stippledocumentation
stipplecreates a hatch filling or stippling within a grid. This function is designed primarily to show regions of statistical significance in spatial maps.
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Contents
Syntax
stipple(x,y,mask) stipple(...,MarkerProperty,MarkerValue,...) stipple(...,'density',DensityValue) stipple(...,'resize',false) h = stipple(...)
Description
stipple(x,y,mask)plots black dots inx,ylocations wherever amaskcontainstruevalues. Dimensions ofx, y,andmaskmust all match.
stipple(...,MarkerProperty,MarkerValue,...)specifies any marker properties that are accepted by the plot function (e.g.,'color','marker','markersize', etc).
stipple(...,'density',DensityValue)指定点刻标记密度。默认的德nsity is100, but if your plot is too crowded you may specify a lower density value (and/or adjust the markersize).
stipple(...,'resize',false)overrides the'density'option and plots stippling at the exact resolution of the input grid. By default, the grids are resized because any grid larger than about 100x100 would produce so many stippling dots it would black out anything under them.
h = stipple(...)returns a handle of the plotted stippling objects.
Example 1
Here's an example you can try at home, which usespeaksto create a 1000x1000 grid of data:
% Load some sample data:[X,Y,Z] = peaks(1000); pcolor(X,Y,Z) shadinginterpholdon
Let's say everywhere Z exceeds 2.5 should have stippling:
mask = Z>2.5; stipple(X,Y,mask)
Example 2: Specify color
If you prefer gray dots to the default black dots, do this:
stipple(X,Y,mask,'color',0.5*[1 1 1])
Example 3: Specify density
Too many dots? Specify whatever density you'd like. The default density is 100, so values lower than that will produce fewer dots, whereas higher values produce more dots. (Specify'resize',falseif you want the density to exactly match the input grids.).
figure pcolor(X,Y,Z) shadinginterpholdonstipple(X,Y,mask,'density',30)
Example 4: Specify several options at once:
To set everything just the way you like it, specify as many options as you'd like. Here we'll plot big red plus signs:
stipple(X,Y,mask,'density',75,'color','r','marker','+','markersize',9)
Example 5: Regions of statistical significance
Here's a real-world application of stippling: Denote regions of statistically significant sea surface temperature trends with stipples. First, load the sample pacific_sst.mat dataset, which contains monthly gridded sea surface temperature data, and calculate trends and corresponding p-values withtrend:
loadpacific_sst[tr,p] = trend(sst,12); figure imagescn(lon,lat,tr) cb = colorbar; ylabel(cb,'SST trend \circC yr^{-1}') cmocean('balance','pivot')% sets the colormap with zero in the middle
Define statistical significance by, let's say, anything with a p-value of less than 0.01:
StatisticallySignificant = p<0.01;
Now plot withstipple:
holdonstipple(lon,lat,StatisticallySignificant)
Author Info
This function was written by Chad A. Greene, August 2018.