{ "cells": [ { "cell_type": "markdown", "id": "d99e28b8-e335-4e56-952a-ddbef596b1d4", "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 *Pb-loss-aware* 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", "

If running this notebook as an online Binder notebook and the webpage times out, click the badge at left to relaunch (refreshing will not work). Note that any changes will be lost!

\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, "id": "2cdabf6e-04fb-4082-a8e9-a24b6f60d2f2", "metadata": {}, "outputs": [], "source": [ "# Load (and install if necessary) the Chron.jl package\n", "using Chron\n", "using Plots, DelimitedFiles" ] }, { "cell_type": "markdown", "id": "cdee6490-0e43-4c38-b334-bb1e141f80dc", "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, "id": "6382e055-7c34-4878-8e90-71c5204abe6b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"m\"" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nsamples = 3 # The number of samples you have data for\n", "smpl = ChronAgeData(nsamples)\n", "smpl.Name = (\"KR18-04\", \"KR18-01\", \"KR18-05\")\n", "smpl.Height .= [ 0.0, 100.0, 200.0] # Arbitrary example heights\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?\n", "smpl.inputSigmaLevel = 1 # i.e., are the data files 1-sigma or 2-sigma. Integer.\n", "smpl.Age_Unit = \"Ma\" # Unit of measurement for ages and errors in the data files\n", "smpl.Height_Unit = \"m\" # Unit of measurement for Height and Height_sigma" ] }, { "cell_type": "markdown", "id": "89c5c1a2-dfdd-4745-8d87-686a538dd99f", "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, "id": "8d66999a-c19f-4bb4-8cbe-f483309d0338", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chron1.0Coupled.ipynb\n", "Chron1.0Coupled.jl\n", "Chron1.0CoupledConcordia.ipynb\n", "Chron1.0CoupledConcordia.jl\n", "Chron1.0CoupledSystematic.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", "ConcordiaExampleData\n", "DenverUPbExampleData\n", "EruptionDepositionAgeDemonstration.ipynb\n", "EruptionDepositionAgeDemonstration.jl\n", "FCT\n", "Manifest.toml\n", "MyData\n", "PlutonEmplacement.ipynb\n", "Project.toml\n", "archive.tar.gz\n", "ffsend\n" ] } ], "source": [ ";ls" ] }, { "cell_type": "markdown", "id": "d34a15e4-4874-4761-84fa-0a9c1b554552", "metadata": {}, "source": [ "Equivalently, we can also run unix commands using the `run()` function, i.e.,\n", "```\n", "run(`ls`);\n", "```" ] }, { "cell_type": "markdown", "id": "a4cc88e6-4581-4949-a495-9326bdc966ca", "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": 4, "id": "914297ee-a0cf-49b8-851d-cc48046f62c4", "metadata": {}, "outputs": [], "source": [ ";mkdir -p MyData/" ] }, { "cell_type": "markdown", "id": "4be2117f-bf68-4bb0-b678-1d04397738e8", "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 MacLennan et al. (2020), [10.1126/sciadv.aay6647](https://doi.org/10.1126/sciadv.aay6647)" ] }, { "cell_type": "code", "execution_count": 5, "id": "b2d06f69-6298-440b-a747-f8e616695498", "metadata": {}, "outputs": [], "source": [ "# You can just paste your data in here, in the following five-column format:\n", "# ²⁰⁷Pb/²³⁵U, ²⁰⁷Pb/²³⁵U sigma, ²⁰⁶Pb/²³⁸U, ²⁰⁶Pb/²³⁸U sigma, correlation\n", "# You should generally exclude all systematic uncertainties here, and all uncertainties\n", "# (sigma) must be absolute uncertainties\n", "data = [\n", "1.1002 0.00060511 0.123908 0.00001982528 0.333\n", "1.1003 0.0005226425 0.123893 0.000020442345 0.421\n", "1.1 0.0011 0.123874 0.00002353606 0.281\n", "1.1002 0.00060511 0.123845 0.000025388225 0.418\n", "1.1007 0.0005338395 0.123833 0.000025385765 0.534\n", "1.0991 0.001154055 0.123797 0.000031568235 0.298\n", "1.09931 0.0004067447 0.123762 0.00003898503 0.709\n", "1.09947 0.0004617774 0.123752 0.00002598792 0.579\n", "1.0986 0.00093381 0.123738 0.00003650271 0.288\n", "1.09883 0.00047799105 0.123735 0.0000222723 0.506\n", "1.09904 0.000384664 0.123733 0.000021653275 0.404\n", "1.0758 0.0005379 0.121175 0.00002302325 0.427\n", "]\n", "\n", "# Now, let's write this data to a file, delimited by commas (',')\n", "# In this example the filename is KR18-01.csv, in the folder called MyData\n", "writedlm(\"MyData/KR18-01.csv\", data, ',')" ] }, { "cell_type": "code", "execution_count": 6, "id": "054f2fe6-9f67-44fd-85c4-058508428fe1", "metadata": {}, "outputs": [], "source": [ "data = [\n", "1.1009 0.000935765 0.123906 0.00002849838 0.319\n", "1.1003 0.00077021 0.123901 0.000035311785 0.415\n", "1.0995 0.000494775 0.123829 0.000025384945 0.434\n", "1.0992 0.00060456 0.123813 0.000036524835 0.616\n", "1.1006 0.00071539 0.123813 0.00002228634 0.321\n", "1.0998 0.00076986 0.123802 0.00002537941 0.418\n", "1.0992 0.00065952 0.123764 0.00003589156 0.509\n", "1.0981 0.0010981 0.123727 0.00003959264 0.232\n", "1.0973 0.000526704 0.123612 0.00002966688 0.47\n", "1.0985 0.0008788 0.123588 0.00002842524 0.341\n", "1.0936 0.0005468 0.123193 0.000032646145 0.575\n", "1.0814 0.000513665 0.121838 0.0000304595 0.587\n", "]\n", "writedlm(\"MyData/KR18-04.csv\",data,',')" ] }, { "cell_type": "code", "execution_count": 7, "id": "5181cfcd-d8cd-4ee4-843e-b8911f563ade", "metadata": {}, "outputs": [], "source": [ "data = [\n", "1.09741 0.00038958055 0.123676 0.00002226168 0.601\n", "1.097 0.00060335 0.123629 0.000024107655 0.575\n", "1.0967 0.00054835 0.123618 0.0000247236 0.41\n", "1.0952 0.00120472 0.123594 0.00003151647 0.366\n", "1.0969 0.000712985 0.123553 0.000035212605 0.581\n", "1.0956 0.0005478 0.123547 0.000032739955 0.562\n", "1.0958 0.00071227 0.123546 0.00002779785 0.489\n", "1.09621 0.00046588925 0.123539 0.000033973225 0.75\n", "1.09612 0.0004822928 0.123533 0.000032736245 0.747\n", "1.0969 0.00076783 0.123505 0.0000419917 0.552\n", "1.0958 0.00060269 0.123469 0.00002345911 0.342\n", "1.09517 0.00037783365 0.123447 0.000030244515 0.783\n", "1.0948 0.000525504 0.123352 0.00003885588 0.737\n", "1.09413 0.0003720042 0.123288 0.00002527404 0.508\n", "1.09321 0.00046461425 0.123179 0.00003202654 0.69\n", "1.08592 0.0004017904 0.122429 0.00002938296 0.634\n", "]\n", "writedlm(\"MyData/KR18-05.csv\",data,',')" ] }, { "cell_type": "markdown", "id": "36cc91c7-77e1-413c-8e87-13f03bd13dc4", "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": "markdown", "id": "78ade5fb-6487-4842-90b0-e77a3ff1300f", "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 Samperton (2018)](https://doi.org/10.7185/geochemlet.1826) and for the Pb-loss-aware version we are using in this notebook, [Isoplot.jl](https://github.com/JuliaGeochronology/Isoplot.jl)\n", "\n", "#### (optional) Boostrap relative pre-eruptive (or pre-depositional) mineral age distribution" ] }, { "cell_type": "code", "execution_count": 8, "id": "8918d67a-fb38-46a6-97a4-3fc584a4eab6", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInterpreting the five columns of KR18-04.csv as:\n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | ²⁰⁷Pb/²³⁵U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | ²⁰⁶Pb/²³⁸U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | correlation coefficient |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInterpreting the five columns of KR18-01.csv as:\n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | ²⁰⁷Pb/²³⁵U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | ²⁰⁶Pb/²³⁸U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | correlation coefficient |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInterpreting the five columns of KR18-05.csv as:\n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | ²⁰⁷Pb/²³⁵U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | ²⁰⁶Pb/²³⁸U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | correlation coefficient |\n" ] }, { "data": { "image/png": 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BootstrapCrystDistributionKDE(smpl, tpbloss=0)\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, framestyle=:box)\n", "savefig(h, joinpath(smpl.Path,\"BootstrappedDistribution.pdf\"))\n", "display(h)" ] }, { "cell_type": "markdown", "id": "d9d6edda-09be-4bbd-ab82-835dc5a9471e", "metadata": {}, "source": [ "#### Run eruption/deposition age model" ] }, { "cell_type": "code", "execution_count": 9, "id": "c5b1a1ef-6c98-46d2-9dc4-a1fa92cf20a0", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mEstimating eruption/deposition age distributions...\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m1: KR18-04\n", "\u001b[36m\u001b[1m│ \u001b[22m\u001b[39mInterpreting the five columns of KR18-04.csv as:\n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | ²⁰⁷Pb/²³⁵U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | ²⁰⁶Pb/²³⁸U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | correlation coefficient |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m2: KR18-01\n", "\u001b[36m\u001b[1m│ \u001b[22m\u001b[39mInterpreting the five columns of KR18-01.csv as:\n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | ²⁰⁷Pb/²³⁵U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | ²⁰⁶Pb/²³⁸U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | correlation coefficient |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m3: KR18-05\n", "\u001b[36m\u001b[1m│ \u001b[22m\u001b[39mInterpreting the five columns of KR18-05.csv as:\n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | ²⁰⁷Pb/²³⁵U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | ²⁰⁶Pb/²³⁸U | 1-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 | correlation coefficient |\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 closure/crystallization distribution to use\n", "dist = HalfNormalDistribution\n", "## You might alternatively consider:\n", "# dist = BootstrappedDistribution \n", "# dist = UniformDistribution # A reasonable default\n", "# dist = MeltsVolcanicZirconDistribution # A single magmatic pulse, truncated by eruption\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(joinpath(smpl.Path, \"results.csv\"), results, ',');" ] }, { "cell_type": "code", "execution_count": 10, "id": "59fcf977-4a2a-4abb-8687-46098df7fc65", "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", "KR18-01.csv\n", "KR18-01_Concordia.pdf\n", "KR18-01_Concordia.svg\n", "KR18-01_Pbloss.pdf\n", "KR18-01_Pbloss.svg\n", "KR18-01_distribution.pdf\n", "KR18-01_distribution.svg\n", "KR18-04.csv\n", "KR18-04_Concordia.pdf\n", "KR18-04_Concordia.svg\n", "KR18-04_Pbloss.pdf\n", "KR18-04_Pbloss.svg\n", "KR18-04_distribution.pdf\n", "KR18-04_distribution.svg\n", "KR18-05.csv\n", "KR18-05_Concordia.pdf\n", "KR18-05_Concordia.svg\n", "KR18-05_Pbloss.pdf\n", "KR18-05_Pbloss.svg\n", "KR18-05_distribution.pdf\n", "KR18-05_distribution.svg\n", "agedist.csv\n", "distresults.csv\n", "height.csv\n", "lldist.csv\n", "results.csv\n" ] }, { "data": { "text/plain": [ "4×5 Matrix{Any}:\n", " \"Sample\" \"Age\" \"2.5% CI\" \"97.5% CI\" \"sigma\"\n", " \"KR18-04\" 751.96 751.274 752.449 0.304138\n", " \"KR18-01\" 752.126 751.698 752.494 0.200616\n", " \"KR18-05\" 750.723 750.381 750.964 0.148518" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see what that did\n", "run(`ls $(smpl.Path)`);\n", "results = readdlm(joinpath(smpl.Path, \"results.csv\"),',')" ] }, { "cell_type": "markdown", "id": "1a1d4317-4424-42bd-b047-0fa6331c9598", "metadata": {}, "source": [ "To see what one of these plots looks like, try pasting this into a markdown cell like the one below\n", "```\n", "\n", "```" ] }, { "cell_type": "markdown", "id": "f6080a62-ba47-4242-8bea-c8944a890d4f", "metadata": {}, "source": [ "\n", " " ] }, { "cell_type": "markdown", "id": "f80c2d88-3ab5-4a0d-86ca-00ba32e85438", "metadata": {}, "source": [ "For each sample, the eruption/deposition age distribution is inherently asymmetric, because of the one-sided relationship between mineral closure and eruption/deposition. For example (KJ04-70):\n", "\n", " \n", "\n", "(if no figure appears, double-click to enter this markdown cell and re-evaluate (`shift`-`enter`) after running the model above" ] }, { "cell_type": "markdown", "id": "bda79d8d-285d-409e-9e71-12f62ce47fb4", "metadata": {}, "source": [ "Consequently, rather than simply taking a mean and standard deviation of the stationary distribtuion of the Markov Chain, the histogram of the stationary distribution is fit to an empirical distribution function of the form \n", "\n", "$\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", "id": "98071d00-59e0-48c7-b667-7379d3935e30", "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": 11, "id": "2708fe2c-76ac-4225-a45a-512e0810468b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mGenerating stratigraphic age-depth model...\n", "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mBurn-in: 840000 steps\n", "\u001b[32mBurn-in... 100%|█████████████████████████████████████████| Time: 0:00:00\u001b[39m\n", "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mCollecting sieved stationary distribution: 1260000 steps\n", "\u001b[32mCollecting... 100%|██████████████████████████████████████| Time: 0:00:00\u001b[39m\n" ] }, { "data": { "image/png": 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", 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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), framestyle=:box)" ] }, { "cell_type": "markdown", "id": "7b4c5419-9848-4a82-a128-1effa5497344", "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": 12, "id": "65170b57-82cd-4993-a1db-d93afd5856dc", "metadata": {}, "outputs": [], "source": [ "writedlm(joinpath(smpl.Path,\"agedist.csv\"), agedist, ',') # Stationary distribution of the age-depth model\n", "writedlm(joinpath(smpl.Path,\"height.csv\"), mdl.Height, ',') # Stratigraphic heights corresponding to each row of agedist\n", "writedlm(joinpath(smpl.Path,\"lldist.csv\"), lldist, ',') # Log likelihood distribution (to check for stationarity)" ] }, { "cell_type": "markdown", "id": "e85c9082-8ebb-4c69-ac38-b757f68a5ad9", "metadata": {}, "source": [ "***\n", "## Plot results" ] }, { "cell_type": "code", "execution_count": 13, "id": "04581791-eb89-4eff-b682-a8e86e3874ea", "metadata": {}, "outputs": [ { "data": { "image/png": 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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))\", framestyle=:box)\n", "savefig(hdl,joinpath(smpl.Path,\"AgeDepthModel.svg\"))\n", "savefig(hdl,joinpath(smpl.Path,\"AgeDepthModel.pdf\"))\n", "display(hdl)" ] }, { "cell_type": "code", "execution_count": 14, "id": "3fb530d0-296d-4bd6-97b2-f34f230a7745", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interpolated age: 752.0700313718455 +0.3446157426226364/-0.34727421019431404 Ma" ] }, { "data": { "image/png": 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"\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Interpolate results at a given height\n", "height = 50\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", "id": "58916b09-048e-490a-b009-1d61fe3cb653", "metadata": {}, "source": [ "There are other things we can plot as well, such as deposition rate:" ] }, { "cell_type": "code", "execution_count": 15, "id": "a1bd1d8b-9833-4fbf-a8ed-a203d7e409da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.522807 seconds (1.71 M allocations: 234.527 MiB, 9.51% gc time, 73.11% compilation time)\n" ] }, { "data": { "image/png": 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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", "\n", "agebinedges = collect(minimum(mdl.Age):binwidth/binoverlap:maximum(mdl.Age))\n", "agebincenters = (agebinedges[1:end-binoverlap] + agebinedges[1+binoverlap:end])/2\n", "\n", "# Calculate rates for the stratigraphy of each markov chain step\n", "dhdt_dist = zeros(length(agebincenters), config.nsteps)\n", "@time for i=1:config.nsteps\n", " heights = linterp1(reverse(agedist[:,i]), reverse(mdl.Height), agebinedges, extrapolate=NaN)\n", " dhdt_dist[:,i] .= (heights[1:end-binoverlap] - heights[binoverlap+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", "\n", "# Plot results\n", "hdl = plot(agebincenters,dhdt, label=\"Mean\", color=:black, linewidth=2)\n", "plot!(hdl,[agebincenters; reverse(agebincenters)],[dhdt_16p; reverse(dhdt_84p)], fill=(0,0.2,:darkblue), linealpha=0, label=\"68% CI\")\n", "for lci in 20:5:45\n", " dhdt_lp = nanpctile(dhdt_dist,lci,dim=2)\n", " dhdt_up = nanpctile(dhdt_dist,100-lci,dim=2)\n", " plot!(hdl,[agebincenters; reverse(agebincenters)],[dhdt_lp; reverse(dhdt_up)], fill=(0,0.2,:darkblue), linealpha=0, label=\"\")\n", "end\n", "plot!(hdl, agebincenters,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,joinpath(smpl.Path,\"DepositionRateModelCI.svg\"))\n", "savefig(hdl,joinpath(smpl.Path,\"DepositionRateModelCI.pdf\"))\n", "display(hdl)" ] }, { "cell_type": "markdown", "id": "aaf5d5a7-9700-49d8-a0ca-573c5971485e", "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": 16, "id": "e9bba28d-c1db-42f2-98fb-1e105a318191", "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", "KR18-01.csv\n", "KR18-01_Concordia.pdf\n", "KR18-01_Concordia.svg\n", "KR18-01_Pbloss.pdf\n", "KR18-01_Pbloss.svg\n", "KR18-01_distribution.pdf\n", "KR18-01_distribution.svg\n", "KR18-04.csv\n", "KR18-04_Concordia.pdf\n", "KR18-04_Concordia.svg\n", "KR18-04_Pbloss.pdf\n", "KR18-04_Pbloss.svg\n", "KR18-04_distribution.pdf\n", "KR18-04_distribution.svg\n", "KR18-05.csv\n", "KR18-05_Concordia.pdf\n", "KR18-05_Concordia.svg\n", "KR18-05_Pbloss.pdf\n", "KR18-05_Pbloss.svg\n", "KR18-05_distribution.pdf\n", "KR18-05_distribution.svg\n", "agedist.csv\n", "distresults.csv\n", "height.csv\n", "lldist.csv\n", "results.csv\n" ] } ], "source": [ ";ls MyData" ] }, { "cell_type": "markdown", "id": "bd5b117b-7735-450e-a353-2eb41e3426ec", "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", "id": "a85bd8f4-d6df-4c4e-b63c-2f319e4f0c42", "metadata": {}, "source": [] }, { "cell_type": "markdown", "id": "e03880a6-5ed5-4cf6-90f8-03cb5a64b01e", "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": 17, "id": "dfea7a78-20b5-4737-8d9e-0b6818ac8005", "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": 18, "id": "89be10c4-d6fc-4fe1-9369-6540a5619099", "metadata": {}, "outputs": [], "source": [ "# Download prebuilt ffsend linux binary\n", "isfile(\"ffsend\") || 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": 19, "id": "8f83c20a-2736-4cae-99b2-36157065ab55", "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", "id": "6645f9f0-4147-4980-b565-4ae0d6fd8086", "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": "markdown", "id": "233b1e6b-74b3-448e-b03c-d122f35aca43", "metadata": {}, "source": [ "Keep in mind that, if running online, any changes you make to this 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" ] }, { "cell_type": "code", "execution_count": null, "id": "756e8ba5-d551-4f57-a4ff-bf60c920df7f", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "29a14bd1-1d5f-4cdc-a335-35e1d08fae5d", "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": { "kernelspec": { "display_name": "Julia 1.9.2", "language": "julia", "name": "julia-1.9" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.9.2" } }, "nbformat": 4, "nbformat_minor": 5 }