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Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.
Multivariate Kernel Smoothing and Its Applications is a comprehensive book on many topics in kernel smoothing, including density estimation. Includes ks package code snippets in R. kde2d.m A Matlab function for bivariate kernel density estimation. libagf A C++ library for multivariate, variable bandwidth kernel density estimation.
In 2008, developers of LabPlot and SciDAVis (another Origin clone, forked from QtiPlot) "found their project goals to be very similar" and decided to merge their code into a common backend while maintaining two frontends: LabPlot, integrated with the KDE desktop environment (DE); and SciDAVis, written in DE-independent Qt with fewer dependencies for easier cross-platform use.
KWayland is the KDE library for implementing Wayland support in KDE applications, it fulfills needs beyond what QtWayland provides. All the KDE applications in a plasma-wayland-session use this library and LXQt maybe as well. KWayland has been part of KDE Frameworks since 5.22 (May 2016); it was formerly distributed as part of KDE Plasma 5.
Kig comes up with a little program (written in Python) called pykig.py which can load a Python script, e.g. MyScript.py; build a Kig figure, described by this script; open Kig and display the figure. For example, here is how a Sierpinski triangle can be made (as an IFS) with pykig:
KDevelop 5 has parser backends for C, C++, Objective-C, OpenCL and JavaScript/QML, with plugins supporting PHP, Python 3 and Ruby. [6] Basic syntax highlighting and code folding are available for dozens of other source-code and markup formats, but without semantic analysis. KDevelop is part of the KDE project, and is based on KDE Frameworks and Qt.
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Core Python Programming is a textbook on the Python programming language, written by Wesley J. Chun. The first edition of the book was released on December 14, 2000. [1] The second edition was released several years later on September 18, 2006. [2] Core Python Programming is mainly targeted at higher education students and IT professionals. [3]