{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quickstart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An interactive session with IOSACal in Jupyter Notebook is the best way to get familiar with the software and create beautiful reports that are also self-explaining. Reproducible science![^download]\n",
    "[^download]: This notebook can be downloaded as {nb-download}`quickstart.ipynb`.\n",
    "\n",
    "\n",
    "This is the standard import needed to start working with IOSACal and Jupyter Notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from iosacal import R, combine, iplot\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radiocarbon data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now define three radiocarbon determinations and combine them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = [R(3320, 65, 'LTL-32131'), R(3320,65,'LTL-123414'), R(3325,55,'LRS-8384')]\n",
    "cr = combine(rs)\n",
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a combined age, calibrate it using the _IntCal20_ curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "calcr = cr.calibrate('intcal20')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calibrated date can be plotted as normal:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iplot(calcr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple dates\n",
    "\n",
    "IOSACal can plot multiple dates for comparing, and you can use Python as normal "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rs = [R(7729, 80, \"P-1365\"),\n",
    "      R(7661, 99, \"P-1375\"),\n",
    "      R(7579, 86, \"P-827\"),\n",
    "      R(7572, 92, \"P-772\"),\n",
    "      R(7538, 89, \"P-778\"),\n",
    "      R(7505, 93, \"P-769\")]\n",
    "multiple = [r.calibrate('intcal13') for r in rs]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's also possible to change Matplotlib settings without need to edit the underlying source code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And create the resulting plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iplot(multiple)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any plot can be saved directly to a file on disk, if needed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iplot(multiple, output=\"Catalhöyük East level VI A.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This was a quick tour of IOSACal used interactively in Jupyter. Thank you!"
   ]
  }
 ],
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