{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Here I am going to look at \n",
    "\n",
    "- a)\n",
    "- b)\n",
    "- c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (x: 10, y: 20)>\n",
       "array([[6.089512e-01, 5.921867e-01, 2.887199e-01, 9.124592e-02, 9.271311e-01,\n",
       "        1.852007e-01, 5.549645e-01, 6.716445e-01, 9.405254e-01, 4.138753e-01,\n",
       "        4.651151e-01, 9.805317e-01, 1.132029e-01, 8.954666e-01, 4.230709e-01,\n",
       "        7.235119e-01, 7.597193e-01, 6.979327e-01, 5.570699e-02, 9.337514e-02],\n",
       "       [9.139808e-02, 8.131817e-01, 3.089330e-02, 5.717526e-01, 1.066662e-01,\n",
       "        5.657157e-01, 2.625941e-01, 9.896213e-01, 6.721665e-01, 5.564167e-01,\n",
       "        2.982966e-01, 1.308679e-01, 7.642018e-01, 5.684872e-01, 8.352758e-01,\n",
       "        3.317794e-01, 2.541880e-01, 4.921978e-03, 7.704251e-01, 3.362494e-01],\n",
       "       [3.111530e-01, 5.371339e-01, 8.077625e-01, 3.862287e-01, 4.009356e-01,\n",
       "        7.556269e-01, 9.047113e-01, 2.531478e-01, 4.379836e-01, 7.610534e-01,\n",
       "        6.318273e-03, 8.214699e-01, 2.902651e-01, 7.712718e-01, 8.999661e-01,\n",
       "        4.464614e-01, 7.098822e-02, 1.800008e-01, 5.808595e-02, 6.221029e-01],\n",
       "       [1.137917e-01, 9.279731e-01, 7.208999e-01, 7.674465e-01, 8.406247e-01,\n",
       "        8.498908e-01, 4.015754e-01, 6.817999e-01, 1.533586e-01, 9.221862e-01,\n",
       "        4.611826e-01, 5.863989e-01, 7.464779e-01, 1.398929e-01, 6.754868e-01,\n",
       "        2.731296e-02, 7.797667e-01, 1.670179e-01, 9.328458e-01, 3.503391e-01],\n",
       "       [8.348758e-02, 7.487514e-01, 5.056826e-02, 9.780706e-01, 6.783454e-01,\n",
       "        6.472068e-01, 8.483019e-01, 3.792755e-01, 7.031391e-01, 4.176098e-01,\n",
       "        1.328022e-01, 7.313341e-01, 1.641179e-02, 9.542104e-01, 7.993335e-01,\n",
       "        8.518552e-01, 7.076589e-01, 7.445155e-01, 4.720021e-01, 5.093500e-01],\n",
       "       [2.778791e-01, 9.808745e-02, 8.998674e-01, 2.798906e-01, 2.503440e-01,\n",
       "        3.451778e-01, 3.797488e-01, 6.584744e-01, 6.882464e-01, 6.176427e-01,\n",
       "        2.928799e-01, 7.657277e-02, 8.867912e-01, 8.939456e-01, 6.136981e-01,\n",
       "        4.207783e-01, 4.616000e-01, 7.854896e-01, 4.293707e-02, 9.572357e-01],\n",
       "       [8.406274e-02, 1.504745e-01, 6.030014e-02, 6.721167e-01, 1.835887e-01,\n",
       "        2.012850e-01, 7.604827e-01, 8.764416e-01, 1.732119e-01, 9.787593e-01,\n",
       "        4.089368e-01, 7.592372e-01, 1.652323e-01, 3.969846e-01, 4.787644e-01,\n",
       "        5.058492e-01, 6.943746e-01, 2.988215e-02, 8.585814e-01, 3.036220e-01],\n",
       "       [8.457635e-01, 2.561272e-01, 8.443130e-01, 4.350447e-01, 8.528330e-01,\n",
       "        1.783162e-01, 2.864370e-01, 3.807775e-01, 1.392894e-01, 4.269599e-01,\n",
       "        5.872172e-02, 7.361147e-02, 7.646812e-01, 5.440484e-01, 4.036109e-01,\n",
       "        7.461635e-01, 1.698425e-01, 1.519611e-01, 4.929727e-01, 6.600693e-01],\n",
       "       [3.109070e-01, 3.286642e-01, 7.161659e-01, 7.897546e-01, 4.583196e-01,\n",
       "        1.163877e-01, 4.411938e-01, 3.903958e-01, 3.126172e-02, 4.600458e-02,\n",
       "        3.738154e-01, 6.498342e-01, 8.994535e-01, 4.974055e-01, 8.896595e-01,\n",
       "        8.878730e-01, 3.126902e-01, 3.511536e-01, 4.980407e-01, 3.351488e-02],\n",
       "       [7.245638e-01, 3.379322e-01, 8.100103e-01, 3.593644e-02, 3.076934e-01,\n",
       "        4.975979e-01, 2.115820e-04, 8.443818e-01, 4.922226e-01, 3.827802e-01,\n",
       "        9.822423e-01, 4.598063e-01, 1.124241e-01, 2.174470e-01, 5.273616e-01,\n",
       "        7.051374e-01, 1.175613e-02, 8.015048e-01, 1.618827e-01, 3.250856e-01]])\n",
       "Dimensions without coordinates: x, y"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rand = np.random.rand(10,20)\n",
    "da = xr.DataArray(rand, dims=['x', 'y'])\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZWa6coM3MMjTMN+MvwgnazGpJuIvDzCxbTtBmZrlygjYzy5QTtJlZhryiiplZxpygzczyVGQa93Bygjaz2nIXh5lZjjxRxcwsY07QZmb58UxCM7OMqTfvDO0EbWb15D5oM7N8uYvDzCxXTtDFbRg3iufePXHwHZvef9PnSpVz2rt/nhxzzVVHlSrr1lEDLQTc3uEfvjc55oq/+1ZyDMDZ55yeHLPyQyNLlXXpqr9Kjtn7/PRV0Y99cG1yDMA1/3t6csyh31tUqqwTJ6THnfrF9M9qxAceS44BiH3Slyv9zF/MKFXWun9/IT3o26WK2oRb0GZmuXKCNjPLUHiqt5lZljwO2swsZ5F3hnaCNrPacgvazCxHb4KJKuljaQqSNFnSbZKWSVoi6ayqyjIzK0O9xbbhUmULegNwTkTcLWk8cJekWyJiaYVlmpkVVttRHBGxEljZfPyipGXAHoATtJkNv8AXCQEk7QUcCNzZ5r1ZwCyArcdOGIrqmJkB+V8krKwPeiNJ44AfAZ+NiE3mdEbE7IjoioiuUWPGVl0dM7M/ioLbMKm0BS1pKxrJ+eqIuKHKsszMUtR6oookAd8HlkXEv1RVjplZKRHZ37C/yi6Ow4BPAP9D0r3N7dgKyzMzS1PXLo6IuIPGXxFmZlmqbReHmVnWAqhxF4eZWd462MUhabqkhyStkHRem/fPlrRU0v2SbpX0J4Md0wnazGpLUWwb9DjSSOBS4BhgKnCSpKl9drsH6IqIdwLXAxcPdlwnaDOrLfVGoa2AacCKiHg0ItYB1wJvWAMsIm6LiFeaTxcCkwY7qBO0mdVT0e6NYl0cewBPtjzvbr7Wn1OBGwc7qC8SmlktNSaqFL5IOFHS4pbnsyNidp/D9dX24JI+DnQB7xms0KwS9KgX1rPzz54uvP9Hz15Sqpxbp+2SHPPSZa+VKmu/f/xdcszNb9svOebBn74zOQbgjn/7TnLMXz5wQqmybnpg/+SYnT+5VXLMgpnlVvXuuuLu5JifPHhAqbKmTiv+c77RuO70cl44ZM/0IGDbH6evOr7v4nJ/kD/z3X1LxXVE8bvZrY6IrgHe7wYmtzyfBGzyIUs6GvgS8J6IGDSpZJWgzcyGUkILejCLgCmS9gaeAmYCH3tDWdKBwHeA6RGxqshB3QdtZvXUwT7oiNgAnAEsAJYBcyNiiaQLJR3X3O0bwDjguubM6nmDHdctaDOrqc7eiyMi5gPz+7x2Qcvjo1OP6QRtZvXlG/abmWUoarzklZlZ9tyCNjPLVN752QnazOpLvXn3cThBm1k9BSkTVYaFE7SZ1ZKITk5UqYQTtJnVlxO0mVmmnKDNzDLkPmgzs3x5FIeZWZbCXRxmZlkKnKDNzLKVdw+HE7SZ1ZfHQZuZ5coJ2swsQxHQk3cfhxO0mdWXW9DFvTZpJI9cvH3h/R9+4PBS5Uz88JjkmP2+kr4KM8AlP78mOebMT5yeHPPIKRuSY6DcCt1X7/eDUmUd84svJMesTV/gnDu+fnV6EHD8UTOTY66af3mpsv7uyr9PjnnHJx5Ojtlp9EvJMQCPnbl7cszD0wddpLqt3caX+251hBO0mVmGAujgmoRVcII2s5oKCPdBm5nlJ/BFQjOzbLkP2swsU5kn6BFVHlzSdEkPSVoh6bwqyzIzS9O8WVKRbZhU1oKWNBK4FHgv0A0skjQvIpZWVaaZWWEBZH670Spb0NOAFRHxaESsA64FZlRYnplZmrq2oIE9gCdbnncD76qwPDOzBPWe6q02r23yq0jSLGAWwKiJxWcRmpltloDIfBx0lV0c3cDklueTgE3mdEbE7IjoioiukduNrbA6ZmZ99EaxbZhUmaAXAVMk7S1pa2AmMK/C8szM0tS1DzoiNkg6A1gAjAQuj4glVZVnZpYkIvtRHJVOVImI+cD8KsswMyst84kqnkloZjUVRE/PcFdiQE7QZlZPvt2omVnGMh9m5wRtZrUUQLgFbWaWofAN+83MspX7RUJFRsNMJD0HPNHmrYnA6iGuTjs51COHOkAe9cihDpBHPXKoAwxdPf4kInbenANIuolGfYtYHRHTN6e8MrJK0P2RtDgiulyPPOqQSz1yqEMu9cihDjnVY0tR6Q37zcysPCdoM7NMvVkS9OzhrkBTDvXIoQ6QRz1yqAPkUY8c6gD51GOL8KbogzYzq6M3SwvazKx2skrQg60CLmm0pDnN9++UtFcFdZgs6TZJyyQtkXRWm32OlPR7Sfc2twsqqMfjkh5oHn9xm/cl6VvNc3G/pIMqqMOftfwf75X0gqTP9tmn4+dC0uWSVkl6sOW1HSXdIml5898J/cSe3NxnuaSTK6jHNyT9pnnOfyxph35iB/z8NrMOX5X0VMs5P7af2AG/Tx2ox5yWOjwu6d5+YjtyLmopIrLYaNwz+hHgrcDWwH3A1D77fBr49+bjmcCcCuqxO3BQ8/F44OE29TgS+GnF5+NxYOIA7x8L3EhjabFDgDuH4PN5hsb400rPBXAEcBDwYMtrFwPnNR+fB1zUJm5H4NHmvxOajyd0uB7vA0Y1H1/Urh5FPr/NrMNXgXMLfF4Dfp82tx593r8EuKDKc1HHLacWdJFVwGcAVzUfXw8cJand2oelRcTKiLi7+fhFYBmNBXBzMwP4QTQsBHaQtHuF5R0FPBIR7SYSdVRE/BfwfJ+XWz/7q4Dj24S+H7glIp6PiLXALUDpyQXt6hERN0fEhubThTSWcqtMP+eiiCLfp47Uo/kd/ChwTdnjW3s5Jeh2q4D3TYyv79P8kvwe2KmqCjW7UA4E7mzz9qGS7pN0o6S3V1B8ADdLuqu5sG5fRc5XJ82k/y9g1ecCYNeIWAmNX6LALm32Gepz8rc0/oppZ7DPb3Od0exmubyf7p6hPBd/CTwbEcv7eb/qc7HFyilBF1kFvNBK4Z0gaRzwI+CzEfFCn7fvpvGn/gHAt4H/rKAKh0XEQcAxwOmSjuhbxTYxVZ2LrYHjgOvavD0U56KooTwnXwI2AFf3s8tgn9/muAzYB/hzYCWN7oVNqtjmtaqGbJ3EwK3nKs/FFi2nBF1kFfDX95E0Ctiecn/+DUjSVjSS89URcUPf9yPihYh4qfl4PrCVpKJz+guJiKeb/64CfkzjT9ZWhVZN75BjgLsj4tk29az8XDQ9u7ELp/nvqjb7DMk5aV58/CDwv6LZydpXgc+vtIh4NiJ6IqIX+G4/xx6qczEK+DAwp799qjwXW7qcEnSRVcDnARuvzJ8I/Ky/L0hZzf607wPLIuJf+tlnt41935Km0TiPazpYh7GSxm98TOPC1IN9dpsHfLI5muMQ4PcbuwAq0G8Lqepz0aL1sz8Z+EmbfRYA75M0ofln//uar3WMpOnAPwDHRcQr/exT5PPbnDq0Xms4oZ9jF/k+dcLRwG8iorvdm1Wfiy3ecF+lbN1ojEx4mMbV5y81X7uQxpcBYBsaf2avAH4NvLWCOhxO40/B+4F7m9uxwKeATzX3OQNYQuPK+ELg3R2uw1ubx76vWc7Gc9FaBwGXNs/VA0BXRZ/JtjQS7vYtr1V6Lmj8MlgJrKfREjyVxrWGW4HlzX93bO7bBXyvJfZvmz8fK4C/qaAeK2j07W782dg4qugtwPyBPr8O1uGHzc/8fhpJd/e+dejv+9TJejRfv3Ljz0LLvpWcizpunkloZpapnLo4zMyshRO0mVmmnKDNzDLlBG1mliknaDOzTDlBm5llygnazCxTTtCWJUlfU8u9uCX9k6TPDGedzIaaJ6pYlpp3ErwhIg6SNILGDMJpEVHFNHKzLI0a7gqYtRMRj0taI+lAYFfgHidnqxsnaMvZ94BTgN2Ay4e3KmZDz10clq3mXdgeALYCpkREzzBXyWxIuQVt2YqIdZJuA37n5Gx15ARt2WpeHDwE+OvhrovZcPAwO8uSpKk07r18a/S/1p3ZFs190GZmmXIL2swsU07QZmaZcoI2M8uUE7SZWaacoM3MMuUEbWaWqf8PFZ0j237qMGkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "da.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some cool figure caption or other interpretational stuff. YEAH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}