Subplots are created without too much empty space in between (and resize properly !) Ellipses of confidence (stat_ellipse()) Custom fits with user-provided anonymous function (stat_fit(), requires curve fitting toolbox) GLM fits (stat_glm(), requires statistics toolbox)
2D binning with contour or heatmap output (stat_bin2d()) spline-smoothed y data with optional confidence interval (stat_smooth()) quantile-quantile plots (stat_qq()) of x data distribution against theoretical distribution or y data distribution. histograms of x-y differences (stat_cornerhist()) histograms and density plots of x values (stat_bin() and stat_density()) y data summarized by x values (uniques or binned) with confidence intervals (stat_summary()) Multiple ways of plotting statistical visualizations of the data: scatter plots (geom_point()) and jittered scatter plot (geom_jitter()) Multiple ways of directly plotting the data: Multiple options for consistent axis limits across facets, rows, columns, etc. Subplots by row and/or columns, or wrapping columns (facet_grid() and facet_wrap()). Colors, lightness, point markers, line styles, and point/line size ('color', 'lightness', 'marker', 'linestyle', 'size') Multiple ways of separating data by groups: Gramm works best with table-like data: separate variables/fields/columns for the variables of interest, with each variable having as many elements as observations. Accepts grouping data as arrays or cellstr. Accepts X,Y and Z data as arrays, matrices or cells of arrays In the last step, gramm draws the figure, and takes care of all the annoying parts: no need to loop over colors or subplots, colors and legends are generated automatically, axes limits are taken care of, etc. One instruction is enough to add each layer, and all layers offer many customization options. In the next steps, add graphical layers to your figure: raw data layers (directly plot data as points, lines.) or statistical layers (plot fits, histograms, densities, summaries with confidence intervals.). In a first step, provide gramm with the relevant data for the figure: X and Y variables, but also grouping variables that will determine color, subplot rows/columns, etc. The typical workflow to generate a figure with gramm is the following (the example figures in the vignette are generated using 6 lines of code): Journal of Open Source Software, 3(23), 568, Gramm: grammar of graphics plotting in Matlab. USE CASES AND EXAMPLE SCREENSHOTS ON THE GITHUB README: As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab.
Producing this, while this changes the color order to MATLAB's previous default co = [ 0 0 1.Gramm is a powerful plotting toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library.
This uses the default R2015 color order x = linspace(0,4*pi)
MATLAB 2014A PLOT CYCLE THROUGH COLORS CODE
Multiple lines in the same plot should then cycle through the RGB triplets specified in co.Įdit: Reading your post again, it seems you've tried this, so I tested it myself to show you it works and give you some sample code to look at. What you want here is set(groot,'defaultAxesColorOrder',co), as shown here: You're actually trying to change the default 'LineColor' cycle. If you want to use plot handles, I think colormap(handle, name) is what you're looking for, though I've never used it personally.Įdit: On second thought, I think I completely misinterpreted your question. % as the default colormap (in R2015 at least) As in sphere(10) % Creates a sphere surface plot using parula Assuming I'm understanding correctly what you're trying to do, you should be able to do colormap name.
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