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Matplotlib (portmanteau of MATLAB, plot, and library [3]) is a plotting library for the Python programming language and its numerical mathematics extension NumPy.It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.
Source code has been (very) slightly modified into fully object-oriented matplotlib interface. A pseudo-random generator is used with a constant seed to ensure reproducibility when updating in the futre. The original shebang was also removed. The matplotlib (mpl) version is 1.5.3, with Python 2.7 and numpy 1.10
Python and Matplotlib are cross-platform, and are therefore available for Windows, OS X, and the Unix-like operating systems like Linux and FreeBSD. Matplotlib can create plots in a variety of output formats, such as PNG and SVG. Matplotlib mainly does 2-D plots (such as line, contour, bar, scatter, etc.), but 3-D functionality is also available.
A scatter plot, also called a scatterplot, scatter graph, scatter chart, scattergram, or scatter diagram, [2] is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. If the points are coded (color/shape/size), one additional variable can be displayed.
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Gnuplot allows the user to display or store plots in several ways: [23] [24] On the console, such as dumb, sixel. In a desktop window, such as Qt, wxt, X11, aquaterm, win. Embedded in a web page, such as SVG, HTML5, PNG, JPEG, animated GIF. File formats designed for document processing, such as PostScript, PDF, cgm, emf, LaTeX variants.
The root locus plots the poles of the closed loop transfer function in the complex s-plane as a function of a gain parameter (see pole–zero plot). Evans also invented in 1948 an analog computer to compute root loci, called a "Spirule" (after "spiral" and " slide rule "); it found wide use before the advent of digital computers .
This line attempts to display the non-random component of the association between the variables in a 2D scatter plot. Smoothing attempts to separate the non-random behaviour in the data from the random fluctuations, removing or reducing these fluctuations, and allows prediction of the response based value of the explanatory variable .