examples/plotmcvariable.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Secular evolution of the continental crust\n",
    "\n",
    "This Jupyter notebook uses the <a href=\"https://github.com/brenhinkeller/StatGeochem.jl\" target=\"_blank\">StatGeochem</a> package to calculate and plot the geochemical evolution of igneous whole-rock geochemical compositions preserved in the extant continental crust, for any element or ratio included in the dataset of <a href=\"https://doi.org/10.1038/nature11024\" target=\"_blank\">Keller & Schoene 2012</a> and <a href=\"https://doi.org/10.1016/j.epsl.2017.10.031\" target=\"_blank\">2018</a>, using the weighted boostrap resampling algorithm therefrom, implemented in the <a href=\"https://julialang.org\" target=\"_blank\">Julia language</a>.\n",
    "\n",
    "<a href=\"https://mybinder.org/v2/gh/brenhinkeller/StatGeochem.jl/main?filepath=examples%2Fplotmcvariable.ipynb\" target=\"_blank\"><img src=\"https://mybinder.org/badge_logo.svg\" alt=\"Launch Binder notebook\" align=\"left\"></a> \n",
    "<p style=\"text-indent: 5px\">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!</p>\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",
    "### Load required Julia packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## --- Load the StatGeochem package which has the resampling functions we'll want\n",
    "using StatGeochem\n",
    "using Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dict{String, Any} with 117 entries:\n",
       "  \"Lower_Vp\"   => [7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2, 7.2  …  7.2, 7.…\n",
       "  \"Pd\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  0.01, N…\n",
       "  \"MgO\"        => [0.89, 0.5, 0.69, 2.83, 3.5, 2.81, 2.97, 2.32, 2.33, 1.74  … …\n",
       "  \"C\"          => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  NaN, Na…\n",
       "  \"Nb\"         => [3.5, 1.3, 3.4, 11.6, 10.4, 12.2, 11.5, 8.3, 7.0, 4.9  …  11.…\n",
       "  \"Ag\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  0.66, N…\n",
       "  \"Gd\"         => [NaN, NaN, NaN, 7.99, 5.92, 6.12, 5.94, 5.46, 2.01, 2.7  …  N…\n",
       "  \"Middle_Rho\" => [2.9, 2.9, 2.9, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.8  …  2.9, 2.…\n",
       "  \"Age\"        => [1.0, 1.0, 1.0, 1650.0, 1650.0, 1650.0, 1650.0, 1650.0, 1650.…\n",
       "  \"Sb\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  NaN, 1.…\n",
       "  \"TiO2\"       => [0.27, 0.25, 0.24, 0.6, 0.63, 0.63, 0.59, 0.39, 0.48, 0.38  ……\n",
       "  \"Cs\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  NaN, 7.…\n",
       "  \"Be\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  2.7, Na…\n",
       "  \"Locality\"   => [\"Adachi, Japan tonalite \", \"Adachi, Japan tonalite \", \"Adach…\n",
       "  \"Sr\"         => [312.0, 396.0, 272.0, 887.4, 798.0, 761.4, 698.7, 602.0, 790.…\n",
       "  \"Material\"   => [\"IGNEOUS\", \"IGNEOUS\", \"IGNEOUS\", \"IGNEOUS\", \"IGNEOUS\", \"IGNE…\n",
       "  \"Ga\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  29.0, N…\n",
       "  \"Ta\"         => [NaN, NaN, NaN, 0.62, 0.59, 0.98, 0.67, 0.38, 0.41, 0.21  …  …\n",
       "  \"Te\"         => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  NaN, Na…\n",
       "  \"Eustar\"     => [NaN, NaN, NaN, 3.18574, 2.14821, 2.45012, 2.3435, 2.07734, 0…\n",
       "  \"Al2O3\"      => [19.19, 14.9, 15.77, 16.26, 17.65, 16.87, 16.21, 15.67, 15.38…\n",
       "  \"CO2\"        => [NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN, NaN  …  7.3, 0.…\n",
       "  \"Freeair\"    => [31.0, 31.0, 31.0, 140.0, 140.0, 140.0, 140.0, 140.0, 140.0, …\n",
       "  \"Y\"          => [22.4, 7.5, 17.6, 29.1, 21.9, 22.0, 23.4, 19.3, 6.3, 8.2  …  …\n",
       "  \"U\"          => [NaN, NaN, NaN, 1.18, 1.24, 3.33, 1.25, 1.09, 0.84, 1.42  …  …\n",
       "  ⋮            => ⋮"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## --- Download and unzip the Keller and Schoene (2012) dataset\n",
    "if ~isfile(\"ign.h5\") # Unless it already exists\n",
    "    download(\"https://storage.googleapis.com/statgeochem/ign.h5.gz\",\"./ign.h5.gz\")\n",
    "    download(\"https://storage.googleapis.com/statgeochem/err2srel.csv\",\"./err2srel.csv\")\n",
    "    run(`gunzip -f ign.h5.gz`) # Unzip file\n",
    "end\n",
    "\n",
    "# Read HDF5 file\n",
    "using HDF5\n",
    "ign = h5read(\"ign.h5\",\"vars\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68696-element Vector{Float64}:\n",
       " 0.5\n",
       " 0.5\n",
       " 0.5\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " 0.2\n",
       " ⋮\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01\n",
       " 0.01"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## --- Compute proximity coefficients (inverse weights)\n",
    "\n",
    "# # Calculate inverse weights based on spatiotemporal sample density\n",
    "# k = invweight(ign[\"Latitude\"] .|> Float32, ign[\"Longitude\"] .|> Float32, ign[\"Age\"] .|> Float32)\n",
    "\n",
    "# Since this is pretty computatually intensive, let's load a precomputed version instead\n",
    "k = ign[\"k\"]\n",
    "\n",
    "# Probability of keeping a given data point when sampling\n",
    "p = 1.0./((k.*nanmedian(5.0./k)) .+ 1.0); # Keep roughly one-fith of the data in each resampling\n",
    "p[vec(ign[\"Elevation\"].<-100)] .= 0 # Consider only continental crust\n",
    "\n",
    "# Set absolute uncertainties for each element where possible, using errors defined inerr2srel.csv\n",
    "err2srel = importdataset(\"err2srel.csv\", ',', importas=:Dict)\n",
    "for e in ign[\"elements\"]\n",
    "    # If there's an err2srel for this variable, create a \"_sigma\" if possible\n",
    "    if haskey(err2srel, e) && !haskey(ign, e*\"_sigma\")\n",
    "        ign[e*\"_sigma\"] = ign[e] .* (err2srel[e] / 2);\n",
    "    end\n",
    "end\n",
    "\n",
    "# Special cases: age uncertainty\n",
    "ign[\"Age_sigma\"] = (ign[\"Age_Max\"]-ign[\"Age_Min\"])/2;\n",
    "t = (ign[\"Age_sigma\"] .< 50) .| isnan.(ign[\"Age_sigma\"]) # Find points with < 50 Ma absolute uncertainty\n",
    "ign[\"Age_sigma\"][t] .= 50 # Set 50 Ma minimum age uncertainty (1-sigma)\n",
    "\n",
    "# Special cases: location uncertainty\n",
    "ign[\"Latitude_sigma\"] = ign[\"Loc_Prec\"]\n",
    "ign[\"Longitude_sigma\"] = ign[\"Loc_Prec\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Hh4fr6irwpGmpKy1KamoZ/Zm8h7a2jJvJMoPN7ty51E2b7klWp+PxBF98cfXRo0IaTg0A8LHkVQifP38eFxdXU1OTlpYWFxdXWVlJCFm1atVvv/22ceNGKpiamiqns8ublZUCrIcydKizVERHh9uvnx0Np75wIU0qIhKJGwYBAFoCeRXCrVu3hoSEGBkZHT9+PCQkhKp5KSkpNjY2X3/9dUhISEhIyK+//iqns8vbxIltpFYl1dXlTprUlql8ZJo50zMkpIPk9rORkWZo6GAzM20aTs3jCT8wCADAOHkNlpFZ5I4cOSKn09HM1rbV5ctj5827fP9+PiGkc2fznTv729q2rIXBOBzWr78GLFjQeerUi3372ixf3s3QUJOeU/v4WP7xR4JUsFs3S3rODgDwUbAfYSP5+FjGxU1ZtixaKBRt3NiH6XTeqW1bYwcHvW7drGirgoSQ6dPbHTyYEBPzUhIZOdIlMBAz3wGgJVKJtUblR0tLTUsLHyakaWqqRUdP+PXXADc3I29vi6NHh5w6NezdY4QBAJiEN3FV9+BBwd27r+dU3LiR3bu3DVWxOnY07dq18Tcz1dTYISEdkpJKbG31Jkxwb5ZUAQDkAVeE8MbJk0nv2rUYAEBZ4YpQ1XXsaNaxoxnVXrjwyqxZHpqa+L8CAFQIrggBAECl4bM/MCYrq+Lff1+PLK2srLt4Mf3BgwJCiJ2dno8P5loAAE0UuxAKheKjR5/dvJmTkVHh6KhPz7Ip0FwqK/lpaa8Xq+ve3aq8vLamRkAIwb1ZAKCTAr/j1NQIAgLCbt3KIYQkJpZcvpw5f36n7dv7MZ2XSuDxBE0vV23bGrdta9ws+QAANJoCF8K9ex9RVVBix474qVPbdeliwVRKym327EvUrcvKSn5eXpVk/8X9+wMlw20AABSOAhfCmzezGwajo7NRCOVk//5AqnH5cuaGDXcjI8cwmw8AQLNQ4FGjamoykpcZBAAAeBcFLhuBgQ5SETabFRBgz0QuAACgqBS4EE6Z0m7SpDaSl1wue9OmPm3aYPAFAAB8BAUuhBwO6/DhoJs3J3TpYjFhgntCwowvvvBmOimQl9pa4Xff3enb90RycpmXV+iZMylMZwQASkKBCyGlZ0/rDh1M/f3tXV0Nmc4F5Gj27EurVt3Kza0Ui8X37+ePGHH61KnnTCcFAMpA4QshqIKXL6uOHHlaPyIWk82b7zGVDwAoExRCUADp6eVisXQwJaWMiVwAQNn8RyEUCAT05AHwHlZWOg2D1ta69GcCAMpHRiHMyMhYu3atv7+/sbExl8tVU1OztLQcPnz4rl27SktL6U8RwMFBv+HEmLlz2zOSDAAombcK4dOnT4cPH+7s7Lx+/Xo+nz9ixIglS5YsWLAgMDAwMzNz4cKF1tbWn332WUFBAVPpgso6dmzI5Mlt2WwWIURfX2PzZr958zoxnRQAKIM3S6xdunRp6NChQUFBf//998CBA9XV1aW6lpeX//nnn/v27XNxcXn69Km1tTW9qcJHSEkpGz/+HNXOyKh49Kjwhx/+JYS0bm14/PgQuZ66pkawaNEVqh0Tk6urq56YWEwI0dJS+/ln/0Yf1thYKzR08JIlXYcP/ys5eQ6Hw2qedAFA5b0phObm5rGxsZ6enu/qqq+vP3PmzJkzZ166dInL5dKSHjRS69YGsbFTGDm1ujpn7twOVHv4cBcNDY6+vgYhpFlKl6Ymh8vloAoCQDN6Uwg7duz4gV8TGBgon2RAGXA4LC8vc6azAAD4UP+9+4RIJMrOzmaxWNbW1mw2plsAAIBS+Y/CFhkZ6ejoaG9vb2dn5+TkdPnyZXrSApqJROJr114IhaKrV18IhQ2m7AEAKK/3FcLKyspx48bNmzcvOTk5OTn5k08+GTduXHV1NW3JAT1yciq7dTvSt+8JPl80ePCfnTsfSksrZzopAACavLk1WlZWFhERMXbsWEkkOTnZyspq6dKl1Muvv/760KFDKSkp7dtj/pZSmTcv8t69PMnLR48KZ8365+rVcQymBCqrrKw2MjKDaj95UmRr20pPT4MQYmCgiU3WQE7eXBGy2ewvv/wyICAgKSmJijg6OmZlZR0/fry2tra2tvbo0aPZ2dkODg7MZAryIRCILl5Mlwpev/6ivLyWkXxAxdXVCdPSyqn/Tp16fv16DtXOzn7FdGqgtN5cEerp6T179uy7777z8vL6/PPPv/nmGwMDg127ds2dO3fChAlUh127dunp6TGXLTQ/Pl/E54ukgmIxqa4WUNMeFEufPserqviEkOLiGj5fZGHxem2269fH6+hgzo8CMDPTXrq0K9WOickdOdJl+PDWzKYESu+tUaO6urobN26cPn36/Pnz27Zt+9NPP02ePDk4OPjx48eEkPbt26MKKh8tLbV27UyePCmqH7Sz05OUEMVy/fp4qrF1a2xOTuWWLX6MpgMACkDG9Im2bdteuXLl3Llzn3322c6dO3fs2NGrVy/6M2suf/2VvH59DNV+9apu8OA/qenYI0e6LF/ejdHUWoqtW/2Cg/+uqxNSL9XU2D//3Jel1HPW4+ML5sy5RLWzsl6ZmmppaakRQjp1Mtu3D9NkAVTLO+cRBgcH+/v7b9q0qUuXLrNmzVq3bp2urkIu9j9ypMvIkS5MZ9GiDRjg8Pjx9O3b7+/a9WDWLI8FC7w8PExk9qyq4q9fH/PHH08KC6sHDAhbu7ZX166WNGfbLDp1MpOsvNO//8lly7r162fHbEoAwJS3pk/k5+dPnz7dysrK0NDQ398/ISFh9erVd+7cefDgQfv27c+ePfuBBxWLxWvXrh0xYoS3t/ejR48k8bi4uM8//9zX13fWrFnN+U1Ak7m6Gm7f3k9dnbNtW793VUFCyKhRZ9av/zc3t5LPF0VGZvbuffzu3Zd05gkA0OzeKoQTJ07MysrasGHDgQMH2rZtO2jQoPz8fE9Pz2vXrq1ZsyYkJCQ4OLi4uPhDjpuXlzd06NDnz59XVlbWD5qZmXl4eEgGpoICuX8//9KljPqRujrhli2xDKUDANA83hTC6urqO3fuXLx4ccqUKSNHjtyxY0fXrl3v3LlDCGGxWFOmTElMTHR0dExJSfnPg7JYrB07dsyYMUNqbe6goKCVK1d27ty52b8NoIHMHeGTk7FFJQAotjeFUFNTU11dnap8hJD8/PzExERTU1NJB319/W3btvn4+NCdI7QMZmbaDYMKOrgUAEDirQn1GzduDAgIaNOmTceOHR0cHLy9vXv06MFIWhkZGR06dGD9P19fX0bSgPp69rR2czOSCs6c+c59uwAAFMJbo0bnzp3r6+t79epVHo/XoUMHf//Gb6PaRA4ODqGhoVjLrUXhctnh4SPnzIm4ejWLEGJoqLluXa/Ro12ZzgsAoEmkp0+4u7u7u7szkgq0fM7OBleujD116vnWrbHXr4/ncrEtFwAovP/ej7BxXrx4wefzRSJRbm5uWlqara0tl8utrq7Oy8srKiri8XhpaWk6Ojrm5gq5g2vXrodFIjEhJD+/WiwWh4enUfG7dyez2Uo9EZ0QQoiBgYaODhdVEACUg7wK4axZs1JTU42MjKjNK65evWpnZxcTEzNnzhyqQ0BAQGBg4K5du+SUgFzdvTuZ6RRUS3p6eUkJjxCSlVXB4wnj4vKpuKOjvpGRJqOpAYDCk1chjIiIaBj09/dPTU2V0xlBiV25kkXN3K+rExoaauzd+5CKz5rlqaBL2wBAyyGvQgjQjGbN8pw1C8NTAUAu8JgHAABUGgohAACotA8thEKhcNy4cePGjZNrNgAAADT70GeEIpHo5MmThJATJ07IMx8AUE6DB/9ZUFBNCCkt5VVV8W1sWlHxCxdGyVy9D4A2H1oIuVxuXV2dXFNh3MqVN2NiXhJCXr2qS0kpCwgIo+Lff9+rWzcMTQRokgsXRlGNffse3buXt3fvAGbzAZD4iFGjUltJKJ+lS334/Ne7tJeU8CQT1HR11ZlLCgAA5Et2IfTx8dm9e7fUfknx8fGjR49uIRMBi4trPv30MtWOi8t/+LAwIiKDEGJsrLl7d0DjjqmryyXkdbE3NMQ0bQAAlSC7EGZmZvJ4PKlgTU1NRkaG3DP6MPr6Gj/++HpLiuLiGi0trra2GiFETU35VziD/5SbW/ngQWFxcU16ermjoz7T6QBAi/YRt0afPHliZmYmv1Q+ipoa28np9RucpAGNkJFRLtlxVyQSX7mSpa7OIYQ4OOi3bm3AaGqNtHv3g//973p1NZ8Q4uaWsWJFt1WrmNlNDAAUwluF8Lffflu3bh0hpKioaMyYMZqab24PCoXCrKysadOm0Z0gyFlGRsXly5lU28fH8saNbKrt52eriIUwIaFo/vwoakl0QgifL1q9+naPHtYBAfbMJgYALdZbhdDc3NzLy4sQkpub6+7ubmxsLPknU1NTDw+P6dOn05wfyJufn62fny3TWTSb8+dTJVVQ4syZFBRC5cPjCWpqBFS7/ug2TU01LS0sHgkf4a3/XYKCgoKCggghY8eOXb16ddu2bRnKCqCRqqsFsoJ8mZ0fPixcterWzZu5ISERs2d7Ll7sraHBkXOC0Gz+/DP5jz8SqPbVq1l+fnYsFiGETJrUZvp0DyYzA0Uj+3PTgQMHWrVqRXMqAE3XpYtFw6DMHSri4vJ79TrG4wkIIampZcuWRd+7l/fnn8PkniI0k0mT2kya1IZqc7lb//lnlJoa1oyExpD9/42bm5u3t/fChQvDwsIqKipozgmg0YKCnAYNcqwf6dLFYvr0dg17btx4l6qCEn/9lZyQUCTf/ACg5ZF9Rbhu3bqLFy8ePXp027ZtGhoa3bt39/f379evX9euXdXUcPNdRc2efenBgwJCSFUVPy+vyts7lIrv3x/YsWNLGU7MZrPOnx958OCTX365X11dN39+508+6UCNg5WSlFTSMPjsWbGHh4n80wSAFkR2VZsxY8aMGTNEItHDhw+joqKioqI2bNjw7bfftmrVCheIKmv//kCmU/ggbDZrxgyP0lJeTk7lggWd39XN1FTG+pZY9BJABb3v8o7NZrdv357P5/P5/Orq6hs3btTW1tKWGYBcTZnSVjJvhOLiYtijhzVT+QAAU2Q/I0xKStqxY8eIESNMTEy6d+9+8uTJLl26XLhwoaRExt0kAEU0dWq7jRv76Oi8XlSvRw+rc+dGcLkYbQGgcmRfEfbp06e0tHTSpEl79+7t27eviQmemoASWrKky7x5Hf39TyxY4CUZfwgAqkZ2IezYsePly5dPnjyZl5eXlZXVr1+/9u3bs9n4sAzKRkeH26qVuoWFDtOJAABjZNe2f/75p7i4+OjRoy4uLr///nunTp0sLCzGjRu3b98+mvMDYFZNjaC0lEf9l5paJmlLTb0AAMX1zsEy+vr6Q4cOHTp0KCEkPj5+xYoVYWFhJ0+enDNnDo3pATDsyJFnJ04kEkJEIvGNG9mS5ehmz/YcN86d0dQAoHm8sxBWVFRcu3aNmjvx5MkTQoiLi0u/fv1ozA2AebNne86e7UkIqakRGBvviIwcw3RGANDMZBfCgQMHRkVFCQQCS0vLfv36/e9//+vXr5+trfIszQwAAECRXQitra1/+uknf39/rLsNAADK7Z2LbtOcBwAAACPejBqtrKz8wK/h8/lYYgYAAJTDm0L4999/9+jR4+LFi0Kh8F29q6ur9+/f7+bmlpmZ+a4+AAAACuTNrdGhQ4c+fvx4xIgRxsbGw4cP79atm6urq5GRkUAgKC4ufvTo0e3bt8+ePauhobF69WonJycGkwaAFuLIkWc3brwghAgEort383r0sKLikya19fW1YTQ1gA/1phDq6+tv3LhxwYIFe/bsOXjw4K5du6S6dujQYf369VOnTtXT06M3SQBooQIDHbp3tyKEFBRUnz+ftnSpDxU3MdFiNC+AjyA9WMbGxub777///vvvU1NTHz9+XFhYqKamZm5u7u3tbWbWUvacA4AWwsREi6p5WlpqHA7LyUmf6Uw6dwgAACAASURBVIwAPto7J9Q7Ozs7OzvTmQoAAAD95LKOdk5Ozg8//DB69OjRo0fXj7969Wru3Llubm59+/aNiYmRx6kBAAA+ilwKYWZm5osXLxwcHK5fv14/vnjx4ry8vEuXLk2ePDkoKAib3QMAAOPkUgh79Oixa9eu4cOH1w9WVFQcPXp048aNDg4Os2bNcnNzO3HihDzODgAA8OHo22IwPT2dxWK5u79esN/LyyshIYG2swMAAMj0zsEyza6wsFBf/82IMgMDg5SUlHd1zsrK6t27N4fDoV526dLl0qVLck8RFN/ChVeonQIfPy6qqqoLCYmg4r/84q+pSd//7QCgQN731sDj8YRCoY5O82zebWBgUFVVJXn56tUrIyOjd3W2sbHZvXt3u3btqJcaGhrNkgMovRkzPIRCMSGkvLy2rk5oaqpNxblcDqN5AUDLJaMQ3r17d+vWrVFRUUVFRYQQPT09X1/f+fPnBwYGNuVM9vb2VVVVubm5VlZWhJCkpKT+/fu/qzObzdbT0zM0NGzKGUEFdeyI2a4A8HGknxFu2bKlR48ep06dsrS0DA4OHjlypLOzc2Rk5MCBA+fPny8Wiz/koCKRqLS09NWrV2KxuLS0lBodampqOnDgwC1btojF4ri4uOjo6AkTJjT/NwQAAPAx3roiDA8PX7JkSXBw8M6dO21s3qwTWFxcvHz58p07d7q5uX3++ef/edCMjAxvb2+q7ezs7O7ufvv2bULI9u3bx40bZ2pqymKxdu3aRV0aAtAvO/vVkSPPqHZmZsWJE4mxsXmEEBubVpMmtWE0NQCg21uFcP369b6+vn/99ZdklArF2Nh4z549NTU1P/zww/z581ks1vsP6uTkVFJS0jDu6Oh49+7dyspKHR2d/zwIgPxoaqpJFgObMqWtg4O+lpYawQqZACrpTSHk8/kxMTHHjx+XqoISCxYsCA0NTUlJcXFxacopdXV1m/LlAE1nYqI1Zowb01kAQIvw5hlhRUWFSCSytLR8V1dra2tCSFlZGR15AQAA0OJNITQwMFBXV3/P3L7nz58TQszNzenICwAAgBZvCiGHw/H399+8eXNlZWXDfgKBYO3ata6urnZ2djSmBwAAIF9vTZ9Ys2bN8+fP+/TpI7VY9oMHDwYPHnz58uV169bRmx4AAIB8vTVqtGvXrseOHZsxY4afn5+mpqajoyOHw8nMzHz16hWXy/3pp5+ktlUCAABQdNIry4waNapbt2579uyJiop68eKFUCh0dXX19fUNCQlxc8MoOwAAUDYylliztrb+7rvvvvvuO/qzAQAAoBl92zABAAC0QLJ3n5gzZ055eXnDuKGhoYODw7Bhw9q2bSvnxABAdVVX83/99WFsbF51tUBHhxsQYM90RqDMZBfCzMzMu3fvlpeXW1lZmZmZ5ebmFhQUmJqampmZHTp0aOXKlbt27Zo7dy7NuQKAKigqqunZ8+jz56WEkOzsyoiIjCVLumzc2IfpvEBpyb41OmHCBHNz89jY2JycnPj4+Pz8/MjISHV19a1bt+bl5Y0ePXrRokXFxcU05wrAFJFIHB2dLRSKb9/O/bAtWKDxNm++R1XB+pGkJBnLFwM0CxmFUCAQfPXVV9u3b/fy8pIE+/fvv3LlymXLlunr6+/du1cgENy6dYvGPAEYk55e7u0dGhh4qq5O2LPn0d69j+XnVzOdlDKLiXkpFRGLyb//SgcBmouMQlhYWFhUVNRwBRk7O7unT58SQvT09Ozt7UtLSxt+LYDymTnzn/j4AsnLW7dy5s+/zGA+Sk9DQ8a6/zKDAM1CxjNCAwMDTU3NsLCwlStX1o+HhYVJluQuKSkxMjKiI0EARpWV1V6//kIqeO5cqlAo5nDe2kosIiKjvLyWEFJRUZufX+3iYkjFBwxw0NfXoCdb5RAU5BQRkVE/oq3N7dsXizuCvMgohFpaWp988snq1atTUlKGDBliYmLy8uXLEydOnD179ueffyaE3L17t6SkpFOnTrRnC0C36mp+w4eCtbVCPl/I4bz15/PixauiohpCSEJC0cOHBZMmvR5ZzeeLaMlUeXz6aceYmNxjxxKpl3p66vv2BZqZaTObFSgx2aNGN2/erK2tvW3btkOHDlERExOTX375ZcGCBYQQe3v7+/fv19/CHkBZWVjoWFvr5uS8tRK9h4eJpqb0386sWZ5U49Sp5zyeYOnSrjSlqHS4XPbRo0MWL/aePftS3752y5b5mJu3rCr48mXVkydFVPvp0+I2bYyoncYtLXXatTNhNDVoDNmFkMPhrFu37ttvv01LS8vLy7OxsXFwcOByudS/mpubYzMmUBFsNmvLFr9Jk8KFwtcXhlwue8sWP0aTUmDPnhVfvpyZnl4eHZ3du/f7Pkx36WLh5KTv52fb0qogIeTly8rLlzOp9p49D8ePd6fufnfubI5CqIhkF0KKhoZGmzZt2rRpQ1s2AC3QuHHurq5G27bFHTr0dO7c9gsXerm74wF5Y/z4478rV94SCESEEF/f4xMntjl0aLDUo1aF0LmzeefOry8GwsKSlizp6uSkz2xK0BRvFcLHjx/X1dW9/wvqz6kAUBGdOpnt2hVw4kTS7t0BTOeiqBISilasuCkSvXnievToswEDHKZNa8dgVgBEqhAGBwdnZma+/wvEmE4MAB8vKiqrfhWkRERkoBAC46RvjWpra48aNWrQoEFqau+7awoA8FGoO6JSJE9eARj0VrXbsWPHvn37jh8/HhERMWnSpBkzZnh4eDCVGQAok969rRsGfX0x+ByY99bKMkOGDDlz5kx+fv5333139epVT0/Pdu3abdiwobCwkKn8AEA5dO1quWBB5/qRvn3tJHNOABgkY4k1Q0PDuXPn3r9/PzY21t/ff+PGjba2titWrKA/OQBQJr/84n/lytiAAPu2bY2PHx9y+fIYLJwGLcH7HgR6eXnp6+tra2tv3br1/v37tOUEAMqqb1+7lJSye/fyxo1zZzoXgNdkb8NUUVFx6NChgIAAV1fX48ePf/nll7t27aI5MwCA/1RdzT948IlIJD548ElVFZ/pdEAhvXVFKBKJbt++HRoaevToUTabPWzYsIiIiH79+lGrBwEAncLCkjZsuEu1ExNLXF0N2WwWIWTMGDes30Z58qRo0KA/X7x4RQiZPfvSqlW3wsNHdehgynReoGDeKoTdunWLi4vz8/PbtWvXqFGjtLVb3MpGAKpjzBi3MWPcqLaZ2a5Ll0abmuJP8i3z5l2mqiAlJ6cyJCQiJmYSgymBInqrEBYUFHA4nPj4+Pj4+IULF8r8gpIS7BMNAMzj8QTR0dlSwX//fVleXot9r+CjvFUIg4KCMFMCABSCSCR7nStM0oeP9VYh3LlzJ1N5AAB8FG1trre3RWxsXv2gp6eJkZEmUymBgpI9ahQAoOXbsaOfoeGbsqevr7FrF1ZFh49GayEsKCj47LPPevToMWHChKSkJDpPDQDKx8fH8vnzWRs2+LLZZN26Xs+fz+rVS8ZCbgDvR9/K2kKhcNCgQT4+PgcOHIiIiPD3909NTdXUxE0MUC25uZVDh/5NtV++rNLR4erpqRNCLC11z50bwWhqCsnEROurr7quWHHzq6+6qqnhFhc0Bn2F8MmTJ0+fPr1z5466unqbNm0OHz588uTJqVOn0pYAQEtgZaUbGzuFas+Y8U+fPjbTp2NpewAm0fcBqrq6Wl1dXV1dnXrZqlWr+Ph42s4OAHJVWckXCER1dUKmEwH4aPRdEXp4eKirqx8+fHjy5Mnx8fG3b9+2tLR8V+fs7OygoCBJ1ezYseOff/5JV6YA8BFKS3mffx519OgzsZjo62//8kvvNWt6cjhYjkoucnMrz59Po9qPHxc5O+tra3MJIVZWOkOGODOamgKjrxDq6uqeOHFi3rx5X331lb29fWBgoImJybs6W1hYbNq0yd3dXfK1dKUJAITPF1VW1lHt0tJaQ8PX89O5XI6uLleq89SpF8+fT6XaPJ5g3boYDQ3OypXdacv2PyUmllDLkNbVifLzq2xtW1Fxd3cjHR3pb4dx1PzID/kkERGRERzs7OpqSENWyo3Wbej9/f0TExMFAoGamlr37t2DgoLemZaamo2NjZOTE53pAQAlNjbv229vUe27d/PatjWm6l/37lbffdezfs+Cgurw8FSpLz9w4HGLKoRnzqSkpZURQrKzKx8/Lhw0yJGKL13q4+Skz2hqb/TufaymRkAIKSys5vNFVlavP/1HR0/Q0nrzRm1lpTt3bnuqffTos+Bgpz59bOnPVsnQWghfvnxpaWmppqZ24MCB9PT0sWPH0nl2APhA3btbRUaOodpeXqE7d/br3NlcZs/8/OqGy7vk5VXJNb2PJVmj/NKljJ9+it2zZwBtp96791FcXB4hhMcTPHxY6OPz+nnQ3LkdvLze+pFGR0+gGps23SssrN64sQ9tSQKthXDx4sXXrl0TCoWmpqYXLlwwMDCg8+wA0Ozs7fW4XDafL6ofdHHBzbrXhg1r3b+/PSEkJaXs9u3LS5f6UHFzc6yf3oLQWgiPHz9eUFDA5/OtrTHpFUAZ6Ompf/ZZp59/jqsfXLbMh6l8WhpJwePxBOrq7JZzJxbqo3v+qZmZGaoggDLZtKnPzz/7t25twGKRLl0sTp8ePnFiG6aTAvgItF4RAoDyUVNjL1zYeexYNy+vQ3fvTmY6HVqJxUQgEPH5mD2p2LAiEcD71NQISkt5paW8sjKeWCym2qWlPB5PwHRqwCSxmGzefM/IaHtW1isPjz/mzLlUXl7LdFLQSLgiBHifI0eenTiRSAgRi4mensbYseeo+OzZnuPGuTOaGjDp55/jliy5TrUFAtH+/Y8LCqrPnMFqsQoJhRDgfWbP9pw925PZHNLSyuvqhImJJaamGGrYUuzd+1Aqcu5cam5upWT+HygQ3BoFaDYCgWjnzvjvv79z5UrWwoVX8vOrm3jAykr+uHHnnJ33lZfX+voe9/M7kZ39qllShSbKza2UiojFMoIfq6ZG8OJFRW5uZWUlv4mHgg+HQgjQbMaMOTt/ftSjR4UlJbxt2+536PBHTk6T3hmXLLl28uSbnTuvX38xbdrFJqcJzcDRUXoiBJvNcnBo0uyI06dTnJ33bd8ef+TIMweHvYcPP23K0eDDoRACNI87d3JPn06pH8nPr966NbYpx6xfBSlXr2YVFDT1QhOa7ssvu0hFpk/3MDHRavQBU1LKxo079/Ll60V5iotrpk27GBeX3/gU4YOhEAI0j8ePixoGHz4saPQBxWLScCCiWEzKyjA6kXlTprQ9fnxIu3YmhBBzc+01a3ru2tW/KQf866/nUptYiURiaqAWyBsKIUDz0NdXbxg0MNBs9AFZLOLhIb1Di56euoODXqOPCc1o3Dj3hITpTk76t29P+vbb7hoanKYcraSE94FBaHYYNQrQPAICHAwMNKQu18aMcW3KMdeu7TVs2GmR6M2y1mvW9FRXl37D3b79fkJCESGkupr/7FmJZDXnzz/v3LCUQstEXVxKwa+PHiiEAM3DyEjzzz+HzZjxT1ZWBSFEQ4OzbJlPE+caDhniHBMzaePGu6dPp/j52S5e7DV4sIy9yQYNcurRw5oQkpBQ9OBB4dy5Hai4ZOM9aPnGjnXbvPneo0eFkoizs8HMmQxP3VERKIQAzcbf3y4xceaWLbGXLqWfOjWsWXYY6NLFIixsqJnZrqNHg941j7B169cbuQgEIh0drtT+PqAQNDQ4N29O+PHHfw8delJXJ5o8ue2KFd309GTcb4dmh0II0Jy0tNTc3Y0ePizAPjvwsVq1Ul+3rreBgSb2I6QZBssAACiYoqKa5cujHz8uXLcu5sKFNKbTUXi4IgQAUCRpaeXdux+hppNGRmZGRmZ+/bXPDz/0ZjovBYZCCABACCE8nuDQodeLuaSnl2trc6n725qanKlT2zXx4IWFNTyeoLKSr6vLbeKh1q69I7WowqZNd+fN64ixUY2GW6MAzCgsrM7NrUxOLq2pwY5OLc6//758+lTGCgmNkJ39auDAU35+x9PSyk1Nd65cebP+fJhGiI3Nk4oIheL797EGTePhihCAARs33l29+nZNjSAiIuPgwScHDgwMDHRgOilVp6mpNndue6qdkFDk4mIgedkUo0advXv3JdXm8QRr18YYGGh++aV3ow+ooyPjmlJmED4QrggB6HbuXOrSpTckF4I5OZWjR59p+lYV0AIlJpZIqqDEwYMJTTnm0KGtpSKmpto9elg15ZgqDoUQgG5hYdJLaVdW8jH2TykVFsr4fNPEZdO//NJ74sQ2kpdWVronTgzR1sYVYeOhEALQreFS2oSQ0lKsKqmEXFwM2WyWVNDd3bgpx1RX5xw5EvTo0XR3d6MffuidlDSrb1+7phwQ8IwQgG6enqZnz6ZKBTt0MGMkGZArCwud2bM99+59JIlwOKwVK7o17Pntt7eo3Sfu3s2rruZ//fUNKv7ddzJWlyWEeHqamJvrdO9u1fRhqIBCCEC3BQs6//FHQv09ewMDHfz98aFeOe3c2b9dO5Nff32QnFzq72+/cmX3Xr2sG3YbMcKFGk3q729XVyeytNSh4mpquG8ndyiEAHQzM9OOj5/6ww//hoY+NTHRmju3w/z5nVjS988+WlRU1s8/xyYllaxceXPRIi9j48ZvEgvNSE2NvWBB5/797ceMOXvp0uh3devUCbcEGINCCMAAU1PtrVv7lpbW9uljM326R9MP+P33d7799hbVXrs2Zs+eR3fuTHR2NmjKMWtrhUePPsvOfvX77wn6+hpNPBpAi4VCCKDw8vOrv//+Tv1IYWH1mjW3Dx0a3OhjvnxZ5e9/IjGxhBCyY0f8vn2PDhwYOGlSm//8QoAPt3TpDWopgKoqfnp6uWT/xQ0bfDt3pm8TFRRCAIX34EEBny+SCv77r/T0tY+yatUtqgpSamuF8+ZFDhvWWqGHZohEYqGwSau6QPPasMGXaty6lbN06Y3IyDGMpIFCCKDwtLVl/CE3XGpELCbLlr0ei5ieXs5ms+zt9QghLBar4ZLN1669kIpUVNTFxeX16WPbPEnTKz+/+ssvr548mcTni9q2/X3DBt/gYGemk4KWAoUQQOF16WJhYaGTl1dVP9hw/RFCSP/+9lTj4MEnHA6LesmSNVBH5uAdmT1bPqFQPGjQqfj4Aurls2fFw4efjowcg5G6QEEhBFB4mppqx48PmTgxPDf39ZSMkSNdli7tKtWNxXpTCKOjszkctuRlQ/362T9/Xlo/YmCg4e1N32ObZnTrVo6kClJEIvGuXQ9QCIGCQgjQQt2/nx8ZmUm1q6v527fHU3c7O3c2DwiQLmB9+tgmJc3avfvB7t0Pw8KCvbyaWrHWrOlx61bOo0eF1Ettbe6+fYEKuo7XixevGgYzMyvozwRkio7O3ro1NimpZP36mPnzO+vpqdOcAAohQAulp6fh5KRPtefP7+ThYUzdmTQxkT1BUFeX6+tr8+efz5teBQkhpqbacXFTwsKeL158deJE98WLvZu43V10dHZtrZAQ8uxZcU5O5eXLr2t87942GhpvVk6pqxNmZ7++rq2qEuTnV6ellRNC1NXZNjaNTMDOTsYXOjjoNe5ohJDw8LRVq15PVikp4Y0ceZqa9h4U5LRmTc9GH1Y1rV8f8803N8ViQghZseLm7t0Pb9+eSPPeirQWwuLi4i1btjx8+NDY2HjGjBl9+/al8+wAiqV1a4PWrV9P3Rszxo3+BNTU2BMmuG/efG/y5LZNf2OKjs6uqKgjhJSV1WpqciSFsGtXy/qFMDe3UrK6WEFB9V9/PY+KyiSEODjobdzYp3Gn7tHD2tvbov42fhwOa/78To07GiEkKMgpKMip0V8OEnl5VatX3xbXG8mbnf3qu+9u79sXSGcatBbCESNGWFhYfPPNN4mJicHBwdevX/fy8qIzAQBgyvLlMhbYbMjBQf/kyeDmPTWHwwoPH7lkyfUTJ5JqawWeniYbNvRR0OGvSub+/fyGM3/u3GnSzJ9GoK8QCoXCW7duJSQktGnTpnv37seOHbt16xYKIQDQwMxM++DBQRMmuG/Zci8ycux/9q+rEzZ8g4Zmp6EhowZpaspYZFyu6FvOlcPh9OnT5+TJk3w+/8mTJwkJCb17S09dAgCQHxaLxeH8x5vezZs53t6he/Y8XLLkWkBAWP1VBaDZdetm2XBR3CFD6J7iSeu65nv27Pntt9+0tbU9PT0XLVrUqdM779G/fPkyJCQk4P+FhITQmScAqKbExJKAgLC4uHxCiEhELl/O7Nv3RFmZjP0joVno6HAPHx5cf/zXkCHODWf+yBt9t0ZramoGDBiwbNmyuXPnZmZmDhgwwMHBYexY2fcojIyMZsyY4eT0+nG0paUlbXkCNM64cedSU8sIIRUVdSUlPG/vUCp+4kQwlqtWFL//nsDjCepH8vKq/vrr+cyZnkylJKWmRvDy5euVE3g8YW5uFTWqVktLTbJzk2IZONDx+fNZ27bdP3ToyaFDg3v2lLFHlbzRVwifPHmSl5cXEhLCYrEcHR1HjBhx/vz5dxVCDQ2Nbt26tW/fnrb0AJroxIlmHuIB9MvOljHjMCtLRpApaWlla9a8XmC9srLu8OGnf/+dTAhp18541aoejKbWeIaGmv3720dGZjJSBQmdhdDW1pbP58fFxXl7ewsEgpiYGEyfAIBGi48vuHfv9YwIkYjs3/+YzWYRQjp1MuvSxaJxx7SzkzG5sCkzDptdu3YmzT6qttkVFFRLFjF48qSoXbvXe0qYmWnTPEHwA9FXCM3Nzbds2TJgwID27dtnZmba2NgsXryYtrMDgBKbNq0dVQWbaPZszx077ldW8iURW9tWo0a5Nv3IKiUhoejEiUSqffDgkwkT3NXVOYQQf3+7cePcGU1NNlrnES5cuHDmzJnp6ekGBgZ2dljlDwAar1MnM8mu7nPnNs9jFGdng2vXxi9efPXWrRwOhzV4sNPWrX1btWr8il9nzqRQ404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       "<circle clip-path=\"url(#clip452)\" cx=\"482.844\" cy=\"278.596\" r=\"14.4\" fill=\"#00008b\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"none\"/>\n",
       "<circle clip-path=\"url(#clip452)\" cx=\"428.024\" cy=\"450.257\" r=\"14.4\" fill=\"#00008b\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"none\"/>\n",
       "<circle clip-path=\"url(#clip452)\" cx=\"373.204\" cy=\"404.141\" r=\"14.4\" fill=\"#00008b\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"none\"/>\n",
       "<circle clip-path=\"url(#clip452)\" cx=\"318.384\" cy=\"424.789\" r=\"14.4\" fill=\"#00008b\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"none\"/>\n",
       "<circle clip-path=\"url(#clip452)\" cx=\"263.564\" cy=\"507.754\" r=\"14.4\" fill=\"#00008b\" fill-rule=\"evenodd\" fill-opacity=\"1\" stroke=\"none\"/>\n",
       "</svg>\n"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## --- Resample a single variable\n",
    "tmin = 50 # Minimum age\n",
    "tmax = 3850 # Maximum age\n",
    "nbins = 38\n",
    "elem = \"MgO\"\n",
    "\n",
    "# Look only at samples in the basaltic silica range\n",
    "t = 43 .< ign[\"SiO2\"] .< 51 # Mafic\n",
    "\n",
    "# Resample, returning binned means and uncertainties\n",
    "# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)\n",
    "(c,m,el,eu) = bin_bsr_means(ign[\"Age\"][t], ign[elem][t], tmin,tmax,nbins, p=p[t], x_sigma=ign[\"Age_sigma\"][t])\n",
    "\n",
    "# Plot results\n",
    "plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:darkblue,label=\"\")\n",
    "plot!(xlabel=\"Age (Ma)\", ylabel=\"$elem (wt. %)\",framestyle=:box,grid=:off,xflip=true) # Format plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## ---  Resample a ratio\n",
    "tmin = 50 # Minimum age\n",
    "tmax = 3850 # Maximum age\n",
    "nbins = 38\n",
    "num = \"La\" # Numerator\n",
    "denom = \"Yb\" # Denominator\n",
    "\n",
    "# Look only at samples in the basaltic silica range\n",
    "t = 43 .< ign[\"SiO2\"] .< 51 # Mafic\n",
    "\n",
    "# Exclude outliers\n",
    "t = t .& inpctile(ign[num], 99) .& inpctile(ign[denom], 99)\n",
    "\n",
    "# Resample, returning binned means and uncertainties \n",
    "# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)\n",
    "(c,m,el,eu) = bin_bsr_ratios(ign[\"Age\"][t], ign[num][t], ign[denom][t], tmin,tmax,nbins, p=p[t], x_sigma=ign[\"Age_sigma\"][t], num_sigma=ign[num][t]*0.05, denom_sigma=ign[denom][t]*0.05)\n",
    "\n",
    "# Plot results\n",
    "h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkred,markerstrokecolor=:darkred,label=\"\")\n",
    "plot!(h, xlabel=\"Age (Ma)\", ylabel=\"$(num) / $(denom)\",framestyle=:box,grid=:off,xflip=true) # Format plot\n",
    "display(h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "## --- Ratio differentiation\n",
    "xelem = \"SiO2\"\n",
    "xmin = 40 # Minimum age\n",
    "xmax = 80 # Maximum age\n",
    "nbins = 20\n",
    "num = \"Sc\" # Numerator\n",
    "denom = \"Yb\" # Denominator\n",
    "\n",
    "# Exclude outliers\n",
    "t = inpctile(ign[num], 99) .& inpctile(ign[denom], 99)\n",
    "\n",
    "# Resample, returning binned means and uncertainties\n",
    "# (c = bincenters, m = mean, el = lower 95% CI, eu = upper 95% CI)\n",
    "(c,m,el,eu) = bin_bsr_ratios(ign[xelem][t], ign[num][t], ign[denom][t], xmin,xmax,nbins, p=p[t], x_sigma=ign[xelem][t]*0.01, num_sigma=ign[num][t]*0.05, denom_sigma=ign[denom][t]*0.05)\n",
    "\n",
    "# Plot results\n",
    "h = plot(c,m,yerror=(el,eu),seriestype=:scatter,color=:darkblue,markerstrokecolor=:darkblue,label=\"\")\n",
    "plot!(h, xlabel=xelem, ylabel=\"$(num) / $(denom)\",xlims=(xmin,xmax),framestyle=:box,grid=:off) # Format plot\n",
    "display(h)\n"
   ]
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   "outputs": [],
   "source": []
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