{ "cells": [ { "cell_type": "markdown", "id": "b1b21308-6a9c-45b3-b8ec-2d393199c3f8", "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", " \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": "db4e99dd-ea0f-44c2-88ba-7d1e3bf581f8", "metadata": {}, "outputs": [], "source": [ "# Load (and install if necessary) the Chron.jl package\n", "using Chron\n", "using Plots, DelimitedFiles" ] }, { "cell_type": "markdown", "id": "7cc47d81-dfd4-4639-a521-494f7db0c398", "metadata": {}, "source": [ "***\n", "## Enter sample information\n", "First, let's enter some basic information about your samples. How many are there, what are the sample names (needs to be a valid filename, BTW), and what are the stratigraphic heights and uncertainties? Then, we'll enter the ages as .csv files." ] }, { "cell_type": "code", "execution_count": 2, "id": "aede5308-e728-430b-83b8-16fff444fd05", "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", "id": "009c0996-c7d5-4136-829a-780761f32e8c", "metadata": {}, "source": [ "Note that smpl.Height *must* increase with increasing stratigraphic height -- i.e., stratigraphically younger samples must be more positive. For this reason, it is convenient to represent depths below surface as negative numbers.\n", "***\n", "Now let's see what's in our current directory (we'll use `;` to activate Julia's command-line shell mode, followed by a unix command, in this case `ls`" ] }, { "cell_type": "code", "execution_count": 3, "id": "9f9e5167-45bc-4c74-a794-b74da8938dff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Chron1.0Coupled.ipynb\n", "Chron1.0Coupled.jl\n", "Chron1.0CoupledConcordia.ipynb\n", "Chron1.0CoupledConcordia.jl\n", "Chron1.0CoupledSystematic.jl\n", "Chron1.0DistOnly.ipynb\n", "Chron1.0DistOnly.jl\n", "Chron1.0Radiocarbon.ipynb\n", "Chron1.0Radiocarbon.jl\n", "Chron1.0StratOnly.ipynb\n", "Chron1.0StratOnly.jl\n", "ConcordiaExampleData\n", "DenverUPbExampleData\n", "EruptionDepositionAgeDemonstration.ipynb\n", "EruptionDepositionAgeDemonstration.jl\n", "FCT\n", "Manifest.toml\n", "MyData\n", "PlutonEmplacement.ipynb\n", "Project.toml\n", "archive.tar.gz\n", "ffsend\n" ] } ], "source": [ ";ls" ] }, { "cell_type": "markdown", "id": "b30dd0a4-1d84-4c52-a3da-e47a272b7c80", "metadata": {}, "source": [ "Equivalently, we can also run unix commands using the `run()` function\n", "```\n", "run(`ls`);\n", "```" ] }, { "cell_type": "markdown", "id": "9f82e4ed-d7b6-4ea3-8d40-c7b8675fc403", "metadata": {}, "source": [ "Now that we know how to access the command line, let's make a new folder for our example data. This can be called whatever you want, just make sure it matches `smpl.Path` above" ] }, { "cell_type": "code", "execution_count": 4, "id": "f2cb2d3a-a5d3-4ea5-8d22-1d84b6ee6ffd", "metadata": {}, "outputs": [], "source": [ ";mkdir -p MyData/" ] }, { "cell_type": "markdown", "id": "847e96be-bf21-483b-823f-2a6cab7a4b2b", "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": 5, "id": "a4e06b71-fb44-449e-a6e5-ecc935a67e75", "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": 6, "id": "ad748fd2-7e00-4efa-8322-14182f56b575", "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": 7, "id": "85352b84-7a11-49b4-a000-eb55c914a8e9", "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": 8, "id": "2c47629a-c1d7-4d9c-a7ef-7a958073824a", "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": 9, "id": "921d844b-0cc1-4514-9c4c-1cc3549b0e21", "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", "id": "783b72a7-e1c3-451a-8f76-e2864ee31122", "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, "id": "14ebc55f-e308-452c-b0df-c18aaf6d9443", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "e8bc8140-9161-4949-be73-30c075d7d72b", "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 has no knowledge of open-system behaviour, so *e.g.*, Pb-loss will lead to erroneous results. If you are using U-Pb data and expect your ages have experienced Pb-loss, consider instead [Chron1.0CoupledConcordia](https://mybinder.org/v2/gh/brenhinkeller/Chron.jl/main?filepath=examples%2FChron1.0CoupledConcordia.ipynb) and [Isoplot.jl](https://github.com/JuliaGeochronology/Isoplot.jl).\n", "\n", "#### Boostrap relative pre-eruptive (or pre-depositional) mineral age distribution" ] }, { "cell_type": "code", "execution_count": 10, "id": "e9e04d05-7ea5-46bd-9c77-33c0a2cf43e5", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInterpreting first two columns of KJ08-157.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInterpreting first two columns of KJ04-75.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInterpreting first two columns of KJ09-66.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInterpreting first two columns of KJ04-72.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mInterpreting first two columns of KJ04-70.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n" ] }, { "data": { "image/png": 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FRY8ePYRCISFEMqemV69ehJDKysqZM2fu37//zJkzM2fOlBzs1NfXDw4Ojo2NvXfv3qJFiwwMDMLCwhiG6dSpU3FxcWpqaqtWrezt7auqqsLCwgghgwYNysnJ2bNnD8uymzZtkhQvmURjb28veUddXV3JgdLXNfzvPDk5+caNG8OHD3/zblS947nN0+PHj0NCQmqd2vIGpaWl0t8jjTLhNOuoT33+nqI7Z2EJ4mGO9GR3Fe4UvnWbw1tDmzd/R48enT9/vmQmS009evSYOnWqZDYNvFnD/8737du3bdu2vXv3vnk3Ff6eVYDscn7fI25qIy832CT+24756hr3Qqz4dwYA0Cw4RvgmK29zE1xpU2UMNQWaUe3NqXX3uLk++LECoD7CwsJe7w4SQo4dO6b4YkACX7J1KqokW1O4WV5Ka6Jvg+jvbrBFlcp6fwAAjSCvHmF1dfXt27dv3rzp4uIiOb20lpKSksTExOTkZFNT06FDh0rX74mNjS0uLpbctrKyGjBggJwqrNeau9xgO9pWT2nzVdwMqf629Krb7BKcXw8AIDfyCsJRo0ZdvHhRLBZHRETIDMJhw4YJhcLAwMALFy4sXLjw/PnzXl5ehJDFixe7uLhIcrHWlF9FeiEma++yx/speej46wDab694uifTSle5hQAAqC15fdFv375dW1t7+vTpde0QGxsrXUQgKirq119/lS69+sknn4SEhMipsAbanMKFtKI9jZR8+oJNS2qUM/3tdfaHYHQKAQDkQl4HwOo9yUOagoQQmqZr7v/3339v3Ljx4sWLcqqtXmKOrLzFfeLbLA6gLvBjfnvAZZWr5FkuAADNn/JnjZ47dy4xMfHq1auSu66urrm5uU+fPl24cGFUVNTatWtlPqu6urqkpGTu3LnSLd26dZOsjyBTRUWF5MzQhtj9kGrTgmprIJZ1tTJF0ydklCP1/fWqpQEqloWNanNoEkpvc4ZhGEazRi/EYrFAoPwvUg1RXV0tObu/gX/nkv3r3U3J/3737t2LjIzctGmTo6OjZMu+ffskNxYuXOju7j5p0qT33nvv9SdSFEXTtHTtV0KImZnZG/4PbNT/n1vSyFQP0nz+f57jTQIOkAV+xEhL2aU0hgZ+Jypdc2jzBq5XqTbKysqwiIHCSJbqbvjfucyrZ7xOmUGYmpraq1evZcuWSS5uWYudnZ2Tk1NKSorMIBQIBPr6+g2/TodQKGzgL4iHpfytIvEQB6Gw2XyH2xmSvm3YLWlUMxmtbaCGtzk0FbS54qHNFa/hbc4wTM1l0Oui0C/WioqKy5cvS34wPnr0KDw8/PPPP5de15gQIha/XEklIyMjLS3Nzc1NkRUSQrakcCOdae1mk4IS83zpH+9wVZyy6wAAUDvy6hHu2LFj+/btycnJFEXdvXt3/PjxI0aMyMjICAoKKioqMjIyGjt2bFFRUVxcXFxcHCEkODh4yZIl169fHzt2bIcOHXie379/f0xMjMyrZMkPx5Ntqfy+ns0sBgnxMaG8jcmuB9w4V1XqFAIANH/yCsKQkBArKyvpXcnlpuzs7JKSkvT09Agh33333fPnz6U7SK4A6efnt3HjxpSUFIZh5syZ4+PjI6fy6nL0CW+mQ/xMm+NFH+b5MjPOs2NcVPuSFAAAzY28gtDR0VE6/0WqRYsWkmtwEEJkXu9KIBCEhIQo8STCzfe58c21y9XDmmopIIlZfN82SEIAgCbTTL/0laKwkhzJ4kY4Nd82metDL7+pWVPyAADkrfl+6Svebw+4Pm1o42Z8WdNIR/pxObmQp2InFAIANGcIwpe2pHATmuu4qARDkVle9MrbmDwKANBkmvX3viJdL+ALK0l36+Z++G2CK33iCZdeik4hAEDTQBD+z+YUbpwr1fwnZOoJyUQ3es0ddAoBAJoGgpAQQqo48kcaN8ZFNVpjqge94wFXLq5/TwAAqJdqfPXL296HXFtTylG/2fcHCSGE2OpRHVvRf6ShUwgA0AQQhIQQsiOVG6ci3UGJaR70unsIQgCAJqBK3/5yUlxFzuXyg+xUqSl6tabKqsm/OI8CAOCdqdK3v5zse8j1sKH1VGr5eIqQD9zpn9EpBAB4ZwhCEpfBRTqoxtHBmia40fGZXJ5I2XUAAKg4TQ/C4ipyNpfv20b12sFIiwyxo7ekoFMIAPBOVC8AmtaBR1wPa1pfpcZFpWZ60T/f41gcKAQAeAeaHoS7VXNcVMLPlLJsQQ4/RhICALw9jQ7C59XkTA7f11aFG2GaB73uHq5HAQDw9lQ4A97d/kdcNyvaQDXHRSWGOdJX8/nUEnQKAQDekkYHYVwGP1Rlx0UltBky1oX+5T6mzAAAvCXNDcLSanL6KTdApc6jl+kDD3prCleJ8VEAgLei8jHw1vY/4rqo+LiohKM+5WNCHchEpxAA4G1obhDGZfCqO1+0lklu9K/JCEIAgLehoUFYWk1OPeX6q/J80Zred6CvF/JpzzFlBgCg0dQkCRorPpMLtaINtZRdRxPRokm0E70tFZ1CAIBG09Ag3J2uPuOiEh+401tSeKwyAwDQWJoYhOVicvyJ+oyLSngYUW1aYpUZAIBGU6swaKCkbK6dBWWkLuOiUhPd6E04oRAAoJE0MQgPZvL9VfByE/Ua7kSfzuGevECnEACgEdQwD96MJ+TwY76frVodIJRoKSBDHehtqQhCAIBG0LggvPyMN9QizgZqGITk/08o5BCFAAANpnFBeDCTG6CO3UGJIHPKUIucfIokBABoKI0LwvhMXs3mi9YywY3+FVNmAAAaTJ0j4XXZ5XxmGR9sobY9QkLIKGf68GOuoFLZdQAAqAjNCsKDj/nebWiBWn9oIy3Stw296wE6hQAADaLWmfCag5lc/zbq3B2UGO+K0VEAgIbSoCAUicmZHD68tfp/5B42VFk1uVaAKTMAAPVT/1SQOvqE8zeljLWVXYf8UYSMdqG2pKBTCABQPw0KwoPqPl+0pvGu9O9pXAUuWw8AUB9NCQaekITHfH/1PYOwFjs9ys+U2v8InUIAgHpoShDeLKJ1GOJqqClBSAgZ70pjdBQAoF6aEoQJ2fQgOw1KQUJIhD19+RmfWYYpMwAAb6IpQZj4hNacA4QSOgwZ5oQ1uAEA6qER2ZAjIhnlVEgrzeoREkImuNJbUrAENwDAm2hEEAoossJfLNSIz/qKADNKX0hOPEEUAgDUSSPCwUyHRNlp6JkE413prZgyAwBQN40IQk02ypmOz+RKqpRdBwBAc4UgVHNmOiTMhv49DZ1CAADZEITqDycUAgC8AYJQ/fVuTT19QW4WYsoMAIAMCEL1R1NkjAu1LRWdQgAAGeQYhJWVlenp6eXl5XXtIBKJTp48efnyZZ5/pbOSmpp69OjRvLw8+dWmaSa60TtSuUoNnTkLAPAm8grC7t27GxgYuLq6HjlyROYOaWlprq6uX3/99dixY/v27VtdXS3Z/vXXX4eGhq5evdrT03P//v1yKk/TOOhTnsbUocfoFAIA1CavIPzPf/6Tl5fn5uZW1w5ff/31oEGDjh07dvXq1cePH//111+EkOzs7KVLl549e/bgwYMbN2788MMPOQ7f3U0DU2YAAGSSVxB27NjR0NDwDTv89ddfY8aMIYRoa2tHRkZKgvDAgQNBQUFOTk6EkIEDBxYVFV29elVOFWqaKAf6fC7/uBxTZgAAXiGQufWjjz4aOnRox44d5fSuxcXFpaWldnZ2krt2dnZ///03ISQrK8ve3v5/lQkE1tbWjx8/DgwMfP0VeJ4XiUSxsbHSLYGBgdLnvo7jOA3vXGrTJMKe2pHKfeqroDVX0eaKhzZXPLS54jW8zWtNQKmL7CBMTExctWqVj49PTEzM6NGj39y3ewsVFRWEEKFQKLmrra0tEokk2wWClyVJt7+usrLyxYsXf/75p3SLSCSytLSs6x0rKyulb6exRtpRk/8VzHatVkwSos0VD22ueGhzxWt4m1dVVTUkC2UH4a1bt06cOCE5Sjdv3rwBAwbExMSEhYU1rti6mZubMwxTUFBgYmJCCHn27JmVlRUhxNLSMi0tTbpbfn6+ZPvrdHR0TE1N9+zZ08B3ZFm2RYsW71y4autqS7Quiq8+1+1sqYgoRJsrHtpc8dDmitfwNtfW1qao+r/uZB8jZBgmLCwsNjb20aNHixcvvnDhQs+ePT09PZctW1ZYWNi4kut4/Xbt2p08eVJy99SpU8HBwYSQ4ODg8+fPi8ViQsiDBw8KCgr8/Pze/e1AarwbpswAALyinskyNjY28+fPf/DgwUcffXTv3r1PP/3Uzs5u9uzZubm5b37i77//vmzZsvz8/L179y5btiw7O5sQsnHjxnbt2kl2mDt37sKFC3fs2LFkyZKTJ09OnDiRENKpUycnJ6dRo0bFxcWNHj160qRJxsbGTfEx4X/GONN7H3Kl1cquAwCg2ZA9NCpVVFS0ffv2jRs33r1718vL64MPPsjOzv7ll1/++uuvW7duGRkZ1fXEsrKyoqKi8ePHS15E0snz9/efMGGCZIeIiAhtbe24uDgDA4Nz585Jh0APHz68evXq/fv3jxw5csqUKU3zKeH/WeiSrlZ0bDo30Q2LCgEAEEIIVdeBxLNnz27cuHH37t0cx0VEREydOjU0NFTy0NOnT11cXLZs2RIZGanAUl/x+PHjkJCQzMzMBu5fWlqqr68v15JUxcFM/tvr7PmB9fwGendoc8VDmyse2lzxGt7m+/bt27Zt2969e9+8m+xvw44dO/7zzz92dnZffPHFxIkTW7VqVfNRKysrW1vbkpKSBhYNzUqfNtT08+RGId/WREHnUQAANGeyg9DFxWXBggV9+vRhGEbmDv/++6+2trY8CwN5YSgy1oXaksKt7iD7HxcAQKPIPlA0ceLEzp0710rBkpKSo0ePSm4bGBggCFXXZHd61wOuAmtwAwDUFYRRUVF37typtfHu3bs9e/aUf0kgd21aUu+ZUnsf4jwKAIDGrDVaWVmpo6Mjv1JAkSa507/eRxACALx6jDA3NzcrK4sQUl1dff/+/ZqDn4WFhRs2bHBwcFB0gSAfg+zomefZ1BLexRBTZgBAo70ShLt27froo48kt6Un/Em1aNHi119/VVBdIGdaNBntTG9O4ZYGYcoMAGi0V4Lw/fff9/HxIYRERUUtXrzY09NT+pCpqamTk5OBgYGiCwS5mexOdzkoXhLACHFuPQBosFeC0NbW1tbWlhCybdu2jh07mpqaKqkqUARXQ8rFkDr0mBtshyQEAM0l+xtwwIABSEFNMNmd/iUZU2YAQKO97BEmJSV9//33EyZMiIqKio6OLioqkvmExMRERdUGcjfUnp7zD5tZxtvqYcoMAGiolz1ClmUrKyslq2NXVVVV1kF5pULT0xWQ4U701tQGXcQZAEAtvewR9u7du3fv3pLbDb/gLai6ye70wL/ZhX40gz4hAGgkzJLQdG1NqNYtyaFMHCkEAA0lOwjj4uIOHDgguV1YWDhs2DB7e/vIyMhnz54psDZQkKke9M/3EIQAoKFkB+GsWbOkmffpp58eOHCgU6dO//77r+RCu6BmIh3oawV8agmOFAKAJpIRhM+fP3/69GlgYCAhpLq6OjY29tNPP925c2dcXFxCQkJBQYHCiwT50mbIWBf6Fyw9CgAaSUYQlpeXE0IMDQ0JIf/++29JScnAgQMJIe+99x7P8w2/KDyokA886K0puDATAGgiGUFoYWEhFApv3rxJCImNjTU3N2/bti0hpLCwkBCCC1CoJUd9KtCc2p2BTiEAaBwZQcgwTGRk5KRJk0aOHPnLL7+MGDGCpmlCyJUrV4RCoWQNNlA/Uz3on+8iCAFA48ieLLN+/fqoqKiMjIxJkyZ9/fXXko1///13v379WrZsqcDyQHH6taGfisjVfEyZAQDNIpC5VV9ff82aNbU2/vDDD/KvB5SGpsgkN3pjMre+Ey7MBAAaBCfUw0sx7nRcBldSpew6AAAUSHaPsLq6euvWrfv27Xv06JFIJFuaUrYAACAASURBVKr5UFpamkIKAyUw1yFhNvTOB9x0T/xCAgBNITsIJ0+evG3bNj8/P39/f11dXQXXBEo01YOefp5FEAKA5pARhNXV1X/88ceiRYu++uorxRcEytXFiqIIOf6E726NRbgBQCPI+OFfWFhYWVk5ePBgxVcDzcFsb3r1bZxHAQCaQkYQmpub29nZpaSkKL4aaA5GO9OXnnH3sfQoAGgGGUFI0/SGDRsWL1585coVxRcESqfNkEnu9FqcXA8AmkH2ZJlFixZlZ2cHBgaamZkZGBjUfAizRjXBTE/GI656sT9joq3sUgAA5Ex2EIaGhvr5+Sm4FGg+LHRJvzb05hRurg+mjwKAmpMdhMuXL1dwHdDcfOhNRxxlP/SiBYhCAFBr+JID2fzNKDs9su8RjhQCgJqrMwj379/fqVMnExOT1q1bS7Z89913q1evVlRhoHw4jwIANIHsINy2bdvgwYN1dXUHDRok3Whpabl06VKWxcVbNcUgOzpHRC4+w3kUAKDOZAQhz/MLFiyYPXt2UlLSuHHjpNtDQkLy8vKys7MVVx0oFUOR6Z70D+gUAoBakxGEubm5T548GT9+fK3tlpaWhJC8vDxF1AXNw0Q3+kgWl12OTiEAqC0ZQailpUUIqXXRCULIw4cPCSGGhobyrwqaCwMhGeNCr0KnEADUl4wgNDEx8fT0XLduHc/zFPW/lZd5nl+2bFnr1q2dnZ0VWyEo2Uc+9OYULr9C2XUAAMiH7PMIly1bNmjQoOzsbE9PT5FI9NNPP+3evfvMmTM7duyQRiNoiNYtqQh7es1ddrE/rlwPAGpI9qzR/v3779+//8mTJ2vWrCksLJw1a1ZGRsaOHTtGjRql4PqgOfisLb3uLldarew6AADkQHaPkBDSv3///v37Z2Vl5ebmGhgYODs7oy+osZwMqO7W9MZk7mOsuAYAaqfOIJRo3bq19IR60GSf+dF9EsXTPWkdjI8CgHqR8QP/yZMnn3/+eefOnR0cHOzt7Tt06PDxxx/johMarq0J5W9KbUvF9FEAUDe1g/D06dOenp7ffPPNzZs3DQwMjI2NU1NTV65c6eXlFRcXp5QSoZn4/D3muxucGFEIAOrllSAsKSmJiooyMDA4dOhQUVHRjRs3rl27lp+ff/r0aWdn53Hjxj169EhZhYLSdbCgWrckf6YjCQFArbwShLt27SooKDh8+HDfvn1p+n8PURTVuXPnpKQkgUCwceNGZRQJzcVnfsw31zkO68wAgBp5JQhPnjzZq1cvLy+v1/ezsrIaPnz4iRMnFFUYNEe9W1MtBeRgJjqFAKA+XgnC1NTUgICAunYNCAhISUlp1KtXVFTwPLoPamWBH73kGvqEAKA+XgnC58+fGxkZ1bWrsbFxSUlJA1+3oKAgPDzc0tLSxMRk1apVr+8watQokxo6duwo2d6hQwfpxgEDBjT4g4CCDLGnBTTZk4FOIQCoiVfOIxSLxW84a56mabFY3MDX/eKLLwwMDAoKCtLT09u3b9+9e/e2bdvW3GH9+vXV1f9bqiQqKqp9+/aS28+fP9+1a5fkrkBQz2mOoBRLApiZ59nBdrQAp9cDgOqrnTQHDx7Mzc2VuWvDx0VZlt25c+fRo0cZhnFxcYmIiNi+ffv3339fcx89PT3JjZycnNOnT69fv176kL6+vrGxcUM/AShcLxvKpgXZ+YAb54okBACVVzsIjx8/fvz48Xd80by8vNLSUg8PD8ldDw+PM2fO1LXzli1bQkJCnJycpFuGDBkiFov9/PyWL1/+hmOWHMcVFRVJ7+rp6QmFwnesHBrov+2YYcfZaCdaGwvNAICKeyUIb9y4wXFNcOynuLiYoqiWLVtK7urp6RUWFta18/bt2xcuXCi9u379eh8fH57nV61a1adPn+TkZBMTk9efJRKJcnJyHB0dpVs++eSTGTNm1PUuZWVlb/NJoA4eOsRDX2vNzeoY5zpHy9Hmioc2Vzy0ueI1vM1FIlFDQu2VIGyqi+6am5vzPC+delNcXNyqVSuZe54+ffrp06cRERHSLaGhoZIbX3/99c6dO8+ePTtw4MDXn6irq2ttbZ2ZmdnwqvT19RvxGaA+yzrwPQ+LY7x19evuh6PNFQ9trnhoc8VrYJvr6upKz4l/A7kc4zE1NW3VqtXVq1cld69duyYdJq1l8+bNI0aMaNGixesPcRwnFosxX6bZ8jGhulvTa+5i+igAqDa5BCFFUTExMYsWLXr48OHBgwcTEhImTJhACMnMzOzRo0d5eblkt7Kysj179kgeknj06NH27dtTUlLu378vGefs1KmTPCqEJvGVP73yFltYqew6AADegbz6WwsXLiwrKwsPDzcyMvr999/t7e0l22uennHp0qXBgwcHBgZKt9A0HRcX98033wgEAn9//+PHjxsYGMipQnh3LobUEHt65S32P4GYMwMAqopS0ZVfHj9+HBIS0vBjhKWlpRjHl4esct7vL/HVIQJbvdpnoKLNFQ9trnhoc8VreJvv27dv27Zte/fuffNuOA8M3knrltQML3reRRwpBABVJTsIf/jhh+zsbAWXAipqvi9z6Rl/8qlKDi0AAMgOwuXLl9vZ2Q0ePDghIaFJziwENaYrIN+1o2ecZ3HNXgBQRbKD8Pbt2+vWrUtLS+vXr5+tre2nn37aqDP2QNMMdaBtWpANyUhCAFA9soPQyMgoJibm1q1bly9f7tev348//ujg4NCzZ8/du3ezLKvgEkElrApmllxj8yuUXQcAQCPVM1kmICBgw4YNmZmZM2fOPHr0aFRUlIuLy48//igSiRRTH6gKTyMqyoH+8ip+JwGAiqknCFmWjY+PHz9+/Jo1a4yNjWfPnh0YGPjxxx937NixogI//uEVSwKYvzK4G4WYNQMAqqTOIHz8+PHixYvt7e0HDhyYm5v7yy+/ZGdnr169OjY29sqVK/fu3Tty5IgiC4Xmz1ibfOnPTD/HIgkBQIXIXlkmOjp69+7d2tra0dHRU6ZMqbn4CyHE19fXwcHh2bNnCqkQVMlkd/qX+9z2VG6sC05RBQDVIDsInz59+v33348dO1Zy+YjXxcXF1XVBCdBkDEW2hDJhCeIwawqL4wGASpAdhNu2bWvVqpWOjk7NjRUVFU+ePJFcAtDLy0sR1YEK8jWhpnrQU86xu4KVXQoAQAPIHr9q37699CJKUteuXat5HXmAunzxHpNdTuIysRI3AKiARhzIqa6uFgrrvgYrwP8T0GRTKPPZNUEuzrIBgGbvlaHRiooKyQmCHMeVlpYWFRVJHyouLo6NjW3durWiCwTV9J4pNdKBnXmeje2BfiEANGuvBOHPP//80UcfSW737t271q4URa1YsUJBdYHq+8xL3PWYMC6DG+qAGaQA0Hy9EoRhYWEbNmwghMybN2/KlCk1jwiamJh4e3u7u7srukBQWdoM2dSZGZQk7mJFm+vUvz8AgFK8EoQ+Pj4+Pj6EkKqqqiFDhtjY2CipKlAT7S2o0c70B2fZPWFM7ev2AgA0D7LHrGbMmIEUhCbxbRCT84L/8TYuTAEAzdTLHuG///67Y8eOQYMG9erV67PPPnv+/LnMJ6xdu1ZRtYE6ENLkj+5Mu/3idhZUsAW6hQDQ7LwMwsePHx88eNDLy6tXr15JSUl1raCGIITGstWjfu0siD7OXh0iMNFWdjUAAK96GYSRkZGRkZGS25cvX1ZSPaCe+ttSSdnUuFPs/l44WAgAzQvmtYOCrGjPPKvg19zBwUIAaF5e9girq6sbcrldAwOspQxvQ3KwsP1+cZA51QEHCwGg2XgZhDt37pwwYUK9T+B5XGwO3pKdHrUuhIk+wf4zUGCpq+xqAAAIITWDsGPHjuvWrVNiKaAJIuzpW4Wk/xHx6f6CFrKvfQIAoFAvv4rc3Nzc3NyUWApoiEX+dNpzfswpNrY7Q2OIFACUDZNlQNEoQn4NZQoq+C+usMquBQCgRo/wypUrcXFxvXv37tKly7fffltaWirzCUuXLlVUbaC2tGgSFyYIPiC20+Ni3PFrDACU6WUQ3r9//+eff7awsOjSpcvWrVvz8vJkPgFBCE3CVJsc6Ml0OSR2NqC6W2OEFACU5mUQjhgxYsSIEZLbKSkpSqoHNIi7EbWrm2DECXFSH4GPCbIQAJQDo1KgTD2sqXUhTHii+G4xTssBAOWocwK7WCxOSEi4du1adna2paWlt7f3wIEDdXRwWTloYhH2dLmYhCWIT/QTuBmiXwgAiiY7CLOysgYMGHD9+nWGYUxNTYuKiqqrq52cnOLj4z08PBRcIqi90c60mCO9DrMn+zEO+shCAFAo2UOjY8aMycrKio2NraioyM3NraioSExM5DguIiKC47BWJDS98a70PF+612H2yQuMkQKAQskIwsLCwhMnTqxfvz4yMlIgEBBCaJoODw/ftWtXcnLyvXv3FF4kaIQZnnSMO90jgc2pf8lbAIAmIyMIJauJenl51dou2YIeIcjPPF96rAvdKV6cXop+IQAoiIwgNDU1DQwMjI+Pr7X9wIEDrVu3xjFCkKtP29Kf+9GhB9nrBchCAFCEl5NlSktLpVelX7Jkyfjx4x89ehQREWFpaZmfn5+QkLB58+Yff/xRMlgKID/jXGkjbdInUby7h6CTJebOAIB8vUy1uLi4WpdhWrt27dq1a2tuiY6OHj58uIJKAw022I421KIijoo3dGKG2ONsVwCQo5dB2K1bt9jYWCWWAlBTNyvqYLhg0N/ikioyzhVZCADy8jII7e3t7e3tlVcJQG3tzKkT/QQD/mbvFvP/DcI1mwBALvBDG5o1dyPqwiDB1Xy+3xFxSZWyqwEAdVTnzJcbN27ExcWlp6fXugxFUlKS/KsCeMlEmyT2Fnx0gW23Xxzfi3HFMmwA0KRkB+GePXuGDx9uZmbGcZyurq6WllZ6erqenl5QUJCC6wMghAho8mMws/Yu1+Wg+I/ugi5WyMJm53k1SXvOczXOedFmiJM+pYtp5tDsyf4j/eyzz/r37x8bGxsTE2NjY/Of//wnOTk5KioqPDxcwfUBSE33pN0MqeHHxR/7Mh/74IihkpVWk5NPuesF5EYhfy2fz6vgnfQpQY2DLZUsSS/lfUyoYAuqYyuqowVl0xL/aNAcyQjCFy9ePHjwYNu2bUKhkBBSVVVFCHF3d//ll1+6des2ZcoUPT09RZcJQAghJMyGujhYMOwYezaH3xLKGGsruyDNI+ZIUja/8wGX8JgLMqcCzKhIB+qbQNrZgGJeizmRmFzO5//J43c+4GecZ610qTk+dLQTrYXJCdCcyAjC8vJynudNTEwIISYmJgUFBZLtvr6+IpEoJSXF399foTUC1NCmJXWqv+CTi2zgPvGfPZhAM3QyFOROEf/Lfe7PNM5BnxrlTP/YUWha3w8RXQHpbEl1tqQIITxhkrL572+yCy5xM7zoD9xpE/yOgeZBxg8zMzOzli1bZmZmEkJcXFyOHz8uEokIIWfOnCGESAISQImENFnVgVnenu53RLzuLha/lbt7xfzw42xYgthIi5wZIDg/UDDNk643BWuhCOllQx3pI0jszaSW8C6x1XP+ZUur5VMxQGPICEKKorp167Zv3z5CSHR0dHFxcdu2bQcNGjRkyJCOHTva2dkpvEgAGSLs6fMDBFtSuCFJbH6FsqtRUykl/KiTbLdDYn8z6sEw4WJ/xtngXbvgPibU5lDmzlBhuZj4/iU+mo1FZUHJZA/Vr1+/fvr06YQQQ0PDo0ePBgYGPnv2bNy4cfv27aOoBv1v8OzZs4kTJwYGBkZHR0s6l7WsXbs26v+NHj1auj01NXXo0KGBgYHTpk0rLi5+qw8FmsLJgDo3UOBmRPz2io9k4fu0KeWJyITTbOeDYk8j6kGU8BNfumWTzv+01CUbOzEbOjGTzrBTzqJrCMokOwhtbGw8PT0ltwMCAnbt2nX+/Pm1a9eam5s38HVHjRolFou3b99ubW09cOBAyaWdarp06VKLFi0iIyMjIyOHDBki2ciybJ8+fTw9Pbdt25afnx8TE/NWHwo0iBZN/hvE7OzKfHCW/fBftoJVdkGqj+PJhmTO569qcx2SGiVc4EfrCeX1Xr1sqJvvCzhCfP8SH3uCnzKgHG/6jVdRUZGWlpadnd2qVStnZ+eWLVs28EVTU1NPnTqVl5dnYGDw3XffWVpanj17tnPnzrV2a9u2bWRkZM0tiYmJYrF4yZIlhJAff/zR1tb2yZMn1tbWjflEoIm6WlHXIwRTz7FB+8Q7uzFtTTCD5i3dKOSnnmUZmhzrK/A2VkQzGgjJxk7M39n8+FPsNE/607aYUQqKJvtvjuO4xYsXW1hYeHt7h4eH+/n5mZmZffjhh5WVlQ150Vu3brm5uRkYGBBCGIYJCAi4cePG67v99ttvffr0mTlzZlpammTLzZs3pefsW1pa2tjY3Llz520+FmgeIy3yezdmflu612Hx19e4asyhaaQXYvLxBTb8sHiiG326v4JSUKqXDXVhkGB3BjflLCvGvx0oluwe4bx581atWhUZGTl06NBWrVpJrke4bt26vLy8Xbt21fuieXl5RkZG0rvGxsa11mkjhPTq1atfv356enqHDh0KCAi4efOmra1trSeamJjk5ubKfAuRSJSTk+Pg4CC5S1HU9OnTP/jgg7pKKi8vb+DRTWgqSmnzwZakfS8y86KgXQa9vl21l5Fmjba9dZtfLqAn/ysINOUv9BabavPlZU1eWv30CUnoSo05L+h/uHp7J7YFoxr/dvhuUbyGt3lFRcXrB+ZeJyMIRSLRunXrPv/8c8kQpURERERQUNDUqVOXL19uY2Pz5hc1NDR88eKF9G5ZWZmhoWGtfUaMGCG50adPn3v37u3cuXPBggWGhoY1I7O0tLRmLtakq6trbm5+7Ngx6RYLC4s3nOnP8zzWAVAwZbW5ix5J7Ed2Z3CDTlHjXOmvAxihxgy2vUWbizny/S1u5W12ZXtmpDNNiDJP7tMjJKEPmXme7XlMkBDOqMRKNPhuUbyGt7mOjk5DIlPGN0RxcXFFRUV0dHSt7cOHD+d5Picnp94XtbOzS09PZ9n/zVt48ODBmy/wZGNjU1JSQgixt7dPTU2VbBSJRNnZ2W94IsMwjjXgbxFqinSgLw8WXC/ggw+IrxeoRt9C8e4U8e0PiM/l8jcjhCOdm8XvBYYi60KYKEe680E2pQT/cKAIMv70LSwsLCws0tPTa21PT0/X1tZ2cnKq90U7dOhgbGy8c+dOQkhSUlJeXl7fvn0JIZcvX16/fr1knwsXLkhuXLlyZf/+/d26dSOEDB48+N69e//88w8hZNOmTS4uLt7e3m//4UCztW5JHe4tmOFJhyeKF1zChNJX8ISsvs11OySe5kEf6MW00lV2Qa9a6Ed//h7d8zD7qAxZCPLHy7Jjxw57e/sTJ05It1y+fNnHx+e7776Tuf/rTp8+bWNj4+TkZG5ufuDAAcnGjRs3tm/fXnLbzs7O0NDQxsbGyMho+fLl0if+/vvvJiYmTk5OdnZ2ly5dquv1MzMz27Rp08BieJ5//vx5w3eGJtF82jznBR91TOwaW33yCafsWuSrgW2e84Lvk1jdYX912vNm3SA/3WFdYqufvlB2HW/UfP7ONUfD23zv3r2DBw+udzeK//8DifHx8V999ZU0INPS0oqLi01NTSWTZfLy8lq2bOnu7n758uUGRizLsjk5ORYWFpLFu19XVFQkEolePzuiqqrq2bNnVlZWNF3nWM3jx49DQkJknqovU2lpqb6+fgN3hibR3Nr8wCNuxnmuTxvqv0Fqu1p3Q9o8MYufeJqd4EZ9+R4jaBajoW/yzXXuz3TuZD9Bs12YtLn9nWuChrf5vn37tm3btnfv3jfv9nKyjL6+vqOjo/Ruzdtvh2GYN0+rMTY2NjY2fn27lpZWvfNxABproB3d1ZpecIn12lP9XTtmpLPGXcipkiWfXmL/esjv6saoyjUdF/rRJVV83yPio30E8juvHzTcyyDs2rVr165dlVcJgNwZCMmajsxYF3rqOXZLCrcuhHHTmOvd3y3mR55gnQyo60MEqtUhXtaOmXKWHZQkPhQu0GGUXQ2oo2Y/MgLQ1ILMqQuDBIPs6M7x4kVXWJFY2QXJGU/Iz/e4rgfFMzzpuB6qNyxMEfJzCNNKlxp5gsXMGZCHOoPwzp07Y8eOlawp4+PjEx0dLZ3nCaDqGIrM8qKvRwhSSojnHvHeh2q7lsmzCjLob3ZLCndugGCim6r+8KUpsrULk1fBf3UVc3+h6cn+H+PMmTNBQUG7d++WLJltb2+fkJAQEhLy119/Kbg+APmxbkH90Z3ZEsp8eZXrdVh8r1jd+htHsvj39oq9jcm5AQIXFR8E1qLJnh6CrSn8X+r7qwWURfYSazNnzvTw8Dh06JClpaVkS3Fx8bBhw6ZPnz5o0CCGwTg9qI+uVtTVwYKf73FdD4lHO9OL/BkD1Z+UUVpN5l1gE7P437qqzLyYelnokr/CmD5HxM4GlC/WVYemI6NHWFhYeOPGjRUrVkhTkBBiZGT0008/5eTkJCcnK7A8AEUQ0GSmF337fWFJFXGLrV57V7XX7D7+hG/7l5gj5Ob7ArVJQQl/M+qHDgwuxQxNS0YQVlVVEUIk146oSbKlgRegAFA55jrkl87M8X6CU0957z3i3Rmcyg2Viljq00vsmFPsD8H0xk7q0LV93XAnepgjNeSouEqVf6xAsyJ7iTVLS8uffvqp1vY1a9bo6Oi4ubkppDAA5fAwomJ7MD91ZP5zjetyUHwhT2XSMDGLb39Y65mI3HlfMMBWVefFNMR/AhkjLfLxv5g4A01DxjFCmqYXLVo0bdq0u3fvDhs2zMrK6tmzZ/Hx8ceOHfviiy8afnleANXVy4YKGyLYmspFHWd9TchifybArPmOMWaU8nP+5e4V86sCxYNdtJRdjtzRFPmtmyBwnzg2nYtyVOfIB8WQPVlm6tSpQqFwyZIlc+fOlWyxsLD47rvvPv74YwXWBqBMNEUmuNKjnOmtKdzgJNbdiCwNYgKbWRyKxOTHO9zK2+xUD/rP7oKqFyJlV6QgBkIS14MJSxD7mVKuKj4hFpRORhCKxeKTJ0/27t170qRJGRkZhYWFhoaGjo6Ob1j5E0BdadEkxp0e60JvTOYGJ7FBZtRcXzqklfK/eTmexKZzCy5zQebUlcGC1i0pQkiVsqtSJF8T6vP3mNEn2TMDBFr4coJ3IOPPJy8vr2fPnhkZGYQQBweHgIAAZ2dnpCBoMm2GzPSiH0QJwmyo8afZ9vvFf6QpbWYpy5NdaZz3HvGPd7hNocyf3ZnWqnABW3mY6UVbtqAWXMLBQngnMuLN1NRUV1e3vLxc8dUANGc6DJnuSScPFSz0ozcmc45/ipfd4J4pcB4/y5OdDzjvPeJ1d7nVwcz5gYJu6nV2RGNRhGwJZXZn8Icfq8ycJmiGZAShtrZ2TEzM6tWrJedRAEBNNEUG2tHH+wniezH3S3i33dUD/hb/kcbJdc3SR2X8kmucS6x4YzK3piNzdoCgl41GR6CUiTb5rSsz8Yz4yQtkIbwl2ZNl7O3tY2Nj3dzcevfu3bp1a4Hg5W7z589XVG0AzZqfKbU5lPlJzOx7yG1/wE07zw6yo4c60F0sqaa6YFC5mOzJ4LamcLeK+OGO9O4ezXryqrJ0sqSmeTCjTrBJfQUMmgca7+WFeWuytLTMzc2V+QSZ+yseLszb/Glam+eKyB9p3P5H3KV83s+U6mFN9bCmO1hQwkYeXq/iyKVn/Kmn/Jkc7p88vrMlNc6F7m9LazdgZUNNa3MpjidhCeLw1vT8toqezaCxba5Ecrwwb005OTmNLg1As7XSJbO96dnetEhMzubyR7O5Of+yd4t5J33K1ZByNSSuhpSLAaUrIIZahKYITYihFpVfwT95QTLL+exykl3O3y7ir+Tz7kZUZ0tqqge9qxutcldNUgrJ5SmC9ov7tMEypNBoMoIwPz8/JSXF0tLS3t4ek0UBGktXQHraUD1tGELICzFJfc6nlPApJeTEE/6XZK6KIyVVhOMJy5Pn1byJNmXTgtjqUTYtiashNcCODrag9NVxaTR5s9WjlrdjxpxkLw7G2RTQOK8EoVgsnjhx4o4dOyTjn25ubgcOHHB1dVVSbQAqr4WAtDWh2qKPohBjXOj4TP7LK+zSIFwhBxrhlR9O69at2759e7du3b799tspU6ZkZGSMHz9eWZUBADTWzyHM9lT+dE6zmMoAquKVHuHhw4dDQ0OPHTsmuevj4zN9+vSSkhJDQ0Nl1AYA0DhmOuTnEHr8KfZ6hAAjzNBAr/QIHz582KtXL+nd8PBwQohkiRkAAJUw0I7uYkV9chHLzUBDvRKEIpGoRYsW0ruSC028ePFC0UUBALyD1cFMYhafmIUBUmiQ2rNGs7Kyrly5IrldUFBACLl//7629ssZ3AEBAQorDgDgLRgIyabOzLhT7K33BYbqf1kqeFe1g3DlypUrV66suWXChAk17zaTE+oBAN6guzXVtw019wL7S2fMIIV6vBKE3377bVlZmbJKAQBoQt93YHz3iI9k8eGtcfoKvMkrQThixAhl1QEA0LRaCsjGzsykM+xNzCCFN8ICDACgtnpYU92tqPmYQQpvhCAEAHW2sgNzMJNPysbkBqgTghAA1JmhFlnfifngLFtWrexSoLlCEAKAmuvbhurUilp4GQOkIBuCEADU3+pgJi6DP5+LAVKQAUEIAOrPRJus70RPOM1WoFsIr0EQAoBGGGBLe5tQ31xHEkJtCEIA0BRrOjIbk7mr+RgghVcgCAFAU1jqkmVBzITTbDWn7FKgOUEQAoAGGedKW7UgK28hCeElBCEAaJYNnZgVt9h7xRgghf9BEAKAZrHVoxa9x3xwluUQhUAIQRACgAaa7klzPPn5HgZIgRAEIQBoIJoim0OZxVfZ9FL0CgFBCAAa+BXo+AAAIABJREFUydWQmuvDTD7DIgkBQQgAGupjH7q0mmxNwQCppkMQAoCGEtBkU2dm/kU2qxzdQo2GIAQAzeVjQk3zpKeew7prGg1BCAAabYEf86iM/JGGAVLNhSAEAI2mRZNNnZk5/7LPKpRdCigJghAANF2QOTXGhZ5xHgOkGkogv5c+duxYfHy8qanp5MmTLS0taz2anZ0dHx+fnJxsZmYWHR3t5OQk2f7rr78WFBRIbtva2kZHR8uvQgAAiSUBTOA+cWw6F+WI7oHGkdc/+e7du6Ojo52dnbOysjp06FBWVlZrhylTppw/f97R0TEvL8/X1/fKlSuS7StXrrx9+3ZRUVFRUVFpaamcygMAqEmbIdu6MLP/YXNFyi4FFE5ePcKlS5euWLFizJgxhJDOnTv/9ttvH3zwQc0d4uLitLW1JbcLCwu3bdsWEBAguTtlypSQkBA5FQYAIJO/GTXBjY45y+7vySi7FlAoufQIS0tLr1271rNnT8ndsLCwM2fO1NpHmoKEkBcvXhgYGEjv7tmzZ9myZUeOHJFHbQAAdfnSn3lYyu/CDFINI5ce4dOnTymKMjMzk9y1sLA4depUXTsnJSWdPHnyp59+ktwNCgrS0tIqLCycPHlyp06ddu3aJfNZlZWVRUVFEydOlG4JDw/v379/Xe8iEokYBr/yFAptrnho83e3oT014DjTzlBs3aJB+6PNFa/hbV5ZWcnz9a+WIJcg1NLS4nmeZVmhUEgIqa6urtn/q+n69eujR4/euXOnjY2NZMu2bdskN+bMmePs7Hzx4sV27dq9/kShUKilpRUUFCTd4unpWde7EEKqqqre8CjIA9pc8dDm7y7Ikkzz5D+8Itwf1qABM7S54jW8zYVCIUVR9e4mlyC0tLSkaTo7O1syFzQrK8va2vr13W7evNmnT58NGzb07dtX5ovY29tnZGTIDEKaplu2bDllypQGlsQwDH61KRjaXPHQ5k3i8/dIu/3inenUWJf6sxBtrngNb3OabtCvGbkcI9TR0QkPD//zzz8JIZWVlfv37x84cCAhpLy8PCkpSSwWE0KSk5P79u27atWqQYMGSZ9YWVnJsv87lefOnTsPHjzw9vaWR4UAAHUR0mRbF2beBTYDF2nSDPKaNfrVV1/16dPn1q1bqampdnZ2/fr1I4RkZmb26tWrqKjIyMho0qRJpaWlK1asWLFiBSEkNDR05cqVt27dGjJkSFBQEM/zx48fnz9/vpeXl5wqBACoi68J9UlbZvRJ9lR/AVP/0BqoNnkFYVBQ0L17986cOWNsbBwaGirpxjo4OFy+fFlfX58Q8uuvv5aXl0v3NzIyIoQEBAQcOXIkJSWFpunVq1fb2dnJqTwAgDf7yJtOfMz99wa30A+n2Ks5Oa4sY25uHhERUXOLjo6O9GRBd3f3159CUZSnp6enp6f8qgIAaAiaItu6MAH7xGHWVHsL9ArVGX7pAADIZtOSWhfCjDzJllUruxSQJwQhAECdIuzpkFbU3AtYj1udIQgBAN5kbUfm+FP+wCMsN6O2EIQAAG+iJyRbQpkp59gnL3A2hXpCEAIA1COkFTXdk4k+zorRLVRHCEIAgPp91pZuISBfXsXBQjWEIAQAqB9Nkd+6CXal8fGZ6BWqGwQhAECDmGiT37sxk8+wj8pwsFCtIAgBABqqgwX1oTcz/DhbjW6hGkEQAgA0wvy2tIUutegKDhaqDwQhAEAjUIRs6szsSuP348xCdYEgBABoHDMdsq8nE3OWvVWIg4XqAEEIANBo75lSazsyg5LY/Eqsx63yEIQAAG9jqAM9zJEadU5YhSFSFYcgBAB4S98EMoZC8vG/mDij2hCEAABviabIpuDqE0/5DcnoFaowBCEAwNvTE/B7w5gvr7CnczBxRlUhCAEA3omLIfVbN0HUMfHtImShSkIQAgC8qx7W1OoOTJ9ErL6mkgTKLgAAQB0Md6ILK0nPw+zZ/gILXWVXA42BHiEAQNOY5klH2FMD/haXi5VdCjQGghAAoMksDWK8jalhx8S4hK8KQRACADQZipD1nRiekElnWA6HC1UEghAAoCkJabK7h+BxOT/hNMsiC1UBghAAoIm1EJCDvQRPXvAjT7AYI23+EIQAAE1PV0DiewnKxfzIk8jC5g5BCAAgF9oM2RMmqGBJ9Alc0b5ZQxACAMiLFk1iuzMVLB99gq3E0tzNFYIQAECOJP1CmpCeh8UFlcquBmRBEAIAyJcWTf7swXS3ptrvF98vwUTSZgdBCAAgdxQhi/2ZBX50l4NiXKeiuUEQAgAoyARXemdXQeQx8a40TJ5pRhCEAACKE2ZDJfURfHaJW3gZp1U0FwhCAACF8jWhLg8WXCvgOx0UPyzFMKnyIQgBABTNXIccChdEO9Id48UJj5GFSoYgBABQAoqQ2d703jDBjPPs7H9wxr0yIQgBAJSmvQV1cZAg7Tnf8YD4egG6hsqBIAQAUCYzHXIwXPBJW7pPonj2P2xZtbIL0jwIQgAA5Yt0oO8MFVawpO1f4iNZ6BoqFIIQAKBZMNEmGzoxa0OYqefYkSdYTChVGAQhAEAz0rs1dft9gYshCdwnnv0PmydSdkEaAEEIANC8tBCQxf7M/SihroB4xlV/eoktrlJ2TWoNQQgA0ByZapP/BjFXhwjyRMRtd/UXV9inL5Rdk5pCEAIANF+2etTmUOZsf0FxJfHeUz3mJHs1H8cOmxiCEACguXMxpH7qyKQNE/qaUhFH2dCD4p0POJxo0VQQhAAAqsFIi8z1odOGCWZ70X+mc61/rx55gj30mMeqNO9IoOwCAACgERiKvO9Av+9A51eQ2HRu6XV2/Cl+iD3dtw3Vw5rWEyq7PhWEIAQAUElmOmSaJz3Nk35Yyu99xK+9y40+ybYzp3q3oXu3pryMKUrZFaoKOQZhcnLyrVu3XFxc/Pz8ZO5QWFh46tQpPT29rl27CoUvf8Zcvnw5IyPD39/fyclJfuUBAKgHe31qjjc1x5suF5PjT7jDj/mIo9wzER/cigq2oDu2otqZU/roKdZNXkG4YcOGRYsWhYeHnzhxIiYm5osvvqi1w927d7t27RoaGpqdnU3T9PHjx7W1tQkhc+fO3bNnT6dOnaZPn75q1aqRI0fKqUIAADXTUkAG2NIDbAkhJE9E/snjzufyX13lrhXw1i0oXxPKx4TyNia+JpSDPsWgw/j/5BKEIpFowYIFBw4cCAkJefDgga+v7wcffGBhYVFzn6+//nrcuHHfffedWCxu165dbGzs6NGjHz58uG7dutTUVBsbm8TExEmTJg0bNkwgwPgtAEDjWOiSQXb0IDtCCBFz/9fencc1cewBAJ+chADhCiEJl1REERUR5VBAEJTUq2I8akXrCXi076O9UGv16Ue8Wi/0aUVb5VVFa6UKKB54QT2CihyKohiU+wgEIpB73x/z3r58QohBBYqZ71+7w8zu7GTJL7s7OwOeNmGFjVh+A3b0GShoUFe2Yi7mhI8swEcMQl8LgqsF4NAJjmbA3pRAMb4+lF0SY7Kzs+l0+qhRowAAbm5ugwYNysjImDt3rmaec+fO3bx5EwBAJpP5fP7Zs2fnzJmTlpYWEBDg4OAAABg3blxra+v9+/f9/Py6opIIgiBGgkwEntYET2vCzI/+myJTAaEEK5GAF83YCwl2sxpUt6nLW0BdG2ZjAuxNCSxTwKQRbE2ALQ3YmhCsTQCDAhhUAoMCrEwAg0IwIYEP5nZrlwTCiooKJycnfNXJyam8vFwzQ0NDQ2trK57H0dExLS1NqyCRSORyueXl5ToDoVqtbmlpOXDgAJ4SGBjo4eHRUZVUKpVKpXqHY0I6DbV590Nt3v16aZuTAehnAfpZAMDF0wgAADVGqGkDtVJQ0wZEMkwkBQ1y8ESMieWgWQ6aFVizHDQrCE1yTK4GEgWgk4EJCVhRCQQALCgYifDfFACAJZUAry3NKID6v6tMEgEwqNqVsaSATt2mdaWB6e4GtblabdCbJV0SCBUKBYlE+v8+yGSFQqGVAQCA56FQKHK53JCCmluQy+X37t3DU5hMppubm54qdbQppIugNu9+qM2734fX5kwKYFLAQAuDMrcqgVxNaJRjAACJgqDCQKsSyFQAANCsADAKvVYA/E1HpRq8VmpHvUYp1qnBcszVagPbXKVSYQZsu0sCIYfDqaurw1dra2vDwsI0M9jZ2ZHJ5Lq6OltbW5iBy+XCgsXFxZoFORyOzl2YmJhYW1sfOnTIwCopFAoajdbZA0HeBWrz7ofavPsZeZvDI2d3704lErmBbU6hUAgGvEXSJU9FfX19S0tLX716BQCQSCQCgSAoKAgAoFKpZDIZAIBIJI4aNerKlSsw/5UrV2CGoKCg7OxsqVQKAHj8+LFEIhk2bFhX1BBBEARBoC65ImSxWAsXLuTz+dHR0cnJyTweDz69O3jw4M8///zw4UMAQFxc3GeffaZWq4VC4f37948cOQIA8PX1HTZs2NSpUyMjIxMSEpYvX25hYdj1OYIgCIK8la7qJ7tnz56lS5cWFBTw+fzjx4/DxMDAwG+//RYu83i8c+fOlZaWWlpaCgQCJpMJ08+dOxcREVFQUPDdd9/Fx8e/l8q8fv06KyvrvWwKMVBLSwvsFYx0m9bW1hs3bvR0LYxLW1vb9evXe7oWxkUqlV67du09bxTrnV69euXk5GRg5qysrICAgC6tD6Ll1q1bvr6+PV0L43L37l0fH5+eroVxycnJ8fb27ulaGJcHDx4MGTLEwMwpKSlTpkx5Yzbje3MSQRAEQTSgQIggCIIYNRQIEQRBEKPWW4fxlMvl9fX1Y8eONSRzU1PTs2fPDMyMvBdNTU3FxcWozbtTc3MzOs+7mUQiKSkpQW3enSQSiVAoNLDNNd9o16O3BkJXV9d9+/ZpDuSmh1KprKqqMjAz8l6gNu9+KpWqoqLC2dm5pytiRFCbdz+1Wl1WVubi4mJIZplMRqfT35iNgHVuaBsEQRAE+aCgZ4QIgiCIUUOBEEEQBDFqKBAiCIIgRg0FQgRBEMSo9dZeo1qqqqoyMzMZDEZERISJiUn7DDKZLCMjQyKRhIeHs9n/nzPk+fPn2dnZTk5OY8aMMWS2DgQnFoszMjJIJBKPx9M5Nvrjx48fPnxoamoaHBwM59sCABQUFNTU1MBlCoUyevTo7qtx71dTU3P58mULCwsej9f+PK+trc3Pz8dXvb298WYvKSnJyspycHAICwsjEtHP305oamrKyMggEAg8Ho/BYGj9VSgUlpSUaKaMHj2aQqEUFhZWV1fDFDKZHBIS0j21/TBUVlY+ffrU3d3dwcFBZwaRSHTx4kUTExMej2dmZoan37179/Hjx15eXp2dtuhD6DWam5sbHh4+adIkoVAolUpv3LihNVVVW1tbcHCwmZlZnz590tLSMjMzvby8AABnz55duHDh1KlTBQJB//79T5482UNH0PuUl5f7+/v7+/srFIrCwsLbt2+zWCzNDD/88ENSUtLIkSMlEslff/2Vnp4eEBAAAJg5c2Z+fr6joyMAgMFg/PHHHz1zAL1Qfn5+aGjoxIkTX716JZFIsrKyTE1NNTOkpKQsXLjQx8cHrm7ZsgUup6enf/7555GRkffv33dxcUlJSemB2vdOVVVVfn5+w4cPxzAsNzf3zp07mj+jAQDHjh2DM+cAACorKysrK2tqaqhU6pw5c3JycuDrQ3Q6/ezZs91e995q1KhReXl5arV69+7dixcvbp+hpKRk1KhRoaGhYrG4tLT09u3bVlZW4H/fOTweLy0tbeXKlStXruzEXt9h7NO/iylTpqxbtw7DMKVSOWzYsKSkJK0Mv/zyy/Dhw5VKJYZha9eu5fP5MH3gwIHHjh3DMKypqcnOzu7u3bvdWu/ebOXKlXPmzIHLkZGRsP01CYVC2OAYhn399dcff/wxXJ4xY8b+/fu7q5oflOnTp69evRrDMJVK5evre/jwYa0MZ86cGT16dPuCXl5eR44cwTBMIpFwOJysrKyur+wHIi4u7tNPP4XLM2bMgO3fkTlz5ixduhQuR0VF7d69u8vr9yGCXx2BgYEHDx7UmSE6Ojo2NhbDMLVaPXbs2O3bt2MYVlNTY2pq+uzZMwzDcnNzGQyGRCIxfKe9/iaJWq1OT0/n8/kAABKJNGXKlLS0NK08aWlpU6ZMIZFIAIBp06alp6djGFZSUlJcXBwZGQkAYDAY48aNa18Q6UhaWtq0adPgMp/Pb990ffr0gQ0OAOBwOHK5HP9TaWlpRkaG1g0l5I3w85xIJEZGRuo8XVtaWi5evJiTk6NQKGDKq1ev8vPzYUFzc/OIiAh0nhsuNTUVNh0AYNq0aXqarqmp6Y8//liwYAGe8vLlywsXLjx//rzLa/lh0fzq0An/8iEQCPiHcvnyZQ8PDzc3NwDA0KFD7ezsOjUlWa8PhHV1dQqFAt5qAwA4ODhUVFRo5amoqMDvNTs4OEilUpFIVFFRYWNjg99c0lkQ6UhlZaVmk+ppOrFYnJCQsGjRIrhKpVJv376dkJDg4+OzYMECrPffme8ejY2Nra2t+s9zAIBUKt23b9/s2bO9vLxKS0sBAJWVlVZWVubm5voLIjppfXXoaboTJ0707dsXvy9NoVBycnL27ds3YsSIuXPnqtXq7qiuEVCpVDU1Ne0/lIqKCvy/A3T+PO/1nWVUKhUAAO/nQiKRlEpl+zx4BwH4W0OpVKpUKs3eMToLIh3RbD09TdfW1sbn88PDwz/99FOYcuTIEfgRVFVVeXt7nzlzBv/FjehhyHk+efJkeIdDrVZHRUV98803v//+OzrP34WB5zkA4JdfftF8oJWYmAjP89raWm9v71OnTuH/Asi7UKvVGIa1/1C0znMymdyp87zXXxGyWCwikYiPrFpTU8PlcrXycDic2tpaPAOZTGaxWBwOp7GxEW+smpoaDofTbdXu7dhstv42BwDIZLKpU6ey2ewDBw7giZr3S0NDQ3Nzc7uhth8AW1tbKpWqv83xtiUSiTNnznz48CEAgM1mi8Vi/NY0Os87hcPhvPE8BwAUFhbm5eXNmjULT8E/CxaLFRYWhs7z94VCodja2rb/UDS/5IHeD0unXh8IyWRyUFDQxYsX4eqlS5dgT2W1Wi0SieAdiZCQkEuXLuEZgoODiUSim5sbi8W6du0aAECpVF69ejU0NLRnjqEXCgkJad/mAICGhgb420KhUMyYMYNOpx89elTnHX+lUpmfn49GKzYQgUAIDg5u3+YYhuHnuaYHDx7ALouurq7Ozs5XrlwBAKhUqszMTHSeGy40NFTzq6P9eQ4dOnQoMjKSyWS234JKpcrLy0Pn+TuSy+VisRgua36fX7x4EX4owcHBubm5IpEIAFBWVlZSUjJy5MhO7OBdu/j8DWRkZFhZWW3evHnRokXOzs6NjY0Yhr18+RIAUF5ejmFYQ0ODo6Pj4sWLN2/ebGVldfnyZVhw7969Tk5OP/300+TJk/38/FQqVU8eRq9SUFDAYDC+//77b7/91tra+vnz5zCdTCbfvHkTw7C4uDgKhTJ//vzo6Ojo6GjY3U4qlY4cOfKHH36Ij48PCAjw9PR8/fp1Tx5Gr5KZmWlpaRkfHx8TE+Pg4CASiTAMq6ysBAAIhUIMw5YtW/bll19u37593rx55ubm165dgwV//vlnBweHn376KTIyctiwYXhvXuSNioqKLC0tV69evWrVKisrqydPnsB0U1PTzMxMuCyTyezs7C5duoSXUqlU/v7+a9eu3bx5c2Bg4IABA5qbm3ug9r3TgQMHoqOj2Wx2cHBwdHR0Xl4ehmEnT550cnKCGQQCAYPBWL9+/YoVK5hMZllZGUyPiory9/fftWuXt7f3F1980amdktavX/8e43aPcHNzCwsLKyws5HK5e/fuhb/LyGRynz59fH19KRSKqanpZ599VlZW1tLSsmnTpsDAQFjQ19d34MCBRUVFQ4cO3blzp8438RGdWCzW1KlTi4qKaDRaQkJC3759YbqDg4O/v7+5uTmFQvHz83N0dORyuVwu18nJafDgwSQSydbWtra2ViaT8Xi8PXv2aL0Jh+jh6uo6duzYwsJCe3v7ffv22dnZAQDIZLKLi4ufnx+VSoXRsb6+vl+/fgkJCfBlWQCAj4/PkCFDHj9+PHjw4F27dmm9ZYvowWQy+Xx+UVERhULZs2ePu7s7TOdyuf7+/nAcCZFI5OjoyOfz8WdUBALBzs4Onudjx45NSEjQfOkb0a+xsdHU1DQ0NHTIkCFcLtfT09PKyopOp3t4eAwePBgA4ODgMGnSpMePH1tYWGhOxjd58mQajSYUCvl8/ldffdWpAVI+hBfqEQRBEOSt9fpnhAiCIAjyLlAgRBAEQYwaCoQIgiCIUUOBEEEQBDFqKBAiCIIgRg0FQgRBEMSooUCIGJGjR48WFhZ22+5qa2tPnz4Nhwl9C0VFRQcPHpTJZB1lOHr06KNHj962dn879fX1R48exedt7pQ///yzrKzsvVcJMRIoECIfCJlM9pFeKpVq3rx56enp3Valr7766vjx4/rnlNHj5s2bMTExLS0tOv+KYZjm4dy/f//w4cNvWdG/h+fPn8+bN+/Jkydw9cKFC6mpqQaWTU9Pj42N7bKqIR+4Xj/7BIJAJBJJcza4vXv3kkikJUuW4ClEInHjxo3BwcHdU5+8vLzjx4/fu3evi7ZPIBA2btwYFBQEV1NTU7ds2bJw4cIu2l03cHJy2rhxo6urK1xNSEhoaWmZNGmSIWVXr17t5uZ27do1NJIq8hbQyDLIh2nIkCFUKtXAOFRXV8dgMPAx9uCI7XAMs/bEYrFMJrO3t9e/zejoaIFAAKeA0CSRSNRqtaWlZUcFpVKpWCxmsViJiYmxsbEikcjGxkYkElEoFAaD0VGp9evXb9myRSqV6vxrW1tbU1OTvb09Pu5UbW2ttbU1hULpaIONjY0AAGtraz3H+C7q6uqsra3J5A5/i48fP76lpaX99Koqlaq+vt7CwoJOp2umjxkzxsbG5vTp011SXeSDhm6NIkaEw+Hs27cPLm/YsMHFxSU7O9vd3Z3FYjEYDDgy+MGDB+3t7Vkslp2dXUpKimbxtLS0QYMGWVtbs9lsJyen48ePd7QjuVx+4sQJODsgLjY2ls1mMxgMKysrLpe7ZcsW/GdodXW1jY3NoUOH5s6dy2AwOBwOPq3ow4cPvby8mEymtbX1pEmT6uvrNQ/nX//6FwDgu+++27p1q0wms7GxsbGxgSFcKBTa2NgkJSXNmjULbrOhoeHJkyfjxo2j0Wj29vZ0Ot3Hxyc7OxvfYEJCgo2NjUAgGDp0qI2NzSeffBIXF9enTx+t+BoaGjp58uT2Rw0roDnrllbKihUrvL29MzMz+/bty2KxzM3N58+fr1Ao4F8fPHjA4XDu3LkDAAgPD79y5cqtW7fgEfF4PACASCSaPn06jUZjs9lmZmaurq6ZmZn4vvh8/tmzZ2H8RpBOQbdGESNSXV39+vVruCyVSqurqxcvXrx27doBAwYkJSVt3ry5rq4uNzc3KSnJyspq7dq1c+bMefnypa2tLQAgNTV1ypQps2fPTkxMpNFov/76a1RUlKWl5YQJE9rvSCAQvH79WmsimLa2tr179/bv31+hUJw8eXLVqlUsFgvezlWr1Y2NjWvWrBkzZsz58+fVarWVlRUsFRUVFRcXFx4efu/eveXLl0+fPv3q1asEAgHDMPxwFixYUFVVlZycfOrUKfC/+XtVKlVjY+M333zD4/EyMjLkcjmdTheJRF5eXnFxcVwut7y8/J///OeECROKi4vhBa5UKm1sbJw5c+ayZcv279/f1tbGZrO3bdt2+vTpqKgoWJ/8/Pzr16//9ttvOlu4sbFRK2pqprS2tpaUlCxbtmzDhg39+/dPTU3dsGGDv79/TEwMAEAul1dXV8POQfHx8cuXL5dKpTt27AAAwAvof/zjH3fu3ElJSfHw8BCLxbdv38Yn3AYA+Pv7K5XKrKwsnUEaQfR5X3NnIMjfyuDBg318fLQSAQDwOgzDsFWrVgEA0tPT4apCobCzs6PT6VVVVTAFdsg8ceIEXHV3dw8LC4MTZEOhoaEhISE69w4v1EpLS/XUcOLEiWPGjIHL8PovICBAMwO8kFqzZo1WSnZ2NoZhcA7CrVu3wj+tW7fOxMREs/izZ88AAGFhYXrq0NDQQCaTDx06BFe3bdsGAEhISNDMExgYGBwcjK8uWbLE1ta2ra2t/dZgwNu5c2dHKdHR0QCAW7du4RmGDx8eEREBl2/fvg0AuH79Olz9+OOPNfeLYVj//v2XLl3a0bFIJBIAwIYNG/QcL4LohK4IEeNFIpHCw8PhMplMhpNJsdlsmOLu7k4gEGCn/NLS0uLi4rCwMM17cY6OjufPn9e5ZTiDto2NjWaiUqlMSUkpKCiAbwiUlZXhc41COi9l+Hy+5nJsbOyDBw9GjRpl4DG232Ztbe3JkyeFQiHsj2piYvL8+XPNDJ988onm6pIlS2bPnv3o0SNPT8+WlpZjx44tWrToredysrKyCggIwFc9PT1zcnIMLDts2LCkpCQ6nT59+nQfHx+t7rjm5uYmJiaa05QjiIFQIESMl5mZGZVKxVepVKrmvHFkMplIJMI7dTB0/fvf/05OTtbaiFwu19wIXhYAoDmJeUNDQ1BQUGVlZUREBJvNptFopqamcPpoHB6DO0pkMplUKrW8vNzwY9Ta5vnz56dNm+bs7BwUFGRtbU0kEkkkUnNzs2YerX5A06ZNW7ly5eHDh3fs2HHixInm5uZ36Zuq1fvGxMRELpcbWBZO7Hfo0KEff/yRyWRGRUVt3LjR3Nwc/hXDMJVK1f6zQJA3QoEQQd4MdtfcunXr0qVLDckPY4lIJMK/95OTk588eVJcXIxPYrxgwQL8nTlI51Si9fX1HA4HLovFYrlcjq8aQmub8fHxw4cPv3btGrxCpyqKAAAD7UlEQVScUqvVe/bs0Sqi+eANAEClUufPn5+YmBgfH5+YmBgSEjJw4ECd+yKRSAQCQTP8a4XYd2Rra5uYmLh///579+6dOXNmx44dMpkM3oUGAIjFYqVS+cbevAjSHuo1iiBv1r9/fw6H8/vvv2OGvW40YsQIAEBBQQGeIhQKra2t8SgolUo177LqkZGRobU8aNCg9tksLCwUCsUbR7ERCoXe3t74TcWrV6+2tbW9sQ5LliwRi8Xr1q0TCATwOZ9OZDKZzWaXlJTgKe1ffjCcubm5zrqRyWR/f/9t27ZFRETALqZQfn4+AMDX1/et94gYLRQIEeTNiETipk2brl+/Di/j2trahELhb7/9tnnzZp35Bw0axGaz//rrLzxl6NChIpFo9+7dUqn0xYsXs2fPbmhoMGTXu3btOnfuXGtr640bN77++ushQ4bofGfc09NTrVZv3br1zp07Dx486GhrQ4cOPXXq1L1792QyWWZmZkxMjCFP+5ydnXk83vbt25lM5tSpU/XkHDdu3PHjxy9cuFBXV5eamvr9998bcow6eXp65ufnnzhxIicn5+nTpwCAxYsXX7lypb6+XqFQZGdnCwQCHx8fPP+tW7fMzc39/Pzeeo+I0UKBEEEMMn/+/F9//fXixYseHh50Ov2jjz5asWJFRy+kE4nE+fPnJycn45dos2bN+vzzz1esWGFqatqvXz8ajabn0krTzp07Fy5caGZmFhISwmQy//zzT51jtkVERKxcuXLv3r0jR47Uc1W0c+dOa2vrESNG0Gi0yMjI9evXG3gvMSYmBsOwefPm4cMO6LRp0yZ3d/fx48ezWKyYmJj2910N9+WXX06cOPGLL77w9fWFL5kUFRXxeDw7OzsqlTp69OjAwMAff/wRz5+cnDx79mxTU9O33iNitNDIMsiHCb5doPW4S6VSEYlEnY/iDN/s06dPJRIJi8VycnLSM45oeXm5u7v76dOnx48fjydWVVWVl5c7Ozt36lGWTCZ79OgRjUbz8PDQrPzbHY5SqSwpKWlpafHw8DA8bBw8eDA2NvbJkyfu7u76c2IYJhQKm5qaPD09O9t1RaVS6R+atampqaysTC6Xu7i4wPc7IYFAMHLkyLy8PE9Pz07tEUEACoQI0nXWrFlz6dIlgUDwLqH376ClpcXb23vAgAHnzp3r6broNmHCBCcnJ81BbRDEcCgQIkhXkclkFRUVLi4ubz0Bxd/B6NGjCwsLFQrF3bt3PTw8ero6ur148YLNZmuNPoogBkKBEEEQfY4cOUImk8eMGcPlcnu6LgjSJVAgRBAEQYwa6jWKIAiCGDUUCBEEQRCjhgIhgiAIYtT+A6XTOQ9CiZ+vAAAAAElFTkSuQmCC", "image/svg+xml": [ "\n", "\n" ], "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Bootstrap a KDE of the pre-eruptive (or pre-deposition) zircon distribution\n", "# shape from individual sample datafiles using a KDE of stacked sample data\n", "BootstrappedDistribution = BootstrapCrystDistributionKDE(smpl)\n", "h = plot(range(0,1,length=length(BootstrappedDistribution)), BootstrappedDistribution,\n", " label=\"Bootstrapped distribution\", xlabel=\"Time (arbitrary units)\", ylabel=\"Probability Density\", \n", " fg_color_legend=:white, framestyle=:box)\n", "savefig(h, joinpath(smpl.Path,\"BootstrappedDistribution.pdf\"))\n", "display(h)" ] }, { "cell_type": "markdown", "id": "4a1e0438-2ca8-45fa-a845-69332f076396", "metadata": {}, "source": [ "#### Run eruption/deposition age model" ] }, { "cell_type": "code", "execution_count": 11, "id": "027df1c4-0228-4987-a024-354132691fc3", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mEstimating eruption/deposition age distributions...\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m1: KJ08-157\n", "\u001b[36m\u001b[1m│ \u001b[22m\u001b[39mInterpreting first two columns of KJ08-157.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m2: KJ04-75\n", "\u001b[36m\u001b[1m│ \u001b[22m\u001b[39mInterpreting first two columns of KJ04-75.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m3: KJ09-66\n", "\u001b[36m\u001b[1m│ \u001b[22m\u001b[39mInterpreting first two columns of KJ09-66.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m4: KJ04-72\n", "\u001b[36m\u001b[1m│ \u001b[22m\u001b[39mInterpreting first two columns of KJ04-72.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\n", "\u001b[36m\u001b[1m┌ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39m5: KJ04-70\n", "\u001b[36m\u001b[1m│ \u001b[22m\u001b[39mInterpreting first two columns of KJ04-70.csv as \n", "\u001b[36m\u001b[1m└ \u001b[22m\u001b[39m | Age | Age 2-sigma 𝑎𝑏𝑠𝑜𝑙𝑢𝑡𝑒 |\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(joinpath(smpl.Path,\"results.csv\"), results, ',');" ] }, { "cell_type": "code", "execution_count": 12, "id": "e8d7e424-090c-4186-8d12-62fe94f20bfc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AgeDepthModel.pdf\n", "AgeDepthModel.svg\n", "BootstrappedDistribution.pdf\n", "DepositionRateModelCI.pdf\n", "DepositionRateModelCI.svg\n", "KJ04-70.csv\n", "KJ04-70_distribution.pdf\n", "KJ04-70_distribution.svg\n", "KJ04-70_rankorder.pdf\n", "KJ04-70_rankorder.svg\n", "KJ04-72.csv\n", "KJ04-72_distribution.pdf\n", "KJ04-72_distribution.svg\n", "KJ04-72_rankorder.pdf\n", "KJ04-72_rankorder.svg\n", "KJ04-75.csv\n", "KJ04-75_distribution.pdf\n", "KJ04-75_distribution.svg\n", "KJ04-75_rankorder.pdf\n", "KJ04-75_rankorder.svg\n", "KJ08-157.csv\n", "KJ08-157_distribution.pdf\n", "KJ08-157_distribution.svg\n", "KJ08-157_rankorder.pdf\n", "KJ08-157_rankorder.svg\n", "KJ09-66.csv\n", "KJ09-66_distribution.pdf\n", "KJ09-66_distribution.svg\n", "KJ09-66_rankorder.pdf\n", "KJ09-66_rankorder.svg\n", "agedist.csv\n", "distresults.csv\n", "height.csv\n", "lldist.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\" 66.0718 66.0379 66.0938 0.0140198\n", " \"KJ04-75\" 65.9319 65.8884 65.9691 0.0207222\n", " \"KJ09-66\" 65.9367 65.8944 65.9764 0.0204011\n", " \"KJ04-72\" 65.9565 65.925 65.9769 0.0129446\n", " \"KJ04-70\" 65.8308 65.7565 65.894 0.0351967" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Let's see what that did\n", "run(`ls $(smpl.Path)`); sleep(0.5)\n", "results = readdlm(joinpath(smpl.Path,\"results.csv\"),',')" ] }, { "cell_type": "markdown", "id": "d063b008-2538-40fb-a6bc-2c02bbc548ca", "metadata": {}, "source": [ "To see what one of these plots looks like, try pasting this into a markdown cell like the one below\n", "```\n", "\n", "```" ] }, { "cell_type": "markdown", "id": "dc3ef141-e4f9-4e88-9339-1472235b43c8", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "id": "d6cf9261-79bd-4bb8-9f1c-00db20d84b07", "metadata": {}, "source": [ "For each sample, the eruption/deposition age distribution is inherently asymmetric, because of the one-sided relationship between mineral closure and eruption/deposition. For example (KJ04-70):\n", "\n", "\n", "\n", "(if no figure appears, double-click to enter this markdown cell and re-evaluate (`shift`-`enter`) after running the model above" ] }, { "cell_type": "markdown", "id": "9cedfd1c-795d-4a7c-9aad-24e65dab8bba", "metadata": {}, "source": [ "Consequently, rather than simply taking a mean and standard deviation of the stationary distribtuion of the Markov Chain, the histogram of the stationary distribution is fit to an empirical distribution function of the form \n", "\n", "$\n", "\\begin{align}\n", "f(x) = a * \\exp\\left[d e \\frac{x - b}{c}\\left(\\frac{1}{2} - \\frac{\\arctan\\left(\\frac{x - b}{c}\\right)}{\\pi}\\right) - \\frac{d}{e}\\frac{x - b}{c}\\left(\\frac{1}{2} + \\frac{\\arctan\\left(\\frac{x - b}{c}\\right)}{\\pi}\\right)\\right]\n", "\\end{align}\n", "$\n", "\n", "where \n", "\n", "$\n", "\\begin{align}\n", "\\{a,c,d,e\\} \\geq 0\n", "\\end{align}\n", "$\n", "\n", "*i.e.*, an asymmetric exponential function with two log-linear segments joined with an arctangent sigmoid. After fitting, the five parameters $a$ - $e$ are stored in `smpl.params` and passed to the stratigraphic model" ] }, { "cell_type": "markdown", "id": "1a54297b-357b-41fe-b8f1-8469e52bd414", "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": 13, "id": "809754c1-4d97-42aa-9331-5c2c63189bb9", "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: 5840000 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: 8760000 steps\n", "\u001b[32mCollecting... 100%|██████████████████████████████████████| Time: 0:00:02\u001b[39m\n" ] }, { "data": { "image/png": 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", "image/svg+xml": [ "\n", "\n" ], "text/html": [ "\n", "\n" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Configure the stratigraphic Monte Carlo model\n", "config = StratAgeModelConfiguration()\n", "# If you in doubt, you can probably leave these parameters as-is\n", "config.resolution = 1.0 # 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 = 20000*npoints_approx # Number to discard\n", "config.sieve = round(Int,npoints_approx) # Record one out of every nsieve steps\n", "\n", "# Run the stratigraphic MCMC model\n", "(mdl, agedist, lldist) = StratMetropolisDist(smpl, config); sleep(0.5)\n", "\n", "# Plot the log likelihood to make sure we're converged (n.b burnin isn't recorded)\n", "plot(lldist,xlabel=\"Step number\",ylabel=\"Log likelihood\",label=\"\",line=(0.85,:darkblue), framestyle=:box)" ] }, { "cell_type": "markdown", "id": "f5d195c1-4cf7-4d7a-984c-b37411d724dd", "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": 14, "id": "33984eb6-29dd-4a6a-a5b2-03bed31ee364", "metadata": {}, "outputs": [], "source": [ "writedlm(joinpath(smpl.Path,\"agedist.csv\"), agedist, ',') # Stationary distribution of the age-depth model\n", "writedlm(joinpath(smpl.Path,\"height.csv\"), mdl.Height, ',') # Stratigraphic heights corresponding to each row of agedist\n", "writedlm(joinpath(smpl.Path,\"lldist.csv\"), lldist, ',') # Log likelihood distribution (to check for stationarity)" ] }, { "cell_type": "markdown", "id": "c0b0eaac-7246-449b-a819-9b22c37bbcf3", "metadata": {}, "source": [ "***\n", "## Plot results" ] }, { "cell_type": "code", "execution_count": 15, "id": "20ab7629-1d0a-4211-aba4-b5470a8cc5a8", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "image/svg+xml": [ "\n", "\n" ], "text/html": [ "\n", "\n" ] }, "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))\", framestyle=:box)\n", "savefig(hdl,joinpath(smpl.Path,\"AgeDepthModel.svg\"))\n", "savefig(hdl,joinpath(smpl.Path,\"AgeDepthModel.pdf\"))\n", "display(hdl)" ] }, { "cell_type": "code", "execution_count": 16, "id": "0531451b-75ff-41bd-b2ef-7191a7479ff0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Interpolated age: 66.0023874019763 +0.0609531235834595/-0.05898701005439477 Ma" ] }, { "data": { "image/png": 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", "image/svg+xml": [ "\n", "\n" ], "text/html": [ "\n", "\n" ] }, "execution_count": 16, "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", "id": "9cbd0e41-a39f-4913-98f9-482d4d16e612", "metadata": {}, "source": [ "There are other things we can plot as well, such as deposition rate:" ] }, { "cell_type": "code", "execution_count": 17, "id": "17658169-2a32-440d-9e3c-5a54d3445c00", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 0.543254 seconds (1.71 M allocations: 282.295 MiB, 7.96% gc time, 70.40% compilation time)\n" ] }, { "data": { "image/png": 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", "image/svg+xml": [ "\n", "\n" ], "text/html": [ "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set bin width and spacing\n", "binwidth = round(nanrange(mdl.Age)/10,sigdigits=1) # Can also set manually, commented out below\n", "# binwidth = 0.01 # Same units as smpl.Age\n", "binoverlap = 10\n", "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(dhdt_16p),0.2,:darkblue), linealpha=0, label=\"68% CI\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_20p; reverse(dhdt_80p)], fill=(minimum(dhdt_20p),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_25p; reverse(dhdt_75p)], fill=(minimum(dhdt_25p),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_30p; reverse(dhdt_70p)], fill=(minimum(dhdt_30p),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_35p; reverse(dhdt_65p)], fill=(minimum(dhdt_35p),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_40p; reverse(dhdt_60p)], fill=(minimum(dhdt_40p),0.2,:darkblue), linealpha=0, label=\"\")\n", "plot!(hdl,[bincenters; reverse(bincenters)],[dhdt_45p; reverse(dhdt_55p)], fill=(minimum(dhdt_45p),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,joinpath(smpl.Path,\"DepositionRateModelCI.svg\"))\n", "savefig(hdl,joinpath(smpl.Path,\"DepositionRateModelCI.pdf\"))\n", "display(hdl)" ] }, { "cell_type": "markdown", "id": "5339dc54-eda3-41ca-9a2c-8c27c675a0d8", "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": 18, "id": "d0ae8155-6c2d-463e-85c5-958589f3da5b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AgeDepthModel.pdf\n", "AgeDepthModel.svg\n", "BootstrappedDistribution.pdf\n", "DepositionRateModelCI.pdf\n", "DepositionRateModelCI.svg\n", "KJ04-70.csv\n", "KJ04-70_distribution.pdf\n", "KJ04-70_distribution.svg\n", "KJ04-70_rankorder.pdf\n", "KJ04-70_rankorder.svg\n", "KJ04-72.csv\n", "KJ04-72_distribution.pdf\n", "KJ04-72_distribution.svg\n", "KJ04-72_rankorder.pdf\n", "KJ04-72_rankorder.svg\n", "KJ04-75.csv\n", "KJ04-75_distribution.pdf\n", "KJ04-75_distribution.svg\n", "KJ04-75_rankorder.pdf\n", "KJ04-75_rankorder.svg\n", "KJ08-157.csv\n", "KJ08-157_distribution.pdf\n", "KJ08-157_distribution.svg\n", "KJ08-157_rankorder.pdf\n", "KJ08-157_rankorder.svg\n", "KJ09-66.csv\n", "KJ09-66_distribution.pdf\n", "KJ09-66_distribution.svg\n", "KJ09-66_rankorder.pdf\n", "KJ09-66_rankorder.svg\n", "agedist.csv\n", "distresults.csv\n", "height.csv\n", "lldist.csv\n", "results.csv\n" ] } ], "source": [ ";ls MyData" ] }, { "cell_type": "markdown", "id": "e7e5c7e1-cecd-4976-ae23-e5b2ecb9409a", "metadata": {}, "source": [ "We could use the trick we learned before to view the SVG files in markdown cells, which you should then be able to right click and download as real vector graphics. e.g. pasting something like\n", "```\n", "\n", "```\n", "in a markdown cell such as this one" ] }, { "cell_type": "markdown", "id": "b6b140f9-e98a-4835-b1a3-36381e9d64d7", "metadata": {}, "source": [] }, { "cell_type": "markdown", "id": "405004f9-ee4d-45df-959c-ced43f189c9f", "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": 19, "id": "aee95c0b-0c8f-481a-90ba-cd8b84779685", "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": 20, "id": "303a2480-3df4-4a8a-b0e2-b7f6b92fa97f", "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": 21, "id": "38b1058f-d5bd-4c16-aaf5-f06f160b8011", "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": "9cc315ed-5ba0-4baf-a878-df13b98cb521", "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": "ddc94b56-8e57-4202-9b7a-6c33e3db745c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "98ce0f98-7a4d-4d22-8b18-ad2617977df5", "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": 22, "id": "370c9f1f-bbcc-431b-8069-d3ae9271c3fc", "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: 5840000 steps\n", "\u001b[32mBurn-in... 100%|█████████████████████████████████████████| Time: 0:00:03\u001b[39m\n", "\u001b[36m\u001b[1m[ \u001b[22m\u001b[39m\u001b[36m\u001b[1mInfo: \u001b[22m\u001b[39mCollecting sieved stationary distribution: 8760000 steps\n", "\u001b[32mCollecting... 100%|██████████████████████████████████████| Time: 0:00:04\u001b[39m\n" ] }, { "data": { "image/png": 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", "image/svg+xml": [ "\n", "\n" ], "text/html": [ "\n", "\n" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "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, "id": "955a2ec3-5e5e-4d26-b0ee-f730a9d2fb73", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "206e6b17-e3b0-43ad-9601-81857d3d8591", "metadata": {}, "source": [ "***\n", "[![DOI](https://github.com/brenhinkeller/Chron.jl/raw/main/readme_figures/osf_io_TQX3F.svg?sanitize=true)](https://doi.org/10.17605/osf.io/TQX3F) " ] } ], "metadata": { "kernelspec": { "display_name": "Julia 1.9.2", "language": "julia", "name": "julia-1.9" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "1.9.2" } }, "nbformat": 4, "nbformat_minor": 5 }