Here is what I ported from my ggplot2 to Julia as personal learning exercise 
(you know).
Tried to quick add some description. Far from perfect, as youâll see. Happy to see improvements of the plotting :-).
Btw. I donât understand why it accepts scalar, although I type it to Array{Number}. Putting scalar causes problem with arrrows!
taylor_plot(sdmod_rel::Array{Number}, correl::Array{Number}; bias=0.0, sdObs=1.0, plotBias=false, plotVarianceErr=false)
Create an (extended) Taylorplot.
Given the modelâs standard deviation relative to the observations sdmod_rel, the correlation with the
observations correl, and optionally the bias, an extended Taylor plot is created. Extended means, that apart
from the classical one (zero centered residuals, no bias) a point including the bias (indicated with an arrows
towards this point) and also a point under the scenario â= no variance errorâ (again with arrow) is plotted,
when plotBias and plotVarianceErr are true. sdObs is the standard deviation of the observations (it just scales the
axes essentially.)
Please note that sdmod_rel and correl need to be Arrays (otherwise arrows! does not work), so for only one point
wrap it in [âŚ].
Examples
julia> taylor_plot([0.8,1.2], [0.8, 0.5], bias=0.2, plotBias=true, plotVarianceErr=true)
function taylor_plot(sdmod_rel::Array{Number}, correl::Array{Number}; bias=0.0, sdObs=1.0, plotBias=false, plotVarianceErr=false)
sdmax = max(maximum(sdmod_rel), 1.0)
## Function to transform coordinates
correl_sd2taylor(sd, correl) = (x=correl * sd, y=sqrt(sd^2-(correl*sd)^2))
## Create Taylorplot gridlines
correls = reduce(vcat,[-1, -0.99, -0.95, -0.9:0.1:0.9, 0.95, 0.99, 1.0])
sds = 0:0.1:sdmax |> collect
fig, ax, sc = scatter(0,0, color=(:black, 0));
for c in correls
lines!(ax, c .* sds .* sdObs * 1.02, sqrt.(sds.^2-(c .* sds).^2).*sdObs * 1.02; linestyle= :dot, color=:grey)
end
for s in sds
lines!(ax, correls .* s .* sdObs, sqrt.(s.^2 .- (correls .* s).^2).*sdObs; linestyle= s==1 ? :dash : :dot, color=:grey)
end
## Creat isolines of root mean squared difference (RMSD) and labels
RMSD = 0.2:0.2:2*sdmax+0.2 |> collect
xvec = -1.0:0.01:1 |> collect
rmsd_lab_x=Vector{Float64}()
rmsd_lab_y=Vector{Float64}()
rmsd_lab_text=Vector{String}()
for r in RMSD[1:end-1]
x2 = @. (xvec * r + 1) * sdObs
y2 = @. sqrt(1-xvec^2) * r * sdObs
i_plot = [i for i in 1:length(x2) if sqrt(x2[i]^2+y2[i]^2)<=1.005*maximum(sds)*sdObs]
# This also works, but not if the array sent back by the compr is empty (collect does not work)
# xp, yp = zip([(xi,yi) for (xi, yi) in zip(x2, y2) if sqrt(xi^2+yi^2)<=1.005*maximum(sds)*sdObs]...) |> collect
lines!(ax, x2[i_plot], y2[i_plot], color=(:blue, 0.5))
i_lab=[i for i in 1:length(x2) if (y2[i]>0.3^2*sdObs^2/abs(x2[i])) & (x2[i]>0.0) & (sqrt(x2[i]^2+y2[i]^2)<=1.005*maximum(sds)*sdObs)]
if length(i_lab)>0
push!(rmsd_lab_x, x2[i_lab[1]])
push!(rmsd_lab_y, y2[i_lab[1]])
push!(rmsd_lab_text, "$(round(r, digits=2))")
end
end
#### Plotting data in Taylor space
x,y = collect.(zip(correl_sd2taylor.(sdmod_rel, correl)...) |> collect) .* sdObs
# First the arrows, so that the point is always on top
if plotBias
RMSD = @. sqrt(sdObs^2 + (sdmod_rel*sdObs)^2 - 2*sdObs^2*sdmod_rel*correl)
RMSE = @. sqrt(bias^2+RMSD^2)
RMSDangle = @. asin(y/RMSD)
RMSEangle = @. asin(bias/RMSE)
alpha = @. ifelse(x > sdObs, Ď - RMSDangle - RMSEangle, RMSDangle - RMSEangle)
xb = @. sdObs - cos(alpha)* RMSE
yb = @. sin(alpha) * RMSE
arrows!(x, y, xb.-x, yb.-y; color=1:length(x), linewidth=3)
end
if plotVarianceErr
x_no_var_err, y_no_var_err = collect.(zip(correl_sd2taylor.(1.0, correl)...) |> collect) .* sdObs
arrows!(x,y, x_no_var_err.-x, y_no_var_err.-y; color=1:length(x), linewidth=3)
end
# if (plotNoVarErr) {
# modPointsNoVarErr <- correl_sd2Taylor(1, correl) * sdObs
# p <- p + geom_point(data=modPointsNoVarErr, mapping=aes(x,y), color="red", size=2, alpha=0.5)
## Plot data points (original Taylorplot)
scatter!(ax, x, y, color=1:length(x), strokewidth=0.5, glowwidth = 5.0, glowcolor=(:black,0.5) )
## Plot point of "perfect model"
scatter!(ax, sdObs, 0.0, markersize=20, color=(:blue,0.5))
### Labels for coordinate system
correlTicks = @pipe reduce(vcat, [-0.9:0.1:0.9, 0.95, 0.99]) |> setdiff(_, [0])
text!(correlTicks*sdmax*sdObs*1.04, sqrt.(sdmax^2 .- (correlTicks*sdmax).^2)*sdObs*1.04,
text=["$(round(c, digits=2))" for c in correlTicks], align=(:center,:center), textsize=10)
#scatter!(correlTicks*sdmax*sdObs*1.04, sqrt.(sdmax^2 .- (correlTicks*sdmax).^2)*sdObs*1.04)
scatter!(rmsd_lab_x,rmsd_lab_y, markersize=(30,20), color=:white, strokewidth=0.5)
text!(rmsd_lab_x,rmsd_lab_y, text=rmsd_lab_text, align=(:center,:center), textsize=10)
## Clipping plot
xlims!(ax, min(0, 1.1*minimum(x)), max(sdObs,1.1*maximum(x)))
(fig, ax)
# taylor_grid = @pipe Base.product(correls, sds) |> DataFrame |> rename(_, [:correls, :sds])
# iso_rmsd = @pipe Base.product(0.2:0.2:2*sdmax+0.2 |> collect, -1.0:0.01:1 |> collect) |>
# DataFrame |> rename(_, [:RMSD, :xvec])
# @chain iso_rmsd begin
# @rtransform! begin
# :x2 = (:xvec * :RMSD + 1)* sdObs
# :y2 = sqrt(1-:xvec^2)*:RMSD*sdObs
# end
# @rsubset! sqrt(:x2^2+:y2^2)<=1.005*maximum(taylor_grid.sds)*sdObs
# end
# @rtransform! taylor_grid :xv = :correls*:sds
# @rtransform! taylor_grid begin
# :yv = sqrt(:sds^2-:xv^2)*sdObs
# :xv=:xv*sdObs
# end
# @transform! taylor_grid begin
# :sdsCat = categorical(:sds)
# :correlCat = categorical(:correls)
# end
# #plt = data(taylor_grid) * visual(Lines, linestyle=:dash) * mapping(:xv, :yv, group=(:correlCat, :sdsCat))
# #plt += data(taylor_grid) * visual(Lines, linestyle=:dash) * mapping(:xv, :yv, group=:sdsCat)
# #draw(plt; axis=(width = 1225, height = 825))
# fig=Figure()
end
Youâll get this:
P.S.: Quite proud to have posted my first maybe helpful Julia code