微软发布Python IDE
投递人 曾经的梦 发布于 2011-08-31 21:03 评论(4) 有876人阅读 原文链接 [收藏] « »
微软发布了Python编程开发工具Python Tools for Visual Studio（PTVS）。PTVS是一个开源项目，采用Apache 2.0许可发布。
PTVS的主要特性包括：CPython、IronPython、Jython和PyPy；高级编辑功能如IntelliSense；多重构；内置REPL（read-eval-print loop）窗口；调试和分析功能，等等。
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NumPy and SciPy for IronPython / .Net
The Python NumPy and SciPy packages have been ported to run under IronPython, Microsoft’s Python implementation for .NET. These packages implement a fast and flexible multi-dimensional array package (NumPy) and a large collection of scientific and numerical algorithms built on top of the array (SciPy). Both packages consist of a heavily optimized native core implementation of the algorithms and a custom .NET interface for integrating the packages into IronPython.
The IronPython ports of NumPy and SciPy are full .NET ports and include custom C#/C interfaces to a common native C core. This means that the full functionality is available not only to IronPython but to all .NET languages such as C# or F# by directly accessing the C# interface objects or sometimes by evaluating IronPython expressions from other .NET languages. This means that a multi-dimensional array object (ndarray) can be passed seamlessly between IronPython and C# or F# code. Further, the ndarray object implements the standard IEnumerable interface, allowing the array object to often be used with existing code that is not specific to NumPy.
This release represents an early version of the architecture for the NumPy 2.0 release where there is a common, platform-independent core and multiple interface layers (currently CPython and IronPython/.NET). This version attempts to maintain full compatibility with earlier versions of NumPy, and in general this goal has been achieved. However, some inconsistencies were necessarily as a result of the port and the re-architecting of the NumPy core. See ‘What You Should Know’ below regarding this release.
The first part of this document provides a quick overview of the capabilities of NumPy and SciPy for those unfamiliar with the packages. These examples attempt to highlight a range of capabilities of these packages but are by no means complete. Please see http://numpy.org and http://scipy.org for the complete documentation. The second part of the document describes the known limitations, incompatibilities, and future work for these packages.
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Sho - Interactive data analysis with IronPython
Sho provides those who are working on Technical Computing-styled workloads an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts (in IronPython) with compiled code (in .NET) to enable fast and flexible prototyping. The environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any .NET language, as well as a feature-rich interactive shell for rapid development. The seamless nature of our environment allows for some unique capabilities:
Performing fast linear algebra and statistical computations from either C# or Python
Adding visualizations (such as plots and bar charts) to your C# or Python applications
Creating rich applications by lashing together C# libraries using Python
Debugging mixed python/C# code using Visual Studio 2010
Sho is widely used in Microsoft Research as well as a variety of product groups: we've found the ability to move seamlessly between script and compiled code tremendously useful, and we think you will too. To get you started, Sho comes with packages for large-scale parallel computing (via HPC and the Azure), statistics, and optimization, as well as an extensible package mechanism that makes it easy for you to create and share your own packages.
