver a detalle los resultados de la encuesta. -For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. +Para los puntos destacados, echa un vistazo a **[esta infografía](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/**. +¿Listo para una inmersión profunda? Visita **https://numpy.org/user-survey-2020-details/**. From 411f88dc2f4701ba5335fe1b170bec946aa66c68 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 10 Aug 2023 03:51:19 +0200 Subject: [PATCH 373/711] New translations user-surveys.md (Spanish) --- content/es/user-surveys.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/user-surveys.md b/content/es/user-surveys.md index 89a2aa0460..ef8467358d 100644 --- a/content/es/user-surveys.md +++ b/content/es/user-surveys.md @@ -1,10 +1,10 @@ --- -title: NUMPY USER SURVEYS +title: ENCUESTAS DE USUARIOS DE NUMPY sidebar: false --- -**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/). +**2020** El equipo de encuestas de NumPy, en asociación con estudiantes y profesores de la Universidad de Michigan y de la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad de NumPy. Encuentra los resultados de la encuesta [aquí](https://numpy.org/user-survey-2020/). -**2021** The collected data is currently being analyzed. +**2021** Los datos recolectados están siendo analizados actualmente. -If you have any questions or suggestions for the past or future surveys, please open an issue [here](https://github.com/numpy/numpy-surveys/issues). +Si tienes alguna pregunta o sugerencia sobre las encuestas pasadas o futuras, por favor abre una propuesta [aquí](https://github.com/numpy/numpy-surveys/issues). From 2932ed1e68076a78cb446ec7945a729404692b19 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 12 Aug 2023 14:24:31 +0200 Subject: [PATCH 374/711] New translations about.md (Spanish) --- content/es/about.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index f88e2e6576..02271daca8 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -35,17 +35,17 @@ Eméritos: Para contactar con el Consejo Directivo de NumPy, por favor envía un correo electrónico a numpy-team@googlegroups.com. -## Teams +## Equipos -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: +La dirección del proyecto NumPy trabaja activamente para diversificar las vías de contribución al proyecto.
NumPy cuenta actualmente con los siguientes equipos: -- development -- documentation -- triage -- website -- survey -- translations -- sprint mentors +- desarrollo +- documentación +- clasificación +- página web +- encuesta +- traducción +- mentores de sprints - funding and grants See the [Team]({{< relref "/teams" >}}) page for more info. From 0c589af9060ce18f2e5d62109cd6b85af10e19b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 12 Aug 2023 16:13:23 +0200 Subject: [PATCH 375/711] New translations about.md (Spanish) --- content/es/about.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 02271daca8..a3549c6bd8 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -46,27 +46,27 @@ La dirección del proyecto NumPy trabaja activamente para diversificar las vías - encuesta - traducción - mentores de sprints -- funding and grants +- financiación y subvenciones -See the [Team]({{< relref "/teams" >}}) page for more info. +Visita la página de [Equipos]({{< relref "/teams" >}}) para más información. -## NumFOCUS Subcommittee +## Subcomité NumFOCUS - Charles Harris - Ralf Gommers - Melissa Weber Mendonça - Sebastian Berg -- External member: Thomas Caswell +- Miembro externo: Thomas Caswell -## Sponsors +## Patrocinadores -NumPy receives direct funding from the following sources: +NumPy recibe financiación directa de las siguientes fuentes: {{< sponsors >}} -## Institutional Partners +## Socios institucionales -Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: +Los socios institucionales son organizaciones que apoyan al proyecto empleando a personas que contribuyen a NumPy como parte de su trabajo. Entre los actuales socios institucionales se encuentran: - UC Berkeley (Stéfan van der Walt) - Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça) @@ -75,15 +75,15 @@ Institutional Partners are organizations that support the project by employing p {{< partners >}} -## Donate +## Donar -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. +Si has encontrado NumPy útil en tu trabajo, investigación o empresa, por favor considera una donación al proyecto proporcional a tus recursos. ¡Cualquier cantidad ayuda! Todas las donaciones se utilizarán estrictamente para financiar el desarrollo del software de código abierto, la documentación y la comunidad de NumPy. -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. +NumPy es un proyecto patrocinado por NumFOCUS, una organización benéfica sin fines de lucro 501(c)(3) de Estados Unidos. NumFOCUS proporciona a NumPy apoyo fiscal, legal y administrativo para ayudar a garantizar el bienestar y la sostenibilidad del proyecto. Visita [numfocus.org](https://numfocus.org) para más información. -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. +Las donaciones a NumPy son gestionadas por [NumFOCUS](https://numfocus.org). Para los donantes de Estados Unidos, su donación es deducible de impuestos en la medida prevista por la ley. Al igual que con cualquier donación, debes consultar a tu asesor de impuestos sobre tu situación fiscal particular. -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). +El Consejo Directivo de NumPy tomará las decisiones sobre el mejor uso de los fondos recibidos. Las prioridades técnicas y de infraestructura están documentadas en la [Hoja de Ruta de NumPy](https://www.numpy.org/neps/index.html#roadmap). {{}} From e284e7ef70a1f4788b5b355a396d53cc0ca5cb72 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 12 Aug 2023 16:13:24 +0200 Subject: [PATCH 376/711] New translations learn.md (Spanish) --- content/es/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/learn.md b/content/es/learn.md index 60e91719d6..e41af9c5f3 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -1,9 +1,9 @@ --- -title: Learn +title: Aprende sidebar: false --- -For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). +Para la **documentación oficial de NumPy** visita [numpy.org/doc/stable](https://numpy.org/doc/stable). *** From ff3ab4beba141ad97275311b7c0e1ca57cf4304a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 20 Aug 2023 19:15:38 +0200 Subject: [PATCH 377/711] New translations learn.md (Spanish) --- content/es/learn.md | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/content/es/learn.md b/content/es/learn.md index e41af9c5f3..5140f5c664 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -7,61 +7,61 @@ Para la **documentación oficial de NumPy** visita [numpy.org/doc/stable](https: *** -Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. +A continuación se muestra una colección de recursos educativos, tanto para el autoaprendizaje como para enseñar a otros, desarrollados por colaboradores de NumPy y aprobados por la comunidad. -## Beginners +## Principiantes -There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: +Hay un montón de información sobre NumPy allá afuera. Si eres nuevo, te recomendamos encarecidamente estos: - **Tutorials** + **Tutoriales** * [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). +* [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -* [SciPy Lectures](https://scipy-lectures.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +* [Lecturas de SciPy](https://scipy-lectures.org/) Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python. * [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) - **Books** + **Libros** -* [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). +* [Guide to NumPy *by Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) Esta es la primera versión gratuita de 2006. Para conseguir la última versión (2015) mira [aquí](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. +También puedes echar un vistazo a esta [lista de Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre el tema "Python+SciPy". La mayoría de esos libros son sobre el "ecosistema SciPy", que tiene NumPy en su núcleo. - **Videos** + **Vídeos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** -## Advanced +## Avanzado -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +Pruebe estos recursos avanzados para comprender mejor los conceptos de NumPy como indexación avanzada, división, apilamiento, álgebra lineal y mucho más. - **Tutorials** + **Tutoriales** * [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). +* [Tutoriales de NumPy](https://numpy.org/numpy-tutorials) Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio [numpy-tutorials en GitHub](https://github.com/numpy/numpy-tutorials). - **Books** + **Libros** * [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* * [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* - **Videos** + **Vídeos** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** -## NumPy Talks +## Charlas de NumPy * [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) @@ -71,6 +71,6 @@ Try these advanced resources for a better understanding of NumPy concepts like a *** -## Citing NumPy +## Citando a 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). +Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, consulta esta [información de citado](/citing-numpy). From 05b362ceb476912ad5335ade844d2c13d20bd198 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 26 Aug 2023 14:25:08 +0200 Subject: [PATCH 378/711] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index f2460d3d5b..f1941a4e49 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -1,13 +1,13 @@ --- -title: "Case Study: First Image of a Black Hole" +title: "Caso de estudio: La primera imagen de un Agujero Negro" 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" caption="**Agujero Negro M87**" alt="black hole image" attr="*(Créditos de la imagen: Colaboración del Telescopio de Horizonte de Sucesos)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}
-

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

-
Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
+

Fotografiar el agujero negro M87 es como tratar de ver algo que por definición es imposible de ver.

+
Katie Bouman, Profesor asistente, Ciencias Matemáticas y de Computación, Caltech
## A telescope the size of the earth From eea56d30400b762d37ad0549ffdd4d586be765c7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 26 Aug 2023 15:30:29 +0200 Subject: [PATCH 379/711] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index f1941a4e49..975bc85391 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -10,11 +10,11 @@ sidebar: false
Katie Bouman, Profesor asistente, Ciencias Matemáticas y de Computación, Caltech
-## A telescope the size of the earth +## Un telescopio del tamaño de la Tierra -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! +El [ telescopio de Horizonte de Sucesos (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! -### Key Goals and Results +### Objetivos clave y resultados * **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. From b666a0658834086aa09bcd467c114c0547793033 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Aug 2023 00:28:23 +0200 Subject: [PATCH 380/711] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 975bc85391..124f168b93 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -3,7 +3,7 @@ title: "Caso de estudio: La primera imagen de un Agujero Negro" sidebar: false --- -{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Agujero Negro M87**" alt="black hole image" attr="*(Créditos de la imagen: Colaboración del Telescopio de Horizonte de Sucesos)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Agujero Negro M87**" alt="imagen de un agujero negro" attr="*(Créditos de la imagen: Colaboración del Telescopio de Horizonte de Sucesos)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}

Fotografiar el agujero negro M87 es como tratar de ver algo que por definición es imposible de ver.

@@ -16,25 +16,25 @@ El [ telescopio de Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.or ### Objetivos clave y resultados -* **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. +* **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. -* **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. +* **El agujero negro:** EHT se entrenó en un enorme agujero negro aproximadamente a 55 millones de años luz de la tierra, situada en el centro de la galaxia Messier 87 (M87) en el cúmulo de 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. -* **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. +* **Comparando las observaciones con la teoría:** A partir de la teoría de la relatividad general de Einstein, los científicos esperaban encontrar una región similar a las sombras causadas por la flexión gravitacional y la captura de la luz. Los científicos pudieron utilizarla para medir la enorme masa del agujero negro. -### The Challenges +### Los desafíos -* **Computational scale** +* **Escala computacional** - EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. + EHT plantea desafíos de procesamiento de datos masivos, incluyendo rápidas fluctuaciones de fase atmosféricas, amplio ancho de banda de grabación, y telescopios que son ampliamente disímiles y geográficamente dispersos. -* **Too much information** +* **Demasiada información** - 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. + 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. -* **Into the unknown** +* **Hacia lo desconocido** - When the goal is to see something never before seen, how can scientists be confident the image is correct? + 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" 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" >}} From d1eed7b723e44d4d4b0e1d2312c760dc70c4bf00 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Aug 2023 01:30:41 +0200 Subject: [PATCH 381/711] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 24 +++++++++++----------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 124f168b93..19a0b8adad 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -36,29 +36,29 @@ El [ telescopio de Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.or 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" 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" class="csfigcaption" caption="**Flujo del Procesamiento de Datos del EHT**" alt="flujo de datos" align="middle" attr="(Créditos del Diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}} -## NumPy’s Role +## El Rol de NumPy -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? +¿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? -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. +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. -Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. +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" 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" class="fig-center" alt="el rol de numpy" caption="**El rol de NumPy en la fotografía del Agujero Negro**" >}} -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. +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" 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" class="fig-center" alt="mapa de dependencia de ehtim con numpy resaltado" caption="**Gráfico de dependencias de software del paquete ehtim resaltando a NumPy**" >}} -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. +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. -## Summary +## Resumen -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. +La matriz n-dimensional eficiente y adaptable 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_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="ventajas de numpy" caption="**Capacidades Clave utilizadas de NumPy**" >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 31ce970b842cbcbc8dfbb4af95ed1c1e9e9dd0e4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Aug 2023 01:30:42 +0200 Subject: [PATCH 382/711] New translations arraycomputing.md (Spanish) --- content/es/arraycomputing.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/es/arraycomputing.md b/content/es/arraycomputing.md index abd29d11c1..44ebbf21d9 100644 --- a/content/es/arraycomputing.md +++ b/content/es/arraycomputing.md @@ -1,15 +1,15 @@ --- -title: Array Computing +title: Cómputo Matricial sidebar: false --- -*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* +*El cómputo matricial es la base del cómputo estadístico, matemático y científico en varias aplicaciones contemporáneas de ciencia de datos y aplicaciones de analíticas, como la visualización de datos, el procesamiento digital de señales, el procesamiento de imágenes, la bioinformática, el aprendizaje automático, la IA y entre otras.* -Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** +La manipulación y transformación de datos a gran escala depende de una computación matricial eficiente y de alto rendimiento. El lenguaje de elección para la analítica de datos, el aprendizaje automático y el cómputo numérico productivo es **Python.** -**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. +**Num**erical **Py**thon o NumPy es la biblioteca estándar de-facto del lenguaje de programación Python que soporta conjuntos y matrices multidimensionales de gran tamaño, y viene con una amplia colección de funciones matemáticas de alto nivel para operar sobre estos conjuntos. -Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. +Tras el lanzamiento de NumPy en 2006, Pandas apareció en el panorama en 2008, y no fue hasta hace un par de años que aparecieron sucesivamente varias bibliotecas de cómputo matricial, poblando este escenario. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. Date: Sun, 27 Aug 2023 02:50:36 +0200 Subject: [PATCH 383/711] New translations arraycomputing.md (Spanish) --- content/es/arraycomputing.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/arraycomputing.md b/content/es/arraycomputing.md index 44ebbf21d9..b473f9330f 100644 --- a/content/es/arraycomputing.md +++ b/content/es/arraycomputing.md @@ -9,13 +9,13 @@ La manipulación y transformación de datos a gran escala depende de una computa **Num**erical **Py**thon o NumPy es la biblioteca estándar de-facto del lenguaje de programación Python que soporta conjuntos y matrices multidimensionales de gran tamaño, y viene con una amplia colección de funciones matemáticas de alto nivel para operar sobre estos conjuntos. -Tras el lanzamiento de NumPy en 2006, Pandas apareció en el panorama en 2008, y no fue hasta hace un par de años que aparecieron sucesivamente varias bibliotecas de cómputo matricial, poblando este escenario. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. +Tras el lanzamiento de NumPy en 2006, Pandas apareció en el panorama en 2008, y no fue hasta hace un par de años que aparecieron sucesivamente varias bibliotecas de cómputo matricial, poblando este escenario. Muchas de estas nuevas bibliotecas imitan las características y capacidades de NumPy, y contienen nuevos algoritmos y características orientadas a las aplicaciones de aprendizaje automático e inteligencia artificial. arraycl + title="Panorama del Cómputo Matricial" /> -**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. +El **cómputo matricial** está basado en los **conjuntos** como estructura de datos. *Los conjuntos* se utilizan para organizar grandes cantidades de datos de manera que un conjunto de valores relacionados pueda ordenarse, buscarse, manipularse matemáticamente y transformarse con facilidad y rapidez. -Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. +La computación matricial es *única* ya que implica operar sobre todo el conjunto de datos *al mismo tiempo*. Esto significa que cualquier operación de conjuntos se aplica a un conjunto completo de valores de una sola vez. Este enfoque vectorial proporciona velocidad y simplicidad, al permitir a los programadores codificar y trabajar sobre los datos agregados, sin tener que utilizar bucles de instrucciones escalares individuales. From 010ee3a0958ef835f0289baa713bb1cbf857977d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 31 Aug 2023 02:45:38 +0200 Subject: [PATCH 384/711] New translations news.md (Japanese) --- content/ja/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index 0a1d35d181..03ae78d38a 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -7,7 +7,7 @@ date: 2023-08-02 ### 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: +_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。 This wouldn’t be possible without our dedicated volunteers: _Portuguese:_ * Melissa Weber Mendonça (melissawm) From 9e8f80975f4d567e92ea422d0c5be02fd6afed3e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 31 Aug 2023 17:27:52 +0200 Subject: [PATCH 385/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index b878d3aa95..a2bd1e6dbd 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -74,22 +74,22 @@ params: section5: false navbar: - - title: Install + title: インストール url: /install - - title: Documentation + title: ドキュメント url: https://numpy.org/doc/stable - title: Learn url: /learn - - title: Community + title: コミュニティ url: /community - title: About Us url: /about - - title: News + title: ニュース url: /news - title: Contribute @@ -112,7 +112,7 @@ params: title: "" links: - - text: Install + text: インストール link: /install - text: Documentation From 0c098637840d6c095286dca430745cf7d742cf82 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 11:18:27 +0200 Subject: [PATCH 386/711] New translations news.md (Spanish) --- content/es/news.md | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 5e98ec46af..80a6a8a296 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,10 +1,25 @@ --- title: News sidebar: false -newsHeader: "numpy.org is now available in Japanese and Portuguese" -date: 2023-08-02 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- +### 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: @@ -210,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.0 ([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_. From 0d4258394892456673239570bf5856857add53f8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 11:18:28 +0200 Subject: [PATCH 387/711] New translations news.md (Arabic) --- content/ar/news.md | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 5e98ec46af..80a6a8a296 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,25 @@ --- title: News sidebar: false -newsHeader: "numpy.org is now available in Japanese and Portuguese" -date: 2023-08-02 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- +### 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: @@ -210,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.0 ([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_. From e68235eb0dd6eab26c69bb6dc17353413318ade2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 11:18:29 +0200 Subject: [PATCH 388/711] New translations news.md (Japanese) --- content/ja/news.md | 26 +++++++++++++++++++++----- 1 file changed, 21 insertions(+), 5 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 03ae78d38a..a5613d1de8 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,10 +1,25 @@ --- title: ニュース sidebar: false -newsHeader: "numpy.org is now available in Japanese and Portuguese" -date: 2023-08-02 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- +### 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. 今回のリリースの目玉機能は次のとおりです。 + +* 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 _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。 This wouldn’t be possible without our dedicated volunteers: @@ -56,7 +71,7 @@ _2023年1月6日_ –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudi ### NumPy 1.24.0 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。 今回のリリースの目玉機能は次のとおりです。 +_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。 * 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 @@ -67,7 +82,7 @@ Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモ ### 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 @@ -118,7 +133,7 @@ _2021年7月12日_ -- NumPy ではコミュニティの力を信じています ### NumPy 1.19.0 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。 The highlights of the release are: - 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 @@ -210,6 +225,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - 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_. From 28885ad7b03d8ad060e4ecf45e91c97e88771ea4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 11:18:30 +0200 Subject: [PATCH 389/711] New translations news.md (Korean) --- content/ko/news.md | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index cf6fc16634..3e9f8a97c1 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,25 @@ --- title: News sidebar: false -newsHeader: "numpy.org is now available in Japanese and Portuguese" -date: 2023-08-02 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- +### 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: @@ -210,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023년 7월 31일_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. - NumPy 1.24.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.24.4)) -- _2023년 6월 26일_. From e75d4b30e60a646a8d6309904932e61a4a56e23a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 11:18:32 +0200 Subject: [PATCH 390/711] New translations news.md (Russian) --- content/ru/news.md | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 5e98ec46af..80a6a8a296 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,25 @@ --- title: News sidebar: false -newsHeader: "numpy.org is now available in Japanese and Portuguese" -date: 2023-08-02 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- +### 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: @@ -210,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.0 ([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_. From d1b53095416687041118c2c47b0cac305e4222c7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 11:18:33 +0200 Subject: [PATCH 391/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 20 ++++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 5e98ec46af..80a6a8a296 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,25 @@ --- title: News sidebar: false -newsHeader: "numpy.org is now available in Japanese and Portuguese" -date: 2023-08-02 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- +### 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: @@ -210,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.0 ([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_. From d273225c26ac9df3c7ef7057fd799068c419b33b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 11:18:34 +0200 Subject: [PATCH 392/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 22 +++++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index ada03d87bf..efaba9ea3c 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,12 +1,27 @@ --- title: Notícias sidebar: false -newsHeader: "numpy.org agora está disponível em japonês e português" -date: 2023-08-02 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- ### numpy.org agora está disponível em japonês e português +_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. Os destaques desta versão são: + +* 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 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:_ @@ -118,7 +133,7 @@ Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBX ### 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: +_23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) está disponível. The highlights of the release are: - a continuação do trabalho com SIMD para suportar mais funções e plataformas, - trabalho inicial na infraestrutura e conversão de novos dtypes, @@ -210,6 +225,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 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_. From 2b15cd03da7e6d6ad8bb30ac101a3eb364d951b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 12:25:34 +0200 Subject: [PATCH 393/711] New translations news.md (Japanese) --- content/ja/news.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index a5613d1de8..9acee74883 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,24 +1,24 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 1.26.0 released" +newsHeader: "NumPy 1.26.0 がリリースされました。" date: 2023-09-16 --- ### 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. 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。 -* 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 +* Python 3.12.0 のサポート +* Cython 3.0.0 への互換性 +* 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 -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. +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。 -The Python versions supported by this release are 3.9-3.12. +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 is now available in Japanese and Portuguese @@ -133,7 +133,7 @@ _2021年7月12日_ -- NumPy ではコミュニティの力を信じています ### NumPy 1.19.0 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。 The highlights of the release are: +_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。 - 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 @@ -225,7 +225,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- 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_. From 1d00aa16db37cc380e2df3259d01e9948b497a02 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Sep 2023 17:05:01 +0200 Subject: [PATCH 394/711] New translations news.md (Spanish) --- content/es/news.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 80a6a8a296..752416b8d6 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,24 +1,24 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.26.0 released" +newsHeader: "NumPy 1.26.0 publicado" date: 2023-09-16 --- ### 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: +_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: -* 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 +* 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 -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. +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. -The Python versions supported by this release are 3.9-3.12. +Las versiones de Python compatibles con esta versión son 3.9-3.12. ### numpy.org is now available in Japanese and Portuguese @@ -133,7 +133,7 @@ Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOON ### 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: +_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: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, @@ -225,7 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 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_. From 45300df902919062a5dcc892072093a2ae7847fe Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Sep 2023 13:46:06 +0200 Subject: [PATCH 395/711] New translations config.yaml (Arabic) --- content/ar/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 37562686c0..ffa024a433 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.25. View all releases" + buttontext: "Latest release: NumPy 1.26. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 584755d0cd5b1ace80dcb0d0b79d9caa636caaf3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Sep 2023 13:46:08 +0200 Subject: [PATCH 396/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 66f7f6554c..e1ff62b0a1 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: Python으로 과학적 컴퓨팅을 하기 위한 기초 패키지 #Button text - buttontext: "Latest release: NumPy 1.25. View all releases" + buttontext: "Latest release: NumPy 1.26. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From 329eb22d1a3354f1e47b63efdd69afca0c8dddc3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Sep 2023 13:46:09 +0200 Subject: [PATCH 397/711] New translations config.yaml (Russian) --- content/ru/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index 37562686c0..ffa024a433 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.25. View all releases" + buttontext: "Latest release: NumPy 1.26. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From f130ce61a8ca1553116593a0a1faa1ba23648168 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Sep 2023 13:46:10 +0200 Subject: [PATCH 398/711] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 37562686c0..ffa024a433 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -10,7 +10,7 @@ params: #Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python #Button text - buttontext: "Latest release: NumPy 1.25. View all releases" + buttontext: "Latest release: NumPy 1.26. View all releases" #Where the main hero button links to buttonlink: "/news/#releases" #Hero image (from static/images/___) From ff01edc897eaae60aa1dcbc8d05aa664bdedcf62 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 01:30:31 +0200 Subject: [PATCH 399/711] New translations news.md (Spanish) --- content/es/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/news.md b/content/es/news.md index 752416b8d6..f2a62096da 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.1 ([release notes](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_. From 5d5dacf014274b576cef1071617c2305509fb914 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 01:30:32 +0200 Subject: [PATCH 400/711] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index 80a6a8a296..fe0852a3fd 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.1 ([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_. From 7878c4ab882ce53b3fb61115483c0769f9100f1f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 01:30:33 +0200 Subject: [PATCH 401/711] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 9acee74883..b4d1b5cabe 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -225,6 +225,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. - 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_. From 4abd855d4ef12f14fa4cf26b5419836b30d6a972 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 01:30:34 +0200 Subject: [PATCH 402/711] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index 3e9f8a97c1..f3e3e53b53 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.1 ([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 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023년 7월 31일_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. From deef6925ba7e0683fda350d9d5701e24b09ca671 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 01:30:35 +0200 Subject: [PATCH 403/711] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index 80a6a8a296..fe0852a3fd 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.1 ([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_. From bbf5e8b746b37216f81666ae4124f7d2f1e424fa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 01:30:36 +0200 Subject: [PATCH 404/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index 80a6a8a296..fe0852a3fd 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.1 ([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_. From 0107afe67a17d1490bc2d77c2f8df95404bb45a5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 01:30:37 +0200 Subject: [PATCH 405/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index efaba9ea3c..46ab1067cd 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -225,6 +225,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 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 ([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_. From 322d09e887bf6859c77e9c2125a1efde7d801a63 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 02:59:14 +0200 Subject: [PATCH 406/711] New translations news.md (Japanese) --- content/ja/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index b4d1b5cabe..3fe43c94cb 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -5,7 +5,7 @@ newsHeader: "NumPy 1.26.0 がリリースされました。" date: 2023-09-16 --- -### NumPy 1.26.0 released +### NumPy 1.26.0 がリリースされました。 _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。 @@ -225,7 +225,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- 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_. From 1de0ab7116fff8c991dc95181499c3b7d7267991 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 02:59:15 +0200 Subject: [PATCH 407/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index a2bd1e6dbd..056006ee9a 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -80,19 +80,19 @@ params: title: ドキュメント url: https://numpy.org/doc/stable - - title: Learn + title: 学習方法 url: /learn - title: コミュニティ url: /community - - title: About Us + title: 私たちについて url: /about - title: ニュース url: /news - - title: Contribute + title: 貢献 url: /contribute footer: logo: logo.svg @@ -115,45 +115,45 @@ params: text: インストール link: /install - - text: Documentation + text: ドキュメント link: https://numpy.org/doc/stable - - text: Learn + text: 学習方法 link: /learn - - text: Citing Numpy + text: NumPy を引用する link: /citing-numpy - - text: Roadmap + text: ロードマップ link: https://numpy.org/neps/roadmap.html column2: links: - - text: About us + text: 私達について link: /about - - text: Community + text: コミュニティ link: /community - - text: User surveys + text: ユーザ調査 link: /user-surveys - - text: Contribute + text: 貢献 link: /contribute - - text: Code of conduct + text: 行動規範 link: /code-of-conduct column3: links: - - text: Get help + text: サポートを得る方法 link: /gethelp - - text: Terms of use + text: 利用規約 link: /terms - - text: Privacy + text: プライバシーポリシー link: /privacy - - text: Press kit + text: プレス用資料 link: /press-kit From 569cd42dcdd07456c375e0c6ead50e441cbfe535 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 02:59:16 +0200 Subject: [PATCH 408/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 88 ++++++++++++++++++------------------- 1 file changed, 44 insertions(+), 44 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 688dbe8431..cf2f6597b1 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -2,80 +2,80 @@ 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 — 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) — 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'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. + 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: Array Library + text: 配列ライブラリ - - text: Capabilities & Application areas + text: 機能と応用分野 libraries: - title: Dask - text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + 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: NumPy-compatible array library for GPU-accelerated computing with Python. + 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: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." + 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: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization + 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: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra. + 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: Deep learning framework that accelerates the path from research prototyping to production deployment. + 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: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + 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: Deep learning framework suited for flexible research prototyping and production. + 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: A cross-language development platform for columnar in-memory data and analytics. + 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: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + 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 Array + title: XND text: Manipulate JSON-like data with NumPy-like idioms. img: /images/content_images/arlib/awkward.svg alttext: awkward @@ -138,82 +138,82 @@ params: img: /images/content_images/sc_dom_img/bayesian_inference.svg - title: Mathematical Analysis - alttext: Four mathematical symbols. + alttext: ４つの数学記号 img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - title: Chemistry - alttext: A test tube. + title: 化学 + alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience - alttext: The Earth. + title: 地球科学 + alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg - - title: Geographic Processing - alttext: A map. + title: 地理情報処理 + alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg - - title: Architecture & Engineering - alttext: A microprocessor development board. + 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 lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + 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: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + 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: 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'. + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + text: "モデリングと評価: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "ダッシュボードでのレポート: [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)). + 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: A streamplot made in matplotlib + 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: A scatter-plot graph made in ggpy + 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: A box-plot made in plotly + alttext: plotyで作られた箱ひげ図 - - url: https://altair-viz.github.io/gallery/streamgraph.html + url: https://alta-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png - alttext: A streamgraph made in altair + 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: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn" + 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: A 3D volume rendering made in PyVista. + 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: A multi-dimensionan image made in napari. + 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: A Voronoi diagram made in vispy. + alttext: vispyで作られたボロノイ図 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's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. + 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。 From 05b3badf1c1879cc2c43edc969a9f92c89251c27 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 04:40:56 +0200 Subject: [PATCH 409/711] New translations news.md (Japanese) --- content/ja/news.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 3fe43c94cb..efa65ade2c 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -20,29 +20,29 @@ Numpy 1.26.0 は 1.25 からの互換性を保持しています。Mesonビル 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 is now available in Japanese and Portuguese +### 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。 This wouldn’t be possible without our dedicated volunteers: +_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： -_Portuguese:_ -* Melissa Weber Mendonça (melissawm) +_ポルトガル語_ +* Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) -* Getúlio Silva (getuliosilva) +* Getúlio Silva (getuliosilva) * Julio Batista Silva (jbsilva) * Alexandre de Siqueira (alexdesiqueira) * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japanese:_ +_日本語：_ * 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. +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。 -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.) +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) 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 1.25.0 リリース From 320bd45c305b8954d759702825abed5d22b4634b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 15 Oct 2023 04:40:57 +0200 Subject: [PATCH 410/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 48 ++++++++++++++++++------------------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index cf2f6597b1..bf8fb8c18c 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -76,68 +76,68 @@ params: url: https://github.com/xtensor-stack/xtensor-python - title: XND - text: Manipulate JSON-like data with NumPy-like idioms. - img: /images/content_images/arlib/awkward.svg + 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: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + 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: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. + 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: Nearly every scientist working in Python draws on the power of NumPy. + 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 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." + 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: Quantum Computing - alttext: A computer chip. + 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: Statistical Computing - alttext: A line graph with the line moving up. + 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: Signal Processing - alttext: A bar chart with positive and negative values. + 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: Image Processing - alttext: An photograph of the mountains. + title: 画像処理 + alttext: 山々の写真 img: /images/content_images/sc_dom_img/image_processing.svg - - title: Graphs and Networks - alttext: A simple graph. + 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: Astronomy - alttext: A telescope. + alttext: 望遠鏡 img: /images/content_images/sc_dom_img/astronomy_processes.svg - - title: Cognitive Psychology - alttext: A human head with gears. + 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: Bioinformatics - alttext: A strand of DNA. + title: 生命情報科学 + alttext: DNAの鎖 img: /images/content_images/sc_dom_img/bioinformatics.svg - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + 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: Mathematical Analysis + title: 数学的分析 alttext: ４つの数学記号 img: /images/content_images/sc_dom_img/mathematical_analysis.svg - From 28168414cb6935fc866686dd35054ff369dc5af1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Oct 2023 09:14:23 +0200 Subject: [PATCH 411/711] New translations news.md (Korean) --- content/ko/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index f3e3e53b53..92a91f014d 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -225,7 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 2023_. +- NumPy 1.26.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023년 10월 14일_. - NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. - NumPy 1.25.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.25.2)) -- _2023년 7월 31일_. - NumPy 1.25.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. From 2a151485874c234ab268a64116d0f0ad96f3c192 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 18 Oct 2023 19:53:22 +0200 Subject: [PATCH 412/711] New translations news.md (Spanish) --- content/es/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/news.md b/content/es/news.md index f2a62096da..a25a08c71e 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -225,7 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _14 Oct 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_. From bf8217ebb51e893e4a0a355db0f0996757e00152 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 18 Oct 2023 20:52:33 +0200 Subject: [PATCH 413/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 46ab1067cd..c4fe8eb1a6 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,24 +1,24 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy 1.26.0 released" +newsHeader: "NumPy versão 1.26.0" date: 2023-09-16 --- -### numpy.org agora está disponível em japonês e português +### Lançado o NumPy versão 1.26.0 -_Sep 16, 2023_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html) is now available. 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: -* 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 +* 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 -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. +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. -The Python versions supported by this release are 3.9-3.12. +As versões do Python suportadas por esta versão são 3.9-3.12. ### numpy.org agora está disponível em japonês e português @@ -133,7 +133,7 @@ Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBX ### 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. The highlights of the release are: +_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: - a continuação do trabalho com SIMD para suportar mais funções e plataformas, - trabalho inicial na infraestrutura e conversão de novos dtypes, @@ -225,8 +225,8 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 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 1.26.1 ([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_. From 5df50f43aa5637b5ee8725e0fb11821f2e8df151 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Oct 2023 20:30:18 +0200 Subject: [PATCH 414/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index 688dbe8431..b347bebc9b 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -201,7 +201,7 @@ params: 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/examples/index.html + url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - From 31fcae652819d6684bb3e658b391973061c39f24 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Oct 2023 20:30:19 +0200 Subject: [PATCH 415/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index 688dbe8431..b347bebc9b 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -201,7 +201,7 @@ params: 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/examples/index.html + url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - From 1c109120856e22316f3b0046ee229f8acfbb6ae3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Oct 2023 20:30:20 +0200 Subject: [PATCH 416/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index 688dbe8431..b347bebc9b 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -201,7 +201,7 @@ params: 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/examples/index.html + url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - From a81a5bf6e018c35c236c3c77828a249ce8facb4c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Oct 2023 20:30:21 +0200 Subject: [PATCH 417/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index 688dbe8431..b347bebc9b 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -201,7 +201,7 @@ params: 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/examples/index.html + url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - From 1d2833a921a396d5b69b2c3d854e7f9602cf8bc2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Oct 2023 20:30:22 +0200 Subject: [PATCH 418/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 53716aae26..9976789461 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -201,7 +201,7 @@ params: 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/examples/index.html + url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - From 22ece1d2cda0451a418f4e29b40af4c123bba74a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 05:00:10 +0100 Subject: [PATCH 419/711] New translations about.md (Spanish) --- content/es/about.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/about.md b/content/es/about.md index a3549c6bd8..a6e42626dc 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -46,6 +46,7 @@ La dirección del proyecto NumPy trabaja activamente para diversificar las vías - encuesta - traducción - mentores de sprints +- optimization - financiación y subvenciones Visita la página de [Equipos]({{< relref "/teams" >}}) para más información. From b1379e63a659355c08eed1e49780d95f98fc1ee8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 05:00:11 +0100 Subject: [PATCH 420/711] New translations about.md (Arabic) --- content/ar/about.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/about.md b/content/ar/about.md index 8c769cfc9d..a6e4abb22e 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -46,6 +46,7 @@ The NumPy project leadership is actively working on diversifying contribution pa - survey - translations - sprint mentors +- optimization - funding and grants See the [Team]({{< relref "/teams" >}}) page for more info. From 86a2d9db8ab2214e0f9bf27e8850609e4f3bcad6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 05:00:12 +0100 Subject: [PATCH 421/711] New translations about.md (Japanese) --- content/ja/about.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/about.md b/content/ja/about.md index 3de210caec..5207c32ec2 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -46,6 +46,7 @@ Numpy プロジェクトのコアメンバーは、プロジェクトへの貢 - 調査 - 翻訳 - スプリントのメンター +- optimization - 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。 From 589d182e7d05b0146fb02177228d74eac80be206 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 05:00:13 +0100 Subject: [PATCH 422/711] New translations about.md (Korean) --- content/ko/about.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/about.md b/content/ko/about.md index ddc765e09f..cbf663bed9 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -46,6 +46,7 @@ The NumPy project leadership is actively working on diversifying contribution pa - survey - translations - sprint mentors +- optimization - funding and grants See the [Team]({{< relref "/teams" >}}) page for more info. From 7353494176240a0592041003e51ccfae5e84a0f8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 05:00:14 +0100 Subject: [PATCH 423/711] New translations about.md (Russian) --- content/ru/about.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/about.md b/content/ru/about.md index 8c769cfc9d..a6e4abb22e 100644 --- a/content/ru/about.md +++ b/content/ru/about.md @@ -46,6 +46,7 @@ The NumPy project leadership is actively working on diversifying contribution pa - survey - translations - sprint mentors +- optimization - funding and grants See the [Team]({{< relref "/teams" >}}) page for more info. From 1d7ca367ce8090db193ee6a25598006c258f65e5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 05:00:15 +0100 Subject: [PATCH 424/711] New translations about.md (Chinese Simplified) --- content/zh/about.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/about.md b/content/zh/about.md index 8c769cfc9d..a6e4abb22e 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -46,6 +46,7 @@ The NumPy project leadership is actively working on diversifying contribution pa - survey - translations - sprint mentors +- optimization - funding and grants See the [Team]({{< relref "/teams" >}}) page for more info. From bd5cadb6b0f00028d3d0ea94095397c931c78c31 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 05:00:16 +0100 Subject: [PATCH 425/711] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/about.md b/content/pt/about.md index 40407d3cdf..a6754775a7 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -46,6 +46,7 @@ A liderança do projeto NumPy trabalha ativamente na diversificação dos caminh - pesquisa - traduções - mentores para sprints de desenvolvimento +- optimization - financiamento e bolsas Veja a página sobre os [Times]({{< relref "/teams" >}}) para mais informações. From 948eb2284f641da557a1552b17d86ba318ebb675 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 07:11:03 +0100 Subject: [PATCH 426/711] New translations about.md (Japanese) --- content/ja/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/about.md b/content/ja/about.md index 5207c32ec2..6508ca265d 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -46,7 +46,7 @@ Numpy プロジェクトのコアメンバーは、プロジェクトへの貢 - 調査 - 翻訳 - スプリントのメンター -- optimization +- 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。 From f5c2d6c557aaa8cb9d77f846c166d243eef72ac6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 10:08:08 +0100 Subject: [PATCH 427/711] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/about.md b/content/pt/about.md index a6754775a7..30e0dbdd13 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -46,7 +46,7 @@ A liderança do projeto NumPy trabalha ativamente na diversificação dos caminh - pesquisa - traduções - mentores para sprints de desenvolvimento -- optimization +- otimização - financiamento e bolsas Veja a página sobre os [Times]({{< relref "/teams" >}}) para mais informações. From 3872136093bbd2ca56b2d24df3841101751ab46f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 1 Nov 2023 16:50:18 +0100 Subject: [PATCH 428/711] New translations about.md (Korean) --- content/ko/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/about.md b/content/ko/about.md index cbf663bed9..abef9f7811 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -46,7 +46,7 @@ The NumPy project leadership is actively working on diversifying contribution pa - survey - translations - sprint mentors -- optimization +- 최적화 - funding and grants See the [Team]({{< relref "/teams" >}}) page for more info. From c5fd166e2319577f4bf0e2c64f1fbddebcd45f70 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 7 Nov 2023 06:25:26 +0100 Subject: [PATCH 429/711] New translations install.md (Korean) --- content/ko/install.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index 3500153a64..99e27a6326 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -3,13 +3,13 @@ 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/distribution) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. +NumPy를 설치하는 유일한 선행 조건은 Python 자체입니다. 만약 아직 Python을 설치하지 않았고 가장 간단한 방법으로 시작하려면, [Anaconda 배포판](https://www.anaconda.com/distribution) 을 사용하길 권장합니다. 이 배포판에는 Python 와 NumPy 및 과학 계산 및 데이터 사이언스에 자주 사용되는 다른 패키지들이 포함되어 있습니다. -NumPy can be installed with `conda`, with `pip`, with a package manager on macOS and Linux, or [from source](https://numpy.org/devdocs/user/building.html). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. +NumPy 는 `conda` 나 `pip` 를 통해서 사용하여 설치할 수 있고, 또한 macOS 및 Linux의 패키지 관리자나 [원본 코드](https://numpy.org/devdocs/user/building.html) 를 이용하여 설치할 수 있습니다. 더 자세한 설명은 아래의 [Python 및 NumPy 설치 가이드](#python-numpy-install-guide)를 확인해주세요. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: +`conda`를 사용한다면, NumPy를 `defaults` 또는 `conda-forge` 채널에서 설치할 수 있습니다: ```bash # Best practice, use an environment rather than install in the base env From c5b650a3af389b30eb250616310f418a5b57844b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 7 Nov 2023 07:31:06 +0100 Subject: [PATCH 430/711] New translations install.md (Korean) --- content/ko/install.md | 50 +++++++++++++++++++++---------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index 99e27a6326..df0d19a959 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -12,68 +12,68 @@ NumPy 는 `conda` 나 `pip` 를 통해서 사용하여 설치할 수 있고, 또 `conda`를 사용한다면, NumPy를 `defaults` 또는 `conda-forge` 채널에서 설치할 수 있습니다: ```bash -# Best practice, use an environment rather than install in the base env +# 가장 좋은 방법은 기본 환경 대신에 새로운 환경을 이용하는 것입니다 conda create -n my-env conda activate my-env -# If you want to install from conda-forge +# my-env 에 conda-forge 채널을 더해줍니다 conda config --env --add channels conda-forge -# The actual install command +# my-env 에 NumPy 를 설치합니다 conda install numpy ``` **PIP** -If you use `pip`, you can install NumPy with: +`pip`를 사용한다면, NumPy를 다음과 같이 설치할 수 있습니다: ```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. +또한 `pip`를 사용할 때, 가상 환경을 사용하는 것을 추천합니다. 가상 환경을 사용하는 이유는 [재현 가능한 설치방법들](#reproducible-installs)을 참조해주세요. 가상 환경 사용에 대한 자세한 내용은 [이 가이드](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)에서 확인하실 수 있습니다. -# Python and NumPy installation guide +# Python 및 NumPy 설치 가이드 -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. +Python에서 패키지를 설치하고 관리하는 것은 복잡한 작업이기에, 대부분의 작업에 대해 다양한 대안적인 해결책이 있습니다. 이 가이드는 독자분들에게 가장 좋은 (또는 가장 인기 있는) 해결책을 이해시키고 명확한 권장 사항을 제공하려고 노력합니다. 이 가이드는 일반적인 운영 체제와 하드웨어에서 Python, NumPy 및 PyData (또는 숫자 계산) 사용자에 중점을 둡니다. -## Recommendations +## 권장 사항 -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. +사용자의 경험과 사용하는 운영체제를 기준으로 추천하는 방식을 먼저 이야기 하겠습니다. 만약 당신이 초심자 또는 숙련자범위에 속해있다면, 간단하게 설치하고 싶다면 초심자로, 추후에 작업을 위해서 보다 구체적인 연습을 하고 싶다면 숙련자 자료를 참고하십시오. -### Beginning users +### 초심자 유저 -On all of Windows, macOS, and Linux: +Windows, macOS, Linux 등 일반적인 운영체제: -- Install [Anaconda](https://www.anaconda.com/distribution/) (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. +- [Anaconda](https://www.anaconda.com/distribution/) 를 설치하십시오.(당신이 필요로 하는 패키지를 설치하고, 아래에 언급될 다양한 도구들을 제공합니다.) +- 코드를 작성하거나 실행할 때, 데이터를 분석하거나 대화형으로 코드를 작업하는 경우에는 [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) 의 notebooks를 사용하십시오. 그리고 코드를 작성하거나 패키지를 작성할 때는 [Spyder](https://www.spyder-ide.org/)나 [Visual Studio Code](https://code.visualstudio.com/)를 사용하십시오. +- 패키지를 관리하거나 JupyterLab, Spyder, Visual Studio Code 를 사용하는 경우 [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/)를 사용하십시오. -### Advanced users +### 숙련자 유저 #### 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. +- [Miniforge](https://github.com/conda-forge/miniforge)를 설치하십시오. +- `base` 라는 이름의 콘다 가상환경은 최소 상태를 유지하고, [콘다 가상환경](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)을 만들어서, 해당 가상환경에 필요한 패키지를 설치하십시오. -#### Alternative if you prefer pip/PyPI +#### Pip/PyPI를 활용하는 경우: -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. +개인적인 선호나 아래의 conda 와 pip의 차이점을 설명하는 글을 읽은 유저나 또는 pip/PyPI기반의 설치 방법을 선호하는 경우 참고하십시오. +- [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or Linux package manager를 활용해서 Python을 설치하십시오. +- Conda와 동일한 수준의 가상환경 관리와 패키지 의존성을 해결을 도와주는 [Poetry](https://python-poetry.org/)를 유지관리 도구로 사용하십시오. -## Python package management +## Python 패키지 관리 -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. +패키지 관리는 아주 중요하기 때문에, 사용할 수 있는 도구들이 많습니다. 웹 및 범용 Python 개발을 위해 Pip뿐만 아니라 [다양한 도구](https://packaging.python.org/guides/tool-recommendations/)들이 있습니다. 고성능 컴퓨터 (HPC) 를 사용하는 경우 [Spack](https://github.com/spack/spack)를 사용하는 것을 추천합니다. 대부분 Numpy를 사용하는 유저는, [conda](https://conda.io/en/latest/) 와 [pip](https://pip.pypa.io/en/stable/)를 가장 많이 사용합니다. ### 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. +Python 패키지를 설치하고 관리하는 주요 툴은 `pip` 과 `conda` 입니다. 그 도구들의 기능은 부분적으로 겹칩지만 (e.g. both can install `numpy`), 같이 쓰일 수도 있습니다. 곧 pip와 conda의 차이점에 대해서 논의해볼 것입니다. - 패키지 관리를 효율적으로 하기 위해서는 차이를 이해하는게 중요합니다. -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. +첫번째 차이점은, conda는 cross-language 를 지원하고, Python을 설치할 수 도 있지만, pip는 특정 Python에만 패키지를 설치하고 관리할 수 있다는 것 입니다. 또한 conda는 non-Python 라이브러리나 도구들을 설치할 수 있지만 (e.g. compilers, CUDA, HDF5), pip는 Python이 필요하기 때문에 설치할 수 없습니다. 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. From d902b3d51247e500eeb643b8e27dc7cebb24be5e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 8 Nov 2023 05:37:51 +0100 Subject: [PATCH 431/711] New translations install.md (Korean) --- content/ko/install.md | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index df0d19a959..3415cb7327 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -75,47 +75,47 @@ Python 패키지를 설치하고 관리하는 주요 툴은 `pip` 과 `conda` 첫번째 차이점은, conda는 cross-language 를 지원하고, Python을 설치할 수 도 있지만, pip는 특정 Python에만 패키지를 설치하고 관리할 수 있다는 것 입니다. 또한 conda는 non-Python 라이브러리나 도구들을 설치할 수 있지만 (e.g. compilers, CUDA, HDF5), pip는 Python이 필요하기 때문에 설치할 수 없습니다. -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. +두번째 차이점은 pip는 Python Packaging Index(PyPI) 로 부터 패키지를 다운받아 설치하지만, conda는 conda 만의 채널로 설치합니다 (일반적으로 "defaults" or "conda-forge" 채널을 사용합니다). PyPI 가 가장 큰 패키지 저장소입니다만, 많은 사람들이 사용하는 패키지는 conda에서도 설치할 수 있습니다. -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. +세번째 차이점은 conda는 환경이나 패키지간 의존성을 해결하기 위한 해키지 관리 도구를 제공하지만, pip는 그를 위해서 (아주 많은) 추가적인 도구들이 필요하다는 것 입니다. -### Reproducible installs +### 재현 가능한 설치방법들 -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: +라이브러리가 업데이트되면, 코드의 실행 결과가 바뀌거나, 코드가 완전히 손상될 수 있습니다. 그러므로 사용중인 패키지 및 버전을 재구성할 수 있도록 하는 것이 중요합니다. 권장되는 방법으로는 -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/) +1. 작업 중인 프로젝트마다 다른 환경을 이용하고, +2. 각각 자체 메타 데이터 형식이 있는 패키지 설치 프로그램을 통해 패키지 이름과 버전을 기록해둡니다. + - Conda: [conda 환경들 과 environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html) + - Pip: [가상 환경들](https://docs.python.org/3/tutorial/venv.html) 과 [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [가상 환경들 과 pyproject.toml](https://python-poetry.org/docs/basic-usage/) -## NumPy packages & accelerated linear algebra libraries +## NumPy 패키지 & 고속 선형 대수 라이브러리 -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: +NumPy는 다른 Python 패키지에 의존하지 않습니다. 그러나 고속 선형 대수 라이브러리, 일반적으로 [Inter MKL](https://software.intel.com/en-us/mkl) 또는 [OpenBLAS](https://www.openblas.net/)에 의존하고 있습니다. 사용자는 이를 설치하지 않아도 됩니다 (NumPy 설치 중 설치됩니다). 고급 사용자의 경우 사용한 BLAS가 디스크의 성능, 동작 및 크기에 영향을 끼칠 수 있기 때문에 세부 정보가 필요할 수도 있습니다. -- 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. +- Pip가 설치하는 PyPI의 휠 파일에 있는 NumPy의 경우는 OpenBLAS로 빌드되었습니다. OpenBLAS 라이브러리는 휠 파일에 포함되어 있습니다. 이는 휠 파일의 크기를 더 크게 만들고, 사용자가 (예를 들어) SciPy도 설치하게 되면 디스크에 2개의 OpenBLAS 사본을 가지게 됩니다. -- 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. +- Conda의 기본 채널 내 NumPy는 Interl MKL로 빌드되었습니다. MKL은 NumPy를 설치할 때 사용자의 환경에 같이 설치되는 분할 패키지입니다. -- 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. +- Conda-forge 채널 내 NumPy는 더미 "BLAS" 패키지로 빌드되었습니다. 사용자가 conda-forge에서 NumPy를 설치할 때 해당 BLAS 패키지가 실제 라이브러리와 함께 설치됩니다. 기본값은 OpenBLAS이나, (기본 채널에서는) MKL이 될 수도 있고, 심지어 [BLIS](https://github.com/flame/blis)나 Reference BLAS가 될 수도 있습니다. -- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. +- MKL 패키지가 OpenBLAS에 비해 더욱 큽니다. OpenBLAS가 30MB를 차지하는 반면, MKL 쪽은 700MB에 달하는 디스크 공간을 차지합니다. -- MKL is typically a little faster and more robust than OpenBLAS. +- 보통 MKL이 OpenBLAS보다 더 빠르고 안정적입니다. -Besides install sizes, performance and robustness, there are two more things to consider: +설치 크기, 성능 및 안정성을 제쳐 두더라도, 고려할 사항이 2가지 더 있습니다. -- 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. +- Intel MKL은 오픈소스가 아닙니다. 일반적으로 사용할 때는 문제가 되지 않지만, 사용자가 NumPy로 빌드한 애플리케이션을 재배포하는 경우 문제가 될 수 있습니다. +- MKL과 OpenBLAS 모두 `np.dot`과 같이 함수를 호출하는 데 다중 스레드를 사용하며, 스레드의 수는 빌드 시간 설정과 환경 변수에 의해 결정됩니다. 보통은 모든 CPU 코어가 사용됩니다. 이로 인하여 예기치 않은 일이 발생할 수 있습니다. NumPy 자체적으로는 어떤 함수 호출도 병렬화하지 않습니다. 일반적으로 더 나은 성능을 제공해주지만, 예를 들어 Dask, scikit-learn 또는 멀티프로세싱과 함께 다른 수준의 병렬화를 사용하는 경우 좋지 않은 결과를 초래할 수 있습니다. -## Troubleshooting +## 트러블슈팅 -If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). +아래와 같은 응답과 함께 설치에 실패한다면, [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html)를 참고하시기 바랍니다. ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! From 3eb3b626728ed7b88781315485e335ebd4b540eb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 8 Nov 2023 05:37:52 +0100 Subject: [PATCH 432/711] New translations learn.md (Korean) --- content/ko/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index 60e91719d6..4a2e5fc85d 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -3,13 +3,13 @@ title: Learn sidebar: false --- -For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). +**공식 NumPy 문서**는 [numpy.org/doc/stable](https://numpy.org/doc/stable)에 있습니다. *** 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 +## 초심자 There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: From 7b6988c1dca5dd4053e1b3e728753446fff36b39 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 Nov 2023 09:07:47 +0100 Subject: [PATCH 433/711] New translations learn.md (Korean) --- content/ko/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index 4a2e5fc85d..1599b52a2b 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -7,11 +7,11 @@ sidebar: false *** -Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. +아래는 NumPy 기여자들이 개발하고 커뮤니티에서 승인한 자기 학습 및 교육 자원들을 선별한 컬렉션입니다. ## 초심자 -There's a ton of information about NumPy out there. If you are just starting, we'd strongly recommend the following: +내외적으로 NumPy에 대한 정보가 많이 있습니다. If you are just starting, we'd strongly recommend the following: **Tutorials** From a258b2b1b989ee6619e7c664776738d089cb0c5d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 Nov 2023 11:18:46 +0100 Subject: [PATCH 434/711] New translations install.md (Korean) --- content/ko/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/install.md b/content/ko/install.md index 3415cb7327..c3128cee25 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -1,5 +1,5 @@ --- -title: Installing NumPy +title: NumPy 설치 sidebar: false --- From 5b0417663c5dd724c54ea95ec5fd69986892647b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 Nov 2023 11:18:47 +0100 Subject: [PATCH 435/711] New translations learn.md (Korean) --- content/ko/learn.md | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index 1599b52a2b..3f32c000f3 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -1,5 +1,5 @@ --- -title: Learn +title: 배우기 sidebar: false --- @@ -11,57 +11,57 @@ sidebar: false ## 초심자 -내외적으로 NumPy에 대한 정보가 많이 있습니다. If you are just starting, we'd strongly recommend the following: +내외적으로 NumPy에 대한 정보가 많이 있습니다. 처음 시작하는 경우 다음 자료들을 강력히 추천합니다: - **Tutorials** + **튜토리얼** -* [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) -* [SciPy Lectures](https://scipy-lectures.org/) Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. +* [NumPy 빠른 시작 튜토리얼](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy 튜토리얼](https://numpy.org/numpy-tutorials) - NumPy 문서 팀에서 개발 및 유지보수하는 Jupyter 노트북 형식의 튜토리얼 및 교육 자료 모음입니다. 만약 추가하고 싶은 내용이 생기는 경우 [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials)를 확인해 주십시오. +* [NumPy Illustrated: The Visual Guide to NumPy - *Lev Maximov 저*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [SciPy Lectures](https://scipy-lectures.org/) - 여기서는 NumPy를 다루는 것 외에도 Python 생태계에 대하여 광범위한 소개를 볼 수 있습니다. * [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/) +* [From Python to NumPy - *Nicolas P. Rougier 저*](https://github.com/rougier/numpy-tutorial) +* [Stanford CS231 - *Justin Johnson 저*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) - **Books** + **도서** -* [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* +* [Guide to NumPy - *Travis E. Oliphant 저*](http://web.mit.edu/dvp/Public/numpybook.pdf) 이건 2006년의 무료 버전 초판입니다. 최근 판(2015)은 [여기에서](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007) 볼 수 있습니다. +* [From Python to NumPy - *Nicolas P. Rougier 저*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [Elegant SciPy - ](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) *Juan Nunez-Iglesias, Stefan van der Walt, Harriet Dashnow 저* -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. +"Python+SciPy" 주제에 관한 [Goodreads 목록](https://www.goodreads.com/shelf/show/python-scipy)도 확인해보시기를 권장합니다. 거기서 대부분의 책은 "SciPy 생태계"에 관한 것이며, 이 생태계의 핵심에는 NumPy가 포함되어 있습니다. - **Videos** + **영상** -* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* +* [Introduction to Numerical Computing with NumPy - ](http://youtu.be/ZB7BZMhfPgk) *Alex Chabot-Leclerc 저* *** -## Advanced +## 숙련자 -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +Indexing, Splitting, Stacking, 선형대수 등과 같은 NumPy의 개념을 더 잘 이해하러면 이 고급 자료들을 참조 해보세요. - **Tutorials** + **튜토리얼** * [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). +* [NumPy 튜토리얼](https://numpy.org/numpy-tutorials) - NumPy 문서 팀에서 개발 및 유지보수하는 Jupyter 노트북 형식의 튜토리얼 및 교육 자료 모음입니다. 만약 추가하고 싶은 내용이 생기는 경우 [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials)를 확인해 주십시오. - **Books** + **도서** * [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* * [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* - **Videos** + **영상** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* *** -## NumPy Talks +## NumPy 이야기 * [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) @@ -71,6 +71,6 @@ Try these advanced resources for a better understanding of NumPy concepts like a *** -## Citing NumPy +## NumPy 인용하기 -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). +만약 당신의 연구에서 NumPy가 중요한 역할을 수행하였고 학술 간행물에서 출판하기 위해서는 [이 인용 정보](/citing-numpy)를 참조하세요. From 3738864ddccf1112b48dc8a2cc41fee2473b8dd6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 Nov 2023 11:18:49 +0100 Subject: [PATCH 436/711] New translations news.md (Korean) --- content/ko/news.md | 86 +++++++++++++++++++++++----------------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 92a91f014d..4c29057498 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,30 +1,30 @@ --- -title: News +title: 소식 sidebar: false -newsHeader: "NumPy 1.26.0 released" +newsHeader: "NumPy 1.26.0 출시" date: 2023-09-16 --- -### NumPy 1.26.0 released +### NumPy 1.26.0 출시 -_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: +_2023년 12월 16일_ -- [NumPy 1.26.0](https://numpy.org/doc/stable/release/1.26.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: -* 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 +* 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 -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. +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. -The Python versions supported by this release are 3.9-3.12. +본 릴리즈에서 지원하는 Python 버전은 3.3.9-3.12입니다. -### numpy.org is now available in Japanese and Portuguese +### numpy.org은 이제 일본어와 포르투갈어로도 이용 가능합니다. -_Aug 2, 2023_ -- numpy.org is now available in 2 additional languages: Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers: +_2023년 8월 2일_ - numpy.org은 이제 추가로 일본어와 포르투갈어로 이용 가능합니다. 이는 다음의 헌신적인 자원봉사자들의 노력 없이는 가능하지 않았을 것입니다: -_Portuguese:_ +_포르투갈어_ * Melissa Weber Mendonça (melissawm) * Ricardo Prins (ricardoprins) * Getúlio Silva (getuliosilva) @@ -33,25 +33,25 @@ _Portuguese:_ * Alexandre B A Villares (villares) * Vini Salazar (vinisalazar) -_Japanese:_ +_일본어_ * 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. +번역 인프라에 대한 작업은 CZI로부터의 자금 지원을 받아 진행되었습니다. -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.) +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. The highlights of the release are: +_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. -* Support for the Fujitsu C/C++ compiler. -* Object arrays are now supported in einsum. -* Support for the inplace matrix multiplication (`@=`). +* Fujitsu C/C++ 컴파일러 지원 +* Einsum에서 객체 배열 지원 +* Inplace 행렬 곱셈 (`@=`) 지원 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. @@ -80,7 +80,7 @@ _Dec 18, 2022_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-not 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 +### NumPy 1.23.0 출시 _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: @@ -99,7 +99,7 @@ _Apr 13, 2022_ -- NumPy is working with [NumFOCUS](http://numfocus.org/) on a [r 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 +### Numpy 1.22.0 출시 _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: @@ -122,7 +122,7 @@ This is an ambitious project aiming to discover and implement activities that sh 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 +### 2021년도 NumPy 설문조사 _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. @@ -131,7 +131,7 @@ It’s time for another survey, and we are counting on you once again. It will t Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Numpy 1.21.0 release +### Numpy 1.21.0 출시 _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: @@ -145,72 +145,72 @@ _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-not 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 +### 2020년도 NumPy 설문조사 결과 _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 +### Numpy 1.20.0 출시 _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 +### NumPy 프로젝트 내 다양성 _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! +### Nature에 첫 공식 NumPy 논문 발표! _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? +### Python 3.9가 곧 출시하는데, NumPy는 바이너리 Wheel을 언제 출시합니까? _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 +### NumPy 1.19.2 출시 _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! +### 최초의 NumPy 설문조사가 진행 중입니다! _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! +### NumPy에 새로운 로고가 생겼습니다! -_Jun 24, 2020_ -- NumPy now has a new logo: +_2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. NumPy 로고 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 +### NumPy 1.19.0 출시 _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 +### Season of Docs 승인 _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 +### NumPy 1.18.0 출시 _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 +### NumPy가 Chan Zuckerberg Initiative에서 보조금을 받았습니다 _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. @@ -221,14 +221,14 @@ More details on our proposed initiatives and deliverables can be found in the [f -## Releases +## 릴리즈 -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 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 1.26.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.1)) -- _2023년 10월 14일_. -- NumPy 1.26.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.0)) -- _16 Sep 2023_. +- 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 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.25.1)) -- _8 Jul 2023_. +- 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_. From 690803a8897b0e6576b7d1572c56a622831b0caf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 04:48:49 +0100 Subject: [PATCH 437/711] New translations history.md (Korean) --- content/ko/history.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/history.md b/content/ko/history.md index aa669d375b..21bd82ce14 100644 --- a/content/ko/history.md +++ b/content/ko/history.md @@ -1,9 +1,9 @@ --- -title: History of NumPy +title: NumPy의 역사 sidebar: false --- -NumPy is a foundational Python library that provides array data structures and related fast numerical routines. When started, the library had little funding, and was written mainly by graduate students—many of them without computer science education, and often without a blessing of their advisors. To even imagine that a small group of “rogue” student programmers could upend the already well-established ecosystem of research software—backed by millions in funding and many hundreds of highly qualified engineers — was preposterous. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. +NumPy는 배열 데이터 구조와 이에 대한 빠른 수치적 루틴을 제공하는 Python의 기초적인 라이브러리입니다. 처음 시작했을 때는 라이브러리를 개발할 자금이 거의 없었고, 주로 컴퓨터 공학 교육을 받지 못했고, 교수의 승인조차 받지 못한 대학원생이 이를 제작해나갔습니다. 소규모 "불량" 학생 프로그래머 집단이 이미 잘 정립되었으며 엄청난 자본과 많은 우수한 기술자들이 뒷받침하는 연구 소프트웨어 생태계를 뒤바꾼다고 상상해보세요. 정말 터무니없는 일입니다. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). From aa323eba3e08295b20c7932c35b1143b121ba5ed Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 05:47:38 +0100 Subject: [PATCH 438/711] New translations history.md (Korean) --- content/ko/history.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ko/history.md b/content/ko/history.md index 21bd82ce14..015495f328 100644 --- a/content/ko/history.md +++ b/content/ko/history.md @@ -3,19 +3,19 @@ title: NumPy의 역사 sidebar: false --- -NumPy는 배열 데이터 구조와 이에 대한 빠른 수치적 루틴을 제공하는 Python의 기초적인 라이브러리입니다. 처음 시작했을 때는 라이브러리를 개발할 자금이 거의 없었고, 주로 컴퓨터 공학 교육을 받지 못했고, 교수의 승인조차 받지 못한 대학원생이 이를 제작해나갔습니다. 소규모 "불량" 학생 프로그래머 집단이 이미 잘 정립되었으며 엄청난 자본과 많은 우수한 기술자들이 뒷받침하는 연구 소프트웨어 생태계를 뒤바꾼다고 상상해보세요. 정말 터무니없는 일입니다. Yet, the philosophical motivations behind a fully open tool stack, in combination with the excited, friendly community with a singular focus, have proven auspicious in the long run. Nowadays, NumPy is relied upon by scientists, engineers, and many other professionals around the world. For example, the published scripts used in the analysis of gravitational waves import NumPy, and the M87 black hole imaging project directly cites NumPy. +NumPy는 배열 데이터 구조와 이에 대한 빠른 수치적 루틴을 제공하는 Python의 기초적인 라이브러리입니다. 처음 시작했을 때는 라이브러리를 개발할 자금이 거의 없었고, 주로 컴퓨터 공학 교육을 받지 못했고, 교수의 승인조차 받지 못한 대학원생이 이를 제작해나갔습니다. 소규모 "불량" 학생 프로그래머 집단이 이미 잘 정립되었으며 엄청난 자본과 많은 우수한 기술자들이 뒷받침하는 연구 소프트웨어 생태계를 뒤바꾼다고 상상해보세요. 정말 터무니없는 일입니다. 그러나 완전 개방형 도구 속에 감추어졌던 철학적 동기들이, 친근하고 들떴으며 특별한 목표를 가진 공동체와 결합되어, 장기적으로 유의미한 것이 드러났습니다. 오늘날 NumPy는 전 세계의 과학자, 기술자 및 기타 많은 전문가들의 신뢰를 받고 있습니다. 예를 들어, 중력파 분석에 사용되며 출시된 스크립트는 NumPy 패키지를 가져 왔고, M87 블랙홀 시각화 프로젝트에서는 NumPy를 직접 인용하였습니다. -For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](https://arxiv.org/abs/1907.10121). +NumPy 및 관련 라이브러리의 개발 단계에 대한 자세한 설명은 [arxiv.org](https://arxiv.org/abs/1907.10121)를 참고하십시오. -If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below: +원본 Numeric 및 Numarray 라이브러리의 사본을 얻으려면 아래 링크를 들어가십시오. -[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* +[*Numeric* 다운로드 페이지](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* +[*Numarray* 다운로드 페이지](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* -*Please note that these older array packages are no longer maintained, and users are strongly advised to use NumPy for any array-related purposes or refactor any pre-existing code to utilize the NumPy library. +*이런 오래된 배열 패키지는 더 이상 지원되지 않으며, 배열 관련 기능을 이용하기 위해서는 NumPy를 사용하거나 NumPy 라이브러리를 활용하기 위해서는 기존 코드를 리팩토링하는 것이 좋습니다. -### Historic Documentation +### 역사적 문서 -[Download *`Numeric'* Manual](static/numeric-manual.pdf) +[*`Numeric'* 메뉴얼 다운로드](static/numeric-manual.pdf) From f6781389786cc26c8587e7a77e2a78e06a1e1d0a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 05:47:39 +0100 Subject: [PATCH 439/711] New translations contribute.md (Korean) --- content/ko/contribute.md | 64 ++++++++++++++++++++-------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 6efff53624..51ce2bd4aa 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -1,66 +1,66 @@ --- -title: Contribute to NumPy +title: NumPy 프로젝트에 기여하기 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. +NumPy 프로젝트에서는 당신의 경험과 의욕을 환영합니다! 당신의 선택지는 프로그래밍에만 국한되어 있지않습니다. 아래의 참여방법들을 확인하면 많은 부분에서 **당신의 도움**이 필요한 것을 알수있습니다. -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). +시작점을 찾기 힘들거나 재능을 어떻게 활용해야 할지 잘 모르겠다면, _물어보세요!_ [메일링 리스트](https://mail.python.org/mailman/listinfo/numpy-discussion)나 [GitHub](http://github.com/numpy/numpy) ([이슈](https://github.com/numpy/numpy/issues)를 생성하거나 관련 이슈에 답글을 다세요)에서 질문하시면 됩니다. -Those are our preferred channels (open source is open by nature), but if you prefer to talk privately, contact our community coordinators at or on [Slack](https://numpy-team.slack.com) (write for an invite). +앞서 소개드린 것들이 저희가 선호하는 연락 채널입니다. (오픈 소스는 원래 개방되어 있으니까요) 하지만 비공개적으로 대화를 나누고 싶으시다면, 을 통해 커뮤니티 코디네이터로 연락하시거나 [Slack](https://numpy-team.slack.com)을 이용하시면 됩니다. (초대를 받으시려면 을 쓰시면 됩니다). -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/). +또한 저희는 격주마다 _커뮤니티 연락_을 합니다. 자세한 정보는 [메일링 리스트](https://mail.python.org/mailman/listinfo/numpy-discussion)로 알립니다. 당신의 참여를 매우 환영합니다. 오픈소스에 기여하는 게 처음이시라면 [이 도움말](https://opensource.guide/how-to-contribute/)을 읽어 보시는 것을 적극 권장합니다. -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. +저희 커뮤니티는 모두를 평등하게 대하고 모든 기여의 가치를 인정하려는 뜻을 품고 있습니다. 개방적이고 참여를 환영하는 분위기를 조성하기 위해 [이용약관](/code-of-conduct)을 만들었습니다. -### Writing code +### 코드 작성 -Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the NumPy codebase.
Check out also our [YouTube channel](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS) for additional advice. +프로그래머 여러분, 이 [도움말](https://numpy.org/devdocs/dev/index.html#development-process-summary)에 어떻게 코드베이스에 기여하는지 설명되어 있습니다.
저희의 [유투브 채널](https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS)을 통해서 추가적인 정보를 확인 해주세요. -### 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 +### Pull Request 리뷰 +프로젝트의 열린 풀 요청만 250개가 넘습니다. 즉 많은 잠재적 개선점과 오픈소스 기여자들이 피드백을 기다리고 있다는 것입니다. NumPy를 알고 있는 개발자라면, 코드베이스에 대해 잘 알지 못해도 기여할 수 있습니다. 아래와 같은 기여를 해 보십시오. +* 늘어지는 토론 요약 +* 문서의 풀 요청 심사 +* 제안된 변경 사항 테스트 -### Developing educational materials +### 교육 자료 개발 -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. +NumPy의 [사용자 도움말](https://numpy.org/devdocs)은 현재 대규모로 재구성되고 있습니다. 현재 새로운 튜토리얼, 방법, 심층적 설명이 필요하고, 사이트의 구조를 다시 짜야 합니다. 글을 쓰는 사람에게만 기회가 주어지는 것은 아닙니다. 코드 예제와 노트북, 동영상 등을 통한 기여도 환영합니다. [NEP 44 — NumPy 문서의 재구성](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)에 사이트 재구성에 대하여 자세한 내용이 설명되어 있습니다. -### Issue triaging +### 이슈 확인 -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: +[NumPy 이슈 트래커](https://github.com/numpy/numpy/issues)에는 _정말 많은_ 이슈들이 현재 열린 상태로 있습니다. 일부는 더 이상 유효하지 않은 이슈고, 일부는 우선 순위를 지정해야 하며, 일부는 새로운 기여자들이 볼 만한 좋은 이슈가 될 수 있을 것입니다. 아래와 같은 기여를 해 보십시오. -* 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) +* 오래된 버그가 현재도 남아 있는지 확인 +* 중복된 이슈를 찾아 하나로 묶기 +* 이슈를 재현하는 코드를 추가 +* 이슈를 올바르게 라벨링 (이 작업에는 심사 권한이 필요합니다. 필요한 경우 요청하십시오) -Please just dive in. +한 번 참여해 보시길 바랍니다. -### Website development +### 웹사이트 개발 -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. +사이트를 막 뜯어 고친 상태이지만, 아직 끝이라기엔 멀었습니다. 웹 개발을 좋아하신다면, [여기](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign)에서 저희가 이루지 못했던 사항의 목록을 볼 수 있습니다. 자신만의 아이디어를 마음껏 공유해 주십시오. -### Graphic design +### 그래픽 디자인 -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. +그래픽 디자이너분들이 할 수 있는 기여의 목록을 여기에 열거하는 건 어렵습니다. 저희 문서에는 일러스트가 많이 부족합니다. 성장하는 사이트에는 이미지가 필요하기 때문에, 기여할 수 있는 기회가 많을 것입니다. -### Translating website content +### 웹사이트 번역 -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. +사용자가 모국어로 NumPy를 이용할 수 있도록 [numpy.org](https://numpy.org)의 여러 번역을 계획하고 있습니다. 이를 위해서는 자원봉사자분들의 통역이 필요합니다. 자세한 내용은 [여기](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)를 참고하십시오. [이 GitHub 이슈](https://github.com/numpy/numpy.org/issues/55)에 댓글을 달아 번역에 참여하십시오. -### Community coordination and outreach +### 커뮤니티 조직 및 확산 -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. +우리는 커뮤니티 연락처를 통해 작업물을 더 널리 공유하고 미흡한 부분을 배워 나갑니다. 우리는 [Twitter](https://twitter.com/numpy_team) 계정, NumPy [코드 스프린트](https://scisprints.github.io/) 개최, 뉴스레터 발행, 그리고 아마 블로그 등을 통해서 더 많은 사람들이 커뮤니티에 참여하기를 간절히 바라고 있습니다. -### Fundraising +### 모금 -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. +NumPy는 오랜 기간 동안 자원봉사의 형태로 유지되었으나, 그 중요성이 커짐에 따라 안정성 및 성장을 보장하려면 경제적 지원이 필요함이 분명해졌습니다. 이런 지원이 얼마나 큰 차이를 만들어 냈는지 [SciPy'19 강연](https://www.youtube.com/watch?v=dBTJD_FDVjU)에서 확인하실 수 있습니다. 모든 비영리 조직과 마찬가지로 저희는 지속적으로 보조금, 후원 및 기타 종류의 지원을 끊임없이 찾고 있습니다. 모금을 받을 아이디어가 몇 개 있지만 당연히 더 많은 자금을 받게 된다면 좋을 것입니다. 모금도 정말 희귀한 능력 중 하나입니다 - 도움을 주신다면 감사드리겠습니다. From 9e44753f88dc23e776f02f51e2fcec57ac327ed3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 18:47:32 +0100 Subject: [PATCH 440/711] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index 4c29057498..a0972afe8d 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - 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_. From 7200541df3ddaaa3da985b28ec77999187280523 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 18:47:33 +0100 Subject: [PATCH 441/711] New translations news.md (Spanish) --- content/es/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/news.md b/content/es/news.md index a25a08c71e..b38f3d98b5 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.2 ([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_. From e5f740c8b3d7eb9183b4fad742afdf67ceb1a14b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 18:47:34 +0100 Subject: [PATCH 442/711] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index fe0852a3fd..a17cba9749 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.2 ([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_. From 3f3b2ea1706e26eb87b9cbfe132d5f195c6c4fba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 18:47:35 +0100 Subject: [PATCH 443/711] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index efa65ade2c..13fdd13d78 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -225,6 +225,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - 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_. From 6be740b592dacd23e1969b8f0288a5f48baa959b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 18:47:37 +0100 Subject: [PATCH 444/711] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index fe0852a3fd..a17cba9749 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.2 ([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_. From f0b6eaea31d0934477919453d23ef5cea1166247 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 18:47:38 +0100 Subject: [PATCH 445/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index fe0852a3fd..a17cba9749 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -225,6 +225,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.2 ([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_. From 29db111dd913573caf5cc221d002b2c3d605942f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 18:47:39 +0100 Subject: [PATCH 446/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index c4fe8eb1a6..6ff634c72a 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -225,6 +225,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 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_. From 2439cddc69c5255aaef4060beb0ebb8d399a9b7f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 13 Nov 2023 19:52:32 +0100 Subject: [PATCH 447/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 6ff634c72a..57738c9856 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -225,7 +225,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- 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_. From 06f850ad1a5076fb0886b91af556436806ac489e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 14 Nov 2023 13:12:25 +0100 Subject: [PATCH 448/711] New translations news.md (Japanese) --- content/ja/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index 13fdd13d78..34d198c65b 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -225,7 +225,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- 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_. From 6e9b0fbf80d1b7195530ef840e1c88f5861103ac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 14 Nov 2023 20:33:55 +0100 Subject: [PATCH 449/711] New translations config.yaml (Spanish) --- content/es/config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/config.yaml b/content/es/config.yaml index 87498cf6aa..4055852f1d 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -3,6 +3,7 @@ params: description: '¿Por qué NumPy? Potentes matrices n-dimensionales. Herramientas de cálculo numérico. Interoperable. Rendimiento. Código abierto.' navbarlogo: image: logo.svg + text: NumPy link: / hero: #Main hero title From 7a8d5dae9a3b28f3b7ff0a6c4f9e227c7f385996 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 14 Nov 2023 20:33:56 +0100 Subject: [PATCH 450/711] New translations config.yaml (Arabic) --- content/ar/config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index ffa024a433..483f5a0c14 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -3,6 +3,7 @@ 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 From d83136e174cb7bc5ffe610c288ba190cd5a7186c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 14 Nov 2023 20:33:57 +0100 Subject: [PATCH 451/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 056006ee9a..75b01e8105 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -3,6 +3,7 @@ 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 From 2fa06e4d4e06ce7cf527d0af9dfd30327fb737f5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 14 Nov 2023 20:33:58 +0100 Subject: [PATCH 452/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index e1ff62b0a1..b2e7e10ee4 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -3,6 +3,7 @@ params: description: 왜 NumPy인가? 강력한 n차원 배열. 수치 컴퓨팅 도구. 상호운용성. 고성능. 오픈소스. navbarlogo: image: logo.svg + text: NumPy link: /ko/ hero: #Main hero title From 214a278dffb3d05dc4fbe9693c6ed11e177bed68 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 14 Nov 2023 20:33:59 +0100 Subject: [PATCH 453/711] New translations config.yaml (Russian) --- content/ru/config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index ffa024a433..483f5a0c14 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -3,6 +3,7 @@ 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 From b6936b3dabda148955f5daf75943d038bf910c77 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 14 Nov 2023 20:34:00 +0100 Subject: [PATCH 454/711] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index ffa024a433..483f5a0c14 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -3,6 +3,7 @@ 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 From b75423ffcc5aaf261043862a441c5e99b28a4140 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 14 Nov 2023 20:34:01 +0100 Subject: [PATCH 455/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 77753c979c..24a9a301ce 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -3,6 +3,7 @@ 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 From 3c95a716da80f7e95092a86b3b67b148cfb643e8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 15 Nov 2023 19:39:59 +0100 Subject: [PATCH 456/711] New translations learn.md (Arabic) --- content/ar/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 60e91719d6..a10aa18aac 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -18,7 +18,7 @@ There's a ton of information about NumPy out there. If you are just starting, we * [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) -* [SciPy Lectures](https://scipy-lectures.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/) From e1f0edbe7a695b720003011f6ae8de7f4c655f7c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 15 Nov 2023 19:40:01 +0100 Subject: [PATCH 457/711] New translations learn.md (Russian) --- content/ru/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/learn.md b/content/ru/learn.md index 60e91719d6..a10aa18aac 100644 --- a/content/ru/learn.md +++ b/content/ru/learn.md @@ -18,7 +18,7 @@ There's a ton of information about NumPy out there. If you are just starting, we * [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) -* [SciPy Lectures](https://scipy-lectures.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/) From 2fb949380efcd26a59005f1eea8bb366ec4e6460 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 15 Nov 2023 19:40:02 +0100 Subject: [PATCH 458/711] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/learn.md b/content/zh/learn.md index 60e91719d6..a10aa18aac 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -18,7 +18,7 @@ There's a ton of information about NumPy out there. If you are just starting, we * [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) -* [SciPy Lectures](https://scipy-lectures.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/) From 15d8e2c5df909a27dfdabc9c857227416e97e414 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Nov 2023 19:42:34 +0100 Subject: [PATCH 459/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 24a9a301ce..4b0173e909 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -11,7 +11,7 @@ params: #Hero subtitle (optional) subtitle: A biblioteca fundamental para computação científica com Python #Button text - buttontext: "Última versão: NumPy 1.25. Veja todas as versões" + 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/___) From ddce6bfeba612d442139b8df552d68915ba6889e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Nov 2023 19:42:35 +0100 Subject: [PATCH 460/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 106 ++++++++++++++++++------------------ 1 file changed, 53 insertions(+), 53 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 9976789461..31a5a15032 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -52,25 +52,25 @@ params: url: https://pytorch.org/ - title: TensorFlow - text: An end-to-end platform for machine learning to easily build and deploy ML powered applications. + 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: Deep learning framework suited for flexible research prototyping and production. + 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: A cross-language development platform for columnar in-memory data and analytics. + 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: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + 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 @@ -82,138 +82,138 @@ params: url: https://awkward-array.org/ - title: uarray - text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. + 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: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. + 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: Nearly every scientist working in Python draws on the power of NumPy. + text: Quase todos os cientistas que trabalham em Python se baseiam na potência do 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." + 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: Quantum Computing - alttext: A computer chip. + title: Computação quântica + alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg - - title: Statistical Computing - alttext: A line graph with the line moving up. + title: Computação estatística + alttext: Um gráfico com uma linha em movimento para cima. img: /images/content_images/sc_dom_img/statistical_computing.svg - - title: Signal Processing - alttext: A bar chart with positive and negative values. + 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: Image Processing - alttext: An photograph of the mountains. + title: Processamento de imagens + alttext: Uma fotografia das montanhas. img: /images/content_images/sc_dom_img/image_processing.svg - - title: Graphs and Networks - alttext: A simple graph. + title: Gráficos e Redes + alttext: Um grafo simples. img: /images/content_images/sc_dom_img/sd6.svg - title: Astronomia - alttext: A telescope. + alttext: Um telescópio. img: /images/content_images/sc_dom_img/astronomy_processes.svg - - title: Cognitive Psychology - alttext: A human head with gears. + title: Psicologia Cognitiva + alttext: Uma cabeça humana com engrenagens. img: /images/content_images/sc_dom_img/cognitive_psychology.svg librariesrow2: - - title: Bioinformatics - alttext: A strand of DNA. + title: Bioinformática + alttext: Um pedaço de DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: Inferência Bayesiana + alttext: Um gráfico com uma curva em forma de sino. img: /images/content_images/sc_dom_img/bayesian_inference.svg - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: Análise Matemática + alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - title: Chemistry - alttext: A test tube. + title: Química + alttext: Um tubo de ensaio. img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience - alttext: The Earth. + title: Geociências + alttext: A Terra. img: /images/content_images/sc_dom_img/geoscience.svg - - title: Geographic Processing - alttext: A map. + title: Processamento Geográfico + alttext: Um mapa. img: /images/content_images/sc_dom_img/GIS.svg - - title: Architecture & Engineering - alttext: A microprocessor development board. + title: Arquitetura e Engenharia + alttext: Uma placa de desenvolvimento de microprocessador. img: /images/content_images/sc_dom_img/robotics.svg datascience: - intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" + 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: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'. + 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 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'. + 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "Report in a dashboard: [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)). + 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: A streamplot made in matplotlib + alttext: Um streamplot feito em matplotlib - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png - alttext: A scatter-plot graph made in ggpy + 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: A box-plot made in plotly + alttext: Um box-plot feito no plotly - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png - alttext: A streamgraph made in altair + alttext: Um gráfico streamgraph feito em 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" + 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: A 3D volume rendering made in PyVista. + alttext: Uma renderização de volume 3D feita no PyVista. - url: https://napari.org img: /images/content_images/v_napari.png - alttext: A multi-dimensionan image made in napari. + alttext: Uma imagem multidimensional, feita em napari. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png - alttext: A Voronoi diagram made in vispy. + alttext: Diagrama de Voronoi feito com 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 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: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. + 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. From 9fdeb8cdcdb306782ca0e3c45732302f9c836262 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Nov 2023 20:43:20 +0100 Subject: [PATCH 461/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 4b0173e909..3242697b5d 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -60,7 +60,7 @@ params: text: O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais. - title: Interoperabilidade - text: O NumPy suporta um grande número de plataformas de hardware e computação, e pode ser combinada com bibliotecas de computação com arrays esparsas, distribuidas ou em GPUs. + text: 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. - title: Alto desempenho text: O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. From e213d3e469ac65ffd2508ddb09e621bbd84b6207 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 18 Nov 2023 22:26:11 +0100 Subject: [PATCH 462/711] New translations code-of-conduct.md (Korean) --- content/ko/code-of-conduct.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/code-of-conduct.md b/content/ko/code-of-conduct.md index 28dc308ac0..59522aadd0 100644 --- a/content/ko/code-of-conduct.md +++ b/content/ko/code-of-conduct.md @@ -1,11 +1,11 @@ --- -title: NumPy Code of Conduct +title: NumPy 이용 약관 sidebar: false aliases: - /conduct.html --- -### Introduction +### 소개 This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. From a1b15328864358071dbf7b41d2ed47dbcd1df0ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 18 Nov 2023 22:26:12 +0100 Subject: [PATCH 463/711] New translations privacy.md (Korean) --- content/ko/privacy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/privacy.md b/content/ko/privacy.md index 6064e4c4f1..b2fc57fcfd 100644 --- a/content/ko/privacy.md +++ b/content/ko/privacy.md @@ -1,8 +1,8 @@ --- -title: Privacy Policy +title: 개인정보 정책 sidebar: false --- -**numpy.org** is operated by [NumFOCUS, Inc.](https://numfocus.org), the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to https://numfocus.org/privacy-policy. +**numpy.org**는 NumPy 프로젝트의 재정적 후원자인 [NumFOCUS, Inc.](https://numfocus.org)가 관리합니다. 이 웹 사이트에 대한 개인정보 정책을 확인하려면 https://numfocus.org/privacy-policy를 참고하세요. -If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at privacy@numfocus.org. +NumFOCUS의 데이터 수집, 이용, 공개 정책에 대하여 아무 질문이나 있으시다면, NumFOCUS 스태프인 privacy@numfocus.org로 연락해주세요. From 68323e982c7f97de88edc95bf001eb65f0c4ffb1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 29 Nov 2023 05:25:30 +0100 Subject: [PATCH 464/711] New translations arraycomputing.md (Korean) --- content/ko/arraycomputing.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/arraycomputing.md b/content/ko/arraycomputing.md index abd29d11c1..9c661978d2 100644 --- a/content/ko/arraycomputing.md +++ b/content/ko/arraycomputing.md @@ -1,21 +1,21 @@ --- -title: Array Computing +title: 배열 연산 sidebar: false --- -*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.* +*배열 연산은 통계와 수학 뿐만 아니라 현대의 다양한 분야에 적용되는 데이터 사이언스, 데이터 시각화나 디지털 신호 처리, 영상 처리, 의생명 정보 공학, 기계학습, AI 등 다양한 분야에서 적용되는 데이터 분석 어플리케이션의 기반입니다.* -Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.** +대규모 데이터의 조작과 연산은 고효율, 고성능의 배열 연산에 달려있습니다. **Python**은 데이터 과학자, 머신 러닝 개발자, 그리고 효율적인 수치 계산을 필요로 하는 분야에서 선택되는 프로그래밍 언어입니다. -**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays. +**Num**erical **Py**thon 또는 NumPy 는 파이썬의 표준라이브러리에는 포함되지 않지만, 대규모, 다차원 행렬을 표현할 수 있고, 배열 연산을 위한 고수준의 수학 함수들을 포함한 라이브러리입니다. -Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications. +2006년에 NumPy가 출시된 이후로, 2008년에 이를 기반으로 Pandas가 나타났습니다. 그리고 몇년전까지도, 다양한 배열 연산 라이브러리가 잇따라 나오며 배열 연산 분야가 더욱 활발해 졌습니다. 최신의 라이브러리들중 대부분은 NumPy 같은 특징과 성능을 모방하고, 새로운 알고리즘이나 머신러닝이나 인공지능 어플리케이션을 위한 특화된 기능을 포함하고 있습니다. arraycl -**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly. +**배열 연산**의 기반은 **array ** 자료구조 입니다. *배열*은 대규모의 데이터를 정렬, 검색, 수학 계산, 그리고 변형을 쉽고 빠르게 처리하는데 사용됩니다. -Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations. +배열 연산은 *한번에 * 데이터 배열에 *모든 연산이* 계산 됩니다. 다시 말해서, 모든 배열 연산은 전체 데이터에 한번에 적용됩니다. 이 벡터화 접근법은 배열 연산을 위해 루프를 활용하여 개별적인 데이터에 접근하여 연산하는 코드를 작성하지 않고, 배열에 바로 연산하는 코드를 작성하여, 개발자가 보다 개발 빠르고 간단하게 할수 있게 해줍니다. From a6de70200b33f84173eed1aff4ac0df29bcbc1ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 29 Nov 2023 05:25:31 +0100 Subject: [PATCH 465/711] New translations citing-numpy.md (Korean) --- content/ko/citing-numpy.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/citing-numpy.md b/content/ko/citing-numpy.md index 5bb5d791b4..cf1458e657 100644 --- a/content/ko/citing-numpy.md +++ b/content/ko/citing-numpy.md @@ -1,13 +1,13 @@ --- -title: Citing NumPy +title: NumPy 인용하기 sidebar: false --- -If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: +진행한 연구에서 NumPy가 중요한 부분을 차지하고 있고 학술지에 출판한다면, 아래의 논문을 참조문헌에 써주시길 바랍니다. -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([링크](https://www.nature.com/articles/s41586-020-2649-2)). -_In BibTeX format:_ +_BibTeX 형식:_ ``` @Article{ harris2020array, From 96cb43371ba5fc44ce84fdab79d2a1c6e8c7b473 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:03 +0100 Subject: [PATCH 466/711] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 19a0b8adad..d7d45436e6 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -5,10 +5,10 @@ sidebar: false {{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Agujero Negro M87**" alt="imagen de un agujero negro" attr="*(Créditos de la imagen: Colaboración del Telescopio de Horizonte de Sucesos)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} -
-

Fotografiar el agujero negro M87 es como tratar de ver algo que por definición es imposible de ver.

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Katie Bouman, Profesor asistente, Ciencias Matemáticas y de Computación, Caltech
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+{{< 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 >}} ## Un telescopio del tamaño de la Tierra From cd08f5b2d03ca1123583e76266074fdef2b4b2e0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:04 +0100 Subject: [PATCH 467/711] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index db140f858c..993e19e1cb 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -5,10 +5,10 @@ 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" >}} -
-

You don't play for the crowd, you play for the country.

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—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
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+{{< 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 From 5b218e371c9f83171ea3e3ef412bef778a6b9d78 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:05 +0100 Subject: [PATCH 468/711] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index b40ed2af50..ba8c558e30 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -5,10 +5,10 @@ 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">}} -
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Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

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—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
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+{{< 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 From 0914b71e71451d17a3a76dda8092edf4e8b517d3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:06 +0100 Subject: [PATCH 469/711] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index b992584c87..62ce66ca39 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -5,10 +5,8 @@ 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" >}} -
-

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

-
David Shoemaker, LIGO Scientific Collaboration
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+{{< 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) From 63f001b7cd899d23675e3df16bbec89ad8c9dae8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:07 +0100 Subject: [PATCH 470/711] New translations blackhole-image.md (Arabic) --- content/ar/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/case-studies/blackhole-image.md b/content/ar/case-studies/blackhole-image.md index f2460d3d5b..856564a5fb 100644 --- a/content/ar/case-studies/blackhole-image.md +++ b/content/ar/case-studies/blackhole-image.md @@ -5,10 +5,10 @@ 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" >}} -
-

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

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Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
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+{{< 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 From 8bd38ef30c79df7120d95f1aa52c6e754e9ebd7e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:08 +0100 Subject: [PATCH 471/711] New translations cricket-analytics.md (Arabic) --- content/ar/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/case-studies/cricket-analytics.md b/content/ar/case-studies/cricket-analytics.md index db140f858c..993e19e1cb 100644 --- a/content/ar/case-studies/cricket-analytics.md +++ b/content/ar/case-studies/cricket-analytics.md @@ -5,10 +5,10 @@ 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" >}} -
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You don't play for the crowd, you play for the country.

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—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
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+{{< 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 From a97b27ce6dd11b59a72bd2616b8bc33f8dbe98b1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:09 +0100 Subject: [PATCH 472/711] New translations deeplabcut-dnn.md (Arabic) --- content/ar/case-studies/deeplabcut-dnn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/case-studies/deeplabcut-dnn.md b/content/ar/case-studies/deeplabcut-dnn.md index b40ed2af50..ba8c558e30 100644 --- a/content/ar/case-studies/deeplabcut-dnn.md +++ b/content/ar/case-studies/deeplabcut-dnn.md @@ -5,10 +5,10 @@ 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">}} -
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Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

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—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
-
+{{< 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 From 13c7c52b1e6476997717a5ad54d980d6d3ef33f3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:10 +0100 Subject: [PATCH 473/711] New translations gw-discov.md (Arabic) --- content/ar/case-studies/gw-discov.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/content/ar/case-studies/gw-discov.md b/content/ar/case-studies/gw-discov.md index b992584c87..62ce66ca39 100644 --- a/content/ar/case-studies/gw-discov.md +++ b/content/ar/case-studies/gw-discov.md @@ -5,10 +5,8 @@ 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" >}} -
-

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

-
David Shoemaker, LIGO Scientific Collaboration
-
+{{< 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) From facfe7263e3231a3cafdfbd8f4ff1ed3ca14881d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:11 +0100 Subject: [PATCH 474/711] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 4e03d6bea2..03a1ae6567 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -5,10 +5,10 @@ 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" >}} -
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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。

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カリフォルニア工科大学 計算・数理学部のKatie Bouman助教授
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+{{< 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 >}} ## 地球大の望遠鏡 From 872d07f3f6b52518b8ade9c3854a80e4f133d540 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:12 +0100 Subject: [PATCH 475/711] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 8b57e07065..7b7fb8b40a 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -5,10 +5,10 @@ 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" >}} -
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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。

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—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
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+{{< 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 >}} ## クリケットについて From 31213f21a5593efc645679fc9dcea418d8658738 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:13 +0100 Subject: [PATCH 476/711] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index 2174db2e54..c1a5d6fd57 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -5,10 +5,10 @@ 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">}} -
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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。

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—Alexander Mathis、 准教授、École polytechnology fe’rale de Lausanne (EPFL)
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+{{< 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 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 From be6b57e866e70b57db3e843a6d1079d43fabdd80 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:14 +0100 Subject: [PATCH 477/711] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index fe1e634e44..28245f2dc0 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -5,16 +5,14 @@ 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" >}} -
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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。

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David Shoemaker, LIGOサイエンティフィック・コラボレーション
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+{{< 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 >}} ## [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。 +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. -\[レーザー干渉計重力波天文台(LIGO)\](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。 +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。 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. ### 主な目的 @@ -39,13 +37,13 @@ sidebar: false 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" 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" >}} ## 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。 +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. -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。 +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: * [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。 @@ -56,14 +54,14 @@ Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波 * 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" class="fig-center" alt="gwpy-numpy depgraph" caption="**Dependency graph showing how GwPy package depends on NumPy**" >}} ---- -{{< 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/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} ## 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/)。 +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_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_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} From 83b0fd13b0e00afbc6fcdd6f3c53ab660628e2ac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:15 +0100 Subject: [PATCH 478/711] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index f16b121250..74d36af6bc 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -5,10 +5,10 @@ sidebar: false {{< figure src="/images/content_images/cs/blackhole.jpg" caption="**블랙홀 M87**" alt="블랙홀 사진" attr="*(사진 크레딧: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} -
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M87 블랙홀을 시각화하는 것은 정의상 볼 수 없는 것을 보려고 하는 것과도 같다.

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Katie Bouman, 캘리포니아 공과대학 컴퓨터과학부 겸 수리과학부 연구 조교수
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+{{< 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 >}} ## 지구 크기의 망원경 From 590334615d006694680694b5b825da81603e62a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:16 +0100 Subject: [PATCH 479/711] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 6e7e54aa2a..be0007eefd 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -5,10 +5,10 @@ sidebar: false {{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**인도 최대의 크리켓 축제인 IPLT20**" alt="인도 프리미어 리그 크리켓 컵 및 경기장" attr="*(사진 출처: IPLT20 (컵 및 로고) & Akash Yadav (경기장))*" attrlink="https://unsplash.com/@aksh1802" >}} -
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군중을 위해서가 아니라, 국가를 위해 뛰는 겁니다.

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—M S Dhoni, 국제 크리켓 선수, 전 팀장, 인도 팀, IPL의 Chennai Super Kings에서 활약
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+{{< 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 >}} ## 크리켓이란 From 0236fa1e88af7105ffa33063b84ecc4c38087940 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:17 +0100 Subject: [PATCH 480/711] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md index d8008a244c..1d144c919d 100644 --- a/content/ko/case-studies/deeplabcut-dnn.md +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -5,10 +5,10 @@ sidebar: false {{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCut을 활용한 쥐의 손 움직임 분석**" alt="쥐 손 애니메이션" attr="*(출처: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} -
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오픈 소스 소프트웨어는 생물 의학을 가속화하고 있습니다. DeepLabCut은 딥 러닝을 사용하여 동물 행동에 대한 자동화된 비디오 분석을 가능하게 합니다.

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—알렉산더 마티스, 조교수, 로잔 연방 공과대학교 (EPFL)
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+{{< 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. DeepLabCut은 딥 러닝을 사용하여 동물 행동에 대한 자동화된 비디오 분석을 가능하게 합니다. +> +> {{< /blockquote >}} ## DeepLabCut 소개 From 4a2440689a3c95c0b6dd2d3994551e0dd9feb334 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:18 +0100 Subject: [PATCH 481/711] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index 7713993442..8cb5ba4e94 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -5,16 +5,14 @@ sidebar: false {{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**중력파**" alt="이항 결합하며 중력파를 생성하는 블랙홀" attr="*(사진 크레딧: LIGO의 Simulating eXtreme Spacetimes (SXS) 프로젝트)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} -
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과학적 Python 생태계는 LIGO 연구에 있어서 중요한 인프라에 해당합니다.

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David Shoemaker, LIGO Scientific Collaboration
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+{{< 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 >}} ## [중력파](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) 그리고 [LIGO](https://www.ligo.caltech.edu)에 대해 -중력파는 '시공간 천막'의 물결이라고 할 수 있으며, 두 블랙홀의 충돌이나 병합, 쌍성의 결합 혹은 초신성과 같이 우주가 대격변하는 사건으로부터 생성됩니다. 중력파를 관측하는 것은 비단 중력 연구에 도움을 줄 뿐만 아니라 먼 우주에서의 모호한 현상들과 이것이 미치는 영향에 대해서도 이해할 수 있게 해 줍니다. +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. -[레이저 간섭계 중력파 관측소(LIGO)](https://www.ligo.caltech.edu) 아인슈타인의 일반상대성이론으로 예측한 중력파를 직접 탐지해 중력파 천체물리학의 장을 열기 위해 고안되었습니다. 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. +병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. 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. ### 주요 목표 @@ -43,9 +41,9 @@ sidebar: false ## 중력파 검출에서 NumPy의 역할 -병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. +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. -Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝트 동안 수행되는 다양한 작업에 사용되는 소프트웨어에서 활용되었습니다. NumPy는 복잡한 수학 및 데이터 조작을 고속으로 해결하는 데 도움이 되었습니다. 몇 가지 예시를 들자면: +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: * [신호 처리](https://www.uv.es/virgogroup/Denoising_ROF.html): 글리치 검출, [잡음 식별 및 데이터 결정](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) * 데이터 수집: 어떤 데이터를 분석할 수 있을지 결정하고, 모래 속 바늘과 같이 미미한 신호가 있는지 파악 @@ -56,14 +54,14 @@ Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝 * 상관 분석 연산 * [GwPy](https://gwpy.github.io/docs/stable/overview.html) 및 [PyCBC](https://pycbc.org)와 같은 중력파 데이터 분석에서 개발된 주요 [소프트웨어](https://github.com/lscsoft)는 후드 아래에서 NumPy 및 AstroPy를 사용하여 중력파 검출기에서 데이터를 연구하기 위한 유틸리티, 도구 및 방법에 객체 기반 인터페이스를 제공합니다. -{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy 종속성" caption="**GwPy 패키지가 어떻게 NumPy에 종속하는지를 나타내는 종속성 그래프**" >}} +{{< 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy 종속성" caption="**PyCBC 패키지가 어떻게 NumPy에 종속하는지를 나타내는 종속성 그래프**" >}} +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} ## 요약 -중력파 검출을 통하여 연구자들은 완전히 예상치 못한 현상을 발견하게 됨으로써, 알려진 것 중 가장 난해한 천체물리학적 현상에 대하여 많은 사람들에게 새로운 통찰을 주었습니다. 숫자 계산 및 데이터 시각화는 과학자가 과학적 관찰에서 수집한 데이터에 대한 통찰력을 얻고 결과를 이해하는 데 도움이 되는 중요한 단계입니다. 계산은 복잡하며 실제 관찰 데이터 및 분석을 제공하는 컴퓨터 시뮬레이션을 사용하여 시각화하지 않는 한 사람이 이해할 수 없습니다. NumPy는 matplotlib, pandas 및 scikit-learn과 같은 다른 Python 패키지와 함께 [연구원](https://www.gw-openscience.org/events/GW150914/)이 복잡한 질문에 답하고 우주에 대한 이해의 새로운 지평을 발견할 수 있도록 합니다. +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="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} From dca4c733fbc0706bad33b98ca69fc087c514267c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:19 +0100 Subject: [PATCH 482/711] New translations blackhole-image.md (Russian) --- content/ru/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ru/case-studies/blackhole-image.md b/content/ru/case-studies/blackhole-image.md index f2460d3d5b..856564a5fb 100644 --- a/content/ru/case-studies/blackhole-image.md +++ b/content/ru/case-studies/blackhole-image.md @@ -5,10 +5,10 @@ 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" >}} -
-

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

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Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
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+{{< 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 From 30a637c507d1a806e1be6a43bc2de5c4b74d7d9f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:20 +0100 Subject: [PATCH 483/711] New translations cricket-analytics.md (Russian) --- content/ru/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ru/case-studies/cricket-analytics.md b/content/ru/case-studies/cricket-analytics.md index db140f858c..993e19e1cb 100644 --- a/content/ru/case-studies/cricket-analytics.md +++ b/content/ru/case-studies/cricket-analytics.md @@ -5,10 +5,10 @@ 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" >}} -
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You don't play for the crowd, you play for the country.

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—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
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+{{< 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 From 819854090d7d6d8922d40e8f6ee747b94c572759 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:21 +0100 Subject: [PATCH 484/711] New translations deeplabcut-dnn.md (Russian) --- content/ru/case-studies/deeplabcut-dnn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ru/case-studies/deeplabcut-dnn.md b/content/ru/case-studies/deeplabcut-dnn.md index b40ed2af50..ba8c558e30 100644 --- a/content/ru/case-studies/deeplabcut-dnn.md +++ b/content/ru/case-studies/deeplabcut-dnn.md @@ -5,10 +5,10 @@ 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">}} -
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Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

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—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
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+{{< 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 From 3db4aff154ccb146b759c33e27c208c547e28f88 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:23 +0100 Subject: [PATCH 485/711] New translations gw-discov.md (Russian) --- content/ru/case-studies/gw-discov.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/content/ru/case-studies/gw-discov.md b/content/ru/case-studies/gw-discov.md index b992584c87..62ce66ca39 100644 --- a/content/ru/case-studies/gw-discov.md +++ b/content/ru/case-studies/gw-discov.md @@ -5,10 +5,8 @@ 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" >}} -
-

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

-
David Shoemaker, LIGO Scientific Collaboration
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+{{< 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) From 5d3eadf39c5e9e83a081ed1bcf77fec394d6effd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:24 +0100 Subject: [PATCH 486/711] New translations blackhole-image.md (Chinese Simplified) --- content/zh/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/case-studies/blackhole-image.md b/content/zh/case-studies/blackhole-image.md index f2460d3d5b..856564a5fb 100644 --- a/content/zh/case-studies/blackhole-image.md +++ b/content/zh/case-studies/blackhole-image.md @@ -5,10 +5,10 @@ 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" >}} -
-

Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see.

-
Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
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+{{< 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 From 4a5600e36a96791fc62714ea0e4e3d2a38b0def7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:25 +0100 Subject: [PATCH 487/711] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md index db140f858c..993e19e1cb 100644 --- a/content/zh/case-studies/cricket-analytics.md +++ b/content/zh/case-studies/cricket-analytics.md @@ -5,10 +5,10 @@ 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" >}} -
-

You don't play for the crowd, you play for the country.

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—M S Dhoni, International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL
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+{{< 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 From 3a4583347b1586e13cf5948df6f7fec322253e08 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:26 +0100 Subject: [PATCH 488/711] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index b40ed2af50..ba8c558e30 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -5,10 +5,10 @@ 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">}} -
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Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

-
—Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
-
+{{< 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 From 9455ca0a139bb0bcb417379e78f2383315d71239 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:27 +0100 Subject: [PATCH 489/711] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md index b992584c87..62ce66ca39 100644 --- a/content/zh/case-studies/gw-discov.md +++ b/content/zh/case-studies/gw-discov.md @@ -5,10 +5,8 @@ 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" >}} -
-

The scientific Python ecosystem is critical infrastructure for the research done at LIGO.

-
David Shoemaker, LIGO Scientific Collaboration
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+{{< 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) From 41bc18825df346bcfba0b282388a31e1b262ef47 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:28 +0100 Subject: [PATCH 490/711] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index d84528f550..f4e1d3717f 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -5,10 +5,10 @@ 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" >}} -
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Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver.

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Katie Bouman, Professora Assistente, Ciências da Computação e Matemática, Caltech
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+{{< 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 >}} ## Um telescópio do tamanho da Terra From ff0faeba9bae318edceaa09810a3528470552ec6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:29 +0100 Subject: [PATCH 491/711] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index e3821f1aa7..29c39e3462 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -5,10 +5,10 @@ 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" >}} -
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Você não joga para a torcida, joga para o país.

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—M S Dhoni, Jogador Internacional de Críquete, ex-capitão, Time Indiano, joga pelo Chennai Super Kings na IPL
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+{{< 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 >}} ## Sobre Críquete From 9ecaab5008a1f5b5d00b2880c315202759a95b55 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:30 +0100 Subject: [PATCH 492/711] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index 1dd02b9f92..9ffb48ea5b 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -5,10 +5,10 @@ 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">}} -
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Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning.

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—Alexander Mathis, Professor Assistente, École polytechnique fédérale de Lausanne (EPFL)
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+{{< 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 permite a análise automática de vídeos de comportamento animal usando Deep Learning. +> +> {{< /blockquote >}} ## Sobre o DeepLabCut From 20a3bb15eb278cad81fb203f84a88dbf07ce9eee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Dec 2023 00:08:31 +0100 Subject: [PATCH 493/711] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 24 +++++++++++------------- 1 file changed, 11 insertions(+), 13 deletions(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index 6de7efed50..6f78786e43 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -5,16 +5,14 @@ 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" >}} -
-

O ecossistema científico Python é uma infraestrutura crítica para a pesquisa feita no LIGO.

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David Shoemaker, Colaborador Científico no LIGO
-
+{{< 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 >}} ## 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. +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. -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. +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. 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. ### Objetivos @@ -39,13 +37,13 @@ O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://w 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" 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" >}} ## 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. +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, 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: +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: * [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 @@ -56,14 +54,14 @@ NumPy, o pacote padrão de análise numérica para Python, foi parte do software * 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" class="fig-center" alt="gwpy-numpy depgraph" caption="**Dependency graph showing how GwPy package depends on NumPy**" >}} ---- -{{< 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/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} ## 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. +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="**Recursos chave da NumPy utilizados**" >}} +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} From d871f61b4187385f7c4d80b9620c97838a73c871 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 4 Dec 2023 08:30:13 +0100 Subject: [PATCH 494/711] New translations community.md (Korean) --- content/ko/community.md | 46 ++++++++++++++++++++--------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/content/ko/community.md b/content/ko/community.md index 5034fba239..1fd796053c 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -1,66 +1,66 @@ --- -title: Community +title: 커뮤니티 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. +NumPy는 다양한 [기여자](/teams/) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 운영진들은 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울여오고 있습니다. 커뮤니티를 발전시키기 위한 다른 이용자들과의 상호작용에 대한 가이드라인은 [NumPy 이용약관](/code-of-conduct)을 통해 확인하실 수 있습니다. -We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. +NumPy 커뮤니티에서는 배우고, 지식을 공유하고, 다른 사람들과 협력할 수 있는 여러 커뮤니케이션 채널을 제공합니다. -## Participate online +## 온라인으로 참여하기 -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 프로젝트 및 커뮤니티에 곧장 참여할 수 있는 방법들입니다. _사용자와 커뮤니티 회원이 사용 중 질문에 대하여 서로 도움을 주고받기를 권장한다는 것을 명심하십시오. [도움말](/gethelp)을 참고하세요._ -### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) +### [NumPy 메일링 리스트](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. +이 리스트는 NumPy 신기능 추가, NumPy 로드맵 변경 등 모든 종류의 프로젝트 전체 의사 결정과 같은 장기적인 토론을 이끄는 주요 포럼이라 할 수 있습니다. 출시, 개발자 모임, 일반 모임, 컨퍼런스 강연과 같은 NumPy에 대한 공지도 이 리스트를 통해 받아볼 수 있습니다. -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/numpy-discussion@python.org/). +리스트에 회신하려면 (다른 발신자에게 회신하기보다는) 하단의 게시물을 이용하십시오. 또, 자동 발신 메일에 회신하지 마십시오. 검색 가능한 아카이브는 [여기](https://mail.python.org/archives/list/numpy-discussion@python.org/)에서 이용할 수 있습니다. *** ### [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`"). +- 버그 제보 (예: "`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- 문서 관련 문제점 (예: "I found this section unclear"); +- 기능 요청 (예: "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)._ +_GitHub은 보안 취약점을 제보하는 곳이 아님을 명심하십시오. NumPy의 보안 취약점을 발견한 것 같으시다면, [여기](https://tidelift.com/docs/security)에서 제보하십시오._ *** ### [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. +NumPy에 _기여하는_ 방법에 대하여 질문하는 실시간 채팅방입니다. 여기는 비공개 공간으로, 공용 메일링 리스트나 GitHub에 질문 또는 아이디어를 올리는 것을 주저하는 사람들을 위한 곳입니다. [여기](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy)에서 자세한 내용과 초대를 받는 방법을 알아보세요. -## Study Groups and Meetups +## 학술 그룹 및 모임 -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). +NumPy와 데이터 과학 및 과학적 컴퓨팅을 위한 Python 패키지의 생태계에 대해 자세히 알아보기 위하여, 지역 모임이나 학술 그룹을 찾고 싶다면 [PyData 모임](https://www.meetup.com/pro/pydata/) (150개 이상의 모임, 10만 명 이상의 회원) 사이트를 돌아보시는 것을 추천해 드립니다. -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 [Twitter](https://twitter.com/numpy_team). +NumPy에서도 가끔 자체 팀이나 관심 있는 기여자들을 위하여 직접 모임을 조직하기도 합니다. 보통 몇 달 전부터 미리 계획되며 [메일링 리스트](https://mail.python.org/mailman/listinfo/numpy-discussion) 및 [트위터](https://twitter.com/numpy_team)로 해당 사실을 알립니다. -## Conferences +## 컨퍼런스 -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: +NumPy 프로젝트에서는 자체 컨퍼런스를 추진하지 않습니다. 보통 NumPy 관리자나 기여자, 사용자들에게 가장 인기 있는 컨퍼런스는 SciPy나 PyData 쪽 컨퍼런스입니다. - [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) +- [PyData 컨퍼런스](https://pydata.org/event-schedule/) (세계 곳곳의 여러 나라에서 1년에 15~20개의 이벤트를 개최합니다) -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. +이런 컨퍼런스 대부분에는 NumPy를 배우는 튜토리얼의 날이나 NumPy 혹은 관련 오픈소스 프로젝트에 기여하는 방법을 배울 수 있는 장이 마련되어 있습니다. -## Join the NumPy community +## NumPy 커뮤니티에 가입하기 -To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. +더욱 성장하기 위해, NumPy 프로젝트에서는 당신의 경험과 의욕을 필요로 합니다. 프로그래머가 아니라고요? 걱정하지 마세요! NumPy에 기여하는 방법에는 여러 가지가 있습니다. -If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. +NumPy 기여자가 되는 데 관심이 있으시다면 [기여](/contribute) 페이지를 방문하시는 것을 추천해 드립니다. 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/). From 27624ed962089945ac763f6ba78277f9b47aea26 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 4 Dec 2023 10:28:01 +0100 Subject: [PATCH 495/711] New translations community.md (Korean) --- content/ko/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/community.md b/content/ko/community.md index 1fd796053c..7a12d27e8d 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -63,4 +63,4 @@ NumPy 프로젝트에서는 자체 컨퍼런스를 추진하지 않습니다. NumPy 기여자가 되는 데 관심이 있으시다면 [기여](/contribute) 페이지를 방문하시는 것을 추천해 드립니다. -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/). +또한, 부담없이 커뮤니티 미팅에 참석 해주시길 바랍니다. 정확한 날짜들은 [행사달력](https://scientific-python.org/calendars/)을 확인해주세요. From d5ced0e2b0bdd188b8c5cf5c26b141831a0d58b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 5 Dec 2023 09:08:47 +0100 Subject: [PATCH 496/711] New translations code-of-conduct.md (Korean) --- content/ko/code-of-conduct.md | 62 +++++++++++++++++------------------ 1 file changed, 31 insertions(+), 31 deletions(-) diff --git a/content/ko/code-of-conduct.md b/content/ko/code-of-conduct.md index 59522aadd0..812f7ba7b2 100644 --- a/content/ko/code-of-conduct.md +++ b/content/ko/code-of-conduct.md @@ -7,53 +7,53 @@ aliases: ### 소개 -This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one. +이 이용 약관은 모든 공개 및 비공개 메일링 리스트, 이슈 트래커, 위키, 블로그, 트위터 및 커뮤니티에서 사용하는 기타 커뮤니케이션 채널을 포함하여 NumPy 프로젝트에서 관리하는 모든 공간에 적용됩니다. NumPy 프로젝트는 대면 이벤트를 조직하지 않지만 커뮤니티와 관련된 이벤트에는 이와 유사한 약관이 있어야 합니다. -This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role. +이 이용 약관은 NumPy 커뮤니티에 공식적으로 또는 비공식적으로 참여하거나 프로젝트 관련 활동에서, 특히 프로젝트를 대표할 때 어떤 역할에서든 프로젝트와의 관계를 주장하는 모든 사람이 준수해야 합니다. -This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community. +이 약관는 철저하거나 완전하지 않습니다. 협력적이고 공유되는 환경과 목표에 대한 우리의 공통적인 이해를 요약하는데 도움이 됩니다. 문자 그대로 뿐만 아니라, 정신에도 이 약관을 따르려 노력해 주시기 바랍니다. 이를 통해 주변 커뮤니티를 더욱 풍요롭게 하는 친근하고 생산적인 환경을 조성해주세요. -### Specific Guidelines +### 구체적인 가이드라인 -We strive to: +우리는 다음을 위해 노력합니다: -1. Be open. We invite anyone to participate in our community. We prefer to use public methods of communication for project-related messages, unless discussing something sensitive. This applies to messages for help or project-related support, too; not only is a public support request much more likely to result in an answer to a question, it also ensures that any inadvertent mistakes in answering are more easily detected and corrected. -2. Be empathetic, welcoming, friendly, and patient. We work together to resolve conflict, and assume good intentions. We may all experience some frustration from time to time, but we do not allow frustration to turn into a personal attack. A community where people feel uncomfortable or threatened is not a productive one. -3. Be collaborative. Our work will be used by other people, and in turn we will depend on the work of others. When we make something for the benefit of the project, we are willing to explain to others how it works, so that they can build on the work to make it even better. Any decision we make will affect users and colleagues, and we take those consequences seriously when making decisions. -4. Be inquisitive. Nobody knows everything! Asking questions early avoids many problems later, so we encourage questions, although we may direct them to the appropriate forum. We will try hard to be responsive and helpful. -5. Be careful in the words that we choose. We are careful and respectful in our communication, and we take responsibility for our own speech. Be kind to others. Do not insult or put down other participants. We will not accept harassment or other exclusionary behaviour, such as: - * Violent threats or language directed against another person. - * Sexist, racist, or otherwise discriminatory jokes and language. - * Posting sexually explicit or violent material. - * Posting (or threatening to post) other people’s personally identifying information (“doxing”). - * Sharing private content, such as emails sent privately or non-publicly, or unlogged forums such as IRC channel history, without the sender’s consent. - * Personal insults, especially those using racist or sexist terms. - * Unwelcome sexual attention. - * Excessive profanity. Please avoid swearwords; people differ greatly in their sensitivity to swearing. - * Repeated harassment of others. In general, if someone asks you to stop, then stop. - * Advocating for, or encouraging, any of the above behaviour. +1. 개방적이 되세요. 우리 커뮤니티에 누구든 참여할 수 있습니다. 민감한 내용이 아닌 이상, 프로젝트 관련 메시지에는 공개적인 의사소통 방법을 사용하는 것을 선호합니다. 이는 도움이나 프로젝트 지원과 관련된 메시지에도 적용됩니다. 공개적인 지원 요청은 질문에 대한 대답이 더욱 쉽게 이루어지며, 대답에 대한 우발적인 오류가 보다 쉽게 감지되고 수정될 수 있도록 도와줍니다. +2. 공감하며, 환영하며, 친절하며, 인내심을 가지세요. 우리는 갈등을 해결하기 위해 함께 노력하며, 선의의 의도를 기대합니다. 때로는 모두가 어떤 형태의 좌절을 경험할 수 있지만, 우리는 그것이 개인적인 공격으로 이어지지 않도록 허용하지 않습니다. 사람들이 불편하거나 위협받는 느낌을 가지는 커뮤니티는 생산적이지 않습니다. +3. 협력적이 되세요. 우리의 작업은 다른 사람들에 의해 사용될 것이며, 우리 역시 다른 사람들의 작업에 의존할 것입니다. 프로젝트의 이익을 위해 무언가를 만들 때, 우리는 다른 사람들에게 어떻게 작동하는지 설명할 의향이 있습니다. 이를 통해 다른 사람들이 그 작업을 발전시켜 더욱 좋은 결과물을 만들 수 있도록 도와줄 수 있습니다. 우리가 내린 어떤 결정도 사용자와 동료에게 영향을 미치며, 이러한 결과를 신중하게 고려하여 결정을 내리게 됩니다. +4. 호기심을 가져보세요. 아무도 모든 것을 알수는 없습니다! 미리 질문을 하면 나중에 많은 문제를 예방할 수 있으므로, 우리는 질문하는 것을 장려합니다. 단, 적절한 포럼으로 질문을 옮길 수도 있습니다. 우리는 답변을 빠르고 유용하게 제공하기 위해 최선을 다할 것입니다. +5. 사용하는 말에 신중하게 대해주세요. 우리는 의사소통 시 신중하고 예의 바른 태도를 유지하며, 우리 자신의 언어에 대한 책임을 집니다. 다른 사람들에게 친절하게 대하세요. 다른 참가자들을 모욕하거나 비하하지 마세요. 우리는 괴롭힘 및 다른 형태의 배제 행위를 용인하지 않습니다. 이러한 행위에는 다음과 같은 것들이 포함됩니다: + * 다른 사람을 향한 폭력적인 위협이나 언어. + * 성 차별, 인종 차별 또는 기타 차별적인 농담 및 언어. + * 성적으로 노골적이거나 폭력적인 자료 게시. + * 다른 사람의 개인 식별 정보를 게시(또는 게시하겠다고 위협)("신상 털기"). + * 발신자의 동의 없이 비공개 또는 비공개로 전송된 이메일과 같은 비공개 콘텐츠 또는 IRC 채널 기록과 같은 기록되지 않은 포럼을 공유. + * 개인적인 모욕, 특히 인종차별적이거나 성차별적인 용어를 사용하는 경우. + * 달갑지 않은 성적 관심. + * 과도한 욕설. 욕설은 삼가해주세요; 사람들은 욕에 대한 민감도가 크게 다릅니다. + * 타인에 대한 반복적인 괴롭힘. 일반적으로 누군가가 중지를 요청하면 중지합니다. + * 위의 행동을 옹호하거나 장려하는 행위. -### Diversity Statement +### 다양성 선언 -The NumPy project welcomes and encourages participation by everyone. We are committed to being a community that everyone enjoys being part of. Although we may not always be able to accommodate each individual’s preferences, we try our best to treat everyone kindly. +NumPy 프로젝트는 모든 사람의 참여를 환영하고 장려합니다. 우리는 모두가 참여하는 것을 즐기는 커뮤니티가 되기 위해 최선을 다하고 있습니다. 항상 개인의 취향을 수용할 수는 없지만 모든 분들께 친절하게 대할 수 있도록 최선을 다하고 있습니다. -No matter how you identify yourself or how others perceive you: we welcome you. Though no list can hope to be comprehensive, we explicitly honour diversity in: age, culture, ethnicity, genotype, gender identity or expression, language, national origin, neurotype, phenotype, political beliefs, profession, race, religion, sexual orientation, socioeconomic status, subculture and technical ability, to the extent that these do not conflict with this code of conduct. +당신이 자신을 어떻게 식별하든 다른 사람들이 당신을 어떻게 인식하든 상관없이 우리는 당신을 환영합니다. 어떤 목록도 포괄적이기를 바랄 수는 없지만 다음과 같은 다양성을 명시적으로 존중합니다: 연령, 문화, 민족, 유전자형, 성 정체성 또는 성 표현, 언어, 출신 국가, 신경형, 표현형, 정치적 신념, 직업, 인종, 종교, 성적 지향, 사회 경제적 지위, 하위 문화 및 기술적 능력 등을 이용 약관과 상충하지 않는 범위 안에서. -Though we welcome people fluent in all languages, NumPy development is conducted in English. +우리는 모든 언어에 능통한 사람들을 환영하지만 NumPy 개발은 영어로 진행됩니다. -Standards for behaviour in the NumPy community are detailed in the Code of Conduct above. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). +NumPy 커뮤니티의 행동 표준은 위의 이용 약관에 자세히 설명되어 있습니다. 커뮤니티의 참가자는 모든 상호 작용에서 이러한 표준을 유지하고 다른 사람들도 그렇게 하도록 도와야 합니다(다음 섹션 참조). -### Reporting Guidelines +### 신고 지침 -We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We also recognize that sometimes people may have a bad day, or be unaware of some of the guidelines in this Code of Conduct. Please keep this in mind when deciding on how to respond to a breach of this Code. +우리는 인터넷 통신이 명백하고 노골적인 남용에서 시작되거나 악화되는 것이 매우 흔한 일이라는 것을 알고 있습니다. 우리는 또한 때때로 사람들이 나쁜 하루를 보내거나 이 이용 약관의 일부 지침을 인식하지 못할 수 있음을 알고 있습니다. 본 약관 위반에 대응하는 방법을 결정할 때 이 점을 염두에 두십시오. -For clearly intentional breaches, report those to the Code of Conduct Committee (see below). For possibly unintentional breaches, you may reply to the person and point out this code of conduct (either in public or in private, whatever is most appropriate). If you would prefer not to do that, please feel free to report to the Code of Conduct Committee directly, or ask the Committee for advice, in confidence. +명백히 의도적인 위반의 경우 이용 약관 위원회(아래 참조) 에 보고하십시오. 의도하지 않은 위반일 가능성이 있는 경우, 그 사람에게 회신하고 이 이용 약관을 지적할 수 있습니다(공개적이든 비공개적이든 가장 적절한 방식으로). 그렇게 하고 싶지 않다면 이용 약관 위원회에 직접 보고하거나 비밀리에 위원회에 조언을 구하십시오. -You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. +numpy-conduct@googlegroups.com 으로 NumPy 윤리 강령 위원회에 문제를 보고할 수 있습니다. -Currently, the Committee consists of: +현재 위원회는 다음과 같이 구성되어 있습니다. -* Stefan van der Walt +* Stéfan van der Walt * Melissa Weber Mendonça * Rohit Goswami From d2bc9d7a4a5575e95bb666cb5e81d9d9058c3426 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 7 Dec 2023 19:49:16 +0100 Subject: [PATCH 497/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 239 +++++++++++++++++++++++++++++------- 1 file changed, 198 insertions(+), 41 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index f0167cd571..140b663144 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -98,64 +98,221 @@ params: text: Casi todos los científicos que trabajan en Python recurren a la potencia de NumPy. - text: "NumPy lleva 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." - librariesrow1: + libraries: - title: Quantum Computing - alttext: Un chip de ordenador. + alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Computación Estadística - alttext: Un gráfico lineal con la línea moviéndose hacia arriba. + 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 - - - title: Procesamiento de Señales - alttext: Un gráfico de barras con valores positivos y negativos. + 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 - - - title: Procesamiento de Imágenes - alttext: Una fotografía de unas montañas. + 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 - - - title: Grafos y Redes - alttext: Un simple grafo. + 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 - - - title: Astronomía - alttext: Un telescopio. + 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 - - - title: Psicología Cognitiva - alttext: Una cabeza humana con engranajes. + 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 - librariesrow2: + links: + - + url: https://www.psychopy.org/ + label: PsychoPy - - title: Bioinformática - alttext: Una cadena de ADN. + title: Bioinformatics + alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Inferencia Bayesiana - alttext: El gráfico de una curva en forma de campana. + 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 - - - title: Análisis Matemático - alttext: Cuatro símbolos matemáticos. + 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 - - - title: Química - alttext: Un tubo de ensayo. + 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 - - - title: Geociencia - alttext: La Tierra. + 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 - - - title: Procesamiento Geográfico - alttext: Un mapa. + 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 - - - title: Arquitectura e Ingeniería - alttext: Una placa de desarrollo de microprocesadores. + 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 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: From c441e0e8e676e35a5e764fdcf60bdf37eba81ef7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 7 Dec 2023 19:49:17 +0100 Subject: [PATCH 498/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 161 +++++++++++++++++++++++++++++++++++- 1 file changed, 159 insertions(+), 2 deletions(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index b347bebc9b..f9d19bcf5c 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -98,64 +98,221 @@ params: 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." - librariesrow1: + 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 - 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 - librariesrow2: + 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: From d2de020b64b9bbdc537ff88950150a7974d1bb98 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 7 Dec 2023 19:49:18 +0100 Subject: [PATCH 499/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 237 ++++++++++++++++++++++++++++++------ 1 file changed, 197 insertions(+), 40 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index bf8fb8c18c..6befd85a5e 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -98,64 +98,221 @@ params: 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: + libraries: - - title: 量子コンピューティング - alttext: コンピューターチップ + title: Quantum Computing + alttext: A computer chip. 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。 + 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 - - - title: 信号処理 - alttext: 正と負の値を持つ棒グラフ。 + 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 - - - title: 画像処理 - alttext: 山々の写真 + 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 - - - title: グラフとネットワーク - alttext: シンプルなグラフ + 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: 望遠鏡 + alttext: A telescope. 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 + 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 - librariesrow2: + links: + - + url: https://www.psychopy.org/ + label: PsychoPy - - title: 生命情報科学 - alttext: DNAの鎖 + title: Bioinformatics + alttext: A strand of DNA. 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 + 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 - - - title: 数学的分析 - alttext: ４つの数学記号 + 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 - - - title: 化学 - alttext: 試験管 + 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 - - - title: 地球科学 - alttext: 地球 + 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 - - - title: 地理情報処理 - alttext: 地図 + 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 - - - 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 + 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 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: From 98a7a22bf6debf0daab64620b9e4f1e903b4ecc3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 7 Dec 2023 19:49:19 +0100 Subject: [PATCH 500/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 161 +++++++++++++++++++++++++++++++++++- 1 file changed, 159 insertions(+), 2 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index b347bebc9b..f9d19bcf5c 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -98,64 +98,221 @@ params: 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." - librariesrow1: + 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 - 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 - librariesrow2: + 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: From 8105d2410b660055ed3b55e5665d487aa3b2bb72 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 7 Dec 2023 19:49:20 +0100 Subject: [PATCH 501/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 161 +++++++++++++++++++++++++++++++++++- 1 file changed, 159 insertions(+), 2 deletions(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index b347bebc9b..f9d19bcf5c 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -98,64 +98,221 @@ params: 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." - librariesrow1: + 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 - 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 - librariesrow2: + 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: From 8d744420d48b804fae01f583c1400806123e8076 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 7 Dec 2023 19:49:21 +0100 Subject: [PATCH 502/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 161 +++++++++++++++++++++++++++++++++++- 1 file changed, 159 insertions(+), 2 deletions(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index b347bebc9b..f9d19bcf5c 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -98,64 +98,221 @@ params: 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." - librariesrow1: + 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 - 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 - librariesrow2: + 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: From 32f9d2a6cf21d21a1a52f6caa60134170d6dec9b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 7 Dec 2023 19:49:22 +0100 Subject: [PATCH 503/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 241 +++++++++++++++++++++++++++++------- 1 file changed, 199 insertions(+), 42 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 31a5a15032..b86598b56a 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -98,64 +98,221 @@ params: 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: + libraries: - - title: Computação quântica - alttext: Um chip de computador. + title: Quantum Computing + alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Computação estatística - alttext: Um gráfico com uma linha em movimento para cima. + 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 - - - title: Processamento de sinais - alttext: Um gráfico de barras com valores positivos e negativos. + 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 - - - title: Processamento de imagens - alttext: Uma fotografia das montanhas. + 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 - - - title: Gráficos e Redes - alttext: Um grafo simples. + 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 - - - title: Astronomia - alttext: Um telescópio. + 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 - - - title: Psicologia Cognitiva - alttext: Uma cabeça humana com engrenagens. + 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 - librariesrow2: + links: + - + url: https://www.psychopy.org/ + label: PsychoPy - - title: Bioinformática - alttext: Um pedaço de DNA. + title: Bioinformatics + alttext: A strand of DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Inferência Bayesiana - alttext: Um gráfico com uma curva em forma de sino. + 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 - - - title: Análise Matemática - alttext: Quatro símbolos matemáticos. + 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 - - - title: Química - alttext: Um tubo de ensaio. + 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 - - - title: Geociências - alttext: A Terra. + 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 - - - title: Processamento Geográfico - alttext: Um mapa. + 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 - - - title: Arquitetura e Engenharia - alttext: Uma placa de desenvolvimento de microprocessador. + 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 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: From 417e7f46bc7a9d0e5be3272298edd88c246d4014 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 7 Dec 2023 21:02:49 +0100 Subject: [PATCH 504/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 44 ++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index b86598b56a..338e49061a 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -100,8 +100,8 @@ params: 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: Quantum Computing - alttext: A computer chip. + title: Computação quântica + alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - @@ -117,8 +117,8 @@ params: url: https://pennylane.ai label: PennyLane - - title: Statistical Computing - alttext: A line graph with the line moving up. + title: Computação estatística + alttext: Um gráfico com uma linha em movimento para cima. img: /images/content_images/sc_dom_img/statistical_computing.svg links: - @@ -134,8 +134,8 @@ params: url: https://github.com/mwaskom/seaborn label: Seaborn - - title: Signal Processing - alttext: A bar chart with positive and negative values. + 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: - @@ -148,8 +148,8 @@ params: url: https://python-control.org/ label: python-control - - title: Image Processing - alttext: An photograph of the mountains. + title: Processamento de imagens + alttext: Uma fotografia das montanhas. img: /images/content_images/sc_dom_img/image_processing.svg links: - @@ -193,16 +193,16 @@ params: url: https://github.com/spacepy/spacepy label: SpacePy - - title: Cognitive Psychology - alttext: A human head with gears. + 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: Bioinformatics - alttext: A strand of DNA. + title: Bioinformática + alttext: Uma fita de DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg links: - @@ -218,8 +218,8 @@ params: url: http://etetoolkit.org/ label: ETE - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: Inferência Bayesiana + alttext: Um gráfico com uma curva em forma de sino. img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - @@ -235,8 +235,8 @@ params: url: https://emcee.readthedocs.io/ label: emcee - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: Análise Matemática + alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - @@ -253,7 +253,7 @@ params: label: FEniCS - title: Chemistry - alttext: A test tube. + alttext: Um tubo de ensaio. img: /images/content_images/sc_dom_img/chemistry.svg links: - @@ -270,7 +270,7 @@ params: label: PyBaMM - title: Geoscience - alttext: The Earth. + alttext: A Terra. img: /images/content_images/sc_dom_img/geoscience.svg links: - @@ -286,8 +286,8 @@ params: url: https://www.fatiando.org/ label: Fatiando a Terra - - title: Geographic Processing - alttext: A map. + title: Processamento Geográfico + alttext: Um mapa. img: /images/content_images/sc_dom_img/GIS.svg links: - @@ -300,8 +300,8 @@ params: url: https://python-visualization.github.io/folium label: Folium - - title: Architecture & Engineering - alttext: A microprocessor development board. + title: Arquitetura e Engenharia + alttext: Uma placa de desenvolvimento de microprocessador. img: /images/content_images/sc_dom_img/robotics.svg links: - From a11e14fdc9705122a76bc008bd2651bdc956fac3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 8 Dec 2023 04:15:58 +0100 Subject: [PATCH 505/711] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md index 1d144c919d..904b1fe4ad 100644 --- a/content/ko/case-studies/deeplabcut-dnn.md +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -6,7 +6,7 @@ sidebar: false {{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCut을 활용한 쥐의 손 움직임 분석**" alt="쥐 손 애니메이션" attr="*(출처: 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. DeepLabCut은 딥 러닝을 사용하여 동물 행동에 대한 자동화된 비디오 분석을 가능하게 합니다. +> }} 오픈소스 소프트웨어는 생물 의학의 발전을 가속화하고 있습니다. DeepLabCut은 딥 러닝을 사용하여 동물 행동에 대한 자동화된 비디오 분석을 가능하게 합니다. > > {{< /blockquote >}} From 6032ff3916700c13fb7f868d4d34801d933bdab9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 8 Dec 2023 04:15:59 +0100 Subject: [PATCH 506/711] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index 8cb5ba4e94..4b950a6b99 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -10,9 +10,9 @@ sidebar: false ## [중력파](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) 그리고 [LIGO](https://www.ligo.caltech.edu)에 대해 -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. +중력파는 '시공간 천막'의 물결이라고 할 수 있으며, 두 블랙홀의 충돌이나 병합, 쌍성의 결합 혹은 초신성과 같이 우주가 대격변하는 사건으로부터 생성됩니다. 중력파를 관측하는 것은 비단 중력 연구에 도움을 줄 뿐만 아니라 먼 우주에서의 모호한 현상들과 이것이 미치는 영향에 대해서도 이해할 수 있게 해 줍니다. -병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. 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. +병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. 그들 각각은 레이저 간섭계를 사용하는 수 킬로미터 규모의 중력파 탐지기를 가지고 있습니다. LIGO Scientific Collaboration(LSC) 은 미국 전역과 90개 이상의 대학 및 연구 기관에서 지원하는 14개국의 대학에서 온 1000명 이상의 과학자 그룹입니다. 약 250명의 학생들이 협력에 적극적으로 기여하고 있습니다. LIGO의 새로운 발견은 중력파가 지구를 통과할 때 공간과 시간에 미치는 미세한 교란을 측정함으로써 중력파 자체를 처음으로 관찰한 것입니다. 그것은 우주의 뒤틀린 측면, 즉 뒤틀린 시공간에서 만들어진 물체와 현상을 탐구하는 새로운 천체물리학의 영역을 열었습니다. ### 주요 목표 @@ -35,15 +35,15 @@ Gravitational waves are ripples in the fabric of space and time, generated by ca * **시각화** - 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. + 아인슈타인의 방정식을 슈퍼컴퓨터를 사용하여 풀 수 있을 만큼 충분히 이해하는 것과 관련된 장애물이 처리되면 다음 큰 과제는 데이터를 인간의 두뇌가 이해할 수 있도록 만드는 것입니다. 시뮬레이션 모델링과 신호 감지에는 효과적인 시각화 기술이 필요합니다. 시각화는 또한 이미징 및 시뮬레이션을 통해 더 많은 청중이 결과를 더 쉽게 이해할 수 있게 될 때까지 수치 상대성에 충분한 중요성을 부여하지 않은 순수 과학 애호가의 눈에 수치 상대성에 더 많은 신뢰성을 제공하는 역할을 합니다. 최신 실험 입력 및 통찰력을 사용하여 복잡한 계산 및 렌더링, 이미지 다시 렌더링 및 시뮬레이션의 속도는 이 영역의 연구자에게 도전이 되는 시간 소모적인 활동이 될 수 있습니다. {{< 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" >}} ## 중력파 검출에서 NumPy의 역할 -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. +병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. -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: +Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝트 동안 수행되는 다양한 작업에 사용되는 소프트웨어에서 활용되었습니다. NumPy는 복잡한 수학 및 데이터 조작을 고속으로 해결하는 데 도움이 되었습니다. Here are some examples: * [신호 처리](https://www.uv.es/virgogroup/Denoising_ROF.html): 글리치 검출, [잡음 식별 및 데이터 결정](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) * 데이터 수집: 어떤 데이터를 분석할 수 있을지 결정하고, 모래 속 바늘과 같이 미미한 신호가 있는지 파악 @@ -62,6 +62,6 @@ NumPy, the standard numerical analysis package for Python, was utilized by the ## 요약 -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. +중력파 검출을 통하여 연구자들은 완전히 예상치 못한 현상을 발견하게 됨으로써, 알려진 것 중 가장 난해한 천체물리학적 현상에 대하여 많은 사람들에게 새로운 통찰을 주었습니다. 숫자 계산 및 데이터 시각화는 과학자가 과학적 관찰에서 수집한 데이터에 대한 통찰력을 얻고 결과를 이해하는 데 도움이 되는 중요한 단계입니다. 계산은 복잡하며 실제 관찰 데이터 및 분석을 제공하는 컴퓨터 시뮬레이션을 사용하여 시각화하지 않는 한 사람이 이해할 수 없습니다. NumPy는 matplotlib, pandas 및 scikit-learn과 같은 다른 Python 패키지와 함께 [연구원](https://www.gw-openscience.org/events/GW150914/)이 복잡한 질문에 답하고 우주에 대한 이해의 새로운 지평을 발견할 수 있도록 합니다. -{{< 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" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} From bbe6c66d1d00afa284ae93b95ea48e945995e867 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 8 Dec 2023 04:16:00 +0100 Subject: [PATCH 507/711] New translations code-of-conduct.md (Korean) --- content/ko/code-of-conduct.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/ko/code-of-conduct.md b/content/ko/code-of-conduct.md index 812f7ba7b2..b8dea1fd9d 100644 --- a/content/ko/code-of-conduct.md +++ b/content/ko/code-of-conduct.md @@ -57,27 +57,27 @@ numpy-conduct@googlegroups.com 으로 NumPy 윤리 강령 위원회에 문제를 * Melissa Weber Mendonça * Rohit Goswami -If your report involves any members of the Committee, or if they feel they have a conflict of interest in handling it, then they will recuse themselves from considering your report. Alternatively, if for any reason you feel uncomfortable making a report to the Committee, then you can also contact senior NumFOCUS staff at [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). +귀하의 보고서가 위원회 구성원과 관련이 있거나 보고서를 처리하는 데 이해 상충이 있다고 생각하는 경우 귀하의 보고서를 고려하지 않을 것입니다. 또는 어떤 이유로든 위원회에 보고하는 것이 불편하다고 느끼면 NumFOCUS 고위 직원에게 연락하셔도 됩니다. [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). -### Incident reporting resolution & Code of Conduct enforcement +### 신고 해결 & 이용약관 강령 -_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). +_이 섹션은 가장 중요한 사항을 요약하고 자세한 내용_은 [NumPy 행동 강령 - 보고에 대한 후속 조치 방법](/report-handling-manual)에서 찾을 수 있습니다. -We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). +우리는 모든 불만을 조사하고 대응할 것입니다. NumPy 행동 강령 위원회와 NumPy 운영 위원회(관련된 경우) 는 신고자의 신원을 보호하고 신고 내용을 기밀로 취급합니다(신고자가 달리 동의하지 않는 한). -In case of severe and obvious breaches, e.g. personal threat or violent, sexist or racist language, we will immediately disconnect the originator from NumPy communication channels; please see the manual for details. +심각하고 명백한 위반의 경우, 예: 개인적인 위협이나 폭력적, 성차별적 또는 인종차별적 언어를 사용하는 경우 NumPy 통신 채널에서 발신자를 즉시 연결 해제합니다. 자세한 내용은 설명서를 참조하십시오. -In cases not involving clear severe and obvious breaches of this Code of Conduct the process for acting on any received Code of Conduct violation report will be: +본 행동 강령의 명백하고 명백한 위반과 관련되지 않은 경우 접수된 행동 강령 위반 보고서에 따라 조치를 취하는 절차는 다음과 같습니다. -1. acknowledge report is received, -2. reasonable discussion/feedback, -3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), -4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. +1. 보고서를 수신 확인, +2. 합리적인 토론/피드백, +3. 중재(피드백이 도움이 되지 않은 경우, 신고자와 피신고자 모두 동의하는 경우에만), +4. 행동 강령 위원회의 투명한 결정([해결책](/report-handling-manual/#resolutions) 참조) 을 통한 집행. -The Committee will respond to any report as soon as possible, and at most within 72 hours. +위원회는 가능한 한 빨리, 최대 72시간 이내에 보고에 응답할 것입니다. -### Endnotes +### 끝내며 -We are thankful to the groups behind the following documents, from which we drew content and inspiration: +내용과 영감을 얻은 다음 문서의 배후에 있는 그룹에 감사드립니다. -- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) +- [SciPy 이용 약관](https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html) From 3a7de9fbec177a1e4d5ba146759f500a355487eb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 11 Dec 2023 23:48:30 +0100 Subject: [PATCH 508/711] New translations about.md (Arabic) --- content/ar/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/about.md b/content/ar/about.md index a6e4abb22e..f4841c1045 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -55,7 +55,7 @@ See the [Team]({{< relref "/teams" >}}) page for more info. - Charles Harris - Ralf Gommers -- Melissa Weber Mendonça +- Inessa Pawson - Sebastian Berg - External member: Thomas Caswell From 26c22385b828dc883346491f668779392bf540ba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 11 Dec 2023 23:48:32 +0100 Subject: [PATCH 509/711] New translations about.md (Korean) --- content/ko/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/about.md b/content/ko/about.md index abef9f7811..1c5e49d441 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -55,7 +55,7 @@ See the [Team]({{< relref "/teams" >}}) page for more info. - Charles Harris - Ralf Gommers -- Melissa Weber Mendonça +- Inessa Pawson - Sebastian Berg - External member: Thomas Caswell From 71623a9e31d162618c16770904d0167995e54639 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 11 Dec 2023 23:48:33 +0100 Subject: [PATCH 510/711] New translations about.md (Russian) --- content/ru/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/about.md b/content/ru/about.md index a6e4abb22e..f4841c1045 100644 --- a/content/ru/about.md +++ b/content/ru/about.md @@ -55,7 +55,7 @@ See the [Team]({{< relref "/teams" >}}) page for more info. - Charles Harris - Ralf Gommers -- Melissa Weber Mendonça +- Inessa Pawson - Sebastian Berg - External member: Thomas Caswell From 299a62ce59aad156c3992bd9acfb0e8929c140bd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 11 Dec 2023 23:48:34 +0100 Subject: [PATCH 511/711] New translations about.md (Chinese Simplified) --- content/zh/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/about.md b/content/zh/about.md index a6e4abb22e..f4841c1045 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -55,7 +55,7 @@ See the [Team]({{< relref "/teams" >}}) page for more info. - Charles Harris - Ralf Gommers -- Melissa Weber Mendonça +- Inessa Pawson - Sebastian Berg - External member: Thomas Caswell From a3d93898e6e967505455dc32009e0920fa617a66 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 14 Dec 2023 19:16:06 +0100 Subject: [PATCH 512/711] New translations install.md (Spanish) --- content/es/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/install.md b/content/es/install.md index 3500153a64..a199b2f91a 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -5,7 +5,7 @@ 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/distribution) - 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/user/building.html). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. +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** From 68b1aaa6d4c4f1f4b4f758d9429a732e2e98d340 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 14 Dec 2023 19:16:07 +0100 Subject: [PATCH 513/711] New translations install.md (Arabic) --- content/ar/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/install.md b/content/ar/install.md index 3500153a64..a199b2f91a 100644 --- a/content/ar/install.md +++ b/content/ar/install.md @@ -5,7 +5,7 @@ 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/distribution) - 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/user/building.html). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. +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** From 55143f0e84f573ae09b54ea369cfdafe81c99fd2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 14 Dec 2023 19:16:10 +0100 Subject: [PATCH 514/711] New translations install.md (Russian) --- content/ru/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/install.md b/content/ru/install.md index 3500153a64..a199b2f91a 100644 --- a/content/ru/install.md +++ b/content/ru/install.md @@ -5,7 +5,7 @@ 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/distribution) - 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/user/building.html). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. +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** From a4559576ec54cf8c5007195df2f42bc74e4f3299 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 14 Dec 2023 19:16:11 +0100 Subject: [PATCH 515/711] New translations install.md (Chinese Simplified) --- content/zh/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/install.md b/content/zh/install.md index 3500153a64..a199b2f91a 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -5,7 +5,7 @@ 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/distribution) - 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/user/building.html). For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. +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** From c35ef54780a64aa85c37228588f2efcd1ca51187 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Dec 2023 19:52:22 +0100 Subject: [PATCH 516/711] New translations news.md (Spanish) --- content/es/news.md | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index b38f3d98b5..d299230d29 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,11 +1,16 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.26.0 publicado" -date: 2023-09-16 +newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" +date: 2023-12-19 --- -### NumPy 1.26.0 released +### 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 publicado _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: From 61ed5d9195e58c5c8a0465c464922ec4f0f2f4e7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Dec 2023 19:52:23 +0100 Subject: [PATCH 517/711] New translations news.md (Arabic) --- content/ar/news.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index a17cba9749..c3ac55d097 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,15 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.26.0 released" -date: 2023-09-16 +newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" +date: 2023-12-19 --- +### 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: From 5ea63fac19e70a7137886f712ee017c05f721031 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Dec 2023 19:52:24 +0100 Subject: [PATCH 518/711] New translations news.md (Japanese) --- content/ja/news.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index 34d198c65b..42bc326160 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,10 +1,15 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 1.26.0 がリリースされました。" +newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" date: 2023-09-16 --- +### NumPy 1.26.0 がリリースされました。 +_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 がリリースされました。 _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。 From 882429b6d45c00bf5f6aa316e2c8728cb4ed1374 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Dec 2023 19:52:26 +0100 Subject: [PATCH 519/711] New translations news.md (Korean) --- content/ko/news.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index a0972afe8d..b376ba632e 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,15 @@ --- title: 소식 sidebar: false -newsHeader: "NumPy 1.26.0 출시" +newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" date: 2023-09-16 --- +### NumPy 1.26.0 출시 +_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 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: From 307c350feb98a58c5bb3dd1882ce32e03b72d4bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Dec 2023 19:52:27 +0100 Subject: [PATCH 520/711] New translations news.md (Russian) --- content/ru/news.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index a17cba9749..c3ac55d097 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,15 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.26.0 released" -date: 2023-09-16 +newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" +date: 2023-12-19 --- +### 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: From 189034b684944f0d733a9fc9fdbd7ba88f5db07f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Dec 2023 19:52:28 +0100 Subject: [PATCH 521/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index a17cba9749..c3ac55d097 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,15 @@ --- title: News sidebar: false -newsHeader: "NumPy 1.26.0 released" -date: 2023-09-16 +newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" +date: 2023-12-19 --- +### 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: From 16f0c76c3f8aab80f117397ca1016f78352519e5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Dec 2023 19:52:29 +0100 Subject: [PATCH 522/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 57738c9856..86642b2f47 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,11 +1,16 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy versão 1.26.0" +newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" date: 2023-09-16 --- ### Lançado o NumPy versão 1.26.0 +_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 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: From 0c12f6aec02b78b20ed39669217102c4561b5d65 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Dec 2023 12:29:26 +0100 Subject: [PATCH 523/711] New translations news.md (Spanish) --- content/es/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index d299230d29..2d4a2788d3 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,14 +1,14 @@ --- title: News sidebar: false -newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" -date: 2023-12-19 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- ### 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  +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 publicado From be5a933fed5e14b0313bafd9a4478a3375e50085 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Dec 2023 12:29:27 +0100 Subject: [PATCH 524/711] New translations news.md (Arabic) --- content/ar/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index c3ac55d097..4a321ee89d 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,14 +1,14 @@ --- title: News sidebar: false -newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" -date: 2023-12-19 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- ### 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  +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 From a9700a1ff4579efed860f86c43b2d140392be21e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Dec 2023 12:29:28 +0100 Subject: [PATCH 525/711] New translations news.md (Japanese) --- content/ja/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 42bc326160..42f0fa1ca2 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,14 +1,14 @@ --- title: ニュース sidebar: false -newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" +newsHeader: "NumPy 1.26.0 released" date: 2023-09-16 --- ### NumPy 1.26.0 がリリースされました。 _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  +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 がリリースされました。 From 87df8cdd5e1c37448f3924f16bd436581e94d1d2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Dec 2023 12:29:29 +0100 Subject: [PATCH 526/711] New translations news.md (Korean) --- content/ko/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index b376ba632e..ed28cb97d4 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,14 +1,14 @@ --- title: 소식 sidebar: false -newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" +newsHeader: "NumPy 1.26.0 released" date: 2023-09-16 --- ### NumPy 1.26.0 출시 _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  +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 출시 From 1efbfa61d343402b0c3eebd6947b1f52ee8f07df Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Dec 2023 12:29:31 +0100 Subject: [PATCH 527/711] New translations news.md (Russian) --- content/ru/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index c3ac55d097..4a321ee89d 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,14 +1,14 @@ --- title: News sidebar: false -newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" -date: 2023-12-19 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- ### 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  +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 From 1ab7639ff4bce48bec60dd4b0ae36ac3782e5df0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Dec 2023 12:29:32 +0100 Subject: [PATCH 528/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index c3ac55d097..4a321ee89d 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,14 +1,14 @@ --- title: News sidebar: false -newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" -date: 2023-12-19 +newsHeader: "NumPy 1.26.0 released" +date: 2023-09-16 --- ### 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  +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 From d65032d2bf69440b43deae4a571f67c6df5edf48 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Dec 2023 12:29:33 +0100 Subject: [PATCH 529/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 86642b2f47..644b264b8b 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,14 +1,14 @@ --- title: Notícias sidebar: false -newsHeader: "NumFOCUS + PyCharm end of the year fundraiser" +newsHeader: "NumPy 1.26.0 released" date: 2023-09-16 --- ### Lançado o NumPy versão 1.26.0 _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  +Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  ### NumPy versão 1.26.0 From 2b1a5953ea95e5c8a9814101114b4a2f6a30d784 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 30 Dec 2023 23:45:42 +0100 Subject: [PATCH 530/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 138 ++++++++++++++++++++--------------------- 1 file changed, 69 insertions(+), 69 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index b2e7e10ee4..f78803ca31 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -11,9 +11,9 @@ params: #Hero subtitle (optional) subtitle: Python으로 과학적 컴퓨팅을 하기 위한 기초 패키지 #Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "최신 릴리: NumPy 1.26. 모든 릴리즈 보기" #Where the main hero button links to - buttonlink: "/news/#releases" + buttonlink: "/ko/news/#releases" #Hero image (from static/images/___) image: logo.svg shell: @@ -22,79 +22,79 @@ params: - title: NumPy 써 보기 text: Use the interactive shell to try NumPy in the browser - docslink: Don't forget to check out the docs. + docslink: 문서도 한 번 열람해보세요. casestudies: - title: CASE STUDIES + title: 사례 연구 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 + title: 최초의 블랙홀 사진 + text: NumPy 및 NumPy에 의존하는 SciPy, Matplotlib와 같은 라이브러리가 사건의 지평선 망원경으로 최초의 블랙홀 사진을 생성할 수 있었던 방법 img: /images/content_images/case_studies/blackhole.png - alttext: First image of a black hole. It is an orange circle in a black background. + alttext: 최초의 블랙홀 사진. 검은 배경의 주황색 원입니다. 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. + title: 중력파 검출 + text: 1916년, 알베르트 아인슈타인이 중력파를 예측했습니다. LIGO 과학자들이 NumPy를 이용하여 이것이 존재함을 증명하기 100년 전이었습니다. img: /images/content_images/case_studies/gravitional.png - alttext: Two orbs orbiting each other. They are displacing gravity around them. + alttext: 서로의 궤도를 도는 두 구체. 주위의 중력을 변화시키고 있습니다. 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. + title: 스포츠 분석 + text: 크리켓 분석은 통계적 모델링과 예측 분석을 통해 선수와 팀의 성과를 개선하여 게임을 바꾸고 있습니다. NumPy는 이런 많은 분석을 가능하게 합니다. img: /images/content_images/case_studies/sports.jpg - alttext: Cricket ball on green field. - url: /case-studies/cricket-analytics + alttext: 크리켓 공이 녹지 위에 있습니다. + url: /ko/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. + title: 딥러닝을 통한 자세 추정 + text: DeepLabCut은 동물의 행동을 관찰하는 과학 연구의 속도를 개선하기 위해, NumPy를 사용하여 종이나 시간에 따른 운동 제어 방식을 잘 이해할 수 있도록 하였습니다. img: /images/content_images/case_studies/deeplabcut.png - alttext: Cheetah pose analysis - url: /case-studies/deeplabcut-dnn + alttext: 치타 자세 분석 + url: /ko/case-studies/deeplabcut-dnn keyfeatures: features: - - title: Powerful N-dimensional arrays - text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. + title: 강력한 n차원 배열 + text: 빠르고 다재다능한 NumPy의 벡터화, 인덱싱, 전송 구성은 오늘날 배열 컴퓨팅의 사실상 표준입니다. - - title: Numerical computing tools - text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + title: 수치적 컴퓨팅 도구 + text: NumPy는 포괄적인 수학 함수, 난수 생성기, 선형 대수 루틴, 푸리에 변환 등을 제공합니다. - - title: Open source - text: 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). + title: 오픈소스 + text: 자유 [BSD 라이선스](https://github.com/numpy/numpy/blob/main/LICENSE.txt)에 따라, NumPy는 흥미에 찼으며, 반응이 빠르고, 다양성이 넘치는 [커뮤니티](/community)에 의하여 [GitHub](https://github.com/numpy/numpy)에서 공개적으로 개발되고 유지됩니다. - - title: Interoperable - text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + title: 상호운용성 + text: NumPy는 광범위한 하드웨어 및 컴퓨팅 플랫폼을 지원합니다. 또 분산형, GPU, 희소 배열 라이브러리와도 잘 작동합니다. - - title: Performant - text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + title: 고성능 + text: NumPy의 핵심은 최적화된 C 코드로 구성되어 있습니다. 컴파일된 코드의 속도와 함께 Python의 유연함을 즐기세요. - - title: Easy to use - text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + title: 쉬운 사용성 + text: NumPy의 고수준 문법은 어떤 배경이나 수준을 가지고 있는 프로그래머든 쉽게 접근하여 생산적인 일을 할 수 있도록 만들어줍니다. tabs: title: 생태계 section5: false navbar: - - title: Install - url: /install + title: 설치 + url: /ko/install - - title: Documentation + title: 문서 url: https://numpy.org/doc/stable - - title: Learn - url: /learn + title: 배움 + url: /ko/learn - - title: Community - url: /community + title: 커뮤니티 + url: /ko/community - - title: About Us - url: /about + title: NumPy 정보 + url: /ko/about - - title: News - url: /news + title: 소식 + url: /ko/news - - title: Contribute - url: /contribute + title: 기여 + url: /ko/contribute footer: logo: logo.svg socialmediatitle: "" @@ -113,48 +113,48 @@ params: title: "" links: - - text: Install - link: /install + text: 설치 + link: /ko/install - - text: Documentation + text: 문서 link: https://numpy.org/doc/stable - - text: Learn - link: /learn + text: 배움 + link: /ko/learn - - text: Citing Numpy - link: /citing-numpy + text: Numpy 인용 + link: /ko/citing-numpy - - text: Roadmap + text: 로드맵 link: https://numpy.org/neps/roadmap.html column2: links: - - text: About us - link: /about + text: 정보 + link: /ko/about - - text: Community - link: /community + text: 커뮤니티 + link: /ko/community - - text: User surveys - link: /user-surveys + text: 사용자 설문조사 + link: /ko/user-surveys - - text: Contribute - link: /contribute + text: 기여 + link: /ko/contribute - - text: Code of conduct - link: /code-of-conduct + text: 이용약관 + link: /ko/code-of-conduct column3: links: - - text: Get help - link: /gethelp + text: 도움받기 + link: /ko/gethelp - - text: Terms of use - link: /terms + text: 이용약관 + link: /ko/terms - - text: Privacy - link: /privacy + text: 개인정보처리방침 + link: /ko/privacy - - text: Press kit - link: /press-kit + text: 홍보 자료 + link: /ko/press-kit From 2a1433e46340bb69e92df2b916ab7dc2e0e6aa50 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 30 Dec 2023 23:45:43 +0100 Subject: [PATCH 531/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index f9d19bcf5c..67cf18ccac 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -2,7 +2,7 @@ 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 — 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. + para1: NumPy는 [scikit-learn](https://scikit-learn.org) 및 [SciPy](https://www.scipy.org)와 같은 강력한 기계 학습 라이브러리의 기반을 형성합니다. 기계 학습이 성장함에 따라 NumPy에 구축된 라이브러리 목록도 늘어납니다. [TensorFlow의](https://www.tensorflow.org) 딥 러닝 기능은 폭넓게 응용할 수 있습니다. — 그 중에는 음성 및 이미지 인식, 텍스트 기반 애플리케이션, 시계열 분석 및 비디오 감지가 있습니다. 또 다른 딥 러닝 라이브러리인 [PyTorch](https://pytorch.org)는 컴퓨터 비전 및 자연어 처리 연구자들 사이에서 인기가 있습니다. [MXNet](https://github.com/apache/incubator-mxnet)은 딥 러닝을 위한 청사진과 템플릿을 제공하는 또 다른 AI 패키지입니다. 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) — 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: @@ -10,61 +10,61 @@ params: 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: 배열 라이브러리 - - text: Capabilities & Application areas + text: 기능 및 응용 분야 libraries: - title: Dask - text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale. + text: 분석을 위한 분산 배열 및 고급 병렬 처리를 통해 규모에 맞는 성능을 구현합니다. 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. + text: Python에서 GPU 가속 컴퓨팅을 구현해주며 NumPy와 호환되는 배열 라이브러리. 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." + text: "NumPy 프로그램을 부분적으로 변환하여 벡터화, GPU/TPU의 적시 컴파일을 제공하는 라이브러리." 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: 고급 통계 및 시각화를 구동하기 위하여 라벨링 및 인덱싱이 이뤄진 다차원 배열을 제공 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. + text: Dask 및 SciPy의 희소 선형 대수와 통합되는 NumPy 호환 희소 배열 라이브러리입니다. 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. + text: 연구 프로토타이핑에서 프로덕션 배포로의 경로를 가속화하는 딥 러닝 프레임워크입니다. 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. + text: 기계 학습을 위한 엔드 투 엔드 플랫폼으로 ML 기반 애플리케이션을 쉽게 구축하고 배포할 수 있습니다. 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. + text: 유연한 연구 프로토타이핑 및 생산에 적합한 딥 러닝 프레임워크입니다. 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. + text: 열 기반 메모리 내 데이터 및 분석을 위한 교차 언어 개발 플랫폼입니다. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow From 12c211750b1bacb86f1ff65457324e4788f72043 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 30 Dec 2023 23:45:44 +0100 Subject: [PATCH 532/711] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index 4b950a6b99..d5888d6225 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -43,7 +43,7 @@ sidebar: false 병합에서 방출되는 중력파는 슈퍼컴퓨터를 사용하는 무차별 대입 수치 상대성 이론을 제외하고는 어떤 기술로도 계산할 수 없습니다. LIGO가 수집하는 데이터의 양은 중력파 신호가 작은 만큼 이해할 수 없을 정도로 많습니다. -Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝트 동안 수행되는 다양한 작업에 사용되는 소프트웨어에서 활용되었습니다. NumPy는 복잡한 수학 및 데이터 조작을 고속으로 해결하는 데 도움이 되었습니다. Here are some examples: +Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝트 동안 수행되는 다양한 작업에 사용되는 소프트웨어에서 활용되었습니다. NumPy는 복잡한 수학 및 데이터 조작을 고속으로 해결하는 데 도움이 되었습니다. 몇 가지 예시를 들자면: * [신호 처리](https://www.uv.es/virgogroup/Denoising_ROF.html): 글리치 검출, [잡음 식별 및 데이터 결정](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) * 데이터 수집: 어떤 데이터를 분석할 수 있을지 결정하고, 모래 속 바늘과 같이 미미한 신호가 있는지 파악 From a0e5b6805a612f16762d042102befd1406463419 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Dec 2023 00:41:41 +0100 Subject: [PATCH 533/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index f78803ca31..35f1038767 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -21,7 +21,7 @@ params: intro: - title: NumPy 써 보기 - text: Use the interactive shell to try NumPy in the browser + text: 대화형 셸을 이용해 브라우저에서 NumPy를 사용해보세요 docslink: 문서도 한 번 열람해보세요. casestudies: title: 사례 연구 From 4b1acf8562ab28f67ecf08fd4081c4f81784bf95 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jan 2024 20:47:26 +0100 Subject: [PATCH 534/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 644b264b8b..780898fc1b 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,14 +1,14 @@ --- title: Notícias sidebar: false -newsHeader: "NumPy 1.26.0 released" +newsHeader: "Lançado o NumPy versão 1.26.0" date: 2023-09-16 --- ### Lançado o NumPy versão 1.26.0 -_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. +_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 unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  +Use a URL única que permitirá rastrear as compras https://lp.jetbrains.com/support-data-science/ ou um código de cupom ISUPPORTDATASCIENCE  ### NumPy versão 1.26.0 From 216c4c82901cf38878fcd50ec5d275b7dd7f7975 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jan 2024 22:00:26 +0100 Subject: [PATCH 535/711] New translations news.md (Spanish) --- content/es/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/news.md b/content/es/news.md index 2d4a2788d3..9d0a4ad0d0 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -230,6 +230,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _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_. From ff262c85d0cbb82ac673964831b0ac1f5f413c35 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jan 2024 22:00:27 +0100 Subject: [PATCH 536/711] New translations news.md (Arabic) --- content/ar/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/news.md b/content/ar/news.md index 4a321ee89d..7a5ad39f38 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -230,6 +230,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _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_. From ce08738a33cb9a4f6a64511b2284949e222cadd1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jan 2024 22:00:29 +0100 Subject: [PATCH 537/711] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 42f0fa1ca2..b6dc5f1cef 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -230,6 +230,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _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_. From 3e4adec3802dd2b8c9f3a451c3bd89b4c9fd9bdd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jan 2024 22:00:30 +0100 Subject: [PATCH 538/711] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index ed28cb97d4..f13b97d509 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -230,6 +230,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _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 ([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_. From 6a84b2b2e085776b195a63722434db629b8b0a8b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jan 2024 22:00:31 +0100 Subject: [PATCH 539/711] New translations news.md (Russian) --- content/ru/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ru/news.md b/content/ru/news.md index 4a321ee89d..7a5ad39f38 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -230,6 +230,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _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_. From 3256849a5ba7c54a7c9d40fbd8503edfa10419b4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jan 2024 22:00:32 +0100 Subject: [PATCH 540/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/news.md b/content/zh/news.md index 4a321ee89d..7a5ad39f38 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -230,6 +230,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _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_. From a223399bd07dd3215bae99bf08f140d01e76bcdc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 3 Jan 2024 22:00:34 +0100 Subject: [PATCH 541/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/pt/news.md b/content/pt/news.md index 780898fc1b..4e9a44f7ed 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -230,6 +230,7 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _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_. From 124c969ac770f5d8ae8acbfa17f22a729af21da1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 00:25:01 +0100 Subject: [PATCH 542/711] New translations news.md (Japanese) --- content/ja/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index b6dc5f1cef..f593842697 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -230,7 +230,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2 Jan 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 1.26.2 ([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_. From 4c3e9d2a76af5f1472e61e1a82c8c984c857d7ca Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 00:25:02 +0100 Subject: [PATCH 543/711] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 28245f2dc0..c7ded3c08d 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -5,14 +5,14 @@ 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" >}} -{{< 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 cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGOの科学調査協力により*" >}} LIGOで行われる研究には、科学的なPythonエコシステムが重要なインフラとなっています。 {{< /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 -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. +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。 -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。 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. +[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。 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。 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. ### 主な目的 From 4e14c5645a86da0c34af2868006ae97e946e51dc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 01:28:01 +0100 Subject: [PATCH 544/711] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 03a1ae6567..7b32fb30d4 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -5,8 +5,8 @@ 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" >}} -{{< 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 cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, * カリフォルニア工科大学の計算数理科学の助教授 *" +> }} 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 >}} From d70b266af5025837644d0e1bc1eb514ca8a4e386 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 01:28:03 +0100 Subject: [PATCH 545/711] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 7b7fb8b40a..ee0c62f285 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -5,8 +5,8 @@ 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" >}} -{{< 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 cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *世界的なクリケットプレイヤーであり、前世界王者、インドチームで活躍し、IPLにおけるチェンナイスーパー王者となった*" +> }} 観客のために競技しているのではなく、国のために競技しているのです。 > > {{< /blockquote >}} From 9b9b4560d6442c424ad78c9f5d36f033a8614ae9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 03:25:58 +0100 Subject: [PATCH 546/711] New translations news.md (Japanese) --- content/ja/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index f593842697..08f4e6c795 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,14 +1,14 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 1.26.0 released" +newsHeader: "NumPy 1.26.0 がリリースされました。" date: 2023-09-16 --- -### NumPy 1.26.0 がリリースされました。 -_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. +### 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。 -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  +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 1.26.0 がリリースされました。 From b21aaa789d7eb1badc14a439335c94eef8baa1d8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 03:25:59 +0100 Subject: [PATCH 547/711] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index c7ded3c08d..781c17a2c4 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -12,7 +12,7 @@ sidebar: false 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。 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。 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. +[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。 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。 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伴う現象を切り拓くものです。 ### 主な目的 @@ -37,13 +37,13 @@ sidebar: false 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="**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" 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="(**図のクレジット:** 「バイナリーブラックホール合成からの重力波の観測」、ResearchGateにて発表。)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}} ## 重力波の検出におけるNumPyの役割 -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. +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。 -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: +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。 @@ -54,7 +54,7 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * 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="**Dependency graph showing how GwPy package depends on NumPy**" >}} +{{< 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**" >}} ---- @@ -62,6 +62,6 @@ NumPy, the standard numerical analysis package for Python, was utilized by the ## まとめ -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. +GW検出により、研究者は完全に予期せぬ現象 を発見することができ、同時に知られている最も深遠な天体物理学 現象の多くに新たな洞察を提供しています。 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、研究者が複雑な現象の疑問に答え、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" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} +{{< 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**" >}} From 36231b8c5d05de6c4ee432924e2bd6d1f911b8f7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 03:26:00 +0100 Subject: [PATCH 548/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 58 ++++++++++++++++++------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 6befd85a5e..848d1a8806 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -100,8 +100,8 @@ params: 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: Quantum Computing - alttext: A computer chip. + 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: - @@ -117,8 +117,8 @@ params: url: https://pennylane.ai label: PennyLane - - title: Statistical Computing - alttext: A line graph with the line moving up. + 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: - @@ -134,8 +134,8 @@ params: url: https://github.com/mwaskom/seaborn label: Seaborn - - title: Signal Processing - alttext: A bar chart with positive and negative values. + 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: - @@ -148,8 +148,8 @@ params: url: https://python-control.org/ label: python-control - - title: Image Processing - alttext: An photograph of the mountains. + title: 画像処理 + alttext: 山々の写真 img: /images/content_images/sc_dom_img/image_processing.svg links: - @@ -162,8 +162,8 @@ params: url: https://mahotas.rtfd.io/ label: Mahotas - - title: Graphs and Networks - alttext: A simple graph. + 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: - @@ -179,8 +179,8 @@ params: url: https://pygsp.rtfd.io/ label: PyGSP - - title: Astronomy - alttext: A telescope. + title: 天文学 + alttext: 望遠鏡 img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - @@ -193,16 +193,16 @@ params: url: https://github.com/spacepy/spacepy label: SpacePy - - title: Cognitive Psychology - alttext: A human head with gears. + 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: Bioinformatics - alttext: A strand of DNA. + title: 生命情報科学 + alttext: DNAの鎖 img: /images/content_images/sc_dom_img/bioinformatics.svg links: - @@ -218,8 +218,8 @@ params: url: http://etetoolkit.org/ label: ETE - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + 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: - @@ -235,8 +235,8 @@ params: url: https://emcee.readthedocs.io/ label: emcee - - title: Mathematical Analysis - alttext: Four mathematical symbols. + 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: - @@ -252,8 +252,8 @@ params: url: https://fenicsproject.org/ label: FEniCS - - title: Chemistry - alttext: A test tube. + title: 化学 + alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg links: - @@ -269,8 +269,8 @@ params: url: https://www.pybamm.org/ label: PyBaMM - - title: Geoscience - alttext: The Earth. + title: 地球科学 + alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg links: - @@ -286,8 +286,8 @@ params: url: https://www.fatiando.org/ label: Fatiando a Terra - - title: Geographic Processing - alttext: A map. + title: 地理情報処理 + alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg links: - @@ -300,8 +300,8 @@ params: url: https://python-visualization.github.io/folium label: Folium - - title: Architecture & Engineering - alttext: A microprocessor development board. + 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: - @@ -309,7 +309,7 @@ params: label: COMPAS - url: https://cityenergyanalyst.com/ - label: City Energy Analyst + label: 都市エネルギー分析 - url: https://nortikin.github.io/sverchok/ label: Sverchok From feb99ee465e69384264e24bcb0f0731e04e93156 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 03:26:02 +0100 Subject: [PATCH 549/711] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index c1a5d6fd57..aee51e07ba 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -5,8 +5,8 @@ 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">}} -{{< 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 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 cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, * ローザンヌ連邦工科大学([EPFL](https://www.epfl.ch/en/))の助教授 *" +> }} 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 >}} From 856ae6e84ce60b09a516e08c65c21f04f5054ba6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 20:46:55 +0100 Subject: [PATCH 550/711] New translations gethelp.md (Spanish) --- content/es/gethelp.md | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/content/es/gethelp.md b/content/es/gethelp.md index 80e2603df5..5da0e518f7 100644 --- a/content/es/gethelp.md +++ b/content/es/gethelp.md @@ -3,11 +3,9 @@ title: Buscar Ayuda sidebar: false --- -**Preguntas de los usuarios:** La mejor manera de obtener ayuda es publicando tu pregunta en sitios como [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), con miles de usuarios disponibles para responder. Alternativas más pequeñas incluyen [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), y [Reddit](https://www.reddit.com/r/Numpy/). Nos gustaría poder estar al pendiente en estos sitios, o responder preguntas de manera directa, ¡pero el volumen es un poco abrumador! - -**Cuestiones de desarrollo:** Para asuntos relacionados con el desarrollo de NumPy (por ejemplo, informes de errores), por favor visita la [Comunidad](/community). - +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) @@ -20,15 +18,3 @@ Un foro para hacer preguntas de uso, como por ejemplo: "¿Cómo hago X cosa en N Otro foro para hacer preguntas de uso. *** - -### [Gitter](https://gitter.im/numpy/numpy) - -Una sala de chat en tiempo real en donde usuarios y miembros de la comunidad se ayudan mutuamente. - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -Otra sala de chat en tiempo real en donde los usuarios y los miembros de la comunidad se ayudan mutuamente. - -*** From 6dae5f2dc001c4a362f2441309d88416907f71d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 20:46:56 +0100 Subject: [PATCH 551/711] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index a427b5b1f5..da3bff67a5 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -3,11 +3,9 @@ title: Get Help sidebar: false --- -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! - -**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). - +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) @@ -20,15 +18,3 @@ A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [us Another forum for usage questions. *** - -### [Gitter](https://gitter.im/numpy/numpy) - -A real-time chat room where users and community members help each other. - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -Another real-time chat room where users and community members help each other. - -*** From 3c4adeb0a4204d7301a00f1c7cf719ab72e9e983 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 20:46:57 +0100 Subject: [PATCH 552/711] New translations gethelp.md (Japanese) --- content/ja/gethelp.md | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/content/ja/gethelp.md b/content/ja/gethelp.md index 0a77e294c0..6105ed9eb4 100644 --- a/content/ja/gethelp.md +++ b/content/ja/gethelp.md @@ -3,11 +3,9 @@ title: サポートを得る方法 sidebar: false --- -**ユーザーからの質問:** ユーザーからの質問に対して回答を得る最も良い方法は、[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)に質問を投稿することです。 規模は小さいですが、下記のような質問をする場所もあります: [IRC](https://webchat.freenode.net/?channels=%23numpy)、 [Gitter](https://gitter.im/numpy/numpy)、 [Reddit](https://www.reddit.com/r/Numpy/)。 私たちはこれらのサイトを定期的に確認して、直接質問に答えるようにしていますが、質問の数は膨大です。 - -**開発関連の問題:** NumPyの開発関連の問題 (例: バグレポート) については、[コミュニティ](/community) のページを参照してください。 - +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) @@ -20,15 +18,3 @@ NumPyの使用方法に関する質問をするためのフォーラムです。 もう一つの使い方に関する質問の場です。 *** - -### [Gitter](https://gitter.im/numpy/numpy) - -ユーザーとコミュニティメンバーがお互いに助け合うリアルタイムのチャットルームです。 - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -ユーザーとコミュニティメンバーがお互いを助け合うもう一つのリアルタイムチャットルームです。 - -*** From 1922a4b33341b516599d2b03f311309a934fe9fb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 20:46:58 +0100 Subject: [PATCH 553/711] New translations gethelp.md (Korean) --- content/ko/gethelp.md | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/content/ko/gethelp.md b/content/ko/gethelp.md index 7fcf03e099..eb52f31b6e 100644 --- a/content/ko/gethelp.md +++ b/content/ko/gethelp.md @@ -3,11 +3,9 @@ title: 도움 구하기 sidebar: false --- -**사용 시 질문:** 도움을 받는 가장 좋은 방법은 [StackOverflow](http://stackoverflow.com/questions/tagged/numpy)와 같이 수많은 사용자들이 답변할 수 있는 사이트에 질문을 게시하는 것입니다. 규모가 좀 더 작은 대체 사이트로는 [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), [Reddit](https://www.reddit.com/r/Numpy/)이 있습니다. 저희가 직접 이런 사이트들을 주시하거나 질문에 대해 답해드리고 싶지만, 그러기에는 질문의 양이 너무 많습니다! - -**개발 이슈:** NumPy 개발 관련 문제(버그 제보 등)의 경우, [커뮤니티](/community)를 방문해주시기 바랍니다. - +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) @@ -20,15 +18,3 @@ sidebar: false 사용 중 질문을 올리는 또다른 포럼입니다. *** - -### [Gitter](https://gitter.im/numpy/numpy) - -사용자와 커뮤니티 구성원이 서로를 돕는 실시간 채팅방입니다. - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -사용자와 커뮤니티 구성원이 서로를 돕는 또다른 실시간 채팅방입니다. - -*** From c56633f2f27212ee824e31218e07dbdc83ee9b99 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 20:46:59 +0100 Subject: [PATCH 554/711] New translations gethelp.md (Russian) --- content/ru/gethelp.md | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/content/ru/gethelp.md b/content/ru/gethelp.md index a427b5b1f5..da3bff67a5 100644 --- a/content/ru/gethelp.md +++ b/content/ru/gethelp.md @@ -3,11 +3,9 @@ title: Get Help sidebar: false --- -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! - -**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). - +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) @@ -20,15 +18,3 @@ A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [us Another forum for usage questions. *** - -### [Gitter](https://gitter.im/numpy/numpy) - -A real-time chat room where users and community members help each other. - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -Another real-time chat room where users and community members help each other. - -*** From 33d8ba372872cc1a670e50b3553f03b52bb1a1c1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 20:47:00 +0100 Subject: [PATCH 555/711] New translations gethelp.md (Chinese Simplified) --- content/zh/gethelp.md | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/content/zh/gethelp.md b/content/zh/gethelp.md index a427b5b1f5..da3bff67a5 100644 --- a/content/zh/gethelp.md +++ b/content/zh/gethelp.md @@ -3,11 +3,9 @@ title: Get Help sidebar: false --- -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), with thousands of users available to answer. Smaller alternatives include [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming! - -**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). - +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) @@ -20,15 +18,3 @@ A forum for asking usage questions, e.g. "How do I do X in NumPy?”. Please [us Another forum for usage questions. *** - -### [Gitter](https://gitter.im/numpy/numpy) - -A real-time chat room where users and community members help each other. - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -Another real-time chat room where users and community members help each other. - -*** From f9da53457fd6836aadf535a2fe05553df5421931 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 20:47:01 +0100 Subject: [PATCH 556/711] New translations gethelp.md (Portuguese, Brazilian) --- content/pt/gethelp.md | 18 ++---------------- 1 file changed, 2 insertions(+), 16 deletions(-) diff --git a/content/pt/gethelp.md b/content/pt/gethelp.md index bba586e7f2..f9f0e0322a 100644 --- a/content/pt/gethelp.md +++ b/content/pt/gethelp.md @@ -3,11 +3,9 @@ title: Obter ajuda sidebar: false --- -**Perguntas de usuários:** A melhor maneira de obter ajuda é postar sua pergunta em um site como [StackOverflow](http://stackoverflow.com/questions/tagged/numpy), com milhares de usuários disponíveis para responder. Outras alternativas incluem [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy)e [Reddit](https://www.reddit.com/r/Numpy/). Gostaríamos de poder ficar de olho nestes sites, ou responder perguntas diretamente, mas o volume é imenso! - -**Issues sobre desenvolvimento:** Para assuntos relacionados ao desenvolvimento do NumPy (por exemplo, relatórios de bugs), veja a [Comunidade](/community). - +**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) @@ -20,15 +18,3 @@ Um fórum para fazer perguntas sobre a utilização da biblioteca, por exemplo: Outro fórum para perguntas de utilização. *** - -### [Gitter](https://gitter.im/numpy/numpy) - -Uma sala de bate-papo em tempo real onde usuários e membros da comunidade se ajudam uns aos outros. - -*** - -### [IRC](https://webchat.freenode.net/?channels=%23numpy) - -Outra sala de bate-papo em tempo real onde usuários e membros da comunidade se ajudam uns aos outros. - -*** From 156a56a21a8f59593e3eb78821eb86464d4dfeff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Jan 2024 22:57:20 +0100 Subject: [PATCH 557/711] New translations gethelp.md (Korean) --- content/ko/gethelp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/gethelp.md b/content/ko/gethelp.md index eb52f31b6e..f216b532d6 100644 --- a/content/ko/gethelp.md +++ b/content/ko/gethelp.md @@ -3,9 +3,9 @@ title: 도움 구하기 sidebar: false --- -**Development issues:** For NumPy development-related matters (e.g., bug reports), please see [Community](/community). +**개발 이슈:** NumPy 개발 관련 문제(버그 제보 등)의 경우, [커뮤니티](/community)를 방문해주시기 바랍니다. -**User questions:** The best way to get help is to post your question to a site like [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) or [Reddit](https://www.reddit.com/r/Numpy/). We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! +**사용자 질문:** 도움을 받을 수 있는 가장 좋은 방법은 링크된 사이트에 질문을 게시하는 것입니다. [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) 또는 [Reddit](https://www.reddit.com/r/Numpy/). 저희가 직접 이런 사이트들을 주시하거나 질문에 대해 답해드리고 싶지만, 그러기에는 질문의 양이 너무 많습니다! ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) From be3eb106124cb02a2e5e8916aeddf7373ee0d2c7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Jan 2024 08:58:02 +0100 Subject: [PATCH 558/711] New translations news.md (Korean) --- content/ko/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index f13b97d509..eb3d6cf56c 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -230,7 +230,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2 Jan 2024_. +- NumPy 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2024년 1월 2일_. - NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. - 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_. From b3542b07ce6b52046040602e0daa1f9a3f7226c3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jan 2024 06:07:29 +0100 Subject: [PATCH 559/711] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 32 ++++++++++++++-------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md index 3836540453..7bc11e4786 100644 --- a/content/ko/report-handling-manual.md +++ b/content/ko/report-handling-manual.md @@ -18,32 +18,32 @@ NumPy 행동 강령 위원회는 본 설명을 따릅니다. 문제를 해결할 ## 중재 -Voluntary informal mediation is a tool at our disposal. In contexts such as when two or more parties have all escalated to the point of inappropriate behavior (something sadly common in human conflict), it may be useful to facilitate a mediation process. This is only an example: the Committee can consider mediation in any case, mindful that the process is meant to be strictly voluntary and no party can be pressured to participate. If the Committee suggests mediation, it should: +자발적인 비공식 중재는 우리가 사용할 수 있는 도구입니다. 두 명 이상의 당사자가 모두 부적절한 행동 (인간 갈등에서 슬프게도 흔한 일) 의 지점까지 확대된 경우와 같은 맥락에서 중재 프로세스를 촉진하는 것이 유용할 수 있습니다. 이것은 단지 예일 뿐입니다. 위원회는 어떤 경우에도 중재를 고려할 수 있으며, 그 과정은 엄격하게 자발적으로 이루어지며 어느 당사자도 참여하도록 압력을 받을 수 없다는 점을 염두에 두어야 합니다. 위원회가 중재를 제안하는 경우 위원회는 다음을 수행해야 합니다. -* Find a candidate who can serve as a mediator. -* Obtain the agreement of the reporter(s). The reporter(s) have complete freedom to decline the mediation idea or to propose an alternate mediator. -* Obtain the agreement of the reported person(s). -* Settle on the mediator: while parties can propose a different mediator than the suggested candidate, only if a common agreement is reached on all terms can the process move forward. -* Establish a timeline for mediation to complete, ideally within two weeks. +* 중재자 역할을 할 수 있는 후보를 찾으십시오. +* 신고자(들) 의 동의를 얻습니다. 신고자는 중재 아이디어를 거부하거나 대체 중재자를 제안할 수 있는 완전한 자유가 있습니다. +* 피신고자의 동의를 얻습니다. +* 중재자 결정: 당사자는 제안된 후보와 다른 중재자를 제안할 수 있지만 모든 조건에 대해 공통된 합의에 도달한 경우에만 프로세스를 진행할 수 있습니다. +* 중재가 완료되기 위한 일정을 설정합니다 (이상적으로는 2주 이내). -The mediator will engage with all the parties and seek a resolution that is satisfactory to all. Upon completion, the mediator will provide a report (vetted by all parties to the process) to the Committee, with recommendations on further steps. The Committee will then evaluate these results (whether a satisfactory resolution was achieved or not) and decide on any additional action deemed necessary. +중재자는 모든 당사자와 관계를 맺고 모두가 만족할 만한 해결책을 모색합니다. 완료되면 조정자는 추가 단계에 대한 권장 사항과 함께 보고서(프로세스의 모든 당사자가 심사한) 를 위원회에 제공합니다. 그런 다음 위원회는 이러한 결과를 평가하고(만족스러운 해결 여부에 관계없이) 필요하다고 판단되는 추가 조치를 결정합니다. -## How the Committee will respond to reports +## 위원회가 신고에 응답하는 방법 -When the Committee (or a Committee member) receives a report, they will first determine whether the report is about a clear and severe breach (as defined below). If so, immediate action needs to be taken in addition to the regular report handling process. +위원회(또는 위원회 위원) 는 보고서를 받으면 먼저 보고서가 명확하고 심각한 위반에 관한 것인지 여부를 결정합니다(아래에 정의됨). 만약 그렇다면, 정기적인 신고 처리 프로세스와 더불어 즉각적인 조치가 취해져야 합니다. -## Clear and severe breach actions +## 명확하고 심각한 권리침해 행위 -We know that it is painfully common for internet communication to start at or devolve into obvious and flagrant abuse. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. +우리는 인터넷 통신이 명백하고 노골적인 남용에서 시작되거나 악화되는 것이 매우 흔한 일이라는 것을 알고 있습니다. 우리는 개인적인 위협, 폭력적, 성차별적 또는 인종차별적 언어와 같은 명확하고 심각한 위반을 신속하게 처리할 것입니다. -When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: +행동 강령 위원회 위원이 명백하고 심각한 위반 사실을 알게 되면 다음과 같은 조치를 취합니다. -* Immediately disconnect the originator from all NumPy communication channels. -* Reply to the reporter that their report has been received and that the originator has been disconnected. -* In every case, the moderator should make a reasonable effort to contact the originator, and tell them specifically how their language or actions qualify as a “clear and severe breach”. The moderator should also say that, if the originator believes this is unfair or they want to be reconnected to NumPy, they have the right to ask for a review, as below, by the Code of Conduct Committee. The moderator should copy this explanation to the Code of Conduct Committee. -* The Code of Conduct Committee will formally review and sign off on all cases where this mechanism has been applied to make sure it is not being used to control ordinary heated disagreement. +* 모든 NumPy 통신 채널에서 발신자를 즉시 연결 해제합니다. +* 보고자에게 보고가 접수되었으며 발신자의 연결이 끊어졌다고 회신합니다. +* 모든 경우에 중재자는 작성자에게 연락하기 위해 합당한 노력을 기울여야 하며, 그들의 언어나 행동이 어떻게 "명백하고 심각한 위반"에 해당하는지 구체적으로 알려야 합니다. 중재자는 또한 작성자가 이것이 불공평하다고 생각하거나 NumPy에 다시 연결되기를 원하는 경우 아래와 같이 행동 강령 위원회에 검토를 요청할 권리가 있다고 말해야 합니다. The moderator should copy this explanation to the Code of Conduct Committee. +* 행동 강령 위원회는 이 메커니즘이 일반적인 열띤 의견 불일치를 통제하는 데 사용되지 않도록 하기 위해 이 메커니즘이 적용된 모든 사례를 공식적으로 검토하고 승인할 것입니다. ## Report handling From a93d1923464c6d10ba88142e53905248b5a0a51b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jan 2024 07:36:47 +0100 Subject: [PATCH 560/711] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 60 ++++++++++++++-------------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md index 7bc11e4786..cdd686956e 100644 --- a/content/ko/report-handling-manual.md +++ b/content/ko/report-handling-manual.md @@ -42,54 +42,54 @@ NumPy 행동 강령 위원회는 본 설명을 따릅니다. 문제를 해결할 * 모든 NumPy 통신 채널에서 발신자를 즉시 연결 해제합니다. * 보고자에게 보고가 접수되었으며 발신자의 연결이 끊어졌다고 회신합니다. -* 모든 경우에 중재자는 작성자에게 연락하기 위해 합당한 노력을 기울여야 하며, 그들의 언어나 행동이 어떻게 "명백하고 심각한 위반"에 해당하는지 구체적으로 알려야 합니다. 중재자는 또한 작성자가 이것이 불공평하다고 생각하거나 NumPy에 다시 연결되기를 원하는 경우 아래와 같이 행동 강령 위원회에 검토를 요청할 권리가 있다고 말해야 합니다. The moderator should copy this explanation to the Code of Conduct Committee. +* 모든 경우에 중재자는 작성자에게 연락하기 위해 합당한 노력을 기울여야 하며, 그들의 언어나 행동이 어떻게 "명백하고 심각한 위반"에 해당하는지 구체적으로 알려야 합니다. 중재자는 또한 작성자가 이것이 불공평하다고 생각하거나 NumPy에 다시 연결되기를 원하는 경우 아래와 같이 행동 강령 위원회에 검토를 요청할 권리가 있다고 말해야 합니다. 중재자는 이 설명을 행동 강령 위원회에 발송해야 합니다. * 행동 강령 위원회는 이 메커니즘이 일반적인 열띤 의견 불일치를 통제하는 데 사용되지 않도록 하기 위해 이 메커니즘이 적용된 모든 사례를 공식적으로 검토하고 승인할 것입니다. -## Report handling +## 신고 대응 -When a report is sent to the Committee they will immediately reply to the reporter to confirm receipt. This reply must be sent within 72 hours, and the group should strive to respond much quicker than that. +보고서가 위원회에 보내지면 위원회는 접수 확인을 위해 즉시 보고자에게 회신할 것입니다. 이 회신은 72시간 이내에 보내야 하며 그룹은 그보다 훨씬 빨리 회신하기 위해 노력해야 합니다. -If a report doesn’t contain enough information, the Committee will obtain all relevant data before acting. The Committee is empowered to act on the Steering Council’s behalf in contacting any individuals involved to get a more complete account of events. +보고서에 충분한 정보가 포함되어 있지 않은 경우 위원회는 조치를 취하기 전에 모든 관련 데이터를 수집합니다. 위원회는 사건에 대한 보다 완전한 설명을 얻기 위해 관련된 모든 개인에게 연락할 때 운영 위원회를 대신하여 행동할 권한이 있습니다. -The Committee will then review the incident and determine, to the best of their ability: +그런 다음 위원회는 사건을 검토하고 최선을 다해 다음을 결정할 것입니다. -* What happened. -* Whether this event constitutes a Code of Conduct violation. -* Who are the responsible party(ies). -* Whether this is an ongoing situation, and there is a threat to anyone’s physical safety. +* 무슨 일이 일어났는지 +* 이 이벤트가 행동 강령 위반에 해당하는지 여부 +* 책임자는 누구인지 +* 이것이 현재 진행 중인 상황인지, 누군가의 신체적 안전에 대한 위협이 있는지 여부 -This information will be collected in writing, and whenever possible the group’s deliberations will be recorded and retained (i.e. chat transcripts, email discussions, recorded conference calls, summaries of voice conversations, etc). +이 정보는 서면으로 수집되며 가능할 때마다 그룹의 심의가 녹음 및 보관됩니다(예: 채팅 내용, 이메일 토론, 녹음된 회의 통화, 음성 대화 요약 등). -It is important to retain an archive of all activities of this Committee to ensure consistency in behavior and provide institutional memory for the project. To assist in this, the default channel of discussion for this Committee will be a private mailing list accessible to current and future members of the Committee as well as members of the Steering Council upon justified request. If the Committee finds the need to use off-list communications (e.g. phone calls for early/rapid response), it should in all cases summarize these back to the list so there’s a good record of the process. +행동의 일관성을 보장하고 프로젝트에 대한 제도적 기억을 제공하기 위해 이 위원회의 모든 활동 기록을 보관하는 것이 중요합니다. 이를 지원하기 위해 이 위원회의 기본 토론 채널은 정당한 요청이 있는 경우 현재 및 미래의 위원회 구성원과 운영 위원회 구성원이 액세스할 수 있는 개인 메일링 리스트가 될 것입니다. 위원회가 목록 외 커뮤니케이션(예: 조기/신속한 응답을 위한 전화 통화)을 사용해야 할 필요성을 발견하는 경우 모든 경우에 이를 목록에 다시 요약하여 프로세스에 대한 좋은 기록이 남도록 해야 합니다. -The Code of Conduct Committee should aim to have a resolution agreed upon within two weeks. In the event that a resolution can’t be determined in that time, the Committee will respond to the reporter(s) with an update and projected timeline for resolution. +행동 강령 위원회는 2주 이내에 결의안에 동의하는 것을 목표로 해야 합니다. 그 시간 내에 해결 방법을 결정할 수 없는 경우 위원회는 보고자에게 해결을 위한 업데이트 및 예상 일정에 대해 응답합니다. -## Resolutions +## 해결 -The Committee must agree on a resolution by consensus. If the group cannot reach consensus and deadlocks for over a week, the group will turn the matter over to the Steering Council for resolution. +위원회는 반드시 합의를 바탕으로 결의를 내야 합니다. 그룹에서 합의가 이루어지지 못하고 1주 넘게 교착 상태에 빠진 경우, 결의를 내기 위해 해당 의제는 조정위원회로 이양됩니다. -Possible responses may include: +가능한 응답은 다음과 같습니다. -* Taking no further action: - - if we determine no violations have occurred; - - if the matter has been resolved publicly while the Committee was considering responses. -* Coordinating voluntary mediation: if all involved parties agree, the Committee may facilitate a mediation process as detailed above. -* Remind publicly, and point out that some behavior/actions/language have been judged inappropriate and why in the current context, or can but hurtful to some people, requesting the community to self-adjust. -* A private reprimand from the Committee to the individual(s) involved. In this case, the group chair will deliver that reprimand to the individual(s) over email, cc’ing the group. -* A public reprimand. In this case, the Committee chair will deliver that reprimand in the same venue that the violation occurred, within the limits of practicality. E.g., the original mailing list for an email violation, but for a chat room discussion where the person/context may be gone, they can be reached by other means. The group may choose to publish this message elsewhere for documentation purposes. -* A request for a public or private apology, assuming the reporter agrees to this idea: they may at their discretion refuse further contact with the violator. The chair will deliver this request. The Committee may, if it chooses, attach “strings” to this request: for example, the group may ask a violator to apologize in order to retain one’s membership on a mailing list. -* A “mutually agreed upon hiatus” where the Committee asks the individual to temporarily refrain from community participation. If the individual chooses not to take a temporary break voluntarily, the Committee may issue a “mandatory cooling off period”. -* A permanent or temporary ban from some or all NumPy spaces (mailing lists, gitter.im, etc.). The group will maintain records of all such bans so that they may be reviewed in the future or otherwise maintained. +* 추가 조치 없음: + - 위반이 발생하지 않았다고 판단되는 경우 + - 위원회가 응답을 고려하는 동안 문제가 공개적으로 해결된 경우. +* 자발적 중재 조정: 모든 관련 당사자가 동의하는 경우 위원회는 위에서 설명한 대로 중재 절차를 촉진할 수 있습니다. +* 공개적으로 상기시키고 일부 행동/행동/언어가 부적절하다고 판단되었으며 그 이유를 현재 상황에서 지적하거나 일부 사람들에게 상처를 줄 수 있으며 커뮤니티가 자체 조정하도록 요청합니다. +* 관련된 개인에 대한 위원회의 사적인 견책. 이 경우 그룹 의장은 그룹을 참조하여 이메일을 통해 개인(들) 에게 질책을 전달합니다. +* 공개 질책. 이 경우 위원회 위원장은 위반이 발생한 동일한 장소에서 실행 가능한 범위 내에서 질책합니다. 예를 들어 이메일 위반에 대한 원래 메일링 리스트이지만 사람/컨텍스트가 없을 수 있는 채팅방 토론의 경우 다른 방법으로 도달할 수 있습니다. 그룹은 문서화 목적으로 이 메시지를 다른 곳에 게시하도록 선택할 수 있습니다. +* 신고자가 이 생각에 동의한다고 가정하고 공개 또는 비공개 사과 요청: 신고자는 재량에 따라 위반자와의 추가 연락을 거부할 수 있습니다. 의장이 이 요청을 전달할 것입니다. 위원회는 선택하는 경우 이 요청에 "문자열"을 첨부할 수 있습니다. 예를 들어 그룹은 위반자에게 메일링 리스트의 회원 자격을 유지하기 위해 사과를 요청할 수 있습니다. +* 위원회가 개인에게 일시적으로 커뮤니티 참여를 자제하도록 요청하는 "상호 합의 중단". 개인이 자발적으로 일시적인 휴식을 취하지 않기로 선택한 경우 위원회는 "필수 냉각 기간"을 발행할 수 있습니다. +* 일부 또는 모든 NumPy 공간(메일링 리스트, gitter.im 등)에서 영구적 또는 일시적 금지. 그룹은 이러한 모든 금지에 대한 기록을 유지하여 나중에 검토하거나 다른 방법으로 유지할 수 있습니다. -Once a resolution is agreed upon, but before it is enacted, the Committee will contact the original reporter and any other affected parties and explain the proposed resolution. The Committee will ask if this resolution is acceptable, and must note feedback for the record. +일단 결의안이 합의되면, 그러나 그것이 시행되기 전에 위원회는 원래 신고자와 영향을 받는 다른 당사자들에게 연락하여 제안된 결의안을 설명합니다. 위원회는 이 결의안이 수용 가능한지 묻고 기록을 위해 피드백을 기록해야 합니다. -Finally, the Committee will make a report to the NumPy Steering Council (as well as the NumPy core team in the event of an ongoing resolution, such as a ban). +최종적으로 위원회는 NumPy 조정위원회에 보고서를 만들어 제출하게 됩니다 (추방 등 효력이 지속되는 결의가 발생하는 경우 NumPy 핵심 팀에게도 보고합니다). -The Committee will never publicly discuss the issue; all public statements will be made by the chair of the Code of Conduct Committee or the NumPy Steering Council. +위원회는 문제를 반드시 비공개 상태로 다룰 것입니다. 모든 공개 성명은 행동강령 위원회 혹은 NumPy 조정위원회에서 담당합니다. -## Conflicts of Interest +## 이해관계 충돌 -In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. +이해관계 충돌이 일어난 경우, 위원회 회원은 즉시 이 사실을 다른 회원에게 고지하고 필요한 경우 자진 사퇴해야 합니다. From 90dbd37e2be0f97c38418e78b63d020c03978db5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jan 2024 07:36:48 +0100 Subject: [PATCH 561/711] New translations about.md (Korean) --- content/ko/about.md | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index 1c5e49d441..c05b97d1b8 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -3,14 +3,14 @@ title: NumPy 정보 sidebar: false --- -NumPy는 Python을 통해 수치적 컴퓨팅을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. 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). +NumPy는 Python을 통해 수치적 컴퓨팅을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. NumPy는 항상 100% 오픈 소스 소프트웨어이며 누구나 무료로 사용할 수 있습니다 [수정된 BSD 라이선스](https://github.com/numpy/numpy/blob/main/LICENSE.txt)의 자유로운 조건에 따라 릴리스됩니다. -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). +NumPy는 NumPy와 더 넓은 과학 Python 커뮤니티의 합의를 통해 GitHub의 공개적으로 개발되었습니다. 거버넌스 접근 방식에 대한 자세한 내용은 [거버넌스 문서](https://www.numpy.org/devdocs/dev/governance/index.html)를 참조하세요. ## 운영 위원회 -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): +NumPy 운영 위원회는 프로젝트를 관리하는 기관입니다. 그 역할은 더 넓은 NumPy 커뮤니티와 협력하고 서비스를 제공함으로써 소프트웨어 패키지와 커뮤니티로서 프로젝트의 장기적인 지속 가능성을 보장하는 것입니다. NumPy 운영 위원회는 현재 다음과 같은 회원들로 구성되어 있습니다. (성씨의 알파벳 순서) - Sebastian Berg - Ralf Gommers @@ -37,27 +37,27 @@ NumPy 운영 위원회에 문의하려면, numpy-team@googlegroups.com 주소로 ## 팀 -The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams: +NumPy 프로젝트 리더십은 프로젝트에 대한 기여 경로를 다양화하기 위해 적극적으로 노력하고 있습니다.
NumPy에는 현재 다음 팀이 있습니다: -- development -- documentation -- triage -- website -- survey -- translations -- sprint mentors +- 개발 +- 문서 +- 심사 +- 웹사이트 +- 설문조사 +- 번역 +- 스프린트 멘토링 - 최적화 -- funding and grants +- 자원 및 보조금 -See the [Team]({{< relref "/teams" >}}) page for more info. +스프린트 멘토링 -## NumFOCUS Subcommittee +## NumFOCUS 소위원회 - Charles Harris - Ralf Gommers - Inessa Pawson - Sebastian Berg -- External member: Thomas Caswell +- 외부 회원: Thomas Caswell ## 스폰서 @@ -78,13 +78,13 @@ NumPy는 다음과 같은 곳들에서 직접적으로 자금을 받습니다. ## 후원 -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. +만약 NumPy가 당신의 업무, 연구 혹은 회사에서 유용하다고 판단된다면 당신의 자원에 맞는 프로젝트에 기여하는 것을 고려해보세요. 그것이 얼마든 도움이 됩니다! 모든 후원은 NumPy의 소프트웨어 개발, 문서 작성과 커뮤니티 운영의 자금으로 엄격하게 사용될 것입니다. -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. +NumPy는 미국의 501(c)(3) 비영리 단체인 NumFOCUS의 후원 프로젝트입니다. NumFOCUS는 NumPy에 재정적, 법적, 행정적 지원을 제공하고 프로젝트의 건강과 지속 가능성을 보장할 수 있도록 도와줍니다. 더 자세한 정보를 알고싶다면 [numfocus.org](https://numfocus.org)를 방문하세요. -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. +NumPy에 대한 후원은 [NumFOCUS](https://numfocus.org)가 관리합니다. 미국에 거주하는 후원자의 경우에는, 당신의 후원은 법이 제공하는 한도 내에서 세금 공제를 받을 수 있습니다. 기부와 마찬가지로 특정 세금 상황에 대해서는 세금 전문가와 상담해야합니다. -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). +NumPy 운영 위원회는 후원받은 후원금을 가장 잘 활용하는 방안을 결정합니다. 기술 및 인프라의 우선 순위는 NumPy [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap)에 문서화되어 있습니다. {{}} From 9523d3dfed17facba68bf07f9af03edf8fa8523b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jan 2024 10:01:29 +0100 Subject: [PATCH 562/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index 67cf18ccac..3bd4162c0b 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -148,8 +148,8 @@ params: url: https://python-control.org/ label: python-control - - title: Image Processing - alttext: An photograph of the mountains. + title: 이미지 처리 + alttext: 산이 찍힌 사진 img: /images/content_images/sc_dom_img/image_processing.svg links: - @@ -162,8 +162,8 @@ params: url: https://mahotas.rtfd.io/ label: Mahotas - - title: Graphs and Networks - alttext: A simple graph. + title: 그래프 및 네트워크 + alttext: 간단한 그래프. img: /images/content_images/sc_dom_img/sd6.svg links: - @@ -179,8 +179,8 @@ params: url: https://pygsp.rtfd.io/ label: PyGSP - - title: Astronomy - alttext: A telescope. + title: 천문학 + alttext: 망원경 img: /images/content_images/sc_dom_img/astronomy_processes.svg links: - From e2db7377a77a415edae80cc2aee5de935394f224 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jan 2024 11:06:13 +0100 Subject: [PATCH 563/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 96 ++++++++++++++++++------------------- 1 file changed, 48 insertions(+), 48 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index 3bd4162c0b..4abf39d9c6 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -3,11 +3,11 @@ params: paras: - para1: NumPy는 [scikit-learn](https://scikit-learn.org) 및 [SciPy](https://www.scipy.org)와 같은 강력한 기계 학습 라이브러리의 기반을 형성합니다. 기계 학습이 성장함에 따라 NumPy에 구축된 라이브러리 목록도 늘어납니다. [TensorFlow의](https://www.tensorflow.org) 딥 러닝 기능은 폭넓게 응용할 수 있습니다. — 그 중에는 음성 및 이미지 인식, 텍스트 기반 애플리케이션, 시계열 분석 및 비디오 감지가 있습니다. 또 다른 딥 러닝 라이브러리인 [PyTorch](https://pytorch.org)는 컴퓨터 비전 및 자연어 처리 연구자들 사이에서 인기가 있습니다. [MXNet](https://github.com/apache/incubator-mxnet)은 딥 러닝을 위한 청사진과 템플릿을 제공하는 또 다른 AI 패키지입니다. - 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) — 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. + para2: '[ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) 메서드라고 하는 비닝(binning), 배깅(bagging), 스태킹(stacking), 부스팅(boosting)과 같은 통계 기법은 다음과 같은 도구로 구현되는 ML 알고리즘에 속합니다. [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) 및 [CatBoost](https://catboost.ai) — – 가장 빠른 추론 엔진 중 하나입니다. [Yellowbrick](https://www.scikit-yb.org/en/latest/) 및 [Eli5](https://eli5.readthedocs.io/en/latest/)는 기계 학습 시각화를 제공합니다.' 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. + text: NumPy의 API는 라이브러리가 혁신적인 하드웨어를 활용하거나, 특수 배열 유형을 생성하거나, NumPy가 제공하는 것 이상의 기능을 추가하도록 작성되는 출발점입니다. headers: - text: 배열 라이브러리 @@ -70,19 +70,19 @@ params: url: https://github.com/apache/arrow - title: xtensor - text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. + text: 수치 분석을 위한 브로드캐스팅 및 지연 컴퓨팅이 포함된 다차원 배열. 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. + text: NumPy와 유사한 관용어로 JSON과 유사한 데이터를 조작합니다. 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. + text: 구현에서 API를 분리하는 Python 백엔드 시스템; unumpy는 NumPy API를 제공합니다. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ @@ -95,13 +95,13 @@ params: scientificdomains: intro: - - text: Nearly every scientist working in Python draws on the power of NumPy. + text: Python으로 작업하는 거의 모든 과학자는 NumPy의 힘을 이용합니다. - - 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." + text: "NumPy는 C 및 Fortran과 같은 언어의 계산 능력을 배우고 사용하기 훨씬 쉬운 언어인 Python으로 가져옵니다. 이 힘에는 단순함이 있습니다. NumPy의 솔루션은 종종 명확하고 우아합니다." libraries: - - title: Quantum Computing - alttext: A computer chip. + title: 양자 컴퓨팅 + alttext: 컴퓨터 칩 img: /images/content_images/sc_dom_img/quantum_computing.svg links: - @@ -117,8 +117,8 @@ params: url: https://pennylane.ai label: PennyLane - - title: Statistical Computing - alttext: A line graph with the line moving up. + title: 통계적 컴퓨팅 + alttext: 선이 위로 이동하는 선그래프 img: /images/content_images/sc_dom_img/statistical_computing.svg links: - @@ -134,8 +134,8 @@ params: url: https://github.com/mwaskom/seaborn label: Seaborn - - title: Signal Processing - alttext: A bar chart with positive and negative values. + title: 신호 처리 + alttext: 양의 값과 음의 값을 가지는 막대 차트 img: /images/content_images/sc_dom_img/signal_processing.svg links: - @@ -193,16 +193,16 @@ params: url: https://github.com/spacepy/spacepy label: SpacePy - - title: Cognitive Psychology - alttext: A human head with gears. + title: 인지심리학 + alttext: 톱니바퀴가 안에서 돌아가는 사람의 머리 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. + title: 생물정보학 + alttext: DNA 가닥 img: /images/content_images/sc_dom_img/bioinformatics.svg links: - @@ -218,8 +218,8 @@ params: url: http://etetoolkit.org/ label: ETE - - title: Bayesian Inference - alttext: A graph with a bell-shaped curve. + title: 베이지안 추론 + alttext: 종 모양 곡선이 그려진 그래프 img: /images/content_images/sc_dom_img/bayesian_inference.svg links: - @@ -235,8 +235,8 @@ params: url: https://emcee.readthedocs.io/ label: emcee - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: 수학적 분석 + alttext: 수학 기호 4개 img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - @@ -252,8 +252,8 @@ params: url: https://fenicsproject.org/ label: FEniCS - - title: Chemistry - alttext: A test tube. + title: 화학 + alttext: 시험관 img: /images/content_images/sc_dom_img/chemistry.svg links: - @@ -269,8 +269,8 @@ params: url: https://www.pybamm.org/ label: PyBaMM - - title: Geoscience - alttext: The Earth. + title: 지구과학 + alttext: 지구 img: /images/content_images/sc_dom_img/geoscience.svg links: - @@ -286,8 +286,8 @@ params: url: https://www.fatiando.org/ label: Fatiando a Terra - - title: Geographic Processing - alttext: A map. + title: 지리학적 처리 + alttext: 지도 img: /images/content_images/sc_dom_img/GIS.svg links: - @@ -300,8 +300,8 @@ params: url: https://python-visualization.github.io/folium label: Folium - - title: Architecture & Engineering - alttext: A microprocessor development board. + title: 아키텍처 및 엔지니어링 + alttext: 마이크로프로세서 개발 보드 img: /images/content_images/sc_dom_img/robotics.svg links: - @@ -309,68 +309,68 @@ params: label: COMPAS - url: https://cityenergyanalyst.com/ - label: City Energy Analyst + label: 도시 에너지 분석가 - 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:" + intro: "NumPy는 풍부한 데이터 과학 라이브러리 생태계의 핵심에 있습니다. 일반적인 탐색적 데이터 과학 워크플로는 다음과 같습니다." 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'. + alttext: Python 라이브러리 다이어그램. 다섯 가지 범주는 '추출, 변환, 로드', '데이터 탐색', '데이터 모델링', '데이터 평가' 및 '데이터 프레젠테이션'입니다. 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'. + alttext: 세 개의 겹치는 원의 다이어그램입니다. 원에는 '수학', '컴퓨터 과학' 및 '영역 전문성'이라는 레이블이 지정되어 있습니다. 세 개의 원이 겹치는 다이어그램의 중간에는 'Data Science'라는 레이블이 붙은 영역이 있습니다. examples: - - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "추출, 변환, 로드: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https:/ /pyjanitor.readthedocs.io/)" - - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + text: "탐색적 분석: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + text: "모델링 및 평가: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "대시보드에서 보고: [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)). + text: 대용량 데이터의 경우 [Dask](https://dask.org) 및 [Ray](https://ray.io/)가 확장되도록 설계되었습니다. 안정적인 배포는 데이터 버전 관리([DVC](https://dvc.org)), 실험 추적([MLFlow](https://mlflow.org)) 및 워크플로 자동화([Airflow](https:// airflow.apache.org), Dagster](https://dagster.io) 와 [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 + alttext: Matplotlib으로 만든 streamplot - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png - alttext: A scatter-plot graph made in ggpy + alttext: ggpy로 만든 산점도 - url: https://www.journaldev.com/19692/python-plotly-tutorial img: /images/content_images/v_plotly.png - alttext: A box-plot made in plotly + alttext: plotly로 만든 상자 그림 - url: https://altair-viz.github.io/gallery/streamgraph.html img: /images/content_images/v_altair.png - alttext: A streamgraph made in altair + alttext: altair로 만든 스트림 그래프 - 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" + alttext: Seaborn에서 만든 두 가지 유형의 그래프, 플롯 그래프와 빈도 그래프의 pairplot - url: https://docs.pyvista.org/ img: /images/content_images/v_pyvista.png - alttext: A 3D volume rendering made in PyVista. + alttext: PyVista로 만든 3D 볼륨 렌더링. - url: https://napari.org img: /images/content_images/v_napari.png - alttext: A multi-dimensionan image made in napari. + alttext: napari로 만든 다차원 이미지. - url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png - alttext: A Voronoi diagram made in vispy. + alttext: vispy로 만든 보로노이 다이어그램. 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: 몇 가지만 예를 들자면 NumPy는 [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) 등이 포함되어 있으며 급격히 성장해나가고 있는 [Python visualization landscape](https://pyviz.org/overviews/index.html)의 핵심 구성 요소 중 하나입니다. - - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. + text: NumPy는 큰 배열을 고속으로 처리할 수 있어 연구자가 기존 Python이 처리할 수 있는 데이터셋보다 훨씬 큰 것도 시각화할 수 있도록 합니다. From 4266d31387342b7da4ebe646f6e3bcdd7874b4ce Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Jan 2024 11:06:14 +0100 Subject: [PATCH 564/711] New translations news.md (Korean) --- content/ko/news.md | 66 +++++++++++++++++++++++----------------------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index eb3d6cf56c..d841ec4a1a 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,7 +1,7 @@ --- title: 소식 sidebar: false -newsHeader: "NumPy 1.26.0 released" +newsHeader: "NumPy 1.26.0 출시" date: 2023-09-16 --- @@ -129,64 +129,64 @@ The two-year project is expected to start by November 2021, and we are excited t ### 2021년도 NumPy 설문조사 -_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. +_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. -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. +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. -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +시작하려면 아래 링크를 눌러 주세요: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 출시 -_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: +_2021년 9월 23일_ -- [NumPy 1.1.21](https://numpy.org/doc/stable/release/1.21.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: -- 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. +- 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. -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. +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 -_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/. +_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 -_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)). +_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 -_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). +_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! -_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. +_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? -_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. +_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 -_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. +_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! -_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. +_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. -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +NumPy를 개선하게 도와주시고 이를위해 설문조사에 참여해 주세요. [여기](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). ### NumPy에 새로운 로고가 생겼습니다! @@ -195,33 +195,33 @@ _2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. NumPy 로고 -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. +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 -_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. +_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 -_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). +_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 -_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`. +_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. -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +자세한 내용은 [출시 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)를 참조하세요. ### NumPy가 Chan Zuckerberg Initiative에서 보조금을 받았습니다 -_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. +_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. -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. +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. -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. +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. @@ -231,7 +231,7 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2024년 1월 2일_. -- NumPy 1.26.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _12 Nov 2023_. +- NumPy 1.126.2 ([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_. From 043b999014a34e81843f5f0bb74f265a99d14664 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Jan 2024 06:27:57 +0100 Subject: [PATCH 565/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index 4abf39d9c6..b3062e58b0 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -88,7 +88,7 @@ params: url: https://uarray.org/en/latest/ - title: tensorly - text: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. + text: NumPy, MXNet, PyTorch, TensorFlow 또는 CuPy를 원활하게 사용하기 위한 Tensor 학습, 대수 및 백엔드. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html From 932fd963118e2ebfb6058b4c2394cfb1d0020357 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Jan 2024 06:27:58 +0100 Subject: [PATCH 566/711] New translations news.md (Korean) --- content/ko/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index d841ec4a1a..1efd61e8b1 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -6,7 +6,7 @@ date: 2023-09-16 --- ### NumPy 1.26.0 출시 -_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. +_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. Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  From f63a48b04fd915bd63e775318c37b7bd405fa893 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Jan 2024 07:35:43 +0100 Subject: [PATCH 567/711] New translations news.md (Korean) --- content/ko/news.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 1efd61e8b1..5310c74928 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -8,7 +8,7 @@ date: 2023-09-16 ### 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. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE  +구매를 추적할 수 있는 고유 URL을 이용하거나: https://lp.jetbrains.com/support-data-science/ 쿠폰 코드를 사용하세요: ISUPPORTDATASCIENCE  ### NumPy 1.26.0 출시 @@ -106,26 +106,26 @@ Please complete this brief [“Participant Interest” form](https://numfocus.ty ### Numpy 1.22.0 출시 -_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: +_2021년 12월 31일_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.22.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: -* 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. +* 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. * 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. +* 새 구성 가능한 메모리 할당자를 다운스트림 프로젝트에서 사용할 수 있습니다. -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. +NumPy 1.22.0은 153명의 기여자가 생성한 609개의 풀 요청을 바탕으로 만들어진 대형 릴리즈입니다. 본 릴리즈에서 지원하는 Python 버전은 3.8-3.10입니다. -### Advancing an inclusive culture in the scientific Python ecosystem +### 과학 Python 생태계에서 포용적 문화 발전 -_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. +_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. -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). +[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. -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. +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. -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). +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 From a08b1ac357b03842b2081d6c6bed02c7b4e07410 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Jan 2024 16:21:03 +0100 Subject: [PATCH 568/711] New translations news.md (Korean) --- content/ko/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 5310c74928..ee5905469e 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -58,11 +58,11 @@ _2023년 6월 17일_ -- 이제 [NumPy 1.25.0](https://numpy.org/doc/stable/relea * Einsum에서 객체 배열 지원 * Inplace 행렬 곱셈 (`@=`) 지원 -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. +NumPy 1.25.0 릴리스에서는 자료형의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업을 계속하고 있습니다. 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. +총 148명의 사람들이 이 릴리스에 기여하였으며 530개의 풀 리퀘스트가 병합되었습니다. -The Python versions supported by this release are 3.9-3.11. +본 릴리즈에서 지원하는 Python 버전은 3.9-3.11입니다. ### Fostering an Inclusive Culture: Call for Participation From 961b2c88ea6684bca2baf332d0113d9869cb1e2f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Jan 2024 18:07:49 +0100 Subject: [PATCH 569/711] New translations news.md (Korean) --- content/ko/news.md | 52 +++++++++++++++++++++++----------------------- 1 file changed, 26 insertions(+), 26 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index ee5905469e..b4ab8e20b8 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -58,61 +58,61 @@ _2023년 6월 17일_ -- 이제 [NumPy 1.25.0](https://numpy.org/doc/stable/relea * Einsum에서 객체 배열 지원 * Inplace 행렬 곱셈 (`@=`) 지원 -NumPy 1.25.0 릴리스에서는 자료형의 처리 및 형변환을 개선하고 실행 속도를 높이는 작업, 문서를 보다 명료하게 다듬는 작업을 계속하고 있습니다. There has also been preparatory work for the future NumPy 2.0.0, resulting in a large number of new and expired deprecations. +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. -### Fostering an Inclusive Culture: Call for Participation +### 포용적인 문화 조성: 참여 요청 -_May 10, 2023_ -- Fostering an Inclusive Culture: Call for Participation +_2023년 5월 10일_ -- 포용적인 문화 조성: 참여 요청 -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/). +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 documentation team leadership transition +### NumPy 문서 팀 리더 변경 -_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. +_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 -_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: +_2022년 12월 18일_ -- [NumPy 1.24.0](https://numpy.org/doc/stable/release/1.24.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: -* New "dtype" and "casting" keywords for stacking functions. -* New F2PY features and fixes. -* Many new deprecations, check them out. -* Many expired deprecations, +* 스태킹 함수를 위한 새 "dtype" 및 "casting" 키워드. +* 새 F2PY 기능 및 수정. +* 수많은 지원 종료 예정 기능, 확인하세요. +* 수많은 만료된 기능, -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 1.24.0 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 -_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: +_2022년 6월 22일_ -- [NumPy 1.23.0](https://numpy.org/doc/stable/release/1.23.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: -* 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. +* `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. -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. +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 research study: call for participation +### NumFOCUS DEI 연구: 참여 요청 -_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. +_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. -**Interested in sharing your experiences?** +**경험을 공유하고 싶으신가요?** -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. +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. -* 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. +* 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. From 1ee07f53d4e5d35e36c5b8b4107ee7ac04686828 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Feb 2024 02:27:04 +0100 Subject: [PATCH 570/711] New translations news.md (Korean) --- content/ko/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/news.md b/content/ko/news.md index b4ab8e20b8..f31a383a45 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -231,6 +231,7 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 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 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2024년 1월 2일_. +- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. - NumPy 1.126.2 ([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_. From eab815d71f0e9b5abcecd6c621ec752bee25ffd7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Feb 2024 02:27:05 +0100 Subject: [PATCH 571/711] New translations news.md (Spanish) --- content/es/news.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/es/news.md b/content/es/news.md index 9d0a4ad0d0..ceac249579 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -230,7 +230,8 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2 Jan 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_. From dec996d693d9308d24d55cc7b733313753ca2676 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Feb 2024 02:27:06 +0100 Subject: [PATCH 572/711] New translations news.md (Arabic) --- content/ar/news.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/ar/news.md b/content/ar/news.md index 7a5ad39f38..9525ba5830 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -230,7 +230,8 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2 Jan 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_. From 5d5c69f65c4ddeddc3e698acdd509a9fd2d33525 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Feb 2024 02:27:07 +0100 Subject: [PATCH 573/711] New translations news.md (Japanese) --- content/ja/news.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ja/news.md b/content/ja/news.md index 08f4e6c795..cc26220220 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -231,6 +231,7 @@ _2019年11月15日_ -- NumPyと、NumPyの重要な依存ライブラリの1つ 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 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 ([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_. From 5b2f1ddf0525bd619aa1a169e91b542e7312c7ef Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Feb 2024 02:27:08 +0100 Subject: [PATCH 574/711] New translations news.md (Russian) --- content/ru/news.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/ru/news.md b/content/ru/news.md index 7a5ad39f38..9525ba5830 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -230,7 +230,8 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2 Jan 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_. From 72bfa593387b05e7cb1a73f159feb00187e23a0b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Feb 2024 02:27:09 +0100 Subject: [PATCH 575/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index 7a5ad39f38..9525ba5830 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -230,7 +230,8 @@ More details on our proposed initiatives and deliverables can be found in the [f 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2 Jan 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_. From f17e404395dcc688b54983271e75cb75df26cd69 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Feb 2024 02:27:10 +0100 Subject: [PATCH 576/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index 4e9a44f7ed..80e98e742d 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -230,7 +230,8 @@ Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrado 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 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2 Jan 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_. From bb4c92f6dc572bcfaf69ba56b46a81f7f89915c4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:48 +0100 Subject: [PATCH 577/711] New translations config.yaml (Spanish) --- content/es/config.yaml | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 4055852f1d..1205ea6f24 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -50,26 +50,6 @@ params: img: /images/content_images/case_studies/deeplabcut.png alttext: Análisis de la pose de un Guepardo url: /case-studies/deeplabcut-dnn - keyfeatures: - features: - - - title: Matrices N-dimensionales Poderosas - text: Rápidos y versátiles, los conceptos de vectorización, indexación y retransmisión de NumPy son los estándares de facto de la computación de matrices en la actualidad. - - - title: Herramientas de Cómputo Numérico - text: NumPy ofrece funciones matemáticas integrales, generadores de números aleatorios, rutinas de álgebra lineal, transformadas de Fourier y mucho más. - - - title: Código abierto - text: Distribuida bajo la licencia liberal [BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy es desarrollado y mantenido [públicamente en GitHub](https://github.com/numpy/numpy) por una [comunidad](/community) vibrante, responsable y diversa. - - - title: Interoperable - text: NumPy es compatible con una amplia gama de plataformas informáticas y de hardware, y funciona bien con bibliotecas distribuidas, de GPU y de matrices dispersas. - - - title: Rendimiento - text: El núcleo de NumPy es código C bien optimizado. Disfrute de la flexibilidad de Python con la velocidad del código compilado. - - - title: Fácil de usar - text: La sintaxis de alto nivel de NumPy lo hace accesible y productivo para programadores de cualquier formación o nivel de experiencia. tabs: title: ECOSISTEMA section5: false From a87816990233c7f076d20e7aafff2a5be6be2da9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:49 +0100 Subject: [PATCH 578/711] New translations _index.md (Spanish) --- content/es/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/es/_index.md diff --git a/content/es/_index.md b/content/es/_index.md new file mode 100644 index 0000000000..01649c02d8 --- /dev/null +++ b/content/es/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +{{< 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. +''' +{{< /card >}} + +{{< card >}} +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' +{{< /card >}} + +{{< 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). +''' +{{< /card >}} + +{{< card >}} +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' +{{< /card >}} + +{{< card >}} +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' +{{< /card >}} + +{{< card >}} +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' +{{< /card >}} + +{{< /grid >}} From a80db62d88d971494b62d950d5c904370136640a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:50 +0100 Subject: [PATCH 579/711] New translations config.yaml (Arabic) --- content/ar/config.yaml | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 483f5a0c14..4aaf75b2e6 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -50,26 +50,6 @@ params: img: /images/content_images/case_studies/deeplabcut.png alttext: Cheetah pose analysis url: /case-studies/deeplabcut-dnn - keyfeatures: - features: - - - title: Powerful N-dimensional arrays - text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. - - - title: Numerical computing tools - text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. - - - title: Open source - text: 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). - - - title: Interoperable - text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. - - - title: Performant - text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. - - - title: Easy to use - text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. tabs: title: ECOSYSTEM section5: false From def6d47dc26c8468cb38da305cb5ba712aa6fa7d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:51 +0100 Subject: [PATCH 580/711] New translations _index.md (Arabic) --- content/ar/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/ar/_index.md diff --git a/content/ar/_index.md b/content/ar/_index.md new file mode 100644 index 0000000000..01649c02d8 --- /dev/null +++ b/content/ar/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +{{< 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. +''' +{{< /card >}} + +{{< card >}} +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' +{{< /card >}} + +{{< 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). +''' +{{< /card >}} + +{{< card >}} +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' +{{< /card >}} + +{{< card >}} +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' +{{< /card >}} + +{{< card >}} +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' +{{< /card >}} + +{{< /grid >}} From 32edc931593b533899d1771afa831b9a021031b8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:52 +0100 Subject: [PATCH 581/711] New translations config.yaml (Japanese) --- content/ja/config.yaml | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 75b01e8105..ec90ad7023 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -50,26 +50,6 @@ params: img: /images/content_images/case_studies/deeplabcut.png alttext: チータの姿勢推定 url: /ja/case-studies/deeplabcut-dnn - keyfeatures: - features: - - - title: 強力な多次元配列 - text: 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。 - - - title: 数値計算ツール群 - text: 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。 - - - title: 相互運用性 - text: 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。 - - - title: 高パフォーマンス - text: 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。 - - - title: 使いやすさ - text: 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。 - - - title: オープンソース - text: 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. tabs: title: NumPyのエコシステム section5: false From b68ab0f95b7c0cc36448edf1e282456897d5fe0e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:53 +0100 Subject: [PATCH 582/711] New translations _index.md (Japanese) --- content/ja/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/ja/_index.md diff --git a/content/ja/_index.md b/content/ja/_index.md new file mode 100644 index 0000000000..01649c02d8 --- /dev/null +++ b/content/ja/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +{{< 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. +''' +{{< /card >}} + +{{< card >}} +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' +{{< /card >}} + +{{< 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). +''' +{{< /card >}} + +{{< card >}} +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' +{{< /card >}} + +{{< card >}} +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' +{{< /card >}} + +{{< card >}} +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' +{{< /card >}} + +{{< /grid >}} From 350698e58fb4def80cc2f34d5b1b261c397ed1ac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:53 +0100 Subject: [PATCH 583/711] New translations config.yaml (Korean) --- content/ko/config.yaml | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 35f1038767..9f0b9cfdc3 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -50,26 +50,6 @@ params: img: /images/content_images/case_studies/deeplabcut.png alttext: 치타 자세 분석 url: /ko/case-studies/deeplabcut-dnn - keyfeatures: - features: - - - title: 강력한 n차원 배열 - text: 빠르고 다재다능한 NumPy의 벡터화, 인덱싱, 전송 구성은 오늘날 배열 컴퓨팅의 사실상 표준입니다. - - - title: 수치적 컴퓨팅 도구 - text: NumPy는 포괄적인 수학 함수, 난수 생성기, 선형 대수 루틴, 푸리에 변환 등을 제공합니다. - - - title: 오픈소스 - text: 자유 [BSD 라이선스](https://github.com/numpy/numpy/blob/main/LICENSE.txt)에 따라, NumPy는 흥미에 찼으며, 반응이 빠르고, 다양성이 넘치는 [커뮤니티](/community)에 의하여 [GitHub](https://github.com/numpy/numpy)에서 공개적으로 개발되고 유지됩니다. - - - title: 상호운용성 - text: NumPy는 광범위한 하드웨어 및 컴퓨팅 플랫폼을 지원합니다. 또 분산형, GPU, 희소 배열 라이브러리와도 잘 작동합니다. - - - title: 고성능 - text: NumPy의 핵심은 최적화된 C 코드로 구성되어 있습니다. 컴파일된 코드의 속도와 함께 Python의 유연함을 즐기세요. - - - title: 쉬운 사용성 - text: NumPy의 고수준 문법은 어떤 배경이나 수준을 가지고 있는 프로그래머든 쉽게 접근하여 생산적인 일을 할 수 있도록 만들어줍니다. tabs: title: 생태계 section5: false From bed9d9298ba0b249e974c411d1a9d32aa96b79bf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:54 +0100 Subject: [PATCH 584/711] New translations _index.md (Korean) --- content/ko/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/ko/_index.md diff --git a/content/ko/_index.md b/content/ko/_index.md new file mode 100644 index 0000000000..01649c02d8 --- /dev/null +++ b/content/ko/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +{{< 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. +''' +{{< /card >}} + +{{< card >}} +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' +{{< /card >}} + +{{< 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). +''' +{{< /card >}} + +{{< card >}} +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' +{{< /card >}} + +{{< card >}} +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' +{{< /card >}} + +{{< card >}} +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' +{{< /card >}} + +{{< /grid >}} From bc4b5b77b38e6b1b79a9e4dfa4c92af2014eeb36 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:55 +0100 Subject: [PATCH 585/711] New translations config.yaml (Russian) --- content/ru/config.yaml | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/content/ru/config.yaml b/content/ru/config.yaml index 483f5a0c14..4aaf75b2e6 100644 --- a/content/ru/config.yaml +++ b/content/ru/config.yaml @@ -50,26 +50,6 @@ params: img: /images/content_images/case_studies/deeplabcut.png alttext: Cheetah pose analysis url: /case-studies/deeplabcut-dnn - keyfeatures: - features: - - - title: Powerful N-dimensional arrays - text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. - - - title: Numerical computing tools - text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. - - - title: Open source - text: 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). - - - title: Interoperable - text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. - - - title: Performant - text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. - - - title: Easy to use - text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. tabs: title: ECOSYSTEM section5: false From c06d7cfa5b576b44dcef28550c407c670ca0c51b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:56 +0100 Subject: [PATCH 586/711] New translations _index.md (Russian) --- content/ru/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/ru/_index.md diff --git a/content/ru/_index.md b/content/ru/_index.md new file mode 100644 index 0000000000..01649c02d8 --- /dev/null +++ b/content/ru/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +{{< 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. +''' +{{< /card >}} + +{{< card >}} +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' +{{< /card >}} + +{{< 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). +''' +{{< /card >}} + +{{< card >}} +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' +{{< /card >}} + +{{< card >}} +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' +{{< /card >}} + +{{< card >}} +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' +{{< /card >}} + +{{< /grid >}} From 1f16e7dad292b3c27dde292a288a34caf99ee202 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:57 +0100 Subject: [PATCH 587/711] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 483f5a0c14..4aaf75b2e6 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -50,26 +50,6 @@ params: img: /images/content_images/case_studies/deeplabcut.png alttext: Cheetah pose analysis url: /case-studies/deeplabcut-dnn - keyfeatures: - features: - - - title: Powerful N-dimensional arrays - text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. - - - title: Numerical computing tools - text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. - - - title: Open source - text: 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). - - - title: Interoperable - text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. - - - title: Performant - text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. - - - title: Easy to use - text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. tabs: title: ECOSYSTEM section5: false From 86bde1c3c3997a5e77fe1a4d5f9f7dfc09afa78c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:58 +0100 Subject: [PATCH 588/711] New translations _index.md (Chinese Simplified) --- content/zh/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/zh/_index.md diff --git a/content/zh/_index.md b/content/zh/_index.md new file mode 100644 index 0000000000..01649c02d8 --- /dev/null +++ b/content/zh/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +{{< 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. +''' +{{< /card >}} + +{{< card >}} +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' +{{< /card >}} + +{{< 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). +''' +{{< /card >}} + +{{< card >}} +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' +{{< /card >}} + +{{< card >}} +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' +{{< /card >}} + +{{< card >}} +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' +{{< /card >}} + +{{< /grid >}} From a236bf004c9a74d6f80b2854cda0db0f605df5b4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:56:59 +0100 Subject: [PATCH 589/711] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 3242697b5d..fadb422094 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -50,26 +50,6 @@ params: img: /images/content_images/case_studies/deeplabcut.png alttext: Análise de pose de um guepardo url: /pt/case-studies/deeplabcut-dnn - keyfeatures: - features: - - - title: Arrays n-dimensionais poderosas - text: Rápidos e versáteis, os conceitos de vetorização, indexação e broadcasting do NumPy são, na prática, o padrão em computação com arrays. - - - title: Ferramentas de computação numérica - text: O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais. - - - title: Interoperabilidade - text: 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. - - - title: Alto desempenho - text: O núcleo do NumPy é feito de código otimizado em C. Experimente a flexibilidade do Python com a velocidade de código compilado. - - - title: Fácil de usar - text: A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação. - - - title: Código aberto - text: 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. tabs: title: ECOSSISTEMA section5: false From c89a6abfa85746e8a94486fe92341e99d1b18946 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Feb 2024 23:57:00 +0100 Subject: [PATCH 590/711] New translations _index.md (Portuguese, Brazilian) --- content/pt/_index.md | 49 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 49 insertions(+) create mode 100644 content/pt/_index.md diff --git a/content/pt/_index.md b/content/pt/_index.md new file mode 100644 index 0000000000..01649c02d8 --- /dev/null +++ b/content/pt/_index.md @@ -0,0 +1,49 @@ +--- +title: null +--- + +{{< grid columns="1 2 2 3" >}} + +{{< 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. +''' +{{< /card >}} + +{{< card >}} +title = 'Numerical computing tools' +body = ''' +NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +''' +{{< /card >}} + +{{< 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). +''' +{{< /card >}} + +{{< card >}} +title = 'Interoperable' +body = ''' +NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +''' +{{< /card >}} + +{{< card >}} +title = 'Performant' +body = ''' +The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +''' +{{< /card >}} + +{{< card >}} +title = 'Easy to use' +body = ''' +NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +''' +{{< /card >}} + +{{< /grid >}} From ce07d8bf658beb5f60f2bacfdf898183cd592909 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Feb 2024 09:17:27 +0100 Subject: [PATCH 591/711] New translations _index.md (Portuguese, Brazilian) --- content/pt/_index.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/pt/_index.md b/content/pt/_index.md index 01649c02d8..76c4b7afd1 100644 --- a/content/pt/_index.md +++ b/content/pt/_index.md @@ -5,23 +5,23 @@ title: null {{< grid columns="1 2 2 3" >}} {{< card >}} -title = 'Powerful N-dimensional arrays' +title = 'Potentes arrays N-dimensionais.' body = ''' -Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +Rápido e versátil, os conceitos de vetorização, indexação e broadcasting do NumPy são os padrões de fato da computação de arrays hoje em dia. ''' {{< /card >}} {{< card >}} -title = 'Numerical computing tools' +title = 'Ferramentas numéricas de computação' body = ''' -NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +O NumPy oferece funções matemáticas abrangentes, geradores de números aleatórios, rotinas de álgebra linear, transformações de Fourier e muito mais. ''' {{< /card >}} {{< card >}} -title = 'Open source' +title = 'Código aberto' 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). +Distribuído sob uma licença liberal [BSD](https://github.com/numpy/numpy/blob/main/LICENSE.txt), o NumPy é desenvolvido e mantido [publicamente no GitHub](https://github.com/numpy/numpy) por uma [comunidade](/community) vibrante, responsiva e diversa. ''' {{< /card >}} @@ -35,7 +35,7 @@ NumPy supports a wide range of hardware and computing platforms, and plays well {{< card >}} title = 'Performant' body = ''' -The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +The core of NumPy is well-optimized C code. Aproveite a flexibilidade do Python com a velocidade de código compilado. ''' {{< /card >}} From 6ead5898298a4806f46266bf0610843f8e1115d1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Feb 2024 11:56:20 +0100 Subject: [PATCH 592/711] New translations _index.md (Portuguese, Brazilian) --- content/pt/_index.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/pt/_index.md b/content/pt/_index.md index 76c4b7afd1..96a58860fc 100644 --- a/content/pt/_index.md +++ b/content/pt/_index.md @@ -26,23 +26,23 @@ Distribuído sob uma licença liberal [BSD](https://github.com/numpy/numpy/blob/ {{< /card >}} {{< card >}} -title = 'Interoperable' +title = 'Interoperável' body = ''' -NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +O NumPy suporta uma ampla gama de plataformas de hardware e computação, e se integra bem com bibliotecas distribuídas, de GPU e de arrays esparsos. ''' {{< /card >}} {{< card >}} -title = 'Performant' +title = 'Eficiente' body = ''' -The core of NumPy is well-optimized C code. Aproveite a flexibilidade do Python com a velocidade de código compilado. +O núcleo do NumPy é composto por código C bem otimizado. Aproveite a flexibilidade do Python com a velocidade de código compilado. ''' {{< /card >}} {{< card >}} -title = 'Easy to use' +title = 'Fácil de usar' body = ''' -NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +A sintaxe de alto nível do NumPy o torna acessível e produtivo para programadores de qualquer formação ou nível de experiência. ''' {{< /card >}} From ccef798309aeb70079f1c17d8c6fef7b071c5b3a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Feb 2024 20:28:04 +0100 Subject: [PATCH 593/711] New translations news.md (Korean) --- content/ko/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index f31a383a45..c1ea37596e 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -230,9 +230,9 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 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 1.26.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2024년 1월 2일_. -- NumPy 1.26.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.26.3)) -- _2 Jan 2024_. -- NumPy 1.126.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.26.2)) -- _2023년 1월 2일_. +- 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_. From 1001c4c543bb802b637a5851621f25211668154b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 25 Feb 2024 07:27:57 +0100 Subject: [PATCH 594/711] New translations _index.md (Korean) --- content/ko/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/_index.md b/content/ko/_index.md index 01649c02d8..ef4502e751 100644 --- a/content/ko/_index.md +++ b/content/ko/_index.md @@ -5,9 +5,9 @@ title: null {{< grid columns="1 2 2 3" >}} {{< card >}} -title = 'Powerful N-dimensional arrays' +title = '강력한 N차원 배열' body = ''' -Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. +빠르고 다재다능한 NumPy의 벡터화, 인덱싱, 전송 구성은 오늘날 배열 컴퓨팅의 사실상 표준입니다. ''' {{< /card >}} From 0105a423862f28b8b76905119056807d8616770e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 25 Feb 2024 08:23:41 +0100 Subject: [PATCH 595/711] New translations _index.md (Korean) --- content/ko/_index.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ko/_index.md b/content/ko/_index.md index ef4502e751..0f06c2c787 100644 --- a/content/ko/_index.md +++ b/content/ko/_index.md @@ -12,37 +12,37 @@ body = ''' {{< /card >}} {{< card >}} -title = 'Numerical computing tools' +title = '수치적 컴퓨팅 도구' body = ''' -NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. +NumPy는 포괄적인 수학 함수, 난수 생성기, 선형 대수 루틴, 푸리에 변환 등을 제공합니다. ''' {{< /card >}} {{< card >}} -title = 'Open source' +title = '오픈 소스' 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). +자유 [BSD 라이선스](https://github.com/numpy/numpy/blob/main/LICENSE.txt)에 따라, NumPy는 흥미에 찼으며, 반응이 빠르고, 다양성이 넘치는 [커뮤니티](/community)에 의하여 [GitHub](https://github.com/numpy/numpy)에서 공개적으로 개발되고 유지됩니다. ''' {{< /card >}} {{< card >}} -title = 'Interoperable' +title = '상호운용성' body = ''' -NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. +NumPy는 광범위한 하드웨어 및 컴퓨팅 플랫폼을 지원합니다. 또 분산형, GPU, 희소 배열 라이브러리와도 잘 작동합니다. ''' {{< /card >}} {{< card >}} -title = 'Performant' +title = '효율성' body = ''' -The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. +NumPy의 핵심은 최적화된 C 코드로 구성되어 있습니다. 컴파일된 코드의 속도와 함께 Python의 유연함을 즐기세요. ''' {{< /card >}} {{< card >}} -title = 'Easy to use' +title = '쉬운 사용법' body = ''' -NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. +NumPy의 고수준 문법은 어떤 배경이나 수준을 가지고 있는 프로그래머든 쉽게 접근하여 생산적인 일을 할 수 있도록 만들어줍니다. ''' {{< /card >}} From 8e3486b7813d0d9dab71e49565bb8e99bda153a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Feb 2024 08:40:34 +0100 Subject: [PATCH 596/711] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index 28dc308ac0..5ee1f4bcbe 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -61,7 +61,7 @@ If your report involves any members of the Committee, or if they feel they have ### Incident reporting resolution & Code of Conduct enforcement -_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). @@ -72,7 +72,7 @@ In cases not involving clear severe and obvious breaches of this Code of Conduct 1. acknowledge report is received, 2. reasonable discussion/feedback, 3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), -4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. The Committee will respond to any report as soon as possible, and at most within 72 hours. From 1334840ef9df4875bdeddb919ffb0ce6de269e10 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Feb 2024 08:40:35 +0100 Subject: [PATCH 597/711] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index 28dc308ac0..5ee1f4bcbe 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -61,7 +61,7 @@ If your report involves any members of the Committee, or if they feel they have ### Incident reporting resolution & Code of Conduct enforcement -_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). @@ -72,7 +72,7 @@ In cases not involving clear severe and obvious breaches of this Code of Conduct 1. acknowledge report is received, 2. reasonable discussion/feedback, 3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), -4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. The Committee will respond to any report as soon as possible, and at most within 72 hours. From 59e1f28649f421f2cbd0b1ea4145a6d5524baf9d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Feb 2024 08:40:37 +0100 Subject: [PATCH 598/711] New translations code-of-conduct.md (Russian) --- content/ru/code-of-conduct.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ru/code-of-conduct.md b/content/ru/code-of-conduct.md index 28dc308ac0..5ee1f4bcbe 100644 --- a/content/ru/code-of-conduct.md +++ b/content/ru/code-of-conduct.md @@ -61,7 +61,7 @@ If your report involves any members of the Committee, or if they feel they have ### Incident reporting resolution & Code of Conduct enforcement -_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). @@ -72,7 +72,7 @@ In cases not involving clear severe and obvious breaches of this Code of Conduct 1. acknowledge report is received, 2. reasonable discussion/feedback, 3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), -4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. The Committee will respond to any report as soon as possible, and at most within 72 hours. From 65fc930256ed3e0d73f7e1434099937b6eb9a14e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Feb 2024 08:40:38 +0100 Subject: [PATCH 599/711] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index 28dc308ac0..5ee1f4bcbe 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -61,7 +61,7 @@ If your report involves any members of the Committee, or if they feel they have ### Incident reporting resolution & Code of Conduct enforcement -_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](/report-handling-manual). +_This section summarizes the most important points, more details can be found in_ [NumPy Code of Conduct - How to follow up on a report](report-handling-manual). We will investigate and respond to all complaints. The NumPy Code of Conduct Committee and the NumPy Steering Committee (if involved) will protect the identity of the reporter, and treat the content of complaints as confidential (unless the reporter agrees otherwise). @@ -72,7 +72,7 @@ In cases not involving clear severe and obvious breaches of this Code of Conduct 1. acknowledge report is received, 2. reasonable discussion/feedback, 3. mediation (if feedback didn’t help, and only if both reporter and reportee agree to this), -4. enforcement via transparent decision (see [Resolutions](/report-handling-manual/#resolutions)) by the Code of Conduct Committee. +4. enforcement via transparent decision (see [Resolutions](report-handling-manual/#resolutions)) by the Code of Conduct Committee. The Committee will respond to any report as soon as possible, and at most within 72 hours. From c0884f0eb9cb1045e204e2531e0b13e0610375d6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 12 Mar 2024 17:04:50 +0100 Subject: [PATCH 600/711] New translations _index.md (Spanish) --- content/es/_index.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/es/_index.md b/content/es/_index.md index 01649c02d8..7fecd5f039 100644 --- a/content/es/_index.md +++ b/content/es/_index.md @@ -2,48 +2,48 @@ title: null --- -{{< grid columns="1 2 2 3" >}} +{{< grid1 columns="1 2 2 3" >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[item]] +type = 'card' title = 'Numerical computing tools' body = ''' NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. ''' -{{< /card >}} -{{< card >}} +[[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). ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< /grid >}} +{{< /grid1>}} From afe57b5947644c666f12a3c27e08216051ed3e8b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 12 Mar 2024 17:05:12 +0100 Subject: [PATCH 601/711] New translations _index.md (Arabic) --- content/ar/_index.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/ar/_index.md b/content/ar/_index.md index 01649c02d8..7fecd5f039 100644 --- a/content/ar/_index.md +++ b/content/ar/_index.md @@ -2,48 +2,48 @@ title: null --- -{{< grid columns="1 2 2 3" >}} +{{< grid1 columns="1 2 2 3" >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[item]] +type = 'card' title = 'Numerical computing tools' body = ''' NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. ''' -{{< /card >}} -{{< card >}} +[[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). ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< /grid >}} +{{< /grid1>}} From 9b10dde65d8544f139cabd3dca5cd60395c208d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 12 Mar 2024 17:05:33 +0100 Subject: [PATCH 602/711] New translations _index.md (Japanese) --- content/ja/_index.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/ja/_index.md b/content/ja/_index.md index 01649c02d8..7fecd5f039 100644 --- a/content/ja/_index.md +++ b/content/ja/_index.md @@ -2,48 +2,48 @@ title: null --- -{{< grid columns="1 2 2 3" >}} +{{< grid1 columns="1 2 2 3" >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[item]] +type = 'card' title = 'Numerical computing tools' body = ''' NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. ''' -{{< /card >}} -{{< card >}} +[[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). ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< /grid >}} +{{< /grid1>}} From 1cd8a42729248a5e011fda5b24aa1c8cef56df45 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 12 Mar 2024 17:06:24 +0100 Subject: [PATCH 603/711] New translations _index.md (Russian) --- content/ru/_index.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/ru/_index.md b/content/ru/_index.md index 01649c02d8..7fecd5f039 100644 --- a/content/ru/_index.md +++ b/content/ru/_index.md @@ -2,48 +2,48 @@ title: null --- -{{< grid columns="1 2 2 3" >}} +{{< grid1 columns="1 2 2 3" >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[item]] +type = 'card' title = 'Numerical computing tools' body = ''' NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. ''' -{{< /card >}} -{{< card >}} +[[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). ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< /grid >}} +{{< /grid1>}} From 141b7e99c77941b386a4db12d5a6e1f4eacbb5e0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 12 Mar 2024 17:06:44 +0100 Subject: [PATCH 604/711] New translations _index.md (Chinese Simplified) --- content/zh/_index.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/zh/_index.md b/content/zh/_index.md index 01649c02d8..7fecd5f039 100644 --- a/content/zh/_index.md +++ b/content/zh/_index.md @@ -2,48 +2,48 @@ title: null --- -{{< grid columns="1 2 2 3" >}} +{{< grid1 columns="1 2 2 3" >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[item]] +type = 'card' title = 'Numerical computing tools' body = ''' NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. ''' -{{< /card >}} -{{< card >}} +[[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). ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< card >}} +[[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. ''' -{{< /card >}} -{{< /grid >}} +{{< /grid1>}} From 76624bd9eeeb368b3d8516d64c90ffc3d765eec6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 16 Mar 2024 21:29:18 +0100 Subject: [PATCH 605/711] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index ba8c558e30..0c6deac529 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -3,7 +3,7 @@ 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. From 036f2bb14977354638869b0eb9881bb14c7eb57d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 16 Mar 2024 21:29:19 +0100 Subject: [PATCH 606/711] New translations user-survey-2020.md (Arabic) --- content/ar/user-survey-2020.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/user-survey-2020.md b/content/ar/user-survey-2020.md index fe431e845c..ce2decb371 100644 --- a/content/ar/user-survey-2020.md +++ b/content/ar/user-survey-2020.md @@ -5,7 +5,7 @@ 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. From 1f544f7a772cbd82d1df72edb91ed5759269e637 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 16 Mar 2024 21:29:20 +0100 Subject: [PATCH 607/711] New translations deeplabcut-dnn.md (Arabic) --- content/ar/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/case-studies/deeplabcut-dnn.md b/content/ar/case-studies/deeplabcut-dnn.md index ba8c558e30..0c6deac529 100644 --- a/content/ar/case-studies/deeplabcut-dnn.md +++ b/content/ar/case-studies/deeplabcut-dnn.md @@ -3,7 +3,7 @@ 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. From fb1d0f606218b1a3e52b88996a6f67ee01dade20 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 16 Mar 2024 21:29:23 +0100 Subject: [PATCH 608/711] New translations user-survey-2020.md (Russian) --- content/ru/user-survey-2020.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/user-survey-2020.md b/content/ru/user-survey-2020.md index fe431e845c..ce2decb371 100644 --- a/content/ru/user-survey-2020.md +++ b/content/ru/user-survey-2020.md @@ -5,7 +5,7 @@ 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. From 982414b5f36663ce08c33d0d241857a1d415aa84 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 16 Mar 2024 21:29:24 +0100 Subject: [PATCH 609/711] New translations deeplabcut-dnn.md (Russian) --- content/ru/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/case-studies/deeplabcut-dnn.md b/content/ru/case-studies/deeplabcut-dnn.md index ba8c558e30..0c6deac529 100644 --- a/content/ru/case-studies/deeplabcut-dnn.md +++ b/content/ru/case-studies/deeplabcut-dnn.md @@ -3,7 +3,7 @@ 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. From 14bf20a2a6990d12e0dc0f4a1fa752509fb29908 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 16 Mar 2024 21:29:25 +0100 Subject: [PATCH 610/711] New translations user-survey-2020.md (Chinese Simplified) --- content/zh/user-survey-2020.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/user-survey-2020.md b/content/zh/user-survey-2020.md index fe431e845c..ce2decb371 100644 --- a/content/zh/user-survey-2020.md +++ b/content/zh/user-survey-2020.md @@ -5,7 +5,7 @@ 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. From c981a5360459bed1db36ab58a90e42be4a16e2fc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 16 Mar 2024 21:29:26 +0100 Subject: [PATCH 611/711] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index ba8c558e30..0c6deac529 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -3,7 +3,7 @@ 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. From da357d81df9466dd1846ab173d23f4d9b2cdb8db Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Mar 2024 11:09:38 +0100 Subject: [PATCH 612/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 140b663144..1ec8e9483a 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -147,6 +147,9 @@ params: - url: https://python-control.org/ label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy - title: Image Processing alttext: An photograph of the mountains. From 7872ea90ae87c00c9b8e58a1c36dd18b21386fee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Mar 2024 11:09:39 +0100 Subject: [PATCH 613/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index f9d19bcf5c..04864bf49e 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -34,7 +34,7 @@ params: 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 @@ -147,6 +147,9 @@ params: - url: https://python-control.org/ label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy - title: Image Processing alttext: An photograph of the mountains. From 268c0e1d2cc673dd337d10747fd3d66cb48024f5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Mar 2024 11:09:40 +0100 Subject: [PATCH 614/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 848d1a8806..22b50fd0db 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -147,6 +147,9 @@ params: - 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 From fb2ddcedd16082869b42f675163098a9edf743bc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Mar 2024 11:09:41 +0100 Subject: [PATCH 615/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index b3062e58b0..c861dd5acb 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -147,6 +147,9 @@ params: - url: https://python-control.org/ label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy - title: 이미지 처리 alttext: 산이 찍힌 사진 From bc097b11b187c99b38f02a9d594c6c57e317a52f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Mar 2024 11:09:41 +0100 Subject: [PATCH 616/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index f9d19bcf5c..04864bf49e 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -34,7 +34,7 @@ params: 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 @@ -147,6 +147,9 @@ params: - url: https://python-control.org/ label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy - title: Image Processing alttext: An photograph of the mountains. From 9e4ab0a60c14bfd27ba04a911b45e538e15c2a50 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Mar 2024 11:09:42 +0100 Subject: [PATCH 617/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index f9d19bcf5c..04864bf49e 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -34,7 +34,7 @@ params: 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 @@ -147,6 +147,9 @@ params: - url: https://python-control.org/ label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy - title: Image Processing alttext: An photograph of the mountains. From 17c326a8b56bebe8afdb68b68fe796d03cb2bcab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 19 Mar 2024 11:09:43 +0100 Subject: [PATCH 618/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 338e49061a..0b5f303c91 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -147,6 +147,9 @@ params: - url: https://python-control.org/ label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy - title: Processamento de imagens alttext: Uma fotografia das montanhas. From 54acd520bf531d3d4b1b25bc30d1c1126141a46c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:23 +0100 Subject: [PATCH 619/711] New translations user-survey-2020.md (Spanish) --- content/es/user-survey-2020.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md index aaf35e521a..d6d502b29a 100644 --- a/content/es/user-survey-2020.md +++ b/content/es/user-survey-2020.md @@ -6,6 +6,8 @@ sidebar: false En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto. {{< 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">}} +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 >}} **[Descarga el informe](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver a detalle los resultados de la encuesta. From e259dc3a1d0a75ec699ba0d0cca39e0f20ad105e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:24 +0100 Subject: [PATCH 620/711] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index d7d45436e6..0087c7b632 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -4,6 +4,8 @@ sidebar: false --- {{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Agujero Negro M87**" alt="imagen de un agujero negro" attr="*(Créditos de la imagen: Colaboración del Telescopio de Horizonte de Sucesos)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +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. @@ -36,7 +38,9 @@ El [ telescopio de Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.or 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" class="csfigcaption" caption="**Flujo del Procesamiento de Datos del EHT**" alt="flujo de datos" align="middle" attr="(Créditos del 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 = '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 src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="el rol de numpy" caption="**El rol de NumPy en la fotografía del Agujero Negro**" >}} ## El Rol de NumPy @@ -46,11 +50,15 @@ La colaboración del EHT respondió a estos desafíos haciendo que los equipos i 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" class="fig-center" alt="el rol de numpy" caption="**El rol de NumPy en la fotografía del Agujero Negro**" >}} +{{< figure >}} +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="ventajas de numpy" caption="**Capacidades Clave utilizadas de NumPy**" >}} +{{< /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" class="fig-center" alt="mapa de dependencia de ehtim con numpy resaltado" caption="**Gráfico de dependencias de software del paquete ehtim resaltando a 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 >}} 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. @@ -58,7 +66,9 @@ Además de NumPy, muchos otros paquetes, como [SciPy](https://www.scipy.org) y [ La matriz n-dimensional eficiente y adaptable 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_bh_benefits.png" class="fig-center" alt="ventajas de numpy" caption="**Capacidades Clave utilizadas de NumPy**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 03b36a406ebd2f7b9a4313162492cc1d45c8a36d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:25 +0100 Subject: [PATCH 621/711] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index 993e19e1cb..d0be271f50 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -3,7 +3,9 @@ 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. @@ -25,7 +27,9 @@ Today, there are rich and almost infinite troves of cricket game records and sta * 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 @@ -33,7 +37,9 @@ Today, there are rich and almost infinite troves of cricket game records and sta * 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 @@ -61,4 +67,6 @@ Sports Analytics is a thriving field. Many researchers and companies [use NumPy] 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 >}} From a43f73ddcfbaeb2e5d992f99e61ea22faa21b353 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:26 +0100 Subject: [PATCH 622/711] New translations deeplabcut-dnn.md (Spanish) --- content/es/case-studies/deeplabcut-dnn.md | 24 +++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 0c6deac529..257905de91 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -3,7 +3,9 @@ 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. @@ -16,7 +18,9 @@ sidebar: false 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. @@ -45,7 +49,9 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) - 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 >}} ### The Challenges @@ -61,7 +67,9 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) 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 @@ -77,13 +85,17 @@ The following features of NumPy played a key role in addressing the image proces 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 >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From ef415e2848e601541c0ff618871e820bd3917a5b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:27 +0100 Subject: [PATCH 623/711] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 62ce66ca39..08f54b7547 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -3,7 +3,9 @@ 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 >}} @@ -37,7 +39,9 @@ The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.lig 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 @@ -54,14 +58,20 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * 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 >}} From 288ff03bf0c45a60effcf7687d6e34fcff6cd3fa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:28 +0100 Subject: [PATCH 624/711] New translations _index.md (Spanish) --- content/es/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/_index.md b/content/es/_index.md index 7fecd5f039..be88f9e642 100644 --- a/content/es/_index.md +++ b/content/es/_index.md @@ -2,7 +2,7 @@ title: null --- -{{< grid1 columns="1 2 2 3" >}} +{{< grid columns="1 2 2 3" >}} [[item]] type = 'card' @@ -46,4 +46,4 @@ body = ''' NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. ''' -{{< /grid1>}} +{{< /grid>}} From 904d5b9c3117e4ef230314454f3299d1d01ebe61 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:29 +0100 Subject: [PATCH 625/711] New translations user-survey-2020.md (Arabic) --- content/ar/user-survey-2020.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/content/ar/user-survey-2020.md b/content/ar/user-survey-2020.md index ce2decb371..b4349bcb7d 100644 --- a/content/ar/user-survey-2020.md +++ b/content/ar/user-survey-2020.md @@ -5,7 +5,9 @@ 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. From 46b204c033484143f1864187696166fdfa56eb5c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:30 +0100 Subject: [PATCH 626/711] New translations blackhole-image.md (Arabic) --- content/ar/case-studies/blackhole-image.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/ar/case-studies/blackhole-image.md b/content/ar/case-studies/blackhole-image.md index 856564a5fb..22334ebed0 100644 --- a/content/ar/case-studies/blackhole-image.md +++ b/content/ar/case-studies/blackhole-image.md @@ -3,7 +3,9 @@ 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. @@ -36,7 +38,9 @@ The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an arr 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 @@ -46,11 +50,15 @@ The EHT collaboration met these challenges by having independent teams evaluate 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 >}} 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. @@ -58,7 +66,9 @@ Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [ 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 >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From d5b4202bcd0497ca2226558a181ad5526072c4b1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:31 +0100 Subject: [PATCH 627/711] New translations cricket-analytics.md (Arabic) --- content/ar/case-studies/cricket-analytics.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/content/ar/case-studies/cricket-analytics.md b/content/ar/case-studies/cricket-analytics.md index 993e19e1cb..d0be271f50 100644 --- a/content/ar/case-studies/cricket-analytics.md +++ b/content/ar/case-studies/cricket-analytics.md @@ -3,7 +3,9 @@ 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. @@ -25,7 +27,9 @@ Today, there are rich and almost infinite troves of cricket game records and sta * 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 @@ -33,7 +37,9 @@ Today, there are rich and almost infinite troves of cricket game records and sta * 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 @@ -61,4 +67,6 @@ Sports Analytics is a thriving field. Many researchers and companies [use NumPy] 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 >}} From 0da89fde6eeec32a1a0af6c314912c0e86e62c1e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:32 +0100 Subject: [PATCH 628/711] New translations deeplabcut-dnn.md (Arabic) --- content/ar/case-studies/deeplabcut-dnn.md | 24 +++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/content/ar/case-studies/deeplabcut-dnn.md b/content/ar/case-studies/deeplabcut-dnn.md index 0c6deac529..257905de91 100644 --- a/content/ar/case-studies/deeplabcut-dnn.md +++ b/content/ar/case-studies/deeplabcut-dnn.md @@ -3,7 +3,9 @@ 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. @@ -16,7 +18,9 @@ sidebar: false 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. @@ -45,7 +49,9 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) - 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 >}} ### The Challenges @@ -61,7 +67,9 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) 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 @@ -77,13 +85,17 @@ The following features of NumPy played a key role in addressing the image proces 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 >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 766141d03ecb88032b898805d1d0cb269c63875d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:33 +0100 Subject: [PATCH 629/711] New translations gw-discov.md (Arabic) --- content/ar/case-studies/gw-discov.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/ar/case-studies/gw-discov.md b/content/ar/case-studies/gw-discov.md index 62ce66ca39..08f54b7547 100644 --- a/content/ar/case-studies/gw-discov.md +++ b/content/ar/case-studies/gw-discov.md @@ -3,7 +3,9 @@ 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 >}} @@ -37,7 +39,9 @@ The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.lig 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 @@ -54,14 +58,20 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * 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 >}} From 36b0534e24bf496daa676271c6418a9d3ea81183 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:34 +0100 Subject: [PATCH 630/711] New translations _index.md (Arabic) --- content/ar/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/_index.md b/content/ar/_index.md index 7fecd5f039..be88f9e642 100644 --- a/content/ar/_index.md +++ b/content/ar/_index.md @@ -2,7 +2,7 @@ title: null --- -{{< grid1 columns="1 2 2 3" >}} +{{< grid columns="1 2 2 3" >}} [[item]] type = 'card' @@ -46,4 +46,4 @@ body = ''' NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. ''' -{{< /grid1>}} +{{< /grid>}} From f2ec931cf70e72831c8e84886e8ab4d72c40ffc0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:35 +0100 Subject: [PATCH 631/711] New translations user-survey-2020.md (Japanese) --- content/ja/user-survey-2020.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md index 370138d6e7..a6533d3cb8 100644 --- a/content/ja/user-survey-2020.md +++ b/content/ja/user-survey-2020.md @@ -6,6 +6,8 @@ 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">}} +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 >}} 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。 From 4b8f61468557e44efb02dc6a2da1092b47f4a787 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:36 +0100 Subject: [PATCH 632/711] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 7b32fb30d4..f435ef2b2b 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -4,6 +4,8 @@ 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" >}} +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, * カリフォルニア工科大学の計算数理科学の助教授 *" > }} 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。 @@ -36,7 +38,9 @@ sidebar: false 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 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 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**" >}} ## NumPyが果たした役割 @@ -46,11 +50,15 @@ EHTの共同研究では、最先端の画像再構成技術を使用して、 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 >}} +{{< 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 >}} 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 = 'Software dependency chart of ehtim package highlighting NumPy' +{{< /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。 @@ -58,7 +66,9 @@ NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas. 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 = 'Key NumPy Capabilities utilized' +{{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 53eb5b5af560c27691f57ca11bae909bda286f04 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:37 +0100 Subject: [PATCH 633/711] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index ee0c62f285..2e366ad8d1 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -4,6 +4,8 @@ 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" >}} +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, *世界的なクリケットプレイヤーであり、前世界王者、インドチームで活躍し、IPLにおけるチェンナイスーパー王者となった*" > }} 観客のために競技しているのではなく、国のために競技しているのです。 @@ -25,7 +27,9 @@ sidebar: false * 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 = '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 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 **" >}} ### データ分析の主要な目標 @@ -33,7 +37,9 @@ sidebar: false * 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 = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} ### 課題 @@ -61,4 +67,6 @@ sidebar: false 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 = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From d9c354316c306fdb75fe728cb607d7e6aa967539 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:38 +0100 Subject: [PATCH 634/711] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index aee51e07ba..d5db28843a 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -4,6 +4,8 @@ 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">}} +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, * ローザンヌ連邦工科大学([EPFL](https://www.epfl.ch/en/))の助教授 *" > }} 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。 @@ -16,7 +18,9 @@ sidebar: false 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 = 'Colored dots track the positions of a racehorse’s body part' 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。 @@ -45,7 +49,9 @@ DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術 - 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 >}} +{{< 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 >}} ### 課題 @@ -61,7 +67,9 @@ DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術 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/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**NumPyの主要機能**" >}} +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 >}} ## 姿勢推定の課題に対応するためのNumPyの役割 @@ -77,13 +85,17 @@ NumPy は DeepLabCutにおける、行動分析の高速化のための数値計 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 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 >}} ## 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 = 'Key NumPy Capabilities utilized' +{{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 3b6fe7bb4a1a5e602fc5ca32a1cdd8c9160a2047 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:39 +0100 Subject: [PATCH 635/711] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 781c17a2c4..e7faa49de9 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -4,6 +4,8 @@ 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" >}} +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の科学調査協力により*" >}} LIGOで行われる研究には、科学的なPythonエコシステムが重要なインフラとなっています。 {{< /blockquote >}} @@ -37,7 +39,9 @@ sidebar: false 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="(**図のクレジット:** 「バイナリーブラックホール合成からの重力波の観測」、ResearchGateにて発表。)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}} +{{< figure >}} +{{< 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/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} ## 重力波の検出におけるNumPyの役割 @@ -54,14 +58,20 @@ Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波 * 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 = 'Dependency graph showing how GwPy package depends on NumPy' +{{< /figure >}} ---- -{{< 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/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Dependency graph showing how PyCBC package depends on NumPy' +{{< /figure >}} ## まとめ GW検出により、研究者は完全に予期せぬ現象 を発見することができ、同時に知られている最も深遠な天体物理学 現象の多くに新たな洞察を提供しています。 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、研究者が複雑な現象の疑問に答え、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_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 7d8d3dd2e220d42e8b29162ffa855166e133efc7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:40 +0100 Subject: [PATCH 636/711] New translations _index.md (Japanese) --- content/ja/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/_index.md b/content/ja/_index.md index 7fecd5f039..be88f9e642 100644 --- a/content/ja/_index.md +++ b/content/ja/_index.md @@ -2,7 +2,7 @@ title: null --- -{{< grid1 columns="1 2 2 3" >}} +{{< grid columns="1 2 2 3" >}} [[item]] type = 'card' @@ -46,4 +46,4 @@ body = ''' NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. ''' -{{< /grid1>}} +{{< /grid>}} From d51f5dad1d7f36415e43c975c211eedc7b041c63 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:41 +0100 Subject: [PATCH 637/711] New translations user-survey-2020.md (Korean) --- content/ko/user-survey-2020.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/ko/user-survey-2020.md b/content/ko/user-survey-2020.md index 902100702b..75e7d93a77 100644 --- a/content/ko/user-survey-2020.md +++ b/content/ko/user-survey-2020.md @@ -6,6 +6,8 @@ sidebar: false 2020년, NumPy 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. 75개국 내 1200명 이상의 사용자 여러분들께서 저희가 NumPy 커뮤니티의 가닥을 잡을 수 있도록 도와주기 위해 참여해주셨으며 프로젝트의 미래에 대한 생각을 표현해주셨습니다. {{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="'NumPy Community Survey 2020 - results'라는 제목이 붙은 2020년 NumPy 사용자 설문조사 보고서 표지" width="250">}} +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 >}} **[보고서를 내려받아서](/surveys/NumPy_usersurvey_2020_report.pdf)** 설문조사 결과를 자세히 들여다 보세요. From 198d1e6ff5b87499cdb6b500e4319bdd613dc8ae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:42 +0100 Subject: [PATCH 638/711] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index 74d36af6bc..ac16cf6dc8 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -4,6 +4,8 @@ sidebar: false --- {{< figure src="/images/content_images/cs/blackhole.jpg" caption="**블랙홀 M87**" alt="블랙홀 사진" attr="*(사진 크레딧: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +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. @@ -36,7 +38,9 @@ sidebar: false 만약 목표가 이전에 본 적이 없는 것을 보는 것이라면, 과학자들은 어떻게 이 사진이 옳다고 입증할 수 있을까요? -{{< figure src="/images/content_images/cs/dataprocessbh.png" class="csfigcaption" caption="**EHT 데이터 처리 파이프라인**" alt="데이터 파이프라인" align="middle" attr="(다이어그램 크레딧: 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 src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="numpy의 역할" caption="**블랙홀 시각화에서 NumPy의 역할**" >}} ## NumPy의 역할 @@ -46,11 +50,15 @@ EHT는 기존 및 최첨된 이미지 재구성 기술을 모두 사용한 뒤, 그들의 연구는 협업 데이터 분석을 통해 과학을 발전시키는 과학적인 Python 생태계의 역할을 보여줍니다. -{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="numpy의 역할" caption="**블랙홀 시각화에서 NumPy의 역할**" >}} +{{< figure >}} +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} +{{< /figure >}} 예를 들어, [`eht-imaging`][ehtim] Python 패키지는 VLBI 데이터를 통해 실험이나 이미지 재구성을 수행할 때 필요한 도구를 제공합니다. NumPy는 아래 소프트웨어 종속성 차트에 나와 있는 것처럼 이 패키지에서 사용되는 배열 데이터 처리의 핵심 역할을 합니다. -{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="numpy를 강조하는 ehtim의 종속성 맵" caption="**NumPy를 강조하는 ehtim 패키지의 소프트웨어 종속성 차트**" >}} +{{< 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 >}} NumPy 외에도 [SciPy](https://www.scipy.org)와 [Pandas](https://pandas.io) 등의 다른 많은 패키지가 블랙홀을 시각화하는 데이터 처리 파이프라인의 일부입니다. 표준 천문 파일 형식과 시간/좌표 변환에는 [Astropy][astropy]가 쓰였고 [Matplotlib][mpl]는 분석 과정 전체에서 블랙홀의 최종 사진을 생성하는 등 데이터를 시각화하는 데 쓰였습니다. @@ -58,7 +66,9 @@ NumPy 외에도 [SciPy](https://www.scipy.org)와 [Pandas](https://pandas.io) NumPy의 핵심 기능인 효율적이고 유용한 n차원 배열은 연구자들이 대규모 수치 데이터셋을 다룰 수 있도록 하여 최초의 블랙홀 사진을 만드는 데 토대를 제공했습니다. 이번 관측은 아인슈타인의 이론에 훌륭한 시각적 증거를 준 관측으로, 과학계에 한 획을 그은 순간이었습니다. 기술적 혁신뿐만 아니라 200명 이상의 과학자와 세계 최고의 전파 관측소 간의 국제 협력도 이루어 냈습니다. 기존의 천문학 모델을 개선한 혁신적인 알고리즘과 데이터 처리 기술이 우주의 비밀을 알아내는 데 도움을 주었습니다. -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 47ca6df1d5a8d06c98caaea3a51feb81ab39f9be Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:43 +0100 Subject: [PATCH 639/711] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index be0007eefd..cfd1f993e6 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -4,6 +4,8 @@ sidebar: false --- {{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**인도 최대의 크리켓 축제인 IPLT20**" alt="인도 프리미어 리그 크리켓 컵 및 경기장" attr="*(사진 출처: IPLT20 (컵 및 로고) & Akash Yadav (경기장))*" attrlink="https://unsplash.com/@aksh1802" >}} +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. @@ -25,7 +27,9 @@ sidebar: false * 다른 상대에 맞서 선수의 체력과 경기력에 대한 통찰력을 얻기, * 팀 구성에 대한 전략적 결정을 내리는 플레이어의 승패 기여도 -{{< figure src="/images/content_images/cs/cricket-pitch.png" class="csfigcaption" caption="**경기장의 중심이 되는 크리켓 피치**" alt="볼러와 배트맨으로 이루어진 크리켓 피치" align="middle" attr="*(사진 출처: 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 src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="NumPy를 크리켓 분석에 사용했을 때의 이익을 보여주는 다이어그램" caption="**활용된 주요 NumPy 기능**" >}} ### 데이터 분석의 주요 목표 @@ -33,7 +37,9 @@ sidebar: false * 실시간 데이터 분석은 경기 중에도 팀과 관련 사업의 변화하는 전략에 대한 통찰력을 확보하여 경제적 이익과 성장을 도모하는 데 도움이 될 수 있습니다. * 과거 분석 외에도 예측 모델을 활용하여 상당한 수의 크런칭과 데이터 과학 노하우, 시각화 도구 및 분석에 더 새로운 관찰을 포함시킬 수 있는 기능이 필요한 가능한 일치 결과를 결정합니다. -{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="자세 예측" caption="**크리켓 자세 예측**" attr="*(사진 출처: 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 >}} ### 도전 @@ -61,4 +67,6 @@ sidebar: false 스포츠 분석은 프로 게임의 판도를 바꿀 것입니다. 특히 최근까지는 주로 "직감"이나 과거부터 내려오던 것을 답습하는 식으로 이뤄진 전략적 의사 결정에 대해서 말입니다. NumPy는 데이터 분석, 기계 학습 및 AI 알고리즘과 관련하여 더욱 높은 수준의 기능을 제공하는 Python 패키지들의 견고한 기반을 제공합니다. 이들 패키지는 크리켓 경기뿐 아니라 크리켓 관련 추론이나 사업을 추진하면서, 판도를 바꿀만한 결정을 이끌어 내는 영감을 실시간으로 제공하는 데 널리 이용되고 있습니다. 크리켓 경기의 결과로 이어지는 숨겨진 매개변수, 패턴이나 속성을 찾는 것은 관계자가 숫자와 통계에 숨겨진 게임을 분석하는 방법을 파악하는 데 도움이 됩니다. -{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="NumPy를 크리켓 분석에 사용했을 때의 이익을 보여주는 다이어그램" caption="**활용된 주요 NumPy 기능**" >}} +{{< 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 >}} From a14439a9e1eb1b308a72c6e34a5fe30abe0c020e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:44 +0100 Subject: [PATCH 640/711] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md index 904b1fe4ad..86a92b4bc5 100644 --- a/content/ko/case-studies/deeplabcut-dnn.md +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -4,6 +4,8 @@ sidebar: false --- {{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCut을 활용한 쥐의 손 움직임 분석**" alt="쥐 손 애니메이션" attr="*(출처: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} +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/))" > }} 오픈소스 소프트웨어는 생물 의학의 발전을 가속화하고 있습니다. DeepLabCut은 딥 러닝을 사용하여 동물 행동에 대한 자동화된 비디오 분석을 가능하게 합니다. @@ -16,7 +18,9 @@ sidebar: false 신경 과학, 의학 및 생체 역학을 포함한 여러 연구 분야에서 동물의 움직임을 추적한 데이터를 사용합니다. DeepLabCut은 필름에 기록된 동작을 구문 분석하여 인간과 다른 동물이 하는 일을 이해하는 데 도움을 줍니다. 심층 신경망 기반 데이터 분석과 함께 태깅 및 모니터링의 힘든 작업에 자동화를 사용하는 DeepLabCut은 영장류, 생쥐, 물고기, 파리 등과 같은 동물 관찰과 관련된 과학적 연구를 훨씬 빠르고 정확하게 만듭니다. -{{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**경주마 신체 부위의 위치를 트래킹하는 색 점**" alt="경주마 애니메이션" attr="*(출처: 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 >}} DeeDeepLabCut의 동물 자세 추출을 통한 동물의 비침습적 행동 추적은 생체 역학, 유전학, 행동학 & 신경 과학과 같은 영역에서 과학적 추구에 매우 중요합니다. 동적으로 변화하는 배경에서 마커 없이 비디오에서 동물 포즈를 비침습적으로 측정하는 것은 기술적으로 뿐만 아니라 필요한 리소스 요구 사항 및 필요한 훈련 데이터 측면에서 계산적으로 어려운 일입니다. @@ -45,7 +49,9 @@ DeepLabCut은 [전이 학습](https://arxiv.org/pdf/1909.11229), 이라는 원 - 동영상에 대한 대규모 추론을 위한 코드 - 통합 시각화 도구를 사용하여 추론 도출 -{{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**포즈 추정 단계 - DeepLabCut**" alt="DLC 단계" align="middle" attr="(출처: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} +{{< figure >}} +{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCut 워크플로우**" alt="워크플로우" attr="*(출처: 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 >}} ### 도전 @@ -61,7 +67,9 @@ DeepLabCut은 [전이 학습](https://arxiv.org/pdf/1909.11229), 이라는 원 마지막으로 배열 조작 - 다양한 이미지, 대상 텐서 및 키포인트에 해당하는 대규모 배열 스택을 처리하는 것은 상당히 어렵습니다. -{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**포즈 추정 변수 및 복잡도**" alt="난점 설명" align="middle" attr="(출처: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} +{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용한 주요 NumPy 기능**" >}} +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 >}} ## 포즈 추정 문제를 해결하는 NumPy의 역할 @@ -77,13 +85,17 @@ NumPy의 다음 기능은 이미지 처리, 조합 요구 사항 및 DeepLabCut DeepLabCut은 툴킷에서 제공하는 워크플로 전체에서 NumPy의 어레이 기능을 활용합니다. 특히, NumPy는 사람의 주석 레이블 지정을 위한 개별 프레임 샘플링과 주석 데이터 작성, 편집 및 처리에 사용됩니다. TensorFlow 내에서 신경망은 DeepLabCut 기술로 수천 번의 반복을 통해 훈련되어 프레임에서 실측 주석을 예측합니다. 이를 위해 이미지 대 이미지 변환 문제로 포즈 추정을 캐스팅하기 위해 목표 밀도(점수 지도)가 생성됩니다. 신경망을 강력하게 만들기 위해 다양한 기하 및 이미지 처리 단계에 따라 대상 스코어맵을 계산해야 하는 데이터 확대가 사용됩니다. 학습을 빠르게 하기 위해 NumPy의 벡터화 기능이 활용됩니다. 추론을 위해 대상 스코어맵에서 가장 가능성이 높은 예측을 추출해야 하며 효율적으로 "개별 동물을 조립하기 위해 예측을 연결"해야 합니다. -{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCut 워크플로우**" alt="워크플로우" attr="*(출처: 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 >}} ## 요약 행동을 관찰하고 효율적으로 설명하는 것은 현대 행동학, 신경과학, 의학 및 기술의 핵심 테넌트입니다. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf)을 사용하면 연구자가 피험자의 자세를 추정하여 행동을 정량화할 수 있습니다. DeepLabCut Python 툴킷은 작은 훈련 이미지 세트만으로 인간 수준의 라벨링 정확도 내에서 신경망을 훈련할 수 있습니다. 따라서 실험실에서의 행동 분석뿐만 아니라 잠재적으로 스포츠, 보행 분석, 의학 및 재활 연구에도 응용 분야를 확장합니다. DeepLabCut 알고리즘이 직면한 복잡한 조합, 데이터 처리 문제는 NumPy의 배열 조작 기능을 사용하여 해결됩니다. -{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용한 주요 NumPy 기능**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 516b0d6cb83f78ced0b7a9e29614543f80ee0782 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:44 +0100 Subject: [PATCH 641/711] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index d5888d6225..e91bcec2e9 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -4,6 +4,8 @@ sidebar: false --- {{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**중력파**" alt="이항 결합하며 중력파를 생성하는 블랙홀" attr="*(사진 크레딧: LIGO의 Simulating eXtreme Spacetimes (SXS) 프로젝트)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} +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 >}} @@ -37,7 +39,9 @@ sidebar: false 아인슈타인의 방정식을 슈퍼컴퓨터를 사용하여 풀 수 있을 만큼 충분히 이해하는 것과 관련된 장애물이 처리되면 다음 큰 과제는 데이터를 인간의 두뇌가 이해할 수 있도록 만드는 것입니다. 시뮬레이션 모델링과 신호 감지에는 효과적인 시각화 기술이 필요합니다. 시각화는 또한 이미징 및 시뮬레이션을 통해 더 많은 청중이 결과를 더 쉽게 이해할 수 있게 될 때까지 수치 상대성에 충분한 중요성을 부여하지 않은 순수 과학 애호가의 눈에 수치 상대성에 더 많은 신뢰성을 제공하는 역할을 합니다. 최신 실험 입력 및 통찰력을 사용하여 복잡한 계산 및 렌더링, 이미지 다시 렌더링 및 시뮬레이션의 속도는 이 영역의 연구자에게 도전이 되는 시간 소모적인 활동이 될 수 있습니다. -{{< 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 >}} +{{< 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/numpy_gw_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} ## 중력파 검출에서 NumPy의 역할 @@ -54,14 +58,20 @@ Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝 * 상관 분석 연산 * [GwPy](https://gwpy.github.io/docs/stable/overview.html) 및 [PyCBC](https://pycbc.org)와 같은 중력파 데이터 분석에서 개발된 주요 [소프트웨어](https://github.com/lscsoft)는 후드 아래에서 NumPy 및 AstroPy를 사용하여 중력파 검출기에서 데이터를 연구하기 위한 유틸리티, 도구 및 방법에 객체 기반 인터페이스를 제공합니다. -{{< 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/numpy_gw_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} +{{< 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 >}} ## 요약 중력파 검출을 통하여 연구자들은 완전히 예상치 못한 현상을 발견하게 됨으로써, 알려진 것 중 가장 난해한 천체물리학적 현상에 대하여 많은 사람들에게 새로운 통찰을 주었습니다. 숫자 계산 및 데이터 시각화는 과학자가 과학적 관찰에서 수집한 데이터에 대한 통찰력을 얻고 결과를 이해하는 데 도움이 되는 중요한 단계입니다. 계산은 복잡하며 실제 관찰 데이터 및 분석을 제공하는 컴퓨터 시뮬레이션을 사용하여 시각화하지 않는 한 사람이 이해할 수 없습니다. NumPy는 matplotlib, pandas 및 scikit-learn과 같은 다른 Python 패키지와 함께 [연구원](https://www.gw-openscience.org/events/GW150914/)이 복잡한 질문에 답하고 우주에 대한 이해의 새로운 지평을 발견할 수 있도록 합니다. -{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} +{{< figure >}} +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 852936f07adca90d7a5d12b39bfa8dbfecc8b8df Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:46 +0100 Subject: [PATCH 642/711] New translations user-survey-2020.md (Russian) --- content/ru/user-survey-2020.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/content/ru/user-survey-2020.md b/content/ru/user-survey-2020.md index ce2decb371..b4349bcb7d 100644 --- a/content/ru/user-survey-2020.md +++ b/content/ru/user-survey-2020.md @@ -5,7 +5,9 @@ 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. From 41d41c6828445d1cff66944c87887d3db6b84ba5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:47 +0100 Subject: [PATCH 643/711] New translations blackhole-image.md (Russian) --- content/ru/case-studies/blackhole-image.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/ru/case-studies/blackhole-image.md b/content/ru/case-studies/blackhole-image.md index 856564a5fb..22334ebed0 100644 --- a/content/ru/case-studies/blackhole-image.md +++ b/content/ru/case-studies/blackhole-image.md @@ -3,7 +3,9 @@ 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. @@ -36,7 +38,9 @@ The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an arr 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 @@ -46,11 +50,15 @@ The EHT collaboration met these challenges by having independent teams evaluate 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 >}} 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. @@ -58,7 +66,9 @@ Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [ 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 >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From baf6f2c1523a50d3bf78a66748f8530d173b6bfb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:48 +0100 Subject: [PATCH 644/711] New translations cricket-analytics.md (Russian) --- content/ru/case-studies/cricket-analytics.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/content/ru/case-studies/cricket-analytics.md b/content/ru/case-studies/cricket-analytics.md index 993e19e1cb..d0be271f50 100644 --- a/content/ru/case-studies/cricket-analytics.md +++ b/content/ru/case-studies/cricket-analytics.md @@ -3,7 +3,9 @@ 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. @@ -25,7 +27,9 @@ Today, there are rich and almost infinite troves of cricket game records and sta * 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 @@ -33,7 +37,9 @@ Today, there are rich and almost infinite troves of cricket game records and sta * 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 @@ -61,4 +67,6 @@ Sports Analytics is a thriving field. Many researchers and companies [use NumPy] 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 >}} From 5d956408897d4e8c5fcbead0a7fc96dde5f2448e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:49 +0100 Subject: [PATCH 645/711] New translations deeplabcut-dnn.md (Russian) --- content/ru/case-studies/deeplabcut-dnn.md | 24 +++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/content/ru/case-studies/deeplabcut-dnn.md b/content/ru/case-studies/deeplabcut-dnn.md index 0c6deac529..257905de91 100644 --- a/content/ru/case-studies/deeplabcut-dnn.md +++ b/content/ru/case-studies/deeplabcut-dnn.md @@ -3,7 +3,9 @@ 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. @@ -16,7 +18,9 @@ sidebar: false 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. @@ -45,7 +49,9 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) - 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 >}} ### The Challenges @@ -61,7 +67,9 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) 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 @@ -77,13 +85,17 @@ The following features of NumPy played a key role in addressing the image proces 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 >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From b5034a3a5359fd034b17d371c6ae9c44485ae3cb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:50 +0100 Subject: [PATCH 646/711] New translations gw-discov.md (Russian) --- content/ru/case-studies/gw-discov.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/ru/case-studies/gw-discov.md b/content/ru/case-studies/gw-discov.md index 62ce66ca39..08f54b7547 100644 --- a/content/ru/case-studies/gw-discov.md +++ b/content/ru/case-studies/gw-discov.md @@ -3,7 +3,9 @@ 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 >}} @@ -37,7 +39,9 @@ The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.lig 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 @@ -54,14 +58,20 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * 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 >}} From 7443133dc79a9c35926602af263645a7236d0ddf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:51 +0100 Subject: [PATCH 647/711] New translations _index.md (Russian) --- content/ru/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ru/_index.md b/content/ru/_index.md index 7fecd5f039..be88f9e642 100644 --- a/content/ru/_index.md +++ b/content/ru/_index.md @@ -2,7 +2,7 @@ title: null --- -{{< grid1 columns="1 2 2 3" >}} +{{< grid columns="1 2 2 3" >}} [[item]] type = 'card' @@ -46,4 +46,4 @@ body = ''' NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. ''' -{{< /grid1>}} +{{< /grid>}} From 589ff6c1fa00dd621a021b54fcec849e35ad2509 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:52 +0100 Subject: [PATCH 648/711] New translations user-survey-2020.md (Chinese Simplified) --- content/zh/user-survey-2020.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/content/zh/user-survey-2020.md b/content/zh/user-survey-2020.md index ce2decb371..b4349bcb7d 100644 --- a/content/zh/user-survey-2020.md +++ b/content/zh/user-survey-2020.md @@ -5,7 +5,9 @@ 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. From 5f4447dceea329e79d45f1303034a7c68c6f73c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:53 +0100 Subject: [PATCH 649/711] New translations blackhole-image.md (Chinese Simplified) --- content/zh/case-studies/blackhole-image.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/zh/case-studies/blackhole-image.md b/content/zh/case-studies/blackhole-image.md index 856564a5fb..22334ebed0 100644 --- a/content/zh/case-studies/blackhole-image.md +++ b/content/zh/case-studies/blackhole-image.md @@ -3,7 +3,9 @@ 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. @@ -36,7 +38,9 @@ The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an arr 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 @@ -46,11 +50,15 @@ The EHT collaboration met these challenges by having independent teams evaluate 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 >}} 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. @@ -58,7 +66,9 @@ Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [ 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 >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 9b23a3174932df5f4c2b9e35527dd34d12d02833 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:54 +0100 Subject: [PATCH 650/711] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md index 993e19e1cb..d0be271f50 100644 --- a/content/zh/case-studies/cricket-analytics.md +++ b/content/zh/case-studies/cricket-analytics.md @@ -3,7 +3,9 @@ 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. @@ -25,7 +27,9 @@ Today, there are rich and almost infinite troves of cricket game records and sta * 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 @@ -33,7 +37,9 @@ Today, there are rich and almost infinite troves of cricket game records and sta * 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 @@ -61,4 +67,6 @@ Sports Analytics is a thriving field. Many researchers and companies [use NumPy] 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 >}} From 8702f5a5a11a0d6cf11d07c9b9649776ccea7c18 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:55 +0100 Subject: [PATCH 651/711] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 24 +++++++++++++++++------ 1 file changed, 18 insertions(+), 6 deletions(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index 0c6deac529..257905de91 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -3,7 +3,9 @@ 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. @@ -16,7 +18,9 @@ sidebar: false 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. @@ -45,7 +49,9 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) - 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 >}} ### The Challenges @@ -61,7 +67,9 @@ Recently, the [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) 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 @@ -77,13 +85,17 @@ The following features of NumPy played a key role in addressing the image proces 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 >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 3b5a9ee37e095b2a0d3abdfbb73b353d324a3109 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:56 +0100 Subject: [PATCH 652/711] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md index 62ce66ca39..08f54b7547 100644 --- a/content/zh/case-studies/gw-discov.md +++ b/content/zh/case-studies/gw-discov.md @@ -3,7 +3,9 @@ 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 >}} @@ -37,7 +39,9 @@ The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.lig 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 @@ -54,14 +58,20 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * 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 >}} From 018aac5c54e0b71e83136e8cdb4d9c6c84ca7cfa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:57 +0100 Subject: [PATCH 653/711] New translations _index.md (Chinese Simplified) --- content/zh/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/_index.md b/content/zh/_index.md index 7fecd5f039..be88f9e642 100644 --- a/content/zh/_index.md +++ b/content/zh/_index.md @@ -2,7 +2,7 @@ title: null --- -{{< grid1 columns="1 2 2 3" >}} +{{< grid columns="1 2 2 3" >}} [[item]] type = 'card' @@ -46,4 +46,4 @@ body = ''' NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. ''' -{{< /grid1>}} +{{< /grid>}} From e0a2d1fe9b5c0d518b7042d792eb68171c0272fc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:58 +0100 Subject: [PATCH 654/711] New translations user-survey-2020.md (Portuguese, Brazilian) --- content/pt/user-survey-2020.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md index 0cb175d668..c722abce1d 100644 --- a/content/pt/user-survey-2020.md +++ b/content/pt/user-survey-2020.md @@ -6,6 +6,8 @@ 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">}} +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 >}} **[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados. From 63702064c134d2ffebee776f7a2e093b672f7a1e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:29:59 +0100 Subject: [PATCH 655/711] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index f4e1d3717f..5df1635b70 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -4,6 +4,8 @@ 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" >}} +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. @@ -36,7 +38,9 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co 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 = '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 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**" >}} ## O papel do NumPy @@ -46,11 +50,15 @@ A colaboração do EHT venceu esses desafios ao estabelecer equipes independente 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 >}} +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Funcionalidades-chave do NumPy utilizadas**" >}} +{{< /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 = 'Software dependency chart of ehtim package highlighting 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. @@ -58,7 +66,9 @@ Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pan 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 = 'Key NumPy Capabilities utilized' +{{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 737ac70859cd502e7599c599e356b7121ca6c24c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:30:00 +0100 Subject: [PATCH 656/711] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 29c39e3462..080082c4ab 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -4,6 +4,8 @@ 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" >}} +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. @@ -25,7 +27,9 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações * 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 = '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 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**" >}} ### Objetivos Principais da Análise de Dados @@ -33,7 +37,9 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações * 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 = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +{{< /figure >}} ### Desafios @@ -61,4 +67,6 @@ A análise de dados esportivos é um campo próspero. Muitos pesquisadores e emp 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 = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' +{{< /figure >}} From 8b2504783f90f38a190b736f4d0057daebe9316b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:30:01 +0100 Subject: [PATCH 657/711] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 22 +++++++++++++++++----- 1 file changed, 17 insertions(+), 5 deletions(-) diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index 9ffb48ea5b..72212c84e7 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -4,6 +4,8 @@ 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">}} +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 permite a análise automática de vídeos de comportamento animal usando Deep Learning. @@ -16,7 +18,9 @@ sidebar: false 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 = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: 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. @@ -45,7 +49,9 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab - 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 >}} +{{< 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 >}} ### Desafios @@ -61,7 +67,9 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab 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/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave do NumPy utilizados**" >}} +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 >}} ## O papel da NumPy nos desafios da estimação de poses @@ -77,13 +85,17 @@ As seguintes características da NumPy desempenharam um papel fundamental para a 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 = '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 >}} ## 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 = 'Key NumPy Capabilities utilized' +{{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 343531e4980fc41b3e529c49a044545843d8e9ad Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 13:30:02 +0100 Subject: [PATCH 658/711] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index 6f78786e43..3e5c0a5401 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -4,6 +4,8 @@ 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" >}} +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 >}} @@ -37,7 +39,9 @@ Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma 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="**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 src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} ## O papel da NumPy na detecção de ondas gravitacionais @@ -54,14 +58,20 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * 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="**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/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} +{{< 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 >}} ## Resumo 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 >}} From 8056fe9f5065a42ee3c3c479b05bde3e6dc210f6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 17:30:33 +0100 Subject: [PATCH 659/711] New translations user-survey-2020.md (Portuguese, Brazilian) --- content/pt/user-survey-2020.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md index c722abce1d..bbf3cb6a17 100644 --- a/content/pt/user-survey-2020.md +++ b/content/pt/user-survey-2020.md @@ -6,7 +6,7 @@ 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">}} -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' +src = '/surveys/NumPy_usersurvey_2020_report_cover. ng' alt = 'Página de rosto do relatório da pesquisa de usuário NumPy de 2020, intitulado "NumPy Community Survey 2020 - resultados"' width = '250' {{< /figure >}} **[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados. From 01b2831dd9c2bc426069826dec56a168fc028fba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 17:30:35 +0100 Subject: [PATCH 660/711] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index 5df1635b70..3f4031312d 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -4,7 +4,7 @@ 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" >}} -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' +src = '/images/content_images/cs/blackhole.jpg' title = 'Buraco Negro M87' alt = 'imagem do buraco negro' attribution = '(Créditos de Imagem: 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*" @@ -39,7 +39,7 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co 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 = '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' +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Pipeline de Processamento de Dados' alt = 'pipeline de dados' 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 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**" >}} ## O papel do NumPy @@ -57,7 +57,7 @@ O trabalho desse grupo ilustra o papel do ecossistema científico do Python no a 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 = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'mapa de dependência ehtim destacando o numpy' title = 'Gráfico de dependência de software do pacote ehtim destacando 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. @@ -67,7 +67,7 @@ Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pan 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 = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'benefícios do numpy' title = 'Principais recursos do NumPy utilizados' {{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 36921406af79a8b8f4a7cbad9b6d79de5d38d7b5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 17:30:36 +0100 Subject: [PATCH 661/711] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 080082c4ab..e7140e6c26 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -4,7 +4,7 @@ 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" >}} -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' +src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, o maior Festival de Críquete da Índia' alt = 'Taça e estádio de críquete da Premier League indiana' attribution = '(Créditos de imagem: IPLT20 (taça e logo) & Akash Yadav (estádio))' 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*" @@ -28,7 +28,7 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações * 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' 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' +src = '/images/content_images/cs/cricket-pitch.png' title = 'Campo de críquete, o ponto focal no campo' alt = 'Campo de críquete com lançador e batedor' align = 'center' attribution = '(Crédito de imagem: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< 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**" >}} ### Objetivos Principais da Análise de Dados @@ -38,7 +38,7 @@ src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the * 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' alt = 'pose estimator' title = 'Cricket Pose Estimator' attribution = '(Image credit: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = 'estimador de poses' title = 'Estimador de poses no críquete' attribution = '(Créditos da imagem: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### Desafios @@ -68,5 +68,5 @@ A análise de dados esportivos é um campo próspero. Muitos pesquisadores e emp 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' alt = 'Diagram showing benefits of using NumPy for cricket analytics' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'Diagrama mostrando os benefícios do uso do NumPy para análise de críquete' title = 'Principais recursos do NumPy utilizados' {{< /figure >}} From 059334f28ace6edf6ab2f16ae63a0fb54d90904e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 17:30:37 +0100 Subject: [PATCH 662/711] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index 72212c84e7..299c321f1a 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -4,7 +4,7 @@ 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">}} -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' +src = '/images/content_images/cs/mice-hand.gif' title = 'Analisando o movimento das mãos de ratos 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, *Assistant Professor, École polytechnique fédérale de Lausanne* ([EPFL](https://www.epfl.ch/en/))" @@ -19,7 +19,7 @@ src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-mov 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' title = 'Colored dots track the positions of a racehorse’s body part' alt = 'horserideranim' attribution = '(Source: Mackenzie Mathis)' +src = '/images/content_images/cs/race-horse.gif' title = ‘Pontos coloridos rastreiam as posições de 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. @@ -68,7 +68,7 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab 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/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave do NumPy utilizados**" >}} -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' +src = '/images/content_images/cs/pose-estimation.png' title = 'Variedade e complexidade da estimativa de pose' 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 @@ -86,7 +86,7 @@ As seguintes características da NumPy desempenharam um papel fundamental para a 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' 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' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'Fluxo de trabalho do 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 @@ -94,7 +94,7 @@ src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Wo 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' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'benefícios do numpy' title = 'Principais recursos do NumPy utilizados' {{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 033d820978c37324adfc6c44e9421740ceb25490 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 20 Mar 2024 17:30:38 +0100 Subject: [PATCH 663/711] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index 3e5c0a5401..bf4be55545 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -4,7 +4,7 @@ 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" >}} -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' +src = '/images/content_images/cs/gw_sxs_image.png' title = 'Ondas Gravitacionais' alt = 'buraco negro coalesce binário gerando ondas gravitacionais' attribution = '(Créditos da Imagem: 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. @@ -59,13 +59,13 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * [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' alt = 'gwpy-numpy depgraph' title = 'Dependency graph showing how GwPy package depends on NumPy' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy depgraph' title = 'Gráfico de dependência mostrando como o pacote GwPy depende do NumPy' {{< /figure >}} ---- {{< 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' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy depgraph' title = 'Gráfico de dependência mostrando como o pacote PyCBC depende do NumPy' {{< /figure >}} ## Resumo @@ -73,5 +73,5 @@ src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy d 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' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'benefícios do numpy' title = 'Principais recursos do NumPy utilizados' {{< /figure >}} From 67444a116be2384c2ffa1fd4e7104f86a0591628 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 21 Mar 2024 19:48:50 +0100 Subject: [PATCH 664/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 1ec8e9483a..88f3c0a6c8 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -125,13 +125,13 @@ params: 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 @@ -190,10 +190,10 @@ params: 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 @@ -249,7 +249,7 @@ params: url: https://www.sympy.org/ label: SymPy - - url: https://github.com/cvxgrp/cvxpy + url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ From 22fca8030af4f979256f312f249c8ff26fdba604 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 21 Mar 2024 19:48:51 +0100 Subject: [PATCH 665/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index 04864bf49e..b4131489aa 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -2,8 +2,8 @@ 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 — 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) — 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 — 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) — 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: - @@ -31,7 +31,7 @@ params: 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. @@ -67,7 +67,7 @@ params: 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. @@ -125,13 +125,13 @@ params: 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 @@ -190,10 +190,10 @@ params: 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 @@ -249,7 +249,7 @@ params: url: https://www.sympy.org/ label: SymPy - - url: https://github.com/cvxgrp/cvxpy + url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ @@ -334,7 +334,7 @@ params: - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "Report in a dashboard: [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)). @@ -374,6 +374,6 @@ params: 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 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. From a5a228f88a2aef2438e109e0bd2e4fa2e594fc53 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 21 Mar 2024 19:48:53 +0100 Subject: [PATCH 666/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index 04864bf49e..b4131489aa 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -2,8 +2,8 @@ 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 — 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) — 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 — 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) — 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: - @@ -31,7 +31,7 @@ params: 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. @@ -67,7 +67,7 @@ params: 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. @@ -125,13 +125,13 @@ params: 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 @@ -190,10 +190,10 @@ params: 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 @@ -249,7 +249,7 @@ params: url: https://www.sympy.org/ label: SymPy - - url: https://github.com/cvxgrp/cvxpy + url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ @@ -334,7 +334,7 @@ params: - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "Report in a dashboard: [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)). @@ -374,6 +374,6 @@ params: 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 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. From bb48719a133f2bcfa5303cab5b0ce9e6df6e97b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 21 Mar 2024 19:48:54 +0100 Subject: [PATCH 667/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index 04864bf49e..b4131489aa 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -2,8 +2,8 @@ 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 — 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) — 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 — 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) — 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: - @@ -31,7 +31,7 @@ params: 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. @@ -67,7 +67,7 @@ params: 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. @@ -125,13 +125,13 @@ params: 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 @@ -190,10 +190,10 @@ params: 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 @@ -249,7 +249,7 @@ params: url: https://www.sympy.org/ label: SymPy - - url: https://github.com/cvxgrp/cvxpy + url: https://www.cvxpy.org/ label: cvxpy - url: https://fenicsproject.org/ @@ -334,7 +334,7 @@ params: - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" - - text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + text: "Report in a dashboard: [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)). @@ -374,6 +374,6 @@ params: 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 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. From 79a680131cc173de76d40ce34240e8fb62696e3b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 11 Apr 2024 16:25:34 +0200 Subject: [PATCH 668/711] New translations about.md (Arabic) --- content/ar/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/about.md b/content/ar/about.md index f4841c1045..243f7083ae 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -49,7 +49,7 @@ The NumPy project leadership is actively working on diversifying contribution pa - optimization - funding and grants -See the [Team]({{< relref "/teams" >}}) page for more info. +See the [Team](/teams) page for more info. ## NumFOCUS Subcommittee From 7a7fc0258f511b9826eda1525e35cbac157dd4a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 11 Apr 2024 16:25:36 +0200 Subject: [PATCH 669/711] New translations about.md (Russian) --- content/ru/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/about.md b/content/ru/about.md index f4841c1045..243f7083ae 100644 --- a/content/ru/about.md +++ b/content/ru/about.md @@ -49,7 +49,7 @@ The NumPy project leadership is actively working on diversifying contribution pa - optimization - funding and grants -See the [Team]({{< relref "/teams" >}}) page for more info. +See the [Team](/teams) page for more info. ## NumFOCUS Subcommittee From 4b78bda087858b926f269d0ee7b292cddf6d586c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 11 Apr 2024 16:25:37 +0200 Subject: [PATCH 670/711] New translations about.md (Chinese Simplified) --- content/zh/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/about.md b/content/zh/about.md index f4841c1045..243f7083ae 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -49,7 +49,7 @@ The NumPy project leadership is actively working on diversifying contribution pa - optimization - funding and grants -See the [Team]({{< relref "/teams" >}}) page for more info. +See the [Team](/teams) page for more info. ## NumFOCUS Subcommittee From 874ed1f1c14e3f06d57b29ccdd1355d20b6b20ca Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 May 2024 05:05:11 +0200 Subject: [PATCH 671/711] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 6 ------ 1 file changed, 6 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 88f3c0a6c8..7eabca58e1 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -86,12 +86,6 @@ params: img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly - text: Aprendizaje tensorial, álgebra y backends para utilizar NumPy, MXNet, PyTorch, TensorFlow o CuPy. - img: /images/content_images/arlib/tensorly.png - alttext: tensorly - url: http://tensorly.org/stable/home.html scientificdomains: intro: - From 20fe73bf1c8826aa90b1d853a31079da42d16762 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 May 2024 05:05:12 +0200 Subject: [PATCH 672/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index b4131489aa..4abd5a2d06 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -2,7 +2,7 @@ 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 — 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 — 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) — 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: @@ -56,12 +56,6 @@ params: 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. @@ -88,7 +82,7 @@ params: 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 From e347c8943cf005d76dee375d596933aba522bc76 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 May 2024 05:05:13 +0200 Subject: [PATCH 673/711] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 6 ------ 1 file changed, 6 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 22b50fd0db..5a208d9f6b 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -86,12 +86,6 @@ params: 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: - From 7114fe4a0421d967f920376564659aee1e2c1819 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 May 2024 05:05:14 +0200 Subject: [PATCH 674/711] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 6 ------ 1 file changed, 6 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index c861dd5acb..4fa0164021 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -86,12 +86,6 @@ params: img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly - text: NumPy, MXNet, PyTorch, TensorFlow 또는 CuPy를 원활하게 사용하기 위한 Tensor 학습, 대수 및 백엔드. - img: /images/content_images/arlib/tensorly.png - alttext: tensorly - url: http://tensorly.org/stable/home.html scientificdomains: intro: - From a5c79e308ebc5455b4b695d3e0742829a52a588d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 May 2024 05:05:15 +0200 Subject: [PATCH 675/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index b4131489aa..4abd5a2d06 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -2,7 +2,7 @@ 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 — 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 — 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) — 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: @@ -56,12 +56,6 @@ params: 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. @@ -88,7 +82,7 @@ params: 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 From d2572728e7eb3fb812a9e73fbc661f20703b533e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 May 2024 05:05:16 +0200 Subject: [PATCH 676/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 10 ++-------- 1 file changed, 2 insertions(+), 8 deletions(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index b4131489aa..4abd5a2d06 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -2,7 +2,7 @@ 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 — 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 — 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) — 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: @@ -56,12 +56,6 @@ params: 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. @@ -88,7 +82,7 @@ params: 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 From b9c1e153c490d2d2e84417dd8931cd649c0b10cf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 10 May 2024 05:05:17 +0200 Subject: [PATCH 677/711] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 0b5f303c91..b84da40386 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -76,7 +76,7 @@ params: url: https://github.com/xtensor-stack/xtensor-python - title: Awkward Array - text: Manipulate JSON-like data with NumPy-like idioms. + text: Python backend system that decouples API from implementation; unumpy provides a NumPy API. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ @@ -86,12 +86,6 @@ params: 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: - From a844d94d1b3a23423f079078de9498b9745cbe34 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 May 2024 19:07:14 +0200 Subject: [PATCH 678/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index 4abd5a2d06..a7ea900602 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -322,7 +322,7 @@ params: 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - From b2ebb98e484f786fe644cff006ecf0641e4e2f1e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 May 2024 19:07:16 +0200 Subject: [PATCH 679/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index 4abd5a2d06..a7ea900602 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -322,7 +322,7 @@ params: 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - From 28fb6cc366add071b0349928d78de312419c22cb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 May 2024 19:07:17 +0200 Subject: [PATCH 680/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index 4abd5a2d06..a7ea900602 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -322,7 +322,7 @@ params: 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: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - From 746f1ff2bef40ec3e135587fe2777a9444fa3f51 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 23 May 2024 19:18:21 +0200 Subject: [PATCH 681/711] New translations news.md (Spanish) --- content/es/news.md | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index ceac249579..7fc9ef2d82 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,16 +1,25 @@ --- 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 publicado +### NumPy 1.26.0 released _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: From c4638380519ad32452d768cfeb7b34e7e5bcfe86 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 23 May 2024 19:18:22 +0200 Subject: [PATCH 682/711] New translations news.md (Arabic) --- content/ar/news.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 9525ba5830..793619c0d1 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,19 @@ --- 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. From 9c343cf9df2057e932695c8d0d5277610a754b25 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 23 May 2024 19:18:23 +0200 Subject: [PATCH 683/711] New translations news.md (Japanese) --- content/ja/news.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index cc26220220..69add71c22 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,10 +1,19 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 1.26.0 がリリースされました。" +newsHeader: "NumPy 2.0 release date: June 16" date: 2023-09-16 --- +### 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。 From bae8a8d677c5e33b1a12849f18ea75c82d34b70c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 23 May 2024 19:18:24 +0200 Subject: [PATCH 684/711] New translations news.md (Korean) --- content/ko/news.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index c1ea37596e..b83a467344 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,19 @@ --- title: 소식 sidebar: false -newsHeader: "NumPy 1.26.0 출시" +newsHeader: "NumPy 2.0 release date: June 16" date: 2023-09-16 --- +### 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) + + ### 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. From 908bc903caf14bbedc097f537108d74ae5552e4b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 23 May 2024 19:18:25 +0200 Subject: [PATCH 685/711] New translations news.md (Russian) --- content/ru/news.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/content/ru/news.md b/content/ru/news.md index 9525ba5830..793619c0d1 100644 --- a/content/ru/news.md +++ b/content/ru/news.md @@ -1,10 +1,19 @@ --- 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. From 52b638d67170911e7dd55c7abb0a454925762f69 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 23 May 2024 19:18:26 +0200 Subject: [PATCH 686/711] New translations news.md (Chinese Simplified) --- content/zh/news.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 9525ba5830..793619c0d1 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,19 @@ --- 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. From 671c4b542992984d5b9ad2396312cf9da78c1f1d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 23 May 2024 19:18:28 +0200 Subject: [PATCH 687/711] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 80e98e742d..085a49d625 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,16 +1,25 @@ --- title: Notícias sidebar: false -newsHeader: "Lançado o NumPy versão 1.26.0" +newsHeader: "NumPy 2.0 release date: June 16" date: 2023-09-16 --- +### 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) + + ### 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 1.26.0 +### 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: From f6d6354ddee86416cf8fe47ac6d29f9679ff6154 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 24 May 2024 13:15:10 +0200 Subject: [PATCH 688/711] New translations news.md (Japanese) --- content/ja/news.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 69add71c22..4145f10bea 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,13 +1,13 @@ --- title: ニュース sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" +newsHeader: "NumPy 2.0 リリース日: 6月16日" date: 2023-09-16 --- -### NumPy 2.0 release date: June 16 +### NumPy 2.0 リリース日: 6月16日 -_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:** +_ 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。 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) From d5c3d5d4c6ec34b40b2e2ef78de0c94910c262d8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 24 May 2024 14:39:39 +0200 Subject: [PATCH 689/711] New translations news.md (Japanese) --- content/ja/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 4145f10bea..8d0cfb5b39 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -7,11 +7,11 @@ date: 2023-09-16 ### NumPy 2.0 リリース日: 6月16日 -_ 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。 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:** +_ 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:** -- 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) +- [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 From 2731e3fdf0f9bb4266da6ec227a5f652b3b18f28 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 09:59:22 +0200 Subject: [PATCH 690/711] New translations learn.md (Arabic) --- content/ar/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index a10aa18aac..ca45c144c5 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -52,7 +52,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **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* **Videos** From 8749f635302673077a6cb52656f29b82a0921b14 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 09:59:24 +0200 Subject: [PATCH 691/711] New translations learn.md (Russian) --- content/ru/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/learn.md b/content/ru/learn.md index a10aa18aac..ca45c144c5 100644 --- a/content/ru/learn.md +++ b/content/ru/learn.md @@ -52,7 +52,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **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* **Videos** From 3cea4178a22ce37409550d30a39591b61ef04f3e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 09:59:25 +0200 Subject: [PATCH 692/711] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/learn.md b/content/zh/learn.md index a10aa18aac..ca45c144c5 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -52,7 +52,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **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* **Videos** From 84c277149bb78676d9fedb7b3eeb6673fa7ab655 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 11:29:40 +0200 Subject: [PATCH 693/711] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index a7ea900602..d74cba9bce 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -25,7 +25,7 @@ params: 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 + url: https://cupy.dev - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." From fb20e39c7550c3c404043436c064aeef4fc04be9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 11:29:42 +0200 Subject: [PATCH 694/711] New translations install.md (Spanish) --- content/es/install.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/install.md b/content/es/install.md index a199b2f91a..f64a17841d 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -3,7 +3,7 @@ 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/distribution) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. +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. @@ -45,7 +45,7 @@ We'll start with recommendations based on the user's experience level and operat On all of Windows, macOS, and Linux: -- Install [Anaconda](https://www.anaconda.com/distribution/) (it installs all packages you need and all other tools mentioned below). +- 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. From fd7c464c95829f1d111f8e0234cdfba6026668db Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 11:29:43 +0200 Subject: [PATCH 695/711] New translations install.md (Arabic) --- content/ar/install.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/install.md b/content/ar/install.md index a199b2f91a..f64a17841d 100644 --- a/content/ar/install.md +++ b/content/ar/install.md @@ -3,7 +3,7 @@ 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/distribution) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. +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. @@ -45,7 +45,7 @@ We'll start with recommendations based on the user's experience level and operat On all of Windows, macOS, and Linux: -- Install [Anaconda](https://www.anaconda.com/distribution/) (it installs all packages you need and all other tools mentioned below). +- 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. From aa4a1be681162d24b2e04566f9b6a0f8c866c7ba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 11:29:45 +0200 Subject: [PATCH 696/711] New translations tabcontents.yaml (Russian) --- content/ru/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/tabcontents.yaml b/content/ru/tabcontents.yaml index a7ea900602..d74cba9bce 100644 --- a/content/ru/tabcontents.yaml +++ b/content/ru/tabcontents.yaml @@ -25,7 +25,7 @@ params: 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 + url: https://cupy.dev - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." From 98ce984d79e580980814d6930ccdaaf573f87473 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 11:29:46 +0200 Subject: [PATCH 697/711] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index a7ea900602..d74cba9bce 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -25,7 +25,7 @@ params: 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 + url: https://cupy.dev - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." From 0956caf1af076fe688d50081608c57c3e9064c83 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 11:29:48 +0200 Subject: [PATCH 698/711] New translations install.md (Russian) --- content/ru/install.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ru/install.md b/content/ru/install.md index a199b2f91a..f64a17841d 100644 --- a/content/ru/install.md +++ b/content/ru/install.md @@ -3,7 +3,7 @@ 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/distribution) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. +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. @@ -45,7 +45,7 @@ We'll start with recommendations based on the user's experience level and operat On all of Windows, macOS, and Linux: -- Install [Anaconda](https://www.anaconda.com/distribution/) (it installs all packages you need and all other tools mentioned below). +- 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. From c14cc4f08e1209fc11782793060921b443f89e2b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 26 May 2024 11:29:49 +0200 Subject: [PATCH 699/711] New translations install.md (Chinese Simplified) --- content/zh/install.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/install.md b/content/zh/install.md index a199b2f91a..f64a17841d 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -3,7 +3,7 @@ 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/distribution) - it includes Python, NumPy, and many other commonly used packages for scientific computing and data science. +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. @@ -45,7 +45,7 @@ We'll start with recommendations based on the user's experience level and operat On all of Windows, macOS, and Linux: -- Install [Anaconda](https://www.anaconda.com/distribution/) (it installs all packages you need and all other tools mentioned below). +- 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. From ee8913ad9d23a0506b9ff43727683eeb1cb43666 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 09:53:13 +0200 Subject: [PATCH 700/711] New translations learn.md (Arabic) --- content/ar/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index ca45c144c5..390fdcf6bc 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -51,7 +51,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Books** -* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *by Jake Vanderplas* +* [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* From 9cc656f47bc419dca9e6dfbedff5be92d951897b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 09:53:16 +0200 Subject: [PATCH 701/711] New translations learn.md (Russian) --- content/ru/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/learn.md b/content/ru/learn.md index ca45c144c5..390fdcf6bc 100644 --- a/content/ru/learn.md +++ b/content/ru/learn.md @@ -51,7 +51,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Books** -* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *by Jake Vanderplas* +* [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* From 0fd1497fbbffef8d35a66495029cff168abba278 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 09:53:17 +0200 Subject: [PATCH 702/711] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/learn.md b/content/zh/learn.md index ca45c144c5..390fdcf6bc 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -51,7 +51,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Books** -* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *by Jake Vanderplas* +* [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* From 24995657d4ee000cbb74711d616d2b176388f253 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 17:38:26 +0200 Subject: [PATCH 703/711] New translations news.md (Korean) --- content/ko/news.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index b83a467344..6f52f07394 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,17 +1,17 @@ --- title: 소식 sidebar: false -newsHeader: "NumPy 2.0 release date: June 16" +newsHeader: "NumPy 2.0 출시일: 6월 16일" date: 2023-09-16 --- -### NumPy 2.0 release date: June 16 +### NumPy 2.0 출시일: 6월 16일 -_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:** +_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.** -- 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) +- [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 From 394839339eb7707072383670ff2b46729c49623b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 17:38:27 +0200 Subject: [PATCH 704/711] New translations user-survey-2020.md (Korean) --- content/ko/user-survey-2020.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/user-survey-2020.md b/content/ko/user-survey-2020.md index 75e7d93a77..68505b30f9 100644 --- a/content/ko/user-survey-2020.md +++ b/content/ko/user-survey-2020.md @@ -6,7 +6,7 @@ sidebar: false 2020년, NumPy 팀은 조사방법론 학사 과정의 학생 및 교수와 협력하여 미시간 대학과 매릴렌드 대학이 공동으로 개최한 첫 공식 NumPy 커뮤니티 조사를 실시했습니다. 75개국 내 1200명 이상의 사용자 여러분들께서 저희가 NumPy 커뮤니티의 가닥을 잡을 수 있도록 도와주기 위해 참여해주셨으며 프로젝트의 미래에 대한 생각을 표현해주셨습니다. {{< figure src="/surveys/NumPy_usersurvey_2020_report_cover.png" class="fig-left" alt="'NumPy Community Survey 2020 - results'라는 제목이 붙은 2020년 NumPy 사용자 설문조사 보고서 표지" width="250">}} -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' +src = '/surveys/NumPy_usersurvey_2020_report_cover.png' alt = '"2020 NumPy 커뮤니티 설문조사 - 결과"라는 제목이 붙은 2020 NumPy 사용자 설문조사 보고서의 표지' width = '250' {{< /figure >}} **[보고서를 내려받아서](/surveys/NumPy_usersurvey_2020_report.pdf)** 설문조사 결과를 자세히 들여다 보세요. From 04ab835ee290f7e0b5c949388cd3594811f33936 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 18:52:14 +0200 Subject: [PATCH 705/711] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index e91bcec2e9..e72252a423 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -4,7 +4,7 @@ sidebar: false --- {{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**중력파**" alt="이항 결합하며 중력파를 생성하는 블랙홀" attr="*(사진 크레딧: LIGO의 Simulating eXtreme Spacetimes (SXS) 프로젝트)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} -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' +src = '/images/content_images/cs/gw_sxs_image.png' title = '중력파' alt = '이항 결합하며 중력파를 생성하는 블랙홀' attribution = '(사진 크레딧: LIGO의 Simulating eXtreme Spacetimes (SXS) 프로젝트)' 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. @@ -59,13 +59,13 @@ Python용 표준 수치 분석 패키지인 NumPy는 LIGO의 GW 탐지 프로젝 * [GwPy](https://gwpy.github.io/docs/stable/overview.html) 및 [PyCBC](https://pycbc.org)와 같은 중력파 데이터 분석에서 개발된 주요 [소프트웨어](https://github.com/lscsoft)는 후드 아래에서 NumPy 및 AstroPy를 사용하여 중력파 검출기에서 데이터를 연구하기 위한 유틸리티, 도구 및 방법에 객체 기반 인터페이스를 제공합니다. {{< 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' +src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' alt = 'gwpy-numpy 종속성' title = 'GwPy 패키지가 어떻게 NumPy에 종속하는지를 나타내는 종속성 그래프' {{< /figure >}} ---- {{< 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' +src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy 종속성' title = 'PyCBC 패키지가 NumPy에 종속하는지를 나타내는 종속성 그래프' {{< /figure >}} ## 요약 @@ -73,5 +73,5 @@ src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' alt = 'PyCBC-numpy d 중력파 검출을 통하여 연구자들은 완전히 예상치 못한 현상을 발견하게 됨으로써, 알려진 것 중 가장 난해한 천체물리학적 현상에 대하여 많은 사람들에게 새로운 통찰을 주었습니다. 숫자 계산 및 데이터 시각화는 과학자가 과학적 관찰에서 수집한 데이터에 대한 통찰력을 얻고 결과를 이해하는 데 도움이 되는 중요한 단계입니다. 계산은 복잡하며 실제 관찰 데이터 및 분석을 제공하는 컴퓨터 시뮬레이션을 사용하여 시각화하지 않는 한 사람이 이해할 수 없습니다. NumPy는 matplotlib, pandas 및 scikit-learn과 같은 다른 Python 패키지와 함께 [연구원](https://www.gw-openscience.org/events/GW150914/)이 복잡한 질문에 답하고 우주에 대한 이해의 새로운 지평을 발견할 수 있도록 합니다. {{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_gw_benefits.png' alt = 'numpy를 통한 이익' title = '활용된 주요 NumPy 기능' {{< /figure >}} From 67b2d156e617061228793d28d1cba916fd8dae97 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 18:52:15 +0200 Subject: [PATCH 706/711] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index ac16cf6dc8..14ddb59afc 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -4,7 +4,7 @@ sidebar: false --- {{< figure src="/images/content_images/cs/blackhole.jpg" caption="**블랙홀 M87**" alt="블랙홀 사진" attr="*(사진 크레딧: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} -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' +src = '/images/content_images/cs/blackhole.jpg' title = '블랙홀 M87' alt = '블랙홀 사진' attribution = '(사진 크레딧: 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*" @@ -39,7 +39,7 @@ src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = ' 만약 목표가 이전에 본 적이 없는 것을 보는 것이라면, 과학자들은 어떻게 이 사진이 옳다고 입증할 수 있을까요? {{< 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' +src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT 데이터 처리 파이프라인' alt = '데이터 파이프라인' align = 'center' attribution = '(다이어그램 크레딧: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' {{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="numpy의 역할" caption="**블랙홀 시각화에서 NumPy의 역할**" >}} ## NumPy의 역할 @@ -57,7 +57,7 @@ EHT는 기존 및 최첨된 이미지 재구성 기술을 모두 사용한 뒤, 예를 들어, [`eht-imaging`][ehtim] Python 패키지는 VLBI 데이터를 통해 실험이나 이미지 재구성을 수행할 때 필요한 도구를 제공합니다. NumPy는 아래 소프트웨어 종속성 차트에 나와 있는 것처럼 이 패키지에서 사용되는 배열 데이터 처리의 핵심 역할을 합니다. {{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy' +src = '/images/content_images/cs/ehtim_numpy.png' alt = 'numpy를 강조하는 ehtim의 종속성 지도' title = 'NumPy를 강조하는 ehtim 패키지의 소프트웨어 종속성 차트' {{< /figure >}} NumPy 외에도 [SciPy](https://www.scipy.org)와 [Pandas](https://pandas.io) 등의 다른 많은 패키지가 블랙홀을 시각화하는 데이터 처리 파이프라인의 일부입니다. 표준 천문 파일 형식과 시간/좌표 변환에는 [Astropy][astropy]가 쓰였고 [Matplotlib][mpl]는 분석 과정 전체에서 블랙홀의 최종 사진을 생성하는 등 데이터를 시각화하는 데 쓰였습니다. @@ -67,7 +67,7 @@ NumPy 외에도 [SciPy](https://www.scipy.org)와 [Pandas](https://pandas.io) NumPy의 핵심 기능인 효율적이고 유용한 n차원 배열은 연구자들이 대규모 수치 데이터셋을 다룰 수 있도록 하여 최초의 블랙홀 사진을 만드는 데 토대를 제공했습니다. 이번 관측은 아인슈타인의 이론에 훌륭한 시각적 증거를 준 관측으로, 과학계에 한 획을 그은 순간이었습니다. 기술적 혁신뿐만 아니라 200명 이상의 과학자와 세계 최고의 전파 관측소 간의 국제 협력도 이루어 냈습니다. 기존의 천문학 모델을 개선한 혁신적인 알고리즘과 데이터 처리 기술이 우주의 비밀을 알아내는 데 도움을 주었습니다. {{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy를 통한 이익' title = '활용된 주요 NumPy 기능' {{< /figure >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From f391a009797a1402928728abc5232fcd104b23d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 18:52:16 +0200 Subject: [PATCH 707/711] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index cfd1f993e6..7dfb7ea1df 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -4,7 +4,7 @@ sidebar: false --- {{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**인도 최대의 크리켓 축제인 IPLT20**" alt="인도 프리미어 리그 크리켓 컵 및 경기장" attr="*(사진 출처: IPLT20 (컵 및 로고) & Akash Yadav (경기장))*" attrlink="https://unsplash.com/@aksh1802" >}} -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' +src = '/images/content_images/cs/ipl-stadium.png' title = '인도 최대의 크리켓 축제인 IPLT20' alt = '인도 프리미어 리그 크리켓 컵 및 경기장' attribution = '(사진 크레딧: IPLT20 (컵 및 로고) & Akash Yadav (경기장))' 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*" @@ -28,7 +28,7 @@ src = '/images/content_images/cs/ipl-stadium.png' title = 'IPLT20, the biggest C * 팀 구성에 대한 전략적 결정을 내리는 플레이어의 승패 기여도 {{< 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' +src = '/images/content_images/cs/cricket-pitch.png' title = '경기장의 중심이 되는 크리켓 피치' alt = '볼러와 배트맨으로 이루어진 크리켓 피치' align = 'center' attribution = '(사진 크레딧: Debarghya Das)' attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' {{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="NumPy를 크리켓 분석에 사용했을 때의 이익을 보여주는 다이어그램" caption="**활용된 주요 NumPy 기능**" >}} ### 데이터 분석의 주요 목표 @@ -38,7 +38,7 @@ src = '/images/content_images/cs/cricket-pitch.png' title = 'Cricket Pitch, the * 과거 분석 외에도 예측 모델을 활용하여 상당한 수의 크런칭과 데이터 과학 노하우, 시각화 도구 및 분석에 더 새로운 관찰을 포함시킬 수 있는 기능이 필요한 가능한 일치 결과를 결정합니다. {{< 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/' +src = '/images/content_images/cs/player-pose-estimator.png' alt = '자세 예측' title = '크리켓 자세 예측' attribution = '(사진 크레딧: connect.vin)' attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' {{< /figure >}} ### 도전 @@ -68,5 +68,5 @@ src = '/images/content_images/cs/player-pose-estimator.png' alt = 'pose estimato 스포츠 분석은 프로 게임의 판도를 바꿀 것입니다. 특히 최근까지는 주로 "직감"이나 과거부터 내려오던 것을 답습하는 식으로 이뤄진 전략적 의사 결정에 대해서 말입니다. NumPy는 데이터 분석, 기계 학습 및 AI 알고리즘과 관련하여 더욱 높은 수준의 기능을 제공하는 Python 패키지들의 견고한 기반을 제공합니다. 이들 패키지는 크리켓 경기뿐 아니라 크리켓 관련 추론이나 사업을 추진하면서, 판도를 바꿀만한 결정을 이끌어 내는 영감을 실시간으로 제공하는 데 널리 이용되고 있습니다. 크리켓 경기의 결과로 이어지는 숨겨진 매개변수, 패턴이나 속성을 찾는 것은 관계자가 숫자와 통계에 숨겨진 게임을 분석하는 방법을 파악하는 데 도움이 됩니다. {{< 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' +src = '/images/content_images/cs/numpy_ca_benefits.png' alt = 'NumPy를 크리켓 분석에 적용했을 때의 이익을 보여주는 다이어그램' title = '활용된 주요 NumPy 기능' {{< /figure >}} From a3ae48f9c732108d4013778bc0efe252405b2c48 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 May 2024 18:52:18 +0200 Subject: [PATCH 708/711] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md index 86a92b4bc5..217e1565f2 100644 --- a/content/ko/case-studies/deeplabcut-dnn.md +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -4,7 +4,7 @@ sidebar: false --- {{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**DeepLapCut을 활용한 쥐의 손 움직임 분석**" alt="쥐 손 애니메이션" attr="*(출처: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} -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' +src = '/images/content_images/cs/mice-hand.gif' title = 'DeepLapCut을 활용한 쥐의 손 움직임 분석' alt = '쥐 손 애니메이션' attribution = '(출처: 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/))" @@ -19,7 +19,7 @@ src = '/images/content_images/cs/mice-hand.gif' title = 'Analyzing mice hand-mov 신경 과학, 의학 및 생체 역학을 포함한 여러 연구 분야에서 동물의 움직임을 추적한 데이터를 사용합니다. DeepLabCut은 필름에 기록된 동작을 구문 분석하여 인간과 다른 동물이 하는 일을 이해하는 데 도움을 줍니다. 심층 신경망 기반 데이터 분석과 함께 태깅 및 모니터링의 힘든 작업에 자동화를 사용하는 DeepLabCut은 영장류, 생쥐, 물고기, 파리 등과 같은 동물 관찰과 관련된 과학적 연구를 훨씬 빠르고 정확하게 만듭니다. {{< 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)' +src = '/images/content_images/cs/race-horse.gif' title = '경주마 신체 부위의 위치를 추적하는 색 점' alt = '경주마 애니메이션' attribution = '(출처: Mackenzie Mathis)' {{< /figure >}} DeeDeepLabCut의 동물 자세 추출을 통한 동물의 비침습적 행동 추적은 생체 역학, 유전학, 행동학 & 신경 과학과 같은 영역에서 과학적 추구에 매우 중요합니다. 동적으로 변화하는 배경에서 마커 없이 비디오에서 동물 포즈를 비침습적으로 측정하는 것은 기술적으로 뿐만 아니라 필요한 리소스 요구 사항 및 필요한 훈련 데이터 측면에서 계산적으로 어려운 일입니다. @@ -51,7 +51,7 @@ DeepLabCut은 [전이 학습](https://arxiv.org/pdf/1909.11229), 이라는 원 {{< figure >}} {{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCut 워크플로우**" alt="워크플로우" attr="*(출처: 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 >}} +{{< figure >}} ### 도전 @@ -68,7 +68,7 @@ DeepLabCut은 [전이 학습](https://arxiv.org/pdf/1909.11229), 이라는 원 마지막으로 배열 조작 - 다양한 이미지, 대상 텐서 및 키포인트에 해당하는 대규모 배열 스택을 처리하는 것은 상당히 어렵습니다. {{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy를 통한 이익" caption="**활용한 주요 NumPy 기능**" >}} -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' +src = '/images/content_images/cs/pose-estimation.png' title = '포즈 추정 변수 및 복잡도' alt = '난점 설명' align = 'center' attribution = '(출처: Mackenzie Mathis)' attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' {{< /figure >}} ## 포즈 추정 문제를 해결하는 NumPy의 역할 @@ -86,7 +86,7 @@ NumPy의 다음 기능은 이미지 처리, 조합 요구 사항 및 DeepLabCut DeepLabCut은 툴킷에서 제공하는 워크플로 전체에서 NumPy의 어레이 기능을 활용합니다. 특히, NumPy는 사람의 주석 레이블 지정을 위한 개별 프레임 샘플링과 주석 데이터 작성, 편집 및 처리에 사용됩니다. TensorFlow 내에서 신경망은 DeepLabCut 기술로 수천 번의 반복을 통해 훈련되어 프레임에서 실측 주석을 예측합니다. 이를 위해 이미지 대 이미지 변환 문제로 포즈 추정을 캐스팅하기 위해 목표 밀도(점수 지도)가 생성됩니다. 신경망을 강력하게 만들기 위해 다양한 기하 및 이미지 처리 단계에 따라 대상 스코어맵을 계산해야 하는 데이터 확대가 사용됩니다. 학습을 빠르게 하기 위해 NumPy의 벡터화 기능이 활용됩니다. 추론을 위해 대상 스코어맵에서 가장 가능성이 높은 예측을 추출해야 하며 효율적으로 "개별 동물을 조립하기 위해 예측을 연결"해야 합니다. {{< 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' +src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut 워크플로우' alt = '워크플로우' attribution = '(출처: 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 >}} ## 요약 @@ -94,7 +94,7 @@ src = '/images/content_images/cs/deeplabcut-workflow.png' title = 'DeepLabCut Wo 행동을 관찰하고 효율적으로 설명하는 것은 현대 행동학, 신경과학, 의학 및 기술의 핵심 테넌트입니다. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf)을 사용하면 연구자가 피험자의 자세를 추정하여 행동을 정량화할 수 있습니다. DeepLabCut Python 툴킷은 작은 훈련 이미지 세트만으로 인간 수준의 라벨링 정확도 내에서 신경망을 훈련할 수 있습니다. 따라서 실험실에서의 행동 분석뿐만 아니라 잠재적으로 스포츠, 보행 분석, 의학 및 재활 연구에도 응용 분야를 확장합니다. DeepLabCut 알고리즘이 직면한 복잡한 조합, 데이터 처리 문제는 NumPy의 배열 조작 기능을 사용하여 해결됩니다. {{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized' +src = '/images/content_images/cs/numpy_dlc_benefits.png' alt = 'numpy를 통한 이익' title = '활용된 주요 NumPy 기능' {{< /figure >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 023d2d9bb7e1cb7e4c6cb7c7dd448d911e305b7d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 28 May 2024 20:20:35 +0200 Subject: [PATCH 709/711] New translations learn.md (Arabic) --- content/ar/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 390fdcf6bc..4f9fa53ae3 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -26,7 +26,7 @@ There's a ton of information about NumPy out there. If you are just starting, we **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). +* [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). * [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* From eff67f647c0ab825b8e9ec0f7bf7c5816d39e329 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 28 May 2024 20:20:38 +0200 Subject: [PATCH 710/711] New translations learn.md (Russian) --- content/ru/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ru/learn.md b/content/ru/learn.md index 390fdcf6bc..4f9fa53ae3 100644 --- a/content/ru/learn.md +++ b/content/ru/learn.md @@ -26,7 +26,7 @@ There's a ton of information about NumPy out there. If you are just starting, we **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). +* [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). * [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* From 3ac33604306619770f63362d138af9d9a61e369a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 28 May 2024 20:20:39 +0200 Subject: [PATCH 711/711] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/learn.md b/content/zh/learn.md index 390fdcf6bc..4f9fa53ae3 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -26,7 +26,7 @@ There's a ton of information about NumPy out there. If you are just starting, we **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). +* [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). * [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*