/
plotmcvariable.jl
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/
plotmcvariable.jl
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## --- Load (and install if neccesary) the StatGeochem package which has the resampling functions we'll want
using StatGeochem
using Plots; gr();
if VERSION>=v"0.7"
using Statistics, DelimitedFiles, SpecialFunctions
end
## --- Download and unzip Keller and Schoene (2012) dataset
if ~isfile("ign.h5") # Unless it already exists
download("https://storage.googleapis.com/statgeochem/ign.h5.gz","./ign.h5.gz")
download("https://storage.googleapis.com/statgeochem/err2srel.csv","./err2srel.csv")
run(`gunzip -f ign.h5.gz`) # Unzip file
end
# Read HDF5 file
using HDF5
ign = h5read("ign.h5","vars")
## --- Compute proximity coefficients (inverse weights)
# # Compute inverse weights
# k = invweight(ign["Latitude"] .|> Float32, ign["Longitude"] .|> Float32, ign["Age"] .|> Float32)
# Since this is pretty computatually intensive, let's load a precomputed version instead
k = ign["k"]
# Probability of keeping a given data point when sampling
p = 1.0 ./ ((k .* median(5.0 ./ k)) .+ 1.0) # Keep rougly one-fith of the data in each resampling
# Set absolute uncertainties for each element where possible, using errors defined inerr2srel.csv
err2srel = importdataset("err2srel.csv", ',')
for e in ign["elements"]
# If there's an err2srel for this variable, create a "_sigma" if possible
if haskey(err2srel, e) && !haskey(ign, e*"_sigma")
ign[e*"_sigma"] = ign[e] .* (err2srel[e] / 2);
end
end
# Special cases: age uncertainty
ign["Age_sigma"] = (ign["Age_Max"]-ign["Age_Min"])/2;
t = (ign["Age_sigma"] .< 50) .| isnan.(ign["Age_sigma"]) # Find points with < 50 Ma absolute uncertainty
ign["Age_sigma"][t] .= 50 # Set 50 Ma minimum age uncertainty (1-sigma)
# Special cases: location uncertainty
ign["Latitude_sigma"] = ign["Loc_Prec"]
ign["Longitude_sigma"] = ign["Loc_Prec"]
## --- Resample a single variable
xmin = 0 # Minimum Age
xmax = 3900 # Maximum Age
nbins = 39
elem = "K2O" # Element to plot
# Look only at samples from a specific silica range
t = 43 .< ign["SiO2"] .< 51 # Mafic
# t = 51 .< ign["SiO2"] .< 62 # Intermediate
# t = 62 .< ign["SiO2"] .< 74 # Felsic
# t = 40 .< ign["SiO2"] .< 80 # All normal igneous
# t = trues(size(ign[elem])) # Everything
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign["Age"][t],ign[elem][t],xmin,xmax,nbins, p=p[t], x_sigma=ign["Age_sigma"][t])
# Plot results
plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:darkblue,label="")
plot!(xlabel="Age (Ma)", ylabel="$elem (wt. %)",xlims=(xmin,xmax),framestyle=:box,grid=:off,xflip=true) # Format plot
## --- Multiple silica ranges together
xmin = 0 # Minimum Age
xmax = 800 # Maximum Age
nbins = 40
elem = "Al2O3" # Element to plot
rsi = [43,51,62,74,80] # Ranges of silica to plot together
t = trues(size(ign[elem]))
h = plot(xlabel="Age (Ma)", ylabel="$elem (wt. %)",xlims=(xmin,xmax),framestyle=:box,grid=:off,xflip=true) # Format plot
for i=1:length(rsi)-1
t .= rsi[i] .< ign["SiO2"] .< rsi[i+1]
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign["Age"][t],ign[elem][t],xmin,xmax,nbins, p=p[t], x_sigma=ign["Age_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),seriestype=:scatter,color=lines[i],markerstrokecolor=lines[i],label="$(rsi[i])-$(rsi[i+1]) % SiO2")
end
savefig("$(elem)_$(xmin)-$(xmax) Ma.pdf")
display(h)
## --- Resample a ratio
tmin = 0 # Minimum age
tmax = 3900 # Maximum age
nbins = 39
num = "La" # Numerator
denom = "Yb" # Denominator
# Look only at samples from a specific silica range
t = 43 .< ign["SiO2"] .< 51 # Mafic
# t = 51 .< ign["SiO2"] .< 62 # Intermediate
# t = 62 .< ign["SiO2"] .< 74 # Felsic
# Exclude outliers
t = t .& inpctile(ign[num], 99) .& inpctile(ign[denom], 99)
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_ratios(ign["Age"][t],ign[num][t],ign[denom][t],tmin,tmax,nbins, p=p[t],
x_sigma=ign["Age_sigma"][t])
# Plot results
h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkred,markerstrokecolor=:darkred,label="")
plot!(h, xlabel="Age (Ma)", ylabel="$(num) / $(denom)",xlims=(tmin,tmax),framestyle=:box,grid=:off,xflip=true) # Format plot
display(h)
# savefig(h,"$(num)$(denom)_$(tmax)-$(tmin)Ma.pdf")
## --- Single element differentiation example
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 8
elem = "K2O"
h = plot(xlabel=xelem, ylabel="$(elem)",xlims=(xmin,xmax),framestyle=:box,grid=:off,fg_color_legend=:white) # Format plot
rt = [0,1,2,3,4] # Time range (Ga)
colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_means(ign[xelem][t],ign[elem][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], y_sigma=ign[elem*"_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),color=colors[i],seriestype=:scatter,markerstrokecolor=:auto,label="$(rt[i])-$(rt[i+1]) Ga")
plot!(h, c,m,style=:dot,color=colors[i],markerstrokecolor=:auto,label="")
end
# savefig(h,"$(xelem)_$(num)$(denom).pdf")
display(h)
## --- Ratio differentiation example
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 8
num = "Sc" # Numerator
denom = "Yb" # Denominator
h = plot(xlabel=xelem, ylabel="$(num) / $(denom)",xlims=(xmin,xmax),framestyle=:box,grid=:off,legend=:topleft,fg_color_legend=:white) # Format plot
rt = [0,1,2,3,4]
colors = reverse(resize_colormap(viridis[1:end-20],length(rt)-1))
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_ratio_medians(ign[xelem][t],ign[num][t],ign[denom][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], num_sigma=ign[num*"_sigma"][t], denom_sigma=ign[denom*"_sigma"][t])
# Plot results
plot!(h, c,m,yerror=(el,eu),color=colors[i],seriestype=:scatter,markerstrokecolor=:auto,label="$(rt[i])-$(rt[i+1]) Ga")
plot!(h, c,m,style=:dot,color=colors[i],markerstrokecolor=:auto,label="")
end
display(h)
## --- Ratio differentiation
xelem = "SiO2"
xmin = 40 # Minimum age
xmax = 80 # Maximum age
nbins = 20
num = "Sc" # Numerator
denom = "Yb" # Denominator
# Exclude outliers
t = inpctile(ign[num], 99) .& inpctile(ign[denom], 99)
# Resample, returning binned means and uncertainties
# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)
(c,m,el,eu) = bin_bsr_ratios(ign[xelem][t],ign[num][t],ign[denom][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem][t]*0.01, num_sigma=ign[num][t]*0.05, denom_sigma=ign[denom][t]*0.05)
# Plot results
h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:darkblue,label="")
plot!(h, xlabel=xelem, ylabel="$(num) / $(denom)",xlims=(xmin,xmax),framestyle=:box,grid=:off) # Format plot
display(h)
## --- Export differentiation trends
xelem = "SiO2"
xmin = 45
xmax = 75
nbins = 10
rt = [0,1,2,3,4] # Time range (Ga)
data = Dict()
for elem in ["Al2O3", "MgO", "Na2O", "Fe2O3T", "K2O", "CaO"]
for i=1:length(rt)-1
t = rt[i]*1000 .< ign["Age"] .< rt[i+1]*1000
# Resample, returning binned means and uncertainties
(c,m,e) = bin_bsr(ign[xelem][t],ign[elem][t],xmin,xmax,nbins, p=p[t],
x_sigma=ign[xelem*"_sigma"][t], y_sigma=ign[elem*"_sigma"][t])
data[xelem] = c
data[elem*"_$(rt[i])-$(rt[i+1])Ga"] = m
data[elem*"_$(rt[i])-$(rt[i+1])Ga_sigma"] = e
end
end
exportdataset(data,"MajorDifferentiation.csv",',')
## --- End of File
""