If you think something should be here, you can open an issue on GitHub.
\ No newline at end of file
+NumPy - 404
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/CNAME b/CNAME
index 5c2bcb5d..363e399e 100644
--- a/CNAME
+++ b/CNAME
@@ -1 +1 @@
-numpy.org
+numpy.org
\ No newline at end of file
diff --git a/about/index.html b/about/index.html
index 18a5bd91..411c0c63 100644
--- a/about/index.html
+++ b/about/index.html
@@ -1,26 +1,18 @@
-NumPy
Some information about the NumPy project and community
NumPy is an open source project aiming to enable numerical computing with Python. It was created in 2005, building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the modified BSD license.
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 role of the NumPy Steering Council is to ensure, through working with and serving the broader NumPy community, the long-term well-being of the project, both technically and as a community. The NumPy Steering Council currently consists of the following members (in alphabetical order):
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:
-
Donate
If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community.
NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit numfocus.org 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.
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.
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):
Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include:
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 4c876f99..6e7015f5 100644
--- a/arraycomputing/index.html
+++ b/arraycomputing/index.html
@@ -1,16 +1,16 @@
-NumPy
Array computing is the foundation of statistical, mathematical, scientific 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,
@@ -28,12 +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.
\ No newline at end of file
+to use loops of individual scalar operations.
On this page
\ No newline at end of file
diff --git a/case-studies/blackhole-image/index.html b/case-studies/blackhole-image/index.html
index c5a1d157..0267f999 100644
--- a/case-studies/blackhole-image/index.html
+++ b/case-studies/blackhole-image/index.html
@@ -1,23 +1,22 @@
-NumPy
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
-from a sidewalk café in Paris!
Key Goals and Results
A New View of the Universe:
+from a sidewalk café in Paris!
A New View of the Universe:
The groundwork for the EHT’s groundbreaking image had been laid 100 years
earlier when Sir Arthur Eddington yielded the first
observational support of Einstein’s theory of general relativity.
The Black Hole: EHT was trained on a supermassive black hole
@@ -28,29 +27,28 @@
had a black hole been visually observed.
Comparing Observations to Theory: From Einstein’s general theory of
relativity, scientists expected to find a shadow-like region caused by
gravitational bending and capture of light. Scientists could
-use it to measure the black hole’s enormous mass.
The Challenges
Computational scale
EHT poses massive data-processing challenges, including rapid atmospheric
+use it to measure the black hole’s enormous mass.
EHT poses massive data-processing challenges, including rapid atmospheric
phase fluctuations, large recording bandwidth, and telescopes that are
widely dissimilar and geographically dispersed.
Too much information
Each day EHT generates over 350 terabytes of observations, stored on
helium-filled hard drives. Reducing the volume and complexity of this much
data is enormously difficult.
Into the unknown
When the goal is to see something never before seen, how can scientists be
-confident the image is correct?
What if there’s a problem with the data? Or perhaps an algorithm relies too
heavily on a particular assumption. Will the image change drastically if a
single parameter is changed?
The EHT collaboration met these challenges by having independent teams
evaluate the data, using both established and cutting-edge image reconstruction
techniques. When results proved consistent, they were combined to yield the
first-of-a-kind image of the black hole.
Their work illustrates the role the scientific Python ecosystem plays in
-advancing science through collaborative data analysis.
The role of NumPy in Black Hole imaging
For example, the eht-imaging Python package provides tools for
+advancing science through collaborative data analysis.
For example, the eht-imaging Python package provides tools for
simulating and performing image reconstruction on VLBI data.
NumPy is at the core of array data processing used
in this package, as illustrated by the partial software
-dependency chart below.
Software dependency chart of ehtim package highlighting NumPy
Besides NumPy, many other packages, such as
-SciPy and Pandas, are part of the
+dependency chart below.
Software dependency chart of ehtim package highlighting NumPy#
Besides NumPy, many other packages, such as
+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
in visualizing data throughout the analysis pipeline, including the generation
-of the final image of the black hole.
Summary
The efficient and adaptable n-dimensional array that is NumPy’s central feature
+of the final image of the black hole.
The efficient and adaptable n-dimensional array that is NumPy’s central feature
enabled researchers to manipulate large numerical datasets, providing a
foundation for the first-ever image of a black hole. A landmark moment in
science, it gives stunning visual evidence of Einstein’s theory. The
@@ -58,12 +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.
Key NumPy Capabilities utilized
\ No newline at end of file
+mystery of the universe.Key NumPy Capabilities utilized#
On this page
\ No newline at end of file
diff --git a/case-studies/cricket-analytics/index.html b/case-studies/cricket-analytics/index.html
index 61328a88..a0624bd1 100644
--- a/case-studies/cricket-analytics/index.html
+++ b/case-studies/cricket-analytics/index.html
@@ -1,17 +1,16 @@
-NumPy
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
@@ -37,16 +36,14 @@
using the latest machine learning and predictive modelling algorithms.
Media and entertainment platforms along with professional sports bodies
associated with the game use technology and analytics for determining key
-metrics for improving match winning chances:
batting performance moving average,
score forecasting,
gaining insights into fitness and performance of a player against different opposition,
player contribution to wins and losses for making strategic decisions on team composition
Sports data analytics are used not only in cricket but many other
sports for
improving the overall team performance and maximizing winning chances.
Real-time data analytics can help in gaining insights even during the game
for changing tactics by the team and by associated businesses for economic
benefits and growth.
Besides historical analysis, predictive models are
harnessed to determine the possible match outcomes that require significant
number crunching and data science know-how, visualization tools and capability
-to include newer observations in the analysis.
IPL has expanded cricket beyond the classic test match format to a much
larger scale. The number of matches played every season across various
formats has increased and so has the data, the algorithms, newer sports data
analysis technologies and simulation models. Cricket data analysis requires
@@ -68,7 +65,7 @@
batsman responds to his delivery in a certain way”.
This kind of predictive analytics query requires highly granular dataset
availability and the capability to synthesize data and create generative
-models that are highly accurate.
NumPy’s Role in Cricket Analytics
Sports Analytics is a thriving field. Many researchers and companies
+models that are highly accurate.
Sports Analytics is a thriving field. Many researchers and companies
use NumPy
and other PyData packages like Scikit-learn, SciPy, Matplotlib, and Jupyter,
besides using the latest machine learning and AI techniques. NumPy has been used
@@ -78,9 +75,9 @@
with a generative or static model.
Causal analysis
and big data approaches
-are used for tactical analysis.
Data Visualization: Data graphing and visualization provides 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.
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
+was primarily done based on “gut feeling" or adherence to past traditions. NumPy
forms a solid foundation for a large set of Python packages which provide higher
level functions related to data analytics, machine learning, and AI algorithms.
These packages are widely deployed to gain real-time insights that help in
@@ -88,12 +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.
Key NumPy Capabilities utilized
\ No newline at end of file
+otherwise hidden in numbers and statistics.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 4052b59a..d0deb171 100644
--- a/case-studies/deeplabcut-dnn/index.html
+++ b/case-studies/deeplabcut-dnn/index.html
@@ -1,17 +1,16 @@
-NumPy
Open Source Software is accelerating Biomedicine. DeepLabCut enables automated video analysis of animal behavior using Deep Learning.
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.
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
+
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.
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.
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
@@ -23,8 +22,7 @@
as well. These enable not only automation of pose-estimation but also
managing the project end-to-end by helping the DeepLabCut Toolkit user right
from the dataset collection stage to creating shareable and reusable analysis
-pipelines.
Fast processing of animal behavior videos in order to measure their behavior
and at the same time make scientific experiments more efficient, accurate.
Extracting detailed animal poses for laboratory experiments, without
markers, in dynamically changing backgrounds, can be challenging, both
@@ -37,8 +35,7 @@
is a complex process that requires heavy-duty numerical analysis, especially
in case of multi-animal movement tracking in experiment videos.
Data Processing
Last but not the least, array manipulation - processing large stacks of
arrays corresponding to various images, target tensors and keypoints is
-fairly challenging.
NumPy’s Role in meeting Pose Estimation Challenges#
NumPy addresses DeepLabCut technology’s core need of numerical computations at
high speed for behavioural analytics. Besides NumPy, DeepLabCut employs
various Python software that utilize NumPy at their core, such as
SciPy, Pandas,
@@ -60,10 +57,9 @@
scoremaps subject to various geometric and image processing steps. To make
training fast, NumPy’s vectorization capabilities are leveraged. For inference,
the most likely predictions from target scoremaps need to extracted and one
-needs to efficiently “link predictions to assemble individual animals”.
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
@@ -71,12 +67,5 @@
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.
Key NumPy Capabilities utilized
\ No newline at end of file
+NumPy’s array manipulation capabilities.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 69f8ec1d..99200852 100644
--- a/case-studies/gw-discov/index.html
+++ b/case-studies/gw-discov/index.html
@@ -1,24 +1,23 @@
-NumPy
Gravitational waves are ripples in the fabric of space and time, generated by
cataclysmic events in the universe such as collision and merging of two black
holes or coalescing binary stars or supernovae. Observing GW can not only help
in studying gravity but also in understanding some of the obscure phenomena in
the distant universe and its impact.
The Laser Interferometer Gravitational-Wave Observatory (LIGO)
was designed to open the field of gravitational-wave astrophysics through the
direct detection of gravitational waves predicted by Einstein’s General Theory
-of Relativity. It comprises two widely-separated interferometers within the
+of Relativity. It comprises two widely separated interferometers within the
United States — one in Hanford, Washington and the other in Livingston,
Louisiana — operated in unison to detect gravitational waves. Each of them has
multi-kilometer-scale gravitational wave detectors that use laser
@@ -30,21 +29,21 @@
made by measuring the tiny disturbances the waves make to space and time as
they pass through the earth. It has opened up new astrophysical frontiers
that explore the warped side of the universe—objects and phenomena that are
-made from warped spacetime.
Key Objectives
Though its mission is to
+made from warped spacetime.
Though its mission is to
detect gravitational waves from some of the most violent and energetic
processes in the Universe, the data LIGO collects may have far-reaching
effects on many areas of physics including gravitation, relativity,
-astrophysics, cosmology, particle physics, and nuclear physics.
Crunch observed data via numerical relativity computations that involves
+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
-comprehended.
The Challenges
Computation
Gravitational Waves are hard to detect as they produce a very small effect
+signal and statistically estimate the significance of observed data.
Data visualization so that the binary / numerical results can be
+comprehended.
Gravitational Waves are hard to detect as they produce a very small effect
and have tiny interaction with matter. Processing and analyzing all of
-LIGO’s data requires a vast computing infrastructure.After taking care of
+LIGO’s data requires a vast computing infrastructure. After taking care of
noise, which is billions of times of the signal, there is still very
complex relativity equations and huge amounts of data which present a
computational challenge:
O(10^7) CPU hrs needed for binary merger analyses
-spread on 6 dedicated LIGO clusters
Data Deluge
As observational devices become more sensitive and reliable, the challenges
+spread on 6 dedicated LIGO clusters.
Data Deluge
As observational devices become more sensitive and reliable, the challenges
posed by data deluge and finding a needle in a haystack rise multi-fold.
LIGO generates terabytes of data every day! Making sense of this data
requires an enormous effort for each and every detection. For example, the
@@ -59,8 +58,7 @@
simulations made it easier to comprehend results for a larger audience.
Speed of complex computations and rendering, re-rendering images and
simulations using latest experimental inputs and insights can be a time
-consuming activity that challenges researchers in this domain.
NumPy’s Role in the Detection of Gravitational Waves#
Gravitational waves emitted from the merger cannot be computed using any
technique except brute force numerical relativity using supercomputers.
The amount of data LIGO collects is as incomprehensibly large as gravitational
wave signals are small.
NumPy, the standard numerical analysis package for Python, was utilized by
@@ -72,25 +70,18 @@
contains a signal - needle in a haystack
Statistical analysis: estimate the statistical significance of observational
data, estimating the signal parameters (e.g. masses of stars, spin velocity,
and distance) by comparison with a model.
Visualization of data
Time series
Spectrograms
Compute Correlations
Key Software developed in GW data analysis
-such as GwPy and
+such as GwPy and
PyCBC uses NumPy and AstroPy under the hood for
providing object based interfaces to utilities, tools, and methods for
-studying data from gravitational-wave detectors.
Dependency graph showing how GwPy package depends on NumPy
Dependency graph showing how PyCBC package depends on NumPy
Summary
GW detection has enabled researchers to discover entirely unexpected phenomena
+studying data from gravitational-wave detectors.Dependency graph showing how GwPy package depends on NumPy#
Dependency graph showing how PyCBC package depends on NumPy#
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.
Key NumPy Capabilities utilized
\ No newline at end of file
+universe.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
new file mode 100644
index 00000000..81977474
--- /dev/null
+++ b/case-studies/index.html
@@ -0,0 +1,12 @@
+NumPy - Case-Studies
\ No newline at end of file
diff --git a/case-studies/index.xml b/case-studies/index.xml
index b8e55e25..feababce 100644
--- a/case-studies/index.xml
+++ b/case-studies/index.xml
@@ -1,9 +1,90 @@
-Case-studies on NumPyhttp://numpy.org/case-studies/Recent content in Case-studies on NumPyHugo -- gohugo.ioen-usCase Study: Cricket Analytics, the game changer!http://numpy.org/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/case-studies/cricket-analytics/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.Case Study: DeepLabCut 3D Pose Estimationhttp://numpy.org/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/case-studies/deeplabcut-dnn/Analyzing mice hand-movement using DeepLapCut (Source: www.deeplabcut.org )
+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) & 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.
-—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 Waveshttp://numpy.org/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/case-studies/gw-discov/Gravitational Waves (Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)
+</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.
-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.Case Study: First Image of a Black Holehttp://numpy.org/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/case-studies/blackhole-image/Black Hole M87 (Image Credits: Event Horizon Telescope Collaboration)
+</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.
-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 sensitivity and resolution.
\ No newline at end of file
+</p>
+ <p class="attribution">—Katie Bouman, <em>Assistant Professor, Computing & 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 6130fedf..5d963469 100644
--- a/citing-numpy/index.html
+++ b/citing-numpy/index.html
@@ -1,25 +1,25 @@
-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: 0.1038/s41586-020-2649-2. (Publisher link).
In BibTeX format:
@Article{ harris2020array,
+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:
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{'{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},
@@ -31,13 +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}
-}
-
\ 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 1706b0f4..f9f827d9 100644
--- a/code-of-conduct/index.html
+++ b/code-of-conduct/index.html
@@ -1,21 +1,13 @@
-NumPy
This Code of Conduct applies to all spaces managed by the NumPy project, including all public and private mailing lists, issue trackers, wikis, blogs, Twitter, and any other communication channel used by our community. The NumPy project does not organise in-person events, however events related to our community should have a code of conduct similar in spirit to this one.
This Code of Conduct should be honored by everyone who participates in the NumPy community formally or informally, or claims any affiliation with the project, in any project-related activities and especially when representing the project, in any role.
This code is not exhaustive or complete. It serves to distill our common understanding of a collaborative, shared environment and goals. Please try to follow this code in spirit as much as in letter, to create a friendly and productive environment that enriches the surrounding community.
Specific Guidelines
We strive to:
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.
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.
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.
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.
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.
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
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:
acknowledge report is received,
reasonable discussion/feedback,
mediation (if feedback didn’t help, and only if both reporter and reportee agree to this),
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:
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.
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.
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.
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.
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.
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.
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).
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.
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#
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:
acknowledge report is received,
reasonable discussion/feedback,
mediation (if feedback didn’t help, and only if both reporter and reportee agree to this),
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.
\ No newline at end of file
diff --git a/community/index.html b/community/index.html
index 9d04b94a..ab96e93f 100644
--- a/community/index.html
+++ b/community/index.html
@@ -1,32 +1,34 @@
-NumPy
NumPy is a community-driven open source project developed by a very 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.
+
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.
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.
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.
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.
For bug reports (e.g. “np.arange(3).shape returns (5,), when it should return (3,)”);
documentation issues (e.g. “I found this section unclear”);
and feature requests (e.g. “I would like to have a new interpolation method in np.percentile”).
Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it here.
For bug reports (e.g. “np.arange(3).shape returns (5,), when it should return (3,)”);
documentation issues (e.g. “I found this section unclear”);
and feature requests (e.g. “I would like to have a new interpolation method in np.percentile”).
Please note that GitHub is not the right place to report a security vulnerability. If you think you have found a security vulnerability in NumPy, please report it here.
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.
\ No newline at end of file
+details and how to get an invite.
Engage in good faith, and be mindful of how your contributions impact the overall
+progress of the discussion.
Critique ideas, not individuals. Avoid personal attributions or speculation about
+motives.
Stay evidence-based. Include references from reliable sources. Limit hypotheticals
+to a minimum.
Limit repetition and escalation. Do not restate points without adding references
+from new reliable sources.
Avoid dominating the discussion. This may include limiting posts to no more than
+1-2 per day or refraining from replying separately to every post in a thread.
Minimize figurative and region-specific language that may be unclear to a global audience.
NumPy Code of Conduct applies at all times.
+This includes tone, language, and behavior.
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.
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.
The NumPy project doesn’t organize its own conferences. The conferences that have traditionally been most popular with NumPy maintainers, contributors and users are the SciPy and PyData conference series:
SciPy US
EuroSciPy
SciPy Latin America
SciPy India
SciPyData Japan
PyData conferences (15-20 events a year spread over many countries)
Many of these conferences include tutorial days that cover NumPy and/or sprints where you can learn how to contribute to NumPy or related open source projects.
If you’re unsure where to start or how your skills fit in, reach out! You
+
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
list or
GitHub (open an
@@ -24,44 +25,36 @@
You are very welcome to join.
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
+contributions. We have a Code of Conduct to foster an open
+and welcoming environment.
For a visual guide on how to contribute to NumPy, check out this comic.
Programmers, this
guide
-explains how to contribute to the codebase.
Reviewing pull requests
The project has more than 250 open pull requests – meaning many potential
+explains how to contribute to the NumPy codebase. Check out also our YouTube channel for additional advice.
The project has more than 250 open pull requests – meaning many potential
improvements and many open-source contributors waiting for feedback. If you’re
a developer who knows NumPy, you can help even if you’re not familiar with the
-codebase. You can:
summarize a long-running discussion
triage documentation PRs
test proposed changes
Developing educational materials
NumPy’s User Guide is undergoing rehabilitation.
+codebase. You can:
NumPy’s User Guide is undergoing rehabilitation.
We’re in need of new tutorials, how-to’s, and deep-dive explanations, and the
site needs restructuring. Opportunities aren’t limited to writers. We’d also
welcome worked examples, notebooks, and videos. NEP 44 — Restructuring the
NumPyDocumentation
-lays out our ideas – and you may have others.
Issue triaging
The NumPy issue tracker has a lot
+lays out our ideas – and you may have others.
The NumPy issue tracker has a lot
of open issues. Some are no longer valid, some should be prioritized, and some
-would make good issues for new contributors. You can:
check if older bugs are still present
find duplicate issues and link related ones
add good self-contained reproducers to issues
label issues correctly (this requires triage rights – just ask)
Please just dive in.
Website development
We’ve just revamped our website, but we’re far from done. If you love web
+would make good issues for new contributors. You can:
check if older bugs are still present
find duplicate issues and link related ones
add good self-contained reproducers to issues
label issues correctly (this requires triage rights – just ask)
We’ve just revamped our website, but we’re far from done. If you love web
development, these
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.
+list some of our unmet needs – and feel free to share your own ideas.
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.
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+opportunities abound.
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.
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.
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.
In light of the foregoing discussion on social media after publication of the
+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
+at the core of the NumPy project.
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
@@ -39,7 +36,7 @@
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
+talent to the NumPy team.
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
@@ -52,7 +49,7 @@
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
+NumPy paper will certainly have a more diverse group of authors.
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.
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
\ No newline at end of file
+Stéfan van der Walt, Eric Wieser, on behalf of the NumPy team
On this page
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+https://numpy.org/
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+++ b/en/sitemap.xml
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+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/
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+NumPy - 404
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+++ b/es/about/index.html
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+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.
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):
Sebastian Berg
Ralf Gommers
Charles Harris
Stephan Hoyer
Inessa Pawson
Matti Picus
Stéfan van der Walt
Melissa Weber Mendonça
Eric Wieser
Eméritos:
Alex Griffing (2015-2017)
Allan Haldane (2015-2021)
Marten van Kerkwijk (2017-2019)
Travis Oliphant (project founder, 2005-2012)
Nathaniel Smith (2012-2021)
Julian Taylor (2013-2021)
Jaime Fernández del Río (2014-2021)
Pauli Virtanen (2008-2021)
Para contactar con el Consejo Directivo de NumPy, por favor envía un correo electrónico a numpy-team@googlegroups.com.
La dirección del proyecto NumPy trabaja activamente para diversificar las vías de contribución al proyecto. NumPy cuenta actualmente con los siguientes equipos:
Los socios institucionales son organizaciones que apoyan al proyecto empleando a personas que contribuyen a NumPy como parte de su trabajo. Entre los actuales socios institucionales se encuentran:
UC Berkeley (Stéfan van der Walt)
Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça)
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
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diff --git a/es/arraycomputing/index.html b/es/arraycomputing/index.html
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+++ 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.
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
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@@ -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
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!
Una nueva vista del universo: El trabajo preliminar de la innovadora imagen de EHT se había establecido 100 años antes, cuando Sir Arthur Eddington dio el primer apoyo observacional a la teoría de la relatividad general de Einstein.
El agujero negro: EHT se entrenó en un enorme agujero negro supermasivo aproximadamente a 55 millones de años luz de la Tierra, situado en el centro de la galaxia Messier 87 (M87) en el cúmulo de galaxias Virgo. Su masa es 6.5 mil millones de veces la del sol. Se había estudiado por más de 100 años, pero nunca antes se había observado un agujero negro.
Comparando las observaciones con la teoría: A partir de la teoría general de la relatividad de Einstein, los científicos esperaban encontrar una región similar a una sombra causada por la flexión gravitacional y la captura de la luz. Los científicos pudieron utilizarla para medir la enorme masa del agujero negro.
EHT plantea enormes desafíos de procesamiento de datos, incluyendo las rápidas fluctuaciones de fase atmosféricas, amplio ancho de banda de grabación, y telescopios que son ampliamente disímiles y geográficamente dispersos.
Demasiada información
Cada día, el EHT genera más de 350 terabytes de observaciones, almacenados en discos duros llenos de helio. Reducir el volumen y complejidad de estos datos es enormemente difícil.
Hacia lo desconocido
Cuando el objetivo es ver algo nunca antes visto, ¿cómo pueden los científicos estar seguros de que la imagen es correcta?
¿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.
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.
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.
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.
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@@ -0,0 +1,14 @@
+NumPy - Estudio de caso: Análisis de críquet, ¡el cambio radical!
Estudio de caso: Análisis de críquet, ¡el cambio radical!
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:
promedio móvil del rendimiento de bateo,
previsión del marcador,
obtener información sobre la condición física y el rendimiento de un jugador contra diferentes oponentes,
contribución del jugador a las victorias y derrotas para tomar decisiones estratégicas sobre la composición del equipo
El análisis de datos deportivos se utiliza no solo en el críquet, sino en muchos otros deportes para mejorar el rendimiento general del equipo y maximizar las posibilidades de ganar.
El análisis de datos en tiempo real puede ayudar a obtener información incluso durante el juego para cambiar tácticas por parte del equipo y de las empresas asociadas para beneficios económicos y crecimiento.
Además del análisis histórico, se aprovechan los modelos predictivos para determinar los posibles resultados de los partidos, lo cual requiere una cantidad significativa de procesamiento de datos y conocimientos de ciencia de datos, herramientas de visualización y la capacidad de incluir nuevas observaciones en el análisis.
La IPL ha expandido el críquet más allá del clásico formato de partido de prueba a una escala mucho más grande. El número de partidos jugados cada temporada a través de varios formatos ha incrementado y así también los datos, los algoritmos, las nuevas tecnologías de análisis de datos deportivos y modelos de simulación. El análisis de datos de críquet requiere mapeo del campo, seguimiento de jugadores, seguimiento de la pelota, análisis de tiros de los jugadores y varios otros aspectos relacionados con cómo se lanza la pelota, su ángulo, giro, velocidad y trayectoria. Todos estos factores juntos han incrementado la complejidad de la limpieza de datos y el preprocesamiento.
Modelación Dinámica
En el cricket, al igual que en cualquier otro deporte, puede haber una gran cantidad de variables relacionadas con el seguimiento de varios jugadores en el campo, sus atributos, la pelota y varias posibilidades de acciones potenciales. La complejidad del análisis de datos y la modelación es directamente proporcional al tipo de preguntas predictivas que se plantean durante el análisis y depende en gran medida de la representación de los datos y del modelo. Las cosas se vuelven aún más desafiantes en términos de cálculo y comparación de datos cuando se buscan predicciones dinámicas del juego de críquet, tal como habría sucedido si el bateador hubiera golpeado la bola a un ángulo o velocidad diferente.
Complejidad de Análisis Predictivo
Gran parte de la toma de decisiones en el críquet se basa en preguntas como “¿con qué frecuencia un bateador juega un cierto tipo de golpe si la entrega de la pelota es de un tipo particular?” o “¿cómo cambia un lanzador su línea y longitud si el bateador responde a su entrega de una cierta manera?”. Este tipo de consulta de análisis predictivo requiere una disponibilidad de un conjunto de datos altamente granular y la capacidad de sintetizar datos y crear modelos generativos que sean altamente precisos.
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:
Análisis Estadístico: Las capacidades numéricas de NumPy ayudan a estimar la significancia estadística de los datos observacionales o de eventos de partidos en el contexto de varias tácticas de jugadores y de juego, estimando el resultado del juego mediante la comparación con un modelo generativo o estático. El análisis causal y los enfoques de big data se utilizan para el análisis táctico.
Visualización de Datos: La creación de gráficos y la visualización de datos proporcionan información útil sobre la relación entre varios conjuntos de datos.
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.
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+NumPy - Caso de estudio: DeepLabCut Estimación de Postura 3D
Caso de estudio: DeepLabCut Estimación de Postura 3D
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)
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.
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.
Automatización del análisis de la postura animal para estudios científicos:
El objetivo principal de la tecnología DeepLabCut es medir y rastrear la postura de los animales en diversos entornos. Estos datos se pueden utilizar, por ejemplo, en estudios de neurociencia para entender cómo el cerebro controla el movimiento, o para aclarar como interactúan socialmente los animales. Los investigadores han observado un aumento de rendimiento diez veces mayor con DeepLabCut. Las posturas se pueden inferir sin conexión hasta a 1200 fotogramas por segundo (FPS).
Creación de un conjunto de herramientas de Python de fácil uso para la estimación de postura:
DeepLabCut quería compartir su tecnología de estimación de postura animal en la forma de una herramienta de fácil uso que pueda ser adoptada fácilmente por los investigadores. Así que han creado un conjunto de herramientas de Python completo y de fácil uso, también con características de administración de proyectos. Estas permiten no solo la automatización de la estimación de postura, sino también administrar el proyecto de punta a punta ayudando al usuario del conjunto de herramientas de DeepLabCut desde la etapa de recolección del conjunto de datos para crear flujos de trabajo de análisis compartibles y reutilizables.
Procesamiento rápido de videos de comportamiento animal para medir su comportamiento y al mismo tiempo hacer experimentos científicos más eficientes y precisos. La extracción detallada de posturas del animal para experimentos de laboratorio, sin marcadores, en entornos dinámicamente cambiantes, puede ser un desafío tanto técnico como en términos de recursos necesarios y datos de entrenamiento requeridos. Proponer una herramienta que sea fácil de usar sin necesidad de habilidades como experiencia en visión por computador que permita a los científicos hacer investigaciones en más contextos del mundo real, es un problema que no es trivial de resolver.
Combinatoria
La combinatoria involucra el armado e integración del movimiento de múltiples extremidades en el comportamiento animal individual. Ensamblar puntos clave y sus conexiones en movimientos individuales de animales y vincularlos a lo largo del tiempo es un proceso complejo que requiere un análisis numérico intensivo, especialmente en el caso del seguimiento de movimientos de múltiples animales en videos de experimentos.
Procesamiento de Datos
Por último, pero no menos importante, la manipulación de arreglos - procesamiento de grandes pilas de arreglos correspondientes a varias imágenes, tensores objetivo y puntos clave es bastante desafiante.
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:
Vectorización
Operaciones con Arreglos Enmascarados
Álgebra lineal
Muestreo Aleatorio
Redimensionamiento de arreglos grandes
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”.
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.
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+NumPy - Estudio de Caso: Descubrimiento de Ondas Gravitacionales
Estudio de Caso: Descubrimiento de Ondas Gravitacionales
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.
Aunque su misión es detectar ondas gravitacionales de algunos de los procesos más violentos y energéticos del Universo, los datos que LIGO recopila pueden tener efectos de gran alcance en muchas áreas de la física, incluyendo gravitación, relatividad, astrofísica, cosmología, física de partículas y física nuclear.
Procesar los datos observados mediante cálculos de relatividad numéricos que implican matemáticas complejas para discernir la señal del ruido, filtrar la señal relevante y estimar estadísticamente la significancia de los datos observados
Visualización de datos para que los resultados binarios/numéricos puedan ser comprendidos.
Las Ondas Gravitacionales son difíciles de detectar, ya que producen un efecto muy pequeño y tienen una diminuta interacción con la materia. Procesar y analizar todos los datos de LIGO requiere una vasta infraestructura informática. Después de ocuparse del ruido, que es miles de millones de veces mayor que la señal, aún quedan ecuaciones de relatividad muy complejas y enormes cantidades de datos que suponen un desafío computacional: se necesitan aproximadamente O(10^7) horas de CPU para los análisis de fusiones binarias, distribuidas en 6 clústeres dedicados de LIGO
Avalancha de Datos
A medida que los dispositivos de observación se vuelven más sensibles y confiables, los desafíos planteados por la avalancha de datos y encontrar una aguja en un pajar aumentan exponencialmente. ¡LIGO genera terabytes de datos cada día! Dar sentido a estos datos requiere de un esfuerzo enorme para todas y cada una de las detecciones. Por ejemplo, las señales recolectadas por LIGO deben ser comparadas por supercomputadoras contra cientos de miles de plantillas de posibles señales de ondas gravitacionales.
Visualización
Una vez superados los obstáculos relacionados con comprender suficientemente bien las ecuaciones de Einstein para resolverlas utilizando supercomputadoras, el siguiente gran desafío fue hacer que los datos fueran comprensibles para el cerebro humano. La modelación de simulación, así como la detección de señales, requieren técnicas de visualización efectivas. La visualización también desempeña un papel en otorgar más credibilidad a la relatividad numérica a los ojos de los aficionados a la ciencia pura, los cuales no le daban suficiente importancia a la relatividad numérica hasta que las imágenes y simulaciones facilitaron la comprensión de los resultados para un público más amplio. La velocidad de los cálculos complejos y la renderización, así como la re-renderización de imágenes y simulaciones utilizando los últimos datos experimentales y conocimientos, puede ser una actividad que consume mucho tiempo y que representa un desafío para los investigadores en este campo.
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:
Recuperación de datos: Decidir qué datos pueden ser analizados, y determinar si estos contienen una señal - aguja en un pajar
Análisis estadístico: estimar la significancia estadística de los datos observados, estimación de los parámetros de señal (por ejemplo, masas de estrellas, velocidad de giro y distancia) en comparación con un modelo.
Visualización de datos
Series de tiempo
Espectrogramas
Cálculo de Correlaciones
Software clave desarrollado en análisis de datos de Ondas Gravitacionales como, tales como GwPy y PyCBC utiliza NumPy y AstroPy bajo su cubierta para proporcionar interfaces basadas en objetos a utilidades, herramientas y métodos para el estudio de datos provenientes de detectores de ondas gravitacionales.
Gráfico de dependencias que muestra cómo depende el paquete GwPy de NumPy#
Gráfico de dependencias que muestra cómo el paquete PyCBC depende de NumPy#
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.
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+NumPy - Case-Studies
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+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 & 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) & 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>
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+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:
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).
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
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+NumPy - Código de conducta de NumPy
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.
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.
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.
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.
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.
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.
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).
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.
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#
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:
acusar recibo del informe,
una discusión/retroalimentación razonable,
mediación (si la retroalimentación no fue útil, y únicamente si tanto el denunciante como el denunciado están de acuerdo con ello),
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.
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+NumPy - Comunidad
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.
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.
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.
Para informes de error (por ejemplo, “np.arange(3).shape devuelve (5,), cuando debería devolver (3,)”);
problemas en la documentación (por ejemplo, “Esta sección me pareció poco clara”);
y solicitudes de funcionalidades (por ejemplo, “Me gustaría tener un nuevo método de interpolación en np.percentile”).
Ten en cuenta que GitHub no es el lugar adecuado para reportar una vulnerabilidad de seguridad. Si crees que has encontrado una vulnerabilidad de seguridad en NumPy, por favor repórtalo aquí.
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.
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).
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:
Conferencias PyData (de 15 a 20 eventos al año, repartidos entre muchos países)
Muchas de estas conferencias incluyen tutoriales y/o sprints que cubren NumPy, en donde puedes aprender cómo contribuir a Numpy o proyectos de código abierto relacionados.
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
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+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
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+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.
El proyecto tiene más de 250 solicitudes de cambios abiertos, lo que significa muchas mejoras potenciales y muchos colaboradores de código abierto esperando retroalimentación. Si eres un desarrollador que conoce NumPy, puedes ayudar aunque no estés familiarizado con el código base. Puedes:
resumir un debate extenso
categorizar documentación de solicitudes de incorporación de cambios
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.
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:
revisar si errores antiguos siguen presentes
encontrar problemas duplicados, y enlazar las relacionadas
añadir la forma de reproducir siempre el mismo problema
etiquetar correctamente los problemas (para ello es necesario tener derechos de categorización, solo necesitas preguntar)
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.
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.
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.
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.
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.
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+NumPy - Buscar Ayuda
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!
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+NumPy - Historia de NumPy
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:
*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.
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+NumPy
NumPy
+
El paquete fundamental para la computación científica con Python
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.
Cálculo acelerado con arrays comprimidos en memoria, en disco o remotos.
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).
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.
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.
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+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> – 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: “¿Cómo hago X cosa en NumPy?”. 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 & 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{\'{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}
+}</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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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) & 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 “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.</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 ("<strong>Project</strong>", “<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>
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+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.
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.
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.
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.
Instale Anaconda (esto instala todos los paquetes que necesita y todas las demás herramientas mencionadas a continuación).
Para escribir y ejecutar código, utilice notebooks en JupyterLab para computación exploratoria e interactiva, y Spyder o Visual Studio Code para escribir scripts y paquetes.
Utilice Anaconda Navigator para administrar sus paquetes e iniciar JupyterLab, Spyder o Visual Studio Code.
Mantenga el entorno conda base mínimo, y utilice uno o más entornos conda para instalar el paquete que necesite para la tarea o proyecto en que está trabajando.
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:
Instalar Python desde python.org, Homebrew o su administrador de paquetes Linux.
Utilice Poetry como la herramienta mejor mantenida que proporciona una resolución de dependencias y capacidades de administración de entornos de forma similar a la que lo hace conda.
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.
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.
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:
usar un entorno diferente por cada proyecto en el cual usted esté trabajando,
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:
Las ruedas NumPy en PyPI, que son los que pip instala, están construidas con OpenBLAS. Las librerías de OpenBLAS están incluidas en la rueda. Esto vuelve a la rueda más grande, y si un usuario instala (por ejemplo) SciPy también, tendrá dos copias de OpenBLAS en disco.
En el canal defaults o predeterminado de conda, NumPy está basado en Intel MKL. MKL es un paquete separado que se instalará en el entorno de usuario al instalar NumPy.
En el canal conda-forge, Numpy está basado en un paquete “BLAS” ficticio o dummy. Cuando un usuario instala NumPy desde conda-forge, ese paquete BLAS es instalado junto con la librería - éste por defecto es OpenBLAS, pero también puede ser MKL (desde el canal defaults o predeterminado), o incluso BLIS o referencia BLAS.
El paquete MKL es mucho más grande que OpenBLAS, de alrededor de 700 MB en disco, mientras que OpenBLAS es aproximadamente de 30MB.
MKL es normalmente un poco más rápido y más robusto que OpenBLAS.
Además del tamaño de instalación, desempeño y robustez, hay dos aspectos más a considerar:
Intel MKL no es de código abierto. Para uso normal esto no es un problema, pero si un usuario necesita redistribuir una aplicación construida con NumPy, esto podría ser un inconveniente.
MKL y OpenBLAS utilizan funciones multihilo como np.dot, siendo el número de hilos determinado tanto por una opción de tiempo de compilación como por una variable de entorno. Todos los núcleos de la CPU usualmente serán utilizados. Esto es en ocasiones inesperado para los usuarios. NumPy en sí mismo no paraleliza automáticamente ninguna llamada a función. Normalmente produce un mejor rendimiento, pero también puede ser perjudicial - por ejemplo cuando se utiliza otro nivel de paralelización con Dask, el aprendizaje de la ciencia o multiprocesamiento.
¡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
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+NumPy - Aprende
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.
NumPy Tutorials Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio numpy-tutorials en GitHub.
Elegant SciPypor Juan Nunez-Technesias, Stefan van der Walt, y Harriet Dashnow
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.
Pruebe estos recursos avanzados para comprender mejor los conceptos de NumPy como indexación avanzada, división, apilamiento, álgebra lineal y mucho más.
Tutoriales de NumPy Una colección de tutoriales y materiales educativos en formato de cuadernos Jupyter desarrollados y mantenidos por el equipo de documentación de NumPy. Para enviar tu propio contenido, visita el repositorio numpy-tutorials en GitHub.
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
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+NumPy - Noticias
18 de agosto 2024 – NumPy 2.1.0 provides support for Python 3.13 and drops support for Python 3.9. Además de las habituales correcciones de errores y soporte actualizado de Python, ayuda a que NumPy vuelva a su ciclo de publicación habitual después del extenso desarrollo de 2.0. Los aspectos más destacados son:
Soporte para Python 3.13.
Soporte preliminar para Python 3.13 de hilos libres.
Compatibilidad con la norma array-api 2023.12.
Esta versión es compatible con las versiones 3.10-3.13 de Python.
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:
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:
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.
16 de septiembre de 2023 – NumPy 1.26.0 ahora está disponible. Los aspectos más destacados del lanzamiento son:
Soporte de Python 3.12.0.
Compatibilidad con Cython 3.0.0.
Utilización del sistema de compilación Meson
Actualización del soporte de SIMD
Correcciones de f2py, meson y soporte de bind(x)
Soporte para la librería actualizada Accelerate BLAS/LAPACK
La versión 1.26.0 de NumPy es la continuación de la serie 1.25.x que marca la transición al sistema de compilación Meson y que provee soporte para Cython 3.0.0. Un total de 20 personas contribuyeron a esta versión y 59 solicitudes de cambios fueron fusionadas.
Las versiones de Python compatibles con esta versión son 3.9-3.12.
numpy.org ya está disponible en japonés y portugués#
_ 2 de agosto de 2023_ – numpy.org ya está disponible en 2 idiomas adicionales: japonés y portugués. Esto no sería posible sin nuestros dedicados voluntarios:
Portugués:
Melissa Weber Mendonça (melissawm)
Precios Ricardo (ricardoprins)
Getúlio Silva (getuliosilva)
Julio Batista Silva (jbsilva)
Alexandre de Siqueira (alexdesiqueira)
Alexandre B A Villares (villares)
Vini Salazar (vinisalazar)
Japonés:
Atsushi Sakai (AtsushiSakai)
KKunai
Tom Kelly (TomKellyGenetics)
Yuji Kanagawa (kngwyu)
Tetsuo Koyama (tkoyama010)
El trabajo sobre la infraestructura de traducción se apoya con fondos de CZI.
De cara al futuro, nos encantaría traducir el sitio web a más idiomas. Si quieres ayudar, por favor pone en contacto con el equipo de traducciones de NumPy en Slack: https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w. (Busca el canal #translations) También estamos formando un equipo de traducciones que estará trabajando en la localización de la documentación y el contenido educativo a través de todo el ecosistema de Python científico. Si esto ha despertado tu interés, únete a nosotros en el Discord de Python científico: https://discord.gg/khWtqY6RKr. (Busca el canal #translations)
17 de junio de 2023 – NumPy 1.25.0 ya está disponible. Los aspectos más destacados del lanzamiento son:
Soporte para MUSL, ahora hay ruedas MUSL.
Soporte para el compilador de Fujitsu C/C++.
Los arreglos de objetos ahora están soportadas en einsum.
Soporte para la multiplicación de matrices in situ (@=).
NumPy 1.25. continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. También se ha realizado trabajo preparatorio para el futuro NumPy 2.0.0, resultando en un gran número de nuevas y eliminadas obsolescencias.
Un total de 148 personas contribuyeron a esta versión y 530 solicitudes de incorporación de cambios fueron aceptadas.
Las versiones de Python soportadas por este lanzamiento son 3.9-3.11.
Fomentar una Cultura Inclusiva: Convocatoria de Participación#
10 de mayo de 2023 – Fomentar una Cultura Inclusiva: Convocatoria de Participación
¿Cómo podemos ser mejores cuando se trata de diversidad e inclusión? Lee el informe y averigua cómo involucrarte aquí.
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.
18 de diciembre de 2022 – NumPy 1.24.0 ya está disponible. Los aspectos más destacados del lanzamiento son:
Nuevas palabras clave “dtype” y “casting” para las funciones de apilamiento.
Nuevas características y correcciones de F2PY.
Muchas nuevas obsolescencias, revísalas.
Muchas obsolescencias caducadas,
El lanzamiento de NumPy 1.24.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación. Hay un gran número de obsolescencias nuevas y caducadas debido a los cambios en la limpieza y promoción de tipo dtype. Es el trabajo de 177 colaboradores distribuidos sobre 444 solicitudes de incorporación de cambios. Las versiones Python soportadas son 3.8-3.11.
22 de junio de 2022 – NumPy 1.23.0 ya está disponible. Los aspectos más destacados del lanzamiento son:
Implementación de loadtxt en C, mejorando enormemente su rendimiento.
Exposición de DLPack a nivel Python para facilitar el intercambio de datos.
Cambios a la promoción y comparación de dtypes estructurados.
Mejoras a f2py.
El lanzamiento de NumPy 1.23.0 continúa el trabajo en curso para mejorar el manejo y promoción de dtypes, aumentar la velocidad de ejecución y clarificar la documentación, caducar viejas obsolescencias. Es el trabajo de 151 colaboradores distribuidos sobre 494 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10. Python 3.11 será soportado cuando alcance la etapa rc.
Estudio de investigación NumFOCUS DEI: llamado a participar#
13 de abril de 2022 – NumPy está trabajando con NumFOCUS 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.
31 de diciembre de 2021 – NumPy 1.22.0 ya está disponible. Los aspectos más destacados del lanzamiento son:
Las anotaciones de tipo del espacio de nombres principal están esencialmente completas. El repositorio principal (upstream) es un objetivo en movimiento, así que probablemente habrán más mejoras, pero el mayor trabajo ya está hecho. Esta es probablemente la mejora más visible para el usuario en esta versión.
Una versión preliminar del propuesto Estándar API de Arreglos es suministrada (véase NEP 47). Este es un paso en la creación de una colección estándar de funciones que pueden ser usadas a través de librerías como CuPy y JAX.
NumPy ahora tiene un backend de DLPack. DLPack proporciona un formato de intercambio común para datos de arreglos (tensor).
Nuevos métodos para cuantil, percentil y funciones relacionadas. Los nuevos métodos proporcionan un conjunto completo de los métodos comúnmente encontrados en la literatura.
Las funciones universales se han refactorizado para implementar la mayor parte de NEP 43. Esto también desbloquea la capacidad de experimentar con la futura API DType.
Un nuevo asignador de memoria configurable para el uso de proyectos dependientes o downstream.
NumPy 1.22.0 es un gran lanzamiento que contó con el trabajo de 153 colaboradores distribuidos sobre 609 solicitudes de incorporación de cambios. Las versiones de Python soportadas por este lanzamiento son 3.8-3.10.
Promoviendo una cultura inclusiva en el ecosistema científico de Python#
31 de agosto de 2021 – Nos complace anunciar que la Iniciativa Chan Zuckerberg ha otorgado una subvención 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.
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.
23 de junio de 2021 – NumPy 1.21.0 ya está disponible. Los aspectos más destacados de esta versión son:
trabajo SIMD continuo que cubre más funciones y plataformas,
trabajo inicial sobre la nueva infraestructura dtype y conversiones de tipo,
universal2 wheels para Python 3.8 y Python 3.9 en Mac,
documentación mejorada,
anotaciones mejoradas,
nuevo PCG64DXSM generador de bits para números aleatorios.
Esta versión de NumPy es el resultado de 581 solicitudes de incorporación de cambios contribuidas por 175 personas. Las versiones de Python soportadas por este lanzamiento son las 3.7-3.9, se añadirá soporte para Python 3.10 después del lanzamiento de Python 3.10.
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/.
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:
Anotaciones de tipo para grandes partes de NumPy, y un nuevo submódulo numpy.typing que contiene los alias ArralyLike y DtypeLike que los usuarios y las librerías dependientes o downstream pueden usar al agregar anotaciones de tipo en su propio código.
Optimizaciones de compilador SIMD multiplataforma, con soporte para instrucciones x86 (SSE, AVX), ARM64 (Neon) y PowerPC (VSX). Esto produjo mejoras significativas de rendimiento para muchas funciones (ejemplos: sin/cos, einsum).
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
actualizar su versión de pip al menos a la 20.1 para soportar manylinux2010 y manylinux2014
utiliza --only-binary=numpy o --only-binary=:all: para evitar que pip intente compilar desde la fuente.
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:
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.
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.
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.
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.
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.
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.
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+NumPy - Kit de prensa
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.
On this page
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+NumPy - Política de Privacidad
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.
On this page
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+NumPy - Código de Conducta de NumPy - Cómo hacer el seguimiento de un informe
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:
Actuar de manera personal en lugar de impersonal. El Comité puede involucrar a las partes para que comprendan la situación, respetando al mismo tiempo la privacidad y, en su caso, la confidencialidad de los informadores. Sin embargo, a veces es necesario comunicarse directamente con uno o más individuos: el objetivo del Comité es mejorar la salud de nuestra comunidad en lugar de solo producir una decisión formal.
Enfatizar la empatía hacia los individuos en lugar de juzgar el comportamiento, evitando etiquetas binarias de “bueno” y “malo/malvado”. Existen agresiones y acosos manifiestos y claros, y los abordaremos con firmeza. Pero en muchas circunstancias puede ser complejo resolver estas situaciones, sobre todo aquellas en las que desacuerdos normales se convierten en comportamientos inútiles o perjudiciales para las partes. Comprender el contexto completo y encontrar un camino que vuelva involucrar a todos es difícil, pero es en última instancia lo más productivo para nuestra comunidad.
Comprendemos que el correo electrónico es un medio difícil y puede aislarnos. Recibir críticas por medio de correo electrónico, sin ningún contacto personal, puede ser particularmente doloroso. Esto hace que sea especialmente importante mantener una ambiente de apertura y respeto hacia las opiniones de los demás. También significa que debemos ser transparentes en nuestro actuar, y que haremos todo lo que esté a nuestro alcance para asegurarnos de que todos nuestros miembros reciban un trato justo y comprensivo.
La discriminación puede ser sutil e inconsciente. Ésta puede manifestarse como injusticia y hostilidad en interacciones que, por todo lo demás, serían normales. Sabemos que esto ocurre, y nos ocuparemos de estar pendientes de esto. Nos gustaría mucho saber de usted si cree que ha sido tratado injustamente, y utilizaremos estos procedimientos para asegurarnos de que su queja sea escuchada y atendida.
Ayude a aumentar el compromiso con buenas prácticas de debate: trate de identificar los puntos en los que el debate puede haberse interrumpido y proporcione información práctica, sugerencias y recursos que puedan conducir a un cambio positivo en estos aspectos.
Sea consciente de las necesidades de los nuevos miembros: proporcióneles apoyo y consideración explícitos, con el objetivo de aumentar la participación, particularmente de grupos subrepresentados.
Las personas provienen de entornos culturales y lingüísticos diferentes. Intente identificar cualquier malentendido honesto causado por un hablante no nativo y ayúdele a entender el problema y lo que puede cambiar para evitar causar una ofensa. La discusión compleja en una lengua extranjera puede ser muy intimidante, y queremos aumentar nuestra diversidad también a través de nacionalidades y culturas.
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:
Encontrar un candidato que pueda servir de mediador.
Obtener el acuerdo del informante(s). El informante(s) tienen total libertad para rechazar la propuesta de mediación o para proponer un mediador alternativo.
Obtener el acuerdo de la persona informante(s).
Acuerden el mediador: aunque las partes pueden proponer un mediador diferente al candidato sugerido, solo si se llega a un acuerdo común en todos los términos se puede avanzar en el proceso.
Establezca un marco de tiempo para completar la mediación, idealmente dentro de dos semanas.
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.
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.
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:
Desconectará inmediatamente al originador de todos los canales de comunicación de NumPy.
Responderá al informante que su informe ha sido recibido y que el autor ha sido desconectado.
En todo caso, el moderador deberá hacer un esfuerzo razonable por contactar al originador, y comunicarle específicamente cómo su lenguaje o sus acciones se constituyeron como “violación clara y grave”. El moderador también debe decir que, si el originador cree que esto es injusto o desea reconectarse con NumPy, tiene el derecho a solicitar una revisión, como se indica a continuación, por el Comité de Código de Conducta. El moderador debería copiar esta explicación al Comité de Código de conducta.
El Comité de Código de conducta revisará y aprobará formalmente todos los casos en los que se haya aplicado este mecanismo, para asegurarse de que no se esté utilizando para controlar desacuerdos ordinarios acalorados.
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:
Qué sucedió.
Si este evento constituye una violación del Código de Conducta.
Quiénes son las parte(s) responsables.
Si se trata de una situación en progreso y existe una amenaza para la seguridad física de cualquiera.
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.
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:
No tomar más medidas:
si determinamos que no se han producido violaciones;
si el asunto se ha resuelto públicamente mientras que el Comité estaba estudiando las respuestas.
Coordinación de mediación voluntaria: si todas las partes implicadas están de acuerdo, el Comité podrá facilitar un proceso de mediación como se detalla arriba.
Recordar públicamente y señalar que algunos comportamientos/acciones/usos de lenguaje, han sido juzgados como inapropiados y por qué, bajo el contexto actual, pueden ser hirientes para algunas personas, solicitando a la comunidad que se autorregule.
Una amonestación privada por parte del Comité al individuo(s) involucrado(s). En este caso, el presidente del grupo entregará esa amonestación al (los) individuo(s) por correo electrónico, con copia al grupo.
Una amonestación pública. En este caso, el presidente del Comité entregará la amonestación por el mismo medio por el que se produjo la violación, dentro de los límites de lo posible. Por ejemplo, la lista de correo original para una violación de correo electrónico, pero para una discusión en una sala de chat, en la que la persona o el contexto pueden haber desaparecido, puede buscarse el contacto por otros medios. El grupo puede elegir publicar este mensaje en otro lugar con fines de documentación.
Una solicitud de disculpa pública o privada, asumiendo que el informante esté de acuerdo con esta idea: puede negarse, a su discreción, a continuar contacto con el infractor. El presidente entregará esta solicitud. El Comité puede, si así lo desea, adjuntar “condiciones” a esta petición: por ejemplo, el grupo puede solicitar a un infractor pedir disculpas para preservar su membresía en una lista de correo.
Un “acuerdo mutuo de suspensión” en el que el Comité solicita al individuo la abstención temporal de participación en la comunidad. Si el individuo decide no aceptar una suspensión temporal voluntariamente, el Comité puede emitir un “período obligatorio de reflexión”.
Una prohibición permanente o temporal de algunos o de todos los espacios de NumPy (listas de correo, gitter.im, etc.). El grupo mantendrá los registros de todas esas prohibiciones, para que puedan ser revisadas en el futuro o ser mantenidas en caso contrario.
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.
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.
On this page
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+params:
+ machinelearning:
+ paras:
+ - para1: NumPy constituye la base de potentes librerías de aprendizaje automático como [scikit-learn](https://scikit-learn.org) y [SciPy](https://scipy.org). A medida que crece el aprendizaje automático, también lo hace la lista de librerías basadas en NumPy. Las capacidades de aprendizaje profundo de [TensorFlow](https://www.tensorflow.org) tienen amplias aplicaciones— 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
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--- /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!
Para la lista de personas del Consejo Directivo, por favor ve aquí.
On this page
\ No newline at end of file
diff --git a/es/terms/index.html b/es/terms/index.html
new file mode 100644
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+++ b/es/terms/index.html
@@ -0,0 +1,13 @@
+NumPy - Terms of Use
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Exceptions to Informal Negotiations and Arbitration#
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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.
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On this page
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new file mode 100644
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+++ 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.
Encuesta de la Comunidad de NumPy 2020 - resultados#
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@@ -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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-
-
-
-
-
-
-
-
-
- NumPy
-
-
-
-
-
-
-
-
-
-
-
NumPy Team Gallery
-
We are an international team on a mission to support scientific and research communities worldwide by building quality, open-source software. Join us!
User questions: The best way to get help is to post your question to a site
-like StackOverflow, with
-thousands of users available to answer. Smaller alternatives include
-IRC,
-Gitter, and
+
NumPy - Get Help
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 just a little
-overwhelming!
Development issues: For NumPy development-related matters (e.g. bug reports), please
-see Community.
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--- a/history/index.html
+++ b/history/index.html
@@ -1,21 +1,12 @@
-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:
*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.
\ No newline at end of file
+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:
*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.
>>># 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)
-
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.
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 and
-Prefect).
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.
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
+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
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.
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).
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.
\ No newline at end of file
diff --git a/index.xml b/index.xml
index 8894bedc..4a9fed06 100644
--- a/index.xml
+++ b/index.xml
@@ -1,34 +1,1714 @@
-NumPyhttp://numpy.org/Recent content on NumPyHugo -- gohugo.ioen-us404http://numpy.org/404/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/404/Oops! You’ve reached a dead end.
-If you think something should be here, you can open an issue on GitHub.About Ushttp://numpy.org/about/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/about/Some information about the NumPy project and community
-NumPy is an open source project aiming to enable numerical computing with Python. It was created in 2005, building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the modified BSD license.
-NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community.Array Computinghttp://numpy.org/arraycomputing/Mon, 01 Jan 0001 00:00:00 +0000http://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!http://numpy.org/case-studies/cricket-analytics/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/case-studies/cricket-analytics/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.Case Study: DeepLabCut 3D Pose Estimationhttp://numpy.org/case-studies/deeplabcut-dnn/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/case-studies/deeplabcut-dnn/Analyzing mice hand-movement using DeepLapCut (Source: www.deeplabcut.org )
+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> – 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> – 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 'NumPy Community Survey 2020 - results'" 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’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) & 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.
-—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 Waveshttp://numpy.org/case-studies/gw-discov/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/case-studies/gw-discov/Gravitational Waves (Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)
+</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.
-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.Case Study: First Image of a Black Holehttp://numpy.org/case-studies/blackhole-image/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/case-studies/blackhole-image/Black Hole M87 (Image Credits: Event Horizon Telescope Collaboration)
+</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.
-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 sensitivity and resolution.Citing NumPyhttp://numpy.org/citing-numpy/Mon, 01 Jan 0001 00:00:00 +0000http://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: 0.1038/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{'{e}}fan J.Communityhttp://numpy.org/community/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/community/NumPy is a community-driven open source project developed by a very 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 NumPyhttp://numpy.org/contribute/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/contribute/The NumPy project welcomes your expertise and enthusiasm! Your choices aren’t limited to programming – in addition to
-Writing code you can
-Review pull requests Develop tutorials, presentations, and other educational material Triage issues Work on our website Contribute graphic design Translate website content Serve as a community coordinator Write grant proposals and help with other fundraising If you’re unsure where to start or how your skills fit in, reach out!Get Helphttp://numpy.org/gethelp/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/gethelp/User questions: The best way to get help is to post your question to a site like StackOverflow, with thousands of users available to answer. Smaller alternatives include IRC, Gitter, and Reddit. We wish we could keep an eye on these sites, or answer questions directly, but the volume is just a little overwhelming!
-Development issues: For NumPy development-related matters (e.g. bug reports), please see Community.
-StackOverflow A forum for asking usage questions, e.History of NumPyhttp://numpy.org/history/Mon, 01 Jan 0001 00:00:00 +0000http://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 NumPyhttp://numpy.org/install/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/install/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 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.Learnhttp://numpy.org/learn/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/learn/For the official NumPy documentation visit numpy.org/doc/stable.
-Below is a curated collection of external resources. To contribute, see the end of this page.
-Beginners There’s a ton of information about NumPy out there. If you are new, we’d strongly recommend these:
-Tutorials
-NumPy Quickstart Tutorial SciPy Lectures Besides covering NumPy, these lectures offer a broader introduction to the scientific Python ecosystem. NumPy: the absolute basics for beginners Machine Learning Plus - Introduction to ndarray Edureka - Learn NumPy Arrays with Examples Dataquest - NumPy Tutorial: Data Analysis with Python NumPy tutorial by Nicolas Rougier Stanford CS231 by Justin Johnson NumPy User Guide BooksNewshttp://numpy.org/news/Mon, 01 Jan 0001 00:00:00 +0000http://numpy.org/news/Numpy 1.20.0 release Jan 30, 2021 – NumPy 1.20.0 is now available. This is the largest NumPy release to date, thanks to 180+ contributors. The two most exciting new features are:
-Type annotations for large parts of NumPy, and a new numpy.typing submodule containing ArrayLike and DtypeLike aliases that users and downstream libraries can use when adding type annotations in their own code. Multi-platform SIMD compiler optimizations, with support for x86 (SSE, AVX), ARM64 (Neon), and PowerPC (VSX) instructions.NumPy Code of Conducthttp://numpy.org/code-of-conduct/Mon, 01 Jan 0001 00:00:00 +0000http://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 reporthttp://numpy.org/report-handling-manual/Mon, 01 Jan 0001 00:00:00 +0000http://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 Statementhttp://numpy.org/diversity_sep2020/Mon, 01 Jan 0001 00:00:00 +0000http://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.Press kithttp://numpy.org/press-kit/Mon, 01 Jan 0001 00:00:00 +0000http://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 Policyhttp://numpy.org/privacy/Mon, 01 Jan 0001 00:00:00 +0000http://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 Usehttp://numpy.org/terms/Mon, 01 Jan 0001 00:00:00 +0000http://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 ("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.
\ No newline at end of file
+</p>
+ <p class="attribution">—Katie Bouman, <em>Assistant Professor, Computing & 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{\'{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}
+}</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’t limited to programming, as you can
+see below there are many areas where we need <strong>your</strong> help.</p>
+<p>If you’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. “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’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’s a ton of information about NumPy out there. If you are just starting, we’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&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">
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+ <div class="sd-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&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>
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+
+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-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">
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+
+
+Charles Harris
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/charris"></a>
+ </div>
+</div>
+
+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-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>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-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&v=4%22" alt="Avatar of Ganesh Kathiresan">
+
+
+
+Ganesh Kathiresan
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/ganesh-k13"></a>
+ </div>
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+
+ <div class="sd-col sd-d-flex-row">
+
+ <div class="sd-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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&v=4" alt="Avatar of Ralf Gommers">
+
+
+
+Ralf Gommers
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/rgommers"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <img src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&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">
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+ <div class="sd-card-body">
+ <img src="https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&v=4" alt="Avatar of Shekhar Prasad Rajak">
+
+
+
+Shekhar Prasad Rajak
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/Shekharrajak"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card-body">
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+
+
+
+Stefan van der Walt
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/stefanv"></a>
+ </div>
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+
+ <div class="sd-col sd-d-flex-row">
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+
+
+
+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">
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+
+
+
+Andrew Nelson
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/andyfaff"></a>
+ </div>
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+
+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <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>
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+ <div class="sd-col sd-d-flex-row">
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+
+
+
+Aaron Meurer
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/asmeurer"></a>
+ </div>
+</div>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <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>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-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>
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+
+
+
+Anne Bonner
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/bonn0062"></a>
+ </div>
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+
+
+
+Brigitta Sipőcz
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/bsipocz"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+
+
+
+carlkl
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/carlkl"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
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+
+
+
+Ryan C Cooper
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/cooperrc"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
+ <img src="https://avatars.githubusercontent.com/u/36567889?u=cbc76d558d375ebafd4a05a505f500eb94e00611&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>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
+ <img src="https://avatars.githubusercontent.com/u/2190658?u=b85e13f985d0bf87eeb3a7a146b61dcc9586019b&v=4%22" alt="Avatar of Hameer Abbasi">
+
+
+
+Hameer Abbasi
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/hameerabbasi"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+
+
+
+Inessa Pawson
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
+ <img src="https://avatars.githubusercontent.com/u/8078968?v=4%22" alt="Avatar of jbrockmendel">
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+
+
+jbrockmendel
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/jbrockmendel"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
+ <img src="https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&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&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&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&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&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&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&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&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&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&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&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&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&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&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="survey-team">Survey team<a class="headerlink" href="#survey-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&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&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&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="translations-team">Translations team<a class="headerlink" href="#translations-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/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&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="emeritus-maintainers">Emeritus maintainers<a class="headerlink" href="#emeritus-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/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&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&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 ("<strong>Project</strong>", “<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>
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diff --git a/install/index.html b/install/index.html
index ed7cbb1d..79b9d6a5 100644
--- a/install/index.html
+++ b/install/index.html
@@ -1,122 +1,39 @@
-NumPy
The only prerequisite for installing NumPy is Python itself. If you don’t have
+
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
-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.
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.
Unless you’re fine with only the packages in the defaults channel, make conda-forge
-your default channel via setting the channel priority.
Linux
If you’re fine with slightly outdated packages and prefer stability over being
-able to use the latest versions of libraries:
Use your OS package manager for as much as possible (Python itself, NumPy, and
-other libraries).
Install packages not provided by your package manager with pip install somepackage --user.
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.
Use the defaults conda channel (conda-forge doesn’t have good support for
-GPU packages yet).
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, for example, 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:
use a different environment per project you’re working on,
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.
IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE!
+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:
Not recommended for most users, but available for convenience.
macOS (Homebrew):
brew install numpy
Linux (APT):
sudo apt install python3-numpy
Windows (Chocolatey):
choco install numpy
For advanced users and developers who want to customize or debug NumPy.
A word of warning: building Numpy from source can be a nontrivial exercise.
+We recommend using binaries instead if those are available for your platform via one of the above methods.
+For details on how to build from source, see the building from source guide in the Numpy docs.
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.
-
\ No newline at end of file
+different reasons, often due to issues with your setup.
On this page
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+NumPy - 404
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diff --git a/ja/about/index.html b/ja/about/index.html
new file mode 100644
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--- /dev/null
+++ b/ja/about/index.html
@@ -0,0 +1,21 @@
+NumPy - 私たちについて
\ No newline at end of file
diff --git a/ja/arraycomputing/index.html b/ja/arraycomputing/index.html
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+++ b/ja/arraycomputing/index.html
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+NumPy - 配列演算
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new file mode 100644
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+++ b/ja/case-studies/blackhole-image/index.html
@@ -0,0 +1,25 @@
+NumPy - ケーススタディ：世界初のブラックホール画像
\ No newline at end of file
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+++ b/ja/case-studies/cricket-analytics/index.html
@@ -0,0 +1,20 @@
+NumPy - ケーススタディ: クリケット分析、ゲームチェンジャー!
\ No newline at end of file
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+++ b/ja/case-studies/deeplabcut-dnn/index.html
@@ -0,0 +1,24 @@
+NumPy - ケーススタディ: DeepLabCut 三次元姿勢推定
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--- /dev/null
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@@ -0,0 +1,23 @@
+NumPy - ケーススタディ: 重力波の発見
\ No newline at end of file
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@@ -0,0 +1,12 @@
+NumPy - Case-Studies
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{'{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/ja/code-of-conduct/index.html b/ja/code-of-conduct/index.html
new file mode 100644
index 00000000..87db90a2
--- /dev/null
+++ b/ja/code-of-conduct/index.html
@@ -0,0 +1,16 @@
+NumPy - NumPy行動規範
\ No newline at end of file
diff --git a/ja/community/index.html b/ja/community/index.html
new file mode 100644
index 00000000..58e439c6
--- /dev/null
+++ b/ja/community/index.html
@@ -0,0 +1,17 @@
+NumPy - コミュニティ
\ No newline at end of file
diff --git a/ja/gethelp/index.html b/ja/gethelp/index.html
new file mode 100644
index 00000000..c2ce4696
--- /dev/null
+++ b/ja/gethelp/index.html
@@ -0,0 +1,15 @@
+NumPy - サポートを得る方法
\ No newline at end of file
diff --git a/ja/history/index.html b/ja/history/index.html
new file mode 100644
index 00000000..476b729b
--- /dev/null
+++ b/ja/history/index.html
@@ -0,0 +1,18 @@
+NumPy - NumPyの歴史
\ No newline at end of file
diff --git a/ja/index.html b/ja/index.html
new file mode 100644
index 00000000..3bac81c8
--- /dev/null
+++ b/ja/index.html
@@ -0,0 +1,56 @@
+NumPy
"""
+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
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。
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
diff --git a/ja/learn/index.html b/ja/learn/index.html
new file mode 100644
index 00000000..456cc2d2
--- /dev/null
+++ b/ja/learn/index.html
@@ -0,0 +1,19 @@
+NumPy - NumPyの学び方
\ No newline at end of file
diff --git a/ja/news/index.html b/ja/news/index.html
new file mode 100644
index 00000000..2f5f454a
--- /dev/null
+++ b/ja/news/index.html
@@ -0,0 +1,16 @@
+NumPy - ニュース
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。
\ No newline at end of file
diff --git a/ja/press-kit/index.html b/ja/press-kit/index.html
new file mode 100644
index 00000000..2e4add6d
--- /dev/null
+++ b/ja/press-kit/index.html
@@ -0,0 +1,13 @@
+NumPy - プレス用資料
\ No newline at end of file
diff --git a/ja/privacy/index.html b/ja/privacy/index.html
new file mode 100644
index 00000000..2a4ea964
--- /dev/null
+++ b/ja/privacy/index.html
@@ -0,0 +1,13 @@
+NumPy - プライバシーポリシー
\ No newline at end of file
diff --git a/ja/report-handling-manual/index.html b/ja/report-handling-manual/index.html
new file mode 100644
index 00000000..0ae601c3
--- /dev/null
+++ b/ja/report-handling-manual/index.html
@@ -0,0 +1,19 @@
+NumPy - NumPy行動規範 - 報告書のフォローアップ方法
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
new file mode 100644
index 00000000..03ae3f29
--- /dev/null
+++ b/ja/terms/index.html
@@ -0,0 +1,13 @@
+NumPy - Terms of Use
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.
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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.
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.
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.
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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.
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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.
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.
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.
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 90596Austin, TX, USA 78709info@numfocus.org+1 (512) 222-5449
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
new file mode 100644
index 00000000..aa98aea0
--- /dev/null
+++ b/ja/user-survey-2020/index.html
@@ -0,0 +1,15 @@
+NumPy - 2020年 NumPyコミュニティ調査
\ No newline at end of file
diff --git a/ja/user-surveys/index.html b/ja/user-surveys/index.html
new file mode 100644
index 00000000..804fca11
--- /dev/null
+++ b/ja/user-surveys/index.html
@@ -0,0 +1,14 @@
+NumPy - NumPyユーザアンケート
\ No newline at end of file
diff --git a/js/app.js b/js/app.js
deleted file mode 100644
index 53867ef3..00000000
--- a/js/app.js
+++ /dev/null
@@ -1,101 +0,0 @@
-function waitForKernel() {
- let kernelInterval = setInterval(() => {
- try {
- if (thebeKernel) {
- // Hide the enable button & copy, show the shell
- $('#demo-code.fake-shell').css('display', 'none');
- $('#numpy-shell.real-shell').css('display', 'flex');
- $('#numpy-shell').addClass('numpy-shell-border');
-
- // We need a more specific attribute to add the caret.
- $('.thebelab-cell').attr('id', 'demo-code');
- // Adds the caret
- $('#numpy-shell #demo-code').prepend('
>
');
-
- // Style the 'Run' button
- $('.thebelab-button').each(function(idx) {
- if (idx == 0) {
- $(this).attr('id', 'demo-button-run');
- $(this).attr('class', 'shell-button');
- $(this).html('Run (Shift + Enter)');
- } else {
- $(this).remove();
- }
- });
- // Style the output elements
- $('.jp-OutputArea').parent().closest('div').attr('id', 'demo-output-parent');
- $('.jp-OutputArea').attr('id', 'demo-output');
-
- // Show the lesson & hide the 'wait' text
- $('.shell-wait').css('display', 'none');
- $('.shell-lesson').css('display', 'flex');
- clearInterval(kernelInterval);
- }
- } catch (err) {
- if (err != 'ReferenceError: thebeKernel is not defined') {
- console.log('Error loading the shell: ', err)
- }
- }
- }, 500);
-}
-
-function loadShell() {
- $('#shell-loader').css('display', 'inline-block');
- // Add 'wait' text
- $('.shell-intro').css('display', 'none');
- $('.shell-wait').css('display', 'flex');
- thebelab.bootstrap();
- waitForKernel();
-}
-
-// Email Form
-$('.sign-up-input').focus(function(e) {
- if ($(window).width() > 850) {
- $('.submission-instructions').css('display', 'block');
- }
-}).blur(function(e) {
- if ($(window).width() > 850) {
- $('.submission-instructions').css('display', 'none');
- }
-});
-
-
-function sendThankYou() {
- // Hides the email form to show a thank you
- $('.sign-up-container').css('display', 'none');
- $('.thank-you').css('display', 'flex');
-
- setTimeout(() => {
- // Resets email input to empty string
- $('#mce-EMAIL').val('');
- $('.thank-you').css('display', 'none');
- $( ".sign-up-container" ).fadeIn( "slow");
- }, 8000);
-}
-
-// Content Page Shortcuts
-const shortcutsTarget = $('#shortcuts');
-if (shortcutsTarget.length > 0) {
- $('.content-container h2, .content-container h3').map(function(idx, el) {
- const title = el.textContent;
- // transforms title into snake-case
- const elTitle = title.replace(/\s/g, '-').toLowerCase();
- // Gets the element type (e.g. h2, h3)
- const elType = $(el).get(0).tagName;
- // Adds snake-case title as an id attribute to target element
- $(el).attr('id', elTitle);
- shortcutsTarget.append(`
Elegant SciPyby Juan Nunez-Iglesias, Stefan van der Walt, and Harriet Dashnow
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.
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.
Contribute to this list
To add to this collection, submit a recommendation via a pull request. Say why your recommendation deserves mention on this page and also which audience would benefit most.
Below is a curated collection of educational resources, both for self-learning and
+teaching others, developed by NumPy contributors and vetted by the community.
NumPy Tutorials A collection of tutorials and
+educational materials in the format of Jupyter Notebooks developed and maintained by
+the NumPy Documentation team. To submit your own content, visit the
+numpy-tutorials repository on GitHub.
Elegant SciPy
+by Juan Nunez-Iglesias, Stéfan van der Walt, and Harriet Dashnow
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.
NumPy Tutorials A collection of tutorials and educational
+materials in the format of Jupyter Notebooks developed and maintained by the NumPy Documentation team.
+To submit your own content, visit the numpy-tutorials repository on GitHub.
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
\ No newline at end of file
diff --git a/news/index.html b/news/index.html
index 8133e52b..1b28ce6c 100644
--- a/news/index.html
+++ b/news/index.html
@@ -1,16 +1,143 @@
-NumPy
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:
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.
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.
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:
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.
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:
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:
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.
Sep 16, 2023 – NumPy 1.26.0
+is now available. The highlights of the release are:
Python 3.12.0 support.
Cython 3.0.0 compatibility.
Use of the Meson build system
Updated SIMD support
f2py fixes, meson and bind(x) support
Support for the updated Accelerate BLAS/LAPACK library
The NumPy 1.26.0 release is a continuation of the 1.25.x series that marks the
+transition to the Meson build system and provision of support for Cython 3.0.0.
+A total of 20 people contributed to this release and 59 pull requests were
+merged.
The Python versions supported by this release are 3.9-3.12.
numpy.org is now available in Japanese and Portuguese#
Aug 2, 2023 – numpy.org is now available in 2 additional languages:
+Japanese and Portuguese. This wouldn’t be possible without our dedicated volunteers:
Portuguese:
Melissa Weber Mendonça (melissawm)
Ricardo Prins (ricardoprins)
Getúlio Silva (getuliosilva)
Julio Batista Silva (jbsilva)
Alexandre de Siqueira (alexdesiqueira)
Alexandre B A Villares (villares)
Vini Salazar (vinisalazar)
Japanese:
Atsushi Sakai (AtsushiSakai)
KKunai
Tom Kelly (TomKellyGenetics)
Yuji Kanagawa (kngwyu)
Tetsuo Koyama (tkoyama010)
The work on the translation infrastructure is supported with funding from CZI.
Looking ahead, we’d love to translate the website into more languages.
+If you’d like to help, please connect with the NumPy Translations Team on Slack:
+https://join.slack.com/t/numpy-team/shared_invite/zt-1gokbq56s-bvEpo10Ef7aHbVtVFeZv2w.
+(Look for the #translations channel.) We are also building a Translations Team who will be
+working on localizing documentation and educational content across the Scientific Python
+ecosystem. If this piqued your interest, join us on the Scientific Python
+Discord: https://discord.gg/khWtqY6RKr. (Look for the #translation channel.)
Jun 17, 2023 – NumPy 1.25.0
+is now available. The highlights of the release are:
Support for MUSL, there are now MUSL wheels.
Support for the Fujitsu C/C++ compiler.
Object arrays are now supported in einsum.
Support for the inplace matrix multiplication (@=).
The NumPy 1.25.0 release continues the ongoing work to improve the handling and
+promotion of dtypes, increase the execution speed, and clarify the
+documentation. There has also been preparatory work for the future NumPy 2.0.0,
+resulting in a large number of new and expired deprecations.
A total of 148 people contributed to this release and 530 pull requests were
+merged.
The Python versions supported by this release are 3.9-3.11.
Fostering an Inclusive Culture: Call for Participation#
May 10, 2023 – Fostering an Inclusive Culture: Call for Participation
How can we be better when it comes to diversity and inclusion?
+Read the report and find out how to get involved
+here.
Jan 6, 2023 –- Mukulika Pahari and Ross Barnowski are appointed as the new NumPy
+documentation team leads replacing Melissa Mendonça. We thank Melissa for all her
+contributions to the NumPy official documentation and educational materials,
+and Mukulika and Ross for stepping up.
Dec 18, 2022 – NumPy 1.24.0
+is now available. The highlights of the release are:
New “dtype” and “casting” keywords for stacking functions.
New F2PY features and fixes.
Many new deprecations, check them out.
Many expired deprecations,
The NumPy 1.24.0 release continues the ongoing work to improve the handling and
+promotion of dtypes, increase execution speed, and clarify the documentation.
+There are a large number of new and expired deprecations due to changes in
+dtype promotion and cleanups. It is the work of 177 contributors spread over
+444 pull requests. The supported Python versions are 3.8-3.11.
Jun 22, 2022 – NumPy 1.23.0
+is now available. The highlights of the release are:
Implementation of loadtxt in C, greatly improving its performance.
Exposure of DLPack at the Python level for easy data exchange.
Changes to the promotion and comparisons of structured dtypes.
Improvements to f2py.
The NumPy 1.23.0 release continues the ongoing work to improve the handling and
+promotion of dtypes, increase the execution speed, clarify the documentation,
+and expire old deprecations. It is the work of 151 contributors spread over
+494 pull requests. The Python versions supported by this release 3.8-3.10.
+Python 3.11 will be supported when it reaches the rc stage.
NumFOCUS DEI research study: call for participation#
Apr 13, 2022 – NumPy is working with NumFOCUS on a
+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
+community. The research team would like to talk to new contributors, project
+developers and maintainers, and those who have contributed in the past about
+their experiences joining and contributing to NumPy.
Interested in sharing your experiences?
Please complete this brief “Participant Interest” form
+which contains additional information on the research goals, privacy, and
+confidentiality considerations. Your participation will be valuable to the
+growth and sustainability of diverse and inclusive open-source software
+communities. Accepted participants will participate in a 30-minute interview
+with a research team member.
Dec 31, 2021 – NumPy 1.22.0
+is now available. The highlights of the release are:
Type annotations of the main namespace are essentially complete. Upstream is
+a moving target, so there will likely be further improvements, but the major
+work is done. This is probably the most user visible enhancement in this
+release.
A preliminary version of the proposed
+array API Standard is provided
+(see NEP 47).
+This is a step in creating a standard collection of functions that can be
+used across libraries such as CuPy and JAX.
NumPy now has a DLPack backend. DLPack provides a common interchange format
+for array (tensor) data.
New methods for quantile, percentile, and related functions. The new
+methods provide a complete set of the methods commonly found in the
+literature.
The universal functions have been refactored to implement most of
+NEP 43.
+This also unlocks the ability to experiment with the future DType API.
A new configurable memory allocator for use by downstream projects.
NumPy 1.22.0 is a big release featuring the work of 153 contributors spread
+over 609 pull requests. The Python versions supported by this release are
+3.8-3.10.
Advancing an inclusive culture in the scientific Python ecosystem#
August 31, 2021 – We are happy to announce the Chan Zuckerberg Initiative has
+awarded a grant
+to support the onboarding, inclusion, and retention of people from historically
+marginalized groups on scientific Python projects, and to structurally improve
+the community dynamics for NumPy, SciPy, Matplotlib, and Pandas.
As a part of CZI’s Essential Open Source Software for Science program,
+this Diversity & Inclusion supplemental grant
+will support the creation of dedicated Contributor Experience Lead positions to
+identify, document, and implement practices to foster inclusive open-source
+communities. This project will be led by Melissa Mendonça (NumPy), with
+additional mentorship and guidance provided by Ralf Gommers (NumPy, SciPy),
+Hannah Aizenman and Thomas Caswell (Matplotlib), Matt Haberland (SciPy), and
+Joris Van den Bossche (Pandas).
This is an ambitious project aiming to discover and implement activities that
+should structurally improve the community dynamics of our projects. By
+establishing these new cross-project roles, we hope to introduce a new
+collaboration model to the Scientific Python communities, allowing
+community-building work within the ecosystem to be done more efficiently and
+with greater outcomes. We also expect to develop a clearer picture of what
+works and what doesn’t in our projects to engage and retain new contributors,
+especially from historically underrepresented groups. Finally, we plan on
+producing detailed reports on the actions executed, explaining how they have
+impacted our projects in terms of representation and interaction with our
+communities.
The two-year project is expected to start by November 2021, and we are excited
+to see the results from this work!
+You can read the full proposal here.
July 12, 2021 – At NumPy, we believe in the power of our community. 1,236
+NumPy users from 75 countries participated in our inaugural survey last year.
+The survey findings gave us a very good understanding of what we should focus
+on for the next 12 months.
It’s time for another survey, and we are counting on you once again. It will
+take about 15 minutes of your time. Besides English, the survey questionnaire
+is available in 8 additional languages: Bangla, French, Hindi, Japanese,
+Mandarin, Portuguese, Russian, and Spanish.
Jun 23, 2021 – NumPy 1.21.0
+is now available. The highlights of the release are:
continued SIMD work covering more functions and platforms,
initial work on the new dtype infrastructure and casting,
universal2 wheels for Python 3.8 and Python 3.9 on Mac,
improved documentation,
improved annotations,
new PCG64DXSM bitgenerator for random numbers.
This NumPy release is the result of 581 merged pull requests contributed by 175
+people. The Python versions supported for this release are 3.7-3.9, support
+for Python 3.10 will be added after Python 3.10 is released.
Jun 22, 2021 – In 2020, the NumPy survey team in partnership with students
+and faculty from the University of Michigan and the University of Maryland
+conducted the first official NumPy community survey. Find the survey results
+here: https://numpy.org/user-survey-2020/.
Jan 30, 2021 – NumPy 1.20.0
is now available. This is the largest NumPy release to date, thanks to 180+
contributors. The two most exciting new features are:
Type annotations for large parts of NumPy, and a new numpy.typing submodule
containing ArrayLike and DtypeLike aliases that users and downstream
@@ -18,52 +145,45 @@
AVX), ARM64 (Neon), and PowerPC (VSX) instructions. This yielded significant
performance improvements for many functions (examples:
sin/cos,
-einsum).
Sep 16, 2020 – We are pleased to announce the publication of
the first official paper on NumPy
as a review article in Nature. This comes 14 years after the release of NumPy 1.0.
The paper covers applications and fundamental concepts of array programming,
the rich scientific Python ecosystem built on top of NumPy, and the recently added
array protocols to facilitate interoperability with external array and tensor
-libraries like CuPy, Dask, and JAX.
Python 3.9 is coming, when will NumPy release binary wheels?
Sept 14, 2020 – Python 3.9 will be released in a few weeks. If you are an
+libraries like CuPy, Dask, and JAX.
Python 3.9 is coming, when will NumPy release binary wheels?#
Sept 14, 2020 – Python 3.9 will be released in a few weeks. If you are an
early adopter of Python versions, you may be dissapointed to find that NumPy
(and other binary packages like SciPy) will not have binary wheels ready on the
day of the release. It is a major effort to adapt the build infrastructure to a
new Python version and it typically takes a few weeks for the packages to appear
on PyPI and conda-forge. In preparation for this event, please make sure to
update your pip to version 20.1 at least to support manylinux2010 and
manylinux2014
use --only-binary=numpy or --only-binary=:all: to prevent pip from
-trying to build from source.
Sep 10, 2020 – NumPy
1.19.2 is now available.
This latest release in the 1.19 series fixes several bugs, prepares for the
upcoming Cython 3.x
release and pins
setuptools to keep distutils working while upstream modifications are ongoing.
The aarch64 wheels are built with the latest manylinux2014 release that fixes
-the problem of differing page sizes used by different linux distros.
The inaugural NumPy survey is live!
Jul 2, 2020 – This survey is meant to guide and set priorities for
+the problem of differing page sizes used by different linux distros.
Jul 2, 2020 – This survey is meant to guide and set priorities for
decision-making about the development of NumPy as software and as a community.
The survey is available in 8 additional languages besides English:
Bangla, Hindi, Japanese, Mandarin, Portuguese, Russian, Spanish and French.
Please help us make NumPy better and take the survey
-here.
NumPy has a new logo!
Jun 24, 2020 – NumPy now has a new logo:
The logo is a modern take on the old one, with a cleaner design. Thanks to
+here.
The logo is a modern take on the old one, with a cleaner design. Thanks to
Isabela Presedo-Floyd for designing the new logo, as well as to Travis Vaught
-for the old logo that served us well for 15+ years.
NumPy 1.19.0 release
Jun 20, 2020 – NumPy 1.19.0 is now available. This is the first release
+for the old logo that served us well for 15+ years.
Jun 20, 2020 – NumPy 1.19.0 is now available. This is the first release
without Python 2 support, hence it was a “clean-up release”. The minimum
supported Python version is now Python 3.6. An important new feature is that
the random number generation infrastructure that was introduced in NumPy 1.17.0
-is now accessible from Cython.
Season of Docs acceptance
May 11, 2020 – NumPy has been accepted as one of the mentor organizations for
+is now accessible from Cython.
May 11, 2020 – NumPy has been accepted as one of the mentor organizations for
the Google Season of Docs program. We are excited about the opportunity to
work with a technical writer to improve NumPy’s documentation once again! For more
details, please see
the official Season of Docs site and our
-ideas page.
NumPy 1.18.0 release
Dec 22, 2019 – NumPy 1.18.0 is now available. After the major changes in
+ideas page.
Dec 22, 2019 – NumPy 1.18.0 is now available. After the major changes in
1.17.0, this is a consolidation release. It is the last minor release that will
support Python 3.5. Highlights of the release includes the addition of basic
-infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for numpy.random.
NumPy 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. All bugfix
+infrastructure for linking with 64-bit BLAS and LAPACK libraries, and a new C-API for numpy.random.
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.
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.
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diff --git a/press-kit/index.html b/press-kit/index.html
index 1a6bb8ec..23b0cbd3 100644
--- a/press-kit/index.html
+++ b/press-kit/index.html
@@ -1,21 +1,13 @@
-NumPy
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.
\ No newline at end of file
+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
\ No newline at end of file
diff --git a/privacy/index.html b/privacy/index.html
index 73e967e0..69e754a7 100644
--- a/privacy/index.html
+++ b/privacy/index.html
@@ -1,21 +1,13 @@
-NumPy
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.
\ No newline at end of file
+NumPy - 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
\ No newline at end of file
diff --git a/pt/404/index.html b/pt/404/index.html
new file mode 100644
index 00000000..55bdc0c3
--- /dev/null
+++ b/pt/404/index.html
@@ -0,0 +1,13 @@
+NumPy - 404
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
\ No newline at end of file
diff --git a/pt/about/index.html b/pt/about/index.html
new file mode 100644
index 00000000..3058a52b
--- /dev/null
+++ b/pt/about/index.html
@@ -0,0 +1,18 @@
+NumPy - Sobre
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.
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.
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.
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)
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
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diff --git a/pt/arraycomputing/index.html b/pt/arraycomputing/index.html
new file mode 100644
index 00000000..77c6e618
--- /dev/null
+++ b/pt/arraycomputing/index.html
@@ -0,0 +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.
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.
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+NumPy - Estudo de Caso: A Primeira Imagem de um Buraco Negro
Estudo de Caso: A Primeira Imagem de um Buraco Negro
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!
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.
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?
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.
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.
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.
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.
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+NumPy - Estudo de Caso: Análise de Críquete, a revolução!
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
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.
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.
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.
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.
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+NumPy - Estudo de Caso: Estimativa de Pose 3D com DeepLabCut
Estudo de Caso: Estimativa de Pose 3D com DeepLabCut
DeepLabCut é uma toolbox de código aberto que permite que pesquisadores de centenas de instituições em todo o mundo rastreiem o comportamento de animais de laboratório, com muito poucos dados de treinamento, mas com precisão no nível humano. Com a tecnologia DeepLabCut, cientistas podem aprofundar a compreensão científica do controle motor e do comportamento em diversas espécies animais e escalas temporais.
Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas trabalhosas de monitoramento e marcação, junto com análise de dados baseada em redes neurais profundas, a DeepLabCut garante que estudos científicos envolvendo a observação de animais como primatas, camundongos, peixes, moscas etc. sejam mais rápidos e precisos.
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.
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.
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.
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.
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.
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+NumPy - Estudo de Caso: Descoberta de Ondas Gravitacionais
Estudo de Caso: Descoberta de Ondas Gravitacionais
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.
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.
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.
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:
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.
Grafo de dependências mostrando como o pacote GwPy depended da NumPy#
Grafo de dependências mostrando como o pacote PyCBC depended da NumPy#
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.
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+NumPy - Case-Studies
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+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) & 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>
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+NumPy - Citando o 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:
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. 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
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+NumPy - Código de Conduta NumPy
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.
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.
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.
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.
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.
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.
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).
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.
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#
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:
acusar o recebimento do relato,
discussão/feedback razoável,
mediação (se o feedback não ajudar e somente se ambos o relatante e relatado concordarem com isso),
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.
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+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.
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.
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.
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.
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.
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.
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.
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
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+https://numpy.org/pt/code-of-conduct/
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+languageName: Português
+params:
+ description: Por que NumPy? Arrays n-dimensionais poderosas. Ferramentas para computação numérica. Interoperabilidade. Alto desempenho. Código aberto.
+ navbarlogo:
+ image: logo.svg
+ text: NumPy
+ link: /pt/
+ hero:
+ # Main hero title
+ title: NumPy
+ # Hero subtitle (optional)
+ subtitle: A biblioteca fundamental para computação científica com Python
+ # Button text
+ buttontext: "Última versão: NumPy 2.2. Veja todas as versões"
+ # Where the main hero button links to
+ buttonlink: "/pt/news/#releases"
+ # Hero image (from static/images/___)
+ image: logo.svg
+ shell:
+ title: placeholder
+ intro:
+ - title: Experimentar o NumPy
+ text: Use o shell interativo para testar o NumPy no navegador
+ docslink: Não se esqueça de conferir a 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
+ tabs:
+ title: ECOSSISTEMA
+ section5: false
+ navbar:
+ - title: Instalação
+ url: /pt/install
+ - title: Documentação
+ url: https://numpy.org/doc/stable
+ - title: Aprenda
+ url: /pt/learn
+ - title: Comunidade
+ url: /pt/community
+ - title: Sobre
+ url: /pt/about
+ - title: Notícias
+ url: /pt/news
+ - title: Contribuir
+ url: /pt/contribute
+ footer:
+ logo: logo.svg
+ socialmediatitle: ""
+ socialmedia:
+ - link: https://github.com/numpy/numpy
+ icon: github
+ - link: https://www.youtube.com/channel/UCguIL9NZ7ybWK5WQ53qbHng
+ icon: youtube
+ quicklinks:
+ column1:
+ title: ""
+ links:
+ - text: Instalação
+ link: /pt/install
+ - text: Documentação
+ link: https://numpy.org/doc/stable
+ - text: Aprenda
+ link: /pt/learn
+ - text: Citando o Numpy
+ link: /pt/citing-numpy
+ - text: Roadmap
+ link: https://numpy.org/neps/roadmap.html
+ column2:
+ links:
+ - text: Sobre
+ link: /pt/about
+ - text: Comunidade
+ link: /pt/community
+ - text: Pesquisas de usuário
+ link: /pt/user-surveys
+ - text: Contribuir
+ link: /pt/contribute
+ - text: Código de Conduta
+ link: /pt/code-of-conduct
+ column3:
+ links:
+ - text: Ajuda
+ link: /pt/gethelp
+ - text: Termos de uso (EN)
+ link: /pt/terms
+ - text: Privacidade
+ link: /pt/privacy
+ - text: Kit de imprensa
+ link: /pt/press-kit
diff --git a/pt/contribute/index.html b/pt/contribute/index.html
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+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.
Para pessoas programadoras, este guia explica como contribuir para a base de código. Confira também nosso canal do YouTube para obter informações adicionais.
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:
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.
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
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.
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.
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.
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.
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.
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+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.
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.
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.
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.
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
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+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!
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+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:
*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.
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+NumPy
NumPy
+
A biblioteca fundamental para computação científica com Python
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.
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
+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
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.
Cálculo acelerado com arrays comprimidos na memória, no disco ou remotos.
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).
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.
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.
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+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> – 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 (“o básico absoluto para inciantes”)</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{\'{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}
+}</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) & 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 “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.</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: “Como faço X no NumPy?”. 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 "NumPy Community Survey 2020 - results"" 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 ("<strong>Project</strong>", “<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&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&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>
+ </div>
+</div>
+
+ <div class="sd-col sd-d-flex-row">
+
+ <div class="sd-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" 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&v=4" 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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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&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">
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+
+
+
+Albert Steppi
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/steppi"></a>
+ </div>
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+
+ </div>
+</div>
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+<div class="sd-container-fluid sd-mb-4 false">
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+
+
+Andrew Nelson
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/andyfaff"></a>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
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+
+
+Anirudh Subramanian
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/anirudh2290"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+
+
+
+Aaron Meurer
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/asmeurer"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <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">
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+ <div class="sd-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>
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+ <div class="sd-col sd-d-flex-row">
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+
+
+Anne Bonner
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/bonn0062"></a>
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+ <div class="sd-col sd-d-flex-row">
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+
+
+Brigitta Sipőcz
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/bsipocz"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+
+
+
+carlkl
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/carlkl"></a>
+ </div>
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+
+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+
+
+
+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">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
+ <img src="https://avatars.githubusercontent.com/u/36567889?u=cbc76d558d375ebafd4a05a505f500eb94e00611&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">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
+ <img src="https://avatars.githubusercontent.com/u/2190658?u=b85e13f985d0bf87eeb3a7a146b61dcc9586019b&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">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
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+
+
+
+Inessa Pawson
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/InessaPawson"></a>
+ </div>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <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>
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+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
+ <img src="https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&v=4" alt="Avatar of Kai">
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+
+
+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">
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+ <div class="sd-card-body">
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+
+
+
+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">
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+ <div class="sd-card-body">
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+
+
+
+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">
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+ <div class="sd-card-body">
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+
+
+
+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">
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+ <div class="sd-card-body">
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+
+
+
+Lysandros Nikolaou
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/lysnikolaou"></a>
+ </div>
+</div>
+
+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
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+
+
+
+Meekail Zain
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/Micky774"></a>
+ </div>
+</div>
+
+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
+
+ <div class="sd-card-body">
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+
+
+
+Christopher Sidebottom
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/Mousius"></a>
+ </div>
+</div>
+
+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-card sd-w-100 sd-shadow-sm sd-card-hover text-center">
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+ <div class="sd-card-body">
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+
+
+
+Mateusz Sokół
+ </div>
+ <a class="sd-stretched-link" href="https://github.com/mtsokol"></a>
+ </div>
+</div>
+
+ <div class="sd-col sd-d-flex-row">
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+ <div class="sd-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&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">
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+
+
+
+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&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">
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+ <div class="sd-card-body">
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+
+
+
+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">
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+
+
+
+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&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&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&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&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&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">
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+ <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&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&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>
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+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:
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.
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
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+NumPy - Aprenda
Abaixo está uma coleção de recursos educacionais, tanto para autoaprendizado como para ensinar outras pessoas, desenvolvidos pelos colaboradores do NumPy e selecionados pela comunidade.
NumPy Tutorials Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório numpy-tutorials no GitHub.
Elegant SciPypor Juan Nunez-Iglesias, Stefan van der Walt, e Harriet Dashnow
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.
Experimente esses recursos avançados para uma melhor compreensão dos conceitos da NumPy, como indexação avançada, splitting, stacking, álgebra linear e muito mais.
NumPy Tutorials Uma coleção de tutoriais e materiais educacionais no formato de Notebooks Jupyter desenvolvidos e mantidos pelo time de documentação do NumPy. Se você tiver interesse em adicionar o seu próprio conteúdo, verifique o repositório numpy-tutorials no GitHub.
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
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+NumPy - Notícias
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.
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.
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:
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:
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.
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)
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.
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.
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.
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.
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.
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.
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.
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/.
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).
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.
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.
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:
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.
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.
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!
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.
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.
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.
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+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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+NumPy - Política de privacidade
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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+NumPy - Código de Conduta NumPy - Como dar seguimento a um relatório
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.
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.
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.
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.
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.
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.
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.
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+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/
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+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://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.
+ headers:
+ - text: Biblioteca de Arrays
+ - text: Recursos e áreas de aplicação
+ libraries:
+ - title: Dask
+ text: Arrays distribuídas e paralelismo avançado para análise, permitindo desempenho em escala.
+ img: /images/content_images/arlib/dask.png
+ alttext: Dask
+ url: https://dask.org/
+ - title: CuPy
+ text: Biblioteca de matriz compatível com NumPy para computação acelerada pela GPU com Python.
+ img: /images/content_images/arlib/cupy.png
+ alttext: CuPy
+ url: https://cupy.chainer.org
+ - title: JAX
+ text: "Transformações combináveis de programas NumPy: vetorização, compilação just-in-time para GPU/TPU."
+ img: /images/content_images/arlib/jax_logo_250px.png
+ alttext: JAX
+ url: https://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
+ text: Biblioteca de arrays compatíveis com o NumPy que pode ser integrada com Dask e álgebra linear esparsa da SciPy.
+ img: /images/content_images/arlib/sparse.png
+ alttext: sparse
+ url: https://sparse.pydata.org/en/latest/
+ - title: PyTorch
+ text: Framework de deep learning que acelera o caminho entre prototipação de pesquisa e colocação em produção.
+ img: /images/content_images/arlib/pytorch-logo-dark.svg
+ alttext: PyTorch
+ url: https://pytorch.org/
+ - title: TensorFlow
+ text: Uma plataforma completa para aprendizagem de máquina que permite construir e colocar em produção aplicações usando ML facilmente.
+ img: /images/content_images/arlib/tensorflow-logo.svg
+ alttext: TensorFlow
+ url: https://www.tensorflow.org
+ - title: MXNet
+ text: Framework de deep learning voltado para flexibilizar prototipação em pesquisa e produção.
+ img: /images/content_images/arlib/mxnet_logo.png
+ alttext: MXNet
+ url: https://mxnet.apache.org/
+ - title: Arrow
+ text: Uma plataforma de desenvolvimento multi-linguagens para dados e análise para dados armazenados em colunas na memória.
+ img: /images/content_images/arlib/arrow.png
+ alttext: arrow
+ url: https://github.com/apache/arrow
+ - title: xtensor
+ text: Arrays multidimensionais com broadcasting e avaliação preguiçosa (lazy computing) para análise numérica.
+ img: /images/content_images/arlib/xtensor.png
+ alttext: xtensor
+ url: https://github.com/xtensor-stack/xtensor-python
+ - title: Awkward Array
+ text: Manipulação de dados JSON-like com sintaxe NumPy-like.
+ img: /images/content_images/arlib/awkward.svg
+ alttext: awkward
+ url: https://awkward-array.org/
+ - title: uarray
+ text: Sistema de backend Python que dissocia a API da implementação; unumpy fornece uma API NumPy.
+ img: /images/content_images/arlib/uarray.png
+ alttext: uarray
+ url: https://uarray.org/en/latest/
+ - title: tensorly
+ text: Ferramentas para aprendizagem com tensores, algebra e backends para usar NumPy, MXNet, PyTorch, TensorFlow ou CuPy sem esforço.
+ img: /images/content_images/arlib/tensorly.png
+ alttext: tensorly
+ url: http://tensorly.org/stable/home.html
+ - 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."
+ libraries:
+ - title: Computação quântica
+ alttext: Um chip de computador.
+ img: /images/content_images/sc_dom_img/quantum_computing.svg
+ links:
+ - url: https://qutip.org
+ label: QuTiP
+ - url: https://pyquil-docs.rigetti.com/en/stable
+ label: PyQuil
+ - url: https://qiskit.org
+ label: Qiskit
+ - url: https://pennylane.ai
+ label: PennyLane
+ - title: Computação estatística
+ alttext: Um gráfico com uma linha em movimento para cima.
+ img: /images/content_images/sc_dom_img/statistical_computing.svg
+ links:
+ - url: https://pandas.pydata.org/
+ label: Pandas
+ - url: https://www.statsmodels.org/
+ label: statsmodels
+ - url: https://xarray.pydata.org/en/stable/
+ label: Xarray
+ - url: https://seaborn.pydata.org/
+ label: Seaborn
+ - title: Processamento de sinais
+ alttext: Um gráfico de barras com valores positivos e negativos.
+ img: /images/content_images/sc_dom_img/signal_processing.svg
+ links:
+ - url: https://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
+ 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
+ links:
+ - url: https://networkx.org/
+ label: NetworkX
+ - url: https://graph-tool.skewed.de/
+ label: graph-tool
+ - url: https://igraph.org/python/
+ label: igraph
+ - url: https://pygsp.rtfd.io/
+ label: PyGSP
+ - title: Processos de Astronomia
+ alttext: Um telescópio.
+ img: /images/content_images/sc_dom_img/astronomy_processes.svg
+ links:
+ - url: https://www.astropy.org/
+ label: AstroPy
+ - url: https://sunpy.org/
+ label: SunPy
+ - url: https://spacepy.github.io/
+ label: SpacePy
+ - title: Psicologia Cognitiva
+ alttext: Uma cabeça humana com engrenagens.
+ img: /images/content_images/sc_dom_img/cognitive_psychology.svg
+ links:
+ - url: https://www.psychopy.org/
+ label: PsychoPy
+ - title: Bioinformática
+ alttext: Um pedaço de DNA.
+ img: /images/content_images/sc_dom_img/bioinformatics.svg
+ links:
+ - url: https://biopython.org/
+ label: BioPython
+ - url: http://scikit-bio.org/
+ label: Scikit-Bio
+ - url: https://github.com/openvax/pyensembl
+ label: PyEnsembl
+ - url: http://etetoolkit.org/
+ label: ETE
+ - title: Inferência Bayesiana
+ alttext: Um gráfico com uma curva em forma de sino.
+ img: /images/content_images/sc_dom_img/bayesian_inference.svg
+ links:
+ - url: https://pystan.readthedocs.io/en/latest/
+ label: PyStan
+ - url: https://docs.pymc.io/
+ label: PyMC
+ - url: https://arviz-devs.github.io/arviz/
+ label: ArviZ
+ - url: https://emcee.readthedocs.io/
+ label: emcee
+ - title: Análise Matemática
+ alttext: Quatro símbolos matemáticos.
+ img: /images/content_images/sc_dom_img/mathematical_analysis.svg
+ links:
+ - url: https://scipy.org
+ label: SciPy
+ - url: https://www.sympy.org/
+ label: SymPy
+ - url: https://www.cvxpy.org/
+ label: cvxpy
+ - url: https://fenicsproject.org/
+ label: FEniCS
+ - title: Química
+ alttext: Um tubo de ensaio.
+ img: /images/content_images/sc_dom_img/chemistry.svg
+ links:
+ - url: https://cantera.org/
+ label: Cantera
+ - url: https://www.mdanalysis.org/
+ label: MDAnalysis
+ - url: https://github.com/rdkit/rdkit
+ label: RDKit
+ - url: https://www.pybamm.org/
+ label: PyBaMM
+ - title: Geociências
+ alttext: A Terra.
+ img: /images/content_images/sc_dom_img/geoscience.svg
+ links:
+ - url: https://pangeo.io/
+ label: Pangeo
+ - url: https://simpeg.xyz/
+ label: Simpeg
+ - url: https://github.com/obspy/obspy/wiki
+ label: ObsPy
+ - url: https://www.fatiando.org/
+ label: Fatiando a Terra
+ - title: Processamento Geográfico
+ alttext: Um mapa.
+ img: /images/content_images/sc_dom_img/GIS.svg
+ links:
+ - url: https://shapely.readthedocs.io/
+ label: Shapely
+ - url: https://geopandas.org/
+ label: GeoPandas
+ - url: https://python-visualization.github.io/folium
+ label: Folium
+ - title: Arquitetura e Engenharia
+ alttext: Uma placa de desenvolvimento de microprocessador.
+ img: /images/content_images/sc_dom_img/robotics.svg
+ links:
+ - url: https://compas.dev/
+ label: COMPAS
+ - url: https://cityenergyanalyst.com/
+ label: City Energy Analyst - Analista de Energía de Ciudad
+ - url: https://nortikin.github.io/sverchok/
+ label: Sverchok
+ datascience:
+ intro: "NumPy está no centro de um rico ecossistema de bibliotecas de ciência de dados. Um fluxo de trabalho típico de ciência de dados exploratório pode parecer assim:"
+ image1:
+ - img: /images/content_images/ds-landscape.png
+ alttext: Diagrama de bibliotecas Python. As cinco categorias são 'Extrair, Transformar, Carregar', 'Exploração de Dados', 'Modelo de Dados', 'Avaliação de Dados' e 'Apresentação de Dados'.
+ image2:
+ - img: /images/content_images/data-science.png
+ alttext: Diagram of three overlapping circle. The circles labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'.
+ examples:
+ - text: "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)).
+ 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://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.
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
new file mode 100644
index 00000000..615b759c
--- /dev/null
+++ b/pt/teams/index.html
@@ -0,0 +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!
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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 90596Austin, TX, USA 78709info@numfocus.org+1 (512) 222-5449
On this page
\ No newline at end of file
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+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.
\ No newline at end of file
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@@ -0,0 +1,14 @@
+NumPy - PESQUISA DE USUÁRIOS NUMPY
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
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index fde31327..e0ea5e15 100644
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+++ b/report-handling-manual/index.html
@@ -1,21 +1,14 @@
-NumPy
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.
\ No newline at end of file
+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.
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.
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.
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.
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.
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.
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
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index a5530e59..7bb5dc1c 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -1 +1 @@
-http://numpy.org/404/http://numpy.org/about/http://numpy.org/arraycomputing/http://numpy.org/case-studies/cricket-analytics/http://numpy.org/case-studies/deeplabcut-dnn/http://numpy.org/case-studies/gw-discov/http://numpy.org/case-studies/blackhole-image/http://numpy.org/case-studies/http://numpy.org/citing-numpy/http://numpy.org/community/http://numpy.org/contribute/http://numpy.org/gethelp/http://numpy.org/history/http://numpy.org/install/http://numpy.org/learn/http://numpy.org/news/http://numpy.org/http://numpy.org/code-of-conduct/http://numpy.org/report-handling-manual/http://numpy.org/diversity_sep2020/http://numpy.org/press-kit/http://numpy.org/privacy/http://numpy.org/terms/
\ No newline at end of file
+https://numpy.org/en/sitemap.xml2025-12-20T00:00:00+00:00https://numpy.org/pt/sitemap.xml2024-12-08T00:00:00+00:00https://numpy.org/ja/sitemap.xml2024-08-18T00:00:00+00:00https://numpy.org/es/sitemap.xml2024-06-17T00:00:00+00:00
\ No newline at end of file
diff --git a/surveys/NumPy_usersurvey_2020_report.pdf b/surveys/NumPy_usersurvey_2020_report.pdf
new file mode 100644
index 00000000..6a41eb85
Binary files /dev/null and b/surveys/NumPy_usersurvey_2020_report.pdf differ
diff --git a/surveys/NumPy_usersurvey_2020_report_cover.png b/surveys/NumPy_usersurvey_2020_report_cover.png
new file mode 100644
index 00000000..4e64de9c
Binary files /dev/null and b/surveys/NumPy_usersurvey_2020_report_cover.png differ
diff --git a/surveys/README.md b/surveys/README.md
new file mode 100644
index 00000000..66684eb3
--- /dev/null
+++ b/surveys/README.md
@@ -0,0 +1 @@
+This folder contains files related to the announcements about NumPy surveys on numpy.org.
diff --git a/tabcontents.yaml b/tabcontents.yaml
new file mode 100644
index 00000000..54bb92c6
--- /dev/null
+++ b/tabcontents.yaml
@@ -0,0 +1,300 @@
+params:
+ machinelearning:
+ paras:
+ - para1: NumPy forms the basis of powerful machine learning libraries like [scikit-learn](https://scikit-learn.org) and [SciPy](https://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:
+ - text: NumPy's API is the starting point when libraries are written to exploit innovative hardware, create specialized array types, or add capabilities beyond what NumPy provides.
+
+ headers:
+ - text: Array Library
+ - text: Capabilities & Application areas
+
+ libraries:
+ - title: Dask
+ text: Distributed arrays and advanced parallelism for analytics, enabling performance at scale.
+ img: /images/content_images/arlib/dask.png
+ alttext: Dask
+ url: https://dask.org/
+ - title: CuPy
+ text: NumPy-compatible array library for GPU-accelerated computing with Python.
+ img: /images/content_images/arlib/cupy.png
+ alttext: CuPy
+ url: https://cupy.dev
+ - title: JAX
+ text: "Composable transformations of NumPy programs: differentiate, vectorize, just-in-time compilation to GPU/TPU."
+ img: /images/content_images/arlib/jax_logo_250px.png
+ alttext: JAX
+ url: https://docs.jax.dev
+ - title: Xarray
+ text: Labeled, indexed multi-dimensional arrays for advanced analytics and visualization.
+ img: /images/content_images/arlib/xarray.png
+ alttext: xarray
+ url: https://xarray.pydata.org/en/stable/index.html
+ - title: Sparse
+ text: NumPy-compatible sparse array library that integrates with Dask and SciPy's sparse linear algebra.
+ img: /images/content_images/arlib/sparse.png
+ alttext: sparse
+ url: https://sparse.pydata.org/en/latest/
+ - title: PyTorch
+ text: Deep learning framework that accelerates the path from research prototyping to production deployment.
+ img: /images/content_images/arlib/pytorch-logo-dark.svg
+ alttext: PyTorch
+ url: https://pytorch.org/
+ - title: TensorFlow
+ text: An end-to-end platform for machine learning to easily build and deploy ML powered applications.
+ img: /images/content_images/arlib/tensorflow-logo.svg
+ alttext: TensorFlow
+ url: https://www.tensorflow.org
+ - title: Arrow
+ text: A cross-language development platform for columnar in-memory data and analytics.
+ img: /images/content_images/arlib/arrow.png
+ alttext: arrow
+ url: https://arrow.apache.org/
+ - title: xtensor
+ text: Multi-dimensional arrays with broadcasting and lazy computing for numerical analysis.
+ img: /images/content_images/arlib/xtensor.png
+ alttext: xtensor
+ url: https://github.com/xtensor-stack/xtensor-python
+ - title: Awkward Array
+ text: Manipulate JSON-like data with NumPy-like idioms.
+ img: /images/content_images/arlib/awkward.svg
+ alttext: awkward
+ url: https://awkward-array.org/
+ - title: uarray
+ text: Python backend system that decouples API from implementation; unumpy provides a NumPy API.
+ img: /images/content_images/arlib/uarray.png
+ alttext: uarray
+ url: https://uarray.org/en/latest/
+ - title: tensorly
+ text: Tensor learning, algebra and backends to seamlessly use NumPy, PyTorch, TensorFlow or CuPy.
+ img: /images/content_images/arlib/tensorly.png
+ alttext: tensorly
+ url: http://tensorly.org/stable/home.html
+ - 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:
+ - text: Nearly every scientist working in Python draws on the power of NumPy.
+ - text: "NumPy brings the computational power of languages like C and Fortran to Python, a language much easier to learn and use. With this power comes simplicity: a solution in NumPy is often clear and elegant."
+
+ libraries:
+ - title: Quantum Computing
+ alttext: A computer chip.
+ img: /images/content_images/sc_dom_img/quantum_computing.svg
+ links:
+ - url: https://qutip.org
+ label: QuTiP
+ - url: https://pyquil-docs.rigetti.com/en/stable
+ label: PyQuil
+ - url: https://qiskit.org
+ label: Qiskit
+ - url: https://pennylane.ai
+ label: PennyLane
+ - title: Statistical Computing
+ alttext: A line graph with the line moving up.
+ img: /images/content_images/sc_dom_img/statistical_computing.svg
+ links:
+ - url: https://pandas.pydata.org/
+ label: Pandas
+ - url: https://www.statsmodels.org/
+ label: statsmodels
+ - url: https://xarray.pydata.org/en/stable/
+ label: Xarray
+ - url: https://seaborn.pydata.org/
+ label: Seaborn
+ - title: Signal Processing
+ alttext: A bar chart with positive and negative values.
+ img: /images/content_images/sc_dom_img/signal_processing.svg
+ links:
+ - url: https://scipy.org
+ label: SciPy
+ - url: https://pywavelets.readthedocs.io/
+ label: PyWavelets
+ - url: https://python-control.org/
+ label: python-control
+ - url: https://hyperspy.org/
+ label: HyperSpy
+ - title: Image Processing
+ alttext: A photograph of the mountains.
+ img: /images/content_images/sc_dom_img/image_processing.svg
+ links:
+ - url: https://scikit-image.org/
+ label: Scikit-image
+ - url: https://opencv.org/
+ label: OpenCV
+ - url: https://mahotas.rtfd.io/
+ label: Mahotas
+ - title: Graphs and Networks
+ alttext: A simple graph.
+ img: /images/content_images/sc_dom_img/sd6.svg
+ links:
+ - url: https://networkx.org/
+ label: NetworkX
+ - url: https://graph-tool.skewed.de/
+ label: graph-tool
+ - url: https://igraph.org/python/
+ label: igraph
+ - url: https://pygsp.rtfd.io/
+ label: PyGSP
+ - title: Astronomy
+ alttext: A telescope.
+ img: /images/content_images/sc_dom_img/astronomy_processes.svg
+ links:
+ - url: https://www.astropy.org/
+ label: AstroPy
+ - url: https://sunpy.org/
+ label: SunPy
+ - url: https://spacepy.github.io/
+ label: SpacePy
+ - title: Cognitive Psychology
+ alttext: A human head with gears.
+ img: /images/content_images/sc_dom_img/cognitive_psychology.svg
+ links:
+ - url: https://www.psychopy.org/
+ label: PsychoPy
+ - title: Bioinformatics
+ alttext: A strand of DNA.
+ img: /images/content_images/sc_dom_img/bioinformatics.svg
+ links:
+ - url: https://biopython.org/
+ label: BioPython
+ - url: http://scikit-bio.org/
+ label: Scikit-Bio
+ - url: https://github.com/openvax/pyensembl
+ label: PyEnsembl
+ - url: http://etetoolkit.org/
+ label: ETE
+ - title: Bayesian Inference
+ alttext: A graph with a bell-shaped curve.
+ img: /images/content_images/sc_dom_img/bayesian_inference.svg
+ links:
+ - url: https://pystan.readthedocs.io/en/latest/
+ label: PyStan
+ - url: https://docs.pymc.io/
+ label: PyMC
+ - url: https://arviz-devs.github.io/arviz/
+ label: ArviZ
+ - url: https://emcee.readthedocs.io/
+ label: emcee
+ - title: Mathematical Analysis
+ alttext: Four mathematical symbols.
+ img: /images/content_images/sc_dom_img/mathematical_analysis.svg
+ links:
+ - url: https://scipy.org
+ label: SciPy
+ - url: https://www.sympy.org/
+ label: SymPy
+ - url: https://www.cvxpy.org/
+ label: cvxpy
+ - url: https://fenicsproject.org/
+ label: FEniCS
+ - title: Chemistry
+ alttext: A test tube.
+ img: /images/content_images/sc_dom_img/chemistry.svg
+ links:
+ - url: https://cantera.org/
+ label: Cantera
+ - url: https://www.mdanalysis.org/
+ label: MDAnalysis
+ - url: https://github.com/rdkit/rdkit
+ label: RDKit
+ - url: https://www.pybamm.org/
+ label: PyBaMM
+ - title: Geoscience
+ alttext: The Earth.
+ img: /images/content_images/sc_dom_img/geoscience.svg
+ links:
+ - url: https://pangeo.io/
+ label: Pangeo
+ - url: https://simpeg.xyz/
+ label: Simpeg
+ - url: https://github.com/obspy/obspy/wiki
+ label: ObsPy
+ - url: https://www.fatiando.org/
+ label: Fatiando a Terra
+ - title: Geographic Processing
+ alttext: A map.
+ img: /images/content_images/sc_dom_img/GIS.svg
+ links:
+ - url: https://shapely.readthedocs.io/
+ label: Shapely
+ - url: https://geopandas.org/
+ label: GeoPandas
+ - url: https://python-visualization.github.io/folium
+ label: Folium
+ - title: Architecture & Engineering
+ alttext: A microprocessor development board.
+ img: /images/content_images/sc_dom_img/robotics.svg
+ links:
+ - url: https://compas.dev/
+ label: COMPAS
+ - url: https://cityenergyanalyst.com/
+ label: City Energy Analyst
+ - url: https://nortikin.github.io/sverchok/
+ label: Sverchok
+
+ datascience:
+
+ intro: "NumPy lies at the core of a rich ecosystem of data science libraries. A typical exploratory data science workflow might look like:"
+
+ image1:
+ - img: /images/content_images/ds-landscape.png
+ alttext: Diagram of Python Libraries. The five catagories are 'Extract, Transform, Load', 'Data Exploration', 'Data Modeling', 'Data Evaluation' and 'Data Presentation'.
+
+ image2:
+ - img: /images/content_images/data-science.png
+ alttext: Diagram of three overlapping circles. The circles are labeled 'Mathematics', 'Computer Science' and 'Domain Expertise'. In the middle of the diagram, which has the three circles overlapping it, is an area labeled 'Data Science'.
+
+ examples:
+ - text: "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), [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:
+ - text: For high data volumes, [Dask](https://dask.org) and [Ray](https://ray.io/) are designed to scale. Stable deployments rely on data versioning ([DVC](https://dvc.org)), experiment tracking ([MLFlow](https://mlflow.org)), and workflow automation ([Airflow](https://airflow.apache.org), [Dagster](https://dagster.io) and [Prefect](https://www.prefect.io)).
+
+ visualization:
+ images:
+ - url: https://www.fusioncharts.com/blog/best-python-data-visualization-libraries
+ img: /images/content_images/v_matplotlib.png
+ alttext: A streamplot made in matplotlib
+ - url: https://github.com/yhat/ggpy
+ img: /images/content_images/v_ggpy.png
+ alttext: A scatter-plot graph made in ggpy
+ - url: https://www.journaldev.com/19692/python-plotly-tutorial
+ img: /images/content_images/v_plotly.png
+ alttext: A box-plot made in plotly
+ - url: https://altair-viz.github.io/gallery/streamgraph.html
+ img: /images/content_images/v_altair.png
+ alttext: A streamgraph made in altair
+ - url: https://seaborn.pydata.org
+ img: /images/content_images/v_seaborn.png
+ alttext: A pairplot of two types of graph, a plot-graph and a frequency graph made in seaborn"
+ - url: https://pyvista.org
+ img: /images/content_images/v_pyvista.png
+ alttext: A 3D volume rendering made in PyVista.
+ - url: https://napari.org
+ img: /images/content_images/v_napari.png
+ alttext: A multi-dimensionan image made in napari.
+ - url: https://vispy.org/gallery/index.html
+ img: /images/content_images/v_vispy.png
+ alttext: A Voronoi diagram made in vispy.
+
+ content:
+ - text: NumPy is an essential component in the burgeoning
+ [Python visualization landscape](https://pyviz.org/overviews/index.html),
+ which includes [Matplotlib](https://matplotlib.org),
+ [Seaborn](https://seaborn.pydata.org), [Plotly](https://plot.ly),
+ [Altair](https://altair-viz.github.io), [Bokeh](https://docs.bokeh.org/en/latest/),
+ [Holoviz](https://holoviz.org), [Vispy](http://vispy.org), [Napari](https://napari.org/),
+ and [PyVista](https://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
new file mode 100644
index 00000000..7d453fbf
--- /dev/null
+++ b/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/teams/emeritus-maintainers.toml b/teams/emeritus-maintainers.toml
new file mode 100644
index 00000000..d030da98
--- /dev/null
+++ b/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/teams/index.html b/teams/index.html
new file mode 100644
index 00000000..4e93cb21
--- /dev/null
+++ b/teams/index.html
@@ -0,0 +1,110 @@
+NumPy - NumPy Teams
NumPy Teams
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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:
Systematically retrieve data or other content from the Site to create or compile, directly or indirectly, a collection, compilation, database, or directory without written permission from us.
Make any unauthorized use of the Site, including collecting usernames and/or email addresses of users by electronic or other means for the purpose of sending unsolicited email, or creating user accounts by automated means or under false pretenses.
Use the Site to advertise or offer to sell goods and services.
Circumvent, disable, or otherwise interfere with security-related features of the Site.
Engage in unauthorized framing of or linking to the Site.
Trick, defraud, or mislead us and other users, especially in any attempt to learn sensitive account information such as user passwords.
Make improper use of our support services or submit false reports of abuse or misconduct.
Engage in any automated use of the system, such as using scripts to send comments or messages, or using any data mining, robots, or similar data gathering and extraction tools.
Interfere with, disrupt, or create an undue burden on the Site or the networks or services connected to the Site.
Attempt to impersonate another user or person or use the username of another user.
Use any information obtained from the Site in order to harass, abuse, or harm another person.
Disparage, tarnish, or otherwise harm, in our opinion, us and/or the Site.
Except as may be the result of standard search engine or Internet browser usage, use, launch, develop, or distribute any automated system, including without limitation, any spider, robot, cheat utility, scraper, or offline reader that accesses the Site, or using or launching any unauthorized script or other software.
Upload or transmit (or attempt to upload or to transmit) any material that acts as a passive or active information collection or transmission mechanism, including without limitation, clear graphics interchange formats (“gifs”), 1×1 pixels, web bugs, cookies, or other similar devices (sometimes referred to as “spyware” or “passive collection mechanisms” or “pcms”).
Upload or transmit (or attempt to upload or to transmit) viruses, Trojan horses, or other material, including excessive use of capital letters and spamming (continuous posting of repetitive text), that interferes with any party’s uninterrupted use and enjoyment of the Site or modifies, impairs, disrupts, alters, or interferes with the use, features, functions, operation, or maintenance of the Site.
Harass, annoy, intimidate, or threaten any of our employees or agents engaged in providing any portion of the Site to you.
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 90596Austin, TX, USA 78709info@numfocus.org+1 (512) 222-5449
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.
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).
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:
Systematically retrieve data or other content from the Site to create or compile, directly or indirectly, a collection, compilation, database, or directory without written permission from us.
Make any unauthorized use of the Site, including collecting usernames and/or email addresses of users by electronic or other means for the purpose of sending unsolicited email, or creating user accounts by automated means or under false pretenses.
Use the Site to advertise or offer to sell goods and services.
Circumvent, disable, or otherwise interfere with security-related features of the Site.
Engage in unauthorized framing of or linking to the Site.
Trick, defraud, or mislead us and other users, especially in any attempt to learn sensitive account information such as user passwords.
Make improper use of our support services or submit false reports of abuse or misconduct.
Engage in any automated use of the system, such as using scripts to send comments or messages, or using any data mining, robots, or similar data gathering and extraction tools.
Interfere with, disrupt, or create an undue burden on the Site or the networks or services connected to the Site.
Attempt to impersonate another user or person or use the username of another user.
Use any information obtained from the Site in order to harass, abuse, or harm another person.
Disparage, tarnish, or otherwise harm, in our opinion, us and/or the Site.
Except as may be the result of standard search engine or Internet browser usage, use, launch, develop, or distribute any automated system, including without limitation, any spider, robot, cheat utility, scraper, or offline reader that accesses the Site, or using or launching any unauthorized script or other software.
Upload or transmit (or attempt to upload or to transmit) any material that acts as a passive or active information collection or transmission mechanism, including without limitation, clear graphics interchange formats (“gifs”), 1×1 pixels, web bugs, cookies, or other similar devices (sometimes referred to as “spyware” or “passive collection mechanisms” or “pcms”).
Upload or transmit (or attempt to upload or to transmit) viruses, Trojan horses, or other material, including excessive use of capital letters and spamming (continuous posting of repetitive text), that interferes with any party’s uninterrupted use and enjoyment of the Site or modifies, impairs, disrupts, alters, or interferes with the use, features, functions, operation, or maintenance of the Site.
Harass, annoy, intimidate, or threaten any of our employees or agents engaged in providing any portion of the Site to you.
Attempt to bypass any measures of the Site designed to prevent or restrict access to the Site, or any portion of the Site.
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.
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.
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.
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.
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.
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.
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Exceptions to Informal Negotiations and Arbitration#
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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.
\ No newline at end of file
diff --git a/user-surveys/index.html b/user-surveys/index.html
new file mode 100644
index 00000000..0eda4039
--- /dev/null
+++ b/user-surveys/index.html
@@ -0,0 +1,15 @@
+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.
+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
+Inessa Pawson
+Mars Lee
+Inessa Pawson
+Jarrod Millman
+Joe LaChance
+Mars Lee
+Ralf Gommers
+shalz
+Shekhar Prasad Rajak
+Stefan van der Walt
+Albert Steppi
+Anirudh Subramanian
+Aaron Meurer
+Atsushi Sakai
+Ben Nathanson
+Anne Bonner
+Brigitta Sipőcz
+carlkl
+Ryan C Cooper
+ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)
+Hameer Abbasi
+jbrockmendel
+Kai
+Yuji Kanagawa
+Kriti Singh
+Christopher Albert
+Lysandros Nikolaou
+Meekail Zain
+Noa Tamir
+shalz
+Tina Oberoi
+Rakesh Vasudevan
+Zijie (ZJ) Poh
+Allan Haldane
+Ondřej Čertík
+David Cournapeau
+Jaime
+Jarrod Millman
+Julian Taylor
+Mark Wiebe
+Nathaniel J. Smith
+Travis E. Oliphant
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+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
+Robert Kern
+Ross Barnowski
+Sebastian Berg
+Sayed Adel
+Stephan Hoyer
+Tyler Reddy
+Warren Weckesser
+Anirudh Subramanian
+Aaron Meurer
+Atsushi Sakai
+Ben Nathanson
+Anne Bonner
+Brigitta Sipőcz
+carlkl
+Ryan C Cooper
+ਗਗਨਦੀਪ ਸਿੰਘ (Gagandeep Singh)
+Hameer Abbasi
+jbrockmendel
+Kai
+Yuji Kanagawa
+Kriti Singh
+Christopher Albert
+Lysandros Nikolaou
+Meekail Zain
+Noa Tamir
+Tina Oberoi
+Rakesh Vasudevan
+Zijie (ZJ) Poh
+Juan Pablo Duque (@juanpabloduqueo)
+Allan Haldane
+Ondřej Čertík
+David Cournapeau
+Jaime
+Julian Taylor
+Mark Wiebe
+Nathaniel J. Smith
+Travis E. Oliphant
