Programming: RQuantLib, Python and Rust
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RQuantLib 0.4.8: Small updates
A new version 0.4.8 of RQuantLib reached CRAN and Debian. This release was triggered by a CRAN request for an update to the configure.ac script which was easy enough (and which, as it happens, did not result in changes in the configure script produced). I also belatedly updated the internals of RQuantLib to follow suit to an upstream change in QuantLib. We now seamlessly switch between shared_ptr<> from Boost and from C++11 – Luigi wrote about the how and why in an excellent blog post that is part of a larger (and also excellent) series of posts on QuantLib internals.
QuantLib is a very comprehensice free/open-source library for quantitative finance, and RQuantLib connects it to the R environment and language.
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10 Python image manipulation tools
Today's world is full of data, and images form a significant part of this data. However, before they can be used, these digital images must be processed—analyzed and manipulated in order to improve their quality or extract some information that can be put to use.
Common image processing tasks include displays; basic manipulations like cropping, flipping, rotating, etc.; image segmentation, classification, and feature extractions; image restoration; and image recognition. Python is an excellent choice for these types of image processing tasks due to its growing popularity as a scientific programming language and the free availability of many state-of-the-art image processing tools in its ecosystem.
This article looks at 10 of the most commonly used Python libraries for image manipulation tasks. These libraries provide an easy and intuitive way to transform images and make sense of the underlying data.
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PyDev of the Week: Bruno Roche
This week we welcome Bruno Roche (@rochacbruno) as our PyDev of the Week! Bruno works for Red Hat and participates in the Python, Flask and Rust communities.
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HTTP Method and Accept headers
Zato 3.1 includes new means to manage access to REST services based on input Method and Accept headers in HTTP requests - here is how they can be employed in practice.
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Text Processing in Rust
This article is about text processing in Rust, but it also contains a quick introduction to pattern matching, which can be very handy when working with text.
Strings are a huge subject in Rust, which can be easily realized by the fact that Rust has two data types for representing strings as well as support for macros for formatting strings. However, all of this also proves how powerful Rust is in string and text processing.
Apart from covering some theoretical topics, this article shows how to develop some handy yet easy-to-implement command-line utilities that let you work with plain-text files. If you have the time, it'd be great to experiment with the Rust code presented here, and maybe develop your own utilities.
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Plotting the Chaikin AD line graph
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Create a sports score application with python
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