{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chron.jl coupled eruption/deposition age and age-depth model\n", "\n", "This Jupyter notebook demonstrates the `Chron.jl` Julia package for integrated Bayesian eruption age and stratigraphic age-depth modelling, based in part on the work of [Keller, Schoene, and Samperton (2018)](https://doi.org/10.7185/geochemlet.1826), with age-depth modelling capabilities extended for use in [Schoene et al. (2019)](https://doi.org/10.1126/science.aau2422), [Deino et al. (2019a)](https://doi.org/10.1016/j.quascirev.2019.05.009) and [Deino et al. (2019b)](https://doi.org/10.1016/j.palaeo.2019.109258). For more information, see [github.com/brenhinkeller/Chron.jl](https://github.com/brenhinkeller/Chron.jl) and and [doi.org/10.17605/osf.io/TQX3F](https://doi.org/10.17605/osf.io/TQX3F)\n", "\n", "\"Launch \n", "

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\n", "\n", "Hint: `shift`-`enter` to run a single cell, or from the `Cell` menu select `Run All` to run the whole file. Any code from this notebook can be copied and pasted into the Julia REPL or a `.jl` script.\n", "***\n", "\n", "## Load required Julia packages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Load (and install if necessary) the Chron.jl package\n", "try\n", " using Chron\n", "catch\n", " using Pkg\n", " Pkg.add(\"Chron\")\n", " using Chron\n", "end\n", "\n", "using Statistics, StatsBase, DelimitedFiles, SpecialFunctions\n", "using Plots; gr(); default(fmt = :png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "## Enter sample information\n", "First, let's enter some basic information about your samples. How many are there, what are the sample names (needs to be a valid filename, BTW), and what are the stratigraphic heights and uncertainties? Then, we'll enter the ages as .csv files." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "nSamples = 5 # The number of samples you have data for\n", "smpl = NewChronAgeData(nSamples)\n", "smpl.Name = (\"KJ08-157\", \"KJ04-75\", \"KJ09-66\", \"KJ04-72\", \"KJ04-70\",) # These have to match the names used above\n", "smpl.Height .= [ -52.0, 44.0, 54.0, 82.0, 93.0,]\n", "smpl.Height_sigma .= [ 3.0, 1.0, 3.0, 3.0, 3.0,]\n", "smpl.Age_Sidedness .= zeros(nSamples) # Sidedness (zeros by default: geochron constraints are two-sided). Use -1 for a maximum age and +1 for a minimum age, 0 for two-sided\n", "smpl.Path = \"MyData/\" # Where are the data files? Must match where you put the csv files below.\n", "smpl.inputSigmaLevel = 2 # i.e., are the data files 1-sigma or 2-sigma. Integer.\n", "\n", "smpl.Age_Unit = \"Ma\" # Unit of measurement for ages and errors in the data files\n", "smpl.Height_Unit = \"cm\"; # Unit of measurement for Height and Height_sigma" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that smpl.Height *must* increase with increasing stratigraphic height -- i.e., stratigraphically younger samples must be more positive. For this reason, it is convenient to represent depths below surface as negative numbers.\n", "***\n", "Now let's see what's in our current directory (we'll use `;` to activate Julia's command-line shell mode, followed by a unix command, in this case `ls`" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chron1.0Coupled.ipynb\n", "Chron1.0Coupled.jl\n", "Chron1.0DistOnly.ipynb\n", "Chron1.0DistOnly.jl\n", "Chron1.0Radiocarbon.ipynb\n", "Chron1.0Radiocarbon.jl\n", "Chron1.0StratOnly.ipynb\n", "Chron1.0StratOnly.jl\n", "DenverUPbExampleData\n", "EruptionDepositionAgeDemonstration.ipynb\n", "EruptionDepositionAgeDemonstration.jl\n", "MyData\n", "ffsend\n" ] } ], "source": [ ";ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Equivalently, we can also run unix commands using the `run()` function" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chron1.0Coupled.ipynb\n", "Chron1.0Coupled.jl\n", "Chron1.0DistOnly.ipynb\n", "Chron1.0DistOnly.jl\n", "Chron1.0Radiocarbon.ipynb\n", "Chron1.0Radiocarbon.jl\n", "Chron1.0StratOnly.ipynb\n", "Chron1.0StratOnly.jl\n", "DenverUPbExampleData\n", "EruptionDepositionAgeDemonstration.ipynb\n", "EruptionDepositionAgeDemonstration.jl\n", "MyData\n", "ffsend\n" ] } ], "source": [ "run(`ls`);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we know how to access the command line, let's make a new folder for our example data. This can be called whatever you want, just make sure it matches `smpl.Path` above" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "mkdir: MyData/: File exists\n" ] } ], "source": [ ";mkdir MyData/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's make some files and paste in csv data for each one. For now, I'm pasting in example data from Clyde et al. (2016), [10.1016/j.epsl.2016.07.041](https://doi.org/10.1016/j.epsl.2016.07.041)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# You can just paste your data in here, in the following two-column format.\n", "# The first column is age and the second column is two-sigma analytical uncertainty.\n", "# You should generally exclude all systematic uncertainties here.\n", "data = [\n", "66.12 0.14\n", "66.115 0.048\n", "66.11 0.1\n", "66.11 0.17\n", "66.096 0.056\n", "66.088 0.081\n", "66.085 0.076\n", "66.073 0.084\n", "66.07 0.11\n", "66.055 0.043\n", "66.05 0.16\n", "65.97 0.12\n", "]\n", "\n", "# Now, let's write this data to a file, delimited by commas (',')\n", "# In this example the filename is KJ08-157.csv, in the folder called MyData\n", "writedlm(\"MyData/KJ08-157.csv\", data, ',')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data = [\n", "66.24 0.25\n", "66.232 0.046\n", "66.112 0.085\n", "66.09 0.1\n", "66.04 0.18\n", "66.03 0.12\n", "66.016 0.08\n", "66.003 0.038\n", "65.982 0.071\n", "65.98 0.19\n", "65.977 0.042\n", "65.975 0.066\n", "65.971 0.082\n", "65.963 0.074\n", "65.92 0.12\n", "65.916 0.088\n", "]\n", "writedlm(\"MyData/KJ04-75.csv\",data,',')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "data = [\n", "66.13 0.15\n", "66.066 0.052\n", "65.999 0.045\n", "65.989 0.057\n", "65.98 0.11\n", "65.961 0.057\n", "65.957 0.091\n", "65.951 0.066\n", "65.95 0.11\n", "65.929 0.059\n", "]\n", "writedlm(\"MyData/KJ09-66.csv\",data,',')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "data = [\n", "66.11 0.2\n", "66.003 0.063\n", "66.003 0.058\n", "65.98 0.06\n", "65.976 0.089\n", "65.973 0.084\n", "65.97 0.15\n", "65.963 0.055\n", "65.959 0.049\n", "65.94 0.18\n", "65.928 0.066\n", "65.92 0.057\n", "65.91 0.14\n", "]\n", "writedlm(\"MyData/KJ04-72.csv\",data,',')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "data = [\n", "66.22 0.27\n", "66.06 0.11\n", "65.933 0.066\n", "65.918 0.087\n", "65.92 0.34\n", "65.916 0.067\n", "65.91 0.18\n", "65.892 0.09\n", "65.89 0.063\n", "65.89 0.15\n", "65.88 0.2\n", "65.812 0.069\n", "65.76 0.15\n", "]\n", "writedlm(\"MyData/KJ04-70.csv\",data,',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, you could download .csv files that you have posted somewhere online using the Julia `download()` function, or using the unix command `curl` throught the command-line interface" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each sample in `smpl.Name`, we must have a `.csv` file in `smpl.Path` which contains each individual mineral age and uncertainty.\n", "\n", "***\n", "## Configure and run eruption/deposition age model\n", "To learn more about the eruption/deposition age estimation model, see also [Keller, Schoene, and Sameperton (2018)](https://doi.org/10.7185/geochemlet.1826) and the [BayeZirChron demo notebook](http://brenh.in/BayeZirChron). It is important to note that this model (like most if not all others) has no knowledge of open-system behaviour, so *e.g.*, Pb-loss will lead to erroneous results.\n", "\n", "#### Boostrap relative pre-eruptive (or pre-depositional) mineral age distribution" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Bootstrap a KDE of the pre-eruptive (or pre-deposition) zircon distribution\n", "# shape from individual sample datafiles using a KDE of stacked sample data\n", "BootstrappedDistribution = BootstrapCrystDistributionKDE(smpl)\n", "h = plot(range(0,1,length=length(BootstrappedDistribution)), BootstrappedDistribution,\n", " label=\"Bootstrapped distribution\", xlabel=\"Time (arbitrary units)\", ylabel=\"Probability Density\", \n", " fg_color_legend=:white)\n", "savefig(h, joinpath(smpl.Path,\"BootstrappedDistribution.pdf\"))\n", "display(h)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Run eruption/deposition age model" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimating eruption/deposition age distributions...\n", "1: KJ08-157\n", "2: KJ04-75\n", "3: KJ09-66\n", "4: KJ04-72\n", "5: KJ04-70\n" ] } ], "source": [ "# Number of steps to run in distribution MCMC\n", "distSteps = 10^6\n", "distBurnin = distSteps÷100\n", "\n", "# Choose the form of the prior distribution to use\n", "# Some pre-defined possiblilities include UniformDisribution,\n", "# TriangularDistribution, and MeltsVolcanicZirconDistribution\n", "# or you can define your own as with BootstrappedDistribution above\n", "dist = BootstrappedDistribution\n", "\n", "# Run MCMC to estimate saturation and eruption/deposition age distributions\n", "smpl = tMinDistMetropolis(smpl,distSteps,distBurnin,dist)\n", "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))\n", "writedlm(smpl.Path*\"results.csv\", results, ',');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BootstrappedDistribution.pdf\n", "KJ04-70.csv\n", "KJ04-70_distribution.pdf\n", "KJ04-70_distribution.svg\n", "KJ04-70_rankorder.pdf\n", "KJ04-70_rankorder.svg\n", "KJ04-72.csv\n", "KJ04-72_distribution.pdf\n", "KJ04-72_distribution.svg\n", "KJ04-72_rankorder.pdf\n", "KJ04-72_rankorder.svg\n", "KJ04-75.csv\n", "KJ04-75_distribution.pdf\n", "KJ04-75_distribution.svg\n", "KJ04-75_rankorder.pdf\n", "KJ04-75_rankorder.svg\n", "KJ08-157.csv\n", "KJ08-157_distribution.pdf\n", "KJ08-157_distribution.svg\n", "KJ08-157_rankorder.pdf\n", "KJ08-157_rankorder.svg\n", "KJ09-66.csv\n", "KJ09-66_distribution.pdf\n", "KJ09-66_distribution.svg\n", "KJ09-66_rankorder.pdf\n", "KJ09-66_rankorder.svg\n", "results.csv\n" ] }, { "data": { "text/plain": [ "6×5 Array{Any,2}:\n", " \"Sample\" \"Age\" \"2.5% CI\" \"97.5% CI\" \"sigma\"\n", " \"KJ08-157\" 66.0703 66.0343 66.0935 0.0149756\n", " \"KJ04-75\" 65.9318 65.886 65.9707 0.0218798\n", " \"KJ09-66\" 65.9368 65.8915 65.9779 0.0219181\n", " \"KJ04-72\" 65.9551 65.9219 65.9766 0.0138594\n", " \"KJ04-70\" 65.8307 65.7535 65.8949 0.0365122" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see what that did\n", "run(`ls $(smpl.Path)`); sleep(0.5)\n", "results = readdlm(smpl.Path*\"results.csv\",',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see what one of these plots looks like, try pasting this into a markdown cell like the one below\n", "```\n", "\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each sample, the eruption/deposition age distribution is inherently asymmetric, because of the one-sided relationship between mineral closure and eruption/deposition. For example (KJ04-70):\n", "\n", "\n", "\n", "(if no figure appears, double-click to enter this markdown cell and re-evaluate (`shift`-`enter`) after running the model above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Consequently, rather than simply taking a mean and standard deviation of the stationary distribtuion of the Markov Chain, the histogram of the stationary distribution is fit to an empirical distribution function of the form \n", "\n", "$\n", "\\begin{align}\n", "f(x) = a * \\exp\\left[d e \\frac{x - b}{c}\\left(\\frac{1}{2} - \\frac{\\arctan\\left(\\frac{x - b}{c}\\right)}{\\pi}\\right) - \\frac{d}{e}\\frac{x - b}{c}\\left(\\frac{1}{2} + \\frac{\\arctan\\left(\\frac{x - b}{c}\\right)}{\\pi}\\right)\\right]\n", "\\end{align}\n", "$\n", "\n", "where \n", "\n", "$\n", "\\begin{align}\n", "\\{a,c,d,e\\} \\geq 0\n", "\\end{align}\n", "$\n", "\n", "*i.e.*, an asymmetric exponential function with two log-linear segments joined with an arctangent sigmoid. After fitting, the five parameters $a$ - $e$ are stored in `smpl.params` and passed to the stratigraphic model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "## Configure and run stratigraphic model\n", "\n", "To run the stratigraphic MCMC model, we call the `StratMetropolisDist` function. If you want to skip the first step and simply input run the stratigraphic model with Gaussian mean age and standard deviation for some number of stratigraphic horizons, then you can set `smpl.Age` and `smpl.Age_sigma` directly, but then you'll need to call `StratMetropolis` instead of `StratMetropolisDist`" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating stratigraphic age-depth model...\n", "Burn-in: 5840000 steps\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mBurn-in...100%|█████████████████████████████████████████| Time: 0:00:02\u001b[39m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Collecting sieved stationary distribution: 8760000 steps\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mCollecting...100%|██████████████████████████████████████| Time: 0:00:03\u001b[39m\n" ] }, { "data": { "image/png": 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" }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Configure the stratigraphic Monte Carlo model\n", "config = NewStratAgeModelConfiguration()\n", "# If you in doubt, you can probably leave these parameters as-is\n", "config.resolution = 1.0 # Same units as sample height. Smaller is slower!\n", "config.bounding = 0.5 # how far away do we place runaway bounds, as a fraction of total section height\n", "(bottom, top) = extrema(smpl.Height)\n", "npoints_approx = round(Int,length(bottom:config.resolution:top) * (1 + 2*config.bounding))\n", "config.nsteps = 30000 # Number of steps to run in distribution MCMC\n", "config.burnin = 20000*npoints_approx # Number to discard\n", "config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps\n", "\n", "# Run the stratigraphic MCMC model\n", "(mdl, agedist, lldist) = StratMetropolisDist(smpl, config); sleep(0.5)\n", "\n", "# Plot the log likelihood to make sure we're converged (n.b burnin isn't recorded)\n", "plot(lldist,xlabel=\"Step number\",ylabel=\"Log likelihood\",label=\"\",line=(0.85,:darkblue))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most important output of this process is `agedist`, which contains the full stationary distribution of the age-depth model. We can save it to a file, but if this notebook is running remotely, you may have trouble getting it out of here (see section **Getting your data out**)!" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "writedlm(smpl.Path*\"agedist.csv\", agedist, ',') # Stationary distribution of the age-depth model\n", "writedlm(smpl.Path*\"height.csv\", mdl.Height, ',') # Stratigraphic heights corresponding to each row of agedist\n", "writedlm(smpl.Path*\"lldist.csv\", lldist, ',') # Log likelihood distribution (to check for stationarity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "## Plot results" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot results (mean and 95% confidence interval for both model and data)\n", "hdl = plot([mdl.Age_025CI; reverse(mdl.Age_975CI)],[mdl.Height; reverse(mdl.Height)], fill=(round(Int,minimum(mdl.Height)),0.5,:blue), label=\"model\")\n", "plot!(hdl, mdl.Age, mdl.Height, linecolor=:blue, label=\"\", fg_color_legend=:white) # Center line\n", "t = smpl.Age_Sidedness .== 0 # Two-sided constraints (plot in black)\n", "any(t) && plot!(hdl, smpl.Age[t], smpl.Height[t], xerror=(smpl.Age[t]-smpl.Age_025CI[t],smpl.Age_975CI[t]-smpl.Age[t]),label=\"data\",seriestype=:scatter,color=:black)\n", "t = smpl.Age_Sidedness .== 1 # Minimum ages (plot in cyan)\n", "any(t) && plot!(hdl, smpl.Age[t], smpl.Height[t], xerror=(smpl.Age[t]-smpl.Age_025CI[t],zeros(count(t))),label=\"\",seriestype=:scatter,color=:cyan,msc=:cyan)\n", "any(t) && zip(smpl.Age[t], smpl.Age[t].+nanmean(smpl.Age_sigma[t])*4, smpl.Height[t]) .|> x-> plot!([x[1],x[2]],[x[3],x[3]], arrow=true, label=\"\", color=:cyan)\n", "t = smpl.Age_Sidedness .== -1 # Maximum ages (plot in orange)\n", "any(t) && plot!(hdl, smpl.Age[t], smpl.Height[t], xerror=(zeros(count(t)),smpl.Age_975CI[t]-smpl.Age[t]),label=\"\",seriestype=:scatter,color=:orange,msc=:orange)\n", "any(t) && zip(smpl.Age[t], smpl.Age[t].-nanmean(smpl.Age_sigma[t])*4, smpl.Height[t]) .|> x-> plot!([x[1],x[2]],[x[3],x[3]], arrow=true, label=\"\", color=:orange)\n", "plot!(hdl, xlabel=\"Age ($(smpl.Age_Unit))\", ylabel=\"Height ($(smpl.Height_Unit))\")\n", "savefig(hdl,smpl.Path*\"AgeDepthModel.svg\")\n", "savefig(hdl,smpl.Path*\"AgeDepthModel.pdf\")\n", "display(hdl)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interpolated age: 66.00167048497062 +0.06206226408366433/-0.06024663189923274 Ma" ] }, { "data": { "image/png": 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" }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Interpolate results at a given height\n", "height = 0\n", "\n", "interpolated_age = linterp1s(mdl.Height,mdl.Age,height)\n", "interpolated_age_min = linterp1s(mdl.Height,mdl.Age_025CI,height)\n", "interpolated_age_max = linterp1s(mdl.Height,mdl.Age_975CI,height)\n", "print(\"Interpolated age: $interpolated_age +$(interpolated_age_max-interpolated_age)/-$(interpolated_age-interpolated_age_min) Ma\")\n", "\n", "# We can also interpolate the full distribution:\n", "interpolated_distribution = Array{Float64}(undef,size(agedist,2))\n", "for i=1:size(agedist,2)\n", " interpolated_distribution[i] = linterp1s(mdl.Height,agedist[:,i],height)\n", "end\n", "histogram(interpolated_distribution, xlabel=\"Age ($(smpl.Age_Unit))\", ylabel=\"N\", label=\"\", fill=(0.75,:darkblue), linecolor=:darkblue)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are other things we can plot as well, such as deposition rate:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " 0.619619 seconds (1.63 M allocations: 231.080 MiB, 11.73% gc time)\n" ] } ], "source": [ "# Set bin width and spacing\n", "binwidth = round(nanrange(mdl.Age)/10,sigdigits=1) # Can also set manually, commented out below\n", "# binwidth = 0.01 # Same units as smpl.Age\n", "binoverlap = 10\n", "ages = collect(minimum(mdl.Age):binwidth/binoverlap:maximum(mdl.Age))\n", "bincenters = ages[1+Int(binoverlap/2):end-Int(binoverlap/2)]\n", "spacing = binoverlap\n", "\n", "# Calculate rates for the stratigraphy of each markov chain step\n", "dhdt_dist = Array{Float64}(undef, length(ages)-binoverlap, config.nsteps)\n", "@time for i=1:config.nsteps\n", " heights = linterp1(reverse(agedist[:,i]), reverse(mdl.Height), ages)\n", " dhdt_dist[:,i] .= abs.(heights[1:end-spacing] .- heights[spacing+1:end]) ./ binwidth\n", "end\n", "\n", "# Find mean and 1-sigma (68%) CI\n", "dhdt = nanmean(dhdt_dist,dim=2)\n", "dhdt_50p = nanmedian(dhdt_dist,dim=2)\n", "dhdt_16p = nanpctile(dhdt_dist,15.865,dim=2) # Minus 1-sigma (15.865th percentile)\n", "dhdt_84p = nanpctile(dhdt_dist,84.135,dim=2) # Plus 1-sigma (84.135th percentile)\n", "# Other confidence intervals (10:10:50)\n", "dhdt_20p = nanpctile(dhdt_dist,20,dim=2)\n", "dhdt_80p = nanpctile(dhdt_dist,80,dim=2)\n", "dhdt_25p = nanpctile(dhdt_dist,25,dim=2)\n", "dhdt_75p = nanpctile(dhdt_dist,75,dim=2)\n", "dhdt_30p = nanpctile(dhdt_dist,30,dim=2)\n", "dhdt_70p = nanpctile(dhdt_dist,70,dim=2)\n", "dhdt_35p = nanpctile(dhdt_dist,35,dim=2)\n", "dhdt_65p = nanpctile(dhdt_dist,65,dim=2)\n", "dhdt_40p = nanpctile(dhdt_dist,40,dim=2)\n", "dhdt_60p = nanpctile(dhdt_dist,60,dim=2)\n", "dhdt_45p = nanpctile(dhdt_dist,45,dim=2)\n", "dhdt_55p = nanpctile(dhdt_dist,55,dim=2)\n", "\n", "# Plot results\n", "hdl = plot(bincenters,dhdt, label=\"Mean\", color=:black, linewidth=2)\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_16p; reverse(dhdt_84p)], fill=(minimum(mdl.Height),0.2,:darkblue), linealpha=0, label=\"68% CI\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_20p; reverse(dhdt_80p)], fill=(minimum(mdl.Height),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_25p; reverse(dhdt_75p)], fill=(minimum(mdl.Height),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_30p; reverse(dhdt_70p)], fill=(minimum(mdl.Height),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_35p; reverse(dhdt_65p)], fill=(minimum(mdl.Height),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_40p; reverse(dhdt_60p)], fill=(minimum(mdl.Height),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_45p; reverse(dhdt_55p)], fill=(minimum(mdl.Height),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,bincenters,dhdt_50p, label=\"Median\", color=:grey, linewidth=1)\n", "plot!(hdl, xlabel=\"Age ($(smpl.Age_Unit))\", ylabel=\"Depositional Rate ($(smpl.Height_Unit) / $(smpl.Age_Unit) over $binwidth $(smpl.Age_Unit))\", fg_color_legend=:white)\n", "savefig(hdl,smpl.Path*\"DepositionRateModelCI.svg\")\n", "savefig(hdl,smpl.Path*\"DepositionRateModelCI.pdf\")\n", "display(hdl)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "## Getting your data out\n", "As before, we can use the unix command `ls` to see all the files we have written. Actually getting them out of here may be harder though." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AgeDepthModel.pdf\n", "AgeDepthModel.svg\n", "BootstrappedDistribution.pdf\n", "DepositionRateModelCI.pdf\n", "DepositionRateModelCI.svg\n", "KJ04-70.csv\n", "KJ04-70_distribution.pdf\n", "KJ04-70_distribution.svg\n", "KJ04-70_rankorder.pdf\n", "KJ04-70_rankorder.svg\n", "KJ04-72.csv\n", "KJ04-72_distribution.pdf\n", "KJ04-72_distribution.svg\n", "KJ04-72_rankorder.pdf\n", "KJ04-72_rankorder.svg\n", "KJ04-75.csv\n", "KJ04-75_distribution.pdf\n", "KJ04-75_distribution.svg\n", "KJ04-75_rankorder.pdf\n", "KJ04-75_rankorder.svg\n", "KJ08-157.csv\n", "KJ08-157_distribution.pdf\n", "KJ08-157_distribution.svg\n", "KJ08-157_rankorder.pdf\n", "KJ08-157_rankorder.svg\n", "KJ09-66.csv\n", "KJ09-66_distribution.pdf\n", "KJ09-66_distribution.svg\n", "KJ09-66_rankorder.pdf\n", "KJ09-66_rankorder.svg\n", "agedist.csv\n", "height.csv\n", "lldist.csv\n", "results.csv\n" ] } ], "source": [ ";ls MyData" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could use the trick we learned before to view the SVG files in markdown cells, which you should then be able to right click and download as real vector graphics. e.g. pasting something like\n", "```\n", "\n", "```\n", "in a markdown cell such as this one" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Meanwhile, for the csv files we could try something like `; cat agedist.csv`, but agedist is probably too big to print. Let's try using ffsend instead, which should give you a download link. In fact, while we're at it, we might as well archive and zip the entire directory!" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Make gzipped tar archive of the the whole MyData directory\n", "run(`tar -zcf archive.tar.gz ./MyData`);" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Download prebuilt ffsend linux binary\n", "download(\"https://github.com/timvisee/ffsend/releases/download/v0.2.65/ffsend-v0.2.65-linux-x64-static\", \"ffsend\")\n", "\n", "# Make ffsend executable\n", "run(`chmod +x ffsend`);" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/bin/bash: ./ffsend: cannot execute binary file\n" ] } ], "source": [ "; ./ffsend upload archive.tar.gz" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You could alternatively use the ffsend command in this way to transfer individual files, for instance `; ./ffsend upload MyData/agedist.csv`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Keep in mind that any changes you make to this online notebook won't persist after you close the tab (or after it times out) even if you save your changes! You have to either copy-paste or `file`>`Download as` a copy.\n", "***\n", "## Stratigraphic model including hiatuses\n", "We can also deal with discrete hiatuses in the stratigraphic section if we know where they are and about how long they lasted. We need some different models and methods though. In particular, in addition to the `ChronAgeData` struct, we also need a `HiatusData` struct now, and will have a new output `hiatusdist` as well. This section is commented out, but you can uncomment, edit, and run if you have hiata of know duration in your section" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating stratigraphic age-depth model...\n", "Burn-in: 5840000 steps\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mBurn-in...100%|█████████████████████████████████████████| Time: 0:00:04\u001b[39m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Collecting sieved stationary distribution: 8760000 steps\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\u001b[32mCollecting...100%|██████████████████████████████████████| Time: 0:00:07\u001b[39m\n" ] }, { "data": { "image/png": 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" }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Data about hiatuses\n", "nHiatuses = 2 # The number of hiatuses you have data for\n", "hiatus = NewHiatusData(nHiatuses) # Struct to hold data\n", "hiatus.Height = [20.0, 35.0 ]\n", "hiatus.Height_sigma = [ 0.0, 0.0 ]\n", "hiatus.Duration = [ 0.2, 0.43]\n", "hiatus.Duration_sigma = [ 0.05, 0.07]\n", "\n", "# Run the model. Note the new `hiatus` argument and `hiatusdist` result\n", "(mdl, agedist, hiatusdist, lldist) = StratMetropolisDist(smpl, hiatus, config); sleep(0.5)\n", "\n", "# Plot results (mean and 95% confidence interval for both model and data)\n", "hdl = plot([mdl.Age_025CI; reverse(mdl.Age_975CI)],[mdl.Height; reverse(mdl.Height)], fill=(minimum(mdl.Height),0.5,:blue), label=\"model\")\n", "plot!(hdl, mdl.Age, mdl.Height, linecolor=:blue, label=\"\", fg_color_legend=:white)\n", "plot!(hdl, smpl.Age, smpl.Height, xerror=(smpl.Age-smpl.Age_025CI,smpl.Age_975CI-smpl.Age),label=\"data\",seriestype=:scatter,color=:black)\n", "plot!(hdl, xlabel=\"Age ($(smpl.Age_Unit))\", ylabel=\"Height ($(smpl.Height_Unit))\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "[![DOI](https://github.com/brenhinkeller/Chron.jl/raw/main/readme_figures/osf_io_TQX3F.svg?sanitize=true)](https://doi.org/10.17605/osf.io/TQX3F) " ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Julia 1.5.0", "language": "julia", "name": "julia-1.5" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.5.0" } }, "nbformat": 4, "nbformat_minor": 2 }