{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0,
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Read-in-the-concentration-dataset\" data-toc-modified-id=\"Read-in-the-concentration-dataset-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Read in the concentration dataset</a></span></li><li><span><a href=\"#Set-the-pallete\" data-toc-modified-id=\"Set-the-pallete-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Set the pallete</a></span></li><li><span><a href=\"#Make-a-pair-of-plots\" data-toc-modified-id=\"Make-a-pair-of-plots-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Make a pair of plots</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "# Repeat the colormap example with separate colorbars\n",
    "\n",
    "Same approach as with the colormap_example notebook, but make a separate \n",
    "colorbar for each subplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setting context.cookbook_dir to /Users/phil/repos/eosc213_students/notebooks/cookbook_examples\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import context"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in the concentration dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "cp = np.load(context.cookbook_dir / \"colormap_data.npz\")\n",
    "x, y, c = cp[\"x\"], cp[\"y\"], cp[\"c\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the pallete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "pal = plt.get_cmap(\"RdYlBu\").reversed()\n",
    "pal.set_bad(\"0.75\")  # 75% grey for np.nan (missing data)\n",
    "pal.set_over(\"xkcd:white\")  # color cells > vmax white\n",
    "pal.set_under(\"xkcd:teal\")  # color cells < vmin teal\n",
    "vmax = 1.5\n",
    "vmin = 0.2\n",
    "the_norm = matplotlib.colors.Normalize(vmin=vmin, vmax=vmax, clip=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Make a pair of plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "# copy the data array for safety\n",
    "#\n",
    "plot_c = np.array(c)\n",
    "plot_c[plot_c < 1.0e-4] = np.nan\n",
    "m2km = 1.0e-3\n",
    "time_steps = np.array([0, 1])\n",
    "nfig = len(time_steps)\n",
    "fig, grid = plt.subplots(1, 2, figsize=(18, 10))\n",
    "ntimesteps = len(time_steps)\n",
    "\n",
    "for time_index, the_ax in zip(time_steps, grid.flat):\n",
    "    im = the_ax.pcolormesh(\n",
    "        x * m2km, y * m2km, plot_c[:, :, time_index], norm=the_norm, cmap=pal\n",
    "    )\n",
    "    the_ax.set_aspect(1)\n",
    "    cax = fig.colorbar(im, ax=the_ax, extend=\"both\", shrink=0.5)\n",
    "    cax.set_label(\"ylabel here\", rotation=270, va=\"bottom\", size=20)\n",
    "    fig.subplots_adjust(bottom=0.1, top=0.8)\n",
    "\n",
    "# cax = fig.add_axes([0.92, 0.1, 0.025, 0.55], frameon=False)\n",
    "# cbar = matplotlib.colorbar.Colorbar(cax, im, extend=\"both\")\n",
    "# cbar.ax.set_ylabel(\"Concentration ($kg/m^3$)\", rotation=270, size=20, va=\"bottom\")\n",
    "fig.suptitle(\n",
    "    \"Evolution of the oil concentration through time\", y=0.7, size=25, va=\"top\"\n",
    ")\n",
    "fig.savefig(\"Oil_Concentration_Evolution.png\")"
   ]
  }
 ],
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