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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. In some situations, sina ...
The filename extensions used vary, though .q is common for Q-files. Grid files may use .g, .x, .xy, or .xyz, among other extensions. The grid file contains the coordinates of the solution grid, while the solution file contains information typical of a CFD solution, flow density, flow momentum (a vector), and flow energy. [2]
A Ragone plot (/ r ə ˈ ɡ oʊ n iː / rə-GOH-nee) [1] is a plot used for comparing the energy density of various energy-storing devices. On such a chart the values of specific energy (in W·h / kg ) are plotted versus specific power (in W/kg).
For k > 1, the density function tends to zero as x approaches zero from above, increases until its mode and decreases after it. The density function has infinite negative slope at x = 0 if 0 < k < 1, infinite positive slope at x = 0 if 1 < k < 2 and null slope at x = 0 if k > 2. For k = 1 the density has a finite negative slope at x = 0.
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.
In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the ...
The goal of density estimation is to take a finite sample of data and to make inferences about the underlying probability density function everywhere, including where no data are observed. In kernel density estimation, the contribution of each data point is smoothed out from a single point into a region of space surrounding it.
In signal processing, a periodogram is an estimate of the spectral density of a signal. The term was coined by Arthur Schuster in 1898. [1] Today, the periodogram is a component of more sophisticated methods (see spectral estimation).