{ "cells": [ { "cell_type": "markdown", "id": "f5983484-4fdb-476c-8ab5-2fcdaa7b0fd0", "metadata": {}, "source": [ "# Chron.jl standalone age-depth model with simple Gaussian age constraints\n", "\n", "This Jupyter notebook demonstrates an example age-depth model using [Chron.jl](https://github.com/brenhinkeller/Chron.jl). For more information, see [github.com/brenhinkeller/Chron.jl](https://github.com/brenhinkeller/Chron.jl) 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, "id": "f2af162e-4998-4f1c-bfdd-19bdffba802e", "metadata": {}, "outputs": [], "source": [ "# Load (and install if necessary) the Chron.jl package\n", "using Chron\n", "using Plots, DelimitedFiles" ] }, { "cell_type": "markdown", "id": "eda0a00a-60d7-434a-b782-56dd2bc44b21", "metadata": {}, "source": [ "***\n", "## Enter sample information\n", "Paste your data in here!" ] }, { "cell_type": "code", "execution_count": 2, "id": "1b6ff424-a166-4deb-8b52-ebca0bffa67a", "metadata": {}, "outputs": [], "source": [ "# Input the number of samples we wish to model (must match below)\n", "nSamples = 4\n", "\n", "# Make an instance of a ChronSection object for nSamples\n", "smpl = ChronAgeData(nSamples)\n", "smpl.Name = (\"Sample 1\", \"Sample 2\", \"Sample 3\", \"Sample 4\") # Et cetera\n", "smpl.Age = [ 6991, 7088, 7230, 7540,] # Measured ages\n", "smpl.Age_sigma = [ 30, 70, 50, 50,] # Measured 1-σ uncertainties\n", "smpl.Height[:] = [ -355, -380,-397.0,-411.5,] # Depths below surface should be negative\n", "smpl.Height_sigma[:] = fill(0.01, nSamples) # Usually assume little or no sample height uncertainty\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 do you want output files to be stored\n", "\n", "smpl.Age_Unit = \"ka\"; # Unit of measurement for ages\n", "smpl.Height_Unit = \"m\"; # Unit of measurement for Height and Height_sigma" ] }, { "cell_type": "markdown", "id": "615e9e01-406a-415b-8d30-ab0ba9b4a408", "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", "***\n", "## Option A: Configure and run stratigraphic model, no hiatuses\n", "\n", "To run the stratigraphic MCMC model, we call the `StratMetropolis` function, which uses the Gaussian mean age and standard deviation set in `smpl.Age` and `smpl.Age_sigma`" ] }, { "cell_type": "code", "execution_count": 3, "id": "d0e8d460-0c0f-4360-ae66-c601044ba0b1", "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: 5660000 steps\n", "\u001b[32mBurn-in... 100%|█████████████████████████████████████████| Time: 0:00:02\u001b[39m\n", "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mCollecting sieved stationary distribution: 16980000 steps\n", "\u001b[32mCollecting... 100%|██████████████████████████████████████| Time: 0:00:06\u001b[39m\n" ] }, { "data": { "image/png": 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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 = 10000*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) = StratMetropolis(smpl, config)\n", "\n", "# Write the results to file\n", "run(`mkdir -p $(smpl.Path)`) # Make sure that the path exists\n", "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*\"age.csv\", mdl.Age, ',') # Mean age of resulting model\n", "writedlm(smpl.Path*\"age_025CI.csv\", mdl.Age_025CI, ',') # 2.5% confidence interval of resulting model\n", "writedlm(smpl.Path*\"age_975CI.csv\", mdl.Age_975CI, ',') # 97.5% confidence interval of resulting model\n", "writedlm(smpl.Path*\"lldist.csv\", lldist, ',') # Log likelihood distribution (to check for stationarity)\n", "\n", "# Plot the log likelihood to make sure we're converged (n.b burnin isn't recorded)\n", "hdl = plot(lldist,xlabel=\"Step number\",ylabel=\"Log likelihood\",label=\"\",line=(0.85,:darkblue),framestyle=:box)\n", "savefig(hdl,smpl.Path*\"lldist.pdf\")\n", "display(hdl)" ] }, { "cell_type": "markdown", "id": "87d41c2c-e70f-4458-a9cf-2c3ce070625a", "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**)!\n", "***\n", "## Plot results" ] }, { "cell_type": "code", "execution_count": 4, "id": "d7465541-c88b-44a1-ae83-f21c227fe461", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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"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=2*smpl.Age_sigma[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=(2*smpl.Age_sigma[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)),2*smpl.Age_sigma[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 ($(smpl.Height_Unit))\", framestyle=:box)\n", "savefig(hdl,smpl.Path*\"AgeDepthModel.svg\")\n", "savefig(hdl,smpl.Path*\"AgeDepthModel.pdf\")\n", "display(hdl)" ] }, { "cell_type": "code", "execution_count": 5, "id": "1a311da9-ffd6-4f39-bf4c-f4b2a86b23dd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interpolated age at height=-404: 7383.812457480191 +140.90578308057047/-136.42163393857118 ka" ] }, { "data": { "image/png": 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$age_at_height +$(age_at_height_max-age_at_height)/-$(age_at_height-age_at_height_min) $(smpl.Age_Unit)\")\n", "\n", "# Optional: interpolate full age distribution\n", "interpolated_distribution = Array{Float64}(undef,size(agedist,2))\n", "for i=1:size(agedist,2)\n", " interpolated_distribution[i] = linterp1s(mdl.Height,agedist[:,i],height)\n", "end\n", "hdl = histogram(interpolated_distribution, nbins=50, fill=(0.75,:darkblue), label=\"\", framestyle=:box)\n", "plot!(hdl, xlabel=\"Age ($(smpl.Age_Unit)) at height=$height\", ylabel=\"Likelihood (unnormalized)\")" ] }, { "cell_type": "markdown", "id": "0b69a3f9-9446-42bc-b4cb-6b4a6265a412", "metadata": {}, "source": [ "There are other things we can plot as well, such as deposition rate:" ] }, { "cell_type": "code", "execution_count": 6, "id": "76a13415-e1a5-4ee7-9500-2117605dd867", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.564304 seconds (1.72 M allocations: 420.885 MiB, 10.11% gc time, 65.55% compilation time)\n" ] }, { "data": { "image/png": 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Can also set manually, commented out below\n", "# binwidth = 100 # 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=(0,0.2,:darkblue), linealpha=0, label=\"68% CI\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_20p; reverse(dhdt_80p)], fill=(0,0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_25p; reverse(dhdt_75p)], fill=(0,0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_30p; reverse(dhdt_70p)], fill=(0,0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_35p; reverse(dhdt_65p)], fill=(0,0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_40p; reverse(dhdt_60p)], fill=(0,0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_45p; reverse(dhdt_55p)], fill=(0,0.2,: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.pdf\")\n", "display(hdl)" ] }, { "cell_type": "markdown", "id": "9b5f1048-2e97-4641-adca-45f7205d0de7", "metadata": {}, "source": [ "***\n", "## Option B: Configure and run 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 `hiatusdist` output." ] }, { "cell_type": "code", "execution_count": 7, "id": "6a92e9f6-1cde-4b12-a2df-455437f1e58b", "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: 3400000 steps\n", "\u001b[32mBurn-in... 100%|█████████████████████████████████████████| Time: 0:00:01\u001b[39m\n", "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mCollecting sieved stationary distribution: 10200000 steps\n", "\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 = [-371.5, -405.0 ]\n", "hiatus.Height_sigma = [ 0.0, 0.0 ]\n", "hiatus.Duration = [ 100.0, 123.0]\n", "hiatus.Duration_sigma = [ 30.5, 20.0]\n", "\n", "# Configure the stratigraphic Monte Carlo model\n", "config = StratAgeModelConfiguration()\n", "# If in doubt, you can probably leave these parameters as-is\n", "config.resolution = 0.2 # Same units as sample height. Smaller is slower!\n", "config.bounding = 0.1 # 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 = 10000*npoints_approx # Number to discard\n", "config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps\n", "\n", "# Run the model. Note the new `hiatus` argument and `hiatusdist` result\n", "(mdl, agedist, hiatusdist, lldist) = StratMetropolis(smpl, hiatus, config); sleep(0.5)\n", "\n", "# Write the results to file\n", "run(`mkdir -p $(smpl.Path)`) # Make sure that the path exists\n", "writedlm(smpl.Path*\"agedist_hiatus.csv\", agedist, ',') # Stationary distribution of the age-depth model\n", "writedlm(smpl.Path*\"height_hiatus.csv\", mdl.Height, ',') # Stratigraphic heights corresponding to each row of agedist\n", "writedlm(smpl.Path*\"age_hiatus.csv\", mdl.Age, ',') # Mean age of resulting model\n", "writedlm(smpl.Path*\"age_025CI_hiatus.csv\", mdl.Age_025CI, ',') # 2.5% confidence interval of resulting model\n", "writedlm(smpl.Path*\"age_975CI_hiatus.csv\", mdl.Age_975CI, ',') # 97.5% confidence interval of resulting model\n", "writedlm(smpl.Path*\"lldist_hiatus.csv\", lldist, ',') # Log likelihood distribution (to check for stationarity)\n", "\n", "# Plot results (mean and 95% confidence interval for both model and data)\n", "hdl = plot([mdl.Age_025CI; reverse(mdl.Age_975CI)],[mdl.Height; reverse(mdl.Height)], fill=(minimum(mdl.Height),0.5,:blue), label=\"model\")\n", "plot!(hdl, mdl.Age, mdl.Height, linecolor=:blue, label=\"\", fg_color_legend=:white)\n", "plot!(hdl, smpl.Age, smpl.Height, xerror=smpl.Age_sigma*2,label=\"data\",seriestype=:scatter,color=:black)\n", "plot!(hdl, xlabel=\"Age ($(smpl.Age_Unit))\", ylabel=\"Height ($(smpl.Height_Unit))\", framestyle=:box)\n", "savefig(hdl,smpl.Path*\"AgeDepthModelHiatus.pdf\");\n", "display(hdl)" ] }, { "cell_type": "markdown", "id": "65520551-bd1c-4e89-af9e-5c9fdbed3e51", "metadata": {}, "source": [ "***\n", "## Getting your data out\n", "As shown below, we have access to the system command line, so we can use the unix command `ls` to see all the files we have written. We'll try this first using `;` to access Julia's command-line shell mode:" ] }, { "cell_type": "code", "execution_count": 8, "id": "f75ed893-eb56-4ab3-a1c2-18927c7a812f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AgeDepthModel.pdf\n", "AgeDepthModel.svg\n", "AgeDepthModelHiatus.pdf\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", "age.csv\n", "age_025CI.csv\n", "age_025CI_hiatus.csv\n", "age_975CI.csv\n", "age_975CI_hiatus.csv\n", "age_hiatus.csv\n", "agedist.csv\n", "agedist_hiatus.csv\n", "distresults.csv\n", "height.csv\n", "height_hiatus.csv\n", "lldist.csv\n", "lldist.pdf\n", "lldist_hiatus.csv\n", "results.csv\n" ] } ], "source": [ "; ls MyData" ] }, { "cell_type": "markdown", "id": "a5d75792-6933-4b0f-a3c3-90f24438887a", "metadata": {}, "source": [ "Alternatively, we could do the same thing using the `run` function:" ] }, { "cell_type": "code", "execution_count": 9, "id": "6f9b1463-0f69-4f67-ae75-aea8ff6ad240", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AgeDepthModel.pdf\n", "AgeDepthModel.svg\n", "AgeDepthModelHiatus.pdf\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", "age.csv\n", "age_025CI.csv\n", "age_025CI_hiatus.csv\n", "age_975CI.csv\n", "age_975CI_hiatus.csv\n", "age_hiatus.csv\n", "agedist.csv\n", "agedist_hiatus.csv\n", "distresults.csv\n", "height.csv\n", "height_hiatus.csv\n", "lldist.csv\n", "lldist.pdf\n", "lldist_hiatus.csv\n", "results.csv\n" ] }, { "data": { "text/plain": [ "Process(`\u001b[4mls\u001b[24m \u001b[4mMyData\u001b[24m`, ProcessExited(0))" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run(`ls MyData`)" ] }, { "cell_type": "markdown", "id": "0c4bd957-bac1-4363-8f4d-c8f079d9d8f8", "metadata": {}, "source": [ "If you're running this notebook online, getting these files out of here may be harder though. For SVG files, one option is to view them 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": "2dfb82b8-c7fb-446c-a336-23ee9bbf206a", "metadata": {}, "source": [] }, { "cell_type": "markdown", "id": "4da5495d-0e72-4d93-8edd-a351db2ab96c", "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": 10, "id": "4b123df8-5fa7-4703-873f-2e11f85052a9", "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": 11, "id": "9746000f-e866-466b-80f5-0ac35dfcf387", "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": 12, "id": "1b0b965e-5950-4a69-a2c5-d1b8687ffbdc", "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": "4443d758-9e18-49d7-9fb9-4f104bdbba3d", "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, "id": "6ddc12e6-3eee-4a75-b59d-e68a4a09d727", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "6a6d82ba-0a0f-4d75-9a52-e7cc3e2e09e9", "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", "[![DOI](https://raw.githubusercontent.com/brenhinkeller/Chron.jl/main/readme_figures/osf_io_TQX3F.svg?sanitize=true)](https://doi.org/10.17605/osf.io/TQX3F)" ] }, { "cell_type": "code", "execution_count": null, "id": "376b6637-6e51-4704-b696-f2157d91b2fe", "metadata": {}, "outputs": [], "source": [] } ], "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 }