pavo : p erceptual a nalysis, v isualization and o rganization of spectral colour data in r
DESCRIPTION
pavo : P erceptual A nalysis, V isualization and O rganization of Spectral Colour Data in R. V. 0.5-1. Workflow: Organising (import, bin, trim, aggregate) Visualising (overlay plot, stack plot, heatmap , aggregated plot) Analysing ( tristimulus variables, visual models). - PowerPoint PPT PresentationTRANSCRIPT
pavo: Perceptual Analysis, Visualization and Organization of Spectral Colour Data in R
Workflow:
Organising (import, bin, trim, aggregate)
Visualising (overlay plot, stack plot, heatmap, aggregated plot)
Analysing (tristimulus variables, visual models)
V. 0.5-1
Example data set
Eurema heacbe Lomandra sp.30 ♂ 30 ♀ 50 leaves
Organising
Functions:getspec – importing spectra (e.g. ‘.ttt’)as.rspec – convert object to ‘rspec’, interpolate, trimaggspec – aggregate spectra by a functionplotsmooth – visually explore levels of LOESS smoothingprocspec – smoothing, normalising, trimming, binning, remove neg. values
Visualising
Function plot‘overlay’ – all spectra in one plot‘stack’ – individual stacked plots for comparison‘heatmap’ – heatmap (best for 3d data)
Function aggplotplot aggregated spectra – default mean ± s.d (can be customised)
Analysing
Trichromatic variables (hue, saturation, brightness)Function summary
Returns 28 (!) variables: know what you want
Hue (x 5)
Saturation (x 15)
Brightness (x 3)
Visual Modelling
Some popular approaches:
• Segment analysis (Endler 1990) – NB. Not really ‘visual’ • Receptor-noise (Vorobyev & Osorio 1998)• Colour hexagon (Chittka 1992) (coming)
• Tetrahedral colour-space (Endler & Mielke 2005, Goldsmith 1990, Stoddard & Prum 2008)
-Be aware of assumptions, limitations etc. & justify all choices. -Consider using multiple approaches & exploring effects of parameter variation (e.g. ‘noise’ where noise is uncertain)-Test your results where possible!
e.g Segment analysis (Endler 1990)
Function segclass
Eg. Receptor-noise (Vorobyev & Osorio 1998)
Q: Can this be seen against this by them
♂
♀
Predator
Friend
Another predator?
Eg. Receptor-noise (Vorobyev & Osorio 1998)
Function: vismodel
Quantum catch
x x
Stimulus Receptor sens.Illuminant
Eg. Receptor-noise (Vorobyev & Osorio 1998)Receptor adaptation
von-Kries correction -
Function: vismodel
Eg. Receptor-noise (Vorobyev & Osorio 1998)
Noise
Function: coldist
signal/noise ratio of receptor
relative density of receptor
Eg. Receptor-noise (Vorobyev & Osorio 1998)Calculating chromatic contrasts:
Eucilidean distance between points (Qi’s) weighted by noise (weber fraction)
Units = Just Noticeable Distances (JND’s)
Function: coldist
Dichromatic -
Eg. Receptor-noise (Vorobyev & Osorio 1998)
Function: coldist
Tetrachromat -
Trichromat -
Eg. Receptor-noise (Vorobyev & Osorio 1998)
Function: coldist
Achromatic contrastreceptor/s used in achromatic vision
Eg. Tetrahedral colour-space
Calculations:• Quantum catch as per receptor noise (inc. all specified
assumptions) with RELATIVE cone stimulation values instead of absolute
• Plot ‘em as co-ordinates• Descriptors of points (angles & euc. distance from achromatic
origin) are your colour variables (hue, saturation)
-Useful for all sorts of stuff, flexible (e.g. Endler & Mielke 2005 vs Stoddard & Prum 2008)-Cannot tell you about ‘discrimination’ as no measure of noise included-Chromatic only
Eg. Tetrahedral colour-space Functions:
vismodel – RELATIVE quantum catch in visual system
tcs – calculate tetrahedral coordinates using results of vismodel
dist – eucilidean distance between points in tetrahedron
Visualising:
tcsplot – 3d interactive plot
projplot – 2d projection plot
tcsvol – calculate volume overlap between clouds of points