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The presence of one of these features, or the presence of multiple features, will cause for multiple lines to be plotted in a various possibility of configurations, depending on the features present in the sequences. A feature that will cause a very different result on the dot plot is the presence of low-complexity region/regions.
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.
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.
|color-even= Sets every other dot to a specific color (default red) |color-odd= Sets every odd dot to a specific color (default red) |square= Makes the chart/plot a square (default no) |width= The width of the chart |picture= The picture for the background of the chart, excluding File: or Image: (default Blank.png) |size= The size of the dots ...
The simplest method of drawing a line involves directly calculating pixel positions from a line equation. Given a starting point (,) and an end point (,), points on the line fulfill the equation = +, with = = being the slope of the line.
Dot plot may refer to: Dot plot (bioinformatics), for comparing two sequences; Dot plot (statistics), data points on a simple scale; Dot plot graphic for Federal ...
A dot chart or dot plot is a statistical chart consisting of data points plotted on a fairly simple scale, typically using filled in circles. There are two common, yet very different, versions of the dot chart. The first has been used in hand-drawn (pre-computer era) graphs to depict distributions going back to 1884. [1]
The Lineweaver–Burk plot derives from a transformation of the Michaelis–Menten equation, = + in which the rate is a function of the substrate concentration and two parameters , the limiting rate, and , the Michaelis constant.