Base Options

Control Options

Color Settings

H1
H2
C1
CX
C2
L1
L2
P1
P1
N
Graphical representation of the selected color palette within the HCL color space using the hclplot function of the colorspace package. Depending on the palette type the "color pane" goes along different dimensions. Please note that this feature is in its beta state and still under development.
** - Setting region to load 'set lon -20 40' 'set lat 30 85' ** - Map projection 'set mpdset hires' 'set mproj nps' 'set mpvals -10 30 34 52' ** - Plotting relative humidity on 850 hPa 'set gxout shaded' 'set lev 850' 'd rhprs' 'set gxout contour' 'd rhprs' ** Labeling 'set string 3 l' 'draw string 1 1 NCEP GFS 1 deg analysis: relative humidity 850hPa'
Note that there is also a python implementation of the colorspace package available on github. More information about the development status of the python colorspace package can be found here: Alternatively you can use code similar to the snippet shown below:
## Note: example as seen on matplotlib webpage (2013-08-26): ## - http://matplotlib.org/examples/pylab_examples/griddata_demo.html ## Only adapted color handling ## Loading necessary python modules for this example from numpy.random import uniform, seed from matplotlib.mlab import griddata import matplotlib.pyplot as plt import numpy as np ## make up data. seed(0) npts = 200 x = uniform(-2,2,npts) y = uniform(-2,2,npts) z = x*np.exp(-x**2-y**2) ## define grid. xi = np.linspace(-2.1,2.1,100) yi = np.linspace(-2.1,2.1,200) ## grid the data. zi = griddata(x,y,z,xi,yi,interp='linear') ## contour the gridded data, plotting dots at the nonuniform data points. CS = plt.contour(xi,yi,zi,len(colors)-1,linewidths=0.5,colors='k') CS = plt.contourf(xi,yi,zi,len(colors)-1,colors=colors, vmax=abs(zi).max(), vmin=-abs(zi).max()) plt.colorbar() # draw colorbar ## plot data points. plt.scatter(x,y,marker='o',c='b',s=5,zorder=10) plt.xlim(-2,2) plt.ylim(-2,2) plt.title('griddata test (%d points)' % npts) plt.show()
%% Create surface data [X,Y] = meshgrid(-8:.5:8); R = sqrt(X.^2 + Y.^2) + eps; Z = sin(R)./R; %% Plotting surface surf(X,Y,Z,'EdgeColor','black') %% Adding your color palette and colorbar colormap(colors) colorbar()
If you use R the preferred way to handle color palettes is to use choose_palette() or hclwizard() on your local machine. Both graphical user interfaces return an R color palette function. However, you can also use the function call below to use the current palette in your R scripts.
## Custom color palette
Custom color palettes can also be registered to be able to call custom palettes by name. If the optional argument register = "Custom-Palette" is set (where "Custom-Palette" is the name of your new palette) the palette will be added to the list of available color palettes. Existing palettes can also be overruled. Note that the graphical interfaces (choose_palette()) will not use custom palettes. These register-calls can also be added to your local ~/.Rprofile.
## Register custom color palette
tab-5223-6

Authors

Jason C. Fisher, Reto Stauffer, Achim Zeileis

Graphical User Interface for Choosing HCL Color Palettes

Description

A graphical user interface (GUI) for viewing, manipulating, and choosing HCL color palettes.

Details

Computes palettes based on the HCL (hue-chroma-luminance) color model (as implemented by polarLUV). The GUIs interface the palette functions qualitative_hcl for qualitative palettes, sequential_hcl for sequential palettes with a single or multiple hues, and diverging_hcl for diverging palettes (composed from two single-hue sequential palettes).

Two different GUIs are implemented and can be selected using the function input argument gui ("tcltk" or "shiny"). Both GUIs allows for interactive modification of the arguments of the respective palette-generating functions, i.e., starting/ending hue (wavelength, type of color), minimal/maximal chroma (colorfulness), minimal maximal luminance (brightness, amount of gray), and a power transformations that control how quickly/slowly chroma and/or luminance are changed through the palette. Subsets of the parameters may not be applicable depending on the type of palette chosen. See qualitative_hcl and Zeileis et al. (2009) for a more detailed explanation of the different arguments. Stauffer et al. (2015) provide more examples and guidance.

Optionally, active palette can be illustrated by using a range of examples such as a map, heatmap, scatter plot, perspective 3D surface etc.

To demonstrate different types of deficiencies, the active palette may be desaturated (emulating printing on a grayscale printer) and collapsed to emulate different types of color-blindness (without red-green or green-blue contrasts) using the simulate_cvd functions.

choose_palette by default starts the Tcl/Tk version of the GUI while hclwizard by default starts the shiny version. hcl_wizard is an alias for hclwizard.

Value

Returns a palette-generating function with the selected arguments. Thus, the returned function takes an integer argument and returns the corresponding number of HCL colors by traversing HCL space through interpolation of the specified hue/chroma/luminance/power values.

HCL Color Palettes

Description

Qualitative, sequential (single-hue and multi-hue), and diverging color palettes based on the HCL (hue-chroma-luminance) color model.

Details

The HCL (hue-chroma-luminance) color model is a perceptual color model obtained by using polar coordinates in CIE LUV space (i.e., polarLUV), where steps of equal size correspond to approximately equal perceptual changes in color. By taking polar coordinates the resulting three dimensions capture the three perceptual axes very well: hue is the type of color, chroma the colorfulness compared to the corresponding gray, and luminance the brightness. This makes it relatively easy to create balanced palettes through trajectories in this HCL space. In contrast, in the more commonly-used HSV (hue-saturation-value) model (a simple transformation of RGB), the three axes are confounded so that luminance changes along with the hue leading to very unbalanced palettes (see rainbow_hcl for further illustrations).

Three types of palettes are derived based on the HCL model:

  • Qualitative: Designed for coding categorical information, i.e., where no particular ordering of categories is available and every color should receive the same perceptual weight.

  • Sequential: Designed for coding ordered/numeric information, i.e., where colors go from high to low (or vice versa).

  • Diverging: Designed for coding numeric information around a central neutral value, i.e., where colors diverge from neutral to two extremes.

The corresponding functions are qualitative_hcl, sequential_hcl, and diverging_hcl. Their construction principles are explained in more detail below. At the core is the luminance axis (i.e., light-dark contrasts): These are easily decoded by humans and matched to high-low differences in the underlying data. Therefore, sequential_hcl palettes are always based on a monotonic luminance sequence whereas the colors in a qualitative_hcl palette all have the same luminance. Finally, diverging_hcl palettes use the same monotonic luminance sequence in both “arms” of the palette, i.e., correspond to two balanced sequential palettes diverging from the same neutral value. The other two axes, hue and chroma, are used to enhance the luminance information and/or to further discriminate the color.

All three palette functions specify trajectories in HCL space and hence need either single values or intervals of the coordinates h, c, l. Their interfaces are always designed such that h, c, l can take vector arguments (as needed) but alternatively or additionally h1/h2, c1/c2/cmax, and l1/l2 can be specified. If so, the latter coordinates overwrite the former.

qualitative_hcl distinguishes the underlying categories by a sequence of hues while keeping both chroma and luminance constant to give each color in the resulting palette the same perceptual weight. Thus, h should be a pair of hues (or equivalently h1 and h2 can be used) with the starting and ending hue of the palette. Then, an equidistant sequence between these hues is employed, by default spanning the full color wheel (i.e, the full 360 degrees). Chroma c (or equivalently c1) and luminance l (or equivalently l1) are constants.

sequential_hcl codes the underlying numeric values by a monotonic sequence of increasing (or decreasing) luminance. Thus, the l argument should provide a vector of length 2 with starting and ending luminance (equivalently, l1 and l2 can be used). Without chroma (i.e., c = 0), this simply corresponds to a grayscale palette like gray.colors. For adding chroma, a simple strategy would be to pick a single hue (via h or h1) and then decrease chroma from some value (c or c1) to zero (i.e., gray) along with increasing luminance. For bringing out the extremes (a dark high-chroma color vs. a light gray) this is already very effective. For distinguishing also colors in the middle two strategies can be employed: (a) Hue can be varied as well by specifying an interval of hues in h (or beginning hue h1 and ending hue h2). (b) Instead of a decreasing chroma a triangular chroma trajectory can be employed from c1 over cmax to c2 (or equivalently a vector c of length 3). This yields high-chroma colors in the middle of the palette that are more easily distinguished from the dark and light extremes. Finally, instead of employing linear trajectories, power transformations are supported in chroma and luminance via a vector power (or separate p1 and p2). If power[2] (or p2) for the luminance trajectory is missing, it defaults to power[1]/p1 from the chroma trajectory.

diverging_hcl codes the underlying numeric values by a triangular luminance sequence with different hues in the left and in the right arm of the palette. Thus, it can be seen as a combination of two sequential palettes with some restrictions: (a) a single hue is used for each arm of the palette, (b) chroma and luminance trajectory are balanced between the two arms, (c) the neutral central value has zero chroma. To specify such a palette a vector of two hues h (or equivalently h1 and h2), either a single chroma value c (or c1) or a vector of two chroma values c (or c1 and cmax), a vector of two luminances l (or l1 and l2), and power parameter(s) power (or p1 and p2) are used. For more flexible diverging palettes without the restrictrictions above (and consequently more parameters) divergingx_hcl is available. For backward compatibility, diverge_hcl is a copy of diverging_hcl.

To facilitate using HCL-based palettes a wide range of example palettes are provided in the package and can be specified by a name instead of a set of parameters/coordinates. The examples have been taken from the literature and many approximate color palettes from other software packages such as ColorBrewer.org (RColorBrewer), CARTO colors (rcartocolor), or scico. The function hcl_palettes can be used to query the available pre-specified palettes. It comes with a print method (listing names and types), a summary method (additionally listing the underlying parameters/coordinates), and a plot method that creates a swatchplot with suitable labels.

Color Spectrum Plot

Description

Visualization of color palettes (given as hex codes) in HCL and/or RGB coordinates.

Details

The function specplot transforms a given color palette in hex codes into their HCL (polarLUV) and/or RGB (sRGB) coordinates. As the hues for low-chroma colors are not (or poorly) identified, by default a smoothing is applied to the hues (fix = TRUE). Also, to avoid jumps from 0 to 360 or vice versa, the hue coordinates are shifted suitably.

By default (plot = TRUE), the resulting HCL and optionally RGB coordinates are visualized by simple line plots along with the color palette x itself.

For comparing two palettes, specplot(x, y) can be used which adds lines (dashed, by default) corresponding to the y palette HCL/RGB coordinates in the display.

Value

specplot invisibly returns a list with components

HCL

a matrix of HCL coordinates,

RGB

a matrix of sRGB coordinates,

hex

original color palette x.

Palette Plot in HCL Space

Description

Visualization of color palettes in HCL space projections.

Details

The function hclplot is an auxiliary function for illustrating the trajectories of color palettes in two-dimensional HCL space projections. It collapses over one of the three coordinates (either the hue H or the luminance L) and displays a heatmap of colors combining the remaining two dimensions. The coordinates for the given color palette are highlighted to bring out its trajectory.

The function hclplot has been designed to work well with the hcl_palettes in this package. While it is possible to apply it to other color palettes as well, the results might look weird or confusing if these palettes are constructed very differently (e.g., as in the highly saturated base R palettes).

More specifically, the following palettes can be visualized well:

  • Qualitative with (approximately) constant luminance. In this case, hclplot shows a hue-chroma plane (in polar coordinates), keeping luminance at a fixed level (by default displayed in the main title of the plot). If the luminance is, in fact, not approximately constant, the luminance varies along with hue and chroma, using a simple linear function (fitted by least squares). hclplot shows a chroma-luminance plane, keeping hue at a fixed level (by default displayed in the main title of the plot). If the hue is, in fact, not approximately constant, the hue varies along with chroma and luminance, using a simple linear function (fitted by least squares.

  • Diverging with two (approximately) constant hues: This case is visualized with two back-to-back sequential displays.

To infer the type of display to use, by default, the following heuristic is used: If luminance is not approximately constant (range > 10) and follows rougly a triangular pattern, a diverging display is used. If luminance is not constant and follows roughly a linear pattern, a sequential display is used. Otherwise a qualitative display is used.

Value

hclplot invisibly returns a matrix with the HCL coordinates corresponding to x.

References

Zeileis A, Hornik K, Murrell P (2009). Escaping RGBland: Selecting Colors for Statistical Graphics. Computational Statistics & Data Analysis, 53, 3259–3270. doi: 10.1016/j.csda.2008.11.033 Preprint available from https://eeecon.uibk.ac.at/~zeileis/papers/Zeileis+Hornik+Murrell-2009.pdf.

Stauffer R, Mayr GJ, Dabernig M, Zeileis A (2015). Somewhere over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations. Bulletin of the American Meteorological Society, 96(2), 203–216. doi: 10.1175/BAMS-D-13-00155.1