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KDE GUI Addons; Utilities for graphical user interfaces; The KDE GUI addons provide utilities for graphical user interfaces in the areas of colors, fonts, text, images, keyboard input. KHTML: 4 khtml.git: KHTML is the HTML rendering engine from which WebKit was forked. It is based on the KParts technology and uses KJS for JavaScript support ...
You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.
Data visualization libraries Plotly.js is an open-source JavaScript library for creating graphs and powers Plotly.py for Python, as well as Plotly.R for R, MATLAB, Node.js, Julia, and Arduino and a REST API.
It supports graphing multiple functions simultaneously. It also provides some numerical and visual features like filling and calculating the area between the plot and the first axis, finding maximum and minimum values, changing function parameters dynamically, and plotting derivatives and integral functions. [4] [2] [3]
The magazine said that the book was not easy to read, but that it would expose experienced programmers to both old and new topics. [ 8 ] A review of SICP as an undergraduate textbook by Philip Wadler noted the weaknesses of the Scheme language as an introductory language for a computer science course. [ 9 ]
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.
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:
Violin plots are similar to box plots, except that they also show the probability density of the data at different values, usually smoothed by a kernel density estimator.A violin plot will include all the data that is in a box plot: a marker for the median of the data; a box or marker indicating the interquartile range; and possibly all sample points, if the number of samples is not too high.