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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.
The simplest kind of linking, one-to-one, where both plots show different projections of the same data, and a point in one plot corresponds to exactly one point in the other. When using area plots, brushing any part of an area has the same effect as brushing it all and is equivalent to selecting all cases in the corresponding category.
A bagplot, or starburst plot, [1] [2] is a method in robust statistics for visualizing two-or three-dimensional statistical data, analogous to the one-dimensional box plot. Introduced in 1999 by Rousseuw et al., the bagplot allows one to visualize the location, spread, skewness , and outliers of a data set.
C++, Python: Implements GPML: OpenStereo [18] [19] Geoscience plotting tool Carlos Grohmann, University of São Paulo: GPL: Cross-platform: Python: Depends on NumPy and Matplotlib: OpendTect [20] Geoscience interpretation and visualization dGB Earth Sciences GPL or custom Cross-platform: C++: Interfaces with GMT Modelgeo [21]
The radar chart is a chart and/or plot that consists of a sequence of equi-angular spokes, called radii, with each spoke representing one of the variables. The data length of a spoke is proportional to the magnitude of the variable for the data point relative to the maximum magnitude of the variable across all data points.
A sina plot is a type of diagram in which numerical data are depicted by points distributed in such a way that the width of the point distribution is proportional to the kernel density. [ 1 ] [ 2 ] Sina plots are similar to violin plots , but while violin plots depict kernel density, sina plots depict the points themselves.
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Plotting the partial autocorrelation function and drawing the lines of the confidence interval is a common way to analyze the order of an AR model. To evaluate the order, one examines the plot to find the lag after which the partial autocorrelations are all within the confidence interval. This lag is determined to likely be the AR model's order ...