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A simple way to parallelize single-color line rasterization is to let multiple line-drawing algorithms draw offset pixels of a certain distance from each other. [2] Another method involves dividing the line into multiple sections of approximately equal length, which are then assigned to different processors for rasterization. The main problem ...
plots and charts from data Plotly: GUI, command line Python: Commercial: No 2012: Any (web-based) plots and charts in browser, web-sharing and exporting, drag-and-drop data import, Python command line plotutils: command line, C/ C++: GPL: Yes 1989: September 27, 2009 / 2.6: Linux, Mac, Windows: Collection of command line programs, C/C++ API PLplot
Marching Square Matlab algorithm – An easy to understand open-source marching square algorithm. implementation in Java; Marching Squares code in Java. Given a 2D data set and thresholds, returns GeneralPath[] for easy plotting. Meandering Triangles explanation and sample Python implementation.
Plotting the line from (0,1) to (6,4) showing a plot of grid lines and pixels. All of the derivation for the algorithm is done. One performance issue is the 1/2 factor in the initial value of D. Since all of this is about the sign of the accumulated difference, then everything can be multiplied by 2 with no consequence.
Graphic types include line, bar or point plots in 2D and 3D, including anaglyph plots of 3D surfaces and other 3D plots. Euler has an API to use the open raytracer POV-Ray. Euler handles symbolic computations via Maxima, which is loaded as a separate process, communicating with Euler through pipes.
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.
SciDAVis can generate different types of 2D and 3D plots (such as line, scatter, bar, pie, and surface plots) from data that is either imported from ASCII files, entered by hand, or calculated using formulas. [2]
The left plot, titled 'Concave Line with Log-Normal Noise', displays a scatter plot of the observed data (y) against the independent variable (x). The red line represents the 'Median line', while the blue line is the 'Mean line'. This plot illustrates a dataset with a power-law relationship between the variables, represented by a concave line.