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content/ar/404.md

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+---
+title: 404
+sidebar: false
+---
+
+Oops! You've reached a dead end.
+
+If you think something should be here, you can [open an issue](https://github.com/numpy/numpy.org/issues) on GitHub. 

content/ar/_index.md

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+---
+title: null
+---
+
+{{< grid columns="1 2 2 3" >}}
+
+[[item]]
+type = 'card'
+title = 'Powerful N-dimensional arrays'
+body = '''
+Fast and versatile, the NumPy vectorization, indexing, and broadcasting concepts are the de-facto standards of array computing today.
+'''
+
+[[item]]
+type = 'card'
+title = 'Numerical computing tools'
+body = '''
+NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more.
+'''
+
+[[item]]
+type = 'card'
+title = 'Open source'
+body = '''
+Distributed under a liberal [BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt), NumPy is developed and maintained [publicly on GitHub](https://github.com/numpy/numpy) by a vibrant, responsive, and diverse [community](/community).
+'''
+
+[[item]]
+type = 'card'
+title = 'Interoperable'
+body = '''
+NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.
+'''
+
+[[item]]
+type = 'card'
+title = 'Performant'
+body = '''
+The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code.
+'''
+
+[[item]]
+type = 'card'
+title = 'Easy to use'
+body = '''
+NumPy's high level syntax makes it accessible and productive for programmers from any background or experience level.
+'''
+
+{{< /grid>}}

content/ar/about.md

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+---
+title: About Us
+sidebar: false
+---
+
+NumPy is an open source project that enables numerical computing with Python. It was created in 2005 building on the early work of the Numeric and Numarray libraries. NumPy will always be 100% open source software and free for all to use. It is released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt).
+
+NumPy is developed in the open on GitHub, through the consensus of the NumPy and wider scientific Python community. For more information on our governance approach, please see our [Governance Document](https://www.numpy.org/devdocs/dev/governance/index.html).
+
+
+## Steering Council
+
+The NumPy Steering Council is the project's governing body. Its role is to ensure, through working with and serving the broader NumPy community, the long-term sustainability of the project, both as a software package and community. The NumPy Steering Council currently consists of the following members (in alphabetical order, by last name):
+
+- Sebastian Berg
+- Ralf Gommers
+- Charles Harris
+- Stephan Hoyer
+- Inessa Pawson
+- Matti Picus
+- Stéfan van der Walt
+- Melissa Weber Mendonça
+- Eric Wieser
+
+Emeritus:
+
+- Alex Griffing (2015-2017)
+- Allan Haldane (2015-2021)
+- Marten van Kerkwijk (2017-2019)
+- Travis Oliphant (project founder, 2005-2012)
+- Nathaniel Smith (2012-2021)
+- Julian Taylor (2013-2021)
+- Jaime Fernández del Río (2014-2021)
+- Pauli Virtanen (2008-2021)
+
+To contact the NumPy Steering Council, please email numpy-team@googlegroups.com.
+
+## Teams
+
+The NumPy project leadership is actively working on diversifying contribution pathways to the project.<br> NumPy currently has the following teams:
+
+- development
+- documentation
+- triage
+- website
+- survey
+- translations
+- sprint mentors
+- optimization
+- funding and grants
+
+See the [Team](/teams) page for more info.
+
+## NumFOCUS Subcommittee
+
+- Charles Harris
+- Ralf Gommers
+- Inessa Pawson
+- Sebastian Berg
+- External member: Thomas Caswell
+
+## Sponsors
+
+NumPy receives direct funding from the following sources:
+{{< sponsors >}}
+
+
+## Institutional Partners
+
+Institutional Partners are organizations that support the project by employing people that contribute to NumPy as part of their job. Current Institutional Partners include:
+
+- UC Berkeley (Stéfan van der Walt)
+- Quansight (Nathan Goldbaum, Ralf Gommers, Matti Picus, Melissa Weber Mendonça)
+- NVIDIA (Sebastian Berg)
+
+{{< partners >}}
+
+
+## Donate
+
+If you have found NumPy useful in your work, research, or company, please consider a donation to the project commensurate with your resources. Any amount helps! All donations will be used strictly to fund the development of NumPy’s open source software, documentation, and community.
+
+NumPy is a Sponsored Project of NumFOCUS, a 501(c)(3) nonprofit charity in the United States. NumFOCUS provides NumPy with fiscal, legal, and administrative support to help ensure the health and sustainability of the project. Visit [numfocus.org](https://numfocus.org) for more information.
+
+Donations to NumPy are managed by [NumFOCUS](https://numfocus.org). For donors in the United States, your gift is tax-deductible to the extent provided by law. As with any donation, you should consult with your tax advisor about your particular tax situation.
+
+NumPy's Steering Council will make the decisions on how to best use any funds received. Technical and infrastructure priorities are documented on the [NumPy Roadmap](https://www.numpy.org/neps/index.html#roadmap).
+
+{{<opencollective>}}
+

content/ar/arraycomputing.md

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+---
+title: Array Computing
+sidebar: false
+---
+
+*Array computing is the foundation of statistical, mathematical, scientific computing in various contemporary data science and analytics applications such as data visualization, digital signal processing, image processing, bioinformatics, machine learning, AI, and several others.*
+
+Large scale data manipulation and transformation depends on efficient, high-performance array computing. The language of choice for data analytics, machine learning, and productive numerical computing is **Python.**
+
+**Num**erical **Py**thon or NumPy is its de-facto standard Python programming language library that supports large, multi-dimensional arrays and matrices, and comes with a vast collection of high-level mathematical functions to operate on these arrays.
+
+Since the launch of NumPy in 2006, Pandas appeared on the landscape in 2008, and it was not until a couple of years ago that several array computing libraries showed up in succession, crowding the array computing landscape. Many of these newer libraries mimic NumPy-like features and capabilities, and pack newer algorithms and features geared towards machine learning and artificial intelligence applications.
+
+<img
+  src="/images/content_images/array_c_landscape.png"
+  alt="arraycl"
+  title="Array Computing Landscape" />
+
+**Array computing** is based on **arrays** data structures. *Arrays* are used to organize vast amounts of data such that a related set of values can be easily sorted, searched, mathematically manipulated, and transformed easily and quickly.
+
+Array computing is *unique* as it involves operating on the data array *at once*. What this means is that any array operation applies to an entire set of values in one shot. This vectorized approach provides speed and simplicity by enabling programmers to code and operate on aggregates of data, without having to use loops of individual scalar operations.

content/ar/case-studies/blackhole-image.md

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+---
+title: "Case Study: First Image of a Black Hole"
+sidebar: false
+---
+
+{{< figure >}}
+src = '/images/content_images/cs/blackhole.jpg' title = 'Black Hole M87' alt = 'black hole image' attribution = '(Image Credits: Event Horizon Telescope Collaboration)' attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg'
+{{< /figure >}}
+
+{{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Assistant Professor, Computing & Mathematical Sciences, Caltech*"
+> }} Imaging the M87 Black Hole is like trying to see something that is by definition impossible to see. 
+> 
+> {{< /blockquote >}}
+
+## A telescope the size of the earth
+
+The [Event Horizon telescope (EHT)](https://eventhorizontelescope.org) is an array of eight ground-based radio telescopes forming a computational telescope the size of the earth, studing the universe with unprecedented sensitivity and resolution.  The huge virtual telescope,  which uses a technique called very-long-baseline interferometry (VLBI), has an angular resolution of [20 micro-arcseconds][resolution] — enough to read a newspaper in New York from a sidewalk café in Paris!
+
+### Key Goals and Results
+
+* **A New View of the Universe:** The groundwork for the EHT's groundbreaking image had been laid 100 years earlier when [Sir Arthur Eddington][eddington] yielded the first observational support of Einstein's theory of general relativity.
+
+* **The Black Hole:** EHT was trained on a supermassive black hole approximately 55 million light-years from Earth, lying at the center of the galaxy Messier 87 (M87) in the Virgo galaxy cluster. Its mass is 6.5 billion times the Sun's. It had been studied for [over 100 years](https://www.jpl.nasa.gov/news/news.php?feature=7385), but never before had a black hole been visually observed.
+
+* **Comparing Observations to Theory:** From Einstein’s general theory of relativity, scientists expected to find a shadow-like region caused by gravitational bending and capture of light. Scientists could use it to measure the black hole's enormous mass.
+
+### The Challenges
+
+* **Computational scale**
+
+    EHT poses massive data-processing challenges, including rapid atmospheric phase fluctuations, large recording bandwidth, and telescopes that are widely dissimilar and geographically dispersed.
+
+* **Too much information**
+
+    Each day EHT generates over 350 terabytes of observations, stored on helium-filled hard drives. Reducing the volume and complexity of this much data is enormously difficult.
+
+* **Into the unknown**
+
+    When the goal is to see something never before seen, how can scientists be confident the image is correct?
+
+{{< figure >}}
+src = '/images/content_images/cs/dataprocessbh.png' title = 'EHT Data Processing Pipeline' alt = 'data pipeline' align = 'center' attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57'
+{{< /figure >}}
+
+## NumPy’s Role
+
+What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed?
+
+The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole.
+
+Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis.
+
+{{< figure >}}
+src = '/images/content_images/cs/bh_numpy_role.png' alt = 'role of numpy' title = 'The role of NumPy in Black Hole imaging'
+{{< /figure >}}
+
+For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below.
+
+{{< figure >}}
+src = '/images/content_images/cs/ehtim_numpy.png' alt = 'ehtim dependency map highlighting numpy' title = 'Software dependency chart of ehtim package highlighting NumPy'
+{{< /figure >}}
+
+Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole.
+
+## Summary
+
+The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories.  Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe.
+
+{{< figure >}}
+src = '/images/content_images/cs/numpy_bh_benefits.png' alt = 'numpy benefits' title = 'Key NumPy Capabilities utilized'
+{{< /figure >}}
+
+[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole
+
+[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment
+
+[ehtim]: https://github.com/achael/eht-imaging
+
+[astropy]: https://www.astropy.org/
+[mpl]: https://matplotlib.org/