Search results
Results from the WOW.Com Content Network
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.
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.
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.
The template offers complex formatting and labeling options to control the output. Typically, each use is made into its own template, and the template is then transcluded into the article. See an example here, and an example of it being used in an article here. The use of fixed images, such as File:Narnia Timeline.svg, was common in the past ...
The top row is a series of plots using the escape time algorithm for 10000, 1000 and 100 maximum iterations per pixel respectively. The bottom row uses the same maximum iteration values but utilizes the histogram coloring method. Notice how little the coloring changes per different maximum iteration counts for the histogram coloring method plots.
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 .
The first scatterplot is formed from the points (d 1 α u 1i, d 2 α u 2i), for i = 1,...,n. The second plot is formed from the points (d 1 1−α v 1j, d 2 1−α v 2j), for j = 1,...,p. This is the biplot formed by the dominant two terms of the SVD, which can then be represented in a two-dimensional display.