diff --git a/404/index.html b/404/index.html index f43d9ee9..d0f2e2e1 100644 --- a/404/index.html +++ b/404/index.html @@ -1,5 +1,5 @@ NumPy - 404 -

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

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404

Oops! You’ve reached a dead end.

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

On this page
\ No newline at end of file diff --git a/about/index.html b/about/index.html index 7326fbfc..5c002abd 100644 --- a/about/index.html +++ b/about/index.html @@ -1,6 +1,6 @@ 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):

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

Emeritus:

  • Alex Griffing (2015-2017)
  • Allan Haldane (2015-2021)
  • Travis Oliphant (project founder, 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)

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

Teams#

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

  • development
  • documentation
  • triage
  • website
  • survey
  • translations
  • sprint mentors
  • optimization
  • funding and grants

See the Team page for more info.

NumFOCUS Subcommittee#

  • Charles Harris
  • Ralf Gommers
  • Inessa Pawson
  • Sebastian Berg
  • External member: Thomas Caswell

Sponsors#

NumPy receives direct funding from the following sources:

Institutional Partners#

Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include:

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

  • UC Berkeley (Stéfan van der Walt)
  • Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça, Mateusz Sokol)
  • NVIDIA (Sebastian Berg)
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.

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\ 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 f1175c9c..d6422575 100644 --- a/arraycomputing/index.html +++ b/arraycomputing/index.html @@ -1,5 +1,5 @@ NumPy - Array Computing -

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#

  • Automation of animal pose analysis for scientific studies:

    The primary objective of DeepLabCut technology is to measure and track posture of animals in a diverse settings. This data can be used, for example, in neuroscience studies to understand how the brain controls movement, or to elucidate how animals socially interact. Researchers have observed a @@ -60,7 +60,7 @@ the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”.

    workflow
    DeepLabCut Workflow#
    (Source: Mackenzie Mathis)

    Summary#

    Observing and efficiently describing behavior is a core tenant of modern ethology, neuroscience, medicine, and technology. -DeepLabCut +DeepLabCut allows researchers to estimate the pose of the subject, efficiently enabling them to quantify the behavior. With only a small set of training images, the DeepLabCut Python toolbox allows training a neural network to within human @@ -68,5 +68,5 @@ analysis in the laboratory, but to potentially also in sports, gait analysis, medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of -NumPy’s array manipulation capabilities.

    numpy benefits
    Key NumPy Capabilities utilized#

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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 24ca8a43..be35d52c 100644 --- a/case-studies/gw-discov/index.html +++ b/case-studies/gw-discov/index.html @@ -1,5 +1,5 @@ NumPy - Case Study: Discovery of Gravitational Waves -