numpy · rgommers · Aug 9, 2021 · Aug 9, 2021 · Aug 9, 2021 · Aug 9, 2021
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+---
+title: 404
+sidebar: false
+---
+
+عفواً! لقد وصلت إلى طريق مسدود.
+
+إذا كنت تعتقد أنه يجب أن يكون هناك شيء ما هنا ، فيمكنك [فتح مشكلة](https://github.com/numpy/numpy.org/issues) على GitHub. 
diff --git a/content/ar/about.md b/content/ar/about.md
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+---
+title: من نحن
+sidebar: false
+---
+
+_بعض المعلومات حول مشروع ومجتمع نمباي_
+
+نمباي هو مشروع مفتوح المصدر يهدف إلي إتاحة الحوسبة الرقمية باستخدام لغة برمجة بايثون. وقد أنشئت في عام 2005، استنادا علي العمل المبكر للمكتبتان Numeric و Numarray. NumPy will always be 100% open source software, free for all to use and released under the liberal terms of the [modified BSD license](https://github.com/numpy/numpy/blob/main/LICENSE.txt).
+
+وقد تم تطوير نمباي في العلن على GitHub ومن خلال توافق آراء مجتمع نمباي ونطاق أوسع لمجتمع بايثون العلمي. لمزيد من المعلومات حول نهج الإدارة، يرجى الاطلاع على [الوثيقة الإدارية](https://www.numpy.org/devdocs/dev/governance/index.html) الخاصة بنا.
+
+
+## المجلس التوجيهي
+
+ويتمثل دور المجلس التوجيهي في ضمان ازدهار المشروع على المدى الطويل، على كلا الصعيدين التقنى والاجتماعى وذلك من خلال العمل فى مجتمع نمباى الواسع وخدمته. ويتألف المجلس التوجيهي المعني بالمشروع حاليا من الأعضاء التالية (بالترتيب الأبجدي):
+
+- سيباستيان بيرج
+- رالف غومرس
+- تشارلز هاريس
+- ستيفان هوير
+- ميليسا فيبر ميندونسا (Melissa Weber Mendonça)
+- إينيسا باوسون
+- ماتى بيكاس
+- ستيفان فان دير والت(Stefan van der Walt)
+- إريك وايزر
+
+الأعضاء الفخريون:
+
+- ترافيس أوليفانت (مؤسس المشروع، 2005-2012)
+- ألكس غريفينغ (2015-2017)
+- مارتن فان كيركويك (2017-2019)
+- آلان هالدين (2015-2021)
+- ناثانييل سميث (2012-2021)
+- جوليان تايلور (2013-2021)
+- باولي فيرتانين (2008-2021)
+- جايمي فرنانديز ديل ريو(2014-2021)
+
+
+## الأقسام
+
+The NumPy project is growing! &#127881; We have teams for:
+
+- الشفرة(الكود)
+- الوثائق
+- المواقع الالكترونية
+- الفرز
+- survey
+- funding and grants
+
+See the [}}">Team]({{< relref) page for individual team members.
+
+## اللجنة الفرعية ل NumFOCUS
+
+- تشارليز هاريس
+- رالف غومرس
+- ميليسا ويبر ميندوكا
+- سيباستيشان بيرج
+- عضو خارجي: توماس كاسويل
+
+## الرعاة
+
+ويتلقى المشروع تمويلا مباشرا من المصادر التالية:
+{{< sponsors >}}
+
+
+## الشركاء المؤسيسون
+
+الشركاء المؤسسيون هم المنظمات التي تدعم المشروع وذلك بتوظيف الأشخاص الذين يساهمون في "نمباي" كجزء من عملهم. ويشمل الشركاء المؤسسيون الحاليون ما يلي:
+
+- جامعة كاليفورنيا في بركلي( ستيفان فان دير والت وسيباستيان بيرغ وروس بارنوفسكي)
+- نظام الواجهة الخلفية( رالف غومرس وميليسا وييبر ميندوكا ومارس لي وماتي بيكاس وبيروا بيتنسون)
+
+{{< partners >}}
+
+
+## التبرع
+
+يرجى النظر في التبرع للمشروع بما يتناسب مع مواردك إذا كنت وجدته مفيد في عملك أو بحثك أو شركتك. ،أي مبلغ قد يساعد، وستستخدم جميع التبرعات بشكل صارم لتطوير برمجيات المشروع مفتوحة المصدر، ووثائقه، ومجتمعه.
+
+نمباي هو مشروع ممول برعاية شركةNumFOCUS, 501(c)(3) وهي مؤسسة خيرية غير ربحية في الولايات المتحدة. فهى تدعم مشروع نمباي ماليا وقانونيا وإداريا للمساعدة في ضمان ازدهاره واستدامته. قم بزيارة [numfocus.org](https://numfocus.org) لمزيد من المعلومات.
+
+يمكنك التبرع من خلال: [](https://numfocus.org). وبخصوص المتبرعين في الولايات المتحدة، فإن هديتكم تخصم من الضرائب بالقدر الذي ينص عليه القانون. كما هو الحال في أي تبرع، وعلى هذا فيتوجب عليك التشاور مع مستشارك الضريبى.
+
+وسيتخذ المجلس التوجيهي لنمباى القرارات المتعلقة بكيفية استخدام أي أموال يتلقاها على أفضل وجه. وتوثق الأولويات التقنية وأولويات البنية التحتية على [](https://www.numpy.org/neps/index.html#roadmap).
+{{< numfocus >}}
diff --git a/content/ar/arraycomputing.md b/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.
diff --git a/content/ar/case-studies/blackhole-image.md b/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" caption="**Black Hole M87**" alt="black hole image" attr="*(Image Credits: Event Horizon Telescope Collaboration)*" attrlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" >}}
+
+<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>
+    <footer align="right">Katie Bouman, <cite>Assistant Professor, Computing & Mathematical Sciences, Caltech</cite></footer>
+</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" class="csfigcaption" caption="**EHT Data Processing Pipeline**" alt="data pipeline" align="middle" attr="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" attrlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" >}}
+
+## NumPy’s Role
+
+What if there's a problem with the data? Or perhaps an algorithm relies too heavily on a particular assumption. Will the image change drastically if a single parameter is changed?
+
+The EHT collaboration met these challenges by having independent teams evaluate the data, using both established and cutting-edge image reconstruction techniques. When results proved consistent, they were combined to yield the first-of-a-kind image of the black hole.
+
+Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis.
+
+{{< figure src="/images/content_images/cs/bh_numpy_role.png" class="fig-center" alt="role of numpy" caption="**The role of NumPy in Black Hole imaging**" >}}
+
+For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below.
+
+{{< figure src="/images/content_images/cs/ehtim_numpy.png" class="fig-center" alt="ehtim dependency map highlighting numpy" caption="**Software dependency chart of ehtim package highlighting NumPy**" >}}
+
+Besides NumPy, many other packages, such as [SciPy](https://www.scipy.org) and [Pandas](https://pandas.io), are part of the data processing pipeline for imaging the black hole. The standard astronomical file formats and time/coordinate transformations were handled by [Astropy][astropy], while [Matplotlib][mpl] was used in visualizing data throughout the analysis pipeline, including the generation of the final image of the black hole.
+
+## Summary
+
+The efficient and adaptable n-dimensional array that is NumPy's central feature enabled researchers to manipulate large numerical datasets, providing a foundation for the first-ever image of a black hole. A landmark moment in science, it gives stunning visual evidence of Einstein’s theory. The achievement encompasses not only technological breakthroughs but also international collaboration among over 200 scientists and some of the world's best radio observatories.  Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe.
+
+{{< figure src="/images/content_images/cs/numpy_bh_benefits.png" class="fig-center" alt="numpy benefits" caption="**Key NumPy Capabilities utilized**" >}}
+
+[resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole
+
+[eddington]: https://en.wikipedia.org/wiki/Eddington_experiment
+
+[ehtim]: https://github.com/achael/eht-imaging
+
+[astropy]: https://www.astropy.org/
+[mpl]: https://matplotlib.org/