diff --git a/404/index.html b/404/index.html index 28960d2a..50bb1a01 100644 --- a/404/index.html +++ b/404/index.html @@ -1,14 +1,13 @@ -NumPy - 404 -

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Oops! You’ve reached a dead end.

If you think something should be here, you can open an issue on GitHub.

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\ No newline at end of file diff --git a/about/index.html b/about/index.html index ee6f6b9b..411c0c63 100644 --- a/about/index.html +++ b/about/index.html @@ -1,19 +1,18 @@ -NumPy - About Us -

About Us

NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the modified BSD license.

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.

Steering Council#

The NumPy Steering Council is the project’s governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name):

Emeritus:

To contact the NumPy Steering Council, please email numpy-team@googlegroups.com.

Teams#

The NumPy project leadership is actively working on diversifying contribution pathways to the project.
NumPy currently has the following teams:

See the Team page for more info.

NumFOCUS Subcommittee#

Sponsors#

NumPy receives direct funding from the following sources:

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:

Logo of UC Berkeley +Logo of Tidelift

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:

Logo of UC Berkeley Logo of Quansight -Logo of NVIDIA

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 for more information.

Donations to NumPy are managed by NumFOCUS. 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.

On this page
\ No newline at end of file +Logo of NVIDIA

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 for more information.

Donations to NumPy are managed by NumFOCUS. 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.

On this page
\ No newline at end of file diff --git a/arraycomputing/index.html b/arraycomputing/index.html index 901f638e..6e7015f5 100644 --- a/arraycomputing/index.html +++ b/arraycomputing/index.html @@ -1,16 +1,16 @@ -NumPy - Array Computing -NumPy - Array Computing

Array Computing

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, @@ -28,6 +28,5 @@ 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.

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\ No newline at end of file +to use loops of individual scalar operations.

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Case Study: First Image of a Black Hole

black hole image
Black Hole M87#
(Image Credits: Event Horizon Telescope Collaboration)

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) is an array of eight ground-based radio telescopes forming a computational telescope -the size of the earth, studing the universe with unprecedented +the size of the earth, studying 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 — enough to read a newspaper in New York @@ -43,7 +43,7 @@ NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below.

ehtim dependency map highlighting numpy
Software dependency chart of ehtim package highlighting NumPy#

Besides NumPy, many other packages, such as -SciPy and Pandas, are part of the +SciPy and Pandas, 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, while Matplotlib was used @@ -56,6 +56,5 @@ 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.

numpy benefits
Key NumPy Capabilities utilized#

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\ No newline at end of file +mystery of the universe.

numpy benefits
Key NumPy Capabilities utilized#

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\ No newline at end of file diff --git a/case-studies/cricket-analytics/index.html b/case-studies/cricket-analytics/index.html index 3facb5ac..a0624bd1 100644 --- a/case-studies/cricket-analytics/index.html +++ b/case-studies/cricket-analytics/index.html @@ -1,16 +1,16 @@ -NumPy - Case Study: Cricket Analytics, the game changer! -NumPy - Case Study: Cricket Analytics, the game changer!

Case Study: Cricket Analytics, the game changer!

Indian Premier League Cricket cup and stadium
IPLT20, the biggest Cricket Festival in India#
(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))

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 @@ -75,7 +75,7 @@ with a generative or static model. Causal analysis and big data approaches -are used for tactical analysis.

  • Data Visualization: Data graphing and visualization provide useful insights into relationship between various datasets.

    • Summary#

    Sports Analytics is a game changer when it comes to how professional games are +are used for tactical analysis.

  • Data Visualization: Data graphing and visualization provide useful insights into relationship between various datasets.

    • Summary#

    Sports Analytics is a game changer when it comes to how professional games are played, especially how strategic decision making happens, which until recently was primarily done based on “gut feeling" or adherence to past traditions. NumPy forms a solid foundation for a large set of Python packages which provide higher @@ -85,6 +85,5 @@ 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.

    Diagram showing benefits of using NumPy for cricket analytics
    Key NumPy Capabilities utilized#

    On this page
    \ No newline at end of file +otherwise hidden in numbers and statistics.

    Diagram showing benefits of using NumPy for cricket analytics
    Key NumPy Capabilities utilized#

    On this page
    \ No newline at end of file diff --git a/case-studies/deeplabcut-dnn/index.html b/case-studies/deeplabcut-dnn/index.html index 12ed2384..d0deb171 100644 --- a/case-studies/deeplabcut-dnn/index.html +++ b/case-studies/deeplabcut-dnn/index.html @@ -1,16 +1,16 @@ -NumPy - Case Study: DeepLabCut 3D Pose Estimation -NumPy - Case Study: DeepLabCut 3D Pose Estimation

    Case Study: DeepLabCut 3D Pose Estimation

    micehandanim
    Analyzing mice hand-movement using DeepLapCut#
    (Source: www.deeplabcut.org )

    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 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.

    horserideranim
    Colored dots track the positions of a racehorse’s body part#
    (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.

    DeepLabCut uses a principle called transfer learning, 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, 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 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#

    On this page
    \ No newline at end of file +NumPy’s array manipulation capabilities.

    numpy benefits
    Key NumPy Capabilities utilized#

    On this page
    \ No newline at end of file diff --git a/case-studies/gw-discov/index.html b/case-studies/gw-discov/index.html index fb7049dc..99200852 100644 --- a/case-studies/gw-discov/index.html +++ b/case-studies/gw-discov/index.html @@ -1,16 +1,16 @@ -NumPy - Case Study: Discovery of Gravitational Waves -NumPy - Case Study: Discovery of Gravitational Waves

    Case Study: Discovery of Gravitational Waves

    binary coalesce black hole generating gravitational waves
    Gravitational Waves#
    (Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)

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

    —David Shoemaker, LIGO Scientific Collaboration

    About Gravitational Waves and LIGO#

    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 @@ -33,17 +33,17 @@ 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 +astrophysics, cosmology, particle physics, and nuclear physics.
  • Crunch observed data via numerical relativity computations that involve 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 +signal and statistically estimate the significance of observed data.
  • Data visualization so that the binary / numerical results can be comprehended.

    • The Challenges#

    gwpy-numpy depgraph
    Dependency graph showing how GwPy package depends on NumPy#

    PyCBC-numpy depgraph
    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 +phenomena known. Number crunching and data visualization are crucial steps +that help scientists gain insights into data gathered from 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 to answer complex questions and discover new horizons in our understanding of the -universe.

    numpy benefits
    Key NumPy Capabilities utilized#

    On this page
    \ No newline at end of file +universe.

    numpy benefits
    Key NumPy Capabilities utilized#

    On this page
    \ No newline at end of file diff --git a/case-studies/index.html b/case-studies/index.html index afb985d9..81977474 100644 --- a/case-studies/index.html +++ b/case-studies/index.html @@ -1,13 +1,12 @@ -NumPy - Case-Studies -

    Case-Studies

    \ No newline at end of file diff --git a/case-studies/index.xml b/case-studies/index.xml index 63075930..feababce 100644 --- a/case-studies/index.xml +++ b/case-studies/index.xml @@ -1,9 +1,90 @@ -Case-Studies on NumPyhttps://numpy.org/case-studies/Recent content in Case-Studies on NumPyHugo -- gohugo.ioenCase Study: Cricket Analytics, the game changer!https://numpy.org/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/cricket-analytics/IPLT20, the biggest Cricket Festival in India# -(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium)) 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.Case Study: DeepLabCut 3D Pose Estimationhttps://numpy.org/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/deeplabcut-dnn/Analyzing mice hand-movement using DeepLapCut# -(Source: www.deeplabcut.org ) 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 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.Case Study: Discovery of Gravitational Waveshttps://numpy.org/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/gw-discov/Gravitational Waves# -(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO) The scientific Python ecosystem is critical infrastructure for the research done at LIGO. —David Shoemaker, LIGO Scientific Collaboration -About Gravitational Waves and LIGO# 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.Case Study: First Image of a Black Holehttps://numpy.org/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/blackhole-image/Black Hole M87# -(Image Credits: Event Horizon Telescope Collaboration) Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. —Katie Bouman, Assistant Professor, Computing &amp; Mathematical Sciences, Caltech -A telescope the size of the earth# The Event Horizon telescope (EHT) 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. \ No newline at end of file +Case-Studies on NumPyhttps://numpy.org/case-studies/Recent content in Case-Studies on NumPyHugoenCase Study: Cricket Analytics, the game changer!https://numpy.org/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/cricket-analytics/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/ipl-stadium.png" alt="Indian Premier League Cricket cup and stadium" class="align-center"> + + + +<figcaption><strong class="caption-title">IPLT20, the biggest Cricket Festival in India</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://unsplash.com/@aksh1802">(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium))</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/"> + <p> + You don't play for the crowd, you play for the country. +</p> + <p class="attribution">—M S Dhoni, <em>International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL</em></p> +</blockquote> + +<h2 id="about-cricket">About Cricket<a class="headerlink" href="#about-cricket" title="Link to this heading">#</a></h2> +<p>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 +<a href="https://www.statista.com/topics/4543/indian-premier-league-ipl/">money</a> 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.</p>Case Study: DeepLabCut 3D Pose Estimationhttps://numpy.org/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/deeplabcut-dnn/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/mice-hand.gif" alt="micehandanim" class="align-center"> + + + +<figcaption><strong class="caption-title">Analyzing mice hand-movement using DeepLapCut</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="http://www.mousemotorlab.org/deeplabcut">(Source: www.deeplabcut.org )</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"> + <p> +Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +</p> + <p class="attribution">—Alexander Mathis, <em>Assistant Professor, École polytechnique fédérale de Lausanne</em> (<a href="https://www.epfl.ch/en/">EPFL</a>)</p> +</blockquote> + +<h2 id="about-deeplabcut">About DeepLabCut<a class="headerlink" href="#about-deeplabcut" title="Link to this heading">#</a></h2> +<p><a href="https://github.com/DeepLabCut/DeepLabCut">DeepLabCut</a> 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.</p>Case Study: Discovery of Gravitational Waveshttps://numpy.org/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/gw-discov/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/gw_sxs_image.png" alt="binary coalesce black hole generating gravitational waves" class="align-center"> + + + +<figcaption><strong class="caption-title">Gravitational Waves</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://youtu.be/Zt8Z_uzG71o">(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +</p> + <p class="attribution">—David Shoemaker, <em>LIGO Scientific Collaboration</em></p> +</blockquote> + +<h2 id="about-gravitational-waves-and-ligo">About <a href="https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/">Gravitational Waves</a> and <a href="https://www.ligo.caltech.edu">LIGO</a><a class="headerlink" href="#about-gravitational-waves-and-ligo" title="Link to this heading">#</a></h2> +<p>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.</p>Case Study: First Image of a Black Holehttps://numpy.org/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/blackhole-image/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/blackhole.jpg" alt="black hole image" class="align-center"> + + + +<figcaption><strong class="caption-title">Black Hole M87</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg">(Image Credits: Event Horizon Telescope Collaboration)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +</p> + <p class="attribution">—Katie Bouman, <em>Assistant Professor, Computing &amp; Mathematical Sciences, Caltech</em></p> +</blockquote> + +<h2 id="a-telescope-the-size-of-the-earth">A telescope the size of the earth<a class="headerlink" href="#a-telescope-the-size-of-the-earth" title="Link to this heading">#</a></h2> +<p>The <a href="https://eventhorizontelescope.org">Event Horizon telescope (EHT)</a> is an +array of eight ground-based radio telescopes forming a computational telescope +the size of the earth, studying 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 +<a href="https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole">20 micro-arcseconds</a> — enough to read a newspaper in New York +from a sidewalk café in Paris!</p> \ No newline at end of file diff --git a/citing-numpy/index.html b/citing-numpy/index.html index faf80287..5d963469 100644 --- a/citing-numpy/index.html +++ b/citing-numpy/index.html @@ -1,16 +1,16 @@ -NumPy - Citing NumPy -NumPy - Citing NumPy

    Citing NumPy

    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:

    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
@@ -31,6 +31,5 @@
  doi           = {10.1038/s41586-020-2649-2},
  publisher     = {Springer Science and Business Media {LLC}},
  url           = {https://doi.org/10.1038/s41586-020-2649-2}
-}
    On this page
    \ No newline at end of file +}
    On this page
    \ No newline at end of file diff --git a/code-of-conduct/index.html b/code-of-conduct/index.html index e8330b71..f9f827d9 100644 --- a/code-of-conduct/index.html +++ b/code-of-conduct/index.html @@ -1,14 +1,13 @@ -NumPy - NumPy Code of Conduct -

    NumPy Code of Conduct

    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, X, 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:

    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.

    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.

    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) 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:

    On this page
    \ No newline at end of file diff --git a/community/index.html b/community/index.html index 1b759b31..ab96e93f 100644 --- a/community/index.html +++ b/community/index.html @@ -1,25 +1,34 @@ -NumPy - Community -

    Community

    NumPy is a community-driven open source project developed by a diverse group of contributors. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the NumPy Code of Conduct for guidance on how to interact with others in a way that makes 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.

    NumPy mailing list#

    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. +for usage questions - see Get Help.

    NumPy mailing list#

    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.

    GitHub issue tracker#

    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.

    Slack#

    A real-time chat room to ask questions about contributing to NumPy. +is available here.

    GitHub issue tracker#

    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.

    Slack#

    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 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 (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 and Twitter.

    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:

    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 page.

    Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar here.

    On this page
    \ No newline at end of file +details and how to get an invite.

    Community Communication Guidelines#

    If the NumPy community moderators determine that a participant is not adhering to +the principles above, they will reach out to the participant with a polite request +for the behavior to be adjusted. If the behavior continues, moderators may consider +additional steps as appropriate.

    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 (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.

    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:

    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 page.

    Also, feel free to stop by and say hi at one of our community meetings. To keep track of them, check out our events calendar here.

    On this page
    \ No newline at end of file diff --git a/conduct.html b/conduct.html index 64d9eff8..9c299463 100644 --- a/conduct.html +++ b/conduct.html @@ -1,2 +1 @@ -https://numpy.org/code-of-conduct/ - \ No newline at end of file +https://numpy.org/es/code-of-conduct/ \ No newline at end of file diff --git a/config.yaml b/config.yaml index a1be22d8..4c97f502 100644 --- a/config.yaml +++ b/config.yaml @@ -11,7 +11,7 @@ params: # Hero subtitle (optional) subtitle: The fundamental package for scientific computing with Python # Button text - buttontext: "Latest release: NumPy 1.26. View all releases" + buttontext: "Latest release: NumPy 2.4. View all releases" # Where the main hero button links to buttonlink: "/news/#releases" # Hero image (from static/images/___) @@ -77,10 +77,8 @@ params: socialmedia: - link: https://github.com/numpy/numpy icon: github - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + - link: https://www.youtube.com/@NumPy_team icon: youtube - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" @@ -91,7 +89,7 @@ params: link: https://numpy.org/doc/stable - text: Learn link: /learn - - text: Citing Numpy + - text: Citing NumPy link: /citing-numpy - text: Roadmap link: https://numpy.org/neps/roadmap.html diff --git a/contribute/index.html b/contribute/index.html index fc28c3a9..7d9c4a01 100644 --- a/contribute/index.html +++ b/contribute/index.html @@ -1,16 +1,16 @@ -NumPy - Contribute to NumPy -

    Contribute to NumPy

    The NumPy project welcomes your expertise and enthusiasm! Your choices aren’t limited to programming, as you can see below there are many areas where we need your help.

    If you’re unsure where to start or how your skills fit in, reach out! You can ask on the mailing @@ -26,7 +26,7 @@ If you are new to contributing to open source, we also highly recommend reading this guide.

    Our community aspires to treat everyone equally and to value all contributions. We have a Code of Conduct to foster an open -and welcoming environment.

    Writing code#

    Programmers, this +and welcoming environment.

    For a visual guide on how to contribute to NumPy, check out this comic.

    NumPy Contributor comic book cover

    Writing code#

    Programmers, this guide explains how to contribute to the NumPy codebase.
    Check out also our YouTube channel for additional advice.

    Reviewing pull requests#

    The project has more than 250 open pull requests – meaning many potential improvements and many open-source contributors waiting for feedback. If you’re @@ -43,20 +43,18 @@ issues 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 to make NumPy -accessible to users in their native language. Volunteer translators are at the heart -of this effort. See -here -for background; comment on this GitHub -issue 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 account, organizing NumPy code -sprints, 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 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.

    On this page
    \ No newline at end of file +opportunities abound.

    Translating website content#

    We are working on translating numpy.org into multiple languages to make +its content more accessible to NumPy users all over the globe. (See +NEP 28 +for background.) Volunteer translators are at the heart of this effort. If you’d like to help, +join the translation channel on the +Scientific Python Discord server.

    To get familiar with our translation process, read the guide +How to translate content using Crowdin.

    Community coordination and outreach#

    Through community contact we share our work more widely and learn where we’re +falling short. We’re eager to get more people involved in efforts like organizing NumPy code +sprints, a newsletter, and perhaps a blog.

    Fundraising#

    For many years, NumPy was maintained by dedicated volunteers, but as its importance grew it +became clear that to ensure stability and growth we would need financial support. +This SciPy'19 talk explains how much difference +that support has made. Like most nonprofits, we are constantly seeking grants, sponsorships, +and other kinds of funding. We have a number of ideas and of course we welcome more. +Fundraising is a scarce skill here – we’d appreciate your help.

    If you’d like to contribute to NumPy by making a donation, visit https://numpy.org/about/#donate.

    On this page
    \ No newline at end of file diff --git a/casestudies.min.92b0bafc1e58181b02c23f14b861767269e505eadc85a123b4eb79e2527bf2e0.css b/css/casestudies.min.92b0bafc1e58181b02c23f14b861767269e505eadc85a123b4eb79e2527bf2e0.css similarity index 100% rename from casestudies.min.92b0bafc1e58181b02c23f14b861767269e505eadc85a123b4eb79e2527bf2e0.css rename to css/casestudies.min.92b0bafc1e58181b02c23f14b861767269e505eadc85a123b4eb79e2527bf2e0.css diff --git a/css/custom.min.cf0f0187caa046832f55197d09d0ad54a98eebc7758bbb354fb1c8fb8541b5bb.css b/css/custom.min.cf0f0187caa046832f55197d09d0ad54a98eebc7758bbb354fb1c8fb8541b5bb.css new file mode 100644 index 00000000..76d09559 --- /dev/null +++ b/css/custom.min.cf0f0187caa046832f55197d09d0ad54a98eebc7758bbb354fb1c8fb8541b5bb.css @@ -0,0 +1 @@ +.hero-title,.navbar-logo-text{font-family:lato} \ No newline at end of file diff --git a/css/mailchimp.min.96f403ea4c8be10747beb4c33a219da2fa8234a3b98882983bd2569da8eeb9e1.css 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svg.icon{fill:var(--spht-color-dark);width:1.6rem;height:1.6rem;padding-bottom:2px;padding-left:8px}.thank-you{display:none;height:75px;color:var(--spht-color-light);align-items:center;color:var(--spht-color-light)}@media only screen and (max-width:1150px){.sign-up-input{font-size:12px}}@media only screen and (max-width:850px){.sign-up-input{width:100%}.thank-you{justify-content:center}.submission-instructions{display:none}.signup-button{display:block;height:35px;border-radius:5px;margin-left:5px;width:60px;color:#000;border:none;outline:none}} \ No newline at end of file diff --git a/shell.min.173478d133f6f5990705f3ed2f48714422de15754d813df6aa2a047bf62a51da.css b/css/shell.min.173478d133f6f5990705f3ed2f48714422de15754d813df6aa2a047bf62a51da.css similarity index 100% rename from shell.min.173478d133f6f5990705f3ed2f48714422de15754d813df6aa2a047bf62a51da.css rename to css/shell.min.173478d133f6f5990705f3ed2f48714422de15754d813df6aa2a047bf62a51da.css diff --git a/css/tabs.scss b/css/tabs.scss index 489341fa..560da2b1 100644 --- a/css/tabs.scss +++ b/css/tabs.scss @@ -50,8 +50,7 @@ table td, table th { } .grid-container > div { - /* White, with 80% opacity */ - background-color: rgba(255, 255, 255, 0.8); + background-color: var(--pst-color-background); text-align: middle; } @@ -73,8 +72,8 @@ table td, table th { } .image-grid > div { - background-color: rgb(238, 238, 238); - border: 2px solid rgb(255, 255, 255); + background-color: var(--pst-color-surface); + border: 2px solid var(--pst-color-surface); border-radius: 10px; padding: 10px; } @@ -96,10 +95,11 @@ section.scientific-domains { line-height: 130%; margin: 0.2em 0.4em; flex-basis: 13%; + align-self: baseline; & header { // FIXME: Use appropriate PST color for this header text. - color: var(--colorPrimaryDark); + color: var(--pst-color-text-base); font-weight: 700; // Ensure headers are the same minimum height (some wrap // to two lines). diff --git 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ul{align-content:left;display:flex;flex-direction:column;padding-inline-start:0}section.scientific-domains ul li ul li{margin-left:0}img.first-column-layout{max-width:100px;max-height:30px;margin:0 20px 0 10px}td.left-text{vertical-align:middle} \ No newline at end of file diff --git a/css/tabs.scss.min.789baca49dcaed7f117290ddfbb257f1622a868114c85b0e0e9dd25b7fcc9199.css b/css/tabs.scss.min.789baca49dcaed7f117290ddfbb257f1622a868114c85b0e0e9dd25b7fcc9199.css deleted file mode 100644 index 1a661810..00000000 --- a/css/tabs.scss.min.789baca49dcaed7f117290ddfbb257f1622a868114c85b0e0e9dd25b7fcc9199.css +++ /dev/null @@ -1 +0,0 @@ -[role=tablist]{justify-content:center}table td:not([align]),table th:not([align]){text-align:inherit}table td,table th{vertical-align:top}.tabs-section{display:flex;flex-direction:column}.tabs-section .container{display:flex;flex-direction:column}.tabs-title{display:flex;justify-content:center;letter-spacing:1.5px;font-size:27px;margin:30px 0}.visualization,.data-science,.machine-learning,.array-libraries{max-width:900px;margin:15px auto}@media only screen and (max-width:1200px){.tabs-section{margin:30px 10px}.tabs-title{margin:30px}}.grid-container{display:grid;grid-template-columns:auto auto;grid-gap:20px}.grid-container>div{background-color:rgba(255,255,255,.8);text-align:middle}@media only screen and (max-width:600px){.grid-container{display:block}}.visualization-images>img{border-radius:10px}.image-grid{display:grid;grid-template-columns:auto auto auto auto;grid-gap:10px}.image-grid>div{background-color:#eee;border:2px solid #fff;border-radius:10px;padding:10px}section.scientific-domains{max-width:900px!important}section.scientific-domains ul{display:flex;flex-wrap:wrap;list-style:none;margin:15px auto;padding-inline-start:0}section.scientific-domains ul li{align-content:center;font-size:.8rem;line-height:130%;margin:.2em .4em;flex-basis:13%}section.scientific-domains ul li header{color:var(--colorPrimaryDark);font-weight:700;min-height:3.3em;text-align:left}section.scientific-domains ul li img{width:50px;height:50px;margin-bottom:.5em}section.scientific-domains ul li ul{align-content:left;display:flex;flex-direction:column;padding-inline-start:0}section.scientific-domains ul li ul li{margin-left:0}img.first-column-layout{max-width:100px;max-height:30px;margin:0 20px 0 10px}td.left-text{vertical-align:middle} \ No newline at end of file diff --git a/diversity_sep2020/index.html b/diversity_sep2020/index.html index 19c4d4f6..db74445a 100644 --- a/diversity_sep2020/index.html +++ b/diversity_sep2020/index.html @@ -1,14 +1,13 @@ -NumPy - NumPy Diversity and Inclusion Statement -

    NumPy Diversity and Inclusion Statement

    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 @@ -59,6 +58,5 @@ bi-weekly community meeting.

    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

    On this page
    \ No newline at end of file +Stéfan van der Walt, Eric Wieser, on behalf of the NumPy team

    On this page
    \ No newline at end of file diff --git a/en/index.html b/en/index.html index d2c8b7bb..589ac254 100644 --- a/en/index.html +++ b/en/index.html @@ -1,2 +1 @@ -https://numpy.org/ - \ No newline at end of file +https://numpy.org/ \ No newline at end of file diff --git a/en/sitemap.xml b/en/sitemap.xml index 1e8f234d..5451a8b4 100644 --- a/en/sitemap.xml +++ b/en/sitemap.xml @@ -1 +1 @@ -https://numpy.org/2023-09-16T00:00:00+00:00https://numpy.org/news/2023-09-16T00:00:00+00:00https://numpy.org/user-survey-2020/https://numpy.org/404/https://numpy.org/about/https://numpy.org/arraycomputing/https://numpy.org/case-studies/cricket-analytics/https://numpy.org/case-studies/deeplabcut-dnn/https://numpy.org/case-studies/gw-discov/https://numpy.org/case-studies/blackhole-image/https://numpy.org/case-studies/https://numpy.org/citing-numpy/https://numpy.org/community/https://numpy.org/contribute/https://numpy.org/gethelp/https://numpy.org/history/https://numpy.org/install/https://numpy.org/learn/https://numpy.org/code-of-conduct/https://numpy.org/report-handling-manual/https://numpy.org/diversity_sep2020/https://numpy.org/teams/https://numpy.org/user-surveys/https://numpy.org/press-kit/https://numpy.org/privacy/https://numpy.org/terms/ \ No newline at end of file +https://numpy.org/news/2025-12-20T00:00:00+00:00https://numpy.org/2025-12-20T00:00:00+00:00https://numpy.org/user-survey-2020/https://numpy.org/404/https://numpy.org/about/https://numpy.org/arraycomputing/https://numpy.org/case-studies/cricket-analytics/https://numpy.org/case-studies/deeplabcut-dnn/https://numpy.org/case-studies/gw-discov/https://numpy.org/case-studies/blackhole-image/https://numpy.org/case-studies/https://numpy.org/citing-numpy/https://numpy.org/community/https://numpy.org/contribute/https://numpy.org/gethelp/https://numpy.org/history/https://numpy.org/install/https://numpy.org/learn/https://numpy.org/code-of-conduct/https://numpy.org/report-handling-manual/https://numpy.org/diversity_sep2020/https://numpy.org/teams/https://numpy.org/user-surveys/https://numpy.org/press-kit/https://numpy.org/privacy/https://numpy.org/terms/ \ No newline at end of file diff --git a/es/404/index.html b/es/404/index.html new file mode 100644 index 00000000..ef7f0637 --- /dev/null +++ b/es/404/index.html @@ -0,0 +1,13 @@ +NumPy - 404

    404

    ¡Oh, oh! Has llegado a un callejón sin salida.

    Si crees que algo debería estar aquí, puedes reportar este problema en GitHub.

    On this page
    \ No newline at end of file diff --git a/es/about/index.html b/es/about/index.html new file mode 100644 index 00000000..e3297992 --- /dev/null +++ b/es/about/index.html @@ -0,0 +1,18 @@ +NumPy - Quiénes Somos

    Quiénes Somos

    NumPy es un proyecto de código abierto cuyo objetivo es permitir la computación numérica en Python. Fue creado en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy siempre será un software 100% de código abierto y de uso libre para todos. Fue liberado bajo los términos liberales de la licencia BSD modificada.

    NumPy es desarrollado de forma abierta en GitHub, mediante el consenso de las comunidades NumPy y Python científico en general. Para más información sobre nuestro enfoque de gobernanza, por favor consulta nuestro Documento de Gobernanza.

    Consejo Directivo#

    El Consejo de Dirección de NumPy es el órgano de gobernanza del proyecto. Su papel 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):

    Eméritos:

    Para contactar con el Consejo Directivo de NumPy, por favor envía un correo electrónico a numpy-team@googlegroups.com.

    Equipos#

    La dirección del proyecto NumPy trabaja activamente para diversificar las vías de contribución al proyecto.
    NumPy cuenta actualmente con los siguientes equipos:

    Visita la página de Equipos para más información.

    Subcomité NumFOCUS#

    Patrocinadores#

    NumPy recibe financiación directa de las siguientes fuentes:

    Socios institucionales#

    Los socios institucionales son organizaciones que apoyan al proyecto empleando a personas que contribuyen a NumPy como parte de su trabajo. Entre los actuales socios institucionales se encuentran:

    Logo of UC Berkeley +Logo of Quansight +Logo of NVIDIA

    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.

    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 para más información.

    Las donaciones a NumPy son gestionadas por NumFOCUS. 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.

    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.

    On this page
    \ No newline at end of file diff --git a/es/arraycomputing/index.html b/es/arraycomputing/index.html new file mode 100644 index 00000000..85973aee --- /dev/null +++ b/es/arraycomputing/index.html @@ -0,0 +1,13 @@ +NumPy - Computación con Arreglos

    Computación con Arreglos

    La computación con arreglos es la base del cómputo estadístico, matemático y científico en varias aplicaciones contemporáneas de ciencia de datos y aplicaciones de analíticas, tales como la visualización de datos, el procesamiento digital de señales, el procesamiento de imágenes, la bioinformática, el aprendizaje automático, la inteligencia artificial, entre muchas otras.

    La manipulación y transformación de datos a gran escala depende de una computación con arreglos eficiente y de alto rendimiento. El lenguaje de elección para la analítica de datos, el aprendizaje automático y el cómputo numérico productivo es Python.

    Numerical Python o NumPy es la biblioteca estándar de-facto del lenguaje de programación Python que soporta arreglos y matrices multidimensionales de gran tamaño, y viene con una amplia colección de funciones matemáticas de alto nivel para operar sobre estos arreglos.

    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 computación con arreglos, 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

    La computación con arreglos está basada en los arreglos como estructura de datos. Los arreglos son utilizados 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.

    La computación con arreglos es única ya que implica operar sobre todos los datos del arreglo al mismo tiempo. 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 operar sobre los datos agregados, sin tener que utilizar bucles de instrucciones escalares individuales.

    On this page
    \ No newline at end of file diff --git a/es/case-studies/blackhole-image/index.html b/es/case-studies/blackhole-image/index.html new file mode 100644 index 00000000..ffdad349 --- /dev/null +++ b/es/case-studies/blackhole-image/index.html @@ -0,0 +1,14 @@ +NumPy - Caso de estudio: La primera imagen de un Agujero Negro

    Caso de estudio: La primera imagen de un Agujero Negro

    Imagen de agujero negro
    Agujero Negro M87#
    (Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)

    Capturar imágenes del Agujero Negro M87 es como intentar ver algo que por definición es imposible de ver.

    —Katie Bouman, Profesora Asistente, Ciencias de la Computación & Matemáticas, Caltech

    Un telescopio del tamaño de la Tierra#

    El Telescopio Event Horizon (EHT) , es un conjunto de ocho radiotelescopios terrestres que forman un telescopio computacional del tamaño de la Tierra, estudiando al universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada Interferometría de línea de base muy larga (VLBI), tiene una resolución angular de 20 microsegundos de arco — ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París!

    Objetivos clave y resultados#

    Los desafíos#

    flujo de datos
    Flujo de Trabajo de Procesamiento de Datos EHT#
    (Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)

    El Rol de NumPy#

    ¿Qué pasa si hay un problema con los datos? O tal vez un algoritmo depende demasiado de una suposición en particular. ¿Cambiará drásticamente la imagen si se cambia un solo parámetro?

    La colaboración del EHT respondió a estos desafíos haciendo que los equipos independientes evaluaran los datos, utilizando técnicas de reconstrucción de imágenes ya establecidas y de vanguardia. Cuando los resultados se mostraron consistentes, se combinaron para producir la primera imagen de su tipo de un agujero negro.

    Su trabajo ilustra el rol que desempeña el ecosistema científico de Python en el avance de la ciencia a través del análisis de datos colaborativos.

    rol de numpy
    El rol de NumPy en la imagen del agujero negro#

    Por ejemplo, el paquete de Python eht-imaging proporciona herramientas para simular y realizar reconstrucción de imágenes en datos VLBI. NumPy está en el núcleo del procesamiento de datos de matrices utilizados en este paquete, como se muestra a continuación en el gráfico parcial de dependencias de software.

    mapa de dependencias de ehtim resaltando a numpy
    Gráfico de dependencias de software del paquete ehtim resaltando a NumPy#

    Además de NumPy, muchos otros paquetes, como SciPy y Pandas, son parte del flujo de procesamiento de datos para fotografiar el agujero negro. Los formatos estándar de archivos astronómicos y transformaciones de tiempo/coordenadas fueron manejados por Astropy, mientras que Matplotlib fue utilizado en la visualización de datos a través del flujo de análisis, incluyendo la generación de la imagen final del agujero negro.

    Resumen#

    El eficiente y adaptable arreglo n-dimensional que es la característica central de NumPy, permitió a los investigadores manipular grandes conjuntos de datos numéricos, proporcionando una base para la primera imagen de un agujero negro. Un momento histórico en la ciencia ofrece una impresionante evidencia visual de la teoría de Einstein. Este logro abarca no solo los avances tecnológicos sino también la colaboración internacional de más de 200 científicos y algunos de los mejores radio observatorios del mundo. Algoritmos innovadores y técnicas de procesamiento de datos, mejorando los modelos astronómicos existentes, ayudaron a desvelar un misterio del universo.

    beneficios de numpy
    Capacidades clave de NumPy utilizadas#

    On this page
    \ No newline at end of file diff --git a/es/case-studies/cricket-analytics/index.html b/es/case-studies/cricket-analytics/index.html new file mode 100644 index 00000000..7c768fd7 --- /dev/null +++ b/es/case-studies/cricket-analytics/index.html @@ -0,0 +1,14 @@ +NumPy - Estudio de caso: Análisis de críquet, ¡el cambio radical!

    Estudio de caso: Análisis de críquet, ¡el cambio radical!

    Copa y estadio de la Premier League de Críquet de India
    IPLT20, el festival de críquet más grande en India#
    (Créditos de imagen: IPLT20 (copa y logo) & Akash Yadav (estadio))

    No juegas para el público, juegas para el país.

    —M S Dhoni, Jugador Internacional de críquet, ex-capitán del equipo de India, juega para Chennai Super Kings en IPL

    Acerca del críquet#

    Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi todos los rincones de la India, rurales o urbanos, es popular entre los jóvenes y ancianos por igual, conectando miles de millones de personas en India como ningún otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de dinero y fama en juego. En los últimos años, la tecnología ha cambiado literalmente las reglas del juego. El público tiene muchas opciones para elegir entre streaming de medios, torneos, acceso asequible a la visualización de críquet en vivo desde dispositivos móviles y más.

    La Indian Premier League (IPL) es una liga profesional 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 en 2019.

    El críquet es un juego de números - las carreras anotadas por un bateador, los wickets tomados 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 profundizar en los números del críquet tanto para mejorar el rendimiento como para estudiar las oportunidades de negocio, el mercado en general y la economía del cricket mediante potentes herramientas de análisis, impulsadas por software de computación numérica como NumPy, es algo muy importante. El análisis del críquet proporciona ideas interesantes sobre el juego e inteligencia predictiva respecto a los resultados del juego.

    Hoy en día, hay abundantes y casi infinitos tesoros de registros y estadísticas de juegos de críquet disponibles, por ejemplo, en ESPN cricinfo y cricsheet. Estas y muchas otras bases de datos de cricket se han utilizado para el análisis de cricket utilizando los últimos algoritmos de aprendizaje automático y modelación predictiva. Las plataformas de medios y entretenimiento, junto con los organismos deportivos profesionales asociados con el juego, utilizan la tecnología y el análisis para determinar métricas clave que mejoren las posibilidades de ganar los partidos:

    Un campo de cricket con lanzador y bateadores
    El campo de críquet, el punto focal en el terreno de juego#
    (Image credit: Debarghya Das)

    Objetivos Clave de Análisis de Datos#

    estimador de postura
    Estimador de postura en críquet#
    (Crédito de imagen: connect.vin)

    Los Desafíos#

    El Papel de NumPy en el Análisis del Críquet#

    El análisis deportivo es un campo en desarrollo. Muchos investigadores y compañías utilizan NumPy y otros paquetes de PyData como Scikit-learn, SciPy, Matplotlib y Jupyter, además de utilizar las últimas técnicas de aprendizaje automático e inteligencia artificial. NumPy se ha utilizado para varios tipos de análisis deportivos relacionados con el críquet tales como:

    Resumen#

    El análisis deportivo ha revolucionado la forma en que se juegan los partidos profesionales, especialmente en cuanto a la toma de decisiones estratégicas, que hasta hace poco se basaba principalmente en la “intuición” o en la adherencia a tradiciones pasadas. NumPy constituye una base sólida para un gran conjunto de paquetes de Python que brindan funciones de nivel superior relacionadas con análisis de datos, el aprendizaje automático y los algoritmos de IA. Estos paquetes están ampliamente desplegados para obtener información en tiempo real que ayudan en la toma de decisiones para resultados revolucionarios, tanto en el campo como para sacar conclusiones y hacer negocios alrededor del juego del críquet. Encontrar los parámetros ocultos, patrones y atributos que conducen al resultado de un partido de críquet ayuda a los interesados a tomar nota de la información del juego que de otra forma estarían ocultos en números y estadísticas.

    Diagrama que muestra los beneficios de usar NumPy para análisis de críquet
    Capacidades claves de NumPy utilizadas#

    On this page
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    Caso de estudio: DeepLabCut Estimación de Postura 3D

    micehandanim
    Analizar movimiento de las manos de los ratones usando DeepLapCut#
    (Fuente: www.deeplabcut.org)

    El software de código abierto está acelerando la biomedicina. DeepLabCut permite el análisis automatizado de video del comportamiento animal utilizando Aprendizaje Profundo.

    —Alexander Mathis, Profesor Asistente, Escuela Politécnica Federal de Lausana (EPFL)

    Acerca de DeepLabCut#

    DeepLabCut es una caja de herramientas de código abierto que permite a los investigadores de cientos de instituciones de todo el mundo rastrear el comportamiento de animales de laboratorio, con muy pocos datos de entrenamiento, con una precisión de nivel humana. Con la tecnología DeepLabCut, los científicos pueden profundizar en la comprensión científica del control motriz y el comportamiento a través de especies animales y escalas temporales.

    Muchas áreas de investigación, incluyendo la neurociencia, la medicina y la biomecánica, utilizan datos para rastrear el movimiento animal. DeepLabCut ayuda a entender lo que los humanos y otros animales están haciendo, analizando las acciones que han sido grabadas en la filmación. Utilizando la automatización para tareas laboriosas de etiquetado y monitoreo, junto con el análisis de datos basado en redes neuronales profundas, DeepLabCut realiza estudios científicos que involucran la observación de animales, tales como primates, ratones, peces, moscas, etc. de manera mucho más rápida y precisa.

    horserideranim
    Puntos de colores rastrean las posiciones de una parte del cuerpo de un caballo de carreras#
    (Fuente: Mackenzie Mathis)

    El rastreo del comportamiento no invasivo de animales de DeepLabCut por medio de la extracción de posturas de animales es crucial para propósitos científicos en dominios tales como la biomecánica, genética, etología y & neurociencia. Medir las poses de animales de manera no invasiva a partir de video - sin marcadores - en fondos que cambian dinámicamente es un desafío computacional, tanto técnicamente como en términos de necesidades de recursos y datos de entrenamiento requeridos.

    DeepLabCut permite a los investigadores estimar la postura del sujeto, permitiéndoles eficientemente cuantificar el comportamiento a través de una caja de herramientas de software basada en Python. Con DeepLabCut, los investigadores pueden identificar fotogramas distintos de videos, etiquetar digitalmente partes específicas del cuerpo en unas pocas docenas de fotogramas con una GUI personalizada, y luego las arquitecturas de estimación de posturas basadas en aprendizaje profundo en DeepLabCut aprenden a identificar esas mismas características en el resto del video y en otros videos similares de animales. Funciona en diferentes especies de animales, desde los animales de laboratorio comunes como moscas y ratones hasta animales más inusuales como los guepardos.

    DeepLabCut utiliza un principio llamado aprendizaje por transferencia, que reduce considerablemente la cantidad de datos de entrenamiento requeridos y acelera la convergencia del período de entrenamiento. Dependiendo de las necesidades, los usuarios pueden seleccionar diferentes arquitecturas de red que proporcionan una inferencia más rápida (por ejemplo MobileNetV2), que también pueden combinarse con retroalimentación experimental en tiempo real. DeepLabCut utilizó originalmente los detectores de características de una arquitectura de estimación de postura humana de alto rendimiento, llamada DeeperCut, que inspiró el nombre. El paquete ahora ha sido significativamente modificado para incluir arquitecturas adicionales, métodos de aumento y una experiencia de usuario completa en el front-end. Además, para apoyar los experimentos biológicos a gran escala, DeepLabCut proporciona capacidades de aprendizaje activo para que los usuarios puedan aumentar el conjunto de entrenamiento a lo largo del tiempo para cubrir casos límite y hacer que su algoritmo de estimación de postura sea robusto dentro de un contexto específico.

    Recientemente, se presentó el modelo zoo de DeepLabCut, que proporciona modelos pre-entrenados para varias especies y condiciones experimentales, desde el análisis facial en primates hasta la postura de perro. Esto se puede ejecutar, por ejemplo, en la nube sin ningún etiquetado de datos nuevos o entrenamiento de redes neuronales, y no es necesaria ninguna experiencia de programación.

    Objetivos y Resultados Clave#

    dlcsteps
    Pasos de estimación de la postura con DeepLabCut#
    (Source: DeepLabCut)

    Los Desafíos#

    challengesfig
    Estimación de variedad y complejidad de postura#
    (Fuente: Mackenzie Mathis)

    El Papel de NumPy para afrontar los desafíos de la estimación de postura#

    NumPy aborda la necesidad central de la tecnología de DeepLabCut de realizar cálculos numéricos a alta velocidad para el análisis del comportamiento. Además de NumPy, DeepLabCut emplea varios softwares de Python que utilizan NumPy en su núcleo, como SciPy, Pandas, matplotlib, Tensorpack, imgaug, scikit-learn, scikit-image y Tensorflow.

    Las siguientes características de NumPy jugaron un papel clave en abordar el procesamiento de imágenes, los requisitos de combinatoria y la necesidad de cálculos rápidos en los algoritmos de estimación de posturas de DeepLabCut:

    DeepLabCut utiliza las capacidades de arreglos de NumPy a lo largo del flujo de trabajo ofrecido por el conjunto de herramientas. En particular, NumPy se utiliza para muestrear diferentes fotogramas para etiquetado de anotaciones humanas, y para escribir, editar y procesar datos de anotación. Dentro de TensorFlow, la red neuronal es entrenada por la tecnología DeepLabCut durante miles de iteraciones para predecir las anotaciones de referencia a partir de fotogramas. Para este propósito, se crean densidades objetivo (mapas de puntuación) para plantear la estimación de poses como un problema de traducción de imagen a imagen. Para hacer que las redes neuronales sean robustas, se emplea el aumento de datos, lo que requiere el cálculo de mapas de puntuación objetivo sujetos a varios pasos geométricos y de procesamiento de imágenes. Para hacer que el entrenamiento sea rápido, se aprovechan las capacidades de vectorización de NumPy. Para la inferencia, es necesario extraer las predicciones más probables de los mapas de puntuación objetivo y “vincular eficientemente las predicciones para ensamblar animales individuales”.

    flujo de trabajo
    Flujo de Trabajo de DeepLabCut#
    (Fuente: Mackenzie Mathis)

    Resumen#

    Observar y describir eficientemente el comportamiento es un punto central de la etología moderna, neurociencia, medicina y tecnología. DeepLabCut permite a los investigadores estimar la postura del sujeto, permitiéndoles de manera eficiente cuantificar el comportamiento. Con solo un pequeño conjunto de imágenes de entrenamiento, el conjunto de herramientas de Python de DeepLabCut permite entrenar una red neuronal con una precisión de etiquetado a nivel humano, expandiendo así su aplicación no solo al análisis del comportamiento en el laboratorio, sino también potencialmente en deportes, análisis de marcha, medicina y estudios de rehabilitación. Los desafíos de la combinatoria compleja y procesamiento de datos enfrentados por los algoritmos de DeepLabCut se abordan mediante el uso de las capacidades de manipulación de arreglos de NumPy.

    beneficios de NumPy
    Capacidades claves utilizadas de NumPy#

    On this page
    \ No newline at end of file diff --git a/es/case-studies/gw-discov/index.html b/es/case-studies/gw-discov/index.html new file mode 100644 index 00000000..af4fb916 --- /dev/null +++ b/es/case-studies/gw-discov/index.html @@ -0,0 +1,14 @@ +NumPy - Estudio de Caso: Descubrimiento de Ondas Gravitacionales

    Estudio de Caso: Descubrimiento de Ondas Gravitacionales

    coalescencia de un agujero negro binario generando ondas gravitacionales
    Ondas Gravitacionales#
    (Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)

    El ecosistema científico de Python es una infraestructura crítica para la investigación realizada en LIGO.

    —David Shoemaker, Colaboración científica LIGO

    Acerca de Ondas Gravitacionales y LIGO#

    Las ondas gravitacionales son ondulaciones en el tejido del espacio y el tiempo, generadas por cataclismos en el universo, tales como la colisión y fusión de dos agujeros negros o la coalescencia de estrellas binarias o supernovas. La observación de Ondas Gravitacionales no solo puede ayudar en el estudio de la gravedad, sino también en la comprensión de algunos de los fenómenos oscuros en el universo distante y su impacto.

    El Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO) fue diseñado para abrir el campo de la astrofísica de ondas gravitacionales mediante la detección directa de las ondas gravitacionales predichas por la Teoría General de la Relatividad de Einstein. Comprende dos interferómetros ampliamente separados dentro de los Estados Unidos: uno en Hanford, Washington, y el otro en Livingston, Louisiana, operando al unísono para detectar ondas gravitacionales. Cada uno de ellos tiene detectores de ondas gravitacionales de escala de múltiples kilómetros que utilizan interferometría de láser. La Colaboración Científica de LIGO (LSC) es un grupo de más de 1000 científicos de universidades de los Estados Unidos y de otros 14 países, respaldados por más de 90 universidades e institutos de investigación; aproximadamente 250 estudiantes contribuyen activamente a la colaboración. El nuevo descubrimiento de LIGO es la primera observación de ondas gravitacionales, realizada midiendo las diminutas perturbaciones que las ondas generan en el espacio y el tiempo a medida que pasan a través de la Tierra. Ha abierto nuevas fronteras astrofísicas que exploran el lado deformado del universo: objetos y fenómenos que están hechos de espaciotiempo deformado.

    Objetivos Clave#

    Los desafíos#

    amplitud de deformación de ondas gravitacionales
    Amplitud de deformación de ondas gravitacionales estimada de GW150914#
    (Créditos del gráfico: Observación de Ondas Gravitacionales de la Fusión de un Agujero Negro Binario, Publicación de ResearchGate)

    El Papel de NumPy en la Detección de Ondas Gravitacionales#

    Las ondas gravitacionales emitidas por la fusión no pueden ser calculadas utilizando ninguna técnica excepto la relatividad numérica por fuerza bruta usando supercomputadoras. La cantidad de datos que LIGO recopila es tan incomprensiblemente grande como pequeñas son las señales de onda gravitacionales.

    NumPy, el paquete de análisis numérico estándar para Python, fue utilizado por el software empleado en varias tareas realizadas durante el proyecto de detección de Ondas Gravitacionales en LIGO. NumPy ayudó a resolver las matemáticas complejas y la manipulación de datos a alta velocidad. Aquí hay algunos ejemplos:

    gwpy-numpy depgraph
    Gráfico de dependencias que muestra cómo depende el paquete GwPy de NumPy#

    PyCBC-numpy depgraph
    Gráfico de dependencias que muestra cómo el paquete PyCBC depende de NumPy#

    Resumen#

    La detección de ondas gravitacionales ha permitido a los investigadores descubrir fenómenos completamente inesperados, al tiempo que proporciona nuevos conocimientos sobre muchos de los fenómenos astrofísicos más profundos conocidos. El procesamiento de datos y la visualización de datos son pasos cruciales que ayudan a los científicos a obtener información a partir de los datos recopilados en las observaciones científicas y a comprender los resultados. Los cálculos son complejos y no pueden ser comprendidos por humanos, a menos que sean visualizados utilizando simulaciones por computador que se alimenten con datos y análisis reales observados. NumPy, junto con otros paquetes de Python como matplotlib, pandas y scikit-learn, está permitiendo a los investigadores responder preguntas complejas y descubrir nuevos horizontes en nuestra comprensión del universo.

    beneficios de NumPy
    Capacidades clave de NumPy utilizadas#

    On this page
    \ No newline at end of file diff --git a/es/case-studies/index.html b/es/case-studies/index.html new file mode 100644 index 00000000..a368254a --- /dev/null +++ b/es/case-studies/index.html @@ -0,0 +1,12 @@ +NumPy - Case-Studies

    Case-Studies

    \ No newline at end of file diff --git a/es/case-studies/index.xml b/es/case-studies/index.xml new file mode 100644 index 00000000..e195606b --- /dev/null +++ b/es/case-studies/index.xml @@ -0,0 +1,70 @@ +Case-Studies on NumPyhttps://numpy.org/es/case-studies/Recent content in Case-Studies on NumPyHugoesCaso de estudio: DeepLabCut Estimación de Postura 3Dhttps://numpy.org/es/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/case-studies/deeplabcut-dnn/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/mice-hand.gif" alt="micehandanim" class="align-center"> + + + +<figcaption><strong class="caption-title">Analizar movimiento de las manos de los ratones usando DeepLapCut</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="http://www.mousemASElab.org/deeplabcut">(Fuente: www.deeplabcut.org)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"> + <p> El software de código abierto está acelerando la biomedicina. DeepLabCut permite el análisis automatizado de video del comportamiento animal utilizando Aprendizaje Profundo. +</p> + <p class="attribution">—Alexander Mathis, <em>Profesor Asistente, Escuela Politécnica Federal de Lausana</em> (<a href="https://www.epfl.ch/en/">EPFL</a>)</p> +</blockquote> + +<h2 id="acerca-de-deeplabcut">Acerca de DeepLabCut<a class="headerlink" href="#acerca-de-deeplabcut" title="Link to this heading">#</a></h2> +<p><a href="https://github.com/DeepLabCut/DeepLabCut">DeepLabCut</a> es una caja de herramientas de código abierto que permite a los investigadores de cientos de instituciones de todo el mundo rastrear el comportamiento de animales de laboratorio, con muy pocos datos de entrenamiento, con una precisión de nivel humana. Con la tecnología DeepLabCut, los científicos pueden profundizar en la comprensión científica del control motriz y el comportamiento a través de especies animales y escalas temporales.</p>Caso de estudio: La primera imagen de un Agujero Negrohttps://numpy.org/es/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/case-studies/blackhole-image/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/blackhole.jpg" alt="Imagen de agujero negro" class="align-center"> + + + +<figcaption><strong class="caption-title">Agujero Negro M87</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg">(Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +Capturar imágenes del Agujero Negro M87 es como intentar ver algo que por definición es imposible de ver. +</p> + <p class="attribution">—Katie Bouman, <em>Profesora Asistente, Ciencias de la Computación &amp; Matemáticas, Caltech</em></p> +</blockquote> + +<h2 id="un-telescopio-del-tamaño-de-la-tierra">Un telescopio del tamaño de la Tierra<a class="headerlink" href="#un-telescopio-del-tamaño-de-la-tierra" title="Link to this heading">#</a></h2> +<p>El <a href="https://eventhorizontelescope.org"> Telescopio Event Horizon (EHT) </a>, es un conjunto de ocho radiotelescopios terrestres que forman un telescopio computacional del tamaño de la Tierra, estudiando al universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada Interferometría de línea de base muy larga (VLBI), tiene una resolución angular de <a href="https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole">20 microsegundos de arco</a> — ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París!</p>Estudio de caso: Análisis de críquet, ¡el cambio radical!https://numpy.org/es/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/case-studies/cricket-analytics/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/ipl-stadium.png" alt="Copa y estadio de la Premier League de Críquet de India" class="align-center"> + + + +<figcaption><strong class="caption-title">IPLT20, el festival de críquet más grande en India</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://unsplash.com/@aksh1802">(Créditos de imagen: IPLT20 (copa y logo) &amp; Akash Yadav (estadio))</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/"> + <p> No juegas para el público, juegas para el país. +</p> + <p class="attribution">—M S Dhoni, <em>Jugador Internacional de críquet, ex-capitán del equipo de India, juega para Chennai Super Kings en IPL</em></p> +</blockquote> + +<h2 id="acerca-del-críquet">Acerca del críquet<a class="headerlink" href="#acerca-del-críquet" title="Link to this heading">#</a></h2> +<p>Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi todos los rincones de la India, rurales o urbanos, es popular entre los jóvenes y ancianos por igual, conectando miles de millones de personas en India como ningún otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de <a href="https://www.statista.com/topics/4543/indian-premier-league-ipl/">dinero</a> y fama en juego. En los últimos años, la tecnología ha cambiado literalmente las reglas del juego. El público tiene muchas opciones para elegir entre streaming de medios, torneos, acceso asequible a la visualización de críquet en vivo desde dispositivos móviles y más.</p>Estudio de Caso: Descubrimiento de Ondas Gravitacionaleshttps://numpy.org/es/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/case-studies/gw-discov/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/gw_sxs_image.png" alt="coalescencia de un agujero negro binario generando ondas gravitacionales" class="align-center"> + + + +<figcaption><strong class="caption-title">Ondas Gravitacionales</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://youtu.be/Zt8Z_uzG71o">(Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> El ecosistema científico de Python es una infraestructura crítica para la investigación realizada en LIGO. +</p> + <p class="attribution">—David Shoemaker, <em>Colaboración científica LIGO</em></p> +</blockquote> + +<h2 id="acerca-de-ondas-gravitacionales-y-ligo">Acerca de <a href="https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/">Ondas Gravitacionales</a> y <a href="https://www.ligo.caltech.edu">LIGO</a><a class="headerlink" href="#acerca-de-ondas-gravitacionales-y-ligo" title="Link to this heading">#</a></h2> +<p>Las ondas gravitacionales son ondulaciones en el tejido del espacio y el tiempo, generadas por cataclismos en el universo, tales como la colisión y fusión de dos agujeros negros o la coalescencia de estrellas binarias o supernovas. La observación de Ondas Gravitacionales no solo puede ayudar en el estudio de la gravedad, sino también en la comprensión de algunos de los fenómenos oscuros en el universo distante y su impacto.</p> \ No newline at end of file diff --git a/es/citing-numpy/index.html b/es/citing-numpy/index.html new file mode 100644 index 00000000..98899516 --- /dev/null +++ b/es/citing-numpy/index.html @@ -0,0 +1,35 @@ +NumPy - Citando a NumPy

    Citando a NumPy

    Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, te sugerimos que cites el siguiente documento:

    En 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}
+}
    On this page
    \ No newline at end of file diff --git a/es/code-of-conduct/index.html b/es/code-of-conduct/index.html new file mode 100644 index 00000000..cb9d150f --- /dev/null +++ b/es/code-of-conduct/index.html @@ -0,0 +1,12 @@ +NumPy - Código de conducta de NumPy

    Código de conducta de NumPy

    Introducción#

    Este Código de Conducta aplica en todos los espacios gestionados por el proyecto NumPy, incluyendo todas las listas de correo públicas y privadas, gestores de incidencias, wikis, blogs, X (antes llamado 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.

    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.

    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.

    Directrices Específicas#

    Nos esforzamos por:

    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, cordiales, 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, de manera que puedan construir sobre este trabajo para hacerlo aún mejor. Cualquier decisión que tomemos afectará a usuarios y colegas, y nos tomamos en serio esas consecuencias a la hora de tomar decisiones.
    4. Ser curiosos. ¡Nadie lo sabe todo! Hacer preguntas tempranas evita muchos problemas posteriores, por lo que fomentamos las preguntas, aunque las podamos redirigir al foro adecuado. Nos esforzaremos por ser receptivos y útiles.
    5. Ser cuidadosos con las palabras que elegimos. Somos cuidadosos y respetuosos en nuestra comunicación, y asumimos la responsabilidad del lenguaje que utilizamos. Ser amables con los demás. No insultes ni menosprecies a los demás participantes. No aceptaremos el acoso ni otros comportamientos excluyentes, tales 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 malas palabras.
      • Acoso reiterado a los demás. En general, si alguien le pide que se detenga, entonces deténgase.
      • Abogar o alentar cualquiera de las conductas anteriormente mencionadas.

    Declaración de Diversidad#

    El proyecto NumPy acoge y fomenta la participación de todos. Estamos comprometidos a ser una comunidad de la que todo el mundo disfrute ser parte. Aunque puede que no siempre seamos capaces de satisfacer las preferencias de cada individuo, intentamos al máximo tratar a todos con amabilidad.

    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 éstas no entren en conflicto con este código de conducta.

    Aunque aceptamos a personas con dominio de cualquier idioma, el desarrollo de NumPy se lleva a cabo en inglés.

    Los estándares de comportamiento en la comunidad NumPy se detallan en el Código de Conducta anterior. Los participantes de nuestra comunidad deben respetar estos estándares en todas sus interacciones y ayudar a los demás a hacer lo mismo (véase la siguiente sección).

    Directrices Para La Presentación De Informes#

    Sabemos que es dolorosamente común que la comunicación en Internet comience o se convierta en un abuso evidente y manifiesto. 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.

    En caso de infracciones claramente intencionadas, informe de éstas al Comité de Código de Conducta (ver más abajo). En caso de infracciones posiblemente involuntarias, 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, por favor siéntase libre de informar directamente al Comité de Código de Conducta o de pedirle consejo a éste de forma confidencial.

    Puede informar de los problemas al Comité de Código de Conducta de NumPy en numpy-conduct@googlegroups.com.

    Actualmente, el Comité está compuesto por:

    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. Alternativamente, si por cualquier razón usted se siente incómodo haciendo un informe al Comité, también puede ponerse en contacto con el personal senior de NumFOCUS en conduct@numfocus.org.

    Resolución de reporte de informes & aplicación del Código de Conducta#

    Esta sección resume los puntos más importantes, puede encontrar más detalles en el Código de Conducta de NumPy - Cómo hacer seguimiento a un informe.

    Investigaremos y responderemos a todas las quejas. El Comité de Código de Conducta de NumPy y el Consejo Directivo de NumPy (en caso de estar involucrado) protegerán la identidad del denunciante y tratarán el contenido de las denuncias con carácter confidencial (a menos que el denunciante acuerde lo contrario).

    En caso de infracciones graves y evidentes, por ejemplo, amenaza personal o lenguaje violento, sexista o racista, desconectaremos inmediatamente al originador de los canales de comunicación de NumPy; por favor consulte el manual para obtener más detalles.

    En casos que no impliquen violaciones claras y graves u obvias de este Código de Conducta, el proceso de actuación sobre cualquier informe de violación del Código de Conducta recibido será:

    1. acusar recibo del informe,
    2. una discusión/retroalimentación razonable,
    3. mediación (si la retroalimentación no fue útil, y únicamente si tanto el denunciante como el denunciado están de acuerdo con ello),
    4. aplicación a través de una decisión transparente (ver Resoluciones) por parte del Comité de Código de conducta.

    El Comité responderá a cualquier informe lo antes posible y dentro de un plazo máximo de 72 horas.

    Notas finales#

    Damos las gracias a los grupos que están detrás de los siguientes documentos, de los que hemos sacado contenido e inspiración:

    On this page
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    Comunidad

    NumPy es un proyecto de código abierto impulsado por la comunidad y desarrollado por un grupo diverso de colaboradores. El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el Código de Conducta de NumPy para obtener orientación sobre cómo interactuar con los demás de una manera que haga que la comunidad prospere.

    Ofrecemos varios canales de comunicación para aprender, compartir conocimientos y conectarse con otros dentro de la comunidad de NumPy.

    Participa en línea#

    Las siguientes son formas de relacionarse directamente con el proyecto y la comunidad de NumPy. Ten en cuenta que animamos a los usuarios y a los miembros de la comunidad a apoyarse mutuamente por preguntas de uso - ver Obtener ayuda.

    Lista de correo de NumPy#

    Este es el foro principal para discusiones más extensas, como añadir nuevas características a NumPy, hacer cambios en el mapa de ruta de NumPy, y todo tipo de proceso de toma de decisiones sobre el proyecto. Aquí también se realizan los anuncios sobre NumPy, tales como lanzamientos, reuniones de desarrolladores, sprints o charlas en conferencias.

    En esta lista, por favor, utiliza el botón de envío inferior, responde a la lista (en lugar de a otro remitente) y no respondas a los resúmenes. El archivo de consulta de esta lista está disponible aquí.

    Seguimiento de incidencias en GitHub#

    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.

    Slack#

    Una sala de chat en tiempo real para hacer preguntas sobre las contribuciones a NumPy. Este es un espacio privado, destinado específicamente a las personas que no se atreven a plantear sus preguntas o ideas en la lista de correo pública o en GitHub. Por favor, visita aquí para más detalles, y sobre cómo obtener una invitación.

    Grupos de Estudio y Reuniones#

    Si desea encontrar un grupo de estudio o reunión local para aprender más sobre NumPy y el ecosistema más amplio de paquetes de Python para ciencia de datos y computación científica, te recomendamos que explores los PyData meetups (más de 150 reuniones, más de 100,000 miembros).

    NumPy también organiza ocasionalmente sprints presenciales para su equipo y colaboradores interesados. Estos normalmente se planifican con varios meses de anticipación y se anunciarán en la lista de correo y en X (antes conocido como Twitter).

    Conferencias#

    El proyecto NumPy no organiza sus propias conferencias. Las conferencias que tradicionalmente han sido más populares entre los responsables, colaboradores y usuarios de NumPy son la serie de conferencias de SciPy y PyData:

    Muchas de estas conferencias incluyen tutoriales y/o sprints que cubren NumPy, en donde puedes aprender cómo contribuir a Numpy o proyectos de código abierto relacionados.

    Únete a la comunidad NumPy#

    Para prosperar, el proyecto NumPy necesita tu experiencia y entusiasmo. ¿No sabes programar? ¡No es un problema! Hay muchas maneras de contribuir a NumPy.

    Si te interesa colaborar en NumPy (¡hurra!) te recomendamos que visites nuestra página Contribuir.

    No dudes en pasar a saludarnos en uno de nuestros encuentros de la comunidad. Para enterarte del próximo, consulta nuestro calendario de eventos aquí.

    On this page
    \ No newline at end of file diff --git a/es/config.yaml b/es/config.yaml new file mode 100644 index 00000000..0357ec74 --- /dev/null +++ b/es/config.yaml @@ -0,0 +1,109 @@ +languageName: Español +params: + description: '¿Por qué NumPy? Potentes arreglos n-dimensionales. Herramientas de cálculo numérico. Interoperabilidad. Rendimiento. Código abierto.' + navbarlogo: + image: logo.svg + text: NumPy + link: /es/ + hero: + #Main hero title + title: NumPy + #Hero subtitle (optional) + subtitle: El paquete fundamental para la computación científica con Python + #Button text + buttontext: "Última versión: NumPy 2.0. Ver todas las versiones" + #Where the main hero button links to + buttonlink: "/news/#releases" + #Hero image (from static/images/___) + image: logo.svg + shell: + title: marcador + intro: + - title: Prueba NumPy + text: Utilice el terminal interactivo para probar NumPy en el navegador + docslink: No olvides echarle un ojo a la documentación. + casestudies: + title: CASOS DE ESTUDIO + features: + - title: Primera imagen de un Agujero Negro + text: Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Telescopio del Horizonte de Sucesos producir la primera imagen de un agujero negro + img: /images/content_images/case_studies/blackhole.png + alttext: Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro. + url: /es/case-studies/blackhole-image + - title: Detección de Ondas Gravitacionales + text: En 1916 Albert Einstein predijo las ondas gravitacionales; 100 años después se confirmó su existencia por científicos del LIGO, utilizando NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Dos cuerpos orbitándose mutuamente. Estos desplazan la gravedad a su alrededor. + url: /es/case-studies/gw-discov + - title: Analíticas Deportivas + text: El Análisis de Críquet está cambiando el juego, mejorando el rendimiento de los jugadores y equipos mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis. + img: /images/content_images/case_studies/sports.jpg + alttext: Bola de Cricket sobre un campo verde. + url: /es/case-studies/cricket-analytics + - title: Estimación de la pose mediante aprendizaje profundo + text: DeepLabCut utiliza NumPy para acelerar estudios científicos que implican la observación del comportamiento animal para una mejor comprensión del control motriz, a través de especies y escalas de tiempo. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Análisis de la pose de un Guepardo + url: /es/case-studies/deeplabcut-dnn + tabs: + title: ECOSISTEMA + section5: false + navbar: + - title: Instalar + url: /es/install + - title: Documentación + url: https://numpy.org/doc/stable + - title: Aprende + url: /es/learn + - title: Comunidad + url: /es/community + - title: Quiénes somos + url: /es/about + - title: Noticias + url: /es/news + - title: Contribuye + url: /es/contribute + footer: + logo: logo.svg + socialmediatitle: "" + socialmedia: + - link: https://github.com/numpy/numpy + icon: github + - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube + quicklinks: + column1: + title: "" + links: + - text: Instalar + link: /es/install + - text: Documentación + link: https://numpy.org/doc/stable + - text: Aprende + link: /es/learn + - text: Citando a NumPy + link: /es/citing-numpy + - text: Mapa de ruta + link: https://numpy.org/neps/roadmap.html + column2: + links: + - text: Acerca de nosotros + link: /es/about + - text: Comunidad + link: /es/community + - text: Encuestas a usuarios + link: /es/user-surveys + - text: Contribuye + link: /es/contribute + - text: Código de Conducta + link: /es/code-of-conduct + column3: + links: + - text: Buscar ayuda + link: /es/gethelp + - text: Términos de uso + link: /es/terms + - text: Confidencialidad + link: /es/privacy + - text: Kit de prensa + link: /es/press-kit diff --git a/es/contribute/index.html b/es/contribute/index.html new file mode 100644 index 00000000..0f518844 --- /dev/null +++ b/es/contribute/index.html @@ -0,0 +1,14 @@ +NumPy - Contribuye a NumPy

    Contribuye a NumPy

    ¡El proyecto NumPy agradece tu experiencia y entusiasmo! Tus opciones no se limitan a la programación. Como puedes ver más abajo, existen muchas áreas en las que necesitamos tu ayuda.

    Si no estás seguro por dónde empezar o cómo encajan tus habilidades, ¡acércate! Puedes preguntar en la lista de correos o GitHub (abre una propuesta o comenta en una relevante).

    Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la comunidad en numpy-team@googlegroups.com o en Slack (escribe a numpy-team@googlegroups.com para recibir una invitación).

    También hacemos llamadas a la comunidad de manera quincenal, cuyos detalles se anuncian en la lista de correo. Te invitamos a unirte. Si es la primera vez que contribuyes al código abierto, también te recomendamos encarecidamente que leas esta guía.

    Nuestra comunidad aspira a tratar a todos por igual y a valorar todas las contribuciones. Tenemos un Código de Conducta para fomentar un entorno abierto y acogedor.

    Escribiendo código#

    Programadores, esta guía explica cómo contribuir al código base.
    También revisa nuestro canal de YouTube por consejos adicionales.

    Revisando solicitudes de cambios#

    El proyecto tiene más de 250 solicitudes de cambios abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes:

    Creando material educativo#

    La Guía de usuario de NumPy está en proceso de rehabilitación. Necesitamos nuevos tutoriales, instrucciones y explicaciones detalladas, y la página necesita una reestructuración. Las oportunidades no se limitan a escritores. También ejemplos prácticos, notebooks y vídeos. La propuesta NEP 44 - Reestructuración de la Documentación NumPy expone nuestras ideas – y tal vez tú puedas tener otras.

    Clasificación de propuestas#

    El rastreador de propuestas de NumPy tiene muchos temas abiertos. Algunos ya no son válidos, otros deberían priorizarse y otros serían buenos temas para nuevos colaboradores. Puedes:

    Por favor, solo sumérgete.

    Desarrollo de la Página Web#

    Acabamos de renovar nuestro sitio web, pero aún no hemos terminado. Si te gusta el desarrollo web, estas incidencias enumeran algunas de nuestras necesidades insatisfechas – y no dudes en compartir tus propias ideas.

    Diseño gráfico#

    Apenas podemos empezar a enumerar las aportaciones que puede hacer un diseñador gráfico. Nuestra documentación está sedienta de ilustraciones; nuestro sitio web, en pleno crecimiento, ansía imágenes… las oportunidades abundan.

    Traduciendo el contenido de la página web#

    Planeamos múltiples traducciones de numpy.org para hacer a NumPy accesible a los usuarios en su lengua materna. Los traductores voluntarios son el núcleo de este esfuerzo. Consulta aquí para más información; comenta en este tema de GitHub para inscribirte.

    Coordinación y divulgación de la comunidad#

    A través del contacto con la comunidad compartimos nuestro trabajo más ampliamente, y aprendemos en dónde nos estamos quedando cortos. Estamos ansiosos por conseguir más gente involucrada en esfuerzos como nuestra cuenta de X (antes llamado Twitter), organizando code sprints de NumPy, un boletín y quizás un blog.

    Recaudación de fondos#

    NumPy fue durante muchos años un proyecto voluntario, pero a medida que crecía su importancia se hizo evidente que necesitaríamos apoyo financiero para garantizar su estabilidad y crecimiento. Esta charla en SciPy'19 explica cuánta diferencia ha supuesto este apoyo. Como todo en el mundo sin ánimo de lucro, estamos constantemente en busca de subvenciones, patrocinios y otros tipos de ayuda. Tenemos varias ideas y, por supuesto, aceptamos más. La recaudación de fondos es una habilidad escasa aquí – apreciaríamos tu ayuda.

    Donar#

    Si deseas contribuir a NumPy haciendo una donación, visita https://numpy.org/about/#donate

    On this page
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    Buscar Ayuda

    Problemas de desarrollo: Para asuntos relacionados con el desarrollo de NumPy (por ejemplo, informes de errores), por favor consulte la sección de Comunidad.

    Preguntas de los usuarios: La mejor manera de obtener ayuda es publicar tu pregunta en un sitio como StackOverflow o Reddit. Nos gustaría poder estar al tanto de estos sitios, o responder preguntas de manera directa, ¡pero el volumen es algo abrumador!

    StackOverflow#

    Un foro para hacer preguntas de uso, como por ejemplo: “¿Cómo hago X cosa en NumPy?”. Por favor usa la etiqueta #numpy

    Reddit#

    Otro foro para hacer preguntas de uso.

    On this page
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    Historia de NumPy

    NumPy es una librería fundamental de Python que proporciona estructuras de datos de arreglos y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la librería contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en ciencias de la computación y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores “rebeldes” pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amigable con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados usados en el análisis de ondas gravitacionales importan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy.

    Para conocer en profundidad los hitos en el desarrollo de NumPy y las librerías relacionadas, consulta arxiv.org.

    Si deseas obtener una copia de las librerías originales Numeric y Numarray, sigue los siguientes enlaces:

    Página de Descarga de Numeric*

    Página de Descarga de Numarray*

    *Ten en cuenta que estos paquetes antiguos ya no se mantienen, y se recomienda encarecidamente a los usuarios que utilicen NumPy para cualquier propósito relacionado con arreglos o que refactoricen cualquier código preexistente para utilizar la librería NumPy.

    Documentación Histórica#

    Descarga el Manual de `Numeric’

    On this page
    \ No newline at end of file diff --git a/es/index.html b/es/index.html new file mode 100644 index 00000000..236ff8d5 --- /dev/null +++ b/es/index.html @@ -0,0 +1,53 @@ +NumPy
    NumPy +
    El paquete fundamental para la computación científica con Python
    ¡NumPy 2.0 ha sido lanzado!
    2024-06-17
    Matrices N-dimensionales potentes
    Rápida y versátil, la vectorización, indexación y conceptos de broadcasting de NumPy son los estándares de facto en el cálculo de matrices hoy en día.
    Herramientas de cálculo numérico
    NumPy ofrece funciones matemáticas completas, generadores de números aleatorios, rutinas de álgebra lineal, transformadas de Fourier, y más.
    Código abierto
    Distribuido bajo una [licencia BSD] liberal (https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy es desarrollado y mantenido públicamente en GitHub por una vibrante, receptiva y diversa comunidad.
    Interoperable
    NumPy soporta una amplia gama de hardware y plataformas de computación, y funciona bien con librerías distribuidas, de GPU y de matrices dispersas.
    Óptimo
    El núcleo de NumPy está optimizado adecuadamente con código en C. Disfrute de la flexibilidad de Python con la velocidad del código compilado.
    Fácil de usar
    La sintaxis de alto nivel de NumPy lo hace accesible y productivo para programadores de cualquier formación o nivel de experiencia.
    Prueba NumPy

    Utilice el terminal interactivo para probar NumPy en el navegador

    """
+To try the examples in the browser:
+1. Type code in the input cell and press
+   Shift + Enter to execute
+2. Or copy paste the code, and click on
+   the "Run" button in the toolbar
+"""
+
+# The standard way to import NumPy:
+import numpy as np
+
+# Create a 2-D array, set every second element in
+# some rows and find max per row:
+
+x = np.arange(15, dtype=np.int64).reshape(3, 5)
+x[1:, ::2] = -99
+x
+# array([[  0,   1,   2,   3,   4],
+#        [-99,   6, -99,   8, -99],
+#        [-99,  11, -99,  13, -99]])
+
+x.max(axis=1)
+# array([ 4,  8, 13])
+
+# Generate normally distributed random numbers:
+rng = np.random.default_rng()
+samples = rng.normal(size=2500)
+samples

    ECOSISTEMA

    + + + +

    Casi todos los científicos que trabajan en Python recurren a la potencia de NumPy.

    NumPy aporta la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante.

    La API de NumPy es el punto de partida cuando se escriben librerías para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona.

    Librería de arreglosCapacidades y áreas de aplicación
    DaskDaskArreglos distribuidos y paralelismo avanzado para análisis, que permiten un rendimiento a escala.
    CuPyCuPyLibrería de arreglos compatible con NumPy para cálculo acelerado en la GPU con Python.
    JAXJAXTransformaciones componibles de programas NumPy: diferenciar, vectorizar, compilación justo-a-tiempo a GPU/TPU.
    xarrayXarrayArreglos multidimensionales indexados y etiquetados para análisis y visualización avanzados.
    sparseSparseLibrería de arreglos dispersos compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy.
    PyTorchPyTorchMarco de aprendizaje profundo que acelera el camino desde la creación de prototipos de investigación hasta la implantación en producción.
    TensorFlowTensorFlowUna plataforma integral de aprendizaje automático para crear y desplegar fácilmente aplicaciones basadas en ML.
    arrowArrowPlataforma de desarrollo multilingüe para datos y análisis columnares en memoria.
    xtensorxtensorArreglos multidimensionales con difusión y computación perezosa para análisis numérico.
    awkwardAwkward ArrayManipular datos similares a JSON con expresiones similares a NumPy.
    uarrayuarraySistema de backend de Python que desacopla la API de la implementación; unumpy proporciona una API de NumPy.
    tensorlytensorlyAprendizaje tensorial, álgebra y backends para usar de manera fluida NumPy, PyTorch, TensorFlow o CuPy.
    blosc2Blosc2Cálculo acelerado con arrays comprimidos en memoria, en disco o remotos.
    Diagrama de las librerías de Python. Las cinco categorías son "Extraer, Transformar, Cargar", "Exploración de Datos", "Modelado de Datos", "Evaluación de Datos" y "Presentación de Datos".

    NumPy es el núcleo de un rico ecosistema de librerías de ciencia de datos. Un flujo de trabajo exploratorio típico de ciencia de datos podría verse así:

    Para grandes volúmenes de datos, Dask y Ray están diseñados para escalarse. Las implementaciones estables se basan en el versionado de datos (DVC), rastreo de experimentos (MLFlow), y automatización del flujo de trabajo (Airflow, Dagster y Prefect).

    Diagrama de tres círculos superpuestos. Los círculos se denominan "Matemáticas", "Ciencias de la Computación" y "Conocimientos Especializados". En el centro del diagrama, con los tres círculos superpuestos, hay un área denominada "Ciencia de datos".
    An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

    Source: Google AI Blog

    NumPy constituye la base de potentes librerías de aprendizaje automático como scikit-learn y SciPy. A medida que crece el aprendizaje automático, también lo hace la lista de librerías basadas en NumPy. Las capacidades de aprendizaje profundo de TensorFlow tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. PyTorch, otra librería de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural.

    Las técnicas estadísticas denominadas métodos ensemble, como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como XGBoost, LightGBM y CatBoost — uno de los motores de inferencia más rápidos. Yellowbrick y Eli5 ofrecen visualizaciones de aprendizaje automático.

    Un diagrama de flujo hecho en matplotlib
    Un diagrama de dispersión hecho en ggpy
    Un diagrama de caja hecho en plotly
    Un diagrama de flujo hecho en altair
    Un gráfico de pares de dos tipos de gráficos, un gráfico de trazado y un gráfico de frecuencias hecho en seaborn
    Un renderizado de volumen 3D realizado en PyVista.
    Una imagen multidimensional hecha en napari.
    Un diagrama de Voronoi hecho en vispy.

    NumPy es un componente esencial en el floreciente panorama de visualización de Python, que incluye Matplotlib, Seaborn, Plotly, Altair, Bokeh, Holoviz, Vispy, Napari, y PyVista, por nombrar algunos.

    El procesamiento acelerado de arreglos de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar.

    CASOS DE ESTUDIO

    Primera imagen de un Agujero Negro
    Primera imagen de un agujero negro. Es un círculo anaranjado con fondo negro.
    Cómo NumPy, junto con bibliotecas como SciPy y Matplotlib que dependen de NumPy, permitió al Telescopio del Horizonte de Sucesos producir la primera imagen de un agujero negro
    Detección de Ondas Gravitacionales
    Dos cuerpos orbitándose mutuamente. Estos desplazan la gravedad a su alrededor.
    En 1916 Albert Einstein predijo las ondas gravitacionales; 100 años después se confirmó su existencia por científicos del LIGO, utilizando NumPy.
    Analíticas Deportivas
    Bola de Cricket sobre un campo verde.
    El Análisis de Críquet está cambiando el juego, mejorando el rendimiento de los jugadores y equipos mediante modelos estadísticos y análisis predictivos. NumPy permite realizar muchos de estos análisis.
    Estimación de la pose mediante aprendizaje profundo
    Análisis de la pose de un Guepardo
    DeepLabCut utiliza NumPy para acelerar estudios científicos que implican la observación del comportamiento animal para una mejor comprensión del control motriz, a través de especies y escalas de tiempo.
    \ No newline at end of file diff --git a/es/index.xml b/es/index.xml new file mode 100644 index 00000000..b743bad2 --- /dev/null +++ b/es/index.xml @@ -0,0 +1,1454 @@ +NumPyhttps://numpy.org/es/Recent content on NumPyHugoesMon, 17 Jun 2024 00:00:00 +0000Noticiashttps://numpy.org/es/news/Mon, 17 Jun 2024 00:00:00 +0000https://numpy.org/es/news/<h3 id="lanzamiento-de-numpy-210">Lanzamiento de NumPy 2.1.0<a class="headerlink" href="#lanzamiento-de-numpy-210" title="Link to this heading">#</a></h3> +<p><em>18 de agosto 2024</em> &ndash; NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. Además de las habituales correcciones de errores y soporte actualizado de Python, ayuda a que NumPy vuelva a su ciclo de publicación habitual después del extenso desarrollo de 2.0. Los aspectos más destacados son:</p> +<ul> +<li>Soporte para Python 3.13.</li> +<li>Soporte preliminar para Python 3.13 de hilos libres.</li> +<li>Compatibilidad con la norma array-api 2023.12.</li> +</ul> +<p>Esta versión es compatible con las versiones 3.10-3.13 de Python.</p>404https://numpy.org/es/404/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/404/<p>¡Oh, oh! Has llegado a un callejón sin salida.</p> +<p>Si crees que algo debería estar aquí, puedes <a href="https://github.com/numpy/numpy.org/issues">reportar este problema</a> en GitHub.</p>Aprendehttps://numpy.org/es/learn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/learn/<p>Para la <strong>documentación oficial de NumPy</strong> visita <a href="https://numpy.org/doc/stable">numpy.org/doc/stable</a>.</p> +<hr> +<p>A continuación se muestra una colección de recursos educativos, tanto para el autoaprendizaje como para enseñar a otros, desarrollados por colaboradores de NumPy y aprobados por la comunidad.</p> +<h2 id="principiantes">Principiantes<a class="headerlink" href="#principiantes" title="Link to this heading">#</a></h2> +<p>Hay un montón de información sobre NumPy allá afuera. Si eres nuevo, te recomendamos encarecidamente estos:</p> +<p><i class="fas fa-chalkboard"></i> <strong>Tutoriales</strong></p> +<ul> +<li><a href="https://numpy.org/devdocs/user/quickstart.html">Tutorial de inicio rápido de NumPy</a></li> +<li><a href="https://numpy.org/numpy-tutorials">NumPy Tutorials</a> Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio <a href="https://github.com/numpy/numpy-tutorials">numpy-tutorials en GitHub</a>.</li> +<li><a href="https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b">NumPy Illustrated: La Guía Visual de NumPy <em>por Lev Maximov</em></a></li> +<li><a href="https://scipy-lectures.org/">Scientific Python Lectures</a> Además de cubrir NumPy, estas conferencias ofrecen una introducción más amplia al ecosistema científico de Python.</li> +<li><a href="https://numpy.org/devdocs/user/absolute_beginners.html">NumPy: the absolute basics for beginners.</a></li> +<li><a href="https://github.com/rougier/numpy-tutorial">NumPy tutorial <em>por Nicolas Rougier</em></a></li> +<li><a href="http://cs231n.github.io/python-numpy-tutorial/">Stanford CS231 <em>por Justin Johnson</em></a></li> +<li><a href="https://numpy.org/devdocs">NumPy User Guide.</a></li> +</ul> +<p><i class="fas fa-book"></i> <strong>Libros</strong></p>Buscar Ayudahttps://numpy.org/es/gethelp/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/gethelp/<p><strong>Problemas de desarrollo:</strong> Para asuntos relacionados con el desarrollo de NumPy (por ejemplo, informes de errores), por favor consulte la sección de <a href="https://numpy.org/es/community/">Comunidad</a>.</p> +<p><strong>Preguntas de los usuarios:</strong> La mejor manera de obtener ayuda es publicar tu pregunta en un sitio como <a href="http://stackoverflow.com/questions/tagged/numpy">StackOverflow</a> o <a href="https://www.reddit.com/r/Numpy/">Reddit</a>. Nos gustaría poder estar al tanto de estos sitios, o responder preguntas de manera directa, ¡pero el volumen es algo abrumador!</p> +<h3 id="stackoverflow"><a href="http://stackoverflow.com/questions/tagged/numpy">StackOverflow</a><a class="headerlink" href="#stackoverflow" title="Link to this heading">#</a></h3> +<p>Un foro para hacer preguntas de uso, como por ejemplo: &ldquo;¿Cómo hago X cosa en NumPy?&rdquo;. Por favor <a href="https://stackoverflow.com/help/tagging">usa la etiqueta <code>#numpy</code></a></p>Caso de estudio: DeepLabCut Estimación de Postura 3Dhttps://numpy.org/es/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/case-studies/deeplabcut-dnn/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/mice-hand.gif" alt="micehandanim" class="align-center"> + + + +<figcaption><strong class="caption-title">Analizar movimiento de las manos de los ratones usando DeepLapCut</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="http://www.mousemASElab.org/deeplabcut">(Fuente: www.deeplabcut.org)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"> + <p> El software de código abierto está acelerando la biomedicina. DeepLabCut permite el análisis automatizado de video del comportamiento animal utilizando Aprendizaje Profundo. +</p> + <p class="attribution">—Alexander Mathis, <em>Profesor Asistente, Escuela Politécnica Federal de Lausana</em> (<a href="https://www.epfl.ch/en/">EPFL</a>)</p> +</blockquote> + +<h2 id="acerca-de-deeplabcut">Acerca de DeepLabCut<a class="headerlink" href="#acerca-de-deeplabcut" title="Link to this heading">#</a></h2> +<p><a href="https://github.com/DeepLabCut/DeepLabCut">DeepLabCut</a> es una caja de herramientas de código abierto que permite a los investigadores de cientos de instituciones de todo el mundo rastrear el comportamiento de animales de laboratorio, con muy pocos datos de entrenamiento, con una precisión de nivel humana. Con la tecnología DeepLabCut, los científicos pueden profundizar en la comprensión científica del control motriz y el comportamiento a través de especies animales y escalas temporales.</p>Caso de estudio: La primera imagen de un Agujero Negrohttps://numpy.org/es/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/case-studies/blackhole-image/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/blackhole.jpg" alt="Imagen de agujero negro" class="align-center"> + + + +<figcaption><strong class="caption-title">Agujero Negro M87</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg">(Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +Capturar imágenes del Agujero Negro M87 es como intentar ver algo que por definición es imposible de ver. +</p> + <p class="attribution">—Katie Bouman, <em>Profesora Asistente, Ciencias de la Computación &amp; Matemáticas, Caltech</em></p> +</blockquote> + +<h2 id="un-telescopio-del-tamaño-de-la-tierra">Un telescopio del tamaño de la Tierra<a class="headerlink" href="#un-telescopio-del-tamaño-de-la-tierra" title="Link to this heading">#</a></h2> +<p>El <a href="https://eventhorizontelescope.org"> Telescopio Event Horizon (EHT) </a>, es un conjunto de ocho radiotelescopios terrestres que forman un telescopio computacional del tamaño de la Tierra, estudiando al universo con una sensibilidad y resolución sin precedente. El enorme telescopio virtual, que utiliza una técnica llamada Interferometría de línea de base muy larga (VLBI), tiene una resolución angular de <a href="https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole">20 microsegundos de arco</a> — ¡suficiente para leer un periódico en Nueva York desde un café en la acera en París!</p>Citando a NumPyhttps://numpy.org/es/citing-numpy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/citing-numpy/<p>Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, te sugerimos que cites el siguiente documento:</p> +<ul> +<li>Harris, C.R., Millman, K.J., van der Walt, S.J. et al. _ Array programming with NumPy_. Nature 585, 357–362 (2020). DOI: <a href="https://doi.org/10.1038/s41586-020-2649-2">10.1038/s41586-020-2649-2</a>. (<a href="https://www.nature.com/articles/s41586-020-2649-2">Publisher link</a>).</li> +</ul> +<p><em>En formato BibTeX:</em></p> + +<div class="highlight"> + <pre>@Article{ harris2020array, + title = { Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\&#39;{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{\&#39;{a}}ndez del + R{\&#39;{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\&#39;{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} +}</pre> +</div>Código de conducta de NumPyhttps://numpy.org/es/code-of-conduct/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/code-of-conduct/<h3 id="introducción">Introducción<a class="headerlink" href="#introducción" title="Link to this heading">#</a></h3> +<p>Este Código de Conducta aplica en todos los espacios gestionados por el proyecto NumPy, incluyendo todas las listas de correo públicas y privadas, gestores de incidencias, wikis, blogs, X (antes llamado 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.</p>Código de Conducta de NumPy - Cómo hacer el seguimiento de un informehttps://numpy.org/es/report-handling-manual/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/report-handling-manual/<p>Este es el manual que sigue el Comité de Código de Conducta de NumPy. Se utiliza cuando respondemos a un problema, para asegurarnos de que seamos consistentes y justos.</p> +<p>Hacer cumplir el <a href="https://numpy.org/es/code-of-conduct/">Código de Conducta</a> impacta a nuestra comunidad hoy y en el futuro. Es una acción que no tomamos a la ligera. Al revisar las medidas de cumplimiento, el Comité de Código de Conducta tendrá en cuenta los siguientes valores y directrices:</p>Computación con Arregloshttps://numpy.org/es/arraycomputing/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/arraycomputing/<p><em>La computación con arreglos es la base del cómputo estadístico, matemático y científico en varias aplicaciones contemporáneas de ciencia de datos y aplicaciones de analíticas, tales como la visualización de datos, el procesamiento digital de señales, el procesamiento de imágenes, la bioinformática, el aprendizaje automático, la inteligencia artificial, entre muchas otras.</em></p> +<p>La manipulación y transformación de datos a gran escala depende de una computación con arreglos eficiente y de alto rendimiento. El lenguaje de elección para la analítica de datos, el aprendizaje automático y el cómputo numérico productivo es <strong>Python.</strong></p>Comunidadhttps://numpy.org/es/community/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/community/<p>NumPy es un proyecto de código abierto impulsado por la comunidad y desarrollado por un grupo diverso de <a href="https://numpy.org/es/teams/">colaboradores</a>. El liderazgo de NumPy se ha comprometido firmemente a crear una comunidad abierta, inclusiva y positiva. Por favor, lee el <a href="https://numpy.org/es/code-of-conduct/">Código de Conducta de NumPy</a> para obtener orientación sobre cómo interactuar con los demás de una manera que haga que la comunidad prospere.</p> +<p>Ofrecemos varios canales de comunicación para aprender, compartir conocimientos y conectarse con otros dentro de la comunidad de NumPy.</p>Contribuye a NumPyhttps://numpy.org/es/contribute/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/contribute/<p>¡El proyecto NumPy agradece tu experiencia y entusiasmo! Tus opciones no se limitan a la programación. Como puedes ver más abajo, existen muchas áreas en las que necesitamos <strong>tu</strong> ayuda.</p> +<p>Si no estás seguro por dónde empezar o cómo encajan tus habilidades, <em>¡acércate!</em> Puedes preguntar en la <a href="https://mail.python.org/mailman/listinfo/numpy-discussion">lista de correos</a> o <a href="http://github.com/numpy/numpy">GitHub</a> (abre una <a href="https://github.com/numpy/numpy/issues">propuesta</a> o comenta en una relevante).</p> +<p>Estos son nuestros canales preferidos (el código abierto es abierto por naturaleza), pero si prefieres hablar de manera privada, contacta a nuestros coordinadores de la comunidad en <a href="mailto:numpy-team@googlegroups.com">numpy-team@googlegroups.com</a> o en <a href="https://numpy-team.slack.com">Slack</a> (escribe a <a href="mailto:numpy-team@googlegroups.com">numpy-team@googlegroups.com</a> para recibir una invitación).</p>ENCUESTA DE LA COMUNIDAD NUMPY 2020https://numpy.org/es/user-survey-2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/user-survey-2020/<p>En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto.</p>ENCUESTAS DE USUARIOS DE NUMPYhttps://numpy.org/es/user-surveys/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/user-surveys/<p><strong>2020</strong> El equipo de encuestas de NumPy, en asociación con estudiantes y profesores de la Universidad de Michigan y de la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad de NumPy. Encuentra los resultados de la encuesta <a href="https://numpy.org/user-survey-2020/">aquí</a>.</p> +<p><strong>2021</strong> Los datos recolectados están siendo analizados actualmente.</p> +<p>Si tienes alguna pregunta o sugerencia sobre las encuestas pasadas o futuras, por favor abre una propuesta <a href="https://github.com/numpy/numpy-surveys/issues">aquí</a>.</p>Equipos de NumPyhttps://numpy.org/es/teams/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/teams/<p>Somos un equipo internacional en una misión para apoyar a las comunidades científicas y de investigaciones alrededor del mundo, mediante la construcción de software de código abierto de calidad. +¡<a href="https://numpy.org/es/contribute/">Únete</a>!</p> +<h3 id="mantenedores">Mantenedores<a class="headerlink" href="#mantenedores" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&amp;v=4%22" alt="Avatar of Andrew Nelson"> + + + +Andrew Nelson + </div> + <a class="sd-stretched-link" href="https://github.com/andyfaff"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43369155?u=1f1fcabf979a2f00f403c60b816ba9f573026181&amp;v=4%22" alt="Avatar of Bas van Beek"> + + + +Bas van Beek + </div> + <a class="sd-stretched-link" href="https://github.com/BvB93"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/77272?v=4%22" alt="Avatar of Charles Harris"> + + + +Charles Harris + </div> + <a class="sd-stretched-link" href="https://github.com/charris"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/425260?v=4%22" alt="Avatar of Eric Wieser"> + + + +Eric Wieser + </div> + <a class="sd-stretched-link" href="https://github.com/eric-wieser"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/20969920?u=ec0e4d9dd70227549776ba8209f0e55a35d1fe84&amp;v=4%22" alt="Avatar of Ganesh Kathiresan"> + + + +Ganesh Kathiresan + </div> + <a class="sd-stretched-link" href="https://github.com/ganesh-k13"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&amp;v=4%22" alt="Avatar of Rohit Goswami"> + + + +Rohit Goswami + </div> + <a class="sd-stretched-link" href="https://github.com/HaoZeke"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/67612?v=4%22" alt="Avatar of Matthew Brett"> + + + +Matthew Brett + </div> + <a class="sd-stretched-link" href="https://github.com/matthew-brett"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&amp;v=4%22" alt="Avatar of Matti Picus"> + + + +Matti Picus + </div> + <a class="sd-stretched-link" href="https://github.com/mattip"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6570539?u=cfb3e218754e85c4fac18064d7cfdce0b67ddaa6&amp;v=4%22" alt="Avatar of Matt Haberland"> + + + +Matt Haberland + </div> + <a class="sd-stretched-link" href="https://github.com/mdhaber"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&amp;v=4%22" alt="Avatar of Melissa Weber Mendonça"> + + + +Melissa Weber Mendonça + </div> + <a class="sd-stretched-link" href="https://github.com/melissawm"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/2789820?v=4%22" alt="Avatar of Marten van Kerkwijk"> + + + +Marten van Kerkwijk + </div> + <a class="sd-stretched-link" href="https://github.com/mhvk"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&amp;v=4%22" alt="Avatar of Christopher Sidebottom"> + + + +Christopher Sidebottom + </div> + <a class="sd-stretched-link" href="https://github.com/Mousius"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&amp;v=4%22" alt="Avatar of Mateusz Sokół"> + + + +Mateusz Sokół + </div> + <a class="sd-stretched-link" href="https://github.com/mtsokol"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4%22" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3126246?u=a3c7cd970c0e4cbc4498febe0de777a263c522c5&amp;v=4%22" alt="Avatar of Nathan Goldbaum"> + + + +Nathan Goldbaum + </div> + <a class="sd-stretched-link" href="https://github.com/ngoldbaum"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/402156?u=288a1f206a151f9e2b69f3c0ce11848d3381943e&amp;v=4%22" alt="Avatar of Pearu Peterson"> + + + +Pearu Peterson + </div> + <a class="sd-stretched-link" href="https://github.com/pearu"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/15134881?v=4%22" alt="Avatar of Josh Wilson"> + + + +Josh Wilson + </div> + <a class="sd-stretched-link" href="https://github.com/person142"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/35046?v=4%22" alt="Avatar of Pauli Virtanen"> + + + +Pauli Virtanen + </div> + <a class="sd-stretched-link" href="https://github.com/pv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/15245051?u=54810990f0fdb11ecaade02762c09d5549d72a11&amp;v=4%22" alt="Avatar of Chunlin"> + + + +Chunlin + </div> + <a class="sd-stretched-link" href="https://github.com/Qiyu8"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&amp;v=4%22" alt="Avatar of Raghuveer Devulapalli"> + + + +Raghuveer Devulapalli + </div> + <a class="sd-stretched-link" href="https://github.com/r-devulap"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4%22" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46135?u=305a96a4778daecacbc8ec97ac25a48099a239cc&amp;v=4%22" alt="Avatar of Robert Kern"> + + + +Robert Kern + </div> + <a class="sd-stretched-link" href="https://github.com/rkern"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4%22" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/61977?v=4%22" alt="Avatar of Sebastian Berg"> + + + +Sebastian Berg + </div> + <a class="sd-stretched-link" href="https://github.com/seberg"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/12713707?u=5a3f6a8de4801d7878750cbd0bb2e0427bf0af0b&amp;v=4%22" alt="Avatar of Sayed Adel"> + + + +Sayed Adel + </div> + <a class="sd-stretched-link" href="https://github.com/seiko2plus"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1217238?u=b61e7e0085405ce6d7d53f8f39a1360ef9723f72&amp;v=4%22" alt="Avatar of Stephan Hoyer"> + + + +Stephan Hoyer + </div> + <a class="sd-stretched-link" href="https://github.com/shoyer"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&amp;v=4%22" alt="Avatar of Stefan van der Walt"> + + + +Stefan van der Walt + </div> + <a class="sd-stretched-link" href="https://github.com/stefanv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/7903078?u=2762d9ff13b992dc635f8f190a17f9a90cddfae1&amp;v=4%22" alt="Avatar of Tyler Reddy"> + + + +Tyler Reddy + </div> + <a class="sd-stretched-link" href="https://github.com/tylerjereddy"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/321463?v=4%22" alt="Avatar of Warren Weckesser"> + + + +Warren Weckesser + </div> + <a class="sd-stretched-link" href="https://github.com/WarrenWeckesser"></a> + </div> +</div> + + </div> +</div> + +<h3 id="equipo-de-documentación">Equipo de documentación<a class="headerlink" href="#equipo-de-documentación" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&amp;v=4%22" alt="Avatar of Rohit Goswami"> + + + +Rohit Goswami + </div> + <a class="sd-stretched-link" href="https://github.com/HaoZeke"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4%22" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&amp;v=4%22" alt="Avatar of Mars Lee"> + + + +Mars Lee + </div> + <a class="sd-stretched-link" href="https://github.com/MarsBarLee"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&amp;v=4%22" alt="Avatar of Matti Picus"> + + + +Matti Picus + </div> + <a class="sd-stretched-link" href="https://github.com/mattip"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&amp;v=4%22" alt="Avatar of Melissa Weber Mendonça"> + + + +Melissa Weber Mendonça + </div> + <a class="sd-stretched-link" href="https://github.com/melissawm"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4%22" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4%22" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + </div> +</div> + +<h3 id="equipo-de-la-página-web">Equipo de la página web<a class="headerlink" href="#equipo-de-la-página-web" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/123428?v=4" alt="Avatar of Jarrod Millman"> + + + +Jarrod Millman + </div> + <a class="sd-stretched-link" href="https://github.com/jarrodmillman"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3891660?u=5de0ba1f1adad6f041f6dde1affef5d05bbed80a&amp;v=4" alt="Avatar of Joe LaChance"> + + + +Joe LaChance + </div> + <a class="sd-stretched-link" href="https://github.com/joelachance"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&amp;v=4" alt="Avatar of Mars Lee"> + + + +Mars Lee + </div> + <a class="sd-stretched-link" href="https://github.com/MarsBarLee"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&amp;v=4" alt="Avatar of shalz"> + + + +shalz + </div> + <a class="sd-stretched-link" href="https://github.com/shaloo"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&amp;v=4" alt="Avatar of Shekhar Prasad Rajak"> + + + +Shekhar Prasad Rajak + </div> + <a class="sd-stretched-link" href="https://github.com/Shekharrajak"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&amp;v=4" alt="Avatar of Stefan van der Walt"> + + + +Stefan van der Walt + </div> + <a class="sd-stretched-link" href="https://github.com/stefanv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&amp;v=4" alt="Avatar of Albert Steppi"> + + + +Albert Steppi + </div> + <a class="sd-stretched-link" href="https://github.com/steppi"></a> + </div> +</div> + + </div> +</div> + +<h3 id="equipo-de-clasificación">Equipo de clasificación<a class="headerlink" href="#equipo-de-clasificación" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&amp;v=4%22" alt="Avatar of Andrew Nelson"> + + + +Andrew Nelson + </div> + <a class="sd-stretched-link" href="https://github.com/andyfaff"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1522319?v=4%22" alt="Avatar of Anirudh Subramanian"> + + + +Anirudh Subramanian + </div> + <a class="sd-stretched-link" href="https://github.com/anirudh2290"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/71486?u=cc88e2a4e4c6c496dcb9dd88cead5c0dab496c89&amp;v=4%22" alt="Avatar of Aaron Meurer"> + + + +Aaron Meurer + </div> + <a class="sd-stretched-link" href="https://github.com/asmeurer"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3813847?v=4%22" alt="Avatar of Atsushi Sakai"> + + + +Atsushi Sakai + </div> + <a class="sd-stretched-link" href="https://github.com/AtsushiSakai"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6691888?v=4%22" alt="Avatar of Ben Nathanson"> + + + +Ben Nathanson + </div> + <a class="sd-stretched-link" href="https://github.com/bjnath"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/35413198?u=e67bd9ebc361fb207f914979d935fd1956eb626c&amp;v=4%22" alt="Avatar of Anne Bonner"> + + + +Anne Bonner + </div> + <a class="sd-stretched-link" href="https://github.com/bonn0062"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6788290?u=d9a388224b87d55106cb3e6199d02ebc1d8e0553&amp;v=4%22" alt="Avatar of Brigitta Sipőcz"> + + + +Brigitta Sipőcz + </div> + <a class="sd-stretched-link" href="https://github.com/bsipocz"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5476002?u=5352f057ef8cb5de29e4d2a9fa8b0d0f49580dc8&amp;v=4%22" alt="Avatar of carlkl"> + + + +carlkl + </div> + <a class="sd-stretched-link" href="https://github.com/carlkl"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/11371428?u=9b425a337d076ec86b75ebc759724283f0970d9a&amp;v=4%22" alt="Avatar of Ryan C Cooper"> + + + +Ryan C Cooper + </div> + <a class="sd-stretched-link" href="https://github.com/cooperrc"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/36567889?u=cbc76d558d375ebafd4a05a505f500eb94e00611&amp;v=4%22" alt="Avatar of ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)"> + + + +ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) + </div> + <a class="sd-stretched-link" href="https://github.com/czgdp1807"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/2190658?u=b85e13f985d0bf87eeb3a7a146b61dcc9586019b&amp;v=4%22" alt="Avatar of Hameer Abbasi"> + + + +Hameer Abbasi + </div> + <a class="sd-stretched-link" href="https://github.com/hameerabbasi"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4%22" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8078968?v=4%22" alt="Avatar of jbrockmendel"> + + + +jbrockmendel + </div> + <a class="sd-stretched-link" href="https://github.com/jbrockmendel"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&amp;v=4%22" alt="Avatar of Kai"> + + + +Kai + </div> + <a class="sd-stretched-link" href="https://github.com/Kai-Striega"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/16046705?u=1bf01e87adb556503c1fe07789c194cc04d38490&amp;v=4%22" alt="Avatar of Yuji Kanagawa"> + + + +Yuji Kanagawa + </div> + <a class="sd-stretched-link" href="https://github.com/kngwyu"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/22004158?u=2ebb3919ebaa3d7e0865ea5583032bc08bd0f526&amp;v=4%22" alt="Avatar of Kriti Singh"> + + + +Kriti Singh + </div> + <a class="sd-stretched-link" href="https://github.com/kritisingh1"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/149655?u=249f7995c486de232c34e7970fbea505f518a1be&amp;v=4%22" alt="Avatar of Christopher Albert"> + + + +Christopher Albert + </div> + <a class="sd-stretched-link" href="https://github.com/krystophny"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/20306270?u=235cdf82e88f76ba2f5f4c2d33fa392319c60ad1&amp;v=4%22" alt="Avatar of Lysandros Nikolaou"> + + + +Lysandros Nikolaou + </div> + <a class="sd-stretched-link" href="https://github.com/lysnikolaou"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/34613774?u=61535ebfff07c68ea672cd8cd68c46187a38d3c1&amp;v=4%22" alt="Avatar of Meekail Zain"> + + + +Meekail Zain + </div> + <a class="sd-stretched-link" href="https://github.com/Micky774"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&amp;v=4%22" alt="Avatar of Christopher Sidebottom"> + + + +Christopher Sidebottom + </div> + <a class="sd-stretched-link" href="https://github.com/Mousius"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&amp;v=4%22" alt="Avatar of Mateusz Sokół"> + + + +Mateusz Sokół + </div> + <a class="sd-stretched-link" href="https://github.com/mtsokol"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4%22" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6564007?u=e5fb962de792bbce925c0c94fb7a748803c8bfa0&amp;v=4%22" alt="Avatar of Noa Tamir"> + + + +Noa Tamir + </div> + <a class="sd-stretched-link" href="https://github.com/noatamir"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&amp;v=4%22" alt="Avatar of Raghuveer Devulapalli"> + + + +Raghuveer Devulapalli + </div> + <a class="sd-stretched-link" href="https://github.com/r-devulap"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&amp;v=4%22" alt="Avatar of shalz"> + + + +shalz + </div> + <a class="sd-stretched-link" href="https://github.com/shaloo"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/55803680?u=bb727a0da1f33ed5f2feb58dc0333943430d2318&amp;v=4%22" alt="Avatar of Tina Oberoi"> + + + +Tina Oberoi + </div> + <a class="sd-stretched-link" href="https://github.com/tinaoberoi"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/13260794?u=5421923c831b67c4ef290bbdeb31ebfbdd906abc&amp;v=4%22" alt="Avatar of Rakesh Vasudevan"> + + + +Rakesh Vasudevan + </div> + <a class="sd-stretched-link" href="https://github.com/vrakesh"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8103276?v=4%22" alt="Avatar of Zijie (ZJ) Poh"> + + + +Zijie (ZJ) Poh + </div> + <a class="sd-stretched-link" href="https://github.com/zjpoh"></a> + </div> +</div> + + </div> +</div> + +<h3 id="equipo-de-encuestas">Equipo de encuestas<a class="headerlink" href="#equipo-de-encuestas" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4%22" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4%22" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4%22" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + </div> +</div> + +<h3 id="mantenedores-eméritos">Mantenedores eméritos<a class="headerlink" href="#mantenedores-eméritos" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/9040124?v=4%22" alt="Avatar of Allan Haldane"> + + + +Allan Haldane + </div> + <a class="sd-stretched-link" href="https://github.com/ahaldane"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/20568?v=4%22" alt="Avatar of Ondřej Čertík"> + + + +Ondřej Čertík + </div> + <a class="sd-stretched-link" href="https://github.com/certik"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/25111?v=4%22" alt="Avatar of David Cournapeau"> + + + +David Cournapeau + </div> + <a class="sd-stretched-link" href="https://github.com/cournape"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3343990?v=4%22" alt="Avatar of Jaime"> + + + +Jaime + </div> + <a class="sd-stretched-link" href="https://github.com/jaimefrio"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/123428?v=4%22" alt="Avatar of Jarrod Millman"> + + + +Jarrod Millman + </div> + <a class="sd-stretched-link" href="https://github.com/jarrodmillman"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/542663?v=4%22" alt="Avatar of Julian Taylor"> + + + +Julian Taylor + </div> + <a class="sd-stretched-link" href="https://github.com/juliantaylor"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&amp;v=4%22" alt="Avatar of Mark Wiebe"> + + + +Mark Wiebe + </div> + <a class="sd-stretched-link" href="https://github.com/mwiebe"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&amp;v=4%22" alt="Avatar of Nathaniel J. Smith"> + + + +Nathaniel J. Smith + </div> + <a class="sd-stretched-link" href="https://github.com/njsmith"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/254880?v=4%22" alt="Avatar of Travis E. Oliphant"> + + + +Travis E. Oliphant + </div> + <a class="sd-stretched-link" href="https://github.com/teoliphant"></a> + </div> +</div> + + </div> +</div> + +<h1 id="gobernanza">Gobernanza<a class="headerlink" href="#gobernanza" title="Link to this heading">#</a></h1> +<p>Para la lista de personas del Consejo Directivo, por favor ve <a href="https://numpy.org/about/">aquí</a>.</p>Estudio de caso: Análisis de críquet, ¡el cambio radical!https://numpy.org/es/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/case-studies/cricket-analytics/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/ipl-stadium.png" alt="Copa y estadio de la Premier League de Críquet de India" class="align-center"> + + + +<figcaption><strong class="caption-title">IPLT20, el festival de críquet más grande en India</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://unsplash.com/@aksh1802">(Créditos de imagen: IPLT20 (copa y logo) &amp; Akash Yadav (estadio))</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/"> + <p> No juegas para el público, juegas para el país. +</p> + <p class="attribution">—M S Dhoni, <em>Jugador Internacional de críquet, ex-capitán del equipo de India, juega para Chennai Super Kings en IPL</em></p> +</blockquote> + +<h2 id="acerca-del-críquet">Acerca del críquet<a class="headerlink" href="#acerca-del-críquet" title="Link to this heading">#</a></h2> +<p>Sería una subestimación decir que a los indios les encanta el críquet. El juego se juega en casi todos los rincones de la India, rurales o urbanos, es popular entre los jóvenes y ancianos por igual, conectando miles de millones de personas en India como ningún otro deporte. El críquet disfruta de una gran atención mediática. Hay una cantidad importante de <a href="https://www.statista.com/topics/4543/indian-premier-league-ipl/">dinero</a> y fama en juego. En los últimos años, la tecnología ha cambiado literalmente las reglas del juego. El público tiene muchas opciones para elegir entre streaming de medios, torneos, acceso asequible a la visualización de críquet en vivo desde dispositivos móviles y más.</p>Estudio de Caso: Descubrimiento de Ondas Gravitacionaleshttps://numpy.org/es/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/case-studies/gw-discov/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/gw_sxs_image.png" alt="coalescencia de un agujero negro binario generando ondas gravitacionales" class="align-center"> + + + +<figcaption><strong class="caption-title">Ondas Gravitacionales</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://youtu.be/Zt8Z_uzG71o">(Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> El ecosistema científico de Python es una infraestructura crítica para la investigación realizada en LIGO. +</p> + <p class="attribution">—David Shoemaker, <em>Colaboración científica LIGO</em></p> +</blockquote> + +<h2 id="acerca-de-ondas-gravitacionales-y-ligo">Acerca de <a href="https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/">Ondas Gravitacionales</a> y <a href="https://www.ligo.caltech.edu">LIGO</a><a class="headerlink" href="#acerca-de-ondas-gravitacionales-y-ligo" title="Link to this heading">#</a></h2> +<p>Las ondas gravitacionales son ondulaciones en el tejido del espacio y el tiempo, generadas por cataclismos en el universo, tales como la colisión y fusión de dos agujeros negros o la coalescencia de estrellas binarias o supernovas. La observación de Ondas Gravitacionales no solo puede ayudar en el estudio de la gravedad, sino también en la comprensión de algunos de los fenómenos oscuros en el universo distante y su impacto.</p>Historia de NumPyhttps://numpy.org/es/history/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/history/<p>NumPy es una librería fundamental de Python que proporciona estructuras de datos de arreglos y rutinas numéricas rápidas relacionadas. Cuando se puso en marcha, la librería contaba con escasos fondos y la escribían principalmente estudiantes de posgrado, muchos de ellos sin formación en ciencias de la computación y, a menudo, sin la bendición de sus asesores. Imaginar siquiera que un pequeño grupo de estudiantes programadores &ldquo;rebeldes&rdquo; pudiera derribar el ecosistema de software de investigación, ya establecido y respaldado por millones en financiación y cientos de ingenieros altamente cualificados, era absurdo. Sin embargo, las motivaciones filosóficas detrás de la pila de herramientas totalmente abierta, en combinación con una comunidad entusiasta y amigable con un enfoque singular, han demostrado ser favorable a largo plazo. Hoy en día, científicos, ingenieros y muchos otros profesionales en todo el mundo confían en NumPy. Por ejemplo, los scripts publicados usados en el análisis de ondas gravitacionales importan NumPy, y el proyecto de imagen del agujero negro M87 cita directamente a NumPy.</p>Instalando NumPyhttps://numpy.org/es/install/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/install/<p>El único prerrequisito para instalar NumPy es Python. Si aún no tienes Python y quieres la forma más sencilla de comenzar, te recomendamos que uses la <a href="https://www.anaconda.com/download">Distribución Anaconda</a> - incluye Python, NumPy y muchos otros paquetes comúnmente utilizados para la computación científica y la ciencia de datos.</p> +<p>NumPy se puede instalar con <code>conda</code>, con <code>pip</code>, con un gestor de paquetes en macOS y Linux, o <a href="https://numpy.org/devdocs/building">a partir del código fuente</a>. Para instrucciones más detalladas, consulte nuestra <a href="https://numpy.org/es/install/#python-numpy-install-guide">guía de instalación de Python y NumPy</a> a continuación.</p>Kit de prensahttps://numpy.org/es/press-kit/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/press-kit/<p>Nos gustaría facilitarte el trabajo para incluir la identidad del proyecto NumPy en tu próximo documento académico, material de curso o presentación.</p> +<p><a href="https://github.com/numpy/numpy/tree/main/branding/logo">Aquí</a> encontrarás varias versiones en alta resolución del logo de NumPy. Ten en cuenta que al utilizar los recursos de numpy.org, aceptas el <a href="https://numpy.org/es/code-of-conduct/">Código de Conducta de NumPy</a>.</p>Política de Privacidadhttps://numpy.org/es/privacy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/privacy/<p><strong>numpy.org</strong> está operado por <a href="https://numfocus.org">NumFOCUS, Inc.</a>, el patrocinador fiscal del proyecto NumPy. Para ver la Política de Privacidad de este sitio web, por favor dirígete a <a href="https://numfocus.org/privacy-policy">https://numfocus.org/privacy-policy</a>.</p> +<p>Si tienes alguna pregunta sobre la política o la recolección, uso y prácticas de divulgación de datos de NumFOCUS, por favor ponte en contacto con el personal de NumFOCUS en <a href="mailto:privacy@numfocus.org">privacy@numfocus.org</a>.</p>Quiénes Somoshttps://numpy.org/es/about/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/about/<p>NumPy es un proyecto de código abierto cuyo objetivo es permitir la computación numérica en Python. Fue creado en el 2005, a partir de los primeros trabajos de las bibliotecas Numeric y Numarray. NumPy siempre será un software 100% de código abierto y de uso libre para todos. Fue liberado bajo los términos liberales de la <a href="https://github.com/numpy/numpy/blob/main/LICENSE.txt">licencia BSD modificada</a>.</p> +<p>NumPy es desarrollado de forma abierta en GitHub, mediante el consenso de las comunidades NumPy y Python científico en general. Para más información sobre nuestro enfoque de gobernanza, por favor consulta nuestro <a href="https://www.numpy.org/devdocs/dev/governance/index.html">Documento de Gobernanza</a>.</p>Terms of Usehttps://numpy.org/es/terms/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/es/terms/<p><em>Last updated January 4, 2020</em></p> +<h2 id="agreement-to-terms">AGREEMENT TO TERMS<a class="headerlink" href="#agreement-to-terms" title="Link to this heading">#</a></h2> +<p>These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy (&quot;<strong>Project</strong>&quot;, “<strong>we</strong>”, “<strong>us</strong>”, or “<strong>our</strong>”), 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.</p> \ No newline at end of file diff --git a/es/install/index.html b/es/install/index.html new file mode 100644 index 00000000..65ca01cd --- /dev/null +++ b/es/install/index.html @@ -0,0 +1,21 @@ +NumPy - Instalando NumPy

    Instalando NumPy

    El único prerrequisito para instalar NumPy es Python. Si aún no tienes Python y quieres la forma más sencilla de comenzar, te recomendamos que uses la Distribución Anaconda - incluye Python, NumPy y muchos otros paquetes comúnmente utilizados para la computación científica y la ciencia de datos.

    NumPy se puede instalar con conda, con pip, con un gestor de paquetes en macOS y Linux, o a partir del código fuente. Para instrucciones más detalladas, consulte nuestra guía de instalación de Python y NumPy a continuación.

    CONDA

    Si utiliza conda, puede instalar NumPy desde los canales defaults o conda-forge:

    # La mejor práctica, utilizar un entorno en lugar de instalar en el entorno base
+conda create -n my-env
+conda activate my-env
+# Si desea instalar desde conda-forge
+conda config --env --add channels conda-forge
+# El comando de instalación
+conda install numpy

    PIP

    Si utiliza pip, puede instalar NumPy con:

    pip install numpy

    También al utilizar pip, es buena práctica utilizar un entorno virtual - vea Instalaciones Reproducibles a continuación para saber por qué, y esta guía para más detalles sobre el uso de entornos virtuales.

    Guía de instalación de Python y NumPy#

    Instalar y administrar paquetes en Python es complicado, hay un número de soluciones alternativas para la mayoría de tareas. Esta guía intenta dar al lector una idea de las mejores (o más populares) soluciones y dar recomendaciones claras. Se enfoca en los usuarios de Python, NumPy y del stack de PyData (o computación numérica) en sistemas operativos y hardware comunes.

    Recomendaciones#

    Empezaremos con recomendaciones basadas en el nivel de experiencia del usuario y el sistema operativo de interés. Si se encuentra entre “principiante” y “avanzado”, por favor diríjase a “principiante” si quiere mantener las cosas simples, y a “avanzado” si quiere trabajar de acuerdo a las mejores prácticas que le servirán de mucho en el futuro.

    Usuarios principiantes#

    Tanto en Windows, macOS y Linux:

    Usuarios avanzados#

    Conda#

    Alternativa si prefiere pip/PyPI#

    Para usuarios que conocen, por preferencia personal o leyendo acerca de las diferencias principales entre conda y pip a continuación, y prefieren una solución basada en pip/PyPI, recomendamos:

    Gestión de paquetes de Python#

    La gestión de los paquetes es un problema desafiante y, como resultado, hay muchas herramientas. Para desarrollo web y de propósito general en Python existe un completo conjunto de herramientas complementario a pip. Para computación de alto rendimiento (HPC), Spack amerita ser considerado. Sin embargo, para la mayoría de usuarios de NumPy, conda y pip son las dos herramientas más populares.

    Pip & conda#

    Las dos herramientas principales que instalan paquetes de Python son pip y conda. Sus funcionalidades se traslapan parcialmente (por ejemplo, ambas pueden instalar numpy); no obstante, también pueden trabajar conjuntamente. Discutiremos las principales diferencias entre pip y conda aquí - esto es importante comprenderlo si usted desea gestionar paquetes de manera efectiva.

    La primera diferencia radica en que conda es multi-lenguaje y puede instalar Python, mientras que pip es instalado para una versión particular de Python en su sistema e instala paquetes para esa misma versión de Python solamente. Esto también significa que conda puede instalar librerías que no sean de Python y herramientas que usted pueda necesitar (por ejemplo, compiladores, CUDA, HDF5), mientras que pip no.

    La segunda diferencia es que pip instala desde el Índice de Empaquetado de Python (PyPI - Python Packaging Index), mientras que conda instala desde sus propios canales (típicamente “defaults” o “conda-forge”). PyPI es, de lejos, la colección de paquetes más grande; sin embargo, todos los paquetes populares están también disponibles para conda.

    La tercera diferencia consiste en que conda es una solución integrada para gestionar paquetes, dependencias y entornos; mientras que con pip, usted podría necesitar otra herramienta (hay muchas!) para manejar entornos o dependencias complejas.

    Instalaciones reproducibles#

    En la medida en que las librerías son actualizadas, los resultados al correr su código pueden cambiar, o su código puede fallar por completo. Es importante que sea capaz de reconstruir el conjunto de paquetes y versiones que usted está utilizando. La mejor práctica es:

    1. usar un entorno diferente por cada proyecto en el cual usted esté trabajando,
    2. almacenar los nombres de paquetes y versiones utilizando su instalador de paquetes, cada uno de los cuales tiene su propio formato de metadata para esto:

    Paquetes NumPy & librerías de álgebra lineal aceleradas#

    NumPy no depende de ningún otro paquete de Python; sin embargo, sí depende de una librería de álgebra lineal acelerada - típicamente Intel MKL u OpenBLAS. Los usuarios no tienen que preocuparse por instalar éstas (se incluyen automáticamente en todos los métodos de instalación de NumPy). Los usuarios avanzados podrían querer, de todas maneras, conocer los detalles, ya que la utilización BLAS puede afectar el desempeño, comportamiento y tamaño en disco:

    Además del tamaño de instalación, desempeño y robustez, hay dos aspectos más a considerar:

    Resolución de problemas#

    Si su instalación falla con el siguiente mensaje, revise el siguiente enlace Resolución de problemas ImportError.

    ¡IMPORTANTE: POR FAVOR LEA ESTO COMO SUGERENCIA PARA RESOLVER ESTE PROBLEMA!
+
+La importación de las extensiones-c de numpy falló. Este error puede ocurrir por varias razones, siendo frecuente debido a problemas con su configuración.
    On this page
    \ No newline at end of file diff --git a/es/learn/index.html b/es/learn/index.html new file mode 100644 index 00000000..f1ce2759 --- /dev/null +++ b/es/learn/index.html @@ -0,0 +1,16 @@ +NumPy - Aprende

    Aprende

    Para la documentación oficial de NumPy visita numpy.org/doc/stable.

    A continuación se muestra una colección de recursos educativos, tanto para el autoaprendizaje como para enseñar a otros, desarrollados por colaboradores de NumPy y aprobados por la comunidad.

    Principiantes#

    Hay un montón de información sobre NumPy allá afuera. Si eres nuevo, te recomendamos encarecidamente estos:

    Tutoriales

    Libros

    También puedes echar un vistazo a esta lista de Goodreads sobre el tema “Python+SciPy”. La mayoría de esos libros son sobre el “ecosistema SciPy”, que tiene NumPy en su núcleo.

    Videos

    Avanzado#

    Pruebe estos recursos avanzados para comprender mejor los conceptos de NumPy como indexación avanzada, división, apilamiento, álgebra lineal y mucho más.

    Tutoriales

    Libros

    Videos

    Charlas de NumPy#

    Citando a NumPy#

    Si NumPy ha sido importante en tu investigación y deseas reconocer el proyecto en tu publicación académica, consulta esta información de citado.

    On this page
    \ No newline at end of file diff --git a/es/news/index.html b/es/news/index.html new file mode 100644 index 00000000..44e3992f --- /dev/null +++ b/es/news/index.html @@ -0,0 +1,13 @@ +NumPy - Noticias

    Noticias

    Lanzamiento de NumPy 2.1.0#

    18 de agosto 2024 – NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. Además de las habituales correcciones de errores y soporte actualizado de Python, ayuda a que NumPy vuelva a su ciclo de publicación habitual después del extenso desarrollo de 2.0. Los aspectos más destacados son:

    Esta versión es compatible con las versiones 3.10-3.13 de Python.

    Lanzamiento de NumPy 2.0.0#

    16 de junio de 2024 – NumPy 2.0.0 es el primer lanzamiento importante desde 2006. Es el resultado de 11 meses de desarrollo desde el último lanzamiento de características y es el trabajo de 212 colaboradores distribuidos entre 1078 solicitudes de incorporación de cambios. Contiene un gran número de nuevas características interesantes, así como cambios en las APIs de Python y C. Incluye cambios importantes que no podrían producirse en un lanzamiento menor regular, como una ruptura de ABI, cambios en las reglas de promoción de tipos y cambios en la API que podrían no haber estado emitiendo advertencias de obsolescencia en la versión 1.26.x. Los documentos clave relacionados con cómo adaptarse a los cambios en NumPy 2.0 incluyen:

    La publicación “NumPy 2.0: an evolutionary milestone” cuenta un poco de la historia sobre cómo se llegó a este lanzamiento.

    Fecha de lanzamiento de NumPy 2.0: 16 de junio#

    23 de mayo de 2024 – Estamos encantados de anunciar que NumPy 2.0 está previsto que sea lanzado el 16 de junio de 2024. Esta publicación lleva más de un año en proceso y es el primer lanzamiento importante desde 2006. Es importante destacar que, además de muchas nuevas características y mejoras en el rendimiento, contiene cambios disruptivos frente al ABI, como también a las APIs de Python y C. Es probable que los paquetes dependientes o downstream y código de usuario final necesiten ser adaptados - si puedes, por favor verifica que tu código funciona con NumPy 2.0.0rc2. Por favor, revisa lo siguiente para más detalles:

    Recaudación de fondos de fin de año de NumFOCUS#

    19 de diciembre de 2023 – NumFOCUS se ha asociado con PyCharm durante su campaña de fin de año para ofrecer un 30% de descuento en licencias de primera vez de PyCharm. Todos los ingresos del primer año de las compras de PyCharm desde ahora hasta el 23 de diciembre de 2023 se destinarán directamente a los programas de NumFOCUS.

    Utiliza una URL única que te permitirá rastrear las compras https://lp.jetbrains.com/support-data-science/ o un código de cupón ISUPPORTDATASCIENCE 

    NumPy 1.26.0 ha sido lanzado#

    16 de septiembre de 2023 – NumPy 1.26.0 ahora está disponible. Los aspectos más destacados del lanzamiento son:

    La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la transición al sistema de compilación Meson y que provee soporte para Cython 3.0.0. Un total de 20 personas contribuyeron a esta versión y 59 solicitudes de cambios fueron fusionadas.

    Las versiones de Python compatibles con esta versión son 3.9-3.12.

    numpy.org ya está disponible en japonés y portugués#

    _ 2 de agosto de 2023_ – numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios:

    Portugués:

    Japonés:

    El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI.

    De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations)

    NumPy 1.25.0 ha sido lanzado#

    17 de junio de 2023 – NumPy 1.25.0 ya está disponible. Los aspectos más destacados del lanzamiento son:

    NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias.

    Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas.

    Las versiones de Python soportadas por este lanzamiento son 3.9-3.11.

    Fomentar una Cultura Inclusiva: Convocatoria de Participación#

    10 de mayo de 2023 – Fomentar una Cultura Inclusiva: Convocatoria de Participación

    ¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte aquí.

    Transición en el liderazgo del equipo de documentación de NumPy#

    6 de enero de 2023 –- Mukulika Pahari y Ross Barnowski son nombrados como los nuevos líderes del equipo de documentación de NumPy, reemplazando a Melissa Mendonça. Damos las gracias a Melissa por todas sus contribuciones a la documentación oficial de NumPy y materiales educativos, y a Mukulika y Ross por asumir este rol.

    Lanzamiento de NumPy 1.24.0#

    18 de diciembre de 2022 – NumPy 1.24.0 ya está disponible. Los aspectos más destacados del lanzamiento son:

    El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11.

    NumPy 1.23.0 ha sido lanzado#

    22 de junio de 2022 – NumPy 1.23.0 ya está disponible. Los aspectos más destacados del lanzamiento son:

    El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc.

    Estudio de investigación NumFOCUS DEI: llamado a participar#

    13 de abril de 2022 – NumPy está trabajando con NumFOCUS en un proyecto de investigación financiado por la Fundación Gordon & Betty Moore para entender las barreras de participación que enfrentan los colaboradores, especialmente aquellos de grupos históricamente subrepresentados, en la comunidad de software de código abierto. El equipo de investigación quisiera hablar con nuevos colaboradores, desarrolladores y mantenedores del proyecto, y con aquellos que han contribuido en el pasado acerca de sus experiencias uniéndose y contribuyendo a NumPy.

    ¿Estás interesado en compartir tus experiencias?

    Por favor, completa este breve formulario de “Interés del Participante”, que contiene información adicional sobre los objetivos de la investigación, la privacidad y las consideraciones de confidencialidad. Tu participación será valiosa para el crecimiento y la sostenibilidad de comunidades de software de código abierto diversas e inclusivas. Los participantes aceptados participarán en una entrevista de 30 minutos con un miembro del equipo de investigación.

    Lanzamiento de NumPy 1.22.0#

    31 de diciembre de 2021 – NumPy 1.22.0 ya está disponible. Los aspectos más destacados del lanzamiento son:

    NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10.

    Promoviendo una cultura inclusiva en el ecosistema científico de Python#

    31 de agosto de 2021 – Nos complace anunciar que la Iniciativa Chan Zuckerberg ha otorgado una subvención para apoyar la incorporación, inclusión, y retención de personas de grupos históricamente marginados en proyectos científicos de Python y para mejorar estructuralmente la dinámica de la comunidad para NumPy, SciPy, Matplotlib y Pandas.

    Como parte del Programa de Software Esencial de Código Abierto para la Ciencia de CZI, esta subvención suplementaria de Diversidad &e Inclusión apoyará la creación de posiciones dedicadas de Líder de Experiencia del Colaborador para identificar, documentar e implementar prácticas para fomentar comunidades inclusivas de código abierto. Este proyecto será liderado por Melissa Mendonça (NumPy), con mentoría y orientación adicionales por parte de Ralf Gommers (NumPy, SciPy), Hannah Aizenman y Thomas Caswell (Matplotlib), Matt Haberland (SciPy), y Joris Van den Bossche (Pandas).

    Este es un proyecto ambicioso destinado a descubrir e implementar actividades que deberían mejorar estructuralmente la dinámica comunitaria de nuestros proyectos. Al establecer estos nuevos roles entre proyectos, esperamos introducir un nuevo modelo de colaboración para las comunidades de Python Científico, permitiendo que el trabajo de construcción de comunidades dentro del ecosistema se realice de manera más eficiente y con mejores resultados. También esperamos desarrollar una idea más clara tanto de lo que funciona y lo que no en nuestros proyectos, para atraer y retener nuevos colaboradores, especialmente de grupos históricamente subrepresentados. Finalmente, planeamos producir informes detallados sobre las acciones ejecutadas, explicando cómo éstas han impactado nuestros proyectos en términos de representación e interacción con nuestras comunidades.

    Se espera que este proyecto, de dos años de duración, comience en noviembre de 2021, y estamos emocionados por ver los resultados de este trabajo! Puedes leer la propuesta completa aquí.

    Encuesta de NumPy de 2021#

    12 de julio de 2021 – En NumPy creemos en el poder de nuestra comunidad. 1,236 usuarios de NumPy de 75 países participaron en nuestra encuesta inaugural el año pasado. Los resultados de la encuesta nos dieron una muy buena comprensión acerca de lo que debería ser nuestro enfoque durante los próximos 12 meses.

    Es hora de otra encuesta, y contamos contigo una vez más. Te tomará alrededor de 15 minutos de tu tiempo. Además de inglés, el cuestionario de la encuesta está disponible en 8 idiomas adicionales: Bangla, Francés, Hindi, Japonés, Mandarín, Portugués, Ruso y Español.

    Sigue el enlace para comenzar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q.

    Lanzamiento de NumPy 1.21.0#

    23 de junio de 2021 – NumPy 1.21.0 ya está disponible. Los aspectos más destacados de esta versión son:

    Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10.

    Resultados de la encuesta de NumPy de 2020#

    22 de junio de 2021 – En 2020, el equipo de encuestas de NumPy, en asociación con los estudiantes y profesores de la Universidad de Michigan y la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad NumPy. Encuentra los resultados de la encuesta aquí: https://numpy.org/user-survey-2020/.

    Lanzamiento de NumPy 1.20.0#

    30 de enero de 2021 – NumPy 1.20.0 ya está disponible. Este es el lanzamiento de NumPy más grande hasta la fecha, gracias a los más de 180 colaboradores. Las dos nuevas características más importantes son:

    Diversidad en el proyecto NumPy#

    20 de septiembre de 2020 – Escribimos una declaración sobre el estado de, y discusión en redes sociales, alrededor de la diversidad e inclusión en el proyecto NumPy.

    Primer artículo oficial de NumPy publicado en Nature!#

    16 de septiembre de 2020 – Nos complace anunciar la publicación del primer artículo oficial sobre NumPy como artículo de revisión en Nature. Esto llega 14 años después de la publicación de NumPy 1.0. El documento cubre aplicaciones y conceptos fundamentales de programación de arreglos, el rico ecosistema científico de Python construido sobre NumPy, y los recientemente añadidos protocolos de arreglos que facilitan la interoperabilidad con librerías de arreglos y tensores externas, tales como CuPy, Dask y JAX.

    Python 3.9 está por llegar, ¿cuándo lanzará NumPy ruedas binarias?#

    14 de septiembre de 2020 – Python 3.9 será lanzado dentro de unas pocas semanas. Si eres uno de los primeros en adoptar las más recientes versiones de Python, es posible que te sientas decepcionado al descubrir que NumPy (y otros paquetes binarios como SciPy) no tendrán ruedas binarias listas para el día del lanzamiento. Es un esfuerzo importante el adaptar la infraestructura de compilación a una versión nueva de Python y normalmente tarda unas cuantas semanas para que los paquetes aparezcan en PyPI y conda-forge. En preparación para este evento, por favor asegúrese de

    Lanzamiento de NumPy 1.19.2#

    10 de septiembre de 2020 – NumPy 1.19.2 ya está disponible. Este último lanzamiento de la serie 1.19 corrige varios errores, se prepara para el lanzamiento próximo de Cython 3.x y fija las versiones de setuptools para mantener distutils funcionando mientras las modificaciones hacia el repositorio principal continúan. Las wheels para aarch64 están construidas con la última versión de manylinux2014 que corrige el problema de diferentes tamaños de página utilizados por diferentes distribuciones de linux.

    La encuesta inaugural de NumPy ya está disponible!#

    2 de julio de 2020 – Esta encuesta está destinada a guiar y establecer prioridades para la toma de decisiones sobre el desarrollo de NumPy como software y como comunidad. La encuesta está disponible en 8 idiomas adicionales además del Inglés: Bangla, Hindi, Japonés, Mandarín, Portugués, Ruso, Español y Francés.

    Por favor ayúdanos a mejorar NumPy diligenciando la encuesta: aquí.

    24 de junio de 2020 – NumPy tiene ahora un nuevo logo:

    Logo de NumPy

    El logo es una versión moderna del anterior, con un diseño más limpio. Gracias a Isabela Presedo-Floyd por diseñar el nuevo logo, así como a Travis Vaught por el viejo logo que nos sirvió tanto durante más de 15 años.

    Lanzamiento de NumPy 1.19.0#

    20 de junio de 2020 – NumPy 1.19.0 ya está disponible. Esta es el primer lanzamiento sin soporte para Python 2, por lo que fue una “versión de limpieza”. La versión mínima soportada de Python es ahora Python 3.6. Una nueva característica importante es que la infraestructura de generación de números aleatorios que fue introducida en NumPy 1.17.0 es ahora accesible desde Cython.

    Aceptación a Season of Docs#

    11 de mayo de 2020 – NumPy ha sido aceptado como una de las organizaciones mentoras para el programa Google Season of Docs. ¡Estamos entusiasmados de tener la oportunidad de trabajar con un redactor técnico para mejorar la documentación de NumPy una vez más! Para más detalles, por favor consulte el sitio oficial de Season of Docs y nuestra página de ideas.

    Lanzamiento de NumPy 1.18.0#

    22 de diciembre de 2019 – NumPy 1.18.0 ya está disponible. Después de los grandes cambios en 1.17.0, este es un lanzamiento de consolidación. Es el último lanzamiento menor que soportará Python 3.5. Los aspectos más destacados de la publicación incluyen la adición de la infraestructura básica para enlazar con las librerías BLAS de 64 bits y LAPACK, y un nuevo C-API para numpy.random.

    Por favor revise las notas del lanzamiento para conocer más detalles.

    NumPy recibe una subvención de la Iniciativa Chan Zuckerberg#

    15 de noviembre de 2019 – Nos complace anunciar que NumPy y OpenBLAS, una de las dependencias clave de NumPy, han recibido una subvención conjunta por $195,000 de la Iniciativa Chan Zuckerberg a través de su programa Esencial de Software Abierto para la Ciencia que apoya el mantenimiento de software, crecimiento, desarrollo y compromiso comunitario para herramientas de código abierto críticas para la ciencia.

    Esta subvención se utilizará para acelerar los esfuerzos en la mejora de la documentación de NumPy, rediseño del sitio web y desarrollo de la comunidad para servir mejor a nuestra amplia y creciente base de usuarios, y asegurar la sostenibilidad a largo plazo del proyecto. Mientras que el equipo de OpenBLAS se enfocará en abordar conjuntos de problemas técnicos clave, en particular la seguridad de los hilos, AVX-512, y problemas de almacenamiento local de hilos (TLS), así como mejoras algorítmicas en ReLAPACK (Recursive LAPACK) de las que depende OpenBLAS.

    Puede encontrar más detalles sobre nuestras iniciativas y entregables propuestos en la propuesta completa de subvención. Está previsto que el trabajo comience el 1 de diciembre de 2019 y continúe durante los siguientes 12 meses.

    Lanzamientos#

    Esta es una lista de lanzamientos NumPy, con enlaces a notas de lanzamiento. Los lanzamientos de corrección de errores (solo cambia la z en el número de versión x.y.z) no tienen nuevas características; las versiones menores (aumenta la y) sí las tienen.

    On this page
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    Kit de prensa

    Nos gustaría facilitarte el trabajo para incluir la identidad del proyecto NumPy en tu próximo documento académico, material de curso o presentación.

    Aquí encontrarás varias versiones en alta resolución del logo de NumPy. Ten en cuenta que al utilizar los recursos de numpy.org, aceptas el Código de Conducta de NumPy.

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    Política de Privacidad

    numpy.org está operado por NumFOCUS, Inc., el patrocinador fiscal del proyecto NumPy. Para ver la Política de Privacidad de este sitio web, por favor dirígete a https://numfocus.org/privacy-policy.

    Si tienes alguna pregunta sobre la política o la recolección, uso y prácticas de divulgación de datos de NumFOCUS, por favor ponte en contacto con el personal de NumFOCUS en privacy@numfocus.org.

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    Código de Conducta de NumPy - Cómo hacer el seguimiento de un informe

    Este es el manual que sigue el Comité de Código de Conducta de NumPy. Se utiliza cuando respondemos a un problema, para asegurarnos de que seamos consistentes y justos.

    Hacer cumplir el Código de Conducta impacta a nuestra comunidad hoy y en el futuro. Es una acción que no tomamos a la ligera. Al revisar las medidas de cumplimiento, el Comité de Código de Conducta tendrá en cuenta los siguientes valores y directrices:

    Mediación#

    La mediación informal voluntaria es una herramienta a nuestra disposición. En contextos tales como cuando dos o más partes han escalado hasta el punto de un comportamiento inapropiado (algo tristemente común en el conflicto humano), puede ser útil facilitar un proceso de mediación. Éste es sólo un ejemplo: el Comité puede considerar la mediación en cualquier caso, siendo consciente de que este proceso se entiende como estrictamente voluntario y que ninguna de las partes puede ser presionada a participar. Si el Comité sugiere la mediación, este debería:

    El mediador dialogará con todas las partes y buscará una decisión que sea satisfactoria para todos. Una vez concluido el proceso, el mediador proporcionará un informe (revisado por todas las partes del proceso) al Comité, con recomendaciones sobre pasos a seguir. El comité a su vez evaluará estos resultados (bien se haya logrado una decisión satisfactoria o no) y decidirá sobre cualquier acción que considere necesaria.

    Cómo responderá el Comité a los informes#

    Cuando el Comité (o uno de sus miembros) recibe un informe, primero determinará si éste se refiere a una violación clara y grave (como se define a continuación). En caso afirmativo, será necesario tomar medidas inmediatas adicionales al proceso de gestión de informe habitual.

    Acciones violatorias claras y severas#

    Sabemos que es dolorosamente común que la comunicación en Internet comience o se convierta en un abuso evidente y manifiesto. Nos ocuparemos rápidamente de violaciones claras y graves, tales como amenazas personales, lenguaje violento, sexista o racista.

    Cuando un miembro del Comité de Código de Conducta tenga conocimiento de una violación clara y grave, hará lo siguiente:

    Gestión de informes#

    Cuando se envíe un informe al Comité, éste responderá inmediatamente al informante confirmando su recepción. Esta respuesta deberá enviarse dentro de un plazo de 72 horas, y el grupo deberá esforzarse por responder en un tiempo inferior.

    Si un informe no contiene suficiente información, el Comité obtendrá todos los datos relevantes antes de actuar. El Comité tiene la facultad de actuar en nombre del Consejo Directivo al contactar cualquier individuo implicado para obtener una descripción más completa de los hechos.

    El Comité procederá a revisar el incidente y determinará, en la medida de su capacidad:

    Esta información se recopilará por escrito, y siempre que sea posible se registrarán y conservarán las deliberaciones del grupo (por ejemplo, transcripciones de chat, discusiones por correo electrónico, conferencias telefónicas grabadas, resúmenes de conversaciones de voz, etc.).

    Es importante conservar un archivo de todas las actividades de este Comité para asegurar consistencia en el comportamiento y proporcionar memoria institucional para el proyecto. Para ayudar en esto, el canal de discusión por defecto para este Comité será una lista de correo privada accesible a los miembros actuales y futuros del Comité, así como a los miembros del Consejo Directivo con previa solicitud justificada. Si el Comité considera necesario utilizar comunicaciones fuera de la lista (por ejemplo, para una respuesta temprana/rápida), deberá, en todos los casos, resumirlos y documentarlos de vuelta a la lista, de manera que se mantenga un buen registro del proceso.

    El Comité de Código de Conducta debería aspirar a que se acuerde una resolución en el plazo de dos semanas. Dado el caso de que no se pueda establecer una decisión dentro de dicho plazo, el Comité responderá al informante(s) con una actualización y duración estimada para la decisión.

    Resoluciones#

    El comité debe llegar a un acuerdo sobre una resolución por consenso. Si el grupo no puede alcanzar un consenso y permanece en un punto muerto durante más de una semana, le trasladará el asunto al Consejo Directivo para que lo resuelva.

    Las posibles respuestas pueden incluir:

    Una vez que se acuerda una resolución, pero antes de que se promulgue, el Comité se pondrá en contacto con el informante original y con cualquier otra parte afectada y explicará la resolución propuesta. El Comité preguntará si esta resolución es aceptable y deberá tomar nota de los comentarios para su registro.

    Finalmente, el Comité presentará un informe al Consejo Directivo de NumPy (así como al equipo central de NumPy en caso de una decisión en curso, tal como una prohibición).

    El Comité nunca debatirá públicamente el asunto; todas las declaraciones públicas serán realizadas por el presidente del Comité de Código de Conducta o el Consejo Directivo de NumPy.

    Conflictos de intereses#

    En caso de cualquier conflicto de intereses, el miembro del Comité deberá notificarlo inmediatamente a los demás miembros y excusarse en caso de ser necesario.

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A medida que crece el aprendizaje automático, también lo hace la lista de librerías basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— entre ellas el reconocimiento de voz e imágenes, las aplicaciones basadas en texto, el análisis de series de tiempo y la detección de vídeo. [PyTorch](https://pytorch.org), otra librería de aprendizaje profundo, es popular entre los investigadores de visión artificial y procesamiento del lenguaje natural. + para2: Las técnicas estadísticas denominadas [métodos ensemble](https://scikit-learn.org/stable/modules/ensemble.html), como binning, bagging, stacking y boosting, se encuentran entre los algoritmos de ML implementados por herramientas como [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/) y [CatBoost](https://catboost.ai) — uno de los motores de inferencia más rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) y [Eli5](https://eli5.readthedocs.io/en/latest/) ofrecen visualizaciones de aprendizaje automático. + arraylibraries: + intro: + - text: La API de NumPy es el punto de partida cuando se escriben librerías para explotar hardware innovador, crear tipos de arreglos especializadas o añadir capacidades más allá de lo que NumPy proporciona. + headers: + - text: Librería de arreglos + - text: Capacidades y áreas de aplicación + libraries: + - title: Dask + text: Arreglos distribuidos y paralelismo avanzado para análisis, que permiten un rendimiento a escala. + img: /images/content_images/arlib/dask.png + alttext: Dask + url: https://dask.org/ + - title: CuPy + text: Librería de arreglos compatible con NumPy para cálculo acelerado en la GPU con Python. + img: /images/content_images/arlib/cupy.png + alttext: CuPy + url: https://cupy.dev + - title: JAX + text: "Transformaciones componibles de programas NumPy: diferenciar, vectorizar, compilación justo-a-tiempo a GPU/TPU." + img: /images/content_images/arlib/jax_logo_250px.png + alttext: JAX + url: https://docs.jax.dev + - title: Xarray + text: Arreglos multidimensionales indexados y etiquetados para análisis y visualización avanzados. + img: /images/content_images/arlib/xarray.png + alttext: xarray + url: https://xarray.pydata.org/en/stable/index.html + - title: Sparse + text: Librería de arreglos dispersos compatible con NumPy que se integra con el álgebra lineal dispersa de Dask y SciPy. + img: /images/content_images/arlib/sparse.png + alttext: sparse + url: https://sparse.pydata.org/en/latest/ + - title: PyTorch + text: Marco de aprendizaje profundo que acelera el camino desde la creación de prototipos de investigación hasta la implantación en producción. + img: /images/content_images/arlib/pytorch-logo-dark.svg + alttext: PyTorch + url: https://pytorch.org/ + - title: TensorFlow + text: Una plataforma integral de aprendizaje automático para crear y desplegar fácilmente aplicaciones basadas en ML. + img: /images/content_images/arlib/tensorflow-logo.svg + alttext: TensorFlow + url: https://www.tensorflow.org + - title: Arrow + text: Plataforma de desarrollo multilingüe para datos y análisis columnares en memoria. + img: /images/content_images/arlib/arrow.png + alttext: arrow + url: https://arrow.apache.org/ + - title: xtensor + text: Arreglos multidimensionales con difusión y computación perezosa para análisis numérico. + img: /images/content_images/arlib/xtensor.png + alttext: xtensor + url: https://github.com/xtensor-stack/xtensor-python + - title: Awkward Array + text: Manipular datos similares a JSON con expresiones similares a NumPy. + img: /images/content_images/arlib/awkward.svg + alttext: awkward + url: https://awkward-array.org/ + - title: uarray + text: Sistema de backend de Python que desacopla la API de la implementación; unumpy proporciona una API de NumPy. + img: /images/content_images/arlib/uarray.png + alttext: uarray + url: https://uarray.org/en/latest/ + - title: tensorly + text: Aprendizaje tensorial, álgebra y backends para usar de manera fluida NumPy, PyTorch, TensorFlow o CuPy. + img: /images/content_images/arlib/tensorly.png + alttext: tensorly + url: http://tensorly.org/stable/home.html + - title: Blosc2 + text: Cálculo acelerado con arrays comprimidos en memoria, en disco o remotos. + img: /images/content_images/arlib/blosc-logo.svg + alttext: blosc2 + url: https://www.blosc.org/python-blosc2/ + + scientificdomains: + intro: + - text: Casi todos los científicos que trabajan en Python recurren a la potencia de NumPy. + - text: "NumPy aporta la potencia de cálculo de lenguajes como C y Fortran a Python, un lenguaje mucho más fácil de aprender y utilizar. Con esta potencia viene la sencillez: una solución en NumPy suele ser clara y elegante." + libraries: + - title: Computación Cuántica + alttext: Un chip para computador. + img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - url: https://qutip.org + label: QuTiP + - url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - url: https://qiskit.org + label: Qiskit + - url: https://pennylane.ai + label: PennyLane + - title: Computación Estadística + alttext: Un gráfico lineal con la línea moviéndose hacia arriba. + img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - url: https://pandas.pydata.org/ + label: Pandas + - url: https://www.statsmodels.org/ + label: statsmodels + - url: https://xarray.pydata.org/en/stable/ + label: Xarray + - url: https://seaborn.pydata.org/ + label: Seaborn + - title: Procesamiento de Señales + alttext: Un gráfico de barras con valores positivos y negativos. + img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - url: https://scipy.org + label: SciPy + - url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - url: https://python-control.org/ + label: python-control + - url: https://hyperspy.org/ + label: HiperSpy + - title: Procesamiento de Imágenes + alttext: Una fotografía de las montañas. + img: /images/content_images/sc_dom_img/image_processing.svg + links: + - url: https://scikit-image.org/ + label: Scikit-image + - url: https://opencv.org/ + label: OpenCV + - url: https://mahotas.rtfd.io/ + label: Mahotas + - title: Grafos y Redes + alttext: Un grafo simple. + img: /images/content_images/sc_dom_img/sd6.svg + links: + - url: https://networkx.org/ + label: NetworkX + - url: https://graph-tool.skewed.de/ + label: graph-tool + - url: https://igraph.org/python/ + label: igraph + - url: https://pygsp.rtfd.io/ + label: PyGSP + - title: Astronomía + alttext: Un telescopio. + img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - url: https://www.astropy.org/ + label: AstroPy + - url: https://sunpy.org/ + label: SunPy + - url: https://spacepy.github.io/ + label: SpacePy + - title: Psicología Cognitiva + alttext: Una cabeza humana con engranajes. + img: /images/content_images/sc_dom_img/cognitive_psychology.svg + links: + - url: https://www.psychopy.org/ + label: PsychoPy + - title: Bioinformática + alttext: Una hebra de ADN. + img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - url: https://biopython.org/ + label: BioPython + - url: http://scikit-bio.org/ + label: Scikit-Bio + - url: https://github.com/openvax/pyensembl + label: PyEnsembl + - url: http://etetoolkit.org/ + label: ETE + - title: Inferencia Bayesiana + alttext: Un gráfico con una curva en forma de campanas. + img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - url: https://docs.pymc.io/ + label: PyMC + - url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - url: https://emcee.readthedocs.io/ + label: emcee + - title: Análisis Matemático + alttext: Cuatro símbolos matemáticos. + img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - url: https://scipy.org + label: SciPy + - url: https://www.sympy.org/ + label: SymPy + - url: https://www.cvxpy.org/ + label: cvxpy + - url: https://fenicsproject.org/ + label: FEniCS + - title: Química + alttext: Un tubo de ensayo. + img: /images/content_images/sc_dom_img/chemistry.svg + links: + - url: https://cantera.org/ + label: Cantera + - url: https://www.mdanalysis.org/ + label: MDAnalysis + - url: https://github.com/rdkit/rdkit + label: RDKit + - url: https://www.pybamm.org/ + label: PyBaMM + - title: Geociencia + alttext: La Tierra. + img: /images/content_images/sc_dom_img/geoscience.svg + links: + - url: https://pangeo.io/ + label: Pangeo + - url: https://simpeg.xyz/ + label: Simpeg + - url: https://github.com/obspy/obspy/wiki + label: ObsPy + - url: https://www.fatiando.org/ + label: Fatiando a Terra + - title: Procesamiento Geográfico + alttext: Un mapa. + img: /images/content_images/sc_dom_img/GIS.svg + links: + - url: https://shapely.readthedocs.io/ + label: Shapely + - url: https://geopandas.org/ + label: GeoPandas + - url: https://python-visualization.github.io/folium + label: Folium + - title: Arquitectura e Ingeniería + alttext: Una placa de desarrollo de microprocesadores. + img: /images/content_images/sc_dom_img/robotics.svg + links: + - url: https://compas.dev/ + label: COMPAS + - url: https://cityenergyanalyst.com/ + label: City Energy Analyst - Analista de Energía de Ciudad + - url: https://nortikin.github.io/sverchok/ + label: Sverchok + datascience: + intro: "NumPy es el núcleo de un rico ecosistema de librerías de ciencia de datos. Un flujo de trabajo exploratorio típico de ciencia de datos podría verse así:" + image1: + - img: /images/content_images/ds-landscape.png + alttext: Diagrama de las librerías de Python. Las cinco categorías son "Extraer, Transformar, Cargar", "Exploración de Datos", "Modelado de Datos", "Evaluación de Datos" y "Presentación de Datos". + image2: + - img: /images/content_images/data-science.png + alttext: Diagrama de tres círculos superpuestos. Los círculos se denominan "Matemáticas", "Ciencias de la Computación" y "Conocimientos Especializados". En el centro del diagrama, con los tres círculos superpuestos, hay un área denominada "Ciencia de datos". + examples: + - text: "Extraer, Transformar, Cargar: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - text: "Análisis Exploratorio: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" + - text: "Modelado y evaluación: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC](https://docs.pymc.io), [spaCy](https://spacy.io)" + - text: "Informes en un panel de control: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" + content: + - text: Para grandes volúmenes de datos, [Dask](https://dask.org) y [Ray](https://ray.io/) están diseñados para escalarse. Las implementaciones estables se basan en el versionado de datos ([DVC](https://dvc.org)), rastreo de experimentos ([MLFlow](https://mlflow.org)), y automatización del flujo de trabajo ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) y [Prefect](https://www.prefect.io)). + visualization: + images: + - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: Un diagrama de flujo hecho en matplotlib + - url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: Un diagrama de dispersión hecho en ggpy + - url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: Un diagrama de caja hecho en plotly + - url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: Un diagrama de flujo hecho en altair + - url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: Un gráfico de pares de dos tipos de gráficos, un gráfico de trazado y un gráfico de frecuencias hecho en seaborn + - url: https://pyvista.org + img: /images/content_images/v_pyvista.png + alttext: Un renderizado de volumen 3D realizado en PyVista. + - url: https://napari.org + img: /images/content_images/v_napari.png + alttext: Una imagen multidimensional hecha en napari. + - url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: Un diagrama de Voronoi hecho en vispy. + content: + - text: NumPy es un componente esencial en el floreciente [panorama de visualización de Python](https://pyviz.org/overviews/index.html), que incluye [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), y [PyVista](https://github.com/pyvista/pyvista), por nombrar algunos. + - text: El procesamiento acelerado de arreglos de gran tamaño de NumPy permite a los investigadores visualizar conjuntos de datos mucho mayores a los que el Python nativo podría manejar. diff --git a/es/teams/index.html b/es/teams/index.html new file mode 100644 index 00000000..9809ec57 --- /dev/null +++ b/es/teams/index.html @@ -0,0 +1,99 @@ +NumPy - Equipos de NumPy

    Equipos de NumPy

    Somos un equipo internacional en una misión para apoyar a las comunidades científicas y de investigaciones alrededor del mundo, mediante la construcción de software de código abierto de calidad. +¡Únete!

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    On this page
    \ No newline at end of file diff --git a/es/user-survey-2020/index.html b/es/user-survey-2020/index.html new file mode 100644 index 00000000..d4216cb4 --- /dev/null +++ b/es/user-survey-2020/index.html @@ -0,0 +1,12 @@ +NumPy - ENCUESTA DE LA COMUNIDAD NUMPY 2020

    ENCUESTA DE LA COMUNIDAD NUMPY 2020

    En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto.

    Página de portada del informe de la encuesta de usuarios de NumPy de 2020
    Encuesta de la Comunidad de NumPy 2020 - resultados#

    Descarga el informe para ver a detalle los resultados de la encuesta.

    Para los puntos destacados, echa un vistazo a esta infografía.

    ¿Listo para una inmersión profunda? Visita https://numpy.org/user-survey-2020-details/.

    On this page
    \ No newline at end of file diff --git a/es/user-surveys/index.html b/es/user-surveys/index.html new file mode 100644 index 00000000..eb23a882 --- /dev/null +++ b/es/user-surveys/index.html @@ -0,0 +1,14 @@ +NumPy - ENCUESTAS DE USUARIOS DE NUMPY

    ENCUESTAS DE USUARIOS DE NUMPY

    2020 El equipo de encuestas de NumPy, en asociación con estudiantes y profesores de la Universidad de Michigan y de la Universidad de Maryland, realizó la primera encuesta oficial de la comunidad de NumPy. Encuentra los resultados de la encuesta aquí.

    2021 Los datos recolectados están siendo analizados actualmente.

    Si tienes alguna pregunta o sugerencia sobre las encuestas pasadas o futuras, por favor abre una propuesta aquí.

    On this page
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    Get Help

    Development issues: For NumPy development-related matters (e.g., bug reports), please see Community.

    User questions: The best way to get help is to post your question to a site like StackOverflow or Reddit. We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little -overwhelming!

    StackOverflow#

    A forum for asking usage questions, e.g. “How do I do X in NumPy?”. Please use the #numpy tag

    Reddit#

    Another forum for usage questions.

    On this page
    \ No newline at end of file +overwhelming!

    StackOverflow#

    A forum for asking usage questions, e.g. “How do I do X in NumPy?”. Please use the #numpy tag

    Reddit#

    Another forum for usage questions.

    On this page
    \ No newline at end of file diff --git a/hero.min.9812993c6f7a6d604f195dc04360091765329a177e902c01e0498cb374d59943.css b/hero.min.9812993c6f7a6d604f195dc04360091765329a177e902c01e0498cb374d59943.css deleted file mode 100644 index 3f7b82b5..00000000 --- a/hero.min.9812993c6f7a6d604f195dc04360091765329a177e902c01e0498cb374d59943.css +++ /dev/null @@ -1 +0,0 @@ -.hero{align-items:stretch;display:flex;flex-direction:column;justify-content:space-between}.hero ul{border-bottom:none}.hero-container{width:100vw}.hero-content{display:flex;flex-direction:column}.hero-title-content{align-items:center;display:flex;flex-direction:row;justify-content:center;width:100%;line-height:1.5}.hero-title{align-items:center;display:flex;flex-direction:row;font-family:var(--fontFamily),sans-serif;font-weight:900;font-size:6em;color:var(--pst-color-text-base);justify-content:center;margin-left:1em;margin-bottom:.2em;margin-right:.5em;text-align:right}.hero-logo{align-self:start;margin:.1em;margin-left:.25em;max-height:.8em}.hero-subtitle{color:var(--pst-color-text-base);font-size:1em;max-width:550px}.hero-cta{padding:15px 0;display:flex;flex-wrap:wrap;align-self:center}.cta-button{margin-right:1em;min-width:15rem;padding-left:1.5rem;padding-right:1.5rem;width:auto;height:3rem;text-transform:uppercase;letter-spacing:1.5px;font-weight:500;color:var(--colorSecondaryLight);background-color:var(--colorPrimaryDark);border:none;border-radius:25px;outline:none;cursor:pointer}.cta-button:hover{color:var(--colorPrimaryLight)}@media only screen and (max-width:1090px){.hero-content{flex-direction:column;padding:0;align-items:center}.hero-title-content{flex-direction:column;align-items:center}}@media only screen and (max-width:600px){.flex-column{margin:0 30px}.hero-title{font-size:4em}}@media only screen and (max-width:400px){.hero-title{flex-direction:column}} \ No newline at end of file diff --git a/history/index.html b/history/index.html index 340a95e3..5523a7b6 100644 --- a/history/index.html +++ b/history/index.html @@ -1,13 +1,12 @@ -NumPy - History of NumPy -

    History of NumPy

    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.

    If you’d like to obtain a copy of the original Numeric and Numarray libraries, follow the links below:

    Download Page for Numeric*

    Download Page for Numarray*

    *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

    On this page
    \ No newline at end of file diff --git a/images/content_images/arlib/blosc-logo.svg b/images/content_images/arlib/blosc-logo.svg new file mode 100644 index 00000000..71773534 --- /dev/null +++ b/images/content_images/arlib/blosc-logo.svg @@ -0,0 +1,79 @@ + + + + + + + + image/svg+xml + + + + + + + + + + + + + + + + diff --git a/images/content_images/numpy-comic.png b/images/content_images/numpy-comic.png new file mode 100644 index 00000000..b2cc4175 Binary files /dev/null and b/images/content_images/numpy-comic.png differ diff --git a/images/content_images/sc_dom_img/sd6.svg b/images/content_images/sc_dom_img/sd6.svg index 3a975bd1..cee7b828 100644 --- a/images/content_images/sc_dom_img/sd6.svg +++ b/images/content_images/sc_dom_img/sd6.svg @@ -1,21 +1 @@ - - - - - - - - - - - - - - - - - - - - - + \ No newline at end of file diff --git a/images/logos/quansight.svg b/images/logos/quansight.svg index 88c5bf3b..746358ce 100644 --- a/images/logos/quansight.svg +++ b/images/logos/quansight.svg @@ -1 +1 @@ -QS logo strip vector \ No newline at end of file + \ No newline at end of file diff --git a/index.html b/index.html index 7036aac2..9ca77d42 100644 --- a/index.html +++ b/index.html @@ -1,14 +1,15 @@ -NumPy -
    NumPy +
    The fundamental package for scientific computing with Python
    NumPy 2.4.0 released!
    2025-12-20
    Powerful N-dimensional arrays
    Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.
    Numerical computing tools
    NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
    Open source
    Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.
    Interoperable
    NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
    Performant
    The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
    Easy to use
    NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.
    Try NumPy

    Use the interactive shell to try NumPy in the browser

    """
 To try the examples in the browser:
 1. Type code in the input cell and press
    Shift + Enter to execute
@@ -35,7 +36,7 @@
 # Generate normally distributed random numbers:
 rng = np.random.default_rng()
 samples = rng.normal(size=2500)
-samples

    ECOSYSTEM

    ECOSYSTEM

    Nearly every scientist working in Python draws on the power of NumPy.

    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.

  • 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.

    Array LibraryCapabilities & Application areas
    DaskDaskDistributed arrays and advanced parallelism for analytics, enabling performance at scale.
    CuPyCuPyNumPy-compatible array library for GPU-accelerated computing with Python.
    JAXJAXComposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU.
    xarrayXarrayLabeled, indexed multi-dimensional arrays for advanced analytics and visualization.
    sparseSparseNumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.
    PyTorchPyTorchDeep learning framework that accelerates the path from research prototyping to production deployment.
    TensorFlowTensorFlowAn end-to-end platform for machine learning to easily build and deploy ML powered applications.
    MXNetMXNetDeep learning framework suited for flexible research prototyping and production.
    arrowArrowA cross-language development platform for columnar in-memory data and analytics.
    xtensorxtensorMulti-dimensional arrays with broadcasting and lazy computing for numerical analysis.
    awkwardAwkward ArrayManipulate JSON-like data with NumPy-like idioms.
    uarrayuarrayPython backend system that decouples API from implementation; unumpy provides a NumPy API.
    tensorlytensorlyTensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy.
  • Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'.

    NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

    For high data volumes, Dask and Ray are designed to scale. Stable deployments rely on data versioning (DVC), experiment tracking (MLFlow), and workflow automation (Airflow, Dagster and Prefect).

    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'.
  • An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

    Source: Google AI Blog

    NumPy forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning grows, so does the list of libraries built on NumPy. TensorFlow’s deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. PyTorch, another deep learning library, is popular among researchers in computer vision and natural language processing. MXNet is another AI package, providing blueprints and templates for deep learning.

    Statistical techniques called ensemble methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as XGBoost, LightGBM, and CatBoost — one of the fastest inference engines. Yellowbrick and Eli5 offer machine learning visualizations.

  • A streamplot made in matplotlib
    A scatter-plot graph made in ggpy
    A box-plot made in plotly
    A streamgraph made in altair
    A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn"
    A 3D volume rendering made in PyVista.
    A multi-dimensionan image made in napari.
    A Voronoi diagram made in vispy.

    NumPy is an essential component in the burgeoning Python visualization landscape, which includes Matplotlib, Seaborn, Plotly, Altair, Bokeh, Holoviz, Vispy, Napari, and PyVista, to name a few.

    NumPy’s accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.

    • CASE STUDIES

    First Image of a Black Hole
    First image of a black hole. It is an orange circle in a black background.
    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
    Detection of Gravitational Waves
    Two orbs orbiting each other. They are displacing gravity around them.
    In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy.
    Sports Analytics
    Cricket ball on green field.
    Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses.
    Pose Estimation using deep learning
    Cheetah pose analysis
    DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales.
    \ No newline at end of file +Visualization

    Nearly every scientist working in Python draws on the power of NumPy.

    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.

    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.

    Array LibraryCapabilities & Application areas
    DaskDaskDistributed arrays and advanced parallelism for analytics, enabling performance at scale.
    CuPyCuPyNumPy-compatible array library for GPU-accelerated computing with Python.
    JAXJAXComposable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU.
    xarrayXarrayLabeled, indexed multi-dimensional arrays for advanced analytics and visualization.
    sparseSparseNumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.
    PyTorchPyTorchDeep learning framework that accelerates the path from research prototyping to production deployment.
    TensorFlowTensorFlowAn end-to-end platform for machine learning to easily build and deploy ML powered applications.
    arrowArrowA cross-language development platform for columnar in-memory data and analytics.
    xtensorxtensorMulti-dimensional arrays with broadcasting and lazy computing for numerical analysis.
    awkwardAwkward ArrayManipulate JSON-like data with NumPy-like idioms.
    uarrayuarrayPython backend system that decouples API from implementation; unumpy provides a NumPy API.
    tensorlytensorlyTensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy.
    blosc2Blosc2Accelerated computation for in-memory, on-disk, or remote compressed arrays.
    Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'.

    NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:

    For high data volumes, Dask and Ray are designed to scale. Stable deployments rely on data versioning (DVC), experiment tracking (MLFlow), and workflow automation (Airflow, Dagster and Prefect).

    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'.
    An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

    Source: Google AI Blog

    NumPy forms the basis of powerful machine learning libraries like scikit-learn and SciPy. As machine learning grows, so does the list of libraries built on NumPy. TensorFlow’s deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. PyTorch, another deep learning library, is popular among researchers in computer vision and natural language processing.

    Statistical techniques called ensemble methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as XGBoost, LightGBM, and CatBoost — one of the fastest inference engines. Yellowbrick and Eli5 offer machine learning visualizations.

    A streamplot made in matplotlib
    A scatter-plot graph made in ggpy
    A box-plot made in plotly
    A streamgraph made in altair
    A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn"
    A 3D volume rendering made in PyVista.
    A multi-dimensionan image made in napari.
    A Voronoi diagram made in vispy.

    NumPy is an essential component in the burgeoning Python visualization landscape, which includes Matplotlib, Seaborn, Plotly, Altair, Bokeh, Holoviz, Vispy, Napari, and PyVista, to name a few.

    NumPy’s accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle.

    CASE STUDIES

    First Image of a Black Hole
    First image of a black hole. It is an orange circle in a black background.
    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
    Detection of Gravitational Waves
    Two orbs orbiting each other. They are displacing gravity around them.
    In 1916, Albert Einstein predicted gravitational waves; 100 years later their existence was confirmed by LIGO scientists using NumPy.
    Sports Analytics
    Cricket ball on green field.
    Cricket Analytics is changing the game by improving player and team performance through statistical modelling and predictive analytics. NumPy enables many of these analyses.
    Pose Estimation using deep learning
    Cheetah pose analysis
    DeepLabCut uses NumPy for accelerating scientific studies that involve observing animal behavior for better understanding of motor control, across species and timescales.
    \ No newline at end of file diff --git a/index.xml b/index.xml index e4076690..4a9fed06 100644 --- a/index.xml +++ b/index.xml @@ -1,37 +1,1714 @@ -NumPyhttps://numpy.org/Recent content on NumPyHugo -- gohugo.ioenSat, 16 Sep 2023 00:00:00 +0000Newshttps://numpy.org/news/Sat, 16 Sep 2023 00:00:00 +0000https://numpy.org/news/NumFOCUS end of the year fundraiser# Dec 19, 2023 &ndash; NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs. -Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE NumPy 1.26.0 released# Sep 16, 2023 &ndash; NumPy 1.2020 NUMPY COMMUNITY SURVEYhttps://numpy.org/user-survey-2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/user-survey-2020/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. -Download the report to take a closer look at the survey findings.404https://numpy.org/404/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/404/Oops! You&rsquo;ve reached a dead end. -If you think something should be here, you can open an issue on GitHub.About Ushttps://numpy.org/about/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/about/NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the modified BSD license. -NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community.Array Computinghttps://numpy.org/arraycomputing/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/arraycomputing/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. -Numerical Python 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.Case Study: Cricket Analytics, the game changer!https://numpy.org/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/cricket-analytics/IPLT20, the biggest Cricket Festival in India# -(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium)) 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.Case Study: DeepLabCut 3D Pose Estimationhttps://numpy.org/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/deeplabcut-dnn/Analyzing mice hand-movement using DeepLapCut# -(Source: www.deeplabcut.org ) 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 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.Case Study: Discovery of Gravitational Waveshttps://numpy.org/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/gw-discov/Gravitational Waves# -(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO) The scientific Python ecosystem is critical infrastructure for the research done at LIGO. —David Shoemaker, LIGO Scientific Collaboration -About Gravitational Waves and LIGO# 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.Case Study: First Image of a Black Holehttps://numpy.org/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/blackhole-image/Black Hole M87# -(Image Credits: Event Horizon Telescope Collaboration) Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. —Katie Bouman, Assistant Professor, Computing &amp; Mathematical Sciences, Caltech -A telescope the size of the earth# The Event Horizon telescope (EHT) 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.Citing NumPyhttps://numpy.org/citing-numpy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/citing-numpy/If NumPy has been significant in your research, and you would like to acknowledge the project in your academic publication, we suggest citing the following paper: -Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357–362 (2020). DOI: 10.1038/s41586-020-2649-2. (Publisher link). In BibTeX format: -@Article{ harris2020array, title = {Array programming with {NumPy}}, author = {Charles R. Harris and K. Jarrod Millman and St{\&#39;{e}}fan J.Communityhttps://numpy.org/community/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/community/NumPy is a community-driven open source project developed by a diverse group of contributors. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the NumPy Code of Conduct for guidance on how to interact with others in a way that makes the community thrive. -We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community.Contribute to NumPyhttps://numpy.org/contribute/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/contribute/The NumPy project welcomes your expertise and enthusiasm! Your choices aren&rsquo;t limited to programming, as you can see below there are many areas where we need your help. -If you&rsquo;re unsure where to start or how your skills fit in, reach out! You can ask on the mailing list or GitHub (open an issue 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 numpy-team@googlegroups.Get Helphttps://numpy.org/gethelp/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/gethelp/Development issues: For NumPy development-related matters (e.g., bug reports), please see Community. -User questions: The best way to get help is to post your question to a site like StackOverflow or Reddit. We wish we could keep an eye on these sites, or answer questions directly, but the volume is a little overwhelming! -StackOverflow# A forum for asking usage questions, e.g. &ldquo;How do I do X in NumPy?”. Please use the #numpy tagHistory of NumPyhttps://numpy.org/history/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/history/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.Installing NumPyhttps://numpy.org/install/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/install/The only prerequisite for installing NumPy is Python itself. If you don&rsquo;t have Python yet and want the simplest way to get started, we recommend you use the Anaconda 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. For more detailed instructions, consult our Python and NumPy installation guide below.Learnhttps://numpy.org/learn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/learn/For the official NumPy documentation visit numpy.org/doc/stable. -Below is a curated collection of educational resources, both for self-learning and teaching others, developed by NumPy contributors and vetted by the community. -Beginners# There&rsquo;s a ton of information about NumPy out there. If you are just starting, we&rsquo;d strongly recommend the following: -Tutorials -NumPy Quickstart Tutorial NumPy Tutorials A collection of tutorials and educational materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team.NumPy Code of Conducthttps://numpy.org/code-of-conduct/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/code-of-conduct/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.NumPy Code of Conduct - How to follow up on a reporthttps://numpy.org/report-handling-manual/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/report-handling-manual/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 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.NumPy Diversity and Inclusion Statementhttps://numpy.org/diversity_sep2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/diversity_sep2020/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.NumPy Teamshttps://numpy.org/teams/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/teams/We are an international team on a mission to support scientific and research communities worldwide by building quality, open-source software. Join us! -Maintainers# Andrew Nelson Bas van Beek Charles Harris Eric Wieser Ganesh Kathiresan Rohit Goswami Matthew Brett Matti Picus Matt Haberland Melissa Weber Mendonça Marten van Kerkwijk Christopher Sidebottom Mateusz Sokół Mukulika Nathan Goldbaum Pearu Peterson Josh Wilson Pauli Virtanen Chunlin Raghuveer Devulapalli Ralf Gommers Robert Kern Ross Barnowski Sebastian Berg Sayed Adel Stephan Hoyer Stefan van der Walt Tyler Reddy Warren Weckesser Docs team# Rohit Goswami Inessa Pawson Mars Lee Matti Picus Melissa Weber Mendonça Mukulika Ross Barnowski Web team# Inessa Pawson Jarrod Millman Joe LaChance Mars Lee Ralf Gommers shalz Shekhar Prasad Rajak Stefan van der Walt Albert Steppi Triage team# Andrew Nelson Anirudh Subramanian Aaron Meurer Atsushi Sakai Ben Nathanson Anne Bonner Brigitta Sipőcz carlkl Ryan C Cooper ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) Hameer Abbasi Inessa Pawson jbrockmendel Kai Yuji Kanagawa Kriti Singh Christopher Albert Lysandros Nikolaou Meekail Zain Christopher Sidebottom Mateusz Sokół Mukulika Noa Tamir Raghuveer Devulapalli shalz Tina Oberoi Rakesh Vasudevan Zijie (ZJ) Poh Survey team# Inessa Pawson Ralf Gommers Ross Barnowski Emeritus maintainers# Allan Haldane Ondřej Čertík David Cournapeau Jaime Jarrod Millman Julian Taylor Mark Wiebe Nathaniel J.NUMPY USER SURVEYShttps://numpy.org/user-surveys/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/user-surveys/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. -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.Press kithttps://numpy.org/press-kit/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/press-kit/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. Note that by using the numpy.org resources, you accept the NumPy Code of Conduct.Privacy Policyhttps://numpy.org/privacy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/privacy/numpy.org is operated by NumFOCUS, Inc., 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.Terms of Usehttps://numpy.org/terms/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/terms/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 (&quot;Project&quot;, “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. \ No newline at end of file +NumPyhttps://numpy.org/Recent content on NumPyHugoenSat, 20 Dec 2025 00:00:00 +0000Newshttps://numpy.org/news/Sat, 20 Dec 2025 00:00:00 +0000https://numpy.org/news/<h3 id="numpy-fellowship-program-2025-retrospective">NumPy Fellowship Program 2025 Retrospective<a class="headerlink" href="#numpy-fellowship-program-2025-retrospective" title="Link to this heading">#</a></h3> +<p><em>8 Jan, 2026</em> &ndash; Joren Hammudoglu (<a href="https://github.com/jorenham">@jorenham</a>) has published a +retrospective on his year as a NumPy Fellow. Read the full post on the Scientific Python blog:</p> +<p><a href="https://blog.scientific-python.org/numpy/fellowship-program-2025-retrospective/">A Year of Typing: My NumPy Fellowship Retrospective</a>.</p> +<h3 id="numpy-240-released">NumPy 2.4.0 released<a class="headerlink" href="#numpy-240-released" title="Link to this heading">#</a></h3> +<p><em>20 Dec, 2025</em> &ndash; The NumPy 2.4.0 release continues the work to improve free +threaded Python support, user dtypes implementation, and annotations. There are +many expired deprecations and bug fixes as well. Highlights are:</p>2020 NUMPY COMMUNITY SURVEYhttps://numpy.org/user-survey-2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/user-survey-2020/<p>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.</p> +<figure class="align-default" id="id000"> +<img src="https://numpy.org/surveys/NumPy_usersurvey_2020_report_cover.png" alt="Cover page of the 2020 NumPy user survey report, titled &#39;NumPy Community Survey 2020 - results&#39;" class="align-center" width="250"> + + + +<figcaption> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<p><strong><a href="https://numpy.org/surveys/NumPy_usersurvey_2020_report.pdf">Download the report</a></strong> +to take a closer look at the survey findings.</p>404https://numpy.org/404/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/404/<p>Oops! You&rsquo;ve reached a dead end.</p> +<p>If you think something should be here, you can <a href="https://github.com/numpy/numpy.org/issues">open an issue</a> on GitHub.</p>About Ushttps://numpy.org/about/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/about/<p>NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the <a href="https://github.com/numpy/numpy/blob/main/LICENSE.txt">modified BSD license</a>.</p> +<p>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 <a href="https://www.numpy.org/devdocs/dev/governance/index.html">Governance Document</a>.</p>Array Computinghttps://numpy.org/arraycomputing/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/arraycomputing/<p><em>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.</em></p> +<p>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 <strong>Python.</strong></p> +<p><strong>Num</strong>erical <strong>Py</strong>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.</p>Case Study: Cricket Analytics, the game changer!https://numpy.org/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/cricket-analytics/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/ipl-stadium.png" alt="Indian Premier League Cricket cup and stadium" class="align-center"> + + + +<figcaption><strong class="caption-title">IPLT20, the biggest Cricket Festival in India</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://unsplash.com/@aksh1802">(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium))</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/"> + <p> + You don't play for the crowd, you play for the country. +</p> + <p class="attribution">—M S Dhoni, <em>International Cricket Player, ex-captain, Indian Team, plays for Chennai Super Kings in IPL</em></p> +</blockquote> + +<h2 id="about-cricket">About Cricket<a class="headerlink" href="#about-cricket" title="Link to this heading">#</a></h2> +<p>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 +<a href="https://www.statista.com/topics/4543/indian-premier-league-ipl/">money</a> 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.</p>Case Study: DeepLabCut 3D Pose Estimationhttps://numpy.org/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/deeplabcut-dnn/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/mice-hand.gif" alt="micehandanim" class="align-center"> + + + +<figcaption><strong class="caption-title">Analyzing mice hand-movement using DeepLapCut</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="http://www.mousemotorlab.org/deeplabcut">(Source: www.deeplabcut.org )</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"> + <p> +Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +</p> + <p class="attribution">—Alexander Mathis, <em>Assistant Professor, École polytechnique fédérale de Lausanne</em> (<a href="https://www.epfl.ch/en/">EPFL</a>)</p> +</blockquote> + +<h2 id="about-deeplabcut">About DeepLabCut<a class="headerlink" href="#about-deeplabcut" title="Link to this heading">#</a></h2> +<p><a href="https://github.com/DeepLabCut/DeepLabCut">DeepLabCut</a> 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.</p>Case Study: Discovery of Gravitational Waveshttps://numpy.org/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/gw-discov/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/gw_sxs_image.png" alt="binary coalesce black hole generating gravitational waves" class="align-center"> + + + +<figcaption><strong class="caption-title">Gravitational Waves</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://youtu.be/Zt8Z_uzG71o">(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +</p> + <p class="attribution">—David Shoemaker, <em>LIGO Scientific Collaboration</em></p> +</blockquote> + +<h2 id="about-gravitational-waves-and-ligo">About <a href="https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/">Gravitational Waves</a> and <a href="https://www.ligo.caltech.edu">LIGO</a><a class="headerlink" href="#about-gravitational-waves-and-ligo" title="Link to this heading">#</a></h2> +<p>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.</p>Case Study: First Image of a Black Holehttps://numpy.org/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/case-studies/blackhole-image/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/blackhole.jpg" alt="black hole image" class="align-center"> + + + +<figcaption><strong class="caption-title">Black Hole M87</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg">(Image Credits: Event Horizon Telescope Collaboration)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +</p> + <p class="attribution">—Katie Bouman, <em>Assistant Professor, Computing &amp; Mathematical Sciences, Caltech</em></p> +</blockquote> + +<h2 id="a-telescope-the-size-of-the-earth">A telescope the size of the earth<a class="headerlink" href="#a-telescope-the-size-of-the-earth" title="Link to this heading">#</a></h2> +<p>The <a href="https://eventhorizontelescope.org">Event Horizon telescope (EHT)</a> is an +array of eight ground-based radio telescopes forming a computational telescope +the size of the earth, studying 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 +<a href="https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole">20 micro-arcseconds</a> — enough to read a newspaper in New York +from a sidewalk café in Paris!</p>Citing NumPyhttps://numpy.org/citing-numpy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/citing-numpy/<p>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:</p> +<ul> +<li>Harris, C.R., Millman, K.J., van der Walt, S.J. et al. <em>Array programming with NumPy</em>. Nature 585, 357–362 (2020). DOI: <a href="https://doi.org/10.1038/s41586-020-2649-2">10.1038/s41586-020-2649-2</a>. (<a href="https://www.nature.com/articles/s41586-020-2649-2">Publisher link</a>).</li> +</ul> +<p><em>In BibTeX format:</em></p> + +<div class="highlight"> + <pre>@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\&#39;{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{\&#39;{a}}ndez del + R{\&#39;{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\&#39;{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} +}</pre> +</div>Communityhttps://numpy.org/community/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/community/<p>NumPy is a community-driven open source project developed by a diverse group of <a href="https://numpy.org/teams/">contributors</a>. The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the <a href="https://numpy.org/code-of-conduct/">NumPy Code of Conduct</a> for guidance on how to interact with others in a way that makes the community thrive.</p> +<p>We offer several communication channels to learn, share your knowledge and connect with others within the NumPy community.</p> +<h2 id="participate-online">Participate online<a class="headerlink" href="#participate-online" title="Link to this heading">#</a></h2> +<p>The following are ways to engage directly with the NumPy project and community. +<em>Please note that we encourage users and community members to support each other +for usage questions - see <a href="https://numpy.org/gethelp/">Get Help</a>.</em></p>Contribute to NumPyhttps://numpy.org/contribute/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/contribute/<p>The NumPy project welcomes your expertise and enthusiasm! +Your choices aren&rsquo;t limited to programming, as you can +see below there are many areas where we need <strong>your</strong> help.</p> +<p>If you&rsquo;re unsure where to start or how your skills fit in, <em>reach out!</em> You +can ask on the <a href="https://mail.python.org/mailman/listinfo/numpy-discussion">mailing +list</a> or +<a href="http://github.com/numpy/numpy">GitHub</a> (open an +<a href="https://github.com/numpy/numpy/issues">issue</a> or comment on a relevant +issue).</p> +<p>Those are our preferred channels (open source is open by nature), but +if you prefer to talk privately, contact our community coordinators at +<a href="mailto:numpy-team@googlegroups.com">numpy-team@googlegroups.com</a> or on <a href="https://numpy-team.slack.com">Slack</a> +(write <a href="mailto:numpy-team@googlegroups.com">numpy-team@googlegroups.com</a> for an invite).</p>Get Helphttps://numpy.org/gethelp/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/gethelp/<p><strong>Development issues:</strong> For NumPy development-related matters (e.g., bug reports), please +see <a href="https://numpy.org/community/">Community</a>.</p> +<p><strong>User questions:</strong> The best way to get help is to post your question to a site +like <a href="http://stackoverflow.com/questions/tagged/numpy">StackOverflow</a> or +<a href="https://www.reddit.com/r/Numpy/">Reddit</a>. We wish we could keep an eye on +these sites, or answer questions directly, but the volume is a little +overwhelming!</p> +<h3 id="stackoverflow"><a href="http://stackoverflow.com/questions/tagged/numpy">StackOverflow</a><a class="headerlink" href="#stackoverflow" title="Link to this heading">#</a></h3> +<p>A forum for asking usage questions, e.g. &ldquo;How do I do X in NumPy?”. Please <a href="https://stackoverflow.com/help/tagging">use the <code>#numpy</code> tag</a></p>History of NumPyhttps://numpy.org/history/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/history/<p>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.</p>Installing NumPyhttps://numpy.org/install/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/install/<div class="admonition tip"> + <div class="admonition-title"> Tip</div> + <p> + This page assumes you are comfortable using a terminal and are familiar with package managers. +The only prerequisite for installing NumPy is Python itself. If you don&rsquo;t have +Python yet and want the simplest way to get started, we recommend you use the +<a href="https://www.anaconda.com/download">Anaconda Distribution</a> - it includes +Python, NumPy, and many other commonly used packages for scientific computing +and data science. + </p> +</div> + +<p>The recommended method of installing NumPy depends on your preferred workflow. Below, we break down the installation methods into the following categories:</p>Learnhttps://numpy.org/learn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/learn/<p>For the <strong>official NumPy documentation</strong> visit <a href="https://numpy.org/doc/stable">numpy.org/doc/stable</a>.</p> +<hr> +<p>Below is a curated collection of educational resources, both for self-learning and +teaching others, developed by NumPy contributors and vetted by the community.</p> +<h2 id="beginners">Beginners<a class="headerlink" href="#beginners" title="Link to this heading">#</a></h2> +<p>There&rsquo;s a ton of information about NumPy out there. If you are just starting, we&rsquo;d +strongly recommend the following:</p> +<p><i class="fas fa-chalkboard"></i> <strong>Tutorials</strong></p> +<ul> +<li><a href="https://numpy.org/devdocs/user/quickstart.html">NumPy Quickstart Tutorial</a></li> +<li><a href="https://numpy.org/numpy-tutorials">NumPy Tutorials</a> A collection of tutorials and +educational materials in the format of Jupyter Notebooks developed and maintained by +the NumPy Documentation team. To submit your own content, visit the +<a href="https://github.com/numpy/numpy-tutorials">numpy-tutorials repository on GitHub</a>.</li> +<li><a href="https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b">NumPy Illustrated: The Visual Guide to NumPy</a> +<em>by Lev Maximov</em></li> +<li><a href="https://lectures.scientific-python.org/">Scientific Python Lectures</a> Besides covering +NumPy, these lectures offer a broader introduction to the scientific Python ecosystem.</li> +<li><a href="https://numpy.org/devdocs/user/absolute_beginners.html">NumPy: the absolute basics for beginners</a></li> +<li><a href="https://github.com/rougier/numpy-tutorial">NumPy tutorial</a> <em>by Nicolas Rougier</em></li> +<li><a href="http://cs231n.github.io/python-numpy-tutorial/">Stanford CS231</a> <em>by Justin Johnson</em></li> +<li><a href="https://numpy.org/devdocs">NumPy User Guide</a></li> +</ul> +<p><i class="fas fa-book"></i> <strong>Books</strong></p>NumPy Code of Conducthttps://numpy.org/code-of-conduct/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/code-of-conduct/<h3 id="introduction">Introduction<a class="headerlink" href="#introduction" title="Link to this heading">#</a></h3> +<p>This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, X, 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.</p> +<p>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.</p>NumPy Code of Conduct - How to follow up on a reporthttps://numpy.org/report-handling-manual/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/report-handling-manual/<p>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.</p> +<p>Enforcing the <a href="https://numpy.org/code-of-conduct/">Code of Conduct</a> 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:</p> +<ul> +<li>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.</li> +<li>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.</li> +<li>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.</li> +<li>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.</li> +<li>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.</li> +<li>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.</li> +<li>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.</li> +</ul> +<h2 id="mediation">Mediation<a class="headerlink" href="#mediation" title="Link to this heading">#</a></h2> +<p>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:</p>NumPy Diversity and Inclusion Statementhttps://numpy.org/diversity_sep2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/diversity_sep2020/<p><em>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:</em></p> +<p>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.</p>NumPy Teamshttps://numpy.org/teams/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/teams/<p>We are an international team on a mission to support scientific and research +communities worldwide by building quality, open-source software. +<a href="https://numpy.org/contribute/">Join us</a>!</p> +<h3 id="maintainers">Maintainers<a class="headerlink" href="#maintainers" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&amp;v=4%22" alt="Avatar of Andrew Nelson"> + + + +Andrew Nelson + </div> + <a class="sd-stretched-link" href="https://github.com/andyfaff"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43369155?u=1f1fcabf979a2f00f403c60b816ba9f573026181&amp;v=4%22" alt="Avatar of Bas van Beek"> + + + +Bas van Beek + </div> + <a class="sd-stretched-link" href="https://github.com/BvB93"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/77272?v=4%22" alt="Avatar of Charles Harris"> + + + +Charles Harris + </div> + <a class="sd-stretched-link" href="https://github.com/charris"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/425260?v=4%22" alt="Avatar of Eric Wieser"> + + + +Eric Wieser + </div> + <a class="sd-stretched-link" href="https://github.com/eric-wieser"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/20969920?u=ec0e4d9dd70227549776ba8209f0e55a35d1fe84&amp;v=4%22" alt="Avatar of Ganesh Kathiresan"> + + + +Ganesh Kathiresan + </div> + <a class="sd-stretched-link" href="https://github.com/ganesh-k13"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&amp;v=4%22" alt="Avatar of Rohit Goswami"> + + + +Rohit Goswami + </div> + <a class="sd-stretched-link" href="https://github.com/HaoZeke"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/67612?v=4%22" alt="Avatar of Matthew Brett"> + + + +Matthew Brett + </div> + <a class="sd-stretched-link" href="https://github.com/matthew-brett"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&amp;v=4%22" alt="Avatar of Matti Picus"> + + + +Matti Picus + </div> + <a class="sd-stretched-link" href="https://github.com/mattip"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6570539?u=cfb3e218754e85c4fac18064d7cfdce0b67ddaa6&amp;v=4%22" alt="Avatar of Matt Haberland"> + + + +Matt Haberland + </div> + <a class="sd-stretched-link" href="https://github.com/mdhaber"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&amp;v=4%22" alt="Avatar of Melissa Weber Mendonça"> + + + +Melissa Weber Mendonça + </div> + <a class="sd-stretched-link" href="https://github.com/melissawm"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/2789820?v=4%22" alt="Avatar of Marten van Kerkwijk"> + + + +Marten van Kerkwijk + </div> + <a class="sd-stretched-link" href="https://github.com/mhvk"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&amp;v=4%22" alt="Avatar of Christopher Sidebottom"> + + + +Christopher Sidebottom + </div> + <a class="sd-stretched-link" href="https://github.com/Mousius"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&amp;v=4%22" alt="Avatar of Mateusz Sokół"> + + + +Mateusz Sokół + </div> + <a class="sd-stretched-link" href="https://github.com/mtsokol"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4%22" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3126246?u=a3c7cd970c0e4cbc4498febe0de777a263c522c5&amp;v=4%22" alt="Avatar of Nathan Goldbaum"> + + + +Nathan Goldbaum + </div> + <a class="sd-stretched-link" href="https://github.com/ngoldbaum"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/402156?u=288a1f206a151f9e2b69f3c0ce11848d3381943e&amp;v=4%22" alt="Avatar of Pearu Peterson"> + + + +Pearu Peterson + </div> + <a class="sd-stretched-link" href="https://github.com/pearu"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/15134881?v=4%22" alt="Avatar of Josh Wilson"> + + + +Josh Wilson + </div> + <a class="sd-stretched-link" href="https://github.com/person142"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/35046?v=4%22" alt="Avatar of Pauli Virtanen"> + + + +Pauli Virtanen + </div> + <a class="sd-stretched-link" href="https://github.com/pv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/15245051?u=54810990f0fdb11ecaade02762c09d5549d72a11&amp;v=4%22" alt="Avatar of Chunlin"> + + + +Chunlin + </div> + <a class="sd-stretched-link" href="https://github.com/Qiyu8"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&amp;v=4%22" alt="Avatar of Raghuveer Devulapalli"> + + + +Raghuveer Devulapalli + </div> + <a class="sd-stretched-link" href="https://github.com/r-devulap"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4%22" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46135?u=305a96a4778daecacbc8ec97ac25a48099a239cc&amp;v=4%22" alt="Avatar of Robert Kern"> + + + +Robert Kern + </div> + <a class="sd-stretched-link" href="https://github.com/rkern"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4%22" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/61977?v=4%22" alt="Avatar of Sebastian Berg"> + + + +Sebastian Berg + </div> + <a class="sd-stretched-link" href="https://github.com/seberg"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/12713707?u=5a3f6a8de4801d7878750cbd0bb2e0427bf0af0b&amp;v=4%22" alt="Avatar of Sayed Adel"> + + + +Sayed Adel + </div> + <a class="sd-stretched-link" href="https://github.com/seiko2plus"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1217238?u=b61e7e0085405ce6d7d53f8f39a1360ef9723f72&amp;v=4%22" alt="Avatar of Stephan Hoyer"> + + + +Stephan Hoyer + </div> + <a class="sd-stretched-link" href="https://github.com/shoyer"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&amp;v=4%22" alt="Avatar of Stefan van der Walt"> + + + +Stefan van der Walt + </div> + <a class="sd-stretched-link" href="https://github.com/stefanv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/7903078?u=2762d9ff13b992dc635f8f190a17f9a90cddfae1&amp;v=4%22" alt="Avatar of Tyler Reddy"> + + + +Tyler Reddy + </div> + <a class="sd-stretched-link" href="https://github.com/tylerjereddy"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/321463?v=4%22" alt="Avatar of Warren Weckesser"> + + + +Warren Weckesser + </div> + <a class="sd-stretched-link" href="https://github.com/WarrenWeckesser"></a> + </div> +</div> + + </div> +</div> + +<h3 id="docs-team">Docs team<a class="headerlink" href="#docs-team" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&amp;v=4%22" alt="Avatar of Rohit Goswami"> + + + +Rohit Goswami + </div> + <a class="sd-stretched-link" href="https://github.com/HaoZeke"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4%22" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&amp;v=4%22" alt="Avatar of Mars Lee"> + + + +Mars Lee + </div> + <a class="sd-stretched-link" href="https://github.com/MarsBarLee"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&amp;v=4%22" alt="Avatar of Matti Picus"> + + + +Matti Picus + </div> + <a class="sd-stretched-link" href="https://github.com/mattip"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&amp;v=4%22" alt="Avatar of Melissa Weber Mendonça"> + + + +Melissa Weber Mendonça + </div> + <a class="sd-stretched-link" href="https://github.com/melissawm"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4%22" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4%22" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + </div> +</div> + +<h3 id="web-team">Web team<a class="headerlink" href="#web-team" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/123428?v=4" alt="Avatar of Jarrod Millman"> + + + +Jarrod Millman + </div> + <a class="sd-stretched-link" href="https://github.com/jarrodmillman"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3891660?u=5de0ba1f1adad6f041f6dde1affef5d05bbed80a&amp;v=4" alt="Avatar of Joe LaChance"> + + + +Joe LaChance + </div> + <a class="sd-stretched-link" href="https://github.com/joelachance"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&amp;v=4" alt="Avatar of Mars Lee"> + + + +Mars Lee + </div> + <a class="sd-stretched-link" href="https://github.com/MarsBarLee"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&amp;v=4" alt="Avatar of shalz"> + + + +shalz + </div> + <a class="sd-stretched-link" href="https://github.com/shaloo"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&amp;v=4" alt="Avatar of Shekhar Prasad Rajak"> + + + +Shekhar Prasad Rajak + </div> + <a class="sd-stretched-link" href="https://github.com/Shekharrajak"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&amp;v=4" alt="Avatar of Stefan van der Walt"> + + + +Stefan van der Walt + </div> + <a class="sd-stretched-link" href="https://github.com/stefanv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&amp;v=4" alt="Avatar of Albert Steppi"> + + + +Albert Steppi + </div> + <a class="sd-stretched-link" href="https://github.com/steppi"></a> + </div> +</div> + + </div> +</div> + +<h3 id="triage-team">Triage team<a class="headerlink" href="#triage-team" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&amp;v=4%22" alt="Avatar of Andrew Nelson"> + + + +Andrew Nelson + </div> + <a class="sd-stretched-link" href="https://github.com/andyfaff"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1522319?v=4%22" alt="Avatar of Anirudh Subramanian"> + + + +Anirudh Subramanian + </div> + <a class="sd-stretched-link" href="https://github.com/anirudh2290"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/71486?u=cc88e2a4e4c6c496dcb9dd88cead5c0dab496c89&amp;v=4%22" alt="Avatar of Aaron Meurer"> + + + +Aaron Meurer + </div> + <a class="sd-stretched-link" href="https://github.com/asmeurer"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3813847?v=4%22" alt="Avatar of Atsushi Sakai"> + + + +Atsushi Sakai + </div> + <a class="sd-stretched-link" href="https://github.com/AtsushiSakai"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6691888?v=4%22" alt="Avatar of Ben Nathanson"> + + + +Ben Nathanson + </div> + <a class="sd-stretched-link" href="https://github.com/bjnath"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/35413198?u=e67bd9ebc361fb207f914979d935fd1956eb626c&amp;v=4%22" alt="Avatar of Anne Bonner"> + + + +Anne Bonner + </div> + <a class="sd-stretched-link" href="https://github.com/bonn0062"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6788290?u=d9a388224b87d55106cb3e6199d02ebc1d8e0553&amp;v=4%22" alt="Avatar of Brigitta Sipőcz"> + + + +Brigitta Sipőcz + </div> + <a class="sd-stretched-link" href="https://github.com/bsipocz"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5476002?u=5352f057ef8cb5de29e4d2a9fa8b0d0f49580dc8&amp;v=4%22" alt="Avatar of carlkl"> + + + +carlkl + </div> + <a 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class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&amp;v=4%22" alt="Avatar of Mark Wiebe"> + + + +Mark Wiebe + </div> + <a class="sd-stretched-link" href="https://github.com/mwiebe"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&amp;v=4%22" alt="Avatar of Nathaniel J. Smith"> + + + +Nathaniel J. Smith + </div> + <a class="sd-stretched-link" href="https://github.com/njsmith"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/254880?v=4%22" alt="Avatar of Travis E. Oliphant"> + + + +Travis E. Oliphant + </div> + <a class="sd-stretched-link" href="https://github.com/teoliphant"></a> + </div> +</div> + + </div> +</div> + +<h1 id="governance">Governance<a class="headerlink" href="#governance" title="Link to this heading">#</a></h1> +<p>For the list of the Steering Council members, please see <a href="https://numpy.org/about/">here</a>.</p>NUMPY USER SURVEYShttps://numpy.org/user-surveys/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/user-surveys/<p><strong>2020</strong> +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 <a href="https://numpy.org/user-survey-2020/">here</a>.</p> +<p><strong>2021</strong> The collected data is currently being analyzed.</p> +<p>If you have any questions or suggestions for the past or future surveys, please open an issue <a href="https://github.com/numpy/numpy-surveys/issues">here</a>.</p>Press kithttps://numpy.org/press-kit/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/press-kit/<p>We would like to make it easy for you to include the NumPy project identity in your next academic paper, course materials, or presentation.</p> +<p>You will find several high-resolution versions of the NumPy logo <a href="https://github.com/numpy/numpy/tree/main/branding/logo">here</a>. Note that by using the numpy.org resources, you accept the <a href="https://numpy.org/code-of-conduct/">NumPy Code of Conduct</a>.</p>Privacy Policyhttps://numpy.org/privacy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/privacy/<p><strong>numpy.org</strong> is operated by <a href="https://numfocus.org">NumFOCUS, Inc.</a>, the fiscal sponsor of the NumPy project. For the Privacy Policy of this website please refer to <a href="https://numfocus.org/privacy-policy">https://numfocus.org/privacy-policy</a>.</p> +<p>If you have any questions about the policy or NumFOCUS’s data collection, use, and disclosure practices, please contact the NumFOCUS staff at <a href="mailto:privacy@numfocus.org">privacy@numfocus.org</a>.</p>Terms of Usehttps://numpy.org/terms/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/terms/<p><em>Last updated January 4, 2020</em></p> +<h2 id="agreement-to-terms">AGREEMENT TO TERMS<a class="headerlink" href="#agreement-to-terms" title="Link to this heading">#</a></h2> +<p>These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy (&quot;<strong>Project</strong>&quot;, “<strong>we</strong>”, “<strong>us</strong>”, or “<strong>our</strong>”), 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.</p> \ No newline at end of file diff --git a/install/index.html b/install/index.html index 5dc22171..79b9d6a5 100644 --- a/install/index.html +++ b/install/index.html @@ -1,104 +1,39 @@ -NumPy - Installing NumPy -

    Installing NumPy

    Tip

    This page assumes you are comfortable using a terminal and are familiar with package managers. +The only prerequisite for installing NumPy is Python itself. If you don’t have Python yet and want the simplest way to get started, we recommend you use the -Anaconda Distribution - it includes +Anaconda 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. -For more detailed instructions, consult our Python and NumPy -installation guide below.

    CONDA

    If you use conda, you can install NumPy from the defaults or conda-forge -channels:

    # Best practice, use an environment rather than install in the base env
-conda create -n my-env
+and data science.

    The recommended method of installing NumPy depends on your preferred workflow. Below, we break down the installation methods into the following categories:

    • Project-based (e.g., uv, pixi) (recommended for new users)
    • Environment-based (e.g., pip, conda) (the traditional workflow)
    • System package managers (not recommended for most users)
    • Building from source (for advanced users and development purposes)

    Choose the method that best suits your needs. If you’re unsure, start with the Environment-based method using conda or pip.

    Below are the different methods for installing NumPy. Click on the tabs to explore each method:

    + + +

    Recommended for new users who want a streamlined workflow.

    • uv: A modern Python package manager designed for speed and simplicity.

      uv pip install numpy

    • pixi: A cross-platform package manager for Python and other languages.

      pixi add numpy

    The two main tools that install Python packages are pip and conda. Their functionality partially overlaps (e.g. both can install numpy), however, they can also work together. We’ll discuss the major differences between pip and conda here - this is important to understand if you want to manage packages effectively.

    The first difference is that conda is cross-language and it can install Python, while pip is installed for a particular Python on your system and installs other packages to that same Python install only. This also means conda can install non-Python libraries and tools you may need (e.g. compilers, CUDA, HDF5), while pip can’t.

    The second difference is that pip installs from the Python Packaging Index (PyPI), while conda installs from its own channels (typically “defaults” or “conda-forge”). PyPI is the largest collection of packages by far, however, all popular packages are available for conda as well.

    The third difference is that conda is an integrated solution for managing packages, dependencies and environments, while with pip you may need another tool (there are many!) for dealing with environments or complex dependencies.

    • Conda: If you use conda, you can install NumPy from the defaults or conda-forge channels:
      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:

      pip install numpy

      Also when using pip, it’s good practice to use a virtual environment - -see Reproducible Installs below for why, and -this guide -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 (it installs all -packages you need and all other tools mentioned below).
      • For writing and executing code, use notebooks in -JupyterLab for -exploratory and interactive computing, and -Spyder or Visual Studio Code -for writing scripts and packages.
      • Use Anaconda Navigator to -manage your packages and start JupyterLab, Spyder, or Visual Studio Code.

      Advanced users#

      Conda#

      • Install Miniforge.
      • Keep the base conda environment minimal, and use one or more -conda environments -to install the package you need for the task or project you’re working on.

      Alternative if you prefer pip/PyPI#

      For users who know, from personal preference or reading about the main -differences between conda and pip below, they prefer a pip/PyPI-based solution, -we recommend:

      • Install Python from python.org, -Homebrew, or your Linux package manager.
      • Use Poetry 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 -complementary with pip. For high-performance computing (HPC), -Spack is worth considering. For most NumPy -users though, conda and -pip 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:

      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 or -OpenBLAS. 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 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 +conda install numpy

  • Pip:
    pip install numpy
    • Tip

    Tip: Use a virtual environment for better dependency management

    python -m venv my-env
+source my-env/bin/activate  # macOS/Linux
+my-env\Scripts\activate     # Windows
+pip install numpy

    Verifying the Installation#

    After installing NumPy, verify the installation by running the following in a Python shell or script:

    import numpy as np
+print(np.__version__)

    This should print the installed version of NumPy without errors.

    Troubleshooting#

    If your installation fails with the message below, see Troubleshooting ImportError.

    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.
    On this page
    \ No newline at end of file +different reasons, often due to issues with your setup.
    On this page
    \ No newline at end of file diff --git a/ja/404/index.html b/ja/404/index.html index 1bbae992..067350ac 100644 --- a/ja/404/index.html +++ b/ja/404/index.html @@ -1,14 +1,13 @@ -NumPy - 404 -

    404

    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 を作成してください。

    On this page
    \ No newline at end of file diff --git a/ja/about/index.html b/ja/about/index.html index 63c8282f..b9e314f1 100644 --- a/ja/about/index.html +++ b/ja/about/index.html @@ -1,22 +1,21 @@ -NumPy - 私たちについて -NumPy - 私たちについて

    私たちについて

    NumPy は、Python で数値計算を可能にするためのオープンソースプロジェクトです。 NumPyは、NumericやNumarrayといった初期のライブラリのコードをもとに、2005年から開発が開始されました。 NumPyは完全にオープンソースなソフトウェアです。 そして、NumPyは修正BSD ライセンス 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 をご覧ください。

    運営委員会#

    Numpy運営委員会はこのプロジェクトの管理組織です。 その役割は、Numpy コミュニティと協力し、Numpyのソフトウェアサービスを確実にユーザに提供することです。 ソフトウェアパッケージとコミュニティの両方において、プロジェクトの長期的な持続可能性を保っていきます。 NumPy運営委員会は現在以下のメンバーで構成されています (姓のアルファベット順):

    過去のメンバー

    Numpy運営委員会に連絡するには、numpy-team@googlegroups.comまでメールしてください。

    チーム#

    Numpy プロジェクトのコアメンバーは、プロジェクトへの貢献の方法の多様化に積極的に取り組んでいます。
    Numpyには現在以下のチームがあります:

    個々のチームメンバーについては、 チーム のページを参照してください。

    NumFOCUSサブ委員会#

    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。

    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。

    Logo of UC Berkeley Logo of Quansight -Logo of NVIDIA

    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 をご覧ください。

    NumPy への寄付は NumFOCUS 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 に記載されています。

    On this page
    \ No newline at end of file +Logo of NVIDIA

    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 をご覧ください。

    NumPy への寄付は NumFOCUS 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 に記載されています。

    On this page
    \ No newline at end of file diff --git a/ja/arraycomputing/index.html b/ja/arraycomputing/index.html index 3e3426d8..e4e32e1b 100644 --- a/ja/arraycomputing/index.html +++ b/ja/arraycomputing/index.html @@ -1,18 +1,17 @@ -NumPy - 配列演算 -NumPy - 配列演算

    配列演算

    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。

    Numerical Python: 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。 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 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。

    On this page
    \ No newline at end of file diff --git a/ja/case-studies/blackhole-image/index.html b/ja/case-studies/blackhole-image/index.html index c8ab3bdb..304044b4 100644 --- a/ja/case-studies/blackhole-image/index.html +++ b/ja/case-studies/blackhole-image/index.html @@ -1,5 +1,4 @@ -NumPy - ケーススタディ：世界初のブラックホール画像 -NumPy - ケーススタディ：世界初のブラックホール画像

    ケーススタディ：世界初のブラックホール画像

    black hole image
    Black Hole M87#
    (Image Credits: Event Horizon Telescope Collaboration)

    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

    地球大の望遠鏡#

    Event Horizon telescope(EHT)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#

    課題#

    data pipeline
    EHTのデータ処理パイプライン#
    (Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)

    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。

    role of numpy
    ブラックホール画像化でNumPyが果たした役割#

    例えば、 eht-imaging 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。

    ehtim dependency map highlighting numpy
    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やPandas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。

    numpy benefits
    利用されたNumPyの主要機能#

    On this page
    \ No newline at end of file diff --git a/ja/case-studies/cricket-analytics/index.html b/ja/case-studies/cricket-analytics/index.html index 5b70fb50..5083fd2f 100644 --- a/ja/case-studies/cricket-analytics/index.html +++ b/ja/case-studies/cricket-analytics/index.html @@ -1,5 +1,4 @@ -NumPy - ケーススタディ: クリケット分析、ゲームチェンジャー! -NumPy - ケーススタディ: クリケット分析、ゲームチェンジャー!

    ケーススタディ: クリケット分析、ゲームチェンジャー!

    Indian Premier League Cricket cup and stadium
    IPLT20、インド最大のクリケットフェスティバル#
    (Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))

    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

    クリケットについて#

    インド人はクリケットが大好きだと言っても過言ではないでしょう。 この競技は、他のスポーツと異なり、インドの農村部や都市部を問わず、あらゆる場所でプレイされており、若者から年配の方まで広く人気があり、インドでは何十億人もの人々を結びつける役割を担っています。 クリケットは多くのメディアの注目を集めています。 クリケットは多くのメディアの注目を集め、非常に多額のお金と名声がかかっています。 過去数年間、テクノロジーは文字通りクリケットの試合を変えてきました。 視聴者はストリーミングメディア、トーナメント、モバイルベースの手頃なアクセスによるライブクリケット視聴などを享受しています。

    インドプレミアリーグ (IPL) は、2008年に設立された20チームから成るプロクリケットリーグです。 これは世界で最も参加者が多いクリケットイベントの1つで、2019年の市場規模は67億ドルだと評価されています。

    クリケットは数のゲームです。 バッツマンによってスコアされたランの数、ボウラーによって取られたウィケットの数、クリケットチームによって獲得した試合の数、バッツマンがボウリング攻撃に特定の方法で応答する回数。 クリケットの数字を掘り下げてパフォーマンスを向上させるとともに、NumPyなどの数値計算ソフトウェアを利用した強力な分析ツールを介して、クリケットのビジネスチャンス、市場全体、経済性を研究することは、大きな意味を持ちます。 クリケット分析は、試合に関する興味深い洞察と、ゲームの結果に関する予測AIを提供します。

    現在では、クリケットゲームの記録と 利用可能な統計データは豊富で、ほぼ無限の宝の山だと言えます。 : ESPN cricinfo や cricsheet. 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 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。

    A cricket pitch with bowler and batsmen
    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#
    (Image credit: Debarghya Das)

    データ分析の主要な目標#

    pose estimator
    クリケットの姿勢推定#
    (Image credit: connect.vin)

    課題#

    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。 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。

    まとめ#

    スポーツアナリティクスは、プロの試合についてはまさにゲームチェンジャーです。 特に戦略的な意思決定については、最近まで主に「直感」や過去の伝統的な考え方に基づいて行われていたため、大きな影響があります。 NumPyは、データ分析・機械学習・人工知能のアルゴリズムに関連する高レベル関数を提供する沢山のPythonパッケージ群の、堅固な基盤となっています。 これらのパッケージは、ゲームの結果を変えるような意思決定を支援するリアルタイムのインサイトを得るため、クリケットの試合だけでなく関連する推論やビジネスの推進にも広く使用されています。 クリケットの試合結果につながる隠れたパラメータや、パターン、属性を見つけることは、ステークホルダーが数字や統計に隠されているゲームの洞察方法を見つけるのにも役に立つのです。

    クリケット分析にNumPyを使用するメリットを示す図
    利用されている主なNumPy機能 #

    On this page
    \ No newline at end of file diff --git a/ja/case-studies/deeplabcut-dnn/index.html b/ja/case-studies/deeplabcut-dnn/index.html index dd0f85dd..6123b77c 100644 --- a/ja/case-studies/deeplabcut-dnn/index.html +++ b/ja/case-studies/deeplabcut-dnn/index.html @@ -1,5 +1,4 @@ -NumPy - ケーススタディ: DeepLabCut 三次元姿勢推定 -NumPy - ケーススタディ: DeepLabCut 三次元姿勢推定

    ケーススタディ: DeepLabCut 三次元姿勢推定

    micehandanim
    DeepLapCutを用いたマウスの手の動きの解析#
    (Source: www.deeplabcut.org )

    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は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。

    神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。

    horserideranim
    色のついた点は競走馬の体の位置を追跡#
    (Source: Mackenzie Mathis)

    DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。

    DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から チーターのようなより珍しい動物まで、動物の種類を問わず利用できます。

    DeepLabCutでは転移学習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#

    dlcsteps
    DeepLabCutによる姿勢推定のステップ#
    (Source: DeepLabCut)

    課題#

    challengesfig
    姿勢推定の多様性と難しさ#
    (Source: Mackenzie Mathis)

    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、Pandas、matplotlib、Tensorpack, imgaug、scikit-learn、scikit-image、Tensorflowなどです。

    以下に挙げるNumPyの特徴が、DeepLabCutの姿勢推定アルゴリズムでの画像処理・組み合わせ処理・高速計算において、重要な役割を果たしました。

    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。

    workflow
    DeepLabCutのワークフロー#
    (Source: Mackenzie Mathis)

    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 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。

    numpy benefits
    NumPyの主要機能#

    On this page
    \ No newline at end of file diff --git a/ja/case-studies/gw-discov/index.html b/ja/case-studies/gw-discov/index.html index f6eb2b94..12b626da 100644 --- a/ja/case-studies/gw-discov/index.html +++ b/ja/case-studies/gw-discov/index.html @@ -1,8 +1,7 @@ -NumPy - ケーススタディ: 重力波の発見 -NumPy - ケーススタディ: 重力波の発見

    ケーススタディ: 重力波の発見

    binary coalesce black hole generating gravitational waves
    重力波#
    (Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)

    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

    重力波 と LIGO について#

    重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。

    レーザー干渉計重力波天文台(LIGO)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#

    課題#

    gravitational waves strain amplitude
    GW150914から推定される重力波の歪みの振幅#
    (Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)

    重力波の検出におけるNumPyの役割#

    ブラックホール合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。

    Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波検出プロジェクトで実行される様々なタスクに使用されるソフトウェアで利用されています。 NumPyは、複雑な数学処理や高速なデータ操作に役立ちました。 次にいくつかの例を示します。

    gwpy-numpy depgraph
    GwPyのNumPy依存グラフ#

    PyCBC-numpy depgraph
    PyCBCのNumPy依存グラフ#

    まとめ#

    一方で、これまで知られてきた深遠な天体物理学の現象に、多くに新たな洞察を提供しました。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な質問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを可能にしています。

    numpy benefits
    利用されたNumPyの主要機能#

    On this page
    \ No newline at end of file diff --git a/ja/case-studies/index.html b/ja/case-studies/index.html index c50af12d..7360abc5 100644 --- a/ja/case-studies/index.html +++ b/ja/case-studies/index.html @@ -1,13 +1,12 @@ -NumPy - Case-Studies -

    Case-Studies

    \ No newline at end of file diff --git a/ja/case-studies/index.xml b/ja/case-studies/index.xml index c38fc749..a1513b0d 100644 --- a/ja/case-studies/index.xml +++ b/ja/case-studies/index.xml @@ -1,55 +1,208 @@ -Case-Studies on NumPyhttps://numpy.org/ja/case-studies/Recent content in Case-Studies on NumPyHugo -- gohugo.iojaケーススタディ: DeepLabCut 三次元姿勢推定https://numpy.org/ja/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/deeplabcut-dnn/DeepLapCutを用いたマウスの手の動きの解析# -(Source: www.deeplabcut.org ) 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は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。 -神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 -色のついた点は競走馬の体の位置を追跡# -(Source: Mackenzie Mathis) DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 -DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から チーターのようなより珍しい動物まで、動物の種類を問わず利用できます。 -DeepLabCutでは転移学習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が発表されました。 これは、霊長類の顔分析から犬の姿勢まで、様々な種や実験条件に対応した事前訓練済みモデルを提供しています。 これにより、例えば、新しいデータのラベルを付けることなくクラウドで予測を実行することができたり、ニューラルネットワークの学習を実行することができます。 プログラミング経験は必要ありません。 -主な目標と結果# 科学研究のための動物姿勢解析の自動化: -DeepLabCutという技術の主な目的は、多様な環境で動物の姿勢を測定し追跡することです。 このデータは例えば神経科学の研究において、脳がどのように運動を制御しているかを理解するためのや、動物がどのように社会的に交流しているかを明らかにするために利用することができます。 研究者はDeepLabCutで 10倍のパフォーマンス向上 が可能であると発表しています。 オフラインでは最大1200フレーム/秒(FPS) で姿勢を推定することができます。 -姿勢推定のための使いやすいPythonツールキットの作成: -DeepLabCutは、動物の姿勢推定技術を研究者が簡単に利用できるツールとして共有したいという考えから開発されています。 そこで開発者らはプロジェクト管理機能を備えた、単独で機能し、使いやすいPythonツールボックスとしてこのツールを作成しました。 これにより、姿勢推定を自動化するだけでなく、DeepLabCutツールキットユーザーをデータセット収集段階から共有可能・再利用可能な分析パイプラインを作成する段階まで補助し、プロジェクトをエンドツーエンドで管理することも可能になりました。 -このツールキット はオープンソースとして利用できます。 -典型的なDeepLabCutワークフローは以下のようになります。 -オンライン学習によるトレーニングセットの作成と調整 特定の動物やシナリオに合わせたニューラルネットワークの構築 動画における大規模推論のためのコード作成 統合された可視化ツールを使用した推論の描画 DeepLabCutによる姿勢推定のステップ#ケーススタディ: クリケット分析、ゲームチェンジャー!https://numpy.org/ja/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/cricket-analytics/IPLT20、インド最大のクリケットフェスティバル# -(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium)) 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 -クリケットについて# インド人はクリケットが大好きだと言っても過言ではないでしょう。 この競技は、他のスポーツと異なり、インドの農村部や都市部を問わず、あらゆる場所でプレイされており、若者から年配の方まで広く人気があり、インドでは何十億人もの人々を結びつける役割を担っています。 クリケットは多くのメディアの注目を集めています。 クリケットは多くのメディアの注目を集め、非常に多額のお金と名声がかかっています。 過去数年間、テクノロジーは文字通りクリケットの試合を変えてきました。 視聴者はストリーミングメディア、トーナメント、モバイルベースの手頃なアクセスによるライブクリケット視聴などを享受しています。 -インドプレミアリーグ (IPL) は、2008年に設立された20チームから成るプロクリケットリーグです。 これは世界で最も参加者が多いクリケットイベントの1つで、2019年の市場規模は67億ドルだと評価されています。 -クリケットは数のゲームです。 バッツマンによってスコアされたランの数、ボウラーによって取られたウィケットの数、クリケットチームによって獲得した試合の数、バッツマンがボウリング攻撃に特定の方法で応答する回数。 クリケットの数字を掘り下げてパフォーマンスを向上させるとともに、NumPyなどの数値計算ソフトウェアを利用した強力な分析ツールを介して、クリケットのビジネスチャンス、市場全体、経済性を研究することは、大きな意味を持ちます。 クリケット分析は、試合に関する興味深い洞察と、ゲームの結果に関する予測AIを提供します。 -現在では、クリケットゲームの記録と 利用可能な統計データは豊富で、ほぼ無限の宝の山だと言えます。 : ESPN cricinfo や cricsheet. 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 に使用されています。 メディアやプロスポーツ団体のエンターテインメントプラットフォームは、技術や分析を利用し、試合勝率を向上させるために、下記のような要素が主要なメトリックだと考え始めています。 -バッティング成績の移動平均 スコア予測 プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察 チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 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# -(Image credit: Debarghya Das) 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。 クリケットの姿勢推定# -(Image credit: connect.vin) 課題# データのクリーニングと前処理 -IPLは、クリケットを古典的なテストマッチ形式から、はるかに大規模に拡大させました。 毎シーズン、様々なフォーマットで行われる試合の数は増加しており、データ、アルゴリズム、最新のスポーツデータ分析技術、シミュレーションモデルも増加しています。 クリケットのデータ分析には、フィールドマッピング、プレイヤートラッキング、ボールトラッキング、プレイヤーショット分析、およびボールがどのように動くのか、その角度、スピン、速度、軌道など、他の沢山の種類のデータを必要とします。 これらの要因により、データクリーニングと前処理の複雑さが増してしまいました。 -動的モデリング -クリケットでは、他のスポーツと同様、フィールド上の選手の様々な数字を追跡するために、関連する変数の数が多くなってしまいがちです。 たとえば、ボールやその属性情報、およびいくつかの行動をとるアクションのいくつかの可能性などの変数です。 データ分析とモデリングの複雑さは、分析中に必要となる予測のための質問の種類に正比例しており、データ表現とモデルにも大きく依存しています。 バッツマンが異なる角度や速度でボールを打った場合に何が起こるのかのような、動的なクリケットのプレーの予測が必要な場合、計算量やデータ比較が更に困難になります。ケーススタディ: 重力波の発見https://numpy.org/ja/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/gw-discov/重力波# -(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO) 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 -重力波 と LIGO について# 重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。 -[レーザー干渉計重力波天文台(LIGO)](https://www. ligo. caltech. edu)は、アインシュタインの一般相対性理論によって予測された重力波の直接検出を通して、重力波天体物理学の分野を切り開くために設計されました。 このシステムは、アメリカのワシントン州ハンフォードとルイジアナ州リビングストンにある2つの干渉計が一体となって構成され、重力波を検出します。 それぞれのシステムには、レーザー干渉法を用いた数キロ規模の重力波検出器が設置されています。 LIGO Scientific Collaboration（LSC）は、米国をはじめとする14カ国の大学から1000人以上の科学者が集まり、90以上の大学・研究機関によって支援されています。 また、約250人の学生も参加しています。 今回のLIGOの発見は、重力波が地球を通過する際に生じる空間と時間の微小な乱れの測定により、重力波そのものを初めて観測しました。 これにより、新しい天体物理学のフロンティアが開かれました。 これは、宇宙の歪んだ側面、つまり歪んだ時空から作られた物体とそれに現象を切り拓くものです。 -主な目的# LIGOのミッションは、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 複雑な数学を含む相対性理論の数値計算によって観測データを解析し、信号とノイズを識別し、関連性のある信号をフィルタリングし、観測データの有意性を統計的に推定することで、宇宙の始まりのクランチを観測できるようになります。 バイナリや数値の結果を理解しやすいようにデータを可視化することも必要です。 課題# 計算 -合成により放出される重力波は、スーパーコンピュータを用いて数値相対性を手あたり次第に試すような方法では計算できません。 LIGOが収集するデータ量は、重力波の信号が少ないのと同じくらい不可解です。 -データの氾濫 -観測装置がより高感度で信頼性を持つようになると、データの大洪水によって、干し草の中から針を探すような問題が、多重に発生することがわかります。 LIGOは毎日テラバイトのデータを生成しているのです！ この大量のデータを解釈するには、各検出ごとに多大な労力が必要です。 例えば、LIGOによって収集される信号は、数十万個の重力波シグネチャのテンプレートで構成されており、スーパーコンピュータでしか解析できません。 -可視化 -アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。 画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。 -GW150914から推定される重力波の歪みの振幅# -(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication) 重力波の検出におけるNumPyの役割# 合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。ケーススタディ：世界初のブラックホール画像https://numpy.org/ja/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/blackhole-image/Black Hole M87# -(Image Credits: Event Horizon Telescope Collaboration) 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 -地球大の望遠鏡# Event Horizon telescope(EHT)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がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 -ブラックホール: EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 100年以上に渡る研究が行われてもなお、これまでに視覚的にブラックホールを観測できたことはありませんでした。 -観測と理論の比較: 科学者たちの間で、アインシュタインの一般相対性理論から、重力による光の曲げや光の捕獲による影のような領域が観測できるのではないかと期待されていました。 これはブラックホールの巨大な質量を測定するために利用することができます。 -課題# 大規模な計算 -EHTは膨大なデータ処理の課題を抱えていました。 大気の位相変動は急速で、記録帯域の幅は大きく、望遠鏡はそれぞれ異なっていて地理的にも分散しています。 -大量のデータ -EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 -よくわからないものを観測する -今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? -EHTのデータ処理パイプライン# -(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration) 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。 -ブラックホール画像化でNumPyが果たした役割# -例えば、 eht-imaging 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。 \ No newline at end of file +Case-Studies on NumPyhttps://numpy.org/ja/case-studies/Recent content in Case-Studies on NumPyHugojaケーススタディ: DeepLabCut 三次元姿勢推定https://numpy.org/ja/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/deeplabcut-dnn/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/mice-hand.gif" alt="micehandanim" class="align-center"> + + + +<figcaption><strong class="caption-title">DeepLapCutを用いたマウスの手の動きの解析</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="http://www.mousemotorlab.org/deeplabcut">(Source: www.deeplabcut.org )</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"> + <p> +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。 +</p> + <p class="attribution">—Alexander Mathis、 <em>准教授、École polytechnology fe’rale de Lausanne</em> (<a href="https://www.epfl.ch/en/">EPFL</a>)</p> +</blockquote> + +<h2 id="deeplabcut-について">DeepLabCut について<a class="headerlink" href="#deeplabcut-について" title="Link to this heading">#</a></h2> +<p><a href="https://github.com/DeepLabCut/DeepLabCut">DeepLabCut</a>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。</p> +<p>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。</p> +<figure class="align-default" id="id002"> +<img src="https://numpy.org/images/content_images/cs/race-horse.gif" alt="horserideranim" class="align-center"> + + + +<figcaption><strong class="caption-title">色のついた点は競走馬の体の位置を追跡</strong><a class="headerlink" href="#id002" title="Link to this image">#</a><br>(Source: Mackenzie Mathis) +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<p>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。</p> +<p>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 <a href="https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618">チーター</a>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。</p> +<p>DeepLabCutでは<a href="https://arxiv.org/pdf/1909.11229">転移学習</a>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<a href="https://arxiv.org/abs/1605.03170">DeeperCut</a>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。</p> +<p>最近、<a href="http://www.mousemotorlab.org/dlc-modelzoo">DeepLabCut model zoo</a>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。</p> +<h3 id="主な目標と結果">主な目標と結果<a class="headerlink" href="#主な目標と結果" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>科学研究のための動物姿勢解析の自動化:</strong></p> +<p>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 <a href="https://www.biorxiv.org/content/10.1101/457242v1">10倍のパフォーマンス向上</a> 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。</p> +</li> +<li> +<p><strong>姿勢推定のための使いやすいPythonツールキットの作成:</strong></p> +<p>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。</p>ケーススタディ: クリケット分析、ゲームチェンジャー!https://numpy.org/ja/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/cricket-analytics/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/ipl-stadium.png" alt="Indian Premier League Cricket cup and stadium" class="align-center"> + + + +<figcaption><strong class="caption-title">IPLT20、インド最大のクリケットフェスティバル</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://unsplash.com/@aksh1802">(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium))</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/"> + <p> +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。 +</p> + <p class="attribution">—M S Dhoni、 <em>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</em></p> +</blockquote> + +<h2 id="クリケットについて">クリケットについて<a class="headerlink" href="#クリケットについて" title="Link to this heading">#</a></h2> +<p>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<a href="https://www.statista.com/topics/4543/indian-premier-league-ipl/">多額のお金</a>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。</p> +<p>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<a href="https://en.wikipedia.org/wiki/Indian_Premier_League">67億ドル</a>だと評価されています。</p> +<p>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。</p> +<p>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。 : <a href="https://stats.espncricinfo.com/ci/engine/stats/index.html">ESPN cricinfo や</a> <a href="https://cricsheet.org">cricsheet</a>. 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、 <a href="https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances">クリケット 分析</a> 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。</p> +<ul> +<li>バッティング成績の移動平均</li> +<li>スコア予測</li> +<li>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</li> +<li>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</li> +</ul> +<figure class="align-center" id="id002"> +<img src="https://numpy.org/images/content_images/cs/cricket-pitch.png" alt="A cricket pitch with bowler and batsmen" class="align-center"> + + + +<figcaption><strong class="caption-title">フィールドのフォーカルポイントとなるクリケットピッチ</strong><a class="headerlink" href="#id002" title="Link to this image">#</a><br><a href="http://debarghyadas.com/files/IPLpaper.pdf">(Image credit: Debarghya Das)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<h3 id="データ分析の主要な目標">データ分析の主要な目標<a class="headerlink" href="#データ分析の主要な目標" title="Link to this heading">#</a></h3> +<ul> +<li>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、 <a href="https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx">他のスポーツ</a>でも使用されています。</li> +<li>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。</li> +<li>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。</li> +</ul> +<figure class="align-default" id="id003"> +<img src="https://numpy.org/images/content_images/cs/player-pose-estimator.png" alt="pose estimator" class="align-center"> + + + +<figcaption><strong class="caption-title">クリケットの姿勢推定</strong><a class="headerlink" href="#id003" title="Link to this image">#</a><br><a href="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/">(Image credit: connect.vin)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<h3 id="課題">課題<a class="headerlink" href="#課題" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>データのクリーニングと前処理</strong></p>ケーススタディ: 重力波の発見https://numpy.org/ja/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/gw-discov/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/gw_sxs_image.png" alt="binary coalesce black hole generating gravitational waves" class="align-center"> + + + +<figcaption><strong class="caption-title">重力波</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://youtu.be/Zt8Z_uzG71o">(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> 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。 +</p> + <p class="attribution">—David Shoemaker, <em>LIGOサイエンティフィック・コラボレーション</em></p> +</blockquote> + +<h2 id="重力波-と-ligo-について"><a href="https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/">重力波</a> と <a href="https://www.ligo.caltech.edu">LIGO</a> について<a class="headerlink" href="#重力波-と-ligo-について" title="Link to this heading">#</a></h2> +<p>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。</p> +<p><a href="https://www.ligo.caltech.edu">レーザー干渉計重力波天文台(LIGO)</a>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。</p> +<h3 id="主な目的">主な目的<a class="headerlink" href="#主な目的" title="Link to this heading">#</a></h3> +<ul> +<li>LIGOの<a href="https://www.ligo.caltech.edu/page/what-is-ligo">ミッション</a>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。</li> +<li>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。</li> +<li>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。</li> +</ul> +<h3 id="課題">課題<a class="headerlink" href="#課題" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>計算</strong></p> +<p>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。</p> +</li> +<li> +<p><strong>データの氾濫</strong></p> +<p>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。</p> +</li> +<li> +<p><strong>可視化</strong></p> +<p>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。</p> +</li> +</ul> +<figure class="align-default" id="id002"> +<img src="https://numpy.org/images/content_images/cs/gw_strain_amplitude.png" alt="gravitational waves strain amplitude" class="align-center"> + + + +<figcaption><strong class="caption-title">GW150914から推定される重力波の歪みの振幅</strong><a class="headerlink" href="#id002" title="Link to this image">#</a><br><a href="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger">(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<h2 id="重力波の検出におけるnumpyの役割">重力波の検出におけるNumPyの役割<a class="headerlink" href="#重力波の検出におけるnumpyの役割" title="Link to this heading">#</a></h2> +<p>ブラックホール合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。</p>ケーススタディ：世界初のブラックホール画像https://numpy.org/ja/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/blackhole-image/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/blackhole.jpg" alt="black hole image" class="align-center"> + + + +<figcaption><strong class="caption-title">Black Hole M87</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg">(Image Credits: Event Horizon Telescope Collaboration)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +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。 +</p> + <p class="attribution">—<em>カリフォルニア工科大学 計算・数理学部</em>のKatie Bouman助教授</p> +</blockquote> + +<h2 id="地球大の望遠鏡">地球大の望遠鏡<a class="headerlink" href="#地球大の望遠鏡" title="Link to this heading">#</a></h2> +<p><a href="https:/eventhorizontelescope.org">Event Horizon telescope(EHT)</a>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、<a href="https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole">20マイクロ秒</a>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!</p> +<h3 id="主な目標と結果">主な目標と結果<a class="headerlink" href="#主な目標と結果" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>宇宙の新しい見方:</strong> EHTの画期的な考え方の基礎が築かれたのは、100年前に <a href="https://en.wikipedia.org/wiki/Eddington_experiment">Sir Arthur Eddington</a>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。</p> +</li> +<li> +<p><strong>ブラックホール:</strong> 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。 <a href="https://www.jpl.nasa.gov/news/news.php?feature=7385">100年以上</a>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。</p> +</li> +<li> +<p><strong>観測と理論の比較:</strong> 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。</p> +</li> +</ul> +<h3 id="課題">課題<a class="headerlink" href="#課題" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>大規模な計算</strong></p> +<p>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。</p> +</li> +<li> +<p><strong>大量のデータ</strong></p> +<p>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。</p> +</li> +<li> +<p><strong>よくわからないものを観測する</strong></p> +<p>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?</p> +</li> +</ul> +<figure class="align-center" id="id002"> +<img src="https://numpy.org/images/content_images/cs/dataprocessbh.png" alt="data pipeline" class="align-center"> + + + +<figcaption><strong class="caption-title">EHTのデータ処理パイプライン</strong><a class="headerlink" href="#id002" title="Link to this image">#</a><br><a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57">(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<h2 id="numpyが果たした役割">NumPyが果たした役割<a class="headerlink" href="#numpyが果たした役割" title="Link to this heading">#</a></h2> +<p>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？</p> \ No newline at end of file diff --git a/ja/citing-numpy/index.html b/ja/citing-numpy/index.html index c44e8af5..c06f6bb6 100644 --- a/ja/citing-numpy/index.html +++ b/ja/citing-numpy/index.html @@ -1,16 +1,16 @@ -NumPy - NumPyを引用する -NumPy - NumPyを引用する

    NumPyを引用する

    もしあなたの研究においてNumPyが重要な役割を果たし、論文でこのプロジェクトについて言及したい場合は、こちらの論文を引用して下さい。

    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
@@ -30,6 +30,5 @@
  doi           = {10.1038/s41586-020-2649-2},
  publisher     = {Springer Science and Business Media {LLC}},
  url           = {https://doi.org/10.1038/s41586-020-2649-2}
-}
    On this page
    \ No newline at end of file +}
    On this page
    \ No newline at end of file diff --git a/ja/code-of-conduct/index.html b/ja/code-of-conduct/index.html index 3cdc4bf2..87db90a2 100644 --- a/ja/code-of-conduct/index.html +++ b/ja/code-of-conduct/index.html @@ -1,20 +1,16 @@ -NumPy - NumPy行動規範 -NumPy - NumPy行動規範

    NumPy行動規範

    はじめに#

    この行動規範は、NumPy プロジェクトによって管理されるすべての場所で適用されます。 この場所とは、すべてのパブリックおよびプライベートのメーリングリスト、イシュートラッカー、Wiki、ブログ、Twitter、コミュニティで使用されているその他の通信チャンネルなどを含みます。 NumPy プロジェクトでは対面でのイベントは開催していません。 しかし、我々のコミュニティに関連するものであれば、対面のイベントでも同様の行動規範を持つ必要があります。

    この行動規範は、NumPy コミュニティに正式または非公式に参加するすべての人が順守する必要があります。 その他にも、NumPyとの提携・関連するプロジェクト活動においては、特にそれらのプロジェクトを代表する場合、同様の行動規範に従う必要があります。

    この行動規範は完全ではありません。 しかし、行動規範は我々が理解すべき、互いの協力の仕方や、共通の場所のあるべき姿、我々のゴールなどをまとめるのに重要な役目を果たします。 フレンドリーで生産的な環境を生み出し、周囲のコミュニティにより良い影響を与えるため、ぜひこの行動規範に従ってください。

    ガイドラインの概要#

    私たちは下記の内容に真摯に取り組みます。

    1. 開けたコミュニティにしましょう。 私たちは、誰でもコミュニティに参加できるようにします。 私たちは、公にすべきではない内容を議論する場合以外、プロジェクトに関連するメッセージを公の場で告知することを選びます。 これは、NumPyに関するヘルプやプロジェクトサポートにも適用されます。公式なサポートだけでなく、NumPyに関する質問に答える場合もです。 これにより、質問に答えた際の意図しない間違いを、より簡単に検出し、訂正できるようになります。
    2. 共感し、歓迎し、友好的で、そして我慢強くありましょう。 私たちは互いに争いを解決し合い、互いの善意を信じ合います。 私たちは時折り不満を感じるかもしれません。 しかしそのような場合も、不満を個人的な攻撃に変えることは許容されません。 人々が不快や脅威を感じるコミュニティは、生産的ではないからです。
    3. 互いに協力し合おう。 私たちの開発成果は他の人々によって利用され、一方で、たちは他の人々の開発成果に依存しているのです。 私たちがプロジェクトために何かを作るとき、私たちはそれがどのように動作するかを他の人に説明する必要があります。 しかし、この作業により、より良いものを作り上げることができるのです。 私たちが下す全ての決断は、ユーザと開発コミュニティに影響を与えうるし、その決断がもたらす結果を私たちは真摯に受け止めます。
    4. 好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。
    5. 使う言葉に注意しましょう。 私たちは、コミュニティにおけるコミュニケーションに注意と敬意を払います。 そして、私たちは自分の言葉に責任を持ちます。 他人に優しくしましょう。 他のコミュニティの参加者を侮辱しないでください。 私たちは、以下のようなハラスメントやその他の排斥行為を許しません。 :
      • 他の人に向けられた暴力的な行為や言葉。
      • 性差別や人種差別、その他の差別的なジョークや言動。
      • 性的または暴力的な内容の投稿。
      • 他のユーザーの個人情報を投稿すること。 (または投稿すると脅すこと)。
      • 公開目的のない電子メールや、ICRチャットのようなログの残らないフォーラムの履歴など、プライベートなコンテンツを送信者の同意なしに共有すること。
      • 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。
      • 不快な思いをさせる性的な言動。
      • 過度に粗暴に振る舞うこと。 ひどいな言葉を使うのを避けてください。 人々は怒りを覚える感度が、それぞれ大きく異なります。
      • 他人に対するハラスメントの繰り返し。 一般的に、誰かがあなたにある言動を止めるように要求した場合、その言動をやめて下さい。
      • 上記のいずれかの行動を擁護すること、または奨励すること。

    多様性に関する声明#

    NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。

    あなたの自己認識や、他者のあなたへの認識は関係ありません。 私たちはあなたを歓迎します。 民族、遺伝、性同一性あるいは関連する表現、言語、国籍、神経学的な差異、生物学的な差異、 政治的信条、職業、人種、宗教、性的指向、社会経済的地位、文化的な差異、技術的な能力。

    私たちはすべての種類の言語言語話者の参加を歓迎しますが、NumPy 開発は英語で行われます。

    NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 NumPyコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです (次のセクションを参照)。

    報告ガイドライン#

    私たちは、インターネット上でのやりとりが簡単にひどい誹謗中傷に陥ってしまうことを、痛いほど知っています. 私たちはまた、嫌な日を過ごしてむしゃくしゃしている人や、行動規範ガイドラインの項目を見落としている人がいることも知っています。 行動規範の違反にどのように対処するかを決定する際には、このことを心に留めておく必要があります。

    意図的な行動規範違反については、行動規範委員会に報告してください (下記参照)。 もし、違反が意図的でない可能性がある場合、その人にこの行動規範の存在を知らせることも可能です (パブリックでもプライベートでも、適切な方法で)。 もし直接指摘したくない場合は、ぜひ、行動規範委員会に直接連絡するか、違反の確度について助言を求めて下さい。

    NumPy行動規範委員会に問題を報告する場合は、こちらにご連絡下さい: 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:

    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 。

    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 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( 決議を参照)

    行動規範委員会は、可能な限り速やかに対応し、最大で72時間以内に対応する様にします。

    文末脚注:#

    私たちは下記のドキュメントを作成したグループに感謝します。 内容・発想ともに大いに影響されています。

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    コミュニティ

    NumPy は 常に多様なコントリビュータ 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 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.

    NumPyメーリングリスト：#

    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)。 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 にあります。

    GitHub イシュートラッカー#

    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 から報告してください。

    Slack#

    SlackはNumpyに_ 貢献するための質問をするための_、リアルタイムのチャットルームです。 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合はこちらを確認下さい。

    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 (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 と Twitter 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。

    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 ページをご覧いただくことをお勧めします。

    また、私たちのコミュニティミーティングにもぜひ参加してみてください。 コミュニティミーティングの活動を確認するには、こちらのイベントカレンダーを確認ください。

    On this page
    \ No newline at end of file diff --git a/ja/conduct/index.html b/ja/conduct/index.html index 472e4d5a..7034d950 100644 --- a/ja/conduct/index.html +++ b/ja/conduct/index.html @@ -1,2 +1 @@ -https://numpy.org/ja/code-of-conduct/ - \ No newline at end of file +https://numpy.org/ja/code-of-conduct/ \ No newline at end of file diff --git a/ja/config.yaml b/ja/config.yaml index bb633381..f662552f 100644 --- a/ja/config.yaml +++ b/ja/config.yaml @@ -85,9 +85,6 @@ params: - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter quicklinks: column1: title: "" @@ -138,4 +135,3 @@ params: - text: プレス用資料 link: /ja/press-kit - diff --git a/ja/contribute/index.html b/ja/contribute/index.html index a78a03a2..6a0580c9 100644 --- a/ja/contribute/index.html +++ b/ja/contribute/index.html @@ -1,5 +1,4 @@ -NumPy - NumPy に貢献する -NumPy - NumPy に貢献する

    NumPy に貢献する

    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。 貢献方法はプログラミングに限定されません。 このページにはあなたができる 様々な種類の貢献方法が示されています。

    もしどこから始めればいいか、あなたのスキルをどう生かせばいいかがわからない場合は、 _是非ご連絡下さい。 _ 連絡の方法としては、 メーリングリスト 、 GitHub、 イシューの作成 、関連するイシューへのコメントがあります。

    連絡先としては、 numpy-team@googlegroups.com または、Slack (グループに招待するためにこちらに連絡お願いします: numpy-team@googlegroups.com)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 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 を読んでみて下さい。

    私たちのコミュニティは、誰もが平等に扱われ、すべての貢献を平等に評価することを目指しています。 私たちはオープンで居心地の良いコミュニティを作るために 行動基準 を制定しています。

    コードを書く#

    プログラマーの方には、こちらの ガイドでNumPyのコードに貢献する方法を説明しています。
    追加情報に関しては、 こちらのYouTube チャンネル もご覧ください。

    プルリクエストのレビュー#

    NumPyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumPyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、

    教育用の資料を作成する#

    NumPy の ユーザガイド 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 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:

    ぜひ、やってみて下さい。

    ウェブサイトの開発#

    私たちはちょうどウェブサイトを作り直し始めたところですが、それらはまだ完了していません。 Web開発が好きなら、このイシュー に未完成な要求が列挙されています。 ぜひ、あなたのアイデアを共有してください。

    グラフィックデザイン#

    グラフィックデザイナーの方が可能な貢献は、枚挙にいとまがありません。 しかし、私たちのドキュメントは説明のために可視化が重要であり、私たちの拡大しているウェブサイトは良い画像を求めていることから、 貢献する機会が沢山あると言えます。

    ウェブサイトの翻訳#

    私たちは、numpy.org 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を参照してください。 この GitHubイシュー にコメントしてサインアップしてください。

    コミュニティとの連携とアウトリーチ#

    コミュニティとのコミュニケーションを通じて、私たちは、NumPyより広く知ってもらい、どこに問題があるのかを知りたいと思っています。 私たちは、Twitter アカウントや、NumPyコードスプリントの開催、ニュースレターの発行、そしておそらくブログなどを通じて、より沢山の人にコミュニティに参加して欲しいと思っていす。

    資金調達#

    NumPyは何年にも渡ってボランティアだけ活動していましたが、その重要性が高まるにつれ、安定性と成長のためには資金面での支援が必要であることがわかってきました。 こちらのSciPy'19のプレゼン では、資金的なサポートを受けたことで、どれだけ違いが出たかを説明しています。 他の非営利団体のように、私たちは助成金や、スポンサーシップ、その他の資金支援を常に探しています。 私たちはすでにいくつかの資金調達のアイデアを持っていますが、他にもより多くを資金調達を受けたいと思っています。 資金調達に関する知識は、我々には不足しているスキルです。 是非、あなたのサポートをお待ちしています。

    寄付#

    寄付をすることでNumpyに貢献したい場合は、 https://numpy.org/about/#donate をご覧ください。

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    サポートを得る方法

    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 のページを参照してください。

    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に質問を投稿することです。 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、質問の数は膨大で対応しきれないのが現実です。

    StackOverflow#

    NumPyの使用方法に関する質問をするためのフォーラムです。 例えば、「NumPyでXをするにはどうすればいいですか？ 質問をする時は、 #numpy タグ を使用してください。

    Reddit#

    もう一つの使い方に関する質問の場です。

    On this page
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    NumPyの歴史

    NumPy は配列データ構造と配列に関連する高速な数値ルーチンを提供する Python 基礎的なライブラリです。 開始当初は資金も少なく、主に大学院生により開発されていました。その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。少数の “野良"学生プログラマーのグループが、すでに確立されていた商用研究ソフトウェアのエコシステムをひっくり返すなんて、想像することすら馬鹿げていました。 商用ソフトは、何百万もの資金と何百人もの優秀なエンジニアに支えられていましたから。それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。現在では、NumPyは科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 このライブラリの開発開始当初は資金も少なく、主に大学院生が開発していましたが、その多くはコンピュータサイエンスの教育を受けておらず、指導教官のサポートも受けていませんでした。 何百万もの資金調達と何百人もの優秀なエンジニアに支えられている当時の商用研究ソフトウェアのエコシステムを、少数の “野良"学生プログラマーのグループがひっくり返すことができると想像することさえ、当時は馬鹿げていると考えられていました。 それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。 現在では、Numpy は科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。

    NumPy および関連ライブラリの開発におけるマイルストーンの詳細については、 arxiv.org 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 のダウンロード***

    *Numarray * 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

    On this page
    \ No newline at end of file diff --git a/ja/index.html b/ja/index.html index b9f5cea0..3bac81c8 100644 --- a/ja/index.html +++ b/ja/index.html @@ -1,17 +1,18 @@ -NumPy -
    NumPy +
    Pythonによる科学技術計算の基礎パッケージ
    NumPy 1.26.0 がリリースされました。
    2024-08-18
    強力な多次元配列
    NumPyの高速で多機能なベクトル化計算、インデックス処理、ブロードキャストの考え方は、現在の配列計算におけるデファクト・スタ>ンダードです。
    数値計算ツール群
    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。
    オープンソース
    NumPyは、寛容なBSDライセンス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によって、 GitHub上でオープンに開発されていま +す.
    相互運用性
    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。
    高パフォーマンス
    NumPyの大部分は最適化されたC言語のコードで構成されています。これによりPythonの柔軟性とコンパイルされたコードの高速性の両方 -を享受できます。
    使いやすさ
    NumPyの高水準なシンタックスは、どんなバックグラウンドや経験を持つのプログラマーでも簡単に利用することができ、生産性を高め>ることができます。
    オープンソース
    NumPyは、寛容なBSDライセンス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によって、 GitHub上でオープンに開発されていま -す.
    NumPy を試す

    インタラクティブシェルを使用して、ブラウザ上で Numpy を試してみてください。

    """
+を享受できます。
    使いやすさ
    NumPyの高水準なシンタックスは、どんなバックグラウンドや経験を持つのプログラマーでも簡単に利用することができ、生産性を高め>ることができます。
    NumPy を試す

    インタラクティブシェルを使用して、ブラウザ上で Numpy を試してみてください。

    """
 To try the examples in the browser:
 1. Type code in the input cell and press
    Shift + Enter to execute
@@ -38,7 +39,7 @@
 # Generate normally distributed random numbers:
 rng = np.random.default_rng()
 samples = rng.normal(size=2500)
-samples

    NumPyのエコシステム

    NumPyのエコシステム

    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。

    Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。

    • 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。

      配列ライブラリ機能と応用分野
      DaskDask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。
      CuPyCuPyPython 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
      JAXJAXNumPyコードの合成可能な変換ライブラリ: 微分、ベクトル化、GPU/TPUへのジャストインタイムコンパイル
      xarrayXarray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
      sparseSparseDask 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
      PyTorchPyTorch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
      TensorFlowTensorFlow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
      MXNetMXNet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
      arrowArrow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
      xtensorxtensor数値解析のためのブロードキャスティングと遅延計算を備えた多次元配列
      awkwardAwkwardNumpy のような イディオムを使って JSON のようなデータを操作するライブラリ
      uarrayuarrayAPI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)
      tensorlytensorlyNumpy、MXNet、PyTorch、TensorFlowまたはCupyをシームレスに使用するための、テンソル学習、テンソル代数、およびそれらのテンソル計算のためのバックエンド
    • 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。

      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。

      大規模データに対して、DaskとRayはスケールすることを目指して設計されています。安定したデプロイメントに関しては、データのバージョニング（DVC）、実験の追跡（MLFlow）、ワークフローの自動化（AirflowおよびPrefectが重要ですが様々なNumPyベースのツールが提供されています。

      三つの円が重なり合う図。円はそれぞれ「数学」、「コンピューターサイエンス」、「専門知識」でラベル付けされています。図の中心部には、三つの円が重なり合って形成されるエリアがあり、「データサイエンス」とラベル付けされています。
    • An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

      Source: Google AI Blog

      NumPyは、scikit-learnやSciPy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。

      ensemble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、LightGBM、CatBoost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やEli5は機械学習の可視化機能を提供しています。

    • matplotlibで作られたストリームプロット
      ggpyで作られた散布図グラフ
      plotyで作られた箱ひげ図
      altairで作られたストリームグラフ
      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"
      PyVista製の3Dボリュームレンダリング
      napariで作られた多次元画像
      vispyで作られたボロノイ図

      NumPyは、Matplotlib、Seaborn、Plotly、Altair、Bokeh、Holoviz、Vispy、Napari、PyVistaなどの、急成長しているPython visualization landscapeに欠かせないコンポーネントです。

      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。

      • ケーススタディ

      世界初のブラックホール画像
      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。
      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。
      重力波の検知
      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。
      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。
      スポーツ分析
      緑のフィールド上にあるクリケットボール。
      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。
      深層学習による姿勢推定
      チータの姿勢推定
      DeepLabCutはNumPyを利用し、動物の種類や時間スケールによらない運動制御の理解へ向け、動物の行動観察を含む科学技術研究を加速させています。
      \ No newline at end of file +Visualization

      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。

      Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。

      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。

      配列ライブラリ機能と応用分野
      DaskDask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。
      CuPyCuPyPython 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
      JAXJAXNumPyコードの合成可能な変換ライブラリ: 微分、ベクトル化、GPU/TPUへのジャストインタイムコンパイル
      xarrayXarray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
      sparseSparseDask 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
      PyTorchPyTorch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
      TensorFlowTensorFlow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
      MXNetMXNet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
      arrowArrow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
      xtensorxtensor数値解析のためのブロードキャスティングと遅延計算を備えた多次元配列
      awkwardAwkwardNumpy のような イディオムを使って JSON のようなデータを操作するライブラリ
      uarrayuarrayAPI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)
      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。

      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。

      大規模データに対して、DaskとRayはスケールすることを目指して設計されています。安定したデプロイメントに関しては、データのバージョニング（DVC）、実験の追跡（MLFlow）、ワークフローの自動化（AirflowおよびPrefectが重要ですが様々なNumPyベースのツールが提供されています。

      三つの円が重なり合う図。円はそれぞれ「数学」、「コンピューターサイエンス」、「専門知識」でラベル付けされています。図の中心部には、三つの円が重なり合って形成されるエリアがあり、「データサイエンス」とラベル付けされています。
      An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

      Source: Google AI Blog

      NumPyは、scikit-learnやSciPy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。

      ensemble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、LightGBM、CatBoost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やEli5は機械学習の可視化機能を提供しています。

      matplotlibで作られたストリームプロット
      ggpyで作られた散布図グラフ
      plotyで作られた箱ひげ図
      altairで作られたストリームグラフ
      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"
      PyVista製の3Dボリュームレンダリング
      napariで作られた多次元画像
      vispyで作られたボロノイ図

      NumPyは、Matplotlib、Seaborn、Plotly、Altair、Bokeh、Holoviz、Vispy、Napari、PyVistaなどの、急成長しているPython visualization landscapeに欠かせないコンポーネントです。

      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。

      ケーススタディ

      世界初のブラックホール画像
      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。
      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。
      重力波の検知
      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。
      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。
      スポーツ分析
      緑のフィールド上にあるクリケットボール。
      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。
      深層学習による姿勢推定
      チータの姿勢推定
      DeepLabCutはNumPyを利用し、動物の種類や時間スケールによらない運動制御の理解へ向け、動物の行動観察を含む科学技術研究を加速させています。
      \ No newline at end of file diff --git a/ja/index.xml b/ja/index.xml index e99c6730..8b1152a2 100644 --- a/ja/index.xml +++ b/ja/index.xml @@ -1,143 +1,1795 @@ -NumPyhttps://numpy.org/ja/Recent content on NumPyHugo -- gohugo.iojaSat, 16 Sep 2023 00:00:00 +0000ニュースhttps://numpy.org/ja/news/Sat, 16 Sep 2023 00:00:00 +0000https://numpy.org/ja/news/NumPy 1.26.0 がリリースされました。# 2023年9月16日 &ndash; NumPy 1.26.0がリリースされました。 今回のリリースの目玉機能は次のとおりです。 -Python 3.12.0 のサポート Cython 3.0.0 との互換性 Mesonビルドシステムの利用 SIMD サポートの改善 f2py のバグ修正, meson と bind(x) のサポート 更新された BLAS/LAPACK の高速化ライブラリのサポート Numpy 1.26.0 は 1.25 からの互換性を保持しています。Mesonビルドシステムへの移行とCython 3.0.0のサポートが目的のリリースです。 合計20人がこのリリースに貢献し、59個のプルリクエストがマージされました。 -このリリースでサポートされている Python のバージョンは3.9から 3.12 です。 -numpy.orgが日本語とポルトガル語で利用可能になりました# 2023年4月2日 &ndash; numpy.orgが2つの言語で利用可能になりました： 日本語とポルトガル語。 熱心なボランティアがいなければ、このプロジェクトは不可能でした： -ポルトガル語 -Melissa Weber Mendonça (melissawm) Ricardo Prins (ricardoprins) Getúlio Silva (getuliosilva) Julio Batista Silva (jbsilva) Alexandre de Siqueira (alexdesiqueira) Alexandre B A Villares (villares) Vini Salazar (vinisalazar) 日本語：2020年 NumPyコミュニティ調査https://numpy.org/ja/user-survey-2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/user-survey-2020/2020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。 -調査結果を詳細を知りたい場合は、こちらのレポート をダウンロードしてください。 -結果の概要については、 こちらの図 をチェックしてください。 -より詳細が知りたくなりましたか？ https://numpy.org/user-survey-2020-details/ をご覧ください。404https://numpy.org/ja/404/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/404/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 を作成してください。NumPy に貢献するhttps://numpy.org/ja/contribute/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/contribute/NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 貢献方法はプログラミングに限定されません。 このページにはあなたができる 様々な種類の貢献方法が示されています。 -もしどこから始めればいいか、あなたのスキルをどう生かせばいいかがわからない場合は、 _是非ご連絡下さい。 _ 連絡の方法としては、 メーリングリスト 、 GitHub、 イシューの作成 、関連するイシューへのコメントがあります。 -連絡先としては、 numpy-team@googlegroups.com または、Slack (グループに招待するためにこちらに連絡お願いします: numpy-team@googlegroups.com)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 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 を読んでみて下さい。 -私たちのコミュニティは、誰もが平等に扱われ、すべての貢献を平等に評価することを目指しています。 私たちはオープンで居心地の良いコミュニティを作るために 行動基準 を制定しています。 -コードを書く# プログラマーの方には、こちらの ガイドでNumPyのコードに貢献する方法を説明しています。 追加情報に関しては、 こちらのYouTube チャンネル もご覧ください。 -プルリクエストのレビュー# NumPyプロジェクトには現時点で250以上のオープンなプルリクエストがあり、多くの 改善要求と多くのレビュワーからのフィードバックを待っています。 もしあなたがNumPy を使ったことがある場合、 たとえNumPyコードベースに慣れていない場合でも貢献する方法はあります。 例えば、 -長期にわたる議論をまとめる ドキュメントのPRをトリアージする 提案された変更をテストする 教育用の資料を作成する# NumPy の ユーザガイド 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 には、 _沢山の_Open状態のイシューがあります。 すでに解決されたもの、優先順位付けされるべきもの、 初心者が取り組むのに適したものがあります。 あなたができることは、いくつもあります: -古いバグがまだ残っているか確認する 重複したイシューを見つけ、お互いに関連づける 問題を再現するコードを作成する イシューに正しいラベル付けをする (トリアージ権が必要なので、連絡下さい) ぜひ、やってみて下さい。NumPyユーザアンケートhttps://numpy.org/ja/user-surveys/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/user-surveys/2020 NumPY調査チームは、ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらをご覧ください。 -2021 収集された調査データは現在解析中です。 -過去または今後のNumPyユーザ調査に関する質問や提案がある場合は、こちらにイシューを作成してください。NumPyのインストールhttps://numpy.org/ja/install/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/install/NumPyをインストールするための唯一必要なものは、Pythonそのものだけです。 もしまだPythonをイントールしておらず、最もシンプルなインストール方法をお探しなら、Anaconda Distributionの使用をおすすめします。これにはPython、NumPy、および科学計算やデータサイエンスでよく使われる様々な多くのパッケージが含まれています。 -NumPyはconda、pip 、macOSやLinuxのパッケージマネージャー、または ソースコード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 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。 -# 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 # conda-forgeからインストールする場合 conda config --env --add channels conda-forge # インストールコマンド conda install numpy PIP -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: -pip install numpy またpipを使う場合、仮想環境を使うことをおすすめします。 再現可能なインストールを参照ください。 こちらの記事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。 -推奨方法# まずはユーザの経験レベルと、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」の間の方は、シンプルにインストールしたい場合は「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」を参照ください。 -初級ユーザ# Windows、macOS、Linuxのすべてのユーザー向けには: -Anaconda をインストールします（必要な パッケージと以下に挙げるすべてのツールがインストールされます）。 コードを書いたり、実行してみましょう。 探索的・対話的コンピューティングにはJupyterLab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やVisual Studio Codeを利用できます。 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# Conda# Miniforge をインストールします。 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:NumPyの学び方https://numpy.org/ja/learn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/learn/公式の NumPy ドキュメント については numpy.org/doc/stableを参照してください。 -以下は、Numpyへの貢献者とコミュニティによって開発された、NumPyの自己学習と他人への教育のための資料です。 -初心者向け# 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： -動画 -NumPy Quickstart チュートリアル NumPyチュートリアルで、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リポジトリをチェックしてみて下さい。 イラストで学ぶNumPy by Lev Maximov Scientific Pythonレクチャー 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 NumPy チュートリアル by Nicolas Rougier スタンフォード大学 CS231 by Justin Johnson NumPyユーザーガイド チュートリアル -NumPガイド Travelis E. Oliphant著 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. PythonにおけるNumPy (発展編) エレガントなSciPy Juan Nunez-Iglesias・Stefan van der Walt・Harriet Dashnow 著 また、「Python+SciPy」を題材にした推薦本リスト 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 by Alex Chabot-Leclerc 上級者向け# 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。 -書籍 -https://www.tutorialspoint.com/numpy/numpy_advanced_indexing.htm NumPyとSciPyへのイントロダクション M. Scott Shell著 NumPy救急キット Stéfan van der Walt著 NumPyチュートリアルで、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リポジトリをチェックしてみて下さい。 チュートリアルNumPyの歴史https://numpy.org/ja/history/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/history/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 &ldquo;野良&quot;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 &ldquo;野良&quot;学生プログラマーのグループがひっくり返すことができると想像することさえ、当時は馬鹿げていると考えられていました。 それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。 現在では、Numpy は科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。 -NumPy および関連ライブラリの開発におけるマイルストーンの詳細については、 arxiv.org 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 のダウンロード** -*Numarray * 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NumPyを引用するhttps://numpy.org/ja/citing-numpy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/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: 10.1038/s41586-020-2649-2. (リンク). BibTeX形式: -@Article{ harris2020array, title = {Array programming with {NumPy}}, author = {Charles R. Harris and K. Jarrod Millman and St{&#39;{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.NumPy開発チームhttps://numpy.org/ja/teams/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/teams/私たちは、高品質のオープンソースソフトウェアを構築することで、世界中の科学・研究コミュニティをサポートすることを使命とする国際的なチームです。 是非参加してください! -Maintainers# Andrew Nelson Bas van Beek Charles Harris Eric Wieser Ganesh Kathiresan Rohit Goswami Matthew Brett Matti Picus Matt Haberland Melissa Weber Mendonça Marten van Kerkwijk Christopher Sidebottom Mateusz Sokół Mukulika Nathan Goldbaum Pearu Peterson Josh Wilson Pauli Virtanen Chunlin Raghuveer Devulapalli Ralf Gommers Robert Kern Ross Barnowski Sebastian Berg Sayed Adel Stephan Hoyer Stefan van der Walt Tyler Reddy Warren Weckesser Docs team# Rohit Goswami Inessa Pawson Mars Lee Matti Picus Melissa Weber Mendonça Mukulika Ross Barnowski Web team# Inessa Pawson Jarrod Millman Joe LaChance Mars Lee Ralf Gommers shalz Shekhar Prasad Rajak Stefan van der Walt Albert Steppi Triage team# Andrew Nelson Anirudh Subramanian Aaron Meurer Atsushi Sakai Ben Nathanson Anne Bonner Brigitta Sipőcz carlkl Ryan C Cooper ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) Hameer Abbasi Inessa Pawson jbrockmendel Kai Yuji Kanagawa Kriti Singh Christopher Albert Lysandros Nikolaou Meekail Zain Christopher Sidebottom Mateusz Sokół Mukulika Noa Tamir Raghuveer Devulapalli shalz Tina Oberoi Rakesh Vasudevan Zijie (ZJ) Poh Survey team# Inessa Pawson Ralf Gommers Ross Barnowski Emeritus maintainers# Allan Haldane Ondřej Čertík David Cournapeau Jaime Jarrod Millman Julian Taylor Mark Wiebe Nathaniel J.NumPy行動規範https://numpy.org/ja/code-of-conduct/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/code-of-conduct/はじめに# この行動規範は、NumPy プロジェクトによって管理されるすべての場所で適用されます。 この場所とは、すべてのパブリックおよびプライベートのメーリングリスト、イシュートラッカー、Wiki、ブログ、Twitter、コミュニティで使用されているその他の通信チャンネルなどを含みます。 NumPy プロジェクトでは対面でのイベントは開催していません。 しかし、我々のコミュニティに関連するものであれば、対面のイベントでも同様の行動規範を持つ必要があります。 -この行動規範は、NumPy コミュニティに正式または非公式に参加するすべての人が順守する必要があります。 その他にも、NumPyとの提携・関連するプロジェクト活動においては、特にそれらのプロジェクトを代表する場合、同様の行動規範に従う必要があります。 -この行動規範は完全ではありません。 しかし、行動規範は我々が理解すべき、互いの協力の仕方や、共通の場所のあるべき姿、我々のゴールなどをまとめるのに重要な役目を果たします。 フレンドリーで生産的な環境を生み出し、周囲のコミュニティにより良い影響を与えるため、ぜひこの行動規範に従ってください。 -ガイドラインの概要# 私たちは下記の内容に真摯に取り組みます。 -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。 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。 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。 好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。 使う言葉に注意しましょう。 私たちは、コミュニティにおけるコミュニケーションに注意と敬意を払います。 そして、私たちは自分の言葉に責任を持ちます。 他人に優しくしましょう。 他のコミュニティの参加者を侮辱しないでください。 私たちは、以下のようなハラスメントやその他の排斥行為を許しません。 : 他の人に向けられた暴力的な行為や言葉。 性差別や人種差別、その他の差別的なジョークや言動。 性的または暴力的な内容の投稿。 他のユーザーの個人情報を投稿すること。 (または投稿すると脅すこと)。 公開目的のない電子メールや、ICRチャットのようなログの残らないフォーラムの履歴など、プライベートなコンテンツを送信者の同意なしに共有すること。 個人的な侮辱, 特に人種差別や性差別的な用語を使用して侮辱すること。 不快な思いをさせる性的な言動。 過度に粗暴に振る舞うこと。 ひどいな言葉を使うのを避けてください。 人々は怒りを覚える感度が、それぞれ大きく異なります。 他人に対するハラスメントの繰り返し。 一般的に、誰かがあなたにある言動を止めるように要求した場合、その言動をやめて下さい。 上記のいずれかの行動を擁護すること、または奨励すること。 多様性に関する声明# NumPyプロジェクトは、全ての人々の参加を歓迎しています。 私たちは、誰もがコミュニティの一員であることを楽しめるように尽力します。 全ての人の好みを満足はさせられないかもしれませんが、全員に対し出来るだけ親切な対応ができるよう最善を尽くします。 -あなたの自己認識や、他者のあなたへの認識は関係ありません。 私たちはあなたを歓迎します。 民族、遺伝、性同一性あるいは関連する表現、言語、国籍、神経学的な差異、生物学的な差異、 政治的信条、職業、人種、宗教、性的指向、社会経済的地位、文化的な差異、技術的な能力。 -私たちはすべての種類の言語言語話者の参加を歓迎しますが、NumPy 開発は英語で行われます。 -NumPy コミュニティの標準的なルールは、上記の行動規範で説明されています。 NumPyコミュニティの参加者は、これらの行動基準をすべてのコミュニケーションにおいて順守し、他の人々にも同様な行動をすることを推奨すべきです (次のセクションを参照)。NumPy行動規範 - 報告書のフォローアップ方法https://numpy.org/ja/report-handling-manual/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/report-handling-manual/NumPyの行動規範委員会はこのマニュアルに従います。 このマニュアルは様々な問題に対応する際に使用され、一貫性と公平性を確保します。 -行動規範 を施行することは、私たちのコミュニティの現在のため、未来のために重要です。 この施行は、軽いものではありません。 施行の基準を見直す際、行動規範委員会は以下の考え方とガイドラインに留意するようにします。 -機械的ではなく、人間的に行動します。 委員会は、当事者のプライバシーと報告者の必要なだけの機密性を尊重しながら、状況を理解するように働きかけることができます. ただし、1人以上の個人と直接連絡を取る必要がある場合もあります。 委員会の目標は正しい決定を下すのではなく、コミュニティの健康を改善することなのです。 行動を判断するのではなく、個人への共感を強調し、「良い」と「悪い」の二値評価を避けます。 明確な攻撃性とハラスメントが存在した場合、私たちはそれらに対処します。 しかし、解決が困難なシナリオの多くは、通常の意見の相違が、複数の当事者による無益または有害な行動に発展した場合です。 完全に文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、コミュニティにとって最終的に最も有益な方法です。 私たちは、電子メールが判断に困難な媒体であり、独立して利用できることを理解しています。 個人の情報なしに電子メール上で批判を受けることは、特に苦痛である場合もあります。 そこで、他者の見解に対して、開放的で、敬意を持った雰囲気を保つことが重要になります。 それはまた、私たちの行動が透明でなければならないことを意味します。 全てのメンバーが公平かつ同情をもって扱われるようにするため、私たちは全力を尽くします。 差別の境界は時に曖昧で、また無意識に行われている場合もあります。 これにより、普通の人との関わりの中で、不公平感や敵意として現れてくるのです。 私達は、このようなことが起こることはわかっているので、気をつけて見ていきたいと思います。 不当な扱いを受けたと思われる方は、ぜひご連絡ください。 良い議論を実践することで、エンゲージメントの向上に取り組みます。例えば議論がどこで止まっているのかを特定したり、 実践的な情報、指針、資源を提供することで、これらの問題を前向きな方向に変えていきます。 新しいメンバーが何を必要としているかに留意します。 特に社会的地位の低いグループからの参加を増やすことを目的に、明確なサポートと配慮を提供していきます。 一人一人の文化的背景や母国語は異なります。 ネイティブでない人が起こした悪気のない誤解を確認し、問題を理解してもらい、不快感を与えないために何を変えればよいかを教えてあげてください。 外国語での複雑な議論はとても難しいものであり、国籍や文化を超えて多様性を育てていきたいと考えています。 仲介# 自主的な非公式の調停は、私たちの重要な役割です。 2つのグループ以上の当事者が不適切な行動をエスカレートした場合(人類の紛争では悲しいことに一般的なものですが)、調停プロセスを促進するは非常に重要です。 ちなみに、これは一例に過ぎません。委員会は、どのようなケースでも調停を検討することができますが、このプロセスはあくまでも自発的なものであり、当事者に参加を迫ることはできないことを念頭に置いて下さい。 委員会が調停を提案する場合は、次のようにすべきです。 -調停者として役立つ候補者を見つけます。 報告者の合意を取得します。 報告者は、調停のアイデアを拒否したり、代替の調停者を提案する権利を持ちます。 報告者の同意を取得します。 調停人を決定します。当事者は、提案された候補者とは別の調停人を提案することができます。すべての条件で共通の合意に達した場合のみ、プロセスを進めることができます。 調停が完了するまでのタイムラインを設定し、理想的には2週間以内に完了させます。 調停者は、すべての当事者と関わり、すべての人に満足のいく決議を求めていきます。 終了後、調停人は（プロセスの全当事者によって吟味された）報告書を委員会に提出し、今後のステップに関する推奨事項を提示します。 委員会は、これらの結果(満足のいく決議が達成されたか否か) を評価し、必要と判断される追加的な措置を決定します。 -報告に対する委員会の対応# 委員会(または委員) が行動規範違反報告を受けた時、その報告が明確で深刻な違反であるかどうかは判断されます(以下に違反項目を定義します)。 違反判定された場合は、通常のレポート処理プロセスに加えて、即時の対応が必要になります。 -明確かつ深刻な違反行為の解決# 私たちは、インターネットでの会話が簡単にひどい誹謗中傷になってしまうことを、痛いほど知っています。 個人的な脅迫、暴力的、性差別的、人種差別的な言葉など、明らかで深刻な違反に対しては、迅速に対処します。 -行動規範委員会のメンバーは、明確かつ深刻な違反に気づいた場合、以下のように行動します。 -直ちにすべてのNumPyのオンラインコミュニティから違反者を排除します。 報告が受信され、違反者が排除されたことを報告者に連絡します。 どのような場合でも、モデレーターは違反者に連絡するための合理的な努力を行い、違反者の言葉や行動がどのように「明確かつ重大な違反」に該当するのかを具体的に伝えるべきです。 モデレーターは、違反者がこれは不当だと思う場合、あるいはNumPyチャンネルとの再接続を望む場合には、行動規範委員会による以下のような審査を求める権利があることも述べるべきです。 モデレータは、この説明を行動規範委員会に転送する必要があります。 行動規範委員会は、このプロセスが適用されたすべてのケースを正式にレビューし署名することで、よくある盛り上がりすぎた論争を諫めるためこのプロセスが使用されたのでないことを確認します。 報告の処理# 報告が委員会に送られると、直ちに報告者に返信して報告を受領したことを確認します。 この返信は72時間以内に送信される必要があり、委員会はそれよりもはるかに迅速に対応するよう努める必要があります。 -レポートに十分な情報が含まれていない場合、委員会は行動する前に、関連するすべてのデータを取得するようにします。 委員会は、事件の状況を全て知るために関係する個人に連絡する際に、運営協議会に代わって行動する権限を与えられています。 -その後、委員会は今回の問題を見直し、効果を最大限に発揮する対策を決定します。 -問題の種類 今回の事情が行動規範違反であるかどうか。 責任者が誰であるか これが進行中の状況であるか、誰の物理的安全に脅威があるかどうか。 これらの情報は書面で収集され、可能な限りグループの審議が記録され、保持されます (例えば、チャットの記録、Eメールのディスカッション、会議通話の記録、音声会話の概要など)。Terms of Usehttps://numpy.org/ja/terms/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/terms/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 (&quot;Project&quot;, “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.ケーススタディ: DeepLabCut 三次元姿勢推定https://numpy.org/ja/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/deeplabcut-dnn/DeepLapCutを用いたマウスの手の動きの解析# -(Source: www.deeplabcut.org ) 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は、ごくわずかなトレーニングデータで人間レベルの精度で実験動物の行動を追跡可能にするオープンソースのツールボックスです。 DeepLabCutの技術を使うことで、科学者は動物の種類と時系列のデータをもとに、運動制御と行動に関する科学的な理解を深めることができるようになりました。 -神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 -色のついた点は競走馬の体の位置を追跡# -(Source: Mackenzie Mathis) DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 -DeepLabCutは、研究者が対象の姿勢を推定し、Pythonベースのソフトウェアを使って効率的に対象の行動を定量化することを可能にします。 DeepLabCutを使用すると、研究者は動画から異なるフレームを識別し、数十個のフレームの特定の身体部位を、よくできたGUIによってラベルづけできます。 すると、DeepLabCutの深層学習ベースのポーズ推定アーキテクチャにより、動画の残りの部分や動物の他の類似した動画から同じ特徴を抽出する方法を学習できます。 ハエやマウスなどの一般的な実験動物から チーターのようなより珍しい動物まで、動物の種類を問わず利用できます。 -DeepLabCutでは転移学習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が発表されました。 これは、霊長類の顔分析から犬の姿勢まで、様々な種や実験条件に対応した事前訓練済みモデルを提供しています。 これにより、例えば、新しいデータのラベルを付けることなくクラウドで予測を実行することができたり、ニューラルネットワークの学習を実行することができます。 プログラミング経験は必要ありません。 -主な目標と結果# 科学研究のための動物姿勢解析の自動化: -DeepLabCutという技術の主な目的は、多様な環境で動物の姿勢を測定し追跡することです。 このデータは例えば神経科学の研究において、脳がどのように運動を制御しているかを理解するためのや、動物がどのように社会的に交流しているかを明らかにするために利用することができます。 研究者はDeepLabCutで 10倍のパフォーマンス向上 が可能であると発表しています。 オフラインでは最大1200フレーム/秒(FPS) で姿勢を推定することができます。 -姿勢推定のための使いやすいPythonツールキットの作成: -DeepLabCutは、動物の姿勢推定技術を研究者が簡単に利用できるツールとして共有したいという考えから開発されています。 そこで開発者らはプロジェクト管理機能を備えた、単独で機能し、使いやすいPythonツールボックスとしてこのツールを作成しました。 これにより、姿勢推定を自動化するだけでなく、DeepLabCutツールキットユーザーをデータセット収集段階から共有可能・再利用可能な分析パイプラインを作成する段階まで補助し、プロジェクトをエンドツーエンドで管理することも可能になりました。 -このツールキット はオープンソースとして利用できます。 -典型的なDeepLabCutワークフローは以下のようになります。 -オンライン学習によるトレーニングセットの作成と調整 特定の動物やシナリオに合わせたニューラルネットワークの構築 動画における大規模推論のためのコード作成 統合された可視化ツールを使用した推論の描画 DeepLabCutによる姿勢推定のステップ#ケーススタディ: クリケット分析、ゲームチェンジャー!https://numpy.org/ja/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/cricket-analytics/IPLT20、インド最大のクリケットフェスティバル# -(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium)) 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 -クリケットについて# インド人はクリケットが大好きだと言っても過言ではないでしょう。 この競技は、他のスポーツと異なり、インドの農村部や都市部を問わず、あらゆる場所でプレイされており、若者から年配の方まで広く人気があり、インドでは何十億人もの人々を結びつける役割を担っています。 クリケットは多くのメディアの注目を集めています。 クリケットは多くのメディアの注目を集め、非常に多額のお金と名声がかかっています。 過去数年間、テクノロジーは文字通りクリケットの試合を変えてきました。 視聴者はストリーミングメディア、トーナメント、モバイルベースの手頃なアクセスによるライブクリケット視聴などを享受しています。 -インドプレミアリーグ (IPL) は、2008年に設立された20チームから成るプロクリケットリーグです。 これは世界で最も参加者が多いクリケットイベントの1つで、2019年の市場規模は67億ドルだと評価されています。 -クリケットは数のゲームです。 バッツマンによってスコアされたランの数、ボウラーによって取られたウィケットの数、クリケットチームによって獲得した試合の数、バッツマンがボウリング攻撃に特定の方法で応答する回数。 クリケットの数字を掘り下げてパフォーマンスを向上させるとともに、NumPyなどの数値計算ソフトウェアを利用した強力な分析ツールを介して、クリケットのビジネスチャンス、市場全体、経済性を研究することは、大きな意味を持ちます。 クリケット分析は、試合に関する興味深い洞察と、ゲームの結果に関する予測AIを提供します。 -現在では、クリケットゲームの記録と 利用可能な統計データは豊富で、ほぼ無限の宝の山だと言えます。 : ESPN cricinfo や cricsheet. 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 に使用されています。 メディアやプロスポーツ団体のエンターテインメントプラットフォームは、技術や分析を利用し、試合勝率を向上させるために、下記のような要素が主要なメトリックだと考え始めています。 -バッティング成績の移動平均 スコア予測 プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察 チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 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# -(Image credit: Debarghya Das) 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。 クリケットの姿勢推定# -(Image credit: connect.vin) 課題# データのクリーニングと前処理 -IPLは、クリケットを古典的なテストマッチ形式から、はるかに大規模に拡大させました。 毎シーズン、様々なフォーマットで行われる試合の数は増加しており、データ、アルゴリズム、最新のスポーツデータ分析技術、シミュレーションモデルも増加しています。 クリケットのデータ分析には、フィールドマッピング、プレイヤートラッキング、ボールトラッキング、プレイヤーショット分析、およびボールがどのように動くのか、その角度、スピン、速度、軌道など、他の沢山の種類のデータを必要とします。 これらの要因により、データクリーニングと前処理の複雑さが増してしまいました。 -動的モデリング -クリケットでは、他のスポーツと同様、フィールド上の選手の様々な数字を追跡するために、関連する変数の数が多くなってしまいがちです。 たとえば、ボールやその属性情報、およびいくつかの行動をとるアクションのいくつかの可能性などの変数です。 データ分析とモデリングの複雑さは、分析中に必要となる予測のための質問の種類に正比例しており、データ表現とモデルにも大きく依存しています。 バッツマンが異なる角度や速度でボールを打った場合に何が起こるのかのような、動的なクリケットのプレーの予測が必要な場合、計算量やデータ比較が更に困難になります。ケーススタディ: 重力波の発見https://numpy.org/ja/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/gw-discov/重力波# -(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO) 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 -重力波 と LIGO について# 重力波は、空間と時間の基本構造の波紋です。 2つのブラックホールの衝突や合体、2連星や超新星の合体など、大きな変動現象によって生成されます。 重力波の観測は、重力を研究する上で重要なだけでなく、遠い宇宙におけるいくつかの不明瞭な現象と、その影響を理解するためにも役立ちます。 -[レーザー干渉計重力波天文台(LIGO)](https://www. ligo. caltech. edu)は、アインシュタインの一般相対性理論によって予測された重力波の直接検出を通して、重力波天体物理学の分野を切り開くために設計されました。 このシステムは、アメリカのワシントン州ハンフォードとルイジアナ州リビングストンにある2つの干渉計が一体となって構成され、重力波を検出します。 それぞれのシステムには、レーザー干渉法を用いた数キロ規模の重力波検出器が設置されています。 LIGO Scientific Collaboration（LSC）は、米国をはじめとする14カ国の大学から1000人以上の科学者が集まり、90以上の大学・研究機関によって支援されています。 また、約250人の学生も参加しています。 今回のLIGOの発見は、重力波が地球を通過する際に生じる空間と時間の微小な乱れの測定により、重力波そのものを初めて観測しました。 これにより、新しい天体物理学のフロンティアが開かれました。 これは、宇宙の歪んだ側面、つまり歪んだ時空から作られた物体とそれに現象を切り拓くものです。 -主な目的# LIGOのミッションは、宇宙で最も激しくエネルギーに満ちたプロセスからの重力波を検出することですが、LIGOが収集するデータは、重力、相対性理論、天体物理学、宇宙論、素粒子物理学、原子核物理学など、物理学の多くの分野に広く影響を与える可能性があります。 複雑な数学を含む相対性理論の数値計算によって観測データを解析し、信号とノイズを識別し、関連性のある信号をフィルタリングし、観測データの有意性を統計的に推定することで、宇宙の始まりのクランチを観測できるようになります。 バイナリや数値の結果を理解しやすいようにデータを可視化することも必要です。 課題# 計算 -合成により放出される重力波は、スーパーコンピュータを用いて数値相対性を手あたり次第に試すような方法では計算できません。 LIGOが収集するデータ量は、重力波の信号が少ないのと同じくらい不可解です。 -データの氾濫 -観測装置がより高感度で信頼性を持つようになると、データの大洪水によって、干し草の中から針を探すような問題が、多重に発生することがわかります。 LIGOは毎日テラバイトのデータを生成しているのです！ この大量のデータを解釈するには、各検出ごとに多大な労力が必要です。 例えば、LIGOによって収集される信号は、数十万個の重力波シグネチャのテンプレートで構成されており、スーパーコンピュータでしか解析できません。 -可視化 -アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。 画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。 -GW150914から推定される重力波の歪みの振幅# -(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication) 重力波の検出におけるNumPyの役割# 合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。ケーススタディ：世界初のブラックホール画像https://numpy.org/ja/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/blackhole-image/Black Hole M87# -(Image Credits: Event Horizon Telescope Collaboration) 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 -地球大の望遠鏡# Event Horizon telescope(EHT)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がアインシュタインの一般相対性理論に沿った最初の観測を実施したことが始まりでした。 -ブラックホール: EHTは、おとめ座銀河団のメシエ87銀河 (M87) の中心にある、地球から約5500万光年の距離にある超巨大ブラックホールを観測しました。 その質量は、太陽の65億倍です。 100年以上に渡る研究が行われてもなお、これまでに視覚的にブラックホールを観測できたことはありませんでした。 -観測と理論の比較: 科学者たちの間で、アインシュタインの一般相対性理論から、重力による光の曲げや光の捕獲による影のような領域が観測できるのではないかと期待されていました。 これはブラックホールの巨大な質量を測定するために利用することができます。 -課題# 大規模な計算 -EHTは膨大なデータ処理の課題を抱えていました。 大気の位相変動は急速で、記録帯域の幅は大きく、望遠鏡はそれぞれ異なっていて地理的にも分散しています。 -大量のデータ -EHTは一日で350テラバイトを超える観測データを生成し、ヘリウムで満たされたハードドライブに保存しています。 この大量のデータとデータの複雑さを軽減することは非常に難しいことです。 -よくわからないものを観測する -今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? -EHTのデータ処理パイプライン# -(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration) 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。 -ブラックホール画像化でNumPyが果たした役割# -例えば、 eht-imaging 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。コミュニティhttps://numpy.org/ja/community/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/community/NumPy は 常に多様なコントリビュータ 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 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. -NumPyメーリングリスト：# 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)。 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 にあります。 -GitHub イシュートラッカー# バグレポート（例：”np.arange(3).shape returns (5,), when it should return (3,)&quot;); ドキュメントの問題 (例: &ldquo;I find this section unclear&rdquo;); 機能追加リクエスト (例: &ldquo;I would like to have a new interpolation method in np.percentile&rdquo;). 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 から報告してください。 -Slack# SlackはNumpyに_ 貢献するための質問をするための_、リアルタイムのチャットルームです。 具体的には、 公開のメーリングリストやGitHubで質問やアイデアを持ち出すことを躊躇している人々のためのものです。 Slackに招待してもらいたい場合はこちらを確認下さい。サポートを得る方法https://numpy.org/ja/gethelp/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/gethelp/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に質問を投稿することです。 規模は小さいですが、下記のような質問をする場所もあります: IRC、 Gitter、 Reddit。 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 のページを参照してください。 -StackOverflow# NumPyの使用方法に関する質問をするためのフォーラムです。 例えば、「NumPyでXをするにはどうすればいいですか？ 質問をする時は、 #numpy タグ を使用してください。 -Reddit# もう一つの使い方に関する質問の場です。 -Gitter# 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# 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。プライバシーポリシーhttps://numpy.org/ja/privacy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/privacy/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.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。プレス用資料https://numpy.org/ja/press-kit/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/press-kit/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。 numpy.orgのリソースを使用することで、NumPy行動規範 を受け入れたことになることに注意してください。私たちについてhttps://numpy.org/ja/about/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/about/NumPy は、Python で数値計算を可能にするためのオープンソースプロジェクトです。 NumPyは、NumericやNumarrayといった初期のライブラリのコードをもとに、2005年から開発が開始されました。 NumPyは完全にオープンソースなソフトウェアです。 そして、NumPyは修正BSD ライセンス 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 をご覧ください。 -運営委員会# Numpy運営委員会はこのプロジェクトの管理組織です。 その役割は、Numpy コミュニティと協力し、Numpyのソフトウェアサービスを確実にユーザに提供することです。 ソフトウェアパッケージとコミュニティの両方において、プロジェクトの長期的な持続可能性を保っていきます。 NumPy運営委員会は現在以下のメンバーで構成されています (姓のアルファベット順): -Sebastian Berg Ralf Gommers Charles Harris Stephan Hoyer Inessa Pawson Matti Picus Stéfan van der Walt Melissa Weber Mendonça Eric Wieser 過去のメンバー -Alex Griffing (2015-2017) Allan Haldane (2015-2021) Marten van Kerkwijk (2017-2019) Travis Oliphant (プロジェクト創設者, 2005-2012) Nathaniel Smith (2012-2021) Julian Taylor (2013-2021) Jaime Fernández del Río (2014-2021) Pauli Virtanen (2008-2021) Numpy運営委員会に連絡するには、numpy-team@googlegroups.配列演算https://numpy.org/ja/arraycomputing/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/arraycomputing/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。 -Numerical Python: 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。 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。 -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 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。 \ No newline at end of file +NumPyhttps://numpy.org/ja/Recent content on NumPyHugojaSun, 18 Aug 2024 00:00:00 +0000ニュースhttps://numpy.org/ja/news/Sun, 18 Aug 2024 00:00:00 +0000https://numpy.org/ja/news/<h3 id="numpy-1260-がリリースされました">NumPy 1.26.0 がリリースされました。<a class="headerlink" href="#numpy-1260-がリリースされました" title="Link to this heading">#</a></h3> +<p><em>2024 Aug, 2024</em> &ndash; Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。</p> +<ul> +<li>Python 3.12.0 のサポート</li> +<li>多くの期限切れの非推奨(Deprecation)の削除</li> +<li>Array-api 2023.12 標準のサポート</li> +</ul> +<p>このリリースでは、Pythonのバージョン 3.10-3.13 がサポートされています。</p> +<h3 id="多くの新しい非推奨deprecationの追加">多くの新しい非推奨(Deprecation)の追加<a class="headerlink" href="#多くの新しい非推奨deprecationの追加" title="Link to this heading">#</a></h3> +<p><em>2024年6月16日</em> &ndash; Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。</p> +<ul> +<li><a href="https://numpy.org/devdocs/numpy_2_0_migration_guide.html">NumPy 2.0移行ガイド</a></li> +<li><a href="https://numpy.org/devdocs/release/2.0.0-notes.html">2.0.0 リリース ノート</a></li> +<li>ステータスアップデートお知らせに関する問題: <a href="https://github.com/numpy/numpy/issues/24300">numpy#24300</a></li> +</ul> +<p>ブログ記事 <a href="https://blog.scientific-python.org/numpy/numpy2/">&ldquo;NumPy 2.0: 進化のマイルストーン&rdquo;</a> は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。</p> +<h3 id="numpy-1250-リリース">NumPy 1.25.0 リリース<a class="headerlink" href="#numpy-1250-リリース" title="Link to this heading">#</a></h3> +<p>_ 2024年5月23日_ &ndash; NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 <strong>破壊的な変更</strong> である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy <code>2.0.0rc2</code>で動作するかどうか確認をお願いします。 <strong>詳細は下記をご覧ください:</strong></p>2020年 NumPyコミュニティ調査https://numpy.org/ja/user-survey-2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/user-survey-2020/<p>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。</p> +<figure class="align-default" id="id000"> +<img src="https://numpy.org/surveys/NumPy_usersurvey_2020_report_cover.png" alt="Cover page of the 2020 Numpy User survey report, titled &#34;Numpyコミュニティ調査2020 - 結果&#34;" class="align-center" width="250"> + + + +<figcaption> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<p>調査結果を詳細を知りたい場合は、<strong><a href="https://numpy.org/surveys/NumPy_usersurvey_2020_report.pdf">こちらのレポート</a></strong> をダウンロードしてください。</p> +<p>結果の概要については、 <strong><a href="https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf">こちらの図</a></strong> をチェックしてください。</p> +<p>より詳細が知りたくなりましたか？ <strong><a href="https://numpy.org/user-survey-2020-details/">https://numpy.org/user-survey-2020-details/</a></strong> をご覧ください。</p>404https://numpy.org/ja/404/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/404/<p>おっとっと！ 間違った所にアクセスしているようです。</p> +<p>何かがここにページがあるべきだと思ったら、GitHub で <a href="https://github.com/numpy/numpy.org/issues">issue</a> を作成してください。</p>NumPy に貢献するhttps://numpy.org/ja/contribute/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/contribute/<p>NumPyプロジェクトを成功させるには、あなたの専門知識とプロジェクトに関する熱意が必要です。 貢献方法はプログラミングに限定されません。 このページには<strong>あなたができる</strong> 様々な種類の貢献方法が示されています。</p> +<p>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、 <a href="https://mail.python.org/mailman/listinfo/numpy-discussion">メーリングリスト</a> 、 <a href="http://github.com/numpy/numpy">GitHub</a>、 <a href="https://github.com/numpy/numpy/issues">イシューの作成</a> 、関連するイシューへのコメントがあります。</p> +<p>連絡先としては、 <a href="mailto:numpy-team@googlegroups.com">numpy-team@googlegroups.com</a> または、<a href="https://numpy-team.slack.com">Slack</a> (グループに招待するためにこちらに連絡お願いします: <a href="mailto:numpy-team@googlegroups.com">numpy-team@googlegroups.com</a>)があります。</p> +<p>また、隔週の _コミュニティミーティング_もあり、詳細は <a href="https://mail.python.org/mailman/listinfo/numpy-discussion">メーリングリスト</a> 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、 <a href="https://opensource.guide/how-to-contribute/">このガイド</a> を読んでみて下さい。</p> +<p>私たちのコミュニティは、誰もが平等に扱われ、すべての貢献を平等に評価することを目指しています。 私たちはオープンで居心地の良いコミュニティを作るために <a href="https://numpy.org/ja/code-of-conduct">行動基準</a> を制定しています。</p> +<h3 id="コードを書く">コードを書く<a class="headerlink" href="#コードを書く" title="Link to this heading">#</a></h3> +<p>プログラマーの方には、こちらの <a href="https://numpy.org/devdocs/dev/index.html#development-process-summary">ガイド</a>でNumPyのコードに貢献する方法を説明しています。 <br>追加情報に関しては、 こちらの<a href="https://www.youtube.com/playlist?list=PLCK6zCrcN3GXBUUzDr9L4__LnXZVtaIzS">YouTube チャンネル</a> もご覧ください。</p> +<h3 id="プルリクエストのレビュー">プルリクエストのレビュー<a class="headerlink" href="#プルリクエストのレビュー" title="Link to this heading">#</a></h3> +<p>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、</p> +<ul> +<li>長期にわたる議論をまとめる</li> +<li>ドキュメントのPRをトリアージする</li> +<li>提案された変更をテストする</li> +</ul> +<h3 id="教育用の資料を作成する">教育用の資料を作成する<a class="headerlink" href="#教育用の資料を作成する" title="Link to this heading">#</a></h3> +<p>NumPy の <a href="https://numpy.org/devdocs">ユーザガイド</a> 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。 <a href="https://numpy.org/neps/nep-0044-restructuring-numpy-docs.html">NEP 44 — Restructuring the NumPyDocumentation</a>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。</p> +<h3 id="イシューのトリアージ">イシューのトリアージ<a class="headerlink" href="#イシューのトリアージ" title="Link to this heading">#</a></h3> +<p><a href="https://github.com/numpy/numpy/issues">NumPyのイシュートラッカー</a> 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:</p> +<ul> +<li>古いバグがまだ残っているか確認する</li> +<li>重複したイシューを見つけ、お互いに関連づける</li> +<li>問題を再現するコードを作成する</li> +<li>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)</li> +</ul> +<p>ぜひ、やってみて下さい。</p>NumPyユーザアンケートhttps://numpy.org/ja/user-surveys/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/user-surveys/<p><strong>2020</strong> NumPY調査チームは、ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果は<a href="https://numpy.org/user-survey-2020/">こちら</a>をご覧ください。</p> +<p><strong>2021</strong> 収集された調査データは現在解析中です。</p> +<p>過去または今後のNumPyユーザ調査に関する質問や提案がある場合は、<a href="https://github.com/numpy/numpy-surveys/issues">こちら</a>にイシューを作成してください。</p>NumPyのインストールhttps://numpy.org/ja/install/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/install/<p>NumPyは<code>conda</code>、<code>pip</code> 、macOSやLinuxのパッケージマネージャー、または <a href="https://numpy.org/devdocs/building">ソースコード</a>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 <a href="https://numpy.org/ja/install/#python-numpy-install-guide">Python と Numpyの インストールガイド</a> を参照してください。</p> +<p>NumPyは<code>conda</code>、<code>pip</code> 、macOSやLinuxのパッケージマネージャー、または <a href="https://numpy.org/devdocs/user/building.html">ソースコード</a>からインストールすることが出来ます。 NumPyをインストールするための唯一必要なものは、Pythonそのものだけです。 もしまだPythonをイントールしておらず、最もシンプルなインストール方法をお探しなら、<a href="https://www.anaconda.com/distribution">Anaconda Distribution</a>の使用をおすすめします。</p> +<p><strong>CONDA</strong></p> +<p><code>conda</code>を使用する場合、 <code>defaults</code> または <code>conda-forge</code> のチャンネルから NumPy をインストールできます。</p> + + +<div class="highlight"> + <pre class="chroma"><code><span style="display:flex;"><span><span style="color:#75715e"># 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</span> +</span></span><span style="display:flex;"><span>conda create -n my-env +</span></span><span style="display:flex;"><span>conda activate my-env +</span></span><span style="display:flex;"><span><span style="color:#75715e"># conda-forgeからインストールする場合</span> +</span></span><span style="display:flex;"><span>conda config --env --add channels conda-forge +</span></span><span style="display:flex;"><span><span style="color:#75715e"># インストールコマンド</span> +</span></span><span style="display:flex;"><span>conda install numpy</span></span></code></pre> +</div> +<p><strong>PIP</strong></p> +<p><code>pip</code>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:</p> + + +<div class="highlight"> + <pre class="chroma"><code><span style="display:flex;"><span>pip install numpy</span></span></code></pre> +</div> +<p>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。 <a href="https://numpy.org/ja/install/#reproducible-installs">再現可能なインストール</a>を参照ください。 <a href="https://dev.to/bowmanjd/python-tools-for-managing-virtual-environments-3bko#howto">こちらの記事</a>では仮想環境を使う詳細について説明されています。</p> +<p><a name="python-numpy-install-guide"></a></p> +<h1 id="pythonとnumpyの-インストールガイド">PythonとNumPyの インストールガイド<a class="headerlink" href="#pythonとnumpyの-インストールガイド" title="Link to this heading">#</a></h1> +<p>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。</p> +<h2 id="推奨方法">推奨方法<a class="headerlink" href="#推奨方法" title="Link to this heading">#</a></h2> +<p>まずはユーザの経験レベルと、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」の間の方は、シンプルにインストールしたい場合は「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」を参照ください。</p> +<h3 id="初級ユーザ">初級ユーザ<a class="headerlink" href="#初級ユーザ" title="Link to this heading">#</a></h3> +<p>Windows、macOS、Linuxのすべてのユーザー向けには:</p>NumPyの学び方https://numpy.org/ja/learn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/learn/<p><strong>公式の NumPy ドキュメント</strong> については <a href="https://numpy.org/doc/stable">numpy.org/doc/stable</a>を参照してください。</p> +<hr> +<p>以下は、Numpyへの貢献者とコミュニティによって開発された、NumPyの自己学習と他人への教育のための資料です。</p> +<h2 id="初心者向け">初心者向け<a class="headerlink" href="#初心者向け" title="Link to this heading">#</a></h2> +<p>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：</p> +<p><i class="fas fa-chalkboard"></i> <strong>動画</strong></p> +<ul> +<li><a href="https://numpy.org/devdocs/user/quickstart.html">NumPy Quickstart チュートリアル</a></li> +<li><a href="https://numpy.org/numpy-tutorials">NumPyチュートリアル</a>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、<a href="https://github.com/numpy/numpy-tutorials">Github上のnumpy-tutorialsリポジトリ</a>をチェックしてみて下さい。</li> +<li><a href="https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b">イラストで学ぶNumPy <em>by Lev Maximov</em></a></li> +<li><a href="https://lectures.scientific-python.org/">Scientific Pythonレクチャー</a> 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。</li> +<li><a href="https://numpy.org/devdocs/user/absolute_beginners.html">NumPy: 初心者のための基本</a></li> +<li><a href="https://github.com/rougier/numpy-tutorial">NumPy チュートリアル <em>by Nicolas Rougier</em></a></li> +<li><a href="http://cs231n.github.io/python-numpy-tutorial/">スタンフォード大学 CS231 <em>by Justin Johnson</em></a></li> +<li><a href="https://numpy.org/devdocs">NumPyユーザーガイド</a></li> +</ul> +<p><i class="fas fa-book"></i> <strong>チュートリアル</strong></p> +<ul> +<li><a href="http://web.mit.edu/dvp/Public/numpybook.pdf">NumPガイド <em>Travelis E. Oliphant著</em></a> 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 <a href="https://www.barnesandnoble.com/w/guide-to-numpy-travis-e-oliphant-phd/1122853007">を参照ください</a>.</li> +<li><a href="https://www.labri.fr/perso/nrougier/from-python-to-numpy/">PythonにおけるNumPy (発展編)</a></li> +<li><a href="https://www.amazon.com/Elegant-SciPy-Art-Scientific-Python/dp/1491922877">エレガントなSciPy</a> <em>Juan Nunez-Iglesias・Stefan van der Walt・Harriet Dashnow 著</em></li> +</ul> +<p>また、「Python+SciPy」を題材にした<a href="https://www.goodreads.com/shelf/show/python-scipy">推薦本リスト</a> 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。</p> +<p><i class="far fa-file-video"></i> <strong>動画</strong></p> +<ul> +<li><a href="http://youtu.be/ZB7BZMhfPgk">NumPy を使った数値計算入門</a> <em>by Alex Chabot-Leclerc</em></li> +</ul> +<hr> +<h2 id="上級者向け">上級者向け<a class="headerlink" href="#上級者向け" title="Link to this heading">#</a></h2> +<p>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。</p>NumPyの歴史https://numpy.org/ja/history/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/history/<p>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 &ldquo;野良&quot;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 &ldquo;野良&quot;学生プログラマーのグループがひっくり返すことができると想像することさえ、当時は馬鹿げていると考えられていました。 それでも、独特の視点を持つ熱狂的でフレンドリーなコミュニティに助けられ、完全にオープンなツールスタックの背後にある哲学的な動機は、長い目では日の目を見てきました。 現在では、Numpy は科学者、技術者、および世界中の多くの専門家によって信頼され、使われています。 例えば、重力波の解析に用いられた公開スクリプトはNumPyを利用していますし、「M87ブラックホール画像化プロジェクト」では、直接NumPyを引用しています。</p> +<p>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、 <a href="arxiv.org/abs/1907.10121">arxiv.org</a> を参照してください。</p> +<p>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。</p> +<p><a href="https://sourceforge.net/projects/numpy/files/Old%20Numeric/"> <em>Numeric</em></a> のダウンロード***</p> +<p><a href="https://sourceforge.net/projects/numpy/files/Old%20Numarray/">*Numarray *</a> のダウンロード***</p> +<p>*<sub>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。</p></p> +<h3 spaces-before="0"> + 過去の資料 +</h3> +<p spaces-before="0"> + <a href="static/numeric-manual.pdf"><em x-id="3">Numeric</em>マニュアルのダウンロード</a> +</p>NumPyを引用するhttps://numpy.org/ja/citing-numpy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/citing-numpy/<p>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。</p> +<ul> +<li>Harris, C.R., Millman, K.J., van der Walt, S.J. et al. <em>Array programming with NumPy</em>. Nature 585, 357–362 (2020). DOI: <a href="https://doi.org/10.1038/s41586-020-2649-2">10.1038/s41586-020-2649-2</a>. (<a href="https://www.nature.com/articles/s41586-020-2649-2">リンク</a>).</li> +</ul> +<p><em>BibTeX形式:</em></p> + +<div class="highlight"> + <pre>@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{&#39;{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{&#39;{a}}ndez del + R{&#39;{\i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{&#39;{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} +}</pre> +</div>NumPy開発チームhttps://numpy.org/ja/teams/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/teams/<p>私たちは、高品質のオープンソースソフトウェアを構築することで、世界中の科学・研究コミュニティをサポートすることを使命とする国際的なチームです。 是非<a href="https://numpy.org/ja/contribute">参加してください</a>!</p> +<h3 id="maintainers">Maintainers<a class="headerlink" href="#maintainers" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img 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sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/123428?v=4" alt="Avatar of Jarrod Millman"> + + + +Jarrod Millman + </div> + <a class="sd-stretched-link" href="https://github.com/jarrodmillman"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3891660?u=5de0ba1f1adad6f041f6dde1affef5d05bbed80a&amp;v=4" alt="Avatar of Joe LaChance"> + + + +Joe LaChance + </div> + <a class="sd-stretched-link" href="https://github.com/joelachance"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&amp;v=4" alt="Avatar of Mars Lee"> + + + +Mars Lee + </div> + <a class="sd-stretched-link" href="https://github.com/MarsBarLee"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&amp;v=4" alt="Avatar of shalz"> + + + +shalz + </div> + <a class="sd-stretched-link" href="https://github.com/shaloo"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&amp;v=4" alt="Avatar of Shekhar Prasad Rajak"> + + + +Shekhar Prasad Rajak + </div> + <a class="sd-stretched-link" href="https://github.com/Shekharrajak"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&amp;v=4" alt="Avatar of Stefan van der Walt"> + + + +Stefan van der Walt + </div> + <a class="sd-stretched-link" href="https://github.com/stefanv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&amp;v=4" alt="Avatar of Albert Steppi"> + + + +Albert Steppi + </div> + <a class="sd-stretched-link" href="https://github.com/steppi"></a> + </div> +</div> + + </div> +</div> + +<h3 id="トリアージチーム">トリアージチーム<a class="headerlink" href="#トリアージチーム" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&amp;v=4%22" alt="Avatar of Andrew Nelson"> + + + +Andrew Nelson + </div> + <a class="sd-stretched-link" href="https://github.com/andyfaff"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1522319?v=4%22" alt="Avatar of Anirudh Subramanian"> + + + +Anirudh Subramanian + </div> + <a class="sd-stretched-link" href="https://github.com/anirudh2290"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/71486?u=cc88e2a4e4c6c496dcb9dd88cead5c0dab496c89&amp;v=4%22" alt="Avatar of Aaron Meurer"> + + + +Aaron Meurer + </div> + <a class="sd-stretched-link" href="https://github.com/asmeurer"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3813847?v=4%22" alt="Avatar of Atsushi Sakai"> + + + +Atsushi Sakai + </div> + <a class="sd-stretched-link" href="https://github.com/AtsushiSakai"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6691888?v=4%22" alt="Avatar of Ben Nathanson"> + + + +Ben Nathanson + </div> + <a class="sd-stretched-link" href="https://github.com/bjnath"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/35413198?u=e67bd9ebc361fb207f914979d935fd1956eb626c&amp;v=4%22" alt="Avatar of Anne Bonner"> + + + +Anne Bonner + </div> + <a class="sd-stretched-link" href="https://github.com/bonn0062"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6788290?u=d9a388224b87d55106cb3e6199d02ebc1d8e0553&amp;v=4%22" alt="Avatar of Brigitta Sipőcz"> + + + +Brigitta Sipőcz + </div> + <a class="sd-stretched-link" href="https://github.com/bsipocz"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5476002?u=5352f057ef8cb5de29e4d2a9fa8b0d0f49580dc8&amp;v=4%22" alt="Avatar of carlkl"> + + + +carlkl + </div> + <a class="sd-stretched-link" href="https://github.com/carlkl"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/11371428?u=9b425a337d076ec86b75ebc759724283f0970d9a&amp;v=4%22" alt="Avatar of Ryan C Cooper"> + + + +Ryan C Cooper + </div> + <a class="sd-stretched-link" href="https://github.com/cooperrc"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/36567889?u=cbc76d558d375ebafd4a05a505f500eb94e00611&amp;v=4%22" alt="Avatar of ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)"> + + + +ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) + </div> + <a class="sd-stretched-link" href="https://github.com/czgdp1807"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/2190658?u=b85e13f985d0bf87eeb3a7a146b61dcc9586019b&amp;v=4%22" alt="Avatar of Hameer Abbasi"> + + + +Hameer Abbasi + </div> + <a class="sd-stretched-link" href="https://github.com/hameerabbasi"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4%22" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8078968?v=4%22" alt="Avatar of jbrockmendel"> + + + +jbrockmendel + </div> + <a class="sd-stretched-link" href="https://github.com/jbrockmendel"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&amp;v=4%22" alt="Avatar of Kai"> + + + +Kai + </div> + <a class="sd-stretched-link" href="https://github.com/Kai-Striega"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/16046705?u=1bf01e87adb556503c1fe07789c194cc04d38490&amp;v=4%22" alt="Avatar of Yuji Kanagawa"> + + + +Yuji Kanagawa + </div> + <a class="sd-stretched-link" href="https://github.com/kngwyu"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/22004158?u=2ebb3919ebaa3d7e0865ea5583032bc08bd0f526&amp;v=4%22" alt="Avatar of Kriti Singh"> + + + +Kriti Singh + </div> + <a class="sd-stretched-link" href="https://github.com/kritisingh1"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/149655?u=249f7995c486de232c34e7970fbea505f518a1be&amp;v=4%22" alt="Avatar of Christopher Albert"> + + + +Christopher Albert + </div> + <a class="sd-stretched-link" href="https://github.com/krystophny"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/20306270?u=235cdf82e88f76ba2f5f4c2d33fa392319c60ad1&amp;v=4%22" alt="Avatar of Lysandros Nikolaou"> + + + +Lysandros Nikolaou + </div> + <a class="sd-stretched-link" href="https://github.com/lysnikolaou"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/34613774?u=61535ebfff07c68ea672cd8cd68c46187a38d3c1&amp;v=4%22" alt="Avatar of Meekail Zain"> + + + +Meekail Zain + </div> + <a class="sd-stretched-link" href="https://github.com/Micky774"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&amp;v=4%22" alt="Avatar of Christopher Sidebottom"> + + + +Christopher Sidebottom + </div> + <a class="sd-stretched-link" href="https://github.com/Mousius"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&amp;v=4%22" alt="Avatar of Mateusz Sokół"> + + + +Mateusz Sokół + </div> + <a class="sd-stretched-link" href="https://github.com/mtsokol"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4%22" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6564007?u=e5fb962de792bbce925c0c94fb7a748803c8bfa0&amp;v=4%22" alt="Avatar of Noa Tamir"> + + + +Noa Tamir + </div> + <a class="sd-stretched-link" href="https://github.com/noatamir"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&amp;v=4%22" alt="Avatar of Raghuveer Devulapalli"> + + + +Raghuveer Devulapalli + </div> + <a class="sd-stretched-link" href="https://github.com/r-devulap"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&amp;v=4%22" alt="Avatar of shalz"> + + + +shalz + </div> + <a class="sd-stretched-link" href="https://github.com/shaloo"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/55803680?u=bb727a0da1f33ed5f2feb58dc0333943430d2318&amp;v=4%22" alt="Avatar of Tina Oberoi"> + + + +Tina Oberoi + </div> + <a class="sd-stretched-link" href="https://github.com/tinaoberoi"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/13260794?u=5421923c831b67c4ef290bbdeb31ebfbdd906abc&amp;v=4%22" alt="Avatar of Rakesh Vasudevan"> + + + +Rakesh Vasudevan + </div> + <a class="sd-stretched-link" href="https://github.com/vrakesh"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8103276?v=4%22" alt="Avatar of Zijie (ZJ) Poh"> + + + +Zijie (ZJ) Poh + </div> + <a class="sd-stretched-link" href="https://github.com/zjpoh"></a> + </div> +</div> + + </div> +</div> + +<h3 id="アンケートチーム">アンケートチーム<a class="headerlink" href="#アンケートチーム" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4%22" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4%22" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4%22" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + </div> +</div> + +<h3 id="名誉メンテナー">名誉メンテナー<a class="headerlink" href="#名誉メンテナー" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/9040124?v=4%22" alt="Avatar of Allan Haldane"> + + + +Allan Haldane + </div> + <a class="sd-stretched-link" href="https://github.com/ahaldane"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/20568?v=4%22" alt="Avatar of Ondřej Čertík"> + + + +Ondřej Čertík + </div> + <a class="sd-stretched-link" href="https://github.com/certik"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/25111?v=4%22" alt="Avatar of David Cournapeau"> + + + +David Cournapeau + </div> + <a class="sd-stretched-link" href="https://github.com/cournape"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3343990?v=4%22" alt="Avatar of Jaime"> + + + +Jaime + </div> + <a class="sd-stretched-link" href="https://github.com/jaimefrio"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/123428?v=4%22" alt="Avatar of Jarrod Millman"> + + + +Jarrod Millman + </div> + <a class="sd-stretched-link" href="https://github.com/jarrodmillman"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/542663?v=4%22" alt="Avatar of Julian Taylor"> + + + +Julian Taylor + </div> + <a class="sd-stretched-link" href="https://github.com/juliantaylor"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&amp;v=4%22" alt="Avatar of Mark Wiebe"> + + + +Mark Wiebe + </div> + <a class="sd-stretched-link" href="https://github.com/mwiebe"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&amp;v=4%22" alt="Avatar of Nathaniel J. Smith"> + + + +Nathaniel J. Smith + </div> + <a class="sd-stretched-link" href="https://github.com/njsmith"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/254880?v=4%22" alt="Avatar of Travis E. Oliphant"> + + + +Travis E. Oliphant + </div> + <a class="sd-stretched-link" href="https://github.com/teoliphant"></a> + </div> +</div> + + </div> +</div> + +<h1 id="管理委員会">管理委員会<a class="headerlink" href="#管理委員会" title="Link to this heading">#</a></h1> +<p>For the list of people on the Steering Council, please see <a href="https://numpy.org/devdocs/dev/governance/people.html">here</a>.</p>NumPy行動規範https://numpy.org/ja/code-of-conduct/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/code-of-conduct/<h3 id="はじめに">はじめに<a class="headerlink" href="#はじめに" title="Link to this heading">#</a></h3> +<p>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。</p> +<p>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。</p> +<p>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。</p> +<h3 id="ガイドラインの概要">ガイドラインの概要<a class="headerlink" href="#ガイドラインの概要" title="Link to this heading">#</a></h3> +<p>私たちは下記の内容に真摯に取り組みます。</p> +<ol> +<li>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。</li> +<li>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。</li> +<li>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。</li> +<li>好奇心を大事にしよう。 全てを知っている人はいないのです！ 早め早めに質問をすることで、後に生じうる多くの問題を回避できます。 そのため私たちは質問を奨励しています。 私たちは、出来るだけ質問に良く対応し、手助けできるよう努力します。</li> +<li>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。 : +<ul> +<li>他の人に向けられた暴力的な行為や言葉。</li> +<li>性差別や人種差別、その他の差別的なジョークや言動。</li> +<li>性的または暴力的な内容の投稿。</li> +<li>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)。</li> +<li>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。</li> +<li>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。</li> +<li>不快な思いをさせる性的な言動。</li> +<li>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。</li> +<li>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。</li> +<li>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。</li> +</ul> +</li> +</ol> +<h3 id="多様性に関する声明">多様性に関する声明<a class="headerlink" href="#多様性に関する声明" title="Link to this heading">#</a></h3> +<p>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。</p>NumPy行動規範 - 報告書のフォローアップ方法https://numpy.org/ja/report-handling-manual/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/report-handling-manual/<p>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。</p> +<p><a href="https://numpy.org/ja/code-of-conduct">行動規範</a> を施行することは、私たちのコミュニティの現在のため、未来のために重要です。 この施行は、軽いものではありません。 施行の基準を見直す際、行動規範委員会は以下の考え方とガイドラインに留意するようにします。</p> +<ul> +<li>機械的ではなく、人間的に行動します。 委員会は、当事者のプライバシーと報告者の必要なだけの機密性を尊重しながら、状況を理解するように働きかけることができます. ただし、1人以上の個人と直接連絡を取る必要がある場合もあります。 委員会の目標は正しい決定を下すのではなく、コミュニティの健康を改善することなのです。</li> +<li>行動を判断するのではなく、個人への共感を強調し、「良い」と「悪い」の二値評価を避けます。 明確な攻撃性とハラスメントが存在した場合、私たちはそれらに対処します。 しかし、解決が困難なシナリオの多くは、通常の意見の相違が、複数の当事者による無益または有害な行動に発展した場合です。 完全に文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、コミュニティにとって最終的に最も有益な方法です。</li> +<li>私たちは、電子メールが判断に困難な媒体であり、独立して利用できることを理解しています。 個人の情報なしに電子メール上で批判を受けることは、特に苦痛である場合もあります。 そこで、他者の見解に対して、開放的で、敬意を持った雰囲気を保つことが重要になります。 それはまた、私たちの行動が透明でなければならないことを意味します。 全てのメンバーが公平かつ同情をもって扱われるようにするため、私たちは全力を尽くします。</li> +<li>差別の境界は時に曖昧で、また無意識に行われている場合もあります。 これにより、普通の人との関わりの中で、不公平感や敵意として現れてくるのです。 私達は、このようなことが起こることはわかっているので、気をつけて見ていきたいと思います。 不当な扱いを受けたと思われる方は、ぜひご連絡ください。</li> +<li>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。</li> +<li>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。</li> +<li>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。</li> +</ul> +<h2 id="仲介">仲介<a class="headerlink" href="#仲介" title="Link to this heading">#</a></h2> +<p>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。</p> +<ul> +<li>調停者として役立つ候補者を見つけます。</li> +<li>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。</li> +<li>報告者の同意を取得します。</li> +<li>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。</li> +<li>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。</li> +</ul> +<p>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。</p> +<h2 id="報告に対する委員会の対応">報告に対する委員会の対応<a class="headerlink" href="#報告に対する委員会の対応" title="Link to this heading">#</a></h2> +<p>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。</p> +<h2 id="明確かつ深刻な違反行為の解決">明確かつ深刻な違反行為の解決<a class="headerlink" href="#明確かつ深刻な違反行為の解決" title="Link to this heading">#</a></h2> +<p>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。</p> +<p>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。</p> +<ul> +<li>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。</li> +<li>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。</li> +<li>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。</li> +<li>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。</li> +</ul> +<h2 id="報告の処理">報告の処理<a class="headerlink" href="#報告の処理" title="Link to this heading">#</a></h2> +<p>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。</p>Terms of Usehttps://numpy.org/ja/terms/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/terms/<p><em>Last updated January 4, 2020</em></p> +<h2 id="agreement-to-terms">AGREEMENT TO TERMS<a class="headerlink" href="#agreement-to-terms" title="Link to this heading">#</a></h2> +<p>These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy (&quot;<strong>Project</strong>&quot;, “<strong>we</strong>”, “<strong>us</strong>”, or “<strong>our</strong>”), 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.</p>ケーススタディ: DeepLabCut 三次元姿勢推定https://numpy.org/ja/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/deeplabcut-dnn/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/mice-hand.gif" alt="micehandanim" class="align-center"> + + + +<figcaption><strong class="caption-title">DeepLapCutを用いたマウスの手の動きの解析</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="http://www.mousemotorlab.org/deeplabcut">(Source: www.deeplabcut.org )</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"> + <p> +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。 +</p> + <p class="attribution">—Alexander Mathis、 <em>准教授、École polytechnology fe’rale de Lausanne</em> (<a href="https://www.epfl.ch/en/">EPFL</a>)</p> +</blockquote> + +<h2 id="deeplabcut-について">DeepLabCut について<a class="headerlink" href="#deeplabcut-について" title="Link to this heading">#</a></h2> +<p><a href="https://github.com/DeepLabCut/DeepLabCut">DeepLabCut</a>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。</p> +<p>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。</p> +<figure class="align-default" id="id002"> +<img src="https://numpy.org/images/content_images/cs/race-horse.gif" alt="horserideranim" class="align-center"> + + + +<figcaption><strong class="caption-title">色のついた点は競走馬の体の位置を追跡</strong><a class="headerlink" href="#id002" title="Link to this image">#</a><br>(Source: Mackenzie Mathis) +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<p>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。</p> +<p>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 <a href="https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618">チーター</a>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。</p> +<p>DeepLabCutでは<a href="https://arxiv.org/pdf/1909.11229">転移学習</a>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<a href="https://arxiv.org/abs/1605.03170">DeeperCut</a>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。</p> +<p>最近、<a href="http://www.mousemotorlab.org/dlc-modelzoo">DeepLabCut model zoo</a>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。</p> +<h3 id="主な目標と結果">主な目標と結果<a class="headerlink" href="#主な目標と結果" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>科学研究のための動物姿勢解析の自動化:</strong></p> +<p>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 <a href="https://www.biorxiv.org/content/10.1101/457242v1">10倍のパフォーマンス向上</a> 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。</p> +</li> +<li> +<p><strong>姿勢推定のための使いやすいPythonツールキットの作成:</strong></p> +<p>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。</p>ケーススタディ: クリケット分析、ゲームチェンジャー!https://numpy.org/ja/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/cricket-analytics/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/ipl-stadium.png" alt="Indian Premier League Cricket cup and stadium" class="align-center"> + + + +<figcaption><strong class="caption-title">IPLT20、インド最大のクリケットフェスティバル</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://unsplash.com/@aksh1802">(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium))</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/"> + <p> +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。 +</p> + <p class="attribution">—M S Dhoni、 <em>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</em></p> +</blockquote> + +<h2 id="クリケットについて">クリケットについて<a class="headerlink" href="#クリケットについて" title="Link to this heading">#</a></h2> +<p>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<a href="https://www.statista.com/topics/4543/indian-premier-league-ipl/">多額のお金</a>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。</p> +<p>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<a href="https://en.wikipedia.org/wiki/Indian_Premier_League">67億ドル</a>だと評価されています。</p> +<p>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。</p> +<p>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。 : <a href="https://stats.espncricinfo.com/ci/engine/stats/index.html">ESPN cricinfo や</a> <a href="https://cricsheet.org">cricsheet</a>. 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、 <a href="https://www.researchgate.net/publication/336886516_Data_visualization_and_toss_related_analysis_of_IPL_teams_and_batsmen_performances">クリケット 分析</a> 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。</p> +<ul> +<li>バッティング成績の移動平均</li> +<li>スコア予測</li> +<li>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</li> +<li>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</li> +</ul> +<figure class="align-center" id="id002"> +<img src="https://numpy.org/images/content_images/cs/cricket-pitch.png" alt="A cricket pitch with bowler and batsmen" class="align-center"> + + + +<figcaption><strong class="caption-title">フィールドのフォーカルポイントとなるクリケットピッチ</strong><a class="headerlink" href="#id002" title="Link to this image">#</a><br><a href="http://debarghyadas.com/files/IPLpaper.pdf">(Image credit: Debarghya Das)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<h3 id="データ分析の主要な目標">データ分析の主要な目標<a class="headerlink" href="#データ分析の主要な目標" title="Link to this heading">#</a></h3> +<ul> +<li>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、 <a href="https://adtmag.com/blogs/dev-watch/2017/07/sports-analytics.aspx">他のスポーツ</a>でも使用されています。</li> +<li>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。</li> +<li>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。</li> +</ul> +<figure class="align-default" id="id003"> +<img src="https://numpy.org/images/content_images/cs/player-pose-estimator.png" alt="pose estimator" class="align-center"> + + + +<figcaption><strong class="caption-title">クリケットの姿勢推定</strong><a class="headerlink" href="#id003" title="Link to this image">#</a><br><a href="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/">(Image credit: connect.vin)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<h3 id="課題">課題<a class="headerlink" href="#課題" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>データのクリーニングと前処理</strong></p>ケーススタディ: 重力波の発見https://numpy.org/ja/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/gw-discov/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/gw_sxs_image.png" alt="binary coalesce black hole generating gravitational waves" class="align-center"> + + + +<figcaption><strong class="caption-title">重力波</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://youtu.be/Zt8Z_uzG71o">(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> 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。 +</p> + <p class="attribution">—David Shoemaker, <em>LIGOサイエンティフィック・コラボレーション</em></p> +</blockquote> + +<h2 id="重力波-と-ligo-について"><a href="https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/">重力波</a> と <a href="https://www.ligo.caltech.edu">LIGO</a> について<a class="headerlink" href="#重力波-と-ligo-について" title="Link to this heading">#</a></h2> +<p>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。</p> +<p><a href="https://www.ligo.caltech.edu">レーザー干渉計重力波天文台(LIGO)</a>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。</p> +<h3 id="主な目的">主な目的<a class="headerlink" href="#主な目的" title="Link to this heading">#</a></h3> +<ul> +<li>LIGOの<a href="https://www.ligo.caltech.edu/page/what-is-ligo">ミッション</a>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。</li> +<li>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。</li> +<li>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。</li> +</ul> +<h3 id="課題">課題<a class="headerlink" href="#課題" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>計算</strong></p> +<p>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。</p> +</li> +<li> +<p><strong>データの氾濫</strong></p> +<p>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。</p> +</li> +<li> +<p><strong>可視化</strong></p> +<p>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。</p> +</li> +</ul> +<figure class="align-default" id="id002"> +<img src="https://numpy.org/images/content_images/cs/gw_strain_amplitude.png" alt="gravitational waves strain amplitude" class="align-center"> + + + +<figcaption><strong class="caption-title">GW150914から推定される重力波の歪みの振幅</strong><a class="headerlink" href="#id002" title="Link to this image">#</a><br><a href="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger">(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<h2 id="重力波の検出におけるnumpyの役割">重力波の検出におけるNumPyの役割<a class="headerlink" href="#重力波の検出におけるnumpyの役割" title="Link to this heading">#</a></h2> +<p>ブラックホール合成により放出される重力波は、スーパーコンピュータを用いたブルートフォースの数値相対性処理以外の手法では計算できません。 重力波は非常に小さい効果を生み、物質と微小な相互作用を持つため、検出が困難です。 LIGOのすべてのデータを処理・分析するには、膨大な計算インフラが必要です。 信号の数十億倍のノイズを除去した後も、非常に複雑な相対性理論の方程式と膨大な量のデータがあり、計算上の課題となっています。</p>ケーススタディ：世界初のブラックホール画像https://numpy.org/ja/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/case-studies/blackhole-image/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/blackhole.jpg" alt="black hole image" class="align-center"> + + + +<figcaption><strong class="caption-title">Black Hole M87</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg">(Image Credits: Event Horizon Telescope Collaboration)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +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。 +</p> + <p class="attribution">—<em>カリフォルニア工科大学 計算・数理学部</em>のKatie Bouman助教授</p> +</blockquote> + +<h2 id="地球大の望遠鏡">地球大の望遠鏡<a class="headerlink" href="#地球大の望遠鏡" title="Link to this heading">#</a></h2> +<p><a href="https:/eventhorizontelescope.org">Event Horizon telescope(EHT)</a>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、<a href="https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole">20マイクロ秒</a>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!</p> +<h3 id="主な目標と結果">主な目標と結果<a class="headerlink" href="#主な目標と結果" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>宇宙の新しい見方:</strong> EHTの画期的な考え方の基礎が築かれたのは、100年前に <a href="https://en.wikipedia.org/wiki/Eddington_experiment">Sir Arthur Eddington</a>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。</p> +</li> +<li> +<p><strong>ブラックホール:</strong> 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。 <a href="https://www.jpl.nasa.gov/news/news.php?feature=7385">100年以上</a>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。</p> +</li> +<li> +<p><strong>観測と理論の比較:</strong> 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。</p> +</li> +</ul> +<h3 id="課題">課題<a class="headerlink" href="#課題" title="Link to this heading">#</a></h3> +<ul> +<li> +<p><strong>大規模な計算</strong></p> +<p>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。</p> +</li> +<li> +<p><strong>大量のデータ</strong></p> +<p>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。</p> +</li> +<li> +<p><strong>よくわからないものを観測する</strong></p> +<p>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?</p> +</li> +</ul> +<figure class="align-center" id="id002"> +<img src="https://numpy.org/images/content_images/cs/dataprocessbh.png" alt="data pipeline" class="align-center"> + + + +<figcaption><strong class="caption-title">EHTのデータ処理パイプライン</strong><a class="headerlink" href="#id002" title="Link to this image">#</a><br><a href="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57">(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<h2 id="numpyが果たした役割">NumPyが果たした役割<a class="headerlink" href="#numpyが果たした役割" title="Link to this heading">#</a></h2> +<p>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？</p>コミュニティhttps://numpy.org/ja/community/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/community/<p>NumPy は 常に多様な<a href="https://numpy.org/ja/teams/">コントリビュータ</a> 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、 <a href="https://numpy.org/ja/code-of-conduct">NumPy 行動規範</a> をご覧ください。</p> +<p>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。</p> +<h2 id="オンラインで参加する方法">オンラインで参加する方法<a class="headerlink" href="#オンラインで参加する方法" title="Link to this heading">#</a></h2> +<p>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。 <em>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<a href="https://numpy.org/ja/gethelp/">サポート</a>.</em></p> +<h3 id="numpyメーリングリスト"><a href="https://mail.python.org/mailman/listinfo/numpy-discussion">NumPyメーリングリスト：</a><a class="headerlink" href="#numpyメーリングリスト" title="Link to this heading">#</a></h3> +<p>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。</p> +<p>このメーリングリストでは、一番下のメールを使用し、メーリングリストに返信して下さい( 他の送信者ではなく)。 このメーリングリストの検索可能なアーカイブは <a href="https://mail.python.org/archives/list/numpy-discussion@python.org/">こちら</a> にあります。</p> +<hr> +<h3 id="github-イシュートラッカー"><a href="https://github.com/numpy/numpy/issues">GitHub イシュートラッカー</a><a class="headerlink" href="#github-イシュートラッカー" title="Link to this heading">#</a></h3> +<ul> +<li>バグレポート（例：”<code>np.arange(3).shape</code> returns <code>(5,)</code>, when it should return <code>(3,)</code>&quot;);</li> +<li>ドキュメントの問題 (例: &ldquo;I find this section unclear&rdquo;);</li> +<li>機能追加リクエスト (例: &ldquo;I would like to have a new interpolation method in <code>np.percentile</code>&rdquo;).</li> +</ul> +<p><em>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、 <a href="https://tidelift.com/docs/security">こちら</a> から報告してください。</em></p>サポートを得る方法https://numpy.org/ja/gethelp/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/gethelp/<p><strong>開発関連の問題:</strong> NumPyの開発関連の問題 (例: バグレポート) については、<a href="https://numpy.org/ja/community/">コミュニティ</a> のページを参照してください。</p> +<p><strong>ユーザーからの質問:</strong> 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、<a href="http://stackoverflow.com/questions/tagged/numpy">StackOverflow</a>に質問を投稿することです。 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、質問の数は膨大で対応しきれないのが現実です。</p> +<h3 id="stackoverflow"><a href="http://stackoverflow.com/questions/tagged/numpy">StackOverflow</a><a class="headerlink" href="#stackoverflow" title="Link to this heading">#</a></h3> +<p>NumPyの使用方法に関する質問をするためのフォーラムです。 例えば、「NumPyでXをするにはどうすればいいですか？ 質問をする時は、<a href="https://stackoverflow.com/help/tagging"> <code>#numpy</code> タグ</a> を使用してください。</p> +<hr> +<h3 id="reddit"><a href="https://www.reddit.com/r/Numpy/">Reddit</a><a class="headerlink" href="#reddit" title="Link to this heading">#</a></h3> +<p>もう一つの使い方に関する質問の場です。</p> +<hr>プライバシーポリシーhttps://numpy.org/ja/privacy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/privacy/<p><strong>numpy.org</strong> は、NumPyプロジェクトの資金援助のスポンサーでもある、<a href="https://numfocus.org">NumFOCUS, Inc.</a>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。</p> +<p>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。</p>プレス用資料https://numpy.org/ja/press-kit/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/press-kit/<p>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。</p> +<p>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: <a href="https://github.com/numpy/numpy/tree/main/branding/logo">ロゴリンク</a>。 numpy.orgのリソースを使用することで、<a href="https://numpy.org/ja/code-of-conduct/">NumPy行動規範</a> を受け入れたことになることに注意してください。</p>私たちについてhttps://numpy.org/ja/about/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/about/<p>NumPy は、Python で数値計算を可能にするためのオープンソースプロジェクトです。 NumPyは、NumericやNumarrayといった初期のライブラリのコードをもとに、2005年から開発が開始されました。 NumPyは完全にオープンソースなソフトウェアです。 そして、NumPyは<a href="https://github.com/numpy/numpy/blob/main/LICENSE.txt">修正BSD ライセンス</a> の条項の下で、すべての人が利用可能です。</p> +<p>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、 <a href="https://www.numpy.org/devdocs/dev/governance/index.html">Governance Document</a> をご覧ください。</p> +<h2 id="運営委員会">運営委員会<a class="headerlink" href="#運営委員会" title="Link to this heading">#</a></h2> +<p>Numpy運営委員会はこのプロジェクトの管理組織です。 その役割は、Numpy コミュニティと協力し、Numpyのソフトウェアサービスを確実にユーザに提供することです。 ソフトウェアパッケージとコミュニティの両方において、プロジェクトの長期的な持続可能性を保っていきます。 NumPy運営委員会は現在以下のメンバーで構成されています (姓のアルファベット順):</p> +<ul> +<li>Sebastian Berg</li> +<li>Ralf Gommers</li> +<li>Charles Harris</li> +<li>Stephan Hoyer</li> +<li>Inessa Pawson</li> +<li>Matti Picus</li> +<li>Stéfan van der Walt</li> +<li>Melissa Weber Mendonça</li> +<li>Eric Wieser</li> +</ul> +<p>過去のメンバー</p> +<ul> +<li>Alex Griffing (2015-2017)</li> +<li>Allan Haldane (2015-2021)</li> +<li>Marten van Kerkwijk (2017-2019)</li> +<li>Travis Oliphant (プロジェクト創設者, 2005-2012)</li> +<li>Nathaniel Smith (2012-2021)</li> +<li>Julian Taylor (2013-2021)</li> +<li>Jaime Fernández del Río (2014-2021)</li> +<li>Pauli Virtanen (2008-2021)</li> +</ul> +<p>Numpy運営委員会に連絡するには、numpy-team@googlegroups.comまでメールしてください。</p>配列演算https://numpy.org/ja/arraycomputing/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/ja/arraycomputing/<p><em>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。</em></p> +<p>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 <strong>Python</strong> です。</p> +<p><strong>Num</strong>erical <strong>Py</strong>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。</p> +<p>2006年にNumPyが発表されてから、2008年にPandasが登場し、その後、数年間にいくつかの配列演算関連のライブラリが次々と現れるようになりました。 これらの新しい配列演算ライブラリの多くは、NumPyの機能や能力を模倣しており、機械学習や人工知能向けの新しいアルゴリズムや機能を持っています。</p> +<p><img +src="https://numpy.org/images/content_images/array_c_landscape.png" +alt="arraycl" +title="配列演算の概略" /></p> +<p><strong>配列演算</strong> は <strong>配列</strong> のデータ構造に基づいています。 <em>配列</em> 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。</p> +<p>配列演算は <em>一度に</em> 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。</p> \ No newline at end of file diff --git a/ja/install/index.html b/ja/install/index.html index 7e0f5400..243ed92b 100644 --- a/ja/install/index.html +++ b/ja/install/index.html @@ -1,6 +1,5 @@ -NumPy - NumPyのインストール -NumPy - NumPyのインストール

      NumPyのインストール

      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 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 ソースコードからインストールすることが出来ます。 NumPyをインストールするための唯一必要なものは、Pythonそのものだけです。 もしまだPythonをイントールしておらず、最もシンプルなインストール方法をお探しなら、Anaconda Distributionの使用をおすすめします。

      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。

      # 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
 # conda-forgeからインストールする場合
@@ -26,6 +25,5 @@
 # インストールコマンド
 conda install numpy

      PIP

      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:

      pip install numpy

      またpipを使う場合、仮想環境を使うことをおすすめします。 再現可能なインストールを参照ください。 こちらの記事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。

      推奨方法#

      まずはユーザの経験レベルと、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」の間の方は、シンプルにインストールしたい場合は「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」を参照ください。

      初級ユーザ#

      Windows、macOS、Linuxのすべてのユーザー向けには:

      上級ユーザー#

      Conda#

      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におけるパッケージ管理#

      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 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 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 と pip が最も広く利用されているツールです。

      Pipとconda#

      pip と conda がPythonパッケージをインストールするための2つの主要なツールです。 これら二つのツールの機能は部分的に重複しますが(例えば、両方とも numpyをインストールできます)、一緒に動作することもできます。 ここでは、pip とcond の主要な違いについて説明します。 これは、パッケージをどのように効果的に管理するかを理解したい場合、重要な知識です。

      2つ目の違いは、pipはPython Packaging Index(PyPI) からパッケージをインストールするのに対し、condaは独自のチャンネル(一般的には “defaults “や “conda-forge “など) からインストールすることです。 PyPIは最大のパッケージ管理システムですが、人気のある全てのパッケージがcondaでも利用可能です。

      最初の違いは、condaは複数言語に対応可能で、Python自体をインストールできることです。 pip はシステム上の特定の Python にインストールされ、パッケージはそのPython用にのみインストールします。 PyPIは、最大のパッケージ管理システムですが、すべての代表的なパッケージは、condaにも利用可能です。

      3つ目の違いは、condaはパッケージ、依存関係、環境を管理するための統合されたソリューションであるのに対し、pipでは環境や複雑な依存関係を扱うために別のツール(たくさん存在しています！

      再現可能なインストール#

      ライブラリが更新されると、コードの実行結果が変わったり、コードが完全に 壊れたりする可能性があります。 なので重要なことは、使用しているパッケージの組み合わせと各バージョンのセットを再構築できるようにしておくことです。 ベストプラクティスは次の通りです:

      1. プロジェクトごとに異なる仮想環境を使用して下さい。
      2. パッケージインストーラを使用してパッケージ名とバージョンを記録するようにして下さい。 それぞれ、独自のメタデータフォーマットがあります:

      NumPyパッケージと高速線形代数ライブラリ#

      NumPy は他の Python パッケージに依存していませんが、高速な線形代数ライブラリに依存しています。 典型的には、インテル® MKLやOpenBLAS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。

      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:

      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.
      On this page
      \ No newline at end of file +Importing the numpy c-extensions failed. This error can happen for different reasons, often due to issues with your setup.
      On this page
      \ No newline at end of file diff --git a/ja/learn/index.html b/ja/learn/index.html index 7fca7d2f..456cc2d2 100644 --- a/ja/learn/index.html +++ b/ja/learn/index.html @@ -1,22 +1,19 @@ -NumPy - NumPyの学び方 -NumPy - NumPyの学び方

      NumPyの学び方

      公式の NumPy ドキュメント については numpy.org/doc/stableを参照してください。

      以下は、Numpyへの貢献者とコミュニティによって開発された、NumPyの自己学習と他人への教育のための資料です。

      初心者向け#

      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：

      動画

      チュートリアル

      また、「Python+SciPy」を題材にした推薦本リスト 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

      上級者向け#

      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。

      書籍

      チュートリアル

      書籍

      NumPyに関する講演#

      NumPyを引用する#

      もし、あなたの研究においてNumPyが重要な役割を果たし、論文でこのプロジェクトについて言及したい場合は、こちらのページを参照して下さい。

      On this page
      \ No newline at end of file diff --git a/ja/news/index.html b/ja/news/index.html index 103a93d2..2f5f454a 100644 --- a/ja/news/index.html +++ b/ja/news/index.html @@ -1,18 +1,16 @@ -NumPy - ニュース -

      ニュース

      NumPy 1.26.0 がリリースされました。#

      2024 Aug, 2024 – Numpy 2.1.0 は Python 3.13 をサポートし、Python 3.9をサポート外としました。 今回のリリースは通常のバグ修正やPythonサポートの更新に加えて、NumPyが2.0の長期開発を経て、通常のリリースサイクルに戻るためのリリースでもあります。 今回のリリースのハイライトは下記の通りです。

      • Python 3.12.0 のサポート
      • 多くの期限切れの非推奨(Deprecation)の削除
      • Array-api 2023.12 標準のサポート

      このリリースでは、Pythonのバージョン 3.10-3.13 がサポートされています。

      多くの新しい非推奨(Deprecation)の追加#

      2024年6月16日 – Numpy 2.0.0 は2006年以来のメジャーリリースです。 これは、前回の機能リリースから11か月間の開発の成果であり、1078件のプルリクエストにわたる212人の貢献者の成果となります。 このリリースには、大きく、エキサイティングな新機能と、PythonとCの両方のAPIへの変更が含まれています。 今回のリリースが、通常のマイナーリリースでは実施できなかった互換性を破壊する変更を含んでいます。これには、ABIの破壊、型昇格ルールの変更、および1.26.xでは非推奨警告が出されていなかった可能性のあるAPIの変更が含まれています。 NumPy 2.0の変更に対応する方法に関する主要なドキュメントは次のとおりです。

      ブログ記事 “NumPy 2.0: 進化のマイルストーン” は、今回のメジャーバージョンリリースがどのようにして決定されたかについてのストーリーを少し伝えています。

      NumPy 1.25.0 リリース#

      _ 2024年5月23日_ – NumPy 2.0が2024年6月16日にリリースされる予定になりました！ このリリースは1年以上かけて我々が準備してきたもので、2006年以来のメジャーリリースとなります。 このリリースで重要なことは、多くの新機能とパフォーマンスの向上に加えて、 このリリースは、 破壊的な変更 である Python と C API を含む、ABI への変更 が含まれています。 NumPyに依存しているパッケージやエンドユーザーのコードがこのは破壊的変更に適応する必要がある可能性があります。可能であれば、あなたのコードがNumPy 2.0.0rc2で動作するかどうか確認をお願いします。 詳細は下記をご覧ください:

      NumFOCUSの年末の資金調達#

      2023年12月19日 – NumFOCUSは、年末キャンペーンでPyCharmチームと協力し、PyCharmライセンスの初回購入に30%の割引を提供しています。 2023年12月23日までのPyCharm購入による1年目の収益は全てNumFOCUSのプログラムに直接寄付されます。

      購入される方はこちらのURLか: https://lp.jetbrains.com/support-data-science/ こちらのクーポンコードを利用してください: ISUPPORTDATASCIENCE 

      NumPy 1.20.0 リリース#

      2022年12月18日 – Numpy 1.24.0 がリリースされました。 今回のリリースのハイライトは次のとおりです。

      • Python 3.12.0 のサポート
      • Cython 3.0.0 との互換性
      • Mesonビルドシステムの利用
      • SIMD サポートの改善
      • f2py のバグ修正, meson と bind(x) のサポート
      • 更新された BLAS/LAPACK の高速化ライブラリのサポート

      Numpy 1.26.0 は 1.25 からの互換性を保持しています。Mesonビルドシステムへの移行とCython 3.0.0へのサポートが目的のリリースです。 合計20人がこのリリースに貢献し、59個のプルリクエストがマージされました。

      このリリースでサポートされている Python のバージョンは3.9から 3.12 です。

      numpy.orgが日本語とポルトガル語で利用可能になりました#

      2023年4月2日 – numpy.orgが2つの言語で利用可能になりました： 日本語とポルトガル語。 熱心なボランティアがいなければ、このプロジェクトは不可能でした：

      ポルトガル語

      • Melissa Weber Mendonça (melissawm)
      • Ricardo Prins (ricardoprins)
      • Getúlio Silva (getuliosilva)
      • Julio Batista Silva (jbsilva)
      • Alexandre de Siqueira (alexdesiqueira)
      • Alexandre B A Villares (villares)
      • Vini Salazar (vinisalazar)

      日本語：

      • Atsushi Sakai (AtsushiSakai)
      • KKunai
      • Tom Kelly (TomKellyGenetics)
      • Yuji Kanagawa (kngwyu)
      • Tetsuo Koyama (tkoyama010)

      翻訳インフラストラクチャに関するプロジェクトは、CZIからの資金援助でサポートされています。

      今後も、NumPyのウェブサイトをより多くの言語に翻訳したいと思っています。 もし手伝える場合は、Slack上のNumPy翻訳チームに連絡をお願います: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (#translation チャンネルを探してください) (#translation チャンネルを探してください) また、Scientific Pythonエコシステム全体のドキュメントや教育コンテンツのローカライズに取り組む翻訳チームも 立ち上げています。 このプロジェクトにも興味がある場合は、是非Scientific Python Discordに参加してください: https://discord.gg/khWtqY6RKr. (#translation チャンネルを探してください)

      Numpy 1.23.0 リリース#

      2022年1月22日 – Numpy 1.23.0 がリリースされました。 今回のリリースのハイライトは次のとおりです。

      • MUSLのサポート。 MUSLのWheelが準備されました。
      • 富士通のC/C++コンパイラサポート
      • einsum でオブジェクト配列がサポートされるようになりました.
      • 行列の置き換え(inplace)掛け算のサポート (@=).

      Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 将来の NumPy 2.0.0 に向けた準備作業も行われており、 多数の新規および期限切れの機能廃止が可能となってきています。

      合計148人がこのリリースに貢献し、530個のプルリクエストが マージされました。

      このリリースでサポートされている Python のバージョンは3.3.9 - 3.11 です。

      インクルーシブな文化の育成: 参加の募集#

      2023年5月10日 – インクルーシブ・カルチャーの育成: 参加募集

      NumPyプロジェクトの多様性とインクルージョンに関して、我々はどのようなことを実施すればいいでしょうか？ 興味がある方はこちらの レポート を読んで参加する方法を確認してください。

      NumPy ドキュメンテーションチームのリーダーの変更#

      2023年1月6日 –- Mukulika PahariとRoss Barnowskiは、Melissa MendoncAudioに代わるNumPyドキュメンテーションチームの新しいリーダーとして任命されました。 私たちは、MelissaにNumPyの公式ドキュメントと教育資料に対するすべての貢献に感謝し、MukulikaとRossに新しい役割にステップアップしてもらったことに感謝します。

      NumPy 1.24.0 リリース#

      2021年1月23日 – Numpy 1.21.0 がリリースされました。 今回のリリースのハイライトは下記の通りです。

      • スタッキング関数のための新しい"dtype"と"casting"キーワードの追加
      • F2PYの新機能追加とバグ修正
      • 多くの新しい非推奨(Deprecation)の追加
      • 多くの期限切れの非推奨(Deprecation)の削除

      Numpy 1.25. リリースは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 dtype のプロモーションとクリーンアップの変更により、多数の新規と期限切れの非推奨が存在しています。 今回のリリースは、444個のプルリクエストと177人のコントリビューターによるものです。 サポートされている Python のバージョンは 3.8-3.11 です。

      Numpy 1.26.0 は 1.25 からの互換性を保持しています。#

      2021年12月31日 – Numpy 1.22.0 がリリースされました。 今回のリリースの目玉機能は次のとおりです。

      • loadtxt がCで実装されたことによる、大幅なパフォーマンス向上
      • より簡単なデータ交換のためのPythonレベルでのDLPackの公開
      • 構造化されたdtypesのプロモーションと比較方法の変更
      • f2pyの改善

      Numpy 1.23. リリースでは引き続きdtypeの取り扱いと dtypeのプロモーションを改善し、実行速度を向上させ、 ドキュメントを明確化するための継続的な作業を続けて行く予定です。 今回のリリースは、494個のプルリクエストと151人のコントリビューターによるものです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。 Python 3.11がrc ステージに到達すると Python 3.11 もサポートされます。

      NumFOCUS DEI研究への参加募集#

      2022年4月13日 – NumPyは、NumFOCUSと協力して、ある研究プロジェクトを進めており、これはGordon & Betty Moore Foundationによって資金提供されています。 この研究チームは、新しい貢献者、プロジェクトの開発者およびメンテナー、そして過去に貢献した方々に、NumPyに参加し貢献した経験について話を聞きたいと考えています。

      あなたの経験を共有することに興味がありますか?

      もし興味がある場合は、研究目標、プライバシー、および 守秘義務に関する追加情報が記載されている、この簡単な参加者の興味フォームに記入をお願いします。 多様で包括的なオープンソースソフトウェアコミュニティの 成長と持続可能性のために、このプロジェクトへのあなたの参加は非常に大きな価値があります。 参加を受け入れられた人は、研究チームメンバーと30分間のインタビューに参加することになります。

      NumPy 1.19.2 リリース#

      2023年9月16日 – NumPy 1.26.0がリリースされました。 今回のリリースの目玉機能は次のとおりです。

      • メインの名前空間の型アノテーションは基本的に完了しました。 上流のコードは常に変化するものなので、さらなる改良が必要でしょうが、大きな作業は終わったと考えています。 これはおそらく、今回のリリースで最も目に見える改良でしょう。
      • 以前から提案されていた array API 標準 のベータ版が提供されています ( NEP 47 を参照) 。 これは、CuPy や JAX などのライブラリで使用できる 関数の標準的なコレクションを作成するために必要なステップです。
      • NumPy に DLPack バックエンドが追加されました。 DLPack は、配列(テンソル) データ用の共通のデータ変換フォーマットを提供します。
      • quantile, percentile, および関連する関数に新しいメソッドが追加されました。 これらの新しいメソッドは、論文で一般的に見られる一通りの処理を提供します。
      • ユニバーサル関数は、NEP 43 の多くを実装するためにリファクタリングされました。 これにより将来の DType API の処理も可能にします。
      • ダウンストリームのプロジェクトで使用するための新しい設定可能なメモリー・アロケーターが追加されました。

      NumPy 1.22.0は、153人の貢献者が609のプルリクエストを作成した 非常に大きなリリースです。 このリリースでサポートされている Python のバージョンは 3.8 - 3.10 です。

      科学的なPythonエコシステムにおける包括的な文化の前進#

      _ 2021年8月31日_ – この度、Chan Zuckerberg Initiativeより、科学的なPythonプロジェクトにおいて、歴史的に疎外されてきたグループの人々のオンボーディング、インクルージョン、リテンションを支援し、NumPy、SciPy、Matplotlib、Pandasのコミュニティダイナミクスを構造的に改善するための 助成金を授与されました ことをお知らせします。

      CZIのEssential Open Source Software for Scienceプログラム の一環として、この Diversity & Inclusion補助金 は、開けたなオープンソースコミュニティを育成するためにやるべきことを特定したり、文書化したり、実施したりするためのコントリビュータ体験のリーダー専任職の創設を支援することになります。 このプロジェクトは、Melissa Mendonça (NumPy) が中心となって、下記の方々の追加のメンタリングとサポートにより実施されます。 Ralf Gommers (NumPy、SciPy)、Hannah AizenmanとThomas Caswell (Matplotlib)、Matt Haberland (SciPy)、そして Joris Van den Bossche (Pandas)。

      このプロジェクトは私たちのOSSプロジェクトのコミュニティダイナミクスを構造的に改善する方法を発見し、実施することを目指す野心的なプロジェクトです。 このような複数のプロジェクトの横断的な役割を確立することで、Scientific Pythonコミュニティに新しいコラボレーションモデルを導入し、エコシステム内のコミュニティ構築作業をより効率的に、より大きな成果を生めるようにしたいと考えています。 特にこのプロジェクトにより、歴史的にこれまで代表的ではなかったグループからの新しいコントリビュータを引き付け、貢献を維持するために、何がうまくいき、何がうまくいかないかを、より明確に把握できるようになると期待しています。 最後に、実施したアクションについて詳細な報告書を作成し、プロジェクトの代表者やコミュニティとの交流の面で、プロジェクトにどのような影響を与えたかを説明する予定です。

      2021年11月から2年間のプロジェクトが始まると予想されており、このプロジェクトの成果を楽しみにしています! このプロジェクトの提案書に関しては、こちら から全文を読むことができます.

      2021年度NumPyアンケート#

      2021年7月12日 – NumPy ではコミュニティの力を信じています。 昨年の第1回アンケートには、75カ国から1,236名のNumPyユーザーが参加してくれました。 この調査結果により、今後12ヶ月間、私たちがどのようなことに集中すべきかを、非常に良く理解することができました。

      今年もアンケートの時間が来ました。もう一度アンケートへの回答をお願いいたします。 アンケートへの回答は15分ほどで終了します。 アンケートは英語以外にも、ベンガル語、フランス語、ヒンディー語、日本語、マンダリン、ポルトガル語、ロシア語、スペイン語の8ヶ国語に対応しています。

      こちらのリンク先から、アンケートを始めることができます: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSL4q.

      Numpy 1.18.0 リリース#

      2023年1月17日 – Numpy 1.25.0 がリリースされました。 今回のリリースの目玉機能は次のとおりです。

      • より多くの機能やプラットフォームをカバーするためのSIMD関連の改善が実施されました。
      • dtypeのための新しいインフラとキャストの準備
      • Mac 版の Python 3.8 と Python 3.9 用 universal2 wheel
      • ドキュメントの改善
      • アノテーションの改善
      • 乱数生成用の新しい PCG64DXSM ビット生成機

      今回のNumpy リリースは、175人による581件のプルリクエストのマージの結果です。 このリリースでサポートされている Python のバージョンは 3.7-3.9 です。 Python 3.10 がリリースされた後、Python 3.10 のサポートが追加されます。

      2020年度 NumPy アンケート結果#

      2021年6月22日 – NumPyの調査チームは、2020年に ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらから確認できます。 https://numpy.org/user-survey-2020/

      NumPy 1.19.2 リリース#

      2021年1月30日 – NumPy 1.20.0 がリリースされました。 今回のリリースは180 人以上のコントリビューターのおかげで、これまでで最大の NumPyのリリースとなりました。 最も重要な2つの新機能は次のとおりです。

      • NumPyの大部分のコードに型注釈が追加されました。 そして新しいサブモジュールであるnumpy.typingが追加されました。 このサブモジュールはArrayLike やDtypeLikeという型注釈のエイリアスが定義されており、これによりユーザーやダウンストリームのライブラリはこの型注釈を使うことができます。
      • X86(SSE、AVX)、ARM64(Neon)、およびPowerPC (VSX) 命令をサポートするマルチプラットフォームSIMDコンパイラの最適化が実施されました。 これにより、多くの関数で大きく パフォーマンスが向上しました (例: sin/cos, einsum).

      NumPyプロジェクトの多様性#

      2020年9月20日に 、私たちは NumPyプロジェクトにおけるダイバーシティやインクルージョンの状況や、ソーシャルメディア上での議論についての宣言 について書きました。

      Natureに初の公式NumPy論文が掲載されました!#

      2020年9月16日 – NumPyに関する 最初の公式の論文 がNatureに査読付き論文として掲載されました。 これはNumPy 1.0のリリースから14年後のことになりました。 この論文では、配列プログラミングのアプリケーションと基本的なコンセプト、NumPyの上に構築された様々な科学的Pythonエコシステム、そしてCuPy、Dask、JAXのような外部の配列およびテンソルライブラリとの相互運用を容易にするために最近追加された配列プロトコルについて説明しています。

      Python 3.9のリリースに伴い、いつNumPyのバイナリwheelがリリースされるのですか？#

      2020年9月14日 – Python 3.9 は数週間後にリリースされる予定です。 もしあなたが新しいPythonのバージョンをいち早く利用している場合、NumPy（およびSciPyのような他のパッケージ）がリリース当日にバイナリwheelを用意していないことを知ってがっかりしたかもしれませんね。 ビルド用のインフラを新しいPythonのバージョンに適応させるのは非常に大変な作業で、PyPIやconda-forgeにパッケージが掲載されるまでには通常数週間かかります。 今後のwheelのリリースに備えて、以下を確認してください。

      • pip がmanylinux2010 と manylinux2014 をサポートするためにpipを少なくともバージョン 20.1 に更新する。
      • --only-binary=numpy または --only-binary=:all: をpipがソースからビルドしようとするのを防ぐために使用します。

      NumPy 1.19.2 リリース#

      2020年9月10日 – NumPy 19.2.0 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 への準備が行われ、アップストリームの修正が進行中の間も distutils の動作を維持するためのsetuptoolsのバージョンの固定が実施されています。 aarch64 wheelは最新のmanylinux2014リリースでビルドされており、異なるLinuxディストリビューションで使用される異なるページサイズの問題が修正されています。

      初めてのNumPyの調査が公開されました!!#

      2020年7月2日 – このアンケート調査は、NumPyにおける、ソフトウェアとしてとコミュニティの両方における意思決定の指針となり、優先順位を決定する役に立ちました。 この調査結果は英語以外のこれらの8つの言語で利用可能です: バングラ, ヒンディー語, 日本語, マンダリン, ポルトガル語, ロシア語, スペイン語とフランス語.

      NumPy をより良くするために、こちらの [アンケート](https://umdsurvey. umd. edu/jfe/form/SV_8bJrXjbhXf7saAl) に協力してもらえると助かります。

      NumPy に新しいロゴができました!#

      2020年6月24日 – NumPyのロゴが新しくなりました:

      NumPyのロゴ

      新しいロゴは、古いロゴに比べて、モダンでよりクリーンなデザインになりました。 新しいロゴをデザインしてくれたIsabela Presedo-Floydと、15年以上にわたって使用してきた旧ロゴをデザインしてくれたTravis Vaughtに感謝します。

      NumPy 1.19.0 リリース#

      2020年6月20日 – NumPy 1.19.0 がリリースされました。 このバージョンは Python 2系のサポートがない最初のリリースであり、“クリーンアップ用のリリース” です。 サポートされている一番古いPython のバージョンは Python 3.6 になりました。 また、今回の重要な新機能はNumPy 1.17.0で導入された乱数生成用のインフラにCythonからアクセスできるようになったことです。

      ドキュメント受諾期間#

      2020年5月11日 – NumPyは、 Googleのシーズンオブドキュメントプログラムのメンター団体の1つとして選ばれました。 NumPy のドキュメントを改善するために、テクニカルライターと協力するこの機会を楽しみにしています! 詳細については、 シーズンオブドキュメント公式サイト と アイデアページ をご覧ください。

      NumPy 1.18.0 リリース#

      2019年12月22日 – NumPy 1.18.0 がリリースされました。 このリリースは、1.17.0での主要な変更の後の、まとめのようなリリースです。 Python 3.5 をサポートする最後のマイナーリリースになります。 今回のリリースでは、64ビットのBLASおよびLAPACKライブラリとリンクするためのインフラの追加や、numpy.randomのための新しいC-APIの追加などが行われました。

      詳細については、 リリースノート を参照してください。

      NumPyはChan Zuckerberg財団から助成金を受けました。#

      2019年11月15日 – NumPyと、NumPyの重要な依存ライブラリの1つであるOpenBLASが、Chan Zuckerberg財団のEssential Open Source Software for Scienceプログラムを通じて、科学に不可欠なオープンソースツールのソフトウェアのメンテナンス、成長、開発、コミュニティへの参加などを支援する195,000ドルの共同助成金を獲得したことを発表しました。

      この助成金は、Numpy ドキュメントやウェブサイトの再設計などの改善に向けた取り組みを促進するために使用されます。 大規模かつ急速に拡大するユーザーの体験をより良くし、プロジェクトの長期的な持続可能性を確保するためのコミュニティ開発を行っていきます。 OpenBLASチームは、技術的に非常に重要な問題である、スレッド安全性、AVX-512に対処することに注力します。 また、スレッドローカルストレージ(TLS) の問題や、OpenBLASが依存するReLAPACK(再帰的なLAPACK) のアルゴリズムの改善も実施します。

      提案されたイニシアチブとその成果の詳細については、 フルグラントプロポーザル を参照してください。 この取り組みは2019年12月1日から始まり、今後12ヶ月間継続実施される予定です。

      過去のリリース#

      こちらは、より以前のNumPyリリースのリストで、各リリースノートへのリンクが記載されています。 全てのバグフィックスリリース(バージョン番号x.y.z のzだけが変更されたもの)は新しい機能追加はされず、マイナーリリース (y が増えたもの)は、新しい機能追加されています。

      On this page
      \ No newline at end of file diff --git a/ja/press-kit/index.html b/ja/press-kit/index.html index 696c2adb..2e4add6d 100644 --- a/ja/press-kit/index.html +++ b/ja/press-kit/index.html @@ -1,14 +1,13 @@ -NumPy - プレス用資料 -

      プレス用資料

      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。 numpy.orgのリソースを使用することで、NumPy行動規範 を受け入れたことになることに注意してください。

      On this page
      \ No newline at end of file diff --git a/ja/privacy/index.html b/ja/privacy/index.html index e5cada78..2a4ea964 100644 --- a/ja/privacy/index.html +++ b/ja/privacy/index.html @@ -1,14 +1,13 @@ -NumPy - プライバシーポリシー -

      プライバシーポリシー

      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.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。

      On this page
      \ No newline at end of file diff --git a/ja/report-handling-manual/index.html b/ja/report-handling-manual/index.html index 634ffee4..0ae601c3 100644 --- a/ja/report-handling-manual/index.html +++ b/ja/report-handling-manual/index.html @@ -1,23 +1,19 @@ -NumPy - NumPy行動規範 - 報告書のフォローアップ方法 -NumPy - NumPy行動規範 - 報告書のフォローアップ方法

      NumPy行動規範 - 報告書のフォローアップ方法

      NumPyの行動規範委員会はこのマニュアルに従います。 このマニュアルは様々な問題に対応する際に使用され、一貫性と公平性を確保します。

      行動規範 を施行することは、私たちのコミュニティの現在のため、未来のために重要です。 この施行は、軽いものではありません。 施行の基準を見直す際、行動規範委員会は以下の考え方とガイドラインに留意するようにします。

      • 機械的ではなく、人間的に行動します。 委員会は、当事者のプライバシーと報告者の必要なだけの機密性を尊重しながら、状況を理解するように働きかけることができます. ただし、1人以上の個人と直接連絡を取る必要がある場合もあります。 委員会の目標は正しい決定を下すのではなく、コミュニティの健康を改善することなのです。
      • 行動を判断するのではなく、個人への共感を強調し、「良い」と「悪い」の二値評価を避けます。 明確な攻撃性とハラスメントが存在した場合、私たちはそれらに対処します。 しかし、解決が困難なシナリオの多くは、通常の意見の相違が、複数の当事者による無益または有害な行動に発展した場合です。 完全に文脈を理解し、すべてを再び元に戻す道を見つけることは困難ですが、コミュニティにとって最終的に最も有益な方法です。
      • 私たちは、電子メールが判断に困難な媒体であり、独立して利用できることを理解しています。 個人の情報なしに電子メール上で批判を受けることは、特に苦痛である場合もあります。 そこで、他者の見解に対して、開放的で、敬意を持った雰囲気を保つことが重要になります。 それはまた、私たちの行動が透明でなければならないことを意味します。 全てのメンバーが公平かつ同情をもって扱われるようにするため、私たちは全力を尽くします。
      • 差別の境界は時に曖昧で、また無意識に行われている場合もあります。 これにより、普通の人との関わりの中で、不公平感や敵意として現れてくるのです。 私達は、このようなことが起こることはわかっているので、気をつけて見ていきたいと思います。 不当な扱いを受けたと思われる方は、ぜひご連絡ください。
      • 良い議論を実践することで、エンゲージメントの向上に取り組みます。例えば議論がどこで止まっているのかを特定したり、 実践的な情報、指針、資源を提供することで、これらの問題を前向きな方向に変えていきます。
      • 新しいメンバーが何を必要としているかに留意します。 特に社会的地位の低いグループからの参加を増やすことを目的に、明確なサポートと配慮を提供していきます。
      • 一人一人の文化的背景や母国語は異なります。 ネイティブでない人が起こした悪気のない誤解を確認し、問題を理解してもらい、不快感を与えないために何を変えればよいかを教えてあげてください。 外国語での複雑な議論はとても難しいものであり、国籍や文化を超えて多様性を育てていきたいと考えています。

      仲介#

      自主的な非公式の調停は、私たちの重要な役割です。 2つのグループ以上の当事者が不適切な行動をエスカレートした場合(人類の紛争では悲しいことに一般的なものですが)、調停プロセスを促進するは非常に重要です。 ちなみに、これは一例に過ぎません。委員会は、どのようなケースでも調停を検討することができますが、このプロセスはあくまでも自発的なものであり、当事者に参加を迫ることはできないことを念頭に置いて下さい。 委員会が調停を提案する場合は、次のようにすべきです。

      • 調停者として役立つ候補者を見つけます。
      • 報告者の合意を取得します。 報告者は、調停のアイデアを拒否したり、代替の調停者を提案する権利を持ちます。
      • 報告者の同意を取得します。
      • 調停人を決定します。当事者は、提案された候補者とは別の調停人を提案することができます。すべての条件で共通の合意に達した場合のみ、プロセスを進めることができます。
      • 調停が完了するまでのタイムラインを設定し、理想的には2週間以内に完了させます。

      調停者は、すべての当事者と関わり、すべての人に満足のいく決議を求めていきます。 終了後、調停人は（プロセスの全当事者によって吟味された）報告書を委員会に提出し、今後のステップに関する推奨事項を提示します。 委員会は、これらの結果(満足のいく決議が達成されたか否か) を評価し、必要と判断される追加的な措置を決定します。

      報告に対する委員会の対応#

      委員会(または委員) が行動規範違反報告を受けた時、その報告が明確で深刻な違反であるかどうかは判断されます(以下に違反項目を定義します)。 違反判定された場合は、通常のレポート処理プロセスに加えて、即時の対応が必要になります。

      明確かつ深刻な違反行為の解決#

      私たちは、インターネットでの会話が簡単にひどい誹謗中傷になってしまうことを、痛いほど知っています。 個人的な脅迫、暴力的、性差別的、人種差別的な言葉など、明らかで深刻な違反に対しては、迅速に対処します。

      行動規範委員会のメンバーは、明確かつ深刻な違反に気づいた場合、以下のように行動します。

      • 直ちにすべてのNumPyのオンラインコミュニティから違反者を排除します。
      • 報告が受信され、違反者が排除されたことを報告者に連絡します。
      • どのような場合でも、モデレーターは違反者に連絡するための合理的な努力を行い、違反者の言葉や行動がどのように「明確かつ重大な違反」に該当するのかを具体的に伝えるべきです。 モデレーターは、違反者がこれは不当だと思う場合、あるいはNumPyチャンネルとの再接続を望む場合には、行動規範委員会による以下のような審査を求める権利があることも述べるべきです。 モデレータは、この説明を行動規範委員会に転送する必要があります。
      • 行動規範委員会は、このプロセスが適用されたすべてのケースを正式にレビューし署名することで、よくある盛り上がりすぎた論争を諫めるためこのプロセスが使用されたのでないことを確認します。

      報告の処理#

      報告が委員会に送られると、直ちに報告者に返信して報告を受領したことを確認します。 この返信は72時間以内に送信される必要があり、委員会はそれよりもはるかに迅速に対応するよう努める必要があります。

      レポートに十分な情報が含まれていない場合、委員会は行動する前に、関連するすべてのデータを取得するようにします。 委員会は、事件の状況を全て知るために関係する個人に連絡する際に、運営協議会に代わって行動する権限を与えられています。

      その後、委員会は今回の問題を見直し、効果を最大限に発揮する対策を決定します。

      • 問題の種類
      • 今回の事情が行動規範違反であるかどうか。
      • 責任者が誰であるか
      • これが進行中の状況であるか、誰の物理的安全に脅威があるかどうか。

      これらの情報は書面で収集され、可能な限りグループの審議が記録され、保持されます (例えば、チャットの記録、Eメールのディスカッション、会議通話の記録、音声会話の概要など)。

      行動の一貫性を確保し、プロジェクトのために記録を残すために、委員会のすべての活動のアーカイブを保持することが重要です。 この活動支援するために、委員会のデフォルトの議論チャネルは、正当化された要求に応じて、委員会の現在および将来のメンバー、および運営委員会のメンバーがアクセスできるプライベートメーリングリストにします。 委員会がリストにはない連絡方法を使用する必要がある場合(例: 早期/迅速な対応を求める電話など)、そのプロセスの良い記録となるように、これらをリストにまとめて戻すべきです。

      行動規範委員会は、2週間以内に決議の合意を目指すべきです。 その期間内に決議が確定できない場合。 委員会は、レポーターに対して現状の更新と今後のタイムラインを連絡します。

      解決方法#

      委員会は、合意により決議について決定しなければなりません。 検討グループが一週間以上、合意かデッドロックに達しなかった場合、グループは、ステアリング評議会にこの問題を引き渡すことができます。

      ありうる返答は次のとおりです:

      • これ以上アクションを取らない。
        • 違反が起きていないと判断された
        • 検討中に問題が明らかに解決された
      • 調停の調整。すべての関係者が合意した場合、委員会は上記のように調停プロセスを促進することができます。
      • 公の場における説明。 どの行動・言動・言語が不適切で、現在の状況がなぜか引き起こされ、人々を傷つけたのかを説明し、コミュニティに自省を要求します。
      • 委員会から関係者(複数可) への非公開処分の実施。 この場合、委員会は、電子メールを介して、グループにccを入れながら、対象者に問題の指摘を連絡します。
      • 公の場での指摘。 この場合、委員会の議長は、違反が発生したのと同じ場所で、実用性の範囲内で叱責を行います。 例えば、メールルールの違反の元のメーリングリストなどです。 しかし、人や状況がかわるかもしれないチャットルームなどの場合、他の手段を利用する可能性もあります。 文書化のため、この問題のメッセージを他の場所で公開することを対策グループが選択する場合もあります。
      • 報告者がこの考えに同意することを前提とした、公的または私的な謝罪の要求。 報告者は自分の裁量で、違反者とのさらなる接触を拒否することもできます。 委員会がこの要求をお届けします。 委員会は、必要に応じてこの要求に「条件」を付けることができます。例えば、メーリングリストの会員資格を維持するために、違反者に謝罪を求めることができます。
      • 「相互に合意した休止」の要求。 これは、委員会から個人への、コミュニティへの参加を一時的に控えるような要請です。 対象者が自発的に一時的な休みを取らないことを選択した場合、委員会は「冷却期限」を準備することがあります。
      • これは、一部またはすべてのNumPyオンラインコミュニティ (メーリングリスト、gitter.im など) からの永続的または一時的な出入り禁止。 将来的に禁止が見直されるのか、維持されるか決定できるよう、対策グループは出入り禁止の記録を全て保持します。

      決議が合意されると制定される前に、委員会は、元の報告者およびその他の影響を受けた当事者に連絡し、提案された決議を説明します。 委員会は、この決議が受け入れられるかどうかを尋ねます。 そして、記録のためのフィードバックに注意を払います。

      最後に 委員会は、NumPy Steering Councilに報告を行います(NumPy Coreチームにも、出入り禁止など進行中の出来事については報告します)。

      委員会はこの問題について公に議論することはありません。 すべての公開声明は、行動規範委員会またはNumPy Steering Councilの議長によって行われます。

      利益相反#

      利益相反が発生した場合、委員会メンバーは直ちに他のメンバーに通知し、必要に応じて対応を辞退しなければなりません。

      On this page
      \ No newline at end of file diff --git a/ja/sitemap.xml b/ja/sitemap.xml index 5088fa5e..fbaf0d16 100644 --- a/ja/sitemap.xml +++ b/ja/sitemap.xml @@ -1 +1 @@ -https://numpy.org/ja/2023-09-16T00:00:00+00:00https://numpy.org/ja/news/2023-09-16T00:00:00+00:00https://numpy.org/ja/user-survey-2020/https://numpy.org/ja/404/https://numpy.org/ja/case-studies/https://numpy.org/ja/contribute/https://numpy.org/ja/user-surveys/https://numpy.org/ja/install/https://numpy.org/ja/learn/https://numpy.org/ja/history/https://numpy.org/ja/citing-numpy/https://numpy.org/ja/teams/https://numpy.org/ja/code-of-conduct/https://numpy.org/ja/report-handling-manual/https://numpy.org/ja/terms/https://numpy.org/ja/case-studies/deeplabcut-dnn/https://numpy.org/ja/case-studies/cricket-analytics/https://numpy.org/ja/case-studies/gw-discov/https://numpy.org/ja/case-studies/blackhole-image/https://numpy.org/ja/community/https://numpy.org/ja/gethelp/https://numpy.org/ja/privacy/https://numpy.org/ja/press-kit/https://numpy.org/ja/about/https://numpy.org/ja/arraycomputing/ \ No newline at end of file +https://numpy.org/ja/2024-08-18T00:00:00+00:00https://numpy.org/ja/news/2024-08-18T00:00:00+00:00https://numpy.org/ja/user-survey-2020/https://numpy.org/ja/404/https://numpy.org/ja/case-studies/https://numpy.org/ja/contribute/https://numpy.org/ja/user-surveys/https://numpy.org/ja/install/https://numpy.org/ja/learn/https://numpy.org/ja/history/https://numpy.org/ja/citing-numpy/https://numpy.org/ja/teams/https://numpy.org/ja/code-of-conduct/https://numpy.org/ja/report-handling-manual/https://numpy.org/ja/terms/https://numpy.org/ja/case-studies/deeplabcut-dnn/https://numpy.org/ja/case-studies/cricket-analytics/https://numpy.org/ja/case-studies/gw-discov/https://numpy.org/ja/case-studies/blackhole-image/https://numpy.org/ja/community/https://numpy.org/ja/gethelp/https://numpy.org/ja/privacy/https://numpy.org/ja/press-kit/https://numpy.org/ja/about/https://numpy.org/ja/arraycomputing/ \ No newline at end of file diff --git a/ja/tabcontents.yaml b/ja/tabcontents.yaml index 78f299b0..ef94d78d 100644 --- a/ja/tabcontents.yaml +++ b/ja/tabcontents.yaml @@ -2,8 +2,8 @@ params: machinelearning: paras: - - para1: NumPyは、[scikit-learn](https://scikit-learn.org)や[SciPy](https://www.scipy.org)のような強力な機械学習ライブラリの基礎を形成しています。機械学習の技術分野が成長するにつれ、NumPyをベースにしたライブラリの数も増えています。[TensorFlow](https://www.tensorflow.org)の深層学習機能は、音声認識や画像認識、テキストベースのアプリケーション、時系列分析、動画検出など、幅広い応用用途があります。[PyTorch](https://pytorch.org)も、コンピュータビジョンや自然言語処理の研究者に人気のある深層学習ライブラリです。[MXNet](https://github.com/apache/incubator-mxnet)もAIパッケージの一つで、深層学習の設計図やテンプレート機能を提供しています。 - para2: '[ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205)法と呼ばれる統計的手法であるビンニング、バギング、スタッキングや、[XGBoost](https://github.com/dmlc/xgboost)、[LightGBM](https://lightgbm.readthedocs.io/en/latest/)、[CatBoost](https://catboost.ai)などのツールで実装されているブースティングなどは、機械学習アルゴリズムの一つであり、最速の推論エンジンの一つです。[Yellowbrick](https://www.scikit-yb.org/en/latest/)や[Eli5](https://eli5.readthedocs.io/en/latest/)は機械学習の可視化機能を提供しています。' + para1: NumPyは、[scikit-learn](https://scikit-learn.org)や[SciPy](https://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://scikit-learn.org/stable/modules/ensemble.html)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: - @@ -31,7 +31,7 @@ params: text: "NumPyコードの合成可能な変換ライブラリ: 微分、ベクトル化、GPU/TPUへのジャストインタイムコンパイル" img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX - url: https://github.com/google/jax + url: https://docs.jax.dev - title: Xarray text: 高度な分析と視覚化のためのラベルとインデックス付き多次元配列 @@ -86,76 +86,230 @@ params: img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly - text: Numpy、MXNet、PyTorch、TensorFlowまたはCupyをシームレスに使用するための、テンソル学習、テンソル代数、およびそれらのテンソル計算のためのバックエンド - img: /images/content_images/arlib/tensorly.png - alttext: tensorly - url: http://tensorly.org/stable/home.html scientificdomains: intro: - text: Pythonを使って働くほとんどの科学者はNumPyの力を利用しています。 - text: "Numpy は、 C や Fortran のような言語の計算パフォーマンスを、Pythonにもたらします。 このパワーはNumPyのシンプルさから来ており、NumPyによるソリューションの多くは明確でエレガントになります。" - librariesrow1: + libraries: - title: 量子コンピューティング alttext: コンピューターチップ img: /images/content_images/sc_dom_img/quantum_computing.svg + links: + - + url: https://qutip.org + label: QuTiP + - + url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - + url: https://qiskit.org + label: Qiskit + - + url: https://pennylane.ai + label: PennyLane - title: 統計コンピューティング - alttext: 線グラフで、グラフが上に移動します。 + alttext: 線グラフが上に移動します。 img: /images/content_images/sc_dom_img/statistical_computing.svg + links: + - + url: https://pandas.pydata.org/ + label: Pandas + - + url: https://github.com/statsmodels/statsmodels + label: statsmodels + - + url: https://xarray.pydata.org/en/stable/ + label: Xarray + - + url: https://github.com/mwaskom/seaborn + label: Seaborn - title: 信号処理 alttext: 正と負の値を持つ棒グラフ。 img: /images/content_images/sc_dom_img/signal_processing.svg + links: + - + url: https://scipy.org + label: SciPy + - + url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - + url: https://python-control.org/ + label: python-control + - + url: https://hyperspy.org/ + label: HyperSpy - title: 画像処理 alttext: 山々の写真 img: /images/content_images/sc_dom_img/image_processing.svg + links: + - + url: https://scikit-image.org/ + label: Scikit-image + - + url: https://opencv.org/ + label: OpenCV + - + url: https://mahotas.rtfd.io/ + label: Mahotas - title: グラフとネットワーク alttext: シンプルなグラフ img: /images/content_images/sc_dom_img/sd6.svg + links: + - + url: https://networkx.org/ + label: NetworkX + - + url: https://graph-tool.skewed.de/ + label: graph-tool + - + url: https://igraph.org/python/ + label: igraph + - + url: https://pygsp.rtfd.io/ + label: PyGSP - title: 天文学 alttext: 望遠鏡 img: /images/content_images/sc_dom_img/astronomy_processes.svg + links: + - + url: https://www.astropy.org/ + label: AstroPy + - + url: https://github.com/sunpy/sunpy + label: SunPy + - + url: https://github.com/spacepy/spacepy + label: SpacePy - title: 認知心理学 alttext: ギアをつけた人間の頭部 img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: + links: + - + url: https://www.psychopy.org/ + label: PsychoPy - title: 生命情報科学 alttext: DNAの鎖 img: /images/content_images/sc_dom_img/bioinformatics.svg + links: + - + url: https://biopython.org/ + label: BioPython + - + url: http://scikit-bio.org/ + label: Scikit-Bio + - + url: https://github.com/openvax/pyensembl + label: PyEnsembl + - + url: http://etetoolkit.org/ + label: ETE - title: ベイズ推論 alttext: 鐘形の曲線のグラフ img: /images/content_images/sc_dom_img/bayesian_inference.svg + links: + - + url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - + url: https://docs.pymc.io/ + label: PyMC + - + url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - + url: https://emcee.readthedocs.io/ + label: emcee - title: 数学的分析 alttext: ４つの数学記号 img: /images/content_images/sc_dom_img/mathematical_analysis.svg + links: + - + url: https://scipy.org + label: SciPy + - + url: https://www.sympy.org/ + label: SymPy + - + url: https://github.com/cvxgrp/cvxpy + label: cvxpy + - + url: https://fenicsproject.org/ + label: FEniCS - title: 化学 alttext: 試験管 img: /images/content_images/sc_dom_img/chemistry.svg + links: + - + url: https://cantera.org/ + label: Cantera + - + url: https://www.mdanalysis.org/ + label: MDAnalysis + - + url: https://github.com/rdkit/rdkit + label: RDKit + - + url: https://www.pybamm.org/ + label: PyBaMM - title: 地球科学 alttext: 地球 img: /images/content_images/sc_dom_img/geoscience.svg + links: + - + url: https://pangeo.io/ + label: Pangeo + - + url: https://simpeg.xyz/ + label: Simpeg + - + url: https://github.com/obspy/obspy/wiki + label: ObsPy + - + url: https://www.fatiando.org/ + label: Fatiando a Terra - title: 地理情報処理 alttext: 地図 img: /images/content_images/sc_dom_img/GIS.svg + links: + - + url: https://shapely.readthedocs.io/ + label: Shapely + - + url: https://geopandas.org/ + label: GeoPandas + - + url: https://python-visualization.github.io/folium + label: Folium - title: アーキテクチャとエンジニアリング alttext: マイクロプロセッサ開発ボード img: /images/content_images/sc_dom_img/robotics.svg + links: + - + url: https://compas.dev/ + label: COMPAS + - + url: https://cityenergyanalyst.com/ + label: 都市エネルギー分析 + - + url: https://nortikin.github.io/sverchok/ + label: Sverchok datascience: intro: "Numpy は豊富なデータサイエンスライブラリのエコシステムの中核にあります。一般的なデータサイエンスのワークフローは次のようになります。" image1: @@ -168,11 +322,11 @@ params: alttext: 三つの円が重なり合う図。円はそれぞれ「数学」、「コンピューターサイエンス」、「専門知識」でラベル付けされています。図の中心部には、三つの円が重なり合って形成されるエリアがあり、「データサイエンス」とラベル付けされています。 examples: - - text: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + text: "抽出, 変換, 読み込み: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "探索的解析: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - text: "モデリングと評価: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC3](https://docs.pymc.io), [spaCy](https://spacy.io)" + text: "モデリングと評価: [scikit-learn](https://scikit-learn.org), [statsmodels](https://www.statsmodels.org/stable/index.html), [PyMC](https://docs.pymc.io), [spaCy](https://spacy.io)" - text: "ダッシュボードでのレポート: [Dash](https://plotly.com/dash), [Panel](https://panel.holoviz.org), [Voila](https://github.com/voila-dashboards/voila)" content: @@ -201,7 +355,7 @@ params: img: /images/content_images/v_seaborn.png alttext: 2種類のグラフによるペアプロット。seabornで作られたプロットと周波数グラフ" - - url: https://docs.pyvista.org/ + url: https://pyvista.org img: /images/content_images/v_pyvista.png alttext: PyVista製の3Dボリュームレンダリング - diff --git a/ja/teams/index.html b/ja/teams/index.html index 6221eec8..909d4c3b 100644 --- a/ja/teams/index.html +++ b/ja/teams/index.html @@ -1,15 +1,15 @@ -NumPy - NumPy開発チーム -

      NumPy開発チーム

      私たちは、高品質のオープンソースソフトウェアを構築することで、世界中の科学・研究コミュニティをサポートすることを使命とする国際的なチームです。 是非参加してください!

      Maintainers#

      Avatar of Andrew Nelson Andrew Nelson
      Avatar of Bas van Beek Bas van Beek
      Avatar of Charles Harris Charles Harris
      Avatar of Eric Wieser @@ -38,23 +38,23 @@ Stephan Hoyer
      Avatar of Stefan van der Walt Stefan van der Walt
      Avatar of Tyler Reddy Tyler Reddy
      Avatar of Warren Weckesser -Warren Weckesser

      Docs team#

      Avatar of Rohit Goswami +Warren Weckesser

      ドキュメントチーム#

      Avatar of Rohit Goswami Rohit Goswami
      Avatar of Inessa Pawson Inessa Pawson
      Avatar of Mars Lee Mars Lee
      Avatar of Matti Picus Matti Picus
      Avatar of Melissa Weber Mendonça Melissa Weber Mendonça
      Avatar of Mukulika Mukulika
      Avatar of Ross Barnowski -Ross Barnowski

      Web team#

      Avatar of Inessa Pawson -Inessa Pawson
      Avatar of Jarrod Millman -Jarrod Millman
      Avatar of Joe LaChance -Joe LaChance
      Avatar of Mars Lee -Mars Lee
      Avatar of Ralf Gommers -Ralf Gommers
      Avatar of shalz -shalz
      Avatar of Shekhar Prasad Rajak -Shekhar Prasad Rajak
      Avatar of Stefan van der Walt -Stefan van der Walt
      Avatar of Albert Steppi -Albert Steppi

      Triage team#

      Avatar of Andrew Nelson +Ross Barnowski

      Webチーム#

      Avatar of Inessa Pawson +Inessa Pawson
      Avatar of Jarrod Millman +Jarrod Millman
      Avatar of Joe LaChance +Joe LaChance
      Avatar of Mars Lee +Mars Lee
      Avatar of Ralf Gommers +Ralf Gommers
      Avatar of shalz +shalz
      Avatar of Shekhar Prasad Rajak +Shekhar Prasad Rajak
      Avatar of Stefan van der Walt +Stefan van der Walt
      Avatar of Albert Steppi +Albert Steppi

      トリアージチーム#

      Avatar of Andrew Nelson Andrew Nelson
      Avatar of Anirudh Subramanian Anirudh Subramanian
      Avatar of Aaron Meurer Aaron Meurer
      Avatar of Atsushi Sakai @@ -82,10 +82,10 @@ shalz
      Avatar of Tina Oberoi Tina Oberoi
      Avatar of Rakesh Vasudevan Rakesh Vasudevan
      Avatar of Zijie (ZJ) Poh -Zijie (ZJ) Poh

      Survey team#

      Avatar of Inessa Pawson +Zijie (ZJ) Poh

      アンケートチーム#

      Avatar of Inessa Pawson Inessa Pawson
      Avatar of Ralf Gommers Ralf Gommers
      Avatar of Ross Barnowski -Ross Barnowski

      Emeritus maintainers#

      Avatar of Allan Haldane +Ross Barnowski

      名誉メンテナー#

      Avatar of Allan Haldane Allan Haldane
      Avatar of Ondřej Čertík Ondřej Čertík
      Avatar of David Cournapeau David Cournapeau
      Avatar of Jaime @@ -94,6 +94,5 @@ Julian Taylor
      Avatar of Mark Wiebe Mark Wiebe
      Avatar of Nathaniel J. Smith Nathaniel J. Smith
      Avatar of Travis E. Oliphant -Travis E. Oliphant

      ガバナンス#

      For the list of people on the Steering Council, please see here.

      On this page
      \ No newline at end of file +Travis E. Oliphant

      管理委員会#

      For the list of people on the Steering Council, please see here.

      On this page
      \ No newline at end of file diff --git a/ja/terms/index.html b/ja/terms/index.html index f480ecc1..03ae3f29 100644 --- a/ja/terms/index.html +++ b/ja/terms/index.html @@ -1,14 +1,13 @@ -NumPy - Terms of Use -

      Terms of Use

      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:

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      THIRD-PARTY WEBSITES AND CONTENT#

      The Site may contain (or you may be sent via the Site) links to other websites (“Third-Party Websites”) as well as articles, photographs, text, graphics, pictures, designs, music, sound, video, information, applications, software, and other content or items belonging to or originating from third parties (“Third-Party Content”). Such Third-Party Websites and Third-Party Content are not investigated, monitored, or checked for accuracy, appropriateness, or completeness by us, and we are not responsible for any Third-Party Websites accessed through the Site or any Third-Party Content posted on, available through, or installed from the Site, including the content, accuracy, offensiveness, opinions, reliability, privacy practices, or other policies of or contained in the Third-Party Websites or the Third-Party Content. Inclusion of, linking to, or permitting the use or installation of any Third-Party Websites or any Third-Party Content does not imply approval or endorsement thereof by us. If you decide to leave the Site and access the Third-Party Websites or to use or install any Third-Party Content, you do so at your own risk, and you should be aware these Terms of Use no longer govern. You should review the applicable terms and policies, including privacy and data gathering practices, of any website to which you navigate from the Site or relating to any applications you use or install from the Site. Any purchases you make through Third-Party Websites will be through other websites and from other companies, and we take no responsibility whatsoever in relation to such purchases which are exclusively between you and the applicable third party. You agree and acknowledge that we do not endorse the products or services offered on Third-Party Websites and you shall hold us harmless from any harm caused by your purchase of such products or services. Additionally, you shall hold us harmless from any losses sustained by you or harm caused to you relating to or resulting in any way from any Third-Party Content or any contact with Third-Party Websites.

      SITE MANAGEMENT#

      We reserve the right, but not the obligation, to: (1) monitor the Site for violations of these Terms of Use; (2) take appropriate legal action against anyone who, in our sole discretion, violates the law or these Terms of Use, including without limitation, reporting such user to law enforcement authorities; (3) in our sole discretion and without limitation, refuse, restrict access to, limit the availability of, or disable (to the extent technologically feasible) any of your Contributions or any portion thereof; (4) in our sole discretion and without limitation, notice, or liability, to remove from the Site or otherwise disable all files and content that are excessive in size or are in any way burdensome to our systems; and (5) otherwise manage the Site in a manner designed to protect our rights and property and to facilitate the proper functioning of the Site.

      PRIVACY POLICY#

      We care about data privacy and security. Please review our Privacy Policy. 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. WE MAY TERMINATE YOUR USE OR PARTICIPATION IN THE SITE OR DELETE ANY CONTENT OR INFORMATION THAT YOU POSTED AT ANY TIME, WITHOUT WARNING, IN OUR SOLE DISCRETION.

      MODIFICATIONS AND INTERRUPTIONS#

      We reserve the right to change, modify, or remove the contents of the Site at any time or for any reason at our sole discretion without notice. However, we have no obligation to update any information on our Site. We also reserve the right to modify or discontinue all or part of the Site without notice at any time. We will not be liable to you or any third party for any modification, suspension, or discontinuance of the Site.

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      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. Such informal negotiations commence upon written notice from one Party to the other Party.

      Binding Arbitration#

      If the Parties are unable to resolve a Dispute through informal negotiations, the Dispute (except those Disputes expressly excluded below) will be finally and exclusively resolved by binding arbitration. YOU UNDERSTAND THAT WITHOUT THIS PROVISION, YOU WOULD HAVE THE RIGHT TO SUE IN COURT AND HAVE A JURY TRIAL. The arbitration shall be commenced and conducted under the Commercial Arbitration Rules of the American Arbitration Association (“AAA”) and, where appropriate, the AAA’s Supplementary Procedures for Consumer Related Disputes (“AAA Consumer Rules”), both of which are available at the AAA website www.adr.org. Your arbitration fees and your share of arbitrator compensation shall be governed by the AAA Consumer Rules and, where appropriate, limited by the AAA Consumer Rules. 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. TO THE FULLEST EXTENT PERMITTED BY LAW, WE DISCLAIM ALL WARRANTIES, EXPRESS OR IMPLIED, IN CONNECTION WITH THE SITE AND YOUR USE THEREOF, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WE MAKE NO WARRANTIES OR REPRESENTATIONS ABOUT THE ACCURACY OR COMPLETENESS OF THE SITE’S CONTENT OR THE CONTENT OF ANY WEBSITES LINKED TO THE SITE AND WE WILL ASSUME NO LIABILITY OR RESPONSIBILITY FOR ANY (1) ERRORS, MISTAKES, OR INACCURACIES OF CONTENT AND MATERIALS, (2) PERSONAL INJURY OR PROPERTY DAMAGE, OF ANY NATURE WHATSOEVER, RESULTING FROM YOUR ACCESS TO AND USE OF THE SITE, (3) ANY UNAUTHORIZED ACCESS TO OR USE OF OUR SECURE SERVERS AND/OR ANY AND ALL PERSONAL INFORMATION AND/OR FINANCIAL INFORMATION STORED THEREIN, (4) ANY INTERRUPTION OR CESSATION OF TRANSMISSION TO OR FROM THE SITE, (5) ANY BUGS, VIRUSES, TROJAN HORSES, OR THE LIKE WHICH MAY BE TRANSMITTED TO OR THROUGH THE SITE BY ANY THIRD PARTY, AND/OR (6) ANY ERRORS OR OMISSIONS IN ANY CONTENT AND MATERIALS OR FOR ANY LOSS OR DAMAGE OF ANY KIND INCURRED AS A RESULT OF THE USE OF ANY CONTENT POSTED, TRANSMITTED, OR OTHERWISE MADE AVAILABLE VIA THE SITE. 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
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      On this page
      \ No newline at end of file diff --git a/ja/user-survey-2020/index.html b/ja/user-survey-2020/index.html index ae335cba..aa98aea0 100644 --- a/ja/user-survey-2020/index.html +++ b/ja/user-survey-2020/index.html @@ -1,16 +1,15 @@ -NumPy - 2020年 NumPyコミュニティ調査 -NumPy - 2020年 NumPyコミュニティ調査

      2020年 NumPyコミュニティ調査

      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。

      Cover page of the 2020 Numpy User survey report, titled "Numpyコミュニティ調査2020 - 結果"

      調査結果を詳細を知りたい場合は、こちらのレポート をダウンロードしてください。

      結果の概要については、 こちらの図 をチェックしてください。

      より詳細が知りたくなりましたか？ https://numpy.org/user-survey-2020-details/ をご覧ください。

      On this page
      \ No newline at end of file diff --git a/ja/user-surveys/index.html b/ja/user-surveys/index.html index 0cb301ab..804fca11 100644 --- a/ja/user-surveys/index.html +++ b/ja/user-surveys/index.html @@ -1,15 +1,14 @@ -NumPy - NumPyユーザアンケート -NumPy - NumPyユーザアンケート

      NumPyユーザアンケート

      2020 NumPY調査チームは、ミシガン大学とメリーランド大学の学生や教員と協力して、最初の公式NumPyコミュニティ調査を実施しました。 アンケートの結果はこちらをご覧ください。

      2021 収集された調査データは現在解析中です。

      過去または今後のNumPyユーザ調査に関する質問や提案がある場合は、こちらにイシューを作成してください。

      On this page
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      `);const n=document.getElementById(`${t}-shortcut`);n.addEventListener("click",()=>{event.preventDefault(),unbindScroll(),history.replaceState(null,null,`#${t}`);const s=Array.from(document.querySelectorAll("#shortcuts > div"));s.forEach(e=>e.classList.remove("active")),n.classList.add("active");let o=e.offsetTop-60;scrollToThen(o,()=>{bindScroll()})})});const i=Array.from(document.querySelectorAll("#shortcuts div:not(#shortcuts-header)")),n=document.getElementById("shortcuts-container");n&&(i.length==0?n.style.display="none":n.style.display=""),bindScroll()}function scrollToThen(e,t){const n=throttle(()=>{const s=e.toFixed();window.pageYOffset.toFixed()===s&&(window.removeEventListener("scroll",n),t())},100);window.addEventListener("scroll",n),n(),window.scrollTo({top:e})}"use strict";class TabsAutomatic{constructor(e){this.tablistNode=e,this.tabs=[],this.firstTab=null,this.lastTab=null,this.tabs=Array.from(this.tablistNode.querySelectorAll("[role=tab]")),this.tabpanels=[];for(var t,s,n=0;nNumPy - Learn -NumPy - Learn

      Learn

      For the official NumPy documentation visit numpy.org/doc/stable.

      Below is a curated collection of educational resources, both for self-learning and +teaching others, developed by NumPy contributors and vetted by the community.

      Beginners#

      There’s a ton of information about NumPy out there. If you are just starting, we’d +strongly recommend the following:

      Tutorials

      Books

      You may also want to check out the Goodreads list +on the subject of “Python+SciPy.” Most books there are about the “SciPy ecosystem,” +which has NumPy at its core.

      Videos

      Advanced#

      Try these advanced resources for a better understanding of NumPy concepts like advanced indexing, splitting, stacking, linear algebra, and more.

      Tutorials

      Books

      Videos

      NumPy Talks#

      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.

      On this page
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News -

      News

      NumPy Fellowship Program 2025 Retrospective#

      8 Jan, 2026 – Joren Hammudoglu (@jorenham) has published a +retrospective on his year as a NumPy Fellow. Read the full post on the Scientific Python blog:

      A Year of Typing: My NumPy Fellowship Retrospective.

      NumPy 2.4.0 released#

      20 Dec, 2025 – The NumPy 2.4.0 release continues the work to improve free +threaded Python support, user dtypes implementation, and annotations. There are +many expired deprecations and bug fixes as well. Highlights are:

      • Many annotation improvements. In particular, runtime signature introspection.
      • New casting kwarg 'same_value' for casting by value.
      • New PyUFunc_AddLoopsFromSpec function that can be used to add user sort +loops using the ArrayMethod API.
      • New __numpy_dtype__ protocol.

      This release supports Python versions 3.11-3.14

      NumPy 2.3.0 released#

      7 Jun, 2025 – The NumPy 2.3.0 release improves free threaded Python support +and annotations together with the usual set of bug fixes. It is unusual in the +number of expired deprecations, code modernizations, and style cleanups. The +latter may not be visible to users, but is important for code maintenance over +the long term. Note that we have also upgraded from manylinux2014 to +manylinux_2_28. Highlights are:

      • Interactive examples in the NumPy documentation.
      • Building NumPy with OpenMP Parallelization.
      • Preliminary support for Windows on ARM.
      • Improved support for free threaded Python.
      • Improved annotations.

      This release supports Python versions 3.11-3.13, Python 3.14 will be +supported when it is released.

      NumPy 2.2.0 released#

      8 Dec, 2024 – The NumPy 2.2.0 release is a quick release that brings us back +into sync with the usual twice yearly release cycle. There have been a number +of small cleanups, improvements to the StringDType, and better support for free +threaded Python. Highlights are:

      • New functions matvec and vecmat,
      • Many improved annotations,
      • Improved support for the new StringDType,
      • Improved support for free threaded Python,
      • Fixes for f2py.

      This release supports Python versions 3.10-3.13.

      NumPy 2.1.0 released#

      18 Aug, 2024 – NumPy 2.1.0 provides support for Python 3.13 and +drops support for Python 3.9. In addition to the usual bug fixes and +updated Python support, it helps get NumPy back to its usual release +cycle after the extended development of 2.0. The highlights for this +release are:

      • Support for Python 3.13.
      • Preliminary support for free threaded Python 3.13.
      • Support for the array-api 2023.12 standard.

      Python versions 3.10-3.13 are supported by this release.

      NumPy 2.0.0 released#

      16 Jun, 2024 – NumPy 2.0.0 is the first major release since 2006. It is the +result of 11 months of development since the last feature release and is the +work of 212 contributors spread over 1078 pull requests. It contains a large +number of exciting new features as well as changes to both the Python and C +APIs. It includes breaking changes that could not happen in a regular minor +release - including an ABI break, changes to type promotion rules, and API +changes which may not have been emitting deprecation warnings in 1.26.x. Key +documents related to how to adapt to changes in NumPy 2.0 include:

      The blog post “NumPy 2.0: an evolutionary milestone” +tells a bit of the story about how this release came together.

      NumPy 2.0 release date: June 16#

      23 May, 2024 – We are excited to announce that NumPy 2.0 is planned to be +released on June 16, 2024. This release has been over a year in the making, and +is the first major release since 2006. Importantly, in addition to many new +features and performance improvement, it contains breaking changes to the +ABI as well as the Python and C APIs. It is likely that downstream packages and +end user code needs to be adapted - if you can, please verify whether your code +works with NumPy 2.0.0rc2. Please see the following for more details:

      NumFOCUS end of the year fundraiser#

      Dec 19, 2023 – NumFOCUS has teamed up with PyCharm during their EOY campaign to offer a 30% discount on first-time PyCharm licenses. All year-one revenue from PyCharm purchases from now until December 23rd, 2023 will go directly to the NumFOCUS programs.

      Use unique URL that will allow to track purchases https://lp.jetbrains.com/support-data-science/ or a coupon code ISUPPORTDATASCIENCE 

      NumPy 1.26.0 released#

      Sep 16, 2023 – NumPy 1.26.0 @@ -44,7 +76,7 @@ and expire old deprecations. It is the work of 151 contributors spread over 494 pull requests. The Python versions supported by this release 3.8-3.10. Python 3.11 will be supported when it reaches the rc stage.

      NumFOCUS DEI research study: call for participation#

      Apr 13, 2022 – NumPy is working with NumFOCUS on a -research project +research project funded by the Gordon & Betty Moore Foundation to understand the barriers to participation that contributors, particularly those from historically underrepresented groups, face in the open-source software @@ -153,6 +185,5 @@ 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 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 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. The work is scheduled to start on Dec 1st, 2019 and continue for the next 12 months.

      Releases#

      Here is a list of NumPy releases, with links to release notes. Bugfix releases (only the z changes in the x.y.z version number) have no new -features; minor releases (the y increases) do.

      On this page
      \ No newline at end of file +features; minor releases (the y increases) do.

      On this page
      \ No newline at end of file diff --git a/press-kit/index.html b/press-kit/index.html index 4e893d10..23b0cbd3 100644 --- a/press-kit/index.html +++ b/press-kit/index.html @@ -1,14 +1,13 @@ -NumPy - Press kit -

      Press kit

      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. Note that by using the numpy.org resources, you accept the NumPy Code of Conduct.

      On this page
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      Privacy Policy

      numpy.org is operated by NumFOCUS, Inc., 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.

      On this page
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      404

      Oops! Você atingiu um beco sem saída.

      Se você acha que algo deveria estar aqui, você pode abrir uma issue no GitHub.

      On this page
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      Sobre

      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 100% software de código aberto, livre para que todos usem. É lançado sob os termos liberais da licença BSD modificada.

      O NumPy é desenvolvido abertamente no GitHub, através do consenso da comunidade NumPy e de uma comunidade mais ampla de Python científico. Para obter mais informações sobre nossa abordagem de governança, por favor, consulte nosso Documento de Governança.

      Conselho Diretor (Steering Council)#

      O Conselho Diretor do NumPy é a entidade que governa o projeto. Seu papel é garantir, através do trabalho com e para a comunidade NumPy em geral, a sustentabilidade do projeto a longo prazo, tanto como pacote de software quanto como comunidade. O Conselho Diretor do NumPy atualmente consiste dos seguintes membros (em ordem alfabética, pelo sobrenome):

      • Sebastian Berg
      • Ralf Gommers
      • Charles Harris
      • Inessa Pawson
      • Matti Picus
      • Stéfan van der Walt
      • Melissa Weber Mendonça
      • Marten van Kerkwijk
      • Eric Wieser

      Membros Eméritos:

      • Alex Griffing (2015-2017)
      • Allan Haldane (2015-2021)
      • Travis Oliphant (fundador do projeto, 2005-2012)
      • Nathaniel Smith (2012-2021)
      • Julian Taylor (2013-2021)
      • Jaime Fernández del Río (2014-2021)
      • Pauli Virtanen (2008-2021)
      • Eric Wieser (2017-2025)
      • Stephan Hoyer (2017-2025)

      Para entrar em contato com o conselho diretor do NumPy, por favor envie um email para numpy-team@googlegroups.com.

      Times#

      A liderança do projeto NumPy trabalha ativamente na diversificação dos caminhos possíveis para contribuições.
      Atualmente, o NumPy conta com os seguintes times:

      • desenvolvimento
      • documentação
      • triagem
      • website
      • pesquisa
      • traduções
      • mentores para sprints de desenvolvimento
      • otimização
      • financiamento e bolsas

      Veja a página sobre os Times para mais informações.

      Subcomitê NumFOCUS#

      • Charles Harris
      • Ralf Gommers
      • Inessa Pawson
      • Sebastian Berg
      • Membro externo: Thomas Caswell

      Patrocinadores#

      Parceiros Institucionais#

      Os Parceiros Institucionais são organizações que apoiam o projeto, empregando pessoas que contribuem para a NumPy como parte de seu trabalho. Os parceiros institucionais atuais incluem:

      • UC Berkeley (Stéfan van der Walt)
      • Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça)
      • NVIDIA (Sebastian Berg)
      Logo of UC Berkeley +Logo of Tidelift

      Parceiros Institucionais#

      Os Parceiros Institucionais são organizações que apoiam o projeto, empregando pessoas que contribuem para a NumPy como parte de seu trabalho. Os parceiros institucionais atuais incluem:

      • UC Berkeley (Stéfan van der Walt)
      • Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça)
      • NVIDIA (Sebastian Berg)
      Logo of UC Berkeley Logo of Quansight -Logo of NVIDIA

      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 para obter mais informações.

      Doações para o NumPy são gerenciadas pela NumFOCUS. 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.

      On this page
      \ No newline at end of file +Logo of NVIDIA

      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, documentação e comunidade da NumPy.

      NumPy é um Projeto Patrocinado da NumFOCUS, uma instituição de caridade sem fins lucrativos 501(c)(3) 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 para obter mais informações.

      Doações para o NumPy são gerenciadas pela NumFOCUS. 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 roadmap do NumPy.

      On this page
      \ No newline at end of file diff --git a/pt/arraycomputing/index.html b/pt/arraycomputing/index.html index 245b7c89..77c6e618 100644 --- a/pt/arraycomputing/index.html +++ b/pt/arraycomputing/index.html @@ -1,14 +1,14 @@ -NumPy - Computação com Arrays -

      Computação com Arrays

      A computação com matrizes 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 alto desempenho com arrays. A linguagem mais escolhida para análise de dados, aprendizagem de máquina e computação numérica produtiva é Python.

      Numerical Python (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 matrizes é baseada em estruturas de dados chamadas arrays. Arrays 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 matrizes é única pois envolve operar nos valores de uma matriz 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.

      On this page
      \ No newline at end of file diff --git a/pt/case-studies/blackhole-image/index.html b/pt/case-studies/blackhole-image/index.html index fbcc08d3..22ce90d6 100644 --- a/pt/case-studies/blackhole-image/index.html +++ b/pt/case-studies/blackhole-image/index.html @@ -1,15 +1,16 @@ -NumPy - Estudo de Caso: A Primeira Imagem de um Buraco Negro -

      Estudo de Caso: A Primeira Imagem de um Buraco Negro

      black hole image
      Black Hole M87#
      (Créditos: Event Horizon Telescope Collaboration)

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

      —Katie Bouman, Professora Assistente, Ciências da Computação e Matemática, Caltech

      Um telescópio do tamanho da Terra#

      O telescópio Event Horizon (EHT), 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 — o suficiente para ler um jornal em Nova Iorque a partir de um café em uma calçada de Paris!

      Principais Objetivos e Resultados#

      • Uma nova visão do universo: A imagem inovadora do EHT foi publicada 100 anos após o experimento de Sir Arthur Eddington 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, mas um buraco negro nunca havia sido observado visualmente antes.

      • Comparando observações com a teoria: Pela teoria geral da relatividade de Einstein, os cientistas esperavam encontrar uma região de sombra causada pela distorção e captura da luz causada pela influência gravitacional do buraco negro. Os cientistas poderiam usá-la para medir a enorme massa do mesmo.

      Desafios#

      • Escala computacional

        O EHT representa um desafio imenso em processamento de dados, incluindo rápidas flutuações de fase atmosférica, uma largura grande de banda nas gravações e telescópios que são muito diferentes e geograficamente dispersos.

      • Muitas informações

        A cada dia, o EHT gera mais de 350 terabytes de observações, armazenadas em discos rígidos cheios de hélio. Reduzir o volume e a complexidade desse volume de dados é extremamente difícil.

      • Em direção ao desconhecido

        Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta?

      data pipeline
      Etapas de Processamento de Dados do EHT#
      (Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)

      O papel do NumPy#

      E se houver um problema com os dados? Ou talvez um algoritmo seja muito dependente de uma hipótese em particular. A imagem será alterada drasticamente se um único parâmetro for alterado?

      A colaboração do EHT venceu esses desafios ao estabelecer equipes independentes que avaliaram os dados usando técnicas de reconstrução de imagem estabelecidas e de ponta para verificar se as imagens resultantes eram consistentes. Quando os resultados se provaram consistentes, eles foram combinados para produzir a imagem inédita do buraco negro.

      O trabalho desse grupo ilustra o papel do ecossistema científico do Python no avanço da ciência através da análise de dados colaborativa.

      role of numpy
      O papel do NumPy na criação da primeira imagem de um Buraco Negro#

      Por exemplo, o pacote Python eht-imaging 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.

      ehtim dependency map highlighting numpy
      Diagrama de dependência de software do pacote ehtim evidenciando o NumPy#

      Além do NumPy, muitos outros pacotes como SciPy e Pandas 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 enquanto a Matplotlib foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro.

      Resumo#

      A estrutura de dados n-dimensional que é a funcionalidade central do NumPy permitiu aos pesquisadores manipular grandes conjuntos de dados, fornecendo a base para a primeira imagem de um buraco negro. Esse momento marcante na ciência fornece evidências visuais impressionantes para a teoria de Einstein. Esta conquista abrange não apenas avanços tecnológicos, mas colaboração científica em escala internacional entre mais de 200 cientistas e alguns dos melhores observatórios de rádio do mundo. Eles usaram algoritmos e técnicas de processamento de dados inovadores, que aperfeiçoaram os modelos astronômicos existentes, para ajudar a descobrir um dos mistérios do universo.

      numpy benefits
      Funcionalidades-chave do NumPy utilizadas#

      On this page
      \ No newline at end of file diff --git a/pt/case-studies/cricket-analytics/index.html b/pt/case-studies/cricket-analytics/index.html index 36b93e0b..a9d8de9c 100644 --- a/pt/case-studies/cricket-analytics/index.html +++ b/pt/case-studies/cricket-analytics/index.html @@ -1,15 +1,19 @@ -NumPy - Estudo de Caso: Análise de Críquete, a revolução! -

      Estudo de Caso: Análise de Críquete, a revolução!

      Copa e estádio da Indian Premier League Cricket
      IPLT20, o maior festival de Críquete da Índia#
      (Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))

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

      —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 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, fundada em 2008. É um dos eventos de críquete mais assistidos no mundo, avaliado em $6,7 bilhões de dólares em 2019.

      perdidos por um boleador, as partidas ganhas por uma equipe de críquete, o número de vezes que um batsman responde de certa maneira a um tipo de arremesso do boleador, etc. A capacidade de investigar números de críquete para melhorar o desempenho e estudar as oportunidades de negócio, mercado e economia de críquete através de poderosas ferramentas de análise, alimentadas por softwares numéricos de computação, como o NumPy, é um grande negócio. A capacidade de investigar estatísticas do críquete para melhorar a performance dos times e estudar oportunidades de negócios, o mercado em si, e a economia do críquete através de ferramentas de análise poderosas alimentadas por softwares de computação numérica como o NumPy é um grande negócio. As análises de críquete fornecem informações interessantes sobre o jogo e informações preditivas sobre os resultados do jogo.

      Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações sobre jogos de críquete, por exemplo, ESPN cricinfo e cricsheet. Estes e muitos outros bancos de dados de críquete foram usados para análise de críquete 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
      Um pitch de críquete com um boleador e batsmen
      Pitch de críquete, o ponto focal do campo#
      (Créditos de imagem: Debarghya Das)

      Objetivos Principais da Análise de Dados#

      • A análise de dados esportivos é usada não somente em críquete, mas em muitos outros esportes 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.
      estimador de postura
      Estimador de Postura de Críquete#
      (Créditos de imagem: connect.vin)

      Desafios#

      • Limpeza e pré-processamento de dados

        A IPL expandiu o formato de jogo clássico de cricket para uma escala muito maior. O número de partidas jogadas a cada temporada em vários formatos tem aumentado, assim como os dados, os algoritmos, as tecnologias de análise de dados mais recentes e modelos de simulação. A análise de dados de críquete requer mapeamento de campo, rastreamento do jogador, rastreamento de bola e análise de tiros do jogador, análise de lances do jogador e vários outros aspectos envolvidos em como a bola é lançada, seu ângulo, giro, velocidade e trajetória. Todos esses fatores em conjunto aumentaram a complexidade da limpeza e pré-processamento de dados.

      • Modelagem Dinâmica

        No críquete, como em qualquer outro esporte, pode haver um grande número de variáveis relacionadas ao rastreamento de vários jogadores no campo, seus atributos, a bola e várias possibilidades de ações em potencial. A complexidade da análise e modelagem de dados é diretamente proporcional ao tipo de questões preditivas que são consideradas durante a análise e são altamente dependentes da representação de dados e do modelo. As coisas são ainda mais desafiadoras em termos de computação e comparações de dados quando previsões dinâmicas de jogo de críquete são desejadas, como o que teria acontecido se o batsman tivesse atingido a bola com um ângulo ou velocidade diferentes.

      • Complexidade da análise preditiva

        Muito da tomada de decisões em críquete se baseia em questões como “com que frequência um batsman joga um certo tipo de lance se a recepção da bola for de um determinado tipo”, ou “como um boleador muda a direção e alcance da sua jogada se o batsman responder de uma certa maneira”. +Esse tipo de consulta de análise preditiva requer a disponibilidade de conjuntos de dados altamente granulares e a capacidade de sintetizar dados e criar modelos generativos que sejam altamente precisos.

      O papel do NumPy na análise de críquete#

      A análise de dados esportivos é um campo próspero. Muitos pesquisadores e empresas usam NumPy e outros pacotes PyData como Scikit-learn, SciPy, Matplotlib, e Jupyter, além de usar as últimas técnicas de aprendizagem de máquina e IA. O NumPy foi usado para vários tipos de análise esportiva relacionada a críquete, como:

      • Análise Estatística: Os recursos numéricos do NumPy ajudam a estimar o significado estatístico de dados observados ou de eventos ocorridos em partidas no contexto de vários jogadores e táticas de jogo, bem como estimar o resultado do jogo em comparação com um modelo generativo ou estático. +Análise Causal e abordagens em big data são usados para análise tática.

      • Visualização de dados: Gráficos e visualizações fornecem informações úteis sobre as relações entre vários conjuntos de dados.

      Resumo#

      A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo era principalmente feita com base na “intuição” ou adesão a tradições passadas. O NumPy forma uma fundação sólida para um grande conjunto de pacotes Python que fornecem funções de alto nível relacionadas à análise de dados, aprendizagem de máquina e algoritmos de IA. +Estes pacotes são amplamente implantados para se obter informações em tempo real que ajudam na tomada de decisão para resultados decisivos, tanto em campo como para se derivar inferências e orientar negócios em torno do jogo de críquete. Encontrar os parâmetros ocultos, padrões, e atributos que levam ao resultado de uma partida de críquete ajuda os envolvidos a tomar nota das percepções do jogo que estariam de outra forma ocultas nos números e estatísticas.

      Diagrama mostrando os benefícios de usar a NumPy para análise de críquete
      Recursos principais da NumPy utilizados#

      On this page
      \ No newline at end of file diff --git a/pt/case-studies/deeplabcut-dnn/index.html b/pt/case-studies/deeplabcut-dnn/index.html index 4a2c1bed..61b0c686 100644 --- a/pt/case-studies/deeplabcut-dnn/index.html +++ b/pt/case-studies/deeplabcut-dnn/index.html @@ -1,15 +1,17 @@ -NumPy - Estudo de Caso: Estimativa de Pose 3D com DeepLabCut -

      Estudo de Caso: Estimativa de Pose 3D com DeepLabCut

      micehandanim
      Análise de movimentos de mãos de camundongos usando DeepLapCut#
      (Fonte: www.deeplabcut.org )

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

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

      Sobre o DeepLabCut#

      DeepLabCut é uma toolbox de código aberto que permite que pesquisadores de centenas de instituições em todo o mundo rastreiem o comportamento de animais de laboratório, com muito poucos dados de treinamento, mas com precisão no nível humano. Com a tecnologia DeepLabCut, cientistas podem aprofundar a compreensão científica do controle motor e do comportamento em diversas espécies animais e escalas temporais.

      Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas trabalhosas de monitoramento e marcação, junto com análise de dados baseada em redes neurais profundas, a DeepLabCut garante que estudos científicos envolvendo a observação de animais como primatas, camundongos, peixes, moscas etc. sejam mais rápidos e precisos.

      horserideranim
      Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida#
      (Fonte: Mackenzie Mathis)

      O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários.

      A DeepLabCut permite que pesquisadores façam estimativas de poses para os sujeitos, permitindo que se possa quantificar de maneira eficiente seus comportamentos através de um conjunto de ferramentas de software baseado em Python. Com a DeepLabCut, pesquisadores podem identificar quadros (frames) distintos em vídeos e rotular digitalmente partes específicas do corpo em alguns quadros com uma GUI especializada. A partir disso, a arquitetura de estimação de poses baseada em deep learning da DeepLabCut aprende a selecionar essas mesmas características no resto do vídeo e em outros vídeos similares. A ferramenta funciona para várias espécies de animais, desde animais comuns em laboratórios, como moscas e camundongos, até os mais incomuns, como guepardos.

      A DeepLabCut usa um princípio chamado aprendizado por transferência (transfer learning), 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. 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. 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, 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 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 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
      dlcsteps
      Passos na estimação de poses com DeepLabCut#
      (Fonte: DeepLabCut)

      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.

      challengesfig
      Estimação de poses e complexidade#
      (Fonte: Mackenzie Mathis)

      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, Pandas, matplotlib, Tensorpack, imgaug, scikit-learn, scikit-image e Tensorflow.

      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.

      workflow
      Fluxo de dados DeepLabCut#
      (Fonte: Mackenzie Mathis)

      Resumo#

      Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. +Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. 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.

      numpy benefits
      Recursos chave do NumPy utilizados#

      On this page
      \ No newline at end of file diff --git a/pt/case-studies/gw-discov/index.html b/pt/case-studies/gw-discov/index.html index c80b48b8..b6892a2e 100644 --- a/pt/case-studies/gw-discov/index.html +++ b/pt/case-studies/gw-discov/index.html @@ -1,15 +1,17 @@ -NumPy - Estudo de Caso: Descoberta de Ondas Gravitacionais -

      Estudo de Caso: Descoberta de Ondas Gravitacionais

      binary coalesce black hole generating gravitational waves
      Ondas gravitacionais#
      (Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)

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

      —David Shoemaker, Colaborador Científico no LIGO

      Sobre Ondas Gravitacionais e o LIGO#

      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) 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 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 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.

      gravitational waves strain amplitude
      Amplitude estimada da deformação das ondas gravitacionais do evento GW150914#
      (Créditos do gráfico: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)

      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: Detecção de falhas, Identificação de ruídos e caracterização de dados (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 fundamental desenvolvido na análise de ondas gravitacionais, como GwPy e PyCBC 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.
      gwpy-numpy depgraph
      Grafo de dependências mostrando como o pacote GwPy depended da NumPy#

      PyCBC-numpy depgraph
      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 outras bibliotecas Python, como matplotlib, pandas, e scikit-learn permitem que pesquisadores respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo.

      numpy benefits
      Recursos chave da NumPy utilizados#

      On this page
      \ No newline at end of file diff --git a/pt/case-studies/index.html b/pt/case-studies/index.html index 328cb25b..4e55fbd8 100644 --- a/pt/case-studies/index.html +++ b/pt/case-studies/index.html @@ -1,13 +1,12 @@ -NumPy - Case-Studies -

      Case-Studies

      \ No newline at end of file diff --git a/pt/case-studies/index.xml b/pt/case-studies/index.xml index cb58a3e3..d90e369f 100644 --- a/pt/case-studies/index.xml +++ b/pt/case-studies/index.xml @@ -1,9 +1,74 @@ -Case-Studies on NumPyhttps://numpy.org/pt/case-studies/Recent content in Case-Studies on NumPyHugo -- gohugo.ioptEstudo de Caso: A Primeira Imagem de um Buraco Negrohttps://numpy.org/pt/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/blackhole-image/Black Hole M87# -(Créditos: Event Horizon Telescope Collaboration) Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver. —Katie Bouman, Professora Assistente, Ciências da Computação e Matemática, Caltech -Um telescópio do tamanho da Terra# O telescópio Event Horizon (EHT), 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.Estudo de Caso: Análise de Críquete, a revolução!https://numpy.org/pt/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/cricket-analytics/IPLT20, o maior festival de Críquete da Índia# -(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium)) 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.Estudo de Caso: Descoberta de Ondas Gravitacionaishttps://numpy.org/pt/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/gw-discov/Ondas gravitacionais# -(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO) O ecossistema científico Python é uma infraestrutura crítica para a pesquisa feita no LIGO. —David Shoemaker, Colaborador Científico no LIGO -Sobre Ondas Gravitacionais e o LIGO# 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.Estudo de Caso: Estimativa de Pose 3D com DeepLabCuthttps://numpy.org/pt/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/deeplabcut-dnn/Análise de movimentos de mãos de camundongos usando DeepLapCut# -(Fonte: www.deeplabcut.org ) 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 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. \ No newline at end of file +Case-Studies on NumPyhttps://numpy.org/pt/case-studies/Recent content in Case-Studies on NumPyHugoptEstudo de Caso: A Primeira Imagem de um Buraco Negrohttps://numpy.org/pt/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/blackhole-image/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/blackhole.jpg" alt="black hole image" class="align-center"> + + + +<figcaption><strong class="caption-title">Black Hole M87</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg">(Créditos: Event Horizon Telescope Collaboration)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +</p> + <p class="attribution">—Katie Bouman, <em>Professora Assistente, Ciências da Computação e Matemática, Caltech</em></p> +</blockquote> + +<h2 id="um-telescópio-do-tamanho-da-terra">Um telescópio do tamanho da Terra<a class="headerlink" href="#um-telescópio-do-tamanho-da-terra" title="Link to this heading">#</a></h2> +<p>O <a href="https://eventhorizontelescope.org">telescópio Event Horizon (EHT)</a>, 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 <a href="https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole">20 micro-arcossegundos</a> — o suficiente para ler um jornal em Nova Iorque a partir de um café em uma calçada de Paris!</p>Estudo de Caso: Análise de Críquete, a revolução!https://numpy.org/pt/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/cricket-analytics/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/ipl-stadium.png" alt="Copa e estádio da Indian Premier League Cricket" class="align-center"> + + + +<figcaption><strong class="caption-title">IPLT20, o maior festival de Críquete da Índia</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://unsplash.com/@aksh1802">(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium))</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/"> + <p> + You don't play for the crowd, you play for the country. +</p> + <p class="attribution">—M S Dhoni, <em>Jogador Internacional de Críquete, ex-capitão, Time Indiano, joga pelo Chennai Super Kings na IPL</em></p> +</blockquote> + +<h2 id="sobre-críquete">Sobre Críquete<a class="headerlink" href="#sobre-críquete" title="Link to this heading">#</a></h2> +<p>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 <a href="https://www.statista.com/topics/4543/indian-premier-league-ipl/">dinheiro</a> 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.</p>Estudo de Caso: Descoberta de Ondas Gravitacionaishttps://numpy.org/pt/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/gw-discov/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/gw_sxs_image.png" alt="binary coalesce black hole generating gravitational waves" class="align-center"> + + + +<figcaption><strong class="caption-title">Ondas gravitacionais</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://youtu.be/Zt8Z_uzG71o">(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +</p> + <p class="attribution">—David Shoemaker, <em>Colaborador Científico no LIGO</em></p> +</blockquote> + +<h2 id="sobre-ondas-gravitacionais-e-o-ligo">Sobre <a href="https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/">Ondas Gravitacionais</a> e o <a href="https://www.ligo.caltech.edu">LIGO</a><a class="headerlink" href="#sobre-ondas-gravitacionais-e-o-ligo" title="Link to this heading">#</a></h2> +<p>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.</p>Estudo de Caso: Estimativa de Pose 3D com DeepLabCuthttps://numpy.org/pt/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/deeplabcut-dnn/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/mice-hand.gif" alt="micehandanim" class="align-center"> + + + +<figcaption><strong class="caption-title">Análise de movimentos de mãos de camundongos usando DeepLapCut</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="http://www.mousemotorlab.org/deeplabcut">(Fonte: www.deeplabcut.org )</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"> + <p> +Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +</p> + <p class="attribution">—Alexander Mathis, <em>Professor Assistente, École polytechnique fédérale de Lausanne</em> (<a href="https://www.epfl.ch/en/">EPFL</a>)</p> +</blockquote> + +<h2 id="sobre-o-deeplabcut">Sobre o DeepLabCut<a class="headerlink" href="#sobre-o-deeplabcut" title="Link to this heading">#</a></h2> +<p><a href="https://github.com/DeepLabCut/DeepLabCut">DeepLabCut</a> 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.</p> \ No newline at end of file diff --git a/pt/citing-numpy/index.html b/pt/citing-numpy/index.html index f0457ba6..566c2fe0 100644 --- a/pt/citing-numpy/index.html +++ b/pt/citing-numpy/index.html @@ -1,25 +1,25 @@ -NumPy - Citando a NumPy -

      Citando o Numpy

      Se a NumPy é importante na sua pesquisa, e você gostaria de dar reconhecimento ao projeto na sua publicação acadêmica, sugerimos citar o seguinte artigo:

      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},
@@ -31,6 +31,5 @@
  doi           = {10.1038/s41586-020-2649-2},
  publisher     = {Springer Science and Business Media {LLC}},
  url           = {https://doi.org/10.1038/s41586-020-2649-2}
-}
      On this page
      \ No newline at end of file +}
      On this page
      \ No newline at end of file diff --git a/pt/code-of-conduct/index.html b/pt/code-of-conduct/index.html index 26521e9e..c3942604 100644 --- a/pt/code-of-conduct/index.html +++ b/pt/code-of-conduct/index.html @@ -1,14 +1,13 @@ -NumPy - Código de Conduta NumPy -

      Código de Conduta NumPy

      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
      • Rohit Goswami

      Se o seu relatório envolve algum membro da comissão, ou se você sentir que existe um conflito de interesses em tratá-lo, então os membros abster-se-ão de considerar o seu relatório. Como alternativa, se por qualquer razão você se sentir desconfortável em fazer um relatório à comissão, então você também pode entrar em contato com a equipe sênior da NumFOCUS em conduct@numfocus.org.

      Resolução de Incidentes & Aplicação do Código de Conduta#

      Esta seção resume os pontos mais importantes, mais detalhes podem ser encontrados em Código de Conduta do NumPy - Como dar seguimento a um relatório.

      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) 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:

      On this page
      \ No newline at end of file diff --git a/pt/community/index.html b/pt/community/index.html index 600dd254..ff634fd9 100644 --- a/pt/community/index.html +++ b/pt/community/index.html @@ -1,14 +1,18 @@ -NumPy - Comunidade -

      Comunidade

      NumPy é um projeto de código aberto impulsionado pela comunidade desenvolvido por um grupo muito diversificado de contribuidores. A liderança do NumPy assumiu um forte compromisso de criar uma comunidade aberta, inclusiva e positiva. A liderança do NumPy assumiu um forte compromisso de criar uma comunidade aberta, inclusiva e positiva.

      Oferecemos vários canais de comunicação para aprender, compartilhar seu conhecimento e se conectar com outros na comunidade NumPy.

      Participar online#

      Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a comunidade 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.

      Lista de discussões NumPy#

      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 feitos 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.

      Página de issues do GitHub#

      • 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 acredita ter encontrado uma vulnerabilidade de segurança no NumPy, relate-a aqui.

      Slack#

      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 ideias em uma grande lista de e-mails públicos ou no GitHub. +Por favor, clique aqui 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 (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.

      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
      • EuroSciPy
      • SciPy Latin America
      • SciPy India
      • SciPyData Japão
      • conferências PyData (de 15 a 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.

      Além disso, sinta-se à vontade para passar por aqui e dizer oi em uma de nossas reuniões da comunidade. Para acompanhá-las, confira nosso calendário de eventos aqui.

      On this page
      \ No newline at end of file diff --git a/pt/conduct/index.html b/pt/conduct/index.html index bf27b9b2..68abb02a 100644 --- a/pt/conduct/index.html +++ b/pt/conduct/index.html @@ -1,2 +1 @@ -https://numpy.org/pt/code-of-conduct/ - \ No newline at end of file +https://numpy.org/pt/code-of-conduct/ \ No newline at end of file diff --git a/pt/config.yaml b/pt/config.yaml index fadb4220..d6d231f3 100644 --- a/pt/config.yaml +++ b/pt/config.yaml @@ -6,135 +6,104 @@ params: text: NumPy link: /pt/ hero: - #Main hero title + # Main hero title title: NumPy - #Hero subtitle (optional) + # Hero subtitle (optional) subtitle: A biblioteca fundamental para computação científica com Python - #Button text - buttontext: "Última versão: NumPy 1.26. Veja todas as versões" - #Where the main hero button links to + # Button text + buttontext: "Última versão: NumPy 2.2. Veja todas as versões" + # Where the main hero button links to buttonlink: "/pt/news/#releases" - #Hero image (from static/images/___) + # Hero image (from static/images/___) image: logo.svg shell: title: placeholder intro: - - - title: Experimentar o NumPy - text: Use o shell interativo para testar o NumPy no navegador + - title: Experimentar o NumPy + text: Use o shell interativo para testar o NumPy no navegador docslink: Não se esqueça de conferir a documentação. casestudies: title: ESTUDOS DE CASO features: - - - title: A Primeira Imagem de um Buraco Negro - text: Como o NumPy, junto com outras bibliotecas como SciPy e Matplotlib que dependem do NumPy, permitiram ao Event Horizon Telescope gerar a primeira imagem de um buraco negro da história. - img: /images/content_images/case_studies/blackhole.png - alttext: Primeira imagem de um buraco negro. É um círculo laranja em um fundo preto. - url: /pt/case-studies/blackhole-image - - - title: Descoberta de Ondas Gravitacionais - text: Em 1916, Albert Einstein previu ondas gravitacionais; 100 anos depois, sua existência foi confirmada pelos cientistas do LIGO usando NumPy. - img: /images/content_images/case_studies/gravitional.png - alttext: Duas esferas orbitando a si mesmas. Elas deslocam a gravidade em seu entorno. - url: /pt/case-studies/gw-discov - - - title: Análise Esportiva - text: A análise de críquete está mudando o jogo ao melhorar o desempenho de jogadores e times através de modelagem estatística e análise preditiva. O NumPy possibilita muitas dessas análises. - img: /images/content_images/case_studies/sports.jpg - alttext: Bola de críquete em um campo verde - url: /pt/case-studies/cricket-analytics - - - title: Estimação de poses usando deep learning - text: DeepLabCut usa o NumPy para acelerar estudos científicos que envolvem comportamento animal para entender melhor o controle motor em várias espécies e escalas de tempo. - img: /images/content_images/case_studies/deeplabcut.png - alttext: Análise de pose de um guepardo - url: /pt/case-studies/deeplabcut-dnn + - title: A Primeira Imagem de um Buraco Negro + text: Como o NumPy, junto com outras bibliotecas como SciPy e Matplotlib que dependem do NumPy, permitiram ao Event Horizon Telescope gerar a primeira imagem de um buraco negro da história. + img: /images/content_images/case_studies/blackhole.png + alttext: Primeira imagem de um buraco negro. É um círculo laranja em um fundo preto. + url: /pt/case-studies/blackhole-image + - title: Descoberta de Ondas Gravitacionais + text: Em 1916, Albert Einstein previu ondas gravitacionais; 100 anos depois, sua existência foi confirmada pelos cientistas do LIGO usando NumPy. + img: /images/content_images/case_studies/gravitional.png + alttext: Duas esferas orbitando a si mesmas. Elas deslocam a gravidade em seu entorno. + url: /pt/case-studies/gw-discov + - title: Análise Esportiva + text: A análise de críquete está mudando o jogo ao melhorar o desempenho de jogadores e times através de modelagem estatística e análise preditiva. O NumPy possibilita muitas dessas análises. + img: /images/content_images/case_studies/sports.jpg + alttext: Bola de críquete em um campo verde + url: /pt/case-studies/cricket-analytics + - title: Estimação de poses usando deep learning + text: DeepLabCut usa o NumPy para acelerar estudos científicos que envolvem comportamento animal para entender melhor o controle motor em várias espécies e escalas de tempo. + img: /images/content_images/case_studies/deeplabcut.png + alttext: Análise de pose de um guepardo + url: /pt/case-studies/deeplabcut-dnn tabs: title: ECOSSISTEMA section5: false navbar: - - - title: Instalação - url: /pt/install - - - title: Documentação - url: https://numpy.org/doc/stable - - - title: Aprenda - url: /pt/learn - - - title: Comunidade - url: /pt/community - - - title: Sobre - url: /pt/about - - - title: Notícias - url: /pt/news - - - title: Contribuir - url: /pt/contribute + - title: Instalação + url: /pt/install + - title: Documentação + url: https://numpy.org/doc/stable + - title: Aprenda + url: /pt/learn + - title: Comunidade + url: /pt/community + - title: Sobre + url: /pt/about + - title: Notícias + url: /pt/news + - title: Contribuir + url: /pt/contribute footer: logo: logo.svg socialmediatitle: "" socialmedia: - - - link: https://github.com/numpy/numpy - icon: github - - - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng - icon: youtube - - - link: https://twitter.com/numpy_team - icon: twitter + - link: https://github.com/numpy/numpy + icon: github + - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng + icon: youtube quicklinks: column1: title: "" links: - - - text: Instalação + - text: Instalação link: /pt/install - - - text: Documentação + - text: Documentação link: https://numpy.org/doc/stable - - - text: Aprenda + - text: Aprenda link: /pt/learn - - - text: Citando o Numpy + - text: Citando o Numpy link: /pt/citing-numpy - - - text: Roadmap + - text: Roadmap link: https://numpy.org/neps/roadmap.html column2: links: - - - text: Sobre + - text: Sobre link: /pt/about - - - text: Comunidade + - text: Comunidade link: /pt/community - - - text: Pesquisas de usuário + - text: Pesquisas de usuário link: /pt/user-surveys - - - text: Contribuir + - text: Contribuir link: /pt/contribute - - - text: Código de Conduta + - text: Código de Conduta link: /pt/code-of-conduct column3: links: - - - text: Ajuda + - text: Ajuda link: /pt/gethelp - - - text: Termos de uso (EN) + - text: Termos de uso (EN) link: /pt/terms - - - text: Privacidade + - text: Privacidade link: /pt/privacy - - - text: Kit de imprensa + - text: Kit de imprensa link: /pt/press-kit diff --git a/pt/contribute/index.html b/pt/contribute/index.html index bc338d52..ec6114d4 100644 --- a/pt/contribute/index.html +++ b/pt/contribute/index.html @@ -1,15 +1,21 @@ -NumPy - Contribua com o NumPy -NumPy - Contribua com o NumPy

      Contribua com o NumPy

      O projeto NumPy precisa de sua experiência e entusiasmo! +Suas opções não são limitadas à programação. Como pode ver a seguir, há várias áreas em que precisamos da sua ajuda.

      Se você não sabe por onde começar ou como suas habilidades podem ajudar, fale conosco! Você pode perguntar na nossa lista de emails ou GitHub (abrindo uma issue ou comentando em uma issue relevante).

      Estes são os nossos canais de comunicação preferidos (projetos de código aberto são abertos por natureza!). No entanto, se você preferir discutir em privado, entre em contato com os coordenadores da comunidade em numpy-team@googlegroups.com ou no Slack (envie um e-mail para numpy-team@googlegroups.com para obter um convite antes de entrar).

      Nós também temos uma reunião aberta da comunidade a cada duas semanas. Os detalhes são anunciados na nossa lista de emails. +Convidamos você a participar. +Se você nunca contribuiu para projetos de código aberto, recomendamos fortemente que você leita esse guia.

      Nossa comunidade deseja tratar todos da mesma forma e valorizar todas as contribuições. Temos um Código de Conduta para promover um ambiente aberto e acolhedor.

      Para um guia visual sobre como contribuir com o NumPy, confira este quadrinho.

      NumPy Contributor comic book cover

      Escrevendo código#

      Para pessoas programadoras, este guia explica como contribuir para a base de código.
      Confira também nosso canal do YouTube para obter informações adicionais.

      Revisando 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

      Desenvolvendo materiais educacionais#

      O Guia do Usuário do Numpy está sendo reformado. +Precisamos de novos tutoriais, how-to’s e de explicações de conceitos, e o site precisa de reestruturação. As oportunidades não se limitam a pessoas com experiência em escrita técnica. Também procuramos exemplos práticos, notebooks e vídeos. A NEP 44 explica nossas ideias para reestruturar a documentação do NumPy — talvez você também tenha outras ideias.

      Triagem de Issues#

      O issue tracker do NumPy tem um monte de issues abertas. Algumas não são mais válidas, algumas deveriam ser priorizadas, e algumas poderiam ser boas para pessoas que estão procurando sua primeira contribuição. Você pode:

      • verificar se erros mais antigos ainda estão presentes
      • encontrar issues duplicadas e criar links entre issues relacionadas
      • adicionar bons exemplos autocontidos que reproduzam issues
      • rotular issues corretamente (isso requer direitos de triagem – basta pedir)

      Sinta-se à vontade!

      Desenvolvimento do site#

      Acabamos de renovar o nosso site, mas estamos longe de terminar. Se você adora o desenvolvimento web, estas issues listam algumas de nossas necessidades não atendidas – e sinta-se livre para compartilhar suas próprias ideias.

      Design gráfico#

      Nós mal podemos começar a listar as contribuições que uma pessoa com conhecimento em design gráfico pode fazer aqui. +Nossa documentação precisa de ilustrações; nosso site crescente precisa de imagens – há muitas oportunidades.

      Traduzir conteúdo do site#

      Planejamos várias traduções do numpy.org para tornar o NumPy acessível aos usuários em seu idioma nativo. Tradutores voluntários estão no coração deste esforço. Tradutores voluntários estão no coração deste esforço. Veja aqui para informações; comente nesta issue do GitHub para se envolver.

      Coordenação e promoção na comunidade#

      Através do contato com a comunidade podemos compartilhar nosso trabalho para mais pessoas e descobrir onde precisamos trabalhar mais. Estamos ansiosos para que mais pessoas se envolvam em esforços como a organização de sprints de código sobre o NumPy, uma newsletter, e talvez um blog.

      Financiamento#

      O NumPy foi um projeto totalmente voluntário por muitos anos, mas conforme sua importância cresceu, tornou-se clara a necessidade de apoio financeiro para garantir estabilidade e crescimento. +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. 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.

      Doações#

      Se você gostaria de contribuir com o NumPy fazendo uma doação, visite https://numpy.org/pt/about/#doações.

      On this page
      \ No newline at end of file diff --git a/pt/diversity_sep2020/index.html b/pt/diversity_sep2020/index.html new file mode 100644 index 00000000..5c56e862 --- /dev/null +++ b/pt/diversity_sep2020/index.html @@ -0,0 +1,62 @@ +NumPy - NumPy Diversity and Inclusion Statement

      NumPy Diversity and Inclusion Statement

      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 and +this blog post).

      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 +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, +GitHub, Slack, +in private at numpy-team@googlegroups.com, or join our +bi-weekly community meeting.

      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

      On this page
      \ No newline at end of file diff --git a/pt/gethelp/index.html b/pt/gethelp/index.html index 034ce5f8..ac56331e 100644 --- a/pt/gethelp/index.html +++ b/pt/gethelp/index.html @@ -1,15 +1,14 @@ -NumPy - Obter ajuda -

      Obter ajuda

      Issues sobre desenvolvimento: Para assuntos relacionados ao desenvolvimento do NumPy (por exemplo, relatórios de bugs), veja a Comunidade.

      Perguntas de usuários: A melhor maneira de obter ajuda é postar sua pergunta em um site como StackOverflow ou Reddit. Gostaríamos de poder ficar de olho nestes sites, ou responder perguntas diretamente, mas o volume é imenso!

      StackOverflow#

      Um fórum para fazer perguntas sobre a utilização da biblioteca, por exemplo: “Como faço X no NumPy?”. Por favor use a tag #numpy

      Reddit#

      Outro fórum para perguntas de utilização.

      On this page
      \ No newline at end of file diff --git a/pt/history/index.html b/pt/history/index.html index 934aa3e4..52ab1596 100644 --- a/pt/history/index.html +++ b/pt/history/index.html @@ -1,13 +1,12 @@ -NumPy - Histórico do NumPy -

      Histórico do NumPy

      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.

      Se você quiser obter uma cópia das bibliotecas Numeric e Numarray, siga os links abaixo:

      Página de download para Numeric*

      Página de download para Numarray*

      *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’

      On this page
      \ No newline at end of file diff --git a/pt/index.html b/pt/index.html index c38eebf8..ee2cb825 100644 --- a/pt/index.html +++ b/pt/index.html @@ -1,14 +1,15 @@ -NumPy -
      NumPy +
      A biblioteca fundamental para computação científica com Python
      Lançado o NumPy versão 2.2.0!
      2024-12-08
      Arrays n-dimensionais poderosas
      Rápidos e versáteis, os conceitos de vetorização, indexação e broadcasting do NumPy são, na prática, o padrão em computação com arrays.
      Ferramentas de computação numérica
      O NumPy oferece um conjunto completo de funções matemáticas, geradores de números aleatórios, rotinas de álgebra linear, transformadas de Fourier, e mais.
      Código aberto
      Distribuído sob uma licença BSD liberal, o NumPy é desenvolvido e mantido publicamente no GitHub por uma comunidade vibrante, responsiva, e diversa.
      Interoperabilidade
      O NumPy suporta um grande número de plataformas de equipamento físico e computação, e pode ser combinado com bibliotecas de computação com arrays esparsas, distribuídas ou em GPUs.
      Alto desempenho
      O núcleo do NumPy é feito de código otimizado em C. Aproveite a flexibilidade do Python com a velocidade de código compilado.
      Fácil de usar
      A sintaxe de alto nível do NumPy torna-o acessível e produtivo para programadores de qualquer nível de experiência e formação.
      Experimentar o NumPy

      Use o shell interativo para testar o NumPy no navegador

      """
 To try the examples in the browser:
 1. Type code in the input cell and press
    Shift + Enter to execute
@@ -35,7 +36,7 @@
 # Generate normally distributed random numbers:
 rng = np.random.default_rng()
 samples = rng.normal(size=2500)
-samples

      ECOSSISTEMA

      ECOSSISTEMA

      Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy.

      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.

      • 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.

        Biblioteca de ArraysRecursos e áreas de aplicação
        DaskDaskArrays distribuídas e paralelismo avançado para análise, permitindo desempenho em escala.
        CuPyCuPyBiblioteca de matriz compatível com NumPy para computação acelerada pela GPU com Python.
        JAXJAXTransformações combináveis de programas NumPy: vetorização, compilação just-in-time para GPU/TPU.
        xarrayXarrayArrays multidimensionais rotuladas e indexadas para análise e visualização avançadas
        sparseSparseBiblioteca de arrays compatíveis com o NumPy que pode ser integrada com Dask e álgebra linear esparsa da SciPy.
        PyTorchPyTorchFramework de deep learning que acelera o caminho entre prototipação de pesquisa e colocação em produção.
        TensorFlowTensorFlowUma plataforma completa para aprendizagem de máquina que permite construir e colocar em produção aplicações usando ML facilmente.
        MXNetMXNetFramework de deep learning voltado para flexibilizar prototipação em pesquisa e produção.
        arrowArrowUma plataforma de desenvolvimento multi-linguagens para dados e análise para dados armazenados em colunas na memória.
        xtensorxtensorArrays multidimensionais com broadcasting e avaliação preguiçosa (lazy computing) para análise numérica.
        awkwardAwkward ArrayManipulação de dados JSON-like com sintaxe NumPy-like.
        uarrayuarraySistema de backend Python que dissocia a API da implementação; unumpy fornece uma API NumPy.
        tensorlytensorlyFerramentas para aprendizagem com tensores, algebra e backends para usar NumPy, MXNet, PyTorch, TensorFlow ou CuPy sem esforço.
      • Diagrama de bibliotecas Python. As cinco categorias são 'Extrair, Transformar, Carregar', 'Exploração de Dados', 'Modelo de Dados', 'Avaliação de Dados' e 'Apresentação de Dados'.

        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:

        For high data volumes, Dask andRay are designed to scale. Stabledeployments rely on data versioning (DVC),experiment tracking (MLFlow), andworkflow automation (Airflow andPrefect).

        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'.
      • An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

        Source: Google AI Blog

        O NumPy forma a base de bibliotecas de aprendizagem de máquina poderosas como scikit-learn e SciPy. 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 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, outra biblioteca de deep learning, é popular entre pesquisadores em visão computacional e processamento de linguagem natural. O MXNet é outro pacote de IA, que fornece templates e protótipos para deep learning.

        Técnicas estatísticas chamadas métodos de ensemble tais como binning, bagging, stacking, e boosting estão entre os algoritmos de ML implementados por ferramentas tais como XGBoost, LightGBM, e CatBoost — um dos motores de inferência mais rápidos. Yellowbrick e Eli5 oferecem visualizações para aprendizagem de máquina.

      • Um streamplot feito em matplotlib
        Um gráfico scatter-plot feito em ggpy
        Um box-plot feito no plotly
        Um gráfico streamgraph feito em altair
        A plot duplo com dois tipos de gráficos, um plot-graph e um gráfico de frequência feitos no seaborn
        Uma renderização de volume 3D feita no PyVista.
        Uma imagem multidimensional, feita em napari.
        Diagrama de Voronoi feito com vispy.

        NumPy é um componente essencial no crescente campo de visualização em Python, que inclui Matplotlib, Seaborn, Plotly, Altair, Bokeh, Holoviz, Vispy, Napari, e PyVista, para citar alguns.

        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.

        • ESTUDOS DE CASO

        A Primeira Imagem de um Buraco Negro
        Primeira imagem de um buraco negro. É um círculo laranja em um fundo preto.
        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.
        Descoberta de Ondas Gravitacionais
        Duas esferas orbitando a si mesmas. Elas deslocam a gravidade em seu entorno.
        Em 1916, Albert Einstein previu ondas gravitacionais; 100 anos depois, sua existência foi confirmada pelos cientistas do LIGO usando NumPy.
        Análise Esportiva
        Bola de críquete em um campo verde
        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.
        Estimação de poses usando deep learning
        Análise de pose de um guepardo
        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.
        \ No newline at end of file +Visualization

        Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy.

        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.

        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.

        Biblioteca de ArraysRecursos e áreas de aplicação
        DaskDaskArrays distribuídas e paralelismo avançado para análise, permitindo desempenho em escala.
        CuPyCuPyBiblioteca de matriz compatível com NumPy para computação acelerada pela GPU com Python.
        JAXJAXTransformações combináveis de programas NumPy: vetorização, compilação just-in-time para GPU/TPU.
        xarrayXarrayArrays multidimensionais rotuladas e indexadas para análise e visualização avançadas
        sparseSparseBiblioteca de arrays compatíveis com o NumPy que pode ser integrada com Dask e álgebra linear esparsa da SciPy.
        PyTorchPyTorchFramework de deep learning que acelera o caminho entre prototipação de pesquisa e colocação em produção.
        TensorFlowTensorFlowUma plataforma completa para aprendizagem de máquina que permite construir e colocar em produção aplicações usando ML facilmente.
        MXNetMXNetFramework de deep learning voltado para flexibilizar prototipação em pesquisa e produção.
        arrowArrowUma plataforma de desenvolvimento multi-linguagens para dados e análise para dados armazenados em colunas na memória.
        xtensorxtensorArrays multidimensionais com broadcasting e avaliação preguiçosa (lazy computing) para análise numérica.
        awkwardAwkward ArrayManipulação de dados JSON-like com sintaxe NumPy-like.
        uarrayuarraySistema de backend Python que dissocia a API da implementação; unumpy fornece uma API NumPy.
        tensorlytensorlyFerramentas para aprendizagem com tensores, algebra e backends para usar NumPy, MXNet, PyTorch, TensorFlow ou CuPy sem esforço.
        blosc2Blosc2Cálculo acelerado com arrays comprimidos na memória, no disco ou remotos.
        Diagrama de bibliotecas Python. As cinco categorias são 'Extrair, Transformar, Carregar', 'Exploração de Dados', 'Modelo de Dados', 'Avaliação de Dados' e 'Apresentação de Dados'.

        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:

        For high data volumes, Dask andRay are designed to scale. Stabledeployments rely on data versioning (DVC),experiment tracking (MLFlow), andworkflow automation (Airflow andPrefect).

        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'.
        An animated gif showing a three-dimensional graph of embeddings made in Tensorflow.

        Source: Google AI Blog

        O NumPy forma a base de bibliotecas de aprendizagem de máquina poderosas como scikit-learn e SciPy. 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 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, outra biblioteca de deep learning, é popular entre pesquisadores em visão computacional e processamento de linguagem natural. O MXNet é outro pacote de IA, que fornece templates e protótipos para deep learning.

        Técnicas estatísticas chamadas métodos de ensemble tais como binning, bagging, stacking, e boosting estão entre os algoritmos de ML implementados por ferramentas tais como XGBoost, LightGBM, e CatBoost — um dos motores de inferência mais rápidos. Yellowbrick e Eli5 oferecem visualizações para aprendizagem de máquina.

        Um streamplot feito em matplotlib
        Um gráfico scatter-plot feito em ggpy
        Um box-plot feito no plotly
        Um gráfico streamgraph feito em altair
        A plot duplo com dois tipos de gráficos, um plot-graph e um gráfico de frequência feitos no seaborn
        Uma renderização de volume 3D feita no PyVista.
        Uma imagem multidimensional, feita em napari.
        Diagrama de Voronoi feito com vispy.

        NumPy é um componente essencial no crescente campo de visualização em Python, que inclui Matplotlib, Seaborn, Plotly, Altair, Bokeh, Holoviz, Vispy, Napari, e PyVista, para citar alguns.

        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.

        ESTUDOS DE CASO

        A Primeira Imagem de um Buraco Negro
        Primeira imagem de um buraco negro. É um círculo laranja em um fundo preto.
        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.
        Descoberta de Ondas Gravitacionais
        Duas esferas orbitando a si mesmas. Elas deslocam a gravidade em seu entorno.
        Em 1916, Albert Einstein previu ondas gravitacionais; 100 anos depois, sua existência foi confirmada pelos cientistas do LIGO usando NumPy.
        Análise Esportiva
        Bola de críquete em um campo verde
        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.
        Estimação de poses usando deep learning
        Análise de pose de um guepardo
        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.
        \ No newline at end of file diff --git a/pt/index.xml b/pt/index.xml index d297342c..5f5d1505 100644 --- a/pt/index.xml +++ b/pt/index.xml @@ -1,34 +1,1651 @@ -NumPyhttps://numpy.org/pt/Recent content on NumPyHugo -- gohugo.ioptSat, 16 Sep 2023 00:00:00 +0000Notíciashttps://numpy.org/pt/news/Sat, 16 Sep 2023 00:00:00 +0000https://numpy.org/pt/news/Lançado o NumPy versão 1.26.0# 16 de setembro de 2023 &ndash; NumPy 1.26.0 está disponível. Os destaques desta versão são: -Suporte ao Python 3.12.0. Compatibilidade com Cython 3.0.0. Utilização do sistema Meson para compilação Suport a SIMD atualizado Melhorias para f2py, suporte a meson e bind(x) Suporte à versão mais recente da biblioteca Accelerate BLAS/LAPACK A versão 1.26.0 é uma continuação da série de versões 1.25.x que marcam a transição para o sistema de compilação Meson e oferecem suporte preliminar para o Cython 3.404https://numpy.org/pt/404/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/404/Oops! Você atingiu um beco sem saída. -Se você acha que algo deveria estar aqui, você pode abrir uma issue no GitHub.Aprendahttps://numpy.org/pt/learn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/learn/Para a documentação oficial do NumPy visite numpy.org/doc/stable. -Abaixo está uma coleção de recursos educacionais, tanto para autoaprendizado como para ensinar outras pessoas, desenvolvidos pelos colaboradores do NumPy e selecionados pela comunidade. -Iniciantes# 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) NumPy Tutorials Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy.Citando a NumPyhttps://numpy.org/pt/citing-numpy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/citing-numpy/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. 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. (Link da editora). Em formato BibTeX: -@Article{ harris2020array, title = {Array programming with {NumPy}}, author = {Charles R.Código de Conduta NumPyhttps://numpy.org/pt/code-of-conduct/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/code-of-conduct/Introdução# Este código de conduta aplica-se a todos os espaços gerenciados pelo projeto NumPy, incluindo todas as listas de discussão públicas e privadas, issue tracker, wikis, blogs, Twitter e qualquer outro canal de comunicação usado pela nossa comunidade. O projeto NumPy não organiza eventos presenciais. No entanto, os eventos relacionados à nossa comunidade devem ter um código de conduta semelhante ao atual. -Este Código de Conduta deve ser honrado por todas as pessoas que participam da comunidade NumPy formal ou informalmente, ou que reivindicam qualquer afiliação com o projeto, em qualquer atividade relacionada ao projeto, especialmente ao representar o projeto, em qualquer função.Código de Conduta NumPy - Como dar seguimento a um relatóriohttps://numpy.org/pt/report-handling-manual/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/report-handling-manual/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 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:Computação com Arrayshttps://numpy.org/pt/arraycomputing/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/arraycomputing/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.Comunidadehttps://numpy.org/pt/community/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/community/NumPy é um projeto de código aberto impulsionado pela comunidade desenvolvido por um grupo muito diversificado de contribuidores. A liderança do NumPy assumiu um forte compromisso de criar uma comunidade aberta, inclusiva e positiva. Por favor, leia o Código de Conduta NumPy 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.Contribua com o NumPyhttps://numpy.org/pt/contribute/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/contribute/O projeto NumPy precisa de sua experiência e entusiasmo! Suas opções de não são limitadas à programação &ndash; além de -Se você não sabe por onde começar ou como suas habilidades podem ajudar, fale conosco! Você pode perguntar na nossa lista de emails ou GitHub (abrindo uma issue ou comentando em uma issue relevante). -Estes são os nossos canais de comunicação preferidos (projetos de código aberto são abertos por natureza!). No entanto, se você preferir discutir em privado, entre em contato com os coordenadores da comunidade em numpy-team@googlegroups.Estudo de Caso: A Primeira Imagem de um Buraco Negrohttps://numpy.org/pt/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/blackhole-image/Black Hole M87# -(Créditos: Event Horizon Telescope Collaboration) Criar uma imagem do Buraco Negro M87 é como tentar ver algo que, por definição, é impossível de se ver. —Katie Bouman, Professora Assistente, Ciências da Computação e Matemática, Caltech -Um telescópio do tamanho da Terra# O telescópio Event Horizon (EHT), 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.Estudo de Caso: Análise de Críquete, a revolução!https://numpy.org/pt/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/cricket-analytics/IPLT20, o maior festival de Críquete da Índia# -(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium)) 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.Estudo de Caso: Descoberta de Ondas Gravitacionaishttps://numpy.org/pt/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/gw-discov/Ondas gravitacionais# -(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO) O ecossistema científico Python é uma infraestrutura crítica para a pesquisa feita no LIGO. —David Shoemaker, Colaborador Científico no LIGO -Sobre Ondas Gravitacionais e o LIGO# 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.Estudo de Caso: Estimativa de Pose 3D com DeepLabCuthttps://numpy.org/pt/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/deeplabcut-dnn/Análise de movimentos de mãos de camundongos usando DeepLapCut# -(Fonte: www.deeplabcut.org ) 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 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.Histórico do NumPyhttps://numpy.org/pt/history/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/history/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 &ldquo;desobedientes&rdquo; 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.Instalando o NumPyhttps://numpy.org/pt/install/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/install/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 - 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. Para obter instruções mais detalhadas, consulte nosso guia de instalação do Python e do NumPy abaixo.Kit de imprensahttps://numpy.org/pt/press-kit/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/press-kit/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. Note que usando os recursos numpy.org, você aceita o Código de Conduta do NumPy.Obter ajudahttps://numpy.org/pt/gethelp/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/gethelp/Perguntas de usuários: A melhor maneira de obter ajuda é postar sua pergunta em um site como StackOverflow, com milhares de usuários disponíveis para responder. Outras alternativas incluem IRC, Gittere Reddit. 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. -StackOverflow# Um fórum para fazer perguntas sobre a utilização da biblioteca, por exemplo: &ldquo;Como faço X no NumPy?PESQUISA DE USUÁRIOS NUMPYhttps://numpy.org/pt/user-surveys/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/user-surveys/2020 O time de pesquisas da NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, conduziram a primeira pesquisa oficial sobre a comunidade NumPy. Você pode encontrar os resultados da pesquisa aqui (em inglês). -2021 Os dados coletados estão em análise. -Se você tem dúvidas ou sugestões sobre as pesquisas já realizadas ou futuras, por favor crie uma issue aqui.PESQUISA SOBRE A COMUNIDADE NUMPY 2020https://numpy.org/pt/user-survey-2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/user-survey-2020/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. -Faça o download do relatório para ver os detalhes sobre os resultados encontrados.Política de privacidadehttps://numpy.org/pt/privacy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/privacy/numpy.org é operado por NumFOCUS, Inc., 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.Quem Somoshttps://numpy.org/pt/about/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/about/NumPy é um projeto de código aberto que visa possibilitar a computação numérica com Python. Foi criado em 2005, com base no trabalho inicial das bibliotecas Numeric e Numarray. O NumPy sempre será 100% software de código aberto, livre para que todos usem. É lançado sob os termos liberais da licença BSD modificada. -O NumPy é desenvolvido no GitHub, através do consenso da comunidade NumPy e de uma comunidade mais ampla de Python científico.Termos de Usohttps://numpy.org/pt/terms/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/terms/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 (&quot;Project&quot;, “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”).Times NumPyhttps://numpy.org/pt/teams/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/teams/Somos uma equipe internacional com a missão de apoiar comunidades científicas e de pesquisa em todo o mundo construindo software de código aberto de qualidade. Junte-se a nós! -Maintainers# Andrew Nelson Bas van Beek Charles Harris Eric Wieser Ganesh Kathiresan Rohit Goswami Matthew Brett Matti Picus Matt Haberland Melissa Weber Mendonça Marten van Kerkwijk Christopher Sidebottom Mateusz Sokół Mukulika Nathan Goldbaum Pearu Peterson Josh Wilson Pauli Virtanen Chunlin Raghuveer Devulapalli Ralf Gommers Robert Kern Ross Barnowski Sebastian Berg Sayed Adel Stephan Hoyer Stefan van der Walt Tyler Reddy Warren Weckesser Docs team# Rohit Goswami Inessa Pawson Mars Lee Matti Picus Melissa Weber Mendonça Mukulika Ross Barnowski Web team# Inessa Pawson Jarrod Millman Joe LaChance Mars Lee Ralf Gommers shalz Shekhar Prasad Rajak Stefan van der Walt Albert Steppi Triage team# Andrew Nelson Anirudh Subramanian Aaron Meurer Atsushi Sakai Ben Nathanson Anne Bonner Brigitta Sipőcz carlkl Ryan C Cooper ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) Hameer Abbasi Inessa Pawson jbrockmendel Kai Yuji Kanagawa Kriti Singh Christopher Albert Lysandros Nikolaou Meekail Zain Christopher Sidebottom Mateusz Sokół Mukulika Noa Tamir Raghuveer Devulapalli shalz Tina Oberoi Rakesh Vasudevan Zijie (ZJ) Poh Survey team# Inessa Pawson Ralf Gommers Ross Barnowski Emeritus maintainers# Allan Haldane Ondřej Čertík David Cournapeau Jaime Jarrod Millman Julian Taylor Mark Wiebe Nathaniel J. \ No newline at end of file +NumPyhttps://numpy.org/pt/Recent content on NumPyHugoptSun, 08 Dec 2024 00:00:00 +0000Notíciashttps://numpy.org/pt/news/Sun, 08 Dec 2024 00:00:00 +0000https://numpy.org/pt/news/<h3 id="lançado-o-numpy-versão-220">Lançado o NumPy versão 2.2.0<a class="headerlink" href="#lançado-o-numpy-versão-220" title="Link to this heading">#</a></h3> +<p><em>8 de dezembro de 2024</em> &ndash; A NumPy 2.2.0 é uma versão que nos traz de volta para o calendário habitual de lançamento duas vezes por ano. Ela inclui um número de pequenas limpezas, melhorias para o StringDType, e melhor suporte para +o free-threaded Python. Alguns dos destaques são:</p> +<ul> +<li>Novas funções <code>matvec</code> e <code>vecmat</code>,</li> +<li>Várias melhorias nas anotações de tipos,</li> +<li>Suporte melhorado para o novo StringDType,</li> +<li>Suporte aprimorado para Python free-threaded,</li> +<li>Correções para o f2py.</li> +</ul> +<p>Esta versão suporta as versões 3.10-3.13 do Python.</p>404https://numpy.org/pt/404/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/404/<p>Oops! Você atingiu um beco sem saída.</p> +<p>Se você acha que algo deveria estar aqui, você pode <a href="https://github.com/numpy/numpy.org/issues">abrir uma issue</a> no GitHub.</p>Aprendahttps://numpy.org/pt/learn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/learn/<p>Para a <strong>documentação oficial do NumPy</strong> visite <a href="https://numpy.org/doc/stable">numpy.org/doc/stable</a>.</p> +<hr> +<p>Abaixo está uma coleção de recursos educacionais, tanto para autoaprendizado como para ensinar outras pessoas, desenvolvidos pelos colaboradores do NumPy e selecionados pela comunidade.</p> +<h2 id="iniciantes">Iniciantes<a class="headerlink" href="#iniciantes" title="Link to this heading">#</a></h2> +<p>Há uma tonelada de informações sobre o NumPy lá fora. Se você está começando, recomendamos fortemente estes:</p> +<p><i class="fas fa-chalkboard"></i> <strong>Tutoriais</strong></p> +<ul> +<li><a href="https://numpy.org/devdocs/user/quickstart.html">NumPy Quickstart Tutorial (Tutorial de Início Rápido)</a></li> +<li><a href="https://numpy.org/numpy-tutorials">NumPy Tutorials</a> Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório <a href="https://github.com/numpy/numpy-tutorials">numpy-tutorials no GitHub</a>.</li> +<li><a href="https://betterprogramming.pub/3b1d4976de1d?sk=57b908a77aa44075a49293fa1631dd9b">NumPy Illustrated: The Visual Guide to NumPy <em>por Lev Maximov</em></a></li> +<li><a href="https://lectures.scientific-python.org/">Scientific Python Lectures</a> Além de incluir conteúdo sobre o NumPy, estas aulas oferecem uma introdução mais ampla ao ecossistema científico do Python.</li> +<li><a href="https://numpy.org/devdocs/user/absolute_beginners.html">NumPy: the absolute basics for beginners (&ldquo;o básico absoluto para inciantes&rdquo;)</a></li> +<li><a href="https://github.com/rougier/numpy-tutorial">NumPy tutorial <em>por Nicolas Rougier</em></a></li> +<li><a href="http://cs231n.github.io/python-numpy-tutorial/">Stanford CS231 <em>por Justin Johnson</em></a></li> +<li><a href="https://numpy.org/devdocs">NumPy User Guide (Guia de Usuário NumPy)</a></li> +</ul> +<p><i class="fas fa-book"></i> <strong>Livros</strong></p>Citando o Numpyhttps://numpy.org/pt/citing-numpy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/citing-numpy/<p>Se a NumPy é importante na sua pesquisa, e você gostaria de dar reconhecimento ao projeto na sua publicação acadêmica, sugerimos citar o seguinte artigo:</p> +<ul> +<li>Harris, C.R., Millman, K.J., van der Walt, S.J. et al. <em>Array programming with NumPy</em>. Nature 585, 357–362 (2020). DOI: <a href="https://doi.org/10.1038/s41586-020-2649-2">10.1038/s41586-020-2649-2</a>. (<a href="https://www.nature.com/articles/s41586-020-2649-2">Link da editora</a>).</li> +</ul> +<p><em>Em formato BibTeX:</em></p> + +<div class="highlight"> + <pre>@Article{ harris2020array, + title = {Array programming with {NumPy}}, + author = {Charles R. Harris and K. Jarrod Millman and St{\&#39;{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{\&#39;{a}}ndez del + R{\&#39;{i}}o and Mark Wiebe and Pearu Peterson and Pierre + G{\&#39;{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} +}</pre> +</div>Código de Conduta NumPyhttps://numpy.org/pt/code-of-conduct/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/code-of-conduct/<h3 id="introdução">Introdução<a class="headerlink" href="#introdução" title="Link to this heading">#</a></h3> +<p>Este código de conduta aplica-se a todos os espaços gerenciados pelo projeto NumPy, incluindo todas as listas de discussão públicas e privadas, <em x-id="3">issue tracker</em>, 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.</p> +<p>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.</p>Código de Conduta NumPy - Como dar seguimento a um relatóriohttps://numpy.org/pt/report-handling-manual/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/report-handling-manual/<p>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.</p> +<p>Garantir que o <a href="https://numpy.org/pt/code-of-conduct/">Código de Conduta</a> 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:</p>Computação com Arrayshttps://numpy.org/pt/arraycomputing/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/arraycomputing/<p><em>A computação com matrizes 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.</em></p> +<p>A manipulação e a transformação de dados de grande escala dependem de computação eficiente de alto desempenho com arrays. A linguagem mais escolhida para análise de dados, aprendizagem de máquina e computação numérica produtiva é <strong>Python.</strong></p>Comunidadehttps://numpy.org/pt/community/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/community/<p>NumPy é um projeto de código aberto impulsionado pela comunidade desenvolvido por um grupo muito diversificado de <a href="https://numpy.org/pt/teams/">contribuidores</a>. A liderança do NumPy assumiu um forte compromisso de criar uma comunidade aberta, inclusiva e positiva. A liderança do NumPy assumiu um forte compromisso de criar uma comunidade aberta, inclusiva e positiva.</p> +<p>Oferecemos vários canais de comunicação para aprender, compartilhar seu conhecimento e se conectar com outros na comunidade NumPy.</p> +<h2 id="participar-online">Participar online<a class="headerlink" href="#participar-online" title="Link to this heading">#</a></h2> +<p>Abaixo, listamos algumas formas de se envolver diretamente com o projeto e a comunidade NumPy. +<em>Por favor, note que encorajamos os usuários e membros da comunidade a apoiarem-se uns aos outros para perguntas sobre utilização - veja <a href="https://numpy.org/pt/gethelp">Obter Ajuda</a>.</em></p>Contribua com o NumPyhttps://numpy.org/pt/contribute/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/contribute/<p>O projeto NumPy precisa de sua experiência e entusiasmo! +Suas opções não são limitadas à programação. Como pode ver a seguir, há várias áreas em que precisamos da <strong>sua</strong> ajuda.</p> +<p>Se você não sabe por onde começar ou como suas habilidades podem ajudar, <em>fale conosco!</em> Você pode perguntar na nossa <a href="https://mail.python.org/mailman/listinfo/numpy-discussion">lista de emails</a> ou <a href="http://github.com/numpy/numpy">GitHub</a> (abrindo uma <a href="https://github.com/numpy/numpy/issues">issue</a> ou comentando em uma issue relevante).</p> +<p>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 <a href="mailto:numpy-team@googlegroups.com">numpy-team@googlegroups.com</a> ou no <a href="https://numpy-team.slack.com">Slack</a> (envie um e-mail para <a href="mailto:numpy-team@googlegroups.com">numpy-team@googlegroups.com</a> para obter um convite antes de entrar).</p>Estudo de Caso: A Primeira Imagem de um Buraco Negrohttps://numpy.org/pt/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/blackhole-image/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/blackhole.jpg" alt="black hole image" class="align-center"> + + + +<figcaption><strong class="caption-title">Black Hole M87</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg">(Créditos: Event Horizon Telescope Collaboration)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. +</p> + <p class="attribution">—Katie Bouman, <em>Professora Assistente, Ciências da Computação e Matemática, Caltech</em></p> +</blockquote> + +<h2 id="um-telescópio-do-tamanho-da-terra">Um telescópio do tamanho da Terra<a class="headerlink" href="#um-telescópio-do-tamanho-da-terra" title="Link to this heading">#</a></h2> +<p>O <a href="https://eventhorizontelescope.org">telescópio Event Horizon (EHT)</a>, 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 <a href="https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole">20 micro-arcossegundos</a> — o suficiente para ler um jornal em Nova Iorque a partir de um café em uma calçada de Paris!</p>Estudo de Caso: Análise de Críquete, a revolução!https://numpy.org/pt/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/cricket-analytics/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/ipl-stadium.png" alt="Copa e estádio da Indian Premier League Cricket" class="align-center"> + + + +<figcaption><strong class="caption-title">IPLT20, o maior festival de Críquete da Índia</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://unsplash.com/@aksh1802">(Image credits: IPLT20 (cup and logo) &amp; Akash Yadav (stadium))</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/"> + <p> + You don't play for the crowd, you play for the country. +</p> + <p class="attribution">—M S Dhoni, <em>Jogador Internacional de Críquete, ex-capitão, Time Indiano, joga pelo Chennai Super Kings na IPL</em></p> +</blockquote> + +<h2 id="sobre-críquete">Sobre Críquete<a class="headerlink" href="#sobre-críquete" title="Link to this heading">#</a></h2> +<p>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 <a href="https://www.statista.com/topics/4543/indian-premier-league-ipl/">dinheiro</a> 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.</p>Estudo de Caso: Descoberta de Ondas Gravitacionaishttps://numpy.org/pt/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/gw-discov/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/gw_sxs_image.png" alt="binary coalesce black hole generating gravitational waves" class="align-center"> + + + +<figcaption><strong class="caption-title">Ondas gravitacionais</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="https://youtu.be/Zt8Z_uzG71o">(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs"> + <p> +The scientific Python ecosystem is critical infrastructure for the research done at LIGO. +</p> + <p class="attribution">—David Shoemaker, <em>Colaborador Científico no LIGO</em></p> +</blockquote> + +<h2 id="sobre-ondas-gravitacionais-e-o-ligo">Sobre <a href="https://www.nationalgeographic.com/news/2017/10/what-are-gravitational-waves-ligo-astronomy-science/">Ondas Gravitacionais</a> e o <a href="https://www.ligo.caltech.edu">LIGO</a><a class="headerlink" href="#sobre-ondas-gravitacionais-e-o-ligo" title="Link to this heading">#</a></h2> +<p>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.</p>Estudo de Caso: Estimativa de Pose 3D com DeepLabCuthttps://numpy.org/pt/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/case-studies/deeplabcut-dnn/<figure class="align-default" id="id000"> +<img src="https://numpy.org/images/content_images/cs/mice-hand.gif" alt="micehandanim" class="align-center"> + + + +<figcaption><strong class="caption-title">Análise de movimentos de mãos de camundongos usando DeepLapCut</strong><a class="headerlink" href="#id000" title="Link to this image">#</a><br><a href="http://www.mousemotorlab.org/deeplabcut">(Fonte: www.deeplabcut.org )</a> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/"> + <p> +Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning. +</p> + <p class="attribution">—Alexander Mathis, <em>Professor Assistente, École polytechnique fédérale de Lausanne</em> (<a href="https://www.epfl.ch/en/">EPFL</a>)</p> +</blockquote> + +<h2 id="sobre-o-deeplabcut">Sobre o DeepLabCut<a class="headerlink" href="#sobre-o-deeplabcut" title="Link to this heading">#</a></h2> +<p><a href="https://github.com/DeepLabCut/DeepLabCut">DeepLabCut</a> 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.</p>Histórico do NumPyhttps://numpy.org/pt/history/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/history/<p>NumPy é uma biblioteca Python fundamental que fornece estruturas de <em x-id="3">arrays</em> 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 &ldquo;desobedientes&rdquo; 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.</p>Instalando o NumPyhttps://numpy.org/pt/install/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/install/<div class="admonition tip"> + <div class="admonition-title"> Tip</div> + <p> + Esta página assume que você está confortável usando um terminal e está familiarizado com gerenciadores de pacotes. +O único pré-requisito para instalar o NumPy é o próprio Python. Se você ainda não tem +Python e quer a maneira mais simples de começar, recomendamos que use a +<a href="https://www.anaconda.com/download">Distribuição Anaconda</a> - ela inclui +Python, NumPy e muitos outros pacotes comumente usados para computação científica +e ciência de dados. + </p> +</div> + +<p>O método recomendado de instalar o NumPy depende do seu fluxo de trabalho preferido. A seguir, dividimos os métodos de instalação entre as seguintes categorias:</p>Kit de imprensahttps://numpy.org/pt/press-kit/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/press-kit/<p>Gostaríamos de facilitar a inclusão da identidade do projeto NumPy em seu próximo documento acadêmico, materiais educacionais ou apresentação.</p> +<p>Você encontrará várias versões de alta resolução do logo do NumPy <a href="https://github.com/numpy/numpy/tree/main/branding/logo">aqui</a>. Note que usando os recursos numpy.org, você aceita o <a href="https://numpy.org/pt/code-of-conduct/">Código de Conduta do NumPy</a>.</p>NumPy Diversity and Inclusion Statementhttps://numpy.org/pt/diversity_sep2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/diversity_sep2020/<p><em>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:</em></p> +<p>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.</p>Obter ajudahttps://numpy.org/pt/gethelp/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/gethelp/<p><strong>Issues sobre desenvolvimento:</strong> Para assuntos relacionados ao desenvolvimento do NumPy (por exemplo, relatórios de bugs), veja a <a href="https://numpy.org/pt/community/">Comunidade</a>.</p> +<p><strong>Perguntas de usuários:</strong> A melhor maneira de obter ajuda é postar sua pergunta em um site como <a href="http://stackoverflow.com/questions/tagged/numpy">StackOverflow</a> ou <a href="https://www.reddit.com/r/Numpy/">Reddit</a>. Gostaríamos de poder ficar de olho nestes sites, ou responder perguntas diretamente, mas o volume é imenso!</p> +<h3 id="stackoverflow"><a href="http://stackoverflow.com/questions/tagged/numpy">StackOverflow</a><a class="headerlink" href="#stackoverflow" title="Link to this heading">#</a></h3> +<p>Um fórum para fazer perguntas sobre a utilização da biblioteca, por exemplo: &ldquo;Como faço X no NumPy?&rdquo;. Por favor <a href="https://stackoverflow.com/help/tagging">use a tag <code>#numpy</code></a></p>PESQUISA DE USUÁRIOS NUMPYhttps://numpy.org/pt/user-surveys/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/user-surveys/<p><strong>2020</strong> O time de pesquisas da NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, conduziram a primeira pesquisa oficial sobre a comunidade NumPy. Você pode encontrar os resultados da pesquisa <a href="https://numpy.org/user-survey-2020/">aqui (em inglês)</a>.</p> +<p><strong>2021</strong> Os dados coletados estão em análise.</p> +<p>Se você tem dúvidas ou sugestões sobre as pesquisas já realizadas ou futuras, por favor crie uma issue <a href="https://github.com/numpy/numpy-surveys/issues">aqui</a>.</p>PESQUISA SOBRE A COMUNIDADE NUMPY 2020https://numpy.org/pt/user-survey-2020/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/user-survey-2020/<p>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.</p> +<figure class="align-default" id="id000"> +<img src="https://numpy.org/surveys/NumPy_usersurvey_2020_report_cover.png" alt="Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado &#34;NumPy Community Survey 2020 - results&#34;" class="align-center" width="250"> + + + +<figcaption> +<p><span class="caption-text"></span> +</figcaption> +</figure> + +<p><strong><a href="https://numpy.org/surveys/NumPy_usersurvey_2020_report.pdf">Faça o download do relatório</a></strong> para ver os detalhes sobre os resultados encontrados.</p>Política de privacidadehttps://numpy.org/pt/privacy/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/privacy/<p><strong>numpy.org</strong> é operado por <a href="https://numfocus.org">NumFOCUS, Inc.</a>, o patrocinador fiscal do projeto NumPy. Para a Política de Privacidade deste site, consulte <a href="https://numfocus.org/privacy-policy">https://numfocus.org/privacy-policy</a>.</p> +<p>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 <a href="mailto:privacy@numfocus.org">privacy@numfocus.org</a>.</p>Sobrehttps://numpy.org/pt/about/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/about/<p>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 100% software de código aberto, livre para que todos usem. É lançado sob os termos liberais da <a href="https://github.com/numpy/numpy/blob/main/LICENSE.txt">licença BSD modificada</a>.</p> +<p>O NumPy é desenvolvido abertamente no GitHub, através do consenso da comunidade NumPy e de uma comunidade mais ampla de Python científico. Para obter mais informações sobre nossa abordagem de governança, por favor, consulte nosso <a href="https://www.numpy.org/devdocs/dev/governance/index.html">Documento de Governança</a>.</p>Terms of Usehttps://numpy.org/pt/terms/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/terms/<p><em>Last updated January 4, 2020</em></p> +<h2 id="agreement-to-terms">AGREEMENT TO TERMS<a class="headerlink" href="#agreement-to-terms" title="Link to this heading">#</a></h2> +<p>These Terms of Use constitute a legally binding agreement made between you, whether personally or on behalf of an entity (“you”) and NumPy (&quot;<strong>Project</strong>&quot;, “<strong>we</strong>”, “<strong>us</strong>”, or “<strong>our</strong>”), 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.</p>Times NumPyhttps://numpy.org/pt/teams/Mon, 01 Jan 0001 00:00:00 +0000https://numpy.org/pt/teams/<p>Somos uma equipe internacional com a missão de apoiar comunidades científicas e de pesquisa em todo o mundo construindo software de código aberto de qualidade. +<a href="https://numpy.org/pt/contribute/">Junte-se a nós</a>!</p> +<h3 id="pessoas-mantenedoras">Pessoas Mantenedoras<a class="headerlink" href="#pessoas-mantenedoras" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&amp;v=4" alt="Avatar of Andrew Nelson"> + + + +Andrew Nelson + </div> + <a class="sd-stretched-link" href="https://github.com/andyfaff"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43369155?u=1f1fcabf979a2f00f403c60b816ba9f573026181&amp;v=4" alt="Avatar of Bas van Beek"> + + + +Bas van Beek + </div> + <a class="sd-stretched-link" href="https://github.com/BvB93"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/77272?v=4" alt="Avatar of Charles Harris"> + + + +Charles Harris + </div> + <a class="sd-stretched-link" href="https://github.com/charris"></a> + 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src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&amp;v=4" alt="Avatar of Rohit Goswami"> + + + +Rohit Goswami + </div> + <a class="sd-stretched-link" href="https://github.com/HaoZeke"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/67612?v=4" alt="Avatar of Matthew Brett"> + + + +Matthew Brett + </div> + <a class="sd-stretched-link" href="https://github.com/matthew-brett"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&amp;v=4" alt="Avatar of Matti Picus"> + + + +Matti Picus + </div> + <a class="sd-stretched-link" href="https://github.com/mattip"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6570539?u=cfb3e218754e85c4fac18064d7cfdce0b67ddaa6&amp;v=4" alt="Avatar of Matt Haberland"> + + + +Matt Haberland + </div> + <a class="sd-stretched-link" href="https://github.com/mdhaber"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&amp;v=4" alt="Avatar of Melissa Weber Mendonça"> + + + +Melissa Weber Mendonça + </div> + <a class="sd-stretched-link" href="https://github.com/melissawm"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/2789820?v=4" alt="Avatar of Marten van Kerkwijk"> + + + +Marten van Kerkwijk + </div> + <a class="sd-stretched-link" href="https://github.com/mhvk"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&amp;v=4" alt="Avatar of Christopher Sidebottom"> + + + +Christopher Sidebottom + </div> + <a class="sd-stretched-link" href="https://github.com/Mousius"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&amp;v=4" alt="Avatar of Mateusz Sokół"> + + + +Mateusz Sokół + </div> + <a class="sd-stretched-link" href="https://github.com/mtsokol"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3126246?u=a3c7cd970c0e4cbc4498febe0de777a263c522c5&amp;v=4" alt="Avatar of Nathan Goldbaum"> + + + +Nathan Goldbaum + </div> + <a class="sd-stretched-link" href="https://github.com/ngoldbaum"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/402156?u=288a1f206a151f9e2b69f3c0ce11848d3381943e&amp;v=4" alt="Avatar of Pearu Peterson"> + + + +Pearu Peterson + </div> + <a class="sd-stretched-link" href="https://github.com/pearu"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/15134881?v=4" alt="Avatar of Josh Wilson"> + + + +Josh Wilson + </div> + <a class="sd-stretched-link" href="https://github.com/person142"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/35046?v=4" alt="Avatar of Pauli Virtanen"> + + + +Pauli Virtanen + </div> + <a class="sd-stretched-link" href="https://github.com/pv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/15245051?u=54810990f0fdb11ecaade02762c09d5549d72a11&amp;v=4" alt="Avatar of Chunlin"> + + + +Chunlin + </div> + <a class="sd-stretched-link" href="https://github.com/Qiyu8"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&amp;v=4" alt="Avatar of Raghuveer Devulapalli"> + + + +Raghuveer Devulapalli + </div> + <a class="sd-stretched-link" href="https://github.com/r-devulap"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46135?u=305a96a4778daecacbc8ec97ac25a48099a239cc&amp;v=4" alt="Avatar of Robert Kern"> + + + +Robert Kern + </div> + <a class="sd-stretched-link" href="https://github.com/rkern"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/61977?v=4" alt="Avatar of Sebastian Berg"> + + + +Sebastian Berg + </div> + <a class="sd-stretched-link" href="https://github.com/seberg"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/12713707?u=5a3f6a8de4801d7878750cbd0bb2e0427bf0af0b&amp;v=4" alt="Avatar of Sayed Adel"> + + + +Sayed Adel + </div> + <a class="sd-stretched-link" href="https://github.com/seiko2plus"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1217238?u=b61e7e0085405ce6d7d53f8f39a1360ef9723f72&amp;v=4" alt="Avatar of Stephan Hoyer"> + + + +Stephan Hoyer + </div> + <a class="sd-stretched-link" href="https://github.com/shoyer"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&amp;v=4" alt="Avatar of Stefan van der Walt"> + + + +Stefan van der Walt + </div> + <a class="sd-stretched-link" href="https://github.com/stefanv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/7903078?u=2762d9ff13b992dc635f8f190a17f9a90cddfae1&amp;v=4" alt="Avatar of Tyler Reddy"> + + + +Tyler Reddy + </div> + <a class="sd-stretched-link" href="https://github.com/tylerjereddy"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/321463?v=4" alt="Avatar of Warren Weckesser"> + + + +Warren Weckesser + </div> + <a class="sd-stretched-link" href="https://github.com/WarrenWeckesser"></a> + </div> +</div> + + </div> +</div> + +<h3 id="time-de-documentacão">Time de documentacão<a class="headerlink" href="#time-de-documentacão" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&amp;v=4" alt="Avatar of Rohit Goswami"> + + + +Rohit Goswami + </div> + <a class="sd-stretched-link" href="https://github.com/HaoZeke"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&amp;v=4" alt="Avatar of Mars Lee"> + + + +Mars Lee + </div> + <a class="sd-stretched-link" href="https://github.com/MarsBarLee"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&amp;v=4" alt="Avatar of Matti Picus"> + + + +Matti Picus + </div> + <a class="sd-stretched-link" href="https://github.com/mattip"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&amp;v=4" alt="Avatar of Melissa Weber Mendonça"> + + + +Melissa Weber Mendonça + </div> + <a class="sd-stretched-link" href="https://github.com/melissawm"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + </div> +</div> + +<h3 id="time-web">Time Web<a class="headerlink" href="#time-web" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/123428?v=4" alt="Avatar of Jarrod Millman"> + + + +Jarrod Millman + </div> + <a class="sd-stretched-link" href="https://github.com/jarrodmillman"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3891660?u=5de0ba1f1adad6f041f6dde1affef5d05bbed80a&amp;v=4" alt="Avatar of Joe LaChance"> + + + +Joe LaChance + </div> + <a class="sd-stretched-link" href="https://github.com/joelachance"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&amp;v=4" alt="Avatar of Mars Lee"> + + + +Mars Lee + </div> + <a class="sd-stretched-link" href="https://github.com/MarsBarLee"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&amp;v=4" alt="Avatar of shalz"> + + + +shalz + </div> + <a class="sd-stretched-link" href="https://github.com/shaloo"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&amp;v=4" alt="Avatar of Shekhar Prasad Rajak"> + + + +Shekhar Prasad Rajak + </div> + <a class="sd-stretched-link" href="https://github.com/Shekharrajak"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&amp;v=4" alt="Avatar of Stefan van der Walt"> + + + +Stefan van der Walt + </div> + <a class="sd-stretched-link" href="https://github.com/stefanv"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&amp;v=4" alt="Avatar of Albert Steppi"> + + + +Albert Steppi + </div> + <a class="sd-stretched-link" href="https://github.com/steppi"></a> + </div> +</div> + + </div> +</div> + +<h3 id="time-de-triagem">Time de Triagem<a class="headerlink" href="#time-de-triagem" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&amp;v=4" alt="Avatar of Andrew Nelson"> + + + +Andrew Nelson + </div> + <a class="sd-stretched-link" href="https://github.com/andyfaff"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1522319?v=4" alt="Avatar of Anirudh Subramanian"> + + + +Anirudh Subramanian + </div> + <a class="sd-stretched-link" href="https://github.com/anirudh2290"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/71486?u=cc88e2a4e4c6c496dcb9dd88cead5c0dab496c89&amp;v=4" alt="Avatar of Aaron Meurer"> + + + +Aaron Meurer + </div> + <a class="sd-stretched-link" href="https://github.com/asmeurer"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3813847?v=4" alt="Avatar of Atsushi Sakai"> + + + +Atsushi Sakai + </div> + <a class="sd-stretched-link" href="https://github.com/AtsushiSakai"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6691888?v=4" alt="Avatar of Ben Nathanson"> + + + +Ben Nathanson + </div> + <a class="sd-stretched-link" href="https://github.com/bjnath"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/35413198?u=e67bd9ebc361fb207f914979d935fd1956eb626c&amp;v=4" alt="Avatar of Anne Bonner"> + + + +Anne Bonner + </div> + <a class="sd-stretched-link" href="https://github.com/bonn0062"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6788290?u=d9a388224b87d55106cb3e6199d02ebc1d8e0553&amp;v=4" alt="Avatar of Brigitta Sipőcz"> + + + +Brigitta Sipőcz + </div> + <a class="sd-stretched-link" href="https://github.com/bsipocz"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5476002?u=5352f057ef8cb5de29e4d2a9fa8b0d0f49580dc8&amp;v=4" alt="Avatar of carlkl"> + + + +carlkl + </div> + <a class="sd-stretched-link" href="https://github.com/carlkl"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/11371428?u=9b425a337d076ec86b75ebc759724283f0970d9a&amp;v=4" alt="Avatar of Ryan C Cooper"> + + + +Ryan C Cooper + </div> + <a class="sd-stretched-link" href="https://github.com/cooperrc"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/36567889?u=cbc76d558d375ebafd4a05a505f500eb94e00611&amp;v=4" alt="Avatar of ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)"> + + + +ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) + </div> + <a class="sd-stretched-link" href="https://github.com/czgdp1807"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/2190658?u=b85e13f985d0bf87eeb3a7a146b61dcc9586019b&amp;v=4" alt="Avatar of Hameer Abbasi"> + + + +Hameer Abbasi + </div> + <a class="sd-stretched-link" href="https://github.com/hameerabbasi"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8078968?v=4" alt="Avatar of jbrockmendel"> + + + +jbrockmendel + </div> + <a class="sd-stretched-link" href="https://github.com/jbrockmendel"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&amp;v=4" alt="Avatar of Kai"> + + + +Kai + </div> + <a class="sd-stretched-link" href="https://github.com/Kai-Striega"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/16046705?u=1bf01e87adb556503c1fe07789c194cc04d38490&amp;v=4" alt="Avatar of Yuji Kanagawa"> + + + +Yuji Kanagawa + </div> + <a class="sd-stretched-link" href="https://github.com/kngwyu"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/22004158?u=2ebb3919ebaa3d7e0865ea5583032bc08bd0f526&amp;v=4" alt="Avatar of Kriti Singh"> + + + +Kriti Singh + </div> + <a class="sd-stretched-link" href="https://github.com/kritisingh1"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/149655?u=249f7995c486de232c34e7970fbea505f518a1be&amp;v=4" alt="Avatar of Christopher Albert"> + + + +Christopher Albert + </div> + <a class="sd-stretched-link" href="https://github.com/krystophny"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/20306270?u=235cdf82e88f76ba2f5f4c2d33fa392319c60ad1&amp;v=4" alt="Avatar of Lysandros Nikolaou"> + + + +Lysandros Nikolaou + </div> + <a class="sd-stretched-link" href="https://github.com/lysnikolaou"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/34613774?u=61535ebfff07c68ea672cd8cd68c46187a38d3c1&amp;v=4" alt="Avatar of Meekail Zain"> + + + +Meekail Zain + </div> + <a class="sd-stretched-link" href="https://github.com/Micky774"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&amp;v=4" alt="Avatar of Christopher Sidebottom"> + + + +Christopher Sidebottom + </div> + <a class="sd-stretched-link" href="https://github.com/Mousius"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&amp;v=4" alt="Avatar of Mateusz Sokół"> + + + +Mateusz Sokół + </div> + <a class="sd-stretched-link" href="https://github.com/mtsokol"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&amp;v=4" alt="Avatar of Mukulika"> + + + +Mukulika + </div> + <a class="sd-stretched-link" href="https://github.com/Mukulikaa"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/6564007?u=e5fb962de792bbce925c0c94fb7a748803c8bfa0&amp;v=4" alt="Avatar of Noa Tamir"> + + + +Noa Tamir + </div> + <a class="sd-stretched-link" href="https://github.com/noatamir"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&amp;v=4" alt="Avatar of Raghuveer Devulapalli"> + + + +Raghuveer Devulapalli + </div> + <a class="sd-stretched-link" href="https://github.com/r-devulap"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&amp;v=4" alt="Avatar of shalz"> + + + +shalz + </div> + <a class="sd-stretched-link" href="https://github.com/shaloo"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/55803680?u=bb727a0da1f33ed5f2feb58dc0333943430d2318&amp;v=4" alt="Avatar of Tina Oberoi"> + + + +Tina Oberoi + </div> + <a class="sd-stretched-link" href="https://github.com/tinaoberoi"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/13260794?u=5421923c831b67c4ef290bbdeb31ebfbdd906abc&amp;v=4" alt="Avatar of Rakesh Vasudevan"> + + + +Rakesh Vasudevan + </div> + <a class="sd-stretched-link" href="https://github.com/vrakesh"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/8103276?v=4" alt="Avatar of Zijie (ZJ) Poh"> + + + +Zijie (ZJ) Poh + </div> + <a class="sd-stretched-link" href="https://github.com/zjpoh"></a> + </div> +</div> + + </div> +</div> + +<h3 id="time-de-pesquisa">Time de Pesquisa<a class="headerlink" href="#time-de-pesquisa" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&amp;v=4" alt="Avatar of Inessa Pawson"> + + + +Inessa Pawson + </div> + <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&amp;v=4" alt="Avatar of Ralf Gommers"> + + + +Ralf Gommers + </div> + <a class="sd-stretched-link" href="https://github.com/rgommers"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&amp;v=4" alt="Avatar of Ross Barnowski"> + + + +Ross Barnowski + </div> + <a class="sd-stretched-link" href="https://github.com/rossbar"></a> + </div> +</div> + + </div> +</div> + +<h3 id="time-de-traduções">Time de traduções<a class="headerlink" href="#time-de-traduções" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&amp;v=4" alt="Avatar of Melissa Weber Mendonça"> + + + +Melissa Weber Mendonça + </div> + <a class="sd-stretched-link" href="https://github.com/melissawm"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" alt="Avatar of Juan Pablo Duque"> + + + +Juan Pablo Duque (@juanpabloduqueo) + </div> + <a class="sd-stretched-link" href="https://scientific-python.crowdin.com"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" alt="Avatar of Yeimi Pena"> + + + +Yeimi Pena (@yeimiyaz) + </div> + <a class="sd-stretched-link" href="https://www.linkedin.com/in/yeimipena/"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3813847?v=4" alt="Avatar of Atsushi Sakai"> + + + +Atsushi Sakai (@AtsushiSakai) + </div> + <a class="sd-stretched-link" href="https://github.com/AtsushiSakai"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" alt="Avatar of Getúlio Silva"> + + + +Getúlio Silva (@getuliosilva) + </div> + <a class="sd-stretched-link" href="https://scientific-python.crowdin.com"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" alt="Oriol Abril-Pla"> + + + +Oriol Abril-Pla (@OriolAbril) + </div> + <a class="sd-stretched-link" href="https://scientific-python.crowdin.com"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" alt="Avatar of @julio"> + + + +@julio + </div> + <a class="sd-stretched-link" href="https://scientific-python.crowdin.com"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" alt="Avatar of Ali Faraji"> + + + +Ali Faraji (@ali) + </div> + <a class="sd-stretched-link" href="https://scientific-python.crowdin.com"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" alt="Avatar of Saeed Foroutan"> + + + +Saeed Foroutan (@SaeedForoutan) + </div> + <a class="sd-stretched-link" href="https://scientific-python.crowdin.com"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" alt="Avatar of @pyjavo"> + + + +@pyjavo + </div> + <a class="sd-stretched-link" href="https://scientific-python.crowdin.com"></a> + </div> +</div> + + </div> +</div> + +<h3 id="mantenedores-eméritos">Mantenedores Eméritos<a class="headerlink" href="#mantenedores-eméritos" title="Link to this heading">#</a></h3> +<div class="sd-container-fluid sd-mb-4 false"> + <div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-3 sd-row-cols-md-4 sd-row-cols-lg-5 sd-g-2 sd-g-xs-2 sd-g-sm-3 sd-g-md-4 sd-g-lg-5"> + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/9040124?v=4" alt="Avatar of Allan Haldane"> + + + +Allan Haldane + </div> + <a class="sd-stretched-link" href="https://github.com/ahaldane"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/20568?v=4" alt="Avatar of Ondřej Čertík"> + + + +Ondřej Čertík + </div> + <a class="sd-stretched-link" href="https://github.com/certik"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/25111?v=4" alt="Avatar of David Cournapeau"> + + + +David Cournapeau + </div> + <a class="sd-stretched-link" href="https://github.com/cournape"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/3343990?v=4" alt="Avatar of Jaime"> + + + +Jaime + </div> + <a class="sd-stretched-link" href="https://github.com/jaimefrio"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/123428?v=4" alt="Avatar of Jarrod Millman"> + + + +Jarrod Millman + </div> + <a class="sd-stretched-link" href="https://github.com/jarrodmillman"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/542663?v=4" alt="Avatar of Julian Taylor"> + + + +Julian Taylor + </div> + <a class="sd-stretched-link" href="https://github.com/juliantaylor"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&amp;v=4" alt="Avatar of Mark Wiebe"> + + + +Mark Wiebe + </div> + <a class="sd-stretched-link" href="https://github.com/mwiebe"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&amp;v=4" alt="Avatar of Nathaniel J. Smith"> + + + +Nathaniel J. Smith + </div> + <a class="sd-stretched-link" href="https://github.com/njsmith"></a> + </div> +</div> + + <div class="sd-col sd-d-flex-row"> + + <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center"> + + <div class="sd-card-body"> + <img src="https://avatars.githubusercontent.com/u/254880?v=4" alt="Avatar of Travis E. Oliphant"> + + + +Travis E. Oliphant + </div> + <a class="sd-stretched-link" href="https://github.com/teoliphant"></a> + </div> +</div> + + </div> +</div> + +<h1 id="governança">Governança<a class="headerlink" href="#governança" title="Link to this heading">#</a></h1> +<p>Para a lista de pessoas no Conselho Diretor, veja <a href="https://numpy.org/devdocs/dev/governance/people.html">aqui</a>.</p> \ No newline at end of file diff --git a/pt/install/index.html b/pt/install/index.html index 3096a380..a41342d6 100644 --- a/pt/install/index.html +++ b/pt/install/index.html @@ -1,23 +1,38 @@ -NumPy - Instalando o NumPy -

        Instalando o NumPy

        Tip

        Esta página assume que você está confortável usando um terminal e está familiarizado com gerenciadores de pacotes. +O único pré-requisito para instalar o NumPy é o próprio Python. Se você ainda não tem +Python e quer a maneira mais simples de começar, recomendamos que use a +Distribuição Anaconda - ela inclui +Python, NumPy e muitos outros pacotes comumente usados para computação científica +e ciência de dados.

        O método recomendado de instalar o NumPy depende do seu fluxo de trabalho preferido. A seguir, dividimos os métodos de instalação entre as seguintes categorias:

        • Baseados em projeto (por exemplo, uv, pixi) (recomendados para novos usuários)
        • Baseados em ambientes (por exemplo, pip, conda) (o fluxo de trabalho tradicional)
        • Gerenciadores de pacotes de sistema (não recomendados para a maioria dos usuários)
        • A partir do código-fonte (para usuários avançados e para fins de desenvolvimento)

        Escolha o método mais adequado às suas necessidades. Se não tiver certeza, comece com um método baseado em ambientes usando conda ou pip.

        + + +

        Recomendado para novos usuários que queiram um fluxo de trabalho simplificado.

        • uv: Um gerenciador de pacotes Python moderno projetado para velocidade e simplicidade.

          uv pip install numpy

        • pixi: Um gerenciador de pacotes multiplataforma para Python e outras linguagens.

          pixi add numpy

        As duas principais ferramentas que instalam pacotes do Python são pip e conda. Para o desenvolvimento web e de propósito geral em Python, há uma série de ferramentas complementares ao pip. Para computação de alto desempenho (HPC), vale a pena considerar o Spack.

        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. Elas têm algumas funcionalidades em comum (por exemplo, ambas podem instalar o numpy). No entanto, elas também podem trabalhar juntas.

        A segunda diferença é que o pip instala do Python Packaging Index (PyPI), enquanto o conda instala de seus próprios canais (tipicamente “defaults” ou “conda-forge”). O PyPI é a maior coleção de pacotes, no entanto, todos os pacotes populares estão disponíveis para conda também.

        A terceira diferença é que conda é uma solução integrada para gerenciar pacotes, dependências e ambientes, enquanto com pip você pode precisar de outra ferramenta (há muitas!) para lidar com ambientes ou dependências complexas.

        • Conda: se você usar o conda, você pode instalar o NumPy do canal default ou do conda-forge:
          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
-conda install numpy

          PIP

          Se você usa o pip, você pode instalar o NumPy com:

          pip install numpy

          Também ao usar o pip, é uma boa prática usar um ambiente virtual - veja em Instalações Reprodutíveis abaixo por quê, e esse guia para detalhes sobre o uso de ambientes virtuais.

          Guia de instalação do Python e do NumPy#

          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.

          Recomendações#

          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#

          Em Windows, macOS e Linux:

          • Instale o Anaconda (instala todos os pacotes que você precisa e todas as outras ferramentas mencionadas abaixo).
          • Para escrever e executar código, use notebooks no JupyterLab para a computação exploratória e interativa, e o Spyder ou Visual Studio Code para escrever scripts e pacotes.
          • Use o Anaconda Navigator para gerenciar seus pacotes e iniciar o JupyterLab, Spyder ou o Visual Studio Code.

          Usuários avançados#

          Conda#

          • Instale o Miniforge.
          • Mantenha o ambiente conda base mínimo, e use um ou mais ambientes conda para instalar o pacote que você precisa para a tarefa ou projeto em que você está trabalhando.

          Alternativa se você preferir pip/PyPI#

          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, Homebrew, ou seu gerenciador de pacotes Linux.
          • Use Poetry 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.

          Gerenciamento de pacotes Python#

          Gerenciar pacotes é um problema desafiador e, como resultado, há muitas ferramentas. Para o desenvolvimento web e de propósito geral em Python, há uma série de ferramentas complementares com pip. Para computação de alto desempenho (HPC), vale a pena considerar o Spack. Para computação de alto desempenho (HPC), vale a pena considerar o Spack. Para a maioria dos usuários NumPy, porém, o conda e o pip são as duas ferramentas mais populares.

          Pip & conda#

          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.

          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.

          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.

          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#

          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. 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:

          Pacotes NumPy & bibliotecas de álgebra linear aceleradas#

          O NumPy não depende de quaisquer outros pacotes Python. No entanto, depende de uma biblioteca de álgebra linear acelerada - tipicamente Intel MKL ou OpenBLAS. 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:

          • 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.

          • 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.

          • 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 ou reference BLAS.

          • O pacote MKL é muito maior que o OpenBLAS, ocupa cerca de 700 MB no disco enquanto OpenBLAS ocupa cerca de 30 MB.

          • A MKL é normalmente um pouco mais rápida e mais robusta do que a OpenBLAS.

          Além do tamanho instalado, desempenho e robustez, há mais duas coisas a se considerar:

          • 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.

          Solução de problemas#

          Se sua instalação falhar com a mensagem abaixo, consulte Solucionando ImportError.

          IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
+conda install numpy
        • Pip:
          pip install numpy
        Tip

        Dica: Use um ambiente virtual para melhor gerenciamento de dependências

        python -m venv my-env
+source my-env/bin/activate  # macOS/Linux
+my-env\Scripts\activate     # Windows
+pip install numpy

        Não recomendado para a maioria dos usuários, mas disponível por conveniência.

        macOS (Homebrew):

        brew install numpy

        Linux (APT):

        sudo apt install python3-numpy

        Windows (Chocolatey):

        choco install numpy

        Para usuários avançados e desenvolvedores que querem personalizar ou depurar o NumPy.

        Um pequeno aviso: construir o Numpy a partir do código-fonte pode ser um exercício não-trivial. +Recomendamos o uso de binários se eles estiverem disponíveis para a sua plataforma através de um dos métodos anteriores. +Para obter detalhes sobre como construir a partir do código-fonte, consulte o guia de construção a partir do código-fonte na documentação do Numpy.

        Recomendações#

        Depois de instalar o NumPy, verifique a instalação, executando o seguinte em um shell ou script Python:

        import numpy as np
+print(np.__version__)

        Isto deve imprimir a versão instalada do NumPy sem erros.

        Solução de problemas#

        Se sua instalação falhar com a mensagem abaixo, consulte Solucionando ImportError.

        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.
        On this page
        \ No newline at end of file +different reasons, often due to issues with your setup.
        On this page
        \ No newline at end of file diff --git a/pt/learn/index.html b/pt/learn/index.html index d6cc3518..15e7afc9 100644 --- a/pt/learn/index.html +++ b/pt/learn/index.html @@ -1,17 +1,16 @@ -NumPy - Aprenda -NumPy - Aprenda

        Aprenda

        Para a documentação oficial do NumPy visite numpy.org/doc/stable.

        Abaixo está uma coleção de recursos educacionais, tanto para autoaprendizado como para ensinar outras pessoas, desenvolvidos pelos colaboradores do NumPy e selecionados pela comunidade.

        Iniciantes#

        Há uma tonelada de informações sobre o NumPy lá fora. Se você está começando, recomendamos fortemente estes:

        Tutoriais

        Livros

        Você também pode querer conferir a lista Goodreads sobre o tema “Python+SciPy”. A maioria dos livros lá será sobre o “ecossistema SciPy”, que tem o NumPy em sua essência.

        Vídeos

        Avançado#

        Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais.

        Tutoriais

        Livros

        Vídeos

        Palestras sobre NumPy#

        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.

        On this page
        \ No newline at end of file diff --git a/pt/news/index.html b/pt/news/index.html index 53687318..a7c9c4e5 100644 --- a/pt/news/index.html +++ b/pt/news/index.html @@ -1,16 +1,34 @@ -NumPy - Notícias -

        Notícias

        Lançado o NumPy versão 2.2.0#

        8 de dezembro de 2024 – A NumPy 2.2.0 é uma versão que nos traz de volta para o calendário habitual de lançamento duas vezes por ano. Ela inclui um número de pequenas limpezas, melhorias para o StringDType, e melhor suporte para +o free-threaded Python. Alguns dos destaques são:

        • Novas funções matvec e vecmat,
        • Várias melhorias nas anotações de tipos,
        • Suporte melhorado para o novo StringDType,
        • Suporte aprimorado para Python free-threaded,
        • Correções para o f2py.

        Esta versão suporta as versões 3.10-3.13 do Python.

        Lançado o NumPy versão 2.1.0#

        18 de agosto de 2024 – NumPy 2.1.0 fornece suporte para Python 3.13 e remove suporte para Python 3.9. Além das habituais correções de erros e suporte a Python atualizado, esta versão ajuda a trazer o NumPy de volta ao ciclo habitual de lançamento após o longo desenvolvimento da versão 2.0. Os destaques desta versão são:

        • Suporte ao Python 3.13.
        • Suporte preliminar para Python 3.13 free threaded.
        • Suporte para o padrão array-api 2023.12.

        As versões 3.10-3.13 do Python são suportadas por esta versão.

        NumPy 2.0.0 lançada#

        16 de junho de 2024 – NumPy 2.0.0 é a primeira grande versão desde 2006. É o resultado de 11 meses de desenvolvimento desde a última feature release e é o trabalho de 212 contribuidores espalhado por 1078 pull requests. Esta versão contém um grande número de novas funcionalidades interessantes, bem como mudanças nas APIs Python e C. As mudanças incluem quebras de compatibilidade que não puderam acontecer em uma versão regular menor - incluindo uma quebra na ABI, mudanças nas regras de promoção de tipo e mudanças na API +que poderiam não estar emitindo alertas de fim de suporte nas versões 1.26.x. Documentos-chave, relacionados a como se adaptar às mudanças no NumPy 2.0, incluem:

        A postagem de blog “NumPy 2.0: an evolutionary milestone” conta um pouco da história sobre como esta versão foi construída.

        Data de lançamento da NumPy 2.0: 16 de junho#

        23 de maio de 2024 – Estamos animados em anunciar que planejamos lançar a NumPy 2.0 em 16 de junho de 2024. Este lançamento está em desenvolvimento há mais de um ano, e +é o primeiro grande lançamento desde 2006. Importantly, in addition to many new +features and performance improvement, it contains breaking changes to the +ABI as well as the Python and C APIs. Importante, além de muitas funcionalidades novas e melhoria de desempenho, esta versão contém quebras de compatibilidade com a ABI e com as APIs Python e C. É provável que os pacotes downstream e o código de usuário final precisem ser adaptados - se você puder, por favor, verifique se o seu código funciona com NumPy 2.0.0rc2. Por favor, veja o seguinte para mais detalhes:

        Arrecadação de fundos de fim de ano da NumFOCUS#

        19 de dezembro de 2023 – A NumFOCUS se juntou ao PyCharm durante sua campanha de final de ano para oferecer 30% de desconto em licenças de PyCharm para novos usuários. Todas as receitas do primeiro ano das compras do PyCharm a partir de agora +até 23 de dezembro de 2023 irão diretamente para os programas NumFOCUS.

        Use a URL única que permitirá rastrear as compras https://lp.jetbrains.com/support-data-science/ ou um código de cupom ISUPPORTDATASCIENCE

        Lançada versão 1.26.0 do NumPy#

        17 de junho de 2023 – NumPy 1.25.0 está disponível agora. Os destaques desta versão são:

        • Suporte ao Python 3.12.0.
        • Compatibilidade com Cython 3.0.0.
        • Utilização do sistema Meson para compilação
        • Suporte a SIMD atualizado
        • Melhorias para f2py, suporte a meson e bind(x)
        • Suporte à versão mais recente da biblioteca Accelerate BLAS/LAPACK

        A versão 1.26.0 é uma continuação da série de versões 1.25.x que marcam a transição para o sistema de compilação Meson e o fornecimento de suporte para o Cython 3.0.0. +Um total de 20 pessoas contribuíram para este lançamento e 59 pull requests foram incorporadas.

        As versões do Python suportadas por esta versão são 3.9-3.12.

        numpy.org agora está disponível em japonês e português#

        2 de agosto de 2023 – numpy.org agora está disponível em 2 idiomas adicionais: japonês e português. Isto não seria possível sem nossos voluntários dedicados:

        Português:

        • Melissa Weber Mendonça (melissawm)
        • Ricardo Prins (ricardoprins)
        • Getúlio Silva (getuliosilva)
        • Julio Batista Silva (jbsilva)
        • Alexandre de Siqueira (alexdesiqueira)
        • Alexandre B A Villares (villares)
        • Vini Salazar (vinisalazar)

        Japonês:

        • Atsushi Sakai (AtsushiSakai)
        • KKunai
        • Tom Kelly (TomKellyGenetics)
        • Yuji Kanagawa (kngwyu)
        • Tetsuo Koyama (tkoyama010)

        O trabalho na infraestrutura de tradução é apoiado com financiamento da CZI.

        No futuro, adoraríamos traduzir o site para mais línguas. +Se você quiser ajudar, por favor entre em contato com o time de traduções do NumPy no Slack: +https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. +(Procure pelo canal de #translations.) Também estamos construindo uma Equipe de Traduções que vai trabalhar na localização da documentação e conteúdo educacional por todo o ecossistema de Python científico. Se esse trabalho te interessa, junte-se a nós no Discord do projeto Scientific Python: https://discord.gg/khWtqY6RKr. (Procure pelo canal #translation)

        Lançado o NumPy 1.25.0#

        31 de dezembro de 2021 – NumPy 1.22.0 está agora disponível. Os destaques desta versão são:

        • Suporte para MUSL, agora existem rodas MUSL.
        • Suporte para o compilador Fujitsu C/C++.
        • Arrays de objetos agora são suportados em einsum.
        • Suporte para a multiplicação da matriz inplace (@=).

        A versão 1.25.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. Também tem havido trabalho preparatório para a futura versão 2.0.0, resultando em um grande número de depreciações novas e expiradas.

        Um total de 148 pessoas contribuíram para este lançamento e 530 pull requests foram incorporadas.

        As versões do Python suportadas por esta versão são 3.9-3.11.

        Promovendo uma cultura inclusiva: Chamada de participação#

        10 de maio de 2023 – Promovendo uma Cultura Inclusiva: Chamada de Participação

        Como podemos ser melhores quando se trata de diversidade e de inclusão? +Leia o relatório e descubra como colaborar aqui.

        Transição de liderança do time de documentação do NumPy#

        6 de janeiro de 2023 –- Mukulika Pahari e Ross Barnowski são nomeados como lideres do time de documentação do NumPy, substituindo Melissa Mendonça. Agradecemos a Melissa por todas suas contribuições para a documentação oficial do NumPy e materiais educacionais, e Mukulika e Ross por aceitarem o desafio.

        NumPy versão 1.23.0#

        16 de setembro de 2023 – NumPy 1.26.0 está disponível. Os destaques desta versão são:

        • Novas palavras-chave “dtype” e “casting” para funções que atuam com stacking.
        • Novas funcionalidades e correções do F2PY.
        • Muitas depreciações novas, confira.
        • Muitas depreciações expiradas.

        A versão 1.24.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade e execução, e na documentação. +Há um grande número de depreciações novas e expiradas devido a mudanças na promoção de dtypes e limpezas no código. É o trabalho de 177 contribuidores espalhados em 444 pull requests. As versões suportadas do Python são 3.8-3.11.

        NumPy versão 1.23.0#

        22 de junho de 2022 – O NumPy 1.23.0 está disponível. Os destaques desta versão são:

        • Implementação de loadtxt em C, melhorando muito seu desempenho.
        • Exposição do DLPack ao nível de Python para facilitar a troca de dados.
        • Mudanças na promoção e comparações de dtypes estruturados.
        • Melhorias no f2py.

        A versão 1.23.0 do NumPy continua o trabalho de melhorias no suporte e promoção de dtypes, na velocidade de execução, na documentação e na expiração de depreciações. É o trabalho de 151 contribuidores espalhados em 494 pull requests. As versões do Python suportadas por esta versão são 3.8-3.10. +Python 3.11 será suportado quando chegar na etapa rc.

        Pesquisa NumFOCUS DEI: chamada para participação#

        13 de abril de 2022 – O NumPy está trabalhando com a NumFOCUS em um projeto de pesquisa financiado pela Gordon & Betty Moore Foundation para entender as barreiras à participação que contribuidores, especialmente aqueles de grupos historicamente subrepresentados, enfrentam na comunidade open source. A equipe da pesquisa gostaria de falar com novos colaboradores, desenvolvedores e mantenedores, e aqueles que contribuíram no passado sobre suas experiências contribuindo para o NumPy.

        Quer compartilhar suas experiências?

        Por favor, preencha este breve formulário: “Participant Interest form” que contém informações adicionais sobre os objetivos da pesquisa, privacidade e considerações de confidencialidade. Sua participação será valiosa para o crescimento e sustentabilidade de comunidades de software de código aberto diversas e inclusivas. Os participantes aceitos participarão de uma entrevista de 30 minutos com um membro da equipe de pesquisa.

        NumPy versão 1.22.0#

        23 de junho de 2021 – O NumPy 1.21.0 está disponível. Os destaques desta versão são:

        • Anotações de tipo do namespace principal estão praticamente completas. Upstream é um alvo em movimento, então provavelmente haverá mais melhorias, mas a maior parte do trabalho está feita. Esta é provavelmente a melhoria mais visível para os usuários nesta versão.
        • Uma versão preliminar da proposta do array API Standard está disponível (veja NEP 47). +Este é um passo na criação de uma coleção padrão de funções que podem ser compartilhadas entre bibliotecas como CuPy e JAX.
        • NumPy agora tem um backend de DLPack. DLPack fornece um formato comum de compartilhamento para dados de arrays (tensores).
        • Novos métodos para quantile, percentile, e funções relacionadas. Os novos métodos fornecem um conjunto completo dos métodos comumente encontrados na literatura.
        • As funções universais foram refatoradas para implementar a maior parte da NEP 43. +Isso também desbloqueia a capacidade de experimentar a futura API DType.
        • Um novo alocador de memória configurável para uso pelos projetos downstream.

        NumPy 1.22.0 é uma versão importante com o trabalho de 153 contribuidores espalhados por mais de 609 pull requests. As versões do Python suportadas por esta versão são 3.8-3.10.

        Promovendo uma cultura inclusiva no ecossistema científico de Python#

        31 de agosto de 2021 – Estamos felizes em anunciar que a Chan Zuckerberg Initiative vai financiar um projeto para apoiar a integração, inclusão, e retenção de pessoas de grupos marginalizados historicamente em projetos científicos em Python, e para estruturalmente melhorar a dinâmica das comunidades para o NumPy, SciPy, Matplotlib, e Pandas.

        Como parte do programa CZI’s Essential Open Source Software for Science, esse financiamento adicional para diversidade e inclusão vai apoiar a criação de posições de Contributor Experience Lead para identificar, documentar e implementar práticas para fomentar comunidades open source inclusivas. Este projeto será liderado por Melissa Mendonça (NumPy), com apoio adicional de Ralf Gommers (NumPy, SciPy), Hannah Aizenman e Thomas Caswell (Matplotlib), Matt Haberland (SciPy), e Joris Van den Bossche (Pandas).

        Esse é um projeto ambicioso que visa descobrir e implementar atividades que devem estruturalmente melhorar a dinâmica da comunidade de nossos projetos. Ao criar essas novas funções entre projetos, esperamos introduzir um novo modelo de colaboração às comunidades de Python científico, permitir que o trabalho de construção da comunidade no ecossistema seja feito de forma mais eficiente e com maiores resultados. Também esperamos desenvolver uma imagem mais clara do que funciona e o que não funciona em nossos projetos para engajar e reter novos colaboradores, especialmente de grupos historicamente sub-representados. Finalmente, planejamos produzir relatórios detalhados sobre as ações executadas, explicando como eles afetaram nossos projetos em termos de representação e interação com nossas comunidades.

        O projeto de dois anos deverá começar em novembro de 2021 e estamos animados para ver os resultados deste trabalho! +Você pode ler a proposta completa aqui.

        Pesquisa NumPy 2021#

        12 de julho de 2021 – Nós do NumPy acreditamos no poder da nossa comunidade. 1.236 usuários do NumPy de 75 países participaram da nossa primeira pesquisa ano passado. +Os resultados da pesquisa nos ajudaram a compreender muito bem o que devemos fazer pelos 12 meses seguintes.

        Chegou a hora de fazer outra pesquisa e estamos contando com você novamente. Vai levar cerca de 15 minutos do seu tempo. Além de Inglês, o questionário de pesquisa está disponível em 8 idiomas adicionais: Bangla, Francês, Hindi, Japonês, Mandarim, Português, Russo e Espanhol.

        Siga o link para começar: https://berkeley.qualtrics.com/jfe/form/SV_aaOONjgcBXDSl4q.

        NumPy versão 1.19.0#

        23 de junho de 2021 – O NumPy 1.21.0 está disponível. Os destaques desta versão são:

        • continuamos o trabalho com SIMD para suportar mais funções e plataformas,
        • trabalho inicial na infraestrutura e conversão de novos dtypes,
        • wheels universal2 para Python 3.8 e Python 3.9 no Mac,
        • melhorias na documentação,
        • melhorias nas anotações de tipos,
        • novo bitgenerator PCG64DXSM para números aleatórios.

        Esta versão do NumPy é o resultado de 581 pull requests aceitos, a partir das contribuições de 175 pessoas. As versões do Python suportadas por esta versão são 3.7-3.9; o suporte para o Python 3.10 será adicionado após o lançamento do Python 3.10.

        Resultados da pesquisa NumPy 2020#

        22 de junho de 2021 – Em 2020, o time de pesquisas NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, realizou a primeira pesquisa oficial sobre a comunidade NumPy. Confira os resultados da pesquisa aqui: https://numpy.org/user-survey-2020/.

        NumPy versão 1.20.0#

        30 de janeiro de 2021 – O NumPy 1.20.0 está disponível. Este é o maior lançamento do NumPy até hoje, graças a mais de 180 colaboradores. As duas novidades mais emocionantes são:

        • Anotações de tipos para grandes partes do NumPy, e um novo submódulo numpy.typing contendo aliases ArrayLike e DtypeLike que usuários e bibliotecas downstream podem usar quando quiserem adicionar anotações de tipos em seu próprio código.
        • Otimizações de compilação SIMD multi-plataforma, com suporte para instruções x86 (SSE, AVX), ARM64 (Neon) e PowerPC (VSX). Isso rendeu melhorias significativas de desempenho para muitas funções (exemplos: sen/cos, einsum).

        Diversidade no projeto NumPy#

        20 de setembro de 2020 – Escrevemos uma declaração sobre o estado da diversidade e inclusão no projeto NumPy e discussões em redes sociais sobre isso..

        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 como um artigo de revisão na Nature. Isso ocorre 14 anos após o lançamento do NumPy 1.0. +O artigo abrange aplicações e conceitos fundamentais da programação de matrizes, o rico ecossistema científico de Python construído em cima do NumPy, e os protocolos de array recentemente adicionados para facilitar a interoperabilidade com bibliotecas externas para computação com matrizes e tensores, como CuPy, Dask e JAX.

        O Python 3.9 está chegando, quando o NumPy vai liberar wheels binárias?#

        14 de setembro de 2020 – Python 3.9 será lançado em algumas semanas. Se você for querer 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 ou --only-binary=:all: para impedir pip de tentar compilar a partir do código fonte.

        NumPy versão 1.18.0#

        10 de setembro de 2020 – O NumPy 1.19.2 está disponível. +Essa última versão da série 1.19 corrige vários bugs, inclui preparações para o lançamento do Cython 3 e fixa o setuptools para que o distutils continue funcionando enquanto modificações upstream estão sendo feitas. +As wheels para aarch64 são compiladas com a manylinux2014 mais recente que conserta o problema da diferença no tamanho das páginas usadas por distribuições linux diferentes.

        A primeira pesquisa NumPy está aqui!#

        2 de julho de 2020 – Esta pesquisa tem como objetivo guiar e definir prioridades para tomada de decisões sobre o desenvolvimento do NumPy como software e como comunidade. +A pesquisa está disponível em mais 8 idiomas além do inglês: Bangla, Hindi, Japonês, Mandarim, Português, Russo, Espanhol e Francês.

        Ajude-nos a melhorar o NumPy respondendo à pesquisa aqui.

        24 de junho de 2020 – NumPy agora tem um novo logo:

        NumPy logo

        O logotipo é uma versão moderna do antigo, com um design mais limpo. Obrigado à Isabela Presedo-Floyd por projetar o novo logotipo, bem como ao Travis Vaught pelo o logotipo antigo que nos serviu bem durante mais de 15 anos.

        NumPy versão 1.19.0#

        20 de junho de 2020 – O NumPy 1.19.0 está disponível. Esta é a primeira versão sem suporte ao Python 2, portanto foi uma “versão de limpeza”. A versão mínima de Python suportada agora é Python 3.6. Uma característica nova importante é que a infraestrutura de geração de números aleatórios que foi introduzida na NumPy 1.17.0 agora está acessível a partir do Cython.

        Aceitação no programa Season of Docs#

        11 de maio de 2020 – O NumPy foi aceito como uma das organizações mentoras do programa Google Season of Docs. Estamos animados com a oportunidade de trabalhar com um technical writer para melhorar a documentação do NumPy mais uma vez! Estamos animados com a oportunidade de trabalhar com um technical writer para melhorar a documentação do NumPy mais uma vez!

        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. É a última versão menor que suportará Python 3.5. Destaques dessa versão incluem a adição de uma infraestrutura básica para permitir o link com as bibliotecas BLAS e LAPACK em 64 bits durante a compilação, e uma nova C-API para numpy.random.

        Por favor, veja as notas de lançamento para mais detalhes.

        O NumPy recebe financiamento da Chan Zuckerberg Initiative#

        15 de novembro de 2019 – Estamos felizes em anunciar que o NumPy e a OpenBLAS, uma das dependências-chave do NumPy, receberam um auxílio conjunto de $195,000 da Chan Zuckerberg Initiative através do seu programa Essential Open Source Software for Science que apoia a manutenção, crescimento, desenvolvimento e envolvimento da comunidade em ferramentas de código aberto fundamentais para a ciência.

        Este auxílio será usado para aumentar os esforços de melhoria da documentação do NumPy, reformulação do site, desenvolvimento comunitário para melhor servir a nossa grande, e rapidamente crescente, base de usuários, assim como para garantir a sustentabilidade do projeto a longo prazo. Enquanto a equipe OpenBLAS se concentrará em tratar de um conjunto de questões técnicas fundamentais, em particular relacionadas a thread-safety, AVX-512, e thread-local storage (TLS), bem como melhorias algorítmicas na ReLAPACK (Recursive LAPACK) da qual a OpenBLAS depende.

        Mais detalhes sobre nossas propostas e resultados esperados podem ser encontrados na proposta completa de concessão de auxílio. O trabalho está agendado para começar no dia 1 de dezembro de 2019 e continuar pelos próximos 12 meses.

        Lançamentos#

        Aqui está uma lista de versões do NumPy, com links para notas de lançamento. Lançamentos para correção de falhas (apenas o z muda no número de versão x.y.z) não tem novos recursos; versões menores (o y aumenta) sim.

        On this page
        \ No newline at end of file diff --git a/pt/press-kit/index.html b/pt/press-kit/index.html index a7aefb6b..86ad6f4f 100644 --- a/pt/press-kit/index.html +++ b/pt/press-kit/index.html @@ -1,14 +1,13 @@ -NumPy - Kit de imprensa -

        Kit de imprensa

        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. Note que usando os recursos numpy.org, você aceita o Código de Conduta do NumPy.

        On this page
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        Política de privacidade

        numpy.org é operado por NumFOCUS, Inc., 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.

        On this page
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        Código de Conduta NumPy - Como dar seguimento a um relatório

        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 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.

        On this page
        \ No newline at end of file diff --git a/pt/sitemap.xml b/pt/sitemap.xml index 3524f895..cda4b5ae 100644 --- a/pt/sitemap.xml +++ b/pt/sitemap.xml @@ -1 +1 @@ -https://numpy.org/pt/2023-09-16T00:00:00+00:00https://numpy.org/pt/news/2023-09-16T00:00:00+00:00https://numpy.org/pt/404/https://numpy.org/pt/learn/https://numpy.org/pt/case-studies/https://numpy.org/pt/citing-numpy/https://numpy.org/pt/code-of-conduct/https://numpy.org/pt/report-handling-manual/https://numpy.org/pt/arraycomputing/https://numpy.org/pt/community/https://numpy.org/pt/contribute/https://numpy.org/pt/case-studies/blackhole-image/https://numpy.org/pt/case-studies/cricket-analytics/https://numpy.org/pt/case-studies/gw-discov/https://numpy.org/pt/case-studies/deeplabcut-dnn/https://numpy.org/pt/history/https://numpy.org/pt/install/https://numpy.org/pt/press-kit/https://numpy.org/pt/gethelp/https://numpy.org/pt/user-surveys/https://numpy.org/pt/user-survey-2020/https://numpy.org/pt/privacy/https://numpy.org/pt/about/https://numpy.org/pt/terms/https://numpy.org/pt/teams/ \ No newline at end of file +https://numpy.org/pt/news/2024-12-08T00:00:00+00:00https://numpy.org/pt/2024-12-08T00:00:00+00:00https://numpy.org/pt/404/https://numpy.org/pt/learn/https://numpy.org/pt/case-studies/https://numpy.org/pt/citing-numpy/https://numpy.org/pt/code-of-conduct/https://numpy.org/pt/report-handling-manual/https://numpy.org/pt/arraycomputing/https://numpy.org/pt/community/https://numpy.org/pt/contribute/https://numpy.org/pt/case-studies/blackhole-image/https://numpy.org/pt/case-studies/cricket-analytics/https://numpy.org/pt/case-studies/gw-discov/https://numpy.org/pt/case-studies/deeplabcut-dnn/https://numpy.org/pt/history/https://numpy.org/pt/install/https://numpy.org/pt/press-kit/https://numpy.org/pt/diversity_sep2020/https://numpy.org/pt/gethelp/https://numpy.org/pt/user-surveys/https://numpy.org/pt/user-survey-2020/https://numpy.org/pt/privacy/https://numpy.org/pt/about/https://numpy.org/pt/terms/https://numpy.org/pt/teams/ \ No newline at end of file diff --git a/pt/tabcontents.yaml b/pt/tabcontents.yaml index fffddb25..d458871b 100644 --- a/pt/tabcontents.yaml +++ b/pt/tabcontents.yaml @@ -1,219 +1,286 @@ params: machinelearning: paras: - - - para1: O NumPy forma a base de bibliotecas de aprendizagem de máquina poderosas como [scikit-learn](https://scikit-learn.org) e [SciPy](https://www.scipy.org). À medida que a disciplina de aprendizagem de máquina cresce, a lista de bibliotecas construidas a partir do NumPy também cresce. As funcionalidades de deep learning do [TensorFlow](https://www.tensorflow.org) tem diversas aplicações — entre elas, reconhecimento de imagem e de fala, aplicações baseadas em texto, análise de séries temporais, e detecção de vídeo. O [PyTorch](https://pytorch.org), outra biblioteca de deep learning, é popular entre pesquisadores em visão computacional e processamento de linguagem natural. O [MXNet](https://github.com/apache/incubator-mxnet) é outro pacote de IA, que fornece templates e protótipos para deep learning. - para2: Técnicas estatísticas chamadas métodos de [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) tais como binning, bagging, stacking, e boosting estão entre os algoritmos de ML implementados por ferramentas tais como [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), e [CatBoost](https://catboost.ai) — um dos motores de inferência mais rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) e [Eli5](https://eli5.readthedocs.io/en/latest/) oferecem visualizações para aprendizagem de máquina. + - para1: O NumPy forma a base de bibliotecas de aprendizagem de máquina poderosas como [scikit-learn](https://scikit-learn.org) e [SciPy](https://scipy.org). À medida que a disciplina de aprendizagem de máquina cresce, a lista de bibliotecas construidas a partir do NumPy também cresce. As funcionalidades de deep learning do [TensorFlow](https://www.tensorflow.org) tem diversas aplicações — entre elas, reconhecimento de imagem e de fala, aplicações baseadas em texto, análise de séries temporais, e detecção de vídeo. O [PyTorch](https://pytorch.org), outra biblioteca de deep learning, é popular entre pesquisadores em visão computacional e processamento de linguagem natural. O [MXNet](https://github.com/apache/incubator-mxnet) é outro pacote de IA, que fornece templates e protótipos para deep learning. + para2: Técnicas estatísticas chamadas [métodos de ensemble](https://scikit-learn.org/stable/modules/ensemble.html) tais como binning, bagging, stacking, e boosting estão entre os algoritmos de ML implementados por ferramentas tais como [XGBoost](https://github.com/dmlc/xgboost), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), e [CatBoost](https://catboost.ai) — um dos motores de inferência mais rápidos. [Yellowbrick](https://www.scikit-yb.org/en/latest/) e [Eli5](https://eli5.readthedocs.io/en/latest/) oferecem visualizações para aprendizagem de máquina. arraylibraries: intro: - - - text: A API do NumPy é o ponto de partida quando bibliotecas são escritas para explorar hardware inovador, criar tipos de arrays especializados, ou adicionar capacidades além do que o NumPy fornece. + - text: A API do NumPy é o ponto de partida quando bibliotecas são escritas para explorar hardware inovador, criar tipos de arrays especializados, ou adicionar capacidades além do que o NumPy fornece. headers: - - - text: Biblioteca de Arrays - - - text: Recursos e áreas de aplicação + - text: Biblioteca de Arrays + - text: Recursos e áreas de aplicação libraries: - - - title: Dask + - title: Dask text: Arrays distribuídas e paralelismo avançado para análise, permitindo desempenho em escala. img: /images/content_images/arlib/dask.png alttext: Dask url: https://dask.org/ - - - title: CuPy + - title: CuPy text: Biblioteca de matriz compatível com NumPy para computação acelerada pela GPU com Python. img: /images/content_images/arlib/cupy.png alttext: CuPy url: https://cupy.chainer.org - - - title: JAX + - title: JAX text: "Transformações combináveis de programas NumPy: vetorização, compilação just-in-time para GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX - url: https://github.com/google/jax - - - title: Xarray + url: https://docs.jax.dev + - title: Xarray text: Arrays multidimensionais rotuladas e indexadas para análise e visualização avançadas img: /images/content_images/arlib/xarray.png alttext: xarray url: https://xarray.pydata.org/en/stable/index.html - - - title: Sparse + - title: Sparse text: Biblioteca de arrays compatíveis com o NumPy que pode ser integrada com Dask e álgebra linear esparsa da SciPy. img: /images/content_images/arlib/sparse.png alttext: sparse url: https://sparse.pydata.org/en/latest/ - - - title: PyTorch + - title: PyTorch text: Framework de deep learning que acelera o caminho entre prototipação de pesquisa e colocação em produção. img: /images/content_images/arlib/pytorch-logo-dark.svg alttext: PyTorch url: https://pytorch.org/ - - - title: TensorFlow + - title: TensorFlow text: Uma plataforma completa para aprendizagem de máquina que permite construir e colocar em produção aplicações usando ML facilmente. img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - - - title: MXNet + - title: MXNet text: Framework de deep learning voltado para flexibilizar prototipação em pesquisa e produção. img: /images/content_images/arlib/mxnet_logo.png alttext: MXNet url: https://mxnet.apache.org/ - - - title: Arrow + - title: Arrow text: Uma plataforma de desenvolvimento multi-linguagens para dados e análise para dados armazenados em colunas na memória. img: /images/content_images/arlib/arrow.png alttext: arrow url: https://github.com/apache/arrow - - - title: xtensor + - title: xtensor text: Arrays multidimensionais com broadcasting e avaliação preguiçosa (lazy computing) para análise numérica. img: /images/content_images/arlib/xtensor.png alttext: xtensor url: https://github.com/xtensor-stack/xtensor-python - - - title: Awkward Array + - title: Awkward Array text: Manipulação de dados JSON-like com sintaxe NumPy-like. img: /images/content_images/arlib/awkward.svg alttext: awkward url: https://awkward-array.org/ - - - title: uarray + - title: uarray text: Sistema de backend Python que dissocia a API da implementação; unumpy fornece uma API NumPy. img: /images/content_images/arlib/uarray.png alttext: uarray url: https://uarray.org/en/latest/ - - - title: tensorly + - title: tensorly text: Ferramentas para aprendizagem com tensores, algebra e backends para usar NumPy, MXNet, PyTorch, TensorFlow ou CuPy sem esforço. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html + - title: Blosc2 + text: Cálculo acelerado com arrays comprimidos na memória, no disco ou remotos. + img: /images/content_images/arlib/blosc-logo.svg + alttext: blosc2 + url: https://www.blosc.org/python-blosc2/ + scientificdomains: intro: - - - text: Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy. - - - text: "NumPy traz o poder computacional de linguagens como C e Fortran para Python, uma linguagem muito mais fácil de aprender e usar. Com esse poder vem a simplicidade: uma solução no NumPy é frequentemente clara e elegante." - librariesrow1: - - - title: Computação quântica + - text: Quase todos os cientistas que trabalham em Python se baseiam na potência do NumPy. + - text: "NumPy traz o poder computacional de linguagens como C e Fortran para Python, uma linguagem muito mais fácil de aprender e usar. Com esse poder vem a simplicidade: uma solução no NumPy é frequentemente clara e elegante." + libraries: + - title: Computação quântica alttext: Um chip de computador. img: /images/content_images/sc_dom_img/quantum_computing.svg - - - title: Computação estatística + links: + - url: https://qutip.org + label: QuTiP + - url: https://pyquil-docs.rigetti.com/en/stable + label: PyQuil + - url: https://qiskit.org + label: Qiskit + - url: https://pennylane.ai + label: PennyLane + - title: Computação estatística alttext: Um gráfico com uma linha em movimento para cima. img: /images/content_images/sc_dom_img/statistical_computing.svg - - - title: Processamento de sinais + links: + - url: https://pandas.pydata.org/ + label: Pandas + - url: https://www.statsmodels.org/ + label: statsmodels + - url: https://xarray.pydata.org/en/stable/ + label: Xarray + - url: https://seaborn.pydata.org/ + label: Seaborn + - title: Processamento de sinais alttext: Um gráfico de barras com valores positivos e negativos. img: /images/content_images/sc_dom_img/signal_processing.svg - - - title: Processamento de imagens + links: + - url: https://scipy.org + label: SciPy + - url: https://pywavelets.readthedocs.io/ + label: PyWavelets + - url: https://python-control.org/ + label: python-control + - url: https://hyperspy.org/ + label: HyperSpy + - title: Processamento de imagens alttext: Uma fotografia das montanhas. img: /images/content_images/sc_dom_img/image_processing.svg - - - title: Gráficos e Redes + links: + - url: https://scikit-image.org/ + label: Scikit-image + - url: https://opencv.org/ + label: OpenCV + - url: https://mahotas.rtfd.io/ + label: Mahotas + - title: Gráficos e Redes alttext: Um grafo simples. img: /images/content_images/sc_dom_img/sd6.svg - - - title: Processos de Astronomia + links: + - url: https://networkx.org/ + label: NetworkX + - url: https://graph-tool.skewed.de/ + label: graph-tool + - url: https://igraph.org/python/ + label: igraph + - url: https://pygsp.rtfd.io/ + label: PyGSP + - title: Processos de Astronomia alttext: Um telescópio. img: /images/content_images/sc_dom_img/astronomy_processes.svg - - - title: Psicologia Cognitiva + links: + - url: https://www.astropy.org/ + label: AstroPy + - url: https://sunpy.org/ + label: SunPy + - url: https://spacepy.github.io/ + label: SpacePy + - title: Psicologia Cognitiva alttext: Uma cabeça humana com engrenagens. img: /images/content_images/sc_dom_img/cognitive_psychology.svg - librariesrow2: - - - title: Bioinformática + links: + - url: https://www.psychopy.org/ + label: PsychoPy + - title: Bioinformática alttext: Um pedaço de DNA. img: /images/content_images/sc_dom_img/bioinformatics.svg - - - title: Inferência Bayesiana + links: + - url: https://biopython.org/ + label: BioPython + - url: http://scikit-bio.org/ + label: Scikit-Bio + - url: https://github.com/openvax/pyensembl + label: PyEnsembl + - url: http://etetoolkit.org/ + label: ETE + - title: Inferência Bayesiana alttext: Um gráfico com uma curva em forma de sino. img: /images/content_images/sc_dom_img/bayesian_inference.svg - - - title: Análise Matemática + links: + - url: https://pystan.readthedocs.io/en/latest/ + label: PyStan + - url: https://docs.pymc.io/ + label: PyMC + - url: https://arviz-devs.github.io/arviz/ + label: ArviZ + - url: https://emcee.readthedocs.io/ + label: emcee + - title: Análise Matemática alttext: Quatro símbolos matemáticos. img: /images/content_images/sc_dom_img/mathematical_analysis.svg - - - title: Química + links: + - url: https://scipy.org + label: SciPy + - url: https://www.sympy.org/ + label: SymPy + - url: https://www.cvxpy.org/ + label: cvxpy + - url: https://fenicsproject.org/ + label: FEniCS + - title: Química alttext: Um tubo de ensaio. img: /images/content_images/sc_dom_img/chemistry.svg - - - title: Geociências + links: + - url: https://cantera.org/ + label: Cantera + - url: https://www.mdanalysis.org/ + label: MDAnalysis + - url: https://github.com/rdkit/rdkit + label: RDKit + - url: https://www.pybamm.org/ + label: PyBaMM + - title: Geociências alttext: A Terra. img: /images/content_images/sc_dom_img/geoscience.svg - - - title: Processamento Geográfico + links: + - url: https://pangeo.io/ + label: Pangeo + - url: https://simpeg.xyz/ + label: Simpeg + - url: https://github.com/obspy/obspy/wiki + label: ObsPy + - url: https://www.fatiando.org/ + label: Fatiando a Terra + - title: Processamento Geográfico alttext: Um mapa. img: /images/content_images/sc_dom_img/GIS.svg - - - title: Arquitetura e Engenharia + links: + - url: https://shapely.readthedocs.io/ + label: Shapely + - url: https://geopandas.org/ + label: GeoPandas + - url: https://python-visualization.github.io/folium + label: Folium + - title: Arquitetura e Engenharia alttext: Uma placa de desenvolvimento de microprocessador. img: /images/content_images/sc_dom_img/robotics.svg + links: + - url: https://compas.dev/ + label: COMPAS + - url: https://cityenergyanalyst.com/ + label: City Energy Analyst - Analista de Energía de Ciudad + - url: https://nortikin.github.io/sverchok/ + label: Sverchok datascience: intro: "NumPy está no centro de um rico ecossistema de bibliotecas de ciência de dados. Um fluxo de trabalho típico de ciência de dados exploratório pode parecer assim:" image1: - - - img: /images/content_images/ds-landscape.png - 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'. + - img: /images/content_images/ds-landscape.png + alttext: Diagrama de bibliotecas Python. As cinco categorias são 'Extrair, Transformar, Carregar', 'Exploração de Dados', 'Modelo de Dados', 'Avaliação de Dados' e 'Apresentação de Dados'. image2: - - - img: /images/content_images/data-science.png - alttext: Diagram of three overlapping circle. The circles labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. + - img: /images/content_images/data-science.png + alttext: Diagram of three overlapping circle. The circles labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - - - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor.readthedocs.io/)" - - - text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" - - - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" - - - text: "Report in a dashboard: [Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" + - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org),[ Intake](https://intake.readthedocs.io),[PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" + - text: "Exploratory analysis: [Jupyter](https://jupyter.org),[Seaborn](https://seaborn.pydata.org),[ Matplotlib](https://matplotlib.org),[ Altair](https://altair-viz.github.io)" + - text: "Model and evaluate: [scikit-learn](https://scikit-learn.org),[ statsmodels](https://www.statsmodels.org/stable/index.html),[ PyMC3](https://docs.pymc.io),[ spaCy](https://spacy.io)" + - text: "Report in a dashboard: [Dash](https://plotly.com/dash),[ Panel](https://panel.holoviz.org),[ Voila](https://github.com/voila-dashboards/voila)" content: - - - text: For high data volumes, [Dask](https://dask.org) and[Ray](https://ray.io/) are designed to scale. Stabledeployments rely on data versioning ([DVC](https://dvc.org)),experiment tracking ([MLFlow](https://mlflow.org)), andworkflow automation ([Airflow](https://airflow.apache.org) and[Prefect](https://www.prefect.io)). + - text: For high data volumes, [Dask](https://dask.org) and[Ray](https://ray.io/) are designed to scale. Stabledeployments rely on data versioning ([DVC](https://dvc.org)),experiment tracking ([MLFlow](https://mlflow.org)), andworkflow automation ([Airflow](https://airflow.apache.org) and[Prefect](https://www.prefect.io)). visualization: images: - - - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries - img: /images/content_images/v_matplotlib.png - alttext: Um streamplot feito em matplotlib - - - url: https://github.com/yhat/ggpy - img: /images/content_images/v_ggpy.png - alttext: Um gráfico scatter-plot feito em ggpy - - - url: https://www.journaldev.com/19692/python-plotly-tutorial - img: /images/content_images/v_plotly.png - alttext: Um box-plot feito no plotly - - - url: https://altair-viz.github.io/gallery/streamgraph.html - img: /images/content_images/v_altair.png - alttext: Um gráfico streamgraph feito em altair - - - url: https://seaborn.pydata.org - img: /images/content_images/v_seaborn.png - alttext: A plot duplo com dois tipos de gráficos, um plot-graph e um gráfico de frequência feitos no seaborn - - - url: https://docs.pyvista.org/ - img: /images/content_images/v_pyvista.png - alttext: Uma renderização de volume 3D feita no PyVista. - - - url: https://napari.org - img: /images/content_images/v_napari.png - alttext: Uma imagem multidimensional, feita em napari. - - - url: https://vispy.org/gallery/index.html - img: /images/content_images/v_vispy.png - alttext: Diagrama de Voronoi feito com vispy. + - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries + img: /images/content_images/v_matplotlib.png + alttext: Um streamplot feito em matplotlib + - url: https://github.com/yhat/ggpy + img: /images/content_images/v_ggpy.png + alttext: Um gráfico scatter-plot feito em ggpy + - url: https://www.journaldev.com/19692/python-plotly-tutorial + img: /images/content_images/v_plotly.png + alttext: Um box-plot feito no plotly + - url: https://altair-viz.github.io/gallery/streamgraph.html + img: /images/content_images/v_altair.png + alttext: Um gráfico streamgraph feito em altair + - url: https://seaborn.pydata.org + img: /images/content_images/v_seaborn.png + alttext: A plot duplo com dois tipos de gráficos, um plot-graph e um gráfico de frequência feitos no seaborn + - url: https://pyvista.org + img: /images/content_images/v_pyvista.png + alttext: Uma renderização de volume 3D feita no PyVista. + - url: https://napari.org + img: /images/content_images/v_napari.png + alttext: Uma imagem multidimensional, feita em napari. + - url: https://vispy.org/gallery/index.html + img: /images/content_images/v_vispy.png + alttext: Diagrama de Voronoi feito com vispy. content: - - - text: NumPy é um componente essencial no crescente [campo de visualização em Python](https://pyviz.org/overviews/index.html), que inclui [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), e [PyVista](https://github.com/pyvista/pyvista), para citar alguns. - - - text: O processamento de grandes arrays acelerado pela NumPy permite que os pesquisadores visualizem conjuntos de dados muito maiores do que o Python nativo poderia permitir. + - text: NumPy é um componente essencial no crescente [campo de visualização em Python](https://pyviz.org/overviews/index.html), que inclui [Matplotlib](https://matplotlib.org), [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://github.com/napari/napari), e [PyVista](https://github.com/pyvista/pyvista), para citar alguns. + - text: O processamento de grandes arrays acelerado pela NumPy permite que os pesquisadores visualizem conjuntos de dados muito maiores do que o Python nativo poderia permitir. diff --git a/pt/teams/docs-team.toml b/pt/teams/docs-team.toml new file mode 100644 index 00000000..97f151c4 --- /dev/null +++ b/pt/teams/docs-team.toml @@ -0,0 +1,69 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4" + alt="Avatar of Rohit Goswami" +>}} +Rohit Goswami''' +link = 'https://github.com/HaoZeke' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4" + alt="Avatar of Inessa Pawson" +>}} +Inessa Pawson''' +link = 'https://github.com/InessaPawson' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4" + alt="Avatar of Mars Lee" +>}} +Mars Lee''' +link = 'https://github.com/MarsBarLee' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4" + alt="Avatar of Matti Picus" +>}} +Matti Picus''' +link = 'https://github.com/mattip' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4" + alt="Avatar of Melissa Weber Mendonça" +>}} +Melissa Weber Mendonça''' +link = 'https://github.com/melissawm' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4" + alt="Avatar of Mukulika" +>}} +Mukulika''' +link = 'https://github.com/Mukulikaa' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4" + alt="Avatar of Ross Barnowski" +>}} +Ross Barnowski''' +link = 'https://github.com/rossbar' diff --git a/pt/teams/emeritus-maintainers.toml b/pt/teams/emeritus-maintainers.toml new file mode 100644 index 00000000..ab6097b3 --- /dev/null +++ b/pt/teams/emeritus-maintainers.toml @@ -0,0 +1,89 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/9040124?v=4" + alt="Avatar of Allan Haldane" +>}} +Allan Haldane''' +link = 'https://github.com/ahaldane' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/20568?v=4" + alt="Avatar of Ondřej Čertík" +>}} +Ondřej Čertík''' +link = 'https://github.com/certik' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/25111?v=4" + alt="Avatar of David Cournapeau" +>}} +David Cournapeau''' +link = 'https://github.com/cournape' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3343990?v=4" + alt="Avatar of Jaime" +>}} +Jaime''' +link = 'https://github.com/jaimefrio' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/123428?v=4" + alt="Avatar of Jarrod Millman" +>}} +Jarrod Millman''' +link = 'https://github.com/jarrodmillman' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/542663?v=4" + alt="Avatar of Julian Taylor" +>}} +Julian Taylor''' +link = 'https://github.com/juliantaylor' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&v=4" + alt="Avatar of Mark Wiebe" +>}} +Mark Wiebe''' +link = 'https://github.com/mwiebe' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&v=4" + alt="Avatar of Nathaniel J. Smith" +>}} +Nathaniel J. Smith''' +link = 'https://github.com/njsmith' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/254880?v=4" + alt="Avatar of Travis E. Oliphant" +>}} +Travis E. Oliphant''' +link = 'https://github.com/teoliphant' diff --git a/pt/teams/index.html b/pt/teams/index.html index 1a49d1f9..615b759c 100644 --- a/pt/teams/index.html +++ b/pt/teams/index.html @@ -1,99 +1,109 @@ -NumPy - Times NumPy -

        Times NumPy

        Somos uma equipe internacional com a missão de apoiar comunidades científicas e de pesquisa em todo o mundo construindo software de código aberto de qualidade. +Junte-se a nós!

        Pessoas Mantenedoras#

        Avatar of Andrew Nelson +Andrew Nelson
        Avatar of Bas van Beek +Bas van Beek
        Avatar of Charles Harris +Charles Harris
        Avatar of Eric Wieser +Eric Wieser
        Avatar of Ganesh Kathiresan +Ganesh Kathiresan
        Avatar of Rohit Goswami +Rohit Goswami
        Avatar of Matthew Brett +Matthew Brett
        Avatar of Matti Picus +Matti Picus
        Avatar of Matt Haberland +Matt Haberland
        Avatar of Melissa Weber Mendonça +Melissa Weber Mendonça
        Avatar of Marten van Kerkwijk +Marten van Kerkwijk
        Avatar of Christopher Sidebottom +Christopher Sidebottom
        Avatar of Mateusz Sokół +Mateusz Sokół
        Avatar of Mukulika +Mukulika
        Avatar of Nathan Goldbaum +Nathan Goldbaum
        Avatar of Pearu Peterson +Pearu Peterson
        Avatar of Josh Wilson +Josh Wilson
        Avatar of Pauli Virtanen +Pauli Virtanen
        Avatar of Chunlin +Chunlin
        Avatar of Raghuveer Devulapalli +Raghuveer Devulapalli
        Avatar of Ralf Gommers +Ralf Gommers
        Avatar of Robert Kern +Robert Kern
        Avatar of Ross Barnowski +Ross Barnowski
        Avatar of Sebastian Berg +Sebastian Berg
        Avatar of Sayed Adel +Sayed Adel
        Avatar of Stephan Hoyer +Stephan Hoyer
        Avatar of Stefan van der Walt +Stefan van der Walt
        Avatar of Tyler Reddy +Tyler Reddy
        Avatar of Warren Weckesser +Warren Weckesser

        Time de documentacão#

        Avatar of Rohit Goswami +Rohit Goswami
        Avatar of Inessa Pawson +Inessa Pawson
        Avatar of Mars Lee +Mars Lee
        Avatar of Matti Picus +Matti Picus
        Avatar of Melissa Weber Mendonça +Melissa Weber Mendonça
        Avatar of Mukulika +Mukulika
        Avatar of Ross Barnowski +Ross Barnowski

        Time Web#

        Avatar of Inessa Pawson +Inessa Pawson
        Avatar of Jarrod Millman +Jarrod Millman
        Avatar of Joe LaChance +Joe LaChance
        Avatar of Mars Lee +Mars Lee
        Avatar of Ralf Gommers +Ralf Gommers
        Avatar of shalz +shalz
        Avatar of Shekhar Prasad Rajak +Shekhar Prasad Rajak
        Avatar of Stefan van der Walt +Stefan van der Walt
        Avatar of Albert Steppi +Albert Steppi

        Time de Triagem#

        Avatar of Andrew Nelson +Andrew Nelson
        Avatar of Anirudh Subramanian +Anirudh Subramanian
        Avatar of Aaron Meurer +Aaron Meurer
        Avatar of Atsushi Sakai +Atsushi Sakai
        Avatar of Ben Nathanson +Ben Nathanson
        Avatar of Anne Bonner +Anne Bonner
        Avatar of Brigitta Sipőcz +Brigitta Sipőcz
        Avatar of carlkl +carlkl
        Avatar of Ryan C Cooper +Ryan C Cooper
        Avatar of ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh) +ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)
        Avatar of Hameer Abbasi +Hameer Abbasi
        Avatar of Inessa Pawson +Inessa Pawson
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        Avatar of Kai +Kai
        Avatar of Yuji Kanagawa +Yuji Kanagawa
        Avatar of Kriti Singh +Kriti Singh
        Avatar of Christopher Albert +Christopher Albert
        Avatar of Lysandros Nikolaou +Lysandros Nikolaou
        Avatar of Meekail Zain +Meekail Zain
        Avatar of Christopher Sidebottom +Christopher Sidebottom
        Avatar of Mateusz Sokół +Mateusz Sokół
        Avatar of Mukulika +Mukulika
        Avatar of Noa Tamir +Noa Tamir
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        Avatar of shalz +shalz
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        Avatar of Rakesh Vasudevan +Rakesh Vasudevan
        Avatar of Zijie (ZJ) Poh +Zijie (ZJ) Poh

        Time de Pesquisa#

        Avatar of Inessa Pawson +Inessa Pawson
        Avatar of Ralf Gommers +Ralf Gommers
        Avatar of Ross Barnowski +Ross Barnowski

        Time de traduções#

        Avatar of Melissa Weber Mendonça +Melissa Weber Mendonça
        Avatar of Juan Pablo Duque +Juan Pablo Duque (@juanpabloduqueo)
        Avatar of Yeimi Pena +Yeimi Pena (@yeimiyaz)
        Avatar of Atsushi Sakai +Atsushi Sakai (@AtsushiSakai)
        Avatar of Getúlio Silva +Getúlio Silva (@getuliosilva)
        Oriol Abril-Pla +Oriol Abril-Pla (@OriolAbril)
        Avatar of @julio +@julio
        Avatar of Ali Faraji +Ali Faraji (@ali)
        Avatar of Saeed Foroutan +Saeed Foroutan (@SaeedForoutan)
        Avatar of @pyjavo +@pyjavo

        Mantenedores Eméritos#

        Avatar of Allan Haldane +Allan Haldane
        Avatar of Ondřej Čertík +Ondřej Čertík
        Avatar of David Cournapeau +David Cournapeau
        Avatar of Jaime +Jaime
        Avatar of Jarrod Millman +Jarrod Millman
        Avatar of Julian Taylor +Julian Taylor
        Avatar of Mark Wiebe +Mark Wiebe
        Avatar of Nathaniel J. Smith +Nathaniel J. Smith
        Avatar of Travis E. Oliphant +Travis E. Oliphant

        Governança#

        Para a lista de pessoas no Conselho Diretor, veja aqui.

        On this page
        \ No newline at end of file diff --git a/pt/teams/maintainers.toml b/pt/teams/maintainers.toml new file mode 100644 index 00000000..34e0a224 --- /dev/null +++ b/pt/teams/maintainers.toml @@ -0,0 +1,289 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4" + alt="Avatar of Andrew Nelson" +>}} +Andrew Nelson''' +link = 'https://github.com/andyfaff' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/43369155?u=1f1fcabf979a2f00f403c60b816ba9f573026181&v=4" + alt="Avatar of Bas van Beek" +>}} +Bas van Beek''' +link = 'https://github.com/BvB93' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/77272?v=4" + alt="Avatar of Charles Harris" +>}} +Charles Harris''' +link = 'https://github.com/charris' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/425260?v=4" + alt="Avatar of Eric Wieser" +>}} +Eric Wieser''' +link = 'https://github.com/eric-wieser' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/20969920?u=ec0e4d9dd70227549776ba8209f0e55a35d1fe84&v=4" + alt="Avatar of Ganesh Kathiresan" +>}} +Ganesh Kathiresan''' +link = 'https://github.com/ganesh-k13' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4" + alt="Avatar of Rohit Goswami" +>}} +Rohit Goswami''' +link = 'https://github.com/HaoZeke' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/67612?v=4" + alt="Avatar of Matthew Brett" +>}} +Matthew Brett''' +link = 'https://github.com/matthew-brett' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4" + alt="Avatar of Matti Picus" +>}} +Matti Picus''' +link = 'https://github.com/mattip' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/6570539?u=cfb3e218754e85c4fac18064d7cfdce0b67ddaa6&v=4" + alt="Avatar of Matt Haberland" +>}} +Matt Haberland''' +link = 'https://github.com/mdhaber' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4" + alt="Avatar of Melissa Weber Mendonça" +>}} +Melissa Weber Mendonça''' +link = 'https://github.com/melissawm' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/2789820?v=4" + alt="Avatar of Marten van Kerkwijk" +>}} +Marten van Kerkwijk''' +link = 'https://github.com/mhvk' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/4933431?u=933e774277f53e83ebb3d58dab9851c801fbfacd&v=4" + alt="Avatar of Christopher Sidebottom" +>}} +Christopher Sidebottom''' +link = 'https://github.com/Mousius' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/8431159?u=179d05b307b027da3360c213fcf4f585e1c6d7b9&v=4" + alt="Avatar of Mateusz Sokół" +>}} +Mateusz Sokół''' +link = 'https://github.com/mtsokol' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4" + alt="Avatar of Mukulika" +>}} +Mukulika''' +link = 'https://github.com/Mukulikaa' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + 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        SITE MANAGEMENT#

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        PRIVACY POLICY#

        We care about data privacy and security. Please review our Privacy Policy. 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. WE MAY TERMINATE YOUR USE OR PARTICIPATION IN THE SITE OR DELETE ANY CONTENT OR INFORMATION THAT YOU POSTED AT ANY TIME, WITHOUT WARNING, IN OUR SOLE DISCRETION.

        MODIFICATIONS AND INTERRUPTIONS#

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        We cannot guarantee the Site will be available at all times. We may experience hardware, software, or other problems or need to perform maintenance related to the Site, resulting in interruptions, delays, or errors. We reserve the right to change, revise, update, suspend, discontinue, or otherwise modify the Site at any time or for any reason without notice to you. You agree that we have no liability whatsoever for any loss, damage, or inconvenience caused by your inability to access or use the Site during any downtime or discontinuance of the Site. 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. Such informal negotiations commence upon written notice from one Party to the other Party.

        Binding Arbitration#

        If the Parties are unable to resolve a Dispute through informal negotiations, the Dispute (except those Disputes expressly excluded below) will be finally and exclusively resolved by binding arbitration. YOU UNDERSTAND THAT WITHOUT THIS PROVISION, YOU WOULD HAVE THE RIGHT TO SUE IN COURT AND HAVE A JURY TRIAL. The arbitration shall be commenced and conducted under the Commercial Arbitration Rules of the American Arbitration Association (“AAA”) and, where appropriate, the AAA’s Supplementary Procedures for Consumer Related Disputes (“AAA Consumer Rules”), both of which are available at the AAA website www.adr.org. Your arbitration fees and your share of arbitrator compensation shall be governed by the AAA Consumer Rules and, where appropriate, limited by the AAA Consumer Rules. 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#

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        DISCLAIMER#

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        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
        Austin, TX, USA 78709
        info@numfocus.org
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        On this page
        \ No newline at end of file diff --git a/pt/user-survey-2020/index.html b/pt/user-survey-2020/index.html index f467f638..99515f9c 100644 --- a/pt/user-survey-2020/index.html +++ b/pt/user-survey-2020/index.html @@ -1,14 +1,13 @@ -NumPy - PESQUISA SOBRE A COMUNIDADE NUMPY 2020 -

        PESQUISA SOBRE A COMUNIDADE NUMPY 2020

        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.

        Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado "NumPy Community Survey 2020 - results"

        Faça o download do relatório para ver os detalhes sobre os resultados encontrados.

        Para os destaques, confira este infográfico.

        Quer saber mais? Visite https://numpy.org/user-survey-2020-details/.

        On this page
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        PESQUISA DE USUÁRIOS NUMPY

        2020 O time de pesquisas da NumPy, em parceria com estudantes e professores da Universidade de Michigan e da Universidade de Maryland, conduziram a primeira pesquisa oficial sobre a comunidade NumPy. Você pode encontrar os resultados da pesquisa aqui (em inglês).

        2021 Os dados coletados estão em análise.

        Se você tem dúvidas ou sugestões sobre as pesquisas já realizadas ou futuras, por favor crie uma issue aqui.

        On this page
        \ No newline at end of file diff --git a/report-handling-manual/index.html b/report-handling-manual/index.html index a8dad5c1..e0ea5e15 100644 --- a/report-handling-manual/index.html +++ b/report-handling-manual/index.html @@ -1,15 +1,14 @@ -NumPy - NumPy Code of Conduct - How to follow up on a report -NumPy - NumPy Code of Conduct - How to follow up on a report

        NumPy Code of Conduct - How to follow up on a report

        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 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.

        On this page
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As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. [MXNet](https://mxnet.apache.org/) is another AI package, providing blueprints and templates for deep learning. - para2: Statistical techniques called [ensemble](https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205) methods such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. + - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://scipy.org). As machine learning grows, so does the list of libraries built on NumPy. [TensorFlow’s](https://www.tensorflow.org) deep learning capabilities have broad applications — among them speech and image recognition, text-based applications, time-series analysis, and video detection. [PyTorch](https://pytorch.org), another deep learning library, is popular among researchers in computer vision and natural language processing. + para2: Statistical techniques called [ensemble methods](https://scikit-learn.org/stable/modules/ensemble.html) such as binning, bagging, stacking, and boosting are among the ML algorithms implemented by tools such as [XGBoost](https://xgboost.readthedocs.io/), [LightGBM](https://lightgbm.readthedocs.io/en/latest/), and [CatBoost](https://catboost.ai) — one of the fastest inference engines. [Yellowbrick](https://www.scikit-yb.org/en/latest/) and [Eli5](https://eli5.readthedocs.io/en/latest/) offer machine learning visualizations. arraylibraries: intro: @@ -22,12 +22,12 @@ params: text: NumPy-compatible array library for GPU-accelerated computing with Python. img: /images/content_images/arlib/cupy.png alttext: CuPy - url: https://cupy.chainer.org + url: https://cupy.dev - title: JAX text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU." img: /images/content_images/arlib/jax_logo_250px.png alttext: JAX - url: https://jax.readthedocs.io/ + url: https://docs.jax.dev - title: Xarray text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization. img: /images/content_images/arlib/xarray.png @@ -48,11 +48,6 @@ params: img: /images/content_images/arlib/tensorflow-logo.svg alttext: TensorFlow url: https://www.tensorflow.org - - title: MXNet - text: Deep learning framework suited for flexible research prototyping and production. - img: /images/content_images/arlib/mxnet_logo.png - alttext: MXNet - url: https://mxnet.apache.org/ - title: Arrow text: A cross-language development platform for columnar in-memory data and analytics. img: /images/content_images/arlib/arrow.png @@ -74,10 +69,15 @@ params: alttext: uarray url: https://uarray.org/en/latest/ - title: tensorly - text: Tensor learning, algebra and backends to seamlessly use NumPy, MXNet, PyTorch, TensorFlow or CuPy. + text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy. img: /images/content_images/arlib/tensorly.png alttext: tensorly url: http://tensorly.org/stable/home.html + - title: Blosc2 + text: Accelerated computation for in-memory, on-disk, or remote compressed arrays. + img: /images/content_images/arlib/blosc-logo.svg + alttext: blosc2 + url: https://www.blosc.org/python-blosc2/ scientificdomains: intro: @@ -89,7 +89,7 @@ params: alttext: A computer chip. img: /images/content_images/sc_dom_img/quantum_computing.svg links: - - url: http://qutip.org + - url: https://qutip.org label: QuTiP - url: https://pyquil-docs.rigetti.com/en/stable label: PyQuil @@ -113,7 +113,7 @@ params: alttext: A bar chart with positive and negative values. img: /images/content_images/sc_dom_img/signal_processing.svg links: - - url: https://www.scipy.org/ + - url: https://scipy.org label: SciPy - url: https://pywavelets.readthedocs.io/ label: PyWavelets @@ -122,7 +122,7 @@ params: - url: https://hyperspy.org/ label: HyperSpy - title: Image Processing - alttext: An photograph of the mountains. + alttext: A photograph of the mountains. img: /images/content_images/sc_dom_img/image_processing.svg links: - url: https://scikit-image.org/ @@ -178,7 +178,7 @@ params: - url: https://pystan.readthedocs.io/en/latest/ label: PyStan - url: https://docs.pymc.io/ - label: PyMC3 + label: PyMC - url: https://arviz-devs.github.io/arviz/ label: ArviZ - url: https://emcee.readthedocs.io/ @@ -187,7 +187,7 @@ params: alttext: Four mathematical symbols. img: /images/content_images/sc_dom_img/mathematical_analysis.svg links: - - url: https://www.scipy.org/ + - url: https://scipy.org label: SciPy - url: https://www.sympy.org/ label: SymPy @@ -253,9 +253,9 @@ params: alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'. examples: - - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor.readthedocs.io/)" + - text: "Extract, Transform, Load: [Pandas](https://pandas.pydata.org), [Intake](https://intake.readthedocs.io), [PyJanitor](https://pyjanitor-devs.github.io/pyjanitor/)" - text: "Exploratory analysis: [Jupyter](https://jupyter.org), [Seaborn](https://seaborn.pydata.org), [Matplotlib](https://matplotlib.org), [Altair](https://altair-viz.github.io)" - - 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), [PyMC](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://voila.readthedocs.io/)" content: @@ -278,7 +278,7 @@ params: - 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/ + - url: https://pyvista.org img: /images/content_images/v_pyvista.png alttext: A 3D volume rendering made in PyVista. - url: https://napari.org @@ -295,6 +295,6 @@ params: [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly), [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/), [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/), - and [PyVista](https://docs.pyvista.org/), to name a few. + and [PyVista](https://pyvista.org), to name a few. - text: NumPy's accelerated processing of large arrays allows researchers to visualize datasets far larger than native Python could handle. diff --git a/teams/docs-team.toml b/teams/docs-team.toml index 09abff8a..7d453fbf 100644 --- a/teams/docs-team.toml +++ b/teams/docs-team.toml @@ -1,69 +1,69 @@ [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4"' -alt = 'Avatar of Rohit Goswami' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4\"" + alt="Avatar of Rohit Goswami" +>}} Rohit Goswami''' link = 'https://github.com/HaoZeke' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4"' -alt = 'Avatar of Inessa Pawson' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4\"" + alt="Avatar of Inessa Pawson" +>}} Inessa Pawson''' link = 'https://github.com/InessaPawson' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4"' -alt = 'Avatar of Mars Lee' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4\"" + alt="Avatar of Mars Lee" +>}} Mars Lee''' link = 'https://github.com/MarsBarLee' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4"' -alt = 'Avatar of Matti Picus' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4\"" + alt="Avatar of Matti Picus" +>}} Matti Picus''' link = 'https://github.com/mattip' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4"' -alt = 'Avatar of Melissa Weber Mendonça' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4\"" + alt="Avatar of Melissa Weber Mendonça" +>}} Melissa Weber Mendonça''' link = 'https://github.com/melissawm' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4"' -alt = 'Avatar of Mukulika' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4\"" + alt="Avatar of Mukulika" +>}} Mukulika''' link = 'https://github.com/Mukulikaa' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4"' -alt = 'Avatar of Ross Barnowski' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4\"" + alt="Avatar of Ross Barnowski" +>}} Ross Barnowski''' link = 'https://github.com/rossbar' diff --git a/teams/emeritus-maintainers.toml b/teams/emeritus-maintainers.toml index b61eef1f..d030da98 100644 --- a/teams/emeritus-maintainers.toml +++ b/teams/emeritus-maintainers.toml @@ -1,89 +1,89 @@ [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/9040124?v=4"' -alt = 'Avatar of Allan Haldane' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/9040124?v=4\"" + alt="Avatar of Allan Haldane" +>}} Allan Haldane''' link = 'https://github.com/ahaldane' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/20568?v=4"' -alt = 'Avatar of Ondřej Čertík' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/20568?v=4\"" + alt="Avatar of Ondřej Čertík" +>}} Ondřej Čertík''' link = 'https://github.com/certik' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/25111?v=4"' -alt = 'Avatar of David Cournapeau' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/25111?v=4\"" + alt="Avatar of David Cournapeau" +>}} David Cournapeau''' link = 'https://github.com/cournape' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/3343990?v=4"' -alt = 'Avatar of Jaime' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3343990?v=4\"" + alt="Avatar of Jaime" +>}} Jaime''' link = 'https://github.com/jaimefrio' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/123428?v=4"' -alt = 'Avatar of Jarrod Millman' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/123428?v=4\"" + alt="Avatar of Jarrod Millman" +>}} Jarrod Millman''' link = 'https://github.com/jarrodmillman' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/542663?v=4"' -alt = 'Avatar of Julian Taylor' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/542663?v=4\"" + alt="Avatar of Julian Taylor" +>}} Julian Taylor''' link = 'https://github.com/juliantaylor' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&v=4"' -alt = 'Avatar of Mark Wiebe' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/399551?u=d4a592a0763568448a8eaa06b680ee9584a8c6e0&v=4\"" + alt="Avatar of Mark Wiebe" +>}} Mark Wiebe''' link = 'https://github.com/mwiebe' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&v=4"' -alt = 'Avatar of Nathaniel J. Smith' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/609896?u=935a2bf5f98be8c08d87eaac095f1f3bc3332490&v=4\"" + alt="Avatar of Nathaniel J. Smith" +>}} Nathaniel J. Smith''' link = 'https://github.com/njsmith' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/254880?v=4"' -alt = 'Avatar of Travis E. Oliphant' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/254880?v=4\"" + alt="Avatar of Travis E. Oliphant" +>}} Travis E. Oliphant''' link = 'https://github.com/teoliphant' diff --git a/teams/index.html b/teams/index.html index 3a197a12..4e93cb21 100644 --- a/teams/index.html +++ b/teams/index.html @@ -1,15 +1,15 @@ -NumPy - NumPy Teams -

        NumPy Teams

        We are an international team on a mission to support scientific and research communities worldwide by building quality, open-source software. Join us!

        Maintainers#

        Avatar of Andrew Nelson Andrew Nelson
        Avatar of Bas van Beek @@ -47,15 +47,15 @@ Matti Picus
        Avatar of Melissa Weber Mendonça Melissa Weber Mendonça
        Avatar of Mukulika Mukulika
        Avatar of Ross Barnowski -Ross Barnowski

        Web team#

        Avatar of Inessa Pawson -Inessa Pawson
        Avatar of Jarrod Millman -Jarrod Millman
        Avatar of Joe LaChance -Joe LaChance
        Avatar of Mars Lee -Mars Lee
        Avatar of Ralf Gommers -Ralf Gommers
        Avatar of shalz -shalz
        Avatar of Shekhar Prasad Rajak -Shekhar Prasad Rajak
        Avatar of Stefan van der Walt -Stefan van der Walt
        Avatar of Albert Steppi +Ross Barnowski

        Web team#

        Avatar of Inessa Pawson +Inessa Pawson
        Avatar of Jarrod Millman +Jarrod Millman
        Avatar of Joe LaChance +Joe LaChance
        Avatar of Mars Lee +Mars Lee
        Avatar of Ralf Gommers +Ralf Gommers
        Avatar of shalz +shalz
        Avatar of Shekhar Prasad Rajak +Shekhar Prasad Rajak
        Avatar of Stefan van der Walt +Stefan van der Walt
        Avatar of Albert Steppi Albert Steppi

        Triage team#

        Avatar of Andrew Nelson Andrew Nelson
        Avatar of Anirudh Subramanian Anirudh Subramanian
        Avatar of Aaron Meurer @@ -87,7 +87,17 @@ Zijie (ZJ) Poh

        Survey team#

        Avatar of Inessa Pawson Inessa Pawson
        Avatar of Ralf Gommers Ralf Gommers
        Avatar of Ross Barnowski -Ross Barnowski

        Emeritus maintainers#

        Avatar of Allan Haldane +Ross Barnowski

        Translations team#

        Avatar of Melissa Weber Mendonça +Melissa Weber Mendonça
        Avatar of Juan Pablo Duque +Juan Pablo Duque (@juanpabloduqueo)
        Avatar of Yeimi Pena +Yeimi Pena (@yeimiyaz)
        Avatar of Atsushi Sakai +Atsushi Sakai (@AtsushiSakai)
        Avatar of Getúlio Silva +Getúlio Silva (@getuliosilva)
        Oriol Abril-Pla +Oriol Abril-Pla (@OriolAbril)
        Avatar of @julio +@julio
        Avatar of Ali Faraji +Ali Faraji (@ali)
        Avatar of Saeed Foroutan +Saeed Foroutan (@SaeedForoutan)
        Avatar of @pyjavo +@pyjavo

        Emeritus maintainers#

        Avatar of Allan Haldane Allan Haldane
        Avatar of Ondřej Čertík Ondřej Čertík
        Avatar of David Cournapeau David Cournapeau
        Avatar of Jaime @@ -96,6 +106,5 @@ Julian Taylor
        Avatar of Mark Wiebe Mark Wiebe
        Avatar of Nathaniel J. Smith Nathaniel J. Smith
        Avatar of Travis E. Oliphant -Travis E. Oliphant

        Governance#

        For the list of the Steering Council members, please see here.

        On this page
        \ No newline at end of file +Travis E. Oliphant

        Governance#

        For the list of the Steering Council members, please see here.

        On this page
        \ No newline at end of file diff --git a/teams/maintainers.toml b/teams/maintainers.toml index 03120e82..38a0a3d2 100644 --- a/teams/maintainers.toml +++ b/teams/maintainers.toml @@ -1,289 +1,289 @@ [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4"' -alt = 'Avatar of Andrew Nelson' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4\"" + alt="Avatar of Andrew Nelson" +>}} Andrew Nelson''' link = 'https://github.com/andyfaff' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/43369155?u=1f1fcabf979a2f00f403c60b816ba9f573026181&v=4"' -alt = 'Avatar of Bas van Beek' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/43369155?u=1f1fcabf979a2f00f403c60b816ba9f573026181&v=4\"" + alt="Avatar of Bas van Beek" +>}} Bas van Beek''' link = 'https://github.com/BvB93' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/77272?v=4"' -alt = 'Avatar of Charles Harris' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/77272?v=4\"" + alt="Avatar of Charles Harris" +>}} Charles Harris''' link = 'https://github.com/charris' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/425260?v=4"' -alt = 'Avatar of Eric Wieser' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/425260?v=4\"" + alt="Avatar of Eric Wieser" +>}} Eric Wieser''' link = 'https://github.com/eric-wieser' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 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/image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/67612?v=4\"" + alt="Avatar of Matthew Brett" +>}} Matthew Brett''' link = 'https://github.com/matthew-brett' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4"' -alt = 'Avatar of Matti Picus' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4\"" + alt="Avatar of Matti Picus" +>}} Matti Picus''' link = 'https://github.com/mattip' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/6570539?u=cfb3e218754e85c4fac18064d7cfdce0b67ddaa6&v=4"' -alt = 'Avatar of Matt Haberland' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/6570539?u=cfb3e218754e85c4fac18064d7cfdce0b67ddaa6&v=4\"" + alt="Avatar of Matt 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'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/15245051?u=54810990f0fdb11ecaade02762c09d5549d72a11&v=4"' -alt = 'Avatar of Chunlin' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/15245051?u=54810990f0fdb11ecaade02762c09d5549d72a11&v=4\"" + alt="Avatar of Chunlin" +>}} Chunlin''' link = 'https://github.com/Qiyu8' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&v=4"' -alt = 'Avatar of Raghuveer Devulapalli' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/44766858?u=fcb771cdeac5320fa0c8f40db39c5afb071fdfb0&v=4\"" + alt="Avatar of Raghuveer Devulapalli" +>}} Raghuveer Devulapalli''' link = 'https://github.com/r-devulap' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4"' -alt = 'Avatar of Ralf Gommers' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4\"" + alt="Avatar of Ralf Gommers" +>}} Ralf Gommers''' link = 'https://github.com/rgommers' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/46135?u=305a96a4778daecacbc8ec97ac25a48099a239cc&v=4"' -alt = 'Avatar of Robert Kern' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/46135?u=305a96a4778daecacbc8ec97ac25a48099a239cc&v=4\"" + alt="Avatar of Robert Kern" +>}} Robert Kern''' link = 'https://github.com/rkern' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4"' -alt = 'Avatar of Ross Barnowski' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4\"" + alt="Avatar of Ross Barnowski" +>}} Ross Barnowski''' link = 'https://github.com/rossbar' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/61977?v=4"' -alt = 'Avatar of Sebastian Berg' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/61977?v=4\"" + alt="Avatar of Sebastian Berg" +>}} Sebastian Berg''' link = 'https://github.com/seberg' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/12713707?u=5a3f6a8de4801d7878750cbd0bb2e0427bf0af0b&v=4"' -alt = 'Avatar of Sayed Adel' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/12713707?u=5a3f6a8de4801d7878750cbd0bb2e0427bf0af0b&v=4\"" + alt="Avatar of Sayed Adel" +>}} Sayed Adel''' link = 'https://github.com/seiko2plus' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/1217238?u=b61e7e0085405ce6d7d53f8f39a1360ef9723f72&v=4"' -alt = 'Avatar of Stephan Hoyer' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/1217238?u=b61e7e0085405ce6d7d53f8f39a1360ef9723f72&v=4\"" + alt="Avatar of Stephan Hoyer" +>}} Stephan Hoyer''' link = 'https://github.com/shoyer' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&v=4"' -alt = 'Avatar of Stefan van der Walt' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&v=4\"" + alt="Avatar of Stefan van der Walt" +>}} Stefan van der Walt''' link = 'https://github.com/stefanv' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/7903078?u=2762d9ff13b992dc635f8f190a17f9a90cddfae1&v=4"' -alt = 'Avatar of Tyler Reddy' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/7903078?u=2762d9ff13b992dc635f8f190a17f9a90cddfae1&v=4\"" + alt="Avatar of Tyler Reddy" +>}} Tyler Reddy''' link = 'https://github.com/tylerjereddy' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/321463?v=4"' -alt = 'Avatar of Warren Weckesser' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/321463?v=4\"" + alt="Avatar of Warren Weckesser" +>}} Warren Weckesser''' link = 'https://github.com/WarrenWeckesser' diff --git a/teams/survey-team.toml b/teams/survey-team.toml index d1eb22b7..0d085742 100644 --- a/teams/survey-team.toml +++ b/teams/survey-team.toml @@ -1,29 +1,29 @@ [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4"' -alt = 'Avatar of Inessa Pawson' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4\"" + alt="Avatar of Inessa Pawson" +>}} Inessa Pawson''' link = 'https://github.com/InessaPawson' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4"' -alt = 'Avatar of Ralf Gommers' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4\"" + alt="Avatar of Ralf Gommers" +>}} Ralf Gommers''' link = 'https://github.com/rgommers' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4"' -alt = 'Avatar of Ross Barnowski' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4\"" + alt="Avatar of Ross Barnowski" +>}} Ross Barnowski''' link = 'https://github.com/rossbar' diff --git a/teams/translations-team.toml b/teams/translations-team.toml new file mode 100644 index 00000000..b771fc6b --- /dev/null +++ b/teams/translations-team.toml @@ -0,0 +1,99 @@ +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4" + alt="Avatar of Melissa Weber Mendonça" +>}} +Melissa Weber Mendonça''' +link = 'https://github.com/melissawm' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Juan Pablo Duque" +>}} +Juan Pablo Duque (@juanpabloduqueo)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Yeimi Pena" +>}} +Yeimi Pena (@yeimiyaz)''' +link = 'https://www.linkedin.com/in/yeimipena/' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3813847?v=4" + alt="Avatar of Atsushi Sakai" +>}} +Atsushi Sakai (@AtsushiSakai)''' +link = 'https://github.com/AtsushiSakai' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Getúlio Silva" +>}} +Getúlio Silva (@getuliosilva)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Oriol Abril-Pla" +>}} +Oriol Abril-Pla (@OriolAbril)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of @julio" +>}} +@julio''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Ali Faraji" +>}} +Ali Faraji (@ali)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Saeed Foroutan" +>}} +Saeed Foroutan (@SaeedForoutan)''' +link = 'https://scientific-python.crowdin.com' + +[[item]] +type = 'card' +classcard = 'text-center' +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of @pyjavo" +>}} +@pyjavo''' +link = 'https://scientific-python.crowdin.com' diff --git a/teams/triage-team.toml b/teams/triage-team.toml index 8bfddc64..ea8d74d4 100644 --- a/teams/triage-team.toml +++ b/teams/triage-team.toml @@ -1,279 +1,279 @@ [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4"' -alt = 'Avatar of Andrew Nelson' -{{< /image >}} +body = '''{{< image + 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[[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/3891660?u=5de0ba1f1adad6f041f6dde1affef5d05bbed80a&v=4"' -alt = 'Avatar of Joe LaChance' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3891660?u=5de0ba1f1adad6f041f6dde1affef5d05bbed80a&v=4" + alt="Avatar of Joe LaChance" +>}} Joe LaChance''' link = 'https://github.com/joelachance' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4"' -alt = 'Avatar of Mars Lee' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4" + alt="Avatar of Mars Lee" +>}} Mars Lee''' link = 'https://github.com/MarsBarLee' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4"' -alt = 'Avatar of Ralf Gommers' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4" + alt="Avatar of Ralf Gommers" +>}} Ralf Gommers''' link = 'https://github.com/rgommers' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&v=4"' -alt = 'Avatar of shalz' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&v=4" + alt="Avatar of shalz" +>}} shalz''' link = 'https://github.com/shaloo' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&v=4"' -alt = 'Avatar of Shekhar Prasad Rajak' -{{< 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src="https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&v=4" + alt="Avatar of Albert Steppi" +>}} Albert Steppi''' link = 'https://github.com/steppi' diff --git a/terms/index.html b/terms/index.html index d9761343..78f42eb0 100644 --- a/terms/index.html +++ b/terms/index.html @@ -1,14 +1,13 @@ -NumPy - Terms of Use -

        Terms of Use

        Last updated January 4, 2020

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        17. Attempt to bypass any measures of the Site designed to prevent or restrict access to the Site, or any portion of the Site.

        SUBMISSIONS#

        You acknowledge and agree that any questions, comments, suggestions, ideas, feedback, or other information regarding the Site (“Submissions”) provided by you to us are non-confidential and shall become our sole property. We shall own exclusive rights, including all intellectual property rights, and shall be entitled to the unrestricted use and dissemination of these Submissions for any lawful purpose, commercial or otherwise, without acknowledgment or compensation to you. You hereby waive all moral rights to any such Submissions, and you hereby warrant that any such Submissions are original with you or that you have the right to submit such Submissions. You agree there shall be no recourse against us for any alleged or actual infringement or misappropriation of any proprietary right in your Submissions.

        THIRD-PARTY WEBSITES AND CONTENT#

        The Site may contain (or you may be sent via the Site) links to other websites (“Third-Party Websites”) as well as articles, photographs, text, graphics, pictures, designs, music, sound, video, information, applications, software, and other content or items belonging to or originating from third parties (“Third-Party Content”). Such Third-Party Websites and Third-Party Content are not investigated, monitored, or checked for accuracy, appropriateness, or completeness by us, and we are not responsible for any Third-Party Websites accessed through the Site or any Third-Party Content posted on, available through, or installed from the Site, including the content, accuracy, offensiveness, opinions, reliability, privacy practices, or other policies of or contained in the Third-Party Websites or the Third-Party Content. Inclusion of, linking to, or permitting the use or installation of any Third-Party Websites or any Third-Party Content does not imply approval or endorsement thereof by us. If you decide to leave the Site and access the Third-Party Websites or to use or install any Third-Party Content, you do so at your own risk, and you should be aware these Terms of Use no longer govern. You should review the applicable terms and policies, including privacy and data gathering practices, of any website to which you navigate from the Site or relating to any applications you use or install from the Site. Any purchases you make through Third-Party Websites will be through other websites and from other companies, and we take no responsibility whatsoever in relation to such purchases which are exclusively between you and the applicable third party. You agree and acknowledge that we do not endorse the products or services offered on Third-Party Websites and you shall hold us harmless from any harm caused by your purchase of such products or services. Additionally, you shall hold us harmless from any losses sustained by you or harm caused to you relating to or resulting in any way from any Third-Party Content or any contact with Third-Party Websites.

        SITE MANAGEMENT#

        We reserve the right, but not the obligation, to: (1) monitor the Site for violations of these Terms of Use; (2) take appropriate legal action against anyone who, in our sole discretion, violates the law or these Terms of Use, including without limitation, reporting such user to law enforcement authorities; (3) in our sole discretion and without limitation, refuse, restrict access to, limit the availability of, or disable (to the extent technologically feasible) any of your Contributions or any portion thereof; (4) in our sole discretion and without limitation, notice, or liability, to remove from the Site or otherwise disable all files and content that are excessive in size or are in any way burdensome to our systems; and (5) otherwise manage the Site in a manner designed to protect our rights and property and to facilitate the proper functioning of the Site.

        PRIVACY POLICY#

        We care about data privacy and security. Please review our Privacy Policy. 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. WE MAY TERMINATE YOUR USE OR PARTICIPATION IN THE SITE OR DELETE ANY CONTENT OR INFORMATION THAT YOU POSTED AT ANY TIME, WITHOUT WARNING, IN OUR SOLE DISCRETION.

        MODIFICATIONS AND INTERRUPTIONS#

        We reserve the right to change, modify, or remove the contents of the Site at any time or for any reason at our sole discretion without notice. However, we have no obligation to update any information on our Site. We also reserve the right to modify or discontinue all or part of the Site without notice at any time. We will not be liable to you or any third party for any modification, suspension, or discontinuance of the Site.

        We cannot guarantee the Site will be available at all times. We may experience hardware, software, or other problems or need to perform maintenance related to the Site, resulting in interruptions, delays, or errors. We reserve the right to change, revise, update, suspend, discontinue, or otherwise modify the Site at any time or for any reason without notice to you. You agree that we have no liability whatsoever for any loss, damage, or inconvenience caused by your inability to access or use the Site during any downtime or discontinuance of the Site. 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. Such informal negotiations commence upon written notice from one Party to the other Party.

        Binding Arbitration#

        If the Parties are unable to resolve a Dispute through informal negotiations, the Dispute (except those Disputes expressly excluded below) will be finally and exclusively resolved by binding arbitration. YOU UNDERSTAND THAT WITHOUT THIS PROVISION, YOU WOULD HAVE THE RIGHT TO SUE IN COURT AND HAVE A JURY TRIAL. The arbitration shall be commenced and conducted under the Commercial Arbitration Rules of the American Arbitration Association (“AAA”) and, where appropriate, the AAA’s Supplementary Procedures for Consumer Related Disputes (“AAA Consumer Rules”), both of which are available at the AAA website www.adr.org. Your arbitration fees and your share of arbitrator compensation shall be governed by the AAA Consumer Rules and, where appropriate, limited by the AAA Consumer Rules. 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. TO THE FULLEST EXTENT PERMITTED BY LAW, WE DISCLAIM ALL WARRANTIES, EXPRESS OR IMPLIED, IN CONNECTION WITH THE SITE AND YOUR USE THEREOF, INCLUDING, WITHOUT LIMITATION, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NON-INFRINGEMENT. WE MAKE NO WARRANTIES OR REPRESENTATIONS ABOUT THE ACCURACY OR COMPLETENESS OF THE SITE’S CONTENT OR THE CONTENT OF ANY WEBSITES LINKED TO THE SITE AND WE WILL ASSUME NO LIABILITY OR RESPONSIBILITY FOR ANY (1) ERRORS, MISTAKES, OR INACCURACIES OF CONTENT AND MATERIALS, (2) PERSONAL INJURY OR PROPERTY DAMAGE, OF ANY NATURE WHATSOEVER, RESULTING FROM YOUR ACCESS TO AND USE OF THE SITE, (3) ANY UNAUTHORIZED ACCESS TO OR USE OF OUR SECURE SERVERS AND/OR ANY AND ALL PERSONAL INFORMATION AND/OR FINANCIAL INFORMATION STORED THEREIN, (4) ANY INTERRUPTION OR CESSATION OF TRANSMISSION TO OR FROM THE SITE, (5) ANY BUGS, VIRUSES, TROJAN HORSES, OR THE LIKE WHICH MAY BE TRANSMITTED TO OR THROUGH THE SITE BY ANY THIRD PARTY, AND/OR (6) ANY ERRORS OR OMISSIONS IN ANY CONTENT AND MATERIALS OR FOR ANY LOSS OR DAMAGE OF ANY KIND INCURRED AS A RESULT OF THE USE OF ANY CONTENT POSTED, TRANSMITTED, OR OTHERWISE MADE AVAILABLE VIA THE SITE. 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
        Austin, TX, USA 78709
        info@numfocus.org
        +1 (512) 222-5449

        On this page
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lists.min.b184a4ac27beda08b522818b2b47de2e27960baf979f88dd2028ff959c7b5339.css rename to theme-css/lists.min.83821789384ebadc1a1ff75ef9f4b29ba53fe45eb30a46a228aa55772a393396.css index 23ffcbbe..a04a4fc0 100644 --- a/lists.min.b184a4ac27beda08b522818b2b47de2e27960baf979f88dd2028ff959c7b5339.css +++ b/theme-css/lists.min.83821789384ebadc1a1ff75ef9f4b29ba53fe45eb30a46a228aa55772a393396.css @@ -1 +1 @@ -dl.field-list{display:grid;grid-template-columns:fit-content(30%)auto}dl.field-list>dt{font-weight:700;word-break:break-word;padding-left:.5em;padding-right:5px}dl.field-list>dd{padding-left:.5em;margin-top:0;margin-left:0;margin-bottom:0} \ No newline at end of file +dl.field-list{display:grid;grid-template-columns:fit-content(30%)auto}dl.field-list>dt{font-weight:700;word-break:break-word;padding-left:.5rem;padding-right:.313rem}dl.field-list>dd{padding-left:.5rem;margin-top:0;margin-left:0;margin-bottom:0} \ No newline at end of file diff --git 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+.news-container{display:flex;justify-content:center;align-items:center;width:100vw;min-height:45px;background-color:var(--spht-color-dark);color:var(--spht-color-light);white-space:nowrap}.news-container a{color:var(--spht-color-light)}.news-container a:hover{color:var(--spht-color-link-hover)}.news-title{display:flex;letter-spacing:1.5px;font-size:1.688rem;font-weight:700;margin:.625rem;margin-right:1.25rem}@media only screen and (max-width:1300px){.news-container{white-space:unset}.news-title{text-align:center}}@media only screen and (max-width:1090px){.news-container{flex-direction:column;padding:1.875rem}.news-date{text-align:center}} \ No newline at end of file diff --git a/posts.min.2265a10f248faf175f6c00dfe3122c60f3bd3541010c88cacac894e6c6dacff3.css b/theme-css/posts.min.9505f87d5973f3f08c99c613c0781b3a42411f4795657e8da7ef29c7ad37c23d.css similarity index 95% rename from posts.min.2265a10f248faf175f6c00dfe3122c60f3bd3541010c88cacac894e6c6dacff3.css rename to theme-css/posts.min.9505f87d5973f3f08c99c613c0781b3a42411f4795657e8da7ef29c7ad37c23d.css index f59fcca1..03e70fda 100644 --- a/posts.min.2265a10f248faf175f6c00dfe3122c60f3bd3541010c88cacac894e6c6dacff3.css +++ b/theme-css/posts.min.9505f87d5973f3f08c99c613c0781b3a42411f4795657e8da7ef29c7ad37c23d.css @@ -1 +1 @@ -.post-list{display:flex;flex-wrap:wrap;column-gap:2rem}.post-list article{flex-grow:2;flex-basis:48%;padding-top:1.5rem}.post-title a{color:var(--pst-color-text-base)!important}.post-meta{color:var(--pst-color-text-muted)}.post-list .post-title{font-size:1.25rem;font-weight:700}.post-author{display:inline}.post-author:not(:first-child):before{content:""}.post-date{padding-left:.5rem}.post-tag:nth-child(2){padding-left:.1rem}.post-tag{padding-left:.3rem}.post-author:not(:first-child){padding-left:.5rem}.post-meta{padding-bottom:1rem}.post-meta .icon{font-size:1.5rem}.post-meta svg{fill:gray}.post-content p:not(:last-child){margin-bottom:.25rem}.post-summary img{border:1px solid #bbb;float:right;max-width:15em;margin:.5em}.post-list .post-summary{padding-top:.5rem}.post-featuredImage img{border:1px #bbb solid;max-width:20rem;margin:.5em;float:left;margin-right:1.25rem}.read-more{font-style:italic}.tag-cloud{position:sticky;align-self:flex-start;top:6rem;margin:0;padding:0;line-height:1.3em}.tag-cloud-title{font-size:32px;margin-top:60px;padding-bottom:10px} \ No newline at end of file +.post-list{display:flex;flex-wrap:wrap;column-gap:2rem}.post-list article{flex-grow:2;flex-basis:48%;padding-top:1.5rem}.post-title a{color:var(--pst-color-text-base)!important}.post-meta{color:var(--pst-color-text-muted)}.post-list .post-title{font-size:1.25rem;font-weight:700}.post-author{display:inline}.post-author:not(:first-child):before{content:""}.post-date{padding-left:.5rem}.post-tag:nth-child(2){padding-left:.1rem}.post-tag{padding-left:.3rem}.post-author:not(:first-child){padding-left:.5rem}.post-meta{padding-bottom:1rem}.post-meta .icon{font-size:1.5rem}.post-meta svg{fill:gray}.post-content p:not(:last-child){margin-bottom:.25rem}.post-summary img{border:1px solid #bbb;float:right;max-width:15em;margin:.5em}.post-list .post-summary{padding-top:.5rem}.post-featuredImage img{border:1px #bbb solid;max-width:20rem;margin:.5em;float:left;margin-right:1.25rem}.read-more{font-style:italic}.tag-cloud{position:sticky;align-self:flex-start;top:6rem;margin:0;padding:0;line-height:1.3em}.tag-cloud-title{font-size:2rem;margin-top:3.75rem;padding-bottom:.625rem} \ No newline at end of file diff --git a/theme-css/pst/bootstrap.scss b/theme-css/pst/bootstrap.scss new file mode 100644 index 00000000..1b76f473 --- /dev/null +++ b/theme-css/pst/bootstrap.scss @@ -0,0 +1 @@ +@import "variables/bootstrap"; diff --git a/theme-css/pst/bootstrap.scss.min.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.css b/theme-css/pst/bootstrap.scss.min.e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855.css new file mode 100644 index 00000000..e69de29b diff --git a/theme-css/pst/pydata-sphinx-theme.scss b/theme-css/pst/pydata-sphinx-theme.scss index 958c38dd..86711ed3 100644 --- a/theme-css/pst/pydata-sphinx-theme.scss +++ b/theme-css/pst/pydata-sphinx-theme.scss @@ -1,6 +1,6 @@ //// Import Bootstrap core //@import "~bootstrap/scss/functions"; -//@import "variables/bootstrap"; +@import "variables/bootstrap"; //@import "~bootstrap/scss/variables"; //@import "~bootstrap/scss/xins"; @@ -58,7 +58,7 @@ @import "./content/lists"; @import "./content/quotes"; //@import "./content/spans"; -//@import "./content/tables"; +@import "./content/tables"; @import "./content/toctree"; //@import "./content/math"; diff --git a/theme-css/pst/pydata-sphinx-theme.scss.min.90d9b9febfab06175a710c2d668af971500bba752c73e59f6242a85feefdafb7.css b/theme-css/pst/pydata-sphinx-theme.scss.min.90d9b9febfab06175a710c2d668af971500bba752c73e59f6242a85feefdafb7.css deleted file mode 100644 index a15b724c..00000000 --- a/theme-css/pst/pydata-sphinx-theme.scss.min.90d9b9febfab06175a710c2d668af971500bba752c73e59f6242a85feefdafb7.css +++ /dev/null @@ -1 +0,0 @@ -html{--pst-header-height:4rem;--pst-header-article-height:calc(var(--pst-header-height) * 2 / 3);--pst-sidebar-secondary:17rem}html{--pst-font-size-base:1rem;--pst-font-size-h1:2.5rem;--pst-font-size-h2:2rem;--pst-font-size-h3:1.75rem;--pst-font-size-h4:1.5rem;--pst-font-size-h5:1.25rem;--pst-font-size-h6:1.1rem;--pst-font-size-milli:0.9rem;--pst-sidebar-font-size:0.9rem;--pst-sidebar-font-size-mobile:1.1rem;--pst-sidebar-header-font-size:1.2rem;--pst-sidebar-header-font-weight:600;--pst-admonition-font-weight-heading:600;--pst-font-weight-caption:300;--pst-font-weight-heading:400;--pst-font-family-base-system:-apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue", Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol;--pst-font-family-monospace-system:"SFMono-Regular", Menlo, Consolas, Monaco, Liberation Mono, Lucida 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diff --git a/theme-css/vars.min.3d537d14ea6e6fb59012fa9d357adf4b209dab8c2535fb94ab37afb6a37020fd.css b/theme-css/vars.min.3d537d14ea6e6fb59012fa9d357adf4b209dab8c2535fb94ab37afb6a37020fd.css new file mode 100644 index 00000000..53977454 --- /dev/null +++ b/theme-css/vars.min.3d537d14ea6e6fb59012fa9d357adf4b209dab8c2535fb94ab37afb6a37020fd.css @@ -0,0 +1 @@ +:root{--spht-color-dark:rgb(1, 50, 67);--spht-color-light:rgb(255, 255, 255);--spht-color-link-hover:rgb(77, 171, 207);--spht-footer-height:6em;--fa-copy:"\f0c5"} \ No newline at end of file diff --git a/user-survey-2020/index.html b/user-survey-2020/index.html index 79eae547..8e95b059 100644 --- a/user-survey-2020/index.html +++ b/user-survey-2020/index.html @@ -1,21 +1,20 @@ -NumPy - 2020 NUMPY COMMUNITY SURVEY -

        2020 NUMPY COMMUNITY SURVEY

        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.

        Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"

        Download the report +future of the project.

        Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'

        Download the report to take a closer look at the survey findings.

        For the highlights, check out -this infographic.

        Ready for a deep dive? Visit https://numpy.org/user-survey-2020-details/.

        On this page
        \ No newline at end of file +this infographic.

        Ready for a deep dive? Visit https://numpy.org/user-survey-2020-details/.

        On this page
        \ No newline at end of file diff --git a/user-surveys/index.html b/user-surveys/index.html index 8788fce9..0eda4039 100644 --- a/user-surveys/index.html +++ b/user-surveys/index.html @@ -1,16 +1,15 @@ -NumPy - NUMPY USER SURVEYS -NumPy - NUMPY USER SURVEYS

        NUMPY USER SURVEYS

        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.

        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.

        On this page
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