Search results
Results from the WOW.Com Content Network
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.
Various attributes can be applied to graphs, nodes and edges in DOT files. [3] These attributes can control aspects such as color, shape, and line styles. For nodes and edges, one or more attribute–value pairs are placed in square brackets [] after a statement and before the semicolon (which is optional). Graph attributes are specified as ...
A radar chart is a graphical method of displaying multivariate data in the form of a two-dimensional chart of three or more quantitative variables represented on axes starting from the same point. The relative position and angle of the axes is typically uninformative, but various heuristics, such as algorithms that plot data as the maximal ...
Twisted is a framework to program communications between computers, and is used (for example) by Dropbox. Libraries such as NumPy, SciPy and Matplotlib allow the effective use of Python in scientific computing, [209] [210] with specialized libraries such as Biopython and Astropy providing domain-specific functionality.
This is an example of a color RGB image stored in PPM format. (Not shown are the newline character(s) at the end of each line.) Image (magnified) P3 # "P3" means this is a RGB color image in ASCII # "3 2" is the width and height of the image in pixels # "255" is the maximum value for each color # This, up through the "255" line below are the ...
The header of both ASCII and binary files is ASCII text. Only the numerical data that follows the header is different between the two versions. The header always starts with a "magic number", a line containing: ply which identifies the file as a PLY file. The second line indicates which variation of the PLY format this is.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
For example, a set of points on a line in n-space transforms to a set of polylines in parallel coordinates all intersecting at n − 1 points. For n = 2 this yields a point-line duality pointing out why the mathematical foundations of parallel coordinates are developed in the projective rather than euclidean space.