{ "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", "

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, "metadata": {}, "outputs": [], "source": [ "# Load (and install if necessary) the Chron.jl package\n", "using Chron\n", "using Plots, DelimitedFiles" ] }, { "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 = ChronAgeData(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", "DispersionExample.jl\n", "EruptionDepositionAgeDemonstration.ipynb\n", "EruptionDepositionAgeDemonstration.jl\n", "Manifest.toml\n", "PlutonEmplacement.ipynb\n", "Project.toml\n", "archive.tar.gz\n", "ffsend\n", "histogram.pdf\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", "DispersionExample.jl\n", "EruptionDepositionAgeDemonstration.ipynb\n", "EruptionDepositionAgeDemonstration.jl\n", "Manifest.toml\n", "PlutonEmplacement.ipynb\n", "Project.toml\n", "archive.tar.gz\n", "ffsend\n", "histogram.pdf\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": [], "source": [ ";mkdir -p 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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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", "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 closure/crystallization distribution to use\n", "dist = BootstrappedDistribution\n", "## You might alternatively consider:\n", "# dist = UniformDistribution # A reasonable default\n", "# dist = MeltsVolcanicZirconDistribution # A single magmatic pulse, truncated by eruption\n", "# dist = ExponentialDistribution # Applicable for survivorship processes, potentially including inheritance/dispersion in Ar-Ar dates\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_rankorder.pdf\n", "KJ04-70_rankorder.svg\n", "KJ04-72.csv\n", "KJ04-72_distribution.pdf\n", "KJ04-72_rankorder.pdf\n", "KJ04-72_rankorder.svg\n", "KJ04-75.csv\n", "KJ04-75_distribution.pdf\n", "KJ04-75_rankorder.pdf\n", "KJ04-75_rankorder.svg\n", "KJ08-157.csv\n", "KJ08-157_distribution.pdf\n", "KJ08-157_rankorder.pdf\n", "KJ08-157_rankorder.svg\n", "KJ09-66.csv\n", "KJ09-66_distribution.pdf\n", "KJ09-66_rankorder.pdf\n", "KJ09-66_rankorder.svg\n", "distresults.csv\n", "results.csv\n" ] }, { "data": { "text/plain": [ "6×5 Matrix{Any}:\n", " \"Sample\" \"Age\" \"2.5% CI\" \"97.5% CI\" \"sigma\"\n", " \"KJ08-157\" 66.0702 66.0339 66.0934 0.015132\n", " \"KJ04-75\" 65.932 65.8856 65.971 0.0219183\n", " \"KJ09-66\" 65.937 65.8928 65.9782 0.0215128\n", " \"KJ04-72\" 65.955 65.9226 65.9765 0.0137933\n", " \"KJ04-70\" 65.8307 65.7538 65.8942 0.0362801" ] }, "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: 0:00:01\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: 0:00:02\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", "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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(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))\", framestyle=:box)\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: 66.0019430345121 +0.061905317399791215/-0.05984345868296259 Ma" ] }, { "data": { "image/png": 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"\n", "\n" ] }, "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": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.560548 seconds (1.75 M allocations: 285.193 MiB, 8.38% gc time, 69.44% compilation time)\n" ] }, { "data": { "image/png": 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"# 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, framestyle=:box)\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_rankorder.pdf\n", "KJ04-70_rankorder.svg\n", "KJ04-72.csv\n", "KJ04-72_distribution.pdf\n", "KJ04-72_rankorder.pdf\n", "KJ04-72_rankorder.svg\n", "KJ04-75.csv\n", "KJ04-75_distribution.pdf\n", "KJ04-75_rankorder.pdf\n", "KJ04-75_rankorder.svg\n", "KJ08-157.csv\n", "KJ08-157_distribution.pdf\n", "KJ08-157_rankorder.pdf\n", "KJ08-157_rankorder.svg\n", "KJ09-66.csv\n", "KJ09-66_distribution.pdf\n", "KJ09-66_rankorder.pdf\n", "KJ09-66_rankorder.svg\n", "agedist.csv\n", "distresults.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", "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": 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: 0:00:02\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: 0:00:04\u001b[39m\n" ] }, { "data": { "image/png": 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"source": [ "# Data about hiatuses\n", "nHiatuses = 2 # The number of hiatuses you have data for\n", "hiatus = HiatusData(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))\", framestyle=:box)" ] }, { "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 1.9.0-beta4", "language": "julia", "name": "julia-1.9" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.9.0" } }, "nbformat": 4, "nbformat_minor": 2 }