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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.
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 simple SVG line plot with Matplotlib. Here is a minimal line plot (output image is shown on the right):
Scatterplot : A scatter graph or scatter plot is a type of display using variables for a set of data. The data is displayed as a collection of points, each having the value of one variable determining the position on the horizontal axis and the value of the other variable determining the position on the vertical axis. [9]
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
Scatter plots are often used to highlight the correlation between variables (x and y). Also called "dot plots" Scatter plot: Scatter plot (3D) position x; position y; position z; color; symbol; size; Similar to the 2-dimensional scatter plot above, the 3-dimensional scatter plot visualizes the relationship between typically 3 variables from a ...
A 3D scatter plot allows the visualization of multivariate data. This scatter plot takes multiple scalar variables and uses them for different axes in phase space. The different variables are combined to form coordinates in the phase space and they are displayed using glyphs and coloured using another scalar variable. [1]
The bandwidth of the Gaussian kernels is set in such a way that the entropy of the conditional distribution equals a predefined entropy using the bisection method. As a result, the bandwidth is adapted to the density of the data: smaller values of σ i {\displaystyle \sigma _{i}} are used in denser parts of the data space.
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