pavo (version 1.1.0)

plot.colspace: Plot spectra in a colourspace

Description

Plots reflectance spectra in the appropriate colorspace.

Usage

# S3 method for colspace
plot(x, ...)

Arguments

x

(required) an object of class colspace.

...

additional graphical options, which vary by modelled space. Refer to their individual documentation:

• diplot: dichromat space
• triplot: trichromat space
• tetraplot: tetrahedral space
• catplot: categorical space
• hexplot: colour hexagon
• cocplot: colour-opponent-coding space
• cieplot: cie spaces
• segplot: segment analysis space

Also see par.

Value

A 2D colorspace plot appropriate to the input data.

References

Smith T, Guild J. (1932) The CIE colorimetric standards and their use. Transactions of the Optical Society, 33(3), 73-134.

Westland S, Ripamonti C, Cheung V. (2012). Computational colour science using MATLAB. John Wiley & Sons.

Chittka L. (1992). The colour hexagon: a chromaticity diagram based on photoreceptor excitations as a generalized representation of colour opponency. Journal of Comparative Physiology A, 170(5), 533-543.

Chittka L, Shmida A, Troje N, Menzel R. (1994). Ultraviolet as a component of flower reflections, and the colour perception of Hymenoptera. Vision research, 34(11), 1489-1508.

Troje N. (1993). Spectral categories in the learning behaviour of blowflies. Zeitschrift fur Naturforschung C, 48, 96-96.

Stoddard, M. C., & Prum, R. O. (2008). Evolution of avian plumage color in a tetrahedral color space: A phylogenetic analysis of new world buntings. The American Naturalist, 171(6), 755-776.

Endler, J. A., & Mielke, P. (2005). Comparing entire colour patterns as birds see them. Biological Journal Of The Linnean Society, 86(4), 405-431.

Kelber A, Vorobyev M, Osorio D. (2003). Animal colour vision - behavioural tests and physiological concepts. Biological Reviews, 78, 81 - 118.

Backhaus W. (1991). Color opponent coding in the visual system of the honeybee. Vision Research, 31, 1381-1397.

See Also

plot

Examples

## Not run: ------------------------------------
# data(flowers)
# data(sicalis)
# 
# # Dichromat
# vis.flowers <- vismodel(flowers, visual = 'canis')
# di.flowers <- colspace(vis.flowers, space = 'di')
# plot(di.flowers)
# 
# # Colour hexagon 
# vis.flowers <- vismodel(flowers, visual = 'apis', qcatch = 'Ei', relative = FALSE, 
#                         vonkries = TRUE, achro = 'l', bkg = 'green')
# hex.flowers <- colspace(vis.flowers, space = 'hexagon')
# plot(hex.flowers, sectors = 'coarse')
# 
# # Tetrahedron (static)
# vis.sicalis <- vismodel(sicalis, visual = 'avg.uv')
# tcs.sicalis <- colspace(vis.sicalis, space = 'tcs')
# plot(tcs.sicalis)
# 
# # Tetrahedron (interactive)
# vis.sicalis <- vismodel(sicalis, visual = 'avg.uv')
# tcs.sicalis <- colspace(vis.sicalis, space = 'tcs')
# tcsplot(tcs.sicalis, size = 0.005)
# 
# ## Add points to interactive tetrahedron
# patch <- rep(c('C','T','B'), 7)
# tcs.crown <- subset(tcs.sicalis, 'C')
# tcs.breast <- subset(tcs.sicalis, 'B') 
# tcsplot(tcs.crown, col ='blue')
# tcspoints(tcs.breast, col ='red')
# 
# ## Plot convex hull in interactive tetrahedron
# tcsplot(tcs.sicalis, col = 'blue', size = 0.005)
# tcsvol(tcs.sicalis)
## ---------------------------------------------