# Load the Chron.jl package
using Chron
using Plots, DelimitedFiles

nSamples = 5 # The number of samples you have data for
smpl = ChronAgeData(nSamples)
smpl.Name      =  ("KJ08-157", "KJ04-75", "KJ09-66", "KJ04-72", "KJ04-70",)
smpl.Path = "MyData/" # Where are the data files? Must match where you put the csv files below.
smpl.inputSigmaLevel = 2 # i.e., are the data files 1-sigma or 2-sigma. Integer.

smpl.Age_Unit = "Ma" # Unit of measurement for ages and errors in the data files
smpl.Height_Unit = "cm"; # Unit of measurement for Height and Height_sigma

;ls

;mkdir -p MyData/

# You can just paste your data in here, in the following two-column format.
# The first column is age and the second column is two-sigma analytical uncertainty.
# You should generally exclude all systematic uncertainties here.
data = [
66.12 0.14
66.115 0.048
66.11 0.1
66.11 0.17
66.096 0.056
66.088 0.081
66.085 0.076
66.073 0.084
66.07 0.11
66.055 0.043
66.05 0.16
65.97 0.12
]

# Now, let's write this data to a file, delimited by commas (',')
# In this example the filename is KJ08-157.csv, in the folder called MyData
writedlm("MyData/KJ08-157.csv", data, ',')

data = [
66.24 0.25
66.232 0.046
66.112 0.085
66.09 0.1
66.04 0.18
66.03 0.12
66.016 0.08
66.003 0.038
65.982 0.071
65.98 0.19
65.977 0.042
65.975 0.066
65.971 0.082
65.963 0.074
65.92 0.12
65.916 0.088
]
writedlm("MyData/KJ04-75.csv",data,',')

data = [
66.13 0.15
66.066 0.052
65.999 0.045
65.989 0.057
65.98 0.11
65.961 0.057
65.957 0.091
65.951 0.066
65.95 0.11
65.929 0.059
]
writedlm("MyData/KJ09-66.csv",data,',')

data = [
66.11 0.2
66.003 0.063
66.003 0.058
65.98 0.06
65.976 0.089
65.973 0.084
65.97 0.15
65.963 0.055
65.959 0.049
65.94 0.18
65.928 0.066
65.92 0.057
65.91 0.14
]
writedlm("MyData/KJ04-72.csv",data,',')

data = [
66.22 0.27
66.06 0.11
65.933 0.066
65.918 0.087
65.92 0.34
65.916 0.067
65.91 0.18
65.892 0.09
65.89 0.063
65.89 0.15
65.88 0.2
65.812 0.069
65.76 0.15
]
writedlm("MyData/KJ04-70.csv",data,',')



# Bootstrap a KDE of the pre-eruptive (or pre-deposition) zircon distribution
# shape from individual sample datafiles using a KDE of stacked sample data
BootstrappedDistribution = BootstrapCrystDistributionKDE(smpl)
h = plot(range(0,1,length=length(BootstrappedDistribution)), BootstrappedDistribution,
    label="Bootstrapped distribution", xlabel="Time (arbitrary units)", ylabel="Probability Density", 
    fg_color_legend=:white, framestyle=:box)
savefig(h, joinpath(smpl.Path,"BootstrappedDistribution.pdf"))
display(h)

# Number of steps to run in distribution MCMC
distSteps = 10^6
distBurnin = distSteps÷100

# Choose the form of the prior closure/crystallization distribution to use
dist = BootstrappedDistribution
## You might alternatively consider:
# dist = UniformDistribution              # A reasonable default
# dist = MeltsVolcanicZirconDistribution  # A single magmatic pulse, truncated by eruption
# dist = ExponentialDistribution          # Applicable for survivorship processes, potentially including inheritance/dispersion in Ar-Ar dates

# Run MCMC to estimate saturation and eruption/deposition age distributions
smpl = tMinDistMetropolis(smpl,distSteps,distBurnin,dist)
results = vcat(["Sample" "Age" "2.5% CI" "97.5% CI" "sigma"], hcat(collect(smpl.Name),smpl.Age,smpl.Age_025CI,smpl.Age_975CI,smpl.Age_sigma))
writedlm(smpl.Path*"results.csv", results, ',');

# Let's see what that did
run(`ls $(smpl.Path)`); 
results = readdlm(smpl.Path*"results.csv",',')

;ls MyData

# Make gzipped tar archive of the the whole MyData directory
run(`tar -zcf archive.tar.gz ./MyData`);

# Download prebuilt ffsend linux binary
isfile("ffsend") || download("https://github.com/timvisee/ffsend/releases/download/v0.2.65/ffsend-v0.2.65-linux-x64-static", "ffsend")

# Make ffsend executable
run(`chmod +x ffsend`);

; ./ffsend upload archive.tar.gz