From 6d9354683d896b18720b068d901a1d5bd983abda Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:35:52 +0000 Subject: [PATCH 001/909] New translations community.md (Spanish) --- content/es/community.md | 65 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 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..4e24a83784 --- /dev/null +++ b/content/es/community.md @@ -0,0 +1,65 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a very diverse group of [contributors](/gallery/team.html). 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](http://numpy-discussion.10968.n7.nabble.com/). + +*** + +### [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. + From 000d81ac0fde76270d9b525192914b56331fbc72 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:35:53 +0000 Subject: [PATCH 002/909] New translations diversity_sep2020.md (Chinese Simplified) --- content/zh/diversity_sep2020.md | 48 +++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 content/zh/diversity_sep2020.md diff --git a/content/zh/diversity_sep2020.md b/content/zh/diversity_sep2020.md new file mode 100644 index 0000000000..ef3030d5f7 --- /dev/null +++ b/content/zh/diversity_sep2020.md @@ -0,0 +1,48 @@ +--- +title: NumPy Diversity and Inclusion Statement +sidebar: false +--- + + +_In light of the foregoing discussion on social media after publication of the NumPy paper in Nature and the concerns raised about the state of diversity and inclusion on the NumPy team, we would like to issue the following statement:_ + + +It is our strong belief that we are at our best, as a team and community, when we are inclusive and equitable. Being an international team from the onset, we recognize the value of collaborating with individuals from diverse backgrounds and expertise. A culture where everyone is welcomed, supported, and valued is at the core of the NumPy project. + +## The Past + +Contributing to open source has always been a pastime in which most historically marginalized groups, especially women, faced more obstacles to participate due to a number of societal constraints and expectations. Open source has a severe diversity gap that is well documented (see, e.g., the [2017 GitHub Open Source Survey](https://opensourcesurvey.org/2017/) and [this blog post](https://medium.com/tech-diversity-files/if-you-think-women-in-tech-is-just-a-pipeline-problem-you-haven-t-been-paying-attention-cb7a2073b996)). + +Since its inception and until 2018, NumPy was maintained by a handful of volunteers often working nights and weekends outside of their day jobs. At any one time, the number of active core developers, the ones doing most of the heavy lifting as well as code review and integration of contributions from the community, was in the range of 4 to 8. The project didn't have a roadmap or mechanism for directing resources, being driven by individual efforts to work on what seemed needed. The authors on the NumPy paper are the individuals who made the most significant and sustained contributions to the project over a period of 15 years (2005 - 2019). The lack of diversity on this author list is a reflection of the formative years of the Python and SciPy ecosystems. + +2018 has marked an important milestone in the history of the NumPy project. Receiving funding from The Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation allowed us to provide full-time employment for two software engineers with years of experience contributing to the Python ecosystem. Those efforts brought NumPy to a much healthier technical state. + +This funding also created space for NumPy maintainers to focus on project governance, community development, and outreach to underrepresented groups. [The diversity statement](https://figshare.com/articles/online_resource/Diversity_and_Inclusion_Statement_NumPy_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/12980852) written in mid 2019 for the CZI EOSS program grant application details some of the challenges as well as the advances in our efforts to bring in more diverse talent to the NumPy team. + +## The Present + +Offering employment opportunities is an effective way to attract and retain diverse talent in OSS. Therefore, we used two-thirds of our second grant that became available in Dec 2019 to employ Melissa Weber Mendonça and Mars Lee. + +As a result of several initiatives aimed at community development and engagement led by Inessa Pawson and Ralf Gommers, the NumPy project has received a number of valuable contributions from women and other underrepresented groups in open source in 2020: + +- Melissa Weber Mendonça gained commit rights, is maintaining numpy.f2py and is leading the documentation team, +- Shaloo Shalini created all case studies on numpy.org, +- Mars Lee contributed web design and led our accessibility improvements work, +- Isabela Presedo-Floyd designed our new logo, +- Stephanie Mendoza, Xiayoi Deng, Deji Suolang, and Mame Fatou Thiam designed and fielded the first NumPy user survey, +- Yuki Dunn, Dayane Machado, Mahfuza Humayra Mohona, Sumera Priyadarsini, Shaloo Shalini, and Kriti Singh (former Outreachy intern) helped the survey team to reach out to non-English speaking NumPy users and developers by translating the questionnaire into their native languages, +- Sayed Adel, Raghuveer Devulapalli, and Chunlin Fang are driving the work on SIMD optimizations in the core of NumPy. + +While we still have much more work to do, the NumPy team is starting to look much more representative of our user base. And we can assure you that the next NumPy paper will certainly have a more diverse group of authors. + +## The Future + +We are fully committed to fostering inclusion and diversity on our team and in our community, and to do our part in building a more just and equitable future. + +We are open to dialogue and welcome every opportunity to connect with organizations representing and supporting women and minorities in tech and science. We are ready to listen, learn, and support. + +Please get in touch with us on [our mailing list](https://scipy.org/scipylib/mailing-lists.html#mailing-lists), [GitHub](https://github.com/numpy/numpy/issues), [Slack](https://numpy.org/contribute/), in private at numpy-team@googlegroups.com, or join our [bi-weekly community meeting](https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg). + + +_Sayed Adel, Sebastian Berg, Raghuveer Devulapalli, Chunlin Fang, Ralf Gommers, Allan Haldane, Stephan Hoyer, Mars Lee, Melissa Weber Mendonça, Jarrod Millman, Inessa Pawson, Matti Picus, Nathaniel Smith, Julian Taylor, Pauli Virtanen, Stéfan van der Walt, Eric Wieser, on behalf of the NumPy team_ + From ec7cec44c1a0e753956c3b9b9ee5c3a59ac2423a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:35:55 +0000 Subject: [PATCH 003/909] New translations gethelp.md (Portuguese, Brazilian) --- content/pt/gethelp.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 content/pt/gethelp.md diff --git a/content/pt/gethelp.md b/content/pt/gethelp.md new file mode 100644 index 0000000000..bba586e7f2 --- /dev/null +++ b/content/pt/gethelp.md @@ -0,0 +1,34 @@ +--- +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). + + + +### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) + +Um fórum para fazer perguntas sobre a utilização da biblioteca, por exemplo: "Como faço X no NumPy?". Por favor [use a tag `#numpy`](https://stackoverflow.com/help/tagging) + +*** + +### [Reddit](https://www.reddit.com/r/Numpy/) + +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 7d05e8cdcdd4c77b675243b3d03a4e038402756e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:35:57 +0000 Subject: [PATCH 004/909] New translations report-handling-manual.md (Portuguese, Brazilian) --- content/pt/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/pt/report-handling-manual.md diff --git a/content/pt/report-handling-manual.md b/content/pt/report-handling-manual.md new file mode 100644 index 0000000000..14418d0e11 --- /dev/null +++ b/content/pt/report-handling-manual.md @@ -0,0 +1,95 @@ +--- +title: Código de Conduta NumPy - Como dar seguimento a um relatório +sidebar: false +--- + +Este é o manual seguido pelo Comitê do Código de Conduta do NumPy. É usado quando respondemos a um incidente para nos certificarmos de que somos pessoas consistentes e justas. + +Garantir que o [Código de Conduta](/code-of-conduct) seja respeitado afeta nossa comunidade hoje e no futuro. É uma ação que levamos muito a sério. Ao analisar medidas de aplicação do Código de Conduta, o Comitê terá em mente os seguintes valores e orientações: + +* Agir de forma pessoal e não impessoal. O Comitê pode levar as partes a compreender a situação, respeitando simultaneamente a privacidade e a necessária confidencialidade das pessoas relatantes. No entanto, por vezes, é necessário comunicar diretamente com um ou mais indivíduos: o objetivo do Comitê é melhorar a saúde da nossa comunidade, em vez de produzir apenas uma decisão formal. +* Enfatizar empatia pelos indivíduos ao invés de julgar o comportamento, evitando rótulos binários de "bom" e "mau". Existem atos de agressão e assédio claros e visíveis, e vamos abordá-los com firmeza. Mas muitos cenários que podem ser desafiadores são aqueles em que as discordâncias normais se transformam em comportamento desnecessário ou prejudicial de várias partes. Compreender o contexto completo e encontrar um caminho que traga um entendimento entre as partes é difícil, mas, em última análise, é o resultado mais produtivo para a nossa comunidade. +* Compreendemos que o e-mail é um meio difícil e que pode causar uma sensação de isolamento. Receber críticas por e-mail, sem contato pessoal, pode ser particularmente doloroso. Isto faz com que seja especialmente importante manter um clima de respeito aberto pelas opiniões dos outros. Significa também que temos de ser transparentes nas nossas ações, e que faremos tudo o que estiver ao nosso alcance para garantir que todos os nossos membros sejam tratados de forma justa e com simpatia. +* A discriminação pode ser sutil e pode ser inconsciente. Pode revelar-se em tratamentos injustos e hostis em interações que normalmente seriam ordinárias. Sabemos que isso acontece, e teremos o cuidado de ter isso em mente. Gostaríamos muito de ouvir se você acha que foi tratado injustamente, e usaremos esses procedimentos para garantir que a sua reclamação seja ouvida e abordada. +* Ajudar a aumentar o envolvimento em uma boa prática de discussão: tentar identificar onde a discussão pode ter falhado, e fornecer informações úteis, indicadores e recursos que podem levar a mudanças positivas nestes pontos. +* Estar ciente das necessidades de novos membros: fornecer-lhes apoio e consideração explícitos, com o objetivo de aumentar a participação de grupos sub-representados, em particular. +* As pessoas vêm de meios culturais e linguísticos diferentes. Tentar identificar quaisquer mal-entendidos honestos causados por falantes não-nativos e ajudá-los a entender a questão e o que pode ser modificado para evitar causar ofensa. Uma discussão complexa numa língua estrangeira pode ser muito intimidante, e queremos aumentar a nossa diversidade também entre nacionalidades e culturas. + + +## Mediação + +A mediação informal voluntária é um instrumento à nossa disposição. Em contextos em que duas ou mais partes escalaram ao ponto de demonstrarem comportamento inapropriado (algo tristemente comum no conflito humano), poderá ser útil facilitar um processo de mediação. Isto é apenas um exemplo: em todo caso, o Comitê pode considerar a mediação, tendo em conta que o processo se destina a ser estritamente voluntário e que nenhuma das partes pode ser pressionada a participar. Se o Comitê sugerir mediação, deve: + +* Encontrar uma pessoa candidata que possa servir de mediadora. +* Obter o acordo da(s) pessoa(s) relatante(s). A(s) pessoa(s) relatante(s) têm total liberdade para recusar a ideia de mediação ou propor um mediador alternativo. +* Obter o acordo da(s) pessoa(s) relatada(s). +* Estabelecer uma pessoa mediadora: enquanto as partes podem propor um mediador diferente da pessoa sugerida, o processo só poderá avançar se for alcançado um acordo comum em todos os termos. +* Estabelecer um cronograma para a mediação ser concluida, idealmente dentro de duas semanas. + +A pessoa mediadora entrará em contato com todas as partes e procurará uma resolução satisfatória para todos. Após a sua conclusão, a pessoa mediadora apresentará ao Comitê um relatório (examinado por todas as partes envolvidas no processo) com recomendações sobre outras medidas. O Comitê avaliará então esses resultados (em caso de resolução satisfatória ou não) e decidirá sobre quaisquer medidas adicionais consideradas necessárias. + + +## Como o Comitê responderá aos relatórios + +Quando o Comitê (ou um membro do Comitê) recebe um relatório, será inicialmente determinado se o relatório é sobre uma violação clara e severa (como definido abaixo). Em caso afirmativo, medidas imediatas serão tomadas para além do processo regular de tratamento dos relatórios. + + +## Ações claras e severas de violação + +Sabemos que é mais comum do que o desejado que a comunicação na Internet comece ou se transforme em abusos óbvios e flagrantes. Trataremos rapidamente de violações claras e severas como ameaças pessoais, linguagem violenta, sexista ou racista. + +Quando um membro do Comitê do Código de Conduta tomar conhecimento de uma violação clara e grave, fará o seguinte: + +* Desligará imediatamente a pessoa originadora de todos os canais de comunicação do NumPy. +* Responderá à pessoa relatante para informá-la que seu relatório foi recebido e que a pessoa originadora foi desligada. +* Em todos os casos, a pessoa moderadora deve fazer um esforço razoável para entrar em contato com a pessoa originadora, e dizer-lhes especificamente como sua linguagem ou ações se qualificam como uma "violação clara e severa". A pessoa moderadora deve também dizer que, se a pessoa originadora considerar que isso é injusto ou quiser ser reconectada ao NumPy, tem o direito de solicitar uma revisão, de acordo com as disposições do Comitê do Código de Conduta. A pessoa moderadora deve copiar esta explicação para o Comitê do Código de Conduta. +* O Comitê do Código de Conduta procederá formalmente à análise e decisão em todos os casos em que este mecanismo tenha sido aplicado para garantir que não seja utilizado para controlar desentendimentos acalorados comuns. + + +## Tratamento de relatórios + +Quando um relatório é enviado ao Comitê, ele responderá imediatamente à pessoa relatante para confirmar a sua recepção. Esta resposta deve ser enviada no prazo de 72 horas, e o grupo deve esforçar-se por responder muito mais rapidamente. + +Se um relatório não contiver informações suficientes, o Comitê obterá todos os dados relevantes antes de agir. O Comitê tem poderes para agir em nome do Conselho Diretor ao contactar quaisquer pessoas envolvidas para obter um relato mais completo dos acontecimentos. + +O Comitê analisará então o incidente e determinará, do melhor jeito possível: + +* O que aconteceu. +* Se este evento constitui ou não uma violação do Código de Conduta. +* Quem são as pessoas responsáveis. +* Se se trata de uma situação contínua, e existe uma ameaça para a segurança física de alguém. + +Estas informações serão recolhidas por escrito e, sempre que possível, as deliberações do grupo serão gravadas e armazenadas (por exemplo, transcrições de conversas, discussões por e-mail, chamadas gravadas de videoconferência, resumos de conversas por voz, etc). + +É importante manter um arquivo de todas as atividades deste Comitê para garantir a consistência no comportamento e fornecer memória institucional ao projeto. Para ajudar com isto, o canal de discussão padrão para este Comitê será uma lista de e-mail privada, acessível a atuais e futuros membros do Comitê, bem como aos membros do Conselho Diretor a pedido justificado. Se o Comitê sentir a necessidade de usar comunicações fora da lista (por exemplo, chamadas por telefone para resposta precoce/rápida), deve em todos os casos resumi-las de volta para a lista, para que haja um bom registro do processo. + +O Comitê do Código de Conduta deve ter por objetivo chegar a um acordo sobre uma resolução no prazo de duas semanas. Caso uma resolução não possa ser determinada nesse período, o Comitê responderá à(s) pessoa(s) relatante(s) com uma atualização e cronograma previsto para a resolução. + + +## Resoluções + +O Comitê tem de chegar a um acordo sobre uma resolução por consenso. Se o grupo não conseguir chegar a um consenso e permanece bloqueado durante mais de uma semana, o grupo encaminhará o assunto para o Conselho Diretor para resolução. + +Possíveis respostas podem incluir: + +* Não tomar nenhuma outra ação: + - se determinarmos que não ocorreram violações; + - se a questão tiver sido resolvida publicamente enquanto o Comitê estava considerando uma resposta. +* Coordenação de mediação voluntária: se todas as partes envolvidas concordarem, o Comitê poderá facilitar um processo de mediação, conforme detalhado acima. +* Salientar publicamente que alguns comportamentos, ações ou linguagem foram julgados inapropriados ou podem ser considerados danosos para algumas pessoas, explicando por que no contexto atual e solicitando que a comunidade se auto-ajuste. +* Uma advertência privada do Comitê para a(s) pessoa(s) envolvida(s). Neste caso, a pessoa presidente do Comitê irá entregar essa advertência à(s) pessoa(s) por e-mail, em cópia (CC) ao grupo. +* Uma advertência pública. Neste caso, a pessoa presidente do Comitê vai apresentar essa advertência no mesmo fórum em que ocorreu a violação, dentro dos limites da viabilidade. Exemplo: a lista original para uma violação de e-mail, mas para uma discussão em sala de bate-papo onde a pessoa/contexto pode sumir, isto pode ser feito por outros meios. O grupo pode optar por publicar esta mensagem em outro local para fins de documentação. +* Um pedido de desculpas públicas ou privadas, supondo que a(s) pessoa(s) relatante(s) concorde(m) com esta ideia: a(s) pessoa(s) pode(m), a seu critério, recusar contatos adicionais com a pessoa relatada. A Presidência dará seguimento a este pedido. O Comitê, se escolher, pode anexar condições adicionais a este pedido inicial: por exemplo, o grupo pode pedir à pessoa relatada que se desculpe para que tenha o direito de manter a sua adesão a uma lista de e-mails. +* Um “acordo mútuo de trégua” onde o Comitê solicita à pessoa que se abstenha temporariamente da participação na comunidade. Se a pessoa optar por não fazer uma pausa temporária voluntariamente, o Comitê pode aplicar um “período de afastamento obrigatório”. +* Um banimento permanente ou temporário de alguns ou todos os espaços do NumPy (listas de e-mails, gitter.im, etc.). O grupo manterá registro de todas essas proibições, para que elas possam ser revistas no futuro ou mantidas. + +Uma vez aprovada uma resolução, mas antes de ser efetivamente aplicada, o Comitê entrará em contato com a pessoa relatante original e quaisquer outras partes afetadas e explicará a resolução proposta. O Comitê perguntará se esta resolução é aceitável e terá de tomar nota da sua resposta para registro futuro. + +Finalmente, o Comitê apresentará um relatório ao Conselho Diretor do NumPy (bem como ao time *core* do NumPy no caso de uma resolução em curso, como um banimento). + +O Comitê nunca discutirá publicamente a questão; todas as declarações públicas serão feitas pela pessoa presidente do Comitê do Código de Conduta ou pelo Conselho Diretor do NumPy. + + +## Conflitos de Interesse + +Em caso de conflito de interesses, um membro do Comitê deve notificar imediatamente os outros membros e abdicar de sua participação no processo caso seja necessário. From 7adb5ed0e8b245dffff7d1c0ecf5c23068fe1a57 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:35:58 +0000 Subject: [PATCH 005/909] New translations terms.md (Portuguese, Brazilian) --- content/pt/terms.md | 178 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 content/pt/terms.md diff --git a/content/pt/terms.md b/content/pt/terms.md new file mode 100644 index 0000000000..a294b49483 --- /dev/null +++ b/content/pt/terms.md @@ -0,0 +1,178 @@ +--- +title: Termos de Uso +sidebar: false +--- + +*Última atualização em 4 de janeiro de 2020* + + +## AGREEMENT TO TERMS + +These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy ("**Project**", “**we**”, “**us**”, or “**our**”), concerning your access to and use of the numpy.org website as well as any other media form, media channel, mobile website or mobile application related, linked, or otherwise connected thereto (collectively, the “Site”). You agree that by accessing the Site, you have read, understood, and agreed to be bound by all of these Terms of Use. IF YOU DO NOT AGREE WITH ALL OF THESE TERMS OF USE, THEN YOU ARE EXPRESSLY PROHIBITED FROM USING THE SITE AND YOU MUST DISCONTINUE USE IMMEDIATELY. + + + +Supplemental terms and conditions or documents that may be posted on the Site from time to time are hereby expressly incorporated herein by reference. We reserve the right, in our sole discretion, to make changes or modifications to these Terms of Use at any time and for any reason. We will alert you about any changes by updating the “Last updated” date of these Terms of Use, and you waive any right to receive specific notice of each such change. It is your responsibility to periodically review these Terms of Use to stay informed of updates. 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Our failure to exercise or enforce any right or provision of these Terms of Use shall not operate as a waiver of such right or provision. These Terms of Use operate to the fullest extent permissible by law. We may assign any or all of our rights and obligations to others at any time. We shall not be responsible or liable for any loss, damage, delay, or failure to act caused by any cause beyond our reasonable control. If any provision or part of a provision of these Terms of Use is determined to be unlawful, void, or unenforceable, that provision or part of the provision is deemed severable from these Terms of Use and does not affect the validity and enforceability of any remaining provisions. There is no joint venture, partnership, employment or agency relationship created between you and us as a result of these Terms of Use or use of the Site. You agree that these Terms of Use will not be construed against us by virtue of having drafted them. You hereby waive any and all defenses you may have based on the electronic form of these Terms of Use and the lack of signing by the parties hereto to execute these Terms of Use. + +## CONTACT US + +In order to resolve a complaint regarding the Site or to receive further information regarding use of the Site, please contact us at: + +NumFOCUS, Inc.
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+1 (512) 222-5449 + + + From af7b860c9dcefc8959dfeb4428d57200706db942 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:00 +0000 Subject: [PATCH 006/909] New translations privacy.md (Portuguese, Brazilian) --- content/pt/privacy.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 content/pt/privacy.md diff --git a/content/pt/privacy.md b/content/pt/privacy.md new file mode 100644 index 0000000000..be4b6613da --- /dev/null +++ b/content/pt/privacy.md @@ -0,0 +1,18 @@ +--- +title: Política de privacidade +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 1a856ca1971c75484e1c3b1abe6a328b5dd13cae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:02 +0000 Subject: [PATCH 007/909] New translations press-kit.md (Portuguese, Brazilian) --- content/pt/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/pt/press-kit.md diff --git a/content/pt/press-kit.md b/content/pt/press-kit.md new file mode 100644 index 0000000000..a59064dca5 --- /dev/null +++ b/content/pt/press-kit.md @@ -0,0 +1,8 @@ +--- +title: Kit de imprensa +sidebar: false +--- + +Gostaríamos de facilitar a inclusão da identidade do projeto NumPy em seu próximo documento acadêmico, materiais educacionais ou apresentação. + +Você encontrará várias versões de alta resolução do logo do NumPy [aqui](https://github.com/numpy/numpy/tree/master/branding/logo). Note que usando os recursos numpy.org, você aceita o [Código de Conduta do NumPy](/code-of-conduct). From 17718730255bf276179ae5e794f1240b027946cc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:04 +0000 Subject: [PATCH 008/909] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 84 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) create mode 100644 content/pt/learn.md diff --git a/content/pt/learn.md b/content/pt/learn.md new file mode 100644 index 0000000000..66ef85cc38 --- /dev/null +++ b/content/pt/learn.md @@ -0,0 +1,84 @@ +--- +title: Aprenda +sidebar: false +--- + +**A documentação oficial do NumPy mora [aqui](https://numpy.org/doc/stable).** + +Abaixo está uma coleção de recursos externos selecionados. Para contribuir, veja o [fim desta página](#add-to-this-list). +*** + +## Iniciantes + +Há uma tonelada de informações sobre o NumPy lá fora. Se você está começando, recomendamos fortemente estes: + + **Tutoriais** + +* [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) +* [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) + + **Livros** + +* [Guide to NumPy *de Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) Essa é uma versão free de 2006. Para a última versão (2015) veja [aqui](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +* [From Python to NumPy *por 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) *por Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow* + +Você também pode querer conferir a [lista Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre o tema "Python+SciPy. A maioria dos livros lá serão sobre o "ecossistema SciPy", que tem o NumPy em sua essência. + + **Vídeos** + +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* + +*** + +## Avançado + +Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais. + + **Tutoriais** + +* [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/) + + **Livros** + +* [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* +* [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *por 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) *por Robert Johansson* + + **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* + +*** + +## Palestras sobre NumPy + +* [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) +* [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) +* [NumPy: what has changed and what is going to change?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *por Matti Picus* (2019) +* [Inside NumPy](https://www.youtube.com/watch?v=dBTJD_FDVjU) *por 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) *por Travis Oliphant* (2019) + +*** + +## 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](/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/master/content/en/learn.md). Diga por que sua recomendação merece ser mencionada nesta página e também qual o público que mais se beneficiaria. From 782e6aca86eb4e133c8cedfde930667019bf7859 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:05 +0000 Subject: [PATCH 009/909] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 142 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 142 insertions(+) create mode 100644 content/pt/install.md diff --git a/content/pt/install.md b/content/pt/install.md new file mode 100644 index 0000000000..319caaf9e2 --- /dev/null +++ b/content/pt/install.md @@ -0,0 +1,142 @@ +--- +title: Instalando o 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. + + + +# Guia de instalação do Python e do NumPy + +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. + +### Usuários iniciantes + +On all of Windows, macOS, and Linux: + +- Instale o [Anaconda](https://www.anaconda.com/distribution/) (instala todos os pacotes que você precisa e todas as outras ferramentas mencionadas abaixo). +- Para escrever e executar código, use notebooks no [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) para a computação exploratória e interativa, e o [Spyder](https://www.spyder-ide.org/) ou [Visual Studio Code](https://code.visualstudio.com/) para escrever scripts e pacotes. +- Use o [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) para gerenciar seus pacotes e iniciar o JupyterLab, Spyder ou o Visual Studio Code. + + +### Usuários avançados + +#### Windows ou macOS + +- Instale o [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- Mantenha o ambiente conda `base` mínimo, e use um ou mais [ambientes conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) para instalar o pacote que você precisa para a tarefa ou projeto em que você está trabalhando. +- A menos que você esteja satisfeito com apenas os pacotes no canal `defaults`, faça do `conda-forge` seu canal padrão [definindo a prioridade do canal](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge). + + +#### Linux + +If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: +- Use seu gerenciador de pacotes do SO o máximo possível (para o Python, NumPy e outras bibliotecas). +- Instale pacotes não fornecidos pelo seu gerenciador de pacotes com `pip install algumpacote --user`. + +If you use a GPU: +- Instale o [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- Mantenha o ambiente conda `base` mínimo, e use um ou mais [ambientes conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) para instalar o pacote que você precisa para a tarefa ou projeto em que você está trabalhando. +- Use o canal conda`defaults` (`conda-forge` não tem bom suporte para pacotes de GPU). + +Otherwise: +- Instale o [Miniforge](https://github.com/conda-forge/miniforge). +- Mantenha o ambiente conda `base` mínimo, e use um ou mais [ambientes conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) para instalar o pacote que você precisa para a tarefa ou projeto em que você está trabalhando. + + +#### Alternativa se você preferir 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: +- Instale o Python a partir de, por exemplo, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), ou seu gerenciador de pacotes Linux. +- Use [Poetry](https://python-poetry.org/) como a ferramenta mais bem mantida que fornece um resolvedor de dependências e recursos de gerenciamento de ambiente de forma semelhante ao que o conda faz. + + +## 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. + + +### Instalações reprodutíveis + +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: [ambientes conda e arquivos environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) + - Pip: [ambientes virtuais](https://docs.python.org/3/tutorial/venv.html) e [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) + - Poetry: [ambientes virtuais e 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 7dc5893758ceaa9b3d9a2ebce00b0929c43aa00f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:07 +0000 Subject: [PATCH 010/909] New translations code-of-conduct.md (Portuguese, Brazilian) --- content/pt/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/pt/code-of-conduct.md diff --git a/content/pt/code-of-conduct.md b/content/pt/code-of-conduct.md new file mode 100644 index 0000000000..d0088908a0 --- /dev/null +++ b/content/pt/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: Código de Conduta NumPy +sidebar: false +aliases: + - /conduct.html +--- + +### Introdução + +Este código de conduta aplica-se a todos os espaços gerenciados pelo projeto NumPy, incluindo todas as listas de discussão públicas e privadas, *issue tracker*, wikis, blogs, Twitter e qualquer outro canal de comunicação usado pela nossa comunidade. O projeto NumPy não organiza eventos presenciais. No entanto, os eventos relacionados à nossa comunidade devem ter um código de conduta semelhante ao atual. + +Este Código de Conduta deve ser honrado por todas as pessoas que participam da comunidade NumPy formal ou informalmente, ou que reivindicam qualquer afiliação com o projeto, em qualquer atividade relacionada ao projeto, especialmente ao representar o projeto, em qualquer função. + +Este código não é exaustivo ou completo. Serve para disseminar a nossa compreensão comum de um ambiente colaborativo e de objetivos compartilhados entre nós. Por favor, tente seguir este código tanto na essência quanto ao pé da letra, para criar um ambiente amigável e produtivo que enriqueça a comunidade em geral. + +### Diretrizes específicas + +Nós nos esforçamos para: + +1. Sermos abertos. Convidamos qualquer pessoa a participar da nossa comunidade. Preferimos usar métodos públicos de comunicação para mensagens relacionadas aos projetos, a menos que estejamos discutindo algo sensível. Isso se aplica a mensagens em busca de ajuda ou suporte relacionado ao projeto também; não só é muito mais provável que um pedido de ajuda público resulte em uma resposta, mas isso também garante que qualquer erro involuntário na resposta seja mais facilmente detectado e corrigido. +2. Sermos empáticos, acolhedores, amigáveis e pacientes. Trabalhamos juntos para resolver conflitos e acreditamos em boas intenções. Todos nós podemos sentir alguma frustração de vez em quando, mas não permitimos que a frustração se transforme num ataque pessoal. Uma comunidade onde as pessoas se sentem desconfortáveis ou ameaçadas não é uma comunidade produtiva. +3. Sermos colaborativos. O nosso trabalho será utilizado por outras pessoas e, por sua vez, dependeremos do trabalho dos outros. Quando fazemos algo em benefício do projeto, estamos dispostos a explicar aos outros como esse algo funciona, para que outros possam desenvolver o trabalho e torná-lo ainda melhor. Qualquer decisão que tomemos afetará nossos usuários e os colegas, e levamos essas consequências a sério quando tomamos decisões. +4. Sermos inquisitivos. Ninguém sabe tudo! Fazer perguntas antecipadamente evita muitos problemas mais tarde, por isso encorajamos as perguntas, embora possamos encaminhá-las para um fórum adequado. Vamos nos esforçar para sermos sensíveis e úteis. +5. Termos cuidado com as palavras que escolhemos. Somos cuidadosos e respeitosos na nossa comunicação e assumimos a responsabilidade pelo nosso próprio discurso. Seja gentil com os outros. Não insulte ou deprecie outros participantes. Nós não aceitaremos assédio ou outros comportamentos exclusivos, como: + * Ameaças ou linguagem violenta direcionadas contra outra pessoa. + * Piadas e linguagem sexista, racista ou discriminatória. + * Postagem de material sexualmente explícito ou violento. + * Postar (ou ameaçar postar) informações pessoais de outras pessoas (“doxing”). + * Compartilhar conteúdo privado, como e-mails enviados de maneira privada ou não-pública, ou fóruns não registrados, como histórico de canais IRC, sem o consentimento do remetente. + * Insultos pessoais, especialmente aqueles que utilizam termos racistas ou sexistas. + * Atenção sexual não consentida. + * Profanidade excessiva. Por favor, evite palavrões; as pessoas diferem muito na sua sensibilidade à linguagem. + * Assédio reiterado. Em geral, se alguém pedir que você pare, então pare. + * Advogar em favor ou encorajar qualquer um dos comportamentos acima. + +### Declaração de diversidade + +O projeto NumPy convida e incentiva a participação de todas as pessoas. Estamos empenhados em ser uma comunidade da qual todas as pessoas gostem de fazer parte. Embora nem sempre sejamos capazes de acomodar as preferências de cada indivíduo, nós tentamos o nosso melhor para tratar todos gentilmente. + +Não importa como você se identifica ou como os outros percebem você: nós lhe damos as boas-vindas. Embora nenhuma lista possa esperar ser totalmente abrangente, honramos explicitamente a diversidade em: idade, cultura, etnia, genótipo, identidade ou expressão de gênero, língua, origem, neurotipo, fenotipo, crenças políticas, profissão, raça, religião, orientação sexual, estado socioeconômico, subcultura e capacidade técnica, na medida em que não entrem em conflito com este código de conduta. + +Embora sejamos receptivos às pessoas fluentes em todas as línguas, o desenvolvimento do NumPy é conduzido em inglês. + +Padrões de comportamento na comunidade NumPy estão detalhados no Código de Conduta acima. Os participantes da nossa comunidade devem se comportar de acordo com esses padrões em todas as suas interações e ajudar os outros a fazê-lo também (veja a próxima seção). + +### Diretrizes de Resposta a Incidentes + +Sabemos que é mais comum do que o desejado que a comunicação na Internet comece ou se transforme em abusos óbvios e flagrantes. Reconhecemos também que, por vezes, as pessoas podem ter um dia ruim, ou não conhecer algumas das orientações deste Código de Conduta. Tenha isto em mente ao decidir como responder a uma violação deste Código. + +Em caso de violações claramente intencionais, o Comitê do Código de Conduta (veja abaixo) deve ser informado. Para violações possivelmente não intencionais, você pode responder à pessoa e apontar este código de conduta (seja em público ou em privado, o que for mais apropriado). Se preferir não o fazer, sinta-se à vontade para informar diretamente o Comitê do Código de Conduta, ou peça ao Comitê um conselho, sigilosamente. + +Você pode relatar problemas ao Comitê do Código de Conduta NumPy em numpy-conduct@googlegroups.com. + +Atualmente, o comitê é formato por: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Anirudh Subramanian + +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 + +_Esta seção resume os pontos mais importantes, mais detalhes podem ser encontrados em_ [Código de Conduta do NumPy - Como dar seguimento a um relatório](/report-handling-manual). + +Vamos investigar e responder a todas as queixas. O Comitê do Código de Conduta do NumPy e o Comitê Diretor do NumPy (se envolvido) protegerão a identidade do relatante, e tratarão o conteúdo das reclamações como confidencial (a menos que o relatante aceite o contrário). + +Em caso de violações graves e óbvias, por exemplo, ameaça pessoal ou linguagem violenta, sexista ou racista, vamos imediatamente desconectar a pessoa relatada dos canais de comunicação do NumPy; por favor, consulte o manual para mais detalhes. + +Em casos que não envolvam claras violações graves e óbvias deste Código de Conduta, o processo de ação referente a qualquer relato de violação do Código de Conduta recebido será: + +1. acusar o recebimento do relato, +2. discussão/feedback razoável, +3. mediação (se o feedback não ajudar e somente se ambos o relatante e relatado concordarem com isso), +4. aplicação de solução via decisão transparente (veja as [Resoluções](/report-handling-manual#resolutions)) do Comitê do Código de Conduta. + +O comitê responderá a qualquer relatório o mais rapidamente possível e, no máximo, no prazo de 72 horas. + +### Notas + +Somos gratos aos grupos responsáveis pelos documentos abaixo, dos quais retiramos conteúdo e inspiração: + +- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) From dec7b7f739ac24dd60551dd1e07eb8e151eca238 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:09 +0000 Subject: [PATCH 011/909] New translations citing-numpy.md (Portuguese, Brazilian) --- content/pt/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/pt/citing-numpy.md diff --git a/content/pt/citing-numpy.md b/content/pt/citing-numpy.md new file mode 100644 index 0000000000..d390e925e9 --- /dev/null +++ b/content/pt/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: Citando a NumPy +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: [0.1038/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:_ + + ``` +@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 9286414c656328f594b414ed8c059e22d58a4768 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:11 +0000 Subject: [PATCH 012/909] New translations arraycomputing.md (Portuguese, Brazilian) --- content/pt/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/pt/arraycomputing.md diff --git a/content/pt/arraycomputing.md b/content/pt/arraycomputing.md new file mode 100644 index 0000000000..941f69fe42 --- /dev/null +++ b/content/pt/arraycomputing.md @@ -0,0 +1,21 @@ +--- +title: Computação com Arrays +sidebar: false +--- + +*A computação com arrays é a base para estatística e matemática computacionais, computação científica e suas várias aplicações em ciência e análise de dados, tais como visualização de dados, processamento de sinais digitais, processamento de imagens, bioinformática, aprendizagem de máquina, IA e muitas outras.* + +A manipulação e a transformação de dados de grande escala dependem de computação eficiente de alta performance com arrays. A linguagem mais escolhida para análise de dados, aprendizagem de máquina e computação numérica produtiva é **Python.** + +**Num**erical **Py**thon (Python Numérico) ou NumPy é a biblioteca em Python padrão para o suporte à utilização de matrizes e arrays multidimensionais de grande porte, e vem com uma vasta coleção de funções matemáticas de alto nível para operar nestas arrays. + +Desde o lançamento do NumPy em 2006, o Pandas apareceu em 2008, e nos últimos anos vimos uma sucessão de bibliotecas de computação com arrays aparecerem, ocupando e preenchendo o campo da computação com arrays. Muitas dessas bibliotecas mais recentes imitam recursos e capacidades parecidas com o NumPy e entregam algoritmos e recursos mais recentes voltados para aplicações de aprendizagem de máquina e inteligência artificial. + +arraycl + +A **computação com arrays** é baseada em estruturas de dados chamadas **arrays**. *Arrays* são usadas para organizar grandes quantidades de dados de forma que um conjunto de valores relacionados possa ser facilmente ordenado, obtido, matematicamente manipulado e transformado fácil e rapidamente. + +A computação com arrays é *única* pois envolve operar nos valores de um array de dados *de uma vez*. Isso significa que qualquer operação de array se aplica a todo um conjunto de valores de uma só vez. Esta abordagem vetorizada fornece velocidade e simplicidade por permitir que os programadores organizem o código e operem em agregados de dados, sem ter que usar laços com operações escalares individuais. From ed85c51459992c399e42d3d7dad3e8b00114f81b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:12 +0000 Subject: [PATCH 013/909] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 69 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/pt/about.md diff --git a/content/pt/about.md b/content/pt/about.md new file mode 100644 index 0000000000..bb240c155e --- /dev/null +++ b/content/pt/about.md @@ -0,0 +1,69 @@ +--- +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 Numerical 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/master/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). + + +## Conselho Diretor (Steering Council) + +O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo prazo do projeto, tanto nos aspectos técnicos quanto na comunidade. Isso é feito através do trabalho com e para a comunidade NumPy em geral. O Conselho Diretor do NumPy atualmente consiste dos seguintes membros (em ordem alfabética): + +- Sebastian Berg +- Jaime Fernández del Río +- Ralf Gommers +- Allan Haldane +- Charles Harris +- Stephan Hoyer +- Matti Picus +- Nathaniel Smith +- Julian Taylor +- Pauli Virtanen +- Stéfan van der Walt +- Eric Wieser + +Membros Eméritos: + +- Travis Oliphant (fundador do projeto, 2005-2012) +- Alex Griffing (2015-2017) +- Marten van Kerkwijk (2017-2019) + +## Times + +O projeto NumPy está crescendo; temos equipes para + +- código +- documentação +- website +- triagem +- financiamento e bolsas + +Veja a página de [Times](/gallery/team.html) para membros individuais de cada time. + +## Patrocinadores + +O NumPy recebe financiamento direto das seguintes fontes: +{{< sponsors >}} + + +## Parceiros Institucionais + +Os Parceiros Institucionais são organizações que apoiam o projeto, empregando pessoas que contribuem para a NumPy como parte de seu trabalho. Os parceiros institucionais atuais incluem: +{{< partners >}} + + +## Doações + +Se você achou o NumPy útil no seu trabalho, pesquisa ou empresa, por favor considere fazer uma doação para o projeto que seja compatível com seus recursos. Qualquer quantidade ajuda! Todas as doações serão utilizadas estritamente para financiar o desenvolvimento do software de código aberto da NumPy, documentação e comunidade. + +NumPy é um Projeto Patrocinado da NumFOCUS, uma instituição de caridade sem fins lucrativos nos Estados Unidos. A NumFOCUS fornece ao NumPy apoio fiscal, legal e administrativo para ajudar a garantir a saúde e a sustentabilidade do projeto. Visite [numfocus.org](https://numfocus.org) para obter mais informações. + +Doações para o NumPy são gerenciadas pela [NumFOCUS](https://numfocus.org). Para doadores nos Estados Unidos, sua doação é dedutível para fins fiscais na medida oferecida pela lei. Como em qualquer doação, você deve consultar seu conselheiro fiscal sobre sua situação fiscal em particular. + +O Conselho Diretor da NumPy tomará as decisões sobre a melhor forma de utilizar os fundos recebidos. Prioridades técnicas e de infraestrutura estão documentadas no [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap). +{{< numfocus >}} From 3bbb473c712bf8c768c64a3828cc4bb96922d1af Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:14 +0000 Subject: [PATCH 014/909] New translations 404.md (Portuguese, Brazilian) --- content/pt/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/pt/404.md diff --git a/content/pt/404.md b/content/pt/404.md new file mode 100644 index 0000000000..627cde96d0 --- /dev/null +++ b/content/pt/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +Oops! Você atingiu um beco sem saída. + +Se você acha que algo deveria estar aqui, você pode [abrir uma issue](https://github.com/numpy/numpy.org/issues) no GitHub. From 6bb38b8965e9c54ece4f37d76354e3fdcfff1620 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:15 +0000 Subject: [PATCH 015/909] 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..3d25090e13 --- /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 0c53bbdb7bdbb253cfe54a0fb40108ae9aebd977 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:17 +0000 Subject: [PATCH 016/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 83 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/pt/news.md diff --git a/content/pt/news.md b/content/pt/news.md new file mode 100644 index 0000000000..d922c2e0e2 --- /dev/null +++ b/content/pt/news.md @@ -0,0 +1,83 @@ +--- +title: Notícias +sidebar: false +--- + +### 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 + +_Sept 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. + + +## Lançamentos + +Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 7b30ca681e69c9912292521564b45dd8bc36662b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:19 +0000 Subject: [PATCH 017/909] 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 ec9dc56667fd5c3109cd2d3c67b841a47979deaf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:21 +0000 Subject: [PATCH 018/909] 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..987b38fb68 --- /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) provides 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 66b7e6279d5fa3e59f5f22b68fac5c322d3768ed Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:22 +0000 Subject: [PATCH 019/909] 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 e5987804012542482011ceda7c955caf91bf8f31 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:24 +0000 Subject: [PATCH 020/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 83 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 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..5dcb849596 --- /dev/null +++ b/content/zh/news.md @@ -0,0 +1,83 @@ +--- +title: News +sidebar: false +--- + +### 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 + +_Sept 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 f01a90b86cc5f8288bbfbdc0179a70a083af3ceb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:26 +0000 Subject: [PATCH 021/909] 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..fc79a621af --- /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](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 3b095c1b88d94db4919474c010c6b804d61198f2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:27 +0000 Subject: [PATCH 022/909] 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 dd28bf3fa86be7467f9f5aa5ac4857b1e8c50925 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:29 +0000 Subject: [PATCH 023/909] 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 0c5111ac8c1164ac778e0d66ab70edc52ae734d7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:31 +0000 Subject: [PATCH 024/909] New translations terms.md (Chinese Simplified) --- content/zh/terms.md | 178 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 content/zh/terms.md diff --git a/content/zh/terms.md b/content/zh/terms.md new file mode 100644 index 0000000000..9a66045505 --- /dev/null +++ b/content/zh/terms.md @@ -0,0 +1,178 @@ +--- +title: Terms of Use +sidebar: false +--- + +*Last updated January 4, 2020* + + +## AGREEMENT TO TERMS + +These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy ("**Project**", “**we**”, “**us**”, or “**our**”), concerning your access to and use of the numpy.org website as well as any other media form, media channel, mobile website or mobile application related, linked, or otherwise connected thereto (collectively, the “Site”). You agree that by accessing the Site, you have read, understood, and agreed to be bound by all of these Terms of Use. IF YOU DO NOT AGREE WITH ALL OF THESE TERMS OF USE, THEN YOU ARE EXPRESSLY PROHIBITED FROM USING THE SITE AND YOU MUST DISCONTINUE USE IMMEDIATELY. + + + +Supplemental terms and conditions or documents that may be posted on the Site from time to time are hereby expressly incorporated herein by reference. We reserve the right, in our sole discretion, to make changes or modifications to these Terms of Use at any time and for any reason. We will alert you about any changes by updating the “Last updated” date of these Terms of Use, and you waive any right to receive specific notice of each such change. It is your responsibility to periodically review these Terms of Use to stay informed of updates. 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+1 (512) 222-5449 + + + From a13e723ab76df7536e52aff91203ff36cf8d0b95 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:32 +0000 Subject: [PATCH 025/909] New translations privacy.md (Chinese Simplified) --- content/zh/privacy.md | 18 ++++++++++++++++++ 1 file changed, 18 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..a3674dd48a --- /dev/null +++ b/content/zh/privacy.md @@ -0,0 +1,18 @@ +--- +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 dc5f33309281b60905cd1a766ebce19f7de57a64 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:34 +0000 Subject: [PATCH 026/909] 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..2309040ad2 --- /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/master/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From b9dad28fefb80d078e3717c35973a2e054951a92 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:36 +0000 Subject: [PATCH 027/909] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 84 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 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..264677ac48 --- /dev/null +++ b/content/zh/learn.md @@ -0,0 +1,84 @@ +--- +title: Learn +sidebar: false +--- + +**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** + +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +*** + +## Beginners + +There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: + + **Tutorials** + +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) +* [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 *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 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/) + + **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 Nunuz-Iglesias* +* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* + +*** + +## 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). + +## Contribute to this list + + +To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. From e8197bc414c6aadd61d3de3fdf4fdfccb719971a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:38 +0000 Subject: [PATCH 028/909] New translations install.md (Chinese Simplified) --- content/zh/install.md | 142 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 142 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..43dd44cb12 --- /dev/null +++ b/content/zh/install.md @@ -0,0 +1,142 @@ +--- +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 + +#### Windows or macOS + +- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- 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. +- Unless you're fine with only the packages in the `defaults` channel, make `conda-forge` your default channel via [setting the channel priority](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge). + + +#### Linux + +If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: +- Use your OS package manager for as much as possible (Python itself, NumPy, and other libraries). +- Install packages not provided by your package manager with `pip install somepackage --user`. + +If you use a GPU: +- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- 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. +- Use the `defaults` conda channel (`conda-forge` doesn't have good support for GPU packages yet). + +Otherwise: +- 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, for example, [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 d418b2a8f6fb0b43d291d6b71f993f13225dc57c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:39 +0000 Subject: [PATCH 029/909] 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..efcde754ae --- /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 +* Anirudh Subramanian + +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/reference/dev/conduct/code_of_conduct.html) From 490d0cfb94e8c2eca49670f6f34656726dc4c106 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:41 +0000 Subject: [PATCH 030/909] New translations history.md (Portuguese, Brazilian) --- content/pt/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/pt/history.md diff --git a/content/pt/history.md b/content/pt/history.md new file mode 100644 index 0000000000..2ddc33eb57 --- /dev/null +++ b/content/pt/history.md @@ -0,0 +1,21 @@ +--- +title: Histórico do NumPy +sidebar: false +--- + +NumPy é uma biblioteca Python fundamental que fornece estruturas de *arrays* de dados e rotinas numéricas rápidas relacionadas a estas arrays. Quando começou, a biblioteca tinha pouco financiamento e foi escrita principalmente por estudantes de pós-graduação—muitos deles sem educação em ciência da computação e, muitas vezes, sem autorização dos seus orientadores. Imaginar que um pequeno grupo de programadores estudantis "desobedientes" poderiam subverter o já bem estabelecido ecossistema de software de pesquisa - apoiado por milhões em financiamento e muitas centenas de engenheiros altamente qualificados - era absurdo. No entanto, as motivações filosóficas por trás de uma ferramenta totalmente aberta, em combinação com a vibrante, amigável comunidade com foco singular, provaram ser auspiciosas a longo prazo. Hoje em dia, cientistas, engenheiros e muitos outros profissionais ao redor do mundo confiam no NumPy. Por exemplo, os scripts usados e publicados na análise de ondas gravitacionais importam o NumPy, e o projeto de imagem para buraco negro M87 cita diretamente o NumPy. + +Para um histórico aprofundado dos marcos no desenvolvimento do NumPy e bibliotecas relacionadas, por favor veja [arxiv.org](arxiv.org/abs/1907.10121). + +Se você quiser obter uma cópia das bibliotecas Numeric e Numarray, siga os links abaixo: + +[Página de download para *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* + +[Página de download para *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* + +*Por favor, note que esses pacotes antigos não são mais mantidos, e os usuários são fortemente aconselhados a usar o NumPy para quaisquer propósitos relacionados a arrays e matrizes ou refatorar qualquer código pré-existente para utilizar a biblioteca do NumPy. + +### Documentação Histórica + +[Baixe o manual do *`Numeric'*](static/numeric-manual.pdf) + From 4d6a5dc39196ccea7b472b88019d15bef91cffd1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:43 +0000 Subject: [PATCH 031/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 70 ++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 content/pt/case-studies/blackhole-image.md diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md new file mode 100644 index 0000000000..e59861dfa2 --- /dev/null +++ b/content/pt/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="*(Créditos: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} + +
+

Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver.

+
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! + +### Principais Objetivos e 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. + +* **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. + +### Desafios + +* **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="**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" >}} + +## 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. + +## Resumo + +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 53051e96f90c370cd0789410fe40d9e33da087fc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:44 +0000 Subject: [PATCH 032/909] 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 ba297b9a4a3e7db9fd3611d732159e9680ab1188 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:46 +0000 Subject: [PATCH 033/909] New translations terms.md (Arabic) --- content/ar/terms.md | 178 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 content/ar/terms.md diff --git a/content/ar/terms.md b/content/ar/terms.md new file mode 100644 index 0000000000..9a66045505 --- /dev/null +++ b/content/ar/terms.md @@ -0,0 +1,178 @@ +--- +title: Terms of Use +sidebar: false +--- + +*Last updated January 4, 2020* + + +## AGREEMENT TO TERMS + +These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy ("**Project**", “**we**”, “**us**”, or “**our**”), concerning your access to and use of the numpy.org website as well as any other media form, media channel, mobile website or mobile application related, linked, or otherwise connected thereto (collectively, the “Site”). 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+1 (512) 222-5449 + + + From 4d83fab9cc0c280e170dade45a65041fb9be4074 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:47 +0000 Subject: [PATCH 034/909] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 78 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 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..2533761d12 --- /dev/null +++ b/content/zh/contribute.md @@ -0,0 +1,78 @@ +- - - +title: Contribute to NumPy sidebar: false +- - - + +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming -- in addition to + +- [Writing code](#writing-code) + +you can + +- [Review pull requests](#reviewing-pull-requests) +- [Develop tutorials, presentations, and other educational material](#developing-educational-materials) +- [Triage issues](#issue-triaging) +- [Work on our website](#website-development) +- [Contribute graphic design](#graphic-design) +- [Translate website content](#translating-website-content) +- [Serve as a community coordinator](#community-coordination-and-outreach) +- [Write grant proposals and help with other fundraising](#fundraising) + +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 codebase. + +### 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 ed8cd9e2a6856af44c17d30056f393ab0ab00020 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:49 +0000 Subject: [PATCH 035/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 78 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 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..2533761d12 --- /dev/null +++ b/content/ko/contribute.md @@ -0,0 +1,78 @@ +- - - +title: Contribute to NumPy sidebar: false +- - - + +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming -- in addition to + +- [Writing code](#writing-code) + +you can + +- [Review pull requests](#reviewing-pull-requests) +- [Develop tutorials, presentations, and other educational material](#developing-educational-materials) +- [Triage issues](#issue-triaging) +- [Work on our website](#website-development) +- [Contribute graphic design](#graphic-design) +- [Translate website content](#translating-website-content) +- [Serve as a community coordinator](#community-coordination-and-outreach) +- [Write grant proposals and help with other fundraising](#fundraising) + +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 codebase. + +### 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 19baea4d79de2615ee63a049ba4ada9a5bf21bb4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:51 +0000 Subject: [PATCH 036/909] New translations contribute.md (Japanese) --- content/ja/contribute.md | 78 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 content/ja/contribute.md diff --git a/content/ja/contribute.md b/content/ja/contribute.md new file mode 100644 index 0000000000..040b29464d --- /dev/null +++ b/content/ja/contribute.md @@ -0,0 +1,78 @@ +- - - +title: Numpy に貢献する サイドバー: 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。 + +- [コードを書く。](#writing-code) + +他にも下記の貢献の方法があります: + +- [プラリクエストのレビュー](#reviewing-pull-requests) +- [チュートリアル、プレゼン資料、その他の教育資料の作成](#developing-educational-materials) +- [イシューのトリアージ。](#issue-triaging) +- [ウェブサイトのメンテナンス](#website-development) +- [グラフィックデザインへの貢献](#graphic-design) +- [ウェブサイトの翻訳](#translating-website-content) +- [コミュニティのコーディネーターとしての貢献](#community-coordination-and-outreach) +- [助成金のプロポーザルの作成や他の人の資金調達のサポート](#fundraising) + +もしどの分野で, 自分が貢献出来るか、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。 + +これらが私達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。 是非、参加してみて下さい！ 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、すべての貢献が平等に扱われることを目指しています。 私達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](/code-of-conduct) を制定しています。 + +### コードを書く + +プログラマーの方々に向けて、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か説明されています。 + +### プルリクエストのレビュー +Numpyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumpyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、 +* 長期にわたる議論をまとめる +* ドキュメントのPRをトリアージする +* 提案された変更をテストする + + +### 教育用の資料を作成する + +NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模な再設計中です。 新しいNumpyのWebページは、新しいチュートリアルや、Numpyの使い方、Numpy内部の深い説明など必要としており、サイト全体にも再設計と再構築が必要です。 このウェブサイトの再構築の作業は、ドキュメントを書くだけではありません。 コード例や、ノートブック、ビデオなどの作成も歓迎しています。 [NEP 44 — Restructuring the NumPyDocumentation](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)に、ウェブサイトの再構築についての詳細が説明されています。 + + +### イシューのトリアージ + +[NumPyのイシュートラッカー](https://github.com/numpy/numpy/issues) には、 _沢山の_Open状態のイシューがあります。 いくつかのイシューはすでに解決されており、いくつかは優先順位付けされるべきであり、 いくつかは初心者が取り組むのに良いイシューになるでしょう。 例えば、できる貢献としては、 + +* 古いバグがまだ残っているかを確認する +* 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こと +* イシューに正しいラベル付けをすること(トリアージ権が必要なので、必要で有れば連絡下さい) + +是非参加してみてください。 + + +### ウェブサイトの開発 + +私たちはちょうどウェブサイトの再設計を始めました。しかし、それらはまだ完了していません。 もし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) ではまだ実装されていない要求が列挙されているので、是非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 + +残念ながら、グラフィックデザイナーの方々が可能な貢献をリストアップすることは難しいです。 しかし、私たちのドキュメントは説明のために可視化が重要であり、私たちの拡大しているウェブサイトは良い画像を求めていることから、 貢献する機会が沢山あると言えます。 + + +### ウェブサイトの翻訳 + +私達は、[numpy.org](https://numpy.org) を複数言語に翻訳し、Numpyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 + + +### コミュニティの調整とアウトリーチ + +コミュニティとのコミュニケーションを通じて、私たちは、Numpyより広く知ってもらうようにし、どこに問題があるのかを知りたいと思っています。 私達は、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy [code sprints](https://scisprints.github.io/)の開催, ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っています。 + +### 資金調達 + +NumPyは何年にも渡ってボランティアベースで活動していましたが、Bumpy 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 b1a605510b0b7851de3317f0b034409a8e5d577b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:52 +0000 Subject: [PATCH 037/909] New translations contribute.md (Spanish) --- content/es/contribute.md | 78 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 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..2533761d12 --- /dev/null +++ b/content/es/contribute.md @@ -0,0 +1,78 @@ +- - - +title: Contribute to NumPy sidebar: false +- - - + +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming -- in addition to + +- [Writing code](#writing-code) + +you can + +- [Review pull requests](#reviewing-pull-requests) +- [Develop tutorials, presentations, and other educational material](#developing-educational-materials) +- [Triage issues](#issue-triaging) +- [Work on our website](#website-development) +- [Contribute graphic design](#graphic-design) +- [Translate website content](#translating-website-content) +- [Serve as a community coordinator](#community-coordination-and-outreach) +- [Write grant proposals and help with other fundraising](#fundraising) + +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 codebase. + +### 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 5d0872e8d442dd123a1651969a506b59ddecc2a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:53 +0000 Subject: [PATCH 038/909] New translations diversity_sep2020.md (Arabic) --- content/ar/diversity_sep2020.md | 48 +++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 content/ar/diversity_sep2020.md diff --git a/content/ar/diversity_sep2020.md b/content/ar/diversity_sep2020.md new file mode 100644 index 0000000000..ef3030d5f7 --- /dev/null +++ b/content/ar/diversity_sep2020.md @@ -0,0 +1,48 @@ +--- +title: NumPy Diversity and Inclusion Statement +sidebar: false +--- + + +_In light of the foregoing discussion on social media after publication of the NumPy paper in Nature and the concerns raised about the state of diversity and inclusion on the NumPy team, we would like to issue the following statement:_ + + +It is our strong belief that we are at our best, as a team and community, when we are inclusive and equitable. Being an international team from the onset, we recognize the value of collaborating with individuals from diverse backgrounds and expertise. A culture where everyone is welcomed, supported, and valued is at the core of the NumPy project. + +## The Past + +Contributing to open source has always been a pastime in which most historically marginalized groups, especially women, faced more obstacles to participate due to a number of societal constraints and expectations. Open source has a severe diversity gap that is well documented (see, e.g., the [2017 GitHub Open Source Survey](https://opensourcesurvey.org/2017/) and [this blog post](https://medium.com/tech-diversity-files/if-you-think-women-in-tech-is-just-a-pipeline-problem-you-haven-t-been-paying-attention-cb7a2073b996)). + +Since its inception and until 2018, NumPy was maintained by a handful of volunteers often working nights and weekends outside of their day jobs. At any one time, the number of active core developers, the ones doing most of the heavy lifting as well as code review and integration of contributions from the community, was in the range of 4 to 8. The project didn't have a roadmap or mechanism for directing resources, being driven by individual efforts to work on what seemed needed. The authors on the NumPy paper are the individuals who made the most significant and sustained contributions to the project over a period of 15 years (2005 - 2019). The lack of diversity on this author list is a reflection of the formative years of the Python and SciPy ecosystems. + +2018 has marked an important milestone in the history of the NumPy project. Receiving funding from The Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation allowed us to provide full-time employment for two software engineers with years of experience contributing to the Python ecosystem. Those efforts brought NumPy to a much healthier technical state. + +This funding also created space for NumPy maintainers to focus on project governance, community development, and outreach to underrepresented groups. [The diversity statement](https://figshare.com/articles/online_resource/Diversity_and_Inclusion_Statement_NumPy_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/12980852) written in mid 2019 for the CZI EOSS program grant application details some of the challenges as well as the advances in our efforts to bring in more diverse talent to the NumPy team. + +## The Present + +Offering employment opportunities is an effective way to attract and retain diverse talent in OSS. Therefore, we used two-thirds of our second grant that became available in Dec 2019 to employ Melissa Weber Mendonça and Mars Lee. + +As a result of several initiatives aimed at community development and engagement led by Inessa Pawson and Ralf Gommers, the NumPy project has received a number of valuable contributions from women and other underrepresented groups in open source in 2020: + +- Melissa Weber Mendonça gained commit rights, is maintaining numpy.f2py and is leading the documentation team, +- Shaloo Shalini created all case studies on numpy.org, +- Mars Lee contributed web design and led our accessibility improvements work, +- Isabela Presedo-Floyd designed our new logo, +- Stephanie Mendoza, Xiayoi Deng, Deji Suolang, and Mame Fatou Thiam designed and fielded the first NumPy user survey, +- Yuki Dunn, Dayane Machado, Mahfuza Humayra Mohona, Sumera Priyadarsini, Shaloo Shalini, and Kriti Singh (former Outreachy intern) helped the survey team to reach out to non-English speaking NumPy users and developers by translating the questionnaire into their native languages, +- Sayed Adel, Raghuveer Devulapalli, and Chunlin Fang are driving the work on SIMD optimizations in the core of NumPy. + +While we still have much more work to do, the NumPy team is starting to look much more representative of our user base. And we can assure you that the next NumPy paper will certainly have a more diverse group of authors. + +## The Future + +We are fully committed to fostering inclusion and diversity on our team and in our community, and to do our part in building a more just and equitable future. + +We are open to dialogue and welcome every opportunity to connect with organizations representing and supporting women and minorities in tech and science. We are ready to listen, learn, and support. + +Please get in touch with us on [our mailing list](https://scipy.org/scipylib/mailing-lists.html#mailing-lists), [GitHub](https://github.com/numpy/numpy/issues), [Slack](https://numpy.org/contribute/), in private at numpy-team@googlegroups.com, or join our [bi-weekly community meeting](https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg). + + +_Sayed Adel, Sebastian Berg, Raghuveer Devulapalli, Chunlin Fang, Ralf Gommers, Allan Haldane, Stephan Hoyer, Mars Lee, Melissa Weber Mendonça, Jarrod Millman, Inessa Pawson, Matti Picus, Nathaniel Smith, Julian Taylor, Pauli Virtanen, Stéfan van der Walt, Eric Wieser, on behalf of the NumPy team_ + From e83c4e3fcd51abe438c60c6a61e20cd6335e60ff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:55 +0000 Subject: [PATCH 039/909] 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..3d25090e13 --- /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 766a8786bb30db3293e7fac4bdb1404941767d4c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:56 +0000 Subject: [PATCH 040/909] 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 d0f051f734d3e83aa9853e8515c4603d862b6c24 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:57 +0000 Subject: [PATCH 041/909] 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..987b38fb68 --- /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) provides 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 64acbc42d52907c5b952a79f0582d6f8ece531c4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:36:59 +0000 Subject: [PATCH 042/909] 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 8f7467485f41d258d864bab2b0bf5054c39249da Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:00 +0000 Subject: [PATCH 043/909] New translations news.md (Arabic) --- content/ar/news.md | 83 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 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..5dcb849596 --- /dev/null +++ b/content/ar/news.md @@ -0,0 +1,83 @@ +--- +title: News +sidebar: false +--- + +### 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 + +_Sept 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 9740d697b34422efc5184f64adf6be93bf3f01c7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:01 +0000 Subject: [PATCH 044/909] 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..fc79a621af --- /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](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 b9f3677684d8eb182aae7667886eeb47bea6f098 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:03 +0000 Subject: [PATCH 045/909] 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 f87cec10f5a56cd4c6316b508d8b8d1e292d2da5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:04 +0000 Subject: [PATCH 046/909] 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 a5d263623c991b5bf592d1a79b6efacfdf993bfd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:05 +0000 Subject: [PATCH 047/909] New translations privacy.md (Arabic) --- content/ar/privacy.md | 18 ++++++++++++++++++ 1 file changed, 18 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..a3674dd48a --- /dev/null +++ b/content/ar/privacy.md @@ -0,0 +1,18 @@ +--- +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 d1ce2e38ad301da1c64b2e598f9af69ab0fe13f6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:09 +0000 Subject: [PATCH 048/909] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 64 ++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 content/pt/case-studies/cricket-analytics.md diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..2a6ecda3bd --- /dev/null +++ b/content/pt/case-studies/cricket-analytics.md @@ -0,0 +1,64 @@ +--- +title: "Estudo de Caso: Análise de Críquete, a revolução!" +sidebar: false +--- + +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} + +
+

Você não joga para a torcida, joga para o país.

+
—M S Dhoni, Jogador Internacional de Críquete, ex-capitão, Time Indiano, joga pelo Chennai Super Kings na IPL
+
+ +## Sobre Críquete + +Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. O cricket também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. + +A Primeira Liga Indiana (*Indian Premier League* - IPL) é uma liga profissional de críquete [Twenty20](https://pt.wikipedia.org/wiki/Twenty20), fundada em 2008. É um dos eventos de críquete mais assistidos no mundo avaliado em [$6,7 bilhões de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) em 2019. + +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. + +Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações sobre jogos de críquete, por exemplo, [ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) e [cricsheet](https://cricsheet.org). Estes e muitos outros bancos de dados de críquete foram usados para [análise de críquete](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances) usando os mais modernos algoritmos de aprendizagem de máquina e modelagem preditiva. Plataformas de mídia e entretenimento, juntamente com entidades de esporte profissionais associadas ao jogo usam tecnologia e análise para determinar métricas chave para melhorar as chances de vitória: + +* média móvel do desempenho em rebatidas, +* previsão de pontuação, +* ganho de informações sobre desempenho e condição física de um determinado jogador contra determinado adversário, +* contribuições dos jogadores para vitórias e derrotas para a tomada de decisões estratégicas na composição do time + +{{< figure src="/images/content_images/cs/cricket-pitch.png" class="csfigcaption" caption="**Pitch de críquete, o ponto focal do campo**" alt="Um pitch de críquete com um boleador e batsmen" align="middle" attr="*(Créditos de imagem: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}} + +### Objetivos Principais da Análise de Dados + +* A análise de dados esportivos é usada não somente em críquete mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. +* A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. +* Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise. + +{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="estimador de postura" caption="**Estimador de Postura de Críquete**" attr="*(Créditos de imagem: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} + +### Desafios + +* **Limpeza e pré-processamento de dados** + + A IPL expandiu o cricket para além do formato de jogo clássico para uma escala muito maior. O número de partidas jogadas a cada temporada em vários formatos tem aumentado, assim como os dados, os algoritmos, tecnologias de análise de dados mais recentes e modelos de simulação. A análise de dados de críquete requer mapeamento de campo, rastreamento do jogador, rastreamento de bola e análise de tiros do jogador, análise de lances do jogador e vários outros aspectos envolvidos em como a bola é lançada, seu ângulo, giro, velocidade e trajetória. Todos esses fatores em conjunto aumentaram a complexidade da limpeza e pré-processamento de dados. + +* **Modelagem Dinâmica** + + No críquete, como em qualquer outro esporte, pode haver um grande número de variáveis relacionadas ao rastreamento de vários jogadores no campo, seus atributos, a bola e várias possibilidades de ações em potencial. A complexidade da análise e modelagem de dados é diretamente proporcional ao tipo de questões preditivas que são consideradas durante a análise e são altamente dependentes da representação de dados e do modelo. As coisas são ainda mais desafiadoras em termos de computação e comparações de dados quando previsões dinâmicas de jogo de críquete são desejadas, como o que teria acontecido se o batsman tivesse atingido a bola com um ângulo ou velocidade diferentes. + +* **Complexidade da análise preditiva** + + Muito da tomada de decisões em críquete se baseia em questões como "com que frequência um batsman joga um certo tipo de lance se a recepção da bola for de um determinado tipo", ou "como um boleador muda a direção e alcance da sua jogada se o batsman responder de uma certa maneira". Esse tipo de consulta de análise preditiva requer a disponibilidade de conjuntos de dados altamente granulares e a capacidade de sintetizar dados e criar modelos generativos que sejam altamente precisos. + +## Papel da 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. A NumPy foi usada para vários tipos de análise esportiva relacionada a críquete, como: + +* **Análise Estatística:** Os recursos numéricos da NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em *big data*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. + +* **Visualização de dados:** Gráficos e [visualizações](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) fornecem informações úteis sobre as relações entre vários conjuntos de dados. + +## Resumo + +A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo atrás era principalmente feito com base na "intuição" ou adesão a tradições passadas. A 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**" >}} From bdeeca5156f6f3f2a96e5d7b24f14df232b6ca40 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:10 +0000 Subject: [PATCH 049/909] 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..2309040ad2 --- /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/master/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 746b163d9efb609b5fbaa9bc05375148d7daf8e1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:11 +0000 Subject: [PATCH 050/909] New translations learn.md (Arabic) --- content/ar/learn.md | 84 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 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..264677ac48 --- /dev/null +++ b/content/ar/learn.md @@ -0,0 +1,84 @@ +--- +title: Learn +sidebar: false +--- + +**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** + +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +*** + +## Beginners + +There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: + + **Tutorials** + +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) +* [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 *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 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/) + + **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 Nunuz-Iglesias* +* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* + +*** + +## 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). + +## Contribute to this list + + +To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. From 0203d36e20318bc49ea3e9b31ceb0b8d26d0f77b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:12 +0000 Subject: [PATCH 051/909] New translations install.md (Arabic) --- content/ar/install.md | 142 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 142 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..43dd44cb12 --- /dev/null +++ b/content/ar/install.md @@ -0,0 +1,142 @@ +--- +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 + +#### Windows or macOS + +- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- 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. +- Unless you're fine with only the packages in the `defaults` channel, make `conda-forge` your default channel via [setting the channel priority](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge). + + +#### Linux + +If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: +- Use your OS package manager for as much as possible (Python itself, NumPy, and other libraries). +- Install packages not provided by your package manager with `pip install somepackage --user`. + +If you use a GPU: +- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- 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. +- Use the `defaults` conda channel (`conda-forge` doesn't have good support for GPU packages yet). + +Otherwise: +- 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, for example, [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 4134f336b03c1e10daca62bfaa7ba19f3cc38283 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:14 +0000 Subject: [PATCH 052/909] New translations contribute.md (Arabic) --- content/ar/contribute.md | 78 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 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..2533761d12 --- /dev/null +++ b/content/ar/contribute.md @@ -0,0 +1,78 @@ +- - - +title: Contribute to NumPy sidebar: false +- - - + +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming -- in addition to + +- [Writing code](#writing-code) + +you can + +- [Review pull requests](#reviewing-pull-requests) +- [Develop tutorials, presentations, and other educational material](#developing-educational-materials) +- [Triage issues](#issue-triaging) +- [Work on our website](#website-development) +- [Contribute graphic design](#graphic-design) +- [Translate website content](#translating-website-content) +- [Serve as a community coordinator](#community-coordination-and-outreach) +- [Write grant proposals and help with other fundraising](#fundraising) + +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 codebase. + +### 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 14e0a0daf03144a7f9fa723f5eaabcd3ecf94131 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:15 +0000 Subject: [PATCH 053/909] New translations community.md (Arabic) --- content/ar/community.md | 65 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 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..4e24a83784 --- /dev/null +++ b/content/ar/community.md @@ -0,0 +1,65 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a very diverse group of [contributors](/gallery/team.html). 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](http://numpy-discussion.10968.n7.nabble.com/). + +*** + +### [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. + From 74afed6c204c8f517138e883269fc858e0f6da3c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:16 +0000 Subject: [PATCH 054/909] 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..efcde754ae --- /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 +* Anirudh Subramanian + +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/reference/dev/conduct/code_of_conduct.html) From 96e8c288774d75773bb000069a1356ca28e43869 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:17 +0000 Subject: [PATCH 055/909] 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..cf20ae59cf --- /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: [0.1038/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 10c7aabb45d2640ff12c2276700c91facc5c30b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:19 +0000 Subject: [PATCH 056/909] 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 ceb0d8c276b36b7701f60e672126a6ab374e9fe3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:20 +0000 Subject: [PATCH 057/909] New translations about.md (Arabic) --- content/ar/about.md | 69 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 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..df89bff1f5 --- /dev/null +++ b/content/ar/about.md @@ -0,0 +1,69 @@ +--- +title: About Us +sidebar: false +--- + +_Some information about the NumPy project and community_ + +NumPy is an open source project aiming to enable numerical computing with Python. It was created in 2005, building on the early work of the Numerical and Numarray libraries. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/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 role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. The NumPy Steering Council currently consists of the following members (in alphabetical order): + +- Sebastian Berg +- Jaime Fernández del Río +- Ralf Gommers +- Allan Haldane +- Charles Harris +- Stephan Hoyer +- Matti Picus +- Nathaniel Smith +- Julian Taylor +- Pauli Virtanen +- Stéfan van der Walt +- Eric Wieser + +Emeritus: + +- Travis Oliphant (project founder, 2005-2012) +- Alex Griffing (2015-2017) +- Marten van Kerkwijk (2017-2019) + +## Teams + +The NumPy project is growing; we have teams for + +- code +- documentation +- website +- triage +- funding and grants + +See the [Team](/gallery/team.html) page for individual team members. + +## 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: +{{< 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). +{{< numfocus >}} From 85dfbea6cba1f26372ee4f2fc35a6dfc8b32eda5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:21 +0000 Subject: [PATCH 058/909] 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 050a7866744ba80a900e77c1d843fa6a11e5bff4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:23 +0000 Subject: [PATCH 059/909] New translations diversity_sep2020.md (Portuguese, Brazilian) --- content/pt/diversity_sep2020.md | 48 +++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 content/pt/diversity_sep2020.md diff --git a/content/pt/diversity_sep2020.md b/content/pt/diversity_sep2020.md new file mode 100644 index 0000000000..ef3030d5f7 --- /dev/null +++ b/content/pt/diversity_sep2020.md @@ -0,0 +1,48 @@ +--- +title: NumPy Diversity and Inclusion Statement +sidebar: false +--- + + +_In light of the foregoing discussion on social media after publication of the NumPy paper in Nature and the concerns raised about the state of diversity and inclusion on the NumPy team, we would like to issue the following statement:_ + + +It is our strong belief that we are at our best, as a team and community, when we are inclusive and equitable. Being an international team from the onset, we recognize the value of collaborating with individuals from diverse backgrounds and expertise. A culture where everyone is welcomed, supported, and valued is at the core of the NumPy project. + +## The Past + +Contributing to open source has always been a pastime in which most historically marginalized groups, especially women, faced more obstacles to participate due to a number of societal constraints and expectations. Open source has a severe diversity gap that is well documented (see, e.g., the [2017 GitHub Open Source Survey](https://opensourcesurvey.org/2017/) and [this blog post](https://medium.com/tech-diversity-files/if-you-think-women-in-tech-is-just-a-pipeline-problem-you-haven-t-been-paying-attention-cb7a2073b996)). + +Since its inception and until 2018, NumPy was maintained by a handful of volunteers often working nights and weekends outside of their day jobs. At any one time, the number of active core developers, the ones doing most of the heavy lifting as well as code review and integration of contributions from the community, was in the range of 4 to 8. The project didn't have a roadmap or mechanism for directing resources, being driven by individual efforts to work on what seemed needed. The authors on the NumPy paper are the individuals who made the most significant and sustained contributions to the project over a period of 15 years (2005 - 2019). The lack of diversity on this author list is a reflection of the formative years of the Python and SciPy ecosystems. + +2018 has marked an important milestone in the history of the NumPy project. Receiving funding from The Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation allowed us to provide full-time employment for two software engineers with years of experience contributing to the Python ecosystem. Those efforts brought NumPy to a much healthier technical state. + +This funding also created space for NumPy maintainers to focus on project governance, community development, and outreach to underrepresented groups. [The diversity statement](https://figshare.com/articles/online_resource/Diversity_and_Inclusion_Statement_NumPy_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/12980852) written in mid 2019 for the CZI EOSS program grant application details some of the challenges as well as the advances in our efforts to bring in more diverse talent to the NumPy team. + +## The Present + +Offering employment opportunities is an effective way to attract and retain diverse talent in OSS. Therefore, we used two-thirds of our second grant that became available in Dec 2019 to employ Melissa Weber Mendonça and Mars Lee. + +As a result of several initiatives aimed at community development and engagement led by Inessa Pawson and Ralf Gommers, the NumPy project has received a number of valuable contributions from women and other underrepresented groups in open source in 2020: + +- Melissa Weber Mendonça gained commit rights, is maintaining numpy.f2py and is leading the documentation team, +- Shaloo Shalini created all case studies on numpy.org, +- Mars Lee contributed web design and led our accessibility improvements work, +- Isabela Presedo-Floyd designed our new logo, +- Stephanie Mendoza, Xiayoi Deng, Deji Suolang, and Mame Fatou Thiam designed and fielded the first NumPy user survey, +- Yuki Dunn, Dayane Machado, Mahfuza Humayra Mohona, Sumera Priyadarsini, Shaloo Shalini, and Kriti Singh (former Outreachy intern) helped the survey team to reach out to non-English speaking NumPy users and developers by translating the questionnaire into their native languages, +- Sayed Adel, Raghuveer Devulapalli, and Chunlin Fang are driving the work on SIMD optimizations in the core of NumPy. + +While we still have much more work to do, the NumPy team is starting to look much more representative of our user base. And we can assure you that the next NumPy paper will certainly have a more diverse group of authors. + +## The Future + +We are fully committed to fostering inclusion and diversity on our team and in our community, and to do our part in building a more just and equitable future. + +We are open to dialogue and welcome every opportunity to connect with organizations representing and supporting women and minorities in tech and science. We are ready to listen, learn, and support. + +Please get in touch with us on [our mailing list](https://scipy.org/scipylib/mailing-lists.html#mailing-lists), [GitHub](https://github.com/numpy/numpy/issues), [Slack](https://numpy.org/contribute/), in private at numpy-team@googlegroups.com, or join our [bi-weekly community meeting](https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg). + + +_Sayed Adel, Sebastian Berg, Raghuveer Devulapalli, Chunlin Fang, Ralf Gommers, Allan Haldane, Stephan Hoyer, Mars Lee, Melissa Weber Mendonça, Jarrod Millman, Inessa Pawson, Matti Picus, Nathaniel Smith, Julian Taylor, Pauli Virtanen, Stéfan van der Walt, Eric Wieser, on behalf of the NumPy team_ + From 949c999d4d009cd72ca2fbe8808c7b1c6e809335 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:25 +0000 Subject: [PATCH 060/909] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 69 ++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/pt/case-studies/gw-discov.md diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md new file mode 100644 index 0000000000..d189210057 --- /dev/null +++ b/content/pt/case-studies/gw-discov.md @@ -0,0 +1,69 @@ +--- +title: "Estudo de Caso: Descoberta de Ondas Gravitacionais" +sidebar: false +--- + +{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**Ondas gravitacionais**" alt="binary coalesce black hole generating gravitational waves" attr="*(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} + +
+

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

+
David Shoemaker, Colaborador Científico no LIGO
+
+ +## Sobre [Ondas Gravitacionais](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) e o [LIGO](https://www.ligo.caltech.edu) + +Ondas gravitacionais são ondulações no tecido espaço-tempo, gerado por eventos cataclísmicos no universo, como colisão e fusão de dois buracos negros ou a coalescência de estrelas binárias ou supernovas. A observação de ondas gravitacionais pode ajudar não só no estudo da gravidade, mas também no entendimento de alguns dos fenômenos obscuros existentes no universo distante e seu impacto. + +O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://www.ligo.caltech.edu) foi projetado para abrir o campo da astrofísica das ondas gravitacionais através da detecção direta de ondas gravitacionais previstas pela Teoria Geral da Relatividade de Einstein. O observatório consiste de dois interferômetros amplamente separados dentro dos Estados Unidos - um em Hanford, Washington e o outro em Livingston, Louisiana — operando em uníssono para detectar ondas gravitacionais. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. + + +### Objetivos + +* Embora sua [missão](https://www.ligo.caltech.edu/page/what-is-ligo) seja detectar ondas gravitacionais de alguns dos processos mais violentos e enérgicos no Universo, os dados que o LIGO coleta podem ter efeitos de grande alcance em muitas áreas da física, incluindo gravitação, relatividade, astrofísica, cosmologia, física de partículas e física nuclear. +* Processar dados observados através de cálculos numéricos de relatividade que envolvem matemática complexa para identificar o sinal e o ruído, filtrar o sinal relevante e estimar estatisticamente o significado dos dados observados. +* Visualização de dados para que os resultados binários/numéricos possam ser compreendidos. + + + +### Desafios + +* **Computação** + + As ondas gravitacionais são difíceis de detectar pois produzem um efeito muito pequeno e têm uma pequena interação com a matéria. Processar e analisar todos os dados do LIGO requer uma vasta infraestrutura de computação. Depois de cuidar do ruído, que é bilhões de vezes maior que o sinal, ainda há equações de relatividade complexas e enormes quantidades de dados que apresentam um desafio computacional: [O(10^7) horas de CPU necessárias para análises de fusão binária](https://youtu.be/7mcHknWWzNI) espalhado em 6 clusters LIGO dedicados. + +* **Sobrecarga de dados** + + À medida que os dispositivos observacionais se tornam mais sensíveis e confiáveis, os desafios criados pela sobrecarga de dados e a procura por uma agulha em um palheiro se tornam muito maiores. O LIGO gera terabytes de dados todos os dias! Entender esses dados requer um enorme esforço para cada detecção. Por exemplo, os sinais sendo coletados pelo LIGO devem ser combinados por supercomputadores e comparados a centenas de milhares de modelos de possíveis assinaturas de ondas gravitacionais. + +* **Visualização** + + Uma vez que os obstáculos relacionados a compreender as equações de Einstein bem o suficiente para resolvê-las usando supercomputadores foram ultrapassados, o próximo grande desafio era tornar os dados compreensíveis para o cérebro humano. A modelagem de simulações, assim como a detecção de sinais, exigem técnicas de visualização efetiva. A visualização também desempenha um papel de fornecer mais credibilidade à relatividade numérica aos olhos dos aficionados pela ciência pura, que não dão importância suficiente à relatividade numérica até que a imagem e as simulações tornem mais fácil a compreensão dos resultados para um público maior. A velocidade da computação complexa, e da renderização, re-renderização de imagens e simulações usando as últimas entradas e informações experimentais pode ser uma atividade demorada que desafia pesquisadores neste domínio. + +{{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="gravitational waves strain amplitude" caption="**Amplitude estimada da deformação das ondas gravitacionais do evento GW150914**" attr="(**Créditos do gráfico:** Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" attrlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" >}} + +## O papel da NumPy na detecção de ondas gravitacionais + +Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. + +NumPy, o pacote padrão de análise numérica para Python, foi parte do software utilizado para várias tarefas executadas durante o projeto de detecção de ondas gravitacionais no LIGO. A NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: + +* [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) +* Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro +* Análise estatística: estimar o significado estatístico dos dados observados, estimando os parâmetros do sinal (por exemplo, massa de estrelas, velocidade de giro e distância) em comparação com um modelo. +* Visualização de dados + - Séries temporais + - Espectrogramas +* Cálculo de correlações +* [Software](https://github.com/lscsoft) fundamental desenvolvido na análise de ondas gravitacionais, como [GwPy](https://gwpy.github.io/docs/stable/overview.html) e [PyCBC](https://pycbc.org) usam NumPy e AstroPy internamente para fornecer interfaces baseadas em objetos para utilidades, ferramentas e métodos para o estudo de dados de detectores de ondas gravitacionais. + +{{< figure src="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**Grafo de dependências mostrando como o pacote GwPy depended da NumPy**" >}} + +---- + +{{< figure src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**Grafo de dependências mostrando como o pacote PyCBC depended da NumPy**" >}} + +## 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 outros pacotes Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo. + +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} From fcae610fb35130e69a23d764b88856deca753d32 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:26 +0000 Subject: [PATCH 061/909] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 90 +++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/pt/case-studies/deeplabcut-dnn.md diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..3d54a281e0 --- /dev/null +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -0,0 +1,90 @@ +--- +title: "Estudo de Caso: Estimativa de Pose 3D com DeepLabCut" +sidebar: false +--- + +{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Análise de movimentos de mãos de camundongos usando DeepLapCut**" alt="micehandanim" attr="*(Fonte: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} + +
+

Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning.

+
—Alexander Mathis, Professor Assistente, École polytechnique fédérale de Lausanne (EPFL)
+
+ +## Sobre o DeepLabCut + +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) é uma toolbox de código aberto que permite que pesquisadores de centenas de instituições em todo o mundo rastreiem o comportamento de animais de laboratório, com muito poucos dados de treinamento, mas com precisão no nível humano. Com a tecnologia DeepLabCut, cientistas podem aprofundar a compreensão científica do controle motor e do comportamento em diversas espécies animais e escalas temporais. + +Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas penosas 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 mais precisos. + +{{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida**" alt="horserideranim" attr="*(Fonte: Mackenzie Mathis)*">}} + +O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinamicamente variáveis é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos necessários e dados de treinamento exigidos. + +A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (*frames*) distintos em vídeos, rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI específica, e a partir disso a arquitetura de estimação de poses baseada em deep learning na DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios como moscas e camundongos até os mais incomuns como [guepardos][cheetah-movement]. + +A DeepLabCut usa um princípio chamado [aprendizado por transferência (*transfer learning*)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de estimativa de poses humanas de alto desempenho, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote agora foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experiências biológicas em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. + +Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab.org/dlc-modelzoo), que proporciona modelos pré-treinados para várias espécies e condições experimentais, desde a análise facial em primatas até à posição de cães. Isso pode ser executado na nuvem, por exemplo, sem qualquer rotulagem de novos dados ou treinamento em rede neural, e não é necessária nenhuma experiência em programação. + +### Principais Objetivos e Resultados + +* **Automação da análise de poses animais para estudos científicos:** + + O objetivo principal da tecnologia DeepLabCut é medir e rastrear a postura dos animais em várias configurações. Esses dados podem ser usados, por exemplo, em estudos de neurociência para entender como o cérebro controla o movimento, ou para elucidar como os animais interagem socialmente. Pesquisadores observaram que [desempenho é 10 vezes melhor](https://www.biorxiv.org/content/10.1101/457242v1) com o DeepLabCut. Poses podem ser inferidas off-line em até 1200 quadros por segundo (FPS). + +* **Criação de um kit de ferramentas Python fácil de usar para estimativa de poses:** + + DeepLabCut queria compartilhar sua tecnologia de estimativa de poses animal na forma de uma ferramenta simples de usar que pudesse ser adotada pelos pesquisadores facilmente. Assim, criaram um conjunto de ferramentas em Python completo e fácil de usar, também com recursos de gerenciamento de projeto. Isso permite não apenas a automação de estimação de poses, mas também o gerenciamento do projeto de ponta a ponta, ajudando o usuário do DeepLabCut Toolkit desde a fase de coleta para criar fluxos de dados compartilháveis e reutilizáveis. + + Seu [conjunto de ferramentas][DLCToolkit] agora está disponível como software de código aberto. + + Um fluxo de trabalho típico na DeepLabCut inclui: + + - criação e refinamento de conjuntos de treinamento por meio de aprendizagem ativa + - criação de redes neurais personalizadas para animais e cenários específicos + - código para inferência em larga escala em vídeos + - inferências de desenho usando ferramentas integradas de visualização + +{{< figure src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**Passos na estimação de poses com DeepLabCut**" alt="dlcsteps" align="middle" attr="(Fonte: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} + +### Desafios + +* **Velocidade** + + Processamento rápido de vídeos de animais para medir seu comportamento e, ao mesmo tempo, tornar os experimentos científicos mais eficientes e precisos. Extrair poses animais detalhadas para experimentos em laboratório, sem marcadores, sobre fundos dinâmicos, pode ser desafiador tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. Criar uma ferramenta que seja fácil de usar sem necessidade de habilidades como expertise em visão computacional que permita aos cientistas fazerem pesquisa em contextos mais próximos do mundo real é um problema não-trivial a ser solucionado. + +* **Combinatória** + + Combinatória envolve a junção e integração de movimentos de múltiplos membros em um comportamento animal único. Reunir pontos-chave e suas conexões em movimentos animais individuais e encadeá-los em função do tempo é um processo complexo que exige análise numérica intensa, especialmente nos casos de rastreio de múltiplos animais em vídeos experimentais. + +* **Processamento de dados** + + Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador. + +{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Estimação de poses e complexidade**" alt="challengesfig" align="middle" attr="(Fonte: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} + +## O papel da NumPy nos desafios da estimação de poses + +NumPy supre a principal necessidade da tecnologia DeepLabCut de cálculos numéricos de alta velocidade para análises comportamentais. Além da NumPy, DeepLabCut emprega várias bibliotecas Python que usam a NumPy como sua base, tais como [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) e [Tensorflow](https://www.tensorflow.org). + +As seguintes características da NumPy desempenharam um papel fundamental para atender às necessidades de processamento de imagens, combinatória e cálculos rápidos nos algoritmos de estimação de pose na DeepLabCut: + +* Vetorização +* Operações em arrays com máscaras +* Álgebra linear +* Amostragem aleatória +* Reordenamento de matrizes grandes + +A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação. Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. Para este propósito, densidades de alvo (*scoremaps*) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente. + +{{< figure src="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**Fluxo de dados DeepLabCut**" alt="workflow" attr="*(Fonte: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} + +## Resumo + +Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy. + +{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave do NumPy utilizados**" >}} + +[cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 + +[DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut From 29548871ccb40d93d09fb60466f05630e5b96b5b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:28 +0000 Subject: [PATCH 062/909] 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..cf20ae59cf --- /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: [0.1038/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 f8ed582920f8d5ccd83984d99911fcd91653c9cc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:30 +0000 Subject: [PATCH 063/909] New translations about.md (Chinese Simplified) --- content/zh/about.md | 69 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 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..cccadd7da0 --- /dev/null +++ b/content/zh/about.md @@ -0,0 +1,69 @@ +--- +title: 关于我们 +sidebar: false +--- + +_下面是 NumPy 项目和社区的一些信息：_ + +NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 年，早期由 Numerical 和 Numarray 库发展而来。 NumPy 将始终保证项目完整开源，所有人都可以根据 [修改后的 BSD 条款](https://github.com/numpy/numpy/blob/master/LICENSE.txt) 免费对其进行使用和分发。 + +经过 Numpy 和 Python 科学计算社区协商讨论，最终决定将 Numpy 在 GitHub 上开源。 想要了解更多与社区治理有关的信息，请参阅我们的[治理文件](https://www.numpy.org/devdocs/dev/governance/index.html)。 + + +## 指导委员会 + +指导委员会的成员们通过与 Numpy 社区合作并提供服务的形式来确保项目的长期发展，包括技术层面和社区层面。 Numpy 指导委员会目前由下列成员组成（按字母顺序排列）： + +- Sebastian Berg +- Jaime Fernández del Río +- Ralf Gommers +- Allan Haldane +- Charles Harris +- Stephan Hoyer +- Matti Picus +- Nathaniel Smith +- Julian Taylor +- Pauli Virtanen +- Stéfan van der Walt +- Eric Wieser + +荣誉会员： + +- Travis Oliphant（项目创始人，2005-2012年） +- Alex Griffing（2015-2017年） +- Marten van Kerkwijk （2017-2019年） + +## 团队 + +NumPy 项目正在不断发展中，我们的团队成员负责： + +- 编码 +- 文档 +- 网站 +- 试用 +- 资金和赠款 + +查看[团队](/gallery/team.html)页面以了解每个独立团队的成员信息。 + +## 赞助商 + +NumPy 直接从下列来源获得资金： +{{< sponsors >}} + + +## 机构合作伙伴 + +机构合作伙伴指那些通过雇用为 NumPy 做贡献的人来支持该项目的组织。 目前的机构伙伴包括： +{{< partners >}} + + +## 捐赠 + +如果您发现 NumPy 对您的工作、研究或公司有用，请考虑向该项目发起捐款。 任何金额都有帮助！ 所有捐款将严格用于 NumPy 开源软件、文档和社区的开发。 + +NumPy 是美国 501(c)(3) 非营利慈善机构 NumFOCUS 的一个赞助项目。 NumFOCUS 向 NumPy 提供财政、法律和行政支助，帮助确保该项目的健康和可持续性。 访问 [numfocus.org](https://numfocus.org) 获取更多信息。 + +对 NumPy 的捐赠将由 [NumFOCUS](https://numfocus.org) 进行管理。 对于在美国的捐赠者，在法律规定的范围内，你的赠品可以免税。 如同任何捐赠一样，您应该与您的税务顾问商讨您的特定税务状况。 + +NumPy 指导委员会将就如何最佳利用收到的任何资金作出决定。 技术和基础设施相关的优先事项已记录在 [NumPy 路线图](https://www.numpy.org/neps/index.html#roadmap) 上。 +{{< numfocus >}} From 27758fa32a297384cd68eef750a8e7169fc65571 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:31 +0000 Subject: [PATCH 064/909] New translations community.md (Japanese) --- content/ja/community.md | 65 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 content/ja/community.md diff --git a/content/ja/community.md b/content/ja/community.md new file mode 100644 index 0000000000..5794d5626f --- /dev/null +++ b/content/ja/community.md @@ -0,0 +1,65 @@ +--- +title: コミュニティ +sidebar: false +--- + +Numpy は 常に多様な[コントリビュータ](/gallery/team.html) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/code-of-conduct) をご覧ください。 + +私たちは、NumPyコミュニティ内で学んだり、知識を共有したり、他の人と交流するためのいくつかのコミュニケーション方法を提供しています。 + + +## オンラインで参加する方法 + +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の通りです。 _重要: 私達はユーザとコミュニティメンバーに互いにNumpyの使い方の質問に関して助言し合って欲しいと思っています。 - 参照[サポート](/gethelp)._ + + +### [NumPyメーリングリスト：](https://mail.python.org/mailman/listinfo/numpy-discussion) + +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](http://numpy-discussion.10968.n7.nabble.com/) にあります。 + +*** + +### [GitHub イシュートラッカー](https://github.com/numpy/numpy/issues) + +- バグレポート（例：”`np.arange(3).shape` returns `(5,)`, when it should return `(3,)`"); +- ドキュメントの問題 (例: "I find this section unclear"); +- 機能追加リクエスト (例: "I would like to have a new interpolation method in `np.percentile`"). + +_ちなみに、セキュリティの脆弱性を報告するには、GitHubのイシュートラッカーは適切な場所ではないことに注意してください。 NumPy でセキュリティ上の脆弱性を発見したと思われる場合は、 [こちら](https://tidelift.com/docs/security) から報告してください。_ + +*** + +### [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。 + + +## 勉強会とミートアップ + +NumPyや、データサイエンス、科学技術計算などのより広いエコシステムのためのPythonパッケージついて、もっと学ぶためのローカルミートアップや勉強会を見つけたい場合、 [PyData ミートアップ](https://www.meetup.com/pro/pydata/) (150人以上のミートアップ、10万人以上のメンバーをまとめたもの) を調べてみることをお勧めします。 + +加えて、NumPy では開発チームと参加に興味があるコントリビュータのために、対面でのスプリントを時折開催しています。 この開発スプリントは通常数ヶ月に一度に開催されており、 [ メーリングリスト ](https://mail. python. org/mailman/listinfo/numpy-discussion) と [ Twitter ](https://twitter. com/numpy_team) で開催連絡されます。 + + +## カンファレンス + +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せん。 NumPy の管理者や、コントリビュータ、ユーザーに最も人気があったカンファレンスは、SciPy および PyDataのカンファレンスです。 + +- [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 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。 + + +## NumPy コミュニティに参加する + +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。 プログラマーじゃないから参加できない？ そんなことはありません！ Numpy に貢献する様々な方法があります。 + +もし、Numpyに貢献したい場合は、 [コントリビュート](/contribute) ページをご覧いただくことをお勧めします。 + From 91310e36dcdbd83a6eeec36c3630df90f354a759 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:33 +0000 Subject: [PATCH 065/909] 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 ca92e9d6a74bf63381548f6c1929812336722ec7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:35 +0000 Subject: [PATCH 066/909] New translations install.md (Japanese) --- content/ja/install.md | 166 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 content/ja/install.md diff --git a/content/ja/install.md b/content/ja/install.md new file mode 100644 index 0000000000..61f58002df --- /dev/null +++ b/content/ja/install.md @@ -0,0 +1,166 @@ +--- +title: Numpyのインストール +sidebar: false +--- + +Numpy をインストールするための必ず必要なものは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、こちらがあります: [Anaconda Distribution](https://www.anaconda.com/distribution)。このanacondaはPythonだけでなく、NumPyや、その他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`や`pip` 、Mac OS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** + +`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。 + +```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** + +`pip`を使用している場合は、 NumPy を以下のようにインストールできます: + +```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 - 参考 [再現可能なインストール](#reproducible-installs) 。 [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。 + + + +# Python と Numpy インストールガイド + +Pythonパッケージのインストールと管理は複雑なで、ほとんどのタスクには数多くの代替ツールがあります。 このガイドでは、読者に最適な(または最も人気のある) 方法と明確な指針を提供したいと思います。 このガイドでは、一般的なオペレーティングシステムとハードウェア上での、 Python、NumPy、PyData (または数値計算) スタックのユーザに焦点を当てています。 + +## 推奨方法 + +まずはユーザの経験のレベルと、関心のあるOSに基づいた推奨方法から説明していきたいと思います。 PythonやNumpyの経験が「初級」と「上級」の間の方や、シンプルにインストールしたい方は「初級」を、より長い視点にたったベストプラクティスに沿ってインストールしたい方は「上級」を参照下さい。 + +### 初級ユーザ + +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、実行するために[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) でnotebookを利用することができます。また探索的、対話的コンピューティングも可能です。[Spyder](https://www.spyder-ide.org/) 、[Visual Studio Code](https://code.visualstudio.com/)はスクリプトを作成したり、パッケージを作成することができます。 +- 是非、[Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) を使用して パッケージを管理し、JupyterLab、Spyder、Visual Studio Code を利用してみて下さい。 + + +### 上級ユーザー + +#### WindowsまたはmacOS + +- [Miniconda](https://docs.conda.io/en/latest/miniconda.html) をインストールします。 +- `ベース` のconda環境を出来るだけ小さく保ちます。 そして、作業中のタスクやプロジェクトに必要なパッケージは個別の` ` [conda 環境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) を使用して、インストールするようにします。 +- もし、あなたの必要なパッケージが`defaults` チャンネルだけで足りない場合は、`conda-forge` こちらの [チャンネルプライオリティの設定](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge)でデフォルトチャンネルを設定することができます。 + + +#### Linux + +もしあなたが最新バージョンのライブラリを使用するよりも、少し古いパッケージで安定性を求める場合は: +- 可能な限りOS付帯のパッケージマネージャーを使用してください (Python本体やNumPy、 その他のライブラリのインストールに)。 +- `pip install somepackage --user` でパッケージマネージャによって提供されていないパッケージをインストールすることができます。 + +GPUを使用する場合: +- [Miniconda](https://docs.conda.io/en/latest/miniconda.html) をインストールして下さい。 +- `ベース` のconda環境を出来るだけ小さく保ちます。 そして、作業中のタスクやプロジェクトに必要なパッケージは個別の` ` [conda 環境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) を使用して、インストールするようにします。 +- また、`デフォルトの` conda channel (`conda-forge` は GPU パッケージをまだサポートしていません) を使用してください。 + +上記以外の場合 +- [Miniforge](https://github.com/conda-forge/miniforge) をインストールします。 +- `ベース` のconda環境を出来るだけ小さく保ちます。 そして、作業中のタスクやプロジェクトに必要なパッケージは個別の` ` [conda 環境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) を使用して、インストールするようにします。 + + +#### pip/PyPI を利用したい場合 + +個人的な好みや、下記のcondaとpipの違いを理解した上で、pip/PyPIベースの方法を使いたいユーザーには、下記をお勧めします: +- Pythonをインストールします。例えば、 [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), または Linux パッケージマネージャを使うことができます。 +- 依存関係の解決と環境の管理を提供する最もよくメンテナンスされているツールとして、[Poetry](https://python-poetry. org/) をconda と同様な方法で使用することができます。 + + +## Python パッケージ管理 + +パッケージの管理は難しいので、その結果、たくさんのツールが存在しています。 ウェブ開発と汎用的なPython開発には、こちらのようなpipを補完する [ツール](https://packaging.python.org/guides/tool-recommendations/) があります。 ハイパフォーマンスコンピューティング(HPC)では、 [Spack](https://github.com/spack/spack) を使うことを検討して下さい。 NumPyのほとんどのユーザーにとっては、 [conda](https://conda.io/en/latest/) と [pip](https://pip.pypa.io/en/stable/) が最も広く利用されているツールです。 + + +### Pip & conda + +Python パッケージをインストールするための2 つの主要なツールは `pip` と `conda` です。 これらの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)、これらは一緒に動作することもできます。 ここでは、pip とconda の主要な違いについて説明します。パッケージをどのように効果的に管理するかを理解することが重要です。 + +最初の違いは、condaは複数言語に対応可能であり、condaからPython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にのみインストールします。 また、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。 + +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、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！) が必要になるかもしれないということです。 + + +### 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ください。 +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ます( それぞれに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, 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 & 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 - 一般的には、 [インテル® MKL](https://software.intel.com/en-us/mkl) または [OpenBLAS](https://www.openblas.net/) に依存しています。 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 install メソッドが自動的に実施します)。 パワーユーザーの中には、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、より詳細を知りたいと思っているかもしれません。 + +- 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。 これにより、ユーザが（例えば）SciPyを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 をインストールしたときにユーザーの環境にインストールされるのとは、別のパッケージです。 + +- 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、またはBLASを利用することもできます。

+ +
  • +

    + OpenBLASのサイズは約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。 +

    +
    • + +
  • +

    + MKLは通常、OpenBLASよりも少し速く、よりロバストな結果が得られます。 +

    +
    • + +

    + 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。 これによりユーザーに予期しないことが起こることがあります。例えばNumPy 自体は、関数呼び出しを自動的に並列化しないことです。 線形代数ライブラリの配列処理は、一般的にはより良いパフォーマンスが得られますが、Daskやscikit-learn、マルチプロセシングなどの別のレベルの並列化を使用している場合などに、逆に悪い結果をもたらすことがあります。 +

      +
      • +
    + + + + +

    + 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!
+
+Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup.
+
    + From 7738066fe05d20790260442b0fb66919ee380976 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:37 +0000 Subject: [PATCH 067/909] New translations code-of-conduct.md (Japanese) --- content/ja/code-of-conduct.md | 83 +++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ja/code-of-conduct.md diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md new file mode 100644 index 0000000000..10853e5152 --- /dev/null +++ b/content/ja/code-of-conduct.md @@ -0,0 +1,83 @@ +--- +title: Numpy行動規範 +sidebar: false +aliases: + - /conduct.html +--- + +### 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では、対面でもイベント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、特にそれらのプロジェクトを運営する場合、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。 是非、我々のコミュニティをより豊かにし、フレンドリーで生産的な環境を作るために、この行動規範に従ってください。 + +### ガイドラインの概要 + +私たちは下記の内容に真摯に取り組みます。 + +1. 開けたコミュニティにしましょう。 私たちは、誰でもコミュニティに参加できるようにします。 私たちは、何かあまり公にすべきではない内容を議論しない限り、プロジェクト関連のメッセージにはパブリックな通信方法を使用するように努めます。 この行動規範はNumpyのヘルプやプロジェクト関連のサポートのメッセージにも適用されます。パブリックなサポートだけでなく、Numpyに関する質問に答える場合もこの行動規範に従うことがひです。 これにより、質問に答えた時の、無意識な間違いを、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。 私たちはお互いの意見の尊重しあい、互いの善意を信じ合います。 私達はたまに様々な種類の不満を感じるかもしれません。しかしそんな時でも、私達はそのような不満を個人的な攻撃に変えるのを許しません。 なぜなら人々が不快や脅威を感じるコミュニティは、生産的な場所ではないからです。 +3. 互いに協力し合おう。 私たちが開発したものは、他の人々によって使用され、一方で、私たちは他の人が開発しているもの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。 私達が実施する全ての決断は、全てのユーザと開発コミュニティに影響を与え、その決断による結果を私達は真摯に受け止めます。 +4. 好奇心を大事にしよう。 全てのことを理解している人は存在しません。 早め早めに質問を行うことは、後で多くの問題を回避することができます。なので私達は、それぞれの質問に対して、適切なフォーラムに案内することで、質問を奨励していきます。 私たちは、出来るだけ質問に対する対応を良くし、手助けできるように努力します。 +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つようにします。 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、非公開フォーラム上でものIRCチャネル履歴などのプライベートコンテンツを、送信者の同意なしに共有すること。 + * 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。 + * 不快な思いをさせる性的な言動。 + * 過度に粗暴に振る舞うこと。 ひどいな言葉を使うのを避けてください。 人々は怒りを覚える感度が、それぞれ大きく異なります。 + * 他人に対するハラスメントの繰り返し。 一般的に、誰かがあなたにある言動を止めるように要求した場合、その言動をやめて下さい。 + * 上記のいずれかの行動を擁護すること、または奨励すること。 + +### 多様性に関する声明 + +NumPyプロジェクトは、全ての人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力を注いでいます。 全ての人が満足できるように対応できるとは限りませんが、全員を出来るだけ親切に扱えるように最善を尽くしていきます。 + +あなたがどのようにあなた自身を認識し、他の人があなたをどのように認識していても、私達 はあなたのプロジェクトへの参加を歓迎します。 下記のリストが全てを含んでいるとは言えませんが、私達は行動規範に反しない限り、下記の多様性を尊重することを明言します。: 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。 + +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 開発は英語で実施します。 + +NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 我々のコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです。(次のセクションを参照)。 + +### 報告ガイドライン + +私たちは、インターネット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. また、この行動規範のガイドラインにそのような行為が禁止されていることに気づいていない人もいることを認識しています。 行動規範の違反に対応する方法を決定する際には、この事実を覚心に留めておく必要があります。 + +意図的な行動規範違反については、行動規範委員会に報告してください(下記参照)。 もし、ある行動規範違反が意図的ではない可能性がある場合、あなたはその人にこの行動規範が存在していることを指摘することができます(方法としてはパブリックな方法でもプライベートな方法に、適切な方法であればはどの様な方法でも可能です。)。 もし、直接指摘するのが躊躇われる場合は、是非、行動規範委員会に連絡下さい。 委員会に助言を求めることもできます。 + +Numpy行動規範委員会に問題を報告する場合はこちらにご連絡下さい: numpy-conduct@googlegroups.com + +現在、行動規範委員会は以下のメンバーで構成されています: + +* Stefan van der Walt +* Melissa Weber Mendonça +* Anirudh Subramanian + +もしあなたの違反報告に委員会のメンバーが含まれている場合, または彼らがそれを処理する上で利益相反をしていると感じる場合、そのメンバーはあなたの報告を評価する立場からは辞退してもらいます。 または、もしあなたが行動規範委員会に報告するのが躊躇われるばあい、こちらのNumFOCUSのスタッフに連絡することも可能です。:[conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). + +### インシデント報告の解決 & 行動規範の実施 + +本節では、_最も重要な点のみをまとめます。_詳細については、[Numpy Code of Conduct - How to follow up on a report](/report-handling-manual) をご覧ください。 + +私たちはすべての訴えを調査し、対応するようにします。 NumPy行動規範委員会およびNumPy運営委員会(もし関係する場合) は、報告者の身元を保護します。 また(報告者が同意しない限り) 苦情の内容を機密として扱うこととします。 + +もし深刻で明らかな違反の場合、例えば、 個人的な脅し、または暴力的、性差別的または人種差別的な発言などの場合、我々は直ちにNumPyのコミュニケーションの場から発言者を退場させます。詳細についてはマニュアルを参照してください。 + +もし、行動規範に対して明白な違反がみられない場合、受領された行動規範違反報告に対するプロセスは以下の通りです。 + +1. 報告書の受領を確認 +2. 建設的な議論/フィードバック +3. 調停（報告者と報告を受けたものの両方がフィードバックが役に立たなかったと同意した場合に限る） +4. 行動規範委員会による透明性のある決定と執行( [決議](/report-handling-manual#resolutions)を参照) + +行動規範委員会は、可能な限り速やかに対応し、最大で72時間以内に対応する様にします。 + +### 文末脚注: + +私たちは下記のドキュメントの作成したグループに感謝しています。このドキュメントから私たちは我々の行動規範の内容と発想を得ることが出来ました。 + +- [SciPy行動規範](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) From 60934d1d3a1d261004749495d434c7fe82de8dac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:38 +0000 Subject: [PATCH 068/909] New translations citing-numpy.md (Japanese) --- content/ja/citing-numpy.md | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 content/ja/citing-numpy.md diff --git a/content/ja/citing-numpy.md b/content/ja/citing-numpy.md new file mode 100644 index 0000000000..1aa50fa270 --- /dev/null +++ b/content/ja/citing-numpy.md @@ -0,0 +1,35 @@ +--- +title: NumPy を引用する場合 +sidebar: false +--- + +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、こちらの論文を引用して下さい。 + +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [0.1038/s41586-020-2649-2](https://doi. org/10.1038/s41586-020-2649-2). ([リンク](https://www.nature.com/articles/s41586-020-2649-2)). + +_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 + 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 826e8287b340139473b107366d15147f85e012bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:40 +0000 Subject: [PATCH 069/909] New translations arraycomputing.md (Japanese) --- content/ja/arraycomputing.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ja/arraycomputing.md diff --git a/content/ja/arraycomputing.md b/content/ja/arraycomputing.md new file mode 100644 index 0000000000..214d01e34e --- /dev/null +++ b/content/ja/arraycomputing.md @@ -0,0 +1,21 @@ +--- +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も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。 + +**Num**erical **Py**thon: NumPyは、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する、Pythonにおけるデファクトスタンダードなライブラリです。 + +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 似た機能を模倣しており、機械学習や人工知能に適した、新しいアルゴリズムや機能を有しています。 + +arraycl + +**配列演算** は **配列** のデータ構造に基づいています。 *配列* は、関連する膨大なデータ群を簡単にかつ高速に、ソート、検索、変換、数学処理できるように構成されています。 + +配列演算は *一度に* 配列のデータの複数の要素を操作するため、 * ユニーク* な処理と言えます。 これは、配列操作が一回の処理で、配列内の 値の全体に適用されることを意味しています。 このベクトル演算は、高速で、シンプルな処理を実現し、ループによる配列の個々の要素のスカラー演算無しに、データを処理することを可能にします。 From 5fea0fa4e99507694fe8c95a0c41b5e5d66c495f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:41 +0000 Subject: [PATCH 070/909] New translations about.md (Japanese) --- content/ja/about.md | 69 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/ja/about.md diff --git a/content/ja/about.md b/content/ja/about.md new file mode 100644 index 0000000000..e13172f599 --- /dev/null +++ b/content/ja/about.md @@ -0,0 +1,69 @@ +--- +title: Numpyプロジェクトについて +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 を使った数値計算のためのオープンソースプロジェクトです。 Numpyは、Numerical and Numarrayライブラリの初期のコードを基に、2005年から開発がスタートしました。 NumPyは開発当初から100%オープンソースソフトウェアとして開発されてきました。[修正BSD ライセンス](https://github.com/numpy/numpy/blob/master/LICENSE.txt) の条項の下で、すべての人が利用可能です。 + +Numpy は 、様々な科学Python コミュニティとのコンセンサスを得ながら、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): + +- Sebastian Berg +- Jaime Fernández del Río +- Ralf Gommers +- Allan Haldane +- Charles Harris +- Stephan Hoyer +- Matti Picus +- Nathaniel Smith +- Julian Taylor +- Pauli Virtanen +- Stéfan van der Walt +- Eric Wieser + +終身名誉委員 + +- Travis Oliphant (プロジェクト創設者, 2005-2012) +- Alex Griffing (2015-2017) +- Marten van Kerkwijk (2017-2019) + +## 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。 + +- コード +- ドキュメント +- ウェブサイト +- トリアージ +- 資金と助成金 + +個々のチームメンバーについては、 [チーム](/gallery/team.html) のページを参照してください。 + +## スポンサー情報 + +Numpyは以下の団体から直接資金援助を受けています。 +{{< sponsors >}} + + +## パートナー団体 + +パートナー団体は、Numpyへの開発を仕事の一つとして、社員を雇っている団体です。 現在のパートナー団体としては、下記の通りです。 +{{< partner >}} + + +## 寄付 + +NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、Numpyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 + +Numpy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、Numpyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 + +Numpy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 米国の寄付提供者の場合、その人の寄付は法律によって定められる範囲で免税されます。 但し、他の寄付と同様に、あなたはあなたの税務状況について、あなたの税務担当と相談する必要があることを忘れないで下さい。 + +Numpyの運営委員会は、受け取った資金をどのように使えば良いかを検討し、使用する方法について決定します. Numpyに関する技術とインフラの投資の優先順位に関しては、[Numpy Roadmap](https://www.numpy.org/neps/index.html#roadmap) に記載されています。 +{{< numfocus >}} From 13c060f0b0648a129ae1403a12069ab3383a3013 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:43 +0000 Subject: [PATCH 071/909] New translations 404.md (Japanese) --- content/ja/404.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ja/404.md diff --git a/content/ja/404.md b/content/ja/404.md new file mode 100644 index 0000000000..8e4db85255 --- /dev/null +++ b/content/ja/404.md @@ -0,0 +1,8 @@ +--- +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。 + +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 a9e4e11af7544c03fdebfaa171cf588e7962d94f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:44 +0000 Subject: [PATCH 072/909] New translations diversity_sep2020.md (Spanish) --- content/es/diversity_sep2020.md | 48 +++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 content/es/diversity_sep2020.md diff --git a/content/es/diversity_sep2020.md b/content/es/diversity_sep2020.md new file mode 100644 index 0000000000..ef3030d5f7 --- /dev/null +++ b/content/es/diversity_sep2020.md @@ -0,0 +1,48 @@ +--- +title: NumPy Diversity and Inclusion Statement +sidebar: false +--- + + +_In light of the foregoing discussion on social media after publication of the NumPy paper in Nature and the concerns raised about the state of diversity and inclusion on the NumPy team, we would like to issue the following statement:_ + + +It is our strong belief that we are at our best, as a team and community, when we are inclusive and equitable. Being an international team from the onset, we recognize the value of collaborating with individuals from diverse backgrounds and expertise. A culture where everyone is welcomed, supported, and valued is at the core of the NumPy project. + +## The Past + +Contributing to open source has always been a pastime in which most historically marginalized groups, especially women, faced more obstacles to participate due to a number of societal constraints and expectations. Open source has a severe diversity gap that is well documented (see, e.g., the [2017 GitHub Open Source Survey](https://opensourcesurvey.org/2017/) and [this blog post](https://medium.com/tech-diversity-files/if-you-think-women-in-tech-is-just-a-pipeline-problem-you-haven-t-been-paying-attention-cb7a2073b996)). + +Since its inception and until 2018, NumPy was maintained by a handful of volunteers often working nights and weekends outside of their day jobs. At any one time, the number of active core developers, the ones doing most of the heavy lifting as well as code review and integration of contributions from the community, was in the range of 4 to 8. The project didn't have a roadmap or mechanism for directing resources, being driven by individual efforts to work on what seemed needed. The authors on the NumPy paper are the individuals who made the most significant and sustained contributions to the project over a period of 15 years (2005 - 2019). The lack of diversity on this author list is a reflection of the formative years of the Python and SciPy ecosystems. + +2018 has marked an important milestone in the history of the NumPy project. Receiving funding from The Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation allowed us to provide full-time employment for two software engineers with years of experience contributing to the Python ecosystem. Those efforts brought NumPy to a much healthier technical state. + +This funding also created space for NumPy maintainers to focus on project governance, community development, and outreach to underrepresented groups. [The diversity statement](https://figshare.com/articles/online_resource/Diversity_and_Inclusion_Statement_NumPy_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/12980852) written in mid 2019 for the CZI EOSS program grant application details some of the challenges as well as the advances in our efforts to bring in more diverse talent to the NumPy team. + +## The Present + +Offering employment opportunities is an effective way to attract and retain diverse talent in OSS. Therefore, we used two-thirds of our second grant that became available in Dec 2019 to employ Melissa Weber Mendonça and Mars Lee. + +As a result of several initiatives aimed at community development and engagement led by Inessa Pawson and Ralf Gommers, the NumPy project has received a number of valuable contributions from women and other underrepresented groups in open source in 2020: + +- Melissa Weber Mendonça gained commit rights, is maintaining numpy.f2py and is leading the documentation team, +- Shaloo Shalini created all case studies on numpy.org, +- Mars Lee contributed web design and led our accessibility improvements work, +- Isabela Presedo-Floyd designed our new logo, +- Stephanie Mendoza, Xiayoi Deng, Deji Suolang, and Mame Fatou Thiam designed and fielded the first NumPy user survey, +- Yuki Dunn, Dayane Machado, Mahfuza Humayra Mohona, Sumera Priyadarsini, Shaloo Shalini, and Kriti Singh (former Outreachy intern) helped the survey team to reach out to non-English speaking NumPy users and developers by translating the questionnaire into their native languages, +- Sayed Adel, Raghuveer Devulapalli, and Chunlin Fang are driving the work on SIMD optimizations in the core of NumPy. + +While we still have much more work to do, the NumPy team is starting to look much more representative of our user base. And we can assure you that the next NumPy paper will certainly have a more diverse group of authors. + +## The Future + +We are fully committed to fostering inclusion and diversity on our team and in our community, and to do our part in building a more just and equitable future. + +We are open to dialogue and welcome every opportunity to connect with organizations representing and supporting women and minorities in tech and science. We are ready to listen, learn, and support. + +Please get in touch with us on [our mailing list](https://scipy.org/scipylib/mailing-lists.html#mailing-lists), [GitHub](https://github.com/numpy/numpy/issues), [Slack](https://numpy.org/contribute/), in private at numpy-team@googlegroups.com, or join our [bi-weekly community meeting](https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg). + + +_Sayed Adel, Sebastian Berg, Raghuveer Devulapalli, Chunlin Fang, Ralf Gommers, Allan Haldane, Stephan Hoyer, Mars Lee, Melissa Weber Mendonça, Jarrod Millman, Inessa Pawson, Matti Picus, Nathaniel Smith, Julian Taylor, Pauli Virtanen, Stéfan van der Walt, Eric Wieser, on behalf of the NumPy team_ + From 74e85eb0ada0751f1ff7e7d25173949766ecd5af Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:46 +0000 Subject: [PATCH 073/909] 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..3d25090e13 --- /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 365104e94487874574ad6a3e9f07c7cf5d62c85d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:48 +0000 Subject: [PATCH 074/909] 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 58d2330958272432f97f6b1cc32feb2a44721ed2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:50 +0000 Subject: [PATCH 075/909] 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..987b38fb68 --- /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) provides 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 aacd7c183e0d17358b03d8ecb715f660becbe9fd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:51 +0000 Subject: [PATCH 076/909] 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..2906e12ece --- /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! + +### 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. + +* **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 c19f0e2eb898ae6d80f10b9ae87e9947c905b78f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:53 +0000 Subject: [PATCH 077/909] New translations news.md (Spanish) --- content/es/news.md | 83 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 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..5dcb849596 --- /dev/null +++ b/content/es/news.md @@ -0,0 +1,83 @@ +--- +title: News +sidebar: false +--- + +### 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 + +_Sept 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 217c831234f4f66143b070846ce8c40b58e29311 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:55 +0000 Subject: [PATCH 078/909] 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..fc79a621af --- /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](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 8ff3407049b739c840194fe2d4419a8fd4a74023 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:57 +0000 Subject: [PATCH 079/909] 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 bb4008b0329368c3db053e23ee81f8821a838caa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:37:58 +0000 Subject: [PATCH 080/909] New translations press-kit.md (Japanese) --- content/ja/press-kit.md | 8 ++++++++ 1 file changed, 8 insertions(+) create mode 100644 content/ja/press-kit.md diff --git a/content/ja/press-kit.md b/content/ja/press-kit.md new file mode 100644 index 0000000000..a523b1d296 --- /dev/null +++ b/content/ja/press-kit.md @@ -0,0 +1,8 @@ +--- +title: プレス用資料 +sidebar: false +--- + +私達はユーザーの皆さんの次の学術論文や、コース教材、プレゼンテーションにNumPyプロジェクトのロゴなど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/master/branding/logo). ちなみに、numpy.orgのリソースを使用するということは、 [Numpy行動規範](/code-of-conduct) を受け入れることを意味していることに注意してください。 From de59b95b5294b2c53475846ef0c798f6bbc9aa65 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:00 +0000 Subject: [PATCH 081/909] New translations terms.md (Spanish) --- content/es/terms.md | 178 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 content/es/terms.md diff --git a/content/es/terms.md b/content/es/terms.md new file mode 100644 index 0000000000..9a66045505 --- /dev/null +++ b/content/es/terms.md @@ -0,0 +1,178 @@ +--- +title: Terms of Use +sidebar: false +--- + +*Last updated January 4, 2020* + + +## AGREEMENT TO TERMS + +These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy ("**Project**", “**we**”, “**us**”, or “**our**”), concerning your access to and use of the numpy.org website as well as any other media form, media channel, mobile website or mobile application related, linked, or otherwise connected thereto (collectively, the “Site”). You agree that by accessing the Site, you have read, understood, and agreed to be bound by all of these Terms of Use. IF YOU DO NOT AGREE WITH ALL OF THESE TERMS OF USE, THEN YOU ARE EXPRESSLY PROHIBITED FROM USING THE SITE AND YOU MUST DISCONTINUE USE IMMEDIATELY. + + + +Supplemental terms and conditions or documents that may be posted on the Site from time to time are hereby expressly incorporated herein by reference. We reserve the right, in our sole discretion, to make changes or modifications to these Terms of Use at any time and for any reason. We will alert you about any changes by updating the “Last updated” date of these Terms of Use, and you waive any right to receive specific notice of each such change. It is your responsibility to periodically review these Terms of Use to stay informed of updates. You will be subject to, and will be deemed to have been made aware of and to have accepted, the changes in any revised Terms of Use by your continued use of the Site after the date such revised Terms of Use are posted. + + + +The information provided on the Site is not intended for distribution to or use by any person or entity in any jurisdiction or country where such distribution or use would be contrary to law or regulation or which would subject us to any registration requirement within such jurisdiction or country. Accordingly, those persons who choose to access the Site from other locations do so on their own initiative and are solely responsible for compliance with local laws, if and to the extent local laws are applicable. + + +## USER REPRESENTATIONS + +By using the Site, you represent and warrant that: (1) you have the legal capacity and you agree to comply with these Terms of Use; (2) you will not use the Site for any illegal or unauthorized purpose; and (3) your use of the Site will not violate any applicable law or regulation. + + +If you provide any information that is untrue, inaccurate, not current, or incomplete, we have the right to refuse any and all current or future use of the Site (or any portion thereof). + + +## PROHIBITED ACTIVITIES + +You may not access or use the Site for any purpose other than that for which we make the Site available. + +As a user of the Site, you agree not to: + +1. 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If this provision is found to be illegal or unenforceable, then neither Party will elect to arbitrate any Dispute falling within that portion of this provision found to be illegal or unenforceable and such Dispute shall be decided by a court of competent jurisdiction within the courts listed for jurisdiction above, and the Parties agree to submit to the personal jurisdiction of that court. + + +## CORRECTIONS + +There may be information on the Site that contains typographical errors, inaccuracies, or omissions. We reserve the right to correct any errors, inaccuracies, or omissions and to change or update the information on the Site at any time, without prior notice. + + +## DISCLAIMER + +THE SITE IS PROVIDED ON AN AS-IS AND AS-AVAILABLE BASIS. YOU AGREE THAT YOUR USE OF THE SITE AND OUR SERVICES WILL BE AT YOUR SOLE RISK. 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Notwithstanding the foregoing, we reserve the right, at your expense, to assume the exclusive defense and control of any matter for which you are required to indemnify us, and you agree to cooperate, at your expense, with our defense of such claims. We will use reasonable efforts to notify you of any such claim, action, or proceeding which is subject to this indemnification upon becoming aware of it. + + +## USER DATA + +We will maintain certain data that you transmit to the Site for the purpose of managing the performance of the Site, as well as data relating to your use of the Site. Although we perform regular routine backups of data, you are solely responsible for all data that you transmit or that relates to any activity you have undertaken using the Site. You agree that we shall have no liability to you for any loss or corruption of any such data, and you hereby waive any right of action against us arising from any such loss or corruption of such data. + + +## ELECTRONIC COMMUNICATIONS, TRANSACTIONS, AND SIGNATURES + +Visiting the Site, sending us emails, and completing online forms constitute electronic communications. You consent to receive electronic communications, and you agree that all agreements, notices, disclosures, and other communications we provide to you electronically, via email and on the Site, satisfy any legal requirement that such communication be in writing. YOU HEREBY AGREE TO THE USE OF ELECTRONIC SIGNATURES, CONTRACTS, ORDERS, AND OTHER RECORDS, AND TO ELECTRONIC DELIVERY OF NOTICES, POLICIES, AND RECORDS OF TRANSACTIONS INITIATED OR COMPLETED BY US OR VIA THE SITE. You hereby waive any rights or requirements under any statutes, regulations, rules, ordinances, or other laws in any jurisdiction which require an original signature or delivery or retention of non-electronic records, or to payments or the granting of credits by any means other than electronic means. + + +## CALIFORNIA USERS AND RESIDENTS + +If any complaint with us is not satisfactorily resolved, you can contact the Complaint Assistance Unit of the Division of Consumer Services of the California Department of Consumer Affairs in writing at 1625 North Market Blvd., Suite N 112, Sacramento, California 95834 or by telephone at (800) 952-5210 or (916) 445-1254. + + +## MISCELLANEOUS + +These Terms of Use and any policies or operating rules posted by us on the Site or in respect to the Site constitute the entire agreement and understanding between you and us. Our failure to exercise or enforce any right or provision of these Terms of Use shall not operate as a waiver of such right or provision. These Terms of Use operate to the fullest extent permissible by law. We may assign any or all of our rights and obligations to others at any time. We shall not be responsible or liable for any loss, damage, delay, or failure to act caused by any cause beyond our reasonable control. If any provision or part of a provision of these Terms of Use is determined to be unlawful, void, or unenforceable, that provision or part of the provision is deemed severable from these Terms of Use and does not affect the validity and enforceability of any remaining provisions. There is no joint venture, partnership, employment or agency relationship created between you and us as a result of these Terms of Use or use of the Site. You agree that these Terms of Use will not be construed against us by virtue of having drafted them. You hereby waive any and all defenses you may have based on the electronic form of these Terms of Use and the lack of signing by the parties hereto to execute these Terms of Use. + +## CONTACT US + +In order to resolve a complaint regarding the Site or to receive further information regarding use of the Site, please contact us at: + +NumFOCUS, Inc.
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    +1 (512) 222-5449 + + + From 003752f706c3a77867fa37e1266486172f3fb2c1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:02 +0000 Subject: [PATCH 082/909] New translations privacy.md (Spanish) --- content/es/privacy.md | 18 ++++++++++++++++++ 1 file changed, 18 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..a3674dd48a --- /dev/null +++ b/content/es/privacy.md @@ -0,0 +1,18 @@ +--- +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 0c217298dec674cf05908dce07a5b80d97227487 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:04 +0000 Subject: [PATCH 083/909] 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..2309040ad2 --- /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/master/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 78cb3c10740b7dafe6e809c27b9879bc5bab8fd8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:05 +0000 Subject: [PATCH 084/909] New translations learn.md (Spanish) --- content/es/learn.md | 84 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 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..264677ac48 --- /dev/null +++ b/content/es/learn.md @@ -0,0 +1,84 @@ +--- +title: Learn +sidebar: false +--- + +**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** + +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +*** + +## Beginners + +There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: + + **Tutorials** + +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) +* [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 *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 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/) + + **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 Nunuz-Iglesias* +* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* + +*** + +## 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). + +## Contribute to this list + + +To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. From 8eff61bfc0c5995a25b40ddc4b6d0300ea1f1503 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:07 +0000 Subject: [PATCH 085/909] New translations install.md (Spanish) --- content/es/install.md | 142 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 142 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..43dd44cb12 --- /dev/null +++ b/content/es/install.md @@ -0,0 +1,142 @@ +--- +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 + +#### Windows or macOS + +- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- 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. +- Unless you're fine with only the packages in the `defaults` channel, make `conda-forge` your default channel via [setting the channel priority](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge). + + +#### Linux + +If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: +- Use your OS package manager for as much as possible (Python itself, NumPy, and other libraries). +- Install packages not provided by your package manager with `pip install somepackage --user`. + +If you use a GPU: +- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- 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. +- Use the `defaults` conda channel (`conda-forge` doesn't have good support for GPU packages yet). + +Otherwise: +- 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, for example, [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 c596d42b7dba8e3633152b56491e8a3210f187bf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:09 +0000 Subject: [PATCH 086/909] 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..efcde754ae --- /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 +* Anirudh Subramanian + +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/reference/dev/conduct/code_of_conduct.html) From db8ac7d00cfb11d83993a95b79fc5ed2d115cff1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:11 +0000 Subject: [PATCH 087/909] 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..cf20ae59cf --- /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: [0.1038/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 bb05dbfdbf93720422cfa3bb489b9df1784d3481 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:12 +0000 Subject: [PATCH 088/909] 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 3cad8c76cb072e7565dc75d04cfff2fa988b3b3e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:14 +0000 Subject: [PATCH 089/909] New translations about.md (Spanish) --- content/es/about.md | 69 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 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..df89bff1f5 --- /dev/null +++ b/content/es/about.md @@ -0,0 +1,69 @@ +--- +title: About Us +sidebar: false +--- + +_Some information about the NumPy project and community_ + +NumPy is an open source project aiming to enable numerical computing with Python. It was created in 2005, building on the early work of the Numerical and Numarray libraries. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/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 role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. The NumPy Steering Council currently consists of the following members (in alphabetical order): + +- Sebastian Berg +- Jaime Fernández del Río +- Ralf Gommers +- Allan Haldane +- Charles Harris +- Stephan Hoyer +- Matti Picus +- Nathaniel Smith +- Julian Taylor +- Pauli Virtanen +- Stéfan van der Walt +- Eric Wieser + +Emeritus: + +- Travis Oliphant (project founder, 2005-2012) +- Alex Griffing (2015-2017) +- Marten van Kerkwijk (2017-2019) + +## Teams + +The NumPy project is growing; we have teams for + +- code +- documentation +- website +- triage +- funding and grants + +See the [Team](/gallery/team.html) page for individual team members. + +## 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: +{{< 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). +{{< numfocus >}} From e48e69c74b61313950f3d86c9cb3885385a77157 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:16 +0000 Subject: [PATCH 090/909] 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..e0e7272719 --- /dev/null +++ b/content/es/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +¡Ups! Has llegado a un callejón sin salida. + +Si crees que algo debería estar aquí, puedes [reportar este problema](https://github.com/numpy/numpy.org/issues) en GitHub. From 66727e8d040583932c3031cb39ae5031bb6342d3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:18 +0000 Subject: [PATCH 091/909] New translations community.md (Portuguese, Brazilian) --- content/pt/community.md | 65 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 content/pt/community.md diff --git a/content/pt/community.md b/content/pt/community.md new file mode 100644 index 0000000000..14c5f35420 --- /dev/null +++ b/content/pt/community.md @@ -0,0 +1,65 @@ +--- +title: Comunidade +sidebar: false +--- + +NumPy é um projeto de código aberto impulsionado pela comunidade desenvolvido por um grupo muito diversificado de [contribuidores](/gallery/team.html). A liderança da NumPy assumiu um forte compromisso de criar uma comunidade aberta, inclusiva e positiva. Por favor, leia [o Código de Conduta NumPy](/code-of-conduct) para orientações sobre como interagir com os outros de uma forma que faça a comunidade prosperar. + +Oferecemos vários canais de comunicação para aprender, compartilhar seu conhecimento e se conectar com outros dentro da comunidade NumPy. + + +## Participar online + +Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a comunidade do NumPy. _Por favor, note que encorajamos os usuários e membros da comunidade a apoiarem-se uns aos outros para perguntas sobre utilização - veja [Obter Ajuda](/gethelp)._ + + +### [Lista de discussões NumPy](https://mail.python.org/mailman/listinfo/numpy-discussion) + +Esta lista é o principal fórum para discussões mais longas, como adicionar novos recursos ao NumPy, fazer alterações no roadmap do NumPy e em todos os tipos de tomada de decisão para todo o projeto. Anúncios sobre o NumPy, como novas versões, reuniões de desenvolvedores, sprints ou palestras de conferência também são feitas nesta lista. + +Nesta lista, por favor, use *bottom posting*, responda à lista (em vez de a outro remetente), e não responda aos *digests*. Um arquivo pesquisável desta lista está disponível [aqui](http://numpy-discussion.10968.n7.nabble.com/). + +*** + +### [Página de issues do GitHub](https://github.com/numpy/numpy/issues) + +- Para relatórios de bugs (por exemplo, "`np.arange(3).shape` retorna `(5,)`, quando deveria retornar `(3,)`"); +- problemas de documentação (ex. "Eu achei esta seção confusa"); +- e pedidos de recursos (por exemplo, "Eu gostaria de ter um novo método de interpolação em `np.percentile`"). + +_Por favor, note que o GitHub não é o lugar certo para relatar uma vulnerabilidade de segurança. Se você acha que encontrou uma vulnerabilidade de segurança no NumPy, relate-a [aqui](https://tidelift.com/docs/security)._ + +*** + +### [Slack](https://numpy-team.slack.com) + +Uma sala de bate-papo em tempo real para fazer perguntas sobre _contribuir_ para o NumPy. Este é um fórum privado, especificamente para pessoas hesitantes em levantar suas perguntas ou idéias em uma grande lista de e-mails públicos ou no GitHub. Por favor, clique [aqui](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para mais detalhes e como obter um convite. + + +## Grupos de Estudo e Meetups + +Se você gostaria de encontrar um encontro ou grupo de estudo local para aprender mais sobre o NumPy e o ecossistema mais amplo de pacotes Python para ciência de dados e computação científica, recomendamos explorar os [meetups PyData](https://www.meetup.com/pro/pydata/) (mais de 150 encontros, mais de 100.000 membros). + +O NumPy também organiza sprints presenciais para sua equipe e colaboradores interessados ocasionalmente. Estes eventos são normalmente planejados com vários meses de antecedência e serão anunciados na [lista de discussão](https://mail.python.org/mailman/listinfo/numpy-discussion) e no [Twitter](https://twitter.com/numpy_team). + + +## Conferências + +O projeto NumPy não organiza suas próprias conferências. As conferências que tradicionalmente têm sido mais populares com mantenedores, colaboradores e usuários são as conferências SciPy e PyData: + +- [SciPy US](https://conference.scipy.org) +- [EuroSciPy](https://www.euroscipy.org) +- [SciPy Latin America](https://www.scipyla.org) +- [SciPy India](https://scipy.in) +- [SciPy Japan](https://conference.scipy.org) +- [conferências PyData](https://pydata.org/event-schedule/) (15-20 eventos por ano espalhados por muitos países) + +Muitas dessas conferências incluem dias de tutorial sobre o NumPy e/ou sprints onde você pode aprender como contribuir com o NumPy ou projetos de código aberto relacionados. + + +## Junte-se à comunidade NumPy + +Para prosperar, o projeto NumPy precisa de sua experiência e entusiasmo. Não é uma pessoa programadora? Sem problemas! Existem muitas maneiras de contribuir com o NumPy. + +Se você está interessado em se tornar um contribuidor do NumPy (oba!) recomendamos que você confira nossa página sobre [Contribuições](/contribute). + From 38c3c300a19d089448e9f366df201517ae30c056 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:19 +0000 Subject: [PATCH 092/909] New translations community.md (Chinese Simplified) --- content/zh/community.md | 65 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 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..4e24a83784 --- /dev/null +++ b/content/zh/community.md @@ -0,0 +1,65 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a very diverse group of [contributors](/gallery/team.html). 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](http://numpy-discussion.10968.n7.nabble.com/). + +*** + +### [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. + From 032016043d3dba50ec0d765de78848c73d3f5c82 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:21 +0000 Subject: [PATCH 093/909] New translations community.md (Korean) --- content/ko/community.md | 65 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 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..4e24a83784 --- /dev/null +++ b/content/ko/community.md @@ -0,0 +1,65 @@ +--- +title: Community +sidebar: false +--- + +NumPy is a community-driven open source project developed by a very diverse group of [contributors](/gallery/team.html). 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](http://numpy-discussion.10968.n7.nabble.com/). + +*** + +### [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. + From ba73a90cbc24c11e9de68ae866015c5e8f869360 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:23 +0000 Subject: [PATCH 094/909] New translations learn.md (Japanese) --- content/ja/learn.md | 84 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) create mode 100644 content/ja/learn.md diff --git a/content/ja/learn.md b/content/ja/learn.md new file mode 100644 index 0000000000..264677ac48 --- /dev/null +++ b/content/ja/learn.md @@ -0,0 +1,84 @@ +--- +title: Learn +sidebar: false +--- + +**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** + +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +*** + +## Beginners + +There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: + + **Tutorials** + +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) +* [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 *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 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/) + + **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 Nunuz-Iglesias* +* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* + +*** + +## 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). + +## Contribute to this list + + +To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. From 887552039683019c39dc7f55b9fad0ca0449bee5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:24 +0000 Subject: [PATCH 095/909] New translations privacy.md (Japanese) --- content/ja/privacy.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 content/ja/privacy.md diff --git a/content/ja/privacy.md b/content/ja/privacy.md new file mode 100644 index 0000000000..9f259a4210 --- /dev/null +++ b/content/ja/privacy.md @@ -0,0 +1,18 @@ +--- +title: プライバシーポリシー +sidebar: false +--- + +**numpy.org** は、Numpyプロジェクトの資金援助のスポンサーでもある、[NumFOCUS, Inc.](https://numfocus.org)によって運営されています。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policyを参照してください。 + +ポリシーまたはNumFOCUSのデータ収集、使用、および開示方法についてご質問がある場合は、privacy@numfocus.orgのNumFOCUSスタッフにお問い合わせください。 + + + + + + + + + + From da413f3a75c41fda552bcb8088ec6bd92e3522af Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:26 +0000 Subject: [PATCH 096/909] 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..0f27a36ee9 --- /dev/null +++ b/content/zh/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +抱歉······ 目标网页并不存在。 + +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 238b825712e722dea90f696283e984d4846061c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:27 +0000 Subject: [PATCH 097/909] New translations install.md (Korean) --- content/ko/install.md | 142 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 142 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..43dd44cb12 --- /dev/null +++ b/content/ko/install.md @@ -0,0 +1,142 @@ +--- +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 + +#### Windows or macOS + +- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- 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. +- Unless you're fine with only the packages in the `defaults` channel, make `conda-forge` your default channel via [setting the channel priority](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge). + + +#### Linux + +If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: +- Use your OS package manager for as much as possible (Python itself, NumPy, and other libraries). +- Install packages not provided by your package manager with `pip install somepackage --user`. + +If you use a GPU: +- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). +- 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. +- Use the `defaults` conda channel (`conda-forge` doesn't have good support for GPU packages yet). + +Otherwise: +- 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, for example, [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 789923cf7e4ee9da4cb9601c2f33d5ced53c7328 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:29 +0000 Subject: [PATCH 098/909] New translations diversity_sep2020.md (Korean) --- content/ko/diversity_sep2020.md | 48 +++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 content/ko/diversity_sep2020.md diff --git a/content/ko/diversity_sep2020.md b/content/ko/diversity_sep2020.md new file mode 100644 index 0000000000..ef3030d5f7 --- /dev/null +++ b/content/ko/diversity_sep2020.md @@ -0,0 +1,48 @@ +--- +title: NumPy Diversity and Inclusion Statement +sidebar: false +--- + + +_In light of the foregoing discussion on social media after publication of the NumPy paper in Nature and the concerns raised about the state of diversity and inclusion on the NumPy team, we would like to issue the following statement:_ + + +It is our strong belief that we are at our best, as a team and community, when we are inclusive and equitable. Being an international team from the onset, we recognize the value of collaborating with individuals from diverse backgrounds and expertise. A culture where everyone is welcomed, supported, and valued is at the core of the NumPy project. + +## The Past + +Contributing to open source has always been a pastime in which most historically marginalized groups, especially women, faced more obstacles to participate due to a number of societal constraints and expectations. Open source has a severe diversity gap that is well documented (see, e.g., the [2017 GitHub Open Source Survey](https://opensourcesurvey.org/2017/) and [this blog post](https://medium.com/tech-diversity-files/if-you-think-women-in-tech-is-just-a-pipeline-problem-you-haven-t-been-paying-attention-cb7a2073b996)). + +Since its inception and until 2018, NumPy was maintained by a handful of volunteers often working nights and weekends outside of their day jobs. At any one time, the number of active core developers, the ones doing most of the heavy lifting as well as code review and integration of contributions from the community, was in the range of 4 to 8. The project didn't have a roadmap or mechanism for directing resources, being driven by individual efforts to work on what seemed needed. The authors on the NumPy paper are the individuals who made the most significant and sustained contributions to the project over a period of 15 years (2005 - 2019). The lack of diversity on this author list is a reflection of the formative years of the Python and SciPy ecosystems. + +2018 has marked an important milestone in the history of the NumPy project. Receiving funding from The Gordon and Betty Moore Foundation and Alfred P. Sloan Foundation allowed us to provide full-time employment for two software engineers with years of experience contributing to the Python ecosystem. Those efforts brought NumPy to a much healthier technical state. + +This funding also created space for NumPy maintainers to focus on project governance, community development, and outreach to underrepresented groups. [The diversity statement](https://figshare.com/articles/online_resource/Diversity_and_Inclusion_Statement_NumPy_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/12980852) written in mid 2019 for the CZI EOSS program grant application details some of the challenges as well as the advances in our efforts to bring in more diverse talent to the NumPy team. + +## The Present + +Offering employment opportunities is an effective way to attract and retain diverse talent in OSS. Therefore, we used two-thirds of our second grant that became available in Dec 2019 to employ Melissa Weber Mendonça and Mars Lee. + +As a result of several initiatives aimed at community development and engagement led by Inessa Pawson and Ralf Gommers, the NumPy project has received a number of valuable contributions from women and other underrepresented groups in open source in 2020: + +- Melissa Weber Mendonça gained commit rights, is maintaining numpy.f2py and is leading the documentation team, +- Shaloo Shalini created all case studies on numpy.org, +- Mars Lee contributed web design and led our accessibility improvements work, +- Isabela Presedo-Floyd designed our new logo, +- Stephanie Mendoza, Xiayoi Deng, Deji Suolang, and Mame Fatou Thiam designed and fielded the first NumPy user survey, +- Yuki Dunn, Dayane Machado, Mahfuza Humayra Mohona, Sumera Priyadarsini, Shaloo Shalini, and Kriti Singh (former Outreachy intern) helped the survey team to reach out to non-English speaking NumPy users and developers by translating the questionnaire into their native languages, +- Sayed Adel, Raghuveer Devulapalli, and Chunlin Fang are driving the work on SIMD optimizations in the core of NumPy. + +While we still have much more work to do, the NumPy team is starting to look much more representative of our user base. And we can assure you that the next NumPy paper will certainly have a more diverse group of authors. + +## The Future + +We are fully committed to fostering inclusion and diversity on our team and in our community, and to do our part in building a more just and equitable future. + +We are open to dialogue and welcome every opportunity to connect with organizations representing and supporting women and minorities in tech and science. We are ready to listen, learn, and support. + +Please get in touch with us on [our mailing list](https://scipy.org/scipylib/mailing-lists.html#mailing-lists), [GitHub](https://github.com/numpy/numpy/issues), [Slack](https://numpy.org/contribute/), in private at numpy-team@googlegroups.com, or join our [bi-weekly community meeting](https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg). + + +_Sayed Adel, Sebastian Berg, Raghuveer Devulapalli, Chunlin Fang, Ralf Gommers, Allan Haldane, Stephan Hoyer, Mars Lee, Melissa Weber Mendonça, Jarrod Millman, Inessa Pawson, Matti Picus, Nathaniel Smith, Julian Taylor, Pauli Virtanen, Stéfan van der Walt, Eric Wieser, on behalf of the NumPy team_ + From a0f40fc6b12a77cef680683c21bc915ca9fb36eb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:32 +0000 Subject: [PATCH 099/909] 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..3d25090e13 --- /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 650ee7f1a2a798fc483cc3489f549e32e284dc85 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:33 +0000 Subject: [PATCH 100/909] 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 dd33c60340298c1845c20de078a8023291b670de Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:35 +0000 Subject: [PATCH 101/909] 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..987b38fb68 --- /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) provides 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 c2bdfe372934798f5fb4c4f3b8b769b3456a2c13 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:37 +0000 Subject: [PATCH 102/909] 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 452b861db6572e8a1249e910b6b2b81117bcded9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:39 +0000 Subject: [PATCH 103/909] New translations news.md (Korean) --- content/ko/news.md | 83 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 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..5dcb849596 --- /dev/null +++ b/content/ko/news.md @@ -0,0 +1,83 @@ +--- +title: News +sidebar: false +--- + +### 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 + +_Sept 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 212c24d14476f35ef36cc81c942d97b2043ccf31 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:40 +0000 Subject: [PATCH 104/909] 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..fc79a621af --- /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](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 4d0d0fdf26eb56843e9282442ae9170bf5c8c3d7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:42 +0000 Subject: [PATCH 105/909] 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 2d25def5ff044908833e83af71213a9fb2e934e1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:44 +0000 Subject: [PATCH 106/909] 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 e02d37996d2e61ad56944fb620563d9af758843e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:45 +0000 Subject: [PATCH 107/909] New translations terms.md (Korean) --- content/ko/terms.md | 178 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 content/ko/terms.md diff --git a/content/ko/terms.md b/content/ko/terms.md new file mode 100644 index 0000000000..9a66045505 --- /dev/null +++ b/content/ko/terms.md @@ -0,0 +1,178 @@ +--- +title: Terms of Use +sidebar: false +--- + +*Last updated January 4, 2020* + + +## AGREEMENT TO TERMS + +These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy ("**Project**", “**we**”, “**us**”, or “**our**”), concerning your access to and use of the numpy.org website as well as any other media form, media channel, mobile website or mobile application related, linked, or otherwise connected thereto (collectively, the “Site”). 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Our failure to exercise or enforce any right or provision of these Terms of Use shall not operate as a waiver of such right or provision. These Terms of Use operate to the fullest extent permissible by law. We may assign any or all of our rights and obligations to others at any time. We shall not be responsible or liable for any loss, damage, delay, or failure to act caused by any cause beyond our reasonable control. If any provision or part of a provision of these Terms of Use is determined to be unlawful, void, or unenforceable, that provision or part of the provision is deemed severable from these Terms of Use and does not affect the validity and enforceability of any remaining provisions. There is no joint venture, partnership, employment or agency relationship created between you and us as a result of these Terms of Use or use of the Site. You agree that these Terms of Use will not be construed against us by virtue of having drafted them. You hereby waive any and all defenses you may have based on the electronic form of these Terms of Use and the lack of signing by the parties hereto to execute these Terms of Use. + +## CONTACT US + +In order to resolve a complaint regarding the Site or to receive further information regarding use of the Site, please contact us at: + +NumFOCUS, Inc.
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    +1 (512) 222-5449 + + + From 3ee533343dfc5abc6eee1a8393aa3b3853ad51d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:47 +0000 Subject: [PATCH 108/909] New translations privacy.md (Korean) --- content/ko/privacy.md | 18 ++++++++++++++++++ 1 file changed, 18 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..a3674dd48a --- /dev/null +++ b/content/ko/privacy.md @@ -0,0 +1,18 @@ +--- +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 5c9787da061761cc31d9a54ea83728536ca6f78c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:49 +0000 Subject: [PATCH 109/909] 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..2309040ad2 --- /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/master/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). From 1aed54a172c48beb604324333fd5679609ec4005 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:50 +0000 Subject: [PATCH 110/909] New translations learn.md (Korean) --- content/ko/learn.md | 84 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 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..87d12db866 --- /dev/null +++ b/content/ko/learn.md @@ -0,0 +1,84 @@ +--- +title: Learn +sidebar: false +--- + +**공식 NumPy 문서는 [여기](https://numpy.org/doc/stable)에 있습니다.** + +아래는 선별된 외부 자료들의 모음입니다. 이곳에 기여하고 싶다면, [이 페이지의 끝](#add-to-this-list)을 참조하세요. +*** + +## Beginners + +여기에 NumPy에 대한 많은 자료가 있습니다. NumPy가 처음이라면 이 자료들을 강력하게 권장합니다. + + **튜토리얼** + +* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) +* [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 *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) + + **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* + +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. + + **영상** + +* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* + +*** + +## Advanced + +Indexing, Splitting, Stacking, 선형대수 등과 같은 NumPy의 개념을 더 잘 이해하러면 이 고급 자료들을 참조 해보세요. + + **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 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/) + + **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* + + **영상** + +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunuz-Iglesias* +* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* + +*** + +## 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) + +*** + +## NumPy 인용하기 + +만약 당신의 연구에서 NumPy가 중요한 역할을 수행하였고 학술 간행물에서 출판하기 위해서는 [이 인용 정보](/citing-numpy)를 참조하세요. + +## 이 목록에 기여하기 + + +이 목록에 자료를 추가하려면 [Pull Request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md)를 통해서 제출하세요. 당신이 추천한 자료가 왜 이 페이지에 올라야하는지, 또한 어떤 사람들이 가장 좋아할지 말해주세요. From e944fcce3ebe7566b7bf8b28577b99b0154c30fd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:52 +0000 Subject: [PATCH 111/909] 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..efcde754ae --- /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 +* Anirudh Subramanian + +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/reference/dev/conduct/code_of_conduct.html) From 4a0891c7cea9d5a25ddf8d4c8d08f29caf8857ad Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:53 +0000 Subject: [PATCH 112/909] New translations terms.md (Japanese) --- content/ja/terms.md | 178 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 178 insertions(+) create mode 100644 content/ja/terms.md diff --git a/content/ja/terms.md b/content/ja/terms.md new file mode 100644 index 0000000000..9a66045505 --- /dev/null +++ b/content/ja/terms.md @@ -0,0 +1,178 @@ +--- +title: Terms of Use +sidebar: false +--- + +*Last updated January 4, 2020* + + +## AGREEMENT TO TERMS + +These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy ("**Project**", “**we**”, “**us**”, or “**our**”), concerning your access to and use of the numpy.org website as well as any other media form, media channel, mobile website or mobile application related, linked, or otherwise connected thereto (collectively, the “Site”). 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Please review our [Privacy Policy](/privacy). By using the Site, you agree to be bound by our Privacy Policy, which is incorporated into these Terms of Use. Please be advised the Site is hosted in the United States. If you access the Site from the European Union, Asia, or any other region of the world with laws or other requirements governing personal data collection, use, or disclosure that differ from applicable laws in the United States, then through your continued use of the Site, you are transferring your data to the United States, and you expressly consent to have your data transferred to and processed in the United States. Further, we do not knowingly accept, request, or solicit information from children or knowingly market to children. Therefore, in accordance with the U.S. Children’s Online Privacy Protection Act, if we receive actual knowledge that anyone under the age of 13 has provided personal information to us without the requisite and verifiable parental consent, we will delete that information from the Site as quickly as is reasonably practical. + +## TERM AND TERMINATION + +These Terms of Use shall remain in full force and effect while you use the Site. WITHOUT LIMITING ANY OTHER PROVISION OF THESE TERMS OF USE, WE RESERVE THE RIGHT TO, IN OUR SOLE DISCRETION AND WITHOUT NOTICE OR LIABILITY, DENY ACCESS TO AND USE OF THE SITE (INCLUDING BLOCKING CERTAIN IP ADDRESSES), TO ANY PERSON FOR ANY REASON OR FOR NO REASON, INCLUDING WITHOUT LIMITATION FOR BREACH OF ANY REPRESENTATION, WARRANTY, OR COVENANT CONTAINED IN THESE TERMS OF USE OR OF ANY APPLICABLE LAW OR REGULATION. 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Nothing in these Terms of Use will be construed to obligate us to maintain and support the Site or to supply any corrections, updates, or releases in connection therewith. + + +## GOVERNING LAW + +These Terms of Use and your use of the Site are governed by and construed in accordance with the laws of the State of Texas applicable to agreements made and to be entirely performed within the State of Texas, without regard to its conflict of law principles. + + +## DISPUTE RESOLUTION + +### Informal Negotiations + +To expedite resolution and control the cost of any dispute, controversy, or claim related to these Terms of Use (each a "Dispute" and collectively, the “Disputes”) brought by either you or us (individually, a “Party” and collectively, the “Parties”), the Parties agree to first attempt to negotiate any Dispute (except those Disputes expressly provided below) informally for at least thirty (30) days before initiating arbitration. 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If such costs are determined to by the arbitrator to be excessive, we will pay all arbitration fees and expenses. The arbitration may be conducted in person, through the submission of documents, by phone, or online. The arbitrator will make a decision in writing, but need not provide a statement of reasons unless requested by either Party. The arbitrator must follow applicable law, and any award may be challenged if the arbitrator fails to do so. Except where otherwise required by the applicable AAA rules or applicable law, the arbitration will take place in Travis County, Texas. Except as otherwise provided herein, the Parties may litigate in court to compel arbitration, stay proceedings pending arbitration, or to confirm, modify, vacate, or enter judgment on the award entered by the arbitrator. + +If for any reason, a Dispute proceeds in court rather than arbitration, the Dispute shall be commenced or prosecuted in the state and federal courts located in Travis County, Texas, and the Parties hereby consent to, and waive all defenses of lack of personal jurisdiction, and forum non conveniens with respect to venue and jurisdiction in such state and federal courts. Application of the United Nations Convention on Contracts for the International Sale of Goods and the the Uniform Computer Information Transaction Act (UCITA) are excluded from these Terms of Use. + +In no event shall any Dispute brought by either Party related in any way to the Site be commenced more than one (1) years after the cause of action arose. If this provision is found to be illegal or unenforceable, then neither Party will elect to arbitrate any Dispute falling within that portion of this provision found to be illegal or unenforceable and such Dispute shall be decided by a court of competent jurisdiction within the courts listed for jurisdiction above, and the Parties agree to submit to the personal jurisdiction of that court. + + +### Restrictions + +The Parties agree that any arbitration shall be limited to the Dispute between the Parties individually. To the full extent permitted by law, (a) no arbitration shall be joined with any other proceeding; (b) there is no right or authority for any Dispute to be arbitrated on a class-action basis or to utilize class action procedures; and (c) there is no right or authority for any Dispute to be brought in a purported representative capacity on behalf of the general public or any other persons. + + +### Exceptions to Informal Negotiations and Arbitration + +The Parties agree that the following Disputes are not subject to the above provisions concerning informal negotiations and binding arbitration: (a) any Disputes seeking to enforce or protect, or concerning the validity of, any of the intellectual property rights of a Party; (b) any Dispute related to, or arising from, allegations of theft, piracy, invasion of privacy, or unauthorized use; and (c) any claim for injunctive relief. If this provision is found to be illegal or unenforceable, then neither Party will elect to arbitrate any Dispute falling within that portion of this provision found to be illegal or unenforceable and such Dispute shall be decided by a court of competent jurisdiction within the courts listed for jurisdiction above, and the Parties agree to submit to the personal jurisdiction of that court. + + +## CORRECTIONS + +There may be information on the Site that contains typographical errors, inaccuracies, or omissions. We reserve the right to correct any errors, inaccuracies, or omissions and to change or update the information on the Site at any time, without prior notice. + + +## DISCLAIMER + +THE SITE IS PROVIDED ON AN AS-IS AND AS-AVAILABLE BASIS. YOU AGREE THAT YOUR USE OF THE SITE AND OUR SERVICES WILL BE AT YOUR SOLE RISK. 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WE DO NOT WARRANT, ENDORSE, GUARANTEE, OR ASSUME RESPONSIBILITY FOR ANY PRODUCT OR SERVICE ADVERTISED OR OFFERED BY A THIRD PARTY THROUGH THE SITE, ANY HYPERLINKED WEBSITE, OR ANY WEBSITE OR MOBILE APPLICATION FEATURED IN ANY BANNER OR OTHER ADVERTISING, AND WE WILL NOT BE A PARTY TO OR IN ANY WAY BE RESPONSIBLE FOR MONITORING ANY TRANSACTION BETWEEN YOU AND ANY THIRD-PARTY PROVIDERS OF PRODUCTS OR SERVICES. AS WITH THE PURCHASE OF A PRODUCT OR SERVICE THROUGH ANY MEDIUM OR IN ANY ENVIRONMENT, YOU SHOULD USE YOUR BEST JUDGMENT AND EXERCISE CAUTION WHERE APPROPRIATE. + + +## LIMITATIONS OF LIABILITY + +IN NO EVENT WILL WE OR OUR DIRECTORS, EMPLOYEES, OR AGENTS BE LIABLE TO YOU OR ANY THIRD PARTY FOR ANY DIRECT, INDIRECT, CONSEQUENTIAL, EXEMPLARY, INCIDENTAL, SPECIAL, OR PUNITIVE DAMAGES, INCLUDING LOST PROFIT, LOST REVENUE, LOSS OF DATA, OR OTHER DAMAGES ARISING FROM YOUR USE OF THE SITE, EVEN IF WE HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. NOTWITHSTANDING ANYTHING TO THE CONTRARY CONTAINED HEREIN, OUR LIABILITY TO YOU FOR ANY CAUSE WHATSOEVER AND REGARDLESS OF THE FORM OF THE ACTION, WILL AT ALL TIMES BE LIMITED TO THE AMOUNT PAID, IF ANY, BY YOU TO US DURING THE SIX (6) MONTH PERIOD PRIOR TO ANY CAUSE OF ACTION ARISING. CERTAIN STATE LAWS DO NOT ALLOW LIMITATIONS ON IMPLIED WARRANTIES OR THE EXCLUSION OR LIMITATION OF CERTAIN DAMAGES. IF THESE LAWS APPLY TO YOU, SOME OR ALL OF THE ABOVE DISCLAIMERS OR LIMITATIONS MAY NOT APPLY TO YOU, AND YOU MAY HAVE ADDITIONAL RIGHTS. + + +## INDEMNIFICATION + +You agree to defend, indemnify, and hold us harmless, including our subsidiaries, affiliates, and all of our respective officers, agents, partners, and employees, from and against any loss, damage, liability, claim, or demand, including reasonable attorneys’ fees and expenses, made by any third party due to or arising out of: (1) use of the Site; (2) breach of these Terms of Use; (3) any breach of your representations and warranties set forth in these Terms of Use; (4) your violation of the rights of a third party, including but not limited to intellectual property rights; or (5) any overt harmful act toward any other user of the Site with whom you connected via the Site. Notwithstanding the foregoing, we reserve the right, at your expense, to assume the exclusive defense and control of any matter for which you are required to indemnify us, and you agree to cooperate, at your expense, with our defense of such claims. We will use reasonable efforts to notify you of any such claim, action, or proceeding which is subject to this indemnification upon becoming aware of it. + + +## USER DATA + +We will maintain certain data that you transmit to the Site for the purpose of managing the performance of the Site, as well as data relating to your use of the Site. Although we perform regular routine backups of data, you are solely responsible for all data that you transmit or that relates to any activity you have undertaken using the Site. You agree that we shall have no liability to you for any loss or corruption of any such data, and you hereby waive any right of action against us arising from any such loss or corruption of such data. + + +## ELECTRONIC COMMUNICATIONS, TRANSACTIONS, AND SIGNATURES + +Visiting the Site, sending us emails, and completing online forms constitute electronic communications. You consent to receive electronic communications, and you agree that all agreements, notices, disclosures, and other communications we provide to you electronically, via email and on the Site, satisfy any legal requirement that such communication be in writing. YOU HEREBY AGREE TO THE USE OF ELECTRONIC SIGNATURES, CONTRACTS, ORDERS, AND OTHER RECORDS, AND TO ELECTRONIC DELIVERY OF NOTICES, POLICIES, AND RECORDS OF TRANSACTIONS INITIATED OR COMPLETED BY US OR VIA THE SITE. You hereby waive any rights or requirements under any statutes, regulations, rules, ordinances, or other laws in any jurisdiction which require an original signature or delivery or retention of non-electronic records, or to payments or the granting of credits by any means other than electronic means. + + +## CALIFORNIA USERS AND RESIDENTS + +If any complaint with us is not satisfactorily resolved, you can contact the Complaint Assistance Unit of the Division of Consumer Services of the California Department of Consumer Affairs in writing at 1625 North Market Blvd., Suite N 112, Sacramento, California 95834 or by telephone at (800) 952-5210 or (916) 445-1254. + + +## MISCELLANEOUS + +These Terms of Use and any policies or operating rules posted by us on the Site or in respect to the Site constitute the entire agreement and understanding between you and us. Our failure to exercise or enforce any right or provision of these Terms of Use shall not operate as a waiver of such right or provision. These Terms of Use operate to the fullest extent permissible by law. We may assign any or all of our rights and obligations to others at any time. We shall not be responsible or liable for any loss, damage, delay, or failure to act caused by any cause beyond our reasonable control. If any provision or part of a provision of these Terms of Use is determined to be unlawful, void, or unenforceable, that provision or part of the provision is deemed severable from these Terms of Use and does not affect the validity and enforceability of any remaining provisions. There is no joint venture, partnership, employment or agency relationship created between you and us as a result of these Terms of Use or use of the Site. You agree that these Terms of Use will not be construed against us by virtue of having drafted them. You hereby waive any and all defenses you may have based on the electronic form of these Terms of Use and the lack of signing by the parties hereto to execute these Terms of Use. + +## CONTACT US + +In order to resolve a complaint regarding the Site or to receive further information regarding use of the Site, please contact us at: + +NumFOCUS, Inc.
    P.O. Box 90596
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    info@numfocus.org
    +1 (512) 222-5449 + + + From b06be866f3c27356e321771282bb5f36e3bc0f4e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:55 +0000 Subject: [PATCH 113/909] 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..cf20ae59cf --- /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: [0.1038/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 64e4af95ee0cd8029742b2b54550f4a40b001e71 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:57 +0000 Subject: [PATCH 114/909] 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 6b3dc2d257ef7ca670278f6ef9c9c5529fbdaa1b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:38:58 +0000 Subject: [PATCH 115/909] New translations about.md (Korean) --- content/ko/about.md | 69 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 69 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..2317add1fe --- /dev/null +++ b/content/ko/about.md @@ -0,0 +1,69 @@ +--- +title: About Us +sidebar: false +--- + +_NumPy 프로젝트와 커뮤니티에 대한 몇가지 정보_ + +NumPy는 Python에서 Numerical Computing을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. NumPy는 항상 100% 오픈소스 소프트웨어 일것이며, [수정된 BSD 라이센스](https://github.com/numpy/numpy/blob/master/LICENSE.txt)에 따라서 누구나 무료로 사용하고 배포할 수 있습니디. + +NumPy는 광범위한 Scientific Python 커뮤니티의 협의를 통해 GitHub에서 공개적으로 개발되었습니다. 우리의 거버넌스 접근 방식에 대한 더 자세한 내용은 [거버넌스 문서](https://www.numpy.org/devdocs/dev/governance/index.html)를 참조해 주세요. + + +## 운영 위원회 + +NumPy 운영 위원회의 역할은 더 광범위한 NumPy 커뮤니티와 협력하고 서비스를 통해서 기술적으로나 커뮤니티로서 프로젝트의 장기적인 안녕을 보장하는 것입니다. NumPy 운영 위원회는 현재 다음과 같은 회원들로 구성되어 있습니다. (알파벳 순서) + +- Sebastian Berg +- Jaime Fernández del Río +- Ralf Gommers +- Allan Haldane +- Charles Harris +- Stephan Hoyer +- Matti Picus +- Nathaniel Smith +- Julian Taylor +- Pauli Virtanen +- Stéfan van der Walt +- Eric Wieser + +명예 회원 + +- Travis Oliphant (project founder, 2005-2012) +- Alex Griffing (2015-2017) +- Marten van Kerkwijk (2017-2019) + +## 팀 + +NumPy 프로젝트는 성장하고 있습니다. 그리고 우리는 다음과 같은 팀들이 있습니다. + +- 코드 +- 문서 +- 웹사이트 +- 심사 +- 자원 및 보조금 + +개발 팀원들은 [팀](/gallery/team.html) 페이지를 참조하세요. + +## 스폰서 + +NumPy는 다음과 같은 곳들에서 직접적으로 자금을 받습니다. +{{< sponsors >}} + + +## 기관 파트너 + +기관 파트너는 그들의 업무의 일환으로 NumPy에 기여하는 직원을 고용하여 프로젝트를 지원하는 조직입니다. 현재 기관 파트너는 다음과 같습니다. +{{< partners >}} + + +## 후원 + +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. + +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. + +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 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. +{{< numfocus >}} From 8b98b4000d1dec405adaed361fa95b543a58b045 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:00 +0000 Subject: [PATCH 116/909] 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..41504d0c8a --- /dev/null +++ b/content/ko/404.md @@ -0,0 +1,8 @@ +--- +title: 404 +sidebar: false +--- + +앗! 잘못된 접근입니다. + +만약 이곳에 어떤 페이지가 있어야 한다면 [Issue 열기](https://github.com/numpy/numpy.org/issues)에서 문제를 제기할 수 있습니다. From cba9667db922ed42653c0c6bf69e9a5c94fc8153 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:01 +0000 Subject: [PATCH 117/909] New translations diversity_sep2020.md (Japanese) --- content/ja/diversity_sep2020.md | 48 +++++++++++++++++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 content/ja/diversity_sep2020.md diff --git a/content/ja/diversity_sep2020.md b/content/ja/diversity_sep2020.md new file mode 100644 index 0000000000..e85b3dfb2e --- /dev/null +++ b/content/ja/diversity_sep2020.md @@ -0,0 +1,48 @@ +--- +title: Numpyの多様性とチームの参加に関する声明 +sidebar: false +--- + + +_Natureに掲載されたNumPyの論文が発表された後のソーシャルメディアで議論が行われ、Numpyの多様性とチームへの参加に関する懸念が提起されました。 そこで以下の声明を発表したいと思います_ + + +私たちがチームに加わりたい人にオープンで平等であるとき、私たちはチームとコミュニティに最善を尽くしていると強く信じることが出来ます。 プロジェクトの発足当初からの私達は国際的なチームでありたいと思っていました。また、私たちは多様なバックグラウンドや専門知識を持つ個人同士のコラボレーションの価値を認識しています。 誰もが歓迎され、助け合い、評価される文化は、Numpy プロジェクトの中核です。 + +## 過去 + +オープンソースへ貢献することは、歴史的に疎外されてきた人達、特に女性や、多くの社会的制約や期待のために参加するためにより多くの障害に直面してきた人達に取って、娯楽であり続けてきました。 オープンソースのプロジェクトでは、大きな多様性の問題があり、レポートとしてもまとめられています。(例えば、 [2017 GitHub Open Source Survey](https://opensourcesurvey.org/2017/) と [こちらのブログ投稿](https://medium.com/tech-diversity-files/if-you-think-women-in-tech-is-just-a-pipeline-problem-you-haven-t-been-paying-attention-cb7a2073b996)). + +プロジェクトがスタートしてから2018年まで、NumPyは一握りのボランティアによって維持されていました。 ほとんどの場合、コードレビューやコミュニティからの貢献をマージする、アクティブなコア開発者の数は、4～8の範囲でした。 Numpyのプロジェクトには当時はロードマップやリソースを配分する仕組みがなく、必要と思われることが個々の努力によって進められていました。 Natureの論文の著者の人達は、15年間(2005年から2019年) の期間に渡って、このプロジェクトに最も重要で持続的な貢献をした人達です。 この著者リストの多様性の欠如は、PythonとSciPyエコシステムの形成する年数を反映しているといえます。 + +2018年はNumPyプロジェクトの歴史において重要な節目となりました。 ゴードン・ベティムーア財団とアルフレッド・P・ソロラン財団により資金援助を受け、Pythonエコシステムに貢献する長年の経験を持つ二人のエンジニア。フルタイム雇用することができました。 これらの取り組みにより、NumPyは技術的により健康的な状態になりました。 + +加えて、この資金調達により、Numpy メンテナーはプロジェクトガバナンス、コミュニティ開発、あまりプロジェクトに参加していないグループへのアウトリーチなどにも取り組めるようになりました。 [CZI EOSSプログラムのために2019年半ばに書かれた多様性に関する声明](https://figshare.com/articles/online_resource/Diversity_and_Inclusion_Statement_NumPy_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/12980852) では、いくつかの課題と、NumPyチームに多様な 才能ある人達を参加してもらうための取り組みについて詳しく説明しています。 + +## 現在 + +雇用機会を提供することは、OSSにおいて、 多様な人材を引き寄せ、維持するために効果的な方法です。 よって、私たちはMelissa Weber Mendonça とMars Leeを採用するために、2019年12月に利用できるようになった2回目の助成金の3分の2を利用することにしました。 + +Inessa PawsonとRalf Gommersによるコミュニティの発展を目的としたいくつかの取り組みの結果として、NumPyプロジェクトには女性や2020年のOSSにはあまり参加してこなかった人から、貴重な投稿が多数寄せられています。 + +- Melissa Weber Mendonça はコミット権限を取得し、numpy.f2py をメンテナンスし、ドキュメンテーションチームを率いています。 +- Shaloo Shaliniはnumpy.org のすべてのケーススタディを作成しました。 +- Mars Leeはウェブデザインに貢献し、アクセシビリティの向上に貢献しました。 +- Isabela Presedo-Floydは新しいNumpyのロゴをデザインしました。 +- Stephanie Mendoza, Xiayoi Deng, Deji Suolang, Mame Fatou Thiamは、Numpy ユーザー調査を設計し実施しました。 +- Yuki Dunn, Dayane Machado, Mahfuza Humayra Mohona, Sumera Priyadarsini, Shaloo Shalini, and Kriti Singh (前回のアウトリーチインターン) は英語を母国語としないNumPyユーザーや開発者にアンケートを母国語に翻訳することで、調査チームを支援しました。 +- Sayed Adel, Raghuveer Devulapalli, Chunlin Fangは、NumpyのコアのためにSIMD最適化に取り組んでいます。 + +私たちにはまだ多くのやるべきことがあるため、より一層の参加者を探しています。 そして私達は次のNumPy論文の著者は、確実に、より多様的になっていることでしょう。 + +## 未来 + +私たちは、チームとコミュニティにおける参加のしやすさと多様性を育成することに力を注ぎ、より公正で公平な未来を築くために我々の役割を果たそうとしています。 + +私たちとの対話は開かれており、技術と 科学分野における女性やマイノリティを代表し支援する組織と連携したいと思っています。 私たちは対話し、学び、サポートする準備ができています。 + +是非以下の連絡手段を使ってご連絡下さい:[メーリングリスト](https://scipy.org/scipylib/mailing-lists.html#mailing-lists), [GitHub](https://github.com/numpy/numpy/issues), [Slack](https://numpy.org/contribute/)。非公開な連絡ではこちらのEmailを使ってください: numpy-team@googlegroups.com, または [2週間に1回のミーティング](https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg)に参加してください。 + + +_以下の人達がNumpyチームの代表してこれらの宣言をします: Sayed Adel, Sebastian Berg, Raghuveer Devulapalli, Chunlin Fang, Ralf Gommers, Allan Haldane, Stephan Hoyer, Mars Lee, Melissa Weber Mendonça, Jarrod Millman, Inessa Pawson, Matti Picus, Nathaniel Smith, Julian Taylor, Pauli Virtanen, Stéfan van der Walt, Eric Wieser_ + From 2587fcdb236c056db346ff8f3b12e6c5c1fa7844 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:03 +0000 Subject: [PATCH 118/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 69 ++++++++++++++++++++++++++++ 1 file changed, 69 insertions(+) create mode 100644 content/ja/case-studies/gw-discov.md diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md new file mode 100644 index 0000000000..3d25090e13 --- /dev/null +++ b/content/ja/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 494109b2908653f5957010609738092979bac889 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:05 +0000 Subject: [PATCH 119/909] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 90 +++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 content/ja/case-studies/deeplabcut-dnn.md diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md new file mode 100644 index 0000000000..b40ed2af50 --- /dev/null +++ b/content/ja/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 38f335951e2a09af5e3ec86755a11311a4c0fec9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:07 +0000 Subject: [PATCH 120/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 64 ++++++++++++++++++++ 1 file changed, 64 insertions(+) create mode 100644 content/ja/case-studies/cricket-analytics.md diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md new file mode 100644 index 0000000000..987b38fb68 --- /dev/null +++ b/content/ja/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) provides 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 bd1c92a40d70ea2be01817f349fef722407201a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:09 +0000 Subject: [PATCH 121/909] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 70 ++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 content/ja/case-studies/blackhole-image.md diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md new file mode 100644 index 0000000000..299d80cf2d --- /dev/null +++ b/content/ja/case-studies/blackhole-image.md @@ -0,0 +1,70 @@ +--- +title: "ケーススタディ：世界初のブラックホールの画像" +sidebar: false +--- + +{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Image Credits: Event Horizon Telescope Collaboration)*" attrk="https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg" >}} + +
    +

    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。

    +
    カリフォルニア工科大学 計算・数理学部のKatie Bouman助教授
    +
    + +## 地球の大きさの望遠鏡。 + +[ Event Horizon telescope(EHT)](https:/eventhorizontelescope.org)は、地球サイズの解析望遠鏡を形成する8台の地上型電波望遠鏡から成るシステムで、これまでに前例のない感度と解像度で宇宙を研究することができます。 超長基線干渉法(VLBI) と呼ばれる手法を用いた巨大な仮想望遠鏡の角度分解能は、[20マイクロ秒][resolution]で、ニューヨークにある新聞をパリの歩道のカフェから読むのに十分な解像度です。 + +### 主な目標と結果 + +* **宇宙の新しい見方:** EHTの画期的な考え方の基礎が築かれたのは、100年前に [Sir Arthur Eddington][eddington] がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 + +* **ブラックホール:** EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 この取り組みは[ 100年以上 ](https://www.jpl.nasa.gov/news/news.php?feature=7385)に渡って研究されてきたが、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:** 科学者達は、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を観測できるのではないかと期待していました。 科学者たちは、ブラックホールの巨大な質量を測定するためにその情報を利用することができます。 + +### 課題 + +* **大規模な計算** + + EHTは、急速な大気の位相変動、大規模な記録帯域幅、広く性能が異なり地理的に分散した望遠鏡などに対して、膨大なデータ処理の課題を抱えていました。 + +* **大量のデータ** + + EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 + +* **よくわからないものを観測する** + + 研究の目標が今までに見たことのないものを見ることであるとき、どのようにし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" >}} + +## NumPyが果たした役割 + +データに問題がある場合はどうなるでしょう？ あるいは、アルゴリズムが特定の仮定に あまりにも大きく依存しているかもしれません。 もしあるパラメータを変更した場合、画像は大きく変化するのでしょうか？ + +EHTの共同研究では、最先端の画像再構成技術を使用して、それぞれのチームがデータを評価することによって、これらの課題に対処しました。 それぞれのチームの解析結果が同じであることが証明されると、それらの結果を組み合わせることで、ブラックホール画像を得ることができました。 + +彼らの研究は、共同のデータ解析を通じて科学を進歩させる、科学的なPythonエコシステムが果たす役割を如実に表しています。 + +{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**ブラックホールの画像化で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。 + +{{< 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**" >}} + +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の画像化の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]で実施し、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 + +## まとめ + +NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 + +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption== "**利用されたNumpyの主要機能**" >}} + +[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 8db5668aed06c7f44ad33e68a3ab3be86c5aef05 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:10 +0000 Subject: [PATCH 122/909] New translations news.md (Japanese) --- content/ja/news.md | 83 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 content/ja/news.md diff --git a/content/ja/news.md b/content/ja/news.md new file mode 100644 index 0000000000..5dcb849596 --- /dev/null +++ b/content/ja/news.md @@ -0,0 +1,83 @@ +--- +title: News +sidebar: false +--- + +### 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 + +_Sept 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 1134d03794f880ceeaaa6cc03e48a385c4d41341 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:12 +0000 Subject: [PATCH 123/909] New translations history.md (Japanese) --- content/ja/history.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 content/ja/history.md diff --git a/content/ja/history.md b/content/ja/history.md new file mode 100644 index 0000000000..0dae1ff01d --- /dev/null +++ b/content/ja/history.md @@ -0,0 +1,21 @@ +--- +title: NumPyの歴史 +sidebar: false +--- + +Numpy は配列形式のデータ構造と配列形式に関連する高速な数値ルーチンを提供する Python の基礎的なライブラリです。 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。 しかし、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と、独特の焦点を持つことによるコミュニティの盛り上がりと、フレンドリーなコミュニティの組み合わせは、長い目で見ると良い結果を得られることが知られていました。 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、Numpyのことを引用しています。 + +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* + +[*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。 + +### 過去の資料 + +[*Numeric*マニュアルのダウンロード](static/numeric-manual.pdf) + From 7291de96b321ecba1d225eaf215498356c566bfc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:14 +0000 Subject: [PATCH 124/909] New translations gethelp.md (Japanese) --- content/ja/gethelp.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) create mode 100644 content/ja/gethelp.md diff --git a/content/ja/gethelp.md b/content/ja/gethelp.md new file mode 100644 index 0000000000..d378333ebb --- /dev/null +++ b/content/ja/gethelp.md @@ -0,0 +1,34 @@ +--- +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。 + +**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。 + + + +### [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。 + +*** + +### [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。 + +*** + +### [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 f0a8e746c12664bea60945344e12d3134d77f67c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:16 +0000 Subject: [PATCH 125/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 95 ++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 content/ja/report-handling-manual.md diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md new file mode 100644 index 0000000000..5586668cba --- /dev/null +++ b/content/ja/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 83ee91ce825bf783e5e32731582714ec424f6f58 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 15:39:18 +0000 Subject: [PATCH 126/909] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 78 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 78 insertions(+) create mode 100644 content/pt/contribute.md diff --git a/content/pt/contribute.md b/content/pt/contribute.md new file mode 100644 index 0000000000..74998f1e44 --- /dev/null +++ b/content/pt/contribute.md @@ -0,0 +1,78 @@ +- - - +title: Contribua com o NumPy sidebar: false +- - - + +The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming -- in addition to + +- [Writing code](#writing-code) + +you can + +- [Review pull requests](#reviewing-pull-requests) +- [Develop tutorials, presentations, and other educational material](#developing-educational-materials) +- [Triage issues](#issue-triaging) +- [Work on our website](#website-development) +- [Contribute graphic design](#graphic-design) +- [Translate website content](#translating-website-content) +- [Serve as a community coordinator](#community-coordination-and-outreach) +- [Write grant proposals and help with other fundraising](#fundraising) + +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 codebase. + +### 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 1a06c79adc297e13af28713b9241de8a2d7d8be9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 23 Jan 2021 23:33:34 +0000 Subject: [PATCH 127/909] 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 987b38fb68..37ea8448cf 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -1,9 +1,9 @@ --- -title: "Case Study: Cricket Analytics, the game changer!" +title: "ケーススタディ: クリケット分析、ゲームチェンジャー!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, the biggest Cricket Festival in India**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="** IPLT20、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" >}}

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

    From ef2492b64c159061209f9732ecc4ea85130df80e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 24 Jan 2021 00:33:00 +0000 Subject: [PATCH 128/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 37ea8448cf..2d0485c69a 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -6,17 +6,17 @@ 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" >}}
    -

    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
    +

    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。

    +
    —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
    -## About Cricket +## クリケットについて -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. +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。 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や、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や、その他の方法に甘やかされています。 -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. +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、2019年に [67億ドル](https://en.wikipedia.org/wiki/Indian_Premier_League) の市場規模と評価される世界で最も参加者が多いクリケットイベントの1つです。 -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. +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など。 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。 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: From 1adbb62f3d46980bc9afac13d25ae8cd187506b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 25 Jan 2021 10:05:31 +0000 Subject: [PATCH 129/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 2d0485c69a..1f8372667f 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -18,10 +18,10 @@ sidebar: false 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など。 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。 -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: +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の主要なメトリックを下記のような要素だと考え始めています: -* batting performance moving average, -* score forecasting, +* バッティングの記録の移動平均 +* スコア予測 * 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 From b932f902829af701cfafc56474f8a76e73b73404 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 25 Jan 2021 11:08:10 +0000 Subject: [PATCH 130/909] 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 1f8372667f..a3845cc7da 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -22,8 +22,8 @@ sidebar: false * バッティングの記録の移動平均 * スコア予測 -* 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 +* プレイヤーの体力やパフォーマンスについての知識を得ること +* 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="**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" >}} From dcfb9cd777ff94e159d12372d86a397f8551581b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 29 Jan 2021 23:23:53 +0000 Subject: [PATCH 131/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index a3845cc7da..6aa76fc7a3 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -25,11 +25,11 @@ sidebar: false * プレイヤーの体力やパフォーマンスについての知識を得ること * 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="**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="** 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" >}} -### Key Data Analytics Objectives +### データ分析の主要な目標 -* 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. +* スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [ 他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 * 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. From c3b52acd7861282e7152a676e2867fa3d2fd87f2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 30 Jan 2021 00:21:04 +0000 Subject: [PATCH 132/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 6aa76fc7a3..8295d1b66d 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -30,20 +30,20 @@ sidebar: false ### データ分析の主要な目標 * スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [ 他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 -* 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. +* 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="**Cricket Pose Estimator**" attr="*(Image credit: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} +{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="pose estimator" caption="**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/" >}} -### The Challenges +### 課題 -* **Data Cleaning and preprocessing** +* **データのクリーニングと前処理** - 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. + IPLは、クリケットを古典的なテストマッチ形式をから、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。 -* **Dynamic Modeling** +* **動的モデリング** - 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. + クリケットも、他のスポーツのように、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、および潜在的な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。 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** From fac5520853e0b92776013547ce5e37653ac1afcc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 30 Jan 2021 07:50:40 +0000 Subject: [PATCH 133/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 8295d1b66d..0cda3c7651 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -43,11 +43,11 @@ sidebar: false * **動的モデリング** - クリケットも、他のスポーツのように、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、および潜在的な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。 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. + クリケットも、他のスポーツのように、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、および潜在的な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。 打者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。 -* **Predictive Analytics Complexity** +* **予測分析の複雑さ** - 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. + クリケットの意思決定の多くは、"ボール運びがある特定のタイプの場合、バッツマン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？"や、"バッツマンが特定の方法であるボール運びに反応した場合、ボウラーはどのように彼の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。 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 From ef297032eb62afa6b5bc797ec9375177e93ea7ba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 30 Jan 2021 08:57:35 +0000 Subject: [PATCH 134/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 0cda3c7651..ceedd0743a 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -47,18 +47,18 @@ sidebar: false * **予測分析の複雑さ** - クリケットの意思決定の多くは、"ボール運びがある特定のタイプの場合、バッツマン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？"や、"バッツマンが特定の方法であるボール運びに反応した場合、ボウラーはどのように彼の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。 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. + クリケットの意思決定の多くは、"ボール運びがある特定のタイプの場合、バッツマン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？"や、"バッツマンが特定の方法であるボール運びに反応した場合、ボウラーはどのように彼の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には、精度の良いデータセットが利用できること、データを合成して高精度な生成モデルを作成する能力が必要です。 -## NumPy’s Role in Cricket Analytics +## クリケット解析におけるNumPyの役割 -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: +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 以下のような様々な種類のクリケット関連のスポーツ分析に使用されています: -* **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. +* **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/) は戦術的分析に使用されています。 -* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provides useful insights into relationship between various datasets. +* **データ可視化:** データのグラフ化と [視覚化](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関する、有益な洞察を与えてくれます。 -## Summary +## まとめ -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. +スポーツアナリティクスは、それがプロの試合がどのように実施されるか、特に最近まで主に "直感 "や過去の伝統的な考え方に基づいて行われていた戦略的な意思決定が、どのように起こるかという面で、世界を変えた技術的であるといえます。 NumPyは、データ分析や機械学習、人工知能のアルゴリズムに関連する高レベルの関数を提供する 沢山のPython パッケージ群に対して、堅固な基盤として利用されています。 これらのパッケージは、クリケットの試合だけでなくクリケットの試合周辺の推論やビジネスを推進しつつ、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に広く利用されています。 クリケットの試合の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="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="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 44f486f2d3371871c96897f4fcbaf8a9fd9ca6dd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 30 Jan 2021 08:57:37 +0000 Subject: [PATCH 135/909] 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 b40ed2af50..8f5e4fb0b4 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -1,5 +1,5 @@ --- -title: "Case Study: DeepLabCut 3D Pose Estimation" +title: "ケーススタディ: DeepLabCut 三次元姿勢推定" sidebar: false --- From 1b5e2a3f8b744c00250139d4a2c8d67d122145d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Jan 2021 10:42:43 +0000 Subject: [PATCH 136/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index 5dcb849596..d45a2fbe06 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -3,6 +3,12 @@ title: News sidebar: false --- +### 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). @@ -22,7 +28,7 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear ### Numpy 1.19.2 release -_Sept 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. +_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! From 3af463cde23d4531ca9c64c3a0a360da94e34a9f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Jan 2021 10:43:07 +0000 Subject: [PATCH 137/909] New translations news.md (Arabic) --- content/ar/news.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/content/ar/news.md b/content/ar/news.md index 5dcb849596..d45a2fbe06 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -3,6 +3,12 @@ title: News sidebar: false --- +### 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). @@ -22,7 +28,7 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear ### Numpy 1.19.2 release -_Sept 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. +_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! From 6c43fb9a2c80b6b501c38cfc48a5193353b0b83f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Jan 2021 10:43:27 +0000 Subject: [PATCH 138/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/content/pt/news.md b/content/pt/news.md index d922c2e0e2..4e7060b82a 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,6 +3,12 @@ title: Notícias sidebar: false --- +### 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). @@ -22,7 +28,7 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear ### Numpy 1.19.2 release -_Sept 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. +_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! From 5dbe92991b31fd114529484921c3bd64b83f8f8b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Jan 2021 10:43:41 +0000 Subject: [PATCH 139/909] New translations news.md (Spanish) --- content/es/news.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/content/es/news.md b/content/es/news.md index 5dcb849596..d45a2fbe06 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -3,6 +3,12 @@ title: News sidebar: false --- +### 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). @@ -22,7 +28,7 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear ### Numpy 1.19.2 release -_Sept 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. +_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! From df2192e43c3a65c73e10a687e608cf61e7e73de1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Jan 2021 10:44:03 +0000 Subject: [PATCH 140/909] New translations news.md (Korean) --- content/ko/news.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/content/ko/news.md b/content/ko/news.md index 5dcb849596..d45a2fbe06 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -3,6 +3,12 @@ title: News sidebar: false --- +### 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). @@ -22,7 +28,7 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear ### Numpy 1.19.2 release -_Sept 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. +_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! From 4f423f320ef313b2b904ce14f7aa0a848321331f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Jan 2021 10:44:22 +0000 Subject: [PATCH 141/909] New translations news.md (Japanese) --- content/ja/news.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/content/ja/news.md b/content/ja/news.md index 5dcb849596..d45a2fbe06 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,6 +3,12 @@ title: News sidebar: false --- +### 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). @@ -22,7 +28,7 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear ### Numpy 1.19.2 release -_Sept 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. +_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! From 775d42f5b87f8f08eb1ce80958d78415b40d296e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Feb 2021 18:56:33 +0000 Subject: [PATCH 142/909] 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 bb240c155e..d24405565f 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -5,7 +5,7 @@ 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 Numerical 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/master/LICENSE.txt). +NumPy é um projeto de código aberto visando habilitar a computação numérica com Python. It was created in 2005, building on the early work of the Numeric and Numarray libraries. 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/master/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). From 39b58eaf03af61ad3b9ecf8fbd5e23e231b7f5a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Feb 2021 18:57:01 +0000 Subject: [PATCH 143/909] 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 cccadd7da0..116caa48c3 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -5,7 +5,7 @@ sidebar: false _下面是 NumPy 项目和社区的一些信息：_ -NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 年，早期由 Numerical 和 Numarray 库发展而来。 NumPy 将始终保证项目完整开源，所有人都可以根据 [修改后的 BSD 条款](https://github.com/numpy/numpy/blob/master/LICENSE.txt) 免费对其进行使用和分发。 +NumPy 是一个使 Python 支持数值计算的开源项目， It was created in 2005, building on the early work of the Numeric and Numarray libraries. NumPy 将始终保证项目完整开源，所有人都可以根据 [修改后的 BSD 条款](https://github.com/numpy/numpy/blob/master/LICENSE.txt) 免费对其进行使用和分发。 经过 Numpy 和 Python 科学计算社区协商讨论，最终决定将 Numpy 在 GitHub 上开源。 想要了解更多与社区治理有关的信息，请参阅我们的[治理文件](https://www.numpy.org/devdocs/dev/governance/index.html)。 From ced92a2fb32d5324bb22b119a498dcbdb006290d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Feb 2021 18:57:27 +0000 Subject: [PATCH 144/909] 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 df89bff1f5..1779f7531f 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -5,7 +5,7 @@ sidebar: false _Some information about the NumPy project and community_ -NumPy is an open source project aiming to enable numerical computing with Python. It was created in 2005, building on the early work of the Numerical and Numarray libraries. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt). +NumPy is an open source project aiming to enable 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, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/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). From 12faa005e944adc05d82801770292dd85b46878c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Feb 2021 18:57:43 +0000 Subject: [PATCH 145/909] 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 e13172f599..95a7507a54 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -5,7 +5,7 @@ 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 を使った数値計算のためのオープンソースプロジェクトです。 Numpyは、Numerical and Numarrayライブラリの初期のコードを基に、2005年から開発がスタートしました。 NumPyは開発当初から100%オープンソースソフトウェアとして開発されてきました。[修正BSD ライセンス](https://github.com/numpy/numpy/blob/master/LICENSE.txt) の条項の下で、すべての人が利用可能です。 +Numpy は Python を使った数値計算の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は開発当初から100%オープンソースソフトウェアとして開発されてきました。[修正BSD ライセンス](https://github.com/numpy/numpy/blob/master/LICENSE.txt) の条項の下で、すべての人が利用可能です。 Numpy は 、様々な科学Python コミュニティとのコンセンサスを得ながら、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。 From 597d64b5108eb8d70a70a24d3f6309ce8070440b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Feb 2021 18:58:04 +0000 Subject: [PATCH 146/909] New translations about.md (Spanish) --- content/es/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/about.md b/content/es/about.md index df89bff1f5..1779f7531f 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -5,7 +5,7 @@ sidebar: false _Some information about the NumPy project and community_ -NumPy is an open source project aiming to enable numerical computing with Python. It was created in 2005, building on the early work of the Numerical and Numarray libraries. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt). +NumPy is an open source project aiming to enable 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, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/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). From 2a72c7d1719232f2e595a2d4124e4eb7fcfe53c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 4 Feb 2021 18:58:28 +0000 Subject: [PATCH 147/909] 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 2317add1fe..871d435f9a 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -5,7 +5,7 @@ sidebar: false _NumPy 프로젝트와 커뮤니티에 대한 몇가지 정보_ -NumPy는 Python에서 Numerical Computing을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. NumPy는 항상 100% 오픈소스 소프트웨어 일것이며, [수정된 BSD 라이센스](https://github.com/numpy/numpy/blob/master/LICENSE.txt)에 따라서 누구나 무료로 사용하고 배포할 수 있습니디. +NumPy는 Python에서 Numerical Computing을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. It was created in 2005, building on the early work of the Numeric and Numarray libraries. NumPy는 항상 100% 오픈소스 소프트웨어 일것이며, [수정된 BSD 라이센스](https://github.com/numpy/numpy/blob/master/LICENSE.txt)에 따라서 누구나 무료로 사용하고 배포할 수 있습니디. NumPy는 광범위한 Scientific Python 커뮤니티의 협의를 통해 GitHub에서 공개적으로 개발되었습니다. 우리의 거버넌스 접근 방식에 대한 더 자세한 내용은 [거버넌스 문서](https://www.numpy.org/devdocs/dev/governance/index.html)를 참조해 주세요. From 1df8ff9b8439076c061061c8af84fd97663aca19 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Feb 2021 23:34:46 +0000 Subject: [PATCH 148/909] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index 8f5e4fb0b4..39e80846d0 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -3,16 +3,16 @@ title: "ケーススタディ: DeepLabCut 三次元姿勢推定" sidebar: false --- -{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**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="**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">}}
    -

    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)
    +

    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、Deep Learning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。

    +
    —Alexander Mathis、 准教授、École polytechnology fe’rale de Lausanne (EPFL)
    -## About DeepLabCut +## DeepLabCut について -[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) は世界中の何百もの研究機関の研究者が、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。 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. From 85ca0b36717b220e5874c3c46fcc21f1624e6785 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 6 Feb 2021 00:41:56 +0000 Subject: [PATCH 149/909] 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 39e80846d0..12dcd07ad9 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -12,7 +12,7 @@ sidebar: false ## DeepLabCut について -[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) は世界中の何百もの研究機関の研究者が、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。 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) は世界中の何百もの研究機関の研究者が、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の技術により、科学者は動物の種類と時系列のデータを元に、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。 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. From 03b49ec0ff323b892c440f97e5fae91fa72a58e4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 6 Feb 2021 23:31:42 +0000 Subject: [PATCH 150/909] 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 12dcd07ad9..699042bff1 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -14,7 +14,7 @@ sidebar: false [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) は世界中の何百もの研究機関の研究者が、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の技術により、科学者は動物の種類と時系列のデータを元に、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。 -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. +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、手間のかかる作業に自動化を利用し、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的研究に利用されており、より速く、正確な結果をもたらしました。 {{< 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)*">}} From 677163f0f1a8a6884affc77184400e5bd05af41b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 7 Feb 2021 00:31:53 +0000 Subject: [PATCH 151/909] 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 699042bff1..d4cd731a3e 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -16,9 +16,9 @@ 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、手間のかかる作業に自動化を利用し、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的研究に利用されており、より速く、正確な結果をもたらしました。 -{{< 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-hore. if" 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'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による動物の姿勢を抽出することによる、非侵襲的な行動追跡は、生体力学や、遺伝学、倫理学、神経科学などの分野における科学的な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、計算処理的に非常に困難です。 例えば、必要な計算リソースやトレーニングデータが問題になります。 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]. From a08cea9d648ce2ce1fcc8348d8b9b63ef0c7001a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 12 Feb 2021 23:37:00 +0000 Subject: [PATCH 152/909] 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 d4cd731a3e..74a585a8cc 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -20,7 +20,7 @@ sidebar: false DeepLabCutによる動物の姿勢を抽出することによる、非侵襲的な行動追跡は、生体力学や、遺伝学、倫理学、神経科学などの分野における科学的な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、計算処理的に非常に困難です。 例えば、必要な計算リソースやトレーニングデータが問題になります。 -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は、研究者が対象の姿勢をを推定することができ、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。 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. From af192c0f0691c872e1cf1cd9782e972429af0832 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 13 Feb 2021 00:37:28 +0000 Subject: [PATCH 153/909] 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 74a585a8cc..badecebde8 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -20,9 +20,9 @@ sidebar: false DeepLabCutによる動物の姿勢を抽出することによる、非侵襲的な行動追跡は、生体力学や、遺伝学、倫理学、神経科学などの分野における科学的な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、計算処理的に非常に困難です。 例えば、必要な計算リソースやトレーニングデータが問題になります。 -DeepLabCutは、研究者が対象の姿勢をを推定することができ、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。 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は、研究者が対象の姿勢をを推定することができ、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にデジタルなラベルを貼ることができます。また、DeepLabCutのディープラーニングベースのポーズ推定アーキテクチャが、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することもできます。 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する事ができます。 -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. +DeepLabCut では [transfer learning](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、ツールの名前の元となった [DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれる、パフォーマンスの高い人像推定アーキテクチャからの特徴検出器を使用しています。 その過程で、このパッケージには、追加のアーキテクチャや、拡張メソッド、および一通りのフロントエンドユーザエクスペリエンスが得られるように大幅に変更されました。 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. From 7c18b005d36c8d95f07fefc8cbc7cbf069b150c4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 13 Feb 2021 23:31:47 +0000 Subject: [PATCH 154/909] 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 badecebde8..a58ee2c0ab 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -22,7 +22,7 @@ DeepLabCutによる動物の姿勢を抽出することによる、非侵襲的 DeepLabCutは、研究者が対象の姿勢をを推定することができ、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にデジタルなラベルを貼ることができます。また、DeepLabCutのディープラーニングベースのポーズ推定アーキテクチャが、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することもできます。 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する事ができます。 -DeepLabCut では [transfer learning](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、ツールの名前の元となった [DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれる、パフォーマンスの高い人像推定アーキテクチャからの特徴検出器を使用しています。 その過程で、このパッケージには、追加のアーキテクチャや、拡張メソッド、および一通りのフロントエンドユーザエクスペリエンスが得られるように大幅に変更されました。 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. +DeepLabCut では [transfer learning](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、ツールの名前の元となった [DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれる、パフォーマンスの高い人像推定アーキテクチャからの特徴検出器を使用しています。 その過程で、このパッケージには、追加のアーキテクチャや、拡張メソッド、および一通りのフロントエンドユーザエクスペリエンスが得られるように大幅に変更されました。 さらに、 大規模な生物学的実験をサポートするために DeepLabCut はアクティブな学習機能を提供しています。例えば、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するために、時間経過しても学習データを増やすことができます。 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. From 6c0e3a88b642ce35e34360cddf16f061f56a6742 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 14 Feb 2021 00:33:14 +0000 Subject: [PATCH 155/909] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 28 +++++++++++------------ 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index a58ee2c0ab..a9f74cbdb2 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -24,30 +24,30 @@ DeepLabCutは、研究者が対象の姿勢をを推定することができ、P DeepLabCut では [transfer learning](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、ツールの名前の元となった [DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれる、パフォーマンスの高い人像推定アーキテクチャからの特徴検出器を使用しています。 その過程で、このパッケージには、追加のアーキテクチャや、拡張メソッド、および一通りのフロントエンドユーザエクスペリエンスが得られるように大幅に変更されました。 さらに、 大規模な生物学的実験をサポートするために DeepLabCut はアクティブな学習機能を提供しています。例えば、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するために、時間経過しても学習データを増やすことができます。 -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. +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学習を実行することができます。また、プログラミング経験は必要ありません。 -### Key Goals and Results +### 主な目標と結果 -* **Automation of animal pose analysis for scientific studies:** +* **科学研究のための動物姿勢解析の自動化:** - 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 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、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神経科学の研究や、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) で姿勢推定することができます。 -* **Creation of an easy-to-use Python toolkit for pose estimation:** +* **姿勢推定のための使いやすいPythonツールキットの作成:** - 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. + 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、姿勢推定の自動化だけでなく、 データセット収集段階から共有可能て、再利用可能な分析パイプラインを作成するDeepLabCut Toolkitを提供し、プロジェクトのエンドツーエンドの管理も可能になりました。 - Their [toolkit][DLCToolkit] is now available as open source. + 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。 - A typical DeepLabCut Workflow includes: + DeepLabCut ワークフローは以下のようになります。 - - 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 + - アクティブ学習によるトレーニングセットの作成と調整を行います + - 特定の動物やシナリオに合わせたニューラルネットワークの構築 + - 動画における大規模推論のためのコード作成 + - 統合された可視化ツールを使用して推論の描画 -{{< 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="**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" >}} -### The Challenges +### 課題 * **Speed** From cab2a9db7578d8bfaccfe2416ad8fea8ffaaaa64 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 18 Feb 2021 11:10:18 +0000 Subject: [PATCH 156/909] New translations learn.md (Spanish) --- content/es/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/learn.md b/content/es/learn.md index 264677ac48..4611d3a402 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -3,7 +3,7 @@ title: Learn sidebar: false --- -**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). *** From 8d16150a813e11116d0358237abf553321b476a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 18 Feb 2021 11:10:19 +0000 Subject: [PATCH 157/909] 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 66ef85cc38..4f339d762f 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -3,7 +3,7 @@ title: Aprenda sidebar: false --- -**A documentação oficial do NumPy mora [aqui](https://numpy.org/doc/stable).** +For the **official NumPy documentation** visit [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). *** From 8f7fb31bd74eb6854167b6f7d8a79f7a70b344db Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 18 Feb 2021 11:11:01 +0000 Subject: [PATCH 158/909] 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 264677ac48..4611d3a402 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -3,7 +3,7 @@ title: Learn sidebar: false --- -**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). *** From c421d99203ff6805bfc8f9318a4f450d5e8a6ae3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 18 Feb 2021 11:11:31 +0000 Subject: [PATCH 159/909] New translations learn.md (Japanese) --- content/ja/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 264677ac48..4611d3a402 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -3,7 +3,7 @@ title: Learn sidebar: false --- -**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). *** From 0d0250d2d0afc7c39344f6fb00abdaa34009e196 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 18 Feb 2021 11:11:44 +0000 Subject: [PATCH 160/909] 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 264677ac48..4611d3a402 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -3,7 +3,7 @@ title: Learn sidebar: false --- -**The official NumPy documentation lives [here](https://numpy.org/doc/stable).** +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). *** From 2ceb7ded958f8d5e395c4c0cb75ea8493ebe8342 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 18 Feb 2021 11:12:06 +0000 Subject: [PATCH 161/909] New translations learn.md (Korean) --- content/ko/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index 87d12db866..04e49d32d6 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -3,7 +3,7 @@ title: Learn sidebar: false --- -**공식 NumPy 문서는 [여기](https://numpy.org/doc/stable)에 있습니다.** +For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). 아래는 선별된 외부 자료들의 모음입니다. 이곳에 기여하고 싶다면, [이 페이지의 끝](#add-to-this-list)을 참조하세요. *** From 0960474fc16b998c4ca5b7d8e19f484ef67e892e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 18 Feb 2021 23:34:52 +0000 Subject: [PATCH 162/909] 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 a9f74cbdb2..de8c976893 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -49,7 +49,7 @@ DeepLabCut では [transfer learning](https://arxiv.org/pdf/1909.11229)という ### 課題 -* **Speed** +* **速度** 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. From ef2bd76601bf4b55de08e2ea7411f7476ce81935 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 19 Feb 2021 00:32:53 +0000 Subject: [PATCH 163/909] 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 de8c976893..cfc7ed1cf8 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -51,7 +51,7 @@ DeepLabCut では [transfer learning](https://arxiv.org/pdf/1909.11229)という * **速度** - 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. + 動物行動動画の高速処理は、彼らの行動を測定し、同時に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。 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** From 67e88e991d9558d1f578d3d0ae7bbee70adbcbc7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 19 Feb 2021 08:46:46 +0000 Subject: [PATCH 164/909] 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 cfc7ed1cf8..0157360e15 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -51,13 +51,13 @@ DeepLabCut では [transfer learning](https://arxiv.org/pdf/1909.11229)という * **速度** - 動物行動動画の高速処理は、彼らの行動を測定し、同時に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。 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. + 動物行動動画の高速処理は、彼らの行動を測定し、同時に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** - 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. + 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。 キーポイントとそ個々の動物の動きを関連性に基づき組み合わせ、それらを時間的に結びつけることは、複雑なプロセスであり、特に実験映像の中で複数の動物の動きを追跡する場合には、非常に膨大な数値解析が必要となります。 -* **Data Processing** +* **データ処理** Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. From 72b789d7c5df976fbf932bea3f5f02cf7e08615e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 19 Feb 2021 10:02:14 +0000 Subject: [PATCH 165/909] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index 0157360e15..f978ebddcb 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -59,11 +59,11 @@ DeepLabCut では [transfer learning](https://arxiv.org/pdf/1909.11229)という * **データ処理** - Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. + 最後に、配列の操作、 様々な画像処理、目標のテンソル処理、キーポイントに対応する大きな配列のスタックを処理することは、かなり難しい問題です。 -{{< 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="**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" >}} -## NumPy's Role in meeting Pose Estimation Challenges +## 姿勢推定の課題に対応するためのNumPyの役割 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). From e1482228f0c1487e5f91c6cc268d20a7d2bf8bf1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 19 Feb 2021 23:39:56 +0000 Subject: [PATCH 166/909] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index f978ebddcb..d301b47ec5 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -65,15 +65,15 @@ DeepLabCut では [transfer learning](https://arxiv.org/pdf/1909.11229)という ## 姿勢推定の課題に対応するためのNumPyの役割 -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). +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) 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: +NumPyの特徴である、画像処理、組み合わせ処理、そして高速計算は、DeepLabCutの姿勢推定アルゴリズムにおいて重要な役割を果たしました。 -* Vectorization -* Masked Array Operations -* Linear Algebra -* Random Sampling -* Reshaping of large arrays +* ベクトル化 +* マスクされた配列操作 +* 線形代数 +* ランダムサンプリング +* 大きな配列の再構成 DeepLabCut 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”. From 2119807ec40f20c69da4a6414ba142cea79d1e22 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Feb 2021 00:56:11 +0000 Subject: [PATCH 167/909] 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 d301b47ec5..a64f843188 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -75,7 +75,7 @@ NumPyの特徴である、画像処理、組み合わせ処理、そして高速 * ランダムサンプリング * 大きな配列の再構成 -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”. +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技術によって何千回も訓練され、 フレームから真アノテーション情報を予測します。 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">}} From 4d757000c1ef3232c383c479d148ed51ecc43477 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Feb 2021 02:15:42 +0000 Subject: [PATCH 168/909] 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 a64f843188..0744c7ba8f 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -75,7 +75,7 @@ NumPyの特徴である、画像処理、組み合わせ処理、そして高速 * 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技術によって何千回も訓練され、 フレームから真アノテーション情報を予測します。 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”. +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技術によって何千回も訓練され、 フレームから真アノテーション情報を予測します。 この目的のために、姿勢推定問題を、画像-画像変換問題として変換するための目標密(スコアマップ) を作成します。 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">}} From 73eda3019351b1a2d1503f51ebd46f5dadb1408f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Feb 2021 03:15:26 +0000 Subject: [PATCH 169/909] 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 0744c7ba8f..ea18ed6dc9 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -75,13 +75,13 @@ NumPyの特徴である、画像処理、組み合わせ処理、そして高速 * 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技術によって何千回も訓練され、 フレームから真アノテーション情報を予測します。 この目的のために、姿勢推定問題を、画像-画像変換問題として変換するための目標密(スコアマップ) を作成します。 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”. +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技術によって何千回も訓練され、 フレームから真アノテーション情報を予測します。 この目的のために、姿勢推定問題を、画像-画像変換問題として変換するための目標密(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に、幾何学・画像的処理を施した、スコアマップの計算を行うデータオーグメンテーションを採用しています。 また学習を高速化するために、NumPyのベクトル化機能が利用されています。 推論のためには、ターゲット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 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">}} -## Summary +## まとめ -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. +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) 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**" >}} From a0f744f5e0f47ff15a061e3c5d40740a6197a57e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Feb 2021 08:01:46 +0000 Subject: [PATCH 170/909] 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 ea18ed6dc9..e3e2750d9b 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -81,7 +81,7 @@ DeepLabCutは、ツールキットが提供する ワークフローを通じて ## 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) 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. +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。 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**" >}} From 1118e9ff750d3471103d8fe8a89df61c05a07b88 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Feb 2021 09:07:24 +0000 Subject: [PATCH 171/909] 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 e3e2750d9b..50b5316673 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -81,9 +81,9 @@ DeepLabCutは、ツールキットが提供する ワークフローを通じて ## 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。 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. +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のPythonツールボックスでは、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、データ処理の問題は、Numpy の配列操作機能を使用して対応することになります。 -{{< 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="numpy benefits" caption="**NumPyの主要機能**" >} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From b4f4e9760549335a4632538ff20c578f1e8c138f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Feb 2021 09:07:25 +0000 Subject: [PATCH 172/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 3d25090e13..6860907164 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -1,5 +1,5 @@ --- -title: "Case Study: Discovery of Gravitational Waves" +title: "ケーススタディ: 重力波の発見" sidebar: false --- From 306749113df31fdedf9da5daed45833878c0fc8e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Feb 2021 03:55:28 +0000 Subject: [PATCH 173/909] 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 116caa48c3..0632e61ae4 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -5,7 +5,7 @@ sidebar: false _下面是 NumPy 项目和社区的一些信息：_ -NumPy 是一个使 Python 支持数值计算的开源项目， It was created in 2005, building on the early work of the Numeric and Numarray libraries. NumPy 将始终保证项目完整开源，所有人都可以根据 [修改后的 BSD 条款](https://github.com/numpy/numpy/blob/master/LICENSE.txt) 免费对其进行使用和分发。 +NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 年，早期由 Numeric 和 Numarray 库发展而来。 NumPy 将始终保证项目完整开源，所有人都可以根据 [修改后的 BSD 条款](https://github.com/numpy/numpy/blob/master/LICENSE.txt) 免费对其进行使用和分发。 经过 Numpy 和 Python 科学计算社区协商讨论，最终决定将 Numpy 在 GitHub 上开源。 想要了解更多与社区治理有关的信息，请参阅我们的[治理文件](https://www.numpy.org/devdocs/dev/governance/index.html)。 From 5be6940873ad66dfa0e77c9f7250d4770bfe5dce Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Feb 2021 04:53:13 +0000 Subject: [PATCH 174/909] New translations arraycomputing.md (Chinese Simplified) --- content/zh/arraycomputing.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/zh/arraycomputing.md b/content/zh/arraycomputing.md index abd29d11c1..c5502d259d 100644 --- a/content/zh/arraycomputing.md +++ b/content/zh/arraycomputing.md @@ -1,21 +1,21 @@ --- -title: Array Computing +title: 数组计算 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.* -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.** +大规模数据操作和转换取决于高效率高性能的数组计算。 数据分析、机器学习和数值计算首选的语言是 **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. +NumPy 是 Python 语言中支持大型、多维数组和矩阵计算、并附有大量高级数学功能的默认标准库。 -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. +自2006年NumPy推出以来，Pandas于2008年出现，直到几年前，更多数组计算库才连续出现，充实数组计算领域。 许多这些较新库都具有类似NumPy的功能，包含较新的算法和功能，适合机器学习和人工智能应用。 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. +**数组计算** 基于 **数组** 这一数据结构。 *数组*用于处理大量数据，使他们便于有效存储、搜索、计算和变换。 -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. +数组计算是 *独特*的 ，因为它需要 *同时*操作整个数据阵列。 这意味着任何数组操作应用于整个数组的每个值。 这种向量化的方法使得程序员能够对数据进行整体操作，无需使用循环操作标量，从而使代码更高效和简洁。 From 0f630256e705ac3e857faa5c0eb28e43a2370193 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Feb 2021 04:53:14 +0000 Subject: [PATCH 175/909] New translations citing-numpy.md (Chinese Simplified) --- content/zh/citing-numpy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/citing-numpy.md b/content/zh/citing-numpy.md index cf20ae59cf..cbfa873b9f 100644 --- a/content/zh/citing-numpy.md +++ b/content/zh/citing-numpy.md @@ -1,13 +1,13 @@ --- -title: Citing NumPy +title: 引用 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: +如果 NumPy 在您的研究中很重要， 您想在您的学术出版物中致谢这个项目，我们建议您引用以下论文： * Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [0.1038/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:_ +_BibTeX 格式：_ ``` @Article{ harris2020array, From 6141eacd2967318a1cf9b6bbeb1de6bc09f645f7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 6 Mar 2021 00:52:10 +0100 Subject: [PATCH 176/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 6860907164..4d6dc6c95a 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -3,7 +3,7 @@ title: "ケーススタディ: 重力波の発見" sidebar: false --- -{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**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="**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" >}}

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

    From cf86b76c2f119b22f226f582b280f6a8c29a11a4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 14 Mar 2021 00:33:40 +0100 Subject: [PATCH 177/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 4d6dc6c95a..4135bee39d 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -6,13 +6,13 @@ 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" >}}
    -

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

    -
    David Shoemaker, LIGO Scientific Collaboration
    +

    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。

    +
    David Shoemaker, LIGOサイエンティフィック・コラボレーション
    -## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) +## [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。 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. From 2780108ecb6ee7244df4e8f04aa93b3ae234d9d5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 14 Mar 2021 01:37:53 +0100 Subject: [PATCH 178/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 4135bee39d..99e2acfb54 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -12,9 +12,9 @@ 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。 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。 重力波を観測することは、重力を研究する上で 重要なだけでなく、遠い宇宙とその影響におけるいくつかの不明瞭な現象の理解するためにも役立ちます。 -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. +[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ように設計されました。 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。 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 From c7e1ca319a46e9c8ec83ebbc12c4227af0e8e364 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 14 Mar 2021 07:21:24 +0100 Subject: [PATCH 179/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 99e2acfb54..e59e2056b4 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -14,7 +14,7 @@ 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。 重力波を観測することは、重力を研究する上で 重要なだけでなく、遠い宇宙とその影響におけるいくつかの不明瞭な現象の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ように設計されました。 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。 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ように設計されました。 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の重要な発見は、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を測定することで、重力波そのものを観測した初めての例であることです。 それは、ゆがんだ時空から作られた 物体と現象を宇宙の歪んだ側面を探索する、新しい天体物理学フロンティア を開きました。 ### Key Objectives From f7442f403fdb375c8fd58b4b31efbe559bdac008 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 14 Mar 2021 08:18:30 +0100 Subject: [PATCH 180/909] 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 e59e2056b4..1e169dc175 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -14,26 +14,26 @@ 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。 重力波を観測することは、重力を研究する上で 重要なだけでなく、遠い宇宙とその影響におけるいくつかの不明瞭な現象の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ように設計されました。 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の重要な発見は、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を測定することで、重力波そのものを観測した初めての例であることです。 それは、ゆがんだ時空から作られた 物体と現象を宇宙の歪んだ側面を探索する、新しい天体物理学フロンティア を開きました。 +[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ように設計されました。 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の重要な発見は、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を測定することで、重力波そのものを観測した初めての例であることです。 これにより、ゆがんだ時空から作られた 物体とそれに伴う現象を、宇宙の歪んだ側面から探索する、新しい天体物理学のフロンティア が開かれました。 -### Key Objectives +### 主な目的 -* 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. +* LIGOの[ミッション](https://www.ligo.caltech.edu/page/what-is-ligo)は、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 +* 複雑な数学を含む相対性理論の数値計算によって観測データを解析し、信号とノイズを識別し、関連性のある信号をフィルタリングし、観測データの有意性を統計的に推定することで、宇宙の始まりのクランチを観測できるようになります。 +* バイナリや数値の結果を理解しやすいのようにデータを可視化することも必要です。 -### The Challenges +### 課題 -* **Computation** +* **計算** - 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 + 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[O(10^7)個のCPU時間](https:/youtu.be7mcHknWWzNI)が必要です。 -* **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 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. + 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からです！ 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** From a7af040742f329674eab8247556564a0f04949c1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Mar 2021 00:54:35 +0100 Subject: [PATCH 181/909] 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 1e169dc175..b292e85aa7 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -33,11 +33,11 @@ sidebar: false * **データの氾濫** - 観測装置がより高感度で信頼性を持つようになると、データの大洪水によって、干し草の中から針を探すような問題が、多重に発生することがわかります。 LIGOは毎日テラバイトのデータを生成しているからです！ 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. + 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からです！ 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。 -* **Visualization** +* **可視化** - 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. + アインシュタインの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. 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" >}} From 6cc0ac6fe0fa57029954e823f33c7ea4b283f01a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Mar 2021 01:53:07 +0100 Subject: [PATCH 182/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index b292e85aa7..9ffbe21861 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -37,7 +37,7 @@ sidebar: false * **可視化** - アインシュタインの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. 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. + アインシュタインの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。 実験データの処理や考察のために、複雑な計算をしたり、画像やシミュレーションの再レンダリングしたりすることは、この分野の研究者にとって時間のかかる作業となります。 {{< 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" >}} From 2f4db6b4d5e3b1866bc47373459ced1a313506f1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Mar 2021 02:52:32 +0100 Subject: [PATCH 183/909] 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 9ffbe21861..64431027d9 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -39,17 +39,17 @@ sidebar: false アインシュタインの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。 実験データの処理や考察のために、複雑な計算をしたり、画像やシミュレーションの再レンダリングしたりすることは、この分野の研究者にとって時間のかかる作業となります。 -{{< 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="(**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 +## 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. +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。 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。 -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、複雑な数学と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。 -* [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. +* [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出, [ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) +* データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 +* 統計解析：観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 * Visualization of data - Time series - Spectrograms From b41fb85594f65c67ef3c34e413dd528607d44cfd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Mar 2021 03:51:21 +0100 Subject: [PATCH 184/909] 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 64431027d9..52332839a7 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -50,20 +50,20 @@ Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波 * [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出, [ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm) * データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 * 統計解析：観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 -* 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. +* データの可視化 + - 時系列データ + - スペクトログラム +* 相関計算 +* 重力波データ解析のために開発された [ソフトウェア](https://github.com/lscsoft)である[GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)はNumPy やAstroPyを重力波検出器からのデータを研究するためのユーティリティー、ツール、およびメソッドへのオブジェクトベースのインターフェースを提供するために利用しています。 -{{< 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**" >}} ---- -{{< 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 depgraph" caption=""**PyCBCのNumPy依存グラフ**" >}} -## Summary +## 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. +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。 データ解釈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、matplotlib、pandasなどの、Pythonパッケージとともに、 scikit-learningは 、研究者 [が](https://www.gw-openscience.org/events/GW150914/) 複雑な質問に答え、 私たちの宇宙についての理解において、新しい地平を発見することを可能にしてきたのです。 -{{< 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 329d32a6fe6b6b212623566f40a60efd9dda59b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 20 Mar 2021 03:51:22 +0100 Subject: [PATCH 185/909] New translations learn.md (Japanese) --- content/ja/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 4611d3a402..fe5ea1b88b 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 --- From 225c918357a318507dba9c89cd438767ec6e7cc6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Mar 2021 00:37:01 +0100 Subject: [PATCH 186/909] 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 fe5ea1b88b..486aa7fb87 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -3,14 +3,14 @@ title: Numpyの学び方 sidebar: false --- -For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). +**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。 -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +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。 *** -## Beginners +## 初心者向け -There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: +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お勧めします： **Tutorials** From ab4793b2c9af7499cf80f563499b4dda6200cc93 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Mar 2021 01:35:24 +0100 Subject: [PATCH 187/909] New translations learn.md (Japanese) --- content/ja/learn.md | 64 ++++++++++++++++++++++----------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 486aa7fb87..6e42b81f4c 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -12,54 +12,54 @@ sidebar: false 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お勧めします： - **Tutorials** - -* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -* [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) -* [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 *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) + **チュートリアル** + +* [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) +* [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 User Guide](https://numpy.org/devdocs) +* [Numpyユーザーガイド](https://numpy.org/devdocs) - **Books** + **書籍** -* [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* +* [NumPガイド*by Travelis E. Oliphant*](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*by Nicolas P. Rougier*](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) *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. +また、"Python+SciPy"を題材にした [おすすめリスト](https://www.goodreads.com/shelf/show/python-scipy) を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。 - **Videos** + **動画** -* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* +* [Numpy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** -## Advanced +## 上級者向け -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +より高度なインデックス作成、分割、スタック、線形代数など、Numpy の概念をより深く理解するためには、これらのリソースを試してみてください。 - **Tutorials** + **チュートリアル** -* [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 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 100演習](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *Nicolas P. Rougier* +* [NumPyとSciPyイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* +* [Numpy救急キット](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [PythonにおけるNumPy (発展編)](https://www.geeksforgeeks.org/numpy-python-set-2-advanced/) +* [高度なインデックシング](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/) - **Books** + **書籍** -* [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* +* [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) *by Jake Vanderplas* +* [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney* +* [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) *by Robert Johansson* - **Videos** + **動画** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunuz-Iglesias* +* [アドバンスドNumPy -](https://www.youtube.com/watch?v=cYugp9IN1-Q) *ブロードキャストルール、ストライド、および高度なインデックシング* by Fan Nunuz-Iglesias * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* *** From b94963e4355861e8274c15be2dcb57427296b7d2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Mar 2021 02:31:58 +0100 Subject: [PATCH 188/909] New translations learn.md (Japanese) --- content/ja/learn.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 6e42b81f4c..665f16893c 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -60,25 +60,25 @@ 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** * [アドバンスドNumPy -](https://www.youtube.com/watch?v=cYugp9IN1-Q) *ブロードキャストルール、ストライド、および高度なインデックシング* by Fan Nunuz-Iglesias -* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* +* [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 Talks +## NumPyに関するトーク -* [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) +* [Numpy Indexing の未来](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernadez* (2016) +* [Python における配列計算革命](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) +* [Numpy: 何が変わり、そして何が今後変わるのか?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) +* [NumPyの内部](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) +* [Python における配列計算の概要](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) *** -## Citing NumPy +## NumPy を引用する場合 -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). +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](/citing-numpy)を参照して下さい。 -## Contribute to this list +## このページへの貢献 -To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. +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/master/content/en/learn.md)を使って提案してみて下さい。 あなたが推薦する情報が、このページで紹介するに値する理由と、その情報によって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 00b8cc0008a786248b632359b5de16550d71c8e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 21 Mar 2021 02:31:59 +0100 Subject: [PATCH 189/909] 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 d45a2fbe06..9d6245b530 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,5 +1,5 @@ --- -title: News +title: ニュース sidebar: false --- From 5420fb712987bf83c640eb900f90dca50b846bec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 22 Mar 2021 00:37:38 +0100 Subject: [PATCH 190/909] 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 9d6245b530..da99c0b51f 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,9 +3,9 @@ title: ニュース sidebar: false --- -### Numpy 1.20.0 release +### Numpy 1.20.0 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。 - 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)). From 2d16f16b609c18463506b78ae12e9ae41d5a27bc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 22 Mar 2021 01:37:22 +0100 Subject: [PATCH 191/909] 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 da99c0b51f..0ebefd34e7 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -6,27 +6,27 @@ sidebar: false ### Numpy 1.20.0 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。 -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +- NumPy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)). -### Diversity in the NumPy project +### 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_ 、私達は[ 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。 -### First official NumPy paper published in Nature! +### Natureに初めての公式の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 is coming, when will NumPy release binary wheels? +### Python 3.9 が登場し、Numpy はいつバイナリホイールをリリースするのか? -_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 -- 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. +_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ホイール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。 ホイールリリースのイベントに備えて、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 release +### 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. From 92a08830fc5ef623df44df6158e880b9a9db1b8f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 22 Mar 2021 02:35:46 +0100 Subject: [PATCH 192/909] 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 0ebefd34e7..90e71b7b9e 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -28,11 +28,11 @@ _2020年9月14日_ -- Python 3.9 は数週間後にリリースされる予定 ### 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ホイールは最新の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。 -### The inaugural NumPy survey is live! +### 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. Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). From 1fb1e3ec9206298d1b6533eee9fa6b39257b7ed9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 22 Mar 2021 08:33:21 +0100 Subject: [PATCH 193/909] 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 90e71b7b9e..1f63d97a9e 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -34,7 +34,7 @@ _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. -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嬉しいです。 ### NumPy has a new logo! From 05d2981cfc094fae6b66f8e493c9a3b347aaf515 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 22 Mar 2021 09:29:59 +0100 Subject: [PATCH 194/909] New translations news.md (Japanese) --- content/ja/news.md | 56 +++++++++++++++++++++++----------------------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 1f63d97a9e..23413d7de7 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -37,53 +37,53 @@ _2020年7月2日_ -- このサーベイは、ソフトウェアとして、ま 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 has a new logo! +### 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 に新しいロゴが作成されました: -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 release +### Numpy 1.19.0 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。 -### Season of Docs acceptance +### 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 -_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。 -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 receives a grant from the Chan Zuckerberg Initiative +### 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. 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、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。 -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。 -## Releases +## 過去のリリース -Here is a list of NumPy releases, with links to release notes. All 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 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年5月3日_. +- NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年4月19日_. +- NumPy 1.18.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020年3月17日_. +- NumPy 1.18.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _2020年1月6日_. +- NumPy 1.17.5 ([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_. +- NumPy 1.14.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018年1月7日_. From 97c8b7fb4785f8357131f239f78eb0f40b198969 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 22 Mar 2021 09:30:01 +0100 Subject: [PATCH 195/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 5586668cba..d29fe66471 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -1,5 +1,5 @@ --- -title: NumPy Code of Conduct - How to follow up on a report +title: NumPy行動規範 - 報告書のフォローアップ方法 sidebar: false --- From cf0cb76f7c5d9405e1dfff5f5a2b6cfebaa1b948 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 23 Mar 2021 07:56:50 +0100 Subject: [PATCH 196/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index d29fe66471..071d9bb036 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -3,11 +3,11 @@ title: NumPy行動規範 - 報告書のフォローアップ方法 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。 -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: +[行動規範](/code-of-conduct) を施行することは、私たちのコミュニティの現在と未来に重要です。 私達はこのルールを重く受け止めています。 施行措置の見直しに際しては、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. 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. +* 機械的ではなく、人間的に行動します。 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. From a448cb60ca96ea76ad8380b51930519b74f648de Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 23 Mar 2021 08:55:48 +0100 Subject: [PATCH 197/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 071d9bb036..3c0b5011fa 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -7,9 +7,9 @@ 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](/code-of-conduct) を施行することは、私たちのコミュニティの現在と未来に重要です。 私達はこのルールを重く受け止めています。 施行措置の見直しに際しては、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。 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. +* 機械的ではなく、人間的に行動します。 委員会は、当事者にプライバシーと報告者に必要な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」のバイナリラベルを避けようとします。 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。 完全な文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、最終的にはコミュニティにとって最も生産的になると考えています。 +* 私たちは、電子メールが判断に困難な媒体であり、分けて利用できることを理解しています。 個人的な連絡なしで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るのです。 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. From 43448ee8b0278ae9782503194da8fb7a45c1d38b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Mar 2021 00:02:53 +0100 Subject: [PATCH 198/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 3c0b5011fa..66053242ed 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -9,7 +9,7 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この * 機械的ではなく、人間的に行動します。 委員会は、当事者にプライバシーと報告者に必要な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」のバイナリラベルを避けようとします。 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。 完全な文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、最終的にはコミュニティにとって最も生産的になると考えています。 -* 私たちは、電子メールが判断に困難な媒体であり、分けて利用できることを理解しています。 個人的な連絡なしで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るのです。 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. +* 私たちは、電子メールが判断に困難な媒体であり、分けて利用できることを理解しています。 個人的な連絡なしで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るのです。 ここでは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、 我々は全力を尽くします * 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. From 5cb2392819eeaa5d99a2f052c8f1641c47dd0d27 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Mar 2021 01:05:42 +0100 Subject: [PATCH 199/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 66053242ed..045cbf3548 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -10,13 +10,13 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この * 機械的ではなく、人間的に行動します。 委員会は、当事者にプライバシーと報告者に必要な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」のバイナリラベルを避けようとします。 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。 完全な文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、最終的にはコミュニティにとって最も生産的になると考えています。 * 私たちは、電子メールが判断に困難な媒体であり、分けて利用できることを理解しています。 個人的な連絡なしで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るのです。 ここでは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、 我々は全力を尽くします -* 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. +* 差別というのは明確には断定できないことがあり、無意識で実施さ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。 +* 新メンバーのニーズに留意し、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: From b65bd5866abf034b3af203473d0406781a25fe6a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Mar 2021 09:16:10 +0100 Subject: [PATCH 200/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 045cbf3548..25110dff82 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -18,10 +18,10 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この ## 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. +* 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。 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. From e3b653050a4c7a8060bc1cdeab5dcf94a9307c07 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Mar 2021 10:14:36 +0100 Subject: [PATCH 201/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 25110dff82..5ccee2b2b5 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -21,15 +21,15 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この 自主的な非公式の調停は、私たちの重要な役割です。 2つのグループ以上の当事者が不適切な行動をエスカレートした場合(人類の紛争では悲しいことに一般的なものですが)、調停プロセスを促進するは非常に重要です。 ちなみに、これは一例に過ぎません。委員会は、どのようなケースでも調停を検討することができますが、このプロセスはあくまでも自発的なものであり、当事者に参加を迫ることはできないことを念頭に置いて下さい。 委員会が調停を提案する場合は、次のようにすべきです。 * 調停者として役立つ候補者を見つけます。 -* 報告者の合意を取得します。 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. +* 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。 +* 調停人の決定：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、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。 -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. +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。 -## How the Committee will respond to reports +## 報告に対する委員会の対応 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. From d42844ec4cdc4fb5ddb40e90ed03faef9ee27cb3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Mar 2021 21:36:18 +0100 Subject: [PATCH 202/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 2906e12ece..47ca6e5a19 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -1,5 +1,5 @@ --- -title: "Case Study: First Image of a Black Hole" +title: "Caso de estudio: Primera imagen de un agujero negro" sidebar: false --- @@ -10,7 +10,7 @@ sidebar: false
    Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
    -## A telescope the size of the earth +## Un telescopio del tamaño del mundo 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! From 5553d40c7f0569b3c3aef609050b3d43405a5a62 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 25 Mar 2021 00:23:25 +0100 Subject: [PATCH 203/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 5ccee2b2b5..6e33baea9c 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -31,12 +31,12 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この ## 報告に対する委員会の対応 -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. +委員会(または委員) が行動規範違反報告を受けた時、その報告が明確で深刻な違反であるかどうかは判断されます(以下に違反項目を定義します)。 違反判定された場合は、通常のレポート処理プロセスに加えて、即時の対応が必要になります。 -## Clear and severe breach actions +## 明確かつ深刻な違反行為の解決 -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. +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. 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: From ba2633edb5df14bb9d58c176e876a53186562d5b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 25 Mar 2021 01:29:44 +0100 Subject: [PATCH 204/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 6e33baea9c..f705037654 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -36,17 +36,17 @@ 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. We will deal quickly with clear and severe breaches like personal threats, violent, sexist or racist language. +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。 -When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: +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. -* 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. +* 直ちにすべてのNumpy 通信チャンネル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、作業完了することで、よくある激しい意見の相違を制御するためにこのプロセスが使用されていないことを確認します。 -## Report handling +## 報告の処理 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. From a054a0c9cf71a64098d4b6eb2e4dc4fc566a2811 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 25 Mar 2021 13:59:45 +0100 Subject: [PATCH 205/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 47ca6e5a19..e5a63062aa 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: 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" >}} +{{
    }}

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

    From c6ed664b349ac33892100945751dc97790541619 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 25 Mar 2021 15:08:10 +0100 Subject: [PATCH 206/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index e5a63062aa..24c352dd18 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: 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 imagen: Colaboración del Telescopio de Horizonte de Eventos)*" 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.

    From ed93955adbbb04f89d0c9d3ed5e0d4b0a511c9e4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Mar 2021 01:34:13 +0100 Subject: [PATCH 207/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 24c352dd18..36e4b92ef0 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: 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 imagen: Colaboración del Telescopio de Horizonte de Eventos)*" 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 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.

    From fcdab5a46852c0f8e9a5fcc4ab1dabd325e1f653 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 26 Mar 2021 02:38:06 +0100 Subject: [PATCH 208/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 36e4b92ef0..e72f0b1f9d 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -12,7 +12,7 @@ sidebar: false ## Un telescopio del tamaño del mundo -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 Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), es un arreglo de ocho radio telescopios terrestres formando un telescopio computacional del tamaño del mundo, estudiando el universo con una sensibilidad y resolución sin precedente. 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! ### Objetivos clave y resultados From 5eb3d6e2d664422ce0a93f4996b1033f5884740d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 27 Mar 2021 00:14:00 +0100 Subject: [PATCH 209/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index f705037654..885c7e9c2c 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -48,9 +48,9 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この ## 報告の処理 -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. +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。 -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. +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。 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: From 6277cd92e3867f24a4c368c81bf5c87885ba91e8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 27 Mar 2021 01:12:59 +0100 Subject: [PATCH 210/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 885c7e9c2c..51e4d74c22 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -50,23 +50,23 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この 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。 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. +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、今回の事象の全ての状況を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. -* 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. +* 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。 -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). +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)。 -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. +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。 -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. +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。 -## Resolutions +## 解決方法 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. From fce1e4a4272ce3237cd562207bbcf823bd16cf78 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 00:02:05 +0100 Subject: [PATCH 211/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 51e4d74c22..85926b1d71 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -68,14 +68,14 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この ## 解決方法 -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. +委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。 -Possible responses may include: +ありうる返答は次のとおりです: -* 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. +* 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。 * 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. From cdcd83ccac907b5a916b5603e16e22bc5b459b3a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 01:01:59 +0100 Subject: [PATCH 212/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 85926b1d71..72648a80b6 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -76,20 +76,20 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この - 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。 -* 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. +* 公の場において、いくつかの行動/言動/言語が不適切で、現在の状況がなぜか引き起こされたのか指摘し、人々を傷つけることができルール言動であったことを説明するなど、コミュニティに自己調整を要求することもあります。 +* 委員会から関係者(複数可) への非公開処分の実施。 この場合、委員会は、電子メールを介して、グループにccを入れながら、対象者に問題の指摘を連絡します。 +* 公の場での指摘。 この場合、委員会の議長は、違反が発生したのと同じ場所で、実用性の範囲内で叱責を行います。 例えば、メールルールの違反の元のメーリングリストなどです。しかし、人や状況がかわるかもしれないチャットルームなどの場合、他の手段を利用する可能性もあります。 対策グループは、文書化のために、この問題のメッセージを他の場所で公開することを選択することもできます。 +* 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。 +* 委員会が個人にコミュニティへの参加を一時的に控える「相互に合意した休止」を要求できます。 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 スペース (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禁止のことです。 対策グループは、将来的な見直しや、または別の方法で対策されるように、すべてのそのような禁止の記録を記録します。 -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. +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。 -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). +最後に 委員会は、Numpy Steering CouncilとNumPy Coreチームに報告を行います。(例えば禁止事項など) -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. +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。 -## Conflicts of Interest +## 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 ebd6c1e28c0e731f4f06e1a0bba938c277f75a61 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 03:22:28 +0200 Subject: [PATCH 213/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 26 ++++++++++++++-------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index e72f0b1f9d..8963bbed5b 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -12,7 +12,11 @@ sidebar: false ## Un telescopio del tamaño del mundo -El [ telescopio Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), es un arreglo de ocho radio telescopios terrestres formando un telescopio computacional del tamaño del mundo, estudiando el universo con una sensibilidad y resolución sin precedente. 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 Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), es un arreglo de ocho radio telescopios terrestres formando un telescopio computacional del tamaño del mundo, estudiando el universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada interferometría de linea de base muy larga (VLBI), tiene una resolución angular de + +20 microsegundos de arco<0> - ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París!

    + + ### Objetivos clave y resultados @@ -22,22 +26,26 @@ El [ telescopio Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), * **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. + + 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. + + 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? + + 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? @@ -54,14 +62,14 @@ For example, the [`eht-imaging`][ehtim] Python package provides tools for simula 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 From 7f3dbf79afe2a962f48fe644cbf11f43b283b2c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 14:42:52 +0200 Subject: [PATCH 214/909] 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 43dd44cb12..3ec0dc58b7 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -77,7 +77,7 @@ Otherwise: #### 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, for example, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- 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. From bceacfa622e0fe7fb561c9e29ef2aaf9a019c7a4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 14:43:10 +0200 Subject: [PATCH 215/909] 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 43dd44cb12..3ec0dc58b7 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -77,7 +77,7 @@ Otherwise: #### 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, for example, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- 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. From b3a1f3d01ceba8d3996979fff57eb789eb7fb292 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 14:43:28 +0200 Subject: [PATCH 216/909] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/install.md b/content/pt/install.md index 319caaf9e2..ccaad9e92f 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -77,7 +77,7 @@ Otherwise: #### Alternativa se você preferir 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: -- Instale o Python a partir de, por exemplo, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), ou seu gerenciador de pacotes Linux. +- 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/) como a ferramenta mais bem mantida que fornece um resolvedor de dependências e recursos de gerenciamento de ambiente de forma semelhante ao que o conda faz. From 79fff2744879f57dd76397dd1b2c6f609c9bf5f3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 14:43:59 +0200 Subject: [PATCH 217/909] 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 43dd44cb12..3ec0dc58b7 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -77,7 +77,7 @@ Otherwise: #### 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, for example, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- 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. From 6956437ab193f1b9a4d3d9cb843aa65587eb995c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 14:44:18 +0200 Subject: [PATCH 218/909] 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 61f58002df..82c9aa0e3e 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -77,7 +77,7 @@ GPUを使用する場合: #### pip/PyPI を利用したい場合 個人的な好みや、下記のcondaとpipの違いを理解した上で、pip/PyPIベースの方法を使いたいユーザーには、下記をお勧めします: -- Pythonをインストールします。例えば、 [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), または Linux パッケージマネージャを使うことができます。 +- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. - 依存関係の解決と環境の管理を提供する最もよくメンテナンスされているツールとして、[Poetry](https://python-poetry. org/) をconda と同様な方法で使用することができます。 From 0826c0979e79be30f6922352b4a44ed9d162cbeb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 14:44:24 +0200 Subject: [PATCH 219/909] 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 43dd44cb12..3ec0dc58b7 100644 --- a/content/ar/install.md +++ b/content/ar/install.md @@ -77,7 +77,7 @@ Otherwise: #### 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, for example, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- 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. From d0812f6c487c7ca5a2c093b610b04e2c6c3869b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 16:51:52 +0200 Subject: [PATCH 220/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index efcde754ae..58e7bc7a7c 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -1,13 +1,13 @@ --- -title: NumPy Code of Conduct +title: القواعد التنظيمية لنمباي sidebar: false aliases: - /conduct.html --- -### Introduction +### المقدمة -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. +هذه القواعد التنظيمية تنطبق علي جميع المجالات التي تدار من قبل مشروع نمباي, بما في ذلك كل قوائم البريد سواء كانت خاصة أم عامة ومتعقبات القضايا والويكي والمدونات وتويتر وأي قناة اتصال تستخدم من قبل مجتمعنا. 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. From d6bac4aa6cd4a71f03914e647f4c7d866e801596 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 17:52:25 +0200 Subject: [PATCH 221/909] 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 58e7bc7a7c..a17e7fe3ae 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -1,5 +1,5 @@ --- -title: القواعد التنظيمية لنمباي +title: القواعد السلوكية لنمباي sidebar: false aliases: - /conduct.html @@ -7,7 +7,7 @@ aliases: ### المقدمة -هذه القواعد التنظيمية تنطبق علي جميع المجالات التي تدار من قبل مشروع نمباي, بما في ذلك كل قوائم البريد سواء كانت خاصة أم عامة ومتعقبات القضايا والويكي والمدونات وتويتر وأي قناة اتصال تستخدم من قبل مجتمعنا. 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. +هذه القواعد السلوكية تنطبق علي جميع المجالات التي تدار من قبل مشروع نمباي, بما في ذلك كل قوائم البريد سواء كانت خاصة أم عامة ومتعقبات القضايا والويكي والمدونات وتويتر وأي قناة اتصال تستخدم من قبل مجتمعنا. مشروع نمباي لا يقوم بتنظيم أي فعاليات شخصية ومع ذلك أي فعاليات متعلقة لمجتمعنا ينبغي أن تكون لها قواعد سلوكية مشابهة لروح هذا المستند. 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. From 19d4cab1eee5f74ddcf0dd0f5b9daf722ebfd8e4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 19:34:56 +0200 Subject: [PATCH 222/909] 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 a17e7fe3ae..d6e85bdcf9 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -9,9 +9,9 @@ aliases: هذه القواعد السلوكية تنطبق علي جميع المجالات التي تدار من قبل مشروع نمباي, بما في ذلك كل قوائم البريد سواء كانت خاصة أم عامة ومتعقبات القضايا والويكي والمدونات وتويتر وأي قناة اتصال تستخدم من قبل مجتمعنا. مشروع نمباي لا يقوم بتنظيم أي فعاليات شخصية ومع ذلك أي فعاليات متعلقة لمجتمعنا ينبغي أن تكون لها قواعد سلوكية مشابهة لروح هذا المستند. -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. +ينبغي علي كل شخص مشارك في مجتمع نمباي أن يحتذي بهذه القواعد سواء كان مشترك بصورة رسمية أو غير رسمية، أو يدعي انتمائه للمشروع في أي انشطه متعلقة للمشروع وخاصة عندما يمثل المشروع تحت أي دور. -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. +هذه القواعد ليست شاملة أو كاملة. 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 From e718ad2eaecaf60aae1bc51aa90571a1fa3ffcf9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 20:36:29 +0200 Subject: [PATCH 223/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index d6e85bdcf9..3fcdbd2288 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -11,14 +11,14 @@ aliases: ينبغي علي كل شخص مشارك في مجتمع نمباي أن يحتذي بهذه القواعد سواء كان مشترك بصورة رسمية أو غير رسمية، أو يدعي انتمائه للمشروع في أي انشطه متعلقة للمشروع وخاصة عندما يمثل المشروع تحت أي دور. -هذه القواعد ليست شاملة أو كاملة. 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. +هذه القواعد ليست شاملة أو كاملة. لكنها تساعد علي استخلاص فهمنا المشترك للتعاون في ظل بيئة وأهداف مشتركة. من فضلك حاول أن تتبع هذه القواعد روحا ونصا من أجل إنشاء بيئة ودودة ومنتجة نثري بها المجتمع المحيط. -### Specific Guidelines +### مبادئ توجيهية محددة -We strive to: +نحن نسعى إلي: -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. +1. أن نكون منفتحين. نحن ندعو الجميع للمشاركة في مجتمعنا. نحن نفضل استخدام وسائل الاتصال العامة للرسائل المتعلقة بالمشروع، ما لم نناقش شيئا حساسا. ينطبق هذا علي الرسائل الخاصة بطلب المساعدة أوالمتعلقة بدعم المشروع ، ليس فقط ﻷن طلبات الدعم العام محببة لاحتمالية الوصول للإجابة عن الاستفسارات بشكل اكبر، ولكن أيضا لضمان سهولة الكشف والتصحيح عن أي أخطاء غير مقصودة في الإجابات. +2. أن نكون عطوفين ومرحبين واكثر ودا وصبرا. نحن نعمل معا لحل الخلافات ونفترض حسن النوايا. قد نواجه بعض الإحباط من حين ألي أخر لكننا لا نسمح للإحباط أن يتحول إلي هجوم شخصي. فالمجتمع الذي يشعر فيه الناس بعدم الارتياح أو بالتهديد ليس مجتمعا منتجا. 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: From 013dd825379db3ba0e019a0e57797fcf66bcaf39 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 21:35:14 +0200 Subject: [PATCH 224/909] 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 3fcdbd2288..5efaa3c351 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -19,8 +19,8 @@ aliases: 1. أن نكون منفتحين. نحن ندعو الجميع للمشاركة في مجتمعنا. نحن نفضل استخدام وسائل الاتصال العامة للرسائل المتعلقة بالمشروع، ما لم نناقش شيئا حساسا. ينطبق هذا علي الرسائل الخاصة بطلب المساعدة أوالمتعلقة بدعم المشروع ، ليس فقط ﻷن طلبات الدعم العام محببة لاحتمالية الوصول للإجابة عن الاستفسارات بشكل اكبر، ولكن أيضا لضمان سهولة الكشف والتصحيح عن أي أخطاء غير مقصودة في الإجابات. 2. أن نكون عطوفين ومرحبين واكثر ودا وصبرا. نحن نعمل معا لحل الخلافات ونفترض حسن النوايا. قد نواجه بعض الإحباط من حين ألي أخر لكننا لا نسمح للإحباط أن يتحول إلي هجوم شخصي. فالمجتمع الذي يشعر فيه الناس بعدم الارتياح أو بالتهديد ليس مجتمعا منتجا. -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. +3. أن نكون متعاونين. كما سيستفيد بعملنا الآخرين سنستفيد نحن أيضا بعملهم. عندما نقوم بصنع شيئاً لمنفعة المشروع ، سوف نكون علي الاستعداد لشرح للآخرين كيفية عمله ، حتي يكونوا قادرين علي البناء عليه لجعله أفضل. أي قرار سنتخذه سيؤثر علي المستخدمين وعلي زملائنا في العمل لذا يجب أن تأخذ العواقب علي محمل الجد عندما نتخذ القرارات. +4. أن نكون اكثر استطلاعاً. لا أحد علي دراية بكل شئ! 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. From 790c9163d7f07ee7039e57534038c90906237528 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 28 Mar 2021 22:35:56 +0200 Subject: [PATCH 225/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index 5efaa3c351..6bed4d737d 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -20,16 +20,16 @@ aliases: 1. أن نكون منفتحين. نحن ندعو الجميع للمشاركة في مجتمعنا. نحن نفضل استخدام وسائل الاتصال العامة للرسائل المتعلقة بالمشروع، ما لم نناقش شيئا حساسا. ينطبق هذا علي الرسائل الخاصة بطلب المساعدة أوالمتعلقة بدعم المشروع ، ليس فقط ﻷن طلبات الدعم العام محببة لاحتمالية الوصول للإجابة عن الاستفسارات بشكل اكبر، ولكن أيضا لضمان سهولة الكشف والتصحيح عن أي أخطاء غير مقصودة في الإجابات. 2. أن نكون عطوفين ومرحبين واكثر ودا وصبرا. نحن نعمل معا لحل الخلافات ونفترض حسن النوايا. قد نواجه بعض الإحباط من حين ألي أخر لكننا لا نسمح للإحباط أن يتحول إلي هجوم شخصي. فالمجتمع الذي يشعر فيه الناس بعدم الارتياح أو بالتهديد ليس مجتمعا منتجا. 3. أن نكون متعاونين. كما سيستفيد بعملنا الآخرين سنستفيد نحن أيضا بعملهم. عندما نقوم بصنع شيئاً لمنفعة المشروع ، سوف نكون علي الاستعداد لشرح للآخرين كيفية عمله ، حتي يكونوا قادرين علي البناء عليه لجعله أفضل. أي قرار سنتخذه سيؤثر علي المستخدمين وعلي زملائنا في العمل لذا يجب أن تأخذ العواقب علي محمل الجد عندما نتخذ القرارات. -4. أن نكون اكثر استطلاعاً. لا أحد علي دراية بكل شئ! 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. +4. أن نكون اكثر استطلاعاً. لا أحد علي دراية بكل شئ! طرح الأسئلة في وقت مبكر قد يجنب العديد من المشاكل اللاحقة ، لذا نحن نشجع الأسئلة علي الرغم من أننا قد نعيد توجهها إلي المنتدي المناسب. وسنحاول جاهدين أن نكون متجاوبين ومفيدين. +5. أن نكون حذريين في اختيار الكلمات. وأن نتوخى الحذر والاحترام في اتصالاتنا، ونتحمل المسؤولية عن خطابنا. وأن نكون عطوفين مع الأخريين. لا تهين أو تحط من قدر المشاركين الآخرين. نحن لن نتقبل المضايقات أو أي سلوك إستباعدي اخر ، مثل: + * التهديدات العنيفة أو الخطاب الموجه ضد الأخر. + * النكات والتلميحات القائمة علي الجنس أو العرق أو اي أشكال التمييز الأخري. + * نشر مواد جنسية صريحة أو مواد تشجع علي العنف. + * نشر (أو التهديد بنشر) المعلومات التعريفية الشخصية لأناس آخرين ("doxing"). + * مشاركة المحتوى الخاص، مثل رسائل البريد الإلكتروني المرسلة بشكل خاص أو غير علني، أو المنتديات غير المسجلة مثل تاريخ قناة IRC، بدون موافقة المرسل. + * الإهانات الشخصية، خاصةً التي تستخدم مصطلحات عنصرية أو متحيزة جنسياً. + * الاهتمام الجنسي الغير مرحب به. + * البذائه المفرطه. 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. From 85b9b1bac2bc8d7b8fe1489cec2da8a7635a140d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Mar 2021 08:09:32 +0200 Subject: [PATCH 226/909] 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 6bed4d737d..ab66993697 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -29,8 +29,8 @@ aliases: * مشاركة المحتوى الخاص، مثل رسائل البريد الإلكتروني المرسلة بشكل خاص أو غير علني، أو المنتديات غير المسجلة مثل تاريخ قناة IRC، بدون موافقة المرسل. * الإهانات الشخصية، خاصةً التي تستخدم مصطلحات عنصرية أو متحيزة جنسياً. * الاهتمام الجنسي الغير مرحب به. - * البذائه المفرطه. 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. + * البذائه المفرطه. يرجى تجنب الكلمات البذيئة، يختلف الناس اختلافا كبيرا في حساسيتهم للبذائه. + * • المضايقة المتكررة للآخرين. In general, if someone asks you to stop, then stop. * Advocating for, or encouraging, any of the above behaviour. ### Diversity Statement From f05cc9e7db49a54ffbe75cbaf57fa98d13e11348 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Mar 2021 09:13:32 +0200 Subject: [PATCH 227/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index ab66993697..3785bef768 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -21,7 +21,7 @@ aliases: 2. أن نكون عطوفين ومرحبين واكثر ودا وصبرا. نحن نعمل معا لحل الخلافات ونفترض حسن النوايا. قد نواجه بعض الإحباط من حين ألي أخر لكننا لا نسمح للإحباط أن يتحول إلي هجوم شخصي. فالمجتمع الذي يشعر فيه الناس بعدم الارتياح أو بالتهديد ليس مجتمعا منتجا. 3. أن نكون متعاونين. كما سيستفيد بعملنا الآخرين سنستفيد نحن أيضا بعملهم. عندما نقوم بصنع شيئاً لمنفعة المشروع ، سوف نكون علي الاستعداد لشرح للآخرين كيفية عمله ، حتي يكونوا قادرين علي البناء عليه لجعله أفضل. أي قرار سنتخذه سيؤثر علي المستخدمين وعلي زملائنا في العمل لذا يجب أن تأخذ العواقب علي محمل الجد عندما نتخذ القرارات. 4. أن نكون اكثر استطلاعاً. لا أحد علي دراية بكل شئ! طرح الأسئلة في وقت مبكر قد يجنب العديد من المشاكل اللاحقة ، لذا نحن نشجع الأسئلة علي الرغم من أننا قد نعيد توجهها إلي المنتدي المناسب. وسنحاول جاهدين أن نكون متجاوبين ومفيدين. -5. أن نكون حذريين في اختيار الكلمات. وأن نتوخى الحذر والاحترام في اتصالاتنا، ونتحمل المسؤولية عن خطابنا. وأن نكون عطوفين مع الأخريين. لا تهين أو تحط من قدر المشاركين الآخرين. نحن لن نتقبل المضايقات أو أي سلوك إستباعدي اخر ، مثل: +5. أن نكون حذريين في اختيار الكلمات. وأن نتوخى الحذر والاحترام في اتصالاتنا، ونتحمل المسؤولية عن خطابنا. وأن نكون عطوفين مع الأخريين. لا تهين أو تحط من قدر المشاركين الآخرين. نحن لن نتقبل المضايقات أو أي سلوك استبعادي أخر ، مثل: * التهديدات العنيفة أو الخطاب الموجه ضد الأخر. * النكات والتلميحات القائمة علي الجنس أو العرق أو اي أشكال التمييز الأخري. * نشر مواد جنسية صريحة أو مواد تشجع علي العنف. @@ -30,14 +30,14 @@ aliases: * الإهانات الشخصية، خاصةً التي تستخدم مصطلحات عنصرية أو متحيزة جنسياً. * الاهتمام الجنسي الغير مرحب به. * البذائه المفرطه. يرجى تجنب الكلمات البذيئة، يختلف الناس اختلافا كبيرا في حساسيتهم للبذائه. - * • المضايقة المتكررة للآخرين. In general, if someone asks you to stop, then stop. - * Advocating for, or encouraging, any of the above behaviour. + * • المضايقة المتكررة للآخرين. بشكل عام، إذا طلب منك شخص ما التوقف فيجب عليك التوقف. + * الدعوة إلى أي من السلوكيات المذكور أعلاه أو التشجيع إليها. -### Diversity Statement +### بيان التنوع -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. +يرحب ويشجع مشروع نمباي بمشاركات الجميع. نحن ملتزمون بأن نكون مجتمعا يتمتع كل فرد فيه بكونه جزء منه. وبالرغم أننا قد لا نكون قادرين دوماً على استيعاب تفضيلات كل فرد، إلا إننا حريصين علي بذل قصارى جهدنا لمعاملة الجميع معاملة كريمة. -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. +بغض النظر عن كيفية تعريفك لنفسك أو كيف يتصورك الآخرون: نحن نرحب بك. 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. From 4ffc64462d4373d672fe2a014d1e6378594b995e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Mar 2021 12:32:15 +0200 Subject: [PATCH 228/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index 3785bef768..6ba1f75d2e 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -37,11 +37,11 @@ aliases: يرحب ويشجع مشروع نمباي بمشاركات الجميع. نحن ملتزمون بأن نكون مجتمعا يتمتع كل فرد فيه بكونه جزء منه. وبالرغم أننا قد لا نكون قادرين دوماً على استيعاب تفضيلات كل فرد، إلا إننا حريصين علي بذل قصارى جهدنا لمعاملة الجميع معاملة كريمة. -بغض النظر عن كيفية تعريفك لنفسك أو كيف يتصورك الآخرون: نحن نرحب بك. 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. +بغض النظر عن كيفية تعريفك لنفسك أو كيف يتصورك الآخرون: نحن نرحب بك. وعلى الرغم من أنه لا يمكن لأي قائمة أن تكون شاملة، فإننا نكرم بوضوح التنوع في: السن والثقافة والأصل العرقي والوراثي والهوية الجنسية واللغة والأصل القومي وتنوع العصبي والتكوين الظاهري والمعتقد السياسي والمهنة والعرق والديانة والتوجه الجنسي والحالة الاجتماعية الاقتصادية وثقافات الفرعية والقدرات التقنية بما لا يتعارض مع هذه القواعد السلوكية. -Though we welcome people fluent in all languages, NumPy development is conducted in English. +على الرغم من أننا نتقبل الناس بجميع اللغات التي يتقنوها، إلا أن تطوير نمباي يجري باستخدام اللغة الإنجليزية. -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). +ترد في قواعد السلوكية المذكورة أعلاه تفاصيل معايير السلوك في مجتمع نمباي. 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 From cbbf0738f31b54eec35854ff151842b5d2b861c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 29 Mar 2021 13:33:14 +0200 Subject: [PATCH 229/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index 6ba1f75d2e..a0d2b7910f 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -13,7 +13,7 @@ aliases: هذه القواعد ليست شاملة أو كاملة. لكنها تساعد علي استخلاص فهمنا المشترك للتعاون في ظل بيئة وأهداف مشتركة. من فضلك حاول أن تتبع هذه القواعد روحا ونصا من أجل إنشاء بيئة ودودة ومنتجة نثري بها المجتمع المحيط. -### مبادئ توجيهية محددة +### القواعد الارشادية المحددة نحن نسعى إلي: @@ -41,9 +41,9 @@ aliases: على الرغم من أننا نتقبل الناس بجميع اللغات التي يتقنوها، إلا أن تطوير نمباي يجري باستخدام اللغة الإنجليزية. -ترد في قواعد السلوكية المذكورة أعلاه تفاصيل معايير السلوك في مجتمع نمباي. Participants in our community should uphold these standards in all their interactions and help others to do so as well (see next section). +ترد في قواعد السلوكية المذكورة أعلاه تفاصيل معايير السلوك في مجتمع نمباي. وينبغي علي المشاركين في مجتمعنا أن يتمسكوا بهذه المعايير في جميع فعاليتهم وأن يساعدوا الآخرين على القيام بالمثل (انظر الفرع التالي). -### Reporting Guidelines +### القواعد الارشادية للإبلاغ 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. From b5e8daf280536e725f340cb6a34ccc6c6f58cabd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Mar 2021 16:26:27 +0200 Subject: [PATCH 230/909] 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 95a7507a54..e13172f599 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -5,7 +5,7 @@ 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 を使った数値計算の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は開発当初から100%オープンソースソフトウェアとして開発されてきました。[修正BSD ライセンス](https://github.com/numpy/numpy/blob/master/LICENSE.txt) の条項の下で、すべての人が利用可能です。 +Numpy は Python を使った数値計算のためのオープンソースプロジェクトです。 Numpyは、Numerical and Numarrayライブラリの初期のコードを基に、2005年から開発がスタートしました。 NumPyは開発当初から100%オープンソースソフトウェアとして開発されてきました。[修正BSD ライセンス](https://github.com/numpy/numpy/blob/master/LICENSE.txt) の条項の下で、すべての人が利用可能です。 Numpy は 、様々な科学Python コミュニティとのコンセンサスを得ながら、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。 From d0ff2fb54cb56a98240c83b50768c42bf2e3cca9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Mar 2021 17:24:09 +0200 Subject: [PATCH 231/909] 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 e13172f599..06843b687b 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -1,5 +1,5 @@ --- -title: Numpyプロジェクトについて +title: 私たちについて sidebar: false --- From 882e5ab90ec37d3d425a86fd6addaf38eb66dc76 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 30 Mar 2021 23:18:43 +0200 Subject: [PATCH 232/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index a0d2b7910f..4f91f7f469 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -45,7 +45,7 @@ aliases: ### القواعد الارشادية للإبلاغ -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. +نحن نعلم أنه شاع بشكل مؤلم إساءة استخدام الاتصالات عبر الإنترنت في انتهاكات واضحة وصارخة. 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. From 12c717b7af3a088d6fcc67d349485f690915212b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 31 Mar 2021 00:26:39 +0200 Subject: [PATCH 233/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index 4f91f7f469..c25bd00d54 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -45,19 +45,19 @@ aliases: ### القواعد الارشادية للإبلاغ -نحن نعلم أنه شاع بشكل مؤلم إساءة استخدام الاتصالات عبر الإنترنت في انتهاكات واضحة وصارخة. 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. +نحن نعلم أنه شاع بشكل مؤلم إساءة استخدام الاتصالات عبر الإنترنت في انتهاكات واضحة وصارخة. وندرك أيضاً أن الناس قد يمرون أحيانا بيوم سيئ أو قد لا يكونون علي دراية ببعض إرشادات القواعد السلوكية. لذا ضع هذا في عين الاعتبار عند اتخاذ القرار بشأن كيفية الرد علي انتهاك هذه القواعد. -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. +أما بالنسبة للانتهاكات المتعمدة بشكل واضح فيجب إبلاغ لجنة قواعد السلوك عليها (انظر أدناه). في حالة حدوث خروفات غير متعمدة فيمكنك الرد على الشخص المعني والإشارة إلى قواعد السلوك هذه (سواء علناً أو سراً، أينما كان ذلك مناسباً). وإذا كنت تفضل عدم القيام بذلك، فلا تتردد في إبلاغ اللجنة المعنية بقواعد السلوك مباشرة ويمكنك أيضاً طلب المشورة من اللجنة بكل ثقة. -You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. +يمكنك إبلاغ لجنة القواعد السلوكية لنمباي عبر numpy-conduct@googlegroups.com. -Currently, the Committee consists of: +وتتألف اللجنة حاليا مما يلي: -* Stefan van der Walt -* Melissa Weber Mendonça -* Anirudh Subramanian +* ستيفان فان دير والت (Stefan van der Walt) +* ميليسا فيبر ميندونسا (Melissa Weber Mendonça) +* أنيروده سوبرامانيان (Anirudh Subramanian) -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). +لو كان بلاغك متورط به أحد أعضاء اللجنة أو إذا كانوا يشعرون بأن لديهم تضارب في المصالح يحدهم عن التعامل معه. 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 From 9dde88bcd0bb3d775ea6caf0e6cde387d6b8a0ad Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 31 Mar 2021 16:15:13 +0200 Subject: [PATCH 234/909] 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 82c9aa0e3e..b2b29802a1 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -77,7 +77,7 @@ GPUを使用する場合: #### pip/PyPI を利用したい場合 個人的な好みや、下記のcondaとpipの違いを理解した上で、pip/PyPIベースの方法を使いたいユーザーには、下記をお勧めします: -- Install Python from [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), or your Linux package manager. +- [python.org](https://www.python.org/downloads/)からや、Macを使っている場合は[Homebrew](https://brew.sh/), Linuxを使っている場合は、Linuxのパッケージマネージャーを使ってPythonをインストールします。 - 依存関係の解決と環境の管理を提供する最もよくメンテナンスされているツールとして、[Poetry](https://python-poetry. org/) をconda と同様な方法で使用することができます。 From 00a091cf07316834bc4b24e475f9f7447d4ddf7f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 31 Mar 2021 17:34:20 +0200 Subject: [PATCH 235/909] New translations about.md (Japanese) --- content/ja/about.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 06843b687b..96dbbc14f6 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -5,9 +5,9 @@ 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 を使った数値計算のためのオープンソースプロジェクトです。 Numpyは、Numerical and Numarrayライブラリの初期のコードを基に、2005年から開発がスタートしました。 NumPyは開発当初から100%オープンソースソフトウェアとして開発されてきました。[修正BSD ライセンス](https://github.com/numpy/numpy/blob/master/LICENSE.txt) の条項の下で、すべての人が利用可能です。 +Numpy は Python を使った数値計算の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/master/LICENSE.txt) の条項の下で、すべての人が利用可能です。 -Numpy は 、様々な科学Python コミュニティとのコンセンサスを得ながら、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。 +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 From 1979f1f759cc6a5b756a7990802a2281e2e90663 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 31 Mar 2021 17:34:22 +0200 Subject: [PATCH 236/909] New translations arraycomputing.md (Japanese) --- content/ja/arraycomputing.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/arraycomputing.md b/content/ja/arraycomputing.md index 214d01e34e..a2ac5c4698 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も配列演算は中核を担っています。* +*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。 -**Num**erical **Py**thon: NumPyは、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する、Pythonにおけるデファクトスタンダードなライブラリです。 +**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 似た機能を模倣しており、機械学習や人工知能に適した、新しいアルゴリズムや機能を有しています。 +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。 Date: Thu, 1 Apr 2021 17:09:31 +0200 Subject: [PATCH 237/909] 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 96dbbc14f6..f10c620a2b 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -5,7 +5,7 @@ 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 を使った数値計算の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/master/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。 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/master/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。 From c42071957a309ac5f54304c24fca9d9657443a9e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Apr 2021 17:09:33 +0200 Subject: [PATCH 238/909] 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 1aa50fa270..587890c75d 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -3,7 +3,7 @@ title: NumPy を引用する場合 sidebar: false --- -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、こちらの論文を引用して下さい。 +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、こちらの論文を引用して下さい。 * Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [0.1038/s41586-020-2649-2](https://doi. org/10.1038/s41586-020-2649-2). ([リンク](https://www.nature.com/articles/s41586-020-2649-2)). From d0204f6fd20eccc860a0f7c0fb5281c0d82c63bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Apr 2021 18:13:45 +0200 Subject: [PATCH 239/909] New translations about.md (Japanese) --- content/ja/about.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index f10c620a2b..530f16d9cb 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -12,7 +12,7 @@ NumPy は 、NumPyコミュニティやより広範な科学計算用Python コ ## 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): +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): - Sebastian Berg - Jaime Fernández del Río @@ -35,7 +35,7 @@ Numpy運営委員会の役割は、Numpyのコミュニティと協力しサポ ## 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。 +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。 - コード - ドキュメント @@ -47,23 +47,23 @@ Numpy プロジェクトは拡大しているため、いくつかのチーム ## 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。 +NumPyは以下の団体から直接資金援助を受けています。 {{< sponsors >}} ## パートナー団体 -パートナー団体は、Numpyへの開発を仕事の一つとして、社員を雇っている団体です。 現在のパートナー団体としては、下記の通りです。 +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。 {{< partner >}} ## 寄付 -NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、Numpyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 +NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 -Numpy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、Numpyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 +NumPy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、NumPyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 -Numpy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 米国の寄付提供者の場合、その人の寄付は法律によって定められる範囲で免税されます。 但し、他の寄付と同様に、あなたはあなたの税務状況について、あなたの税務担当と相談する必要があることを忘れないで下さい。 +NumPy 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。 +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 fc74c3c9f2417a88ec33d6a8abeaf0a7edb7d489 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Apr 2021 18:13:47 +0200 Subject: [PATCH 240/909] New translations code-of-conduct.md (Japanese) --- content/ja/code-of-conduct.md | 36 +++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md index 10853e5152..759b2b56c2 100644 --- a/content/ja/code-of-conduct.md +++ b/content/ja/code-of-conduct.md @@ -7,26 +7,26 @@ aliases: ### 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では、対面でもイベント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、特にそれらのプロジェクトを運営する場合、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。 是非、我々のコミュニティをより豊かにし、フレンドリーで生産的な環境を作るために、この行動規範に従ってください。 +この行動規範は完全ではありません。 しかし、行動規範は我々が理解すべき、互いの協力の仕方や、共通の場所のあるべき姿、我々のゴールなどをまとめるのに重要な役目を果たします。 フレンドリーで生産的な環境を生み出し、周囲のコミュニティにより良い影響を与えるため、ぜひこの行動規範に従ってください。 ### ガイドラインの概要 私たちは下記の内容に真摯に取り組みます。 -1. 開けたコミュニティにしましょう。 私たちは、誰でもコミュニティに参加できるようにします。 私たちは、何かあまり公にすべきではない内容を議論しない限り、プロジェクト関連のメッセージにはパブリックな通信方法を使用するように努めます。 この行動規範はNumpyのヘルプやプロジェクト関連のサポートのメッセージにも適用されます。パブリックなサポートだけでなく、Numpyに関する質問に答える場合もこの行動規範に従うことがひです。 これにより、質問に答えた時の、無意識な間違いを、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。 私たちはお互いの意見の尊重しあい、互いの善意を信じ合います。 私達はたまに様々な種類の不満を感じるかもしれません。しかしそんな時でも、私達はそのような不満を個人的な攻撃に変えるのを許しません。 なぜなら人々が不快や脅威を感じるコミュニティは、生産的な場所ではないからです。 -3. 互いに協力し合おう。 私たちが開発したものは、他の人々によって使用され、一方で、私たちは他の人が開発しているもの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。 私達が実施する全ての決断は、全てのユーザと開発コミュニティに影響を与え、その決断による結果を私達は真摯に受け止めます。 -4. 好奇心を大事にしよう。 全てのことを理解している人は存在しません。 早め早めに質問を行うことは、後で多くの問題を回避することができます。なので私達は、それぞれの質問に対して、適切なフォーラムに案内することで、質問を奨励していきます。 私たちは、出来るだけ質問に対する対応を良くし、手助けできるように努力します。 -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つようにします。 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。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。: * 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、非公開フォーラム上でものIRCチャネル履歴などのプライベートコンテンツを、送信者の同意なしに共有すること。 + * 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。 @@ -35,21 +35,21 @@ aliases: ### 多様性に関する声明 -NumPyプロジェクトは、全ての人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力を注いでいます。 全ての人が満足できるように対応できるとは限りませんが、全員を出来るだけ親切に扱えるように最善を尽くしていきます。 +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。 -あなたがどのようにあなた自身を認識し、他の人があなたをどのように認識していても、私達 はあなたのプロジェクトへの参加を歓迎します。 下記のリストが全てを含んでいるとは言えませんが、私達は行動規範に反しない限り、下記の多様性を尊重することを明言します。: 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。 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。 -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 開発は英語で実施します。 +私たちはすべての種類の言語言語話者の参加を歓迎しますが、NumPy 開発は英語で行われます。 -NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 我々のコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです。(次のセクションを参照)。 +NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 NumPyコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです (次のセクションを参照)。 ### 報告ガイドライン -私たちは、インターネット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. また、この行動規範のガイドラインにそのような行為が禁止されていることに気づいていない人もいることを認識しています。 行動規範の違反に対応する方法を決定する際には、この事実を覚心に留めておく必要があります。 +私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 -意図的な行動規範違反については、行動規範委員会に報告してください(下記参照)。 もし、ある行動規範違反が意図的ではない可能性がある場合、あなたはその人にこの行動規範が存在していることを指摘することができます(方法としてはパブリックな方法でもプライベートな方法に、適切な方法であればはどの様な方法でも可能です。)。 もし、直接指摘するのが躊躇われる場合は、是非、行動規範委員会に連絡下さい。 委員会に助言を求めることもできます。 +意図的な行動規範違反については、行動規範委員会に報告してください (下記参照)。 もし、違反が意図的でない可能性がある場合、その人にこの行動規範の存在を知らせることも可能です (パブリックでもプライベートでも、適切な方法で)。 もし直接指摘したくない場合は、ぜひ、行動規範委員会に直接連絡するか、違反の確度について助言を求めて下さい。 -Numpy行動規範委員会に問題を報告する場合はこちらにご連絡下さい: numpy-conduct@googlegroups.com +NumPy行動規範委員会に問題を報告する場合は、こちらにご連絡下さい: numpy-conduct@googlegroups.com。 現在、行動規範委員会は以下のメンバーで構成されています: @@ -57,7 +57,7 @@ Numpy行動規範委員会に問題を報告する場合はこちらにご連絡 * Melissa Weber Mendonça * Anirudh Subramanian -もしあなたの違反報告に委員会のメンバーが含まれている場合, または彼らがそれを処理する上で利益相反をしていると感じる場合、そのメンバーはあなたの報告を評価する立場からは辞退してもらいます。 または、もしあなたが行動規範委員会に報告するのが躊躇われるばあい、こちらのNumFOCUSのスタッフに連絡することも可能です。:[conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). +もしあなたの違反報告に委員会のメンバーが含まれている場合, または彼らがそれを処理する上で利益相反をしていると感じる場合、そのメンバーはあなたの報告を評価する立場からは辞退してもらいます。 もしくは、行動規範委員会に報告するのが躊躇われる場合は、こちらからNumFOCUSのシニアスタッフに連絡することも可能です:[conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible) 。 ### インシデント報告の解決 & 行動規範の実施 @@ -78,6 +78,6 @@ Numpy行動規範委員会に問題を報告する場合はこちらにご連絡 ### 文末脚注: -私たちは下記のドキュメントの作成したグループに感謝しています。このドキュメントから私たちは我々の行動規範の内容と発想を得ることが出来ました。 +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) From f99a6ba8aea2846790ef20e69b791046361f069d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Apr 2021 23:20:21 +0200 Subject: [PATCH 241/909] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index 74998f1e44..90a4aa3d2d 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -2,26 +2,26 @@ title: Contribua com o NumPy sidebar: false - - - -The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming -- in addition to +O projeto NumPy precisa de sua experiência e entusiasmo! Suas opções de não são limitadas à programação -- além de -- [Writing code](#writing-code) +- [Escrever código](#writing-code) -you can +você pode: -- [Review pull requests](#reviewing-pull-requests) -- [Develop tutorials, presentations, and other educational material](#developing-educational-materials) -- [Triage issues](#issue-triaging) -- [Work on our website](#website-development) -- [Contribute graphic design](#graphic-design) -- [Translate website content](#translating-website-content) -- [Serve as a community coordinator](#community-coordination-and-outreach) -- [Write grant proposals and help with other fundraising](#fundraising) +- [Revisar pull requests](#reviewing-pull-requests) +- [Desenvolver tutoriais, apresentações e outros materiais educacionais](#developing-educational-materials) +- [Fazer triagem em issues](#issue-triaging) +- [Trabalhar no nosso site](#website-development) +- [Contribuir com design gráfico](#graphic-design) +- [Traduzir conteúdo do site](#translating-website-content) +- [Trabalhar coordenando a comunidade](#community-coordination-and-outreach) +- [Escrever propostas e ajudar com outras atividades para financiamento](#fundraising) -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). +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). -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). +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). -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/). +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/). 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. From 4633a44b1e47f870587e4317520cb6ac122270a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Apr 2021 23:20:22 +0200 Subject: [PATCH 242/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 44 +++++++++++----------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index e59861dfa2..d07ddd9776 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -1,5 +1,5 @@ --- -title: "Case Study: First Image of a Black Hole" +title: "Estudo de Caso: A Primeira Imagem de um Buraco Negro" sidebar: false --- @@ -7,58 +7,58 @@ sidebar: false

    Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver.

    -
    Katie Bouman, Assistant Professor, Computing & Mathematical Sciences, Caltech
    +
    Katie Bouman, Professora Assistente, Ciências da Computação e Matemática, Caltech
    -## A telescope the size of the earth +## Um telescópio do tamanho da Terra -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! +O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um conjunto de oito telescópios em solo formando um telescópio computacional do tamanho da Terra, projetado para estudar o universo com sensibilidade e resolução sem precedentes. O enorme telescópio virtual, que usa uma técnica chamada interferometria de longa linha de base (VLBI), tem uma resolução angular de [20 micro-arcossegundos][resolution] — o suficiente para ler um jornal em Nova Iorque a partir de um café em uma calçada de Paris! ### Principais Objetivos e Resultados -* **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. +* **Uma nova visão do universo:**A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. -* **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. +* **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. -* **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 observações com a teoria:** Da teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir sua enorme massa. ### Desafios -* **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. + O EHT representa um desafio imenso em processamento de dados, incluindo flutuações de fase atmosféricas rápidas, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. -* **Too much information** +* **Muitas informações** - 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. + A cada dia, o EHT gera mais de 350 terabytes de observações, armazenadas em discos rígidos cheios de hélio. Reduzir o volume e a complexidade desse volume de dados é extremamente difícil. -* **Into the unknown** +* **Em direção ao desconhecido** - When the goal is to see something never before seen, how can scientists be confident the image is correct? + 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" >}} ## 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? +E se houver um problema com os dados? Ou talvez um algoritmo seja muito dependente de uma hipótese em particular. A imagem será alterada drasticamente se um único parâmetro for alterado? -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. +A colaboração do EHT venceu esses desafios ao estabelecer equipes independentes que avaliaram os dados usando técnicas de reconstrução de imagem estabelecidas e de ponta para verificar se as imagens resultantes eram consistentes. Quando os resultados se provaram consistentes, eles foram combinados para produzir a imagem inédita do buraco negro. -Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. +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="**The role of NumPy in Black Hole imaging**" >}} +{{< 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**" >}} -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 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 vetorial 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="**Software dependency chart of ehtim package highlighting NumPy**" >}} +{{< 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**" >}} -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. +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 formatos de arquivos astronômicos padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a[Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. ## Resumo -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. +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 alguns dos mistérios do 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="numpy benefits" caption="**Funcionalidades-chave do NumPy utilizadas**" >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 342eaed90271ae78361cf4cf5b02babec3e59587 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 00:26:26 +0200 Subject: [PATCH 243/909] 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 d24405565f..163d4eab78 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -5,7 +5,7 @@ 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. It was created in 2005, building on the early work of the Numeric and Numarray libraries. 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/master/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/master/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). From af5399a1cb28d92de13ed5e7890cc022c28b6b78 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 00:26:27 +0200 Subject: [PATCH 244/909] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 54 ++++++++++++++++++++-------------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index 90a4aa3d2d..eef1b6d84c 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -23,56 +23,56 @@ Estes são os nossos canais de comunicação preferidos (projetos de código abe 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/). -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. +Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contribuições. Temos um [Código de Conduta](/code-of-conduct) para promover um ambiente aberto e acolhedor. -### Writing code +### Escrevendo código -Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the codebase. +Para pessoas programadoras, este [guia](https://numpy.org/devdocs/dev/index.html#development-process-summary) explica como contribuir para a base de código. -### 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 +### Revisar pull requests +O projeto tem mais de 250 pull requests abertos -- o que significa que muitas potenciais melhorias e muitos contribuidores de código aberto estão aguardando feedback. Se você é uma pessoa programadora que conhece o NumPy, você pode ajudar, mesmo que não tenha familiaridade com o código. Você pode: +* resumir uma discussão longa +* fazer triagem de PRs de documentação +* testar alterações propostas -### Developing educational materials +### Desenvolvimento de materiais educacionais -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. +O [Guia do Usuário](https://numpy.org/devdocs) do Numpy está sendo reformado. Precisamos de novos tutoriais, how-to's e de explicações de conceitos, e o site precisa de reestruturação. Oportunidades não se limitam a pessoas com experiência em escrita técnica. Também procuramos exemplos práticos, notebooks e vídeos. A [NEP 44](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html) explica nossas ideias para reestruturar a documentação do NumPy — talvez você também tenha outras ideias. -### Issue triaging +### Triagem de Issues -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: +O [*issue tracker* do NumPy](https://github.com/numpy/numpy/issues) tem _um monte_ de issues abertas. Algumas não são mais válidas, algumas deveriam ser priorizadas, e algumas poderiam ser boas para pessoas que estão procurando sua primeira contribuição. Você pode: -* 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) +* verificar se erros mais antigos ainda estão presentes +* encontrar issues duplicadas e criar links entre issues relacionadas +* adicionar bons exemplos autocontidos que reproduzam issues +* rotular issues corretamente (isso requer direitos de triagem -- basta perguntar) -Please just dive in. +Sinta-se à vontade! -### Website development +### Desenvolvimento do site -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 o nosso site, mas estamos longe de terminar. Se você adora o desenvolvimento web, estas [issues](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign) listam algumas de nossas necessidades não atendidas -- e sinta-se livre para compartilhar suas próprias ideias. -### Graphic design +### Design 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. +Nós mal podemos começar a listar as contribuições que uma pessoa com conhecimento em design gráfico pode fazer aqui. Nossa documentação precisa de ilustrações; nosso site crescente precisa de imagens -- há muitas oportunidades. -### Translating website content +### Traduzir conteúdo do site -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. +Planejamos várias traduções do [numpy.org](https://numpy.org) para tornar o NumPy acessível aos usuários em seu idioma nativo. Tradutores voluntários estão no coração deste esforço. Veja [aqui](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n) para informações; comente [nesta issue do GitHub](https://github.com/numpy/numpy.org/issues/55) para se envolver. -### Community coordination and outreach +### Coordenação e promoção na comunidade -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. +Através do contato com a comunidade podemos compartilhar nosso trabalho para mais pessoas e descobrir onde precisamos trabalhar mais. Estamos ansiosos para que mais pessoas se envolvam em esforços como nossa conta no [Twitter](https://twitter.com/numpy_team), na organização de [sprints](https://scisprints.github.io/) sobre o NumPy, uma newsletter, e talvez um blog. -### Fundraising +### Financiamento -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. +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 82c6d9e3cceb9282ef91042a0c04437b071be236 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 00:26:29 +0200 Subject: [PATCH 245/909] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 76 +++++++++++++++++++++---------------------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/content/pt/install.md b/content/pt/install.md index ccaad9e92f..b86910dc56 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -3,46 +3,46 @@ title: Instalando o 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. +O único pré-requisito para instalar o NumPy é o próprio Python. Se você ainda não tem o Python e quer começar do jeito mais simples, nós recomendamos que você use a [Distribuição Anaconda](https://www.anaconda.com/distribution) - inclui Python, NumPy e outros pacotes comumente usados para computação científica e ciência de dados. -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. +O NumPy pode ser instalado com `conda`, com `pip`, com um gerenciador de pacotes no macOS e Linux, ou [da fonte](https://numpy.org/devdocs/user/building.html). Para obter instruções mais detalhadas, consulte nosso [guia de instalação do Python e do NumPy](#python-numpy-install-guide) abaixo. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: +Se você usar o `conda`, você pode instalar o NumPy do canal `default` ou do `conda-forge`: ```bash -# Best practice, use an environment rather than install in the base env +# Recomenda-se usar um ambiente novo ao invés de instalar no ambiente-base conda create -n my-env conda activate my-env -# If you want to install from conda-forge +# Se quiser instalar do conda-forge conda config --env --add channels conda-forge -# The actual install command +# O comando para instação conda install numpy ``` **PIP** -If you use `pip`, you can install NumPy with: +Se você usa o `pip`, você pode instalar o NumPy com: ```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. +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 -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. +Instalar e gerenciar pacotes no Python pode ser complicado. Há várias soluções alternativas para a maioria das tarefas. Este guia tenta dar ao leitor um resumo das melhores (ou mais populares) soluções e dar recomendações claras. Ele se concentra em usuários do Python, NumPy e do PyData (ou computação numérica) em sistemas operacionais e hardware comuns. -## Recommendations +## Recomendações -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. +Vamos começar com recomendações baseadas no nível de experiência do usuário e no sistema operacional de interesse. Se você estiver entre "iniciante" e "avançado", por favor, escolha "iniciante" se você quiser manter as coisas simples, e "avançado" se você quiser trabalhar de acordo com as melhores práticas que te ajudarão a ir mais longe no futuro. ### Usuários iniciantes -On all of Windows, macOS, and Linux: +Em Windows, macOS e Linux: - Instale o [Anaconda](https://www.anaconda.com/distribution/) (instala todos os pacotes que você precisa e todas as outras ferramentas mencionadas abaixo). - Para escrever e executar código, use notebooks no [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) para a computação exploratória e interativa, e o [Spyder](https://www.spyder-ide.org/) ou [Visual Studio Code](https://code.visualstudio.com/) para escrever scripts e pacotes. @@ -60,78 +60,78 @@ On all of Windows, macOS, and Linux: #### Linux -If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: +Se você não tiver problemas em ter pacotes um pouco desatualizados e preferir estabilidade ao invés de ser capaz de usar as últimas versões das bibliotecas: - Use seu gerenciador de pacotes do SO o máximo possível (para o Python, NumPy e outras bibliotecas). - Instale pacotes não fornecidos pelo seu gerenciador de pacotes com `pip install algumpacote --user`. -If you use a GPU: +Se você usa uma GPU: - Instale o [Miniconda](https://docs.conda.io/en/latest/miniconda.html). - Mantenha o ambiente conda `base` mínimo, e use um ou mais [ambientes conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) para instalar o pacote que você precisa para a tarefa ou projeto em que você está trabalhando. - Use o canal conda`defaults` (`conda-forge` não tem bom suporte para pacotes de GPU). -Otherwise: +Caso contrário: - Instale o [Miniforge](https://github.com/conda-forge/miniforge). - Mantenha o ambiente conda `base` mínimo, e use um ou mais [ambientes conda](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) para instalar o pacote que você precisa para a tarefa ou projeto em que você está trabalhando. #### Alternativa se você preferir 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. +Para usuários que preferem uma solução baseada em pip/PyPI, por preferência pessoal ou leitura sobre as principais diferenças entre o conda e o pip, nós recomendamos: +- Instale o Python a partir de, por exemplo, [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/), ou seu gerenciador de pacotes Linux. - Use [Poetry](https://python-poetry.org/) como a ferramenta mais bem mantida que fornece um resolvedor de dependências e recursos de gerenciamento de ambiente de forma semelhante ao que o conda faz. -## Python package management +## Gerenciamento de pacotes Python -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. +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. ### 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. +As duas principais ferramentas que instalam pacotes do Python são `pip` e `conda`. Algumas de suas funcionalidades são redundantes (por exemplo, ambos podem instalar o `numpy`). No entanto, elas também podem trabalhar juntas. Vamos discutir as principais diferenças entre o pip e o conda aqui - é importante entender isso se você deseja gerenciar pacotes de forma efetiva. -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. +A primeira diferença é que "conda" é multilinguagens e pode instalar o Python, enquanto o pip é instalado em um determinado Python em seu sistema e instala outros pacotes apenas para essa mesma instalação de Python. Isto também significa que o conda pode instalar bibliotecas e ferramentas não-Python das quais você pode precisar (por exemplo, compiladores, CUDA, HDF5), enquanto pip não pode. -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. +A segunda diferença é que o pip instala do Índice de Pacotes Python (Python Packaging Index - PyPI), enquanto o conda instala de seus próprios canais (tipicamente "defaults" ou "conda-forge"). PyPI é a maior coleção de pacotes, no entanto, todos os pacotes populares também estão disponíveis para conda. -The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies. +A 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 -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: +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: -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: +1. usar um ambiente diferente para cada projeto em que você trabalha, +2. gravar nomes de pacotes e versões usando seu instalador de pacotes; cada um tem seu próprio formato de metadados para essa tarefa: - Conda: [ambientes conda e arquivos environment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) - Pip: [ambientes virtuais](https://docs.python.org/3/tutorial/venv.html) e [requirements.txt](https://pip.readthedocs.io/en/latest/user_guide/#requirements-files) - Poetry: [ambientes virtuais e pyproject.toml](https://python-poetry.org/docs/basic-usage/) -## NumPy packages & accelerated linear algebra libraries +## Pacotes NumPy & bibliotecas de álgebra linear aceleradas -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: +O NumPy não depende de quaisquer outros pacotes Python. No entanto, depende de uma biblioteca de álgebra linear acelerada - tipicamente [Intel MKL](https://software.intel.com/en-us/mkl) ou [OpenBLAS](https://www.openblas.net/). Os usuários não precisam se preocupar com a instalação desses pacotes (eles são incluídos automaticamente em todos os métodos de instalação do NumPy). No entanto, usuários experientes podem querer saber os detalhes, porque o BLAS usado pode afetar o desempenho, o comportamento e o tamanho em disco: -- 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. +- As wheels da NumPy no PyPI, que é o que o pip instala, são compiladas com OpenBLAS. As bibliotecas da OpenBLAS são empacotadas dentro da wheel. Isso faz com que a wheel fique maior, e se um usário também instalar (por exemplo) a SciPy, terá agora duas cópias da OpenBLAS no disco. -- 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. +- No canal defaults do conda, a NumPy é compilada com a Intel MKL. MKL é um pacote separado que será instalado no ambiente do usuário quando instalar a 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. +- No canal do conda-Forge, a NumPy é compilada com um pacote "BLAS" fictício. Quando um usuário instala o NumPy do conda-forge, esse pacote BLAS então é instalado juntamente com a biblioteca real - o padrão é OpenBLAS, mas também pode ser MKL (do canal defaults), ou até mesmo [BLIS](https://github.com/flame/blis) ou *reference BLAS*. -- The MKL package is a lot larger than OpenBLAS, it's about 700 MB on disk while OpenBLAS is about 30 MB. +- O pacote MKL é muito maior que o OpenBLAS, ocupa cerca de 700 MB no disco enquanto OpenBLAS ocupa cerca de 30 MB. -- MKL is typically a little faster and more robust than OpenBLAS. +- A MKL é normalmente um pouco mais rápida e mais robusta do que a OpenBLAS. -Besides install sizes, performance and robustness, there are two more things to consider: +Além do tamanho instalado, desempenho e robustez, há mais duas coisas a se considerar: -- 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. +- A Intel MKL não é de código aberto. Para uso normal isto não é um problema, mas se um usuário precisa redistribuir uma aplicação compilada com a NumPy, isso pode ser um problema. +- Tanto MKL quanto OpenBLAS usarão multi-threading para chamadas de função como `np.dot`, com o número de threads sendo determinado tanto por uma opção no momento da compilação quanto por uma variável de ambiente. Muitas vezes, todos os núcleos de CPU serão usados. Isto é, às vezes, inesperado para usuários; o NumPy em si não paraleliza automaticamente nenhuma chamada de função. Normalmente, isso produz melhor desempenho, mas também pode ser prejudicial - por exemplo, ao usar outro nível de paralelização com Dask, scikit-learn ou multiprocessamento. -## Troubleshooting +## Solução de problemas -If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). +Se sua instalação falhar com a mensagem abaixo, consulte [Solucionando ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). ``` IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! From c128fa4fe4bb17d11ff2e3022447e0237ca0412f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 00:26:30 +0200 Subject: [PATCH 246/909] 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 4f339d762f..6d8d5b6a24 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -3,7 +3,7 @@ title: Aprenda sidebar: false --- -For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). +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). *** From ff1e889e75374ed788fff45b13cfb7ef18c7366a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 00:26:31 +0200 Subject: [PATCH 247/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 88 +++++++++++++++++++++++----------------------- 1 file changed, 44 insertions(+), 44 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 4e7060b82a..dd7a55e3c8 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,87 +3,87 @@ title: Notícias sidebar: false --- -### Numpy 1.20.0 release +### 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: -- 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)). +_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. +- 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)). -### Diversity in the NumPy project +### 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). -### First official NumPy paper published in Nature! +### 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. -### Python 3.9 is coming, when will NumPy release binary wheels? +### 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 -- 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. +_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 1.19.2 release +### 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. -### The inaugural NumPy survey is live! +### 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). -### NumPy has a new logo! +### 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 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 1.19.0 release +### 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. -### Season of Docs acceptance +### 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 1.18.0 release +### 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. 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`. -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. -### NumPy receives a grant from the Chan Zuckerberg Initiative +### 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 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. -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, 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. -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 -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - -- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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_. +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 1.18.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 de abril de 2020_. +- NumPy 1.18.2 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 de março de 2020_. +- NumPy 1.18.1 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 de janeiro 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 1.15.0 ([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 fff0da7696b311249f579169bbfd71e4b34a1456 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 00:26:33 +0200 Subject: [PATCH 248/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index d07ddd9776..e43effd2ad 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -38,7 +38,7 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co {{< 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" >}} -## NumPy’s Role +## O papel do NumPy E se houver um problema com os dados? Ou talvez um algoritmo seja muito dependente de uma hipótese em particular. A imagem será alterada drasticamente se um único parâmetro for alterado? From d61b9ecb67ac6cb3e480ae2721970c59390d0d53 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 19:07:11 +0200 Subject: [PATCH 249/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index e43effd2ad..04a8e873d1 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -26,7 +26,7 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co * **Escala computacional** - O EHT representa um desafio imenso em processamento de dados, incluindo flutuações de fase atmosféricas rápidas, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. + O EHT representa um desafio imenso em processamento de dados, incluindo flutuações rápidas de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. * **Muitas informações** From ac043a0b220b339bb46f26b34204017baac18842 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 20:12:08 +0200 Subject: [PATCH 250/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index 04a8e873d1..0920410694 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -20,7 +20,7 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co * **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. -* **Comparando observações com a teoria:** Da teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir sua enorme massa. +* **Comparando observações com a teoria:** Pela teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir a enorme massa do mesmo. ### Desafios From dfe26b90120459f1a55ab1585358e34c5c7a3f34 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 21:39:47 +0200 Subject: [PATCH 251/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index 0920410694..3036337e0b 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -26,7 +26,7 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co * **Escala computacional** - O EHT representa um desafio imenso em processamento de dados, incluindo flutuações rápidas de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. + O EHT representa um desafio imenso em processamento de dados, incluindo rápidas flutuações de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos. * **Muitas informações** @@ -48,11 +48,11 @@ O trabalho desse grupo ilustra o papel do ecossistema científico do Python no a {{< 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**" >}} -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 vetorial usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo. +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**" >}} -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 formatos de arquivos astronômicos padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a[Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. +Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a[Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. ## Resumo From 56ae040ec57ea3671890f54e97a36ad3459f0062 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 22:36:41 +0200 Subject: [PATCH 252/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index 3036337e0b..d84528f550 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -52,11 +52,11 @@ Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para sim {{< 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**" >}} -Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a[Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. +Além do NumPy, muitos outros pacotes como [SciPy](https://www.scipy.org) e [Pandas](https://pandas.io) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. ## Resumo -A estrutura de dados n-dimensional que é a funcionalidade central do NumPy permitiu aos pesquisadores manipular grandes conjuntos de dados, fornecendo a base para a primeira imagem de um buraco negro. Esse momento marcante na ciência fornece evidências visuais impressionantes para a teoria de Einstein. Esta conquista abrange não apenas avanços tecnológicos, mas colaboração científica em escala internacional entre mais de 200 cientistas e alguns dos melhores observatórios de rádio do mundo. Eles usaram algoritmos e técnicas de processamento de dados inovadores que aperfeiçoaram os modelos astronômicos existentes para ajudar a descobrir alguns dos mistérios do universo. +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**" >}} From 097a30bfeebce4a3df3e458d9abc331ff6b37e91 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 22:36:42 +0200 Subject: [PATCH 253/909] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 2a6ecda3bd..c2fbcac9c8 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -14,7 +14,7 @@ sidebar: false Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. O cricket também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. -A Primeira Liga Indiana (*Indian Premier League* - IPL) é uma liga profissional de críquete [Twenty20](https://pt.wikipedia.org/wiki/Twenty20), fundada em 2008. É um dos eventos de críquete mais assistidos no mundo avaliado em [$6,7 bilhões de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) em 2019. +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. @@ -29,7 +29,7 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações ### Objetivos Principais da Análise de Dados -* A análise de dados esportivos é usada não somente em críquete mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. +* A análise de dados esportivos é usada não somente em críquete, mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. * A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. * Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise. @@ -39,7 +39,7 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações * **Limpeza e pré-processamento de dados** - A IPL expandiu o cricket para além do formato de jogo clássico para uma escala muito maior. O número de partidas jogadas a cada temporada em vários formatos tem aumentado, assim como os dados, os algoritmos, tecnologias de análise de dados mais recentes e modelos de simulação. A análise de dados de críquete requer mapeamento de campo, rastreamento do jogador, rastreamento de bola e análise de tiros do jogador, análise de lances do jogador e vários outros aspectos envolvidos em como a bola é lançada, seu ângulo, giro, velocidade e trajetória. Todos esses fatores em conjunto aumentaram a complexidade da limpeza e pré-processamento de dados. + A IPL expandiu o formato de jogo clássico de cricket para uma escala muito maior. O número de partidas jogadas a cada temporada em vários formatos tem aumentado, assim como os dados, os algoritmos, as tecnologias de análise de dados mais recentes e modelos de simulação. A análise de dados de críquete requer mapeamento de campo, rastreamento do jogador, rastreamento de bola e análise de tiros do jogador, análise de lances do jogador e vários outros aspectos envolvidos em como a bola é lançada, seu ângulo, giro, velocidade e trajetória. Todos esses fatores em conjunto aumentaram a complexidade da limpeza e pré-processamento de dados. * **Modelagem Dinâmica** @@ -51,14 +51,14 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações ## Papel da 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. A NumPy foi usada para vários tipos de análise esportiva relacionada a críquete, como: +A análise de dados esportivos é um campo próspero. Muitos pesquisadores e empresas [usam NumPy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) e outros pacotes PyData como Scikit-learn, SciPy, Matplotlib, e Jupyter, além de usar as últimas técnicas de aprendizagem de máquina e IA. O NumPy foi usado para vários tipos de análise esportiva relacionada a críquete, como: -* **Análise Estatística:** Os recursos numéricos da NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em *big data*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. +* **Análise Estatística:** Os recursos numéricos do NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em *big data*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. * **Visualização de dados:** Gráficos e [visualizações](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) fornecem informações úteis sobre as relações entre vários conjuntos de dados. ## Resumo -A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo atrás era principalmente feito com base na "intuição" ou adesão a tradições passadas. A 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. +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**" >}} From 8682ece58d5530e49f001e98a2806f3b90e7957e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 22:36:44 +0200 Subject: [PATCH 254/909] 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 3d54a281e0..1dd02b9f92 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -14,15 +14,15 @@ sidebar: false [DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) é uma toolbox de código aberto que permite que pesquisadores de centenas de instituições em todo o mundo rastreiem o comportamento de animais de laboratório, com muito poucos dados de treinamento, mas com precisão no nível humano. Com a tecnologia DeepLabCut, cientistas podem aprofundar a compreensão científica do controle motor e do comportamento em diversas espécies animais e escalas temporais. -Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas penosas 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 mais precisos. +Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas trabalhosas de monitoramento e marcação, junto com análise de dados baseada em redes neurais profundas, a DeepLabCut garante que estudos científicos envolvendo a observação de animais como primatas, camundongos, peixes, moscas etc. sejam mais rápidos e precisos. {{< figure src="/images/content_images/cs/race-horse.gif" class="fig-center" caption="**Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida**" alt="horserideranim" attr="*(Fonte: Mackenzie Mathis)*">}} -O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinamicamente variáveis é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos necessários e dados de treinamento exigidos. +O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. -A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (*frames*) distintos em vídeos, rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI específica, e a partir disso a arquitetura de estimação de poses baseada em deep learning na DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios como moscas e camundongos até os mais incomuns como [guepardos][cheetah-movement]. +A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (*frames*) distintos em vídeos e rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI especializada. A partir disso, a arquitetura de estimação de poses baseada em deep learning da DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios, como moscas e camundongos, até os mais incomuns, como [guepardos][cheetah-movement]. -A DeepLabCut usa um princípio chamado [aprendizado por transferência (*transfer learning*)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de estimativa de poses humanas de alto desempenho, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote agora foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experiências biológicas em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. +A DeepLabCut usa um princípio chamado [aprendizado por transferência (*transfer learning*)](https://arxiv.org/pdf/1909.11229), o que reduz enormemente a quantidade de dados de treinamento necessários e acelera a convergência do período de treinamento. Dependendo das suas necessidades, usuários podem escolher diferentes arquiteturas de rede que forneçam inferência mais rápida (por exemplo, MobileNetV2), e que também podem ser combinadas com feedback experimental em tempo real. A DeepLabCut usou originalmente os detectores de features de uma arquitetura de alto desempenho para estimativa de poses humanas, chamada [DeeperCut](https://arxiv.org/abs/1605.03170), que inspirou seu nome. O pacote foi significativamente alterado para incluir mais arquiteturas, métodos de ampliação e uma experiência de usuário completa no front-end. Além de possibilitar experimentos biológicos em grande escala, DeepLabCut fornece capacidades ativas de aprendizado para que os usuários possam aumentar o conjunto de treinamento ao longo do tempo, para incluir casos particulares e tornar seu algoritmo de estimativa de poses robusto no seu contexto específico. Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab.org/dlc-modelzoo), que proporciona modelos pré-treinados para várias espécies e condições experimentais, desde a análise facial em primatas até à posição de cães. Isso pode ser executado na nuvem, por exemplo, sem qualquer rotulagem de novos dados ou treinamento em rede neural, e não é necessária nenhuma experiência em programação. From 3dfb46ef3f4b3ce52148d133bbae487bacd426ee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Apr 2021 22:36:45 +0200 Subject: [PATCH 255/909] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index d189210057..6de7efed50 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -14,7 +14,7 @@ sidebar: false Ondas gravitacionais são ondulações no tecido espaço-tempo, gerado por eventos cataclísmicos no universo, como colisão e fusão de dois buracos negros ou a coalescência de estrelas binárias ou supernovas. A observação de ondas gravitacionais pode ajudar não só no estudo da gravidade, mas também no entendimento de alguns dos fenômenos obscuros existentes no universo distante e seu impacto. -O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://www.ligo.caltech.edu) foi projetado para abrir o campo da astrofísica das ondas gravitacionais através da detecção direta de ondas gravitacionais previstas pela Teoria Geral da Relatividade de Einstein. O observatório consiste de dois interferômetros amplamente separados dentro dos Estados Unidos - um em Hanford, Washington e o outro em Livingston, Louisiana — operando em uníssono para detectar ondas gravitacionais. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. +O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://www.ligo.caltech.edu) foi projetado para abrir o campo da astrofísica das ondas gravitacionais através da detecção direta de ondas gravitacionais previstas pela Teoria Geral da Relatividade de Einstein. O observatório consiste de dois interferômetros amplamente separados dentro dos Estados Unidos - um em Hanford, Washington e o outro em Livingston, Louisiana — operando em uníssono para detectar ondas gravitacionais. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a Terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. ### Objetivos @@ -64,6 +64,6 @@ NumPy, o pacote padrão de análise numérica para Python, foi parte do software ## 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 outros pacotes 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. +A detecção de ondas gravitacionais permitiu que pesquisadores descobrissem fenômenos totalmente inesperados ao mesmo tempo em que proporcionaram novas idéias sobre muitos dos fenômenos mais profundos conhecidos na astrofísica. O processamento e a visualização de dados é um passo crucial que ajuda cientistas a obter informações coletadas de observações científicas e a entender os resultados. Os cálculos são complexos e não podem ser compreendidos por humanos a não ser que sejam visualizados usando simulações de computador que são alimentadas com dados e análises reais observados. A NumPy, junto com outras bibliotecas Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo. {{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} From 25075d8df5a2001e6777af3475a39b94e84cdcf8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Apr 2021 03:26:13 +0200 Subject: [PATCH 256/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index c25bd00d54..c9ad39a707 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -57,13 +57,13 @@ aliases: * ميليسا فيبر ميندونسا (Melissa Weber Mendonça) * أنيروده سوبرامانيان (Anirudh Subramanian) -لو كان بلاغك متورط به أحد أعضاء اللجنة أو إذا كانوا يشعرون بأن لديهم تضارب في المصالح يحدهم عن التعامل معه. 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). +لو كان بلاغك متورط به أحد أعضاء اللجنة أو إذا كانوا يشعرون بأن لديهم تضارب في المصالح يحدهم عن التعامل معه. أو إذا شعرت بعدم الارتياح لأي سبب من الأسباب لإبلاغ اللجنة ، فبمكانك الاتصال عوضا عن ذلك بفريق NumFOCUS الأعلى على [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). -### Incident reporting resolution & Code of Conduct enforcement +### تسوية بلاغات الحوادث & نفاذ القواعد السلوكية -_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). +_هذا القسم يلخص أهم النقاط، يمكنك العثور على مزيد من التفاصيل في_ [ قواعد سلوكيات نمباي - كيفية متابعة التقرير](/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). +سوف نقوم بالتحقيق في جميع الشكاوى والرد عليها. وستقوم لجنة القواعد السلوكية واللجنة التوجيهية لنمباي (إذا اشتركت) بحماية هوية المبلِّغ وسوف يتم التعامل مع مضمون الشكاوى على أنها سرية (ما لم يوافق المبلغ على غير ذلك). 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. From a27db9c65aad3c320afefc008b716463f16d808a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Apr 2021 04:27:49 +0200 Subject: [PATCH 257/909] New translations 404.md (Arabic) --- content/ar/404.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/404.md b/content/ar/404.md index da192c53c0..a12adc165c 100644 --- a/content/ar/404.md +++ b/content/ar/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -Oops! You've reached a dead end. +عفواً! لقد وصلت إلى طريق مسدود. -If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. +إذا كنت تعتقد أنه يجب أن يكون شيء ما، فيمكنك [فتح مشكلة](https://github.com/numpy/numpy.org/issues) على GitHub. From f8a835e9dc8f9abef755ee22da0bb5de1a101536 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Apr 2021 04:27:50 +0200 Subject: [PATCH 258/909] New translations about.md (Arabic) --- content/ar/about.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 1779f7531f..0171632a52 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -1,18 +1,18 @@ --- -title: About Us +title: من نحن sidebar: false --- -_Some information about the NumPy project and community_ +_بعض المعلومات حول مشروع ومجتمع نمباي_ -NumPy is an open source project aiming to enable 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, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt). +نمباي هو مشروع مفتوح المصدر يهدف إلي إتاحة الحوسبة الرقمية باستخدام لغة برمجة بايثون. وقد أنشئت في عام 2005، استنادا علي العمل المبكر للمكتبتان Numeric و Numarray. ستظل نمباي دائماُ مائة في المائة برمجية مفتوحة المصدر، مجانية للجميع وتصدر بموجب الشروط الليبرالية [لرخصة BSD المعدلة](https://github.com/numpy/numpy/blob/master/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). +وقد تم تطوير نمباي في العلن على GitHub ومن خلال توافق آراء مجتمع نمباي ونطاق أوسع لمجتمع بايثون العلمي. لمزيد من المعلومات حول نهج الإدارة، يرجى الاطلاع على [الوثيقة الإدارية](https://www.numpy.org/devdocs/dev/governance/index.html) الخاصة بنا. -## Steering Council +## المجلس التوجيهي -The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. The NumPy Steering Council currently consists of the following members (in alphabetical order): +The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. ويتألف المجلس التوجيهي المعني لنمباي حاليا علي الأعضاء التالية (بالترتيب الأبجدي): - Sebastian Berg - Jaime Fernández del Río From c48288bfcf59b54f8422376802c422010db67f63 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Apr 2021 04:27:51 +0200 Subject: [PATCH 259/909] New translations code-of-conduct.md (Arabic) --- content/ar/code-of-conduct.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ar/code-of-conduct.md b/content/ar/code-of-conduct.md index c9ad39a707..4811c7c959 100644 --- a/content/ar/code-of-conduct.md +++ b/content/ar/code-of-conduct.md @@ -65,19 +65,19 @@ _هذا القسم يلخص أهم النقاط، يمكنك العثور على سوف نقوم بالتحقيق في جميع الشكاوى والرد عليها. وستقوم لجنة القواعد السلوكية واللجنة التوجيهية لنمباي (إذا اشتركت) بحماية هوية المبلِّغ وسوف يتم التعامل مع مضمون الشكاوى على أنها سرية (ما لم يوافق المبلغ على غير ذلك). -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. +في حالة حدوث إخلالات خطيرة وواضحة، مثل التهديد أو العنف الشخصي أو التحيز جنسياً أو عنصرياً، سنقوم على الفور بفصل المقدم عن قنوات الاتصال الخاصة بنمباي؛ يرجى الاطلاع على الدليل للحصول على التفاصيل. -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: +وفي الحالات التي لا تنطوي على انتهاكات خطيرة وواضحة لقواعد السلوك هذه، تكون عملية التصرف بشأن أي تقرير يرد عن انتهاك القواعد السلوكية علي ما يلي: -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. الإقرار بتلقي التقرير، +2. مناقشات/ملاحظات معقولة، +3. الوساطة (إذا لم تساعد ردود الفعل، وفقط إذا وافق كل من المبلّغ والمبلّغ على ذلك)، +4. • الإنفاذ من خلال قرار شفاف (انظر [القرارات](/report-handling-manual#resolutions))الصادر عن لجنة قواعد السلوك. -The Committee will respond to any report as soon as possible, and at most within 72 hours. +واللجنة سترد على أي تقرير في أقرب وقت ممكن، وفي الأغلب 72 ساعة على الأكثر. -### Endnotes +### تعليق ختامي -We are thankful to the groups behind the following documents, from which we drew content and inspiration: +نحن ممتنون للمجموعات التي تقف وراء الوثائق التالية التي استخلصنا منها المضمون والإلهام: -- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) +- [القواعد السلوكية لسكابي](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) From 2181f11ae7cdfe10466e3000e902b53b12a34cd6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 16:00:48 +0200 Subject: [PATCH 260/909] 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 5794d5626f..9f8734ed38 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -3,7 +3,7 @@ title: コミュニティ sidebar: false --- -Numpy は 常に多様な[コントリビュータ](/gallery/team.html) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/code-of-conduct) をご覧ください。 +NumPy は非常に多様な[コントリビュータ](/gallery/team.html) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/code-of-conduct) をご覧ください。 私たちは、NumPyコミュニティ内で学んだり、知識を共有したり、他の人と交流するためのいくつかのコミュニケーション方法を提供しています。 From 6d105134be2aff74e8275f4fe5235fe3e0217bd0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:02:33 +0200 Subject: [PATCH 261/909] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/zh/learn.md b/content/zh/learn.md index 4611d3a402..ea35371573 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -15,6 +15,7 @@ There's a ton of information about NumPy out there. If you are new, we'd strongl **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) * [Machine Learning Plus - Introduction to ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) From 1b8ef1346f8ccab0419fc3f3f28d4c11612325ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:02:40 +0200 Subject: [PATCH 262/909] New translations about.md (Chinese Simplified) --- content/zh/about.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/content/zh/about.md b/content/zh/about.md index 0632e61ae4..5dfd4e796f 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -15,16 +15,13 @@ NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 指导委员会的成员们通过与 Numpy 社区合作并提供服务的形式来确保项目的长期发展，包括技术层面和社区层面。 Numpy 指导委员会目前由下列成员组成（按字母顺序排列）： - Sebastian Berg -- Jaime Fernández del Río - Ralf Gommers -- Allan Haldane - Charles Harris - Stephan Hoyer +- Melissa Weber Mendonça +- Inessa Pawson - Matti Picus -- Nathaniel Smith -- Julian Taylor -- Pauli Virtanen -- Stéfan van der Walt +- Stéfan van der Walt - Eric Wieser 荣誉会员： @@ -32,6 +29,12 @@ NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 - Travis Oliphant（项目创始人，2005-2012年） - Alex Griffing（2015-2017年） - Marten van Kerkwijk （2017-2019年） +- Allan Haldane (2015-2021) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Pauli Virtanen (2008-2021) +- Jaime Fernández del Río (2014-2021) + ## 团队 From 0928b31840ad569aed418aa2df900a72d4d3b2a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:02:51 +0200 Subject: [PATCH 263/909] New translations learn.md (Korean) --- content/ko/learn.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ko/learn.md b/content/ko/learn.md index 04e49d32d6..2441c963be 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -15,6 +15,7 @@ For the **official NumPy documentation** visit [numpy.org/doc/stable](https://nu **튜토리얼** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) * [Machine Learning Plus - Introduction to ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) From 0ba550f4674e11045233ef35dd81663d620c663a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:02:58 +0200 Subject: [PATCH 264/909] New translations about.md (Korean) --- content/ko/about.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index 871d435f9a..1cfe839c79 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -15,15 +15,12 @@ NumPy는 광범위한 Scientific Python 커뮤니티의 협의를 통해 GitHub NumPy 운영 위원회의 역할은 더 광범위한 NumPy 커뮤니티와 협력하고 서비스를 통해서 기술적으로나 커뮤니티로서 프로젝트의 장기적인 안녕을 보장하는 것입니다. NumPy 운영 위원회는 현재 다음과 같은 회원들로 구성되어 있습니다. (알파벳 순서) - Sebastian Berg -- Jaime Fernández del Río - Ralf Gommers -- Allan Haldane - Charles Harris - Stephan Hoyer +- Melissa Weber Mendonça +- Inessa Pawson - Matti Picus -- Nathaniel Smith -- Julian Taylor -- Pauli Virtanen - Stéfan van der Walt - Eric Wieser @@ -32,6 +29,12 @@ NumPy 운영 위원회의 역할은 더 광범위한 NumPy 커뮤니티와 협 - Travis Oliphant (project founder, 2005-2012) - Alex Griffing (2015-2017) - Marten van Kerkwijk (2017-2019) +- Allan Haldane (2015-2021) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Pauli Virtanen (2008-2021) +- Jaime Fernández del Río (2014-2021) + ## 팀 From 9d16d9f019b8733a56879f53564ced8eff0a889a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:03:11 +0200 Subject: [PATCH 265/909] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index 6d8d5b6a24..ee3d572e04 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -15,14 +15,15 @@ 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) -* [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 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) * [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) +* [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 (Guia de Usuário NumPy)](https://numpy.org/devdocs) +* [NumPy User Guide](https://numpy.org/devdocs) **Livros** From ead18a3d9d6e50acb269575ccb6eb57271c23588 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:03:18 +0200 Subject: [PATCH 266/909] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index 163d4eab78..a90423141f 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -15,15 +15,12 @@ O NumPy é desenvolvido no GitHub, através do consenso da comunidade NumPy e de O papel do Conselho Diretor do NumPy consiste em assegurar o bem-estar a longo prazo do projeto, tanto nos aspectos técnicos quanto na comunidade. Isso é feito através do trabalho com e para a comunidade NumPy em geral. O Conselho Diretor do NumPy atualmente consiste dos seguintes membros (em ordem alfabética): - Sebastian Berg -- Jaime Fernández del Río - Ralf Gommers -- Allan Haldane - Charles Harris - Stephan Hoyer +- Melissa Weber Mendonça +- Inessa Pawson - Matti Picus -- Nathaniel Smith -- Julian Taylor -- Pauli Virtanen - Stéfan van der Walt - Eric Wieser @@ -32,6 +29,12 @@ Membros Eméritos: - Travis Oliphant (fundador do projeto, 2005-2012) - Alex Griffing (2015-2017) - Marten van Kerkwijk (2017-2019) +- Allan Haldane (2015-2021) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Pauli Virtanen (2008-2021) +- Jaime Fernández del Río (2014-2021) + ## Times From 17987868f431f2e74a80aa790fa1580f541f79e5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:03:28 +0200 Subject: [PATCH 267/909] New translations learn.md (Japanese) --- content/ja/learn.md | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 665f16893c..ccbc6bfdb1 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -15,14 +15,15 @@ NumPyについての資料は多数存在しています。 初心者の方に **チュートリアル** * [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) -* [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) +* [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) +* [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 *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) +* [NumPy User Guide](https://numpy.org/devdocs) **書籍** From 27d0cfd0115fac70be4d600b0a1371e7b4889998 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:03:31 +0200 Subject: [PATCH 268/909] New translations about.md (Arabic) --- content/ar/about.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 0171632a52..29d4a19ad9 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -15,15 +15,12 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. ويتألف المجلس التوجيهي المعني لنمباي حاليا علي الأعضاء التالية (بالترتيب الأبجدي): - Sebastian Berg -- Jaime Fernández del Río - Ralf Gommers -- Allan Haldane - Charles Harris - Stephan Hoyer +- Melissa Weber Mendonça +- Inessa Pawson - Matti Picus -- Nathaniel Smith -- Julian Taylor -- Pauli Virtanen - Stéfan van der Walt - Eric Wieser @@ -32,6 +29,12 @@ Emeritus: - Travis Oliphant (project founder, 2005-2012) - Alex Griffing (2015-2017) - Marten van Kerkwijk (2017-2019) +- Allan Haldane (2015-2021) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Pauli Virtanen (2008-2021) +- Jaime Fernández del Río (2014-2021) + ## Teams From c896bdb88758c766f5bae693acbe53043bf7d225 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:03:42 +0200 Subject: [PATCH 269/909] New translations learn.md (Spanish) --- content/es/learn.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/es/learn.md b/content/es/learn.md index 4611d3a402..ea35371573 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -15,6 +15,7 @@ There's a ton of information about NumPy out there. If you are new, we'd strongl **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) * [Machine Learning Plus - Introduction to ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) From b459b3f43f291545b4a3d3459283ef5c6cf46686 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:03:47 +0200 Subject: [PATCH 270/909] New translations about.md (Spanish) --- content/es/about.md | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 1779f7531f..1de6728185 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -15,15 +15,12 @@ NumPy is developed in the open on GitHub, through the consensus of the NumPy and The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. The NumPy Steering Council currently consists of the following members (in alphabetical order): - Sebastian Berg -- Jaime Fernández del Río - Ralf Gommers -- Allan Haldane - Charles Harris - Stephan Hoyer +- Melissa Weber Mendonça +- Inessa Pawson - Matti Picus -- Nathaniel Smith -- Julian Taylor -- Pauli Virtanen - Stéfan van der Walt - Eric Wieser @@ -32,6 +29,12 @@ Emeritus: - Travis Oliphant (project founder, 2005-2012) - Alex Griffing (2015-2017) - Marten van Kerkwijk (2017-2019) +- Allan Haldane (2015-2021) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Pauli Virtanen (2008-2021) +- Jaime Fernández del Río (2014-2021) + ## Teams From 08e0f187e5e0e47bb0f13b9966726b1ffa5f302f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:03:54 +0200 Subject: [PATCH 271/909] New translations about.md (Japanese) --- content/ja/about.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 530f16d9cb..a9c9232830 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -15,16 +15,13 @@ NumPy は 、NumPyコミュニティやより広範な科学計算用Python コ 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): - Sebastian Berg -- Jaime Fernández del Río - Ralf Gommers -- Allan Haldane - Charles Harris - Stephan Hoyer +- Melissa Weber Mendonça +- Inessa Pawson - Matti Picus -- Nathaniel Smith -- Julian Taylor -- Pauli Virtanen -- Stéfan van der Walt +- Stéfan van der Walt - Eric Wieser 終身名誉委員 @@ -32,6 +29,12 @@ NumPy運営委員会の役割は、NumPyのコミュニティと協力しサポ - Travis Oliphant (プロジェクト創設者, 2005-2012) - Alex Griffing (2015-2017) - Marten van Kerkwijk (2017-2019) +- Allan Haldane (2015-2021) +- Nathaniel Smith (2012-2021) +- Julian Taylor (2013-2021) +- Pauli Virtanen (2008-2021) +- Jaime Fernández del Río (2014-2021) + ## チーム From 9e424ef7a7600ae97cb420c4e9b3c020537f8a43 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:04:02 +0200 Subject: [PATCH 272/909] 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 9f8734ed38..b6a1480c45 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -3,7 +3,7 @@ title: コミュニティ sidebar: false --- -NumPy は非常に多様な[コントリビュータ](/gallery/team.html) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/code-of-conduct) をご覧ください。 +NumPy は非常に多様な[コントリビュータ](/gallery/team.html) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 [NumPy 行動規範](/code-of-conduct) をぜひ参照してください。コミュニティの繁栄につながるようなかたちで、人々と交流する方法について書いてあります。 私たちは、NumPyコミュニティ内で学んだり、知識を共有したり、他の人と交流するためのいくつかのコミュニケーション方法を提供しています。 From b7812920f51201bb9f1cc8ce662c1a454d19e011 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 17:04:16 +0200 Subject: [PATCH 273/909] New translations learn.md (Arabic) --- content/ar/learn.md | 1 + 1 file changed, 1 insertion(+) diff --git a/content/ar/learn.md b/content/ar/learn.md index 4611d3a402..ea35371573 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -15,6 +15,7 @@ There's a ton of information about NumPy out there. If you are new, we'd strongl **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) +* [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) * [Machine Learning Plus - Introduction to ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) From 1517109944ffea73e6f013524e5b46c9bb19cc21 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Apr 2021 18:01:10 +0200 Subject: [PATCH 274/909] New translations community.md (Japanese) --- content/ja/community.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/community.md b/content/ja/community.md index b6a1480c45..8e1052ff92 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -15,7 +15,7 @@ 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 ### [NumPyメーリングリスト：](https://mail.python.org/mailman/listinfo/numpy-discussion) -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は、NumPyへの新機能追加するなど、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ロードマップの変更や、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](http://numpy-discussion.10968.n7.nabble.com/) にあります。 @@ -38,14 +38,14 @@ SlackはNumpyに_ 貢献するための質問をする_、リアルタイムの ## 勉強会とミートアップ -NumPyや、データサイエンス、科学技術計算などのより広いエコシステムのためのPythonパッケージついて、もっと学ぶためのローカルミートアップや勉強会を見つけたい場合、 [PyData ミートアップ](https://www.meetup.com/pro/pydata/) (150人以上のミートアップ、10万人以上のメンバーをまとめたもの) を調べてみることをお勧めします。 +NumPy、データサイエンス、科学技術計算などのより広いエコシステムのためのPythonパッケージについてもっと学ぶために、ローカルのミートアップや勉強会を見つけたい場合、 [PyData ミートアップス](https://www.meetup.com/pro/pydata/) (150人以上のミートアップ、10万人以上のメンバーをまとめたもの) を調べてみるのが良いでしょう。 加えて、NumPy では開発チームと参加に興味があるコントリビュータのために、対面でのスプリントを時折開催しています。 この開発スプリントは通常数ヶ月に一度に開催されており、 [ メーリングリスト ](https://mail. python. org/mailman/listinfo/numpy-discussion) と [ Twitter ](https://twitter. com/numpy_team) で開催連絡されます。 ## カンファレンス -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せん。 NumPy の管理者や、コントリビュータ、ユーザーに最も人気があったカンファレンスは、SciPy および PyDataのカンファレンスです。 +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せん。 伝統的には、SciPy および PyDataのカンファレンスシリーズが、NumPy のメンテナ・コントリビュータ・ユーザーに最も人気がありました。 - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) @@ -59,7 +59,7 @@ 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 ## NumPy コミュニティに参加する -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。 プログラマーじゃないから参加できない？ そんなことはありません！ Numpy に貢献する様々な方法があります。 +プロジェクトを成功させるために、NumPyはあなたの専門知識とプロジェクトに関する熱意を必要としています。 プログラマーではないから参加できない？ そんなことはありません！ NumPyに貢献するには、様々な方法があります。 -もし、Numpyに貢献したい場合は、 [コントリビュート](/contribute) ページをご覧いただくことをお勧めします。 +NumPyに貢献したい場合は、 [コントリビュート](/contribute) ページをご覧いただくことをお勧めします。 From a3009cc31da12fcc9de94c77d58f435fe049f7de Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Apr 2021 21:17:24 +0200 Subject: [PATCH 275/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 152 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 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..64c90d9a8b --- /dev/null +++ b/content/zh/config.yaml @@ -0,0 +1,152 @@ +--- +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logos/numpy.svg + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: Get started + #Where the main hero button links to + buttonlink: "/install" + #Hero image (from static/images/___) + image: logos/numpy.svg + #Customizable navbar. For a dropdown, add a "sublinks" list. + news: + title: NumPy v1.20.0 + content: Type annotation support - Performance improvements through multi-platform SIMD + url: /news + shell: + title: placeholder + 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: 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. + - + title: Open source + text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + 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: Contribute + url: /contribute +footer: + logo: numpy.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + 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: 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 fd2174ff71f6259c83634cd826894c61c9fc6ab6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Apr 2021 21:17:25 +0200 Subject: [PATCH 276/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 152 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 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..64c90d9a8b --- /dev/null +++ b/content/ko/config.yaml @@ -0,0 +1,152 @@ +--- +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logos/numpy.svg + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: Get started + #Where the main hero button links to + buttonlink: "/install" + #Hero image (from static/images/___) + image: logos/numpy.svg + #Customizable navbar. For a dropdown, add a "sublinks" list. + news: + title: NumPy v1.20.0 + content: Type annotation support - Performance improvements through multi-platform SIMD + url: /news + shell: + title: placeholder + 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: 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. + - + title: Open source + text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + 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: Contribute + url: /contribute +footer: + logo: numpy.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + 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: 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 3ca361ec6f745a9467bb89fb751063c330d410a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Apr 2021 21:17:26 +0200 Subject: [PATCH 277/909] New translations config.yaml (Japanese) --- content/ja/config.yaml | 152 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 content/ja/config.yaml diff --git a/content/ja/config.yaml b/content/ja/config.yaml new file mode 100644 index 0000000000..64c90d9a8b --- /dev/null +++ b/content/ja/config.yaml @@ -0,0 +1,152 @@ +--- +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logos/numpy.svg + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: Get started + #Where the main hero button links to + buttonlink: "/install" + #Hero image (from static/images/___) + image: logos/numpy.svg + #Customizable navbar. For a dropdown, add a "sublinks" list. + news: + title: NumPy v1.20.0 + content: Type annotation support - Performance improvements through multi-platform SIMD + url: /news + shell: + title: placeholder + 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: 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. + - + title: Open source + text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + 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: Contribute + url: /contribute +footer: + logo: numpy.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + 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: 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 3ca277e3612533d4cf119a350db7330014772eab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Apr 2021 21:17:27 +0200 Subject: [PATCH 278/909] New translations config.yaml (Arabic) --- content/ar/config.yaml | 152 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 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..64c90d9a8b --- /dev/null +++ b/content/ar/config.yaml @@ -0,0 +1,152 @@ +--- +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logos/numpy.svg + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: Get started + #Where the main hero button links to + buttonlink: "/install" + #Hero image (from static/images/___) + image: logos/numpy.svg + #Customizable navbar. For a dropdown, add a "sublinks" list. + news: + title: NumPy v1.20.0 + content: Type annotation support - Performance improvements through multi-platform SIMD + url: /news + shell: + title: placeholder + 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: 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. + - + title: Open source + text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + 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: Contribute + url: /contribute +footer: + logo: numpy.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + 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: 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 9405c3eccae3419d4435c5aa6746e4034b3cda52 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Apr 2021 21:17:28 +0200 Subject: [PATCH 279/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 152 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 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..64c90d9a8b --- /dev/null +++ b/content/es/config.yaml @@ -0,0 +1,152 @@ +--- +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logos/numpy.svg + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: Get started + #Where the main hero button links to + buttonlink: "/install" + #Hero image (from static/images/___) + image: logos/numpy.svg + #Customizable navbar. For a dropdown, add a "sublinks" list. + news: + title: NumPy v1.20.0 + content: Type annotation support - Performance improvements through multi-platform SIMD + url: /news + shell: + title: placeholder + 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: 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. + - + title: Open source + text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + 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: Contribute + url: /contribute +footer: + logo: numpy.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + 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: 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 876a5872acca4c8919dbd3f1113dc3a7f9ac6df5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Apr 2021 21:17:52 +0200 Subject: [PATCH 280/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 152 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 152 insertions(+) create mode 100644 content/pt/config.yaml diff --git a/content/pt/config.yaml b/content/pt/config.yaml new file mode 100644 index 0000000000..64c90d9a8b --- /dev/null +++ b/content/pt/config.yaml @@ -0,0 +1,152 @@ +--- +languageName: English +params: + description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + navbarlogo: + image: logos/numpy.svg + link: / + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: The fundamental package for scientific computing with Python + #Button text + buttontext: Get started + #Where the main hero button links to + buttonlink: "/install" + #Hero image (from static/images/___) + image: logos/numpy.svg + #Customizable navbar. For a dropdown, add a "sublinks" list. + news: + title: NumPy v1.20.0 + content: Type annotation support - Performance improvements through multi-platform SIMD + url: /news + shell: + title: placeholder + 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: 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. + - + title: Open source + text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + 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: Contribute + url: /contribute +footer: + logo: numpy.svg + socialmediatitle: "" + socialmedia: + - + link: https://github.com/numpy/numpy + icon: github + - + 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: 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 2cbc2137955a2335f80688764d369417a2d7185f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Apr 2021 13:49:52 +0200 Subject: [PATCH 281/909] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 299d80cf2d..cd186ad23c 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -12,7 +12,7 @@ sidebar: false ## 地球の大きさの望遠鏡。 -[ 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 From 8c8b660f310515e3ba081bdda6ef75732f35f28f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Apr 2021 21:13:26 +0200 Subject: [PATCH 282/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index d84528f550..b983df6265 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -3,7 +3,7 @@ title: "Estudo de Caso: A Primeira Imagem de um Buraco Negro" sidebar: false --- -{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" attr="*(Créditos: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Buraco Negro M87**" alt="black hole image" attr="*(Créditos: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}

    Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver.

    From d57924edff33e4f276867ef9fbb96a8e3ff4d3f8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Apr 2021 22:13:57 +0200 Subject: [PATCH 283/909] New translations blackhole-image.md (Portuguese, Brazilian) --- content/pt/case-studies/blackhole-image.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index b983df6265..209b669074 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -3,7 +3,7 @@ title: "Estudo de Caso: A Primeira Imagem de um Buraco Negro" sidebar: false --- -{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Buraco Negro M87**" alt="black hole image" attr="*(Créditos: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}} +{{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Buraco Negro M87**" alt="imagem de um buraco negro" attr="*(Créditos: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}

    Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver.

    @@ -16,7 +16,7 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co ### Principais Objetivos e Resultados -* **Uma nova visão do universo:**A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. +* **Uma nova visão do universo:** A imagem inovadora do EHT foi publicada 100 anos após [o experimento de Sir Arthur Eddington][eddington] ter produzido as primeiras evidências observacionais apoiando a teoria da relatividade geral de Einstein. * **O Buraco Negro:** o EHT foi treinado em um buraco negro supermassivo a aproximadamente 55 milhões de anos-luz da Terra, localizado no centro do galáxia Messier 87 (M87) no aglomerado de Virgem. Sua massa é equivalente a 6,5 bilhões de vezes a do Sol. Ele vem sendo estudado [há mais de 100 anos](https://www.jpl.nasa.gov/news/news.php?feature=7385), mas um buraco negro nunca havia sido observado visualmente antes. @@ -36,7 +36,7 @@ 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" 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" class="csfigcaption" caption="**Etapas de Processamento de Dados do EHT**" alt="pipeline de dados" align="middle" attr="(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}} ## O papel do NumPy @@ -46,11 +46,11 @@ 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 src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="o papel do NumPy" caption="**O papel do NumPy na criação da primeira imagem de um Buraco Negro**" >}} 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" class="fig-center" alt="mapa de dependências do ehtim com o numpy em realce" caption="**Diagrama de dependência de software do pacote ehtim evidenciando o NumPy**" >}} 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 +58,7 @@ 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" class="fig-center" alt="numpy benefits" caption="**Funcionalidades-chave do NumPy utilizadas**" >}} +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="funcionalidades do numpy" caption="**Funcionalidades-chave do NumPy utilizadas**" >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 6e3e664296ba3d7412c3f77e49634777401849c5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Apr 2021 22:14:00 +0200 Subject: [PATCH 284/909] 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 6de7efed50..7b55f4a3ef 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -3,7 +3,7 @@ title: "Estudo de Caso: Descoberta de Ondas Gravitacionais" sidebar: false --- -{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**Ondas gravitacionais**" alt="binary coalesce black hole generating gravitational waves" attr="*(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)*" attrlink="https://youtu.be/Zt8Z_uzG71o" >}} +{{< figure src="/images/content_images/cs/gw_sxs_image.png" class="fig-center" caption="**Ondas gravitacionais**" alt="duas esferas orbitando a si mesmas, gerando ondas gravitacionais" 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.

    @@ -39,7 +39,7 @@ 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="amplitude da deformação das ondas gravitacionais" 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" >}} ## O papel da NumPy na detecção de ondas gravitacionais @@ -56,11 +56,11 @@ 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="gráfico de dependências do gwpy com o NumPy em realce" caption="**Gráfico de dependências mostrando como o pacote GwPy depende do 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="gráfico de dependências do PyCBC com NumPy em realce" caption="**Gráfico de dependências mostrando como o pacote PyCBC depende do NumPy**" >}} ## Resumo From f2befcd627a15f5d93a1d3678d55f81916078e39 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Apr 2021 23:11:34 +0200 Subject: [PATCH 285/909] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index c2fbcac9c8..216fcebd26 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -3,7 +3,7 @@ title: "Estudo de Caso: Análise de Críquete, a revolução!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Créditos de imagem: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}}

    Você não joga para a torcida, joga para o país.

    @@ -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 +## 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: @@ -61,4 +61,4 @@ 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" class="fig-center" alt="Diagrama mostrando os benefícios de usar o NumPy para análise de críquete" caption="**Recursos principais do NumPy utilizados**" >}} From 86f1e9650e2c088508fb125039d90816f6d5a155 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Apr 2021 23:11:36 +0200 Subject: [PATCH 286/909] 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..6b66188c5b 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -3,7 +3,7 @@ title: "Estudo de Caso: Estimativa de Pose 3D com DeepLabCut" sidebar: false --- -{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Análise de movimentos de mãos de camundongos usando DeepLapCut**" alt="micehandanim" attr="*(Fonte: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}} +{{< figure src="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**Análise de movimentos de mãos de camundongos usando DeepLapCut**" alt="animação de mãos de camundongos" attr="*(Fonte: www.deeplabcut.org )*" attrlink="http://www.mousemotorlab.org/deeplabcut">}}

    Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a análise automática de vídeos de comportamento animal usando Deep Learning.

    @@ -16,7 +16,7 @@ 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" class="fig-center" caption="**Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida**" alt="animação de um jóquei em um cavalo correndo" attr="*(Fonte: Mackenzie Mathis)*">}} O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. @@ -45,7 +45,7 @@ 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 src="/images/content_images/cs/deeplabcut-toolkit-steps.png" class="csfigcaption" caption="**Passos na estimação de poses com DeepLabCut**" alt="diagrama de passos na estimação de poses" align="middle" attr="(Fonte: DeepLabCut)" attrlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" >}} ### Desafios @@ -61,7 +61,7 @@ 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/pose-estimation.png" class="csfigcaption" caption="**Estimação de poses e complexidade**" alt="challengesfig" align="middle" attr="(Fonte: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} +{{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Estimação de poses e complexidade**" alt="6 imagens com diferentes exemplos de captura de movimento" align="middle" attr="(Fonte: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} ## O papel da NumPy nos desafios da estimação de poses From 9674b5373ee3ebf565108bdfbb4d8899ac024ba3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Apr 2021 23:11:37 +0200 Subject: [PATCH 287/909] 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 7b55f4a3ef..f64b463d0f 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -29,7 +29,7 @@ O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://w * **Computação** - As ondas gravitacionais são difíceis de detectar pois produzem um efeito muito pequeno e têm uma pequena interação com a matéria. Processar e analisar todos os dados do LIGO requer uma vasta infraestrutura de computação. Depois de cuidar do ruído, que é bilhões de vezes maior que o sinal, ainda há equações de relatividade complexas e enormes quantidades de dados que apresentam um desafio computacional: [O(10^7) horas de CPU necessárias para análises de fusão binária](https://youtu.be/7mcHknWWzNI) espalhado em 6 clusters LIGO dedicados. + As ondas gravitacionais são difíceis de detectar pois produzem um efeito muito pequeno e têm uma pequena interação com a matéria. Processar e analisar todos os dados do LIGO requer uma vasta infraestrutura de computação. Depois de cuidar do ruído, que é bilhões de vezes maior que o sinal, ainda há equações de relatividade complexas e enormes quantidades de dados que apresentam um desafio computacional: [O(10^7) horas de CPU necessárias para análises de fusão binária](https://youtu.be/7mcHknWWzNI) espalhado em 6 clusters dedicados ao LIGO. * **Sobrecarga de dados** @@ -45,7 +45,7 @@ O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://w Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. -NumPy, o pacote padrão de análise numérica para Python, foi parte do software utilizado para várias tarefas executadas durante o projeto de detecção de ondas gravitacionais no LIGO. A NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: +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. O NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: * [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) * Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro @@ -64,6 +64,6 @@ NumPy, o pacote padrão de análise numérica para Python, foi parte do software ## 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. +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. O NumPy, junto com outras bibliotecas Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo. -{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="funcionalidades do numpy" caption="**Recursos chave do NumPy utilizados**" >}} From c1fd1765ee212d8a22efbebe3ca45fa60e8376c1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Apr 2021 00:08:34 +0200 Subject: [PATCH 288/909] 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 6b66188c5b..1c02e9b208 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -77,7 +77,7 @@ 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" 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" class="fig-center" caption="**Fluxo de dados DeepLabCut**" alt="diagrama com o fluxo de dados do deeplabcut" attr="*(Fonte: Mackenzie Mathis)*" attrlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962">}} ## Resumo From 6d8e65d81ac01107d7764ddb73cd6706802f0f1a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Apr 2021 21:02:28 +0200 Subject: [PATCH 289/909] New translations citing-numpy.md (Chinese Simplified) --- content/zh/citing-numpy.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/citing-numpy.md b/content/zh/citing-numpy.md index cbfa873b9f..1731e941cf 100644 --- a/content/zh/citing-numpy.md +++ b/content/zh/citing-numpy.md @@ -12,14 +12,14 @@ _BibTeX 格式：_ ``` @Article{ harris2020array, title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. + 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 + 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}, From 39da0fbdfed56eedac9d47888afd00957a592d9a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Apr 2021 21:02:50 +0200 Subject: [PATCH 290/909] 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 cf20ae59cf..9aa943e53e 100644 --- a/content/ko/citing-numpy.md +++ b/content/ko/citing-numpy.md @@ -12,14 +12,14 @@ _In BibTeX format:_ ``` @Article{ harris2020array, title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. + 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 + 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}, From f78b68211cb326e0eda005ff0da15764f801e6c5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Apr 2021 21:03:10 +0200 Subject: [PATCH 291/909] New translations citing-numpy.md (Portuguese, Brazilian) --- content/pt/citing-numpy.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/citing-numpy.md b/content/pt/citing-numpy.md index d390e925e9..1dc8a215f0 100644 --- a/content/pt/citing-numpy.md +++ b/content/pt/citing-numpy.md @@ -12,14 +12,14 @@ _Em formato BibTeX:_ ``` @Article{ harris2020array, title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. + 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 + 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}, From bc1ca750dd9511739c6e328f025f41e22ee4213f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Apr 2021 21:03:42 +0200 Subject: [PATCH 292/909] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md index cf20ae59cf..9aa943e53e 100644 --- a/content/es/citing-numpy.md +++ b/content/es/citing-numpy.md @@ -12,14 +12,14 @@ _In BibTeX format:_ ``` @Article{ harris2020array, title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. + 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 + 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}, From b9061b73f37ed61acde76cf367456f4292810b44 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Apr 2021 21:03:50 +0200 Subject: [PATCH 293/909] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md index cf20ae59cf..9aa943e53e 100644 --- a/content/ar/citing-numpy.md +++ b/content/ar/citing-numpy.md @@ -12,14 +12,14 @@ _In BibTeX format:_ ``` @Article{ harris2020array, title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. + 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 + 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}, From aa52bef8fd81d80762e05edd9430a825a5b43bc9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Apr 2021 21:04:03 +0200 Subject: [PATCH 294/909] New translations citing-numpy.md (Japanese) --- content/ja/citing-numpy.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ja/citing-numpy.md b/content/ja/citing-numpy.md index 587890c75d..752d29d800 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -10,16 +10,16 @@ sidebar: false _BibTeX形式:_ ``` -@Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{'{e}}fan J. +@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 + 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}, From fa9a87b1a76f85f215b3deb5ece92d5b7f240a71 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Apr 2021 00:45:32 +0200 Subject: [PATCH 295/909] New translations 404.md (Spanish) --- content/es/404.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/404.md b/content/es/404.md index e0e7272719..b38d73b758 100644 --- a/content/es/404.md +++ b/content/es/404.md @@ -3,6 +3,6 @@ title: 404 sidebar: false --- -¡Ups! Has llegado a un callejón sin salida. +¡Oh, oh! Has llegado a un callejón sin salida. Si crees que algo debería estar aquí, puedes [reportar este problema](https://github.com/numpy/numpy.org/issues) en GitHub. From 7c4b1665cbb4777de28cbc3a2e87af8a6804811c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Apr 2021 00:45:33 +0200 Subject: [PATCH 296/909] New translations about.md (Spanish) --- content/es/about.md | 50 ++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 1de6728185..34642cf247 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -1,18 +1,18 @@ --- -title: About Us +title: Quiénes somos sidebar: false --- -_Some information about the NumPy project and community_ +_Información sobre el proyecto y la comunidad NumPy_ -NumPy is an open source project aiming to enable 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, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt). +NumPy es un proyecto de código abierto cuyo objetivo es facilitar la computación numérica con Python. Se creó en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy siempre será un software 100% de código abierto, de uso libre para todos y liberado bajo los términos liberales de la [licencia BSD modificada](https://github.com/numpy/numpy/blob/master/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 la comunidad de NumPy y de la comunidad científica de Python en general. Para más información sobre nuestro enfoque de gobernanza, consulta nuestro [Documento de Gobernanza](https://www.numpy.org/devdocs/dev/governance/index.html). -## Steering Council +## Consejo Directivo -The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. The NumPy Steering Council currently consists of the following members (in alphabetical order): +El papel del Consejo Directivo de NumPy es 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 @@ -24,9 +24,9 @@ The role of the NumPy Steering Council is to ensure, through working with and se - Stéfan van der Walt - Eric Wieser -Emeritus: +Eméritos: -- Travis Oliphant (project founder, 2005-2012) +- Travis Oliphant (fundador del proyecto, 2005-2012) - Alex Griffing (2015-2017) - Marten van Kerkwijk (2017-2019) - Allan Haldane (2015-2021) @@ -36,37 +36,37 @@ Emeritus: - Jaime Fernández del Río (2014-2021) -## Teams +## Equipos -The NumPy project is growing; we have teams for +El proyecto NumPy está creciendo; tenemos equipos para -- code -- documentation -- website -- triage -- funding and grants +- código +- documentación +- sitio web +- triaje +- financiación y subvenciones -See the [Team](/gallery/team.html) page for individual team members. +Visita la página [Equipo](/gallery/team.html) para conocer a los miembros de cada equipo. -## 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 el proyecto empleando a personas que contribuyen a NumPy como parte de su trabajo. Entre los actuales socios institucionales se encuentran: {{< 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). {{< numfocus >}} From 12d3edd1c28ea491b9c3385fc2990f6cc19408f5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Apr 2021 00:45:34 +0200 Subject: [PATCH 297/909] New translations arraycomputing.md (Spanish) --- content/es/arraycomputing.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/arraycomputing.md b/content/es/arraycomputing.md index abd29d11c1..ffe0d578ba 100644 --- a/content/es/arraycomputing.md +++ b/content/es/arraycomputing.md @@ -1,11 +1,11 @@ --- -title: Array Computing +title: Cómputo vectorial 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.** +La manipulación y transformación de datos a gran escala depende de una computación vectorial eficiente y de alto rendimiento. El lenguaje de elección para el análisis 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. From e78e4bb7111bb2717a6196597d9c068222d5000a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Apr 2021 01:44:15 +0200 Subject: [PATCH 298/909] New translations arraycomputing.md (Spanish) --- content/es/arraycomputing.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/arraycomputing.md b/content/es/arraycomputing.md index ffe0d578ba..11877b3847 100644 --- a/content/es/arraycomputing.md +++ b/content/es/arraycomputing.md @@ -3,19 +3,19 @@ title: Cómputo vectorial 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 vectorial es la base del cómputo estadístico, matemático y científico en varias aplicaciones contemporáneas de ciencia de datos y análisis, como la visualización de datos, el procesamiento digital de señales, el procesamiento de imágenes, la bioinformática el aprendizaje automático, la IA y muchas otras.* La manipulación y transformación de datos a gran escala depende de una computación vectorial eficiente y de alto rendimiento. El lenguaje de elección para el análisis 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 matrices y arreglos multidimensionales de gran tamaño, y viene con una amplia colección de funciones matemáticas de alto nivel para operar sobre estos arreglos. -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 vectorial, 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 vectorial" /> -**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 vectorial** está basado en los **arreglos** como estructura de datos. *Los arreglos* 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 vectorial es *única* ya que implica operar sobre los arreglos de datos *de una vez*. Esto significa que cualquier operación de arreglos 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 409ce1b1b579f4da3a6d1860161c6d4503148e0f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 21 Apr 2021 15:34:57 +0200 Subject: [PATCH 299/909] 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 1dc8a215f0..390d965d4c 100644 --- a/content/pt/citing-numpy.md +++ b/content/pt/citing-numpy.md @@ -1,5 +1,5 @@ --- -title: Citando a NumPy +title: Citando o NumPy sidebar: false --- From b3447dea52f961604aa030b1184db1dbf7efb293 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 05:06:09 +0200 Subject: [PATCH 300/909] New translations about.md (Arabic) --- content/ar/about.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 29d4a19ad9..b479f17bcc 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -12,7 +12,7 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ ## المجلس التوجيهي -The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. ويتألف المجلس التوجيهي المعني لنمباي حاليا علي الأعضاء التالية (بالترتيب الأبجدي): +The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. ويتألف المجلس التوجيهي المعني بالمشروع حاليا من الأعضاء التالية (بالترتيب الأبجدي): - Sebastian Berg - Ralf Gommers @@ -26,8 +26,8 @@ The role of the NumPy Steering Council is to ensure, through working with and se Emeritus: -- Travis Oliphant (project founder, 2005-2012) -- Alex Griffing (2015-2017) +- ترافيس أوليفانت (مؤسس المشروع، 2005-2012) +- ألكس غريفينغ (2015-2017) - Marten van Kerkwijk (2017-2019) - Allan Haldane (2015-2021) - Nathaniel Smith (2012-2021) From e907d72665eaec65d47e06b70ab69d56f83e96da Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 06:07:17 +0200 Subject: [PATCH 301/909] New translations about.md (Arabic) --- content/ar/about.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index b479f17bcc..fce591bb90 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -28,11 +28,11 @@ Emeritus: - ترافيس أوليفانت (مؤسس المشروع، 2005-2012) - ألكس غريفينغ (2015-2017) -- Marten van Kerkwijk (2017-2019) +- مارتن فان كيركويك (2017-2019) - Allan Haldane (2015-2021) -- Nathaniel Smith (2012-2021) -- Julian Taylor (2013-2021) -- Pauli Virtanen (2008-2021) +- ناثانييل سميث (2012-2021) +- جوليان تايلور (2013-2021) +- باولي فيرتانين (2008-2021) - Jaime Fernández del Río (2014-2021) From 8135c447b27e878999bee02cceda4ce97d8ca076 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 13:19:29 +0200 Subject: [PATCH 302/909] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/content/zh/learn.md b/content/zh/learn.md index ea35371573..0ad006d0f7 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -5,9 +5,14 @@ sidebar: false For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +## NumPy Tutorials + +You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + *** +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). + ## Beginners There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: @@ -60,7 +65,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Videos** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunuz-Iglesias* +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* *** From 80537599aae96ef8641b4deb6d5721509251566d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 13:19:47 +0200 Subject: [PATCH 303/909] New translations learn.md (Korean) --- content/ko/learn.md | 33 +++++++++++++++++++-------------- 1 file changed, 19 insertions(+), 14 deletions(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index 2441c963be..0ad006d0f7 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -5,14 +5,19 @@ sidebar: false For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). -아래는 선별된 외부 자료들의 모음입니다. 이곳에 기여하고 싶다면, [이 페이지의 끝](#add-to-this-list)을 참조하세요. +## NumPy Tutorials + +You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + *** +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). + ## Beginners -여기에 NumPy에 대한 많은 자료가 있습니다. NumPy가 처음이라면 이 자료들을 강력하게 권장합니다. +There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: - **튜토리얼** + **Tutorials** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) @@ -25,7 +30,7 @@ For the **official NumPy documentation** visit [numpy.org/doc/stable](https://nu * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) - **도서** + **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/) @@ -33,7 +38,7 @@ For the **official NumPy documentation** visit [numpy.org/doc/stable](https://nu 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. - **영상** + **Videos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* @@ -41,9 +46,9 @@ You may also want to check out the [Goodreads list](https://www.goodreads.com/sh ## Advanced -Indexing, Splitting, Stacking, 선형대수 등과 같은 NumPy의 개념을 더 잘 이해하러면 이 고급 자료들을 참조 해보세요. +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. - **튜토리얼** + **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* @@ -52,15 +57,15 @@ Indexing, Splitting, Stacking, 선형대수 등과 같은 NumPy의 개념을 더 * [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/) - **도서** + **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* - **영상** + **Videos** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunuz-Iglesias* +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* *** @@ -75,11 +80,11 @@ Indexing, Splitting, Stacking, 선형대수 등과 같은 NumPy의 개념을 더 *** -## NumPy 인용하기 +## Citing NumPy -만약 당신의 연구에서 NumPy가 중요한 역할을 수행하였고 학술 간행물에서 출판하기 위해서는 [이 인용 정보](/citing-numpy)를 참조하세요. +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). -## 이 목록에 기여하기 +## Contribute to this list -이 목록에 자료를 추가하려면 [Pull Request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md)를 통해서 제출하세요. 당신이 추천한 자료가 왜 이 페이지에 올라야하는지, 또한 어떤 사람들이 가장 좋아할지 말해주세요. +To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. From 43218af5689292523c0fb662dcca4c46d02685c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 13:20:06 +0200 Subject: [PATCH 304/909] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 41 +++++++++++++++++++++++------------------ 1 file changed, 23 insertions(+), 18 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index ee3d572e04..7f3d89944d 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -5,14 +5,19 @@ 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). +## NumPy Tutorials + +You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + *** -## Iniciantes +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). + +## Beginners -Há uma tonelada de informações sobre o NumPy lá fora. Se você está começando, recomendamos fortemente estes: +There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: - **Tutoriais** + **Tutorials** * [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) @@ -25,25 +30,25 @@ Há uma tonelada de informações sobre o NumPy lá fora. Se você está começa * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) - **Livros** + **Books** * [Guide to NumPy *de Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) Essa é uma versão free de 2006. Para a última versão (2015) veja [aqui](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). * [From Python to NumPy *por 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) *por Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow* -Você também pode querer conferir a [lista Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre o tema "Python+SciPy. A maioria dos livros lá serão sobre o "ecossistema SciPy", que tem o NumPy em sua essência. +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. - **Vídeos** + **Videos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* *** -## Avançado +## Advanced -Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais. +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. - **Tutoriais** + **Tutorials** * [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* @@ -52,20 +57,20 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos * [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/) - **Livros** + **Books** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* * [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *por 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) *por Robert Johansson* - **Vídeos** + **Videos** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *por Juan Nunuz-Iglesias* +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *por Amuls Academy* *** -## Palestras sobre NumPy +## NumPy Talks * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) @@ -75,11 +80,11 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos *** -## Citando a NumPy +## Citing 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](/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). -## Contribua para esta lista +## Contribute to this list -Para adicionar a essa coleção, envie uma recomendação [através de um pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Diga por que sua recomendação merece ser mencionada nesta página e também qual o público que mais se beneficiaria. +To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. From 272199be39c2aa2c930ed3d2dc18a8c3453efe02 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 13:20:16 +0200 Subject: [PATCH 305/909] New translations learn.md (Japanese) --- content/ja/learn.md | 41 +++++++++++++++++++++++------------------ 1 file changed, 23 insertions(+), 18 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index ccbc6bfdb1..5329bdf3fc 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -5,14 +5,19 @@ 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。 -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。 +## NumPy Tutorials + +You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + *** -## 初心者向け +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). + +## Beginners -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お勧めします： +There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: - **チュートリアル** + **Tutorials** * [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) @@ -25,25 +30,25 @@ NumPyについての資料は多数存在しています。 初心者の方に * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) - **書籍** + **Books** * [NumPガイド*by Travelis E. Oliphant*](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*by Nicolas P. Rougier*](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) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* -また、"Python+SciPy"を題材にした [おすすめリスト](https://www.goodreads.com/shelf/show/python-scipy) を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。 +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. - **動画** + **Videos** * [Numpy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** -## 上級者向け +## Advanced -より高度なインデックス作成、分割、スタック、線形代数など、Numpy の概念をより深く理解するためには、これらのリソースを試してみてください。 +Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. - **チュートリアル** + **Tutorials** * [NumPy 100演習](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *Nicolas P. Rougier* * [NumPyとSciPyイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* @@ -52,20 +57,20 @@ NumPyについての資料は多数存在しています。 初心者の方に * [高度なインデックシング](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/) - **書籍** + **Books** * [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) *by Jake Vanderplas* * [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney* * [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) *by Robert Johansson* - **動画** + **Videos** -* [アドバンスドNumPy -](https://www.youtube.com/watch?v=cYugp9IN1-Q) *ブロードキャストルール、ストライド、および高度なインデックシング* by Fan Nunuz-Iglesias +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [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 Indexing の未来](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernadez* (2016) * [Python における配列計算革命](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) @@ -75,11 +80,11 @@ 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 を引用する場合 +## Citing NumPy -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](/citing-numpy)を参照して下さい。 +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). -## このページへの貢献 +## Contribute to this list -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/master/content/en/learn.md)を使って提案してみて下さい。 あなたが推薦する情報が、このページで紹介するに値する理由と、その情報によってどのような人が最も恩恵を受けるかを説明して下さい。 +To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. From d439c6f0fc84ba312dbee95d3ae9db0ef99063c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 13:20:31 +0200 Subject: [PATCH 306/909] New translations learn.md (Spanish) --- content/es/learn.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/content/es/learn.md b/content/es/learn.md index ea35371573..0ad006d0f7 100644 --- a/content/es/learn.md +++ b/content/es/learn.md @@ -5,9 +5,14 @@ sidebar: false For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +## NumPy Tutorials + +You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + *** +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). + ## Beginners There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: @@ -60,7 +65,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Videos** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunuz-Iglesias* +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* *** From 31c75d6446d491761b633514f3426d95211f2c47 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 13:20:41 +0200 Subject: [PATCH 307/909] New translations learn.md (Arabic) --- content/ar/learn.md | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index ea35371573..0ad006d0f7 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -5,9 +5,14 @@ sidebar: false For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +## NumPy Tutorials + +You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). + *** +Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). + ## Beginners There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: @@ -60,7 +65,7 @@ Try these advanced resources for a better understanding of NumPy concepts like a **Videos** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunuz-Iglesias* +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* *** From 618e01993d4ae1cb825d5109e6062bb239902108 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 14:17:15 +0200 Subject: [PATCH 308/909] 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 1cfe839c79..49d3142c71 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -5,7 +5,7 @@ sidebar: false _NumPy 프로젝트와 커뮤니티에 대한 몇가지 정보_ -NumPy는 Python에서 Numerical Computing을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. It was created in 2005, building on the early work of the Numeric and Numarray libraries. NumPy는 항상 100% 오픈소스 소프트웨어 일것이며, [수정된 BSD 라이센스](https://github.com/numpy/numpy/blob/master/LICENSE.txt)에 따라서 누구나 무료로 사용하고 배포할 수 있습니디. +NumPy는 Python에서 Numerical Computing을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. NumPy는 항상 100% 오픈소스 소프트웨어 일것이며, [수정된 BSD 라이센스](https://github.com/numpy/numpy/blob/master/LICENSE.txt)에 따라서 누구나 무료로 사용하고 배포할 수 있습니디. NumPy는 광범위한 Scientific Python 커뮤니티의 협의를 통해 GitHub에서 공개적으로 개발되었습니다. 우리의 거버넌스 접근 방식에 대한 더 자세한 내용은 [거버넌스 문서](https://www.numpy.org/devdocs/dev/governance/index.html)를 참조해 주세요. From aa5fea0788ec80da3d4b4c3fcaa6e0cb5997bb16 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 15:19:54 +0200 Subject: [PATCH 309/909] New translations arraycomputing.md (Korean) --- content/ko/arraycomputing.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/arraycomputing.md b/content/ko/arraycomputing.md index abd29d11c1..2d8b5673ab 100644 --- a/content/ko/arraycomputing.md +++ b/content/ko/arraycomputing.md @@ -3,19 +3,19 @@ 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.* +*행렬 연산은 통계와 수학 뿐만 아니라 현대의 다양한 분야에 적용되는 데이터 사이언스와 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.** +대규모의 데이터의 조작과 연산은 고효율, 고성능의 행렬 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, 큰 규모, 다 차원 행렬을 표현할 수 있고, 행렬 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 행렬 연산 라이브러리가 잇따라 나오며 행렬 연산 분야가 더욱 활발해 졌습니다. 최신의 라이브러리들중 대부분은 NumPy 같은 특징과 성능을 품고, 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. +**행렬 연산**의 기반은 **배열**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. +행렬 연산은 *한번에 * 데이터 배열에 *모든 연산이* 계산 됩니다. 다시 말해서, 모든 행렬 연산은 전체 데이터에 한번에 적용됩니다. 이 벡터화 접근법은 행렬 연산을 위해 루프를 활용하여 개별적인 데이터에 접근하여 연산하는 코드를 작성하지 않고, 행렬에 바로 연산하는 코드를 작성하여, 개발자가 보다 개발 빠르고 간단하게 할수 있게 해줍니다. From 24bcfa22300e9db5964bf7498526bf802b9ed537 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 15:19:55 +0200 Subject: [PATCH 310/909] 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 3ec0dc58b7..69bdbcb0a0 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -1,11 +1,11 @@ --- -title: Installing NumPy +title: NumPy 설치 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 설치를 위해서는 Python만 필요합니다. 만양 파이썬이 설치되지 않았다면, Python, NumPy, 그리고 다양한 데이터 과학과 과학 계산을 위해 일반적으로 많이 사용되는 패키지를 한번에 설치할 수 있는 [Anaconda Distribution](https://www.anaconda.com/distribution)을 활용하여 설치하는 것을 추천합니다. -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`, `pip` 그리고 macOS과 Linux의 패키지 매니저를 사용하거나 또는 [소스](https://numpy.org/devdocs/user/building.html)로 부터 설치할 수 있습니다. For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. **CONDA** From a9b2390722d71636a79e19847943d721f9889a52 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 15:19:56 +0200 Subject: [PATCH 311/909] New translations learn.md (Korean) --- content/ko/learn.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index 0ad006d0f7..80f6d1fa79 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -3,21 +3,21 @@ 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. -## NumPy Tutorials +## NumPy 튜토리얼 -You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +NumPy 커뮤니티 [NumPy Tutorials](https://numpy.org/numpy-tutorials)에서 다양한 튜토리얼과 교육 자료를 찾을 수 있습니다. 이 페이지의 목적은 개인 학습 자료와 강의 자료 모두로 활용할 수 있도록 양질의 자료를 제공하는 것이며, 자료는 Jupyter Notebooks 형식으로 되어 있습니다. 만약 추가하고 싶은 내용이 생기는 경우 [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials)를 확인해 주십시오. *** -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +아래는 선별된 외부 자료가 모아져 있습니다. 여기에 기여하기 위해서는 [이 페이지](#add-to-this-list)의 마지막 부분을 확인 해주십시오. -## Beginners +## 초심자 -There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: +방대한 자료가 제공되고 있습니다. 만약 처음 접하신다면, 아래의 자료를 강하게 권해드립니다. - **Tutorials** + **튜토리얼** * [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) From 2d0e5b96d56b429c8be6574035e61822242fa8bd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 16:31:54 +0200 Subject: [PATCH 312/909] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md index 9aa943e53e..0d19b30596 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: [0.1038/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: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Enlace del editor](https://www.nature.com/articles/s41586-020-2649-2)). -_In BibTeX format:_ +_En formato BibTeX:_ ``` @Article{ harris2020array, From adbca294422e1271d0a2c619c4fe532462874038 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 16:31:56 +0200 Subject: [PATCH 313/909] 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 efcde754ae..9c1a17b683 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -1,11 +1,11 @@ --- -title: NumPy Code of Conduct +title: Código de conducta de NumPy sidebar: false aliases: - /conduct.html --- -### Introduction +### Introducción 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 96fe1dcd1e31a5ba7b7fb0fe31ba3fd98aa08efb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 16:31:57 +0200 Subject: [PATCH 314/909] New translations citing-numpy.md (Korean) --- content/ko/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/citing-numpy.md b/content/ko/citing-numpy.md index 9aa943e53e..9754abe9df 100644 --- a/content/ko/citing-numpy.md +++ b/content/ko/citing-numpy.md @@ -3,7 +3,7 @@ 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: +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: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). From 49f60df963527cce8edf841f7eaf98f24d4c76c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 16:31:58 +0200 Subject: [PATCH 315/909] New translations install.md (Korean) --- content/ko/install.md | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index 69bdbcb0a0..2694083682 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -5,30 +5,31 @@ sidebar: false NumPy 설치를 위해서는 Python만 필요합니다. 만양 파이썬이 설치되지 않았다면, Python, NumPy, 그리고 다양한 데이터 과학과 과학 계산을 위해 일반적으로 많이 사용되는 패키지를 한번에 설치할 수 있는 [Anaconda Distribution](https://www.anaconda.com/distribution)을 활용하여 설치하는 것을 추천합니다. -NumPy 는 `conda`, `pip` 그리고 macOS과 Linux의 패키지 매니저를 사용하거나 또는 [소스](https://numpy.org/devdocs/user/building.html)로 부터 설치할 수 있습니다. For more detailed instructions, consult our [Python and NumPy installation guide](#python-numpy-install-guide) below. +NumPy 는 `conda`, `pip` 그리고 macOS과 Linux의 패키지 매니저를 사용하거나 또는 [소스](https://numpy.org/devdocs/user/building.html)로 부터 설치할 수 있습니다. 보다 상세한 설치 과정과 방법은 [Python and NumPy 설치 가이드](#python-numpy-install-guide)의 아래쪽에 있습니다. **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: +만약 `conda`를 사용해 설치하는 경우, `defaults` 또는 `conda-forge` 채널을 활용해서 설치할 수 있습니다. ```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 +# 기본 환경보다 가상환경을 설치하여 활용하는 것이 좋습니다. +# Anaconda가 설치된 환경에서 cmd에서 하기 명령어를 입력합니다. +conda create -n my-env # my-env 라는 이름의 가상환경 생성 +conda activate my-env # 활성화 된 가상환경을 my-env로 변경 +# conda-forge로 설치하는 경우하기 명령어 입력 conda config --env --add channels conda-forge -# The actual install command +# 실제 설치 명령어 conda install numpy ``` **PIP** -If you use `pip`, you can install NumPy with: +만약 `pip`로 NumPy를 설치하는 경우 ```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 pip를 사용할 때, 가상환경을 만들어보고, 만들어진 가상환경에 설치하는 것이 좋습니다. 상세한 내용은 [Reproducible Installs](#reproducible-installs)를 참조하십시오. 또한 가상환경을 사용하는 상세한 내용은 [가이드](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto)를 참조하십시오. From fbafb0289a026a154d653663553e163e7c268430 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 17:29:28 +0200 Subject: [PATCH 316/909] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index 9c1a17b683..b2cfe5bc86 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -7,21 +7,21 @@ aliases: ### Introducción -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. +Este Código de Conducta se aplica a todos los espacios gestionados por el proyecto NumPy, incluyendo todas las listas de correo públicas y privadas, gestores de incidencias, wikis, blogs, Twitter y cualquier otro canal de comunicación utilizado por nuestra comunidad. El proyecto NumPy no organiza eventos en persona, sin embargo los eventos relacionados con nuestra comunidad deben tener un código de conducta similar a este. -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. +Este Código de Conducta debe ser respetado por todos los que participan en la comunidad NumPy formalmente o informalmente, o reclaman cualquier afiliación con el proyecto, en cualquier actividad relacionada con el proyecto y especialmente cuando representan el proyecto, de cualquier manera. -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. +Este código no es exhaustivo ni completo. Sirve para sintetizar nuestro entendimiento común de un entorno y unos objetivos compartidos y de colaboración. Por favor, intenta seguir este código tanto en el espíritu como en la letra, para crear un ambiente cordial y productivo que enriquezca a la comunidad circundante. -### Specific Guidelines +### Directrices Específicas -We strive to: +Nos esforzamos por: -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: +1. Ser abiertos. Invitamos a todas las personas a participar en nuestra comunidad. Preferimos utilizar métodos de comunicación públicos para los mensajes relacionados con el proyecto, a menos que se trate de algo delicado. Esto se aplica también a los mensajes de ayuda o soporte relacionados con el proyecto; no sólo es mucho más probable que una solicitud de soporte pública dé lugar a una respuesta a una pregunta, sino que también garantiza que cualquier error involuntario en la respuesta se detecte y corrija más fácilmente. +2. Ser empáticos, receptivos, amables y pacientes. Trabajamos juntos para resolver los conflictos y asumimos que hay buenas intenciones. Todos podemos experimentar cierta frustración de vez en cuando, pero no permitimos que la frustración se convierta en un ataque personal. Una comunidad en la que la gente se siente incómoda o amenazada no es productiva. +3. Ser colaborativos. Nuestro trabajo será utilizado por otras personas, y a su vez dependeremos del trabajo de otros. Cuando hacemos algo en beneficio del proyecto, estamos dispuestos a explicar a otros cómo funciona. para que puedan aprovechar el trabajo y hacerlo aún mejor. Cualquier decisión que tomemos afectará a usuarios y colegas, y nos tomaremos en serio esas consecuencias a la hora de tomar decisiones. +4. Ser curiosos. ¡Nadie lo sabe todo! Plantear las preguntas con antelación evita muchos problemas posteriores, por lo que fomentamos las preguntas, aunque es conveniente dirigirlas al foro adecuado. Nos esforzaremos por ser receptivos y útiles. +5. Ser cuidadosos con las palabras que elijamos. 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. From 7e3ebd0e3eb86f3ee084c6aabc1d2aecf55c7d36 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Apr 2021 18:27:44 +0200 Subject: [PATCH 317/909] New translations code-of-conduct.md (Spanish) --- content/es/code-of-conduct.md | 70 +++++++++++++++++------------------ 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/content/es/code-of-conduct.md b/content/es/code-of-conduct.md index b2cfe5bc86..6bb0dfa637 100644 --- a/content/es/code-of-conduct.md +++ b/content/es/code-of-conduct.md @@ -21,63 +21,63 @@ Nos esforzamos por: 2. Ser empáticos, receptivos, amables y pacientes. Trabajamos juntos para resolver los conflictos y asumimos que hay buenas intenciones. Todos podemos experimentar cierta frustración de vez en cuando, pero no permitimos que la frustración se convierta en un ataque personal. Una comunidad en la que la gente se siente incómoda o amenazada no es productiva. 3. Ser colaborativos. Nuestro trabajo será utilizado por otras personas, y a su vez dependeremos del trabajo de otros. Cuando hacemos algo en beneficio del proyecto, estamos dispuestos a explicar a otros cómo funciona. para que puedan aprovechar el trabajo y hacerlo aún mejor. Cualquier decisión que tomemos afectará a usuarios y colegas, y nos tomaremos en serio esas consecuencias a la hora de tomar decisiones. 4. Ser curiosos. ¡Nadie lo sabe todo! Plantear las preguntas con antelación evita muchos problemas posteriores, por lo que fomentamos las preguntas, aunque es conveniente dirigirlas al foro adecuado. Nos esforzaremos por ser receptivos y útiles. -5. Ser cuidadosos con las palabras que elijamos. 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. +5. Ser cuidadosos con las palabras que elijamos. Somos cuidadosos y respetuosos en nuestra comunicación, y asumimos la responsabilidad del lenguaje que utilizamos. Somos amables con los demás. No insultes ni menosprecies a los demás participantes. No aceptaremos el acoso ni otros comportamientos excluyentes, como: + * Amenazas o expresiones violentas dirigidas a otra persona. + * Bromas y lenguaje sexista, racista o discriminatorio. + * Publicar material sexualmente explícito o violento. + * Publicar (o amenazar con publicar) información de identificación personal de otras personas ("doxing"). + * Compartir contenido privado, como correos electrónicos enviados de forma privada o no pública, o foros no registrados como el historial de canales IRC, sin el consentimiento del remitente. + * Insultos personales, especialmente aquellos que utilizan términos racistas o sexistas. + * Atención sexual no deseada. + * Uso excesivo de lenguaje inapropiado. Por favor, evite las palabras soeces; las personas difieren mucho en su sensibilidad a las palabrotas. + * Acoso reiterado a los demás. En general, si alguien le pide que se detenga, entonces pare. + * Defender o fomentar cualquiera de las conductas anteriores. -### Diversity Statement +### Declaración de diversidad -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. +El proyecto NumPy acoge con satisfacción y fomenta la participación de todos. Estamos comprometidos a ser una comunidad de la que todo el mundo disfruta ser parte. Aunque puede que no siempre seamos capaces de satisfacer las preferencias de cada individuo, intentamos lo mejor para tratar a todo el mundo con amabilidad. -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. +No importa cómo te identifiques o cómo te perciban los demás: te damos la bienvenida. Aunque ninguna lista puede esperar ser exhaustiva, honramos explícitamente la diversidad en: edad, cultura, etnia, genotipo, identidad o expresión de género, lengua, origen nacional, neurotipo, fenotipo, creencias políticas, profesión, raza, religión, orientación sexual, estatus socioeconómico, subcultura y capacidad técnica, en la medida en que no entren en conflicto con este código de conducta. -Though we welcome people fluent in all languages, NumPy development is conducted in English. +Aunque aceptamos a personas con dominio de cualquier idioma, el desarrollo de NumPy se lleva a cabo en inglés. -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). +Los estándares de comportamiento en la comunidad NumPy se detallan en el Código de Conducta anterior. Los participantes en nuestra comunidad deben mantener estas normas en todas sus interacciones y ayudar a los demás a hacer lo mismo (véase la siguiente sección). -### Reporting Guidelines +### Directrices para Informar Incidentes -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. +Sabemos que es dolorosamente común que la comunicación en Internet comience o se convierta en un abuso evidente y flagrante. También reconocemos que a veces la gente puede tener un mal día, o no ser consciente de algunas de las directrices de este Código de Conducta. Por favor, tenga esto en cuenta a la hora de decidir cómo responder a una violación de este Código. -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. +En caso de infracciones claramente intencionadas, informe de las mismas al Comité del Código de Conducta (ver más abajo). Para infracciones posiblemente no intencionadas, Usted puede responder a la persona y señalar este código de conducta (tanto en público como en privado, lo que sea más apropiado). Si prefiere no hacerlo, no dude en informar directamente al Comité del Código de Conducta o pedirle consejo, de forma confidencial. -You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. +Puede informar de los problemas al Comité del Código de Conducta de NumPy en numpy-conduct@googlegroups.com. -Currently, the Committee consists of: +Actualmente, la comisión está formada por: * Stefan van der Walt * Melissa Weber Mendonça * Anirudh Subramanian -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). +Si tu informe implica a algún miembro del Comité, o si éste considera que tiene un conflicto de intereses en su tramitación, se abstendrán de examinar tu denuncia. Si por alguna razón le incomoda hacer un informe al Comité, también puede ponerse en contacto con el personal superior de NumFOCUS en [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible). -### Incident reporting resolution & Code of Conduct enforcement +### Resolución de informes de incidentes y aplicación del Código de Conducta -_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). +_Esta sección resume los puntos más importantes, se pueden encontrar más detalles en el_ [Código de Conducta NumPy - Cómo hacer seguimiento de un incidente](/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). +Vamos a investigar y responder a todas las reclamaciones. El Comité del Código de Conducta de NumPy y el Comité Directivo de NumPy (si está involucrado) protegerán la identidad del denunciante y tratarán el contenido de las denuncias como confidencial (a menos que el denunciante esté de acuerdo con lo contrario). -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. +En caso de infracciones graves y evidentes, por ejemplo, amenazas personales o lenguaje violento, sexista o racista, desconectaremos inmediatamente al emisor de los canales de comunicación de NumPy; consulte el manual para obtener más detalles. -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: +En los casos que no impliquen infracciones claras, graves y evidentes de este Código de Conducta, el proceso para actuar sobre cualquier informe de infracción del Código de Conducta recibido será: -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. acusar recibo del informe, +2. un intercambio de opiniones razonable, +3. mediación (si la retroalimentación no ha servido de nada, y sólo si tanto el denunciante como el denunciado están de acuerdo con ello), +4. aplicación a través de una decisión transparente (ver [Resoluciones](/report-handling-manual#resolutions)) hechas por el Comité del Código de conducta. -The Committee will respond to any report as soon as possible, and at most within 72 hours. +El Comité responderá a cualquier informe lo antes posible y como máximo dentro de 72 horas. -### Endnotes +### Nota final -We are thankful to the groups behind the following documents, from which we drew content and inspiration: +Damos las gracias a los grupos que están detrás de los siguientes documentos, de los que hemos sacado contenido e inspiración: -- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) +- [El Código de Conducta de SciPy](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) From 51997a805e4439bb5653fc4a0ee0e715b24787b6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 25 Apr 2021 07:16:26 +0200 Subject: [PATCH 318/909] New translations install.md (Korean) --- content/ko/install.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index 2694083682..2bd812d29f 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -33,19 +33,19 @@ pip install numpy -# Python and NumPy installation guide +# Python, 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. +파이썬만 활용해서 패키지를 설치하고 관리하는 것은 복잡하기 때문에, 다양한 대안들이 많이 있습니다. 이 가이드에는 가장 보편적이고, 명확한 방식을 알려줍니다. 이 가이드는 통상적으로 사용되는 운영체제와 하드웨어에서 Python과 NumPy 그리고 수치 계산을 해주는 PyData를 사용하는 유저를 위한 자료입니다. ## 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. +사용자의 전문성과 사용하는 운영체제를 기준으로 추천하는 방식을 알려드리겠습니다. 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 모든 일반적인 운영체제: -- Install [Anaconda](https://www.anaconda.com/distribution/) (it installs all packages you need and all other tools mentioned below). +- [Anaconda](https://www.anaconda.com/distribution/) 를 설치하십시오.(당신이 필요로 하는 패키지를 설치하고, 아래에 언급될 다양한 도구들을 제공합니다.) - 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 d09286527622ef602f188c7f31cf640618340991 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 25 Apr 2021 08:16:56 +0200 Subject: [PATCH 319/909] 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 2bd812d29f..05a405f1bc 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -46,40 +46,40 @@ pip install numpy Windows, macOS, Linux 등 모든 일반적인 운영체제: - [Anaconda](https://www.anaconda.com/distribution/) 를 설치하십시오.(당신이 필요로 하는 패키지를 설치하고, 아래에 언급될 다양한 도구들을 제공합니다.) -- 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. +- 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 -#### Windows or macOS +#### Windows, macOS -- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). -- 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. -- Unless you're fine with only the packages in the `defaults` channel, make `conda-forge` your default channel via [setting the channel priority](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge). +- [Miniconda](https://docs.conda.io/en/latest/miniconda.html)를 설치하십시오. +- `base` 라는 이름의 콘다 가상환경은 초기 최소 상태를 유지하고, [콘다 가상환경](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)을 만들어서, 해당 가상환경에 진행하고자 하는 일이나 프로젝트를 위해서 필요한 패키지를 설치하십시오. +- `기본 채널`로 충분하지 않다면, `conda-forge` [채널 우선순위 설정](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge)을 통해서 원하는 채널을설정할 수 있습니다.. #### Linux -If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: -- Use your OS package manager for as much as possible (Python itself, NumPy, and other libraries). -- Install packages not provided by your package manager with `pip install somepackage --user`. +만약 약간 하위 버전의 패키지나, 최신 버전이 아닌 보다 안정적인 패키지를 설치하고 싶은 경우에 참고하십시오. +- OS에서 사용 가능한 패키지 매니저를 활용하여 설치하십시오 (Python itself, NumPy, and other libraries). +- 설치한 패키지 매니저가 라이브러리를 설치해주지 않는다면, `pip install somepackage --user`를 명령 프롬프트에 입력하십시오. -If you use a GPU: -- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). -- 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. -- Use the `defaults` conda channel (`conda-forge` doesn't have good support for GPU packages yet). +GPU를 사용하는 경우: +- [Miniconda](https://docs.conda.io/en/latest/miniconda.html)를 설치하십시오. +- `base` 라는 이름의 콘다 가상환경은 초기 최소 상태를 유지하고, [콘다 가상환경](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)을 만들어서, 해당 가상환경에 진행하고자 하는 일이나 프로젝트를 위해서 필요한 패키지를 설치하십시오. +- `기본 콘다 채널`을 활용해 주십시오.(`conda-forge` GPU 패키지를 지원하는 좋은 채널을 아직 제공하지 않습니다.). -Otherwise: -- 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. +기타: +- [Miniforge](https://github.com/conda-forge/miniforge)를 설치하십시오. +- `base` 라는 이름의 콘다 가상환경은 초기 최소 상태를 유지하고, [콘다 가상환경](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)을 만들어서, 해당 가상환경에 진행하고자 하는 일이나 프로젝트를 위해서 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/) 이나 [Homebrew](https://brew.sh/), 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 From 7843d42745aed16373608f1d38fddad5bf69aabc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 25 Apr 2021 16:27:56 +0200 Subject: [PATCH 320/909] 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 80f6d1fa79..e5d9f7e45e 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -36,7 +36,7 @@ NumPy 커뮤니티 [NumPy Tutorials](https://numpy.org/numpy-tutorials)에서 * [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. +Python+SciPy와 관련된 자료는 [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy)를 확인하시면 좋습니다. 대부분 NumPy를 핵심으로 사용하는 SciPy 에코시스템과 관련된 자료입니다. **Videos** @@ -44,7 +44,7 @@ You may also want to check out the [Goodreads list](https://www.goodreads.com/sh *** -## Advanced +## 숙련자 Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. From ee2459f034c766113dc8015e4e9e28a5e3787612 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 25 Apr 2021 17:27:36 +0200 Subject: [PATCH 321/909] New translations learn.md (Korean) --- content/ko/learn.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index e5d9f7e45e..fda3607db0 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -46,7 +46,7 @@ Python+SciPy와 관련된 자료는 [Goodreads list](https://www.goodreads.com/s ## 숙련자 -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +NumPy에서 제공하는 어레이 인덱싱, 분리, 중첩, 선형 대수 등과 같은 개념들을 보다 깊이 이해하고 싶다면, 아래의 숙련자 자료를 활용하십시오. **Tutorials** From 0651ef7c8af0e3a3b87f153124ac293325c1e230 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Apr 2021 15:45:58 +0200 Subject: [PATCH 322/909] New translations install.md (Korean) --- content/ko/install.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index 05a405f1bc..199c524498 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -82,9 +82,9 @@ GPU를 사용하는 경우: - 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 많다, 웹 및 범용 Python 개발을 위해 Pip뿐만 아니라 [다양한 도구](https://packaging.python.org/guides/tool-recommendations/)들이 있다. 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 From d6ce1f10ebfaf4d4d9b01f737e21a9155ce406f5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 27 Apr 2021 16:41:43 +0200 Subject: [PATCH 323/909] New translations install.md (Korean) --- content/ko/install.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index 199c524498..c65b872205 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -84,18 +84,18 @@ GPU를 사용하는 경우: ## Python 패키지 관리 -패키지 관리는 아주 중요하기 때문에, 사용할 수 있는 도구들이 많다, 웹 및 범용 Python 개발을 위해 Pip뿐만 아니라 [다양한 도구](https://packaging.python.org/guides/tool-recommendations/)들이 있다. 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. +`pip`, `conda`가 Python 패키지를 설치하고 관리하는 주요 툴입니다. 그들의 기능은 대부분 겹칩니다. (e.g. both can install `numpy`), 그리고 같이 쓰일 수도 있습니다. 곧 pip와 conda의 차이점에 대해서 논의해볼 것입니다. - 패키지 관리를 잘 하기 위해서는 알고 계시는 것이 좋습니다. -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 도있습니다. 하지만 pip는 특정 설치된 Python에만 패키지를 설치하고 관리할 수 있습니다. 따라서 해당 Python에 모든 패키지가 설치됩니다. 또한 conda는 non-Python 라이브러리나 도구들을 설치할 수 있습니다. (e.g. compilers, CUDA, HDF5), 하지만 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 설치합니다. 반면에 conda는 conda 만의 채널로 설치합니다. (일반적으로 "defaults" or "conda-forge"). 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 제공합니다. 하지만 pip는 그를 위해서 (아주 많은) 추가적인 도구들이 필요합니다. ### Reproducible installs From f84ec013b407b7a7ed6ffb111ddffb7ada366eab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 29 Apr 2021 08:29:08 +0200 Subject: [PATCH 324/909] New translations learn.md (Japanese) --- content/ja/learn.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 5329bdf3fc..9c2e6aa2eb 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -5,17 +5,17 @@ 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 Tutorials +## NumPy のチュートリアル -You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +[Numpy のチュートリアル](https://numpy.org/numpy-tutorials) では、Numpy コミュニティによるチュートリアルや教材が手に入ります｡ このページの目標は、NumPy プロジェクトによる自己学習と授業のための高品質な教材を Jupyter Notebooks の形式で提供することです。 独自のコンテンツを追加したい場合は、GitHubの [numpy-tutorials リポジトリ](https://github.com/numpy/numpy-tutorials)を確認してください。 *** -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +以下は、厳選された外部の教材です。 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。 -## Beginners +## 初学者向け -There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: +NumPyについての情報はたくさん見つかります。 If you are new, we'd strongly recommend these: **Tutorials** From fcaa9737cadad8f55914fde2d8f60aabae150a44 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 29 Apr 2021 08:29:09 +0200 Subject: [PATCH 325/909] 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 a9c9232830..8b2eb04a17 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -18,7 +18,7 @@ NumPy運営委員会の役割は、NumPyのコミュニティと協力しサポ - Ralf Gommers - Charles Harris - Stephan Hoyer -- Melissa Weber Mendonça +- Melissa Weber Mendonça - Inessa Pawson - Matti Picus - Stéfan van der Walt From 9e298d10796686e94430476b300e347ac041f5da Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 May 2021 00:50:26 +0200 Subject: [PATCH 326/909] 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 fce591bb90..edc0b83f63 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -36,7 +36,7 @@ Emeritus: - Jaime Fernández del Río (2014-2021) -## Teams +## الأقسام The NumPy project is growing; we have teams for From 91eb703ebfcf04f3a3a5f98273d6fa67a64193f5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 May 2021 01:50:43 +0200 Subject: [PATCH 327/909] New translations about.md (Arabic) --- content/ar/about.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index edc0b83f63..539e5ae7e6 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -38,7 +38,7 @@ Emeritus: ## الأقسام -The NumPy project is growing; we have teams for +يزدهر مشروع نمباي حيث أصبح لدينا أقسام لكل من - code - documentation @@ -48,7 +48,7 @@ The NumPy project is growing; we have teams for See the [Team](/gallery/team.html) page for individual team members. -## Sponsors +## الرُعاة NumPy receives direct funding from the following sources: {{< sponsors >}} From 7b24953dfbda640ecbf38cac43b76f4d8de418f2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 May 2021 02:53:06 +0200 Subject: [PATCH 328/909] 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 539e5ae7e6..54320bf2c0 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -50,7 +50,7 @@ See the [Team](/gallery/team.html) page for individual team members. ## الرُعاة -NumPy receives direct funding from the following sources: +ويتلقى المشروع تمويلا مباشرا من المصادر التالية: {{< sponsors >}} From d4380a31f26cafd1870a2fd276495d6bbdf90cb9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 6 May 2021 23:45:15 +0200 Subject: [PATCH 329/909] New translations privacy.md (Chinese Simplified) --- content/zh/privacy.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/content/zh/privacy.md b/content/zh/privacy.md index a3674dd48a..6064e4c4f1 100644 --- a/content/zh/privacy.md +++ b/content/zh/privacy.md @@ -6,13 +6,3 @@ 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 442f064b4b8eb60e5e3f534627fbcdd83e56c1af Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 6 May 2021 23:45:20 +0200 Subject: [PATCH 330/909] New translations privacy.md (Korean) --- content/ko/privacy.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/content/ko/privacy.md b/content/ko/privacy.md index a3674dd48a..6064e4c4f1 100644 --- a/content/ko/privacy.md +++ b/content/ko/privacy.md @@ -6,13 +6,3 @@ 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 9b42b9c248c6c6d997a9622a476246885f2053c4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 6 May 2021 23:45:39 +0200 Subject: [PATCH 331/909] 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 022c2d4d086897ecd2feb1c8b505ab1fab97d83a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 6 May 2021 23:45:57 +0200 Subject: [PATCH 332/909] New translations privacy.md (Spanish) --- content/es/privacy.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/content/es/privacy.md b/content/es/privacy.md index a3674dd48a..6064e4c4f1 100644 --- a/content/es/privacy.md +++ b/content/es/privacy.md @@ -6,13 +6,3 @@ 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 56417d2dbccdd6211eb3dfdbf6915b600536e009 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 6 May 2021 23:46:25 +0200 Subject: [PATCH 333/909] New translations privacy.md (Japanese) --- content/ja/privacy.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/content/ja/privacy.md b/content/ja/privacy.md index 9f259a4210..c1b676d6ef 100644 --- a/content/ja/privacy.md +++ b/content/ja/privacy.md @@ -6,13 +6,3 @@ sidebar: false **numpy.org** は、Numpyプロジェクトの資金援助のスポンサーでもある、[NumFOCUS, Inc.](https://numfocus.org)によって運営されています。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policyを参照してください。 ポリシーまたはNumFOCUSのデータ収集、使用、および開示方法についてご質問がある場合は、privacy@numfocus.orgのNumFOCUSスタッフにお問い合わせください。 - - - - - - - - - - From 6336c3bb313bae85dbb281265dd4a5816c9f76b1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 6 May 2021 23:46:40 +0200 Subject: [PATCH 334/909] New translations privacy.md (Arabic) --- content/ar/privacy.md | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/content/ar/privacy.md b/content/ar/privacy.md index a3674dd48a..6064e4c4f1 100644 --- a/content/ar/privacy.md +++ b/content/ar/privacy.md @@ -6,13 +6,3 @@ 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 0b765ae79815f498257656df4787878374b1773d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 13:17:37 +0200 Subject: [PATCH 335/909] New translations citing-numpy.md (Korean) --- content/ko/citing-numpy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/citing-numpy.md b/content/ko/citing-numpy.md index 9754abe9df..9ff62d54b5 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 --- 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: [0.1038/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: [0.1038/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 a7948f5b981a28061f54aa50306dc8fb6a531693 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 13:17:39 +0200 Subject: [PATCH 336/909] 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 a427b5b1f5..ea867d4a7e 100644 --- a/content/ko/gethelp.md +++ b/content/ko/gethelp.md @@ -1,9 +1,9 @@ --- -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. 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). From 8d0fb7ae9ad57f9d2eec4844176a9b6d885079ab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 14:23:07 +0200 Subject: [PATCH 337/909] 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 efcde754ae..61c024a82c 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 e3d4f2dd890808f0c13bae15a583fcaa2ef438ce Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 14:23:08 +0200 Subject: [PATCH 338/909] 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 c65b872205..d81aec76fe 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -37,7 +37,7 @@ pip install numpy 파이썬만 활용해서 패키지를 설치하고 관리하는 것은 복잡하기 때문에, 다양한 대안들이 많이 있습니다. 이 가이드에는 가장 보편적이고, 명확한 방식을 알려줍니다. 이 가이드는 통상적으로 사용되는 운영체제와 하드웨어에서 Python과 NumPy 그리고 수치 계산을 해주는 PyData를 사용하는 유저를 위한 자료입니다. -## Recommendations +## 권장 사항 사용자의 전문성과 사용하는 운영체제를 기준으로 추천하는 방식을 알려드리겠습니다. 만약 당신이 초심자 또는 숙련자범위에 속해있다면, 간단하게 설치하고 싶다면 초심자로, 추후에 작업을 위해서 보다 구체적인 연습을 하고 싶다면 숙련자 자료를 참고하십시오. @@ -130,9 +130,9 @@ Besides install sizes, performance and robustness, there are two more things to - 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 +## 문제 해결 -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 6633ea8ab85363717a51e5b2a617edfa671ca18f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 14:23:09 +0200 Subject: [PATCH 339/909] New translations learn.md (Korean) --- content/ko/learn.md | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/content/ko/learn.md b/content/ko/learn.md index fda3607db0..ceee075654 100644 --- a/content/ko/learn.md +++ b/content/ko/learn.md @@ -1,5 +1,5 @@ --- -title: Learn +title: 배우기 sidebar: false --- @@ -19,28 +19,28 @@ NumPy 커뮤니티 [NumPy Tutorials](https://numpy.org/numpy-tutorials)에서 **튜토리얼** -* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -* [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 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) * [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 *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) -* [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy tutorial - *Nicolas 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* Python+SciPy와 관련된 자료는 [Goodreads list](https://www.goodreads.com/shelf/show/python-scipy)를 확인하시면 좋습니다. 대부분 NumPy를 핵심으로 사용하는 SciPy 에코시스템과 관련된 자료입니다. - **Videos** + **동영상** -* [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* *** @@ -48,7 +48,7 @@ Python+SciPy와 관련된 자료는 [Goodreads list](https://www.goodreads.com/s NumPy에서 제공하는 어레이 인덱싱, 분리, 중첩, 선형 대수 등과 같은 개념들을 보다 깊이 이해하고 싶다면, 아래의 숙련자 자료를 활용하십시오. - **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* @@ -57,20 +57,20 @@ NumPy에서 제공하는 어레이 인덱싱, 분리, 중첩, 선형 대수 등 * [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/) - **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** + **동영상** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* *** -## 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) @@ -80,11 +80,11 @@ NumPy에서 제공하는 어레이 인덱싱, 분리, 중첩, 선형 대수 등 *** -## 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. -## Contribute to this list +## 이 목록에 기여하기 -To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. +이 목록에 자료를 추가하려면 [Pull Request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md)를 통해서 제출하세요. 당신이 추천한 자료가 왜 이 페이지에 올라야하는지, 또한 어떤 사람들이 가장 좋아할지 말해주세요. From 06c34880f9b813f1884a05fa095dc0d588215978 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 14:23:11 +0200 Subject: [PATCH 340/909] 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 2309040ad2..b014d0e8ca 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/master/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/master/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 d8a589d06842fe5d550422359c50b2c3ad092d0a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 14:23:12 +0200 Subject: [PATCH 341/909] 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..0469768bcc 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 관행에 대하여 아무 질문이나 있으시다면, NumFOCUS 스태프인 privacy@numfocus.org로 연락해주세요. From f487a901a33cf9a36ccae7cb205add021ec0a58f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 14:23:14 +0200 Subject: [PATCH 342/909] New translations gethelp.md (Korean) --- content/ko/gethelp.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/gethelp.md b/content/ko/gethelp.md index ea867d4a7e..7fcf03e099 100644 --- a/content/ko/gethelp.md +++ b/content/ko/gethelp.md @@ -3,32 +3,32 @@ 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. 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 43c784479e809bc61cc3f9faeb7018d45defb2a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 14:23:15 +0200 Subject: [PATCH 343/909] New translations history.md (Korean) --- content/ko/history.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/history.md b/content/ko/history.md index fc79a621af..e6bf4bd6f5 100644 --- a/content/ko/history.md +++ b/content/ko/history.md @@ -1,5 +1,5 @@ --- -title: History of NumPy +title: NumPy의 역사 sidebar: false --- @@ -9,13 +9,13 @@ For the in-depth account on milestones in the development of NumPy and related l 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/)* +[*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 91ca671a8025b92945f635e067e486e2ee47e74f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 8 May 2021 15:53:42 +0200 Subject: [PATCH 344/909] 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 b40ed2af50..b8879d147e 100644 --- a/content/ar/case-studies/deeplabcut-dnn.md +++ b/content/ar/case-studies/deeplabcut-dnn.md @@ -1,5 +1,5 @@ --- -title: "Case Study: DeepLabCut 3D Pose Estimation" +title: "دراسة حالة: تقدير DeepLabCut 3D Pose" sidebar: false --- From 4fce776145c2813081fe760cd7132d6fabdb3710 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 May 2021 06:58:18 +0200 Subject: [PATCH 345/909] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index f2460d3d5b..bb30452351 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -1,5 +1,5 @@ --- -title: "Case Study: First Image of a Black Hole" +title: "사례 연구: 최초의 블랙홀 사진" sidebar: false --- From aeb925497015da6c9cde9fb24fb104fd1d78cf79 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 May 2021 07:57:23 +0200 Subject: [PATCH 346/909] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 44 +++++++++++----------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index bb30452351..b78d073162 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -3,50 +3,50 @@ 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 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 +### 과제 -* **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. 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" 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. @@ -58,11 +58,11 @@ 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" 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 0e8d1044693229032a6cb08547170bb461ca96eb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 May 2021 08:57:40 +0200 Subject: [PATCH 347/909] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index b78d073162..8b7bf49ad1 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -42,9 +42,9 @@ sidebar: false 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="numpy의 역할" caption="**블랙홀 시각화에서 NumPy의 역할**" >}} @@ -54,7 +54,7 @@ For example, the [`eht-imaging`][ehtim] Python package provides tools for simula 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 +## 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. From d343cf05156f9beecac322c5ee476486642959a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 May 2021 09:59:12 +0200 Subject: [PATCH 348/909] New translations history.md (Korean) --- content/ko/history.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/history.md b/content/ko/history.md index e6bf4bd6f5..f9d28d91d6 100644 --- a/content/ko/history.md +++ b/content/ko/history.md @@ -3,11 +3,11 @@ 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. 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](arxiv.org/abs/1907.10121). +NumPy 및 관련 라이브러리의 개발 단계에 대한 자세한 설명은 [arxiv.org](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. [*Numeric* 다운로드 페이지](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* From 51b1e6ca419e2e62495b9ec5a75967daf777ce83 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 9 May 2021 09:59:14 +0200 Subject: [PATCH 349/909] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index 8b7bf49ad1..e1ffece08b 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -48,11 +48,11 @@ EHT는 기존 및 최첨된 이미지 재구성 기술을 모두 사용한 뒤, {{< 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. 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" 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. 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. ## 요약 From e9515d8de59d14f50d847ae413ef91c01ec2d2cc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 10 May 2021 11:13:31 +0200 Subject: [PATCH 350/909] New translations install.md (Korean) --- content/ko/install.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index d81aec76fe..1da45e50e8 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -98,12 +98,12 @@ GPU를 사용하는 경우: 세번째 차이점은 conda는 환경이나 패키지간 의존성을 해결하기 위한 해키지 관리 도구를 제공합니다. 하지만 pip는 그를 위해서 (아주 많은) 추가적인 도구들이 필요합니다. -### Reproducible installs +### 재구성 가능한 설치 -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. 사용중인 패키지 및 버전을 재구성할 수 있도록 하는 것이 중요합니다. 가장 좋은 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: +1. 작업 중인 프로젝트마다 다른 환경을 이용하고, +2. 각각 자체 메타 데이터 형식이 있는 패키지 설치 프로그램을 통해 패키지 이름과 버전을 기록해둡니다. - 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/) From f06550c46c7846ad6ef46e99d96c83cf7e99ad39 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 01:12:29 +0200 Subject: [PATCH 351/909] New translations learn.md (Japanese) --- content/ja/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 9c2e6aa2eb..014185beb9 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -15,9 +15,9 @@ sidebar: false ## 初学者向け -NumPyについての情報はたくさん見つかります。 If you are new, we'd strongly recommend these: +NumPyについての情報はたくさん見つかります。 初心者の方にはこちらの資料をお勧めします： - **Tutorials** + **チュートリアル** * [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) From 525487e321b17e8b7c6d6efa7872361bc2a75fa5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 02:12:29 +0200 Subject: [PATCH 352/909] New translations learn.md (Japanese) --- content/ja/learn.md | 48 ++++++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 24 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 014185beb9..9b03845dd5 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -20,35 +20,35 @@ NumPyについての情報はたくさん見つかります。 初心者の方 **チュートリアル** * [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) -* [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) -* [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 *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** +* [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Pythonソフトウェアのエコシステムを広く紹介しています。 +* [Numpy初心者のための基礎](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 - 例題で学ぶ NumPy配列 ](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) +* [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) + + **書籍** * [NumPガイド*by Travelis E. Oliphant*](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*by Nicolas P. Rougier*](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) *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. +また、PythonとSciPyを題材にした [おすすめリスト](https://www.goodreads.com/shelf/show/python-scipy) もチェックしてみてください。 ほとんどの書籍ではNumPyを核とした「SciPyエコシステム」が説明されています。 - **Videos** + **動画** * [Numpy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** -## Advanced +## 上級者向け -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +インデックス処理、分割、スタック、線形代数などのより高度なNumpy の概念を、より深く理解するためには、これらの資料が参考になると思います。 - **Tutorials** + **チュートリアル** * [NumPy 100演習](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *Nicolas P. Rougier* * [NumPyとSciPyイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* @@ -57,20 +57,20 @@ Try these advanced resources for a better understanding of NumPy concepts like a * [高度なインデックシング](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/) - **Books** + **書籍** * [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) *by Jake Vanderplas* * [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney* * [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) *by Robert Johansson* - **Videos** + **動画** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* +* [アドバンスドNumPy -](https://www.youtube.com/watch?v=cYugp9IN1-Q) *ブロードキャストルール、ストライド、および高度なインデックシング* by Fan Nunuz-Iglesias * [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 Talks +## NumPyに関する講演 * [Numpy Indexing の未来](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernadez* (2016) * [Python における配列計算革命](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) @@ -80,11 +80,11 @@ Try these advanced resources for a better understanding of NumPy concepts like a *** -## Citing NumPy +## NumPy を引用する場合 -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). +もし、あなたの研究においてNumPyが重要な役割を果たし、あなたの論文でNumPyについて言及したい場合は、こちらの[ページ](/citing-numpy)を参照して下さい。 -## Contribute to this list +## このページへの貢献 -To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. +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/master/content/en/learn.md)を使って提案してみて下さい。 PRでは、あなたが推薦する資料が、なぜこのページで言及に値するのか、そして誰がその資料によって最も利益を得るかを説明して下さい。 From 425f94ad573fd349ecfe69d2469d3e8725dd901c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 02:12:30 +0200 Subject: [PATCH 353/909] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index e1ffece08b..0866051e69 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -48,7 +48,7 @@ EHT는 기존 및 최첨된 이미지 재구성 기술을 모두 사용한 뒤, {{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="numpy의 역할" caption="**블랙홀 시각화에서 NumPy의 역할**" >}} -예를 들어, [`eht-imaging`][ehtim] Python 패키지는 VLBI 데이터를 통해 실험이나 이미지 재구성을 수행할 때 필요한 도구를 제공합니다. 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="numpy를 강조하는 ehtim의 종속성 맵" caption="**NumPy를 강조하는 ehtim 패키지의 소프트웨어 종속성 차트**" >}} From 12b5a7c88bc2d92b597683d88e0ef8d61a5a9a6a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 03:12:03 +0200 Subject: [PATCH 354/909] 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 1da45e50e8..4ea56fed34 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -3,9 +3,9 @@ title: NumPy 설치 sidebar: false --- -NumPy 설치를 위해서는 Python만 필요합니다. 만양 파이썬이 설치되지 않았다면, Python, NumPy, 그리고 다양한 데이터 과학과 과학 계산을 위해 일반적으로 많이 사용되는 패키지를 한번에 설치할 수 있는 [Anaconda Distribution](https://www.anaconda.com/distribution)을 활용하여 설치하는 것을 추천합니다. +NumPy 설치를 위해서는 Python만 필요합니다. 만약 파이썬이 설치되지 않았다면, Python, NumPy, 그리고 다양한 데이터 과학과 과학 계산을 위해 일반적으로 많이 사용되는 패키지를 한번에 설치할 수 있는 [Anaconda Distribution](https://www.anaconda.com/distribution)을 활용하여 설치하는 것을 추천합니다. -NumPy 는 `conda`, `pip` 그리고 macOS과 Linux의 패키지 매니저를 사용하거나 또는 [소스](https://numpy.org/devdocs/user/building.html)로 부터 설치할 수 있습니다. 보다 상세한 설치 과정과 방법은 [Python and NumPy 설치 가이드](#python-numpy-install-guide)의 아래쪽에 있습니다. +NumPy는 `conda`, `pip`, macOS와 Linux의 패키지 매니저를 사용하거나 [소스](https://numpy.org/devdocs/user/building.html)로부터 설치할 수 있습니다. 보다 상세한 설치 과정과 방법은 [Python and NumPy 설치 가이드](#python-numpy-install-guide)의 아래쪽에 있습니다. **CONDA** @@ -110,7 +110,7 @@ GPU를 사용하는 경우: -## 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: From 2a4dd5574f41045525e2acd93ca85f0b6d2ca708 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 03:12:04 +0200 Subject: [PATCH 355/909] New translations history.md (Korean) --- content/ko/history.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/history.md b/content/ko/history.md index f9d28d91d6..e7402a34be 100644 --- a/content/ko/history.md +++ b/content/ko/history.md @@ -3,7 +3,7 @@ title: NumPy의 역사 sidebar: false --- -NumPy는 배열 데이터 구조와 이에 대한 빠른 수치적 루틴을 제공하는 Python의 기초적인 라이브러리입니다. 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. 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. NumPy 및 관련 라이브러리의 개발 단계에 대한 자세한 설명은 [arxiv.org](arxiv.org/abs/1907.10121)를 참고하십시오. From 60b05bb8fef4edf1e7ce54f553b4628f582e60c7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 03:12:05 +0200 Subject: [PATCH 356/909] New translations blackhole-image.md (Korean) --- content/ko/case-studies/blackhole-image.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/case-studies/blackhole-image.md b/content/ko/case-studies/blackhole-image.md index 0866051e69..10efe3275b 100644 --- a/content/ko/case-studies/blackhole-image.md +++ b/content/ko/case-studies/blackhole-image.md @@ -52,11 +52,11 @@ EHT는 기존 및 최첨된 이미지 재구성 기술을 모두 사용한 뒤, {{< 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**" >}} -NumPy 외에도 [SciPy](https://www.scipy.org)와 [Pandas](https://pandas.io) 등의 다른 많은 패키지가 블랙홀을 시각화하는 데이터 처리 파이프라인의 일부입니다. 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. ## 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="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} From ea1b1531bf6309affb32bc6e9986211c59df2a68 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 09:56:16 +0200 Subject: [PATCH 357/909] 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 4ea56fed34..8a0733b352 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -112,7 +112,7 @@ GPU를 사용하는 경우: ## 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 저절로 설치됨). 고급 사용자의 경우 사용한 BLAS가 디스크의 성능, 동작 및 크기에 영향을 끼칠 수 있기 때문에 세부 정보를 알고 싶을 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. From f9c79db598e3a4e76c0b0669be1b104ee1f734b5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 10:57:06 +0200 Subject: [PATCH 358/909] New translations community.md (Korean) --- content/ko/community.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/ko/community.md b/content/ko/community.md index 4e24a83784..fea42870c6 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -1,9 +1,9 @@ --- -title: Community +title: 커뮤니티 sidebar: false --- -NumPy is a community-driven open source project developed by a very diverse group of [contributors](/gallery/team.html). 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 매우 다양한 [기여자](/gallery/team.html) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. 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. @@ -33,7 +33,7 @@ _Please note that GitHub is not the right place to report a security vulnerabili ### [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 @@ -43,23 +43,23 @@ If you would like to find a local meetup or study group to learn more about NumP 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 +## 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) +- [SciPy 라틴 아메리카](https://www.scipyla.org) +- [SciPy 인도](https://scipy.in) +- [SciPy 일본](https://conference.scipy.org) +- [PyData 컨퍼런스](https://pydata.org/event-schedule/) (세계 곳곳의 여러 나라에서 1년에 15~20개의 이벤트를 개최) -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 가입 -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. 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. From 7ed79c43751b847b205acc09b7da50f094e6ac5d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 10:57:08 +0200 Subject: [PATCH 359/909] New translations install.md (Korean) --- content/ko/install.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ko/install.md b/content/ko/install.md index 8a0733b352..09fc02f3c0 100644 --- a/content/ko/install.md +++ b/content/ko/install.md @@ -114,20 +114,20 @@ GPU를 사용하는 경우: 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 저절로 설치됨). 고급 사용자의 경우 사용한 BLAS가 디스크의 성능, 동작 및 크기에 영향을 끼칠 수 있기 때문에 세부 정보를 알고 싶을 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 라이브러리가 휠 파일에 포함되어 있습니다. 이는 휠 파일의 크기를 더 크게 만들고, 사용자가 (예를 들어) SciPy도 설치하게 되면 디스크에 2개의 OpenBLAS 사본이 있게 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. ## 문제 해결 From a3c9558497afb59cb5a79ae9312510ee02c070ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 10:57:09 +0200 Subject: [PATCH 360/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 102 ++++++++++++++++++++--------------------- 1 file changed, 51 insertions(+), 51 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 64c90d9a8b..589a7b8b91 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -1,7 +1,7 @@ --- -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: logos/numpy.svg link: / @@ -9,9 +9,9 @@ params: #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: Get started + buttontext: 시작하기 #Where the main hero button links to buttonlink: "/install" #Hero image (from static/images/___) @@ -19,78 +19,78 @@ params: #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: NumPy v1.20.0 - content: Type annotation support - Performance improvements through multi-platform SIMD + content: 타입 어노테이션 지원 - 다중 플랫폼 SIMD를 통해 성능 향상 url: /news shell: - title: placeholder + title: 플레이스홀더 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 통계 + 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. + alttext: 크리켓 공이 녹지 위에 있습니다. 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: 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 + alttext: 치타 자세 분석 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: 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: 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, 희소배열 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. - - title: Open source - text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/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/master/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. tabs: - title: ECOSYSTEM + title: 생태계 section5: false navbar: - - title: Install + title: 설치 url: /install - - title: Documentation + title: 문서 url: https://numpy.org/doc/stable - - title: Learn + title: 배움 url: /learn - - title: Community + title: 커뮤니티 url: /community - - title: About Us + title: 정보 url: /about - - title: Contribute + title: 기여 url: /contribute footer: logo: numpy.svg @@ -107,46 +107,46 @@ footer: title: "" links: - - text: Install + text: 설치 link: /install - - text: Documentation + text: 문서 link: https://numpy.org/doc/stable - - text: Learn + text: 배움 link: /learn - - text: Citing Numpy + text: Numpy 인용 link: /citing-numpy - - text: Roadmap + text: 로드맵 link: https://numpy.org/neps/roadmap.html column2: links: - - text: About us + text: 정보 link: /about - - text: Community + text: 커뮤니티 link: /community - - text: Contribute + text: 기여 link: /contribute - - text: Code of conduct + text: 이용약관 link: /code-of-conduct column3: links: - - text: Get help + text: 도움받기 link: /gethelp - - text: Terms of use + text: 이용약관 link: /terms - - text: Privacy + text: 개인정보처리방침 link: /privacy - - text: Press kit + text: 홍보 자료 link: /press-kit From f356b198e8567d5e4a0d89ce29d53bcd7db629e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 11:57:17 +0200 Subject: [PATCH 361/909] New translations community.md (Korean) --- content/ko/community.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/community.md b/content/ko/community.md index fea42870c6..f4d98f985d 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -3,7 +3,7 @@ title: 커뮤니티 sidebar: false --- -NumPy는 매우 다양한 [기여자](/gallery/team.html) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. 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 매우 다양한 [기여자](/gallery/team.html) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. [NumPy 이용약관](/code-of-conduct)을 읽으면 커뮤니티가 발전하도록 해 주는 상대방과의 상호작용을 어떻게 하는지 그 방법을 알 수 있습니다. We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. @@ -59,7 +59,7 @@ NumPy 프로젝트에서는 자체 컨퍼런스를 추진하지 않습니다. ## NumPy 커뮤니티에 가입 -더욱 성장하기 위해, NumPy 프로젝트에서는 당신의 경험과 의욕을 필요로 합니다. 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 (야호!) [기여](/contribute) 페이지를 방문하시는 것을 추천해 드립니다. From 34da35906de79b9ec11a615cb60365ac39e0319d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 21:31:16 +0200 Subject: [PATCH 362/909] 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 54320bf2c0..d2c9c72dbe 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -33,7 +33,7 @@ Emeritus: - ناثانييل سميث (2012-2021) - جوليان تايلور (2013-2021) - باولي فيرتانين (2008-2021) -- Jaime Fernández del Río (2014-2021) +- جايمي فرنانديز ديل ريو(2014-2021) ## الأقسام From b687158c90cdb5e79c7371ab1fc8d110a33c9a48 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 22:32:00 +0200 Subject: [PATCH 363/909] New translations about.md (Arabic) --- content/ar/about.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index d2c9c72dbe..53166b3a99 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -40,13 +40,13 @@ Emeritus: يزدهر مشروع نمباي حيث أصبح لدينا أقسام لكل من -- code -- documentation -- website -- triage -- funding and grants +- الشفرة(الكود) +- الوثائق +- المواقع الالكترونية +- الفرز +- التمويل والمنح -See the [Team](/gallery/team.html) page for individual team members. +شاهد صفحة [ ](/gallery/team.html) لأعضاء الفريق. ## الرُعاة @@ -54,9 +54,9 @@ See the [Team](/gallery/team.html) page for individual team members. {{< sponsors >}} -## Institutional Partners +## الشركاء المؤسسيون -Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include: +الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. Current Institutional Partners include: {{< partners >}} From aeda3f0277800d614cce3d88ec364e0900996d94 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 15 May 2021 23:31:36 +0200 Subject: [PATCH 364/909] New translations about.md (Arabic) --- content/ar/about.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 53166b3a99..f045a7e558 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -56,13 +56,13 @@ Emeritus: ## الشركاء المؤسسيون -الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. Current Institutional Partners include: +الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. ويشمل الشركاء المؤسسيون الحاليون ما يلي: {{< partners >}} -## Donate +## التبرع -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. +يرجى النظر في التبرع للمشروع بما يتناسب مع مواردك إذا كنت وجدته مفيد في عملك أو بحثك أو شركتك. 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. From 6e977afae0530818fa1fde53ac6bcf052f5327d3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 16 May 2021 00:31:06 +0200 Subject: [PATCH 365/909] New translations about.md (Arabic) --- content/ar/about.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index f045a7e558..feb25c5b7f 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -62,11 +62,11 @@ Emeritus: ## التبرع -يرجى النظر في التبرع للمشروع بما يتناسب مع مواردك إذا كنت وجدته مفيد في عملك أو بحثك أو شركتك. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community. +يرجى النظر في التبرع للمشروع بما يتناسب مع مواردك إذا كنت وجدته مفيد في عملك أو بحثك أو شركتك. ،أي مبلغ قد يساعد، وستستخدم جميع التبرعات بشكل صارم لتطوير برمجيات المشروع مفتوحة المصدر، ووثائقه، ومجتمعه. -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. +نمباي هو مشروع ممول برعاية شركةNumFOCUS, 501(c)(3) وهي مؤسسة خيرية غير ربحية في الولايات المتحدة. فهى تدعم مشروع نمباي ماليا وقانونيا وإداريا للمساعدة في ضمان ازدهاره واستدامته. قم بزيارة [numfocus.org](https://numfocus.org) لمزيد من المعلومات. -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. +يمكنك التبرع من خلال: [](https://numfocus.org). وبخصوص المتبرعين في الولايات المتحدة، فإن هديتكم تخصم من الضرائب بالقدر الذي ينص عليه القانون. 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). {{< numfocus >}} From df04ab9aa1cece047b09990e2357cf0f5f6ee68b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 16 May 2021 01:32:34 +0200 Subject: [PATCH 366/909] New translations about.md (Arabic) --- content/ar/about.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index feb25c5b7f..5a8f7a290e 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -24,12 +24,12 @@ The role of the NumPy Steering Council is to ensure, through working with and se - Stéfan van der Walt - Eric Wieser -Emeritus: +الأعضاء الفخريون: - ترافيس أوليفانت (مؤسس المشروع، 2005-2012) - ألكس غريفينغ (2015-2017) - مارتن فان كيركويك (2017-2019) -- Allan Haldane (2015-2021) +- آلان هالدين (2015-2021) - ناثانييل سميث (2012-2021) - جوليان تايلور (2013-2021) - باولي فيرتانين (2008-2021) @@ -51,13 +51,13 @@ Emeritus: ## الرُعاة ويتلقى المشروع تمويلا مباشرا من المصادر التالية: -{{< sponsors >}} +{{< المتبرعون >}} ## الشركاء المؤسسيون الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. ويشمل الشركاء المؤسسيون الحاليون ما يلي: -{{< partners >}} +شركاء ## التبرع @@ -66,7 +66,7 @@ Emeritus: نمباي هو مشروع ممول برعاية شركةNumFOCUS, 501(c)(3) وهي مؤسسة خيرية غير ربحية في الولايات المتحدة. فهى تدعم مشروع نمباي ماليا وقانونيا وإداريا للمساعدة في ضمان ازدهاره واستدامته. قم بزيارة [numfocus.org](https://numfocus.org) لمزيد من المعلومات. -يمكنك التبرع من خلال: [](https://numfocus.org). وبخصوص المتبرعين في الولايات المتحدة، فإن هديتكم تخصم من الضرائب بالقدر الذي ينص عليه القانون. As with any donation, you should consult with your tax advisor about your particular tax situation. +يمكنك التبرع من خلال: [](https://numfocus.org). وبخصوص المتبرعين في الولايات المتحدة، فإن هديتكم تخصم من الضرائب بالقدر الذي ينص عليه القانون. كما هو الحال في أي تبرع، وعلى هذا فيتوجب عليك التشاور مع مستشارك الضريبى. -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). +وسيتخذ المجلس التوجيهي لنمباى القرارات المتعلقة بكيفية استخدام أي أموال يتلقاها على أفضل وجه. وتوثق الأولويات التقنية وأولويات البنية التحتية على [](https://www.numpy.org/neps/index.html#roadmap). {{< numfocus >}} From d72dce23f0a53668980a2c5b204b15e6a45c3509 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 16 May 2021 02:34:08 +0200 Subject: [PATCH 367/909] New translations about.md (Arabic) --- content/ar/about.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 5a8f7a290e..e0a547202c 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -14,15 +14,15 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. ويتألف المجلس التوجيهي المعني بالمشروع حاليا من الأعضاء التالية (بالترتيب الأبجدي): -- Sebastian Berg -- Ralf Gommers -- Charles Harris -- Stephan Hoyer -- Melissa Weber Mendonça -- Inessa Pawson -- Matti Picus -- Stéfan van der Walt -- Eric Wieser +- سيباستيان بيرج +- رالف غومرس +- تشارلز هاريس +- ستيفان هوير +- ميليسا فيبر ميندونسا (Melissa Weber Mendonça) +- إينيسا باوسون +- ماتى بيكاس +- ستيفان فان دير والت(Stefan van der Walt) +- إريك وايزر الأعضاء الفخريون: From 806b1783a7ba1bbc5df28d5d049f3629c6cfff28 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 16 May 2021 03:33:41 +0200 Subject: [PATCH 368/909] 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 e0a547202c..8f388ff560 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -12,7 +12,7 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ ## المجلس التوجيهي -The role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. ويتألف المجلس التوجيهي المعني بالمشروع حاليا من الأعضاء التالية (بالترتيب الأبجدي): +ويتمثل دور المجلس التوجيهي في ضمان ازدهار المشروع على المدى الطويل، على كلا الصعيدين التقنى والاجتماعى وذلك من خلال العمل فى مجتمع نمباى الواسع وخدمته. ويتألف المجلس التوجيهي المعني بالمشروع حاليا من الأعضاء التالية (بالترتيب الأبجدي): - سيباستيان بيرج - رالف غومرس From 76c55e6eea01875f15e2a32f8d9647c991bfb8fd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 16 May 2021 03:33:42 +0200 Subject: [PATCH 369/909] New translations learn.md (Arabic) --- content/ar/learn.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 0ad006d0f7..76a1428b08 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -1,11 +1,11 @@ --- -title: Learn +title: التعلم sidebar: false --- -For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). +للحصول على وثائق مشروع نمباى الرسمية عليك بزيارة[numpy.org/doc/stable](https://numpy.org/doc/stable). -## NumPy Tutorials +## المحتوى التعليمى لنمباى You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). @@ -13,9 +13,9 @@ You can find a set of tutorials and educational materials by the NumPy community Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). -## Beginners +## للمبتدئين -There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: +يوجد الكثير من المعلومات حول مشروع نمباى هناك. لذا إن كنت جديدا هنا فنوصيك بهذا بشدة: **Tutorials** @@ -57,13 +57,13 @@ Try these advanced resources for a better understanding of NumPy concepts like a * [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/) - **Books** + **الكتب** * [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** + **الفيديو** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* From 93804cda32b022b94361f607008f5e6976a4337e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 May 2021 01:11:34 +0200 Subject: [PATCH 370/909] 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 76a1428b08..badff9424e 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -7,7 +7,7 @@ sidebar: false ## المحتوى التعليمى لنمباى -You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +يقدم مجتمع نمباى مجموعة من الدروس والمواد التعليمية فى[NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). *** From c582d39633260d179f1a474db45c4433c5c7b216 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 May 2021 02:12:51 +0200 Subject: [PATCH 371/909] 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 badff9424e..5ca2d5a0e9 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -7,7 +7,7 @@ sidebar: false ## المحتوى التعليمى لنمباى -يقدم مجتمع نمباى مجموعة من الدروس والمواد التعليمية فى[NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +يقدم مجتمع نمباى مجموعة من الدروس والمواد التعليمية فى [المحتوى التعليمى لنمباى](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). *** From 4ec7f2d1f7ac43a5bbe906ca7041c3624893aa81 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 May 2021 03:13:21 +0200 Subject: [PATCH 372/909] New translations learn.md (Arabic) --- content/ar/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 5ca2d5a0e9..bd81997f6c 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -7,11 +7,11 @@ sidebar: false ## المحتوى التعليمى لنمباى -يقدم مجتمع نمباى مجموعة من الدروس والمواد التعليمية فى [المحتوى التعليمى لنمباى](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +يقدم مجتمع نمباى مجموعة من الدروس والمواد التعليمية فى [المحتوى التعليمى لنمباى](https://numpy.org/numpy-tutorials). الهدف من هذة الصفحة توفير موارد عالية الجودة عن طريق مشروع نمباى، سواء للتعلم الذاتى أو لتدريس الفصول الدراسية بتنسيق مذكرات جوبيتر(Jupyter Notebooks). لذا إن كنت مهتما بإضافة محتوياتك تحقق من هذا[numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). *** -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +وفيما يلى مختارات من المصادر الخارجية، للمساهمة تفحص [ نهاية هذة الصفح](#add-to-this-list). ## للمبتدئين From adc7528358b865e0d9cbe606090d47699bad2710 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 May 2021 04:13:23 +0200 Subject: [PATCH 373/909] New translations learn.md (Arabic) --- content/ar/learn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index bd81997f6c..7d59ba160b 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -11,16 +11,16 @@ sidebar: false *** -وفيما يلى مختارات من المصادر الخارجية، للمساهمة تفحص [ نهاية هذة الصفح](#add-to-this-list). +وفيما يلى مختارات من المصادر الخارجية، للمساهمة تفحص [ نهاية هذة الصفحة](#add-to-this-list). ## للمبتدئين يوجد الكثير من المعلومات حول مشروع نمباى هناك. لذا إن كنت جديدا هنا فنوصيك بهذا بشدة: - **Tutorials** + **المحتوى التعليمى** -* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -* [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [دروس Quickstart](https://numpy.org/devdocs/user/quickstart.html) +* [توضيح لنمباى: الدليل المرئى لمشروع نمباى*من قبل ليف ماكسيموف*](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) * [Machine Learning Plus - Introduction to ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) From 6ba011c70a3f9b696802224ea4e64b17b6a1ebdf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 May 2021 15:24:25 +0200 Subject: [PATCH 374/909] 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 7d59ba160b..c18448d9ad 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -20,7 +20,7 @@ sidebar: false **المحتوى التعليمى** * [دروس Quickstart](https://numpy.org/devdocs/user/quickstart.html) -* [توضيح لنمباى: الدليل المرئى لمشروع نمباى*من قبل ليف ماكسيموف*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [توضيح لنمباى: الدليل المرئى لمشروع نمباى *من قبل ليف ماكسيموف*](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) * [Machine Learning Plus - Introduction to ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) From 0e43c34e7a008127c33d12d8cf2669efe228a3d7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 18 May 2021 16:29:15 +0200 Subject: [PATCH 375/909] New translations learn.md (Arabic) --- content/ar/learn.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index c18448d9ad..874f67db17 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -21,16 +21,16 @@ sidebar: false * [دروس Quickstart](https://numpy.org/devdocs/user/quickstart.html) * [توضيح لنمباى: الدليل المرئى لمشروع نمباى *من قبل ليف ماكسيموف*](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) -* [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/) +* [محاضرات SciPy](https://scipy-lectures.org/)، بجانب التغطية لمشروع نمباى تعرض هذة المحاضرات مقدمة أوسع لمنظومة لغة البايثون العلمية. +* [نمباى: الأساسيات الثابتة للمبتدئين](https://numpy.org/devdocs/user/absolute_beginners.html) +* [بالإضافة إلى التعلم الآلى يوجدمقدمة للمصفوفة ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) +* [إدوريكا - تعلم مصفوفات نمباى بالأمثلة ](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 *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** + **الكتب** * [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/) @@ -38,7 +38,7 @@ sidebar: false 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** + **الفيديو** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* @@ -48,7 +48,7 @@ You may also want to check out the [Goodreads list](https://www.goodreads.com/sh Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. - **Tutorials** + **المحتوى التعليمى** * [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* From 9c774dd6e8ec33d6a99d3a93b23dcd0fb6392392 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 19 May 2021 05:50:07 +0200 Subject: [PATCH 376/909] New translations learn.md (Arabic) --- content/ar/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 874f67db17..2003de6d1f 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -25,8 +25,8 @@ sidebar: false * [نمباى: الأساسيات الثابتة للمبتدئين](https://numpy.org/devdocs/user/absolute_beginners.html) * [بالإضافة إلى التعلم الآلى يوجدمقدمة للمصفوفة ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) * [إدوريكا - تعلم مصفوفات نمباى بالأمثلة ](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 *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [منصةDataquest لعلوم البانات - البرنامج التعليمى لنمباى: تحليل البيانات باستخدام لغة البايثون](https://www.dataquest.io/blog/numpy-tutorial-python/) +* [برنامج نمباى التعليمى *من قبل نيكولاس روجير*](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) From 1868e4122710a60b4b9a30a165f1015d18516e9b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 19 May 2021 06:48:28 +0200 Subject: [PATCH 377/909] New translations learn.md (Arabic) --- content/ar/learn.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 2003de6d1f..2c12d57fbe 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -27,14 +27,14 @@ sidebar: false * [إدوريكا - تعلم مصفوفات نمباى بالأمثلة ](https://www.edureka.co/blog/python-numpy-tutorial/) * [منصةDataquest لعلوم البانات - البرنامج التعليمى لنمباى: تحليل البيانات باستخدام لغة البايثون](https://www.dataquest.io/blog/numpy-tutorial-python/) * [برنامج نمباى التعليمى *من قبل نيكولاس روجير*](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) +* [CS231 لجامعة ستانفورد*من قبل جاستين جونسون*](http://cs231n.github.io/python-numpy-tutorial/) +* [دليل استخدام نمباي](https://numpy.org/devdocs) **الكتب** -* [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* +* [دليل نمباى *ل ترافيس أوليفانت *](http://web.mit.edu/dvp/Public/numpybook.pdf) وهذا هو الإصدار المجانى 1 من 2006. وللإطلاع على أحدث نسخة (2015)انظر هنا [](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +* [لغة البايثون فى نمباى * ل نيكولاس روجير*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [محاضرات SciPy ممتازة](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877)*> لكلا من خوان نونيز إغليسياس وستيفان فان دير والت بالإضافة إلى هارييت داشنوف* 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. From 8d3f4b25b1efe3e0f8de750d7bdb55511c20ab58 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 19 May 2021 07:46:28 +0200 Subject: [PATCH 378/909] New translations learn.md (Arabic) --- content/ar/learn.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 2c12d57fbe..29bd14f707 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -36,22 +36,22 @@ sidebar: false * [لغة البايثون فى نمباى * ل نيكولاس روجير*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) * [محاضرات SciPy ممتازة](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877)*> لكلا من خوان نونيز إغليسياس وستيفان فان دير والت بالإضافة إلى هارييت داشنوف* -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. +يمكنك أيضا مراجعة [ قائمة القراءات الجيدة(Goodreads list) ](https://www.goodreads.com/shelf/show/python-scipy)حول موضوع "Python+SciPy". وتتحدث معظم الكتب فى هذة القائمة عن النظام البيئى لSciPy والذى يمثل نمباى جوهره. **الفيديو** -* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* +* [مقدمة للحوسبة الرقمية مع نمباى ](http://youtu.be/ZB7BZMhfPgk) *أليكساندر شابوت لوكلير* *** ## Advanced -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +لفهم أفضل لمفاهيم مشروع نمباى جرب هذة المصادر المتطورة مثل الفهرسة المتقدمة و والتقسيم والتكامل والجبر الخطى و.. إلخ. **المحتوى التعليمى** -* [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* +* [100 تمرين لنمباى](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) * لنيكولاس بي روجير* +* [مقدمة لنمباى وScipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) * ل ام سكوت شيل* * [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by 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) From 0b66f180234caff00522ae0e831f8a4fd9b0b86a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 19 May 2021 08:49:05 +0200 Subject: [PATCH 379/909] New translations learn.md (Arabic) --- content/ar/learn.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 29bd14f707..67e4153862 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -52,15 +52,15 @@ sidebar: false * [100 تمرين لنمباى](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) * لنيكولاس بي روجير* * [مقدمة لنمباى وScipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) * ل ام سكوت شيل* -* [Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) *by 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/) +* [حقيبة نمباى للإسعافات الأولية ](http://mentat.za.net/numpy/numpy_advanced_slides/) *ل ستيفين فان دير واليت* +* [نمباى بلغة البايثون(متقدم)](https://www.geeksforgeeks.org/numpy-python-set-2-advanced/) +* [فهرسة متقدمة](https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm) +* [التعلم الآلى وتحليل البيانات باستخدام نمباى](https://www.machinelearningplus.com/python/numpy-tutorial-python-part2/) **الكتب** -* [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* +* [مرجع البايثون لعلوم البيانات ل ](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *جيك فاندربلاس* +* [بايثون لتحليل البيانات ](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *من قبل ويس ماكيني* * [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* **الفيديو** From a909db1faeaed5a927ae4c92988b2abc6340b108 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 19 May 2021 09:52:14 +0200 Subject: [PATCH 380/909] New translations learn.md (Arabic) --- content/ar/learn.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 67e4153862..685aea7c3e 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -44,7 +44,7 @@ sidebar: false *** -## Advanced +## خيارات متقدمة لفهم أفضل لمفاهيم مشروع نمباى جرب هذة المصادر المتطورة مثل الفهرسة المتقدمة و والتقسيم والتكامل والجبر الخطى و.. إلخ. @@ -61,12 +61,12 @@ sidebar: false * [مرجع البايثون لعلوم البيانات ل ](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *جيك فاندربلاس* * [بايثون لتحليل البيانات ](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *من قبل ويس ماكيني* -* [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* +* [الحوسبة العلمية بلغة البايثون: تطبيقات باستخدام نمباى وSciPy ومكتبة Matplotlib المُختصة بالإظهار المرئي للبيانات للحوسبة العلمية وتحليل البيانات](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) * من قبل روبرت جونسون* **الفيديو** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* -* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* +* [خيارات نمباي المتقدمة - قواعد البث والمسارات والفهرسة المتقدمة](https://www.youtube.com/watch?v=cYugp9IN1-Q) * لخوان نونيز إغليسياس* +* [عمليات الفهرسة المتقدمة فى مصفوفات نمباى ](https://www.youtube.com/watch?v=2WTDrSkQBng) *لAmuls Academy* *** From 119bf4b02318d2609ebe2d0375ff2eefc62c08bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 21 May 2021 00:05:05 +0200 Subject: [PATCH 381/909] 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 685aea7c3e..0759e37c2b 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -70,7 +70,7 @@ sidebar: false *** -## NumPy Talks +## مناقشات نمباى * [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) From 66e40ad674909094aab901239fa3dc55edabdbb2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 21 May 2021 01:02:07 +0200 Subject: [PATCH 382/909] New translations learn.md (Arabic) --- content/ar/learn.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 0759e37c2b..634c0c9998 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -72,9 +72,9 @@ sidebar: false ## مناقشات نمباى -* [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) +* [مستقبل فهرسة نمباى](https://www.youtube.com/watch?v=o0EacbIbf58) * ل جيمي فيرنانديز*(2016) +* [تطور حوسبة المصفوفات بلغة البايثون](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) * بواسطة رالف غومرس*(2019) +* [نمباى: إلى أى مدى تغير نمباى وما هى التغييرات المستقبلية له؟](https://www.youtube.com/watch?v=YFLVQFjRmPY) *ل ماتى بيكاس* (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) From 6d574c68e9db88f7b4122366e0e71b44526adabd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 May 2021 10:11:50 +0200 Subject: [PATCH 383/909] New translations about.md (Arabic) --- content/ar/about.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 8f388ff560..28d9a22335 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -51,13 +51,13 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ ## الرُعاة ويتلقى المشروع تمويلا مباشرا من المصادر التالية: -{{< المتبرعون >}} +{{< sponsors >}} ## الشركاء المؤسسيون الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. ويشمل الشركاء المؤسسيون الحاليون ما يلي: -شركاء +{{< partners >}} ## التبرع From 16131537c9a90d75755ad05ba3dcd68875c684d3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 May 2021 11:12:47 +0200 Subject: [PATCH 384/909] New translations learn.md (Arabic) --- content/ar/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index 634c0c9998..4c5f5400c8 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -75,8 +75,8 @@ sidebar: false * [مستقبل فهرسة نمباى](https://www.youtube.com/watch?v=o0EacbIbf58) * ل جيمي فيرنانديز*(2016) * [تطور حوسبة المصفوفات بلغة البايثون](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) * بواسطة رالف غومرس*(2019) * [نمباى: إلى أى مدى تغير نمباى وما هى التغييرات المستقبلية له؟](https://www.youtube.com/watch?v=YFLVQFjRmPY) *ل ماتى بيكاس* (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) +* [محتوى نمباى](https://www.youtube.com/watch?v=dBTJD_FDVjU) *بواسطة رالف غومرس وسيباستيان بيرغ وماتى بيكاس وتايلر ريدي وستيفان فان دير والت وتشارلز هاريس* (2019) +* [مراجعة موجزة لحوسبة المصفوفات بلغة البايثون ](https://www.youtube.com/watch?v=f176j2g2eNc) *لترافيس أوليفانت* (2019) *** From 11a5e8b88701a2beed3abd54fafad9729e7d6785 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 May 2021 12:12:54 +0200 Subject: [PATCH 385/909] 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 4c5f5400c8..ba2173d88b 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -84,7 +84,7 @@ sidebar: false 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). -## Contribute to this list +## المساهمة فى هذة القائمة To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. From c8e4e15f62a1bf38d392d273b0c305ceb3868c70 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 22 May 2021 23:18:44 +0200 Subject: [PATCH 386/909] New translations learn.md (Arabic) --- content/ar/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index ba2173d88b..e74415ba19 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -80,9 +80,9 @@ sidebar: false *** -## Citing NumPy +## الاستشهاد بنمباى -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). +إذا كان لنمباى دور كبير فى بحثك وتود الإشارة إليه فى منشورك الأكاديمى،فيرجى الاطلاع على[ معلومات الاستشهاد هذة](/citing-numpy). ## المساهمة فى هذة القائمة From 55c751858336f8a3d7dac2288352ea11d9b60252 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 23 May 2021 00:17:43 +0200 Subject: [PATCH 387/909] 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 e74415ba19..f88f226dbd 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -87,4 +87,4 @@ sidebar: false ## المساهمة فى هذة القائمة -To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. +للإضافة إلى هذة المجموعة، قم بتقديم توصية [ عن طريق طلب سحب](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). اذكر لماذا تستحق توصيتك الذكر فى هذة الصفحة وأيضا من الجمهور الأكثر استفادة. From 7cc8aaad14e0ab9655cdd50679556a2b326f8b94 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 23 May 2021 00:17:44 +0200 Subject: [PATCH 388/909] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md index 9aa943e53e..52dab1bb6e 100644 --- a/content/ar/citing-numpy.md +++ b/content/ar/citing-numpy.md @@ -1,5 +1,5 @@ --- -title: Citing NumPy +title: الاستشهاد بنمباى sidebar: false --- From 0c95088e26c935e7a25b0ac6fffa748d7039e13e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 23 May 2021 05:31:31 +0200 Subject: [PATCH 389/909] New translations about.md (Chinese Simplified) --- content/zh/about.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/zh/about.md b/content/zh/about.md index 5dfd4e796f..2fa875f347 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -15,21 +15,21 @@ NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 指导委员会的成员们通过与 Numpy 社区合作并提供服务的形式来确保项目的长期发展，包括技术层面和社区层面。 Numpy 指导委员会目前由下列成员组成（按字母顺序排列）： - Sebastian Berg -- Ralf Gommers -- Charles Harris -- Stephan Hoyer -- Melissa Weber Mendonça -- Inessa Pawson +- 拉尔夫·戈默斯 +- 查尔斯·哈里斯 +- 史蒂芬·霍耶 +- 梅丽莎·韦伯·门多萨 +- 伊涅萨·波森 - Matti Picus -- Stéfan van der Walt -- Eric Wieser +- 斯特凡·凡·德·沃特 +- 艾瑞克·维瑟 荣誉会员： - Travis Oliphant（项目创始人，2005-2012年） - Alex Griffing（2015-2017年） - Marten van Kerkwijk （2017-2019年） -- Allan Haldane (2015-2021) +- 艾伦·霍尔丹（2015-2021） - Nathaniel Smith (2012-2021) - Julian Taylor (2013-2021) - Pauli Virtanen (2008-2021) From c941af5704c256da159031b5227fb6bfac46afbb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 23 May 2021 06:31:20 +0200 Subject: [PATCH 390/909] New translations about.md (Chinese Simplified) --- content/zh/about.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/zh/about.md b/content/zh/about.md index 2fa875f347..5dfd4e796f 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -15,21 +15,21 @@ NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 指导委员会的成员们通过与 Numpy 社区合作并提供服务的形式来确保项目的长期发展，包括技术层面和社区层面。 Numpy 指导委员会目前由下列成员组成（按字母顺序排列）： - Sebastian Berg -- 拉尔夫·戈默斯 -- 查尔斯·哈里斯 -- 史蒂芬·霍耶 -- 梅丽莎·韦伯·门多萨 -- 伊涅萨·波森 +- Ralf Gommers +- Charles Harris +- Stephan Hoyer +- Melissa Weber Mendonça +- Inessa Pawson - Matti Picus -- 斯特凡·凡·德·沃特 -- 艾瑞克·维瑟 +- Stéfan van der Walt +- Eric Wieser 荣誉会员： - Travis Oliphant（项目创始人，2005-2012年） - Alex Griffing（2015-2017年） - Marten van Kerkwijk （2017-2019年） -- 艾伦·霍尔丹（2015-2021） +- Allan Haldane (2015-2021) - Nathaniel Smith (2012-2021) - Julian Taylor (2013-2021) - Pauli Virtanen (2008-2021) From 6e6e65e04bc2bc177c580b47270b98ce53092b47 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 23 May 2021 06:31:21 +0200 Subject: [PATCH 391/909] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index efcde754ae..14785051b2 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -1,23 +1,23 @@ --- -title: NumPy Code of Conduct -sidebar: false +title: NumPy 行为守则 +sidebar: 假 aliases: - /conduct.html --- -### Introduction +### 引言 -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. +本行为守则适用于 NumPy 项目管理的所有网络空间，包括所有公共和私人的邮件列表、问题追踪器、 维基、 博客、 推特以及本社区使用的任何其他交流频道。 NumPy 项目不组织面对面活动。 然而，与本社区有关的活动应有一个与本社区内涵上类似的行为守则。 -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. +本行为守则应由每个正式或非正式参加本社区的、与项目有关的、与项目的活动有关的、特别是在代表项目时的人来遵守。 -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. +该守则不详尽，也不完整。 它有助于提升我们对协作、共享环境和目标的共识。 请尽量从内涵上遵循这一守则，以创造一个友好、高效益的环境来丰富有关的社区。 -### Specific Guidelines +### 具体准则 -We strive to: +要努力去： -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. +1. 敞开心扉。 我们邀请任何人参加本社区。 我们更喜欢公开交流与项目有关的信息，除非讨论某些敏感问题时。 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. From 780b0fdc83cbdc8fd8fba3f0cd64b63dbaeebb7e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 23 May 2021 06:31:22 +0200 Subject: [PATCH 392/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 64c90d9a8b..2a25bab481 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -1,7 +1,7 @@ --- -languageName: English +languageName: 英语 params: - description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + description: 为什么使用 Numpy？它有强大的高维数组、有数字计算工具、互可操作、高性能、开源。 navbarlogo: image: logos/numpy.svg link: / @@ -9,9 +9,9 @@ params: #Main hero title title: NumPy #Hero subtitle (optional) - subtitle: The fundamental package for scientific computing with Python + subtitle: 使用 Python 进行科学计算的基本包 #Button text - buttontext: Get started + buttontext: 入门 #Where the main hero button links to buttonlink: "/install" #Hero image (from static/images/___) @@ -19,44 +19,44 @@ params: #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: NumPy v1.20.0 - content: Type annotation support - Performance improvements through multi-platform SIMD + content: 支持输入批注 - 通过多平台SIMD实现性能改进 url: /news shell: - title: placeholder + title: 占位符 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 + title: 第一张黑洞图像 + text: NumPy 是怎么配合 SciPy 和 Matplotlib 等库来让事件视界望远镜（Eht）生成第一张黑洞图像的 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: 这是黑洞的第一张图像。它是黑色背景上衬着的一个橙色圆环。 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: 引力波探测 + text: 在1916年，阿尔伯特·爱因斯坦预测了引力波的存在。100年后，激光干涉引力波天文台（LIGO）的科学家利用 NumPy 证明了引力波的存在。 img: /images/content_images/case_studies/gravitional.png - alttext: Two orbs orbiting each other. They are displacing gravity around them. + alttext: 两个相互环绕的天体。它们改变了周围的引力。 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: 运动分析 + text: Cricket Analytics 正在通过统计模型和预测分析来改进队员的团队的表现，改变体育界。NumPy 让其中很多的分析成为了可能。 img: /images/content_images/case_studies/sports.jpg - alttext: Cricket ball on green field. + alttext: 绿色的赛场上的板球。 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: 使用深度学习进行估计 + text: DeepLabCut 使用 NumPy 来加速进行涉及观察动物行为的科学研究，以便跨物种和时间尺度推动研究发展。 img: /images/content_images/case_studies/deeplabcut.png alttext: Cheetah pose analysis url: /case-studies/deeplabcut-dnn keyfeatures: features: - - title: Powerful N-dimensional arrays + title: 强大的高维数组 text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. - - title: Numerical computing tools + title: 数字计算工具 text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. - title: Interoperable From ed8a37d5bc0ccc1bec6b151a0ad90f1d53e50038 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 06:57:54 +0200 Subject: [PATCH 393/909] New translations news.md (Korean) --- content/ko/news.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index d45a2fbe06..b859e149e0 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -72,15 +72,15 @@ 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 +## 릴리즈 -Here is a list of NumPy releases, with links to release notes. All 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. 모든 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 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 2020_. -- NumPy 1.17.5 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _1 Jan 2020_. +- NumPy 1.18.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020년 5월 3일_. +- NumPy 1.18.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _2020년 4월 19일_. +- NumPy 1.18.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020년 3월 17일_. +- NumPy 1.18.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _2020년 1월 6일_. +- NumPy 1.17.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020년 1월 1일_. - NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. - NumPy 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 2019_. - NumPy 1.17.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 Jul 2019_. From 82a0042582d92ae4f594aae5c95cbe19096743eb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 07:59:44 +0200 Subject: [PATCH 394/909] New translations community.md (Korean) --- content/ko/community.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/community.md b/content/ko/community.md index f4d98f985d..facac739f3 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -13,7 +13,7 @@ We offer several communication channels to learn, share your knowledge and conne 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) +### [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. @@ -36,11 +36,11 @@ _Please note that GitHub is not the right place to report a security vulnerabili 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. 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). ## 컨퍼런스 From 74f06810021627c73205bfca75f422e30924f4a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 07:59:45 +0200 Subject: [PATCH 395/909] New translations news.md (Korean) --- content/ko/news.md | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index b859e149e0..8599b0b88b 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,15 +1,15 @@ --- -title: News +title: 소식 sidebar: false --- -### 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: +_2021년 1월 30일_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html)이 출시되었습니다. 역대 최대의 NumPy 릴리즈입니다. 180명이 넘는 기여자분들께 감사드립니다. 흥미롭고 새로운 두 기능이 나왔습니다. - 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). @@ -37,18 +37,18 @@ _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-m 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 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. +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 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. +_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 acceptance @@ -81,9 +81,9 @@ NumPy 릴리즈의 목록입니다. 모든 버그 수정 릴리즈(`x.y.z`에서 - NumPy 1.18.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020년 3월 17일_. - NumPy 1.18.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _2020년 1월 6일_. - NumPy 1.17.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020년 1월 1일_. -- NumPy 1.18.0 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _22 Dec 2019_. -- NumPy 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 1.18.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _2019년 12월 22일_. +- NumPy 1.17.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _2019년 11월 11일_. +- 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 580a253d8b1fe850a2a1cd7df22959f9efe522d0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 09:05:35 +0200 Subject: [PATCH 396/909] New translations community.md (Korean) --- content/ko/community.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ko/community.md b/content/ko/community.md index facac739f3..2a51d7b327 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -21,11 +21,11 @@ On this list please use bottom posting, reply to the list (rather than to anothe *** -### [GitHub issue tracker](https://github.com/numpy/numpy/issues) +### [GitHub 이슈 추적기](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)._ @@ -40,7 +40,7 @@ NumPy에 _기여하는_ 방법에 대하여 질문하는 실시간 채팅방입 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. -NumPy에서도 가끔 자체 팀이나 관심 있는 기여자들을 위하여 직접 모임을 조직하기도 합니다. 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. ## 컨퍼런스 From 3ce0c3a31274de077e7df6e080f399d063815ff6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 10:50:23 +0200 Subject: [PATCH 397/909] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md index 5586668cba..2d34abc23c 100644 --- a/content/ko/report-handling-manual.md +++ b/content/ko/report-handling-manual.md @@ -1,5 +1,5 @@ --- -title: NumPy Code of Conduct - How to follow up on a report +title: NumPy 이용 약관 - 보고서의 후속 조치 방법 sidebar: false --- From d1c5013e15d055bd2f08625f162cd76765ce51bf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 10:50:24 +0200 Subject: [PATCH 398/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 987b38fb68..99b0115402 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -1,16 +1,16 @@ --- -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.

    -
    —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. @@ -57,7 +57,7 @@ Sports Analytics is a thriving field. Many researchers and companies [use NumPy] * **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provides useful insights into relationship between various datasets. -## 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. From eb33e910c5db49895133e01a39011c9bc4ee99bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 11:50:04 +0200 Subject: [PATCH 399/909] New translations community.md (Korean) --- content/ko/community.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/community.md b/content/ko/community.md index 2a51d7b327..584f98043d 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -5,19 +5,19 @@ sidebar: false NumPy는 매우 다양한 [기여자](/gallery/team.html) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. [NumPy 이용약관](/code-of-conduct)을 읽으면 커뮤니티가 발전하도록 해 주는 상대방과의 상호작용을 어떻게 하는지 그 방법을 알 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 참여 -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 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](http://numpy-discussion.10968.n7.nabble.com/). +리스트에 회신하려면 (다른 발신자에게 회신하기보다는) 하단의 게시물을 이용하십시오. 또, 자동 발신 메일에 회신하지 마십시오. 메일링 리스트에 대하여 검색 가능한 아카이브는 [여기](http://numpy-discussion.10968.n7.nabble.com/)에서 이용할 수 있습니다. *** @@ -27,7 +27,7 @@ On this list please use bottom posting, reply to the list (rather than to anothe - 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._ *** From c45195012ed48e610637664edd101fbae30e0e77 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 11:50:06 +0200 Subject: [PATCH 400/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 2533761d12..9ba7434317 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -1,21 +1,21 @@ - - - -title: Contribute to NumPy sidebar: false +title: NumPy에 기여하기 sidebar: false - - - -The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming -- in addition to +NumPy 프로젝트에서는 당신의 경험과 의욕을 환영합니다! NumPy에 기여할 수 있는 방법은 프로그래밍뿐만이 아닙니다. -- [Writing code](#writing-code) +- [코드 작성](#writing-code) -you can +아래와 같이 기여할 수도 있습니다. -- [Review pull requests](#reviewing-pull-requests) -- [Develop tutorials, presentations, and other educational material](#developing-educational-materials) -- [Triage issues](#issue-triaging) -- [Work on our website](#website-development) -- [Contribute graphic design](#graphic-design) -- [Translate website content](#translating-website-content) -- [Serve as a community coordinator](#community-coordination-and-outreach) -- [Write grant proposals and help with other fundraising](#fundraising) +- [풀 요청 검토](#reviewing-pull-requests) +- [튜토리얼, 발표 등 교육 자료 개발](#developing-educational-materials) +- [이슈 선별](#issue-triaging) +- [사이트에서 작업](#website-development) +- [그래픽 디자인에 기여](#graphic-design) +- [사이트 콘텐츠 번역](#translating-website-content) +- [커뮤니티 코디네이터로 기여](#community-coordination-and-outreach) +- [보조금 제안서 작성 및 기타 모금 지원](#fundraising) 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 3c0ac5a22c44658d848d2aea44173f44491af207 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 27 May 2021 11:50:07 +0200 Subject: [PATCH 401/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 99b0115402..0378cc2713 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -35,7 +35,7 @@ Today, there are rich and almost infinite troves of cricket game records and sta {{< 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 +### 과제 * **Data Cleaning and preprocessing** @@ -49,16 +49,16 @@ Today, there are rich and almost infinite troves of cricket game records and sta 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 +## 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) provides 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. ## 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. NumPy forms a solid foundation for a large set of Python packages which provide higher level functions related to data analytics, machine learning, and AI algorithms. These packages are widely deployed to gain real-time insights that help in decision making for game-changing outcomes, both on field as well as to draw inferences and drive business around the game of cricket. Finding out the hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. -{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="Diagram showing benefits of using NumPy for cricket analytics" caption="**Key NumPy Capabilities utilized**" >}} +{{< figure src="/images/content_images/cs/numpy_ca_benefits.png" class="fig-center" alt="NumPy를 크리켓 분석에 사용했을 때의 이익을 보여주는 다이어그램" caption="**활용된 주요 NumPy 기능**" >}} From c6dd4a6c72806351cc230b2ca64d9f47e13b82e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 28 May 2021 10:25:49 +0200 Subject: [PATCH 402/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 0378cc2713..1dc03264f3 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -12,7 +12,7 @@ sidebar: false ## 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. 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. From 40f21102007692e45e03ee890507ef8eb72ec9d5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 28 May 2021 11:30:15 +0200 Subject: [PATCH 403/909] 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 8599b0b88b..61856d3efa 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -63,9 +63,9 @@ _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17 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 받음 -_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. From 9f9b1cbe8e0d8f305524fb20f6b9311599537bd1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 28 May 2021 11:30:16 +0200 Subject: [PATCH 404/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 1dc03264f3..eb874b3d84 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -12,7 +12,7 @@ sidebar: false ## 크리켓이란 -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. 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. 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. @@ -27,25 +27,25 @@ Today, there are rich and almost infinite troves of cricket game records and sta {{< 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 +### 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. +* 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. * Real-time data analytics can help in gaining insights even during the game for changing tactics by the team and by associated businesses for economic benefits and growth. * Besides historical analysis, predictive models are harnessed to determine the possible match outcomes that require significant number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. -{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="pose estimator" caption="**Cricket Pose Estimator**" attr="*(Image credit: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} +{{< figure src="/images/content_images/cs/player-pose-estimator.png" class="fig-center" alt="자세 예측" caption="**크리켓 자세 예측**" attr="*(사진 출처: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} ### 과제 -* **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. -* **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. -* **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. @@ -59,6 +59,6 @@ Today, there are rich and almost infinite troves of cricket game records and sta ## 요약 -스포츠 분석은 프로 게임의 판도를 바꿀 것입니다. 특히 최근까지는 주로 "직감"이나 과거부터 내려오던 것을 답습하는 식으로 이뤄진 전략적 의사 결정에 대해서 말입니다. 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="NumPy를 크리켓 분석에 사용했을 때의 이익을 보여주는 다이어그램" caption="**활용된 주요 NumPy 기능**" >}} From 15cdc9f47efdfbb68ca70f9bd341c981acdefab7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 May 2021 01:09:58 +0200 Subject: [PATCH 405/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index eb874b3d84..d4b15d570f 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -12,9 +12,9 @@ sidebar: false ## 크리켓이란 -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. 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. @@ -30,7 +30,7 @@ Today, there are rich and almost infinite troves of cricket game records and sta ### 데이터 분석의 주요 목표 * 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. -* 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. +* 실시간 데이터 분석은 경기 중에도 팀과 관련 사업의 변화하는 전략에 대한 통찰력을 확보하여 경제적 이익과 성장을 도모하는 데 도움이 될 수 있습니다. * 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="자세 예측" caption="**크리켓 자세 예측**" attr="*(사진 출처: connect.vin)*" attrlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} From 2a5f7e642e8b5a2d67c1ace9356286a4d7c1de79 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 May 2021 15:21:22 +0200 Subject: [PATCH 406/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 9ba7434317..daf8b9a464 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -25,7 +25,7 @@ We also have a biweekly _community call_, details of which are announced on the 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 +### 코드 작성 Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the codebase. From 756cdf2035462407f05aed535426d79fbe7f740a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 29 May 2021 16:22:27 +0200 Subject: [PATCH 407/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 46 ++++++++++++++++++++-------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index daf8b9a464..359d6d8aa9 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -27,52 +27,52 @@ Our community aspires to treat everyone equally and to value all contributions. ### 코드 작성 -Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the codebase. +프로그래머 여러분, 이 [도움말](https://numpy.org/devdocs/dev/index.html#development-process-summary)에서 어떻게 코드베이스에 기여하는지 알 수 있습니다. -### 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 +### 풀 요청 검토 +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 +### 사이트 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 +### 사이트 콘텐츠 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 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 +### 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. +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. 이런 지원이 얼마나 큰 차이를 만들어 냈는지 [SciPy'19 강연](https://www.youtube.com/watch?v=dBTJD_FDVjU)에서 확인하실 수 있습니다. 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 fe0e9cc1220adb166cf11e966f7a1de2c6a45d8c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 30 May 2021 10:31:56 +0200 Subject: [PATCH 408/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index d4b15d570f..0cb22bcb26 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -39,11 +39,11 @@ Today, there are rich and almost infinite troves of cricket game records and sta * **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. * **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. 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. * **예측 분석의 복잡성** From e1d829cccf07e0c2bff092efa6a939dcf2815b9d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 30 May 2021 11:31:38 +0200 Subject: [PATCH 409/909] 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 61c024a82c..48ef3716e7 100644 --- a/content/ko/code-of-conduct.md +++ b/content/ko/code-of-conduct.md @@ -76,8 +76,8 @@ In cases not involving clear severe and obvious breaches of this Code of Conduct The Committee will respond to any report as soon as possible, and at most within 72 hours. -### Endnotes +### 끝내며 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/reference/dev/conduct/code_of_conduct.html) +- [SciPy 이용 약관](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) From 297a9b8778e483f655f6cb472c5e16d0fe69707f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 30 May 2021 11:31:39 +0200 Subject: [PATCH 410/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 0cb22bcb26..51c3ef7097 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -25,7 +25,7 @@ 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" 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" >}} ### 데이터 분석의 주요 목표 @@ -43,7 +43,7 @@ Today, there are rich and almost infinite troves of cricket game records and sta * **동적 모델링** - 크리켓에서는 다른 스포츠와 마찬가지로 다양한 선수의 수, 선수의 속성, 공이나 잠재적 행동의 가능성 등 여러 가능성을 추적할 때 많은 변수가 작용합니다. 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. * **예측 분석의 복잡성** From 56ae2a62f275a138116076d504943f9b3a219557 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 5 Jun 2021 17:52:14 +0200 Subject: [PATCH 411/909] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md index 52dab1bb6e..c16d60c29a 100644 --- a/content/ar/citing-numpy.md +++ b/content/ar/citing-numpy.md @@ -3,9 +3,9 @@ title: الاستشهاد بنمباى 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: +إذا كان لنمباى دور كبير فى بحثك وتود الإشارة إليه فى منشورك الأكاديمى،فبامكانك القاء نظرة على هذة الورقة المقترحة للاستشهاد: -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [0.1038/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. _برمجة المصفوفات بواسطة نمباى_. الطبيعة 585, 357–362 (2020). DOI: [0.1038/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:_ From e915e24f1fac972a2232a59538006d5c8bec0ae4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 5 Jun 2021 18:53:04 +0200 Subject: [PATCH 412/909] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md index c16d60c29a..4e6924e76e 100644 --- a/content/ar/citing-numpy.md +++ b/content/ar/citing-numpy.md @@ -5,9 +5,9 @@ sidebar: false إذا كان لنمباى دور كبير فى بحثك وتود الإشارة إليه فى منشورك الأكاديمى،فبامكانك القاء نظرة على هذة الورقة المقترحة للاستشهاد: -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _برمجة المصفوفات بواسطة نمباى_. الطبيعة 585, 357–362 (2020). DOI: [0.1038/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. _برمجة المصفوفات بواسطة نمباى_. الطبيعة 585, 357–362 (2020). DOI: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([رابط النشر](https://www.nature.com/articles/s41586-020-2649-2)). -_In BibTeX format:_ +_بتنسيق In BibTeX:_ ``` @Article{ harris2020array, From cf7771c93bc459845b0592b855eeb80a34c2e94e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 5 Jun 2021 18:53:05 +0200 Subject: [PATCH 413/909] New translations press-kit.md (Arabic) --- content/ar/press-kit.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/press-kit.md b/content/ar/press-kit.md index 2309040ad2..ee87b81a02 100644 --- a/content/ar/press-kit.md +++ b/content/ar/press-kit.md @@ -1,8 +1,8 @@ --- -title: Press kit +title: الملف الصحفى 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. +نرحب بتسهيل إدراج مشروع نمباى عليك سواء فى بحثك الأكاديمى أو كمادة دراسية أو كعرض. -You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/master/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). +لذلك ستجد عدة إصدارات عالية الجودة من شاعر نمباى[ هنا](https://github.com/numpy/numpy/tree/master/branding/logo). وعليك أن تلاحظ أنه باستخدام موارد numpy.org فأنت توافق على[ قواعد السلوك لنمباى](/code-of-conduct). From b2fe833f1dca4ce63ce33604e69c06631f957a0e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 5 Jun 2021 18:53:06 +0200 Subject: [PATCH 414/909] New translations privacy.md (Arabic) --- content/ar/privacy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/privacy.md b/content/ar/privacy.md index 6064e4c4f1..93c6c713df 100644 --- a/content/ar/privacy.md +++ b/content/ar/privacy.md @@ -1,8 +1,8 @@ --- -title: Privacy Policy +title: سياسة الخصوصية 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** يتم تشغيلة بواسطة [NumFOCUS, Inc.](https://numfocus.org), الراعى المالى لمشروع نمباى. للوصول إلى سياسة الخصوصية لهذا الموقع برجاء زيارة 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. +إذا كان لديك أسئلة بخصوص سياسة أو جمع بيانات NumFOCUS واستخدامها بالإضافة إلى ممارسات الإفصاح، يرجى الاتصال بموظفى NumFOCUS على موقع privacy@numfocus.org. From 1226fe2084ede51bfe78bc3090a76bd9a57755bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 5 Jun 2021 18:53:07 +0200 Subject: [PATCH 415/909] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index a427b5b1f5..9d211d4f4e 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -1,5 +1,5 @@ --- -title: Get Help +title: الحصول على مساعدة sidebar: false --- From 63c5cba7b9ec9ef6b9643af5f63e2541bca8f03a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 6 Jun 2021 07:21:47 +0200 Subject: [PATCH 416/909] 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 8963bbed5b..4375540c13 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -34,7 +34,7 @@ El [ telescopio Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), 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** +* **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. @@ -46,7 +46,7 @@ El [ telescopio Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), -## NumPy’s Role +## Los roles 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? @@ -64,7 +64,7 @@ Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [ -## 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. From e4c477c68a04ff8aba44510da80c39c5a46eefc5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 21:35:14 +0200 Subject: [PATCH 417/909] 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 8bc93d90f4829b6e276ab9605be2fe7b1b38d7b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 21:35:15 +0200 Subject: [PATCH 418/909] 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 83719242dc8f66f4d21efdb37930cf33d8370cae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 21:35:16 +0200 Subject: [PATCH 419/909] 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 95d81f9dfc85f9ad3d414140ad81461c3cbaae03 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 21:35:17 +0200 Subject: [PATCH 420/909] 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 ebe5568d471c30c4e1d3dc69c4f492202ed82d23 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 21:35:18 +0200 Subject: [PATCH 421/909] 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 0fe9ff02c5dc77275c305279d9c40f6ceb1859b3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 21:35:19 +0200 Subject: [PATCH 422/909] 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 5307cf2976cac48bca786365cfb186adc4d94a2e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 22:38:07 +0200 Subject: [PATCH 423/909] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index 7f3d89944d..53d38334d8 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -5,9 +5,9 @@ sidebar: false Para a **documentação oficial do NumPy** visite [numpy.org/doc/stable](https://numpy.org/doc/stable). -## NumPy Tutorials +## Tutoriais NumPy -You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +Você pode encontrar um conjunto de tutoriais e materiais educativos criados pela comunidade do NumPy em [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). *** From d57644bf49a9ddc8300354b23dda93041fe1c41e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 23:44:40 +0200 Subject: [PATCH 424/909] 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 072dfb3117b72a0fb42b2142805d39d769955d9d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 22 Jun 2021 23:44:41 +0200 Subject: [PATCH 425/909] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index 53d38334d8..23c4ac5780 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -7,17 +7,17 @@ Para a **documentação oficial do NumPy** visite [numpy.org/doc/stable](https:/ ## Tutoriais NumPy -Você pode encontrar um conjunto de tutoriais e materiais educativos criados pela comunidade do NumPy em [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +Você pode encontrar um conjunto de tutoriais e materiais educativos criados pela comunidade do NumPy em [NumPy Tutorials](https://numpy.org/numpy-tutorials). O objetivo desta página é fornecer recursos de alta qualidade pelo projeto NumPy, tanto para autoaprendizado como para o ensino, no formato de notebooks Jupyter. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). *** -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +Abaixo você pode encontrar uma coleção de recursos externos selecionados. Para contribuir, veja o [fim desta página](#add-to-this-list). -## Beginners +## Iniciantes -There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: +Há uma tonelada de informações sobre o NumPy por aí. Se você está começando, recomendamos fortemente as fontes seguintes: - **Tutorials** + **Tutoriais** * [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) * [NumPy Illustrated: The Visual Guide to NumPy *by Lev Maximov*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) @@ -30,25 +30,25 @@ There's a ton of information about NumPy out there. If you are new, we'd strongl * [Stanford CS231 *by Justin Johnson*](http://cs231n.github.io/python-numpy-tutorial/) * [NumPy User Guide](https://numpy.org/devdocs) - **Books** + **Livros** * [Guide to NumPy *de Travis E. Oliphant*](http://web.mit.edu/dvp/Public/numpybook.pdf) Essa é uma versão free de 2006. Para a última versão (2015) veja [aqui](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). * [From Python to NumPy *por 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) *por Juan Nunez-Iglesias, Stefan van der Walt, e 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. +Você também pode querer conferir a [lista Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre o tema "Python+SciPy. A maioria dos livros lá serão sobre o "ecossistema SciPy", que tem o NumPy em sua essência. - **Videos** + **Vídeos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* *** -## Advanced +## Avançados -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais. - **Tutorials** + **Tutoriais** * [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* @@ -57,20 +57,20 @@ Try these advanced resources for a better understanding of NumPy concepts like a * [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/) - **Books** + **Livros** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* * [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *por 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) *por Robert Johansson* - **Videos** + **Vídeos** * [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *por Amuls Academy* *** -## NumPy Talks +## Palestras sobre NumPy * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) @@ -80,11 +80,11 @@ Try these advanced resources for a better understanding of NumPy concepts like a *** -## Citing NumPy +## Citando o 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). +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](/citing-numpy). -## Contribute to this list +## Contribua para esta lista -To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. +Para adicionar a essa coleção, envie uma recomendação [através de um pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Diga por que sua recomendação merece ser mencionada nesta página e também qual o público que mais se beneficiaria. From bb15d89934321796abdba09fda2a24b2fe93655c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 10:36:31 +0200 Subject: [PATCH 426/909] 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 a4a48ffe68d032e0881bea54cfbaf0f02a62cbe7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:44 +0200 Subject: [PATCH 427/909] New translations news.md (Spanish) --- content/es/news.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/content/es/news.md b/content/es/news.md index d45a2fbe06..8b6c78b8ea 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -3,6 +3,11 @@ title: News sidebar: false --- +### 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: From 76dd616aa9bf4baf1a0578757aa595e1bf001457 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:45 +0200 Subject: [PATCH 428/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 64c90d9a8b..9a90251b51 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: NumPy v1.20.0 - content: Type annotation support - Performance improvements through multi-platform SIMD + title: 2020 NumPy survey + content: results are in url: /news shell: title: placeholder From efe7590b8c5b0c2c5ef662f152e38e702731f70a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:47 +0200 Subject: [PATCH 429/909] New translations news.md (Arabic) --- content/ar/news.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/content/ar/news.md b/content/ar/news.md index d45a2fbe06..8b6c78b8ea 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -3,6 +3,11 @@ title: News sidebar: false --- +### 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: From 07a74e4e8c64b6778ecef1a355157f09c08a46a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:48 +0200 Subject: [PATCH 430/909] New translations config.yaml (Arabic) --- content/ar/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 64c90d9a8b..9a90251b51 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: NumPy v1.20.0 - content: Type annotation support - Performance improvements through multi-platform SIMD + title: 2020 NumPy survey + content: results are in url: /news shell: title: placeholder From d02428a1dddda889b416aed12124cfe4758a8fb3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:49 +0200 Subject: [PATCH 431/909] New translations news.md (Japanese) --- content/ja/news.md | 63 +++++++++++++++++++++++++--------------------- 1 file changed, 34 insertions(+), 29 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 23413d7de7..3e9fdb6fcf 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,78 +3,83 @@ title: ニュース sidebar: false --- -### Numpy 1.20.0 リリース +### 2020 NumPy survey results -_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。 +_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: - 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 +### Diversity in the NumPy project -_2020年9月20日に_ 、私達は[ 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に初めての公式のNumPy論文が掲載されました! +### First official NumPy paper published in Nature! -_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 が登場し、Numpy はいつバイナリホイールをリリースするのか? +### Python 3.9 is coming, when will NumPy release binary wheels? -_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ホイール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。 ホイールリリースのイベントに備えて、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 +### 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ホイールは最新の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!! +### The inaugural NumPy survey is live! -_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! +### NumPy has a new logo! -_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 1.19.0 リリース +### NumPy 1.19.0 release -_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 +### Season of Docs acceptance -_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 +### 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。 +_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。 +### NumPy receives a grant from the Chan Zuckerberg Initiative -_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、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. 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年5月3日_. - NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年4月19日_. From 33b21ff881964a783204ec6a4aaa01437e6ea18b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:50 +0200 Subject: [PATCH 432/909] 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 64c90d9a8b..9a90251b51 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: NumPy v1.20.0 - content: Type annotation support - Performance improvements through multi-platform SIMD + title: 2020 NumPy survey + content: results are in url: /news shell: title: placeholder From 134900db217b9c237556e0175f232425fd0e1bbb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:51 +0200 Subject: [PATCH 433/909] New translations news.md (Korean) --- content/ko/news.md | 29 +++++++++++++++++------------ 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 61856d3efa..06bf2e794c 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -3,13 +3,18 @@ title: 소식 sidebar: false --- -### Numpy 1.20.0 출시 +### 2020 NumPy survey results -_2021년 1월 30일_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html)이 출시되었습니다. 역대 최대의 NumPy 릴리즈입니다. 180명이 넘는 기여자분들께 감사드립니다. 흥미롭고 새로운 두 기능이 나왔습니다. +_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)). -### NumPy 프로젝트 내 다양성 +### 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). @@ -37,18 +42,18 @@ _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-m Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy에 새로운 로고가 생겼습니다! +### NumPy has a new logo! -_2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. +_Jun 24, 2020_ -- NumPy now has a new logo: -NumPy 로고 +NumPy logo -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. +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 출시 +### NumPy 1.19.0 release -_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. +_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 @@ -63,9 +68,9 @@ _Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17 Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. -### NumPy가 Chan Zuckerberg Initiative에서 보조금을 받음 +### NumPy receives a grant from the Chan Zuckerberg Initiative -_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. +_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. @@ -74,7 +79,7 @@ More details on our proposed initiatives and deliverables can be found in the [f ## 릴리즈 -NumPy 릴리즈의 목록입니다. 모든 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. +Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 1.18.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020년 5월 3일_. - NumPy 1.18.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _2020년 4월 19일_. From 2dfbebfb6f29b3b271ffa94b8cfac16ce4b4d20e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:52 +0200 Subject: [PATCH 434/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 589a7b8b91..9889903404 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: NumPy v1.20.0 - content: 타입 어노테이션 지원 - 다중 플랫폼 SIMD를 통해 성능 향상 + title: 2020 NumPy survey + content: results are in url: /news shell: title: 플레이스홀더 From 885ac77db5c83dd6b18ea372e31fa245a153dff2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:53 +0200 Subject: [PATCH 435/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/content/zh/news.md b/content/zh/news.md index d45a2fbe06..8b6c78b8ea 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -3,6 +3,11 @@ title: News sidebar: false --- +### 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: From c58f27bf4fcba36ea4c8bb0080729e9c59e8f9a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:54 +0200 Subject: [PATCH 436/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 2a25bab481..3924306c40 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: NumPy v1.20.0 - content: 支持输入批注 - 通过多平台SIMD实现性能改进 + title: 2020 NumPy survey + content: results are in url: /news shell: title: 占位符 From 27f11da9e4fa8db92183a7b0dff6bd4ba746031a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:55 +0200 Subject: [PATCH 437/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 61 +++++++++++++++++++++++++--------------------- 1 file changed, 33 insertions(+), 28 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index dd7a55e3c8..b5fc418e46 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,78 +3,83 @@ title: Notícias sidebar: false --- -### NumPy versão 1.20.0 +### 2020 NumPy survey results -_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: +_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: - 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 +### Diversity in the NumPy project -_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! +### First official NumPy paper published in 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? +### Python 3.9 is coming, when will NumPy release binary wheels? -_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 +### 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. +_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! +### The inaugural NumPy survey is live! -_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! +### NumPy has a new 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 1.19.0 +### NumPy 1.19.0 release -_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 +### Season of Docs acceptance -_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 +### 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`. +_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 +### NumPy receives a grant from the 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 1.18.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de maio de 2020_. - NumPy 1.18.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 de abril de 2020_. From 0c4ac4576d642111551c0e71ff6c8e42368a6cc5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 21:10:56 +0200 Subject: [PATCH 438/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 64c90d9a8b..9a90251b51 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: NumPy v1.20.0 - content: Type annotation support - Performance improvements through multi-platform SIMD + title: 2020 NumPy survey + content: results are in url: /news shell: title: placeholder From 4e2d4fb0b418c442d6bf0f2c320f7ba40ff2f58a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 23 Jun 2021 22:18:35 +0200 Subject: [PATCH 439/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 60 +++++++++++++++++++++++----------------------- 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index b5fc418e46..42ebe38f03 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,83 +3,83 @@ title: Notícias sidebar: false --- -### 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 faculdades 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 1.20.0 release +### 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 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. - 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)). -### Diversity in the NumPy project +### 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). -### First official NumPy paper published in Nature! +### 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. -### Python 3.9 is coming, when will NumPy release binary wheels? +### 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 1.19.2 release +### 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. -### The inaugural NumPy survey is live! +### 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). -### NumPy has a new logo! +### 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 logo é uma versão moderna do antigo, com um design mais limpo. Obrigado 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 1.19.0 release +### 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_ -- 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. -### Season of Docs acceptance +### 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 1.18.0 release +### 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_ -- 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`. -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. -### NumPy receives a grant from the Chan Zuckerberg Initiative +### O NumPy receberá um auxílio 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 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. -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, 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. -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 -Here is a list of NumPy releases, with links to release notes. All 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. 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 1.18.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 de abril de 2020_. From 739db30822e249ca6d14bbec81ec76c67b9748a4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 25 Jun 2021 22:32:06 +0200 Subject: [PATCH 440/909] New translations news.md (Arabic) --- content/ar/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/news.md b/content/ar/news.md index 8b6c78b8ea..03dba38d85 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -3,7 +3,7 @@ title: News sidebar: false --- -### 2020 NumPy survey results +### نتائج استطلاع نمباى لعام 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/. From d93e0d501f2bd332294960c5b919ba3c3d8920dc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 25 Jun 2021 23:30:34 +0200 Subject: [PATCH 441/909] New translations news.md (Arabic) --- content/ar/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/news.md b/content/ar/news.md index 03dba38d85..ee8b4e483c 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,5 +1,5 @@ --- -title: News +title: أخبار sidebar: false --- From bbaf0faaddcfaa85780b369c2ab281b15a9853c9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 26 Jun 2021 00:30:15 +0200 Subject: [PATCH 442/909] 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 ee8b4e483c..a798303e4c 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -5,12 +5,12 @@ sidebar: false ### نتائج استطلاع نمباى لعام 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 يونيو2021_ -قام فريق استطلاع نمباى فى عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتى ميشيغان وماريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباى. بامكانك معرفة نتائج الدراسة الاستقصائية من هنا: https://numpy.org/user-survey-2020/. -### Numpy 1.20.0 release +### الإصدار 1.20.0 لنمباى -_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: +_30يناير2021_ -- [إصدار1.20.0 لنمباى](https://numpy.org/doc/stable/release/1.20.0-notes.html) متاح الآن. وهذا هو أكبر اصدار لنمباى حتى الآن بفضل 180+ من المساهمين. والسمتان الجديدتان الأكثر إثارة للاهتمام هما: - 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)). From 1af041093dce9976105587ad53512f7e98cdb0fa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 26 Jun 2021 01:38:57 +0200 Subject: [PATCH 443/909] New translations news.md (Arabic) --- content/ar/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index a798303e4c..5bdf32981c 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -14,7 +14,7 @@ _30يناير2021_ -- [إصدار1.20.0 لنمباى](https://numpy.org/doc/stab - 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 +### التنوع فى مشروع نمباى _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). @@ -77,7 +77,7 @@ 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 +## الإصدارات Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. From 89c2810d7bd4f2710ccba8d377a98c41cc5eebd1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 26 Jun 2021 02:39:53 +0200 Subject: [PATCH 444/909] New translations news.md (Arabic) --- content/ar/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/news.md b/content/ar/news.md index 5bdf32981c..73703a607e 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -79,7 +79,7 @@ More details on our proposed initiatives and deliverables can be found in the [f ## الإصدارات -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +إليك قائمة من إصدارات نمباى، مع روابط لاصدار الملاحظات. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. From 9accf3e9189bb76bfcdb5ff8c28f1acd6caaa63e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 26 Jun 2021 22:51:33 +0200 Subject: [PATCH 445/909] New translations news.md (Arabic) --- content/ar/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 73703a607e..f3a7804590 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -3,9 +3,9 @@ title: أخبار sidebar: false --- -### نتائج استطلاع نمباى لعام 2020 +### نتائج استطلاع نمباي لعام 2020 -_22 يونيو2021_ -قام فريق استطلاع نمباى فى عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتى ميشيغان وماريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباى. بامكانك معرفة نتائج الدراسة الاستقصائية من هنا: https://numpy.org/user-survey-2020/. +_22 يونيو2021_ -قام فريق استطلاع نمباي في عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتى ميتشيغان وميريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباي. بامكانك معرفة نتائج الدراسة الاستقصائية من هنا: https://numpy.org/user-survey-2020/. ### الإصدار 1.20.0 لنمباى From af7f62f6d8b2ec1e05ef2447d82cceaf2f0b141f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 26 Jun 2021 23:55:15 +0200 Subject: [PATCH 446/909] 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 f3a7804590..d3c8c9a888 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -5,7 +5,7 @@ sidebar: false ### نتائج استطلاع نمباي لعام 2020 -_22 يونيو2021_ -قام فريق استطلاع نمباي في عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتى ميتشيغان وميريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباي. بامكانك معرفة نتائج الدراسة الاستقصائية من هنا: https://numpy.org/user-survey-2020/. +_22 يونيو2021_ -قام فريق استطلاع نمباي في عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتي ميتشيجان وميريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباي. بامكانك معرفة نتائج الدراسة الاستقصائية من هنا: https://numpy.org/user-survey-2020/. ### الإصدار 1.20.0 لنمباى @@ -14,9 +14,9 @@ _30يناير2021_ -- [إصدار1.20.0 لنمباى](https://numpy.org/doc/stab - 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)). -### التنوع فى مشروع نمباى +### التنوع في مشروع نمباي -_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 سبتمبر 2020_ -- كتبنا[ تقريرًا وأجرينا نقاشًا على وسائل التواصل الاجتماعي حول التنوع والشمول فى مشروع نمباي](/diversity_sep2020). ### First official NumPy paper published in Nature! From 7775c14e9ce9e05650d39c1210b95caf6e1b9d8b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 00:50:58 +0200 Subject: [PATCH 447/909] 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 d3c8c9a888..987f04917d 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,5 +1,5 @@ --- -title: أخبار +title: الأخبار sidebar: false --- @@ -19,7 +19,7 @@ _30يناير2021_ -- [إصدار1.20.0 لنمباى](https://numpy.org/doc/stab _20 سبتمبر 2020_ -- كتبنا[ تقريرًا وأجرينا نقاشًا على وسائل التواصل الاجتماعي حول التنوع والشمول فى مشروع نمباي](/diversity_sep2020). -### First official NumPy paper published in Nature! +### نشر أول ورقة رسمية لنمباي فى مجلة نيتشر! _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. @@ -79,7 +79,7 @@ More details on our proposed initiatives and deliverables can be found in the [f ## الإصدارات -إليك قائمة من إصدارات نمباى، مع روابط لاصدار الملاحظات. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +إليك قائمة من إصدارات نمباي، مع روابط لملاحظات كل إصدار. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. From 213475bd2eae0f0e59e5cfa90314b0a791bfef6a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 01:50:46 +0200 Subject: [PATCH 448/909] New translations news.md (Arabic) --- content/ar/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/news.md b/content/ar/news.md index 987f04917d..212e266e80 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -79,7 +79,7 @@ More details on our proposed initiatives and deliverables can be found in the [f ## الإصدارات -إليك قائمة من إصدارات نمباي، مع روابط لملاحظات كل إصدار. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +إليك قائمة من إصدارات نمباي، مع روابط لملاحظات كل إصدار. لا توجد مزايا جديدة(التغييرات فقط فى رقم الإصدار `z` `x.y.z`) فى جميع إصدارات إصلاح العيوب على عكس الإصدارات الثانوية(الزيادة `y`). - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. From 2949cac00b49ea12579d098d4d53cd3ee3fb5a93 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 09:23:51 +0200 Subject: [PATCH 449/909] New translations community.md (Chinese Simplified) --- content/zh/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/community.md b/content/zh/community.md index 4e24a83784..d82db503b9 100644 --- a/content/zh/community.md +++ b/content/zh/community.md @@ -1,5 +1,5 @@ --- -title: Community +title: 社区 sidebar: false --- From b7ff68ee6dedce9154990f6991804f8454bdc37a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 10:21:59 +0200 Subject: [PATCH 450/909] New translations community.md (Chinese Simplified) --- content/zh/community.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/zh/community.md b/content/zh/community.md index d82db503b9..18026cda40 100644 --- a/content/zh/community.md +++ b/content/zh/community.md @@ -3,21 +3,21 @@ title: 社区 sidebar: false --- -NumPy is a community-driven open source project developed by a very diverse group of [contributors](/gallery/team.html). 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. +Numby是一个社区驱动的开源项目，由一群十分多样化的[贡献者](/gallery/team.html)开发。 Numpy的领导层承诺要打造一个开放，包容，积极向上的社区。 请阅读 [ NumPy 行为准则](/code-of-conduct) 以了解如何用促进社区繁荣的方式与他人交流互动。 -We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community. +我们提供多种交流渠道，可以用来学习知识、分享您的专业见解、或是与 NumPy 社区中的其他人联系。 ## 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)._ +以下是直接参与 NumPy 项目和社区的方法。 _注意，我们鼓励用户和社区成员在使用问题上相互帮助——参阅 [获取帮助](/gethelp)。_ -### [NumPy mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) +### [NumPy 邮件列表](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. +这个列表是较长形式讨论的主要讨论区，例如将新功能添加到Numpy，更改Numpy 路线图或是各种项目级的决策。 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](http://numpy-discussion.10968.n7.nabble.com/). +On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. 这个列表提供了一个可检索的 [归档](http://numpy-discussion.10968.n7.nabble.com/)。 *** From 7c3e309476ecabb031d328a5805486cf4270a7c2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 11:21:43 +0200 Subject: [PATCH 451/909] New translations community.md (Chinese Simplified) --- content/zh/community.md | 32 ++++++++++++++++---------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/content/zh/community.md b/content/zh/community.md index 18026cda40..e32f2c6699 100644 --- a/content/zh/community.md +++ b/content/zh/community.md @@ -21,45 +21,45 @@ On this list please use bottom posting, reply to the list (rather than to anothe *** -### [GitHub issue tracker](https://github.com/numpy/numpy/issues) +### [Github issue 追踪器](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`"). +- 报告bug (例如："`np.arange(3).shop`本应该返回 `(3),`，却返回结果 `(5),`"); +- 文档问题 (例如："我发现这一节没写清楚"); +- 特性请求 (例如："我想在 `np.percentile` 中加一个新的插值方法")。 -_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)._ +_注意，Github不是报告安全漏洞的正确位置! 如果你认为你在 NumPy 中找到了一个安全漏洞，请在 [这里](https://tidelift.com/docs/security) 报告。_ *** ### [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. +一个用于询问有关为NumPy做 _贡献_ 的问题的实时聊天室。 这是一个私密空间，特别适用于那些在大型公共邮件列表或GitHub上提出他们的问题或想法时犹豫不决的人。 在 [这里](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) 获取更多详情以及如何才能受邀加入这个空间的方法。 -## Study Groups and Meetups +## 学习小组和 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). +如果你想找一个关于NumPy以及更广的数据科学和科学计算python包生态的本地meetup或是学习小组，我们建议你探索一下[PyData meetups](https://www.meetup.com/pro/pydata/)(开展过150+次 meetups, 包含100,000+ 名成员)。 -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还偶尔为其团队和感兴趣的贡献者组织亲身Sprints。 这往往会提前几个月计划，并在[邮件列表](https://mail.python.org/mailman/listinfo/numpy-discussion)和[Twitter](https://twitter.com/numpy_team)上发布通知。 -## Conferences +## 会议 -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 项目不会组织自己的会议。 历来最受 NumPy 维护者、贡献者和用户欢迎的会议是SciPy 和 PyData 系列会议如下： - [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 conferences](https://pydata.org/event-schedule/) (分布在许多国家，每年有15-20个活动) -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. +这些会议大部分都包括一些教程日涵盖 NumPy 和/或 sprints ，您可以从中学习如何为NumPy 或相关的开源项目做贡献。 -## Join the NumPy community +## 加入 NumPy 社区 -To thrive, the NumPy project needs your expertise and enthusiasm. Not a coder? Not a problem! There are many ways to contribute to NumPy. +NumPy 项目的繁荣发展需要您的专业知识和热情。 不是coder? 没关系! 有许多方式可以为NumPy做贡献。 -If you are interested in becoming a NumPy contributor (yay!) we recommend checking out our [Contribute](/contribute) page. +如果您有兴趣成为NumPy贡献者 (好耶!) ，建议查看 [贡献](/contribute) 页面。 From bb86de5f14a74eaab88b12e682078e9e784175cf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 12:29:12 +0200 Subject: [PATCH 452/909] New translations community.md (Chinese Simplified) --- content/zh/community.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/community.md b/content/zh/community.md index e32f2c6699..b380f113e3 100644 --- a/content/zh/community.md +++ b/content/zh/community.md @@ -8,16 +8,16 @@ Numby是一个社区驱动的开源项目，由一群十分多样化的[贡献 我们提供多种交流渠道，可以用来学习知识、分享您的专业见解、或是与 NumPy 社区中的其他人联系。 -## Participate online +## 线上参与 以下是直接参与 NumPy 项目和社区的方法。 _注意，我们鼓励用户和社区成员在使用问题上相互帮助——参阅 [获取帮助](/gethelp)。_ ### [NumPy 邮件列表](https://mail.python.org/mailman/listinfo/numpy-discussion) -这个列表是较长形式讨论的主要讨论区，例如将新功能添加到Numpy，更改Numpy 路线图或是各种项目级的决策。 Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. +这个列表是较长形式讨论的主要讨论区，例如将新功能添加到Numpy，更改Numpy 路线图或是各种项目级的决策。 同时也是NumPy的公告区，例如releases，开发者会议，Sprints 或是会议演讲也在这个列表中。 -On this list please use bottom posting, reply to the list (rather than to another sender), and don't reply to digests. 这个列表提供了一个可检索的 [归档](http://numpy-discussion.10968.n7.nabble.com/)。 +在这个列表上，请用包含引文回复的方式回复邮件列表(而不是另一个发送者)，并且不要回复摘要。 这个列表提供了一个可检索的 [归档](http://numpy-discussion.10968.n7.nabble.com/)。 *** @@ -38,7 +38,7 @@ _注意，Github不是报告安全漏洞的正确位置! 如果你认为你在 N ## 学习小组和 Meetups -如果你想找一个关于NumPy以及更广的数据科学和科学计算python包生态的本地meetup或是学习小组，我们建议你探索一下[PyData meetups](https://www.meetup.com/pro/pydata/)(开展过150+次 meetups, 包含100,000+ 名成员)。 +如果你想找一个用于了解NumPy以及更广泛的数据科学和科学计算python包生态的本地meetup或是学习小组，我们建议你探索一下[PyData meetups](https://www.meetup.com/pro/pydata/)(开展过150+次 meetups, 包含100,000+ 名成员)。 NumPy还偶尔为其团队和感兴趣的贡献者组织亲身Sprints。 这往往会提前几个月计划，并在[邮件列表](https://mail.python.org/mailman/listinfo/numpy-discussion)和[Twitter](https://twitter.com/numpy_team)上发布通知。 From ed36bdf85a494614ffc3cf30b258ff8f94e1eddd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 12:29:13 +0200 Subject: [PATCH 453/909] New translations gethelp.md (Chinese Simplified) --- content/zh/gethelp.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/zh/gethelp.md b/content/zh/gethelp.md index a427b5b1f5..7268b84f77 100644 --- a/content/zh/gethelp.md +++ b/content/zh/gethelp.md @@ -1,34 +1,34 @@ --- -title: Get Help +title: 获取帮助 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! +**用户问题：** 获得帮助的最佳方法是将您的问题发布到[StackOverflow](http://stackoverflow.com/questions/tagged/numpy)这样的有数以千计用户可以回答的网站上。 更轻量的备选方案包括 [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), 和 [Reddit](https://www.reddit.com/r/Numpy/)。 我们也希望我们能够关注这些站点的动向或者直接回答问题，但数量实在是太多了！ -**Development issues:** For NumPy development-related matters (e.g. bug reports), please see [Community](/community). +**开发问题：** 与 Numpy 开发有关的事项(例如bug报告)，请看 [社区](/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) +是一个询问使用问题的论坛，例如"我如何在 NumPy 中执行 X 操作？” 请 [使用 `#numpy` 标签](https://stackoverflow.com/help/tagging) *** ### [Reddit](https://www.reddit.com/r/Numpy/) -Another forum for usage questions. +另一个询问使用问题的论坛。 *** ### [Gitter](https://gitter.im/numpy/numpy) -A real-time chat room where users and community members help each other. +一个用户和社区成员相互帮助的实时聊天室。 *** ### [IRC](https://webchat.freenode.net/?channels=%23numpy) -Another real-time chat room where users and community members help each other. +另一个用户和社区成员相互帮助的实时聊天室。 *** From d3ce93ccdd95ddc66bbc99be075944f50c64ee4e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 12:29:14 +0200 Subject: [PATCH 454/909] New translations history.md (Chinese Simplified) --- content/zh/history.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/zh/history.md b/content/zh/history.md index fc79a621af..f54be68d26 100644 --- a/content/zh/history.md +++ b/content/zh/history.md @@ -1,21 +1,21 @@ --- -title: History of NumPy +title: NumPy的历史 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 是一个提供数组数据结构和相关快速数值计算程序的基础 Python 库。 一开始，这个库并没有多少资金，主要由研究生撰写，并且其中许多人没有接受过计算机科学教育。也常常得不到他们顾问的支持。 很难想象，这样一小群三流学生编程者能够颠覆具有数百万资金和高级工程师支持的成熟研究软件生态。 然而，事实证明，从长远来看，完全开放工具栈背后的哲学依据加上兴奋、友好、专注的社区是更好的。 现在，世界各地的科学家、工程师和许多其他专业人员都依赖于Numpy。 例如，已发表的用于引力波分析的脚本导入了NumPy，M87黑洞成像项目直接引用了NumPy。 -For the in-depth account on milestones in the development of NumPy and related libraries please see [arxiv.org](arxiv.org/abs/1907.10121). +有关NumPy和相关库发展里程碑的详细说明，请参见[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: +如果您想要获得数字和数组库的原始副本，请点击下面的链接： -[Download Page for *Numeric*](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* +[*Numeric* 下载页](https://sourceforge.net/projects/numpy/files/Old%20Numeric/)* -[Download Page for *Numarray*](https://sourceforge.net/projects/numpy/files/Old%20Numarray/)* +[ *Numarray* 下载页 ](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. +*请注意，这些旧的数组包不再维护，强烈建议用户将NumPy用于任何与数组相关的目的，或重构早期代码以利用NumPy库。 -### Historic Documentation +### 历史文档 -[Download *`Numeric'* Manual](static/numeric-manual.pdf) +[下载 *"Numeric"* 手册](static/numeric-manual.pdf) From 19c09525346caf5d42fc9ca998f2b586bb724771 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 13:33:24 +0200 Subject: [PATCH 455/909] New translations install.md (Chinese Simplified) --- content/zh/install.md | 76 ++++++++++++++++++++++--------------------- 1 file changed, 39 insertions(+), 37 deletions(-) diff --git a/content/zh/install.md b/content/zh/install.md index 3ec0dc58b7..5159a3e622 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -1,61 +1,63 @@ --- -title: Installing NumPy +title: 安装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. +安装 NumPy 的唯一前提条件是安装了 Python 。 如果您还没有Python，并且想以最简单的方式开始， 我们建议您使用[Anaconda Distribution](https://www.anaconda.com/distribution) - 它包括 Python, NumPy，以及许多其他常用的科学计算和数据科学软件包。 -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 可以使用 `conda` 安装，用 `pip` 安装， 在macOS 和Linux用软件包管理器安装或用[源码安装](https://numpy.org/devdocs/user/building.html)。 更详细的说明，查阅下方的 [ Python和NumPy安装指南 ](#python-numpy-install-guide)。 **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: +如果您使用 `conda`，您可以从 `defaults` 或 `conda-forge` 频道安装 NumPy ```bash -# Best practice, use an environment rather than install in the base env +# 最佳练习 使用环境而不是在基础环境中安装 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 activer my-env + +# 如果你想从conda-forge频道安装 +conda config --env --add channel conda-full + +# 实际的安装命令 conda install numpy ``` **PIP** -If you use `pip`, you can install NumPy with: +如果您使用 `pip`，您可以用如下命令安装NumPy： ```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. +另外，当使用 pip 时，最好使用虚拟环境。查看 [Rupuable Installs](#reproducible-installs) 了解原因。 查看 [指南](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) 了解关于使用虚拟环境的详情。 -# Python and NumPy installation guide +# Python 和 NumPy 安装指南 -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. +在 Python 上安装和管理软件包很复杂，大多数任务有许多替代解决方案。 本指南试图给读者一种最佳(或最受欢迎) 解决办法，并给出清晰的建议。 It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. -## Recommendations +## 建议 -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. +我们将首先根据用户的经验水平和有兴趣的操作系统提出建议。 如果您在“开始”和“高级”之间纠结，我们建议如果您想要保持简单，请使用"开始"， 如果您想要按照更长远的最佳做法去做，请使用"高级"。 -### Beginning users +### 开始用户 -On all of Windows, macOS, and Linux: +在所有Windows、 macOS和Linux上： -- 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. +- 安装 [Anaconda](https://www.anaconda.com/distribution/) (包含了您需要的所有软件包以及下面提到的所有其他工具)。 +- 编写和执行代码，使用[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) 的notebooks 用于探索式和交互式计算， 使用 [Spyder](https://www.spyder-ide.org/) 或 [Visual Studio Code](https://code.visualstudio.com/) 编写脚本和软件包。 +- 用 [Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) 管理你的软件包并启动JupyterLab, Spyder或Visual Studio Code. -### Advanced users +### 高级用户 -#### Windows or macOS +#### Windows 或 macOS -- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). -- 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. -- Unless you're fine with only the packages in the `defaults` channel, make `conda-forge` your default channel via [setting the channel priority](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge). +- 安装 [Miniconda](https://docs.conda.io/en/latest/miniconda.html)。 +- 保持 `base` conda 环境最小化， 并使用一个或多个[conda 环境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) 用于安装你需要的包以完成你正在做的任务或项目。 +- 除非你只需要 `defaults` 频道的包， 否则请通过 [设置频道优先级](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge) 将 `conda-forge` 设为您的默认频道 #### Linux @@ -64,31 +66,31 @@ If you're fine with slightly outdated packages and prefer stability over being a - Use your OS package manager for as much as possible (Python itself, NumPy, and other libraries). - Install packages not provided by your package manager with `pip install somepackage --user`. -If you use a GPU: -- Install [Miniconda](https://docs.conda.io/en/latest/miniconda.html). -- 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. -- Use the `defaults` conda channel (`conda-forge` doesn't have good support for GPU packages yet). +如果您使用GPU： +- 安装 [Miniconda](https://docs.conda.io/en/latest/miniconda.html)。 +- 保持 `base` conda 环境最小化， 并使用一个或多个[conda 环境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) 用于安装你需要的包以完成你正在做的任务或项目。 +- 使用 `defaults` conda 频道 (`conda-forge` 尚不支持 GPU 软件包)。 -Otherwise: -- 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. +否则： +- 安装[Miniforge](https://github.com/conda-forge/miniforge). +- 保持 `base` conda 环境最小化， 并使用一个或多个[conda 环境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) 用于安装你需要的包以完成你正在做的任务或项目。 #### 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. +- 从 [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/)或 Linux 软件包管理器安装 Python。 +- 使用 [Poetry](https://python-poetry.org/) ，它是具有与conda 相似的依赖解析器和环境管理能力的完善工具。 -## Python package management +## Python 软件包管理 -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. +软件包管理是一个具有挑战性的问题，因此有许多的工具出现。 对于Web和一般Python开发有一整套能与pip互补的[工具](https://packaging.python.org/guides/tool-recommendations/)。 对于高性能计算 (HPC),[Spack](https://github.com/spack/spack) 值得考虑。 但对于大多数NumPy用户来说， [conda](https://conda.io/en/latest/) 和 [pip](https://pip.pypa.io/en/stable/) 是两个最受欢迎的工具。 ### 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. +安装 Python 软件包的两个主要工具是 `pip` and `conda`。 他们的功能部分重叠(例如两者都可以安装 `numpy`)，但他们也可以一起工作。 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. From 6c7f75a5058ea00688bf3a81c83566f917a2ff25 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 27 Jun 2021 14:33:08 +0200 Subject: [PATCH 456/909] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index 9d211d4f4e..ced2a28ac5 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -3,7 +3,7 @@ title: الحصول على مساعدة 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! +**أسئلة المستخدم**: إن أفضل طريقة للحصول على المساعدة هي أن تقوم بنشر سؤالك على الموقع مثل [ ](http://stackoverflow.com/questions/tagged/numpy)حيث يوجد آلاف المستخدمين المتاحين للإجابة على أسئلتك. وتحتوي البدائل الأصغر على [IRC](https://webchat.freenode.net/?channels=%23numpy) [Gitterو](https://gitter.im/numpy/numpy) و [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). From cc44de5afa6f7035a55cf3651cd4fddf02922083 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 28 Jun 2021 10:57:05 +0200 Subject: [PATCH 457/909] New translations blackhole-image.md (Chinese Simplified) --- content/zh/case-studies/blackhole-image.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/case-studies/blackhole-image.md b/content/zh/case-studies/blackhole-image.md index f2460d3d5b..7821b505b5 100644 --- a/content/zh/case-studies/blackhole-image.md +++ b/content/zh/case-studies/blackhole-image.md @@ -1,9 +1,9 @@ --- -title: "Case Study: First Image of a Black Hole" +title: "案例研究：人类有史以来首张黑洞照片" 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" >}} +{{{< figsrc="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" tot="*(Image Credits: Event Horizon Telesmall Collection Collaboration)*" tomlink="https://www.jpl.nasa.gov/images/universse/20190410/blackhole20190410.jpg" >}}

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

    From 2c5ec37ee0a9e8fdcc24aca60282ee54c605ba3f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 28 Jun 2021 11:59:11 +0200 Subject: [PATCH 458/909] New translations blackhole-image.md (Chinese Simplified) --- content/zh/case-studies/blackhole-image.md | 36 +++++++++++----------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/content/zh/case-studies/blackhole-image.md b/content/zh/case-studies/blackhole-image.md index 7821b505b5..adc6980096 100644 --- a/content/zh/case-studies/blackhole-image.md +++ b/content/zh/case-studies/blackhole-image.md @@ -6,41 +6,41 @@ sidebar: false {{{< figsrc="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" tot="*(Image Credits: Event Horizon Telesmall Collection Collaboration)*" tomlink="https://www.jpl.nasa.gov/images/universse/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
    +

    理论上黑洞是不可能被“看见”，M87黑洞的成像正试图打破这种限制

    +
    Katie Bouman, Assistant Professor, Computing & Mathocal Sciences, Caltech
    -## A telescope the size of the earth +## 一架和地球大小相当的望远镜 -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! +[事件视界望远镜(EHT)](https://eventhorizontelescope.org) 是由八个地面射电望远镜组成的虚拟的类似地球大小的望远镜， 可以前所未有的敏感度和分辨率来了解宇宙。 这台巨大的虚拟望远镜使用一种称为超长基线干涉法 (VLBI)的技术， 其角分辨率为 [20 微弧秒][resolution] - EHT的分辨本领相当于从巴黎的一家人行道上的咖啡馆里阅读纽约的报纸！ -### Key Goals and Results +### 关键目标和成果 -* **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. +* **关于宇宙的新观点:** 100年前，当 [亚瑟.爱丁顿爵士][eddington] 提出爱因斯坦的广义相对论的第一个观测证据时，就为EHT的开创性形象奠定了基础. -* **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. +* **黑洞成像：** EHT 在距离地球约5500万光年的超大质量黑洞上进行了训练，该黑洞位于处女座星系团梅西埃87(M87) 的中心。 它的质量是太阳的65亿倍。 它已经被研究了 [100多年](https://www.jpl.nasa.gov/news/news.php?feature=7385)，但从来没有一个黑洞被真正“看见”过。 -* **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. +* **将观察结果与理论进行比较：** 从爱因斯坦的广义相对论来看， 科学家期望找到由引力弯曲和光捕获引发的阴影状区域。 科学家可以用它来测量黑洞的巨大质量。 -### The Challenges +### 面临的挑战 -* **Computational scale** +* **计算规模** - EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. + EHT带来了巨大的数据处理挑战，其中包括快速的大气层相位波动、极高的记录带宽以及相异且地理位置分散的望远镜。 -* **Too much information** +* **巨大的信息量** - 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. + EHT每天生成超过350TB的观测值，这些数据存储在充满氦气的硬盘驱动器中。 减少这么多数据的数量和复杂性是极其困难的。 -* **Into the unknown** +* **探索未知** - When the goal is to see something never before seen, how can scientists be confident the image is correct? + 当目标是看到前所未见的事物时，科学家怎么才能确定图像是正确的？ -{{< 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" >}} +{{< figsrc="/images/content_images/cs/dataprocessbh. ng" class="csfigcaption" caption="**EHT Data Processing Pipeline**" alt="data peline" align="middle" tot="(Diagram Credits: The Astrophysical Journal, Event Horizon Telesrole Collection Collaboration)" tourlink="https://iopscience.op.org/article/10.3847/2041-8213/ab0c57" >}} -## NumPy’s Role +## Numpy的角色 -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? +如果数据有问题，怎么办？ 或者一个算法过于依赖某个特定的假设。 如果单个参数被更改，图像是否会发生剧烈变化？ 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. From 100b2fa9c72ed2b795098df632956bd973458b0d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 28 Jun 2021 13:37:46 +0200 Subject: [PATCH 459/909] 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 adc6980096..47a320fdf9 100644 --- a/content/zh/case-studies/blackhole-image.md +++ b/content/zh/case-studies/blackhole-image.md @@ -42,13 +42,13 @@ sidebar: false 如果数据有问题，怎么办？ 或者一个算法过于依赖某个特定的假设。 如果单个参数被更改，图像是否会发生剧烈变化？ -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. +EHT协作组织为了应对上述挑战，让不同的独立小组使用现有的最先进的图像重建技术来评估数据。 当结果被证明是一致时，将这些结果合并以产生黑洞的第一张图像。 -Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. +他们的工作说明了Python科学生态系统通过协作数据分析在 推进科学方面发挥的重要作用。 -{{< 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**" >}} +{{< figsrc="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**NumPy在黑洞成像中的作用**" >}} -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. +例如， [`eht-imaging`][ehtim] 这个Python 软件包提供了 在 VLBI 数据上模拟和执行图像重建的工具。 NumPy 是这个包中使用的数组数据处理的核心，下面的部分软件 依赖关系图说明了这一点。 {{< 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**" >}} From 99098ff6eb6a2b4c9f4039153033aeb507551081 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 28 Jun 2021 14:43:10 +0200 Subject: [PATCH 460/909] New translations blackhole-image.md (Chinese Simplified) --- content/zh/case-studies/blackhole-image.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/zh/case-studies/blackhole-image.md b/content/zh/case-studies/blackhole-image.md index 47a320fdf9..466504d87a 100644 --- a/content/zh/case-studies/blackhole-image.md +++ b/content/zh/case-studies/blackhole-image.md @@ -6,7 +6,7 @@ sidebar: false {{{< figsrc="/images/content_images/cs/blackhole.jpg" caption="**Black Hole M87**" alt="black hole image" tot="*(Image Credits: Event Horizon Telesmall Collection Collaboration)*" tomlink="https://www.jpl.nasa.gov/images/universse/20190410/blackhole20190410.jpg" >}}
    -

    理论上黑洞是不可能被“看见”，M87黑洞的成像正试图打破这种限制

    +

    理论上黑洞不可能被“看见”，M87黑洞的成像正试图打破这种限制

    Katie Bouman, Assistant Professor, Computing & Mathocal Sciences, Caltech
    @@ -24,7 +24,7 @@ sidebar: false ### 面临的挑战 -* **计算规模** +* **庞大的计算规模** EHT带来了巨大的数据处理挑战，其中包括快速的大气层相位波动、极高的记录带宽以及相异且地理位置分散的望远镜。 @@ -32,7 +32,7 @@ sidebar: false EHT每天生成超过350TB的观测值，这些数据存储在充满氦气的硬盘驱动器中。 减少这么多数据的数量和复杂性是极其困难的。 -* **探索未知** +* **对未知的探索** 当目标是看到前所未见的事物时，科学家怎么才能确定图像是正确的？ @@ -50,15 +50,15 @@ EHT协作组织为了应对上述挑战，让不同的独立小组使用现有 例如， [`eht-imaging`][ehtim] 这个Python 软件包提供了 在 VLBI 数据上模拟和执行图像重建的工具。 NumPy 是这个包中使用的数组数据处理的核心，下面的部分软件 依赖关系图说明了这一点。 -{{< 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**" >}} +{{< figsrc="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="numpy在ehtim软件依赖关系中的地位" caption="**numpy在ehtim软件依赖关系中的重要地位**" >}} -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. +除了NumPy以外，许多其他软件包，例如 [SciPy](https://www.scipy.org) 和 [Pandas](https://pandas.io), 也是用于黑洞成像的数据处理管道的一部分。 标准天文学文件格式和时间/坐标转换 由 [Astropy][astropy]处理， 而 [Matplotlib][mpl] 被用于在整个分析管道中的数据可视化，包括生成黑洞的最终图像。 -## Summary +## 总结 -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. +作为Numpy的核心功能，高效且拓展性强的N维数组使研究人员能够操作大规模数据集，从而为人类有史以来首张黑洞的成像提供坚实基础。 这是整个科学史中具有里程碑意义的时刻，它为爱因斯坦的理论提供了有力的佐证。 这项成就不仅包括技术突破，还见证了包括200多位科学家与世界上最好的无线电观测站之间的国际合作。 创新的算法和数据处理技术改进了现有的天文模型，帮助我们揭开宇宙的神秘面纱。 -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} +{{< figsrc="/images/content_images/cs/numpy_bh_bbh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Numpy核心能力的运用**" >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 1176c2d6dca96a8b8c5cc89bc11a02a5fd8d5775 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 28 Jun 2021 14:43:11 +0200 Subject: [PATCH 461/909] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md index 987b38fb68..95ce7beeba 100644 --- a/content/zh/case-studies/cricket-analytics.md +++ b/content/zh/case-studies/cricket-analytics.md @@ -1,20 +1,20 @@ --- -title: "Case Study: Cricket Analytics, the game changer!" +title: "案例研究：板球赛场上的常胜将军-数据分析！" 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" >}} +{{< figsrc="/images/content_images/cs/ipl-stadium. ng" caption="**IPLT20, 印度最大的板球节**" alt="印度高级板球联赛体育场和冠军奖杯 attr="*(图片来源：IPLT20 (奖杯和标志) & Akash Yadav(体育场))*" totlink="https://unsplash. om/@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
    +

    你不是为人群而战，而是为国家而战。

    +
    — — M S Dhoni, International Cricket Player, ex-Captain, Indian team, plays for Chennai Super Kings in IPL
    -## About Cricket +## 关于板球 -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. +印度人喜欢板球几乎人尽皆知。 这个游戏几乎在印度的任何角落都可以玩，无论是农村还是城市，与其它任何运动项目相比，只有板球可以轻松连接印度的数十亿年轻人和老年人。 板球受到了媒体的广泛关注。 无数 [金钱](https://www.statista.com/topics/4543/indian-premier-league-ipl/) 和 荣誉都压宝在这项运动上。 在过去的几年中，数据技术分析确实成为了运动场上的常胜将军。 流媒体、锦标赛、在移动设备上实时的观看板球等形式让不同观众的观看欲望得到最大满足。 -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. +印度超级联赛(IPL) 是成立于2008年的Twenty20板球职业联赛。 它是世界上参与人数最多的板球赛事之一，2019年价值为 [67亿美元](https://en.wikipedia.org/wiki/Indian_Premier_League) 。 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. From 99d49cf0c7c2b5752749a96a56430239d47f0593 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 29 Jun 2021 04:17:01 +0200 Subject: [PATCH 462/909] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md index 95ce7beeba..b8a74eeb3e 100644 --- a/content/zh/case-studies/cricket-analytics.md +++ b/content/zh/case-studies/cricket-analytics.md @@ -1,5 +1,5 @@ --- -title: "案例研究：板球赛场上的常胜将军-数据分析！" +title: "案例研究：通过数据分析玩转板球！" sidebar: false --- @@ -16,7 +16,7 @@ sidebar: false 印度超级联赛(IPL) 是成立于2008年的Twenty20板球职业联赛。 它是世界上参与人数最多的板球赛事之一，2019年价值为 [67亿美元](https://en.wikipedia.org/wiki/Indian_Premier_League) 。 -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. +板球本质上是关于数字的游戏-击球方的跑动得分，投球手击中门柱的次数，板球队赢得回合的次数，击球手以特定方式还击的次数等等。 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: From f93c01b5a169287a99e8281a4a8c90495d20a250 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 29 Jun 2021 05:16:11 +0200 Subject: [PATCH 463/909] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 42 ++++++++++---------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md index b8a74eeb3e..af00dc45e8 100644 --- a/content/zh/case-studies/cricket-analytics.md +++ b/content/zh/case-studies/cricket-analytics.md @@ -16,42 +16,42 @@ sidebar: false 印度超级联赛(IPL) 是成立于2008年的Twenty20板球职业联赛。 它是世界上参与人数最多的板球赛事之一，2019年价值为 [67亿美元](https://en.wikipedia.org/wiki/Indian_Premier_League) 。 -板球本质上是关于数字的游戏-击球方的跑动得分，投球手击中门柱的次数，板球队赢得回合的次数，击球手以特定方式还击的次数等等。 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. +板球本质上是关于数字的游戏-击球方的跑动得分，投球手击中门柱的次数，板球队赢得回合的次数，击球手以特定方式还击的次数等等。 借助类似Numpy等功能强大的数值计算分析软件，可以充分挖掘板球得分背后的原理，并对板球的商业化、市场化和经济效益提供重要的参考价值。 板球数据分析为比赛提供了独特的视角，并提供了有关比赛结果的智能预测。 -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: +时至今日，板球比赛的记录和统计数据非常丰富，几乎无穷无尽。例如[ESPN cricinfo](https://stats.espncricinfo.com/ci/engine/stats/index.html) 和 [cricsheet](https://cricsheet.org)。 这些板球数据库使用最新的机器学习和预测建模算法来进行 [板球分析](https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances)。 媒体和娱乐平台以及与游戏相关联的专业体育机构使用技术分析来确定关键指标，以提高比赛获胜机率： -* 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 +* 击球时跑动步数均值的表现 +* 分数预测 +* 深入了解球员在面对不同对手时的身体表现状况 +* 在团队组成的决策过程中考察球员对比赛输赢的贡献值 -{{< 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" >}} +{{< figsrc="/images/content_images/cs/cricket-pitch. ng" class="csfigcaption" caption="**万众瞩目的板球场**" alt="板球赛场上投球手和击球手蓄势待发" align="middle" attr="*(Image credit: Debarghya Das)*" attrlink="http://debarghyadas.com/files/IPLpaper.pdf" >}} -### Key Data Analytics Objectives +### 关键数据分析目标 -* 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. +* 运动数据分析不仅用于板球运动，还适用于 [其它运动](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) 中，以改善团队的整体表现并最大程度的提高获胜机会。 +* 实时数据分析甚至可以在比赛过程中帮助提高洞察力，从而使团队和相关投资方改变比赛策略以获取更高的经济效益。 +* 除了历史数据分析之外，预测模型也被使用来确定可能的比赛结果，这些结果需要大量的数值处理和数据科学专业知识， 可视化工具以及在分析中增加新观察项的能力。 -{{< 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/" >}} +{{< figsrc="/images/content_images/cs/player-pose-estimatator. ng" class="fig-center" alt="post estimator" caption="**板球姿势预测**" tot="*(Image credit: connect.vin)*" totlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" >}} -### The Challenges +### 面临的挑战 -* **Data Cleaning and preprocessing** +* **数据清理和预处理** - 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. + IPL已经将板球运动从经典的测试赛扩展到更广的比赛形式。 每个赛季各种形式的比赛场次都有所增加，相应的数据规模、新算法、新的数据分析技术和模拟模型也有所增加。 板球数据分析需要对现场数据进行全方位跟踪，包括球员追踪、球的追踪、球员击球数据分析以及与如何传递球、球的角度、旋转、速度和轨迹有关的方面。 所有这些因素共同增加了数据清理和预处理的复杂性。 -* **Dynamic Modeling** +* **数据动态建模** - 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. + 在板球运动中，就像任何其他体育运动一样，可能存在大量的变量，这些变量包括跟踪球场上各种球员的状态、球员的属性、球本身以及球员多种可能的潜在动作。 数据分析和建模的复杂性与分析过程中提出的预测问题的种类成正比，并且高度依赖数据表示和模型建模能力。 在要求实时动态的预测板球比赛的结果时，对计算性能、数据比对质量提出了更高的挑战，比如击球手以不同角度或速度击球对比赛结果会产生何种影响。 -* **Predictive Analytics Complexity** +* **预测分析的复杂性** - 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. + 板球比赛中的很多决定大都基于如下问题的答案：“针对特定类型的投球手，击球手特殊攻击的频率是多少”或者“如果击球手发动特殊攻击的话，投球手会如何改变他的投球位和投球距离”。 这些预测性分析的答案需要高精度且可用的数据集以及合成数据和构建高精度模型的能力。 -## NumPy’s Role in Cricket Analytics +## NumPy在板球数据分析中的角色 -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: +体育分析是一个蓬勃发展的领域。 除了使用最新的机器学习和AI技术之外，许多研究人员和公司[使用Numpy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)和其它Python数据处理包，例如Scikit-learn, SciPy, Matplotlib和Jupyter。 Numpy已用于各种与板球相关的体育分析中，例如： * **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. From 65ab3ccf046ebb81b384337303c396fc99e2abae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 29 Jun 2021 06:17:24 +0200 Subject: [PATCH 464/909] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md index af00dc45e8..1fd0182272 100644 --- a/content/zh/case-studies/cricket-analytics.md +++ b/content/zh/case-studies/cricket-analytics.md @@ -53,12 +53,12 @@ sidebar: false 体育分析是一个蓬勃发展的领域。 除了使用最新的机器学习和AI技术之外，许多研究人员和公司[使用Numpy](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)和其它Python数据处理包，例如Scikit-learn, SciPy, Matplotlib和Jupyter。 Numpy已用于各种与板球相关的体育分析中，例如： -* **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. +* **统计分析:** NumPy的数值计算功能有助于在各种球员和比赛策略下估算观察数据或比赛事件的统计意义，并通过与生成模型或静态模型进行比较来预测比赛结果。 [因果分析](https://amplitude.com/blog/2017/01/19/causation-correlation) 和 [大数据分析](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) 就常用于战术分析。 -* **Data Visualization:** Data graphing and [visualization](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) provides useful insights into relationship between various datasets. +* **数据可视化：** 数据图形化和 [可视化](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) 对各种数据集之间的关系提供了有价值的见解。 -## Summary +## 总结 -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. +在专业赛事中，特别是需要战略决策的比赛，运动数据分析常常能起到力挽狂澜的效果，哪怕是现在，主要还是依赖“勇气和直觉”或对过去传统的经验来完成分析。 NumPy为大量的 Python 软件包奠定了坚实的基础，这些软件包提供了与数据分析， 机器学习和AI算法相关的更高层级的函数。 这些软件包被广泛应用以获取实时赛况的分析，从而有助于在现场做出最有利于自己的决策，实现在板球比赛过程中的商业利益最大化。 找出影响板球比赛结果的隐藏参数、模式和属性，有助于投资方获得隐藏在数字和统计数据背后的比赛洞察力。 -{{< 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**" >}} +{{< figsrc="/images/content_images/cs/numpy_bh_bbh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Numpy核心能力的运用**" >}} From 1b27c07e17af7a8c477a3f197dd6e918eb71fc08 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 30 Jun 2021 04:33:07 +0200 Subject: [PATCH 465/909] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index b40ed2af50..0b4af6f68f 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -1,12 +1,12 @@ --- -title: "Case Study: DeepLabCut 3D Pose Estimation" +title: "案例研究：使用DeepLabCut预测动物行为" 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">}} +{{{< figsrc="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**使用DeepLapCut**分析老鼠的手部姿势 " alt="micehandanim" tot="*(来源: www.deeplabcut.org)*" totlink="http://www.mousemotorlab.org/deeplabcut">}}
    -

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

    +

    开源软件正在加速生物医学的发展。 DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

    —Alexander Mathis, Assistant Professor, École polytechnique fédérale de Lausanne (EPFL)
    From 83bb766f2aecd9aa9f6f0c32756d70c132ca4f51 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 30 Jun 2021 05:39:50 +0200 Subject: [PATCH 466/909] New translations install.md (Chinese Simplified) --- content/zh/install.md | 41 ++++++++++++++++++++--------------------- 1 file changed, 20 insertions(+), 21 deletions(-) diff --git a/content/zh/install.md b/content/zh/install.md index 5159a3e622..680333ff27 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -90,55 +90,54 @@ For users who know, from personal preference or reading about the main differenc ### Pip & conda -安装 Python 软件包的两个主要工具是 `pip` and `conda`。 他们的功能部分重叠(例如两者都可以安装 `numpy`)，但他们也可以一起工作。 We'll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively. +安装 Python 软件包的两个主要工具是 `pip` and `conda`。 他们的功能部分重叠(例如两者都可以安装 `numpy`)，但他们也可以一起工作。 我们将在这里讨论 pip 与 conda 的主要差异——这对于理解如何有效地管理软件包非常重要。 -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. +第一点不同是conda是跨语言的，它可以安装 Python，然而 pip 安装在您的系统的特定的 Python 之上， 并只为那一个特定的Python安装其他的软件包。 这也意味着conda 可以安装非Python 库和其他您可能需要的工具(例如编译器、CUDA、HDF5)，pip则不行。 -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. +第二个不同是 pip 以Python包索引(PyPI) 作为安装源。 而conda从自己的渠道安装(通常是"defaults"或 "conda-forge")。 PyPI 是迄今为止最大的软件包集合，不过所有流行的软件包也可用于 conda。 -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. +第三个不同点，conda是依赖关系、环境和软件包管理的集成解决方案。而 pip 可能需要其他工具 (很多!) 用于处理环境或复杂的依赖关系。 ### 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: +随着库的更新，代码的运行结果可能会改变，甚至您的代码完全跑不起来。 能重建你使用的对应版本软件包集合就很重要了。 最佳做法如下： -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: +1. 为你的每一个项目构建不同的环境 +2. 用软件包管理器记录软件包名称和版本； 每个包管理器都有自己的元数据格式： - 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) + - 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包 & 快速线性代数库 -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: +NumPy 不依赖任何其他Python 包。 不过它依赖于一个快速线性代数库 - 通常是[Intel MKL](https://software.intel.com/en-us/mkl) 或 [OpenBLAS](https://www.openblas.net/)。 用户不必担心要如何安装那些库 (他们会自动包含在所有NumPy 的安装脚本中)。 高级用户可能仍然想知道详细信息，因为使用 BLAS 会影响磁盘的性能、行为和空间： -- 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. +- 用pip安装的 NumPy，线性代数库是 OpenBLAS。 The OpenBLAS libraries are included in the wheel. 这使得轮子得更大，而且如果用户安装了 (假设) SciPy 他们现在会在磁盘上有两份OpenBLAS 副本。 -- 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 defaults channel, NumPy is built against Intel MKL. MKL 是个分离的软件包，在安装Numpy时会将它安装到用户环境中。 - 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包比OpenBLAS大得多，它在磁盘上有大约700MB，而OpenBLAS 大约30MB。 -- MKL is typically a little faster and more robust than OpenBLAS. +- MKL通常比OpenBLAS更快，更强大。 -Besides install sizes, performance and robustness, there are two more things to consider: +除了安装大小、性能和强大性能外，还有两个东西需要考虑： -- 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 MKL不开源。 对于正常使用，这倒不是一个问题。 但如果用户需要重新发布基于 NumPy 构建的应用程序。这可能是个问题。 +- MKL 和 OpenBLAS 都将使用多线程进行函数调用，如`np.dot`，而线程数量同时由构建时间选项和一个环境变量决定。 通常所有的CPU核心都能用上。 这有时并不是用户期望的；NumPy本身并不进行任何自动并行函数调用。 多线程通常能产生更好的性能，但也可能降低性能――例如，当使用 Dask、scikit-learn 或 multiprocessing 的另一个并行化等级时。 -## Troubleshooting +## 故障排查 -If your installation fails with the message below, see [Troubleshooting ImportError](https://numpy.org/doc/stable/user/troubleshooting-importerror.html). +如果您的安装失败并显示如下信息，请参阅 [故障排查 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. +Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup. ``` From e7d3855ad5885b13139e96c041a16acc20c0beb8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 30 Jun 2021 05:39:52 +0200 Subject: [PATCH 467/909] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index 0b4af6f68f..bc1a149143 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -6,25 +6,25 @@ sidebar: false {{{< figsrc="/images/content_images/cs/mice-hand.gif" class="fig-center" caption="**使用DeepLapCut**分析老鼠的手部姿势 " alt="micehandanim" tot="*(来源: www.deeplabcut.org)*" totlink="http://www.mousemotorlab.org/deeplabcut">}}
    -

    开源软件正在加速生物医学的发展。 DeepLabCut enables automated video analysis of animal behavior using Deep Learning.

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

    开源软件正在加速生物医学的发展。 DeepLabCut能够使用深度学习对动物行为进行自动视频分析。

    +
    — — Alexander Mathis, 洛桑联邦理工大学 (EPFL)助理教授
    -## About DeepLabCut +## 关于 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. +[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) 是一个开放源码工具箱，它使世界各地数以百计的研究人员能够在训练数据非常少的情况下跟踪实验室动物的行为，而且能达到人类水平的准确性。 借助DeepLabCut技术，科学家可以更深入更科学地了解不同动物在不同时间段对运动的控制和表现行为。 -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. +包括神经科学、医学和生物力学在内的若干研究领域都使用了跟踪动物运动的数据。 DeepLabCut通过解析电影上记录的动作，帮助了解人类和其他动物行为背后的内涵。 DeepLabCut将标记和监测的繁重工作自动化，同时进行基于神经网络的深度数据分析，使得涉及观察例如灵长类动物、小鼠、鱼类和苍蝇等动物行为的科学研究更快、更准确。 -{{< 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)*">}} +{{< figsrc="/images/content_images/cs/race-horse. gif" class="fig-center" caption="**通过彩色标点跟踪赛马身体位置的变化**" alt="horserideranim" totel="*(资料来源：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. +对于生物力学、遗传学、人类学和神经科学等领域的科学研究来说，DeepLabCut通过非侵入式手段提取动物姿势至关重要。 无论从技术层面还是从庞大的资源需求和训练集来看，不带标记非侵入式的从视频中检测动物的姿势，预测动物在动态变化背景下的行为表现对计算机都极具挑战。 -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使研究人员能够通过基于 Python 的软件工具包有效地估计该实验对象的姿势，使他们能够对实验对象的行为进行量化。 借助DeepLabCut，研究人员可以从视频中识别出不同的帧，并使用量身定制的GUI数字标记数十个帧中的特定身体部位，然后DeepLabCut中基于深度学习的姿势估计架构将学习如何从剩余视频或类似动物行为的视频中提取出相同的特征。 这种方法适用于各种动物，从常见的苍蝇和老鼠等实验室动物到不常见到的[猎豹][cheetah-movement]等动物。 -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. +DeepLabCut使用一种称为 [转移学习](https://arxiv.org/pdf/1909.11229)的原理，大大减少了所需训练数据的规模，并加快了训练周期的收敛速度。 根据不同需求，用户可以选择不同的网络结构来获得更高性能的推理模型(例如MobileNetV2)，也可以将其与实时的实验反馈相结合。 DeepLabCut最初使用了一个名为[DeeperCut](https://arxiv.org/abs/1605.03170)的高性能人物姿势评估特征探测器, 这也是DeepLabCut这个名字的由来。 现在这套软件已经作了重大更新，包含支持更多架构、算子规模的扩大和全面的前端用户体验提升。 此外， 为了支持大规模生物实验，DeepLabCut提供了主动学习的能力，因此用户可以随着时间的推移增加训练集以覆盖边缘用例，并使他们的姿势估计算法在特定场景下变的更加强大。 -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. +最近，引入了 [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) ，它为不同物种和不同实验条件提供预训练的模型，从灵长类动物的面部分析到狗的姿势。 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 @@ -85,6 +85,6 @@ Observing and efficiently describing behavior is a core tenant of modern etholog {{< 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 +[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 24b0f973c0cd6f100e9ab8ed4f4977ed2bb8292c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 30 Jun 2021 06:39:00 +0200 Subject: [PATCH 468/909] 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 bc1a149143..51ecea5558 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -24,13 +24,13 @@ DeepLabCut使研究人员能够通过基于 Python 的软件工具包有效地 DeepLabCut使用一种称为 [转移学习](https://arxiv.org/pdf/1909.11229)的原理，大大减少了所需训练数据的规模，并加快了训练周期的收敛速度。 根据不同需求，用户可以选择不同的网络结构来获得更高性能的推理模型(例如MobileNetV2)，也可以将其与实时的实验反馈相结合。 DeepLabCut最初使用了一个名为[DeeperCut](https://arxiv.org/abs/1605.03170)的高性能人物姿势评估特征探测器, 这也是DeepLabCut这个名字的由来。 现在这套软件已经作了重大更新，包含支持更多架构、算子规模的扩大和全面的前端用户体验提升。 此外， 为了支持大规模生物实验，DeepLabCut提供了主动学习的能力，因此用户可以随着时间的推移增加训练集以覆盖边缘用例，并使他们的姿势估计算法在特定场景下变的更加强大。 -最近，引入了 [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) ，它为不同物种和不同实验条件提供预训练的模型，从灵长类动物的面部分析到狗的姿势。 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. +最近，引入了 [DeepLabCut model zoo](http://www.mousemotorlab.org/dlc-modelzoo) ，它为不同物种和不同实验条件提供预训练的模型，从灵长类动物的面部分析到狗的姿势。 有了modelzoo之后，模型就可以在云端运行，而且不用给新数据贴上任何标签，也不需要神经网络训练，也不需要任何编程经验。 -### Key Goals and Results +### 关键目标和成果 -* **Automation of animal pose analysis for scientific studies:** +* **对动物进行自动化分析以供科学研究：** - 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技术的主要目标是在各种环境下测量和跟踪动物的姿势。 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:** From 60a0feb39ce1344cdea959f07a24ae9c12471a73 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 30 Jun 2021 10:49:27 +0200 Subject: [PATCH 469/909] 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 51ecea5558..352ea96eb0 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -28,13 +28,13 @@ DeepLabCut使用一种称为 [转移学习](https://arxiv.org/pdf/1909.11229)的 ### 关键目标和成果 -* **对动物进行自动化分析以供科学研究：** +* **对动物行为进行自动化分析以供科学研究：** - DeepLabCut技术的主要目标是在各种环境下测量和跟踪动物的姿势。 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技术的主要目标是在各种环境下测量和跟踪动物的姿势。 这些数据大有用处，比如可以用于神经科学研究以了解大脑是如何控制运动的，或者阐明动物是如何进行社交互动的。 研究人员观察到DeepLabCut的 [性能提升了10倍](https://www.biorxiv.org/content/10.1101/457242v1)。 可以在单机状态下以每秒1200多帧(FPS) 的速度推断出动物姿态。 -* **Creation of an easy-to-use Python toolkit for pose estimation:** +* **创建一个易于使用的 Python 工具包用于姿态估计：** - 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. + DeepLabCut想要以易于使用的工具的形式共享其动物姿态估计技术，使得研究人员可以轻松上手。 因此这个Python工具箱甚至包含有项目管理的功能。 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. From b6a7831c250f22d570c6cb6f16f43b01fb63eb26 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 30 Jun 2021 11:51:43 +0200 Subject: [PATCH 470/909] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 34 +++++++++++------------ 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index 352ea96eb0..54233f1cc0 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -34,36 +34,36 @@ DeepLabCut使用一种称为 [转移学习](https://arxiv.org/pdf/1909.11229)的 * **创建一个易于使用的 Python 工具包用于姿态估计：** - DeepLabCut想要以易于使用的工具的形式共享其动物姿态估计技术，使得研究人员可以轻松上手。 因此这个Python工具箱甚至包含有项目管理的功能。 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. + DeepLabCut想要以易于使用的工具的形式共享其动物姿态估计技术，使得研究人员可以轻松上手。 因此这个Python工具箱甚至包含有项目管理的功能。 通过帮助使用DeepLabCut工具集的用户打通从数据收集阶段到创建可共享可重用的数据分析流程，这样不仅实现了姿态估计的自动化，而且可以端到端的管理整个项目。 - Their [toolkit][DLCToolkit] is now available as open source. + 他们的 [工具包][DLCToolkit] 现在已经完全开源了。 - A typical DeepLabCut Workflow includes: + 典型的DeepLabCut 工作流包括： - - 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 + - 通过主动学习创建和完善训练集 + - 针对特定动物和场景创建量身定制的神经网络 + - 从视频中得到大规模推理所需的代码 + - 使用集成的可视化工具得出结论 -{{< 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" >}} +{{{< figsrc="/images/content_images/cs/deepplabcut-toolkit-steps.png" class="csfigcaption" caption="**DeepLabCut的姿态估计流程**" alt="dlcsteps" align="middle" tot="(来源: DeepLabCut)" tourlink="https://twitter.com/DeepLabCut/status/1198046918284210176/phot/1" >}} -### The Challenges +### 面临的挑战 -* **Speed** +* **性能** - 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. + 在快速处理动物行为视频以测量其行为的同时提高科学实验的效率和精度。 无论从技术层面还是从庞大的资源需求和训练集来看，不带标记非侵入式的从视频中检测动物的姿势，预测动物在动态变化背景下的行为表现对计算性能都极具挑战。 需要提出一种易于使用的工具，但不依赖诸如计算机科学家的专业知识，也不需要在近乎真实的环境中进行研究，要达成这个目标不是一件容易的事儿。 -* **Combinatorics** +* **组合学** - 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. + 组合学涉及到将多个肢体的运动姿势组装并整合到单个动物的行为中去。 将关键姿态及其联系与个体动物不同时段的不同动作整合起来是一个复杂的过程，需要进行繁琐的数值分析，尤其是在实验视频中捕捉多个动物行为的情况下。 -* **Data Processing** +* **数据处理** - Last but not the least, array manipulation - processing large stacks of arrays corresponding to various images, target tensors and keypoints is fairly challenging. + 最后一点但是并非不重要的一点是对数组的处理-处理与各种图像、目标张量和关键点相对应的大型数组具有相当大的挑战性。 -{{< 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" >}} +{{< figsrc="/images/content_images/cs/pose-estimatation.png" class="csfigcaption" caption="**姿势估计的复杂性和多样性**" alt="challesfig" ="middle tot="(来源: Mackenzie Mathis)" tacklink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} -## NumPy's Role in meeting Pose Estimation Challenges +## Numpy在应对姿态估计挑战中的角色 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). From 099701c019cf66cbe6433628372fb3a6eb4c8955 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Jul 2021 03:40:30 +0200 Subject: [PATCH 471/909] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index 54233f1cc0..4261d12811 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -65,15 +65,15 @@ DeepLabCut使用一种称为 [转移学习](https://arxiv.org/pdf/1909.11229)的 ## Numpy在应对姿态估计挑战中的角色 -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). +NumPy 解决了DeepLabCut技术对行为分析进行高性能数值计算的核心需求。 除了NumPy, DeepLabCut 还使用 各种以 NumPy 为核心的 Python 软件， 例如 [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [tensorpack](https://github.com/tensorpack/tensorpack), [imgig](https://github.com/aleju/imgaug), [sikit-learning](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) 和 [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: +NumPy 的以下功能在图像处理、组合计算和高性能DeepLabCut 姿态预测算法等方面发挥了关键作用。 -* Vectorization -* Masked Array Operations -* Linear Algebra -* Random Sampling -* Reshaping of large arrays +* 向量化 +* Mask数组操作 +* 线性代数 +* 随机采样 +* 大规模矩阵变形 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”. From e144f38405bea404a8741bb4c27d0005acf61987 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Jul 2021 04:44:41 +0200 Subject: [PATCH 472/909] New translations deeplabcut-dnn.md (Chinese Simplified) --- content/zh/case-studies/deeplabcut-dnn.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/case-studies/deeplabcut-dnn.md b/content/zh/case-studies/deeplabcut-dnn.md index 4261d12811..0cd2de78d8 100644 --- a/content/zh/case-studies/deeplabcut-dnn.md +++ b/content/zh/case-studies/deeplabcut-dnn.md @@ -75,15 +75,15 @@ NumPy 的以下功能在图像处理、组合计算和高性能DeepLabCut 姿态 * 随机采样 * 大规模矩阵变形 -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”. +DeepLabCut在工具包提供的整个工作流中都使用了NumPy 数组。 需要特别指出的是，为了方便手工注释标注，以及便于编写、编辑和处理这些标注，Numpy被广泛应用于对不同的图像帧进行采样。 DeepLabCut对TensorFlow中的神经网络进行了成千上万次迭代训练，以预测图像 帧中注释的准确性。 为了达成这个目标，需要创建一个目标分布图(得分地图) 将姿态预测问题投射为图像之间变换的问题。 采用数据增强技术可以让神经网络变的更健壮，这就需要对遵循各种几何和图像处理流程的目标积分图进行计算。 为了加快训练速度，NumPy 的向量化功能会被充分利用起来。 在推理阶段，目标积分图中最可能的预测结果会被提取出来，然后就可以有效地“将预测结果映射到某种具体的动物”。 -{{< 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">}} +{{< figsrc="/images/content_images/cs/deeplabcut-workflow.png" class="fig-center" caption="**DeepLabCut工作流程**" alt="workflow" tot="*(资料来源：Mackenzie Mathis)*" tacklink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-lab-lab-the-lab-the-work-flow-as well-the-directory-and_fig1_329185962">}} -## Summary +## 总结 -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. +对行为进行精确的观测和高效的描述是现代伦理学、神经科学、医学和技术的核心内容。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) 让研究人员预测实验对象的行为成为可能，从而高效量化动物行为。 DeepLabCut Python工具箱仅需少量训练图像就可以将神经网络训练达到人类水平的标注准确性，因此它的应用范围绝不局限于实验室的行为分析，而且还可以拓展到运动、步态分析、医学和康复研究中。 通过操作Numpy数组，可以解决DeepLabCut算法面临的复杂组合计算和数据处理难题。 -{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} +{{< figsrc="/images/content_images/cs/numpy_bh_bbh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Numpy核心能力的运用**" >}} [cheetah-movement]: https://www. technologynetworks. com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From c4d1c87da756cff1cf6feaf776a18fa34a63516a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 1 Jul 2021 04:44:42 +0200 Subject: [PATCH 473/909] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/contribute.md b/content/zh/contribute.md index 2533761d12..1ad0bf25f7 100644 --- a/content/zh/contribute.md +++ b/content/zh/contribute.md @@ -1,8 +1,8 @@ - - - -title: Contribute to NumPy sidebar: false +title: Numpy贡献者指南 sidebar: false - - - -The NumPy project welcomes your expertise and enthusiasm! Your choices aren't limited to programming -- in addition to +NumPy 项目的繁荣发展需要您的专业知识和热情。 Your choices aren't limited to programming -- in addition to - [Writing code](#writing-code) From f6c06f4d73d2c840d80ab372401df62f8fc1ea67 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Jul 2021 05:11:58 +0200 Subject: [PATCH 474/909] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md index 3d25090e13..60daedf3d7 100644 --- a/content/zh/case-studies/gw-discov.md +++ b/content/zh/case-studies/gw-discov.md @@ -1,5 +1,5 @@ --- -title: "Case Study: Discovery of Gravitational Waves" +title: "案例研究：发现引力波" sidebar: false --- From 5b18e92d93a7f4428afc4067d76e43b6e1a6f3fa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Jul 2021 06:11:57 +0200 Subject: [PATCH 475/909] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md index 60daedf3d7..da4349fc05 100644 --- a/content/zh/case-studies/gw-discov.md +++ b/content/zh/case-studies/gw-discov.md @@ -3,23 +3,23 @@ title: "案例研究：发现引力波" 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" >}} +{{< figsrc="/images/content_images/cs/gw_sxs_image. ng" class="fig-center" caption="**引力波**" alt="两个黑洞合并生成引力波纹" totel="*(图片来源：LIGO的极端时空模拟 (SXS) 项目)*" totlink="https://youtu. e/Zt8Z_uzG71o">}}
    -

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

    -
    David Shoemaker, LIGO Scientific Collaboration
    +

    Python 科学生态系统是 LIGO 研究的关键基础设施。

    +
    David Shoemaker, LIGO科学计算团队
    -## About [Gravitational Waves](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/) and [LIGO](https://www.ligo.caltech.edu) +## 关于 [引力波](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. +引力波是空间和时间结构中的涟漪。由宇宙中的灾难性事件产生，例如两个黑洞的碰撞和合并或双星或超新星的合并。 观测引力波不仅有助于研究引力，而且有助于了解遥远宇宙中一些不为人知的现象及其影响。 -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. +[激光干涉引力波天文台(LIGO)](https://www.ligo.caltech.edu)旨在通过直接探测爱因斯坦广义相对论预测的引力波来打开引力波天体物理学领域。 它由美国境内的两个相距甚远的干涉仪组成—一个位于华盛顿汉福德，另一个位于路易斯安那州利文斯顿—它们同时运行以探测引力波。 每一个仪器都装载使用了激光干涉测量法的公里级引力波探测器。 LIGO科学计算团队(LSC) 是由来自美国各地大学和其他 14 个国家的 1000 多名科学家组成的团体，得到了 90 多所大学和研究机构的支持；大约 250 名学生积极参与项目合作。 LIGO 的新发现是关于对引力波本身的首次观测，通过测量引力波在穿过地球时对空间和时间造成的微小扰动而制成。 它开辟了新的天体物理学研究方向，致力于探索宇宙扭曲的一面—研究由扭曲的时空构成的物体和现象。 -### Key Objectives +### 关键目标 -* 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. +* 虽然它的 [任务](https://www.ligo.caltech.edu/page/what-is-ligo) 是探测宇宙中反应最剧烈和能量最集中的区域产生的引力波，但 LIGO 收集的数据可能会对物理学的许多领域产生深远的影响，包括引力、相对论、天体物理学、宇宙学、粒子物理学和核物理。 * 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. From 61903721a4c458b1c9b0b76c670d0b1f773c7760 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Jul 2021 08:56:51 +0200 Subject: [PATCH 476/909] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md index da4349fc05..505bdf87c4 100644 --- a/content/zh/case-studies/gw-discov.md +++ b/content/zh/case-studies/gw-discov.md @@ -20,24 +20,24 @@ sidebar: false ### 关键目标 * 虽然它的 [任务](https://www.ligo.caltech.edu/page/what-is-ligo) 是探测宇宙中反应最剧烈和能量最集中的区域产生的引力波，但 LIGO 收集的数据可能会对物理学的许多领域产生深远的影响，包括引力、相对论、天体物理学、宇宙学、粒子物理学和核物理。 -* 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. +* 通过涉及复杂数学的数值相对论来计算和处理观测数据，以便从噪声中辨别信号、滤除相关信号并统计估计观测数据的重要性。 +* 数据可视化，以便可以理解二进制/数值结果。 -### The Challenges +### 面临的挑战 -* **Computation** +* **计算** - 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 + 引力波很难被探测到，因为它们产生的影响非常小，与物质的相互作用也很小。 处理和分析 LIGO 的所有数据需要庞大的计算基础设施。在处理数十亿倍于引力波信号的噪声后，仍然需要使用非常复杂的相对论方程来处理海量数据，这带来了计算挑战： [二进制合并分析需要花费O(10^ 7) 级别的 CPU 小时数](https://youtu.be/7mcHknWWzNI)才能完成，这些计算过程由 6 个专用 LIGO 集群分摊解决。 -* **Data Deluge** +* **数据泛滥** - 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. + 随着观测设备变得更加敏感和可靠，数据泛滥和大海捞针所带来的挑战成倍增加。 LIGO 每天生成数 TB 的数据！ 每一次检测之后要理解这些数据都要付出巨大的努力。 例如，LIGO 收集的信号必须由超级计算机与数十万个可能的引力波特征模板进行匹配。 -* **Visualization** +* **可视化** - 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. + 一旦解决了理解爱因斯坦方程以及使用超级计算机求解这些方程相关的障碍，下一个重大挑战就是使人脑能够理解数据。 仿真建模以及信号检测需要有效的可视化技术。 在纯科学爱好者的眼中，可视化在为数值相对论提供更多可信度方面也发挥了作用，在成像和模拟使更多人更容易理解结果之前，他们并没有对数值相对论给予足够的重视。 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" >}} From 55e51798feffe07c3c030b90692bfdf76e4c915e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 5 Jul 2021 10:06:23 +0200 Subject: [PATCH 477/909] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 36 ++++++++++++++-------------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md index 505bdf87c4..ff4e8a3e2f 100644 --- a/content/zh/case-studies/gw-discov.md +++ b/content/zh/case-studies/gw-discov.md @@ -37,33 +37,33 @@ sidebar: false * **可视化** - 一旦解决了理解爱因斯坦方程以及使用超级计算机求解这些方程相关的障碍，下一个重大挑战就是使人脑能够理解数据。 仿真建模以及信号检测需要有效的可视化技术。 在纯科学爱好者的眼中，可视化在为数值相对论提供更多可信度方面也发挥了作用，在成像和模拟使更多人更容易理解结果之前，他们并没有对数值相对论给予足够的重视。 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. + 一旦解决了理解爱因斯坦方程以及使用超级计算机求解这些方程相关的障碍，下一个重大挑战就是使人脑能够理解数据。 仿真建模以及信号检测需要有效的可视化技术。 在纯科学爱好者的眼中，可视化在为数值相对论提供更多可信度方面也发挥了作用，在成像和模拟使更多人更容易理解结果之前，他们并没有对数值相对论给予足够的重视。 使用最新的实验输入和见解来加快复杂计算和渲染、重新渲染图像和模拟的速度可能是一项耗时的活动，给该领域的研究人员带来严峻挑战。 -{{< 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" >}} +{{< figsrc="/images/content_images/cs/gw_strain_amplitude ng" class="fig-center" alt="引力波应变幅值” caption="**来自GW150914的估计引力波应变幅度**"Attorney="(**图表来源:** 从二元黑洞合并中观察引力波，ResearchGate 出版物)" tourlink="https://www. esearchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger” >}} -## NumPy’s Role in the Detection of Gravitational Waves +## Numpy 在引力波检测中的作用 -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. +除了使用超级计算机暴力计算数值相对论之外，目前还无法使用任何其它技术计算黑洞合并发出的引力波。 LIGO 收集的数据量之大，就像无比微弱的引力波信号一样，令人难以置信。 -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: +NumPy 是 Python 的标准数值分析包，被用于 LIGO GW 检测项目期间执行的各种任务的软件所使用。 NumPy 有助于高性能处理复杂的数学问题和数据操作。 这里有一些例子： -* [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. +* [信号处理](https://www.uv.es/virgogroup/Denoising_ROF.html): 毛刺检测, [噪音识别和数据表征](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplab, pandas, pyCharm) +* 数据检索：决定哪些数据可以用于分析，确定它是否包含信号—犹如大海捞针 +* 统计分析：估计观测数据的统计显著性，通过与模型比较来估计信号参数(如恒星质量、自旋速度和距离)。 +* 数据可视化 + - 时间序列 + - 频谱图 +* 计算相关性 +* 在GW 数据分析中开发的关键 [软件](https://github.com/lscsoft) 例如： [GwPy](https://gwpy.github.io/docs/stable/overview.html) 和 [PyCBC](https://pycbc.org) 使用 NumPy 和 AstroPy 为实用程序、工具和方法提供基于对象的接口，用于研究来自引力波探测器的数据。 -{{< 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**" >}} +{{< figsrc="/images/content_images/cs/gwpy-numpy-dep-graph.png" class="fig-center" alt="gwpy-numpy depgraph" caption="**GwPy 包的软件依赖关系**>}} ---- -{{< 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**" >}} +{{< figsrc="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**PyCBC包的软件依赖关系图**>}} -## Summary +## 总结 -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 探测使研究人员能够发现完全出乎意料的现象，同时为许多已知的最深刻的天体物理现象提供了新的见解。 数学运算和数据可视化是帮助科学家深入了解从科学观察中收集到的数据并理解结果的关键步骤。 计算是复杂的，除非使用计算机模拟进行可视化，并提供真实的观察数据和分析，否则人类无法理解。 NumPy 与其他 Python 包（例如 matplotlib、pandas 和 scikit-learn）一起[使研究人员](https://www.gw-openscience.org/events/GW150914/)能够回答复杂的问题并开拓我们理解宇宙的新视角。 -{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}} +{{< figsrc="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Numpy核心能力的应用**" >}} From 1637c94ba418ae5213a30b9ea0f7d85f215dae95 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Jul 2021 04:49:08 +0200 Subject: [PATCH 478/909] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/contribute.md b/content/zh/contribute.md index 1ad0bf25f7..594c77b4e1 100644 --- a/content/zh/contribute.md +++ b/content/zh/contribute.md @@ -2,9 +2,9 @@ title: Numpy贡献者指南 sidebar: false - - - -NumPy 项目的繁荣发展需要您的专业知识和热情。 Your choices aren't limited to programming -- in addition to +NumPy 项目的繁荣发展需要您的专业知识和热情！ 您在社区能做的不仅仅只是编程 - 或者更直白的说 -- [Writing code](#writing-code) +- [编写代码](#writing-code) you can From ad64aa67badc01601ce8b4bcf1b17c71e68039bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Jul 2021 05:49:39 +0200 Subject: [PATCH 479/909] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 70 ++++++++++++++++++++-------------------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/content/zh/contribute.md b/content/zh/contribute.md index 594c77b4e1..414f51d306 100644 --- a/content/zh/contribute.md +++ b/content/zh/contribute.md @@ -2,65 +2,65 @@ title: Numpy贡献者指南 sidebar: false - - - -NumPy 项目的繁荣发展需要您的专业知识和热情！ 您在社区能做的不仅仅只是编程 - 或者更直白的说 +NumPy 项目的繁荣发展需要您的专业知识和热情！ 您在社区能做的不仅限于编程 - 除了 -- [编写代码](#writing-code) +- [写代码](#writing-code) -you can +你还可以 -- [Review pull requests](#reviewing-pull-requests) -- [Develop tutorials, presentations, and other educational material](#developing-educational-materials) -- [Triage issues](#issue-triaging) -- [Work on our website](#website-development) -- [Contribute graphic design](#graphic-design) -- [Translate website content](#translating-website-content) -- [Serve as a community coordinator](#community-coordination-and-outreach) -- [Write grant proposals and help with other fundraising](#fundraising) +- [检视合并请求](#reviewing-pull-requests) +- [开发教程、演示文稿和其它教育材料](#developing-educational-materials) +- [对问题分类](#issue-triaging) +- [优化社区官网](#website-development) +- [贡献图形设计](#graphic-design) +- [翻译网站内容](#translating-website-content) +- [担任社区协调员](#community-coordination-and-outreach) +- [撰写捐款提案并帮助完成其他筹款活动](#fundraising) -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). +如果你不确定从哪里开始或你的技能如何匹配社区， _向我们求助吧！_ 您可以在 [邮件列表](https://mail.python.org/mailman/listinfo/numpy-discussion) 或[GitHub](http://github.com/numpy/numpy) (打开一个[issue](https://github.com/numpy/numpy/issues) 或评论相关的问题)。 -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). +这些是我们的首选联系渠道(开源的本质是开放)，但如果您更喜欢私下交流，请通过 或 [Slack](https://numpy-team.slack.com)联系我们的社区协调员(发送邮件至以获得邀请) -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/). +我们还有一个双周的 _社区电话例会_，详细信息会在[邮件列表 ](https://mail.python.org/mailman/listinfo/numpy-discussion)中公布。 非常欢迎您的加入。 如果您刚开始为开源做贡献，我们也强烈建议您阅读[本指南](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. +我们的社区渴望平等对待每个人并重视所有贡献。 我们有一套 [行为准则 ](/code-of-conduct)来营造一个开放和热情的环境。 -### Writing code +### 编写代码 -Programmers, this [guide](https://numpy.org/devdocs/dev/index.html#development-process-summary) explains how to contribute to the codebase. +面向程序员, 此[指南](https://numpy.org/devdocs/dev/index.html#development-process-summary)解释如何为代码库做出贡献。 -### 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 +### 审核其他人提交的 merge request +本项目有超过250个开放的合入请求 — 这意味着许多潜在的改进和许多等待反馈的开源贡献者。 如果您是一位了解 NumPy 的开发人员，即使您不熟悉代码库，也可以提供帮助。 您可以： +* 对长时间讨论的话题进行总结 +* 对文档的PR进行分类 +* 对做出的修改进行测试 -### Developing educational materials +### 开发教材 -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. +NumPy的 [用户指南](https://numpy.org/devdocs) 正在进行整改。 我们需要新的教程、入门指南和深入细致的解释，并且官网结构也需要重新组织。 贡献机会也不限于编写教材。 我们也欢迎使用示例、学习笔记和教学视频。 [NEP 44 — 重构NumPy文档](https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html)列出了我们目前的想法，您可能还有其他想法。 -### Issue triaging +### 问题分类 -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的问题跟踪器 ](https://github.com/numpy/numpy/issues)有 _很多_未关闭的问题。 有些问题不再合理范围，有些问题应该优先考虑，有些是新贡献者带来的好问题。 您可以： -* 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) +* 检查之前的问题是否仍然存在 +* 找出重复出现的问题并将其关联起来 +* 为问题添加清晰的可复现代码 +* 为问题添加正确的标签(这需要分类权限 - 发邮件咨询即可获取) -Please just dive in. +只管尽情探索吧。 -### Website development +### 网站开发 -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. +我们刚刚更新了我们的网站，但离完成还有很长的距离。 如果您喜欢网站开发，这些[问题](https://github.com/numpy/numpy.org/issues?q=is%3Aissue+is%3Aopen+label%3Adesign)列出了一些我们尚未满足的需求 -- 请随时分享您的想法。 -### Graphic design +### 平面设计 -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. +我们几乎无法开始列出平面设计师可以在这里做出的贡献。 Our docs are parched for illustration; our growing website craves images -- opportunities abound. ### Translating website content From 42805512a920e5c3b79ad5f76b3933f015b629a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Jul 2021 08:23:01 +0200 Subject: [PATCH 480/909] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/contribute.md b/content/zh/contribute.md index 414f51d306..5d092a35fe 100644 --- a/content/zh/contribute.md +++ b/content/zh/contribute.md @@ -60,12 +60,12 @@ NumPy的 [用户指南](https://numpy.org/devdocs) 正在进行整改。 我们 ### 平面设计 -我们几乎无法开始列出平面设计师可以在这里做出的贡献。 Our docs are parched for illustration; our growing website craves images -- opportunities abound. +我们几乎无法开始列出平面设计师可以在这里做出的贡献。 社区文档为了准确生动的描述而生；日益成长壮大的网站迫切需要大量的平面设计图片-这里的机会比比皆是。 -### Translating website content +### 翻译网站内容 -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. +我们计划对 [numpy.org](https://numpy.org) 进行多语种翻译，让用户可以用他们的母语访问 NumPy。 翻译志愿者是这项工作的核心。 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 From 8303946fcbd2c6e069332a071d5ab582d16488a5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 6 Jul 2021 09:20:25 +0200 Subject: [PATCH 481/909] New translations contribute.md (Chinese Simplified) --- content/zh/contribute.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/contribute.md b/content/zh/contribute.md index 5d092a35fe..79e22d590d 100644 --- a/content/zh/contribute.md +++ b/content/zh/contribute.md @@ -65,14 +65,14 @@ NumPy的 [用户指南](https://numpy.org/devdocs) 正在进行整改。 我们 ### 翻译网站内容 -我们计划对 [numpy.org](https://numpy.org) 进行多语种翻译，让用户可以用他们的母语访问 NumPy。 翻译志愿者是这项工作的核心。 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. +我们计划对 [numpy.org](https://numpy.org) 进行多语种翻译，让用户可以用他们的母语访问 NumPy。 翻译志愿者是这项工作的核心。 请参阅[此处](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)了解翻译背景; 对此 [GitHub问题](https://github.com/numpy/numpy.org/issues/55) 发表评论以加入到翻译队伍中. -### Community coordination and outreach +### 社区协调和宣传 -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. +通过社区我们可以更广泛地分享我们的工作，并了解我们的不足之处。 我们渴望让更多的人参与进来，比如关注我们的[Twitter](https://twitter.com/numpy_team) 帐户、组织NumPy [代码比赛](https://scisprints.github.io/)、时事通讯以及博客宣传中。 -### Fundraising +### 筹款活动 -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 多年来一直都是靠志愿者发展起来的，但随着其重要性的增加，很明显，为了确保稳定和成长，我们需要经济上的支持。 [这个SciPy'19 演讲](https://www.youtube.com/watch?v=dBTJD_FDVjU) 解释了这种支持产生了多大的不同。 与所有非营利组织一样，我们一直在寻求捐款、赞助和其他类型的支持。 我们有很多想法，当然我们欢迎大家提供更多意见。 筹款在这里是一项稀缺技能 - 我们迫切需要您的帮助。 From 2d98801b796e8dcd311f6b3fc2b2ee4edebd88f0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 04:06:22 +0200 Subject: [PATCH 482/909] 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 0ad006d0f7..cd4810bc5a 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -1,5 +1,5 @@ --- -title: Learn +title: 学习指南 sidebar: false --- From 293ed35e04f0f53fc481d499042c6a059ca7a969 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 05:11:50 +0200 Subject: [PATCH 483/909] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 64 ++++++++++++++++++++++----------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/content/zh/learn.md b/content/zh/learn.md index cd4810bc5a..58c35be1ab 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -3,58 +3,58 @@ title: 学习指南 sidebar: false --- -For the **official NumPy documentation** visit [numpy.org/doc/stable](https://numpy.org/doc/stable). +有关 **官方NumPy文档**，请访问 [numpy.org/doc/stable](https://numpy.org/doc/stable)。 -## NumPy Tutorials +## Numpy入门教程 -You can find a set of tutorials and educational materials by the NumPy community at [NumPy Tutorials](https://numpy.org/numpy-tutorials). The goal of this page is to provide high-quality resources by the NumPy project, both for self-learning and for teaching classes with, in the format of Jupyter Notebooks. If you’re interested in adding your own content, check the [numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +您可以在[NumPy教程](https://numpy.org/numpy-tutorials)中找到 NumPy 社区的一套教程和教材。 本页面的目标是通过 NumPy 项目以 Jupyter Notebooks 的格式提供高质量的资源，用于自学和教学课程。 如果您有兴趣添加自己的内容，请查看 GitHub上的 [numpy-tutorials项目](https://github.com/numpy/numpy-tutorials) 。 *** -Below is a curated collection of external resources. To contribute, see the [end of this page](#add-to-this-list). +以下是精选的外部资源合集。 要做出贡献，请参阅 [本页末尾](#add-to-this-list)。 -## Beginners +## 初学者 -There's a ton of information about NumPy out there. If you are new, we'd strongly recommend these: +外部有大量关于 NumPy 的信息。 如果您是新手，我们强烈推荐学习这些资源： - **Tutorials** + **教程** -* [NumPy Quickstart Tutorial](https://numpy.org/devdocs/user/quickstart.html) -* [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) -* [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 *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) +* [NumPy 快速入门教程](https://numpy.org/devdocs/user/quickstart.html) +* [NumPy图解： *Lev Maximov编写的*NumPy可视化指南](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [SciPy 讲座](https://scipy-lectures.org/) 除了涵盖NumPy之外，这些讲座还对Python科学生态系统提供了更广泛的介绍。 +* [NumPy：初学者的必读基础课](https://numpy.org/devdocs/user/absolute_beginners.html) +* [机器学习之家-ndarray简介](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) +* [Edureka - 通过示例学习 NumPy 数组 ](https://www.edureka.co/blog/python-numpy-tutorial/) +* [Dataquek - NumPy 教程：使用 Python 进行数据分析](https://www.dataquest.io/blog/numpy-tutorial-python/) +* [*Nicolas Rougier的*NumPy 教程](https://github.com/rougier/numpy-tutorial) +* [*由 Justin Johnson 编写的*斯坦福 CS231。](http://cs231n.github.io/python-numpy-tutorial/) +* [NumPy用户指南](https://numpy.org/devdocs) - **Books** + **图书** -* [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* +* [Travis E. Oliphant的*NumPy 指南*](http://web.mit.edu/dvp/Public/numpybook.pdf)，这是从2006年开始的免费版本。 最新的副本 (2015) 请参见 [此处](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007)。 +* [*Nicolas P. Rougier的*从 Python 到 NumPy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* *由 Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow编写的*[优雅的SciPy](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877) -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. +您也可能想要查看有关“Python+SciPy”主题的 [Goodreads列表](https://www.goodreads.com/shelf/show/python-scipy) 。 那里的大部分书籍都是有关以NumPy为核心构建起来的“SciPy生态系统”。 - **Videos** + **视频** -* [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* +* *由Alex Chabot-Leclerc制作的*[NumPy数值计算导论](http://youtu.be/ZB7BZMhfPgk) *** -## Advanced +## 进阶资源 -Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more. +学习这些进阶资源以更好地理解 NumPy 概念，例如高级索引、拆分、堆叠、线性代数等。 - **Tutorials** + **教程** -* [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 in Python (Advanced)](https://www.geeksforgeeks.org/numpy-python-set-2-advanced/) -* [Advanced Indexing](https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm) +* *Nicolas P. Rougier的*[100 NumPy练习题](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) +* * M. Scott Shell的*[NumPy 和 Scipy 介绍](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) +* *Stéfan van der Walt的*[Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) +* [Python 中的 NumPy(进阶)](https://www.geeksforgeeks.org/numpy-python-set-2-advanced/) +* [高级索引](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/) **Books** From 59860bb922def25a9b7acd49504ba2f541f32873 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 06:11:14 +0200 Subject: [PATCH 484/909] New translations learn.md (Chinese Simplified) --- content/zh/learn.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/content/zh/learn.md b/content/zh/learn.md index 58c35be1ab..ccd7981d25 100644 --- a/content/zh/learn.md +++ b/content/zh/learn.md @@ -55,36 +55,36 @@ sidebar: false * *Stéfan van der Walt的*[Numpy Medkits](http://mentat.za.net/numpy/numpy_advanced_slides/) * [Python 中的 NumPy(进阶)](https://www.geeksforgeeks.org/numpy-python-set-2-advanced/) * [高级索引](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/) +* [使用NumPy进行机器学习和数据分析](https://www.machinelearningplus.com/python/numpy-tutorial-python-part2/) - **Books** + **图书** -* [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* +* *Jake Vanderplas编写的*[Python 数据科学手册](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) +* *Wes McKinney的*[Python数据分析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) +* *Robert Johansson的*[数值Python: 使用 Numpy、SciPy 和 Matplotlib 进行科学计算和数据科学应用](https://www.amazon.com/Numerical-Python-Scientific-Applications-Matplotlib/dp/1484242459) - **Videos** + **视频** -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* -* [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *by Amuls Academy* +* *Juan Nunez-Iglesias的*[NumPy进阶 - 广播机制、步幅和高级索引](https://www.youtube.com/watch?v=cYugp9IN1-Q) +* *AMuls Academy的*[在 NumPy 数组中的高级索引操作](https://www.youtube.com/watch?v=2WTDrSkQBng) *** -## NumPy Talks +## NumPy演讲 -* [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) +* *Jaime Fernadez的*[NumPy索引的未来](https://www.youtube.com/watch?v=o0EacbIbf58) (2016) +* *Ralf Gommers的*[Python数组计算的演变史](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) (2019) +* *Matti Picus的*[NumPy：什么已经改变，什么将要改变？](https://www.youtube.com/watch?v=YFLVQFjRmPY) (2019) +* *Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris谈谈*[NumPy揭秘](https://www.youtube.com/watch?v=dBTJD_FDVjU) (2019) +* *Travis Oliphant的* [ Python 数组计算概述](https://www.youtube.com/watch?v=f176j2g2eNc) (2019) *** -## Citing NumPy +## 引用 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). +如果NumPy在您的研究中具有重要意义，您希望在您的学术出版物中向该项目致谢。 请参阅 [此引用信息](/citing-numpy)。 -## Contribute to this list +## 为本页面的资源列表做出贡献 -To add to this collection, submit a recommendation [via a pull request](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md). Say why your recommendation deserves mention on this page and also which audience would benefit most. +若要添加资源到本页面，请[通过提交合并请求](https://github.com/numpy/numpy.org/blob/master/content/en/learn.md) 来提交建议 。 需要详细说明为什么您的推荐值得在此页面上被提及，以及哪些受众最受益。 From 260379e632858f6945b7ae109e3560f818f3dda1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 16:29:50 +0200 Subject: [PATCH 485/909] New translations about.md (Spanish) --- content/es/about.md | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 34642cf247..2b11a8d69c 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -48,19 +48,31 @@ El proyecto NumPy está creciendo; tenemos equipos para Visita la página [Equipo](/gallery/team.html) para conocer a los miembros de cada equipo. -## Patrocinadores +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors NumPy recibe financiación directa de las siguientes fuentes: {{< sponsors >}} -## Socios institucionales +## Institutional Partners Los socios institucionales son organizaciones que apoyan el 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, Sebastian Berg, Ross Barnowski) +- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) + {{< partners >}} -## Donar +## Donate 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. From f80909d43ebafe48c3b94f86d4ede72f7b8bb713 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 16:29:51 +0200 Subject: [PATCH 486/909] New translations about.md (Arabic) --- content/ar/about.md | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 28d9a22335..5472ed1599 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -48,19 +48,31 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ شاهد صفحة [ ](/gallery/team.html) لأعضاء الفريق. -## الرُعاة +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors ويتلقى المشروع تمويلا مباشرا من المصادر التالية: {{< sponsors >}} -## الشركاء المؤسسيون +## Institutional Partners الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. ويشمل الشركاء المؤسسيون الحاليون ما يلي: + +- UC Berkeley (Stéfan van der Walt, Sebastian Berg, Ross Barnowski) +- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) + {{< partners >}} -## التبرع +## Donate يرجى النظر في التبرع للمشروع بما يتناسب مع مواردك إذا كنت وجدته مفيد في عملك أو بحثك أو شركتك. ،أي مبلغ قد يساعد، وستستخدم جميع التبرعات بشكل صارم لتطوير برمجيات المشروع مفتوحة المصدر، ووثائقه، ومجتمعه. From 4d5739d82b26e0ac3b2d3e1c0b3dfd26f1e4060e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 16:29:52 +0200 Subject: [PATCH 487/909] New translations about.md (Japanese) --- content/ja/about.md | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 8b2eb04a17..887b859a9a 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -48,19 +48,31 @@ NumPy プロジェクトは拡大しているため、いくつかのチーム 個々のチームメンバーについては、 [チーム](/gallery/team.html) のページを参照してください。 -## スポンサー情報 +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors NumPyは以下の団体から直接資金援助を受けています。 {{< sponsors >}} -## パートナー団体 +## Institutional Partners 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, Sebastian Berg, Ross Barnowski) +- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) + {{< partner >}} -## 寄付 +## Donate NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 From fd1691aab52febbde697f925a5ee56128a54ef5c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 16:29:54 +0200 Subject: [PATCH 488/909] New translations about.md (Korean) --- content/ko/about.md | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index 49d3142c71..6e7e20befc 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -48,19 +48,31 @@ NumPy 프로젝트는 성장하고 있습니다. 그리고 우리는 다음과 개발 팀원들은 [팀](/gallery/team.html) 페이지를 참조하세요. -## 스폰서 +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors NumPy는 다음과 같은 곳들에서 직접적으로 자금을 받습니다. {{< sponsors >}} -## 기관 파트너 +## Institutional Partners 기관 파트너는 그들의 업무의 일환으로 NumPy에 기여하는 직원을 고용하여 프로젝트를 지원하는 조직입니다. 현재 기관 파트너는 다음과 같습니다. + +- UC Berkeley (Stéfan van der Walt, Sebastian Berg, Ross Barnowski) +- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) + {{< partners >}} -## 후원 +## Donate 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. From 7e2837c22c4c37fa6a1894317f18446d872d8958 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 16:29:55 +0200 Subject: [PATCH 489/909] New translations about.md (Chinese Simplified) --- content/zh/about.md | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/content/zh/about.md b/content/zh/about.md index 5dfd4e796f..0ca2b552e6 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -48,19 +48,31 @@ NumPy 项目正在不断发展中，我们的团队成员负责： 查看[团队](/gallery/team.html)页面以了解每个独立团队的成员信息。 -## 赞助商 +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors NumPy 直接从下列来源获得资金： {{< sponsors >}} -## 机构合作伙伴 +## Institutional Partners 机构合作伙伴指那些通过雇用为 NumPy 做贡献的人来支持该项目的组织。 目前的机构伙伴包括： + +- UC Berkeley (Stéfan van der Walt, Sebastian Berg, Ross Barnowski) +- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) + {{< partners >}} -## 捐赠 +## Donate 如果您发现 NumPy 对您的工作、研究或公司有用，请考虑向该项目发起捐款。 任何金额都有帮助！ 所有捐款将严格用于 NumPy 开源软件、文档和社区的开发。 From a9768e0ea4529c76392c8127e6a6fabbc1c7c82c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 16:29:56 +0200 Subject: [PATCH 490/909] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index a90423141f..fc50a29b0b 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -48,19 +48,31 @@ O projeto NumPy está crescendo; temos equipes para Veja a página de [Times](/gallery/team.html) para membros individuais de cada time. -## Patrocinadores +## NumFOCUS Subcommittee + +- Charles Harris +- Ralf Gommers +- Melissa Weber Mendonça +- Sebastian Berg +- External member: Thomas Caswell + +## Sponsors O NumPy recebe financiamento direto das seguintes fontes: {{< sponsors >}} -## Parceiros Institucionais +## Institutional Partners 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, Sebastian Berg, Ross Barnowski) +- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) + {{< partners >}} -## Doações +## Donate Se você achou o NumPy útil no seu trabalho, pesquisa ou empresa, por favor considere fazer uma doação para o projeto que seja compatível com seus recursos. Qualquer quantidade ajuda! Todas as doações serão utilizadas estritamente para financiar o desenvolvimento do software de código aberto da NumPy, documentação e comunidade. From fbc00cf971e91537732da0cab64daa04d4a37767 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 17:35:18 +0200 Subject: [PATCH 491/909] New translations about.md (Spanish) --- content/es/about.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 2b11a8d69c..b6c041f384 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -48,21 +48,21 @@ El proyecto NumPy está creciendo; tenemos equipos para Visita la página [Equipo](/gallery/team.html) para conocer a los miembros de cada equipo. -## NumFOCUS Subcommittee +## Subcomité NumFOCUS - Charles Harris - Ralf Gommers - Melissa Weber Mendonça - Sebastian Berg -- External member: Thomas Caswell +- Miembro externo: Thomas Caswell -## Sponsors +## Patrocinadores NumPy recibe financiación directa de las siguientes fuentes: {{< sponsors >}} -## Institutional Partners +## Socios institucionales Los socios institucionales son organizaciones que apoyan el proyecto empleando a personas que contribuyen a NumPy como parte de su trabajo. Entre los actuales socios institucionales se encuentran: @@ -72,7 +72,7 @@ Los socios institucionales son organizaciones que apoyan el proyecto empleando a {{< partners >}} -## Donate +## Donar 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. From aca39390b0fed6ac9c9e7051b9ebe6758e95a890 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 7 Jul 2021 23:29:40 +0200 Subject: [PATCH 492/909] New translations about.md (Arabic) --- content/ar/about.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 5472ed1599..c5a667eeba 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -50,9 +50,9 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ ## NumFOCUS Subcommittee -- Charles Harris -- Ralf Gommers -- Melissa Weber Mendonça +- تشارلز هاريس +- رالف غومرس +- ميليسا ويبر ميندوكا - Sebastian Berg - External member: Thomas Caswell From f4262a27c2eeb3d0da78a1d832d2714c33a98839 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 00:36:43 +0200 Subject: [PATCH 493/909] New translations about.md (Arabic) --- content/ar/about.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index c5a667eeba..6cb018828a 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -48,21 +48,21 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ شاهد صفحة [ ](/gallery/team.html) لأعضاء الفريق. -## NumFOCUS Subcommittee +## اللجنة الفرعية ل NumFOCUS - تشارلز هاريس - رالف غومرس - ميليسا ويبر ميندوكا -- Sebastian Berg -- External member: Thomas Caswell +- سيباستيشان بيرج +- عضو خارجي: توماس كاسويل -## Sponsors +## الرعاة ويتلقى المشروع تمويلا مباشرا من المصادر التالية: {{< sponsors >}} -## Institutional Partners +## الشركاء المؤسيسون الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. ويشمل الشركاء المؤسسيون الحاليون ما يلي: @@ -72,7 +72,7 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ {{< partners >}} -## Donate +## التبرع يرجى النظر في التبرع للمشروع بما يتناسب مع مواردك إذا كنت وجدته مفيد في عملك أو بحثك أو شركتك. ،أي مبلغ قد يساعد، وستستخدم جميع التبرعات بشكل صارم لتطوير برمجيات المشروع مفتوحة المصدر، ووثائقه، ومجتمعه. From 5e58a7bfc1b8bdcb667924d1489140a889fd9e7b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 01:36:43 +0200 Subject: [PATCH 494/909] New translations news.md (Arabic) --- content/ar/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 212e266e80..3af51d2bb5 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -31,9 +31,9 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear - 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 +### إصدار 1.19.2 لنمباي -_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 سيبتمبر لعام 2020 _- [أصبح نمباي 1.19.2 ](https://numpy.org/devdocs/release/1.19.2-notes.html) متاح الآن. 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! From 1cf3cc37bd5402c5572d6a2b62ccbf87536c288b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 01:36:45 +0200 Subject: [PATCH 495/909] New translations about.md (Arabic) --- content/ar/about.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 6cb018828a..9257aba36c 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -50,7 +50,7 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ ## اللجنة الفرعية ل NumFOCUS -- تشارلز هاريس +- تشارليز هاريس - رالف غومرس - ميليسا ويبر ميندوكا - سيباستيشان بيرج @@ -66,8 +66,8 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. ويشمل الشركاء المؤسسيون الحاليون ما يلي: -- UC Berkeley (Stéfan van der Walt, Sebastian Berg, Ross Barnowski) -- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) +- جامعة كاليفورنيا في بركلي( ستيفان فان دير والت وسيباستيان بيرغ وروس بارنوفسكي) +- نظام الواجهة الخلفية( رالف غومرس وميليسا وييبر ميندوكا ومارس لي وماتي بيكاس وبيروا بيتنسون) {{< partners >}} From 636538648ec8d59495ddafaddceeefe04fdeeed1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 02:34:59 +0200 Subject: [PATCH 496/909] 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 680333ff27..06f57209a9 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -63,7 +63,7 @@ pip install numpy #### Linux If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: -- Use your OS package manager for as much as possible (Python itself, NumPy, and other libraries). +- 尽可能使用您操作系统自带的包管理器进行管理(python本身、NumPy和其他库)。 - Install packages not provided by your package manager with `pip install somepackage --user`. 如果您使用GPU： From 30e5e87f0efb077de523775ea722ecd690a07536 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 03:43:02 +0200 Subject: [PATCH 497/909] New translations install.md (Chinese Simplified) --- content/zh/install.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/zh/install.md b/content/zh/install.md index 06f57209a9..d8eb2369bb 100644 --- a/content/zh/install.md +++ b/content/zh/install.md @@ -36,11 +36,11 @@ pip install numpy # Python 和 NumPy 安装指南 -在 Python 上安装和管理软件包很复杂，大多数任务有许多替代解决方案。 本指南试图给读者一种最佳(或最受欢迎) 解决办法，并给出清晰的建议。 It focuses on users of Python, NumPy, and the PyData (or numerical computing) stack on common operating systems and hardware. +在 Python 上安装和管理软件包很复杂，大部分工作任务都有许多可选择的解决方案。 本指南试图给读者一种最佳(或最受欢迎) 解决方案，并给出清晰的建议。 它侧重于在通用操作系统和硬件上使用Python、NumPy和PyData (或数学计算) 这些技术栈的用户。 ## 建议 -我们将首先根据用户的经验水平和有兴趣的操作系统提出建议。 如果您在“开始”和“高级”之间纠结，我们建议如果您想要保持简单，请使用"开始"， 如果您想要按照更长远的最佳做法去做，请使用"高级"。 +我们将首先根据用户的经验水平和有兴趣的操作系统提出建议。 如果您在“开始”和“高级”之间纠结，我们建议，如果您想简单点请使用"开始"，如果您想按长期最佳实践去做，请看"高级"。 ### 开始用户 @@ -62,9 +62,9 @@ pip install numpy #### Linux -If you're fine with slightly outdated packages and prefer stability over being able to use the latest versions of libraries: +如果您觉得稍旧点的库还不错，并且相比于使用最新版本的库更喜欢稳定性： - 尽可能使用您操作系统自带的包管理器进行管理(python本身、NumPy和其他库)。 -- Install packages not provided by your package manager with `pip install somepackage --user`. +- 使用 `pip install somepackage --user` 安装来包管理器未提供的包。 如果您使用GPU： - 安装 [Miniconda](https://docs.conda.io/en/latest/miniconda.html)。 @@ -76,9 +76,9 @@ If you're fine with slightly outdated packages and prefer stability over being a - 保持 `base` conda 环境最小化， 并使用一个或多个[conda 环境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) 用于安装你需要的包以完成你正在做的任务或项目。 -#### Alternative if you prefer pip/PyPI +#### 如果您更喜欢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: +对出于个人喜好或看完下面 conda 和 pip之间的主要差异后更喜欢基于 pip/PyPI 的解决方案的用户，我们建议： - 从 [python.org](https://www.python.org/downloads/), [Homebrew](https://brew.sh/)或 Linux 软件包管理器安装 Python。 - 使用 [Poetry](https://python-poetry.org/) ，它是具有与conda 相似的依赖解析器和环境管理能力的完善工具。 @@ -99,7 +99,7 @@ For users who know, from personal preference or reading about the main differenc 第三个不同点，conda是依赖关系、环境和软件包管理的集成解决方案。而 pip 可能需要其他工具 (很多!) 用于处理环境或复杂的依赖关系。 -### Reproducible installs +### 可复现安装 随着库的更新，代码的运行结果可能会改变，甚至您的代码完全跑不起来。 能重建你使用的对应版本软件包集合就很重要了。 最佳做法如下： @@ -115,11 +115,11 @@ For users who know, from personal preference or reading about the main differenc NumPy 不依赖任何其他Python 包。 不过它依赖于一个快速线性代数库 - 通常是[Intel MKL](https://software.intel.com/en-us/mkl) 或 [OpenBLAS](https://www.openblas.net/)。 用户不必担心要如何安装那些库 (他们会自动包含在所有NumPy 的安装脚本中)。 高级用户可能仍然想知道详细信息，因为使用 BLAS 会影响磁盘的性能、行为和空间： -- 用pip安装的 NumPy，线性代数库是 OpenBLAS。 The OpenBLAS libraries are included in the wheel. 这使得轮子得更大，而且如果用户安装了 (假设) SciPy 他们现在会在磁盘上有两份OpenBLAS 副本。 +- 用pip安装的 NumPy，线性代数库是 OpenBLAS。 OpenBLAS 库包含在NumPy的轮子中。 这让轮子变得更大，而且如果用户安装了 (假设) SciPy，他们现在会在磁盘上有两份OpenBLAS 副本。 -- In the conda defaults channel, NumPy is built against Intel MKL. MKL 是个分离的软件包，在安装Numpy时会将它安装到用户环境中。 +- 在 conda 的默认频道中，NumPy 是用 Intel MKL 构建的。 MKL 是个单独的软件包，在安装Numpy时会将它安装到用户环境中。 -- 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. +- 在 conda-forge 通道中，NumPy 是用虚构的“BLAS”软件包构建的。 当用户从conda-forge安装NumPy时，BLAS 软件包就会与实际库一起安装 - 默认是OpenBLAS ，不过它也可以是 MKL (默认频道)，乃至是[BLIS](https://github.com/flame/blis) 或reference BLAS(Netlib的参考实现版本)。 - MKL包比OpenBLAS大得多，它在磁盘上有大约700MB，而OpenBLAS 大约30MB。 From 86d2da106532b65a918b9518623e5cfd06aa330b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 03:43:03 +0200 Subject: [PATCH 498/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 8b6c78b8ea..44be73dcaa 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -42,7 +42,7 @@ _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-m Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy has a new logo! +### NumPy 有新logo了! _Jun 24, 2020_ -- NumPy now has a new logo: @@ -61,9 +61,9 @@ _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release witho _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 发布 -_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`. +_Decc 22, 2019_ -- NumPy 1.18.0 现在可用了。 After the major changes in 1.17.0, this is a consolidation release. 这是最后一个支持 Python 3.5小版本。 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. @@ -77,9 +77,9 @@ 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 +## 版本发布 -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +这是NumPy 版本列表，包含了对应版本发布说明的链接。 所有的 bug修复版本(即在 `x.y.z`格式版本号中只有 `z`改变)没有新功能；小版本更新(`y` 改变)有新功能。 - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. From 10d82cfbfec2539465e8d016fda2899d567a066b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 04:42:48 +0200 Subject: [PATCH 499/909] New translations about.md (Chinese Simplified) --- content/zh/about.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/zh/about.md b/content/zh/about.md index 0ca2b552e6..5fb88f0e63 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -43,36 +43,36 @@ NumPy 项目正在不断发展中，我们的团队成员负责： - 编码 - 文档 - 网站 -- 试用 +- 分类 - 资金和赠款 查看[团队](/gallery/team.html)页面以了解每个独立团队的成员信息。 -## NumFOCUS Subcommittee +## NumFOCUS小组委员会 - Charles Harris - Ralf Gommers - Melissa Weber Mendonça - Sebastian Berg -- External member: Thomas Caswell +- 外部成员：Thomas Caswell -## Sponsors +## 赞助商 NumPy 直接从下列来源获得资金： {{< sponsors >}} -## Institutional Partners +## 机构合作伙伴 机构合作伙伴指那些通过雇用为 NumPy 做贡献的人来支持该项目的组织。 目前的机构伙伴包括： -- UC Berkeley (Stéfan van der Walt, Sebastian Berg, Ross Barnowski) -- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) +- UC Berkeley (Stefan van der Walt, Sebastian Berg, Ross Barnowski) +- Quansight(Ralf Gommers、Melissa Weber Mendonceda、Mars Lee、Matti Picus、Pearu Peterson) {{< partners >}} -## Donate +## 捐赠 如果您发现 NumPy 对您的工作、研究或公司有用，请考虑向该项目发起捐款。 任何金额都有帮助！ 所有捐款将严格用于 NumPy 开源软件、文档和社区的开发。 From c1c4c97058e5c7fa737845e18865a2bdc385af6f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 04:42:49 +0200 Subject: [PATCH 500/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 44be73dcaa..795cd67555 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -63,9 +63,9 @@ _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for ### NumPy 1.18.0 发布 -_Decc 22, 2019_ -- NumPy 1.18.0 现在可用了。 After the major changes in 1.17.0, this is a consolidation release. 这是最后一个支持 Python 3.5小版本。 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`. +_Decc 22, 2019_ -- NumPy 1.18.0 现在可用了。 After the major changes in 1.17.0, this is a consolidation release. 这是最后一个支持 Python 3.5小版本。 该版本的重要更新包括两个，添加了与64位 BLAS 和 LAPACK 库有关的底层更新， 添加 一个用于`numpy.random`的新C-API更新。 -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +详情请看 [版本说明](https://github.com/numpy/numpy/releases/tag/v1.18.0)。 ### NumPy receives a grant from the Chan Zuckerberg Initiative From c9c14db566a14cab89b6e97a346fce56ea644392 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 05:39:09 +0200 Subject: [PATCH 501/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 795cd67555..07b4534d4c 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,16 +1,16 @@ --- -title: News +title: 社区快讯 sidebar: false --- -### 2020 NumPy survey results +### 2020 Numpy调研结果出炉 -_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, 2021_ -- 2020, NumPy调研小组与密歇根大学和马里兰大学的学生和教职员工合作，进行了第一次官方NumPy社区调查。 在这里可以查看调查结果：https://numpy.org/user-survey-2020/。 -### Numpy 1.20.0 release +### NumPy 1.20.0 发布 -_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) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢社区的180+贡献者。 最令人振奋的两个新特性是： - 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)). From 36ef6c27aad48e29e927728ce5220b33e4363530 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 05:39:10 +0200 Subject: [PATCH 502/909] New translations arraycomputing.md (Chinese Simplified) --- content/zh/arraycomputing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/arraycomputing.md b/content/zh/arraycomputing.md index c5502d259d..09dd3ab2fc 100644 --- a/content/zh/arraycomputing.md +++ b/content/zh/arraycomputing.md @@ -3,7 +3,7 @@ title: 数组计算 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.* +*数组计算是统计学、数学和当代数据科学及应用(如数据可视化、数字信号处理、图像处理、生物信息学、机器学习、AI等) 中的科学计算领域的基础。* 大规模数据操作和转换取决于高效率高性能的数组计算。 数据分析、机器学习和数值计算首选的语言是 **Python**。 From 1658285efef5504f7f64ac635f9af4456fa5e92d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 08:19:25 +0200 Subject: [PATCH 503/909] New translations privacy.md (Chinese Simplified) --- content/zh/privacy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/privacy.md b/content/zh/privacy.md index 6064e4c4f1..ef0dfe8c96 100644 --- a/content/zh/privacy.md +++ b/content/zh/privacy.md @@ -1,8 +1,8 @@ --- -title: Privacy Policy +title: 隐私政策 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** 由NumPy 项目的财政赞助者 [NumFOCUS, Inc.](https://numfocus.org)运营。 关于本网站的隐私政策，请访问 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. +如果您对隐私政策或NumFOCUS的数据收集、使用和披露做法有任何疑问，请通过privacy@numfocus.org联系NumFOCUS工作人员。 From 316373ebc35313b825bf149ccb90df8357177594 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 08:19:26 +0200 Subject: [PATCH 504/909] 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..7cccb58d4b 100644 --- a/content/zh/user-survey-2020.md +++ b/content/zh/user-survey-2020.md @@ -3,7 +3,7 @@ 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. +2020 年，NumPy 调查团队与密歇根大学和马里兰大学联合主办的调查方法学硕士课程的师生合作，进行了第一次官方 NumPy 社区调查。 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">}} From 49fe78ccc9e283381f3454e18ec1b0d527e39216 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 09:39:19 +0200 Subject: [PATCH 505/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 07b4534d4c..be61b35a7c 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -11,29 +11,29 @@ _22, 2021_ -- 2020, NumPy调研小组与密歇根大学和马里兰大学的学 ### NumPy 1.20.0 发布 _2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢社区的180+贡献者。 最令人振奋的两个新特性是： -- Type annotations for large parts of NumPy, and a new `numpy.typing` submodule containing `ArrayLike` and `DtypeLike` aliases that users and downstream libraries can use when adding type annotations in their own code. -- Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant performance improvements for many functions (examples: [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194)). +- Numpy的大部分代码都做了类型注解，添加了一个全新的包含 `ArrayLike` 和 `DtypeLike`别名系统的 `numpy.typing` 子模块，使得用户和下游依赖库可以在自己的代码中添加类型注解。 +- 新增多架构SIMD编译优化框架，同时支持X86(SSE、AVX)、ARM64(Neon) 和PowerPC(VSX) 指令集。 大大提高了许多函数的性能(例如： [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194))。 -### Diversity in the NumPy project +### NumPy项目的多样性 -_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_ -- 我们就NumPy项目中的多样性和包容性的现状以及社交媒体相关的讨论写了一份[声明](/diversity_sep2020) -### First official NumPy paper published in Nature! +### 在Nature中发表的第一篇官方的NumPy论文！ -_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_ - 我们高兴地宣布 [Numpy的第一篇官方论文](https://www.nature.com/articles/s41586-020-2649-2)刊登在自然杂志的评论文章。 这距离NumPy 1.0发布已经过去了整整14年。 该论文涵盖数组编程的应用和基本概念，丰富的Python科学计算生态系统建立在NumPy之上，包括最近添加的数组标准协议，大大提高了与外部数组和张量库(如CuPy, Dask 和 JAX) 的互操作性 。 -### Python 3.9 is coming, when will NumPy release binary wheels? +### Python 3.9 即将来临，新版本的NumPy 何时发布？ -_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. +_2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Python版本的忠实拥趸， 您可能会失望的发现NumPy(以及其他二进制软件包，如SciPy) 在Python新版发布后数天内不会有版本发布。 使构建基础设施兼容新的 Python 版本需要付出重大努力，通常需要几周时间才能让包出现在 PyPI 和 conda-forge 上。 为了准备这次重大事件得以顺利进行，请确保： +- 将您的 `pip` 升级到 20.1 版本，至少要支持`manylinux2010` 和 `manylinux2014` +- 使用 [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) 或 `--only-binary=:all:` 选项来防止 `pip` 从源码构建的尝试。 -### Numpy 1.19.2 release +### NumPy 1.19.2 发布 -_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) 正式发布。 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! From 189178b9e96271d2d48cff7819ef0fa21b75967c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 09:39:20 +0200 Subject: [PATCH 506/909] New translations user-survey-2020.md (Chinese Simplified) --- content/zh/user-survey-2020.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/user-survey-2020.md b/content/zh/user-survey-2020.md index 7cccb58d4b..6a5d862f55 100644 --- a/content/zh/user-survey-2020.md +++ b/content/zh/user-survey-2020.md @@ -1,16 +1,16 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: 2020年NumPy社区调研 sidebar: false --- -2020 年，NumPy 调查团队与密歇根大学和马里兰大学联合主办的调查方法学硕士课程的师生合作，进行了第一次官方 NumPy 社区调查。 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，NumPy 调查团队与密歇根大学和马里兰大学联合主办的调查方法学硕士课程的师生合作，进行了第一次官方 NumPy 社区调查。 来自 75 个国家/地区的 1,200 多名用户参与其中，帮助我们勾勒出一幅 NumPy 社区的全景图，并表达了他们对项目未来的看法。 {{< 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. +**[下载报告](/surveys/NumPy_usersurvey_2020_report.pdf)** 以更仔细地查看调查结果。 -For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. +重点部分，请参阅 **[信息图](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/**. +准备仔细研究？ 访问 **https://numpy.org/user-survey-2020-details/**。 From 8ed4c6ae739d47526972b1868dbc3196648d1261 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 13:36:38 +0200 Subject: [PATCH 507/909] 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 887b859a9a..04ecaedd20 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -48,7 +48,7 @@ NumPy プロジェクトは拡大しているため、いくつかのチーム 個々のチームメンバーについては、 [チーム](/gallery/team.html) のページを参照してください。 -## NumFOCUS Subcommittee +## NumFOCUS分科会 - Charles Harris - Ralf Gommers From 536d3c6933745b1a9d554b392ef59a82677831a1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 8 Jul 2021 14:56:25 +0200 Subject: [PATCH 508/909] New translations about.md (Japanese) --- content/ja/about.md | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 04ecaedd20..59af31b383 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -50,29 +50,29 @@ NumPy プロジェクトは拡大しているため、いくつかのチーム ## NumFOCUS分科会 -- Charles Harris -- Ralf Gommers -- Melissa Weber Mendonça -- Sebastian Berg -- External member: Thomas Caswell +- チャールズ　ハリス +- ラルフ　ゴマーズ +- メリッサ　ウェーバー　メンドンサ +- セバスチャン　バーグ +- 外部メンバー: トーマス・カスウェル -## Sponsors +## スポンサー NumPyは以下の団体から直接資金援助を受けています。 {{< sponsors >}} -## Institutional Partners +## 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, Sebastian Berg, Ross Barnowski) -- Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) +- カルフォルニア大学バークレー校(ステファン・ヴァン・デル・ウォルト、セバスチャン・バーグ、ロス・バルノフスキ) +- クアンサイト(ラルフ　ゴマーズ、メリッサ　ウェーバー　メンドンサ、マーズ　リー、マッティ　ピカス、ピアウ　ピーターソン) {{< partner >}} -## Donate +## 寄付 NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 From 0c770432201ef7770ced069b2b7bfd9daf6e8a6c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 03:34:35 +0200 Subject: [PATCH 509/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index be61b35a7c..2488e29890 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -33,7 +33,7 @@ _2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Pyth ### NumPy 1.19.2 发布 -_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) 正式发布。 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) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 和 pins安装工具做好准备，以确保正在进行上游修改时用户仍然可以正常安装运行。 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! From ebfc7c0e3b94922d63d419701d88306cd2fe9d16 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 04:35:01 +0200 Subject: [PATCH 510/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 2488e29890..0d9c7e9129 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -33,46 +33,46 @@ _2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Pyth ### NumPy 1.19.2 发布 -_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 和 pins安装工具做好准备，以确保正在进行上游修改时用户仍然可以正常安装运行。 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) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 和 pins安装工具做好准备，以确保正在进行上游修改时用户仍然可以正常安装运行。 Aarch64架构的安装包是用最新的 manylinux2014 版本构建的，它修复了 linux 发行版之间使用不同大小内存页的问题。 -### The inaugural NumPy survey is live! +### 首次NumPy调研即将开始！ -_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_ - 本次调查旨在指导并确定 关于使用社区方式还是软件方式来开发NumPy的决策。 除英文外，调查还提供了另外8种语言的版本：孟加拉语、印地语、日语、曼达林语、葡萄牙语、俄语、西班牙语和法语。 -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +请帮助我们让 NumPy 变得更好，在[这里](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)参与调查。 ### NumPy 有新logo了! -_Jun 24, 2020_ -- NumPy now has a new logo: +_2020年7月24日_ -- NumPy 现在有一个新的标志： 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. +这是一个更时髦更纯净的标志。 感谢Isabela Presedo-Floryd的设计方案， 同时感谢Travis Vaugh设计的旧图标为我们提供了很好的15年以上服务。 -### NumPy 1.19.0 release +### NumPy 1.19.0 发布 -_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_ -- NumPy 1.19.0 正式发布。 这是第一个不支持Python 2的版本，因此它是一个“清理版本”。 目前支持的最小Python 版本是 Python 3.6。 本版本拥有一个重要的新特性，NumPy 1.17.0引进的随机数字生成基础模块现在可以通过Cython访问。 -### Season of Docs acceptance +### 文档整改时间段 -_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 已成为Google Season 文档项目的mentor组织之一。 我们很高兴看到有机会和技术写作者一起再次改进NumPy的技术文档！ 更多详情，请参考 [GsoD网站的官方赛期](https://developers.google.com/season-of-docs/) 和我们的 [意见页面](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)。 ### NumPy 1.18.0 发布 -_Decc 22, 2019_ -- NumPy 1.18.0 现在可用了。 After the major changes in 1.17.0, this is a consolidation release. 这是最后一个支持 Python 3.5小版本。 该版本的重要更新包括两个，添加了与64位 BLAS 和 LAPACK 库有关的底层更新， 添加 一个用于`numpy.random`的新C-API更新。 +_2019年12月22日_ -- NumPy 1.18.0 正式发布。 在1.17.0发生重大变化后，这是一个合并版本。 这是最后一个支持 Python 3.5的小版本。 该版本的重要更新包括两个，添加了与64位 BLAS 和 LAPACK 库有关的底层更新， 添加 一个用于`numpy.random`的新C-API更新。 详情请看 [版本说明](https://github.com/numpy/numpy/releases/tag/v1.18.0)。 -### NumPy receives a grant from the Chan Zuckerberg Initiative +### NumPy 从Chan Zuckerberg Initiative获得了一笔捐款 -_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_ -- 我们高兴地宣布NumPy和 OpenBLAS (Numpy的一个核心依赖库)已经收到一笔19,5000美元的联合赠款。 捐款来自于Chan Zuckerberg Initiative通过的[基础开源科学计算软件项目](https://chanzuckerberg.com/eoss/)，用来支持对科学发展起到关键作用的开源软件的维护、增长、开发和社区参与。 -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. +这笔赠款将用来加速改进NumPy文档、网站重构和社区开发，进而更好地为我们庞大和迅速增长的用户基础服务，并确保项目的长期可持续性。 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. From e4d973b6a94b219c84cee84608a2cd015d67ffe4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 05:36:16 +0200 Subject: [PATCH 511/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 0d9c7e9129..3f06d0ed4c 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -46,7 +46,7 @@ _2020年7月2日_ - 本次调查旨在指导并确定 关于使用社区方式 _2020年7月24日_ -- NumPy 现在有一个新的标志： -NumPy logo +NumPy logo 这是一个更时髦更纯净的标志。 感谢Isabela Presedo-Floryd的设计方案， 同时感谢Travis Vaugh设计的旧图标为我们提供了很好的15年以上服务。 @@ -72,23 +72,23 @@ _2019年12月22日_ -- NumPy 1.18.0 正式发布。 在1.17.0发生重大变化 _2019年11月15日_ -- 我们高兴地宣布NumPy和 OpenBLAS (Numpy的一个核心依赖库)已经收到一笔19,5000美元的联合赠款。 捐款来自于Chan Zuckerberg Initiative通过的[基础开源科学计算软件项目](https://chanzuckerberg.com/eoss/)，用来支持对科学发展起到关键作用的开源软件的维护、增长、开发和社区参与。 -这笔赠款将用来加速改进NumPy文档、网站重构和社区开发，进而更好地为我们庞大和迅速增长的用户基础服务，并确保项目的长期可持续性。 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. +这笔赠款将用来加速改进NumPy文档、网站重构和社区开发，进而更好地为我们庞大和迅速增长的用户基础服务，并确保项目的长期可持续性。 OpenBLAS 团队将侧重于处理几个关键技术问题，特别是线程安全问题、AVX-512和 thread-local 存储(TLS) 问题，以及OpenBLAS 依赖的 ReLAPACK (递归的LAPACK) 算法改进。 -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. +若想查看更多关于捐款的倡议和交付件的详情，可在 [全额赠款提案](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) 中找到。 项目开始于2019年12月1日，今后12个月将持续运作下去。 ## 版本发布 这是NumPy 版本列表，包含了对应版本发布说明的链接。 所有的 bug修复版本(即在 `x.y.z`格式版本号中只有 `z`改变)没有新功能；小版本更新(`y` 改变)有新功能。 -- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. -- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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_. +- NumPy1.18.4 (<0">发行说明) -- _2020年5月3日_. +- NumPy1.18.3 (<0">发行说明) -- _2020年4月19日_. +- NumPy1.18.2 (<0">发行说明) -- _2020年3月17日_. +- NumPy1.18.1 (<0">发行说明) -- _2020年1月6日_. +- NumPy1.17.5 (<0">发行说明) -- _2020年1月1日_. +- NumPy1.18.0 (<0">发行说明) -- _2019年12月22日_. +- NumPy1.17.4 (<0">发行说明) -- _2019年11月11日_. +- NumPy1.17.0 (<0">发行说明) -- _2019年7月26日_. +- NumPy1.16.0 (<0">发行说明) -- _2019年1月14日_. +- NumPy1.15.0 (<0">发行说明) -- _2018年7月23日_. +- NumPy1.14.0 (<0">发行说明) -- _2018年1月7日_. From b2551ad8fd426ca39d4ce1b1777328e4c2652d4d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 06:34:36 +0200 Subject: [PATCH 512/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index 3f06d0ed4c..fc32c2302f 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -10,7 +10,7 @@ _22, 2021_ -- 2020, NumPy调研小组与密歇根大学和马里兰大学的学 ### NumPy 1.20.0 发布 -_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢社区的180+贡献者。 最令人振奋的两个新特性是： +_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢社区的180+位贡献者。 最令人振奋的两个新特性是： - Numpy的大部分代码都做了类型注解，添加了一个全新的包含 `ArrayLike` 和 `DtypeLike`别名系统的 `numpy.typing` 子模块，使得用户和下游依赖库可以在自己的代码中添加类型注解。 - 新增多架构SIMD编译优化框架，同时支持X86(SSE、AVX)、ARM64(Neon) 和PowerPC(VSX) 指令集。 大大提高了许多函数的性能(例如： [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194))。 From cf32e8963da6e1f6272a79662103207cfbcbe7b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 06:34:37 +0200 Subject: [PATCH 513/909] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index 14785051b2..c0618c454f 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -1,6 +1,6 @@ --- title: NumPy 行为守则 -sidebar: 假 +sidebar: false aliases: - /conduct.html --- From 9fc96cc088817277caf1c69a0c3b81fc30ccd74a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 06:34:38 +0200 Subject: [PATCH 514/909] New translations press-kit.md (Chinese Simplified) --- content/zh/press-kit.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/press-kit.md b/content/zh/press-kit.md index 2309040ad2..500a14e46b 100644 --- a/content/zh/press-kit.md +++ b/content/zh/press-kit.md @@ -1,8 +1,8 @@ --- -title: Press kit +title: 宣传材料 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. +我们希望能让您在下一篇学术论文、课程材料或演示文稿中轻松地加入NumPy项目标识。 -You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/master/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). +您可以在[这里](https://github.com/numpy/numpy/tree/master/branding/logo)找到一些高分辨率的 NumPy logo。 注意，使用 numpy.org 资源意味着你接受 [NumPy 行为准则](/code-of-conduct)。 From 5c7c7264dc4457a0d6f7421315ef1c0a10177d62 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 06:34:39 +0200 Subject: [PATCH 515/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 5586668cba..becd1b20a1 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -3,9 +3,9 @@ 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. +这是NumPy行为准则委员会的指导手册。 它保证我们对问题做出一致且公正的回应。 -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: +[行为守则](/code-of-conduct) 的执行影响到我们的社区现在和未来。 我们很重视它。 在审查执行措施时，行为准则委员会将牢记以下价值观和准则： * 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. @@ -16,7 +16,7 @@ Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today an * 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 +## 调解 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 8fd741ba865bc95a7ad88006cbe219de6a1f6f51 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 14:56:12 +0200 Subject: [PATCH 516/909] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md index 4e6924e76e..e28581451e 100644 --- a/content/ar/citing-numpy.md +++ b/content/ar/citing-numpy.md @@ -1,9 +1,9 @@ --- -title: الاستشهاد بنمباى -sidebar: false +title: الاستشهاد بنمباي +sidebar: خطأ --- -إذا كان لنمباى دور كبير فى بحثك وتود الإشارة إليه فى منشورك الأكاديمى،فبامكانك القاء نظرة على هذة الورقة المقترحة للاستشهاد: +إذا كان لنمباي دور كبير فى بحثك وتود الإشارة إليه فى منشورك الأكاديمى،فبامكانك إلقاء نظرة على هذة الورقة المقترحة للاستشهاد: * Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _برمجة المصفوفات بواسطة نمباى_. الطبيعة 585, 357–362 (2020). DOI: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([رابط النشر](https://www.nature.com/articles/s41586-020-2649-2)). From aa828a37e2ebcd908bb1eb19c914b525effb1248 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 15:53:43 +0200 Subject: [PATCH 517/909] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index ced2a28ac5..f0b9922a03 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -3,9 +3,9 @@ title: الحصول على مساعدة sidebar: false --- -**أسئلة المستخدم**: إن أفضل طريقة للحصول على المساعدة هي أن تقوم بنشر سؤالك على الموقع مثل [ ](http://stackoverflow.com/questions/tagged/numpy)حيث يوجد آلاف المستخدمين المتاحين للإجابة على أسئلتك. وتحتوي البدائل الأصغر على [IRC](https://webchat.freenode.net/?channels=%23numpy) [Gitterو](https://gitter.im/numpy/numpy) و [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! +**أسئلة المستخدم**: إن أفضل طريقة للحصول على المساعدة هي أن تقوم بنشر سؤالك على الموقع مثل [ ](http://stackoverflow.com/questions/tagged/numpy)حيث يوجد آلاف المستخدمين المتاحين للإجابة على أسئلتك. وتحتوي البدائل الأصغر على [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). +**مشاكل التطوير:**للاطلاع على المشاكل المتعلقة بتطوير نمباي(مثل تقارير الأخطاء) برجاء انظر هنا[Community](/community). From bd3d68e4f0a5702a78466bc462aae767bd781653 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 15:53:45 +0200 Subject: [PATCH 518/909] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md index e28581451e..a249049af1 100644 --- a/content/ar/citing-numpy.md +++ b/content/ar/citing-numpy.md @@ -5,7 +5,7 @@ sidebar: خطأ إذا كان لنمباي دور كبير فى بحثك وتود الإشارة إليه فى منشورك الأكاديمى،فبامكانك إلقاء نظرة على هذة الورقة المقترحة للاستشهاد: -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _برمجة المصفوفات بواسطة نمباى_. الطبيعة 585, 357–362 (2020). DOI: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([رابط النشر](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _برمجة المصفوفات بواسطة نمباي_. Nature 585, 357–362 (2020). DOI: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([رابط النشر](https://www.nature.com/articles/s41586-020-2649-2)). _بتنسيق In BibTeX:_ From 668ddcf0a692106796a4c83d0a518f08cb2b3aa6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 15:53:46 +0200 Subject: [PATCH 519/909] New translations 404.md (Arabic) --- content/ar/404.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/404.md b/content/ar/404.md index a12adc165c..299593ff09 100644 --- a/content/ar/404.md +++ b/content/ar/404.md @@ -5,4 +5,4 @@ sidebar: false عفواً! لقد وصلت إلى طريق مسدود. -إذا كنت تعتقد أنه يجب أن يكون شيء ما، فيمكنك [فتح مشكلة](https://github.com/numpy/numpy.org/issues) على GitHub. +إذا كنت تعتقد أنه يجب أن يكون هناك شيء ما هنا ، فيمكنك [فتح مشكلة](https://github.com/numpy/numpy.org/issues) على GitHub. From 00fe776cd5c6d4cbdd59b3207743be5afd5d4458 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 17:02:20 +0200 Subject: [PATCH 520/909] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index f0b9922a03..a2d48e545d 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -9,7 +9,7 @@ sidebar: false -### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) +### [موقع ستاك أوفرفلو](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) From c04e22ecfc752bb804394956bc15980550412c23 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 9 Jul 2021 23:24:37 +0200 Subject: [PATCH 521/909] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index a2d48e545d..2a71170630 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -9,9 +9,9 @@ sidebar: false -### [موقع ستاك أوفرفلو](http://stackoverflow.com/questions/tagged/numpy) +### [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) +A forum for asking usage questions, e.g. "How do I do X in NumPy?”. برجاء [استخدم `#numpy` tag](https://stackoverflow.com/help/tagging) *** From d74ff66ce3803c10084026b38fcf114d91356c22 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Jul 2021 00:23:19 +0200 Subject: [PATCH 522/909] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index 2a71170630..7af19f187d 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -11,24 +11,24 @@ sidebar: false ### [StackOverflow](http://stackoverflow.com/questions/tagged/numpy) -A forum for asking usage questions, e.g. "How do I do X in NumPy?”. برجاء [استخدم `#numpy` tag](https://stackoverflow.com/help/tagging) +منتدى لطرح أسئلة الاستخدام مثل" كيف أستطيع أن أفعل x في نمباي؟". برجاء [استخدم `#numpy` tag](https://stackoverflow.com/help/tagging) *** ### [Reddit](https://www.reddit.com/r/Numpy/) -Another forum for usage questions. +منتدى آخر لأسئلة الاستخدام. *** ### [Gitter](https://gitter.im/numpy/numpy) -A real-time chat room where users and community members help each other. +غرفة دردشة فورية حيث يساعد المستخدمون وأعضاء المجتمع بعضهم البعض. *** ### [IRC](https://webchat.freenode.net/?channels=%23numpy) -Another real-time chat room where users and community members help each other. +غرفة أخرى للدردشة فورية حيث يساعد المستخدمون وأعضاء المجتمع بعضهم البعض. *** From 338e06c62cc165dddad14589002a6914364b3a50 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 10 Jul 2021 00:23:22 +0200 Subject: [PATCH 523/909] New translations report-handling-manual.md (Arabic) --- content/ar/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/report-handling-manual.md b/content/ar/report-handling-manual.md index 5586668cba..c94be4486f 100644 --- a/content/ar/report-handling-manual.md +++ b/content/ar/report-handling-manual.md @@ -1,5 +1,5 @@ --- -title: NumPy Code of Conduct - How to follow up on a report +title: قواعد السلوك لنمباي - كيفية متابعة تقرير sidebar: false --- From 46e0beddf4b7a5c640b69f1db5b7286fd65483a5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jul 2021 00:36:48 +0200 Subject: [PATCH 524/909] 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..29028864a7 100644 --- a/content/ar/user-survey-2020.md +++ b/content/ar/user-survey-2020.md @@ -1,5 +1,5 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: استطلاع مجتمع نمباي لعام 2020 sidebar: false --- From 1654f082f2b17b439b62bf05f5be169acb3fdeab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jul 2021 08:36:13 +0200 Subject: [PATCH 525/909] 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 6a5d862f55..82bc33417c 100644 --- a/content/zh/user-survey-2020.md +++ b/content/zh/user-survey-2020.md @@ -5,7 +5,7 @@ sidebar: false 2020 年，NumPy 调查团队与密歇根大学和马里兰大学联合主办的调查方法学硕士课程的师生合作，进行了第一次官方 NumPy 社区调查。 来自 75 个国家/地区的 1,200 多名用户参与其中，帮助我们勾勒出一幅 NumPy 社区的全景图，并表达了他们对项目未来的看法。 -{{< 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="2020年Numpy用户调查报告的封面，标题是“NumPy Community Survey 2020 - results” width="250">}} **[下载报告](/surveys/NumPy_usersurvey_2020_report.pdf)** 以更仔细地查看调查结果。 From 689ca9545baaa99391f6481f1cb0abf5eeda6b00 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jul 2021 09:31:47 +0200 Subject: [PATCH 526/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index becd1b20a1..bfa91f8702 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -1,5 +1,5 @@ --- -title: NumPy Code of Conduct - How to follow up on a report +title: Numpy行为准则应知应会 sidebar: false --- From 79e3391b718b466e8d5a06500e9726f14fab89d5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jul 2021 10:30:48 +0200 Subject: [PATCH 527/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index bfa91f8702..a97be7a7a9 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -7,7 +7,7 @@ sidebar: false [行为守则](/code-of-conduct) 的执行影响到我们的社区现在和未来。 我们很重视它。 在审查执行措施时，行为准则委员会将牢记以下价值观和准则： -* 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. +* 人性化方式运作而非不近人情。 委员会会让各方了解情况，同时尊重举报者的隐私和任何必要的保密性。 然而，仍然存在着这种情况。 有时有必要与一个或多个人直接沟通：委员会的目标是改善我们社区的健康状况，而不仅仅是作出正式决定。 * 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. From 2df4cec095a3beb464a0d09288db65a22a14eb46 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 13 Jul 2021 12:43:07 +0200 Subject: [PATCH 528/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index a97be7a7a9..99455feb5e 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -8,8 +8,8 @@ sidebar: false [行为守则](/code-of-conduct) 的执行影响到我们的社区现在和未来。 我们很重视它。 在审查执行措施时，行为准则委员会将牢记以下价值观和准则： * 人性化方式运作而非不近人情。 委员会会让各方了解情况，同时尊重举报者的隐私和任何必要的保密性。 然而，仍然存在着这种情况。 有时有必要与一个或多个人直接沟通：委员会的目标是改善我们社区的健康状况，而不仅仅是作出正式决定。 -* 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. +* 强调对个人遭遇的同情，而不是一味批评行为，避免出现“好”和“坏/邪恶”的二元对立标签。 我们将坚定地处理公开、明显的挑衅和骚扰问题。 但许多可能被证明具有挑战性的情形是有些正常的分歧会转化为多方无助或有害的行为。 了解整个背景并找到一条使所有人都能重新参与进来的道路是困难的，但最终对我们社区最有成效。 +* 我们都知道，电子邮件是一个孤立的、沟通困难的媒介。 在没有个人联系方式的情况下收到对电子邮件的批判，可能特别令人痛苦。 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. From f2df1e7a67f390a7c1f5bce23bfc898104d1b8bc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jul 2021 04:52:08 +0200 Subject: [PATCH 529/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 99455feb5e..05f103a32f 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -9,7 +9,7 @@ sidebar: false * 人性化方式运作而非不近人情。 委员会会让各方了解情况，同时尊重举报者的隐私和任何必要的保密性。 然而，仍然存在着这种情况。 有时有必要与一个或多个人直接沟通：委员会的目标是改善我们社区的健康状况，而不仅仅是作出正式决定。 * 强调对个人遭遇的同情，而不是一味批评行为，避免出现“好”和“坏/邪恶”的二元对立标签。 我们将坚定地处理公开、明显的挑衅和骚扰问题。 但许多可能被证明具有挑战性的情形是有些正常的分歧会转化为多方无助或有害的行为。 了解整个背景并找到一条使所有人都能重新参与进来的道路是困难的，但最终对我们社区最有成效。 -* 我们都知道，电子邮件是一个孤立的、沟通困难的媒介。 在没有个人联系方式的情况下收到对电子邮件的批判，可能特别令人痛苦。 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. +* 我们都知道，电子邮件是一个孤立的、沟通困难的媒介。 在没有个人联系方式的情况下收到匿名举报邮件，可能特别令人痛苦。 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. From 34ca7024cabf8435785e4b9043694b8b422c27bd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jul 2021 06:04:47 +0200 Subject: [PATCH 530/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 05f103a32f..f5ae58f668 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -9,24 +9,24 @@ sidebar: false * 人性化方式运作而非不近人情。 委员会会让各方了解情况，同时尊重举报者的隐私和任何必要的保密性。 然而，仍然存在着这种情况。 有时有必要与一个或多个人直接沟通：委员会的目标是改善我们社区的健康状况，而不仅仅是作出正式决定。 * 强调对个人遭遇的同情，而不是一味批评行为，避免出现“好”和“坏/邪恶”的二元对立标签。 我们将坚定地处理公开、明显的挑衅和骚扰问题。 但许多可能被证明具有挑战性的情形是有些正常的分歧会转化为多方无助或有害的行为。 了解整个背景并找到一条使所有人都能重新参与进来的道路是困难的，但最终对我们社区最有成效。 -* 我们都知道，电子邮件是一个孤立的、沟通困难的媒介。 在没有个人联系方式的情况下收到匿名举报邮件，可能特别令人痛苦。 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. +* 我们都知道，电子邮件是一个孤立的、沟通困难的媒介。 在没有个人联系方式的情况下收到匿名举报邮件，可能特别令人痛苦。 这使得保持一种尊重他人意见的开放气氛变得特别重要。 这还意味着我们的所有行为必须公开透明。 我们将竭尽全力确保我们所有成员得到公平和同情的待遇。 +* 歧视可能是微妙且不负责任的。 它在一定程度上表现出不公平和敌意的态度，而不是正常的互动。 我们知道这种情况确实发生了，我们将仔细寻找并应对这种情况。 如果你觉得受到了不公平的待遇，我们非常希望听到你们的声音， 我们将利用这些流程确保你的申诉得到关注和处理。 +* 帮助增加良性讨论的实践：想方设法识别出讨论可能已经产生歧义和偏斜的地方。 并提供可实施的信息、方向和资源，以便在这些方面产生积极的影响。 +* 重点关注新成员的需求：向他们提供明确的支持和关心， 目的是增加代表人数不足的群体的参与度。 +* 每个人都拥有不同的文化背景和语言习惯。 尽可能识别对非英文为母语的参与者造成的任何不必要的误解，并帮助他们了解这一问题以及作出哪些改变以避免造成冒犯。 用一门外语进行复杂的讨论可能非常具有威胁性，我们希望在不同民族和不同文化之间扩大我们的多样性。 ## 调解 -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: +自愿非正式调解是一种好的调解方式。 在两个或多个当事方都已升级到不适当行为程度的情况下(可悲的是在人类冲突中很常见)， 促进调解进程可能是有益的。 这只是一个例子：委员会在任何情况下都可以考虑调解。 考虑到这一进程完全是自愿的，任何一方都不能被迫参与。 如果委员会建议进行调解，它应当： -* 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. +* 找到一个可以担任调解员的候选人。 +* 取得举报者的同意。 举报者完全可以自由地拒绝调解或提出一名候补调解人。 +* 取得被举报人的同意。 +* 调解人的问题：各方可以提出不同于推荐候选人的调解人， 只有就所有条件达成共同协议，该进程才能向前推进。 +* 最好在两周内确定完成调解的时间计划表。 -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. +调解人将与所有参与方接触，寻求各方都满意的解决办法。 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 From 8df7dbe2b9513a1dc872ed9e39379ff0d4297b78 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jul 2021 22:40:36 +0200 Subject: [PATCH 531/909] New translations gw-discov.md (Arabic) --- content/ar/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/case-studies/gw-discov.md b/content/ar/case-studies/gw-discov.md index 3d25090e13..fb623d0fdb 100644 --- a/content/ar/case-studies/gw-discov.md +++ b/content/ar/case-studies/gw-discov.md @@ -1,5 +1,5 @@ --- -title: "Case Study: Discovery of Gravitational Waves" +title: "دراسة حالة: اكتشاف الأمواج الثقالية" sidebar: false --- From c339ca91bfb9673d4ce2510978fd7dd11808ee59 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 14 Jul 2021 23:54:39 +0200 Subject: [PATCH 532/909] New translations gw-discov.md (Arabic) --- content/ar/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/case-studies/gw-discov.md b/content/ar/case-studies/gw-discov.md index fb623d0fdb..c40043e305 100644 --- a/content/ar/case-studies/gw-discov.md +++ b/content/ar/case-studies/gw-discov.md @@ -10,7 +10,7 @@ sidebar: false
    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) +## حول الموجات الثقالية والLIGO[](https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/)[](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. From 390d2c502e32e37cb880b28b39df88a7b698ae4b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jul 2021 01:05:55 +0200 Subject: [PATCH 533/909] New translations news.md (Arabic) --- content/ar/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/news.md b/content/ar/news.md index 3af51d2bb5..7d40eccd28 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -10,7 +10,7 @@ _22 يونيو2021_ -قام فريق استطلاع نمباي في عام 2020 ### الإصدار 1.20.0 لنمباى -_30يناير2021_ -- [إصدار1.20.0 لنمباى](https://numpy.org/doc/stable/release/1.20.0-notes.html) متاح الآن. وهذا هو أكبر اصدار لنمباى حتى الآن بفضل 180+ من المساهمين. والسمتان الجديدتان الأكثر إثارة للاهتمام هما: +_30يناير2021_ -- [إصدار1.20.0 لنمباى](https://numpy.org/doc/stable/release/1.20.0-notes.html) متاح الآن. وهذا هو أكبر إصدار لنمباي حتى الآن بفضل 180+ من المساهمين. والسمتان الجديدتان الأكثر إثارة للاهتمام هما: - 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)). From 4c7c0d26ec2d8f955494936dd0010ae6ee4cf8c3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jul 2021 01:05:56 +0200 Subject: [PATCH 534/909] New translations gethelp.md (Arabic) --- content/ar/gethelp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/gethelp.md b/content/ar/gethelp.md index 7af19f187d..a3eb24c50f 100644 --- a/content/ar/gethelp.md +++ b/content/ar/gethelp.md @@ -3,7 +3,7 @@ title: الحصول على مساعدة sidebar: false --- -**أسئلة المستخدم**: إن أفضل طريقة للحصول على المساعدة هي أن تقوم بنشر سؤالك على الموقع مثل [ ](http://stackoverflow.com/questions/tagged/numpy)حيث يوجد آلاف المستخدمين المتاحين للإجابة على أسئلتك. وتحتوي البدائل الأصغر على [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! +**أسئلة المستخدم**: إن أفضل طريقة للحصول على المساعدة هي أن تقوم بنشر سؤالك على الموقع مثل [ ](http://stackoverflow.com/questions/tagged/numpy)حيث يوجد آلاف المستخدمين المتاحين للإجابة على أسئلتك. وتحتوي البدائل الأصغر على [IRC](https://webchat.freenode.net/?channels=%23numpy), [Gitter](https://gitter.im/numpy/numpy), and [Reddit](https://www.reddit.com/r/Numpy/). ونتمنى أن نستطيع مراقبة الموقع أو الإجابة على الأسئلة مباشرة ولكن المجلد كبير نوعًا ما! **مشاكل التطوير:**للاطلاع على المشاكل المتعلقة بتطوير نمباي(مثل تقارير الأخطاء) برجاء انظر هنا[Community](/community). From 388772fc0513cb16d10b8431cb34a9ec91b9c52b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jul 2021 01:05:57 +0200 Subject: [PATCH 535/909] New translations learn.md (Arabic) --- content/ar/learn.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/ar/learn.md b/content/ar/learn.md index f88f226dbd..a87b92b63e 100644 --- a/content/ar/learn.md +++ b/content/ar/learn.md @@ -5,35 +5,35 @@ sidebar: false للحصول على وثائق مشروع نمباى الرسمية عليك بزيارة[numpy.org/doc/stable](https://numpy.org/doc/stable). -## المحتوى التعليمى لنمباى +## المحتوى التعليمي لنمباي -يقدم مجتمع نمباى مجموعة من الدروس والمواد التعليمية فى [المحتوى التعليمى لنمباى](https://numpy.org/numpy-tutorials). الهدف من هذة الصفحة توفير موارد عالية الجودة عن طريق مشروع نمباى، سواء للتعلم الذاتى أو لتدريس الفصول الدراسية بتنسيق مذكرات جوبيتر(Jupyter Notebooks). لذا إن كنت مهتما بإضافة محتوياتك تحقق من هذا[numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). +يقدم مجتمع نمباي مجموعة من الدروس والمواد التعليمية في [المحتوى التعليمى لنمباى](https://numpy.org/numpy-tutorials). الهدف من هذة الصفحة توفير موارد عالية الجودة عن طريق مشروع نمباي، سواء للتعلم الذاتي أو لتدريس الفصول الدراسية بتنسيق مذكرات جوبيتر(Jupyter Notebooks). لذا إن كنت مهتمًا بإضافة محتوياتك تحقق من هذا[numpy-tutorials repository on GitHub](https://github.com/numpy/numpy-tutorials). *** -وفيما يلى مختارات من المصادر الخارجية، للمساهمة تفحص [ نهاية هذة الصفحة](#add-to-this-list). +وفيما يلي مختارات من المصادر الخارجية. للمساهمة تفحص [ نهاية هذه الصفحة](#add-to-this-list). ## للمبتدئين -يوجد الكثير من المعلومات حول مشروع نمباى هناك. لذا إن كنت جديدا هنا فنوصيك بهذا بشدة: +يوجد الكثير من المعلومات حول مشروع نمباي هناك. لذا إن كنت جديدا هنا فنوصيك بهذا بشدة: - **المحتوى التعليمى** + **المحتوى التعليمي** * [دروس Quickstart](https://numpy.org/devdocs/user/quickstart.html) -* [توضيح لنمباى: الدليل المرئى لمشروع نمباى *من قبل ليف ماكسيموف*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) -* [محاضرات SciPy](https://scipy-lectures.org/)، بجانب التغطية لمشروع نمباى تعرض هذة المحاضرات مقدمة أوسع لمنظومة لغة البايثون العلمية. +* [توضيح لنمباي: الدليل المرئي لمشروع نمباي *من قبل ليف ماكسيموف*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [محاضرات SciPy](https://scipy-lectures.org/)، بجانب التغطية لمشروع نمباي تعرض هذة المحاضرات مقدمة أوسع لمنظومة لغة البايثون العلمية. * [نمباى: الأساسيات الثابتة للمبتدئين](https://numpy.org/devdocs/user/absolute_beginners.html) -* [بالإضافة إلى التعلم الآلى يوجدمقدمة للمصفوفة ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) -* [إدوريكا - تعلم مصفوفات نمباى بالأمثلة ](https://www.edureka.co/blog/python-numpy-tutorial/) -* [منصةDataquest لعلوم البانات - البرنامج التعليمى لنمباى: تحليل البيانات باستخدام لغة البايثون](https://www.dataquest.io/blog/numpy-tutorial-python/) -* [برنامج نمباى التعليمى *من قبل نيكولاس روجير*](https://github.com/rougier/numpy-tutorial) +* [بالإضافة إلى التعلم الآلي يوجد مقدمة للمصفوفة ndarray](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) +* [إدوريكا - تعلم مصفوفات نمباي بالأمثلة ](https://www.edureka.co/blog/python-numpy-tutorial/) +* [منصةDataquest لعلوم البيانات - البرنامج التعليمي لنمباي: تحليل البيانات باستخدام لغة البايثون](https://www.dataquest.io/blog/numpy-tutorial-python/) +* [برنامج نمباي التعليمي *من قبل نيكولاس روجير*](https://github.com/rougier/numpy-tutorial) * [CS231 لجامعة ستانفورد*من قبل جاستين جونسون*](http://cs231n.github.io/python-numpy-tutorial/) * [دليل استخدام نمباي](https://numpy.org/devdocs) **الكتب** -* [دليل نمباى *ل ترافيس أوليفانت *](http://web.mit.edu/dvp/Public/numpybook.pdf) وهذا هو الإصدار المجانى 1 من 2006. وللإطلاع على أحدث نسخة (2015)انظر هنا [](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). -* [لغة البايثون فى نمباى * ل نيكولاس روجير*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) +* [دليل نمباى *ل ترافيس أوليفانت *](http://web.mit.edu/dvp/Public/numpybook.pdf) وهذا هو الإصدار المجاني 1 من 2006. وللإطلاع على أحدث نسخة (2015)انظر هنا [](https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007). +* [لغة البايثون فى نمباي* ل نيكولاس روجير*](https://www.labri.fr/perso/nrougier/from-python-to-numpy/) * [محاضرات SciPy ممتازة](https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877)*> لكلا من خوان نونيز إغليسياس وستيفان فان دير والت بالإضافة إلى هارييت داشنوف* يمكنك أيضا مراجعة [ قائمة القراءات الجيدة(Goodreads list) ](https://www.goodreads.com/shelf/show/python-scipy)حول موضوع "Python+SciPy". وتتحدث معظم الكتب فى هذة القائمة عن النظام البيئى لSciPy والذى يمثل نمباى جوهره. From ab3e119a9eb22677cf9a57ce5413352d593c6c12 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jul 2021 08:38:57 +0200 Subject: [PATCH 536/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index f5ae58f668..6944909bf5 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -26,10 +26,10 @@ sidebar: false * 调解人的问题：各方可以提出不同于推荐候选人的调解人， 只有就所有条件达成共同协议，该进程才能向前推进。 * 最好在两周内确定完成调解的时间计划表。 -调解人将与所有参与方接触，寻求各方都满意的解决办法。 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. +调解人将与所有参与方接触，寻求各方都满意的解决办法。 在调解完成后，调解方将向委员会提交一份书面报告(经调解过程中所有各方审查通过)，并就今后的实施细则提出建议。 然后委员会将评估调解结果(无论是否达成了令人满意的解决方案)，并对必要的额外的调解动作作出决定。 -## How the Committee will respond to reports +## 委员会将如何对举报作出回应 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. From 8b850dc459a299ca830b2dba711d29e292b475d9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jul 2021 09:41:14 +0200 Subject: [PATCH 537/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 6944909bf5..03c400fef1 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -31,10 +31,10 @@ sidebar: false ## 委员会将如何对举报作出回应 -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. +当委员会(或委员会成员) 收到举报时， 他们将首先要确定该报告是否涉及明显和严重的违约行为（定义见下文）。 如果确认属实，需要立即采取行动并启动常规的举报调解流程。 -## Clear and severe breach actions +## 明确和严重的违约行为 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. From 350c98d605b38b9be12abc6801dafc96b3cc9fe0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 15 Jul 2021 10:47:26 +0200 Subject: [PATCH 538/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 03c400fef1..7c930fbeae 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -36,13 +36,13 @@ sidebar: false ## 明确和严重的违约行为 -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. +我们知道，互联网通信平台从诞生开始就演变为非常普遍的辱骂恶意中伤的场所。 我们将迅速处理明显和严重的侵权行为，如人身威胁、暴力、性别歧视或种族主义语言。 -When a member of the Code of Conduct Committee becomes aware of a clear and severe breach, they will do the following: +如果行为守则委员会的一名成员发现明显和严重的违反行为，他们将采取下列行动： -* 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. +* 立即断开始作俑者与所有NumPy 通信频道的连接。 +* 告知举报者，他们的报告已经收到，被举报人已经断开和Numpy的联系。 +* 在每种情况下，调解人都应作出努力与被举报人联系。 并明确的告诉他们，他们的语言或行动是如何构成“明显和严重的违反行为”的。 调解人还应指出，如果被举报人认为这是不公平的，或者他们想要重新和NumPy取得联系, 他们有权要求行为守则委员会进行下面描述的审查。 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. From a52616ae9906e09853203503c56339b19a10cdf0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Jul 2021 04:56:19 +0200 Subject: [PATCH 539/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 7c930fbeae..a9638384e1 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -42,8 +42,8 @@ sidebar: false * 立即断开始作俑者与所有NumPy 通信频道的连接。 * 告知举报者，他们的报告已经收到，被举报人已经断开和Numpy的联系。 -* 在每种情况下，调解人都应作出努力与被举报人联系。 并明确的告诉他们，他们的语言或行动是如何构成“明显和严重的违反行为”的。 调解人还应指出，如果被举报人认为这是不公平的，或者他们想要重新和NumPy取得联系, 他们有权要求行为守则委员会进行下面描述的审查。 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. +* 在每种情况下，调解人都应作出努力与被举报人联系。 并明确的告诉他们，他们的语言或行动是如何构成“明显和严重的违反行为”的。 调解人还应指出，如果被举报人认为这是不公平的，或者他们想要重新和NumPy取得联系, 他们有权要求行为守则委员会进行下面描述的审查。 调解人应将这一审查结果报送给行为守则委员会。 +* 行为守则委员会将正式审查和签署所有适用这一行为的案件，以确保其不被用来进行普通程度争执的仲裁。 ## Report handling From f36b8dcee9c3211ad6a78cfab91d27f04624fb29 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 16 Jul 2021 06:01:27 +0200 Subject: [PATCH 540/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index a9638384e1..2f7b93c43f 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -46,31 +46,31 @@ sidebar: false * 行为守则委员会将正式审查和签署所有适用这一行为的案件，以确保其不被用来进行普通程度争执的仲裁。 -## Report handling +## 举报处理 -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. +当报告送交委员会时，他们将立即答复报告人以确认收到。 这种答复必须在72小时内完成，委员会会尽最大努力比这更快一些。 -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. +如果报告没有提供足够多的资料，委员会将在采取行动之前获得所有相关数据。 委员有权代表指导委员会与任何相关个体联系，以更完整地了解事件的来龙去脉。 -The Committee will then review the incident and determine, to the best of their ability: +委员会随后将审查这一事件，并尽力确定如下事项： -* 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. +* 发生了什么。 +* 这一事件是否违反《行为守则》。 +* 谁是责任方。 +* 这种情况是否正在发生，而且对个人的人身安全产生了威胁。 -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). +这种信息将以书面形式收集，只要有可能，委员会的任何相关动作都将记录并保存(比如聊天记录、电子邮件讨论、会议录音、语音对话总结等)。 -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. +必须保留委员会所有活动的档案，以确保行为上的一致性，并为项目提供机构记忆。 为了协助这方面的工作， 本委员会的默认沟通渠道将是一份私人邮件列表，供委员会现有和未来成员以及指导委员会成员在提出正当要求时查阅。 如果委员会认为有必要使用列表外的沟通方式(例如： 早期/快速响应的电话呼叫)，在所有情况下都应该及时将沟通方式收敛到列表中，以便有一个良好的过程记录。 -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. +行为守则委员会应争取在两周内商定一项决议。 如果在那个时候无法确定某项决议， 委员会将向报告者提供最新情况和预计的决议时间表。 -## Resolutions +## 解决方案 -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. +委员会必须以协商一致方式商定一项决议。 如果该小组在一个多星期内无法达成共识和陷入僵局，该小组将把问题提交给指导委员会解决。 -Possible responses may include: +可能的原因包括： * Taking no further action: - if we determine no violations have occurred; From 66e6e749cbb239eea23970ba52813b24aebec416 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jul 2021 03:22:44 +0200 Subject: [PATCH 541/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 2f7b93c43f..8adddeb5a3 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -70,10 +70,10 @@ sidebar: false 委员会必须以协商一致方式商定一项决议。 如果该小组在一个多星期内无法达成共识和陷入僵局，该小组将把问题提交给指导委员会解决。 -可能的原因包括： +可能的应对措施包括： -* Taking no further action: - - if we determine no violations have occurred; +* 不采取进一步行动： + - 如果确定没有发生侵犯行为； - 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. From d5bcd3823952c89fa93b9760f51062bf3ceb67f1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 19 Jul 2021 04:23:18 +0200 Subject: [PATCH 542/909] New translations report-handling-manual.md (Chinese Simplified) --- content/zh/report-handling-manual.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/zh/report-handling-manual.md b/content/zh/report-handling-manual.md index 8adddeb5a3..a86407d368 100644 --- a/content/zh/report-handling-manual.md +++ b/content/zh/report-handling-manual.md @@ -74,22 +74,22 @@ sidebar: false * 不采取进一步行动： - 如果确定没有发生侵犯行为； - - 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. + - 如果问题在委员会审议答复时已经公开解决了。 +* 协商自愿调解：如果所有有关各方同意，委员会可促进上面详述的调解进程。 +* 公开提醒并指出某些行为/行动/语言被认为是不恰当的，在目前情况下发生的合理性说明， 或者只能伤害特定人群，要求社区进行自我调整。 +* 委员会对有关个体进行私下训诫。 在这种情况下，小组主席将通过电子邮件向个人发出谴责，并抄送小组成员。 +* 公开谴责。 在这种情况下，委员会主席将在实际可行的限度内在同一地点对侵权行为进行申斥。 例如，在原始邮件列表中进行邮件控诉， 有可能被控诉对象未参与到聊天室的讨论中，虽然可以通过其他方式联系到。 该群组可以选择在别处发布控诉消息作为文档备份。 +* 要求公开或私下道歉， 假定举报者同意这一想法：他们可酌情拒绝与侵权者进一步接触。 委员会主席将传递这一请求。 委员会可按其意愿对这项要求附加“严格”条件：该群组可能要求侵权者道歉，以便在邮件列表中保留一个会员资格。 +* 委员会要求个人暂时停止社区参与的“相互商定的间歇”。 如果个人选择不自愿暂停，委员会可发布“强制性冷却期”。 +* 永远或暂时禁止参与NumPy （如邮件列表、gitter.im等等）。 工作组将保存所有这类禁令的记录，以便今后对其进行审查或备案。 -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. +一旦商定了一项决议，在该决议颁布之前， 委员会将与原报告人和任何其他受影响的各方进行联系，并解释拟议的决议。 委员会将询问这项决议是否可以接受，并且必须将反馈意见记录在案。 -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). +最后，委员会将向NumPy指导委员会进行汇报（也将向NumPy核心小组以诸如禁令的例行事件形式汇报）。 -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. +委员会绝不公开讨论这个问题。 所有公开声明将由行为守则委员会主席或NumPy指导委员会作出。 -## Conflicts of Interest +## 利益冲突 -In the event of any conflict of interest, a Committee member must immediately notify the other members, and recuse themselves if necessary. +如果出现利益冲突，委员会成员必须立即通知其他成员，必要时需要回避。 From c8459896dbd23b4be454548e8f3f98f29b4480bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jul 2021 08:44:08 +0200 Subject: [PATCH 543/909] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index c0618c454f..e74c306aff 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -17,10 +17,10 @@ aliases: 要努力去： -1. 敞开心扉。 我们邀请任何人参加本社区。 我们更喜欢公开交流与项目有关的信息，除非讨论某些敏感问题时。 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. +1. 敞开心扉。 我们邀请任何人参加本社区。 我们更喜欢公开交流与项目有关的信息，除非讨论某些敏感问题时。 这也适用于帮助或项目支持的信息； 向公众寻求支持帮助不仅更有可能得到对某个问题的答复， 它还能确保更容易发现和纠正任何无意中的错误。 +2. 充满热情、欢迎、友善和耐心。 我们怀着美好的意愿共同努力解决冲突。 我们都可能时不时遭受某种挫折，但我们不允许把沮丧变成个人攻击的工具。 一个让人感到不舒服或受到威胁的社区并不是一个富有成效的社区。 +3. 保持合作。 我们的工作成果将被其他人使用，反过来，我们依靠其他人的工作来进行改进。 当我们在项目中添加一些有用的功能时，我们愿意向其他人解释它是如何运作的。 这样他们就能够在这些功能的基础上进一步改善工作效率。 我们作出的任何决定都将影响到用户和开发者，因此在作出决定时必须认真考虑后果。 +4. 勤于向人请教。 没人知道所有事！ 提早提问可以避免很多麻烦的发生，所以我们鼓励提问，尽管我们可能把它们引向更合适的论坛中。 我们将努力做出反应并提供帮助。 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. From 6e26d4802eb985a4bd63c45a9562edec6c8b7c57 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 20 Jul 2021 09:44:45 +0200 Subject: [PATCH 544/909] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index e74c306aff..04e97c09f7 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -21,23 +21,23 @@ aliases: 2. 充满热情、欢迎、友善和耐心。 我们怀着美好的意愿共同努力解决冲突。 我们都可能时不时遭受某种挫折，但我们不允许把沮丧变成个人攻击的工具。 一个让人感到不舒服或受到威胁的社区并不是一个富有成效的社区。 3. 保持合作。 我们的工作成果将被其他人使用，反过来，我们依靠其他人的工作来进行改进。 当我们在项目中添加一些有用的功能时，我们愿意向其他人解释它是如何运作的。 这样他们就能够在这些功能的基础上进一步改善工作效率。 我们作出的任何决定都将影响到用户和开发者，因此在作出决定时必须认真考虑后果。 4. 勤于向人请教。 没人知道所有事！ 提早提问可以避免很多麻烦的发生，所以我们鼓励提问，尽管我们可能把它们引向更合适的论坛中。 我们将努力做出反应并提供帮助。 -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. +5. 谨慎用词 我们在沟通过程中保持谨慎和尊重，我们对自己的发言负有全部责任。 善待他人。 不要侮辱或贬低其他参与者。 我们不接受骚扰或其他排斥行为，例如： + * 针对他人的暴力威胁或语言。 + * 性别、种族主义或其他歧视性笑话和语言。 + * 露骨或粗暴的素材; + * 发布（或威胁发布）他人个人身份信息（“敲诈”）。 + * 未经发件人同意分享私人内容，例如私下发送电子邮件、或发送至非公开/未登录论坛，如IRC 频道。 + * 个人侮辱，尤其是使用种族主义或性别歧视术语的侮辱。 + * 不受欢迎的性关注。 + * 过于夸张。 请避免使用骂人的话；人们对咒骂的敏感度差异很大。 + * 对他人的反复骚扰。 一般来说，如果有人要求你停止，你就要停止了。 + * 鼓吹或鼓励上述任何行为。 + +### 多样性声明 + +NumPy项目欢迎并鼓励每个人参与。 我们致力于成为一个人人都参与的共同体。 虽然我们可能并不总是能够照顾到每个人的喜好，但我们尽力善待每一个人。 + +无论你如何看待自己，也无论其他人如何看待你：我们欢迎你的参与。 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. From 29a05596ebb2465da90923680dd06f381631b972 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Jul 2021 04:17:53 +0200 Subject: [PATCH 545/909] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index 04e97c09f7..1d316dc7e2 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -37,17 +37,17 @@ aliases: NumPy项目欢迎并鼓励每个人参与。 我们致力于成为一个人人都参与的共同体。 虽然我们可能并不总是能够照顾到每个人的喜好，但我们尽力善待每一个人。 -无论你如何看待自己，也无论其他人如何看待你：我们欢迎你的参与。 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. +无论你如何看待自己，也无论其他人如何看待你：我们欢迎你的参与。 虽然社区文化不可能做到包罗万象，但我们明确尊重在年龄、文化、族裔、基因、性别认同或表达、语言、民族血统、神经型、表型、政治信仰、职业、种族、宗教、性取向、社会经济地位、亚文化和技术水平等方面的多样性，在不违反本行为守则的情况下，任何人都可以参与到社区中。 -Though we welcome people fluent in all languages, NumPy development is conducted in English. +虽然我们欢迎精通多语种的人群，但NumPy的开发是用英语进行的。 -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). +上面的《行为守则》详细介绍了NumPy社区的行为标准。 我们社区的参与者应在其所有互动过程中遵守这些标准，并帮助其他参与者也这样做（见下一节）。 -### Reporting Guidelines +### 举报指南 -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. +我们知道，互联网通信平台从诞生开始就演变为非常普遍的辱骂恶意中伤的场所。 我们还认识到，有时人们可能会有不愉快的时候，或不知道本行为守则中的一些准则。 在决定如何应对违反本守则的行为时，请铭记这一点。 -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. +关于明显故意违反行为，向行为守则委员会报告（见下文）。 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. From 0a9cff5989a14a8c4fc218bdb03d10fdead8260e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Jul 2021 21:38:34 +0200 Subject: [PATCH 546/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/content/es/config.yaml b/content/es/config.yaml index 9a90251b51..dcd9eb3ed8 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -23,6 +23,21 @@ params: url: /news shell: title: placeholder + promptlabel: interactive shell prompt + button: + - + label: Enables the interactive tutorial shell + text: Enable + shellcontent: + intro: + - + title: Try NumPy + text: Enable the interactive shell + loading: + - + title: While we wait... + text: Launching container on mybinder.org... + docslink: Don't forget to check out the docs. casestudies: title: CASE STUDIES features: From 10c846cccd0f6a4316f7876ac9720a18dfeb2dfb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Jul 2021 21:38:35 +0200 Subject: [PATCH 547/909] New translations config.yaml (Arabic) --- content/ar/config.yaml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 9a90251b51..dcd9eb3ed8 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -23,6 +23,21 @@ params: url: /news shell: title: placeholder + promptlabel: interactive shell prompt + button: + - + label: Enables the interactive tutorial shell + text: Enable + shellcontent: + intro: + - + title: Try NumPy + text: Enable the interactive shell + loading: + - + title: While we wait... + text: Launching container on mybinder.org... + docslink: Don't forget to check out the docs. casestudies: title: CASE STUDIES features: From 389f6b3a46a6591715f177d4644620974e2b32a6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Jul 2021 21:38:36 +0200 Subject: [PATCH 548/909] New translations config.yaml (Japanese) --- content/ja/config.yaml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 9a90251b51..dcd9eb3ed8 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -23,6 +23,21 @@ params: url: /news shell: title: placeholder + promptlabel: interactive shell prompt + button: + - + label: Enables the interactive tutorial shell + text: Enable + shellcontent: + intro: + - + title: Try NumPy + text: Enable the interactive shell + loading: + - + title: While we wait... + text: Launching container on mybinder.org... + docslink: Don't forget to check out the docs. casestudies: title: CASE STUDIES features: From c07ad54657e3c55349022e1270ca0a6792fe2af7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Jul 2021 21:38:37 +0200 Subject: [PATCH 549/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 9889903404..b3465a37e8 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -23,6 +23,21 @@ params: url: /news shell: title: 플레이스홀더 + promptlabel: interactive shell prompt + button: + - + label: Enables the interactive tutorial shell + text: Enable + shellcontent: + intro: + - + title: Try NumPy + text: Enable the interactive shell + loading: + - + title: While we wait... + text: Launching container on mybinder.org... + docslink: Don't forget to check out the docs. casestudies: title: 사례 연구 features: From 34cb9bfe56a880f2f3caad796d65e8cab09de96e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Jul 2021 21:38:38 +0200 Subject: [PATCH 550/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 3924306c40..2535cd5db9 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -23,6 +23,21 @@ params: url: /news shell: title: 占位符 + promptlabel: interactive shell prompt + button: + - + label: Enables the interactive tutorial shell + text: Enable + shellcontent: + intro: + - + title: Try NumPy + text: Enable the interactive shell + loading: + - + title: While we wait... + text: Launching container on mybinder.org... + docslink: Don't forget to check out the docs. casestudies: title: CASE STUDIES features: From 63e3dd2a1a230e0cfbb774b319a1e7d1adef6157 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Jul 2021 21:38:40 +0200 Subject: [PATCH 551/909] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index fc50a29b0b..8c21ea9add 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -48,21 +48,21 @@ O projeto NumPy está crescendo; temos equipes para Veja a página de [Times](/gallery/team.html) para membros individuais de cada time. -## NumFOCUS Subcommittee +## Subcomitê NumFOCUS - Charles Harris - Ralf Gommers - Melissa Weber Mendonça - Sebastian Berg -- External member: Thomas Caswell +- Membro externo: Thomas Caswell -## Sponsors +## Patrocinadores O NumPy recebe financiamento direto das seguintes fontes: {{< sponsors >}} -## Institutional Partners +## 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: @@ -72,7 +72,7 @@ Os Parceiros Institucionais são organizações que apoiam o projeto, empregando {{< partners >}} -## Donate +## Doações Se você achou o NumPy útil no seu trabalho, pesquisa ou empresa, por favor considere fazer uma doação para o projeto que seja compatível com seus recursos. Qualquer quantidade ajuda! Todas as doações serão utilizadas estritamente para financiar o desenvolvimento do software de código aberto da NumPy, documentação e comunidade. From 78290c8dc0ec658ca6e634d79d48cd31d5bb9b61 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 22 Jul 2021 21:38:41 +0200 Subject: [PATCH 552/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 9a90251b51..dcd9eb3ed8 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -23,6 +23,21 @@ params: url: /news shell: title: placeholder + promptlabel: interactive shell prompt + button: + - + label: Enables the interactive tutorial shell + text: Enable + shellcontent: + intro: + - + title: Try NumPy + text: Enable the interactive shell + loading: + - + title: While we wait... + text: Launching container on mybinder.org... + docslink: Don't forget to check out the docs. casestudies: title: CASE STUDIES features: From 5558f5c588daa614a3ea909f25ed58c324d91cdd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 01:07:15 +0200 Subject: [PATCH 553/909] New translations config.yaml (Japanese) --- content/ja/config.yaml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index dcd9eb3ed8..c4e1fe083f 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -23,21 +23,21 @@ params: url: /news shell: title: placeholder - promptlabel: interactive shell prompt + promptlabel: 対話的なシェルプロンプト button: - - label: Enables the interactive tutorial shell - text: Enable + label: 対話的なチュートリアルシェルを有効にします。 + text: 有効化 shellcontent: intro: - - title: Try NumPy - text: Enable the interactive shell + title: NumPy を試す。 + text: 対話的なチュートリアルシェルを有効にします。 loading: - - title: While we wait... - text: Launching container on mybinder.org... - docslink: Don't forget to check out the docs. + title: しばらくお待ちください... + text: mybinder.orgでコンテナを起動しています... + 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: CASE STUDIES features: From ebb2ada0497b1dd2f9896ef5202aef48620f44e3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 19:50:41 +0200 Subject: [PATCH 554/909] New translations news.md (Arabic) --- content/ar/news.md | 34 ++++++++++++++++++++++++---------- 1 file changed, 24 insertions(+), 10 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 7d40eccd28..5d4e396b31 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -3,23 +3,37 @@ title: الأخبار sidebar: false --- -### نتائج استطلاع نمباي لعام 2020 +### Numpy 1.21.0 release -_22 يونيو2021_ -قام فريق استطلاع نمباي في عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتي ميتشيجان وميريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباي. بامكانك معرفة نتائج الدراسة الاستقصائية من هنا: https://numpy.org/user-survey-2020/. +_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. -### الإصدار 1.20.0 لنمباى +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. -_30يناير2021_ -- [إصدار1.20.0 لنمباى](https://numpy.org/doc/stable/release/1.20.0-notes.html) متاح الآن. وهذا هو أكبر إصدار لنمباي حتى الآن بفضل 180+ من المساهمين. والسمتان الجديدتان الأكثر إثارة للاهتمام هما: + +### 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)). -### التنوع في مشروع نمباي +### Diversity in the NumPy project -_20 سبتمبر 2020_ -- كتبنا[ تقريرًا وأجرينا نقاشًا على وسائل التواصل الاجتماعي حول التنوع والشمول فى مشروع نمباي](/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). -### نشر أول ورقة رسمية لنمباي فى مجلة نيتشر! +### 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. @@ -31,9 +45,9 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear - 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. -### إصدار 1.19.2 لنمباي +### Numpy 1.19.2 release -_10 سيبتمبر لعام 2020 _- [أصبح نمباي 1.19.2 ](https://numpy.org/devdocs/release/1.19.2-notes.html) متاح الآن. 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. +_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! @@ -79,7 +93,7 @@ More details on our proposed initiatives and deliverables can be found in the [f ## الإصدارات -إليك قائمة من إصدارات نمباي، مع روابط لملاحظات كل إصدار. لا توجد مزايا جديدة(التغييرات فقط فى رقم الإصدار `z` `x.y.z`) فى جميع إصدارات إصلاح العيوب على عكس الإصدارات الثانوية(الزيادة `y`). +Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. - NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. - NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. From 72d0dd758e9f41d6497c1d3afdf68e46ecfa1602 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 19:50:42 +0200 Subject: [PATCH 555/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 108 +++++++++++++++++++++++++-------------------- 1 file changed, 61 insertions(+), 47 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index fc32c2302f..56cc7b459d 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -3,92 +3,106 @@ title: 社区快讯 sidebar: false --- -### 2020 Numpy调研结果出炉 +### Numpy 1.21.0 release -_22, 2021_ -- 2020, NumPy调研小组与密歇根大学和马里兰大学的学生和教职员工合作，进行了第一次官方NumPy社区调查。 在这里可以查看调查结果：https://numpy.org/user-survey-2020/。 +_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. -### NumPy 1.20.0 发布 +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. -_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢社区的180+位贡献者。 最令人振奋的两个新特性是： -- Numpy的大部分代码都做了类型注解，添加了一个全新的包含 `ArrayLike` 和 `DtypeLike`别名系统的 `numpy.typing` 子模块，使得用户和下游依赖库可以在自己的代码中添加类型注解。 -- 新增多架构SIMD编译优化框架，同时支持X86(SSE、AVX)、ARM64(Neon) 和PowerPC(VSX) 指令集。 大大提高了许多函数的性能(例如： [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194))。 -### NumPy项目的多样性 +### 2020 NumPy survey results -_2020年9月20日_ -- 我们就NumPy项目中的多样性和包容性的现状以及社交媒体相关的讨论写了一份[声明](/diversity_sep2020) +_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/. -### 在Nature中发表的第一篇官方的NumPy论文！ +### Numpy 1.20.0 release -_2020年9月16日_ - 我们高兴地宣布 [Numpy的第一篇官方论文](https://www.nature.com/articles/s41586-020-2649-2)刊登在自然杂志的评论文章。 这距离NumPy 1.0发布已经过去了整整14年。 该论文涵盖数组编程的应用和基本概念，丰富的Python科学计算生态系统建立在NumPy之上，包括最近添加的数组标准协议，大大提高了与外部数组和张量库(如CuPy, Dask 和 JAX) 的互操作性 。 +_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 -### Python 3.9 即将来临，新版本的NumPy 何时发布？ +_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14日_ -- Python 3.9 将在几周后发布。 如果您是这个Python版本的忠实拥趸， 您可能会失望的发现NumPy(以及其他二进制软件包，如SciPy) 在Python新版发布后数天内不会有版本发布。 使构建基础设施兼容新的 Python 版本需要付出重大努力，通常需要几周时间才能让包出现在 PyPI 和 conda-forge 上。 为了准备这次重大事件得以顺利进行，请确保： -- 将您的 `pip` 升级到 20.1 版本，至少要支持`manylinux2010` 和 `manylinux2014` -- 使用 [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) 或 `--only-binary=:all:` 选项来防止 `pip` 从源码构建的尝试。 +### First official NumPy paper published in Nature! -### NumPy 1.19.2 发布 +_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10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 和 pins安装工具做好准备，以确保正在进行上游修改时用户仍然可以正常安装运行。 Aarch64架构的安装包是用最新的 manylinux2014 版本构建的，它修复了 linux 发行版之间使用不同大小内存页的问题。 -### 首次NumPy调研即将开始！ +### Python 3.9 is coming, when will NumPy release binary wheels? -_2020年7月2日_ - 本次调查旨在指导并确定 关于使用社区方式还是软件方式来开发NumPy的决策。 除英文外，调查还提供了另外8种语言的版本：孟加拉语、印地语、日语、曼达林语、葡萄牙语、俄语、西班牙语和法语。 +_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. -请帮助我们让 NumPy 变得更好，在[这里](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)参与调查。 +### Numpy 1.19.2 release -### NumPy 有新logo了! +_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7月24日_ -- NumPy 现在有一个新的标志： +### The inaugural NumPy survey is live! -NumPy logo +_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. -这是一个更时髦更纯净的标志。 感谢Isabela Presedo-Floryd的设计方案， 同时感谢Travis Vaugh设计的旧图标为我们提供了很好的15年以上服务。 +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy 1.19.0 发布 +### NumPy has a new logo! -_2020年6月_ -- NumPy 1.19.0 正式发布。 这是第一个不支持Python 2的版本，因此它是一个“清理版本”。 目前支持的最小Python 版本是 Python 3.6。 本版本拥有一个重要的新特性，NumPy 1.17.0引进的随机数字生成基础模块现在可以通过Cython访问。 +_Jun 24, 2020_ -- NumPy now has a new logo: +NumPy logo -### 文档整改时间段 +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. -_2020年5月11日_ -- NumPy 已成为Google Season 文档项目的mentor组织之一。 我们很高兴看到有机会和技术写作者一起再次改进NumPy的技术文档！ 更多详情，请参考 [GsoD网站的官方赛期](https://developers.google.com/season-of-docs/) 和我们的 [意见页面](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)。 +### NumPy 1.19.0 release -### NumPy 1.18.0 发布 +_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. -_2019年12月22日_ -- NumPy 1.18.0 正式发布。 在1.17.0发生重大变化后，这是一个合并版本。 这是最后一个支持 Python 3.5的小版本。 该版本的重要更新包括两个，添加了与64位 BLAS 和 LAPACK 库有关的底层更新， 添加 一个用于`numpy.random`的新C-API更新。 -详情请看 [版本说明](https://github.com/numpy/numpy/releases/tag/v1.18.0)。 +### 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 从Chan Zuckerberg Initiative获得了一笔捐款 -_2019年11月15日_ -- 我们高兴地宣布NumPy和 OpenBLAS (Numpy的一个核心依赖库)已经收到一笔19,5000美元的联合赠款。 捐款来自于Chan Zuckerberg Initiative通过的[基础开源科学计算软件项目](https://chanzuckerberg.com/eoss/)，用来支持对科学发展起到关键作用的开源软件的维护、增长、开发和社区参与。 +### NumPy 1.18.0 release -这笔赠款将用来加速改进NumPy文档、网站重构和社区开发，进而更好地为我们庞大和迅速增长的用户基础服务，并确保项目的长期可持续性。 OpenBLAS 团队将侧重于处理几个关键技术问题，特别是线程安全问题、AVX-512和 thread-local 存储(TLS) 问题，以及OpenBLAS 依赖的 ReLAPACK (递归的LAPACK) 算法改进。 +_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`. -若想查看更多关于捐款的倡议和交付件的详情，可在 [全额赠款提案](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) 中找到。 项目开始于2019年12月1日，今后12个月将持续运作下去。 +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. ## 版本发布 -这是NumPy 版本列表，包含了对应版本发布说明的链接。 所有的 bug修复版本(即在 `x.y.z`格式版本号中只有 `z`改变)没有新功能；小版本更新(`y` 改变)有新功能。 - -- NumPy1.18.4 (<0">发行说明) -- _2020年5月3日_. -- NumPy1.18.3 (<0">发行说明) -- _2020年4月19日_. -- NumPy1.18.2 (<0">发行说明) -- _2020年3月17日_. -- NumPy1.18.1 (<0">发行说明) -- _2020年1月6日_. -- NumPy1.17.5 (<0">发行说明) -- _2020年1月1日_. -- NumPy1.18.0 (<0">发行说明) -- _2019年12月22日_. -- NumPy1.17.4 (<0">发行说明) -- _2019年11月11日_. -- NumPy1.17.0 (<0">发行说明) -- _2019年7月26日_. -- NumPy1.16.0 (<0">发行说明) -- _2019年1月14日_. -- NumPy1.15.0 (<0">发行说明) -- _2018年7月23日_. -- NumPy1.14.0 (<0">发行说明) -- _2018年1月7日_. +Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 6e7cc3b1161c52eb3d8f2d9eba0fa10b6f4a2b8d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 19:50:43 +0200 Subject: [PATCH 556/909] New translations news.md (Spanish) --- content/es/news.md | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/content/es/news.md b/content/es/news.md index 8b6c78b8ea..3b4dad1e36 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -3,6 +3,20 @@ title: News sidebar: false --- +### 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/. From 8e7c3a2c05cec590b5262dc9b0860c06cb52d7bd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 19:50:46 +0200 Subject: [PATCH 557/909] New translations news.md (Japanese) --- content/ja/news.md | 44 +++++++++++++++++++++++++++++--------------- 1 file changed, 29 insertions(+), 15 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 3e9fdb6fcf..5f19c806ba 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,6 +3,20 @@ title: ニュース sidebar: false --- +### 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/. @@ -11,8 +25,8 @@ _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students an ### 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: -- 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)). +- 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 @@ -27,8 +41,8 @@ _Sep 16, 2020_ -- We are pleased to announce the publication of [the first offic ### 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 -- `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。 +- 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 @@ -81,14 +95,14 @@ 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. -- NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年5月3日_. -- NumPy 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年4月19日_. -- NumPy 1.18.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020年3月17日_. -- NumPy 1.18.1 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _2020年1月6日_. -- NumPy 1.17.5 ([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_. -- NumPy 1.14.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018年1月7日_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 a085a1023320ff54cdcdfad16109addb96ccfdd7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 19:50:47 +0200 Subject: [PATCH 558/909] New translations news.md (Korean) --- content/ko/news.md | 36 +++++++++++++++++++++++++----------- 1 file changed, 25 insertions(+), 11 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 06bf2e794c..0552170fc7 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -3,6 +3,20 @@ title: 소식 sidebar: false --- +### 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/. @@ -81,14 +95,14 @@ 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. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. -- NumPy 1.18.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020년 5월 3일_. -- NumPy 1.18.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _2020년 4월 19일_. -- NumPy 1.18.2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020년 3월 17일_. -- NumPy 1.18.1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _2020년 1월 6일_. -- 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 1.17.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _2019년 11월 11일_. -- NumPy 1.17.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _2019년 7월 26일_. -- NumPy 1.16.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019년 1월 14일_. -- NumPy 1.15.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018년 7월 23일_. -- NumPy 1.14.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018년 1월 7일_. +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 e0993cacb838c24b35f7a3c72ddce01a5f0b7b82 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 19:50:48 +0200 Subject: [PATCH 559/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 106 +++++++++++++++++++++++++-------------------- 1 file changed, 60 insertions(+), 46 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 42ebe38f03..24de5e2a78 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,92 +3,106 @@ title: Notícias sidebar: false --- -### Resultados da pesquisa NumPy 2020 +### Numpy 1.21.0 release -_22 de junho de 2021_ -- Em 2020, o time de pesquisas NumPy, em parceria com estudantes e faculdades 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 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. -### NumPy versão 1.20.0 +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. -_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. -- 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 +### 2020 NumPy survey results -_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). +_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/. -### Primeiro artigo oficial do NumPy publicado na Nature! +### Numpy 1.20.0 release -_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. +_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 -### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? +_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). -_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. +### First official NumPy paper published in Nature! -### NumPy versão 1.19.2 +_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. -_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! +### Python 3.9 is coming, when will NumPy release binary wheels? -_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. +_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. -Ajude-nos a melhorar o NumPy respondendo à pesquisa [aqui](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +### Numpy 1.19.2 release -### O NumPy tem um novo logo! +_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. -_24 de junho de 2020_ -- NumPy agora tem um novo logo: +### 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 -O logo é uma versão moderna do antigo, com um design mais limpo. Obrigado 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 1.19.0 +### NumPy 1.19.0 release -_20 de junho de 2020_ -- 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 +### Season of Docs acceptance -_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 +### NumPy 1.18.0 release -_22 de dezembro de 2019_ -- 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 receberá um auxílio da Chan Zuckerberg Initiative +### NumPy receives a grant from the 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. - -- NumPy 1.18.4 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 de maio de 2020_. -- NumPy 1.18.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 de abril de 2020_. -- NumPy 1.18.2 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 de março de 2020_. -- NumPy 1.18.1 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 de janeiro 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 1.15.0 ([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_. +Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. + +- NumPy 1.18.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _3 May 2020_. +- NumPy 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. +- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. +- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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 72d9f349432071421db1422bf0482905ea39b7ed Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:16:28 +0200 Subject: [PATCH 560/909] New translations news.md (Arabic) --- content/ar/news.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 5d4e396b31..35906b7dfc 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -3,6 +3,15 @@ title: الأخبار sidebar: false --- +### 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: @@ -93,15 +102,16 @@ More details on our proposed initiatives and deliverables can be found in the [f ## الإصدارات -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +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 1.21.0 ([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 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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_. From f6d03dd63b928eee87d2aa036725fe5d72cc9ffd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:20 +0200 Subject: [PATCH 561/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 24de5e2a78..8078b31e32 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,6 +3,15 @@ title: Notícias sidebar: false --- +### 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: @@ -93,15 +102,16 @@ More details on our proposed initiatives and deliverables can be found in the [f ## Lançamentos -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +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 1.21.0 ([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 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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_. From 15af6d9c0b269b9ed7d52ceb4e03f4e45209cf3d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:21 +0200 Subject: [PATCH 562/909] New translations news.md (Korean) --- content/ko/news.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 0552170fc7..7b8b01824c 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -3,6 +3,15 @@ title: 소식 sidebar: false --- +### 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: @@ -93,15 +102,16 @@ More details on our proposed initiatives and deliverables can be found in the [f ## 릴리즈 -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +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 1.21.0 ([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 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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_. From 7dc2fba68bcd19db00c03eb5c3d5c98d47125764 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:23 +0200 Subject: [PATCH 563/909] New translations news.md (Japanese) --- content/ja/news.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 5f19c806ba..112ee6eb1c 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,6 +3,15 @@ title: ニュース sidebar: false --- +### 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: @@ -93,15 +102,16 @@ More details on our proposed initiatives and deliverables can be found in the [f ## 過去のリリース -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +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 1.21.0 ([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 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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_. From 15cf77c7d559f99e847f66477363ab52dd84e66a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:24 +0200 Subject: [PATCH 564/909] New translations news.md (Spanish) --- content/es/news.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 3b4dad1e36..beae21e17a 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -3,6 +3,15 @@ title: News sidebar: false --- +### 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: @@ -93,15 +102,16 @@ More details on our proposed initiatives and deliverables can be found in the [f ## Releases -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +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 1.21.0 ([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 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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_. From b5d19848c05cd0df382f12bf7c4039e5ae5146a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:25 +0200 Subject: [PATCH 565/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index dcd9eb3ed8..26261e1794 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2020 NumPy survey - content: results are in + title: 2021 NumPy survey + content: Your voice matters url: /news shell: title: placeholder From d77379123ffff3630eaf32a994609d43cdaf7a56 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:27 +0200 Subject: [PATCH 566/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 2535cd5db9..7571788813 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2020 NumPy survey - content: results are in + title: 2021 NumPy survey + content: Your voice matters url: /news shell: title: 占位符 From 901596c000224fa0612787ff9a96f5439e3d248c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:28 +0200 Subject: [PATCH 567/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index b3465a37e8..7a4d1949fc 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2020 NumPy survey - content: results are in + title: 2021 NumPy survey + content: Your voice matters url: /news shell: title: 플레이스홀더 From 6a4d34a66843e655f45b4bf39fe08fa0ae90437c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:29 +0200 Subject: [PATCH 568/909] 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 c4e1fe083f..282e0dc14c 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2020 NumPy survey - content: results are in + title: 2021 NumPy survey + content: Your voice matters url: /news shell: title: placeholder From 5a8a1fa062a335ccdbbd00071fd16aa9131eb8a9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:29 +0200 Subject: [PATCH 569/909] New translations config.yaml (Arabic) --- content/ar/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index dcd9eb3ed8..26261e1794 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2020 NumPy survey - content: results are in + title: 2021 NumPy survey + content: Your voice matters url: /news shell: title: placeholder From 9d0fb094d7cdd78b393cdff6aa946d691ca2bdf8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:31 +0200 Subject: [PATCH 570/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index dcd9eb3ed8..26261e1794 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2020 NumPy survey - content: results are in + title: 2021 NumPy survey + content: Your voice matters url: /news shell: title: placeholder From f01cdcaec1e8611b8c6a54482e2412aa810a953a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:17:42 +0200 Subject: [PATCH 571/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 20 +++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 56cc7b459d..15f345c121 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -3,6 +3,15 @@ title: 社区快讯 sidebar: false --- +### 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: @@ -93,15 +102,16 @@ More details on our proposed initiatives and deliverables can be found in the [f ## 版本发布 -Here is a list of NumPy releases, with links to release notes. All bugfix releases (only the `z` changes in the `x.y.z` version number) have no new features; minor releases (the `y` increases) do. +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 1.21.0 ([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 1.18.3 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 Apr 2020_. -- NumPy 1.18.2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 Mar 2020_. -- NumPy 1.18.1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 Jan 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 1.17.4 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.17.4)) -- _11 Nov 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_. From 22cf56aeb88acdf9fb214f8ea47992a99dad1cf1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:39 +0200 Subject: [PATCH 572/909] New translations report-handling-manual.md (Portuguese, Brazilian) --- content/pt/report-handling-manual.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/report-handling-manual.md b/content/pt/report-handling-manual.md index 14418d0e11..f5047bdca7 100644 --- a/content/pt/report-handling-manual.md +++ b/content/pt/report-handling-manual.md @@ -9,7 +9,7 @@ Garantir que o [Código de Conduta](/code-of-conduct) seja respeitado afeta noss * Agir de forma pessoal e não impessoal. O Comitê pode levar as partes a compreender a situação, respeitando simultaneamente a privacidade e a necessária confidencialidade das pessoas relatantes. No entanto, por vezes, é necessário comunicar diretamente com um ou mais indivíduos: o objetivo do Comitê é melhorar a saúde da nossa comunidade, em vez de produzir apenas uma decisão formal. * Enfatizar empatia pelos indivíduos ao invés de julgar o comportamento, evitando rótulos binários de "bom" e "mau". Existem atos de agressão e assédio claros e visíveis, e vamos abordá-los com firmeza. Mas muitos cenários que podem ser desafiadores são aqueles em que as discordâncias normais se transformam em comportamento desnecessário ou prejudicial de várias partes. Compreender o contexto completo e encontrar um caminho que traga um entendimento entre as partes é difícil, mas, em última análise, é o resultado mais produtivo para a nossa comunidade. -* Compreendemos que o e-mail é um meio difícil e que pode causar uma sensação de isolamento. Receber críticas por e-mail, sem contato pessoal, pode ser particularmente doloroso. Isto faz com que seja especialmente importante manter um clima de respeito aberto pelas opiniões dos outros. Significa também que temos de ser transparentes nas nossas ações, e que faremos tudo o que estiver ao nosso alcance para garantir que todos os nossos membros sejam tratados de forma justa e com simpatia. +* Compreendemos que o e-mail é um meio difícil e que pode causar uma sensação de isolamento. Receber críticas por e-mail, sem contato pessoal, pode ser particularmente doloroso. Significa também que temos de ser transparentes nas nossas ações, e que faremos tudo o que estiver ao nosso alcance para garantir que todos os nossos membros sejam tratados de forma justa e com simpatia. Isto faz com que seja especialmente importante manter um clima de respeito aberto pelas opiniões dos outros. * A discriminação pode ser sutil e pode ser inconsciente. Pode revelar-se em tratamentos injustos e hostis em interações que normalmente seriam ordinárias. Sabemos que isso acontece, e teremos o cuidado de ter isso em mente. Gostaríamos muito de ouvir se você acha que foi tratado injustamente, e usaremos esses procedimentos para garantir que a sua reclamação seja ouvida e abordada. * Ajudar a aumentar o envolvimento em uma boa prática de discussão: tentar identificar onde a discussão pode ter falhado, e fornecer informações úteis, indicadores e recursos que podem levar a mudanças positivas nestes pontos. * Estar ciente das necessidades de novos membros: fornecer-lhes apoio e consideração explícitos, com o objetivo de aumentar a participação de grupos sub-representados, em particular. From 0c27b745f5c7d0d3fa3f87b66ae6b270ea8870f7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:41 +0200 Subject: [PATCH 573/909] New translations report-handling-manual.md (Japanese) --- content/ja/report-handling-manual.md | 30 ++++++++++++++-------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/ja/report-handling-manual.md b/content/ja/report-handling-manual.md index 72648a80b6..1d561bdcb2 100644 --- a/content/ja/report-handling-manual.md +++ b/content/ja/report-handling-manual.md @@ -3,16 +3,16 @@ title: NumPy行動規範 - 報告書のフォローアップ方法 sidebar: false --- -NumPyの行動規範委員会はこのマニュアルに従います。 このマニュアルは様々な問題に対応する際に使用され、一貫性と公平性を確保します。 +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。 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](/code-of-conduct) を施行することは、私たちのコミュニティの現在と未来に重要です。 私達はこのルールを重く受け止めています。 施行措置の見直しに際しては、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、当事者にプライバシーと報告者に必要な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、当事者にプライバシーと報告者に必要な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」のバイナリラベルを避けようとします。 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。 完全な文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、最終的にはコミュニティにとって最も生産的になると考えています。 * 私たちは、電子メールが判断に困難な媒体であり、分けて利用できることを理解しています。 個人的な連絡なしで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るのです。 ここでは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、 我々は全力を尽くします * 差別というのは明確には断定できないことがあり、無意識で実施さ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。 -* 新メンバーのニーズに留意し、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。 +* 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。 @@ -23,7 +23,7 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この * 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。 -* 調停人の決定：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、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。 @@ -40,17 +40,17 @@ 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 通信チャンネルから違反者を排除します。 +* 直ちにすべてのNumPyのオンラインコミュニティから違反者を排除します。 * 報告が受信され、違反者が排除されたことを報告者に連絡します。 * どのような場合でも、モデレーターは違反者に連絡するための合理的な努力を行い、違反者の言葉や行動がどのように「明確かつ重大な違反」に該当するのかを具体的に伝えるべきです。 モデレーターは、違反者がこれは不当だと思う場合、あるいはNumPyチャンネルとの再接続を望む場合には、行動規範委員会による以下のような審査を求める権利があることも述べるべきです。 モデレータは、この説明を行動規範委員会に転送する必要があります。 -* 行動規範委員会は、このプロセスが適用されたすべてのケースを正式にレビューし、作業完了することで、よくある激しい意見の相違を制御するためにこのプロセスが使用されていないことを確認します。 +* 行動規範委員会は、このプロセスが適用されたすべてのケースを正式にレビューし署名することで、よくある盛り上がりすぎた論争を諫めるためこのプロセスが使用されたのでないことを確認します。 ## 報告の処理 報告が委員会に送られると、直ちに報告者に返信して報告を受領したことを確認します。 この返信は72時間以内に送信される必要があり、委員会はそれよりもはるかに迅速に対応するよう努める必要があります。 -レポートに十分な情報が含まれていない場合、委員会は行動する前に、関連するすべてのデータを取得するようにします。 委員会は、今回の事象の全ての状況を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。 委員会は、今回の事象の全ての状況を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。 @@ -68,26 +68,26 @@ NumPyの行動規範委員会はこのマニュアルに従います。 この ## 解決方法 -委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。 +委員会は、合意により決議について決定しなければなりません。 委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。 ありうる返答は次のとおりです: -* これ以上アクションを取らない: +* 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。 -* 公の場において、いくつかの行動/言動/言語が不適切で、現在の状況がなぜか引き起こされたのか指摘し、人々を傷つけることができルール言動であったことを説明するなど、コミュニティに自己調整を要求することもあります。 +* 調停の調整。すべての関係者が合意した場合、委員会は上記のように調停プロセスを促進することができます。 +* 公の場における説明。どの行動・言動・言語が不適切で、現在の状況がなぜか引き起こされ、人々を傷つけたのかを説明し、コミュニティに自省を要求します。 * 委員会から関係者(複数可) への非公開処分の実施。 この場合、委員会は、電子メールを介して、グループにccを入れながら、対象者に問題の指摘を連絡します。 * 公の場での指摘。 この場合、委員会の議長は、違反が発生したのと同じ場所で、実用性の範囲内で叱責を行います。 例えば、メールルールの違反の元のメーリングリストなどです。しかし、人や状況がかわるかもしれないチャットルームなどの場合、他の手段を利用する可能性もあります。 対策グループは、文書化のために、この問題のメッセージを他の場所で公開することを選択することもできます。 * 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。 -* 委員会が個人にコミュニティへの参加を一時的に控える「相互に合意した休止」を要求できます。 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。 +* 委員会が個人にコミュニティへの参加を一時的に控える「相互に合意した休止」を要求できます。 「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 スペース (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禁止のことです。 対策グループは、将来的な見直しや、または別の方法で対策されるように、すべてのそのような禁止の記録を記録します。 決議が合意されると制定される前に、委員会は、元の報告者およびその他の影響を受けた当事者に連絡し、提案された決議を説明します。 委員会は、この決議が受け入れられるかどうかを尋ねます。そして、記録のためのフィードバックに注意を払います。 -最後に 委員会は、Numpy Steering CouncilとNumPy Coreチームに報告を行います。(例えば禁止事項など) +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 From 41cfbef0512744354b9a747edb79c949b6c9f1a6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:44 +0200 Subject: [PATCH 574/909] New translations privacy.md (Japanese) --- content/ja/privacy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/privacy.md b/content/ja/privacy.md index c1b676d6ef..f70c032c59 100644 --- a/content/ja/privacy.md +++ b/content/ja/privacy.md @@ -3,6 +3,6 @@ title: プライバシーポリシー sidebar: false --- -**numpy.org** は、Numpyプロジェクトの資金援助のスポンサーでもある、[NumFOCUS, Inc.](https://numfocus.org)によって運営されています。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policyを参照してください。 +**numpy.org** は、NumPyプロジェクトの資金援助のスポンサーでもある、[NumFOCUS, Inc.](https://numfocus.org)によって運営されています。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policy を参照してください。 このウェブサイトのプライバシーポリシーについては、https://numfocus.org/privacy-policyを参照してください。 ポリシーまたはNumFOCUSのデータ収集、使用、および開示方法についてご質問がある場合は、privacy@numfocus.orgのNumFOCUSスタッフにお問い合わせください。 From 750c74f8ca9540cb0a9c834c3c4845c795b525bc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:48 +0200 Subject: [PATCH 575/909] New translations press-kit.md (Japanese) --- content/ja/press-kit.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ja/press-kit.md b/content/ja/press-kit.md index a523b1d296..81b0bc6704 100644 --- a/content/ja/press-kit.md +++ b/content/ja/press-kit.md @@ -3,6 +3,6 @@ title: プレス用資料 sidebar: false --- -私達はユーザーの皆さんの次の学術論文や、コース教材、プレゼンテーションにNumPyプロジェクトのロゴなどを簡単に盛り込めるようにしたいと考えています。 +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/master/branding/logo). ちなみに、numpy.orgのリソースを使用するということは、 [Numpy行動規範](/code-of-conduct) を受け入れることを意味してい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/master/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。 ちなみに、numpy.orgのリソースを使用するということは、 [Numpy行動規範](/code-of-conduct) を受け入れることを意味していることに注意してください。 From 92fd64613422553c6e1a752ff44c8ba7f6239ec3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:50 +0200 Subject: [PATCH 576/909] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index 23c4ac5780..915a712533 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -5,30 +5,30 @@ sidebar: false Para a **documentação oficial do NumPy** visite [numpy.org/doc/stable](https://numpy.org/doc/stable). -## Tutoriais NumPy +## Iniciantes Você pode encontrar um conjunto de tutoriais e materiais educativos criados pela comunidade do NumPy em [NumPy Tutorials](https://numpy.org/numpy-tutorials). O objetivo desta página é fornecer recursos de alta qualidade pelo projeto NumPy, tanto para autoaprendizado como para o ensino, no formato de notebooks Jupyter. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório [numpy-tutorials no GitHub](https://github.com/numpy/numpy-tutorials). *** -Abaixo você pode encontrar uma coleção de recursos externos selecionados. Para contribuir, veja o [fim desta página](#add-to-this-list). +Abaixo está uma coleção de recursos externos selecionados. Para contribuir, veja o [fim desta página](#add-to-this-list). -## Iniciantes +## Avançado -Há uma tonelada de informações sobre o NumPy por aí. Se você está começando, recomendamos fortemente as fontes seguintes: +Há uma tonelada de informações sobre o NumPy lá fora. Se você está começando, recomendamos fortemente estes: - **Tutoriais** + **Livros** * [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) -* [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 *por Nicolas Rougier*](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 *by Nicolas Rougier*](https://github.com/rougier/numpy-tutorial) +* [**Tutoriais**](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) +* [NumPy User Guide (Guia de Usuário NumPy)](https://numpy.org/devdocs) **Livros** @@ -36,19 +36,19 @@ Há uma tonelada de informações sobre o NumPy por aí. Se você está começan * [From Python to NumPy *por 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) *por Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow* -Você também pode querer conferir a [lista Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre o tema "Python+SciPy. A maioria dos livros lá serão sobre o "ecossistema SciPy", que tem o NumPy em sua essência. +**Vídeos** - **Vídeos** + Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais. * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* *** -## Avançados +## Palestras sobre NumPy -Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais. +**Tutoriais** - **Tutoriais** + **Livros** * [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* @@ -57,20 +57,20 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos * [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/) - **Livros** + **Vídeos** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* * [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *por 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) *por Robert Johansson* - **Vídeos** + 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). -* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *by Juan Nunez-Iglesias* +* [Advanced NumPy - broadcasting rules, strides, and advanced indexing](https://www.youtube.com/watch?v=cYugp9IN1-Q) *por Juan Nunuz-Iglesias* * [Advanced Indexing Operations in NumPy Arrays](https://www.youtube.com/watch?v=2WTDrSkQBng) *por Amuls Academy* *** -## Palestras sobre NumPy +## Citando a NumPy * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) @@ -80,7 +80,7 @@ Experimente esses recursos avançados para uma melhor compreensão dos conceitos *** -## Citando o NumPy +## Contribua para esta lista 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](/citing-numpy). From 21b3b66322d99750565122d2733ee5b491d9561b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:52 +0200 Subject: [PATCH 577/909] New translations learn.md (Japanese) --- content/ja/learn.md | 70 ++++++++++++++++++++++----------------------- 1 file changed, 35 insertions(+), 35 deletions(-) diff --git a/content/ja/learn.md b/content/ja/learn.md index 9b03845dd5..b08672364b 100644 --- a/content/ja/learn.md +++ b/content/ja/learn.md @@ -1,90 +1,90 @@ --- -title: Numpyの学び方 +title: NumPyの学び方 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。 +**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のチュートリアル [Numpy のチュートリアル](https://numpy.org/numpy-tutorials) では、Numpy コミュニティによるチュートリアルや教材が手に入ります｡ このページの目標は、NumPy プロジェクトによる自己学習と授業のための高品質な教材を Jupyter Notebooks の形式で提供することです。 独自のコンテンツを追加したい場合は、GitHubの [numpy-tutorials リポジトリ](https://github.com/numpy/numpy-tutorials)を確認してください。 *** -以下は、厳選された外部の教材です。 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。 +以下は、厳選された外部の教材です。 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。 -## 初学者向け +## 初心者向け -NumPyについての情報はたくさん見つかります。 初心者の方にはこちらの資料をお勧めします： +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： 初心者の方にはこちらの資料をお勧めします： **チュートリアル** * [NumPy Quickstart チュートリアル](https://numpy.org/devdocs/user/quickstart.html) * [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Pythonソフトウェアのエコシステムを広く紹介しています。 -* [Numpy初心者のための基礎](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 - 例題で学ぶ NumPy配列 ](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/) +* [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。 +* [機械学習プラス - ndarray入門](https://numpy.org/devdocs/user/absolute_beginners.html) +* [Edureka - NumPy配列を例題で学ぶ](https://www.machinelearningplus.com/python/numpy-tutorial-part1-array-python-examples/) +* [Dataquest - NumPyチュートリアル: Python を使ったデータ解析 ](https://www.edureka.co/blog/python-numpy-tutorial/) +* [NumPy チュートリアル *by Nicolas Rougier*](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) * [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) +* [NumPyユーザーガイド](https://numpy.org/devdocs) **書籍** -* [NumPガイド*by Travelis E. Oliphant*](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*by Nicolas P. Rougier*](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) *by Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow* +* [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). 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/) +* [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とSciPyを題材にした [おすすめリスト](https://www.goodreads.com/shelf/show/python-scipy) もチェックしてみてください。 ほとんどの書籍ではNumPyを核とした「SciPyエコシステム」が説明されています。 +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。 **動画** -* [Numpy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* +* [NumPy を使った数値計算入門](http://youtu.be/ZB7BZMhfPgk) *by Alex Chabot-Leclerc* *** ## 上級者向け -インデックス処理、分割、スタック、線形代数などのより高度なNumpy の概念を、より深く理解するためには、これらの資料が参考になると思います。 +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** -* [NumPy 100演習](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *Nicolas P. Rougier* -* [NumPyとSciPyイントロダクション](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) *by M. Scott Shell* -* [Numpy救急キット](http://mentat.za.net/numpy/numpy_advanced_slides/) *by Stéfan van der Walt* +* [NumPyエクササイズ100](http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html) *Nicolas P. Rougier* +* [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。 +* [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/) -* [高度なインデックシング](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/) +* [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/) **書籍** -* [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) *by Jake Vanderplas* -* [Pythonデータ解析](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *by Wes McKinney* -* [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) *by Robert Johansson* +* [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** -* [アドバンスドNumPy -](https://www.youtube.com/watch?v=cYugp9IN1-Q) *ブロードキャストルール、ストライド、および高度なインデックシング* by Fan Nunuz-Iglesias +* [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に関する講演 +## NumPyに関するトーク -* [Numpy Indexing の未来](https://www.youtube.com/watch?v=o0EacbIbf58) *by Jaime Fernadez* (2016) -* [Python における配列計算革命](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *by Ralf Gommers* (2019) -* [Numpy: 何が変わり、そして何が今後変わるのか?](https://www.youtube.com/watch?v=YFLVQFjRmPY) *by Matti Picus* (2019) -* [NumPyの内部](https://www.youtube.com/watch?v=dBTJD_FDVjU) *by Ralf Gommers, Sebastian Berg, Matti Picus, Tyler Reddy, Stefan van der Walt, Charles Harris* (2019) -* [Python における配列計算の概要](https://www.youtube.com/watch?v=f176j2g2eNc) *by Travis Oliphant* (2019) +* [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 -もし、あなたの研究においてNumPyが重要な役割を果たし、あなたの論文でNumPyについて言及したい場合は、こちらの[ページ](/citing-numpy)を参照して下さい。 +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/master/content/en/learn.md)を使って提案してみて下さい。 PRでは、あなたが推薦する資料が、なぜこのページで言及に値するのか、そして誰がその資料によって最も利益を得るかを説明して下さい。 +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/master/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。 PRでは、あなたが推薦する資料が、なぜこのページで言及に値するのか、そして誰がその資料によって最も利益を得るかを説明して下さい。 From 83cf23f8da8cce2844169478aa94b8d9d1402989 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:56 +0200 Subject: [PATCH 578/909] 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 d378333ebb..d7dad543e4 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。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。 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。 @@ -11,7 +11,7 @@ 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？」というような質問です。 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。 *** From 0f8d3a818baebd2731e0835ee14fcc105d25558f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:58 +0200 Subject: [PATCH 579/909] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 219 ++++++++++++++++++++++++++++++++++++ 1 file changed, 219 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..74bf2ba35c --- /dev/null +++ b/content/zh/tabcontents.yaml @@ -0,0 +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: 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." + 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 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: 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: http://vispy.org/gallery.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 4273b4a5dbd1088b843bda55a4d8eeb3fb3e7d39 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:39:59 +0200 Subject: [PATCH 580/909] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 219 ++++++++++++++++++++++++++++++++++++ 1 file changed, 219 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..74bf2ba35c --- /dev/null +++ b/content/ko/tabcontents.yaml @@ -0,0 +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: 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." + 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 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: 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: http://vispy.org/gallery.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 147e8b41bd067114579dff41a98aa6047ff402bc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:00 +0200 Subject: [PATCH 581/909] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 219 ++++++++++++++++++++++++++++++++++++ 1 file changed, 219 insertions(+) create mode 100644 content/ja/tabcontents.yaml diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml new file mode 100644 index 0000000000..74bf2ba35c --- /dev/null +++ b/content/ja/tabcontents.yaml @@ -0,0 +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: 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." + 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 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: 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: http://vispy.org/gallery.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 0ec4cc6e25fc4d9cbd6d33c0511f448a5645d93b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:02 +0200 Subject: [PATCH 582/909] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 219 ++++++++++++++++++++++++++++++++++++ 1 file changed, 219 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..74bf2ba35c --- /dev/null +++ b/content/ar/tabcontents.yaml @@ -0,0 +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: 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." + 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 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: 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: http://vispy.org/gallery.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 a832c76fee73330fc2b46b721207dc5010cd62b1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:03 +0200 Subject: [PATCH 583/909] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 219 ++++++++++++++++++++++++++++++++++++ 1 file changed, 219 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..74bf2ba35c --- /dev/null +++ b/content/es/tabcontents.yaml @@ -0,0 +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: 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." + 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 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: 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: http://vispy.org/gallery.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 c9f375ef6cc557ad7c4ea3c85c9e17e6fddc6824 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:07 +0200 Subject: [PATCH 584/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 146 ++++++++++++++++++++--------------------- 1 file changed, 73 insertions(+), 73 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 26261e1794..9c4765dfd9 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -1,26 +1,26 @@ --- -languageName: English +languageName: Português params: - description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + description: Por que NumPy? Arrays n-dimensionais poderosas. Ferramentas para computação numérica. Interoperabilidade. Alto desempenho. Código aberto. navbarlogo: image: logos/numpy.svg - link: / + link: /pt/ hero: #Main hero title title: NumPy #Hero subtitle (optional) - subtitle: The fundamental package for scientific computing with Python + subtitle: A biblioteca fundamental para computação científica com Python #Button text - buttontext: Get started + buttontext: Comece aqui #Where the main hero button links to - buttonlink: "/install" + buttonlink: "/pt/install" #Hero image (from static/images/___) image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2021 NumPy survey - content: Your voice matters - url: /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 promptlabel: interactive shell prompt @@ -39,74 +39,74 @@ params: text: Launching container on mybinder.org... docslink: Don't forget to check out the docs. casestudies: - title: CASE STUDIES + title: ESTUDOS DE CASO 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: 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: First image of a black hole. It is an orange circle in a black background. - url: /case-studies/blackhole-image + alttext: Primeira imagem de um buraco negro. É um círculo laranja em um fundo preto. + url: /pt/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: 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: Two orbs orbiting each other. They are displacing gravity around them. - url: /case-studies/gw-discov + alttext: Duas esferas orbitando a si mesmas. Elas deslocam a gravidade em seu entorno. + url: /pt/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: 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: Cricket ball on green field. - url: /case-studies/cricket-analytics + alttext: Bola de críquete em um campo verde + url: /pt/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: 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: Cheetah pose analysis - url: /case-studies/deeplabcut-dnn + alttext: Análise de pose de um guepardo + url: /pt/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: 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: Numerical computing tools - text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + 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: Interoperable - text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + 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: Performant - text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + 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: 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: A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação. - - title: Open source - text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/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 aberto + text: Distribuido com uma [licença BSD](https://github.com/numpy/numpy/blob/master/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: ECOSYSTEM + title: ECOSSISTEMA 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: Contribute - url: /contribute + title: Contribuir + url: /pt/contribute footer: logo: numpy.svg socialmediatitle: "" @@ -122,46 +122,46 @@ footer: 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: 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 f75893d3c711fe1e9855357b5c00153035704c5c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:08 +0200 Subject: [PATCH 585/909] New translations config.yaml (Japanese) --- content/ja/config.yaml | 148 ++++++++++++++++++++--------------------- 1 file changed, 74 insertions(+), 74 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index 282e0dc14c..a3c20fe79f 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -1,26 +1,26 @@ --- -languageName: English +languageName: 日本語 (Japanese) params: - description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. + 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: logos/numpy.svg - link: / + link: /ja/ hero: #Main hero title title: NumPy #Hero subtitle (optional) - subtitle: The fundamental package for scientific computing with Python + subtitle: Pythonによる科学技術計算の基礎パッケージ #Button text - buttontext: Get started + buttontext: 使い始める #Where the main hero button links to - buttonlink: "/install" + buttonlink: "/ja/install" #Hero image (from static/images/___) image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2021 NumPy survey - content: Your voice matters - url: /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 promptlabel: 対話的なシェルプロンプト @@ -39,74 +39,74 @@ params: text: mybinder.orgでコンテナを起動しています... 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: CASE STUDIES + title: ケーススタディ 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: 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: First image of a black hole. It is an orange circle in a black background. - url: /case-studies/blackhole-image + 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: 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: 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: Two orbs orbiting each other. They are displacing gravity around them. - url: /case-studies/gw-discov + 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: 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: 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: Cricket ball on green field. - url: /case-studies/cricket-analytics + 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: 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: 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: Cheetah pose analysis - url: /case-studies/deeplabcut-dnn + alttext: チータの姿勢推定 + url: /ja/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: 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: Numerical computing tools - text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + 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: Interoperable - text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + 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: Performant - text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + 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: Easy to use - text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + 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: Open source - text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + title: オープンソース + text: 寛容な[BSDライセンス](https://github.com/numpy/numpy/blob/master/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: ECOSYSTEM + title: エコシステム section5: false navbar: - - title: Install - url: /install + title: インストール + url: /ja/install - - title: Documentation + title: ドキュメント url: https://numpy.org/doc/stable - - title: Learn - url: /learn + title: 学び方 + url: /ja/learn - - title: Community - url: /community + title: コミュニティ + url: /ja/community - - title: About Us - url: /about + title: 私達について + url: /ja/about - - title: Contribute - url: /contribute + title: NumPyに貢献する + url: /ja/contribute footer: logo: numpy.svg socialmediatitle: "" @@ -122,46 +122,46 @@ footer: title: "" links: - - text: Install - link: /install + text: インストール + link: /ja/install - - text: Documentation + text: ドキュメント link: https://numpy.org/doc/stable - - text: Learn - link: /learn + text: 学び方 + link: /ja/learn - - text: Citing Numpy - link: /citing-numpy + text: 引用する + link: /ja/citing-numpy - - text: Roadmap + text: ロードマップ link: https://numpy.org/neps/roadmap.html column2: links: - - text: About us - link: /about + text: 私達について + link: /ja/about - - text: Community - link: /community + text: コミュニティ + link: /ja/community - - text: Contribute - link: /contribute + text: NumPyに貢献する + link: /ja/contribute - - text: Code of conduct - link: /code-of-conduct + text: 行動規範 + link: /ja/code-of-conduct column3: links: - - text: Get help - link: /gethelp + text: サポートを得る方法 + link: /ja/gethelp - - text: Terms of use - link: /terms + text: 利用規約 + link: /ja/terms - - text: Privacy - link: /privacy + text: プライバシーポリシー + link: /ja/privacy - - text: Press kit - link: /press-kit + text: プレス用資料 + link: /ja/press-kit From 1a44a71cce497f503c27ab5faa98bd941d90b404 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:11 +0200 Subject: [PATCH 586/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 86 +++++++++++++++++++++++----------------------- 1 file changed, 43 insertions(+), 43 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 8078b31e32..beb139caba 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,7 +3,7 @@ title: Notícias sidebar: false --- -### 2021 NumPy survey +### NumPy versão 1.20.0 _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. @@ -12,11 +12,11 @@ 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 +### Diversidade no projeto NumPy _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, +- 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. - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, @@ -26,93 +26,93 @@ _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 +### Primeiro artigo oficial do NumPy publicado na Nature! _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 +### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? -_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 release do NumPy até agora, graças a mais de 180 contribuidores. As duas novidades mais emocionantes são: - 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)). +- 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)). -### Diversity in the NumPy project +### NumPy versão 1.19.2 -_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). +_24 de junho de 2020_ -- NumPy agora tem um novo logo: -### First official NumPy paper published in Nature! +### A primeira pesquisa NumPy está aqui! -_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. -### Python 3.9 is coming, when will NumPy release binary wheels? +### O NumPy tem um novo logo! -_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. +_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 1.19.2 release +### NumPy versão 1.19.0 -_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. -### The inaugural NumPy survey is live! +### Aceitação no programa Season of Docs -_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). -### NumPy has a new logo! +### NumPy versão 1.18.0 -_Jun 24, 2020_ -- NumPy now has a new logo: +Por favor, veja as [notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0) para mais detalhes. 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 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 1.19.0 release +### O NumPy recebe financiamento da Chan Zuckerberg Initiative -_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. ### 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). +_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 -_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. 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`. -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +NumPy 1.15.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julho de 2018_. -### NumPy receives a grant from the 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. +NumPy 1.18.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 de abril de 2020_. -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, 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. -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 -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 1.21.0 ([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_. +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 1.16.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 de janeiro de 2019_. +- 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 1.18.1 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 de janeiro de 2020_. +- NumPy 1.18.2 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 de março de 2020_. +- NumPy 1.14.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de janeiro de 2018_. +- NumPy 1.17.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julho de 2019_. +- 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 1.16.0 ([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 347de71d0e8e2299059ffe7991037c2f9a6bc6bf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:13 +0200 Subject: [PATCH 587/909] New translations news.md (Japanese) --- content/ja/news.md | 88 +++++++++++++++++++++++----------------------- 1 file changed, 44 insertions(+), 44 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 112ee6eb1c..ace105f5c3 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,7 +3,7 @@ title: ニュース sidebar: false --- -### 2021 NumPy survey +### NumPy 1.20.0 リリース _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. @@ -12,7 +12,7 @@ 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プロジェクトの多様性 _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: @@ -26,93 +26,93 @@ _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 +### Natureに初の公式NumPy論文が掲載されました! _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 +### 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？ -_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)). +_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。 +- 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)). -### Diversity in the NumPy project +### NumPy 1.19.2 リリース -_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6月24日_ -- NumPy に新しいロゴが作成されました: -### First official NumPy paper published in Nature! +### 初めてのNumPyの調査が公開されました!! -_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 is coming, when will NumPy release binary wheels? +### NumPy に新しいロゴができました! -_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. +_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 release +### NumPy 1.19.0 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。 -### The inaugural NumPy survey is live! +### ドキュメント受諾期間 -_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. -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嬉しいです。 -### NumPy has a new logo! +### NumPy 1.18.0 リリース -_Jun 24, 2020_ -- NumPy now has a new logo: +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 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 release +### NumPyはChan Zuckerberg財団から助成金を受けました。 -_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。 ### 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). +_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 1.16.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019年1月14日_. -_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。 -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +NumPy 1.15.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018年7月23日_. ### NumPy 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. +_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. 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. +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。 -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。 ## 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. - -- 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_. +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 1.18.1 ([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 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年4月19日_. +- NumPy 1.18.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020年3月17日_. +- NumPy 1.14.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018年1月7日_. +- NumPy 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 1.18.0 ([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 1.16.0 ([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 5902ca5a9e9656015bfc9817b0a1fd68cb0f1df5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:17 +0200 Subject: [PATCH 588/909] New translations history.md (Japanese) --- content/ja/history.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/history.md b/content/ja/history.md index 0dae1ff01d..0fc9c85cc2 100644 --- a/content/ja/history.md +++ b/content/ja/history.md @@ -3,15 +3,15 @@ title: NumPyの歴史 sidebar: false --- -Numpy は配列形式のデータ構造と配列形式に関連する高速な数値ルーチンを提供する Python の基礎的なライブラリです。 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。 しかし、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と、独特の焦点を持つことによるコミュニティの盛り上がりと、フレンドリーなコミュニティの組み合わせは、長い目で見ると良い結果を得られることが知られていました。 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、Numpyのことを引用しています。 +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。 しかし、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と、独特の焦点を持つことによるコミュニティの盛り上がりと、フレンドリーなコミュニティの組み合わせは、長い目で見ると良い結果を得られることが知られていました。 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、Numpyのことを引用しています。 -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 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。 +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。 From d131f83025b509d910117ef13d05110b7550320f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:19 +0200 Subject: [PATCH 589/909] New translations install.md (Japanese) --- content/ja/install.md | 76 +++++++++++++++++++++---------------------- 1 file changed, 38 insertions(+), 38 deletions(-) diff --git a/content/ja/install.md b/content/ja/install.md index b2b29802a1..9fe9491150 100644 --- a/content/ja/install.md +++ b/content/ja/install.md @@ -1,40 +1,40 @@ --- -title: Numpyのインストール +title: NumPyのインストール sidebar: false --- -Numpy をインストールするための必ず必要なものは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、こちらがあります: [Anaconda Distribution](https://www.anaconda.com/distribution)。このanacondaはPythonだけでなく、NumPyや、その他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 をインストールするための必ず必要なものはPython本体です。 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`や`pip` 、Mac OS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。 +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。 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** `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。 ```bash -# Best practice, use an environment rather than install in the base env +# base envにインストールするのでなく、environmentを作成するのがベストプラクティスです conda create -n my-env conda activate my-env -# If you want to install from conda-forge +# conda-forgeからインストールする場合 conda config --env --add channels conda-forge -# The actual install command +# インストールコマンド conda install numpy ``` **PIP** -`pip`を使用している場合は、 NumPy を以下のようにインストールできます: +`pip`を使用する場合、以下のようにNumPyをインストールできます: ```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 - 参考 [再現可能なインストール](#reproducible-installs) 。 [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。 -# Python と Numpy インストールガイド +# PythonとNumPyの インストールガイド -Pythonパッケージのインストールと管理は複雑なで、ほとんどのタスクには数多くの代替ツールがあります。 このガイドでは、読者に最適な(または最も人気のある) 方法と明確な指針を提供したいと思います。 このガイドでは、一般的なオペレーティングシステムとハードウェア上での、 Python、NumPy、PyData (または数値計算) スタックのユーザに焦点を当てています。 +Pythonパッケージのインストールと管理は複雑なので、ほとんどのタスクには数多くの代替ツールがあります。 このガイドでは、読者に最適な(または最も人気のある) 方法と明確な指針を提供したいと思います。 このガイドでは、一般的なオペレーティングシステムとハードウェア上での、 Python、NumPy、PyData (または数値計算) スタックのユーザに焦点を当てています。 このガイドでは、読者に最適な(または最も人気のある) 方法と明確な指針を提供したいと思います。 このガイドでは、一般的なオペレーティングシステムとハードウェア上での、 Python、NumPy、PyData (または数値計算) スタックのユーザに焦点を当てています。 ## 推奨方法 @@ -45,8 +45,8 @@ Pythonパッケージのインストールと管理は複雑なで、ほとん 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、実行するために[JupyterLab](https://jupyterlab.readthedocs.io/en/stable/index.html) でnotebookを利用することができます。また探索的、対話的コンピューティングも可能です。[Spyder](https://www.spyder-ide.org/) 、[Visual Studio Code](https://code.visualstudio.com/)はスクリプトを作成したり、パッケージを作成することができます。 -- 是非、[Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/) を使用して パッケージを管理し、JupyterLab、Spyder、Visual Studio Code を利用してみて下さい。 +- 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。 ### 上級ユーザー @@ -54,66 +54,66 @@ Windows、macOS、Linuxのすべてのユーザー向けには: #### WindowsまたはmacOS - [Miniconda](https://docs.conda.io/en/latest/miniconda.html) をインストールします。 -- `ベース` のconda環境を出来るだけ小さく保ちます。 そして、作業中のタスクやプロジェクトに必要なパッケージは個別の` ` [conda 環境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) を使用して、インストールするようにします。 -- もし、あなたの必要なパッケージが`defaults` チャンネルだけで足りない場合は、`conda-forge` こちらの [チャンネルプライオリティの設定](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge)でデフォルトチャンネルを設定することができます。 +- `base` のconda環境を出来るだけ小さく保って下さい。 そして、一つか二つ個別の[`conda environment`](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)を使って、作業中のタスクやプロジェクトに必要なパッケージをインストールしましょう。 +- もし、あなたの必要なパッケージが`defaults` チャンネルだけで足りない場合は、`conda-forge`をこちらの [チャンネルプライオリティの設定](https://conda-forge.org/docs/user/introduction.html#how-can-i-install-packages-from-conda-forge)でデフォルトチャンネルに設定できます。 #### Linux もしあなたが最新バージョンのライブラリを使用するよりも、少し古いパッケージで安定性を求める場合は: -- 可能な限りOS付帯のパッケージマネージャーを使用してください (Python本体やNumPy、 その他のライブラリのインストールに)。 +- Python本体やNumPy、その他のライブラリのインストールに、可能な限りOSのパッケージマネージャーを使用してください。。 - `pip install somepackage --user` でパッケージマネージャによって提供されていないパッケージをインストールすることができます。 GPUを使用する場合: - [Miniconda](https://docs.conda.io/en/latest/miniconda.html) をインストールして下さい。 -- `ベース` のconda環境を出来るだけ小さく保ちます。 そして、作業中のタスクやプロジェクトに必要なパッケージは個別の` ` [conda 環境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) を使用して、インストールするようにします。 +- `base` のconda環境を出来るだけ小さく保って下さい。 そして、一つか二つ個別の[`conda environment`](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)を使って、作業中のタスクやプロジェクトに必要なパッケージをインストールしましょう。 - また、`デフォルトの` conda channel (`conda-forge` は GPU パッケージをまだサポートしていません) を使用してください。 上記以外の場合 - [Miniforge](https://github.com/conda-forge/miniforge) をインストールします。 -- `ベース` のconda環境を出来るだけ小さく保ちます。 そして、作業中のタスクやプロジェクトに必要なパッケージは個別の` ` [conda 環境](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) を使用して、インストールするようにします。 +- `base` のconda環境を出来るだけ小さく保って下さい。 そして、一つか二つ個別の[`conda environment`](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)を使って、作業中のタスクやプロジェクトに必要なパッケージをインストールしましょう。 #### pip/PyPI を利用したい場合 個人的な好みや、下記のcondaとpipの違いを理解した上で、pip/PyPIベースの方法を使いたいユーザーには、下記をお勧めします: -- [python.org](https://www.python.org/downloads/)からや、Macを使っている場合は[Homebrew](https://brew.sh/), Linuxを使っている場合は、Linuxのパッケージマネージャーを使ってPythonをインストールします。 -- 依存関係の解決と環境の管理を提供する最もよくメンテナンスされているツールとして、[Poetry](https://python-poetry. org/) をconda と同様な方法で使用することができます。 +- [python.org](https://www.python.org/downloads/)からや、Macを使っている場合は[Homebrew](https://brew.sh/)、 Linuxを使っている場合は、Linuxのパッケージマネージャーを使ってPythonをインストールします。 +- 依存関係の解決と環境の管理を提供する最もよくメンテナンスされているツールとして、[Poetry](https://python-poetry.org/) をconda と同様な方法で使用することができます。 -## Python パッケージ管理 +## Pythonにおけるパッケージ管理 -パッケージの管理は難しいので、その結果、たくさんのツールが存在しています。 ウェブ開発と汎用的なPython開発には、こちらのようなpipを補完する [ツール](https://packaging.python.org/guides/tool-recommendations/) があります。 ハイパフォーマンスコンピューティング(HPC)では、 [Spack](https://github.com/spack/spack) を使うことを検討して下さい。 NumPyのほとんどのユーザーにとっては、 [conda](https://conda.io/en/latest/) と [pip](https://pip.pypa.io/en/stable/) が最も広く利用されているツールです。 +パッケージの管理は難しいので、その結果、たくさんのツールが存在しています。 パッケージの管理は難しいため、たくさんのツールが存在しています。 ウェブ開発と汎用的なPython開発には、こちらのようなpipを補完する [ツール](https://packaging.python.org/guides/tool-recommendations/) があります。 ハイパフォーマンスコンピューティング(HPC)では、 [Spack](https://github.com/spack/spack) を使うことを検討して下さい。 NumPyのほとんどのユーザーにとっては、 [conda](https://conda.io/en/latest/) と [pip](https://pip.pypa.io/en/stable/) が最も広く利用されているツールです。 ハイパフォーマンスコンピューティング(HPC)では、 [Spack](https://github.com/spack/spack) を使うことを検討して下さい。 NumPyのほとんどのユーザーにとっては、 [conda](https://conda.io/en/latest/) と [pip](https://pip.pypa.io/en/stable/) が最も広く利用されているツールです。 -### Pip & conda +### Pipとconda -Python パッケージをインストールするための2 つの主要なツールは `pip` と `conda` です。 これらの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)、これらは一緒に動作することもできます。 ここでは、pip とconda の主要な違いについて説明します。パッケージをどのように効果的に管理するかを理解することが重要です。 +`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。 これらの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)、これらは一緒に動作することもできます。 ここでは、pip とconda の主要な違いについて説明します。パッケージをどのように効果的に管理するかを理解することが重要です。 -最初の違いは、condaは複数言語に対応可能であり、condaからPython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にのみインストールします。 また、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。 +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。 また、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。 -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、すべての代表的なパッケージは、condaにも利用可能です。 +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。 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！) が必要になるかもしれないということです。 ### 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: 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ください。 -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ます( それぞれに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, 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/) +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: + - condaの場合: [conda environmentsとenvironment.yml](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#) + - 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。 -## Numpyパッケージ & 高速線形代数ライブラリ +## 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 - 一般的には、 [インテル® MKL](https://software.intel.com/en-us/mkl) または [OpenBLAS](https://www.openblas.net/) に依存しています。 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 install メソッドが自動的に実施します)。 パワーユーザーの中には、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、より詳細を知りたいと思っているかもしれません。 +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。 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 install メソッドが自動的に実施します)。 パワーユーザーの中には、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、より詳細を知りたいと思っているかもしれません。 -- 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。 これにより、ユーザが（例えば）SciPyを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 つまりwheelにはOpenBLASライブラリが含まれています。 これにより、ユーザが（例えば）SciPyを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 をインストールしたときにユーザーの環境にインストールされるのとは、別のパッケージです。 @@ -121,13 +121,13 @@ Numpy は他の Python パッケージに依存していませんが、高速な

  • - OpenBLASのサイズは約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。 + 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。

  • - MKLは通常、OpenBLASよりも少し速く、よりロバストな結果が得られます。 + MKLは通常、OpenBLASよりも少し速く、より安定した解を得られます。

    • @@ -138,12 +138,12 @@ 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。 + 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。 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。 これによりユーザーに予期しないことが起こることがあります。例えばNumPy 自体は、関数呼び出しを自動的に並列化しないことです。 線形代数ライブラリの配列処理は、一般的にはより良いパフォーマンスが得られますが、Daskやscikit-learn、マルチプロセシングなどの別のレベルの並列化を使用している場合などに、逆に悪い結果をもたらすことがあります。 + 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。 多くの場合、すべての CPU コアが使用されます。 これによりユーザーに予期しないことが起こることがあります。例えばNumPy 自体は、関数呼び出しを自動的に並列化しないことです。 線形代数ライブラリの配列処理は、一般的にはより良いパフォーマンスが得られますが、Daskやscikit-learn、マルチプロセシングなどの別のレベルの並列化を使用している場合などに、逆に悪い結果をもたらすことがあります。

    From df310793f0b4320261233db3f3db48bf4fe14f1b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:25 +0200 Subject: [PATCH 590/909] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index f64b463d0f..d70614f955 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -45,7 +45,7 @@ O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://w Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. -NumPy, o pacote padrão de análise numérica para Python, foi parte do software utilizado para várias tarefas executadas durante o projeto de detecção de ondas gravitacionais no LIGO. O NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: +NumPy, o pacote padrão de análise numérica para Python, foi parte do software utilizado para várias tarefas executadas durante o projeto de detecção de ondas gravitacionais no LIGO. A NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: * [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) * Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro @@ -56,14 +56,14 @@ 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="gráfico de dependências do gwpy com o NumPy em realce" caption="**Gráfico de dependências mostrando como o pacote GwPy depende do NumPy**" >}} +{{< 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="gráfico de dependências do PyCBC com NumPy em realce" caption="**Gráfico de dependências mostrando como o pacote PyCBC depende do 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**" >}} ## 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. O 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. +A detecção de ondas gravitacionais permitiu que pesquisadores descobrissem fenômenos totalmente inesperados ao mesmo tempo em que proporcionaram novas idéias sobre muitos dos fenômenos mais profundos conhecidos na astrofísica. O processamento e a visualização de dados é um passo crucial que ajuda cientistas a obter informações coletadas de observações científicas e a entender os resultados. Os cálculos são complexos e não podem ser compreendidos por humanos a não ser que sejam visualizados usando simulações de computador que são alimentadas com dados e análises reais observados. A NumPy, junto com outras bibliotecas Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo. -{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="funcionalidades do numpy" caption="**Recursos chave do NumPy utilizados**" >}} +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} From c56e7ffc486f4e190cf8dc7692e86229f4d4b75a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:27 +0200 Subject: [PATCH 591/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 32 ++++++++++++++-------------- 1 file changed, 16 insertions(+), 16 deletions(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index 52332839a7..fe1e634e44 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -12,16 +12,16 @@ 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。 重力波を観測することは、重力を研究する上で 重要なだけでなく、遠い宇宙とその影響におけるいくつかの不明瞭な現象の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ように設計されました。 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の重要な発見は、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を測定することで、重力波そのものを観測した初めての例であることです。 これにより、ゆがんだ時空から作られた 物体とそれに伴う現象を、宇宙の歪んだ側面から探索する、新しい天体物理学のフロンティア が開かれました。 +\[レーザー干渉計重力波天文台(LIGO)\](https://www. ligo. caltech. edu)は、アインシュタインの一般相対性理論によって予測された重力波の直接検出を通して、重力波天体物理学の分野を切り開くために設計されました。 このシステムは、アメリカのワシントン州ハンフォードとルイジアナ州リビングストンにある2つの干渉計が一体となって構成され、重力波を検出します。 それぞれのシステムには、レーザー干渉法を用いた数キロ規模の重力波検出器が設置されています。 LIGO Scientific Collaboration（LSC）は、米国をはじめとする14カ国の大学から1000人以上の科学者が集まり、90以上の大学・研究機関によって支援されています。 また、約250人の学生も参加しています。 今回のLIGOの発見は、重力波が地球を通過する際に生じる空間と時間の微小な乱れの測定により、重力波そのものを初めて観測しました。 これにより、新しい天体物理学のフロンティアが開かれました。 これは、宇宙の歪んだ側面、つまり歪んだ時空から作られた物体とそれに現象を切り拓くものです。 ### 主な目的 * LIGOの[ミッション](https://www.ligo.caltech.edu/page/what-is-ligo)は、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 * 複雑な数学を含む相対性理論の数値計算によって観測データを解析し、信号とノイズを識別し、関連性のある信号をフィルタリングし、観測データの有意性を統計的に推定することで、宇宙の始まりのクランチを観測できるようになります。 -* バイナリや数値の結果を理解しやすいのようにデータを可視化することも必要です。 +* 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。 @@ -29,41 +29,41 @@ 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[O(10^7)個の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** - 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からです！ 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** - アインシュタインの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。 実験データの処理や考察のために、複雑な計算をしたり、画像やシミュレーションの再レンダリングしたりすることは、この分野の研究者にとって時間のかかる作業となります。 + 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 -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。 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。 +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、複雑な数学と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) +* [信号処理](https://www.uv.es/virgogroup/Denoising_ROF.html): グリッジ検出、[ノイズ同定とデータ判定](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, pyCharm)。 * データ取得: どのデータが解析できるかを決定し、干し草の中の針のような信号が入っているかどうかを突き止める。 -* 統計解析：観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 -* データの可視化 +* 統計解析: 観測データの統計的有意性を推定し、モデルとの比較により信号パラメータ（星の質量、スピン速度、距離など）を推定する。 +* データ可視化 - 時系列データ - スペクトログラム * 相関計算 -* 重力波データ解析のために開発された [ソフトウェア](https://github.com/lscsoft)である[GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)はNumPy やAstroPyを重力波検出器からのデータを研究するためのユーティリティー、ツール、およびメソッドへのオブジェクトベースのインターフェースを提供するために利用しています。 +* 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="**GwPyのNumPy依存グラフ**" >}} ---- -{{< figure src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption=""**PyCBCのNumPy依存グラフ**" >}} +{{< figure src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" class="fig-center" alt="PyCBC-numpy depgraph" caption="**PyCBCのNumPy依存グラフ**" >}} ## 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。 データ解釈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、matplotlib、pandasなどの、Pythonパッケージとともに、 scikit-learningは 、研究者 [が](https://www.gw-openscience.org/events/GW150914/) 複雑な質問に答え、 私たちの宇宙についての理解において、新しい地平を発見することを可能にしてきたのです。 +一方で、これまで知られてきた深遠な天体物理学の現象に、多くに新たな洞察を提供しました。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な質問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。 -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption== "**利用されたNumpyの主要機能**" >}} +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} From 599c70e0153da138914e47700a24c4f28802f6c5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:30 +0200 Subject: [PATCH 592/909] New translations about.md (Japanese) --- content/ja/about.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 59af31b383..db5c917b5b 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -21,7 +21,7 @@ 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 - Inessa Pawson - Matti Picus -- Stéfan van der Walt +- Nathaniel Smith - Eric Wieser 終身名誉委員 @@ -29,11 +29,11 @@ NumPy運営委員会の役割は、NumPyのコミュニティと協力しサポ - Travis Oliphant (プロジェクト創設者, 2005-2012) - Alex Griffing (2015-2017) - Marten van Kerkwijk (2017-2019) -- Allan Haldane (2015-2021) +- Allan Haldane - Nathaniel Smith (2012-2021) -- Julian Taylor (2013-2021) -- Pauli Virtanen (2008-2021) -- Jaime Fernández del Río (2014-2021) +- Julian Taylor +- Pauli Virtanen +- Jaime Fernández del Río ## チーム @@ -48,7 +48,7 @@ NumPy プロジェクトは拡大しているため、いくつかのチーム 個々のチームメンバーについては、 [チーム](/gallery/team.html) のページを参照してください。 -## NumFOCUS分科会 +## スポンサー情報 - チャールズ　ハリス - ラルフ　ゴマーズ @@ -56,15 +56,15 @@ NumPy プロジェクトは拡大しているため、いくつかのチーム - セバスチャン　バーグ - 外部メンバー: トーマス・カスウェル -## スポンサー +## パートナー団体 NumPyは以下の団体から直接資金援助を受けています。 {{< sponsors >}} -## パートナー団体 +## 寄付 -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。 +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。 現在のパートナー団体としては、下記の通りです。 - カルフォルニア大学バークレー校(ステファン・ヴァン・デル・ウォルト、セバスチャン・バーグ、ロス・バルノフスキ) - クアンサイト(ラルフ　ゴマーズ、メリッサ　ウェーバー　メンドンサ、マーズ　リー、マッティ　ピカス、ピアウ　ピーターソン) @@ -74,11 +74,11 @@ NumPyは以下の団体から直接資金援助を受けています。 ## 寄付 -NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 +NumPy があなたの仕事や研究、ビジネスで役に立った場合、できる範囲で良いので、是非、NumPyプロジェクトへの寄付を検討して頂けると助かります。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 少額の寄付でも大きな助けになります。 すべての寄付は、NumPyのオープンソースソフトウェア、ドキュメント、コミュニティの開発のために使用されることが約束されています。 NumPy は NumFOCUS にスポンサーされたプロジェクトであり、米国の 501(c)(3) 非営利の慈善団体でもあります。 NumFOCUSは、NumPyプロジェクトに財政、法務、管理面でのサポートを提供し、プロジェクトの安定と持続可能性を保つ手助けをしています。 詳細については、 [numfocus.org](https://numfocus.org) をご覧ください。 -NumPy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 米国の寄付提供者の場合、その人の寄付は法律によって定められる範囲で免税されます。 但し、他の寄付と同様に、あなたはあなたの税務状況について、あなたの税務担当と相談する必要があることを忘れないで下さい。 +NumPy への寄付は [NumFOCUS](https://numfocus.org) によって管理されています。 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。 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 2ba2385c6725ceb65b94177e886f19b42c6281d9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:31 +0200 Subject: [PATCH 593/909] New translations deeplabcut-dnn.md (Japanese) --- content/ja/case-studies/deeplabcut-dnn.md | 42 +++++++++++------------ 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index 50b5316673..2174db2e54 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -6,44 +6,44 @@ 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">}}
    -

    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、Deep Learning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。

    +

    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。

    —Alexander Mathis、 准教授、École polytechnology fe’rale de Lausanne (EPFL)
    ## DeepLabCut について -[DeepLabCut](https://github.com/DeepLabCut/DeepLabCut) は世界中の何百もの研究機関の研究者が、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の技術により、科学者は動物の種類と時系列のデータを元に、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、手間のかかる作業に自動化を利用し、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的研究に利用されており、より速く、正確な結果をもたらしました。 +神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 -{{< figure src="/images/content_images/cs/race-hore. if" 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" 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による動物の姿勢を抽出することによる、非侵襲的な行動追跡は、生体力学や、遺伝学、倫理学、神経科学などの分野における科学的な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、計算処理的に非常に困難です。 例えば、必要な計算リソースやトレーニングデータが問題になります。 +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を推定することができ、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にデジタルなラベルを貼ることができます。また、DeepLabCutのディープラーニングベースのポーズ推定アーキテクチャが、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することもできます。 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する事ができます。 +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 [transfer learning](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、ツールの名前の元となった [DeeperCut](https://arxiv.org/abs/1605.03170)と呼ばれる、パフォーマンスの高い人像推定アーキテクチャからの特徴検出器を使用しています。 その過程で、このパッケージには、追加のアーキテクチャや、拡張メソッド、および一通りのフロントエンドユーザエクスペリエンスが得られるように大幅に変更されました。 さらに、 大規模な生物学的実験をサポートするために DeepLabCut はアクティブな学習機能を提供しています。例えば、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するために、時間経過しても学習データを増やすことができます。 +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学習を実行することができます。また、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 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、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神経科学の研究や、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、姿勢推定の自動化だけでなく、 データセット収集段階から共有可能て、再利用可能な分析パイプラインを作成するDeepLabCut Toolkitを提供し、プロジェクトのエンドツーエンドの管理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。 - DeepLabCut ワークフローは以下のようになります。 + 典型的なDeepLabCutワークフローは以下のようになります。 - - アクティブ学習によるトレーニングセットの作成と調整を行います + - オンライン学習によるトレーニングセットの作成と調整 - 特定の動物やシナリオに合わせたニューラルネットワークの構築 - 動画における大規模推論のためのコード作成 - - 統合された可視化ツールを使用して推論の描画 + - 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" >}} @@ -51,23 +51,23 @@ DeepLabCut では [transfer learning](https://arxiv.org/pdf/1909.11229)という * **速度** - 動物行動動画の高速処理は、彼らの行動を測定し、同時に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** - 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。 キーポイントとそ個々の動物の動きを関連性に基づき組み合わせ、それらを時間的に結びつけることは、複雑なプロセスであり、特に実験映像の中で複数の動物の動きを追跡する場合には、非常に膨大な数値解析が必要となります。 + 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、 様々な画像処理、目標のテンソル処理、キーポイントに対応する大きな配列のスタックを処理することは、かなり難しい問題です。 + 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) and [Tensorflow](https://www.tensorflow.org). +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の特徴である、画像処理、組み合わせ処理、そして高速計算は、DeepLabCutの姿勢推定アルゴリズムにおいて重要な役割を果たしました。 +以下に挙げるNumPyの特徴が、DeepLabCutの姿勢推定アルゴリズムでの画像処理・組み合わせ処理・高速計算において、重要な役割を果たしました。 * ベクトル化 * マスクされた配列操作 @@ -75,15 +75,15 @@ NumPyの特徴である、画像処理、組み合わせ処理、そして高速 * 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技術によって何千回も訓練され、 フレームから真アノテーション情報を予測します。 この目的のために、姿勢推定問題を、画像-画像変換問題として変換するための目標密(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に、幾何学・画像的処理を施した、スコアマップの計算を行うデータオーグメンテーションを採用しています。 また学習を高速化するために、NumPyのベクトル化機能が利用されています。 推論のためには、ターゲット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のPythonツールボックスでは、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、データ処理の問題は、Numpy の配列操作機能を使用して対応することになります。 +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**" >} +{{< figure src="/images/content_images/cs/numpy_dlc_benefits.png" class="fig-center" alt="numpy benefits" caption="**NumPyの主要機能**" >}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 From 0972be39d19d2dedfddd2f64445689b818b4100e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:35 +0200 Subject: [PATCH 594/909] 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 216fcebd26..c22be26be0 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -3,7 +3,7 @@ title: "Estudo de Caso: Análise de Críquete, a revolução!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Créditos de imagem: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< 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" >}}

    Você não joga para a torcida, joga para o país.

    @@ -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. 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. 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 do NumPy na Análise de Críquete +## Papel da 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: @@ -61,4 +61,4 @@ 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 o NumPy para análise de críquete" caption="**Recursos principais do NumPy utilizados**" >}} +{{< 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**" >}} From 38c744aa8f82dc086b6e1078fa0a70654ebd2324 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:37 +0200 Subject: [PATCH 595/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 30 ++++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index ceedd0743a..8b57e07065 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -12,24 +12,24 @@ 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ています。 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や、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や、その他の方法に甘やかされています。 +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、2019年に [67億ドル](https://en.wikipedia.org/wiki/Indian_Premier_League) の市場規模と評価される世界で最も参加者が多いクリケットイベントの1つです。 +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など。 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。 +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の主要なメトリックを下記のような要素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。 -* バッティングの記録の移動平均 +* バッティング成績の移動平均 * スコア予測 -* プレイヤーの体力やパフォーマンスについての知識を得ること +* 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" >}} ### データ分析の主要な目標 -* スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [ 他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 +* スポーツデータ分析はクリケットだけでなく、チーム全体のパフォーマンスを向上させ、勝利率を最大限に高めるために、 [他のスポーツ](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx)でも使用されています。 * リアルタイムデータ分析は、ゲーム中の洞察を得ることができ、チームや関連ビジネスが経済的利益と成長のために戦術を変更するためも役立ちます。 * 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。 @@ -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をから、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** - クリケットも、他のスポーツのように、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、および潜在的な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。 打者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** - クリケットの意思決定の多くは、"ボール運びがある特定のタイプの場合、バッツマン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？"や、"バッツマンが特定の方法であるボール運びに反応した場合、ボウラーはどのように彼の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には、精度の良いデータセットが利用できること、データを合成して高精度な生成モデルを作成する能力が必要です。 + 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 以下のような様々な種類のクリケット関連の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/) は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:** データのグラフ化と [視覚化](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関する、有益な洞察を与えてくれます。 +* **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、それがプロの試合がどのように実施されるか、特に最近まで主に "直感 "や過去の伝統的な考え方に基づいて行われていた戦略的な意思決定が、どのように起こるかという面で、世界を変えた技術的であるといえます。 NumPyは、データ分析や機械学習、人工知能のアルゴリズムに関連する高レベルの関数を提供する 沢山のPython パッケージ群に対して、堅固な基盤として利用されています。 これらのパッケージは、クリケットの試合だけでなくクリケットの試合周辺の推論やビジネスを推進しつつ、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に広く利用されています。 クリケットの試合の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 **" >} +{{< 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 f4d362f5e2719127756ca06951357bdcf0bf1850 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:40 +0200 Subject: [PATCH 596/909] New translations blackhole-image.md (Japanese) --- content/ja/case-studies/blackhole-image.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index cd186ad23c..b126e9ab74 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -16,17 +16,17 @@ sidebar: false ### 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。 +* **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。 この取り組みは[ 100年以上 ](https://www.jpl.nasa.gov/news/news.php?feature=7385)に渡って研究されてきたが、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:** 科学者達は、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を観測できるのではないかと期待していました。 科学者たちは、ブラックホールの巨大な質量を測定するためにその情報を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、急速な大気の位相変動、大規模な記録帯域幅、広く性能が異なり地理的に分散した望遠鏡などに対して、膨大なデータ処理の課題を抱えていました。 + 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** @@ -34,7 +34,7 @@ sidebar: false * **よくわからないものを観測する** - 研究の目標が今までに見たことのないものを見ることであるとき、どのようにし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? + 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" >}} @@ -46,19 +46,19 @@ 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="**ブラックホールの画像化でNumpyが果たした役割**" >}} +{{< 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。 -{{< 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" 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**" >}} -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の画像化の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]で実施し、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 +NumPyだけでなく、[SciPy](https://www.scipy.org)や[Pandas](https://pandas.io)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 ## まとめ NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 -{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption== "**利用されたNumpyの主要機能**" >}} +{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**利用されたNumPyの主要機能**" >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 8f898620eaf42490aa258bf6d5ef0c7ca19bf3b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:42 +0200 Subject: [PATCH 597/909] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index eef1b6d84c..a02f8cbc09 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -23,7 +23,7 @@ Estes são os nossos canais de comunicação preferidos (projetos de código abe 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/). -Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contribuições. Temos um [Código de Conduta](/code-of-conduct) para promover um ambiente aberto e acolhedor. +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 From 589402b8bd1d79e36fa84703d7e9fd24dc8f0367 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:43 +0200 Subject: [PATCH 598/909] New translations code-of-conduct.md (Japanese) --- content/ja/code-of-conduct.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/code-of-conduct.md b/content/ja/code-of-conduct.md index 759b2b56c2..a05fd397a9 100644 --- a/content/ja/code-of-conduct.md +++ b/content/ja/code-of-conduct.md @@ -1,5 +1,5 @@ --- -title: Numpy行動規範 +title: NumPy行動規範 sidebar: false aliases: - /conduct.html @@ -20,7 +20,7 @@ aliases: 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。 +4. 好奇心を大事にしよう。 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。 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。: * 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。 @@ -35,7 +35,7 @@ aliases: ### 多様性に関する声明 -NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。 +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。 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。 @@ -45,7 +45,7 @@ NumPy コミュニティの標準的なルールは、上記の行動規範で ### 報告ガイドライン -私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 +私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。 意図的な行動規範違反については、行動規範委員会に報告してください (下記参照)。 もし、違反が意図的でない可能性がある場合、その人にこの行動規範の存在を知らせることも可能です (パブリックでもプライベートでも、適切な方法で)。 もし直接指摘したくない場合は、ぜひ、行動規範委員会に直接連絡するか、違反の確度について助言を求めて下さい。 @@ -61,7 +61,7 @@ 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。 From 06b90e333f2211f8a822ff54a784a0f918040387 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:45 +0200 Subject: [PATCH 599/909] New translations contribute.md (Japanese) --- content/ja/contribute.md | 50 ++++++++++++++++++++-------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ja/contribute.md b/content/ja/contribute.md index 040b29464d..1a7281d840 100644 --- a/content/ja/contribute.md +++ b/content/ja/contribute.md @@ -1,36 +1,36 @@ - - - -title: Numpy に貢献する サイドバー: false +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。 - [コードを書く。](#writing-code) -他にも下記の貢献の方法があります: +以外にも、下記の貢献の方法があります: -- [プラリクエストのレビュー](#reviewing-pull-requests) -- [チュートリアル、プレゼン資料、その他の教育資料の作成](#developing-educational-materials) -- [イシューのトリアージ。](#issue-triaging) -- [ウェブサイトのメンテナンス](#website-development) -- [グラフィックデザインへの貢献](#graphic-design) -- [ウェブサイトの翻訳](#translating-website-content) -- [コミュニティのコーディネーターとしての貢献](#community-coordination-and-outreach) -- [助成金のプロポーザルの作成や他の人の資金調達のサポート](#fundraising) +- [プルリクエストをレビューする](#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](#developing-educational-materials) +- [イシューをトリアージする](#issue-triaging) +- [ウェブサイトをメンテナンスをする](#website-development) +- [グラフィックデザインに貢献する](#graphic-design) +- [ウェブサイトを翻訳する](#translating-website-content) +- [コミュニティのコーディネーターをつとめる](#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](#fundraising) -もしどの分野で, 自分が貢献出来るか、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。 +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。 -これらが私達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。 +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。 是非、参加してみて下さい！ 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。 是非、参加してみて下さい！ 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、すべての貢献が平等に扱われることを目指しています。 私達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](/code-of-conduct) を制定しています。 ### コードを書く -プログラマーの方々に向けて、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か説明されています。 +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。 ### 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、 +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。 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。 例えば、 * 長期にわたる議論をまとめる * ドキュメントのPRをトリアージする * 提案された変更をテストする @@ -43,19 +43,19 @@ NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模 ### 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。 いくつかのイシューはすでに解決されており、いくつかは優先順位付けされるべきであり、 いくつかは初心者が取り組むのに良いイシューになるでしょう。 例えば、できる貢献としては、 +[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: いくつかのイシューはすでに解決されており、いくつかは優先順位付けされるべきであり、 いくつかは初心者が取り組むのに良いイシューになるでしょう。 例えば、できる貢献としては、 -* 古いバグがまだ残っているかを確認する -* 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こと -* イシューに正しいラベル付けをすること(トリアージ権が必要なので、必要で有れば連絡下さい) +* 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) -是非参加してみてください。 +ぜひ、やってみて下さい。 ### ウェブサイトの開発 -私たちはちょうどウェブサイトの再設計を始めました。しかし、それらはまだ完了していません。 もし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) ではまだ実装されていない要求が列挙されているので、是非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の再設計を始めました。しかし、それらはまだ完了していません。 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 @@ -65,12 +65,12 @@ NumPy の [ユーザガイド](https://numpy.org/devdocs) は現在、大規模 ### ウェブサイトの翻訳 -私達は、[numpy.org](https://numpy.org) を複数言語に翻訳し、Numpyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 +私達は、[numpy.org](https://numpy.org) を複数言語に翻訳し、Numpyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 私たちは、[numpy.org](https://numpy.org) を複数言語に翻訳し、NumPyを母国語でアクセスできるようにしたいと思っています。 これを実現するには、ボランティアの翻訳者が必要です。 詳しくは[このイシュー](https://numpy.org/neps/nep-0028-website-redesign.html#translation-multilingual-i18n)を参照してください。 [この GitHubイシュー](https://github.com/numpy/numpy.org/issues/55) にコメントしてサインアップしてください。 -### コミュニティの調整とアウトリーチ +### コミュニティとの連携とアウトリーチ -コミュニティとのコミュニケーションを通じて、私たちは、Numpyより広く知ってもらうようにし、どこに問題があるのかを知りたいと思っています。 私達は、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy [code sprints](https://scisprints.github.io/)の開催, ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っています。 +コミュニティとのコミュニケーションを通じて、私たちは、Numpyより広く知ってもらうようにし、どこに問題があるのかを知りたいと思っています。 コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、[Twitter](https://twitter.com/numpy_team) アカウントや、NumPy[コードスプリント](https://scisprints.github.io/)の開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。 ### 資金調達 From ac8b7c337689c01c622ff463209e376a35e44c80 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:47 +0200 Subject: [PATCH 600/909] New translations community.md (Portuguese, Brazilian) --- content/pt/community.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/community.md b/content/pt/community.md index 14c5f35420..ee466892d9 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](/gallery/team.html). A liderança da NumPy assumiu um forte compromisso de criar uma comunidade aberta, inclusiva e positiva. Por favor, leia [o Código de Conduta NumPy](/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](/gallery/team.html). 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. Oferecemos vários canais de comunicação para aprender, compartilhar seu conhecimento e se conectar com outros dentro da comunidade NumPy. @@ -61,5 +61,5 @@ Muitas dessas conferências incluem dias de tutorial sobre o NumPy e/ou sprints Para prosperar, o projeto NumPy precisa de sua experiência e entusiasmo. Não é uma pessoa programadora? Sem problemas! Existem muitas maneiras de contribuir com o NumPy. -Se você está interessado em se tornar um contribuidor do NumPy (oba!) recomendamos que você confira nossa página sobre [Contribuições](/contribute). +Se você está interessado em se tornar um contribuidor do NumPy (oba!) recomendamos que você confira nossa página sobre [Contribuições](/pt/contribute). From 7b82f1733df86f4a8cd8b30d1fd3b04b983b8d55 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:49 +0200 Subject: [PATCH 601/909] New translations community.md (Japanese) --- content/ja/community.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ja/community.md b/content/ja/community.md index 8e1052ff92..c6ed459f85 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -3,19 +3,19 @@ title: コミュニティ sidebar: false --- -NumPy は非常に多様な[コントリビュータ](/gallery/team.html) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 [NumPy 行動規範](/code-of-conduct) をぜひ参照してください。コミュニティの繁栄につながるようなかたちで、人々と交流する方法について書いてあります。 +NumPy は 常に多様な[コントリビュータ](/gallery/team.html) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 NumPy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/ja/code-of-conduct) をご覧ください。 Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 [NumPy 行動規範](/code-of-conduct) をぜひ参照してください。コミュニティの繁栄につながるようなかたちで、人々と交流する方法について書いてあります。 私たちは、NumPyコミュニティ内で学んだり、知識を共有したり、他の人と交流するためのいくつかのコミュニケーション方法を提供しています。 ## オンラインで参加する方法 -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の通りです。 _重要: 私達はユーザとコミュニティメンバーに互いにNumpyの使い方の質問に関して助言し合って欲しいと思っています。 - 参照[サポート](/gethelp)._ +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の通りです。 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の通りです。 _重要: 私たちはユーザとコミュニティメンバーに互いにNumPyの使い方の質問に関して助言し合って欲しいと思っています。 - 参照[サポート](/gethelp)._ ### [NumPyメーリングリスト：](https://mail.python.org/mailman/listinfo/numpy-discussion) -このメーリングリストは、NumPyへの新機能追加するなど、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ロードマップの変更や、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、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](http://numpy-discussion.10968.n7.nabble.com/) にあります。 @@ -27,7 +27,7 @@ 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 - ドキュメントの問題 (例: "I find this section unclear"); - 機能追加リクエスト (例: "I would like to have a new interpolation method in `np.percentile`"). -_ちなみに、セキュリティの脆弱性を報告するには、GitHubのイシュートラッカーは適切な場所ではないことに注意してください。 NumPy でセキュリティ上の脆弱性を発見したと思われる場合は、 [こちら](https://tidelift.com/docs/security) から報告してください。_ +_ちなみに、セキュリティの脆弱性を報告するには、GitHubのイシュートラッカーは適切な場所ではないことに注意してください。 NumPy でセキュリティ上の脆弱性を発見したと思われる場合は、 [こちら](https://tidelift.com/docs/security) から報告してください。 NumPy でセキュリティ上の脆弱性を発見したと思われる場合は、 [こちら](https://tidelift.com/docs/security) から報告してください。_ *** @@ -45,7 +45,7 @@ NumPy、データサイエンス、科学技術計算などのより広いエコ ## カンファレンス -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せん。 伝統的には、SciPy および PyDataのカンファレンスシリーズが、NumPy のメンテナ・コントリビュータ・ユーザーに最も人気がありました。 +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せん。 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せん。 NumPy の管理者や、コントリビュータ、ユーザーに最も人気があったカンファレンスは、SciPy および PyDataのカンファレンスです。 - [SciPy US](https://conference.scipy.org) - [EuroSciPy](https://www.euroscipy.org) @@ -54,12 +54,12 @@ 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 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。) -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。 +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。 ## NumPy コミュニティに参加する プロジェクトを成功させるために、NumPyはあなたの専門知識とプロジェクトに関する熱意を必要としています。 プログラマーではないから参加できない？ そんなことはありません！ NumPyに貢献するには、様々な方法があります。 -NumPyに貢献したい場合は、 [コントリビュート](/contribute) ページをご覧いただくことをお勧めします。 +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。 From 802320a899eb00d0e904da338ff1b7a1f50e5751 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:54 +0200 Subject: [PATCH 602/909] New translations citing-numpy.md (Portuguese, Brazilian) --- content/pt/citing-numpy.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/pt/citing-numpy.md b/content/pt/citing-numpy.md index 390d965d4c..cdc9b2de43 100644 --- a/content/pt/citing-numpy.md +++ b/content/pt/citing-numpy.md @@ -12,14 +12,14 @@ _Em formato BibTeX:_ ``` @Article{ harris2020array, title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. + 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 + 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}, From b4e6a08aa491425aa061ed57e73cc55dd240d30c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:40:57 +0200 Subject: [PATCH 603/909] New translations citing-numpy.md (Japanese) --- content/ja/citing-numpy.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ja/citing-numpy.md b/content/ja/citing-numpy.md index 752d29d800..ee48d610d4 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -5,21 +5,21 @@ sidebar: false 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、こちらの論文を引用して下さい。 -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [0.1038/s41586-020-2649-2](https://doi. org/10.1038/s41586-020-2649-2). ([リンク](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: \[0.1038/s41586-020-2649-2\](https://doi. ([リンク](https://www.nature.com/articles/s41586-020-2649-2)). _BibTeX形式:_ ``` -@Article{ harris2020array, - title = {Array programming with {NumPy}}, - author = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J. +@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 + 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}, From da9d87a85b10b53320e5b7495dc45a4fddfbce21 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:41:00 +0200 Subject: [PATCH 604/909] 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 a2ac5c4698..45df2a55cf 100644 --- a/content/ja/arraycomputing.md +++ b/content/ja/arraycomputing.md @@ -5,17 +5,17 @@ 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。* -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。 +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。 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。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。 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。 arraycl -**配列演算** は **配列** のデータ構造に基づいています。 *配列* は、関連する膨大なデータ群を簡単にかつ高速に、ソート、検索、変換、数学処理できるように構成されています。 +**配列演算** は **配列** のデータ構造に基づいています。 *配列* は、関連する膨大なデータ群を簡単にかつ高速に、ソート、検索、変換、数学処理できるように構成されています。 *配列* は、関連する膨大なデータ群を簡単にかつ高速に、ソート、検索、変換、数学処理できるように構成されています。 配列演算は *一度に* 配列のデータの複数の要素を操作するため、 * ユニーク* な処理と言えます。 これは、配列操作が一回の処理で、配列内の 全ての値に適用されることを意味しています。 このベクトル化手法は、速さと単純さという恩恵をもたらします。プログラマーはループを回して個々の要素のスカラー演算を行うことなく、データの集合を操作しコーディングすることができるのです。 From 18645c1d3e7ede7287b7ab8110d2c86b19a0b842 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:41:02 +0200 Subject: [PATCH 605/909] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index 8c21ea9add..2054ec30c4 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -29,11 +29,11 @@ Membros Eméritos: - Travis Oliphant (fundador do projeto, 2005-2012) - Alex Griffing (2015-2017) - Marten van Kerkwijk (2017-2019) -- Allan Haldane (2015-2021) -- Nathaniel Smith (2012-2021) -- Julian Taylor (2013-2021) -- Pauli Virtanen (2008-2021) -- Jaime Fernández del Río (2014-2021) +- Allan Haldane +- Nathaniel Smith +- Julian Taylor +- Pauli Virtanen +- Jaime Fernández del Río ## Times @@ -48,7 +48,7 @@ O projeto NumPy está crescendo; temos equipes para Veja a página de [Times](/gallery/team.html) para membros individuais de cada time. -## Subcomitê NumFOCUS +## Patrocinadores - Charles Harris - Ralf Gommers @@ -56,13 +56,13 @@ Veja a página de [Times](/gallery/team.html) para membros individuais de cada t - Sebastian Berg - Membro externo: Thomas Caswell -## Patrocinadores +## Parceiros Institucionais O NumPy recebe financiamento direto das seguintes fontes: {{< sponsors >}} -## Parceiros Institucionais +## Doações 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: From fcb6ea498f097ed16f3ce60fc4fc88ada9d3493d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 23 Jul 2021 20:41:03 +0200 Subject: [PATCH 606/909] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 219 ++++++++++++++++++++++++++++++++++++ 1 file changed, 219 insertions(+) create mode 100644 content/pt/tabcontents.yaml diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml new file mode 100644 index 0000000000..74bf2ba35c --- /dev/null +++ b/content/pt/tabcontents.yaml @@ -0,0 +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: 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." + 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 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: 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: http://vispy.org/gallery.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 2fae5745f56fa73370b851095a181f4ef40ae423 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 01:25:36 +0200 Subject: [PATCH 607/909] 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 ace105f5c3..d6e7c50ac3 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -5,7 +5,7 @@ sidebar: false ### NumPy 1.20.0 リリース -_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ユーザーが参加しました。 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. From 861287c474461fd55b3f75bd584379a159fb778e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 01:34:13 +0200 Subject: [PATCH 608/909] 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 d6e7c50ac3..23c2686e2f 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -5,7 +5,7 @@ sidebar: false ### NumPy 1.20.0 リリース -_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ユーザーが参加しました。 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ユーザーが参加しました。 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. From f0503362cbfdee1ca1f4dd7a82c20b751097d489 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 01:44:04 +0200 Subject: [PATCH 609/909] 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 23c2686e2f..cdf6039595 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,16 +3,16 @@ title: ニュース sidebar: false --- -### NumPy 1.20.0 リリース +### 2021年度NumPy調査 _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ユーザーが参加しました。 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プロジェクトの多様性 +### NumPy 1.21.0 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: From e7b3e8fc2049535b024a1c006135395dd6a95469 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 01:51:45 +0200 Subject: [PATCH 610/909] 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 cdf6039595..f71afde606 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -14,9 +14,9 @@ _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 1.21.0 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) が利用可能になりました。 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, +- より多くの機能やプラットフォームをカバーするSIMD関連の作業が継続されました。 - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, From 410b8c90b8aac11f0cac12099176ef1a658e51ae Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 02:13:14 +0200 Subject: [PATCH 611/909] New translations news.md (Japanese) --- content/ja/news.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index f71afde606..854f8d71f6 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -17,16 +17,16 @@ _2021年7月12日_ -- NumPy ではコミュニティの力を信じています _2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) が利用可能になりました。 今回のリリースのハイライトは次のとおりです。 - より多くの機能やプラットフォームをカバーするSIMD関連の作業が継続されました。 -- 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. +- 新しいdtypeインフラとキャストの初期作業 +- mac 版の Python 3.8 と Python 3.9 用 universal2 wheels +- 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人が貢献した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。 -### Natureに初の公式NumPy論文が掲載されました! +### 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/. From 2c5043cffb08e18c5bb56b6cf5fb109851650807 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 06:16:22 +0200 Subject: [PATCH 612/909] New translations news.md (Japanese) --- content/ja/news.md | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 854f8d71f6..e5db6299aa 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,7 +3,7 @@ title: ニュース sidebar: false --- -### 2021年度NumPy調査 +### 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ユーザーが参加しました。 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。 @@ -28,44 +28,44 @@ _2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0 ### 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/ -### 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？ +### Numpy 1.20.0 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。 - 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 1.19.2 リリース +### NumPyプロジェクトにおける多様性 _2020年6月24日_ -- NumPy に新しいロゴが作成されました: -### 初めてのNumPyの調査が公開されました!! +### Natureに初めての公式の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。 -### NumPy に新しいロゴができました! +### 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。 - `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 1.19.0 リリース +### 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。 -### ドキュメント受諾期間 +### 初めてのNumPyのアンケートが公開されました!! _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. 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 1.18.0 リリース +### NumPyのロゴが新しくなりました。 詳細については、 [リリース ノート](https://github.com/numpy/numpy/releases/tag/v1.18.0) を参照してください。 @@ -74,28 +74,28 @@ NumPy をより良くするために、こちらの \[アンケート\](https:// 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はChan Zuckerberg財団から助成金を受けました。 +### Numpy 1.19.0 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。 -### Season of Docs acceptance +### ドキュメント改善期間 _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。 -### 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 1.18.0 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。 NumPy 1.15.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018年7月23日_. -### NumPy receives a grant from the Chan Zuckerberg Initiative +### 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。 -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、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。 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。 @@ -113,6 +113,6 @@ This grant will be used to ramp up the efforts in improving NumPy documentation, - 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 1.18.0 ([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 1.16.0 ([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 1.16.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019年1月14日_. +- NumPy 1.15.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018年7月23日_. +- NumPy 1.14.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018年1月7日_. From 77f8da74e76170b0ea2da15dbeb855fc2658bfde Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 06:32:36 +0200 Subject: [PATCH 613/909] 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 74bf2ba35c..9294502ae0 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -2,7 +2,7 @@ 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。\n\nwww.DeepL.com/Translator（無料版）で翻訳しました。" 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: @@ -10,7 +10,7 @@ arraylibraries: 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: 配列ライブラリ - text: Capabilities & Application areas libraries: From 4b23d8a7090c94b93cddc7c23568e2295af3de8c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 06:42:06 +0200 Subject: [PATCH 614/909] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 9294502ae0..ed364aad88 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -3,26 +3,26 @@ 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。\n\nwww.DeepL.com/Translator（無料版）で翻訳しました。" - 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)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。\n\nwww.DeepL.com/Translator（無料版）で翻訳しました。" 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: 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 From 15eff5eafacdf2933441dd256c2523348d5bf9bb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 06:52:10 +0200 Subject: [PATCH 615/909] 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 ed364aad88..f5a26ead27 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -28,13 +28,13 @@ 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プログラムの部分的な変換により、微分可能化、ベクトル化、GPU/TPUへのジャストインタイム・コンパイルを実現します。" 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 From 721cd7ceac53db53d84f5f682cabf2dcc43cac6e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 07:03:58 +0200 Subject: [PATCH 616/909] 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 f5a26ead27..131fded5b0 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -40,33 +40,33 @@ arraylibraries: 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 + alttext: 矢 url: https://github.com/apache/arrow - title: xtensor From b73ee6773b17125f9af05f4bcc2503238788c232 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 07:16:06 +0200 Subject: [PATCH 617/909] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 54 ++++++++++++++++++------------------- 1 file changed, 27 insertions(+), 27 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 131fded5b0..ab30969ce2 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -2,8 +2,8 @@ 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。\n\nwww.DeepL.com/Translator（無料版）で翻訳しました。" - 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。\n\nwww.DeepL.com/Translator（無料版）で翻訳しました。" + 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: - @@ -71,7 +71,7 @@ arraylibraries: - title: xtensor text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis. - img: /images/content_images/arlib/xtensor.png + img: /images/content_images/case_studies/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - @@ -100,44 +100,44 @@ scientificdomains: 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. + 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 + title: 統計コンピューティング 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. + 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. - img: /images/content_images/sc_dom_img/astronomy_processes.svg + title: 天文学における計算 + alttext: 望遠鏡。 + img: /images/content_images/sc_dom_img/天文学_processes.svg - - title: Cognitive Psychology - alttext: A human head with gears. - img: /images/content_images/sc_dom_img/cognitive_psychology.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_personicy.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. - img: /images/content_images/sc_dom_img/bayesian_inference.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_conference.svg - - title: Mathematical Analysis + title: 数学的分析 alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - @@ -207,13 +207,13 @@ visualization: - 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: http://vispy.org/gallery.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. From 9a5aee42576219219e9ca4ce64d57abaa3d6f311 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 07:21:45 +0200 Subject: [PATCH 618/909] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index ab30969ce2..846cf1a195 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -95,7 +95,7 @@ arraylibraries: 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." librariesrow1: @@ -138,23 +138,23 @@ scientificdomains: img: /images/content_images/sc_dom_img/bayesian_conference.svg - title: 数学的分析 - 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. + 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. + 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:" @@ -170,7 +170,7 @@ datascience: - 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: "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)" - @@ -183,7 +183,7 @@ 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 From 7e966f15b75bdb234dd359ef3aa796ed72c63aad Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 07:32:00 +0200 Subject: [PATCH 619/909] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 846cf1a195..20950099c5 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -70,13 +70,13 @@ 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による多次元配列 img: /images/content_images/case_studies/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. + text: NumPyの基本的なコンセプトを再現した、配列計算用のライブラリを開発する。 img: /images/content_images/arlib/xnd.png alttext: xnd url: https://xnd.io @@ -187,15 +187,15 @@ visualization: - 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 @@ -203,7 +203,7 @@ visualization: - 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 @@ -216,4 +216,4 @@ visualization: - 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 2fc85a84455a56ad6d47879c23ef78412a9a06bc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 07:52:33 +0200 Subject: [PATCH 620/909] 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 20950099c5..6e6551dbe3 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -82,7 +82,7 @@ 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 ed4e850ca3b7d818a7518f0e6430d841ce2e6231 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 08:03:50 +0200 Subject: [PATCH 621/909] 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 6e6551dbe3..62113e9305 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -88,7 +88,7 @@ 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 @@ -97,7 +97,7 @@ scientificdomains: - 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のソリューションは、多くの場合、明確でエレガントです。" librariesrow1: - title: 量子コンピューティング @@ -105,7 +105,7 @@ scientificdomains: img: /images/content_images/sc_dom_img/quantum_computing.svg - title: 統計コンピューティング - alttext: A line graph with the line moving up. + alttext: 上に移動している、線グラフ img: /images/content_images/sc_dom_img/statistical_computing.svg - title: 信号処理 @@ -145,7 +145,7 @@ scientificdomains: alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience + title: 地球科学 alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg - @@ -153,11 +153,11 @@ scientificdomains: alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg - - title: Architecture & Engineering + 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 From 0e831ea745a7e1b2cbf2899308f27280d6a16637 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 08:17:15 +0200 Subject: [PATCH 622/909] 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 62113e9305..3c596b17de 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -161,7 +161,7 @@ datascience: 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 From da61f3697b71caf5689da81bd57cd294a66a7747 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 09:33:54 +0200 Subject: [PATCH 623/909] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 3c596b17de..769a544661 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -165,16 +165,16 @@ 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: 3つの重なり合う円の図. 「数学」,「コンピュータサイエンス」,「ドメインの専門知識」とラベルされた円. 図の途中には、3つの円が重なっており、「データサイエンス」と表示された領域があります。 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.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: "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: "モデルと評価: [scikit-learn](https://scikit-learn.org),[ statsmodels.org/stable/index.html),[ PyMC3](https://docs.pym.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)). From e8ef9202ec295d2fa4b269f66dc8e8d61403220c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 24 Jul 2021 09:42:26 +0200 Subject: [PATCH 624/909] 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 769a544661..284c3fb978 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -177,7 +177,7 @@ datascience: 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: 大量のデータの場合は, [Dask](https://dask.org) や[Ray](https://ray.io/) がスケールする様に設計されています。データのバージョンに応じた安定したデプロイには[DVC](https://dvc.org)が利用できます。実験結果のトラッキングには[MLFlow](https://mlflow.org), ワークフローの自動化には [Airflow](https://airflow.apache.org)や、[Prefect](https://www.prefect.io)が利用できます。 visualization: images: - @@ -199,7 +199,7 @@ visualization: - 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 From 5622311014b40f191598b73a5b7a99d174592cd6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jul 2021 16:45:05 +0200 Subject: [PATCH 625/909] 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 2054ec30c4..32ba4392ba 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -56,13 +56,13 @@ Veja a página de [Times](/gallery/team.html) para membros individuais de cada t - Sebastian Berg - Membro externo: Thomas Caswell -## Parceiros Institucionais +## Patrocinadores O NumPy recebe financiamento direto das seguintes fontes: {{< sponsors >}} -## Doações +## 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: From de8aad0442d42b0ef5560cde69d8ed1a185d6c60 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jul 2021 16:45:06 +0200 Subject: [PATCH 626/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 9c4765dfd9..90402734ed 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -26,18 +26,18 @@ params: promptlabel: interactive shell prompt button: - - label: Enables the interactive tutorial shell - text: Enable + label: Habilita o tutorial com console interativo + text: Habilitar shellcontent: intro: - - title: Try NumPy - text: Enable the interactive shell + title: Experimentar o NumPy + text: Ativar o console interativo loading: - - title: While we wait... - text: Launching container on mybinder.org... - docslink: Don't forget to check out the docs. + title: Enquanto esperamos... + text: Iniciando container em mybinder.org... + docslink: Não se esqueça de conferir a documentação. casestudies: title: ESTUDOS DE CASO features: From 9616f437b11a8e7aec07886b6019503b0db2ce52 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 26 Jul 2021 17:08:16 +0200 Subject: [PATCH 627/909] 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 beb139caba..024a0bd88b 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -5,11 +5,11 @@ sidebar: false ### NumPy versão 1.20.0 -_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. ### Diversidade no projeto NumPy From a2a513caf15feb0617458a7633b276d695e8b92a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 30 Jul 2021 09:39:20 +0200 Subject: [PATCH 628/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 42 +++++++++++++++++++++--------------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 7571788813..8028393569 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -18,28 +18,28 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2021 NumPy survey - content: Your voice matters + title: 2021 Numpy调查 + content: 您的意见很重要 url: /news shell: title: 占位符 - promptlabel: interactive shell prompt + promptlabel: 交互式shell提示 button: - - label: Enables the interactive tutorial shell - text: Enable + label: 启用交互式教程shell。 + text: 启用 shellcontent: intro: - - title: Try NumPy - text: Enable the interactive shell + title: 尝试使用NumPy + text: 启用交互式教程shell。 loading: - - title: While we wait... - text: Launching container on mybinder.org... - docslink: Don't forget to check out the docs. + title: 当我们等待时... + text: 正在前往 mybinder.org 启动容器... + docslink: 别忘了查看 用户手册。 casestudies: - title: CASE STUDIES + title: 案例研究 features: - title: 第一张黑洞图像 @@ -63,27 +63,27 @@ params: title: 使用深度学习进行估计 text: DeepLabCut 使用 NumPy 来加速进行涉及观察动物行为的科学研究，以便跨物种和时间尺度推动研究发展。 img: /images/content_images/case_studies/deeplabcut.png - alttext: Cheetah pose analysis + alttext: 猎豹姿势分析 url: /case-studies/deeplabcut-dnn keyfeatures: features: - title: 强大的高维数组 - text: Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today. + text: 快速、多面性、NumPy向量化、索引化和广播概念是当今数组计算的事实标准。 - title: 数字计算工具 - text: NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. + text: NumPy 提供了丰富全面的数学函数、随机数生成器、线性代数函数、傅式变换等等。 - - title: Interoperable - text: NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. + title: 互操作性 + text: NumPy 支持范围广泛的硬件和计算平台，并且在分布式、GPU和稀疏数组库中也能得到很好的应用。 - - title: Performant - text: The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. + title: 高性能 + text: NumPy 的核心是高度优化的 C 代码，同时兼顾了Python语言的灵活性和编译代码的高性能。 - - title: Easy to use - text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. + title: 简单易用 + text: NumPy的高度模块化的语法使得任何背景或经验级别的程序员都能够快速上手。 - - title: Open source + title: 开放源代码 text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). tabs: title: ECOSYSTEM From f9b640577be3d31ade2e48899b703127a7036a15 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 30 Jul 2021 09:58:12 +0200 Subject: [PATCH 629/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 8028393569..02a3736879 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -84,22 +84,22 @@ params: text: NumPy的高度模块化的语法使得任何背景或经验级别的程序员都能够快速上手。 - title: 开放源代码 - text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + text: 以自由的 [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt)下发布。NumPy 是由一个生气勃勃的、响应性的和多样化的 [community](/community)开发和维护的[在 GitHub开源](https://github.com/numpy/numpy)。 tabs: - title: ECOSYSTEM + title: 生态系统 section5: false navbar: - - title: Install + title: 安装 url: /install - - title: Documentation + title: 文档 url: https://numpy.org/doc/stable - - title: Learn + title: 学习指南 url: /learn - - title: Community + title: 社区 url: /community - title: About Us From b3e5f1d1e93f940000fa889a531e6757a1aa87d6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 30 Jul 2021 10:24:53 +0200 Subject: [PATCH 630/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 02a3736879..92d8ddd2e1 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -102,10 +102,10 @@ navbar: title: 社区 url: /community - - title: About Us + title: 关于我们 url: /about - - title: Contribute + title: 参与贡献 url: /contribute footer: logo: numpy.svg @@ -116,25 +116,25 @@ footer: icon: github - link: https://twitter.com/numpy_team - icon: twitter + icon: 推特 quicklinks: column1: title: "" links: - - text: Install + text: 安装 link: /install - - text: Documentation + text: 文档 link: https://numpy.org/doc/stable - - text: Learn + text: 学习指南 link: /learn - - text: Citing Numpy + text: 引用 NumPy link: /citing-numpy - - text: Roadmap + text: 版本规划 link: https://numpy.org/neps/roadmap.html column2: links: From f08543fe06aefd25887d1e211589a00e0b5cecfb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 6 Aug 2021 19:15:19 +0200 Subject: [PATCH 631/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 30 ++++++++-------------- 1 file changed, 11 insertions(+), 19 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 4375540c13..ca88d88011 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -6,17 +6,13 @@ sidebar: false {{< figure src="/images/content_images/cs/blackhole.jpg" caption="**Agujero Negro M87**" alt=black hole image" attr="*(Créditos de 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
    +

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

    +
    Katie Bouman, Profesor asistente, Ciencias matemáticas y computación, Caltech
    ## Un telescopio del tamaño del mundo -El [ telescopio Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), es un arreglo de ocho radio telescopios terrestres formando un telescopio computacional del tamaño del mundo, estudiando el universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada interferometría de linea de base muy larga (VLBI), tiene una resolución angular de - -20 microsegundos de arco<0> - ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París!

    - - +El [ telescopio del Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), es un arreglo de ocho radiotelescopios terrestres que forman un telescopio computacional del tamaño del mundo, estudiando el universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada interferometría de línea de base muy larga (VLBI), tiene una resolución angular de [20 microsegundos de arco][resolution] — ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París! ### Objetivos clave y resultados @@ -26,25 +22,21 @@ El [ telescopio Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.org), * **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. + + EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed. * **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. -* **Into the unknown** - - When the goal is to see something never before seen, how can scientists be confident the image is correct? + 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. -{{< 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" >}} +* **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" >}} ## Los roles de Numpy @@ -62,14 +54,14 @@ For example, the [`eht-imaging`][ehtim] Python package provides tools for simula 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. - - ## 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. {{< 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 From 038517ace9650100c9ad2c79030575dbc0eef70b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 6 Aug 2021 20:12:02 +0200 Subject: [PATCH 632/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index ca88d88011..4a3e246b6b 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -16,7 +16,7 @@ El [ telescopio del Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.o ### 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 para 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. From 8c4d2acc9bf211bb44eab411f2111ad861e0930d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 6 Aug 2021 20:19:48 +0200 Subject: [PATCH 633/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 4a3e246b6b..9f5f86da2a 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -18,7 +18,7 @@ El [ telescopio del Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.o * **Una nueva vista del universo:** El trabajo preliminar para 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 apuntó a 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. From 9afcf3f36d11c5ab7a7b27604ffafc9e34c4a61a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 6 Aug 2021 20:35:10 +0200 Subject: [PATCH 634/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 9f5f86da2a..51f6b97233 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -20,11 +20,11 @@ El [ telescopio del Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.o * **El agujero negro:** EHT apuntó a 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 causada 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. @@ -32,13 +32,13 @@ El [ telescopio del Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.o 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** +* **Hacia lo desconocido** 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" >}} -## Los roles de Numpy +## 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? From d09e1773bad6b906842c8a3c7ee14e901518bafd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:11:34 +0200 Subject: [PATCH 635/909] 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 fe431e845c..be877a2271 100644 --- a/content/ko/user-survey-2020.md +++ b/content/ko/user-survey-2020.md @@ -1,5 +1,5 @@ --- -title: 2020 NUMPY COMMUNITY SURVEY +title: 2020 NUMPY 커뮤니티 설문조사 sidebar: false --- From 72f425554a6dc035cfcc4470d7291f8570a4ab42 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:21:08 +0200 Subject: [PATCH 636/909] New translations user-survey-2020.md (Korean) --- content/ko/user-survey-2020.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ko/user-survey-2020.md b/content/ko/user-survey-2020.md index be877a2271..c3e1ccf23b 100644 --- a/content/ko/user-survey-2020.md +++ b/content/ko/user-survey-2020.md @@ -3,14 +3,14 @@ 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. +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. 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 2a329084f860bda17780a3c2a405e9b222c72da9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:21:09 +0200 Subject: [PATCH 637/909] New translations about.md (Korean) --- content/ko/about.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index 6e7e20befc..4cfb4720f0 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -1,11 +1,11 @@ --- -title: About Us +title: NumPy 정보 sidebar: false --- _NumPy 프로젝트와 커뮤니티에 대한 몇가지 정보_ -NumPy는 Python에서 Numerical Computing을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. NumPy는 항상 100% 오픈소스 소프트웨어 일것이며, [수정된 BSD 라이센스](https://github.com/numpy/numpy/blob/master/LICENSE.txt)에 따라서 누구나 무료로 사용하고 배포할 수 있습니디. +NumPy는 Python을 통해 수치적 컴퓨팅을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. NumPy는 항상 100% 오픈소스 소프트웨어일 것이며, [수정된 BSD 라이선스](https://github.com/numpy/numpy/blob/master/LICENSE.txt)에 따라서 누구나 무료로 사용하고 배포할 수 있습니다. NumPy는 광범위한 Scientific Python 커뮤니티의 협의를 통해 GitHub에서 공개적으로 개발되었습니다. 우리의 거버넌스 접근 방식에 대한 더 자세한 내용은 [거버넌스 문서](https://www.numpy.org/devdocs/dev/governance/index.html)를 참조해 주세요. @@ -56,23 +56,23 @@ NumPy 프로젝트는 성장하고 있습니다. 그리고 우리는 다음과 - Sebastian Berg - External member: Thomas Caswell -## Sponsors +## 스폰서 NumPy는 다음과 같은 곳들에서 직접적으로 자금을 받습니다. {{< sponsors >}} -## Institutional Partners +## 기관 파트너 기관 파트너는 그들의 업무의 일환으로 NumPy에 기여하는 직원을 고용하여 프로젝트를 지원하는 조직입니다. 현재 기관 파트너는 다음과 같습니다. -- UC Berkeley (Stéfan van der Walt, Sebastian Berg, Ross Barnowski) +- UC 버클리 (Stéfan van der Walt, Sebastian Berg, Ross Barnowski) - Quansight (Ralf Gommers, Melissa Weber Mendonça, Mars Lee, Matti Picus, Pearu Peterson) {{< partners >}} -## Donate +## 후원 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. From 912818a1b3960d1c84200061660d549c9252a59b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:31:52 +0200 Subject: [PATCH 638/909] New translations about.md (Korean) --- content/ko/about.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index 4cfb4720f0..2a1b7b801b 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -48,13 +48,13 @@ NumPy 프로젝트는 성장하고 있습니다. 그리고 우리는 다음과 개발 팀원들은 [팀](/gallery/team.html) 페이지를 참조하세요. -## NumFOCUS Subcommittee +## NumFOCUS 소위원회 - Charles Harris - Ralf Gommers - Melissa Weber Mendonça - Sebastian Berg -- External member: Thomas Caswell +- 외부 회원: Thomas Caswell ## 스폰서 From c07fe033fb801fbd6f8ec893ab1582fb1007caaa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:31:53 +0200 Subject: [PATCH 639/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 359d6d8aa9..06ec9faf9b 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -68,7 +68,7 @@ NumPy의 [사용자 도움말](https://numpy.org/devdocs)은 현재 대규모로 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. From 2bfad274595d16a85cc4c46607b31803f03a97a5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:31:54 +0200 Subject: [PATCH 640/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 7a4d1949fc..ee79c41a11 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -18,26 +18,26 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2021 NumPy survey - content: Your voice matters + title: 2021년도 NumPy 설문조사 + content: 소중한 의견을 들려주세요 url: /news shell: title: 플레이스홀더 - promptlabel: interactive shell prompt + promptlabel: 대화형 쉘 프롬프트 button: - - label: Enables the interactive tutorial shell - text: Enable + label: 대화형 튜토리얼 쉘을 켭니다 + text: 사용 shellcontent: intro: - - title: Try NumPy - text: Enable the interactive shell + title: NumPy 써 보기 + text: 대화형 쉘을 켭니다 loading: - - title: While we wait... - text: Launching container on mybinder.org... - docslink: Don't forget to check out the docs. + title: 잠시만요... + text: mybinder.org의 컨테이너를 실행하는 중입니다... + docslink: 문서도 한 번 열람해보세요. casestudies: title: 사례 연구 features: From f5d5b11a35d7d7946440fdeb5c6896185a371a4d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:31:54 +0200 Subject: [PATCH 641/909] 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 74bf2ba35c..5443d3917f 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -10,7 +10,7 @@ arraylibraries: 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 libraries: From 7eab72bae33edef375a1339af9bca09a57c40ced Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:47:19 +0200 Subject: [PATCH 642/909] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 50 ++++++++++++++++++------------------- 1 file changed, 25 insertions(+), 25 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index 5443d3917f..27f9f3c32a 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -22,19 +22,19 @@ arraylibraries: 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 @@ -100,48 +100,48 @@ scientificdomains: 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. + title: 양자 컴퓨팅 + alttext: 컴퓨터 칩. img: /images/content_images/sc_dom_img/quantum_computing.svg - - 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 - - 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 - - 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 - - title: Graphs and Networks - alttext: A simple graph. + title: 그래프 및 네트워크 + alttext: 간단한 그래프. img: /images/content_images/sc_dom_img/sd6.svg - - title: Astronomy Processes - alttext: A telescope. + title: 천문 데이터 처리 + alttext: 망원경. img: /images/content_images/sc_dom_img/astronomy_processes.svg - - 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 librariesrow2: - - title: Bioinformatics - alttext: A strand of DNA. + title: 생물정보학 + alttext: DNA 가닥. img: /images/content_images/sc_dom_img/bioinformatics.svg - - 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 - - title: Mathematical Analysis - alttext: Four mathematical symbols. + title: 수학적 분석 + alttext: 수학 기호 4개. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - title: Chemistry + title: 화학 alttext: A test tube. img: /images/content_images/sc_dom_img/chemistry.svg - @@ -183,7 +183,7 @@ 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: matplotlib으로 만든 streamplot - url: https://github.com/yhat/ggpy img: /images/content_images/v_ggpy.png From 0e8c1b413ebdabcf4f12a1206d8b6c03db3fb580 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 8 Aug 2021 03:52:46 +0200 Subject: [PATCH 643/909] 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 27f9f3c32a..aa1a6c0d39 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -142,19 +142,19 @@ scientificdomains: img: /images/content_images/sc_dom_img/mathematical_analysis.svg - title: 화학 - alttext: A test tube. + alttext: 시험관. img: /images/content_images/sc_dom_img/chemistry.svg - - title: Geoscience - alttext: The Earth. + title: 지구과학 + alttext: 지구. img: /images/content_images/sc_dom_img/geoscience.svg - - title: Geographic Processing - alttext: A map. + title: 지리학적 처리 + alttext: 지도. img: /images/content_images/sc_dom_img/GIS.svg - - 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 datascience: intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:" @@ -187,15 +187,15 @@ visualization: - 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 @@ -207,11 +207,11 @@ visualization: - 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: http://vispy.org/gallery.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. From bb4562fd702b1ac4ee8f3a3494b1f0bd727e55e2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 16:42:27 +0200 Subject: [PATCH 644/909] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index c22be26be0..cdbd16e885 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -12,11 +12,11 @@ sidebar: false ## Sobre Críquete -Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. O cricket também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. +Dizer que os indianos adoram o críquete seria subestimar este sentimento. O jogo é jogado praticamente em todas as localidades da Índia, rurais ou urbanas, e é popular com os jovens e os anciões, conectando bilhões de pessoas na Índia como nenhum outro esporte. O críquete também recebe muita atenção da mídia. Há uma quantidade significativa de [dinheiro](https://www.statista.com/topics/4543/indian-premier-league-ipl/) e fama em jogo. Ao longo dos últimos anos, a tecnologia foi literalmente uma revolução. As audiências tem uma ampla possibilidade de escolha, com mídias de streaming, torneios, acesso barato a jogos de críquete ao vivo em dispositivos móveis, e mais. A Primeira Liga Indiana (*Indian Premier League* - IPL) é uma liga profissional de críquete [Twenty20](https://pt.wikipedia.org/wiki/Twenty20), fundada em 2008. É um dos eventos de críquete mais assistidos no mundo, avaliado em [$6,7 bilhões de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) em 2019. -perdidos por um boleador, as partidas ganhas por uma equipe de críquete, o número de vezes que um batsman responde de certa maneira a um tipo de arremesso do boleador, etc. A capacidade de investigar números de críquete para melhorar o desempenho e estudar as oportunidades de negócio, mercado e economia de críquete através de poderosas ferramentas de análise, alimentadas por softwares numéricos de computação, como o NumPy, é um grande negócio. A capacidade de investigar 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. +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. 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 +## 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 d321ae4fea9409d1a34c80a94bea9797a0ee25f3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 16:56:19 +0200 Subject: [PATCH 645/909] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index cdbd16e885..7181b28c9f 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -3,7 +3,7 @@ title: "Estudo de Caso: Análise de Críquete, a revolução!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, o maior festival de Críquete da Índia**" alt="Copa e estádio da Indian Premier League Cricket" attr="*(Créditos de imagem: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}}

    Você não joga para a torcida, joga para o país.

    From 45263929e12bdd3ca77dd302310c2baa74f80286 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 17:18:08 +0200 Subject: [PATCH 646/909] 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 7181b28c9f..6c67336d5a 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -30,7 +30,7 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações ### Objetivos Principais da Análise de Dados * A análise de dados esportivos é usada não somente em críquete, mas em muitos [outros esportes](https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx) para melhorar o desempenho geral da equipe e maximizar as chances de vitória. -* A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. +* A análise de dados em tempo real pode ajudar na 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/" >}} @@ -61,4 +61,4 @@ 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" class="fig-center" alt="Diagrama mostrando os benefícios de usar o NumPy para análise de críquete" caption="**Recursos principais da NumPy utilizados**" >}} From 765f64c8aad9e51cacb08aad9a1769426cebfbe6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 17:18:10 +0200 Subject: [PATCH 647/909] New translations deeplabcut-dnn.md (Portuguese, Brazilian) --- content/pt/case-studies/deeplabcut-dnn.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index 1c02e9b208..e94880b925 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -34,7 +34,7 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab * **Criação de um kit de ferramentas Python fácil de usar para estimativa de poses:** - DeepLabCut queria compartilhar sua tecnologia de estimativa de poses animal na forma de uma ferramenta simples de usar que pudesse ser adotada pelos pesquisadores facilmente. Assim, criaram um conjunto de ferramentas em Python completo e fácil de usar, também com recursos de gerenciamento de projeto. Isso permite não apenas a automação de estimação de poses, mas também o gerenciamento do projeto de ponta a ponta, ajudando o usuário do DeepLabCut Toolkit desde a fase de coleta para criar fluxos de dados compartilháveis e reutilizáveis. + DeepLabCut queria compartilhar sua tecnologia de estimativa de poses de animais na forma de uma ferramenta simples de usar que pudesse ser adotada pelos pesquisadores facilmente. Assim, criaram um conjunto de ferramentas em Python completo e fácil de usar, também com recursos de gerenciamento de projeto. Isso permite não apenas a automação de estimação de poses, mas também o gerenciamento do projeto de ponta a ponta, ajudando o usuário do DeepLabCut Toolkit desde a fase de coleta para criar fluxos de dados compartilháveis e reutilizáveis. Seu [conjunto de ferramentas][DLCToolkit] agora está disponível como software de código aberto. @@ -63,7 +63,7 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab {{< figure src="/images/content_images/cs/pose-estimation.png" class="csfigcaption" caption="**Estimação de poses e complexidade**" alt="6 imagens com diferentes exemplos de captura de movimento" align="middle" attr="(Fonte: Mackenzie Mathis)" attrlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" >}} -## O papel da NumPy nos desafios da estimação de poses +## O papel do NumPy nos desafios da estimação de poses NumPy supre a principal necessidade da tecnologia DeepLabCut de cálculos numéricos de alta velocidade para análises comportamentais. Além da NumPy, DeepLabCut emprega várias bibliotecas Python que usam a NumPy como sua base, tais como [SciPy](https://www.scipy.org), [Pandas](https://pandas.pydata.org), [matplotlib](https://matplotlib.org), [Tensorpack](https://github.com/tensorpack/tensorpack), [imgaug](https://github.com/aleju/imgaug), [scikit-learn](https://scikit-learn.org/stable/), [scikit-image](https://scikit-image.org) e [Tensorflow](https://www.tensorflow.org). @@ -75,7 +75,7 @@ As seguintes características da NumPy desempenharam um papel fundamental para a * 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. +A DeepLabCut utiliza as capacidades de manipulação de arrays do 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="diagrama com o fluxo de dados do deeplabcut" 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">}} From ddcd31cac67479f5c73832e3c3f2a778efa912ee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 17:18:11 +0200 Subject: [PATCH 648/909] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index d70614f955..6196390690 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -12,7 +12,7 @@ sidebar: false ## 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. +Ondas gravitacionais são ondulações no tecido espaço-tempo, geradas por eventos cataclísmicos no universo, como a colisão e a fusão de dois buracos negros ou a coalescência de estrelas binárias ou supernovas. A observação de ondas gravitacionais pode ajudar não só no estudo da gravidade, mas também no entendimento de alguns dos fenômenos obscuros existentes no universo distante e seu impacto. O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://www.ligo.caltech.edu) foi projetado para abrir o campo da astrofísica das ondas gravitacionais através da detecção direta de ondas gravitacionais previstas pela Teoria Geral da Relatividade de Einstein. O observatório consiste de dois interferômetros amplamente separados dentro dos Estados Unidos - um em Hanford, Washington e o outro em Livingston, Louisiana — operando em uníssono para detectar ondas gravitacionais. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a Terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. @@ -41,11 +41,11 @@ O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://w {{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="amplitude da deformação das ondas gravitacionais" 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" >}} -## O papel da NumPy na detecção de ondas gravitacionais +## O papel do NumPy na detecção de ondas gravitacionais Ondas gravitacionais emitidas da fusão não podem ser calculadas usando nenhuma técnica a não ser relatividade numérica por força bruta usando supercomputadores. A quantidade de dados que o LIGO coleta é imensa tanto quanto os sinais de ondas gravitacionais são pequenos. -NumPy, o pacote padrão de análise numérica para Python, foi parte do software utilizado para várias tarefas executadas durante o projeto de detecção de ondas gravitacionais no LIGO. A NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: +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. O NumPy ajudou a resolver problemas matemáticos e de manipulação de dados complexos em alta velocidade. Aqui estão alguns exemplos: * [Processamento de sinais](https://www.uv.es/virgogroup/Denoising_ROF.html): Detecção de falhas, [Identificação de ruídos e caracterização de dados](https://ep2016.europython.eu/media/conference/slides/pyhton-in-gravitational-waves-research-communities.pdf) (NumPy, scikit-learn, scipy, matplotlib, pandas, PyCharm) * Recuperação de dados: Decidir quais dados podem ser analisados, compreender se os dados contém um sinal - como uma agulha em um palheiro @@ -56,14 +56,14 @@ 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="gráfico de dependências do gwpy com o NumPy em realce" caption="**Gráfico de dependências mostrando como o pacote GwPy depende do 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/PyCBC-numpy-dep-graph.png" class="fig-center" alt="gráfico de dependências do PyCBC com NumPy em realce" caption="**Gráfico de dependências mostrando como o pacote PyCBC depende do NumPy**" >}} ## 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. +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. O NumPy, junto com outras bibliotecas Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo. -{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="numpy benefits" caption="**Recursos chave da NumPy utilizados**" >}} +{{< figure src="/images/content_images/cs/numpy_gw_benefits.png" class="fig-center" alt="funcionalidades do numpy" caption="**Recursos chave do NumPy utilizados**" >}} From dde254bccbb1bf2f87c704cc49ce44287c978eb8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 18:57:10 +0200 Subject: [PATCH 649/909] 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 32ba4392ba..08af62cca0 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -48,7 +48,7 @@ O projeto NumPy está crescendo; temos equipes para Veja a página de [Times](/gallery/team.html) para membros individuais de cada time. -## Patrocinadores +## Subcomitê NumFOCUS - Charles Harris - Ralf Gommers @@ -80,5 +80,5 @@ 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). +O Conselho Diretor do 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 c2af1aec7798de069834670c80ed655b57499452 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 18:57:11 +0200 Subject: [PATCH 650/909] New translations arraycomputing.md (Portuguese, Brazilian) --- content/pt/arraycomputing.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/arraycomputing.md b/content/pt/arraycomputing.md index 941f69fe42..bf4bb3baa3 100644 --- a/content/pt/arraycomputing.md +++ b/content/pt/arraycomputing.md @@ -7,7 +7,7 @@ sidebar: false A manipulação e a transformação de dados de grande escala dependem de computação eficiente de alta performance com arrays. A linguagem mais escolhida para análise de dados, aprendizagem de máquina e computação numérica produtiva é **Python.** -**Num**erical **Py**thon (Python Numérico) ou NumPy é a biblioteca em Python padrão para o suporte à utilização de matrizes e arrays multidimensionais de grande porte, e vem com uma vasta coleção de funções matemáticas de alto nível para operar nestas arrays. +**Num**erical **Py**thon (Python Numérico) ou NumPy é a biblioteca padrão em Python que dá suporte à utilização de matrizes e arrays multidimensionais de grande porte, e vem com uma vasta coleção de funções matemáticas de alto nível para operar nestas arrays. Desde o lançamento do NumPy em 2006, o Pandas apareceu em 2008, e nos últimos anos vimos uma sucessão de bibliotecas de computação com arrays aparecerem, ocupando e preenchendo o campo da computação com arrays. Muitas dessas bibliotecas mais recentes imitam recursos e capacidades parecidas com o NumPy e entregam algoritmos e recursos mais recentes voltados para aplicações de aprendizagem de máquina e inteligência artificial. From 49a80532f8f2655c2cff901b843202cd80220eee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 18:57:12 +0200 Subject: [PATCH 651/909] 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 cdc9b2de43..61ab60cace 100644 --- a/content/pt/citing-numpy.md +++ b/content/pt/citing-numpy.md @@ -3,7 +3,7 @@ title: Citando o NumPy 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: +Se o 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: [0.1038/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)). From 4a2d9f919e93338e97ebce18ccfbfd4ad48991a3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 19:17:56 +0200 Subject: [PATCH 652/909] 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 d0088908a0..5a29dbde43 100644 --- a/content/pt/code-of-conduct.md +++ b/content/pt/code-of-conduct.md @@ -20,8 +20,8 @@ Nós nos esforçamos para: 1. Sermos abertos. Convidamos qualquer pessoa a participar da nossa comunidade. Preferimos usar métodos públicos de comunicação para mensagens relacionadas aos projetos, a menos que estejamos discutindo algo sensível. Isso se aplica a mensagens em busca de ajuda ou suporte relacionado ao projeto também; não só é muito mais provável que um pedido de ajuda público resulte em uma resposta, mas isso também garante que qualquer erro involuntário na resposta seja mais facilmente detectado e corrigido. 2. Sermos empáticos, acolhedores, amigáveis e pacientes. Trabalhamos juntos para resolver conflitos e acreditamos em boas intenções. Todos nós podemos sentir alguma frustração de vez em quando, mas não permitimos que a frustração se transforme num ataque pessoal. Uma comunidade onde as pessoas se sentem desconfortáveis ou ameaçadas não é uma comunidade produtiva. 3. Sermos colaborativos. O nosso trabalho será utilizado por outras pessoas e, por sua vez, dependeremos do trabalho dos outros. Quando fazemos algo em benefício do projeto, estamos dispostos a explicar aos outros como esse algo funciona, para que outros possam desenvolver o trabalho e torná-lo ainda melhor. Qualquer decisão que tomemos afetará nossos usuários e os colegas, e levamos essas consequências a sério quando tomamos decisões. -4. Sermos inquisitivos. Ninguém sabe tudo! Fazer perguntas antecipadamente evita muitos problemas mais tarde, por isso encorajamos as perguntas, embora possamos encaminhá-las para um fórum adequado. Vamos nos esforçar para sermos sensíveis e úteis. -5. Termos cuidado com as palavras que escolhemos. Somos cuidadosos e respeitosos na nossa comunicação e assumimos a responsabilidade pelo nosso próprio discurso. Seja gentil com os outros. Não insulte ou deprecie outros participantes. Nós não aceitaremos assédio ou outros comportamentos exclusivos, como: +4. Sermos questionadores. Ninguém sabe tudo! Fazer perguntas antecipadamente evita muitos problemas mais tarde, por isso encorajamos as perguntas, embora possamos encaminhá-las para um fórum adequado. Vamos nos esforçar para sermos sensíveis e úteis. +5. Termos cuidado com as palavras que escolhemos. Sejamos cuidadosos e respeitosos na nossa comunicação e tomemos para nós a responsabilidade pelo nosso próprio discurso. Seja gentil com os outros. Não insulte ou deprecie outros participantes. Nós não aceitaremos assédio ou outros comportamentos exclusivos, como: * Ameaças ou linguagem violenta direcionadas contra outra pessoa. * Piadas e linguagem sexista, racista ou discriminatória. * Postagem de material sexualmente explícito ou violento. From 9886387031c5d1cac3b3b4310d4e7d7b3f936491 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 20:05:17 +0200 Subject: [PATCH 653/909] 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 ee466892d9..d77eae3bfd 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](/gallery/team.html). 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](/gallery/team.html). 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 90c74251e0b380a93ba771433239cc6cee81dda5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 20:19:03 +0200 Subject: [PATCH 654/909] 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 90402734ed..66b4c8498c 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -18,12 +18,12 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: NumPy v1.20.0 - content: Suporte a anotações de tipos - Melhorias no desempenho através de SIMD multi-plataformas + title: Pesquisa NumPy 2021 + content: Sua voz é importante url: /pt/news shell: title: placeholder - promptlabel: interactive shell prompt + promptlabel: console interativo button: - label: Habilita o tutorial com console interativo From 23143742129667b39567d2eb126b910a26ea0392 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 20:19:05 +0200 Subject: [PATCH 655/909] New translations contribute.md (Portuguese, Brazilian) --- content/pt/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/contribute.md b/content/pt/contribute.md index a02f8cbc09..be44edae3c 100644 --- a/content/pt/contribute.md +++ b/content/pt/contribute.md @@ -2,7 +2,7 @@ title: Contribua com o NumPy sidebar: false - - - -O projeto NumPy precisa de sua experiência e entusiasmo! Suas opções de não são limitadas à programação -- além de +O projeto NumPy precisa de sua experiência e entusiasmo! Suas opções não são limitadas à programação -- além de - [Escrever código](#writing-code) From 3845ab4f403dfaa059e329aed336bbfb17010140 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 20:27:01 +0200 Subject: [PATCH 656/909] New translations learn.md (Portuguese, Brazilian) --- content/pt/learn.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/pt/learn.md b/content/pt/learn.md index 915a712533..90d8904e4d 100644 --- a/content/pt/learn.md +++ b/content/pt/learn.md @@ -13,20 +13,20 @@ Você pode encontrar um conjunto de tutoriais e materiais educativos criados pel Abaixo está uma coleção de recursos externos selecionados. Para contribuir, veja o [fim desta página](#add-to-this-list). -## Avançado +## Iniciantes Há uma tonelada de informações sobre o NumPy lá fora. Se você está começando, recomendamos fortemente estes: - **Livros** + **Tutoriais** * [NumPy Quickstart Tutorial (Tutorial de Início Rápido)](https://numpy.org/devdocs/user/quickstart.html) -* [NumPy tutorial *por Nicolas Rougier*](https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b) +* [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/) -* [**Tutoriais**](https://github.com/rougier/numpy-tutorial) +* [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 (Guia de Usuário NumPy)](https://numpy.org/devdocs) @@ -36,19 +36,19 @@ Há uma tonelada de informações sobre o NumPy lá fora. Se você está começa * [From Python to NumPy *por 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) *por Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow* -**Vídeos** +Você também pode querer conferir a [lista Goodreads](https://www.goodreads.com/shelf/show/python-scipy) sobre o tema "Python+SciPy. A maioria dos livros lá serão sobre o "ecossistema SciPy", que tem o NumPy em sua essência. - Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais. + **Vídeos** * [Introduction to Numerical Computing with NumPy](http://youtu.be/ZB7BZMhfPgk) *por Alex Chabot-Leclerc* *** -## Palestras sobre NumPy +## Avançados -**Tutoriais** +Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais. - **Livros** + **Tutoriais** * [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* @@ -57,20 +57,20 @@ Há uma tonelada de informações sobre o NumPy lá fora. Se você está começa * [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/) - **Vídeos** + **Livros** * [Python Data Science Handbook](https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057) *por Jake Vanderplas* * [Python for Data Analysis](https://www.amazon.com/Python-Data-Analysis-Wrangling-IPython/dp/1491957662) *por 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) *por Robert Johansson* - 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). + **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* *** -## Citando a NumPy +## Palestras sobre NumPy * [The Future of NumPy Indexing](https://www.youtube.com/watch?v=o0EacbIbf58) *por Jaime Fernández* (2016) * [Evolution of Array Computing in Python](https://www.youtube.com/watch?v=HVLPJnvInzM&t=10s) *por Ralf Gommers* (2019) @@ -80,7 +80,7 @@ Há uma tonelada de informações sobre o NumPy lá fora. Se você está começa *** -## Contribua para esta lista +## Citando o 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](/citing-numpy). From b6f38fd2a3bc72b001763293367ed213726e05d9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 20:32:16 +0200 Subject: [PATCH 657/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 024a0bd88b..61b7b5421c 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,7 +3,7 @@ title: Notícias sidebar: false --- -### NumPy versão 1.20.0 +### 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. @@ -12,13 +12,13 @@ Chegou a hora de fazer outra pesquisa e estamos contando com você novamente. Va Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Diversidade no projeto NumPy +### NumPy versão 1.21.0 -_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: -- 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. -- initial work on the new dtype infrastructure and casting, -- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- 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, - improved documentation, - improved annotations, - new `PCG64DXSM` bitgenerator for random numbers. @@ -34,7 +34,7 @@ _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students an ### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? _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: -- 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. +- 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)). ### NumPy versão 1.19.2 From d891129d17441fba21356e3a4546c2637d9bd121 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 20:44:59 +0200 Subject: [PATCH 658/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 61b7b5421c..8a0d095873 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -19,35 +19,35 @@ _23 de junho de 2021_ -- O [NumPy 1.21.0](https://numpy.org/doc/stable/release/1 - 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, -- improved documentation, -- improved annotations, -- new `PCG64DXSM` bitgenerator for random numbers. +- 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. -### Primeiro artigo oficial do NumPy publicado na Nature! +### 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/. -### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? +### NumPy versão 1.20.0 _30 de janeiro de 2021_ -- O [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html) está disponível. Este é o maior 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. - 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)). -### NumPy versão 1.19.2 +### Diversidade no projeto NumPy -_24 de junho de 2020_ -- NumPy agora tem um novo logo: +_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). -### A primeira pesquisa NumPy está aqui! +### Primeiro artigo oficial do NumPy publicado na Nature! _16 de setembro de 2020_ -- Temos o prazer de anunciar a publicação do [primeiro artigo oficial do NumPy](https://www.nature.com/articles/s41586-020-2649-2) como um artigo de revisão na Nature. Isso ocorre 14 anos após o lançamento do NumPy 1.0. O artigo abrange aplicações e conceitos fundamentais da programação de matrizes, o rico ecossistema científico de Python construído em cima do NumPy, e os protocolos de array recentemente adicionados para facilitar a interoperabilidade com bibliotecas externas para computação com matrizes e tensores, como CuPy, Dask e JAX. -### O NumPy tem um novo logo! +### O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias? _14 de setembro de 2020_ -- Python 3.9 será lançado em algumas semanas. Se você for quiser usar imediatamente a nova versão do Python, você pode ficar desapontado ao descobrir que o NumPy (e outros pacotes binários como SciPy) não terão wheels no dia do lançamento. É um grande esforço adaptar a infraestrutura de compilação a uma nova versão de Python e normalmente leva algumas semanas para que os pacotes apareçam no PyPI e no conda-forge. Em preparação para este evento, por favor, certifique-se de - atualizar seu `pip` para a versão 20.1 pelo menos para suportar `manylinux2010` e `manylinux2014` From 278406376652f47f52ad2b906fd1da9bba9f6c94 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 20:58:14 +0200 Subject: [PATCH 659/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 8a0d095873..ebe2387735 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -54,46 +54,46 @@ _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 1.19.0 +### 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. -### Aceitação no programa Season of Docs +### A primeira pesquisa NumPy está aqui! _2 de julho de 2020_ -- Esta pesquisa tem como objetivo guiar e definir prioridades para tomada de decisões sobre o desenvolvimento do NumPy como software e como comunidade. A pesquisa está disponível em mais 8 idiomas além do inglês: Bangla, Hindi, Japonês, Mandarim, Português, Russo, Espanhol e Francês. Ajude-nos a melhorar o NumPy respondendo à pesquisa [aqui](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy versão 1.18.0 +### O NumPy tem um novo logo! -Por favor, veja as [notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0) para mais detalhes. +_24 de junho de 2020_ -- NumPy agora tem um novo logo: -NumPy 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. -### O NumPy recebe financiamento da Chan Zuckerberg Initiative +### NumPy versão 1.19.0 _20 de junho de 2020_ -- O NumPy 1.19.0 está disponível. Esta é a primeira versão sem suporte ao Python 2, portanto foi uma "versão de limpeza". A versão mínima de Python suportada agora é Python 3.6. Uma característica nova importante é que a infraestrutura de geração de números aleatórios que foi introduzida na NumPy 1.17.0 agora está acessível a partir do Cython. -### Season of Docs acceptance +### Aceitação no programa Season of Docs _11 de maio de 2020_ -- O NumPy foi aceito como uma das organizações mentoras do programa Google Season of Docs. Estamos animados com a oportunidade de trabalhar com um *technical writer* para melhorar a documentação do NumPy mais uma vez! Para mais detalhes, consulte [o site oficial do programa Season of Docs](https://developers.google.com/season-of-docs/) e nossa [página de ideias](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### NumPy 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`. -NumPy 1.15.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _23 de julho de 2018_. +Por favor, veja as [notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.0) para mais detalhes. -### _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. +### O NumPy receberá um auxílio da Chan Zuckerberg Initiative -NumPy 1.18.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.3)) -- _19 de abril de 2020_. +_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. 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. @@ -104,15 +104,15 @@ 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 1.16.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 de janeiro de 2019_. +- NumPy 1.21.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junho de 2021_. +- NumPy 1.20.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de maio de 2021_. +- NumPy 1.20.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de janeiro de 2021_. +- NumPy 1.19.5 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de janeiro de 2021_. +- NumPy 1.19.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junho 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 1.18.1 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.1)) -- _6 de janeiro de 2020_. -- NumPy 1.18.2 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _17 de março de 2020_. -- NumPy 1.14.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _7 de janeiro de 2018_. -- NumPy 1.17.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _26 de julho de 2019_. - 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 1.16.0 ([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 1.17.0 ([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 1.15.0 ([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 e52fb61572f2970fced67214ff782d39ead63c37 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 22:13:23 +0200 Subject: [PATCH 660/909] 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 74bf2ba35c..c472579e8d 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -2,7 +2,7 @@ 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: 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: 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 bc6fc59d54bcf420de79bbefc5d8855825523f82 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 22:30:57 +0200 Subject: [PATCH 661/909] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index c472579e8d..17665ecf4b 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -3,32 +3,32 @@ 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: 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: 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: "Transformações compostas de programas NumPy: diferenciação, vetorização, compilação just-in-time para a GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX url: https://github.com/google/jax From bf074fb544533982bf49f0a0ca3278f5be853548 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 22:49:04 +0200 Subject: [PATCH 662/909] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 17665ecf4b..15b0213852 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -34,31 +34,31 @@ 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/ From 3e4d712f106b47c7f33f0dd35527588b4b921a41 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 23:22:11 +0200 Subject: [PATCH 663/909] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 15b0213852..968bcbe414 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -64,7 +64,7 @@ arraylibraries: 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 @@ -138,43 +138,43 @@ scientificdomains: img: /images/content_images/sc_dom_img/bayesian_inference.svg - title: Mathematical Analysis - alttext: Four mathematical symbols. + 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 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: Diagrama de três círculos sobrepostos. Os círculos estão rotulados como "Matemática", "Ciência da Computação" e "Especialização de Domínio". No meio do diagrama onde se sobrepõem os três círculos, está uma área denominada "Ciência de Dados". examples: - - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "Extrair, Transformar, Carregar: [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: "Análise exploratória: [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: "Modelar e avaliar: [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: "Criar relatórios em dashboards: [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)). From 4dd36be4ada60524703da4cd3eb8fb79633952fa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 23:38:54 +0200 Subject: [PATCH 664/909] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 72 ++++++++++++++++++------------------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index 968bcbe414..dcb5e0933b 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -70,74 +70,74 @@ arraylibraries: 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 - title: XND - text: Develop libraries for array computing, recreating NumPy's foundational concepts. + text: Desenvolva bibliotecas para computação em arrays, recriando os conceitos fundamentais do NumPy. 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. + 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: 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 + title: Análise Matemática alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - @@ -177,43 +177,43 @@ datascience: text: "Criar relatórios em dashboards: [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: '[Dask](https://dask.org) e [Ray](https://ray.io/) são projetados para altos volumes de dados. Ambientes de produção estáveis dependem de versionamento de dados ([DVC](https://dvc.org), rastreamento de experimentos ([MLFlow](https://mlflow.org)) e automação de fluxo de trabalho ([Airflow](https://airflow.apache.org) e [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/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: http://vispy.org/gallery.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 759e7285bc74c5d7af636909cc4eb5dc957be8bd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 23:48:47 +0200 Subject: [PATCH 665/909] 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 08af62cca0..31e0daba3d 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -7,7 +7,7 @@ _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/master/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, por meio 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). ## Conselho Diretor (Steering Council) From 1fe5d082d8c929a852f67e618c8a12de22b0acd2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 23:48:48 +0200 Subject: [PATCH 666/909] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/pt/install.md b/content/pt/install.md index b86910dc56..a861ce80e7 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -3,13 +3,13 @@ title: Instalando o NumPy sidebar: false --- -O único pré-requisito para instalar o NumPy é o próprio Python. Se você ainda não tem o Python e quer começar do jeito mais simples, nós recomendamos que você use a [Distribuição Anaconda](https://www.anaconda.com/distribution) - inclui Python, NumPy e outros pacotes comumente usados para computação científica e ciência de dados. +O único pré-requisito para instalar o NumPy é o próprio Python. Se você ainda não tem o Python e quer começar do jeito mais simples, nós recomendamos que você use a [Distribuição Anaconda](https://www.anaconda.com/distribution) - ela inclui Python, NumPy e outros pacotes comumente usados para computação científica e ciência de dados. -O NumPy pode ser instalado com `conda`, com `pip`, com um gerenciador de pacotes no macOS e Linux, ou [da fonte](https://numpy.org/devdocs/user/building.html). Para obter instruções mais detalhadas, consulte nosso [guia de instalação do Python e do NumPy](#python-numpy-install-guide) abaixo. +O NumPy pode ser instalado com `conda`, com `pip`, com um gerenciador de pacotes no macOS e Linux, ou [pelo código fonte](https://numpy.org/devdocs/user/building.html). Para obter instruções mais detalhadas, consulte nosso [guia de instalação do Python e do NumPy](#python-numpy-install-guide) abaixo. **CONDA** -Se você usar o `conda`, você pode instalar o NumPy do canal `default` ou do `conda-forge`: +Se você usar o `conda`, você pode instalar o NumPy do canal `defaults` ou do `conda-forge`: ```bash # Recomenda-se usar um ambiente novo ao invés de instalar no ambiente-base From 808878ee631fc21ff7ab99d8a4b1431ab5e4b17b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 9 Aug 2021 23:55:03 +0200 Subject: [PATCH 667/909] New translations install.md (Portuguese, Brazilian) --- content/pt/install.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/install.md b/content/pt/install.md index a861ce80e7..179ca126e7 100644 --- a/content/pt/install.md +++ b/content/pt/install.md @@ -17,7 +17,7 @@ conda create -n my-env conda activate my-env # Se quiser instalar do conda-forge conda config --env --add channels conda-forge -# O comando para instação +# O comando para instalação conda install numpy ``` From 359f1f5c69feab0d284d2ae65ab62fd5db758899 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 10 Aug 2021 09:49:32 +0200 Subject: [PATCH 668/909] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index aa1a6c0d39..891a9b1437 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -28,7 +28,7 @@ arraylibraries: url: https://cupy.chainer.org - title: JAX - text: "NumPy 프로그램을 부분적으로 변환하여 벡터화, GPU/TPU의 적시 컴파일을 제공하는 라이브러리." + 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 @@ -165,19 +165,19 @@ 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: 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.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. 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. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org) and [Prefect](https://www.prefect.io)). visualization: images: - From 191e152f7559eb89c9a0ac2fd9f4a232c1a362dc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 10 Aug 2021 09:49:33 +0200 Subject: [PATCH 669/909] New translations tabcontents.yaml (Portuguese, Brazilian) --- content/pt/tabcontents.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/pt/tabcontents.yaml b/content/pt/tabcontents.yaml index dcb5e0933b..2643c627c0 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -28,7 +28,7 @@ arraylibraries: url: https://cupy.chainer.org - title: JAX - text: "Transformações compostas de programas NumPy: diferenciação, vetorização, compilação just-in-time para a 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 @@ -165,19 +165,19 @@ datascience: image2: - img: /images/content_images/data-science.png - alttext: Diagrama de três círculos sobrepostos. Os círculos estão rotulados como "Matemática", "Ciência da Computação" e "Especialização de Domínio". No meio do diagrama onde se sobrepõem os três círculos, está uma área denominada "Ciência de Dados". + 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: "Extrair, Transformar, Carregar: [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: "Análise exploratória: [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: "Modelar e avaliar: [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: "Criar relatórios em dashboards: [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: '[Dask](https://dask.org) e [Ray](https://ray.io/) são projetados para altos volumes de dados. Ambientes de produção estáveis dependem de versionamento de dados ([DVC](https://dvc.org), rastreamento de experimentos ([MLFlow](https://mlflow.org)) e automação de fluxo de trabalho ([Airflow](https://airflow.apache.org) e [Prefect](https://www.prefect.io)).' + 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) and [Prefect](https://www.prefect.io)). visualization: images: - From a05d5d3131ecbb15d6e2aa0d8a3d10f5c5ca2b1f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 10 Aug 2021 09:49:34 +0200 Subject: [PATCH 670/909] New translations tabcontents.yaml (Spanish) --- content/es/tabcontents.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/tabcontents.yaml b/content/es/tabcontents.yaml index 74bf2ba35c..7d7cc1c188 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -28,7 +28,7 @@ arraylibraries: url: https://cupy.chainer.org - title: JAX - text: "Composable transformations of NumPy programs differentiate: vectorize, just-in-time compilation to GPU/TPU." + text: "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 @@ -165,19 +165,19 @@ 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: 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.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. 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. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org) and [Prefect](https://www.prefect.io)). visualization: images: - From dcbc2c4c6660553c4ab3c0606be26ba65863f85a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 10 Aug 2021 09:49:35 +0200 Subject: [PATCH 671/909] New translations tabcontents.yaml (Arabic) --- content/ar/tabcontents.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ar/tabcontents.yaml b/content/ar/tabcontents.yaml index 74bf2ba35c..7d7cc1c188 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -28,7 +28,7 @@ arraylibraries: url: https://cupy.chainer.org - title: JAX - text: "Composable transformations of NumPy programs differentiate: vectorize, just-in-time compilation to GPU/TPU." + text: "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 @@ -165,19 +165,19 @@ 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: 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.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. 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. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org) and [Prefect](https://www.prefect.io)). visualization: images: - From 386627d10835fc8de92df30a8e06d863e6f31e2a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 10 Aug 2021 09:49:36 +0200 Subject: [PATCH 672/909] New translations tabcontents.yaml (Japanese) --- content/ja/tabcontents.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ja/tabcontents.yaml b/content/ja/tabcontents.yaml index 284c3fb978..9b955b584b 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -28,7 +28,7 @@ arraylibraries: url: https://cupy.chainer.org - title: JAX - text: "NumPyプログラムの部分的な変換により、微分可能化、ベクトル化、GPU/TPUへのジャストインタイム・コンパイルを実現します。" + 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 @@ -165,19 +165,19 @@ datascience: image2: - img: /images/content_images/data-science.png - alttext: 3つの重なり合う円の図. 「数学」,「コンピュータサイエンス」,「ドメインの専門知識」とラベルされた円. 図の途中には、3つの円が重なっており、「データサイエンス」と表示された領域があります。 + 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: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org),[ 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: "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: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "モデルと評価: [scikit-learn](https://scikit-learn.org),[ statsmodels.org/stable/index.html),[ PyMC3](https://docs.pym.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: "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)" + text: "Report in a dashboard: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: - - text: 大量のデータの場合は, [Dask](https://dask.org) や[Ray](https://ray.io/) がスケールする様に設計されています。データのバージョンに応じた安定したデプロイには[DVC](https://dvc.org)が利用できます。実験結果のトラッキングには[MLFlow](https://mlflow.org), ワークフローの自動化には [Airflow](https://airflow.apache.org)や、[Prefect](https://www.prefect.io)が利用できます。 + 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) and [Prefect](https://www.prefect.io)). visualization: images: - From a07b41a06caa74d9bc94a6cd1c1e0f032bc1f115 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 10 Aug 2021 09:49:37 +0200 Subject: [PATCH 673/909] New translations tabcontents.yaml (Chinese Simplified) --- content/zh/tabcontents.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/zh/tabcontents.yaml b/content/zh/tabcontents.yaml index 74bf2ba35c..7d7cc1c188 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -28,7 +28,7 @@ arraylibraries: url: https://cupy.chainer.org - title: JAX - text: "Composable transformations of NumPy programs differentiate: vectorize, just-in-time compilation to GPU/TPU." + text: "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 @@ -165,19 +165,19 @@ 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: 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.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. 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. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org) and [Prefect](https://www.prefect.io)). visualization: images: - From 588613b0cfe23f8636db6822b4c02541bd3a56ab Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Aug 2021 04:26:18 +0200 Subject: [PATCH 674/909] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index 1d316dc7e2..b7a571c5ba 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -47,17 +47,17 @@ NumPy项目欢迎并鼓励每个人参与。 我们致力于成为一个人人 我们知道，互联网通信平台从诞生开始就演变为非常普遍的辱骂恶意中伤的场所。 我们还认识到，有时人们可能会有不愉快的时候，或不知道本行为守则中的一些准则。 在决定如何应对违反本守则的行为时，请铭记这一点。 -关于明显故意违反行为，向行为守则委员会报告（见下文）。 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. +关于明显故意违反行为，向行为守则委员会报告（见下文）。 对于可能无意的违规行为，您可以回复此人并指出此行为准则（无论是在公开场合还是私下场合，选择一种最合适的方式）。 如果你不愿意这样做，请随时直接向行为守则委员会汇报， 或以保密方式向委员会征求意见。 -You can report issues to the NumPy Code of Conduct Committee at numpy-conduct@googlegroups.com. +您可以在 numpy-conduct@googlegroups.com上向NumPy行为守则委员会报告问题。 -Currently, the Committee consists of: +目前，该委员会包含如下成员： * Stefan van der Walt -* Melissa Weber Mendonça +* Melissa Weber Mendonça * Anirudh Subramanian -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). +如果你的举报涉及委员会的任何成员，或他们认为对举报的处理存在利益冲突， 他们将回避审议你的报告。 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 From 25aad394c9f3a6cbbd9544083c42feae637dce69 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Aug 2021 04:36:47 +0200 Subject: [PATCH 675/909] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index b7a571c5ba..56ce018161 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -57,21 +57,21 @@ NumPy项目欢迎并鼓励每个人参与。 我们致力于成为一个人人 * Melissa Weber Mendonça * Anirudh Subramanian -如果你的举报涉及委员会的任何成员，或他们认为对举报的处理存在利益冲突， 他们将回避审议你的报告。 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). +如果你的举报涉及委员会的任何成员，或他们认为对举报的处理存在利益冲突， 他们将回避审议你的报告。 或者，如果你出于任何原因感到不方便向委员会提交报告， 那么您也可以通过 [conduct@numfocus.org](https://numfocus.org/code-of-conduct#persons-responsible)联系NumFOCUS高级工作人员。 -### Incident reporting resolution & Code of Conduct enforcement +### 事故报告决议 & 行为守则执行 -_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). +_本节概述最重要的环节。 更多详细信息可在_ [NumPy行为守则-如何对举报采取后续行动](/report-handling-manual) 中找到。 -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). +我们将调查并答复所有指控。 NumPy行为守则委员会和NumPy指导委员会（如果涉及的话）将保护举报者的身份，并将投诉内容视为保密(除非举报人另有约定)。 -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. +如果发生严重和明显的违约行为，例如人身攻击和恐吓、性别或种族歧视， 我们将立即断开发起人与 NumPy 通信频道的联系；详情请参阅手册。 -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: +在不涉及明显严重和明显违反本行为守则行为的情况下，就收到的任何违反行为守则行为报告采取行动的程序将是： -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), +1. 声明已收到举报信息。 +2. 合理的讨论/反馈。 +3. 调解(如果反馈意见没有产生帮助，并且只有当举报方和被举报方都同意这样做时)， 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 02953d0e9ea3ed0d95b23d929b3281702c9a49cf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Aug 2021 04:52:44 +0200 Subject: [PATCH 676/909] New translations code-of-conduct.md (Chinese Simplified) --- content/zh/code-of-conduct.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/code-of-conduct.md b/content/zh/code-of-conduct.md index 56ce018161..65f28b634e 100644 --- a/content/zh/code-of-conduct.md +++ b/content/zh/code-of-conduct.md @@ -72,12 +72,12 @@ _本节概述最重要的环节。 更多详细信息可在_ [NumPy行为守则- 1. 声明已收到举报信息。 2. 合理的讨论/反馈。 3. 调解(如果反馈意见没有产生帮助，并且只有当举报方和被举报方都同意这样做时)， -4. enforcement via transparent decision (see [Resolutions](/report-handling-manual#resolutions)) by the Code of Conduct Committee. +4. 《行为守则》委员会通过公开透明的决定(见 [决议](/report-handling-manual#resolutions)) 来执行。 -The Committee will respond to any report as soon as possible, and at most within 72 hours. +委员会将尽快、至多在72小时内对任何举报作出答复。 -### Endnotes +### 尾注 -We are thankful to the groups behind the following documents, from which we drew content and inspiration: +我们感谢以下文件背后的团体，我们从这些文件中吸取了内容和灵感： -- [The SciPy Code of Conduct](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) +- [《SciPy行为守则》](https://docs.scipy.org/doc/scipy/reference/dev/conduct/code_of_conduct.html) From 90e21d0c2c68f7e6d357751736e65775d3ee4ba3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Aug 2021 04:52:45 +0200 Subject: [PATCH 677/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index 15f345c121..55fa4d0711 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -3,7 +3,7 @@ title: 社区快讯 sidebar: false --- -### 2021 NumPy survey +### 2021 Numpy调查 _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. From 4f4b5c9c021b9964e9a350b33af047d50c1e682c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Aug 2021 04:52:46 +0200 Subject: [PATCH 678/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 92d8ddd2e1..262c4fa815 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -139,29 +139,29 @@ footer: column2: links: - - text: About us + text: 关于我们 link: /about - - text: Community + text: 社区 link: /community - - text: Contribute + text: 参与贡献 link: /contribute - - text: Code of conduct - link: /code-of-conduct + text: 行为准则 + link: /codes-of-conduct column3: links: - - text: Get help + text: 获得帮助 link: /gethelp - - text: Terms of use + text: 使用条款 link: /terms - - text: Privacy + text: 隐私政策 link: /privacy - - text: Press kit + text: 宣传材料 link: /press-kit From 8beed51cfe3a68cc716043a56e674e306e1f3132 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 16 Aug 2021 06:09:20 +0200 Subject: [PATCH 679/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index 55fa4d0711..9f6a2222f3 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -5,7 +5,7 @@ sidebar: false ### 2021 Numpy调查 -_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_ -- 我们相信NumPy社区的力量。 来自75个国家的1236 名用户参加了我们去年的首次调查。 调查结果使我们对今后12个月应该集中注意的问题有了很好的了解。 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. From 208ce0dd04cf2c945a97d5972ae4ca24a1c55f45 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 23 Aug 2021 16:19:56 +0200 Subject: [PATCH 680/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 9f6a2222f3..adc6fb63f1 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -7,14 +7,14 @@ sidebar: false _2021年7月12日_ -- 我们相信NumPy社区的力量。 来自75个国家的1236 名用户参加了我们去年的首次调查。 调查结果使我们对今后12个月应该集中注意的问题有了很好的了解。 -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. +现在是时候进行另一次调查了，我们将再度尋求您的合作。 这份调查将耗费您大约15分钟的时间。 除英文外，调查问卷还提供另外8种语文：孟加拉语、法语、印地语、日语、普通话、葡萄牙语、俄语和西班牙语。 -Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +点击链接开始：https://berkeley.qualtrics.com/jfe/form/SV_aOONjgcBXDSl4q。 -### Numpy 1.21.0 release +### NumPy 1.21.0 发布 -_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) 正式发布。 The highlights of the release are: - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, From 0db3a834a7a848b229c1dbf084b891104b382416 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 23 Aug 2021 16:28:46 +0200 Subject: [PATCH 681/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index adc6fb63f1..58358eaa89 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -14,7 +14,7 @@ _2021年7月12日_ -- 我们相信NumPy社区的力量。 来自75个国家的12 ### NumPy 1.21.0 发布 -_2021年1月23日_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 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) 正式发布。 此次发布的重点是： - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, From 4826be1fc43a4ec7933cc23becb0fca50c41493f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 23 Aug 2021 16:45:31 +0200 Subject: [PATCH 682/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 58358eaa89..db509069c6 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -14,16 +14,16 @@ _2021年7月12日_ -- 我们相信NumPy社区的力量。 来自75个国家的12 ### NumPy 1.21.0 发布 -_2021年1月23日_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 此次发布的重点是： +_2021年6月23日_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 此次发布的重点是： -- 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. +- 继续开展SIMD工作，涵盖更多的功能和平台 +- 新dtype的基础和型态转换初步工作 +- 适用于Mac平台的Python 3.8和Python 3.9的universal2 wheels +- 改进文档 +- 改进注释 +- 新的 `PCG64DXSM` 位元生成器，用于生成随机数字 -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. +这个NumPy版本包含175人所贡献的581个合并请求。 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 From eb7a1b6e154c4cee383be155c1771f358361889a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 23 Aug 2021 17:13:45 +0200 Subject: [PATCH 683/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index db509069c6..590fd04699 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -23,15 +23,15 @@ _2021年6月23日_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0 - 改进注释 - 新的 `PCG64DXSM` 位元生成器，用于生成随机数字 -这个NumPy版本包含175人所贡献的581个合并请求。 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. +这个NumPy版本包含175人所贡献的581个合并请求。 此发布版本支持Python 3.7-3.9，将在Python 3.10发布后添加Python 3.10支持。 -### 2020 NumPy survey results +### 2020 Numpy调研结果出炉 -_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_ -- 在2020年, NumPy调研小组与密歇根大学和马里兰大学的学生和教职员工合作，进行了第一次官方NumPy社区调查。 在这里可以查看调研结果：https://numpy.org/user-survey-2020/。 -### Numpy 1.20.0 release +### NumPy 1.20.0 发布 _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. From f65b0c7ebb283462d7cc31562a0545598b3d32c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Aug 2021 13:32:19 +0200 Subject: [PATCH 684/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 590fd04699..b956da9a04 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -33,13 +33,13 @@ _2021年6月22日_ -- 在2020年, NumPy调研小组与密歇根大学和马里 ### NumPy 1.20.0 发布 -_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)). +_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢180+位贡献者。 最令人振奋的两个特点是： +- 为大部分Numpy代码做了类型注解，並添加了一个全新的`numpy.typing` 子模块，其中包含 `ArrayLike` 和 `DtypeLike`别名 ，使得用户和下游依赖库可以为自己的代码添加类型注解。 +- 为多个架构进行SIMD编译优化，其支持X86(SSE、AVX)、ARM64(Neon) 和PowerPC(VSX) 指令集。 大幅提高许多函数的性能(例如： [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194))。 -### Diversity in the NumPy project +### NumPy项目的多样性 -_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_ -- 我们就NumPy项目的社交媒体、多样性和包容性的现状以及相关的讨论撰写了一份[声明](/diversity_sep2020)。 ### First official NumPy paper published in Nature! From ee60f7b24e2ad0cfc09df2d0efe6b313fae612a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Aug 2021 13:44:28 +0200 Subject: [PATCH 685/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index b956da9a04..4eefed06d6 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -42,14 +42,14 @@ _2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.21.0 _2020年9月20日_ -- 我们就NumPy项目的社交媒体、多样性和包容性的现状以及相关的讨论撰写了一份[声明](/diversity_sep2020)。 -### First official NumPy paper published in Nature! +### NumPy官方第一次在Nature发表论文！ -_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_ - 我们高兴地宣布 [Numpy的第一篇官方论文](https://www.nature.com/articles/s41586-020-2649-2)刊登在Nature的评论文章。 这离NumPy 1.0发布已经过去了整整14年。 该论文涵盖数组编程的应用和基本概念，丰富的Python科学计算生态系统建立在NumPy之上，包括最近添加的数组标准协议，大大提高了与外部数组和张量库(如CuPy, Dask 和 JAX) 的互操作性 。 -### Python 3.9 is coming, when will NumPy release binary wheels? +### Python 3.9 即将来临，新版本的NumPy 将在何时发布？ -_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 将在几周后发布。 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. From 7cd88b913d3811ee3f055702123cca9aac0a3b03 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Aug 2021 14:00:02 +0200 Subject: [PATCH 686/909] 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 4eefed06d6..a5b0433bc0 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -49,9 +49,9 @@ _2020年9月16日_ - 我们高兴地宣布 [Numpy的第一篇官方论文](https ### Python 3.9 即将来临，新版本的NumPy 将在何时发布？ -_2020年9月14日_ -- Python 3.9 将在几周后发布。 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. +_2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Python版本的早期采用者， 您可能会失望的发现NumPy(以及其他二进制软件包，如SciPy) 在Python新版发布当天还不会发布相应的版本。 构建兼容新的 Python 版本的基础设施需要付出重大努力，通常需要几周时间才能让新版本出现在 PyPI 和 conda-forge 上。 为了这次版本升级得以顺利进行，请确保： +- 将您的 `pip` 升级到 20.1 版本，至少要支持`manylinux2010` 和 `manylinux2014` +- 使用 [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) 或 `--only-binary=:all:` 选项来防止 `pip` 尝试从源码构建。 ### Numpy 1.19.2 release From 62cf4a761b3e5c573cfb8128c196b461fb162d12 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Aug 2021 14:27:52 +0200 Subject: [PATCH 687/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index a5b0433bc0..3aefe6b138 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -54,9 +54,9 @@ _2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Pyth - 使用 [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) 或 `--only-binary=:all:` 选项来防止 `pip` 尝试从源码构建。 -### Numpy 1.19.2 release +### NumPy 1.19.2 发布 -_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) 正式发布。 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! From 883a4be69017ba5654b82f1cb087a5aeaf24ef0f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 25 Aug 2021 14:47:50 +0200 Subject: [PATCH 688/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index 3aefe6b138..180f58eb0e 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -56,7 +56,7 @@ _2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Pyth ### NumPy 1.19.2 发布 -_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) 正式发布。 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) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 做准备，並固定设置工具以在上游修改正在进行时保持 distutils 工作。 Aarch64架构的安装包是用最新的 manylinux2014 版本构建的，它修复了 linux 发行版之间使用不同大小内存页的问题。 ### The inaugural NumPy survey is live! From 250709a707c54a3e3c5ba01245dbdd87432a57df Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 26 Aug 2021 10:10:21 +0200 Subject: [PATCH 689/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 180f58eb0e..9e762d3e88 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -58,23 +58,23 @@ _2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Pyth _2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 做准备，並固定设置工具以在上游修改正在进行时保持 distutils 工作。 Aarch64架构的安装包是用最新的 manylinux2014 版本构建的，它修复了 linux 发行版之间使用不同大小内存页的问题。 -### The inaugural NumPy survey is live! +### 首次NumPy调研即将开始！ -_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_ - 本次调查旨在指导并确定将NumPy以社区方式还是软件方式来开发。 除英文外，调查还提供了另外8种语言的版本：孟加拉语、印地语、日语、普通话、葡萄牙语、俄语、西班牙语和法语。 -Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). +请帮助我们让 NumPy 变得更好，在[这里](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)参与调查。 -### NumPy has a new logo! +### NumPy 有新标志了! -_Jun 24, 2020_ -- NumPy now has a new logo: +_2020年7月24日_ -- NumPy 现在有一个新的标志： 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. +这是一个更时髦、纯净的标志。 感谢Isabela Presedo-Floryd的设计方案， 同时感谢Travis Vaugh设计的旧图标为我们服务了15年以上。 -### NumPy 1.19.0 release +### NumPy 1.19.0 发布 _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. @@ -104,8 +104,8 @@ 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 1.21.0 ([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_. +- NumPy1.21.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年1月22日_. +- NumPy1.23.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年1月10日_. - 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_. From c7c8c96e55609f264a7194366051c49bc6873f97 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 26 Aug 2021 10:19:10 +0200 Subject: [PATCH 690/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 9e762d3e88..82ac53c4c9 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -69,29 +69,29 @@ _2020年7月2日_ - 本次调查旨在指导并确定将NumPy以社区方式还 _2020年7月24日_ -- NumPy 现在有一个新的标志： -NumPy logo +NumPy 标志 这是一个更时髦、纯净的标志。 感谢Isabela Presedo-Floryd的设计方案， 同时感谢Travis Vaugh设计的旧图标为我们服务了15年以上。 ### NumPy 1.19.0 发布 -_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 正式发布。 这是第一个不支持Python 2的版本，因此它是一个“清理版本”。 目前支持的最低Python 版本是 Python 3.6。 本版本拥有一个重要的新特性，NumPy 1.17.0引进的随机数字生成基础模块现在可以通过Cython访问。 -### Season of Docs acceptance +### 文档整改时段 -_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 已成为Google Season 文档项目之一。 我们很高兴看到有机会和技术写作者一起再次改进NumPy的技术文档！ 更多详情，请参考 [文档整改时段官方网站](https://developers.google.com/season-of-docs/) 和我们的 [意见页面](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)。 -### NumPy 1.18.0 release +### NumPy 1.18.0 发布 -_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 正式发布。 在1.17.0发生重大变化后，这是一个合并版本。 这是最后一个支持 Python 3.5的小版本。 该版本的重要更新包括两个，添加了与64位 BLAS 和 LAPACK 库有关的底层更新， 添加 一个用于`numpy.random`的新C-API更新。 -Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. +详情请看 [版本说明](https://github.com/numpy/numpy/releases/tag/v1.18.0)。 -### NumPy receives a grant from the Chan Zuckerberg Initiative +### NumPy 从Chan Zuckerberg Initiative获得了一笔捐款 _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. @@ -104,15 +104,15 @@ 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. -- NumPy1.21.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年1月22日_. -- NumPy1.23.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年1月10日_. -- 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_. +- NumPy1.21.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年6月22日_. +- NumPy1.23.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年5月10日_. +- NumPy1.20.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021年1月30日_. +- NumPy1.19.5 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021年1月5日_. +- NumPy1.19.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020年6月20日_. +- NumPy1.18.4 (发行说明) -- _2020年5月3日_. +- NumPy1.17.5 (发行说明) -- _2020年1月1日_. +- NumPy1.18.0 (发行说明) -- _2019年12月22日_. +- NumPy1.17.0 (发行说明) -- _2019年7月26日_. +- NumPy1.16.0 (发行说明) -- _2019年1月14日_. +- NumPy1.15.0 (发行说明) -- _2018年7月23日_. +- NumPy1.14.0 (发行说明) -- _2018年1月7日_. From 06af6458902535a64c866316a7fac74ba9c9aebb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 26 Aug 2021 10:41:46 +0200 Subject: [PATCH 691/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index 82ac53c4c9..ea9edee282 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -93,16 +93,16 @@ _2019年12月22日_ -- NumPy 1.18.0 正式发布。 在1.17.0发生重大变化 ### NumPy 从Chan Zuckerberg Initiative获得了一笔捐款 -_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_ -- 我们高兴地宣布NumPy和 OpenBLAS (Numpy的一个核心依赖库)已经收到一笔19,5000美元的联合赠款。 捐款来自于Chan Zuckerberg Initiative通过的[基础开源科学计算软件项目](https://chanzuckerberg.com/eoss/)，用来支持对科学发展起到关键作用的开源软件的维护、增长、开发和社区参与。 -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. +这笔赠款将用来加速改进NumPy文档、网站重构和社区开发，进而更好地为我们庞大和迅速增长的用户基础服务，并确保项目的长期可持续性。 OpenBLAS 团队将侧重于处理几个关键技术问题，特别是线程安全问题、AVX-512和 thread-local 存储(TLS) 问题，以及OpenBLAS 依赖的 ReLAPACK (递归的LAPACK) 算法改进。 -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. +若想查看更多关于捐款的倡议和交付件的详情，可在 [全额赠款提案](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) 中找到。 项目开始于2019年12月1日，今后12个月将持续运作下去。 ## 版本发布 -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. +这是NumPy 版本列表，包含了对应版本发布说明的链接。 所有的 bug修复版本(即在 `x.y.z`格式版本号中只有 `z`改变)没有新功能；小版本更新(`y` 改变)有新功能。 - NumPy1.21.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年6月22日_. - NumPy1.23.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年5月10日_. From 16e14cee3e5ae0f36091b5dd4501716935cc3603 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 26 Aug 2021 10:41:47 +0200 Subject: [PATCH 692/909] 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 7d7cc1c188..f325309bb2 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -2,7 +2,7 @@ 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 是诸如 [scikit-learn](https://sikit-learn)和[SciPy](https://www.scipy.org)等强大的机器学习库的基础。随着机器学习的增长，函式库列表也在成长。 [TensorFlow's](https://www.tensorflow.org) 深度学习能力有广泛的应用程序 — ，其中包括语音和图像识别、基于文本的应用、时间序列分析和视频检测。 [PyTorch](https://pytorch.org)是另一个深层学习图书馆，在计算机视力和自然语言处理的研究人员中很受欢迎。 [MXNet](https://github.com/apache/incubator-mxnet) 是另一个 AI 包，提供了深入学习的蓝图和模板。 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 1dd4692ae0d244e09c0f0d15f62f6f4343a13134 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 27 Aug 2021 23:16:10 +0200 Subject: [PATCH 693/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 51f6b97233..d20ba58095 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -26,23 +26,23 @@ El [ telescopio del Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.o * **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 registro, y telescopios que son ampliamente disímiles y geográficamente dispersos. * **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 EHT genera más de 350 terabytes de observaciones, almacenados en discos duros llenos de helio. Reducir el volumen y complejidad de estos datos es enormemente difícil. * **Hacia lo desconocido** - 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" >}} +{{< figure src="/images/content_images/cs/dataprocessbh.png" class="csfigcaption" caption="**Pipeline de procesamiento de datos de EHT**" alt="data pipeline" align="middle" attr="(Créditos del diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}} ## 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 un supuesto en particular. ¿Cambiará drásticamente la imagen si se cambia un sólo 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 alianza de EHT respondió a estos desafíos haciendo que los equipos independientes evalúen los datos, utilizando técnicas establecidas y de reconstrucción de imagen de vanguardia. Cuando los resultados se mostraron consistentes, se combinaron para producir la primera imagen de un agujero negro. Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. From 8283665cb82e7e262922291f9c30c81fbbf1cef4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 27 Aug 2021 23:46:36 +0200 Subject: [PATCH 694/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index d20ba58095..bc85841557 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -44,21 +44,21 @@ El [ telescopio del Horizonte de Sucesos (EHT) ](https://eventhorizontelescope.o La alianza de EHT respondió a estos desafíos haciendo que los equipos independientes evalúen los datos, utilizando técnicas establecidas y de reconstrucción de imagen de vanguardia. Cuando los resultados se mostraron consistentes, se combinaron para producir la primera imagen 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="role of 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 arreglos utilizados en este paquete, como se muestra en el gráfico de dependencias de software parcial a continuación. -{{< 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="ehtim dependency map highlighting numpy" caption="**Gráfico de dependencias de software del paquete ehtim destacando 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 pipeline de procesamiento de datos para fotografiar el agujero negro. Los formatos de archivo astronómicos estándar y transformaciones de tiempo/coordenadas fueron manejados por [Astropy][astropy], mientras que [Matplotlib][mpl] fue utilizado en la visualización de datos a través del pipeline de análisis, incluyendo la generación de la imagen final del agujero negro. ## 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. -{{< 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="numpy benefits" caption="**Capacidades clave de NumPy utilizadas**" >}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole From 58bd5fa4518febaf9c02b610af67ddb79ef5ef47 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 27 Aug 2021 23:56:15 +0200 Subject: [PATCH 695/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index bc85841557..9d46131f1d 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -56,7 +56,7 @@ Además de NumPy, muchos otros paquetes, como [SciPy](https://www.scipy.org) y [ ## 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. +El arreglo 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 emblemático en la ciencia, ofrece una impresionante evidencia visual de la teoría de Einstein. El logro abarca no sólo los avances tecnológicos sino también la colaboración internacional entre más de 200 científicos y algunos de los mejores observatorios radiofónicos del mundo. Los algoritmos innovadores y técnicas de procesamiento de datos, mejorando los modelos astronómicos existentes, ayudaron a desarrollar un misterio del universo. {{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Capacidades clave de NumPy utilizadas**" >}} From 5167da4d266c74c2b5bd854e09a987b3b7eefe1a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:43 +0200 Subject: [PATCH 696/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 86 ++++++++++++++++++++++++++-------------------- 1 file changed, 48 insertions(+), 38 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index ea9edee282..7ad7f58516 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -3,18 +3,28 @@ title: 社区快讯 sidebar: false --- -### 2021 Numpy调查 +### Advancing and inclusive culture in the scientific Python ecosystem -_2021年7月12日_ -- 我们相信NumPy社区的力量。 来自75个国家的1236 名用户参加了我们去年的首次调查。 调查结果使我们对今后12个月应该集中注意的问题有了很好的了解。 +_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. -现在是时候进行另一次调查了，我们将再度尋求您的合作。 这份调查将耗费您大约15分钟的时间。 除英文外，调查问卷还提供另外8种语文：孟加拉语、法语、印地语、日语、普通话、葡萄牙语、俄语和西班牙语。 +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). -点击链接开始：https://berkeley.qualtrics.com/jfe/form/SV_aOONjgcBXDSl4q。 +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). -### NumPy 1.21.0 发布 +### 2021 NumPy survey -_2021年6月23日_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 此次发布的重点是： +_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: - 继续开展SIMD工作，涵盖更多的功能和平台 - 新dtype的基础和型态转换初步工作 @@ -23,86 +33,86 @@ _2021年6月23日_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0 - 改进注释 - 新的 `PCG64DXSM` 位元生成器，用于生成随机数字 -这个NumPy版本包含175人所贡献的581个合并请求。 此发布版本支持Python 3.7-3.9，将在Python 3.10发布后添加Python 3.10支持。 +This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. -### 2020 Numpy调研结果出炉 +### 2020 NumPy survey results -_2021年6月22日_ -- 在2020年, NumPy调研小组与密歇根大学和马里兰大学的学生和教职员工合作，进行了第一次官方NumPy社区调查。 在这里可以查看调研结果：https://numpy.org/user-survey-2020/。 +_Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students and faculty from the University of Michigan and the University of Maryland conducted the first official NumPy community survey. Find the survey results here: https://numpy.org/user-survey-2020/. -### NumPy 1.20.0 发布 +### Numpy 1.20.0 release -_2021年1月30日_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) 正式发布。 这是 NumPy到目前为止最大的一次版本更新，感谢180+位贡献者。 最令人振奋的两个特点是： +_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: - 为大部分Numpy代码做了类型注解，並添加了一个全新的`numpy.typing` 子模块，其中包含 `ArrayLike` 和 `DtypeLike`别名 ，使得用户和下游依赖库可以为自己的代码添加类型注解。 - 为多个架构进行SIMD编译优化，其支持X86(SSE、AVX)、ARM64(Neon) 和PowerPC(VSX) 指令集。 大幅提高许多函数的性能(例如： [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194))。 -### NumPy项目的多样性 +### Diversity in the NumPy project -_2020年9月20日_ -- 我们就NumPy项目的社交媒体、多样性和包容性的现状以及相关的讨论撰写了一份[声明](/diversity_sep2020)。 +_Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on social media around, diversity and inclusion in the NumPy project](/diversity_sep2020). -### NumPy官方第一次在Nature发表论文！ +### First official NumPy paper published in Nature! -_2020年9月16日_ - 我们高兴地宣布 [Numpy的第一篇官方论文](https://www.nature.com/articles/s41586-020-2649-2)刊登在Nature的评论文章。 这离NumPy 1.0发布已经过去了整整14年。 该论文涵盖数组编程的应用和基本概念，丰富的Python科学计算生态系统建立在NumPy之上，包括最近添加的数组标准协议，大大提高了与外部数组和张量库(如CuPy, Dask 和 JAX) 的互操作性 。 +_Sep 16, 2020_ -- We are pleased to announce the publication of [the first official paper on NumPy](https://www.nature.com/articles/s41586-020-2649-2) as a review article in Nature. This comes 14 years after the release of NumPy 1.0. The paper covers applications and fundamental concepts of array programming, the rich scientific Python ecosystem built on top of NumPy, and the recently added array protocols to facilitate interoperability with external array and tensor libraries like CuPy, Dask, and JAX. -### Python 3.9 即将来临，新版本的NumPy 将在何时发布？ +### Python 3.9 is coming, when will NumPy release binary wheels? -_2020年9月14日_ -- Python 3.9 将在几周后发布。 如果您是这个Python版本的早期采用者， 您可能会失望的发现NumPy(以及其他二进制软件包，如SciPy) 在Python新版发布当天还不会发布相应的版本。 构建兼容新的 Python 版本的基础设施需要付出重大努力，通常需要几周时间才能让新版本出现在 PyPI 和 conda-forge 上。 为了这次版本升级得以顺利进行，请确保： +_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 - 将您的 `pip` 升级到 20.1 版本，至少要支持`manylinux2010` 和 `manylinux2014` - 使用 [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) 或 `--only-binary=:all:` 选项来防止 `pip` 尝试从源码构建。 -### NumPy 1.19.2 发布 +### Numpy 1.19.2 release -_2020年9月10日_ -- [NumPy 19.2.0](https://numpy.org/devdocs/release/1.19.2-notes.html) 正式发布。 这个最新版本修复了1.19 系列中的几个漏洞，为 [即将发布的Cython3.x](http://docs.cython.org/en/latest/src/changes.html) 做准备，並固定设置工具以在上游修改正在进行时保持 distutils 工作。 Aarch64架构的安装包是用最新的 manylinux2014 版本构建的，它修复了 linux 发行版之间使用不同大小内存页的问题。 +_Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html) is now available. This latest release in the 1.19 series fixes several bugs, prepares for the [upcoming Cython 3.x release](http://docs.cython.org/en/latest/src/changes.html) and pins setuptools to keep distutils working while upstream modifications are ongoing. The aarch64 wheels are built with the latest manylinux2014 release that fixes the problem of differing page sizes used by different linux distros. -### 首次NumPy调研即将开始！ +### The inaugural NumPy survey is live! -_2020年7月2日_ - 本次调查旨在指导并确定将NumPy以社区方式还是软件方式来开发。 除英文外，调查还提供了另外8种语言的版本：孟加拉语、印地语、日语、普通话、葡萄牙语、俄语、西班牙语和法语。 +_Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-making about the development of NumPy as software and as a community. The survey is available in 8 additional languages besides English: Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French. -请帮助我们让 NumPy 变得更好，在[这里](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)参与调查。 +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy 有新标志了! +### NumPy has a new logo! -_2020年7月24日_ -- NumPy 现在有一个新的标志： +_Jun 24, 2020_ -- NumPy now has a new logo: -NumPy 标志 +NumPy logo -这是一个更时髦、纯净的标志。 感谢Isabela Presedo-Floryd的设计方案， 同时感谢Travis Vaugh设计的旧图标为我们服务了15年以上。 +The logo is a modern take on the old one, with a cleaner design. Thanks to Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught for the old logo that served us well for 15+ years. -### NumPy 1.19.0 发布 +### NumPy 1.19.0 release -_2020年6月20日_ -- NumPy 1.19.0 正式发布。 这是第一个不支持Python 2的版本，因此它是一个“清理版本”。 目前支持的最低Python 版本是 Python 3.6。 本版本拥有一个重要的新特性，NumPy 1.17.0引进的随机数字生成基础模块现在可以通过Cython访问。 +_Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release without Python 2 support, hence it was a "clean-up release". The minimum supported Python version is now Python 3.6. An important new feature is that the random number generation infrastructure that was introduced in NumPy 1.17.0 is now accessible from Cython. -### 文档整改时段 +### Season of Docs acceptance -_2020年5月11日_ -- NumPy 已成为Google Season 文档项目之一。 我们很高兴看到有机会和技术写作者一起再次改进NumPy的技术文档！ 更多详情，请参考 [文档整改时段官方网站](https://developers.google.com/season-of-docs/) 和我们的 [意见页面](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)。 +_May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for the Google Season of Docs program. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! For more details, please see [the official Season of Docs site](https://developers.google.com/season-of-docs/) and our [ideas page](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas). -### NumPy 1.18.0 发布 +### NumPy 1.18.0 release -_2019年12月22日_ -- NumPy 1.18.0 正式发布。 在1.17.0发生重大变化后，这是一个合并版本。 这是最后一个支持 Python 3.5的小版本。 该版本的重要更新包括两个，添加了与64位 BLAS 和 LAPACK 库有关的底层更新， 添加 一个用于`numpy.random`的新C-API更新。 +_Dec 22, 2019_ -- NumPy 1.18.0 is now available. After the major changes in 1.17.0, this is a consolidation release. It is the last minor release that will support Python 3.5. Highlights of the release includes the addition of basic infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for `numpy.random`. -详情请看 [版本说明](https://github.com/numpy/numpy/releases/tag/v1.18.0)。 +Please see the [release notes](https://github.com/numpy/numpy/releases/tag/v1.18.0) for more details. -### NumPy 从Chan Zuckerberg Initiative获得了一笔捐款 +### NumPy receives a grant from the Chan Zuckerberg Initiative -_2019年11月15日_ -- 我们高兴地宣布NumPy和 OpenBLAS (Numpy的一个核心依赖库)已经收到一笔19,5000美元的联合赠款。 捐款来自于Chan Zuckerberg Initiative通过的[基础开源科学计算软件项目](https://chanzuckerberg.com/eoss/)，用来支持对科学发展起到关键作用的开源软件的维护、增长、开发和社区参与。 +_Nov 15, 2019_ -- We are pleased to announce that NumPy and OpenBLAS, one of NumPy's key dependencies, have received a joint grant for $195,000 from the Chan Zuckerberg Initiative through their [Essential Open Source Software for Science program](https://chanzuckerberg.com/eoss/) that supports software maintenance, growth, development, and community engagement for open source tools critical to science. -这笔赠款将用来加速改进NumPy文档、网站重构和社区开发，进而更好地为我们庞大和迅速增长的用户基础服务，并确保项目的长期可持续性。 OpenBLAS 团队将侧重于处理几个关键技术问题，特别是线程安全问题、AVX-512和 thread-local 存储(TLS) 问题，以及OpenBLAS 依赖的 ReLAPACK (递归的LAPACK) 算法改进。 +This grant will be used to ramp up the efforts in improving NumPy documentation, website redesign, and community development to better serve our large and rapidly growing user base, and ensure the long-term sustainability of the project. While the OpenBLAS team will focus on addressing sets of key technical issues, in particular thread-safety, AVX-512, and thread-local storage (TLS) issues, as well as algorithmic improvements in ReLAPACK (Recursive LAPACK) on which OpenBLAS depends. -若想查看更多关于捐款的倡议和交付件的详情，可在 [全额赠款提案](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167) 中找到。 项目开始于2019年12月1日，今后12个月将持续运作下去。 +More details on our proposed initiatives and deliverables can be found in the [full grant proposal](https://figshare.com/articles/Proposal_NumPy_OpenBLAS_for_Chan_Zuckerberg_Initiative_EOSS_2019_round_1/10302167). The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months. ## 版本发布 -这是NumPy 版本列表，包含了对应版本发布说明的链接。 所有的 bug修复版本(即在 `x.y.z`格式版本号中只有 `z`改变)没有新功能；小版本更新(`y` 改变)有新功能。 +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. - NumPy1.21.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年6月22日_. - NumPy1.23.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年5月10日_. From c0f1780cb68de8613929a7a30cff16c1d8f58cdb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:44 +0200 Subject: [PATCH 697/909] New translations news.md (Spanish) --- content/es/news.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/content/es/news.md b/content/es/news.md index beae21e17a..a4a6643d7a 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -3,6 +3,16 @@ title: News sidebar: false --- +### Advancing and 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. From ec41d276365310fc3af9c4eafe7d81d00529a7a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:45 +0200 Subject: [PATCH 698/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 26261e1794..c01c434642 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2021 NumPy survey - content: Your voice matters + title: D&I Grant from CZI + content: Including NumPy, SciPy, Matplotlib and Pandas url: /news shell: title: placeholder From 282338fccbb4907de64cedab3b25fa3e524b4603 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:46 +0200 Subject: [PATCH 699/909] New translations news.md (Arabic) --- content/ar/news.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/content/ar/news.md b/content/ar/news.md index 35906b7dfc..1e86bb5c84 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -3,6 +3,16 @@ title: الأخبار sidebar: false --- +### Advancing and 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. From a0a4ae023896cf9f88bc781a0334cb90765a5256 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:47 +0200 Subject: [PATCH 700/909] New translations config.yaml (Arabic) --- content/ar/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 26261e1794..c01c434642 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2021 NumPy survey - content: Your voice matters + title: D&I Grant from CZI + content: Including NumPy, SciPy, Matplotlib and Pandas url: /news shell: title: placeholder From 4b27c31601aa4c66654a9e9d118def8686fadd2f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:48 +0200 Subject: [PATCH 701/909] New translations news.md (Japanese) --- content/ja/news.md | 86 ++++++++++++++++++++++++++-------------------- 1 file changed, 48 insertions(+), 38 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index e5db6299aa..555f5c2472 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,18 +3,28 @@ title: ニュース sidebar: false --- -### 2021年度NumPyアンケート +### Advancing and inclusive culture in the scientific Python ecosystem -_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ユーザーが参加しました。 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。 +_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. -今年もアンケートの時間が来ました。もう一度お願いいたします。 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。 +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. +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). -### NumPy 1.21.0 リリース +### 2021 NumPy survey -_2021年1月23日_ -- [Numpy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) が利用可能になりました。 今回のリリースのハイライトは次のとおりです。 +_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: - より多くの機能やプラットフォームをカバーするSIMD関連の作業が継続されました。 - 新しいdtypeインフラとキャストの初期作業 @@ -23,86 +33,86 @@ _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人が貢献した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 +### 2020 NumPy survey results -_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 1.20.0 リリース +### Numpy 1.20.0 release -_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プロジェクトにおける多様性 +### Diversity in the NumPy project -_2020年6月24日_ -- NumPy に新しいロゴが作成されました: +_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に初めての公式のNumPy論文が掲載されました! +### First official NumPy paper published in Nature! -_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？ +### Python 3.9 is coming, when will NumPy release binary wheels? -_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 +### 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。 +_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のアンケートが公開されました!! +### The inaugural NumPy survey is live! -_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のロゴが新しくなりました。 +### NumPy has a new logo! -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。 +_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 1.19.0 リリース +### NumPy 1.19.0 release -_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. -### ドキュメント改善期間 +### Season of Docs acceptance -_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 +### 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。 +_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`. -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_. +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。 +### NumPy receives a grant from the Chan Zuckerberg Initiative -_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、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. 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 1.18.1 ([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_. From 7481d9c858f77b3f93f917e7269c6ef1e5dddc68 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:50 +0200 Subject: [PATCH 702/909] 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 a3c20fe79f..10f67a83d6 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. 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 + title: D&I Grant from CZI + content: Including NumPy, SciPy, Matplotlib and Pandas url: /ja/news shell: title: placeholder From 0c6715481e74122fd86479ce18eb5396306a3f0e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:51 +0200 Subject: [PATCH 703/909] New translations news.md (Korean) --- content/ko/news.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/content/ko/news.md b/content/ko/news.md index 7b8b01824c..87659eab68 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -3,6 +3,16 @@ title: 소식 sidebar: false --- +### Advancing and 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. From fa7c878c645f47adf4a1d991a3b0d58e4d8202e8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:52 +0200 Subject: [PATCH 704/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index ee79c41a11..3b522b141d 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2021년도 NumPy 설문조사 - content: 소중한 의견을 들려주세요 + title: D&I Grant from CZI + content: Including NumPy, SciPy, Matplotlib and Pandas url: /news shell: title: 플레이스홀더 From 199b57e7aa71a800890792bd43d84f8b326893ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:53 +0200 Subject: [PATCH 705/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 262c4fa815..570a7313ab 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: 2021 Numpy调查 - content: 您的意见很重要 + title: D&I Grant from CZI + content: Including NumPy, SciPy, Matplotlib and Pandas url: /news shell: title: 占位符 From e06e9c1a07bda22fc62852ee0e2670c5b38e6d58 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:54 +0200 Subject: [PATCH 706/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 86 ++++++++++++++++++++++++++-------------------- 1 file changed, 48 insertions(+), 38 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index ebe2387735..f7854c868c 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,18 +3,28 @@ title: Notícias sidebar: false --- -### Pesquisa NumPy 2021 +### Advancing and inclusive culture in the scientific Python ecosystem -_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. +_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. -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. +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). -Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +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). -### NumPy versão 1.21.0 +### 2021 NumPy survey -_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: +_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: - a continuação do trabalho com SIMD para suportar mais funções e plataformas, - trabalho inicial na infraestrutura e conversão de novos dtypes, @@ -23,86 +33,86 @@ _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 +### 2020 NumPy survey results -_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 1.20.0 +### Numpy 1.20.0 release -_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 +### Diversity in the NumPy project -_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! +### First official NumPy paper published in 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? +### Python 3.9 is coming, when will NumPy release binary wheels? -_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 +### 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. +_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! +### The inaugural NumPy survey is live! -_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! +### NumPy has a new logo! -_24 de junho de 2020_ -- NumPy agora tem um novo logo: +_Jun 24, 2020_ -- NumPy now has a new logo: -NumPy 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 1.19.0 +### NumPy 1.19.0 release -_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 +### Season of Docs acceptance -_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 +### 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`. +_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 receberá um auxílio da Chan Zuckerberg Initiative +### NumPy receives a grant from the 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 1.21.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junho de 2021_. - NumPy 1.20.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de maio de 2021_. From 4361f682c17bf7e248d7a949b55fc282427ed863 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:07:55 +0200 Subject: [PATCH 707/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index 66b4c8498c..f989b17e61 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -18,8 +18,8 @@ params: image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: Pesquisa NumPy 2021 - content: Sua voz é importante + title: D&I Grant from CZI + content: Including NumPy, SciPy, Matplotlib and Pandas url: /pt/news shell: title: placeholder From bf22bcbf258c4aa7670544ff5f2b4fd0bab87445 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:23:06 +0200 Subject: [PATCH 708/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/news.md b/content/zh/news.md index 7ad7f58516..8911f1c8e0 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -3,7 +3,7 @@ title: 社区快讯 sidebar: false --- -### Advancing and inclusive culture in the scientific Python ecosystem +### 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. From 7dc2b981d8aed50096b75b1bbaad1217f4f6ea7a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:23:07 +0200 Subject: [PATCH 709/909] 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 a4a6643d7a..c6fe43603d 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -3,7 +3,7 @@ title: News sidebar: false --- -### Advancing and inclusive culture in the scientific Python ecosystem +### 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. From 8f2af66a32729098f06b41a539e3250c80c145ee Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:23:08 +0200 Subject: [PATCH 710/909] New translations news.md (Arabic) --- content/ar/news.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/news.md b/content/ar/news.md index 1e86bb5c84..99eb46f8a6 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -3,7 +3,7 @@ title: الأخبار sidebar: false --- -### Advancing and inclusive culture in the scientific Python ecosystem +### 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. From 983a1fd50007132d64a74d10bb58ae76be18cacb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:23:09 +0200 Subject: [PATCH 711/909] 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 555f5c2472..7d857dcc18 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -3,7 +3,7 @@ title: ニュース sidebar: false --- -### Advancing and inclusive culture in the scientific Python ecosystem +### 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. From 1ec052f41836415d5f368a85d71fe7b33ba72ef7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:23:11 +0200 Subject: [PATCH 712/909] 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 87659eab68..5979c96a30 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -3,7 +3,7 @@ title: 소식 sidebar: false --- -### Advancing and inclusive culture in the scientific Python ecosystem +### 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. From 6ca24b2c5d4434398afdb44f3a2f1009ddbb2f01 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 31 Aug 2021 19:23:12 +0200 Subject: [PATCH 713/909] 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 f7854c868c..5059872d94 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -3,7 +3,7 @@ title: Notícias sidebar: false --- -### Advancing and inclusive culture in the scientific Python ecosystem +### 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. From 65bc578557c37c9502fca8896492bf5b937dc632 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 7 Sep 2021 19:12:07 +0200 Subject: [PATCH 714/909] New translations blackhole-image.md (Spanish) --- content/es/case-studies/blackhole-image.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 9d46131f1d..0392434d67 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -1,5 +1,5 @@ --- -title: "Caso de estudio: Primera imagen de un agujero negro" +title: "Estudio de caso: La primera imagen de un agujero negro" sidebar: false --- From dfb1a89b8e97469c0847566d7ccc9a8db110987d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 7 Sep 2021 19:12:08 +0200 Subject: [PATCH 715/909] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index 987b38fb68..949c3b329b 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -1,18 +1,18 @@ --- -title: "Case Study: Cricket Analytics, the game changer!" +title: "Estudio de caso: Análisis de críquet, ¡el cambio radical!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, the biggest Cricket Festival in India**" alt="Indian Premier League Cricket cup and stadium" attr="*(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, el Festival de Cricket más grande en India**" alt="Copa y estadio de la Premier League de Cricket de India" attr="*(Créditos de imagen: IPLT20 (copa y logo) & Akash Yadav (estadio))*" 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
    +

    No juegas para el público, juegas para el país.

    +
    —M S Dhoni, Jugador de cricket internacional, ex-capitán, Selección de India, juega para Chennai Super Kings en la IPL
    -## About Cricket +## Acerca del 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. +Sería una subestimación decir que a los indios les encanta el críquet. 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. From 34f8acb179dcb3c14a4367126e8f068476f87e42 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 7 Sep 2021 19:23:57 +0200 Subject: [PATCH 716/909] 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 949c3b329b..d3a0b9a98c 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -3,16 +3,16 @@ title: "Estudio de caso: Análisis de críquet, ¡el cambio radical!" sidebar: false --- -{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, el Festival de Cricket más grande en India**" alt="Copa y estadio de la Premier League de Cricket de India" attr="*(Créditos de imagen: IPLT20 (copa y logo) & Akash Yadav (estadio))*" attrlink="https://unsplash.com/@aksh1802" >}} +{{< figure src="/images/content_images/cs/ipl-stadium.png" caption="**IPLT20, el festival de críquet más grande en India**" alt="Copa y estadio de la Premier League de Críquet de India" attr="*(Créditos de imagen: IPLT20 (copa y logo) & Akash Yadav (estadio))*" attrlink="https://unsplash.com/@aksh1802" >}}

    No juegas para el público, juegas para el país.

    -
    —M S Dhoni, Jugador de cricket internacional, ex-capitán, Selección de India, juega para Chennai Super Kings en la IPL
    +
    —M S Dhoni, Jugador de críquet internacional, ex-capitán, Selección de India, juega para Chennai Super Kings en la IPL
    -## Acerca del cricket +## Acerca del críquet -Sería una subestimación decir que a los indios les encanta el críquet. 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. +Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi cada rincón de India, rural o urbano, popular entre los jóvenes y ancianos por igual, conectando miles de millones en India a diferencia de cualquier otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de [dinero](https://www.statista.com/topics/4543/indian-premier-league-ipl/) y fama en juego. Durante los últimos años, la tecnología ha sido literalmente un punto de inflexión. 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. From 1d766b1a8f27efcd2ae73e86a1c7bf09b1efd737 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 7 Sep 2021 19:45:18 +0200 Subject: [PATCH 717/909] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index d3a0b9a98c..996f55b059 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -12,11 +12,11 @@ sidebar: false ## Acerca del críquet -Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi cada rincón de India, rural o urbano, popular entre los jóvenes y ancianos por igual, conectando miles de millones en India a diferencia de cualquier otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de [dinero](https://www.statista.com/topics/4543/indian-premier-league-ipl/) y fama en juego. Durante los últimos años, la tecnología ha sido literalmente un punto de inflexión. Audiences are spoilt for choice with streaming media, tournaments, affordable access to mobile based live cricket watching, and more. +Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi cada rincón de India, rural o urbano, popular entre los jóvenes y ancianos por igual, conectando miles de millones en India a diferencia de cualquier otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de [dinero](https://www.statista.com/topics/4543/indian-premier-league-ipl/) y fama en juego. Durante los últimos años, la tecnología ha sido literalmente un punto de inflexión. El público está plagado de opciones con los medios de streaming, los torneos, acceso asequible a la observación de críquet en vivo basado en móviles, y más. -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. +La Premier League de India (IPL) es una liga de críquet Twenty20, fundada en 2008. Es uno de los eventos de críquet más concurridos en el mundo, valorado en [$6.7 mil millones de dólares](https://en.wikipedia.org/wiki/Indian_Premier_League) en 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. +El críquet es un juego de números - las carreras anotadas por un bateador, los wickets alcanzados por un lanzador, los partidos ganados por un equipo de críquet, el número de veces que un bateador responde de cierta manera a un tipo de ataque de lanzamiento, etc. La capacidad de cavar en los números del críquet para mejorar el rendimiento y estudiar las oportunidades de negocio, el mercado en general y la economía del críquet a través de potentes herramientas de análisis, alimentadas por software de cálculo numérico como NumPy, es un gran negocio. El análisis del críquet proporciona ideas interesantes sobre el juego e inteligencia predictiva respecto a los resultados del juego. 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: From 3bb569d853feaedfcfc8df4d33c60c06e908cf91 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 16 Sep 2021 19:50:32 +0200 Subject: [PATCH 718/909] New translations citing-numpy.md (Spanish) --- content/es/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/citing-numpy.md b/content/es/citing-numpy.md index 0d19b30596..486e90ddd0 100644 --- a/content/es/citing-numpy.md +++ b/content/es/citing-numpy.md @@ -5,7 +5,7 @@ sidebar: false 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: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([Enlace del editor](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). ([Enlace del editor](https://www.nature.com/articles/s41586-020-2649-2)). _En formato BibTeX:_ From ef3b5616092c91a8201799d40d02cc0d047df62e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 16 Sep 2021 19:50:33 +0200 Subject: [PATCH 719/909] New translations citing-numpy.md (Arabic) --- content/ar/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/citing-numpy.md b/content/ar/citing-numpy.md index a249049af1..e1f68bdf3f 100644 --- a/content/ar/citing-numpy.md +++ b/content/ar/citing-numpy.md @@ -5,7 +5,7 @@ sidebar: خطأ إذا كان لنمباي دور كبير فى بحثك وتود الإشارة إليه فى منشورك الأكاديمى،فبامكانك إلقاء نظرة على هذة الورقة المقترحة للاستشهاد: -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _برمجة المصفوفات بواسطة نمباي_. Nature 585, 357–362 (2020). DOI: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([رابط النشر](https://www.nature.com/articles/s41586-020-2649-2)). +* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _برمجة المصفوفات بواسطة نمباي_. Nature 585, 357–362 (2020). DOI: [10.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([رابط النشر](https://www.nature.com/articles/s41586-020-2649-2)). _بتنسيق In BibTeX:_ From 3fe49eecb4b5eb8c407767d28956ff72a541df8f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 16 Sep 2021 19:50:34 +0200 Subject: [PATCH 720/909] 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 ee48d610d4..9696c6e4d1 100644 --- a/content/ja/citing-numpy.md +++ b/content/ja/citing-numpy.md @@ -5,7 +5,7 @@ sidebar: false 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、こちらの論文を引用して下さい。 -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: \[0.1038/s41586-020-2649-2\](https://doi. ([リンク](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). ([リンク](https://www.nature.com/articles/s41586-020-2649-2)). _BibTeX形式:_ From 67cd599eb6ddf61cd1eaad3551431923bba86db7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 16 Sep 2021 19:50:35 +0200 Subject: [PATCH 721/909] New translations citing-numpy.md (Korean) --- content/ko/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/citing-numpy.md b/content/ko/citing-numpy.md index 9ff62d54b5..cf1458e657 100644 --- a/content/ko/citing-numpy.md +++ b/content/ko/citing-numpy.md @@ -5,7 +5,7 @@ sidebar: false 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: [0.1038/s41586-020-2649-2](https://doi.org/10.1038/s41586-020-2649-2). ([링크](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)). _BibTeX 형식:_ From d6bc3ecb48d8ead22594ab7342d977bc9d3ba23c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 16 Sep 2021 19:50:36 +0200 Subject: [PATCH 722/909] New translations citing-numpy.md (Chinese Simplified) --- content/zh/citing-numpy.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/citing-numpy.md b/content/zh/citing-numpy.md index 1731e941cf..a5db3deb31 100644 --- a/content/zh/citing-numpy.md +++ b/content/zh/citing-numpy.md @@ -5,7 +5,7 @@ sidebar: false 如果 NumPy 在您的研究中很重要， 您想在您的学术出版物中致谢这个项目，我们建议您引用以下论文： -* Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: [0.1038/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). ([Publisher link](https://www.nature.com/articles/s41586-020-2649-2)). _BibTeX 格式：_ From e75facf6b6e0a6f603213cdb3f0b33addc569579 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 16 Sep 2021 19:50:37 +0200 Subject: [PATCH 723/909] 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 61ab60cace..f10e1042ca 100644 --- a/content/pt/citing-numpy.md +++ b/content/pt/citing-numpy.md @@ -5,7 +5,7 @@ sidebar: false Se o 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: [0.1038/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. 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 15c904f348b9d316adc3842844808928c1a712f1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Sep 2021 00:09:22 +0200 Subject: [PATCH 724/909] New translations community.md (Spanish) --- content/es/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/community.md b/content/es/community.md index 4e24a83784..80426f0b57 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -3,7 +3,7 @@ title: Community sidebar: false --- -NumPy is a community-driven open source project developed by a very diverse group of [contributors](/gallery/team.html). 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 muy diverso de [colaboradores](/gallery/team.html). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. 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. From 73c692b11a7735df488cf5fa16c5d31434563d17 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Sep 2021 00:15:33 +0200 Subject: [PATCH 725/909] New translations community.md (Spanish) --- content/es/community.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/es/community.md b/content/es/community.md index 80426f0b57..75852dc605 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -3,19 +3,19 @@ title: Community sidebar: false --- -NumPy es un proyecto de código abierto impulsado por la comunidad y desarrollado por un grupo muy diverso de [colaboradores](/gallery/team.html). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. 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 muy diverso de [colaboradores](/gallery/team.html). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el [Código de Conducta 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 NumPy. -## Participate online +## Participar 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 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. +Esta lista es el foro principal para discusiones más largas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones a nivel de proyecto. 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](http://numpy-discussion.10968.n7.nabble.com/). From d1f20f4c8947f70f6d89f5ea9d16eb5210e0ce49 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Sep 2021 00:28:08 +0200 Subject: [PATCH 726/909] 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 75852dc605..74c753871c 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -15,15 +15,15 @@ Las siguientes son formas de relacionarse directamente con el proyecto y la comu ### [Lista de correo de NumPy](https://mail.python.org/mailman/listinfo/numpy-discussion) -Esta lista es el foro principal para discusiones más largas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones a nivel de proyecto. Announcements about NumPy, such as for releases, developer meetings, sprints or conference talks are also made on this list. +Esta lista es el foro principal para discusiones más largas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones a nivel de proyecto. Los anuncios sobre NumPy, tales como lanzamientos, reuniones de desarrolladores, sprints o conferencias también se hacen en esta lista. -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](http://numpy-discussion.10968.n7.nabble.com/). +En esta lista, por favor, utilice el envío inferior, responda a la lista (en lugar de a otro remitente), y no responda a los resúmenes. Un archivo de búsqueda de esta lista está disponible [aquí](http://numpy-discussion.10968.n7.nabble.com/). *** -### [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,)`"); +- Para informes de error (por ejemplo, "`np.arange(3).shape` devuelve `(5,)`, donde debería devolver `(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`"). From ced0716e8077de5e5325ab0a87d37795f6cafc43 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Sep 2021 00:38:52 +0200 Subject: [PATCH 727/909] 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 74c753871c..0e11a59add 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -24,16 +24,16 @@ En esta lista, por favor, utilice el envío inferior, responda a la lista (en lu ### [Seguimiento de incidencias en GitHub](https://github.com/numpy/numpy/issues) - Para informes de error (por ejemplo, "`np.arange(3).shape` devuelve `(5,)`, donde debería devolver `(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`"). +- problemas de 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. 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 From 548df0d9532617df5f6e2419a2c5880d7a6809ec Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Sep 2021 03:46:27 +0200 Subject: [PATCH 728/909] 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 0e11a59add..d26157a3d0 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -33,14 +33,14 @@ _Ten en cuenta que GitHub no es el lugar adecuado para reportar una vulnerabilid ### [Slack](https://numpy-team.slack.com) -Una sala de chat en tiempo real para hacer preguntas sobre las _contribuciones_ a 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 una gran lista de correo pública o en GitHub. Por favor, mira [aquí](https://numpy.org/devdocs/dev/index.html#contributing-to-numpy) para más detalles y 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 la ciencia de los datos y la computación científica, le recomendamos que explore 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 sprints en persona para su equipo y colaboradores interesados de vez en cuando. 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 From 675f5678ab53cbf8caeaacaff235456c1f4d5a1f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Sep 2021 03:53:30 +0200 Subject: [PATCH 729/909] New translations community.md (Spanish) --- content/es/community.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/es/community.md b/content/es/community.md index d26157a3d0..aa1a9a5dcc 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -40,21 +40,21 @@ Una sala de chat en tiempo real para hacer preguntas sobre las _contribuciones_ 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 la ciencia de los datos y la computación científica, le recomendamos que explore los [PyData meetups](https://www.meetup.com/pro/pydata/) (más de 150 reuniones, más de 100.000 miembros). -NumPy también organiza sprints en persona para su equipo y colaboradores interesados de vez en cuando. 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 sprints en persona para su equipo y colaboradores interesados de vez en cuando. Normalmente se planifican con varios meses de antelación 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 mantenedores, colaboradores y usuarios de NumPy son la serie de conferencias 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) +- [SciPy Japón](https://conference.scipy.org) +- [Conferencias PyData](https://pydata.org/event-schedule/) (15 eventos al año repartidos en 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 donde puedes aprender cómo contribuir a Numpy o proyectos de código abierto relacionados. ## Join the NumPy community From 3cbf6f0fc41352ca7dacf89d7dfe02d0a517fab1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Sep 2021 04:13:27 +0200 Subject: [PATCH 730/909] New translations community.md (Spanish) --- content/es/community.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/community.md b/content/es/community.md index aa1a9a5dcc..5cbb75c867 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -57,9 +57,9 @@ El proyecto NumPy no organiza sus propias conferencias. Las conferencias que tra Muchas de estas conferencias incluyen tutoriales y/o sprints que cubren NumPy 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? ¡Ningún 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](/contribuir). From 4d19e20baf20814f044225ead2a28be5252b4b69 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 17 Sep 2021 04:13:28 +0200 Subject: [PATCH 731/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index c01c434642..66fc416157 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -1,7 +1,7 @@ --- -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: logos/numpy.svg link: / @@ -9,35 +9,35 @@ 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: Get started + buttontext: Para empezar #Where the main hero button links to buttonlink: "/install" #Hero image (from static/images/___) image: logos/numpy.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: D&I Grant from CZI - content: Including NumPy, SciPy, Matplotlib and Pandas + title: Subvención D&I de CZI + content: Incluye NumPy, SciPy, Matplotlib y Pandas url: /news shell: - title: placeholder - promptlabel: interactive shell prompt + title: marcador de posición + promptlabel: apuntador interactivo de la consola button: - - label: Enables the interactive tutorial shell - text: Enable + label: Activa el terminal interactivo del tutorial + text: Habilitar shellcontent: intro: - - title: Try NumPy - text: Enable the interactive shell + title: Prueba NumPy + text: Activa el terminal interactivo loading: - - title: While we wait... - text: Launching container on mybinder.org... - docslink: Don't forget to check out the docs. + title: Mientras esperamos... + text: Iniciando contenedor en mybinder.org... + docslink: No te olvides de echar un vistazo a los documentos. casestudies: title: CASE STUDIES features: From f71b6066fad46c698bbe1b5ce30542ff828ff13f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 21 Sep 2021 22:16:40 +0200 Subject: [PATCH 732/909] New translations community.md (Spanish) --- content/es/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/community.md b/content/es/community.md index 5cbb75c867..b74aa6b31a 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -17,7 +17,7 @@ Las siguientes son formas de relacionarse directamente con el proyecto y la comu Esta lista es el foro principal para discusiones más largas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones a nivel de proyecto. Los anuncios sobre NumPy, tales como lanzamientos, reuniones de desarrolladores, sprints o conferencias también se hacen en esta lista. -En esta lista, por favor, utilice el envío inferior, responda a la lista (en lugar de a otro remitente), y no responda a los resúmenes. Un archivo de búsqueda de esta lista está disponible [aquí](http://numpy-discussion.10968.n7.nabble.com/). +En esta lista, por favor, utilice el envío inferior, responda a la lista (en lugar de a otro remitente), y no responda a los resúmenes. A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** From ae81a33bf02d7825bea80a67c4ea8244fbc52846 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 21 Sep 2021 22:16:41 +0200 Subject: [PATCH 733/909] New translations community.md (Arabic) --- content/ar/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/community.md b/content/ar/community.md index 4e24a83784..4fcb70235b 100644 --- a/content/ar/community.md +++ b/content/ar/community.md @@ -17,7 +17,7 @@ The following are ways to engage directly with the NumPy project and community. 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](http://numpy-discussion.10968.n7.nabble.com/). +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/). *** From e4996bce155d5077f74d09b4ec3369529a268c88 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 21 Sep 2021 22:16:42 +0200 Subject: [PATCH 734/909] 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 c6ed459f85..2dcab69a75 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、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](http://numpy-discussion.10968.n7.nabble.com/) 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)。 また、自動送信のメールには返信しないでください。 A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** From 00ac33163bed94aed9b1d9a3313c5fb68f26dbe8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 21 Sep 2021 22:16:43 +0200 Subject: [PATCH 735/909] 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 584f98043d..3625914f79 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -17,7 +17,7 @@ NumPy 프로젝트 및 커뮤니티에 곧장 참여할 수 있는 방법들입 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. -리스트에 회신하려면 (다른 발신자에게 회신하기보다는) 하단의 게시물을 이용하십시오. 또, 자동 발신 메일에 회신하지 마십시오. 메일링 리스트에 대하여 검색 가능한 아카이브는 [여기](http://numpy-discussion.10968.n7.nabble.com/)에서 이용할 수 있습니다. +리스트에 회신하려면 (다른 발신자에게 회신하기보다는) 하단의 게시물을 이용하십시오. 또, 자동 발신 메일에 회신하지 마십시오. A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** From 21468dc816f9c54a2843bf7f6461539bc3398078 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 21 Sep 2021 22:16:44 +0200 Subject: [PATCH 736/909] New translations community.md (Chinese Simplified) --- content/zh/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/community.md b/content/zh/community.md index b380f113e3..e77898c0c2 100644 --- a/content/zh/community.md +++ b/content/zh/community.md @@ -17,7 +17,7 @@ Numby是一个社区驱动的开源项目，由一群十分多样化的[贡献 这个列表是较长形式讨论的主要讨论区，例如将新功能添加到Numpy，更改Numpy 路线图或是各种项目级的决策。 同时也是NumPy的公告区，例如releases，开发者会议，Sprints 或是会议演讲也在这个列表中。 -在这个列表上，请用包含引文回复的方式回复邮件列表(而不是另一个发送者)，并且不要回复摘要。 这个列表提供了一个可检索的 [归档](http://numpy-discussion.10968.n7.nabble.com/)。 +在这个列表上，请用包含引文回复的方式回复邮件列表(而不是另一个发送者)，并且不要回复摘要。 A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** From d81dfa79a460cb9857810c60bf2123e672785ec9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 21 Sep 2021 22:16:45 +0200 Subject: [PATCH 737/909] 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 d77eae3bfd..a6ae36bcef 100644 --- a/content/pt/community.md +++ b/content/pt/community.md @@ -17,7 +17,7 @@ Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a com Esta lista é o principal fórum para discussões mais longas, como adicionar novos recursos ao NumPy, fazer alterações no roadmap do NumPy e em todos os tipos de tomada de decisão para todo o projeto. Anúncios sobre o NumPy, como novas versões, reuniões de desenvolvedores, sprints ou palestras de conferência também são feitas nesta lista. -Nesta lista, por favor, use *bottom posting*, responda à lista (em vez de a outro remetente), e não responda aos *digests*. Um arquivo pesquisável desta lista está disponível [aqui](http://numpy-discussion.10968.n7.nabble.com/). +Nesta lista, por favor, use *bottom posting*, responda à lista (em vez de a outro remetente), e não responda aos *digests*. A searchable archive of this list is available [here](https://mail.python.org/archives/list/numpy-discussion@python.org/). *** From 9f69a5a7d68b5abbb02ff340a5af1e0df5f4e8a1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 17:04:29 +0200 Subject: [PATCH 738/909] New translations cricket-analytics.md (Spanish) --- content/es/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index 996f55b059..d6f01bec93 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -55,7 +55,7 @@ Sports Analytics is a thriving field. Many researchers and companies [use NumPy] * **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) provides useful insights into relationship between various datasets. +* **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 From 59b5a972c3457f264e9bcae89170b1c98ec05cb9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 17:04:30 +0200 Subject: [PATCH 739/909] New translations cricket-analytics.md (Arabic) --- content/ar/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/case-studies/cricket-analytics.md b/content/ar/case-studies/cricket-analytics.md index 987b38fb68..db140f858c 100644 --- a/content/ar/case-studies/cricket-analytics.md +++ b/content/ar/case-studies/cricket-analytics.md @@ -55,7 +55,7 @@ Sports Analytics is a thriving field. Many researchers and companies [use NumPy] * **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) provides useful insights into relationship between various datasets. +* **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 From 179f4a11b84101fea030e86761c34676b95c92ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 17:04:32 +0200 Subject: [PATCH 740/909] New translations cricket-analytics.md (Japanese) --- content/ja/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 8b57e07065..553a0fa314 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -55,7 +55,7 @@ sidebar: false * **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。 +* **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. ## まとめ From 94f972c7f92524b35cd9bf00bd138cc93ecc7b1a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 17:04:33 +0200 Subject: [PATCH 741/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 51c3ef7097..d99bcf8710 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -55,7 +55,7 @@ Today, there are rich and almost infinite troves of cricket game records and sta * **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. -* **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. +* **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. ## 요약 From cf2b18e58ebf63b33f4cc6fddf20a954964f50e8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 17:04:34 +0200 Subject: [PATCH 742/909] New translations cricket-analytics.md (Chinese Simplified) --- content/zh/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/case-studies/cricket-analytics.md b/content/zh/case-studies/cricket-analytics.md index 1fd0182272..5159ce7d17 100644 --- a/content/zh/case-studies/cricket-analytics.md +++ b/content/zh/case-studies/cricket-analytics.md @@ -55,7 +55,7 @@ sidebar: false * **统计分析:** NumPy的数值计算功能有助于在各种球员和比赛策略下估算观察数据或比赛事件的统计意义，并通过与生成模型或静态模型进行比较来预测比赛结果。 [因果分析](https://amplitude.com/blog/2017/01/19/causation-correlation) 和 [大数据分析](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) 就常用于战术分析。 -* **数据可视化：** 数据图形化和 [可视化](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) 对各种数据集之间的关系提供了有价值的见解。 +* **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. ## 总结 From 5a98ce4ff84b0fef81d68e9576952b76363982b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 17:04:35 +0200 Subject: [PATCH 743/909] New translations cricket-analytics.md (Portuguese, Brazilian) --- content/pt/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 6c67336d5a..5b1e3b87c8 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -55,7 +55,7 @@ A análise de dados esportivos é um campo próspero. Muitos pesquisadores e emp * **Análise Estatística:** Os recursos numéricos do NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. [Análise Causal](https://amplitude.com/blog/2017/01/19/causation-correlation) e [abordagens em *big data*](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4996805/) são usados para análise tática. -* **Visualização de dados:** Gráficos e [visualizações](https://towardsdatascience.com/advanced-sports-visualization-with-pandas-matplotlib-and-seaborn-9c16df80a81b) fornecem informações úteis sobre as relações entre vários conjuntos de dados. +* **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. ## Resumo From adc9b9c201f7f97fbe2f690dae269878a5407688 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 21:37:59 +0200 Subject: [PATCH 744/909] 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 29028864a7..3d5e6fe001 100644 --- a/content/ar/user-survey-2020.md +++ b/content/ar/user-survey-2020.md @@ -3,7 +3,7 @@ title: استطلاع مجتمع نمباي لعام 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. +استطلاع نمباي في عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتي ميتشيجان وميريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباي. 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">}} From 3806a06476b3b452024f7a51d4d2651cbb124929 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 22:07:20 +0200 Subject: [PATCH 745/909] New translations user-survey-2020.md (Arabic) --- content/ar/user-survey-2020.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/user-survey-2020.md b/content/ar/user-survey-2020.md index 3d5e6fe001..152fb45437 100644 --- a/content/ar/user-survey-2020.md +++ b/content/ar/user-survey-2020.md @@ -3,11 +3,11 @@ title: استطلاع مجتمع نمباي لعام 2020 sidebar: false --- -استطلاع نمباي في عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتي ميتشيجان وميريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباي. 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. +استطلاع نمباي في عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتي ميتشيجان وميريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباي. شارك أكثر من 1200 مستخدم من 75 دولة لمساعدتنا في تصميم مخطط لمجتمع نمباي كما عبروا عن أفكارهم حول مستقبل المشروع. {{< 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. +**[قم بتحميل هذا التقرير ](/surveys/NumPy_usersurvey_2020_report.pdf)** لإلقاء نظرة أدق على نتائج الاستطلاع. For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. From 7591e2d8cd08d083163abaf0bea35a5ebcf32bde Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 27 Sep 2021 22:22:13 +0200 Subject: [PATCH 746/909] New translations user-survey-2020.md (Arabic) --- content/ar/user-survey-2020.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ar/user-survey-2020.md b/content/ar/user-survey-2020.md index 152fb45437..015193f7a1 100644 --- a/content/ar/user-survey-2020.md +++ b/content/ar/user-survey-2020.md @@ -10,7 +10,7 @@ sidebar: false **[قم بتحميل هذا التقرير ](/surveys/NumPy_usersurvey_2020_report.pdf)** لإلقاء نظرة أدق على نتائج الاستطلاع. -For the highlights, check out **[this infographic](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. +للنقاط الأكثر أهمية، تحقق من **[هذة التصاميم التي تتضمن معلومات](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/**. +أمستعد لأكثر من ذلك؟ قم بزيارة **https://numpy.org/user-survey-2020-details/**. From 9d27e71afb4b6a127abf4dbfe8a96856e93e77f1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 28 Sep 2021 10:50:18 +0200 Subject: [PATCH 747/909] 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 015193f7a1..495e405096 100644 --- a/content/ar/user-survey-2020.md +++ b/content/ar/user-survey-2020.md @@ -3,7 +3,7 @@ title: استطلاع مجتمع نمباي لعام 2020 sidebar: false --- -استطلاع نمباي في عام 2020 بالاشتراك مع طلاب وأعضاء هيئة التدريس من جامعتي ميتشيجان وميريلاند بإجراء أول دراسة استقصائية رسمية لمجتمع نمباي. شارك أكثر من 1200 مستخدم من 75 دولة لمساعدتنا في تصميم مخطط لمجتمع نمباي كما عبروا عن أفكارهم حول مستقبل المشروع. +في عام 2020، شارك فريق استطلاع نمباي بإجراء أول دراسة استقصائية رسمية للمجتمع مع الطلاب وأعضاء هيئة التدريس الملتحقين ببرنامج ماجستير في منهجية الاستطلاع الذي تستضيفه جامعتي ميتشيجان وميريلاند. شارك أكثر من 1200 مستخدم من 75 دولة لمساعدتنا في تصميم مخطط لمجتمع نمباي كما عبروا عن أفكارهم حول مستقبل المشروع. {{< 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">}} From 721655631a82a03d7587ef42fc4292d5532c20a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:38 +0200 Subject: [PATCH 748/909] New translations about.md (Spanish) --- content/es/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/about.md b/content/es/about.md index b6c041f384..12d1887232 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -5,7 +5,7 @@ sidebar: false _Información sobre el proyecto y la comunidad NumPy_ -NumPy es un proyecto de código abierto cuyo objetivo es facilitar la computación numérica con Python. Se creó en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy siempre será un software 100% de código abierto, de uso libre para todos y liberado bajo los términos liberales de la [licencia BSD modificada](https://github.com/numpy/numpy/blob/master/LICENSE.txt). +NumPy es un proyecto de código abierto cuyo objetivo es facilitar la computación numérica con Python. Se creó en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). NumPy se desarrolla de forma abierta en GitHub, mediante el consenso de la comunidad de NumPy y de la comunidad científica de Python en general. Para más información sobre nuestro enfoque de gobernanza, consulta nuestro [Documento de Gobernanza](https://www.numpy.org/devdocs/dev/governance/index.html). From 11c27199584767c16fc78d0769006780a1412f47 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:39 +0200 Subject: [PATCH 749/909] New translations press-kit.md (Korean) --- content/ko/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/press-kit.md b/content/ko/press-kit.md index b014d0e8ca..f1d749a41a 100644 --- a/content/ko/press-kit.md +++ b/content/ko/press-kit.md @@ -5,4 +5,4 @@ sidebar: false 저희는 당신이 NumPy 프로젝트의 상징을 논문, 코스 자료, 발표 자료 등에 삽입하기 쉽도록 하고자 합니다. -[여기에서](https://github.com/numpy/numpy/tree/master/branding/logo) 여러 버전의 고화질 NumPy 로고를 찾을 수 있습니다. numpy.org 자료를 이용하는 경우, [NumPy 이용약관](/code-of-conduct)에 동의하게 됨을 명심하십시오. +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). numpy.org 자료를 이용하는 경우, [NumPy 이용약관](/code-of-conduct)에 동의하게 됨을 명심하십시오. From acd805891ab274f5bab92736a1e11fb136eb555f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:40 +0200 Subject: [PATCH 750/909] New translations press-kit.md (Portuguese, Brazilian) --- content/pt/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/press-kit.md b/content/pt/press-kit.md index a59064dca5..1418154f57 100644 --- a/content/pt/press-kit.md +++ b/content/pt/press-kit.md @@ -5,4 +5,4 @@ sidebar: false Gostaríamos de facilitar a inclusão da identidade do projeto NumPy em seu próximo documento acadêmico, materiais educacionais ou apresentação. -Você encontrará várias versões de alta resolução do logo do NumPy [aqui](https://github.com/numpy/numpy/tree/master/branding/logo). Note que usando os recursos numpy.org, você aceita o [Código de Conduta do NumPy](/code-of-conduct). +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). Note que usando os recursos numpy.org, você aceita o [Código de Conduta do NumPy](/code-of-conduct). From ad9f4220de287dc30eee7c629469071fac8e44de Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:41 +0200 Subject: [PATCH 751/909] 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 31e0daba3d..38211e5742 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -5,7 +5,7 @@ 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/master/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. NumPy will always be 100% open source software, free for all to use and 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, por meio 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). From 933b1b2825b19124cb937decef42c7165d7ef1a7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:42 +0200 Subject: [PATCH 752/909] 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 570a7313ab..7a156a2ae9 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -84,7 +84,7 @@ params: text: NumPy的高度模块化的语法使得任何背景或经验级别的程序员都能够快速上手。 - title: 开放源代码 - text: 以自由的 [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt)下发布。NumPy 是由一个生气勃勃的、响应性的和多样化的 [community](/community)开发和维护的[在 GitHub开源](https://github.com/numpy/numpy)。 + 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). tabs: title: 生态系统 section5: false From 2957b140ea836f51302a94e64f3aacec66e3c634 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:43 +0200 Subject: [PATCH 753/909] New translations press-kit.md (Chinese Simplified) --- content/zh/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/press-kit.md b/content/zh/press-kit.md index 500a14e46b..d95ce2760e 100644 --- a/content/zh/press-kit.md +++ b/content/zh/press-kit.md @@ -5,4 +5,4 @@ sidebar: false 我们希望能让您在下一篇学术论文、课程材料或演示文稿中轻松地加入NumPy项目标识。 -您可以在[这里](https://github.com/numpy/numpy/tree/master/branding/logo)找到一些高分辨率的 NumPy logo。 注意，使用 numpy.org 资源意味着你接受 [NumPy 行为准则](/code-of-conduct)。 +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). 注意，使用 numpy.org 资源意味着你接受 [NumPy 行为准则](/code-of-conduct)。 From e2901f334b5de481931eb7476e1352295310a0bf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:44 +0200 Subject: [PATCH 754/909] 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 5fb88f0e63..62231db733 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -5,7 +5,7 @@ sidebar: false _下面是 NumPy 项目和社区的一些信息：_ -NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 年，早期由 Numeric 和 Numarray 库发展而来。 NumPy 将始终保证项目完整开源，所有人都可以根据 [修改后的 BSD 条款](https://github.com/numpy/numpy/blob/master/LICENSE.txt) 免费对其进行使用和分发。 +NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 年，早期由 Numeric 和 Numarray 库发展而来。 NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). 经过 Numpy 和 Python 科学计算社区协商讨论，最终决定将 Numpy 在 GitHub 上开源。 想要了解更多与社区治理有关的信息，请参阅我们的[治理文件](https://www.numpy.org/devdocs/dev/governance/index.html)。 From 90130560464c58314fb06aa92c6dde19b13053d1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:45 +0200 Subject: [PATCH 755/909] 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 3b522b141d..38fdd8aab9 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -84,7 +84,7 @@ params: text: NumPy의 고수준 문법은 어떤 배경이나 수준을 가지고 있는 프로그래머든 쉽게 접근하여 생산적인 일을 할 수 있도록 만들어줍니다. - title: 오픈소스 - text: 자유 [BSD 라이선스](https://github.com/numpy/numpy/blob/master/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. + 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). tabs: title: 생태계 section5: false From 3b3fe680967724dad9e77a38d2bf4937e4ac5fd9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:47 +0200 Subject: [PATCH 756/909] 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 2a1b7b801b..5afc286852 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -5,7 +5,7 @@ sidebar: false _NumPy 프로젝트와 커뮤니티에 대한 몇가지 정보_ -NumPy는 Python을 통해 수치적 컴퓨팅을 할 수 있도록 도와주는 오픈소스 프로젝트입니다. Numerical와 Numarray라는 라이브러리의 초기 작업을 기반으로 2005년에 만들어졌습니다. NumPy는 항상 100% 오픈소스 소프트웨어일 것이며, [수정된 BSD 라이선스](https://github.com/numpy/numpy/blob/master/LICENSE.txt)에 따라서 누구나 무료로 사용하고 배포할 수 있습니다. +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, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). NumPy는 광범위한 Scientific Python 커뮤니티의 협의를 통해 GitHub에서 공개적으로 개발되었습니다. 우리의 거버넌스 접근 방식에 대한 더 자세한 내용은 [거버넌스 문서](https://www.numpy.org/devdocs/dev/governance/index.html)를 참조해 주세요. From fac86b4bbf3226870518ef22ad856c6798e89b34 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:48 +0200 Subject: [PATCH 757/909] New translations press-kit.md (Spanish) --- content/es/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/press-kit.md b/content/es/press-kit.md index 2309040ad2..2c8970bb29 100644 --- a/content/es/press-kit.md +++ b/content/es/press-kit.md @@ -5,4 +5,4 @@ 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/master/branding/logo). Note that by using the numpy.org resources, you accept the [NumPy Code of Conduct](/code-of-conduct). +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 f2f158de559eee2cd73bb4258d84a1b73ffa9213 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:49 +0200 Subject: [PATCH 758/909] 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 10f67a83d6..c1cf8fb33e 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -84,7 +84,7 @@ params: 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/master/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: 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). tabs: title: エコシステム section5: false From 282cd4e285be799bae2006d9943a430e8596f1e3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:50 +0200 Subject: [PATCH 759/909] 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 81b0bc6704..2213617fce 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/master/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。 ちなみに、numpy.orgのリソースを使用するということは、 [Numpy行動規範](/code-of-conduct) を受け入れることを意味していることに注意してください。 +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). ちなみに、numpy.orgのリソースを使用するということは、 [Numpy行動規範](/code-of-conduct) を受け入れることを意味していることに注意してください。 From 898e1b46ed52b45a6cafbf826b8d4d3b919d50cf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:51 +0200 Subject: [PATCH 760/909] 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 db5c917b5b..02b3e1fa31 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -5,7 +5,7 @@ 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/master/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。 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 will always be 100% open source software, free for all to use and 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。 From d73e93e00b44880503782603105b15f2c0d55b9d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:51 +0200 Subject: [PATCH 761/909] 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 c01c434642..c511ca18ee 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -84,7 +84,7 @@ params: text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. - title: Open source - text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + 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). tabs: title: ECOSYSTEM section5: false From d14f6e2ced0082e32e9abc72cbc00806d7349b75 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:53 +0200 Subject: [PATCH 762/909] New translations press-kit.md (Arabic) --- content/ar/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/press-kit.md b/content/ar/press-kit.md index ee87b81a02..512f472173 100644 --- a/content/ar/press-kit.md +++ b/content/ar/press-kit.md @@ -5,4 +5,4 @@ sidebar: false نرحب بتسهيل إدراج مشروع نمباى عليك سواء فى بحثك الأكاديمى أو كمادة دراسية أو كعرض. -لذلك ستجد عدة إصدارات عالية الجودة من شاعر نمباى[ هنا](https://github.com/numpy/numpy/tree/master/branding/logo). وعليك أن تلاحظ أنه باستخدام موارد numpy.org فأنت توافق على[ قواعد السلوك لنمباى](/code-of-conduct). +You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). وعليك أن تلاحظ أنه باستخدام موارد numpy.org فأنت توافق على[ قواعد السلوك لنمباى](/code-of-conduct). From 184ac312fb056d4331dc2f939a5c37273f49b55f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:54 +0200 Subject: [PATCH 763/909] 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 9257aba36c..30be233592 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -5,7 +5,7 @@ sidebar: false _بعض المعلومات حول مشروع ومجتمع نمباي_ -نمباي هو مشروع مفتوح المصدر يهدف إلي إتاحة الحوسبة الرقمية باستخدام لغة برمجة بايثون. وقد أنشئت في عام 2005، استنادا علي العمل المبكر للمكتبتان Numeric و Numarray. ستظل نمباي دائماُ مائة في المائة برمجية مفتوحة المصدر، مجانية للجميع وتصدر بموجب الشروط الليبرالية [لرخصة BSD المعدلة](https://github.com/numpy/numpy/blob/master/LICENSE.txt). +نمباي هو مشروع مفتوح المصدر يهدف إلي إتاحة الحوسبة الرقمية باستخدام لغة برمجة بايثون. وقد أنشئت في عام 2005، استنادا علي العمل المبكر للمكتبتان Numeric و Numarray. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt). وقد تم تطوير نمباي في العلن على GitHub ومن خلال توافق آراء مجتمع نمباي ونطاق أوسع لمجتمع بايثون العلمي. لمزيد من المعلومات حول نهج الإدارة، يرجى الاطلاع على [الوثيقة الإدارية](https://www.numpy.org/devdocs/dev/governance/index.html) الخاصة بنا. From 9b554ccdaaaa8f3345927df215d4621f425a5a2c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:55 +0200 Subject: [PATCH 764/909] 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 66fc416157..cb5e2b42b6 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -84,7 +84,7 @@ params: text: NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level. - title: Open source - text: Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/master/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community). + 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). tabs: title: ECOSYSTEM section5: false From 868b8b1a26ed5eba2e024a5a4ab78d9a049b08a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 2 Oct 2021 19:41:55 +0200 Subject: [PATCH 765/909] 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 f989b17e61..be0b021798 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -84,7 +84,7 @@ params: 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/master/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. + 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). tabs: title: ECOSSISTEMA section5: false From 06ce3e86ba9ed2660443e841b2dbe48546866978 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:51 +0200 Subject: [PATCH 766/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/content/es/config.yaml b/content/es/config.yaml index cb5e2b42b6..7d76e4d999 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -144,6 +144,9 @@ footer: - text: Community link: /community + - + text: User surveys + link: /user-surveys - text: Contribute link: /contribute From 8a247451a589c9533a5249d9d81fc163f0caa970 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:52 +0200 Subject: [PATCH 767/909] 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 451f9a03624de718f3eb0ef9b722db90349efe7c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:53 +0200 Subject: [PATCH 768/909] New translations config.yaml (Arabic) --- content/ar/config.yaml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index c511ca18ee..5ca4b86872 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -144,6 +144,9 @@ footer: - text: Community link: /community + - + text: User surveys + link: /user-surveys - text: Contribute link: /contribute From 9fbe15654d8117c3bc3fd39518121dc0870f5642 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:54 +0200 Subject: [PATCH 769/909] 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 3dbfeff4c09a62e6508c48631b808ae38e306ad0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:55 +0200 Subject: [PATCH 770/909] New translations config.yaml (Japanese) --- content/ja/config.yaml | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index c1cf8fb33e..a529cba241 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -145,11 +145,14 @@ footer: text: コミュニティ link: /ja/community - - text: NumPyに貢献する - link: /ja/contribute + text: User surveys + link: /user-surveys - - text: 行動規範 - link: /ja/code-of-conduct + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct column3: links: - From 8f37dbc57fcffe9970883d79e5f0d39ef1d4cc0d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:56 +0200 Subject: [PATCH 771/909] 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 7ea13c8143a8529c5ec0b6ae13344b78f824b1c5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:57 +0200 Subject: [PATCH 772/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 38fdd8aab9..31b42b6eaf 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -145,10 +145,13 @@ footer: text: 커뮤니티 link: /community - - text: 기여 + text: User surveys + link: /user-surveys + - + text: Contribute link: /contribute - - text: 이용약관 + text: Code of conduct link: /code-of-conduct column3: links: From 36314b93b5e5911bced0b88b803ab1f89e983294 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:58 +0200 Subject: [PATCH 773/909] 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 940769c92c0a99fdc84a1bed0e9b3072e3c4b748 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:44:59 +0200 Subject: [PATCH 774/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 7a156a2ae9..e7b732b2d8 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -145,11 +145,14 @@ footer: text: 社区 link: /community - - text: 参与贡献 + text: User surveys + link: /user-surveys + - + text: Contribute link: /contribute - - text: 行为准则 - link: /codes-of-conduct + text: Code of conduct + link: /code-of-conduct column3: links: - From b70001d813e2c6f48eb94a2e93f18dceaead89ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:45:00 +0200 Subject: [PATCH 775/909] 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 89132e4e0e150cc612ac363821f6b8be747cf8dd Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:45:01 +0200 Subject: [PATCH 776/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index be0b021798..a76f944546 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -145,11 +145,14 @@ footer: text: Comunidade link: /pt/community - - text: Contribuir - link: /pt/contribute + text: User surveys + link: /user-surveys - - text: Código de Conduta - link: /pt/code-of-conduct + text: Contribute + link: /contribute + - + text: Code of conduct + link: /code-of-conduct column3: links: - From 73899e420f67ae873e036f3ceb4cab4b5b5ec155 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 17 Oct 2021 11:45:02 +0200 Subject: [PATCH 777/909] 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 35fda802b27f4f535017908a0f631cdfac550b2d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 11:49:12 +0200 Subject: [PATCH 778/909] New translations user-surveys.md (Korean) --- content/ko/user-surveys.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/user-surveys.md b/content/ko/user-surveys.md index 89a2aa0460..acdcbc953a 100644 --- a/content/ko/user-surveys.md +++ b/content/ko/user-surveys.md @@ -1,5 +1,5 @@ --- -title: NUMPY USER SURVEYS +title: NUMPY 사용자 설문조사 sidebar: false --- From d720c23c1027a10efd45c02a0ed352c0f8f65618 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 11:49:14 +0200 Subject: [PATCH 779/909] 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 5afc286852..1b1d455290 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -5,7 +5,7 @@ sidebar: false _NumPy 프로젝트와 커뮤니티에 대한 몇가지 정보_ -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, free for all to use and 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% 오픈소스 소프트웨어일 것이며, [수정 BSD 라이선스](https://github.com/numpy/numpy/blob/main/LICENSE.txt) 내 자유 조항에 따라서 누구나 무료로 사용하고 배포할 수 있습니다. NumPy는 광범위한 Scientific Python 커뮤니티의 협의를 통해 GitHub에서 공개적으로 개발되었습니다. 우리의 거버넌스 접근 방식에 대한 더 자세한 내용은 [거버넌스 문서](https://www.numpy.org/devdocs/dev/governance/index.html)를 참조해 주세요. From 658f69a65652f714de9fafa8e4c65f8e21e5ac85 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 11:49:15 +0200 Subject: [PATCH 780/909] 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 3625914f79..738033325e 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -17,7 +17,7 @@ NumPy 프로젝트 및 커뮤니티에 곧장 참여할 수 있는 방법들입 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. -리스트에 회신하려면 (다른 발신자에게 회신하기보다는) 하단의 게시물을 이용하십시오. 또, 자동 발신 메일에 회신하지 마십시오. 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. *** From 097bff142cfbf26ad80c1895e2ce27c6aae317ef Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 11:49:16 +0200 Subject: [PATCH 781/909] New translations press-kit.md (Korean) --- content/ko/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/press-kit.md b/content/ko/press-kit.md index f1d749a41a..ddce954013 100644 --- a/content/ko/press-kit.md +++ b/content/ko/press-kit.md @@ -5,4 +5,4 @@ sidebar: false 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). numpy.org 자료를 이용하는 경우, [NumPy 이용약관](/code-of-conduct)에 동의하게 됨을 명심하십시오. +[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 a06d4f1841497775dcc3f0dc7fad27fe0b7119c8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 11:49:17 +0200 Subject: [PATCH 782/909] 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 c3e1ccf23b..902100702b 100644 --- a/content/ko/user-survey-2020.md +++ b/content/ko/user-survey-2020.md @@ -3,7 +3,7 @@ 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. 75개국 내 1200명 이상의 사용자 여러분들께서 저희가 NumPy 커뮤니티의 가닥을 잡을 수 있도록 도와주기 위해 참여해주셨으며 프로젝트의 미래에 대한 생각을 표현해주셨습니다. +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">}} From 9e4b3d7900e098ba10b4ba005dc915afdc5884a8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 13:10:53 +0200 Subject: [PATCH 783/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 31b42b6eaf..3ab530478b 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -19,7 +19,7 @@ params: #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: D&I Grant from CZI - content: Including NumPy, SciPy, Matplotlib and Pandas + content: NumPy, SciPy, Matplotlib 및 Pandas 포함 url: /news shell: title: 플레이스홀더 @@ -84,7 +84,7 @@ params: text: NumPy의 고수준 문법은 어떤 배경이나 수준을 가지고 있는 프로그래머든 쉽게 접근하여 생산적인 일을 할 수 있도록 만들어줍니다. - title: 오픈소스 - 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). + 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. tabs: title: 생태계 section5: false @@ -145,13 +145,13 @@ footer: text: 커뮤니티 link: /community - - text: User surveys + text: 사용자 설문조사 link: /user-surveys - - text: Contribute + text: 기여 link: /contribute - - text: Code of conduct + text: 이용약관 link: /code-of-conduct column3: links: From fa5eb8b0f046a4e71851ceeafb8ab9c1d9e8de3e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 13:10:54 +0200 Subject: [PATCH 784/909] New translations user-surveys.md (Korean) --- content/ko/user-surveys.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/user-surveys.md b/content/ko/user-surveys.md index acdcbc953a..9fec78190d 100644 --- a/content/ko/user-surveys.md +++ b/content/ko/user-surveys.md @@ -3,8 +3,8 @@ 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 bfea315bdc095fb59df7572b2045932482164357 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 13:10:56 +0200 Subject: [PATCH 785/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index d99bcf8710..70f694c26b 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -21,7 +21,7 @@ Cricket is a game of numbers - the runs scored by a batsman, the wickets taken b 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, +* 점수 예측, * 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 From dce163a42ecf9b985e3ae536be9443a725055d70 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 13:20:01 +0200 Subject: [PATCH 786/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 70f694c26b..05df6aec4f 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -55,7 +55,7 @@ Today, there are rich and almost infinite troves of cricket game records and sta * **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. ## 요약 From 0f33e6de129b1ba150ab839f9ec06f3ad94f8987 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 18:33:46 +0200 Subject: [PATCH 787/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 7d76e4d999..5aae124db1 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -3,7 +3,7 @@ languageName: Inglés params: description: '¿Por qué NumPy? Potentes matrices n-dimensionales. Herramientas de cálculo numérico. Interoperable. Rendimiento. Código abierto.' navbarlogo: - image: logos/numpy.svg + image: logo.svg link: / hero: #Main hero title @@ -15,7 +15,7 @@ params: #Where the main hero button links to buttonlink: "/install" #Hero image (from static/images/___) - image: logos/numpy.svg + image: logo.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: Subvención D&I de CZI @@ -108,7 +108,7 @@ navbar: title: Contribute url: /contribute footer: - logo: numpy.svg + logo: logo.svg socialmediatitle: "" socialmedia: - From 70307ce1c1d7b734e7a465c1e31f408d9759015c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 18:33:47 +0200 Subject: [PATCH 788/909] New translations config.yaml (Arabic) --- content/ar/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index 5ca4b86872..e789c6189f 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -3,7 +3,7 @@ languageName: English params: description: Why NumPy? Powerful n-dimensional arrays. Numerical computing tools. Interoperable. Performant. Open source. navbarlogo: - image: logos/numpy.svg + image: logo.svg link: / hero: #Main hero title @@ -15,7 +15,7 @@ params: #Where the main hero button links to buttonlink: "/install" #Hero image (from static/images/___) - image: logos/numpy.svg + image: logo.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: D&I Grant from CZI @@ -108,7 +108,7 @@ navbar: title: Contribute url: /contribute footer: - logo: numpy.svg + logo: logo.svg socialmediatitle: "" socialmedia: - From 9cdef3aab747b81a60eb348bec1841e280c631f5 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 18:33:48 +0200 Subject: [PATCH 789/909] New translations config.yaml (Japanese) --- content/ja/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index a529cba241..cc302061ac 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -3,7 +3,7 @@ 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: logos/numpy.svg + image: logo.svg link: /ja/ hero: #Main hero title @@ -15,7 +15,7 @@ params: #Where the main hero button links to buttonlink: "/ja/install" #Hero image (from static/images/___) - image: logos/numpy.svg + image: logo.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: D&I Grant from CZI @@ -108,7 +108,7 @@ navbar: title: NumPyに貢献する url: /ja/contribute footer: - logo: numpy.svg + logo: logo.svg socialmediatitle: "" socialmedia: - From 3f61ed2f4495778a7ec4253f65d14450be1b2d12 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 18:33:49 +0200 Subject: [PATCH 790/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 3ab530478b..fceb10a0c3 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -3,7 +3,7 @@ languageName: 한국어 params: description: 왜 NumPy인가? 강력한 n차원 배열. 수치 컴퓨팅 도구. 상호운용성. 고성능. 오픈소스. navbarlogo: - image: logos/numpy.svg + image: logo.svg link: / hero: #Main hero title @@ -15,7 +15,7 @@ params: #Where the main hero button links to buttonlink: "/install" #Hero image (from static/images/___) - image: logos/numpy.svg + image: logo.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: D&I Grant from CZI @@ -108,7 +108,7 @@ navbar: title: 기여 url: /contribute footer: - logo: numpy.svg + logo: logo.svg socialmediatitle: "" socialmedia: - From ccc7bfe0c64a60eaea6014f2977930140fd9a86f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 18:33:50 +0200 Subject: [PATCH 791/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index e7b732b2d8..4f050bdd24 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -3,7 +3,7 @@ languageName: 英语 params: description: 为什么使用 Numpy？它有强大的高维数组、有数字计算工具、互可操作、高性能、开源。 navbarlogo: - image: logos/numpy.svg + image: logo.svg link: / hero: #Main hero title @@ -15,7 +15,7 @@ params: #Where the main hero button links to buttonlink: "/install" #Hero image (from static/images/___) - image: logos/numpy.svg + image: logo.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: D&I Grant from CZI @@ -108,7 +108,7 @@ navbar: title: 参与贡献 url: /contribute footer: - logo: numpy.svg + logo: logo.svg socialmediatitle: "" socialmedia: - From a6992b03c1501f63a6b3433e538f49a49fb668ed Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 27 Oct 2021 18:33:51 +0200 Subject: [PATCH 792/909] 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 a76f944546..b4d0f818ee 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -3,7 +3,7 @@ languageName: Português params: description: Por que NumPy? Arrays n-dimensionais poderosas. Ferramentas para computação numérica. Interoperabilidade. Alto desempenho. Código aberto. navbarlogo: - image: logos/numpy.svg + image: logo.svg link: /pt/ hero: #Main hero title @@ -15,7 +15,7 @@ params: #Where the main hero button links to buttonlink: "/pt/install" #Hero image (from static/images/___) - image: logos/numpy.svg + image: logo.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: title: D&I Grant from CZI @@ -108,7 +108,7 @@ navbar: title: Contribuir url: /pt/contribute footer: - logo: numpy.svg + logo: logo.svg socialmediatitle: "" socialmedia: - From 427018db7433ed09c911959ca4293a359b058ab6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 03:10:26 +0200 Subject: [PATCH 793/909] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md index b40ed2af50..51da1dbd6a 100644 --- a/content/ko/case-studies/deeplabcut-dnn.md +++ b/content/ko/case-studies/deeplabcut-dnn.md @@ -45,7 +45,7 @@ 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" 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 @@ -61,7 +61,7 @@ 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" 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 @@ -77,7 +77,7 @@ 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" 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 From a91dd9043d3a2c3a6db35ecf0f9e176c83c6e0df Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 03:25:59 +0200 Subject: [PATCH 794/909] 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 fceb10a0c3..989b80f414 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -18,7 +18,7 @@ params: image: logo.svg #Customizable navbar. For a dropdown, add a "sublinks" list. news: - title: D&I Grant from CZI + title: CZI에서 D&I 장려금 수여 content: NumPy, SciPy, Matplotlib 및 Pandas 포함 url: /news shell: From a8cb63476a2c688af6eb57d04519fb5d679d911f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 03:26:02 +0200 Subject: [PATCH 795/909] New translations deeplabcut-dnn.md (Korean) --- content/ko/case-studies/deeplabcut-dnn.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/case-studies/deeplabcut-dnn.md b/content/ko/case-studies/deeplabcut-dnn.md index 51da1dbd6a..f6b769cf3f 100644 --- a/content/ko/case-studies/deeplabcut-dnn.md +++ b/content/ko/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="**DeepLapCut을 활용한 쥐의 손 움직임 분석**" alt="쥐 손 애니메이션" attr="*(출처: 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.

    @@ -16,7 +16,7 @@ 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" 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. @@ -83,7 +83,7 @@ DeepLabCut utilizes NumPy’s array capabilities throughout the workflow offered 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" 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 c22b851a068f1a2ee5665474860a688f02ccbb16 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 03:26:05 +0200 Subject: [PATCH 796/909] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index 3d25090e13..a9bbffd32b 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -3,7 +3,7 @@ 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" 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.

    @@ -39,7 +39,7 @@ 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" class="fig-center" alt="중력파 변형 진폭" caption="**GW150914에서 추정된 중력파 변형 진폭**" attr="(**그래프 출처:** 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 @@ -56,14 +56,14 @@ 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" 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 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" class="fig-center" alt="numpy를 통한 이익" caption="**활용된 주요 NumPy 기능**" >}} From f4e32278d06acedbf115f59ece0a0bb49fbdaf8e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 03:36:47 +0200 Subject: [PATCH 797/909] New translations code-of-conduct.md (Korean) --- content/ko/code-of-conduct.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/code-of-conduct.md b/content/ko/code-of-conduct.md index 48ef3716e7..bff6168db5 100644 --- a/content/ko/code-of-conduct.md +++ b/content/ko/code-of-conduct.md @@ -13,7 +13,7 @@ This Code of Conduct should be honored by everyone who participates in the NumPy 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 +### 구체적인 지침 We strive to: @@ -33,7 +33,7 @@ We strive to: * 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 +### 다양성 성명 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. @@ -43,7 +43,7 @@ Though we welcome people fluent in all languages, NumPy development is conducted 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 +### 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. @@ -59,7 +59,7 @@ Currently, the Committee consists of: 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 +### 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). From 8bce9869d812d07923810ca63b9d3795ae3e1941 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 03:36:48 +0200 Subject: [PATCH 798/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 06ec9faf9b..dd1cc3302e 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -17,13 +17,13 @@ NumPy 프로젝트에서는 당신의 경험과 의욕을 환영합니다! NumPy - [커뮤니티 코디네이터로 기여](#community-coordination-and-outreach) - [보조금 제안서 작성 및 기타 모금 지원](#fundraising) -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). -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/). +We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). 당신의 참여를 매우 환영합니다. 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. +저희 커뮤니티는 모두를 평등하게 대하고 모든 기여의 가치를 인정하려는 뜻을 품고 있습니다. 개방적이고 참여를 환영하는 분위기를 조성하기 위해 [이용약관](/code-of-conduct)을 만들었습니다. ### 코드 작성 From c42c0b944909518036f89e5551d3258be4935c59 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 03:46:59 +0200 Subject: [PATCH 799/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index dd1cc3302e..cb60c23d0e 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -21,7 +21,7 @@ NumPy 프로젝트에서는 당신의 경험과 의욕을 환영합니다! NumPy 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). 당신의 참여를 매우 환영합니다. If you are new to contributing to open source, we also highly recommend reading [this guide](https://opensource.guide/how-to-contribute/). +We also have a biweekly _community call_, details of which are announced on the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion). 당신의 참여를 매우 환영합니다. 오픈소스에 기여하는 게 처음이시라면 [이 도움말](https://opensource.guide/how-to-contribute/)을 읽어 보시는 것을 적극 권장합니다. 저희 커뮤니티는 모두를 평등하게 대하고 모든 기여의 가치를 인정하려는 뜻을 품고 있습니다. 개방적이고 참여를 환영하는 분위기를 조성하기 위해 [이용약관](/code-of-conduct)을 만들었습니다. From 6fbd1587a8c4b251c0cbfb7d91b193122058705a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 04:25:56 +0200 Subject: [PATCH 800/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index cb60c23d0e..b283474d53 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -21,7 +21,7 @@ NumPy 프로젝트에서는 당신의 경험과 의욕을 환영합니다! NumPy 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). 당신의 참여를 매우 환영합니다. 오픈소스에 기여하는 게 처음이시라면 [이 도움말](https://opensource.guide/how-to-contribute/)을 읽어 보시는 것을 적극 권장합니다. +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. 저희 커뮤니티는 모두를 평등하게 대하고 모든 기여의 가치를 인정하려는 뜻을 품고 있습니다. 개방적이고 참여를 환영하는 분위기를 조성하기 위해 [이용약관](/code-of-conduct)을 만들었습니다. From d41073f13de2ea4621bb6a49e67ebe983492728c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 04:44:19 +0200 Subject: [PATCH 801/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index b283474d53..71ac98789b 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -74,5 +74,5 @@ Through community contact we share our work more widely and learn where we're fa ### 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. 이런 지원이 얼마나 큰 차이를 만들어 냈는지 [SciPy'19 강연](https://www.youtube.com/watch?v=dBTJD_FDVjU)에서 확인하실 수 있습니다. 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. 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 e1408c6518b439409d63f4e4abfc72fd7d85691f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 07:21:18 +0200 Subject: [PATCH 802/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 71ac98789b..8eeacc7f38 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -19,7 +19,7 @@ NumPy 프로젝트에서는 당신의 경험과 의욕을 환영합니다! NumPy 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). 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. @@ -70,7 +70,7 @@ NumPy의 [사용자 도움말](https://numpy.org/devdocs)은 현재 대규모로 ### 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. ### 모금 From fc92694cf8f5ed83ed26f2a4e8503295eacf09a2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 07:31:36 +0200 Subject: [PATCH 803/909] New translations contribute.md (Korean) --- content/ko/contribute.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/contribute.md b/content/ko/contribute.md index 8eeacc7f38..d789e93c46 100644 --- a/content/ko/contribute.md +++ b/content/ko/contribute.md @@ -74,5 +74,5 @@ NumPy의 [사용자 도움말](https://numpy.org/devdocs)은 현재 대규모로 ### 모금 -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. 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 86b0d5bd0d6758558f5ce579fac03b049b936f47 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 07:31:37 +0200 Subject: [PATCH 804/909] New translations tabcontents.yaml (Korean) --- content/ko/tabcontents.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/tabcontents.yaml b/content/ko/tabcontents.yaml index 891a9b1437..1013a09536 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -203,7 +203,7 @@ visualization: - url: https://docs.pyvista.org/examples/index.html 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 @@ -214,6 +214,6 @@ visualization: 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 8a70e5f4ff894bcee09c0d7b10ec478c83ee4027 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 07:42:56 +0200 Subject: [PATCH 805/909] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md index 2d34abc23c..90f623f5f7 100644 --- a/content/ko/report-handling-manual.md +++ b/content/ko/report-handling-manual.md @@ -3,7 +3,7 @@ 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: @@ -16,7 +16,7 @@ Enforcing the [Code of Conduct](/code-of-conduct) impacts our community today an * 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 +## 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: @@ -29,12 +29,12 @@ Voluntary informal mediation is a tool at our disposal. In contexts such as when 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 +## 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. -## 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. @@ -46,7 +46,7 @@ When a member of the Code of Conduct Committee becomes aware of a clear and seve * 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 +## 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. @@ -66,9 +66,9 @@ It is important to retain an archive of all activities of this Committee to ensu 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 +## 결의 -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: @@ -90,6 +90,6 @@ Finally, the Committee will make a report to the NumPy Steering Council (as well 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 +## 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 ec7ced8cfbde35ac48df96496e9c1a46c5b0c0dc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Thu, 28 Oct 2021 07:52:42 +0200 Subject: [PATCH 806/909] New translations report-handling-manual.md (Korean) --- content/ko/report-handling-manual.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/ko/report-handling-manual.md b/content/ko/report-handling-manual.md index 90f623f5f7..5da70399b1 100644 --- a/content/ko/report-handling-manual.md +++ b/content/ko/report-handling-manual.md @@ -85,9 +85,9 @@ Possible responses may include: 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). +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. ## 이해관계 충돌 From 7afb6ab5bb5deba4973f5747409bbf925c403c86 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Oct 2021 21:32:57 +0100 Subject: [PATCH 807/909] 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 1013a09536..1e7980763c 100644 --- a/content/ko/tabcontents.yaml +++ b/content/ko/tabcontents.yaml @@ -209,7 +209,7 @@ visualization: img: /images/content_images/v_napari.png alttext: napari로 만든 다차원 이미지. - - url: http://vispy.org/gallery.html + url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: vispy로 만든 보로노이 다이어그램. content: From e2ae3c6665da26988183ecb8827b1cd5c6981176 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Oct 2021 21:32:58 +0100 Subject: [PATCH 808/909] 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 7d7cc1c188..3f5d82e888 100644 --- a/content/es/tabcontents.yaml +++ b/content/es/tabcontents.yaml @@ -209,7 +209,7 @@ visualization: img: /images/content_images/v_napari.png alttext: A multi-dimensionan image made in napari. - - url: http://vispy.org/gallery.html + url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: A Voronoi diagram made in vispy. content: From d737a802dbca8e4939157ddf53b4e3d02a621c47 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Oct 2021 21:32:59 +0100 Subject: [PATCH 809/909] 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 7d7cc1c188..3f5d82e888 100644 --- a/content/ar/tabcontents.yaml +++ b/content/ar/tabcontents.yaml @@ -209,7 +209,7 @@ visualization: img: /images/content_images/v_napari.png alttext: A multi-dimensionan image made in napari. - - url: http://vispy.org/gallery.html + url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: A Voronoi diagram made in vispy. content: From 710389149c6a271c2c8f146541141ffa7df39a8f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Oct 2021 21:33:00 +0100 Subject: [PATCH 810/909] 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 9b955b584b..f955e3a5f3 100644 --- a/content/ja/tabcontents.yaml +++ b/content/ja/tabcontents.yaml @@ -209,7 +209,7 @@ visualization: img: /images/content_images/v_napari.png alttext: ナパリで作られた多次元画像 - - url: http://vispy.org/gallery.html + 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: From 409e1fa9935abf38acf4047aa81205d5db5b3cd6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Oct 2021 21:33:02 +0100 Subject: [PATCH 811/909] 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 f325309bb2..3406a94d9d 100644 --- a/content/zh/tabcontents.yaml +++ b/content/zh/tabcontents.yaml @@ -209,7 +209,7 @@ visualization: img: /images/content_images/v_napari.png alttext: A multi-dimensionan image made in napari. - - url: http://vispy.org/gallery.html + url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: A Voronoi diagram made in vispy. content: From 0e7ddc137d4109b832b9e13315a8e56a7ac089b2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 31 Oct 2021 21:33:03 +0100 Subject: [PATCH 812/909] 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 2643c627c0..aa5868769e 100644 --- a/content/pt/tabcontents.yaml +++ b/content/pt/tabcontents.yaml @@ -209,7 +209,7 @@ visualization: img: /images/content_images/v_napari.png alttext: Uma imagem multidimensional, feita em napari. - - url: http://vispy.org/gallery.html + url: https://vispy.org/gallery/index.html img: /images/content_images/v_vispy.png alttext: Diagrama de Voronoi feito com vispy. content: From 8fbc76639c83a3331f525f0cf6bfb132d170111f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Nov 2021 08:34:04 +0100 Subject: [PATCH 813/909] 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 5979c96a30..31bc8f816c 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -13,7 +13,7 @@ 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. @@ -22,26 +22,26 @@ 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: +_2021년 6월 23일_ -- [NumPy 1.21.0](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. +- 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. 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/. +_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 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. @@ -75,13 +75,13 @@ _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-m 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 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. +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 release @@ -114,15 +114,15 @@ 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 1.21.0 ([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 1.21.0 ([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 f78c8751b9de03921817296b7def0aa29953dd5f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Nov 2021 08:43:30 +0100 Subject: [PATCH 814/909] New translations cricket-analytics.md (Korean) --- content/ko/case-studies/cricket-analytics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ko/case-studies/cricket-analytics.md b/content/ko/case-studies/cricket-analytics.md index 05df6aec4f..def42fa146 100644 --- a/content/ko/case-studies/cricket-analytics.md +++ b/content/ko/case-studies/cricket-analytics.md @@ -20,7 +20,7 @@ Cricket is a game of numbers - the runs scored by a batsman, the wickets taken b 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, +* 타격 성적 이동 평균, * 점수 예측, * 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 From 1fc0c52af66a8227628b720b076d3426fdddca7a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Nov 2021 08:43:31 +0100 Subject: [PATCH 815/909] New translations news.md (Korean) --- content/ko/news.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 31bc8f816c..c6e429b1f8 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -64,7 +64,7 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear - 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. @@ -72,7 +72,7 @@ _Sep 10, 2020_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes. _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). +NumPy를 개선할 수 있도록 도와주시고 [여기](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)에서 설문 조사에 참여해주시면 감사드리겠습니다. ### NumPy에 새로운 로고가 생겼습니다! @@ -84,7 +84,7 @@ _2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. 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 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. @@ -94,16 +94,16 @@ _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release witho _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 받음 -_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. @@ -112,7 +112,7 @@ More details on our proposed initiatives and deliverables can be found in the [f ## 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 목록을 볼 수 있으며, 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 1.21.0 ([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_. From 85bc9b806b54d5507ac38c1cc523bc769a83a228 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Nov 2021 08:55:24 +0100 Subject: [PATCH 816/909] 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 c6e429b1f8..2a60993ba3 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -11,15 +11,15 @@ As a part of [CZI's Essential Open Source Software for Science program](https:// 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). +2개년 프로젝트가 2021년 11월부터 시작될 예정입니다. 프로젝트의 결과를 볼 날이 기대되네요! [전체 정보는 여기서 열람하실 수 있습니다](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 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. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. 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 @@ -47,7 +47,7 @@ _Jan 30, 2021_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-not - 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). @@ -66,7 +66,7 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear ### 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. 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! From 4f1038200b7676b545547dede93881041f0492b8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Nov 2021 09:06:58 +0100 Subject: [PATCH 817/909] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index a9bbffd32b..a01f407e89 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -1,23 +1,23 @@ --- -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="**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. 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 +### 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 @@ -25,17 +25,17 @@ The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.lig -### The Challenges +### 과제 -* **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 -* **Data Deluge** +* **데이터 범람** 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** +* **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. @@ -50,10 +50,10 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * [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 +* 데이터의 시각화 - Time series - - Spectrograms -* Compute Correlations + - 스펙트로그램 +* 상관 분석 연산 * 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 종속성" caption="**GwPy 패키지가 어떻게 NumPy에 종속하는지를 나타내는 종속성 그래프**" >}} @@ -62,7 +62,7 @@ NumPy, the standard numerical analysis package for Python, was utilized by the {{< 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. From 01f894081567aa0293696665d5353173d58dbeac Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Nov 2021 09:07:00 +0100 Subject: [PATCH 818/909] 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 2a60993ba3..fa87295a84 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -70,7 +70,7 @@ _2020년 9월 10일_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2- ### 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. +_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. 설문지는 영어 외에도 8개 국어로 제공됩니다: 벵골어, 프랑스어, 힌디어, 일본어, 중국 관화, 포르투갈어, 러시아어, 스페인어. NumPy를 개선할 수 있도록 도와주시고 [여기](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)에서 설문 조사에 참여해주시면 감사드리겠습니다. From 81059c1506e320b8d1949c7ca97bc80d3efe548f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Nov 2021 09:58:54 +0100 Subject: [PATCH 819/909] 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 a01f407e89..61ad5cde55 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -12,7 +12,7 @@ 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. 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. @@ -41,7 +41,7 @@ The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.lig {{< figure src="/images/content_images/cs/gw_strain_amplitude.png" class="fig-center" alt="중력파 변형 진폭" caption="**GW150914에서 추정된 중력파 변형 진폭**" attr="(**그래프 출처:** 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. @@ -51,7 +51,7 @@ NumPy, the standard numerical analysis package for Python, was utilized by the * 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. * 데이터의 시각화 - - Time series + - 시계열 데이터 - 스펙트로그램 * 상관 분석 연산 * 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. @@ -64,6 +64,6 @@ 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. 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**" >}} From 22f63d27c8e2d4f7b3ff8b7791020e6139981ded Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Fri, 5 Nov 2021 10:04:41 +0100 Subject: [PATCH 820/909] New translations gw-discov.md (Korean) --- content/ko/case-studies/gw-discov.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/case-studies/gw-discov.md b/content/ko/case-studies/gw-discov.md index 61ad5cde55..aebb9b443e 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -47,9 +47,9 @@ Gravitational waves emitted from the merger cannot be computed using any techniq 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. +* [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 From 944d1098ab9a925ef016166cb7e7fb47c0ac1d8a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 24 Nov 2021 11:27:36 +0100 Subject: [PATCH 821/909] New translations press-kit.md (Arabic) --- content/ar/press-kit.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/press-kit.md b/content/ar/press-kit.md index 512f472173..f923ebe884 100644 --- a/content/ar/press-kit.md +++ b/content/ar/press-kit.md @@ -5,4 +5,4 @@ sidebar: false نرحب بتسهيل إدراج مشروع نمباى عليك سواء فى بحثك الأكاديمى أو كمادة دراسية أو كعرض. -You will find several high-resolution versions of the NumPy logo [here](https://github.com/numpy/numpy/tree/main/branding/logo). وعليك أن تلاحظ أنه باستخدام موارد numpy.org فأنت توافق على[ قواعد السلوك لنمباى](/code-of-conduct). +سوف تجد عدة إصدارات عالية الدقة من شعار الأرقام [هنا](https://github.com/numpy/numpy/tree/main/branding/logo). وعليك أن تلاحظ أنه باستخدام موارد numpy.org فأنت توافق على[ قواعد السلوك لنمباى](/code-of-conduct). From a6a7a77e8b4bcf6c9e81063dcfcfa0a55f4f2363 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 27 Nov 2021 10:40:53 +0100 Subject: [PATCH 822/909] New translations history.md (Arabic) --- content/ar/history.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/history.md b/content/ar/history.md index fc79a621af..46246ba287 100644 --- a/content/ar/history.md +++ b/content/ar/history.md @@ -1,5 +1,5 @@ --- -title: History of NumPy +title: تاريخ مشروع نمباي sidebar: false --- From c6b002bebcbeb6552a3e635c131f6d81291051d8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 27 Nov 2021 12:21:59 +0100 Subject: [PATCH 823/909] New translations history.md (Arabic) --- content/ar/history.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/history.md b/content/ar/history.md index 46246ba287..041d967485 100644 --- a/content/ar/history.md +++ b/content/ar/history.md @@ -3,7 +3,7 @@ title: تاريخ مشروع نمباي 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. +مشروع نمباي هو مكتبة تأسيسية للغة البايثون يوفر هياكل بيانات المصفوفات وما يتصل بها من إجراءات رقمية سريعة. 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](arxiv.org/abs/1907.10121). From 09bc601e4a393f716058cde3a9baefa11abfc6c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:13 +0100 Subject: [PATCH 824/909] 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 c6fe43603d..80e1618043 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,6 +1,8 @@ --- title: News sidebar: false +newsHeader: D&I Grant from CZI +date: --- ### Advancing an inclusive culture in the scientific Python ecosystem From 8cee23b5d14d845f979ff7601a89bed865b26383 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:14 +0100 Subject: [PATCH 825/909] New translations config.yaml (Spanish) --- content/es/config.yaml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/content/es/config.yaml b/content/es/config.yaml index 5aae124db1..01b9b93562 100644 --- a/content/es/config.yaml +++ b/content/es/config.yaml @@ -16,11 +16,6 @@ params: buttonlink: "/install" #Hero image (from static/images/___) image: logo.svg - #Customizable navbar. For a dropdown, add a "sublinks" list. - news: - title: Subvención D&I de CZI - content: Incluye NumPy, SciPy, Matplotlib y Pandas - url: /news shell: title: marcador de posición promptlabel: apuntador interactivo de la consola From 290279aa11291f9b64795578b926545b4c49b0a7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:15 +0100 Subject: [PATCH 826/909] 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 99eb46f8a6..0fe6e12170 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,6 +1,8 @@ --- title: الأخبار sidebar: false +newsHeader: D&I Grant from CZI +date: --- ### Advancing an inclusive culture in the scientific Python ecosystem From b4f9ff92c0f34f0935c42c7924cfb6ab4ee571f2 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:16 +0100 Subject: [PATCH 827/909] New translations config.yaml (Arabic) --- content/ar/config.yaml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/content/ar/config.yaml b/content/ar/config.yaml index e789c6189f..bda18c3313 100644 --- a/content/ar/config.yaml +++ b/content/ar/config.yaml @@ -16,11 +16,6 @@ params: buttonlink: "/install" #Hero image (from static/images/___) image: logo.svg - #Customizable navbar. For a dropdown, add a "sublinks" list. - news: - title: D&I Grant from CZI - content: Including NumPy, SciPy, Matplotlib and Pandas - url: /news shell: title: placeholder promptlabel: interactive shell prompt From f6f19922264e08940e8bc17ad40d59cc14efa831 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:17 +0100 Subject: [PATCH 828/909] 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 7d857dcc18..aef3146199 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,6 +1,8 @@ --- title: ニュース sidebar: false +newsHeader: D&I Grant from CZI +date: --- ### Advancing an inclusive culture in the scientific Python ecosystem From a5678134b2bb9c94da32dbe7662aacc389fbadde Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:18 +0100 Subject: [PATCH 829/909] New translations config.yaml (Japanese) --- content/ja/config.yaml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/content/ja/config.yaml b/content/ja/config.yaml index cc302061ac..bb625af115 100644 --- a/content/ja/config.yaml +++ b/content/ja/config.yaml @@ -16,11 +16,6 @@ params: buttonlink: "/ja/install" #Hero image (from static/images/___) image: logo.svg - #Customizable navbar. For a dropdown, add a "sublinks" list. - news: - title: D&I Grant from CZI - content: Including NumPy, SciPy, Matplotlib and Pandas - url: /ja/news shell: title: placeholder promptlabel: 対話的なシェルプロンプト From 79455f897e38b9bb415b2e77b22afbe7d08d9cc0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:19 +0100 Subject: [PATCH 830/909] 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 fa87295a84..824196fb87 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,6 +1,8 @@ --- title: 소식 sidebar: false +newsHeader: D&I Grant from CZI +date: --- ### Advancing an inclusive culture in the scientific Python ecosystem From 8a6f685b021914108143bd588de9f3bf64f54cbf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:20 +0100 Subject: [PATCH 831/909] New translations config.yaml (Korean) --- content/ko/config.yaml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/content/ko/config.yaml b/content/ko/config.yaml index 989b80f414..783615da46 100644 --- a/content/ko/config.yaml +++ b/content/ko/config.yaml @@ -16,11 +16,6 @@ params: buttonlink: "/install" #Hero image (from static/images/___) image: logo.svg - #Customizable navbar. For a dropdown, add a "sublinks" list. - news: - title: CZI에서 D&I 장려금 수여 - content: NumPy, SciPy, Matplotlib 및 Pandas 포함 - url: /news shell: title: 플레이스홀더 promptlabel: 대화형 쉘 프롬프트 From a9659768b7bcf55309dffd53888320201acc1d83 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:21 +0100 Subject: [PATCH 832/909] 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 8911f1c8e0..c658a50cdf 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,6 +1,8 @@ --- title: 社区快讯 sidebar: false +newsHeader: D&I Grant from CZI +date: --- ### Advancing an inclusive culture in the scientific Python ecosystem From ce9a92009d03b6892cd9db449e69d3d4a6dfb585 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:22 +0100 Subject: [PATCH 833/909] New translations config.yaml (Chinese Simplified) --- content/zh/config.yaml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/content/zh/config.yaml b/content/zh/config.yaml index 4f050bdd24..fcd0b546f0 100644 --- a/content/zh/config.yaml +++ b/content/zh/config.yaml @@ -16,11 +16,6 @@ params: buttonlink: "/install" #Hero image (from static/images/___) image: logo.svg - #Customizable navbar. For a dropdown, add a "sublinks" list. - news: - title: D&I Grant from CZI - content: Including NumPy, SciPy, Matplotlib and Pandas - url: /news shell: title: 占位符 promptlabel: 交互式shell提示 From 8599812defad710e3f712bdd3934b3504807b71a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:23 +0100 Subject: [PATCH 834/909] 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 5059872d94..7e01b0da93 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,6 +1,8 @@ --- title: Notícias sidebar: false +newsHeader: D&I Grant from CZI +date: --- ### Advancing an inclusive culture in the scientific Python ecosystem From 739e767297cf7899e9b6b4345b52fb654ae35f9f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:01:24 +0100 Subject: [PATCH 835/909] New translations config.yaml (Portuguese, Brazilian) --- content/pt/config.yaml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/content/pt/config.yaml b/content/pt/config.yaml index b4d0f818ee..9ca2f96a65 100644 --- a/content/pt/config.yaml +++ b/content/pt/config.yaml @@ -16,11 +16,6 @@ params: buttonlink: "/pt/install" #Hero image (from static/images/___) image: logo.svg - #Customizable navbar. For a dropdown, add a "sublinks" list. - news: - title: D&I Grant from CZI - content: Including NumPy, SciPy, Matplotlib and Pandas - url: /pt/news shell: title: placeholder promptlabel: console interativo From d92d81c8104eda3bd2ca0e7f4b3fa7698ba2535b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 4 Dec 2021 10:40:30 +0100 Subject: [PATCH 836/909] 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 824196fb87..873dd0d249 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,7 +1,7 @@ --- title: 소식 sidebar: false -newsHeader: D&I Grant from CZI +newsHeader: CZI에서 D&I 장려금 수여 date: --- From 3035308931caa9d5bcb1a413206b23f0d1cfd44d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 11 Dec 2021 14:11:43 +0100 Subject: [PATCH 837/909] 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 80e1618043..85d62e3a74 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -116,6 +116,8 @@ 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. +- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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_. From a69ee538e05dd46603969eb4a3c2e3024edc973a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 11 Dec 2021 14:11:44 +0100 Subject: [PATCH 838/909] 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 0fe6e12170..2c07df170a 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -116,6 +116,8 @@ 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. +- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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_. From 4dee875daf6c5a93fbff0470bbf0452b4147b562 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 11 Dec 2021 14:11:45 +0100 Subject: [PATCH 839/909] New translations news.md (Japanese) --- content/ja/news.md | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index aef3146199..e8c5ffe8b6 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -116,15 +116,17 @@ 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 1.18.1 ([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 1.18.4 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020年4月19日_. -- NumPy 1.18.2 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.18.2)) -- _2020年3月17日_. -- NumPy 1.14.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018年1月7日_. -- NumPy 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 1.18.0 ([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 1.16.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019年1月14日_. -- NumPy 1.15.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018年7月23日_. -- NumPy 1.14.0 ([リリースノート](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018年1月7日_. +- NumPy 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. +- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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 627d6aa28be92d4913d7ce8821e31da24079d400 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 11 Dec 2021 14:11:46 +0100 Subject: [PATCH 840/909] New translations news.md (Korean) --- content/ko/news.md | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 873dd0d249..33770f4c6d 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -116,15 +116,17 @@ More details on our proposed initiatives and deliverables can be found in the [f NumPy 릴리즈의 목록을 볼 수 있으며, 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 1.21.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021년 6월 22일_. -- NumPy 1.20.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021년 5월 10일_. -- NumPy 1.20.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021년 1월 30일_. -- NumPy 1.19.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021년 1월 5일_. -- NumPy 1.19.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020년 6월 20일_. -- NumPy 1.18.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020년 5월 3일_. -- NumPy 1.17.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020년 1월 1일_. -- NumPy 1.18.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _2019년 12월 22일_. -- NumPy 1.17.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _2019년 7월 26일_. -- NumPy 1.16.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019년 1월 14일_. -- NumPy 1.15.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018년 7월 23일_. -- NumPy 1.14.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018년 1월 7일_. +- NumPy 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. +- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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 6d5e2c3eb40a0548c8e2e0cf684be4eb45430868 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 11 Dec 2021 14:11:47 +0100 Subject: [PATCH 841/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index c658a50cdf..bd161e9607 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -116,15 +116,17 @@ 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. -- NumPy1.21.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021年6月22日_. -- NumPy1.23.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021年5月10日_. -- NumPy1.20.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021年1月30日_. -- NumPy1.19.5 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021年1月5日_. -- NumPy1.19.0 ([发行说明](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020年6月20日_. -- NumPy1.18.4 (发行说明) -- _2020年5月3日_. -- NumPy1.17.5 (发行说明) -- _2020年1月1日_. -- NumPy1.18.0 (发行说明) -- _2019年12月22日_. -- NumPy1.17.0 (发行说明) -- _2019年7月26日_. -- NumPy1.16.0 (发行说明) -- _2019年1月14日_. -- NumPy1.15.0 (发行说明) -- _2018年7月23日_. -- NumPy1.14.0 (发行说明) -- _2018年1月7日_. +- NumPy 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. +- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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 2d5660d03ee8bbffd364b9426aca171597044ff3 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 11 Dec 2021 14:11:48 +0100 Subject: [PATCH 842/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 26 ++++++++++++++------------ 1 file changed, 14 insertions(+), 12 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 7e01b0da93..1e5ddec264 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -116,15 +116,17 @@ 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 1.21.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _22 de junho de 2021_. -- NumPy 1.20.3 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _10 de maio de 2021_. -- NumPy 1.20.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _30 de janeiro de 2021_. -- NumPy 1.19.5 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _5 de janeiro de 2021_. -- NumPy 1.19.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _20 de junho 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 1.16.0 ([notas de lançamento](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _14 de janeiro 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. +- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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 a1af21c27023cd4891b8309be08bd70aaeb32ad1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 12 Dec 2021 07:03:08 +0100 Subject: [PATCH 843/909] 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 33770f4c6d..deb9027dfa 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -116,7 +116,7 @@ More details on our proposed initiatives and deliverables can be found in the [f NumPy 릴리즈의 목록을 볼 수 있으며, 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. +- NumPy 1.22.0rc2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _2021년 12월 10일_. - NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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_. From a2730e2f24a5e9d6d81fc8a6df65e8070e400821 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 12 Dec 2021 07:14:50 +0100 Subject: [PATCH 844/909] New translations news.md (Korean) --- content/ko/news.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index deb9027dfa..74f1818d93 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -117,16 +117,16 @@ More details on our proposed initiatives and deliverables can be found in the [f NumPy 릴리즈의 목록을 볼 수 있으며, 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 1.22.0rc2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _2021년 12월 10일_. -- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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 1.22.0rc1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _2021년 11월 23일_. +- 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 5fec6dfdfab58131562102bfbc03e9405ebbc180 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 Dec 2021 22:18:44 +0100 Subject: [PATCH 845/909] New translations news.md (Spanish) --- content/es/news.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index 85d62e3a74..f0160987c4 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -116,8 +116,7 @@ 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. -- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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_. From d7eaace1753cc83d1c592a652d272ab31ba8c69a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 Dec 2021 22:18:45 +0100 Subject: [PATCH 846/909] New translations news.md (Arabic) --- content/ar/news.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 2c07df170a..57de76ce25 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -116,8 +116,7 @@ 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. -- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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_. From d4194614e8e6af591b2fa0b05b4a218068454487 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 Dec 2021 22:18:46 +0100 Subject: [PATCH 847/909] New translations news.md (Japanese) --- content/ja/news.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index e8c5ffe8b6..056a44728c 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -116,8 +116,7 @@ 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. -- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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_. From d769eef888d394648cbffbfa29bac5177a9e5920 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 Dec 2021 22:18:48 +0100 Subject: [PATCH 848/909] New translations news.md (Korean) --- content/ko/news.md | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 74f1818d93..2e260a1174 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -116,17 +116,16 @@ More details on our proposed initiatives and deliverables can be found in the [f NumPy 릴리즈의 목록을 볼 수 있으며, 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 1.22.0rc2 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _2021년 12월 10일_. -- NumPy 1.22.0rc1 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _2021년 11월 23일_. -- NumPy 1.21.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.21.0)) -- _2021년 6월 22일_. -- NumPy 1.20.3 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.3)) -- _2021년 5월 10일_. -- NumPy 1.20.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.20.0)) -- _2021년 1월 30일_. -- NumPy 1.19.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.5)) -- _2021년 1월 5일_. -- NumPy 1.19.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.19.0)) -- _2020년 6월 20일_. -- NumPy 1.18.4 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.4)) -- _2020년 5월 3일_. -- NumPy 1.17.5 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.5)) -- _2020년 1월 1일_. -- NumPy 1.18.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)) -- _2019년 12월 22일_. -- NumPy 1.17.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.17.0)) -- _2019년 7월 26일_. -- NumPy 1.16.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.16.0)) -- _2019년 1월 14일_. -- NumPy 1.15.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.15.0)) -- _2018년 7월 23일_. -- NumPy 1.14.0 ([릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.14.0)) -- _2018년 1월 7일_. +- NumPy 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 a40d9052f6c8af913baa616ed8ff60295503dd91 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 Dec 2021 22:18:49 +0100 Subject: [PATCH 849/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index bd161e9607..b3a92f18fb 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -116,8 +116,7 @@ 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. -- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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_. From ab43a989d0101c29e257e61ff357e8a0e58ad24a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 20 Dec 2021 22:18:50 +0100 Subject: [PATCH 850/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 1e5ddec264..8786afc5cb 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -116,8 +116,7 @@ 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 1.22.0rc2 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc2)) -- _10 Dec 2021_. -- NumPy 1.22.0rc1 ([release notes](https://github.com/numpy/numpy/releases/tag/v1.21.0rc1)) -- _23 Nov 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_. From 9c4ab182ac3ee02aeeb6b89e827d3fa2cd91c949 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 21 Dec 2021 01:24:23 +0100 Subject: [PATCH 851/909] New translations news.md (Korean) --- content/ko/news.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 2e260a1174..3dee56bc51 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -116,16 +116,16 @@ More details on our proposed initiatives and deliverables can be found in the [f NumPy 릴리즈의 목록을 볼 수 있으며, 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 1.21.5 ([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 1.21.5 ([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 c45bc29f34561b1f4f635fb53ca36ea15a525194 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 1 Jan 2022 18:00:21 +0100 Subject: [PATCH 852/909] New translations news.md (Spanish) --- content/es/news.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/content/es/news.md b/content/es/news.md index f0160987c4..63410520fc 100644 --- a/content/es/news.md +++ b/content/es/news.md @@ -1,10 +1,23 @@ --- title: News sidebar: false -newsHeader: D&I Grant from CZI +newsHeader: NumPy 1.22.0 released date: --- +### 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. @@ -35,7 +48,7 @@ _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-not - 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. +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 @@ -116,6 +129,7 @@ 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 1.22.0 ([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_. From 2e60aa889cb99efb05990c9de4ee793d5976022c Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 1 Jan 2022 18:00:23 +0100 Subject: [PATCH 853/909] New translations news.md (Arabic) --- content/ar/news.md | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/content/ar/news.md b/content/ar/news.md index 57de76ce25..103bba370f 100644 --- a/content/ar/news.md +++ b/content/ar/news.md @@ -1,10 +1,23 @@ --- title: الأخبار sidebar: false -newsHeader: D&I Grant from CZI +newsHeader: NumPy 1.22.0 released date: --- +### 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. @@ -35,7 +48,7 @@ _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-not - 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. +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 @@ -116,6 +129,7 @@ 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 1.22.0 ([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_. From 960777d6df67c5ad94d97e1db2c5a79ed385a5bc Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 1 Jan 2022 18:00:24 +0100 Subject: [PATCH 854/909] New translations news.md (Japanese) --- content/ja/news.md | 38 ++++++++++++++++++++++++++------------ 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/content/ja/news.md b/content/ja/news.md index 056a44728c..84084c6900 100644 --- a/content/ja/news.md +++ b/content/ja/news.md @@ -1,10 +1,23 @@ --- title: ニュース sidebar: false -newsHeader: D&I Grant from CZI +newsHeader: NumPy 1.22.0 released date: --- +### 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. @@ -28,14 +41,14 @@ Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOON _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SIMD関連の作業が継続されました。 -- 新しいdtypeインフラとキャストの初期作業 -- mac 版の Python 3.8 と Python 3.9 用 universal2 wheels -- 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 +- 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. +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 @@ -46,8 +59,8 @@ _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students an ### 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: -- 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)). +- 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 @@ -62,8 +75,8 @@ _Sep 16, 2020_ -- We are pleased to announce the publication of [the first offic ### 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 -- `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。 +- 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 @@ -116,6 +129,7 @@ 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 1.22.0 ([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_. From 44a3cf3455422807434644b31688795d74f8ec6d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 1 Jan 2022 18:00:25 +0100 Subject: [PATCH 855/909] New translations news.md (Korean) --- content/ko/news.md | 100 ++++++++++++++++++++++++++------------------- 1 file changed, 57 insertions(+), 43 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 3dee56bc51..b323ec8010 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,10 +1,23 @@ --- title: 소식 sidebar: false -newsHeader: CZI에서 D&I 장려금 수여 +newsHeader: NumPy 1.22.0 released date: --- +### 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. @@ -13,43 +26,43 @@ As a part of [CZI's Essential Open Source Software for Science program](https:// 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. -2개년 프로젝트가 2021년 11월부터 시작될 예정입니다. 프로젝트의 결과를 볼 날이 기대되네요! [전체 정보는 여기서 열람하실 수 있습니다](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). -### 2021년도 NumPy 설문조사 +### 2021 NumPy survey -_2021년 7월 12일_ -- NumPy에서, 우리는 커뮤니티의 힘을 믿습니다. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. 설문 조사 결과를 통해 다음 12개월 동안 우리가 어떤 것에 집중해야 할지 아주 잘 이해할 수 있었습니다. +_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. -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. +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. -시작하려면 아래 링크를 눌러 주세요: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. +Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. -### Numpy 1.21.0 출시 +### Numpy 1.21.0 release -_2021년 6월 23일_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html)이 출시되었습니다. 주요 기능들은 다음과 같습니다: +_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, -- 문서화 향상, -- 주석 향상, -- 난수 생성에 이용되는 새 `PCG64DXSM` 비트 생성기 +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. -이번 NumPy 릴리즈는 175명이 기여해주신 581개의 풀 리퀘스트가 합쳐진 결과입니다. 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. +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. -### 2020년도 NumPy 설문조사 결과 +### 2020 NumPy survey results -_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/. +_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 출시 +### 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)). -### NumPy 프로젝트 내 다양성 +### 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). @@ -66,27 +79,27 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear - 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 출시 +### Numpy 1.19.2 release -_2020년 9월 10일_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html)이 출시되었습니다. 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. +_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. 설문지는 영어 외에도 8개 국어로 제공됩니다: 벵골어, 프랑스어, 힌디어, 일본어, 중국 관화, 포르투갈어, 러시아어, 스페인어. +_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. -NumPy를 개선할 수 있도록 도와주시고 [여기](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)에서 설문 조사에 참여해주시면 감사드리겠습니다. +Please help us make NumPy better and take the survey [here](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl). -### NumPy에 새로운 로고가 생겼습니다! +### NumPy has a new logo! -_2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. +_Jun 24, 2020_ -- NumPy now has a new logo: -NumPy 로고 +NumPy logo -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. +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 출시 +### 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. @@ -96,16 +109,16 @@ _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release witho _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 출시 +### 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. -### NumPy가 Chan Zuckerberg Initiative에서 보조금을 받음 +### NumPy receives a grant from the Chan Zuckerberg Initiative -_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. +_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. @@ -114,18 +127,19 @@ More details on our proposed initiatives and deliverables can be found in the [f ## 릴리즈 -NumPy 릴리즈의 목록을 볼 수 있으며, 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 1.21.5 ([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_. +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 1.22.0 ([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 afc7e761fe86b00e25f6c3d91b969a0d8e752010 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 1 Jan 2022 18:00:26 +0100 Subject: [PATCH 856/909] New translations news.md (Chinese Simplified) --- content/zh/news.md | 38 ++++++++++++++++++++++++++------------ 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/content/zh/news.md b/content/zh/news.md index b3a92f18fb..14f677fcea 100644 --- a/content/zh/news.md +++ b/content/zh/news.md @@ -1,10 +1,23 @@ --- title: 社区快讯 sidebar: false -newsHeader: D&I Grant from CZI +newsHeader: NumPy 1.22.0 released date: --- +### 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. @@ -28,14 +41,14 @@ Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOON _Jun 23, 2021_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21.0-notes.html) is now available. The highlights of the release are: -- 继续开展SIMD工作，涵盖更多的功能和平台 -- 新dtype的基础和型态转换初步工作 -- 适用于Mac平台的Python 3.8和Python 3.9的universal2 wheels -- 改进文档 -- 改进注释 -- 新的 `PCG64DXSM` 位元生成器，用于生成随机数字 +- continued SIMD work covering more functions and platforms, +- initial work on the new dtype infrastructure and casting, +- universal2 wheels for Python 3.8 and Python 3.9 on Mac, +- improved documentation, +- improved annotations, +- new `PCG64DXSM` bitgenerator for random numbers. -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. +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 @@ -46,8 +59,8 @@ _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students an ### 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: -- 为大部分Numpy代码做了类型注解，並添加了一个全新的`numpy.typing` 子模块，其中包含 `ArrayLike` 和 `DtypeLike`别名 ，使得用户和下游依赖库可以为自己的代码添加类型注解。 -- 为多个架构进行SIMD编译优化，其支持X86(SSE、AVX)、ARM64(Neon) 和PowerPC(VSX) 指令集。 大幅提高许多函数的性能(例如： [sin/cos](https://github.com/numpy/numpy/pull/17587), [einsum](https://github.com/numpy/numpy/pull/18194))。 +- 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 @@ -62,8 +75,8 @@ _Sep 16, 2020_ -- We are pleased to announce the publication of [the first offic ### 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 -- 将您的 `pip` 升级到 20.1 版本，至少要支持`manylinux2010` 和 `manylinux2014` -- 使用 [`--only-binary=numpy`](https://pip.pypa.io/en/stable/reference/pip_install/#cmdoption-only-binary) 或 `--only-binary=:all:` 选项来防止 `pip` 尝试从源码构建。 +- 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 @@ -116,6 +129,7 @@ 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 1.22.0 ([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_. From 9ff561951a6f89e1c96419f798e2e6037a9eec60 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 1 Jan 2022 18:00:27 +0100 Subject: [PATCH 857/909] New translations news.md (Portuguese, Brazilian) --- content/pt/news.md | 38 ++++++++++++++++++++++++++------------ 1 file changed, 26 insertions(+), 12 deletions(-) diff --git a/content/pt/news.md b/content/pt/news.md index 8786afc5cb..43f1262292 100644 --- a/content/pt/news.md +++ b/content/pt/news.md @@ -1,10 +1,23 @@ --- title: Notícias sidebar: false -newsHeader: D&I Grant from CZI +newsHeader: NumPy 1.22.0 released date: --- +### 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. @@ -28,14 +41,14 @@ Follow the link to get started: https://berkeley.qualtrics.com/jfe/form/SV_aaOON _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, -- 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. +- 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. +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 @@ -46,8 +59,8 @@ _Jun 22, 2021_ -- In 2020, the NumPy survey team in partnership with students an ### 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: -- 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)). +- 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 @@ -62,8 +75,8 @@ _Sep 16, 2020_ -- We are pleased to announce the publication of [the first offic ### 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 -- 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. +- 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 @@ -116,6 +129,7 @@ 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 1.22.0 ([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_. From 3410a96fe842a1d417c7d817f11caba1ef937428 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 1 Jan 2022 18:40:51 +0100 Subject: [PATCH 858/909] 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 3ec0dc58b7..0e00bb52b0 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -1,9 +1,9 @@ --- -title: Installing NumPy +title: Instalando 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. +El único prerequisito para instalar NumPy es Python. 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. From 661436d857d5cd5b1f53a6bd9ada11185df9a82d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sat, 1 Jan 2022 18:48:48 +0100 Subject: [PATCH 859/909] 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 0e00bb52b0..2ace1de23e 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -3,9 +3,9 @@ title: Instalando NumPy sidebar: false --- -El único prerequisito para instalar NumPy es Python. 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. +El único prerequisito para instalar NumPy es Python. Si aún no tiene Python y quiere empezar de la forma más fácil, recomendamos utilizar la [Distribución Anaconda](https://www.anaconda.com/distribution) - ésta incluye Python, NumPy y muchos otros paquetes utilizados comúnmente para computación científica y ciencia de datos. -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 se puede instalar con `conda`, con `pip`, con un gestor de paquetes en macOS y Linux, o [a partir del código fuente](https://numpy.org/devdocs/user/building.html). Para instrucciones más detalladas, consulte nuestra [guía de instalación de Python y NumPy](#python-numpy-install-guide) a continuación. **CONDA** From 3c49f89c6f2743d779759bd5a44e470f3b3135b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 Jan 2022 00:39:40 +0100 Subject: [PATCH 860/909] New translations news.md (Korean) --- content/ko/news.md | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index b323ec8010..e826dedca7 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -1,13 +1,13 @@ --- title: 소식 sidebar: false -newsHeader: NumPy 1.22.0 released +newsHeader: NumPy 1.22.0 출시 date: --- -### 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: +_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. * 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. @@ -94,7 +94,7 @@ 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. @@ -129,17 +129,17 @@ 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 1.22.0 ([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 1.22.0 ([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 ecd0fe29d605172b3527d19b359cabe925fef742 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 Jan 2022 00:47:07 +0100 Subject: [PATCH 861/909] New translations news.md (Korean) --- content/ko/news.md | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index e826dedca7..bf8f338b68 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -37,9 +37,9 @@ 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: +_2021년 6월 23일_ -- [NumPy 1.21.0](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, @@ -51,14 +51,14 @@ _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/. +_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 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: +_2021년 1월 30일_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20.0-notes.html)이 출시되었습니다. 역대 최대의 NumPy 릴리즈입니다. 180명이 넘는 기여자분들께 감사드립니다. 흥미롭고 새로운 두 기능이 나왔습니다. - 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)). @@ -79,9 +79,9 @@ _Sept 14, 2020_ -- Python 3.9 will be released in a few weeks. If you are an ear - 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. +_2020년 9월 10일_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2-notes.html)이 출시되었습니다. 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! @@ -90,18 +90,18 @@ _Jul 2, 2020_ -- This survey is meant to guide and set priorities for decision-m 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. +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 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. +_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 acceptance @@ -109,9 +109,9 @@ _Jun 20, 2020_ -- NumPy 1.19.0 is now available. This is the first release witho _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`. +_2019년 12월 22일_ -- NumPy 1.18.0이 출시되었습니다. 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. From 60c1a243490b3ef6fc479523994cb652938c2ff7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 Jan 2022 04:01:28 +0100 Subject: [PATCH 862/909] 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 bf8f338b68..12871626d1 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -34,7 +34,7 @@ _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. +시작하려면 아래 링크를 눌러 주세요: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. ### Numpy 1.21.0 출시 @@ -127,7 +127,7 @@ More details on our proposed initiatives and deliverables can be found in the [f ## 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 1.22.0 ([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_. From 838baafdb68938f12aa222171514ef20fc73cdc0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 Jan 2022 06:01:27 +0100 Subject: [PATCH 863/909] New translations news.md (Korean) --- content/ko/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 12871626d1..a7977f2e1d 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -28,11 +28,11 @@ 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. +_2021년 7월 12일_ -- NumPy에서, 우리는 커뮤니티의 힘을 믿습니다. 1,236 NumPy users from 75 countries participated in our inaugural survey last year. 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. 시작하려면 아래 링크를 눌러 주세요: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q. @@ -48,7 +48,7 @@ _2021년 6월 23일_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21 - 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. +이번 NumPy 릴리즈는 175명이 기여해주신 581개의 풀 리퀘스트가 합쳐진 결과입니다. 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. ### 2020년도 NumPy 설문조사 결과 From 61f1ece0150804873e6bfe16e74e701410933ea4 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 Jan 2022 06:11:28 +0100 Subject: [PATCH 864/909] New translations news.md (Korean) --- content/ko/news.md | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index a7977f2e1d..97b003b61a 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -44,11 +44,11 @@ _2021년 6월 23일_ -- [NumPy 1.21.0](https://numpy.org/doc/stable/release/1.21 - 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. +- 문서화 향상, +- 주석 향상, +- 난수 생성에 이용되는 새 `PCG64DXSM` 비트 생성기. -이번 NumPy 릴리즈는 175명이 기여해주신 581개의 풀 리퀘스트가 합쳐진 결과입니다. 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 @@ -62,7 +62,7 @@ _2021년 1월 30일_ -- [NumPy 1.20.0](https://numpy.org/doc/stable/release/1.20 - 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). @@ -74,7 +74,7 @@ _Sep 16, 2020_ -- We are pleased to announce the publication of [the first offic ### 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 +_2020년 9월 14일_ -- Python 3.9가 몇 주 내로 출시될 것입니다. 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. @@ -85,9 +85,9 @@ _2020년 9월 10일_ -- [NumPy 1.19.2](https://numpy.org/devdocs/release/1.19.2- ### 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. +_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 개선할 수 있도록 도와주시고 [여기](https://umdsurvey.umd.edu/jfe/form/SV_8bJrXjbhXf7saAl)에서 설문 조사에 참여해주시면 감사드리겠습니다. ### NumPy에 새로운 로고가 생겼습니다! @@ -111,14 +111,14 @@ _May 11, 2020_ -- NumPy has been accepted as one of the mentor organizations for ### NumPy 1.18.0 출시 -_2019년 12월 22일_ -- NumPy 1.18.0이 출시되었습니다. 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. After the major changes in 1.17.0, this is a consolidation release. 본 릴리즈는 Python 3.5를 지원하는 마지막 마이너 릴리즈입니다. 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. +자세한 정보는 [릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)를 참고하시기 바랍니다. -### NumPy receives a grant from the Chan Zuckerberg Initiative +### NumPy가 Chan Zuckerberg Initiative에서 보조금을 받음 -_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. From 8896c3f157da4f4b4bfc163f7f3a6bde87e28d75 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 Jan 2022 06:19:53 +0100 Subject: [PATCH 865/909] New translations news.md (Korean) --- content/ko/news.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index 97b003b61a..419a4dbb17 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -104,14 +104,14 @@ _2020년 6월 24일_ -- NumPy에 새로운 로고가 생겼습니다. _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 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). +_2020년 5월 11일_ -- NumPy가 Google Season of Docs 프로그램의 선도 조직으로 승인되었습니다. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! 자세한 내용은 [Season of Docs 공식 사이트](https://developers.google.com/season-of-docs/) 및 저희의 [아이디어 페이지](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)를 참고하시기 바랍니다. ### NumPy 1.18.0 출시 -_2019년 12월 22일_ -- NumPy 1.18.0이 출시되었습니다. After the major changes in 1.17.0, this is a consolidation release. 본 릴리즈는 Python 3.5를 지원하는 마지막 마이너 릴리즈입니다. 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. 자세한 정보는 [릴리즈 노트](https://github.com/numpy/numpy/releases/tag/v1.18.0)를 참고하시기 바랍니다. @@ -122,7 +122,7 @@ _2019년 11월 15일_ -- NumPy의 주요 종속 패키지 중 하나인 NumPy와 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. +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). 2019년 12월 1일부터 작업을 시작하여 다음 12개월 동안 진행할 예정입니다. ## 릴리즈 From 4c05fad489720aa24c5b0a6ac87e8dbd6f0de935 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 Jan 2022 06:36:24 +0100 Subject: [PATCH 866/909] 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 419a4dbb17..f72f8b03b9 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -72,9 +72,9 @@ _Sep 20, 2020_ -- We wrote a [statement on the state of, and discussion on socia _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? -_2020년 9월 14일_ -- Python 3.9가 몇 주 내로 출시될 것입니다. 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 +_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. 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. From 1382abe7bf92305d59e98135a66542dc48c9da87 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Sun, 2 Jan 2022 06:47:23 +0100 Subject: [PATCH 867/909] New translations news.md (Korean) --- content/ko/news.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/content/ko/news.md b/content/ko/news.md index f72f8b03b9..bb8f152ac8 100644 --- a/content/ko/news.md +++ b/content/ko/news.md @@ -11,12 +11,12 @@ _2021년 12월 31일_ -- [NumPy 1.22.0](https://numpy.org/doc/stable/release/1.2 * 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. +* NumPy가 DLPack 백엔드로 구동됩니다. 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. +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 @@ -26,7 +26,7 @@ As a part of [CZI's Essential Open Source Software for Science program](https:// 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). +2개년 프로젝트가 2021년 11월부터 시작될 예정입니다. 프로젝트의 결과를 볼 날이 기대되네요! [전체 정보는 여기서 열람하실 수 있습니다](https://figshare.com/articles/online_resource/Advancing_an_inclusive_culture_in_the_scientific_Python_ecosystem/16548063). ### 2021년도 NumPy 설문조사 @@ -74,9 +74,9 @@ _Sep 16, 2020_ -- We are pleased to announce the publication of [the first offic ### Python 3.9가 곧 출시하는데, NumPy는 바이너리 Wheel을 언제 출시합니까? -_2020년 9월 14일_ -- Python 3.9가 몇 주 내로 출시될 것입니다. 만약 Python 얼리어답터라면, NumPy (그리고 SciPy 등 다른 바이너리 패키지)가 릴리즈 시일에 바이너리 Wheel을 준비하지 못한다는 것을 알고 실망했을 수 있습니다. 새로운 Python 버전에 빌드 환경을 맞추는 것은 많은 노력을 요하고, 패키지가 PyPI 및 conda-forge에 배포되는 데에는 일반적으로 몇 주가 걸립니다. 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) 혹은 `--only-binary=:all:` 인수를 사용하여 `pip`가 소스로부터 빌드하는 것을 막도록 합니다 ### NumPy 1.19.2 출시 @@ -106,7 +106,7 @@ _2020년 6월 20일_ -- NumPy 1.19.0이 출시되었습니다. Python 2의 지 ### Season of Docs 승인 -_2020년 5월 11일_ -- NumPy가 Google Season of Docs 프로그램의 선도 조직으로 승인되었습니다. We are excited about the opportunity to work with a technical writer to improve NumPy's documentation once again! 자세한 내용은 [Season of Docs 공식 사이트](https://developers.google.com/season-of-docs/) 및 저희의 [아이디어 페이지](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)를 참고하시기 바랍니다. +_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! 자세한 내용은 [Season of Docs 공식 사이트](https://developers.google.com/season-of-docs/) 및 저희의 [아이디어 페이지](https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2020-Project-Ideas)를 참고하시기 바랍니다. ### NumPy 1.18.0 출시 From 9dbab878ce07567b08c48a0d8f88f945ad185b7f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 3 Jan 2022 00:17:51 +0100 Subject: [PATCH 868/909] New translations install.md (Spanish) --- content/es/install.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/content/es/install.md b/content/es/install.md index 2ace1de23e..1826efab39 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -9,21 +9,21 @@ NumPy se puede instalar con `conda`, con `pip`, con un gestor de paquetes en mac **CONDA** -If you use `conda`, you can install NumPy from the `defaults` or `conda-forge` channels: +Si utiliza `conda`, puede instalar NumPy desde los canales `defaults` o `conda-forge`: ```bash -# Best practice, use an environment rather than install in the base env +# La mejor práctica, utilizar un entorno en lugar de instalar en el entorno base conda create -n my-env conda activate my-env -# If you want to install from conda-forge +# Si quiere instalar desde conda-forge conda config --env --add channels conda-forge -# The actual install command +# El verdadero comando de instalación conda install numpy ``` **PIP** -If you use `pip`, you can install NumPy with: +Si utiliza `pip`, puede instalar NumPy con: ```bash pip install numpy From 840a74364a84be60c9f3aff12a8e0c3c3c5effd9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 3 Jan 2022 00:29:06 +0100 Subject: [PATCH 869/909] 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 1826efab39..9e74270ecf 100644 --- a/content/es/install.md +++ b/content/es/install.md @@ -28,7 +28,7 @@ Si utiliza `pip`, puede instalar NumPy con: ```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. +También al utilizar pip, es buena práctica utilizar un entorno virtual - vea [Instalaciones reproducibles](#reproducible-installs) a continuación para saber por qué, y [esta guía](https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto) para más detalles sobre el uso de entornos virtuales. From 6f6f4f4aec8012f3ba174ad6d7895f9d6e5302b0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 3 Jan 2022 16:58:01 +0100 Subject: [PATCH 870/909] New translations about.md (Spanish) --- content/es/about.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/content/es/about.md b/content/es/about.md index 12d1887232..34183cbf5a 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -38,13 +38,14 @@ Eméritos: ## Equipos -El proyecto NumPy está creciendo; tenemos equipos para +The NumPy project is growing! 🎉 We have teams for: - código - documentación - sitio web - triaje -- financiación y subvenciones +- survey +- funding and grants Visita la página [Equipo](/gallery/team.html) para conocer a los miembros de cada equipo. From edc40da0c47ae2a41a11eba5bfef629ef07fcc28 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 3 Jan 2022 16:58:02 +0100 Subject: [PATCH 871/909] New translations about.md (Arabic) --- content/ar/about.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/content/ar/about.md b/content/ar/about.md index 30be233592..33d161de72 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -38,13 +38,14 @@ _بعض المعلومات حول مشروع ومجتمع نمباي_ ## الأقسام -يزدهر مشروع نمباي حيث أصبح لدينا أقسام لكل من +The NumPy project is growing! 🎉 We have teams for: - الشفرة(الكود) - الوثائق - المواقع الالكترونية - الفرز -- التمويل والمنح +- survey +- funding and grants شاهد صفحة [ ](/gallery/team.html) لأعضاء الفريق. From b126513f2fbfb1f46334fef301a8c64bc5ffe5f9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 3 Jan 2022 16:58:03 +0100 Subject: [PATCH 872/909] New translations about.md (Japanese) --- content/ja/about.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/content/ja/about.md b/content/ja/about.md index 02b3e1fa31..dd407a8aff 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -38,13 +38,14 @@ NumPy運営委員会の役割は、NumPyのコミュニティと協力しサポ ## 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 is growing! 🎉 We have teams for: - コード - ドキュメント - ウェブサイト - トリアージ -- 資金と助成金 +- survey +- funding and grants 個々のチームメンバーについては、 [チーム](/gallery/team.html) のページを参照してください。 From c621db0af2057d5320aca629f73eb65798325daf Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 3 Jan 2022 16:58:05 +0100 Subject: [PATCH 873/909] New translations about.md (Korean) --- content/ko/about.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index 1b1d455290..cc85b061a8 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -38,13 +38,14 @@ NumPy 운영 위원회의 역할은 더 광범위한 NumPy 커뮤니티와 협 ## 팀 -NumPy 프로젝트는 성장하고 있습니다. 그리고 우리는 다음과 같은 팀들이 있습니다. +The NumPy project is growing! 🎉 We have teams for: - 코드 - 문서 - 웹사이트 - 심사 -- 자원 및 보조금 +- survey +- funding and grants 개발 팀원들은 [팀](/gallery/team.html) 페이지를 참조하세요. From b0c3e06343815ab355db715f6c2d1ac94ca8b625 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 3 Jan 2022 16:58:06 +0100 Subject: [PATCH 874/909] New translations about.md (Chinese Simplified) --- content/zh/about.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/content/zh/about.md b/content/zh/about.md index 62231db733..4a929bae98 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -38,13 +38,14 @@ NumPy 是一个使 Python 支持数值计算的开源项目， 它诞生于 2005 ## 团队 -NumPy 项目正在不断发展中，我们的团队成员负责： +The NumPy project is growing! 🎉 We have teams for: - 编码 - 文档 - 网站 - 分类 -- 资金和赠款 +- survey +- funding and grants 查看[团队](/gallery/team.html)页面以了解每个独立团队的成员信息。 From 09d14c30109dc12d4cac7c9a7c7d330921c80e80 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Mon, 3 Jan 2022 16:58:06 +0100 Subject: [PATCH 875/909] New translations about.md (Portuguese, Brazilian) --- content/pt/about.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/content/pt/about.md b/content/pt/about.md index 38211e5742..3adc0beb76 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -38,13 +38,14 @@ Membros Eméritos: ## Times -O projeto NumPy está crescendo; temos equipes para +The NumPy project is growing! 🎉 We have teams for: - código - documentação - website - triagem -- financiamento e bolsas +- survey +- funding and grants Veja a página de [Times](/gallery/team.html) para membros individuais de cada time. From d7e10286e0d83c34ba06836ecd005ee6b3063c2f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 03:30:27 +0100 Subject: [PATCH 876/909] New translations gw-discov.md (Spanish) --- content/es/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index 3d25090e13..b992584c87 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -14,7 +14,7 @@ sidebar: false 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. +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 From 04b436d77a6d6bd28de9856839f72ebdcd4abd27 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 03:30:28 +0100 Subject: [PATCH 877/909] New translations gw-discov.md (Arabic) --- content/ar/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/case-studies/gw-discov.md b/content/ar/case-studies/gw-discov.md index c40043e305..fd4fc688f8 100644 --- a/content/ar/case-studies/gw-discov.md +++ b/content/ar/case-studies/gw-discov.md @@ -14,7 +14,7 @@ sidebar: false 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. +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 From 0a82a4579bfb981b2f551da0077ef5b6f53aaea0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 03:30:30 +0100 Subject: [PATCH 878/909] New translations gw-discov.md (Japanese) --- content/ja/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index fe1e634e44..98197a74e7 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -14,7 +14,7 @@ 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。 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。 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. 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 From cf323ebad0a93221a77701b15b2cf76b2623dd35 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 03:30:31 +0100 Subject: [PATCH 879/909] 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 aebb9b443e..fef175b417 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -14,7 +14,7 @@ sidebar: false 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. +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. ### 주요 목표 From 03c5f0824a837c46170c913f6ba1f47d80f8a13f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 03:30:32 +0100 Subject: [PATCH 880/909] New translations gw-discov.md (Chinese Simplified) --- content/zh/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/case-studies/gw-discov.md b/content/zh/case-studies/gw-discov.md index ff4e8a3e2f..fd33c7a37f 100644 --- a/content/zh/case-studies/gw-discov.md +++ b/content/zh/case-studies/gw-discov.md @@ -14,7 +14,7 @@ sidebar: false 引力波是空间和时间结构中的涟漪。由宇宙中的灾难性事件产生，例如两个黑洞的碰撞和合并或双星或超新星的合并。 观测引力波不仅有助于研究引力，而且有助于了解遥远宇宙中一些不为人知的现象及其影响。 -[激光干涉引力波天文台(LIGO)](https://www.ligo.caltech.edu)旨在通过直接探测爱因斯坦广义相对论预测的引力波来打开引力波天体物理学领域。 它由美国境内的两个相距甚远的干涉仪组成—一个位于华盛顿汉福德，另一个位于路易斯安那州利文斯顿—它们同时运行以探测引力波。 每一个仪器都装载使用了激光干涉测量法的公里级引力波探测器。 LIGO科学计算团队(LSC) 是由来自美国各地大学和其他 14 个国家的 1000 多名科学家组成的团体，得到了 90 多所大学和研究机构的支持；大约 250 名学生积极参与项目合作。 LIGO 的新发现是关于对引力波本身的首次观测，通过测量引力波在穿过地球时对空间和时间造成的微小扰动而制成。 它开辟了新的天体物理学研究方向，致力于探索宇宙扭曲的一面—研究由扭曲的时空构成的物体和现象。 +[激光干涉引力波天文台(LIGO)](https://www.ligo.caltech.edu)旨在通过直接探测爱因斯坦广义相对论预测的引力波来打开引力波天体物理学领域。 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. 每一个仪器都装载使用了激光干涉测量法的公里级引力波探测器。 LIGO科学计算团队(LSC) 是由来自美国各地大学和其他 14 个国家的 1000 多名科学家组成的团体，得到了 90 多所大学和研究机构的支持；大约 250 名学生积极参与项目合作。 LIGO 的新发现是关于对引力波本身的首次观测，通过测量引力波在穿过地球时对空间和时间造成的微小扰动而制成。 它开辟了新的天体物理学研究方向，致力于探索宇宙扭曲的一面—研究由扭曲的时空构成的物体和现象。 ### 关键目标 From 947dae2cb26b0c7f9a14f5b627518a15aec10b07 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 03:30:33 +0100 Subject: [PATCH 881/909] New translations gw-discov.md (Portuguese, Brazilian) --- content/pt/case-studies/gw-discov.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index 6196390690..1064e5ca32 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -14,7 +14,7 @@ sidebar: false Ondas gravitacionais são ondulações no tecido espaço-tempo, geradas por eventos cataclísmicos no universo, como a colisão e a fusão de dois buracos negros ou a coalescência de estrelas binárias ou supernovas. A observação de ondas gravitacionais pode ajudar não só no estudo da gravidade, mas também no entendimento de alguns dos fenômenos obscuros existentes no universo distante e seu impacto. -O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://www.ligo.caltech.edu) foi projetado para abrir o campo da astrofísica das ondas gravitacionais através da detecção direta de ondas gravitacionais previstas pela Teoria Geral da Relatividade de Einstein. O observatório consiste de dois interferômetros amplamente separados dentro dos Estados Unidos - um em Hanford, Washington e o outro em Livingston, Louisiana — operando em uníssono para detectar ondas gravitacionais. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a Terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. +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. 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. Cada um deles tem detectores em escala quilométrica de ondas gravitacionais que usam interferometria laser. A Colaboração Científica LIGO (LSC), é um grupo de mais de 1000 cientistas de universidades dos Estados Unidos e em 14 outros países apoiados por mais de 90 universidades e institutos de pesquisa; aproximadamente 250 estudantes contribuem ativamente com a colaboração. A nova descoberta do LIGO é a primeira observação de ondas gravitacionais em si, feita medindo os pequenos distúrbios que as ondas fazem ao espaço-tempo enquanto atravessam a Terra. A descoberta abriu novas fronteiras astrofísicas que exploram o lado "curvado" do universo - objetos e fenômenos que são feitos a partir da curvatura do espaço-tempo. ### Objetivos From 5d6355836a2ab1e77bb70e06099ee31352dffa13 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 06:08:50 +0100 Subject: [PATCH 882/909] New translations about.md (Korean) --- content/ko/about.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/ko/about.md b/content/ko/about.md index cc85b061a8..3655d06dbf 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -38,14 +38,14 @@ NumPy 운영 위원회의 역할은 더 광범위한 NumPy 커뮤니티와 협 ## 팀 -The NumPy project is growing! 🎉 We have teams for: +NumPy 프로젝트가 성장하고 있습니다! 🎉 아래 활동들을 하는 팀이 있습니다: - 코드 - 문서 - 웹사이트 - 심사 -- survey -- funding and grants +- 설문조사 +- 자원 및 보조금 개발 팀원들은 [팀](/gallery/team.html) 페이지를 참조하세요. From cfe413f2b7a106aea1939f4fbd79779ca70236a0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 06:49:29 +0100 Subject: [PATCH 883/909] 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 fef175b417..4d129bc9a9 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -14,7 +14,7 @@ sidebar: false 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. +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. 이는 미국 내의 멀리 떨어져 있는 간섭계 2개로 구성되어 있습니다. 하나는 워싱턴 주 핸포드에, 다른 하나는 루이지애나 주 리빙스턴에 있으며 이들은 중력파를 감지하기 위해 함께 작동합니다. 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 From 8ad7f9d90084532f382155efed82184e9c4bbaf7 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 08:34:12 +0100 Subject: [PATCH 884/909] New translations community.md (Spanish) --- content/es/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/community.md b/content/es/community.md index b74aa6b31a..d72608327b 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -3,7 +3,7 @@ title: Community sidebar: false --- -NumPy es un proyecto de código abierto impulsado por la comunidad y desarrollado por un grupo muy diverso de [colaboradores](/gallery/team.html). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el [Código de Conducta NumPy](/code-of-conduct) para obtener orientación sobre cómo interactuar con los demás de una manera que haga que la comunidad prospere. +NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el [Código de Conducta NumPy](/code-of-conduct) para obtener orientación sobre cómo interactuar con los demás de una manera que haga que la comunidad prospere. Ofrecemos varios canales de comunicación para aprender, compartir conocimientos y conectarse con otros dentro de la comunidad NumPy. From 3744d76b89974d17c1ce0fc1f46d12dd478590c0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 08:34:13 +0100 Subject: [PATCH 885/909] New translations community.md (Arabic) --- content/ar/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/community.md b/content/ar/community.md index 4fcb70235b..014b501502 100644 --- a/content/ar/community.md +++ b/content/ar/community.md @@ -3,7 +3,7 @@ title: Community sidebar: false --- -NumPy is a community-driven open source project developed by a very diverse group of [contributors](/gallery/team.html). 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 is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). 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. From 284b8ce7f021bb8aa5ff1372308c6aaedd3317e0 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 08:34:14 +0100 Subject: [PATCH 886/909] 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 2dcab69a75..4262c8c75c 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -3,7 +3,7 @@ title: コミュニティ sidebar: false --- -NumPy は 常に多様な[コントリビュータ](/gallery/team.html) のグループによって開発されている、コミュニティ主導のオープンソースプロジェクトです。 NumPy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 コミュニティを繁栄させるために、コミュニティの人達と交流する方法については、 [NumPy 行動規範](/ja/code-of-conduct) をご覧ください。 Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 [NumPy 行動規範](/code-of-conduct) をぜひ参照してください。コミュニティの繁栄につながるようなかたちで、人々と交流する方法について書いてあります。 +NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 [NumPy 行動規範](/code-of-conduct) をぜひ参照してください。コミュニティの繁栄につながるようなかたちで、人々と交流する方法について書いてあります。 私たちは、NumPyコミュニティ内で学んだり、知識を共有したり、他の人と交流するためのいくつかのコミュニケーション方法を提供しています。 From 10d5019d7a51e1d1886134c0ad64626991a6480e Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 08:34:16 +0100 Subject: [PATCH 887/909] 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 738033325e..d038c675cf 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -3,7 +3,7 @@ title: 커뮤니티 sidebar: false --- -NumPy는 매우 다양한 [기여자](/gallery/team.html) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. [NumPy 이용약관](/code-of-conduct)을 읽으면 커뮤니티가 발전하도록 해 주는 상대방과의 상호작용을 어떻게 하는지 그 방법을 알 수 있습니다. +NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. [NumPy 이용약관](/code-of-conduct)을 읽으면 커뮤니티가 발전하도록 해 주는 상대방과의 상호작용을 어떻게 하는지 그 방법을 알 수 있습니다. NumPy 커뮤니티에서는 배우고, 지식을 공유하고, 다른 사람들과 협력할 수 있는 여러 커뮤니케이션 채널을 제공합니다. From d8f85e43c6666bdc44f45c33591f943bd47ef0f8 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 08:34:17 +0100 Subject: [PATCH 888/909] New translations community.md (Chinese Simplified) --- content/zh/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/community.md b/content/zh/community.md index e77898c0c2..f81838d612 100644 --- a/content/zh/community.md +++ b/content/zh/community.md @@ -3,7 +3,7 @@ title: 社区 sidebar: false --- -Numby是一个社区驱动的开源项目，由一群十分多样化的[贡献者](/gallery/team.html)开发。 Numpy的领导层承诺要打造一个开放，包容，积极向上的社区。 请阅读 [ NumPy 行为准则](/code-of-conduct) 以了解如何用促进社区繁荣的方式与他人交流互动。 +NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). Numpy的领导层承诺要打造一个开放，包容，积极向上的社区。 请阅读 [ NumPy 行为准则](/code-of-conduct) 以了解如何用促进社区繁荣的方式与他人交流互动。 我们提供多种交流渠道，可以用来学习知识、分享您的专业见解、或是与 NumPy 社区中的其他人联系。 From 52ed8371aee122f7a83b855bb9a6e8f755078ea6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Tue, 4 Jan 2022 08:34:19 +0100 Subject: [PATCH 889/909] 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 a6ae36bcef..3307c4e27d 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](/gallery/team.html). 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. +NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). 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 47929cb5b4e6e6e823565c0b8b61288cbc13adfa Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 03:14:13 +0100 Subject: [PATCH 890/909] 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 4d129bc9a9..0c186b1507 100644 --- a/content/ko/case-studies/gw-discov.md +++ b/content/ko/case-studies/gw-discov.md @@ -45,7 +45,7 @@ The [Laser Interferometer Gravitational-Wave Observatory (LIGO)](https://www.lig 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: +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. 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 6aa576912c27f2d431dd6cdd471b340f310abf32 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 03:14:14 +0100 Subject: [PATCH 891/909] 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 d038c675cf..80f858bd97 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -3,7 +3,7 @@ title: 커뮤니티 sidebar: false --- -NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. [NumPy 이용약관](/code-of-conduct)을 읽으면 커뮤니티가 발전하도록 해 주는 상대방과의 상호작용을 어떻게 하는지 그 방법을 알 수 있습니다. +NumPy는 다양한 [기여자](/gallery/team.html) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. [NumPy 이용약관](/code-of-conduct)을 읽으면 커뮤니티가 발전하도록 해 주는 상대방과의 상호작용을 어떻게 하는지 그 방법을 알 수 있습니다. NumPy 커뮤니티에서는 배우고, 지식을 공유하고, 다른 사람들과 협력할 수 있는 여러 커뮤니케이션 채널을 제공합니다. From d8e2a06128b15f42bb13c6ddf9a0a33df4243450 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:36 +0100 Subject: [PATCH 892/909] New translations about.md (Spanish) --- content/es/about.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/about.md b/content/es/about.md index 34183cbf5a..34880353ec 100644 --- a/content/es/about.md +++ b/content/es/about.md @@ -47,7 +47,7 @@ The NumPy project is growing! 🎉 We have teams for: - survey - funding and grants -Visita la página [Equipo](/gallery/team.html) para conocer a los miembros de cada equipo. +See the [}}">Team]({{< relref) page for individual team members. ## Subcomité NumFOCUS From 121a8848e04efa1a7bfd6c8bf66e8cd13c6ca7fb Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:37 +0100 Subject: [PATCH 893/909] New translations community.md (Chinese Simplified) --- content/zh/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/zh/community.md b/content/zh/community.md index f81838d612..f4904d9cbd 100644 --- a/content/zh/community.md +++ b/content/zh/community.md @@ -3,7 +3,7 @@ title: 社区 sidebar: false --- -NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). Numpy的领导层承诺要打造一个开放，包容，积极向上的社区。 请阅读 [ NumPy 行为准则](/code-of-conduct) 以了解如何用促进社区繁荣的方式与他人交流互动。 +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). Numpy的领导层承诺要打造一个开放，包容，积极向上的社区。 请阅读 [ NumPy 行为准则](/code-of-conduct) 以了解如何用促进社区繁荣的方式与他人交流互动。 我们提供多种交流渠道，可以用来学习知识、分享您的专业见解、或是与 NumPy 社区中的其他人联系。 From e9f80644521fed11dba1cabbf4c2208072f1053a Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:38 +0100 Subject: [PATCH 894/909] New translations teams.md (Chinese Simplified) --- content/zh/teams.md | 20 ++++++++++++++++++++ 1 file changed, 20 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..0c24455d18 --- /dev/null +++ b/content/zh/teams.md @@ -0,0 +1,20 @@ +--- +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/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 people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). From 4cfbb2d710cc63ca7bc092c86a2c7781916e8046 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:39 +0100 Subject: [PATCH 895/909] New translations teams.md (Korean) --- content/ko/teams.md | 20 ++++++++++++++++++++ 1 file changed, 20 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..0c24455d18 --- /dev/null +++ b/content/ko/teams.md @@ -0,0 +1,20 @@ +--- +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/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 people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). From 337f3de748023ce2d75062b043c5e1c557e122ea Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:40 +0100 Subject: [PATCH 896/909] New translations teams.md (Japanese) --- content/ja/teams.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/ja/teams.md diff --git a/content/ja/teams.md b/content/ja/teams.md new file mode 100644 index 0000000000..0c24455d18 --- /dev/null +++ b/content/ja/teams.md @@ -0,0 +1,20 @@ +--- +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/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 people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). From 26d191db40fabd19c909804844a49cddcf994063 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:41 +0100 Subject: [PATCH 897/909] New translations teams.md (Arabic) --- content/ar/teams.md | 20 ++++++++++++++++++++ 1 file changed, 20 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..0c24455d18 --- /dev/null +++ b/content/ar/teams.md @@ -0,0 +1,20 @@ +--- +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/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 people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). From 4ab579f10b4240e2149ef970cb9c9d8dd6ce221d Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:42 +0100 Subject: [PATCH 898/909] New translations teams.md (Spanish) --- content/es/teams.md | 20 ++++++++++++++++++++ 1 file changed, 20 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..0c24455d18 --- /dev/null +++ b/content/es/teams.md @@ -0,0 +1,20 @@ +--- +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/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 people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html). From 85a94954993b20f538babe8870c68c65b29bc037 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:43 +0100 Subject: [PATCH 899/909] 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 3307c4e27d..bc930270be 100644 --- a/content/pt/community.md +++ b/content/pt/community.md @@ -3,7 +3,7 @@ title: Comunidade sidebar: false --- -NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). 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. +NumPy is a community-driven open source project developed by a diverse group of [contributors](/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 e4f58d2597ac04f11d2c9537e4952a510e3affb6 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:44 +0100 Subject: [PATCH 900/909] 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 80f858bd97..f24e878bf5 100644 --- a/content/ko/community.md +++ b/content/ko/community.md @@ -3,7 +3,7 @@ title: 커뮤니티 sidebar: false --- -NumPy는 다양한 [기여자](/gallery/team.html) 집단이 개발하며 커뮤니티에 의해 유지되는 오픈소스 프로젝트입니다. NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. [NumPy 이용약관](/code-of-conduct)을 읽으면 커뮤니티가 발전하도록 해 주는 상대방과의 상호작용을 어떻게 하는지 그 방법을 알 수 있습니다. +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). NumPy 관리자는 개방적이며 포용적이고 긍정적인 커뮤니티를 만들기 위해 상당한 노력을 기울였습니다. [NumPy 이용약관](/code-of-conduct)을 읽으면 커뮤니티가 발전하도록 해 주는 상대방과의 상호작용을 어떻게 하는지 그 방법을 알 수 있습니다. NumPy 커뮤니티에서는 배우고, 지식을 공유하고, 다른 사람들과 협력할 수 있는 여러 커뮤니케이션 채널을 제공합니다. From 64541e9de89bba786fd368f515c5a948d253eaff Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:45 +0100 Subject: [PATCH 901/909] 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 33d161de72..ad34b96460 100644 --- a/content/ar/about.md +++ b/content/ar/about.md @@ -47,7 +47,7 @@ The NumPy project is growing! 🎉 We have teams for: - survey - funding and grants -شاهد صفحة [ ](/gallery/team.html) لأعضاء الفريق. +See the [}}">Team]({{< relref) page for individual team members. ## اللجنة الفرعية ل NumFOCUS From 52b592a0ce0ea5687eaa100f631d2f22989654e9 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:46 +0100 Subject: [PATCH 902/909] 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 4262c8c75c..d1462b8210 100644 --- a/content/ja/community.md +++ b/content/ja/community.md @@ -3,7 +3,7 @@ title: コミュニティ sidebar: false --- -NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 [NumPy 行動規範](/code-of-conduct) をぜひ参照してください。コミュニティの繁栄につながるようなかたちで、人々と交流する方法について書いてあります。 +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). Numpy を主導するグループは、オープンで協力的でポジティブなコミュニティを作ることを、約束しました。 [NumPy 行動規範](/code-of-conduct) をぜひ参照してください。コミュニティの繁栄につながるようなかたちで、人々と交流する方法について書いてあります。 私たちは、NumPyコミュニティ内で学んだり、知識を共有したり、他の人と交流するためのいくつかのコミュニケーション方法を提供しています。 From 28be80fd4f623463821bde41a9ba73eca521b9ba Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:47 +0100 Subject: [PATCH 903/909] New translations community.md (Arabic) --- content/ar/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/ar/community.md b/content/ar/community.md index 014b501502..ebbe0cc802 100644 --- a/content/ar/community.md +++ b/content/ar/community.md @@ -3,7 +3,7 @@ title: Community sidebar: false --- -NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). 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 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. From e5fe9f1f05a4a5aacc172577f27c3c2ae0cca65f Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:48 +0100 Subject: [PATCH 904/909] New translations community.md (Spanish) --- content/es/community.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/es/community.md b/content/es/community.md index d72608327b..1ca7f93870 100644 --- a/content/es/community.md +++ b/content/es/community.md @@ -3,7 +3,7 @@ title: Community sidebar: false --- -NumPy is a community-driven open source project developed by a diverse group of [contributors](/gallery/team.html). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el [Código de Conducta NumPy](/code-of-conduct) para obtener orientación sobre cómo interactuar con los demás de una manera que haga que la comunidad prospere. +NumPy is a community-driven open source project developed by a diverse group of [contributors](/teams/). El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el [Código de Conducta NumPy](/code-of-conduct) para obtener orientación sobre cómo interactuar con los demás de una manera que haga que la comunidad prospere. Ofrecemos varios canales de comunicación para aprender, compartir conocimientos y conectarse con otros dentro de la comunidad NumPy. From dea90c303ff854e01ad252a3c89f58fbc34bb575 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:49 +0100 Subject: [PATCH 905/909] 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 3adc0beb76..5601824cbe 100644 --- a/content/pt/about.md +++ b/content/pt/about.md @@ -47,7 +47,7 @@ The NumPy project is growing! 🎉 We have teams for: - survey - funding and grants -Veja a página de [Times](/gallery/team.html) para membros individuais de cada time. +See the [}}">Team]({{< relref) page for individual team members. ## Subcomitê NumFOCUS From e60a70a377afb8d0c54bbc31dcc57e4054b6a76b Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:50 +0100 Subject: [PATCH 906/909] 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 4a929bae98..fc4063180f 100644 --- a/content/zh/about.md +++ b/content/zh/about.md @@ -47,7 +47,7 @@ The NumPy project is growing! 🎉 We have teams for: - survey - funding and grants -查看[团队](/gallery/team.html)页面以了解每个独立团队的成员信息。 +See the [}}">Team]({{< relref) page for individual team members. ## NumFOCUS小组委员会 From 69a943deb30592175adeb0411efd48abaa1ac9c1 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:51 +0100 Subject: [PATCH 907/909] 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 3655d06dbf..69efce402b 100644 --- a/content/ko/about.md +++ b/content/ko/about.md @@ -47,7 +47,7 @@ NumPy 프로젝트가 성장하고 있습니다! 🎉 아래 활동들을 - 설문조사 - 자원 및 보조금 -개발 팀원들은 [팀](/gallery/team.html) 페이지를 참조하세요. +See the [}}">Team]({{< relref) page for individual team members. ## NumFOCUS 소위원회 From d30d7de39c07b32b29b60b06b434f89e432a6457 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:52 +0100 Subject: [PATCH 908/909] 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 dd407a8aff..ca2c4f4989 100644 --- a/content/ja/about.md +++ b/content/ja/about.md @@ -47,7 +47,7 @@ The NumPy project is growing! 🎉 We have teams for: - survey - funding and grants -個々のチームメンバーについては、 [チーム](/gallery/team.html) のページを参照してください。 +See the [}}">Team]({{< relref) page for individual team members. ## スポンサー情報 From 4dfd6a1ad85ec0b3f8abf26581d3535c5769cb97 Mon Sep 17 00:00:00 2001 From: Ralf Gommers Date: Wed, 5 Jan 2022 21:34:53 +0100 Subject: [PATCH 909/909] New translations teams.md (Portuguese, Brazilian) --- content/pt/teams.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 content/pt/teams.md diff --git a/content/pt/teams.md b/content/pt/teams.md new file mode 100644 index 0000000000..0c24455d18 --- /dev/null +++ b/content/pt/teams.md @@ -0,0 +1,20 @@ +--- +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/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 people on the Steering Council, please see [here](https://numpy.org/devdocs/dev/governance/people.html).