{ "cells": [ { "cell_type": "markdown", "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, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "┌ Info: Precompiling Chron [68885b1f-77b5-52a7-b2e7-6a8014c56b98]\n", "└ @ Base loading.jl:1278\n" ] } ], "source": [ "# Load (and install if necessary) the Chron.jl package\n", "try\n", " using Chron\n", "catch\n", " using Pkg\n", " Pkg.add(PackageSpec(url=\"https://github.com/brenhinkeller/Chron.jl\"))\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", "Paste your data in here!" ] }, { "cell_type": "code", "execution_count": 2, "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 = NewChronAgeData(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", "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, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating stratigraphic age-depth model...\n", "Burn-in: 5660000 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: 16980000 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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" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Configure the stratigraphic Monte Carlo model\n", "config = NewStratAgeModelConfiguration()\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 = 0.5 # 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))\n", "savefig(hdl,smpl.Path*\"lldist.pdf\")\n", "display(hdl)" ] }, { "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**)!\n", "***\n", "## Plot results" ] }, { "cell_type": "code", "execution_count": 4, "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 ($(smpl.Height_Unit))\")\n", "savefig(hdl,smpl.Path*\"AgeDepthModel.svg\")\n", "savefig(hdl,smpl.Path*\"AgeDepthModel.pdf\")\n", "display(hdl)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interpolated age at height=-404: 7384.094999138302 +140.86232924974502/-139.12898687300822 ka" ] }, { "data": { "image/png": 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" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Stratigraphic height at which to interpolate\n", "height = -404\n", "\n", "age_at_height = linterp1s(mdl.Height,mdl.Age,height)\n", "age_at_height_min = linterp1s(mdl.Height,mdl.Age_025CI,height)\n", "age_at_height_max = linterp1s(mdl.Height,mdl.Age_975CI,height)\n", "print(\"Interpolated age at height=$height: $age_at_height +$(age_at_height_max-age_at_height)/-$(age_at_height-age_at_height_min) $(smpl.Age_Unit)\")\n", "\n", "# Optional: interpolate full age distribution\n", "interpolated_distribution = Array{Float64}(undef,size(agedist,2))\n", "for i=1:size(agedist,2)\n", " interpolated_distribution[i] = linterp1s(mdl.Height,agedist[:,i],height)\n", "end\n", "hdl = histogram(interpolated_distribution, nbins=50, fill=(0.75,:darkblue), label=\"\")\n", "plot!(hdl, xlabel=\"Age ($(smpl.Age_Unit)) at height=$height\", ylabel=\"Likelihood (unnormalized)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are other things we can plot as well, such as deposition rate:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ " 0.658503 seconds (1.49 M allocations: 361.509 MiB, 14.97% 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 = 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 = pctile(dhdt_dist,15.865,dim=2) # Minus 1-sigma (15.865th percentile)\n", "dhdt_84p = pctile(dhdt_dist,84.135,dim=2) # Plus 1-sigma (84.135th percentile)\n", "# Other confidence intervals (10:10:50)\n", "dhdt_20p = pctile(dhdt_dist,20,dim=2)\n", "dhdt_80p = pctile(dhdt_dist,80,dim=2)\n", "dhdt_25p = pctile(dhdt_dist,25,dim=2)\n", "dhdt_75p = pctile(dhdt_dist,75,dim=2)\n", "dhdt_30p = pctile(dhdt_dist,30,dim=2)\n", "dhdt_70p = pctile(dhdt_dist,70,dim=2)\n", "dhdt_35p = pctile(dhdt_dist,35,dim=2)\n", "dhdt_65p = pctile(dhdt_dist,65,dim=2)\n", "dhdt_40p = pctile(dhdt_dist,40,dim=2)\n", "dhdt_60p = pctile(dhdt_dist,60,dim=2)\n", "dhdt_45p = pctile(dhdt_dist,45,dim=2)\n", "dhdt_55p = pctile(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)\n", "savefig(hdl,smpl.Path*\"DepositionRateModelCI.pdf\")\n", "display(hdl)" ] }, { "cell_type": "markdown", "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, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating stratigraphic age-depth model...\n", "Burn-in: 3400000 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: 10200000 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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" }, "metadata": {}, "output_type": "display_data" } ], "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 = [-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 = NewStratAgeModelConfiguration()\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))\")\n", "savefig(hdl,smpl.Path*\"AgeDepthModelHiatus.pdf\");\n", "display(hdl)" ] }, { "cell_type": "markdown", "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, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AgeDepthModel.pdf\n", "AgeDepthModel.svg\n", "AgeDepthModelHiatus.pdf\n", "DepositionRateModelCI.pdf\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", "height.csv\n", "height_hiatus.csv\n", "lldist.csv\n", "lldist.pdf\n", "lldist_hiatus.csv\n" ] } ], "source": [ "; ls MyData" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, we could do the same thing using the `run` function:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AgeDepthModel.pdf\n", "AgeDepthModel.svg\n", "AgeDepthModelHiatus.pdf\n", "DepositionRateModelCI.pdf\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", "height.csv\n", "height_hiatus.csv\n", "lldist.csv\n", "lldist.pdf\n", "lldist_hiatus.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", "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", "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": 10, "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, "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": 12, "metadata": {}, "outputs": [], "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", "[![DOI](https://raw.githubusercontent.com/brenhinkeller/Chron.jl/master/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 }