Hi,

thanks to Juan for the Rpy package suggestion

I came up with this, which at least produces the plot. I can't quite work

out R specific bits at the moment (e.g. legend), but perhaps it might help

someone else.

#!/usr/bin/env python

import sys

from rpy2.robjects.packages import importr

import rpy2.robjects as robjects

r = robjects.r

# Note depends on R package plotrix

r.png(filename="x.png" ,width=480, height=480)

# fake some reference data

s = importr('stats')

ref = s.rnorm(30, sd=2)

ref_sd = r.sd(ref)

# add a little noise

model1 = s.rnorm(30, sd=2)

# add more noise

model2 = s.rnorm(30, sd=6)

# display the diagram with the better model

p = importr('plotrix')

#print plot

p.taylor_diagram(ref,model1)

# now add the worse model

p.taylor_diagram(ref,model2, add=True, col="blue")

# get approximate legend position

lpos = 1.5 * ref_sd[0]

# add a legend

#r.legend(lpos,lpos,legend=("Better","Worse"),pch=19,col=("red","blue"))

# now restore par values

#p.par(oldpar)

# show the "all correlation" display

p.taylor_diagram(ref,model1,pos_cor=False)

p.taylor_diagram(ref,model2,add=True,col="blue")

Martin

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