Matplotlib_2-Additional_Features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 id=\"tocheading\">Table of Contents and Notebook Setup</h1>\n",
    "<div id=\"toc\"></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Advanced Subplot Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subplots of Unequal Scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes we might have a figure composed of multiple plots but we don't want them to all be the same size (perhaps we have a main one and something else on the side). The following is a simple solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 10, 0.2)\n",
    "y = np.sin(x)\n",
    "\n",
    "fig, axes = plt.subplots(2,3, gridspec_kw = {'width_ratios':[3, 1, 2]})\n",
    "axes[0, 0].plot(x,y)\n",
    "axes[1, 1].plot(y,x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'gridspec_kw' argument is simple. Notie that we pass in an array of length three; this is the number of columns in the plot grid. This allows us to determine the ratio of the width of the columns. The same thing can be accomplished with the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 10, 0.2)\n",
    "y = np.cos(x)\n",
    "\n",
    "fig, axes = plt.subplots(2,3, gridspec_kw = {'height_ratios':[2, 1]})\n",
    "axes[0, 0].plot(x,y)\n",
    "axes[1, 1].plot(y,x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subplots of Different Shape (and Management of Space) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes we want might two small subplots on the top and one small one on the bottom. This can be accomplished simply as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2MzkRmE3VbZysE4FzgUclvQBcBIz5gbENKycCs6m2AgslLZB0LMV82W/NKR0Rr0XEnIiYHxHzgXFgeUR42FwbSk4EZi0i4hBwPcUgic8DmyJiu6T1kpZXWzuz8pUxH4HZv52I2EwxoGLztm92KHvpIOpk1i++IjAzqzknAjOzmnMiMDOrOScCM7Oay0oE3QbgknSnpGfS54+S/t6073DTvrHWY83MrFo5U1VODsB1BcWLNlsljUXEjskyEfG1pvI3AIubTvGPiDivvCqbmVmZcq4IcgfgmrQKuK+MypmZWf/lJIKuA3BNknQGsAB4pGnz8WnQrXFJn+pwnAfmMjOrSE4iOOoAXC1WAg9ExOGmbadHRAP4HPA9SR+YcjIPzGVmVpmcRNBtAK5mK2m5LRQRe9O/u4FHOfL5gZmZVSwnERx1AK5Jkj4EzAaeaNo2W9JxaXkOcDGwo/VYMzOrTtdeQxFxSNLkAFyzgNHJAbiAiYiYTAqrgI0R0Xzb6BzgbklvUiSdbzX3NjIzs+plDTqXMwBXRNzW5rjHgY/0UD8zM+szv1lsZlZzTgRmZjXnRGBmVnNOBGZmNedEYGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNOBGZmNedEYGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNZiUDSUkk7Je2SdFOb/Wsk7ZP0TPp8sWnfakl/Sp/VZVbezMx613ViGkmzgA3AFRTzF2+VNNZmprH7I+L6lmPfC9wKNCgmvN+Wjj1QSu3NzKxnOVcEFwK7ImJ3RLwBbARWZJ7/SuDhiNif/uf/MLB0elU1M7N+yEkEpwIvNa3vSdtafVrSs5IekDTv7RwraZ2kCUkT+/bty6y6mZmVIScRqM22aFn/JTA/Ij4K/Bq4920cS0SMREQjIhpz587NqJKZmZUlJxHsAeY1rZ8G7G0uEBGvRsTBtPoj4ILcY83MrFo5iWArsFDSAknHAiuBseYCkk5pWl0OPJ+WHwKWSJotaTawJG0zm9EyesrdKGlHuh36G0lnVFFPszJ07TUUEYckXU/xP/BZwGhEbJe0HpiIiDHgq5KWA4eA/cCadOx+SbdTJBOA9RGxvw/fw6w0mT3lngYaEfG6pGuBO4DPDr62Zr3rmggAImIzsLll2zeblm8Gbu5w7Cgw2kMdzQbtrZ5yAJIme8q9lQgiYktT+XHgmoHW0KxEfrPYbKrcnnKT1gIPttvhHnE2DJwIzKbK6u0GIOkaihcmv9Nuv3vE2TDIujVkVjNZvd0kXQ7cAlzS1GvObOj4isBsqpyecouBu4HlEfFKBXU0K40TgVmLiDgETPaUex7YNNlTLvWOg+JW0LuBn6WBFsc6nM5sxvOtIbM2MnrKXT7wSpn1ia8IzMxqzonAzKzmnAjMzGrOicDMrOacCMzMas6JwMys5pwIzMxqzonAzKzmnAjMzGrOicDMrOayEkEv0/ZJOpzGYvF4LGZmM1DXsYZKmLbvHxFxXsn1NjOzkuRcEbw1bV9EvAFMTtv3lojYEhGvp9VxivHbzcxsCOQkgl6n7Ts+TdU3LulT7Q7wdH5mZtXJGYZ6OtP2XdK0+fSI2CvpTOARSX+IiD8fcbKIEWAEoNFotD23mZn1R84Vwdudtm9587R9EbE3/bsbeBRY3EN9zcysZDmJYNrT9kmaLem4tDwHuBhofshsZmYV63prKCIOSZqctm8WMDo5bR8wERFjHDltH8CLEbEcOAe4W9KbFEnnWy29jczMrGJZU1VOd9q+iHgc+EgvFTQzs/7ym8VmZjXnRGBmVnNOBGZmNedEYGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNOBGZmNedEYGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNOBGZmNedEYGZWc1mJQNJSSTsl7ZJ0U5v9x0m6P+1/UtL8pn03p+07JV1ZXtXN+qeXNm82bLomAkmzgA3AVcAiYJWkRS3F1gIHIuKDwJ3At9OxiyjmOP4wsBT4QTqf2YzVS5s3G0Y5VwQXArsiYndEvAFsBFa0lFkB3JuWHwAuUzF58QpgY0QcjIi/ALvS+cxmsl7avNnQyZmz+FTgpab1PcDHOpVJk92/BrwvbR9vOfbU1gCS1gHr0upBSc9l1b58c4C/1ShulbGr/M4f6rK/lzZ/xHdy265d3Cpjd2vXHeUkgnZ/5URmmZxjiYgRYARA0kRENDLqVbqqYvs7Dz52tyJttuW2+SM3uG3XKm6VsTPadUc5t4b2APOa1k8D9nYqI+kY4CRgf+axZjNNL23ebOjkJIKtwEJJCyQdS/Hwd6ylzBiwOi1fDTwSEZG2r0w9LBYAC4Gnyqm6Wd/00ubNhk7XW0Pp/uf1wEPALGA0IrZLWg9MRMQY8D/ATyTtoviraGU6drukTcAO4BBwXUQc7hJyZPpfp2dVxfZ3nkGxe2nzvcTts7r9d/bP+m2Q/4gxM6s3v1lsZlZzTgRmZjVXWSKo8hX+jNg3Stoh6VlJv5F0xiDiNpW7WlJIKqULWk5cSZ9J33m7pJ+WETcntqTTJW2R9HT6eS8rKe6opFc69dtX4fupXs9KOr+MuOnclbTtqtp1Tuymcm7bvcXsT7uOiIF/KB7A/Rk4EzgW+D2wqKXMV4C70vJK4P4Bxv4k8K60fG0ZsXPipnInAo9RvIjXGND3XQg8DcxO6+8f4M96BLg2LS8CXigp9ieA84HnOuxfBjxI8T7ARcCTw9y2q2rXbtuDbdv9atdVXRFU+Qp/19gRsSUiXk+r4xT9yPseN7kduAP4Zwkxc+N+CdgQEQcAIuKVAcYO4D1p+SRKes8kIh7j6P36VwA/jsI4cLKkU0oIXVXbrqpdZ8VO3LZ71K92XVUiaPcKf+vQE0e8wg9MvsI/iNjN1lJk2L7HlbQYmBcRvyohXnZc4CzgLEm/kzQuaekAY98GXCNpD7AZuKGk2N283XZQ5nn70baratdZsd22B9a2p9Wuc4aY6IfSXuHvU+yioHQN0AAu6XdcSe+gGMVyTQmxsuMmx1BcQl9K8VfibyWdGxF/H0DsVcA9EfFdSR+n6Jt/bkS82WPsMurWr/P2I3ZV7bprbLftgbbtabWtqq4IqnyFP2vYC0mXA7cAyyPi4ADingicCzwq6QWK+3tjJTxUy/1Z/yIi/hXFKLE7KX55epUTey2wCSAingCOpxi0q9/6NfxJVW27qnadE9tte3Bte3rtuowHJ9N44HEMsBtYwP8/aPlwS5nrOPKB2qYBxl5M8SBo4SC/c0v5RynngVrO910K3JuW51BcWr5vQLEfBNak5XNSo1VJP/P5dH6o9h8c+VDtqWFu21W1a7ftwbftfrTr0hrDNL7MMuCPqWHekratp/hLBYrs+TOKOQyeAs4cYOxfA/8LPJM+Y4OI21K2lF+WzO8r4L8ohgL5A7BygD/rRcDv0i/SM8CSkuLeB7wM/Ivir6S1wJeBLzd95w2pXn8o62ddZduuql27bQ+ubferXXuICTOzmvObxWZmNedEYGZWc04EZmY150RgZlZzTgRmZjXnRGBmVnNOBGZmNfd/lo2mAt10QAsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "\n",
    "ax1 = plt.subplot(221)\n",
    "ax2 = plt.subplot(223)\n",
    "ax3 = plt.subplot(122)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The numerical arguments work as follows. The first two numbers 22 or 12 represent a 2x2 or 1x2 grid. The third number 1 or 3 or 2 represents the position in that grid that the plot should go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way of accomplishing this is using the subplot2grid feature. The first tuple breaks the figure apart into a 3x3 grid. The second tuple places the top left corner of the 'ax' in that location on the grid. The colspan and rowspan then stretch out the plot so that it covers more area of the grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/CnBfNzjvA66aUz1/ChzrznsHcNHYsb/X9dtx4G3zqKdbfz/wwVXHTb1/GL2z9B3gR4xS/1XAO4B3dPsDXNfVet/4fxSz6Jt5j/8hPTb7uhvaY5LX11jbOxlo+JlwTAT4S0ZfHXMfcGDRNS+oH/YCn+/m1nuANy665hn1w6bn+0kffr2FJElqind4liRJTTH8SJKkphh+JElSUww/kiSpKYYfSZLUFMOPJElqiuFHkiQ1xfAjSZKaYviRJElNMfxIkqSmGH4kSVJTDD+SJKkpvcJPkuuTPJTkq+vsT5KPJDme5N4kr+xzPknS/DjHa6j6vvPzcWDfGfZfCuzpHoeAv+l5PknS/Hwc53gNUK/wU1WfAx4+Q5P9wCdr5Cjw/CQv7HNOSdJ8OMdrqLbP+PnPAx4cW1/ptn1nvFGSQ4x+auA5z3nOqy666KIZlyVJw3D33Xd/t6p2Luj0zvGaq2mN91mHn6yxrZ6yoeowcBhgaWmplpeXZ1yWJA1Dkn9d5OnX2OYcr5mZ1nif9W97rQC7xtbPB07O+JySpPlwjtcz0qzDzxHgd7vfCHg18IOq+s5GB0mSnhGc4/WM1OtjryQ3AK8Dzk2yArwP+CmAqvoocAtwGXAc+CHwtj7nkyTNj3O8hqpX+KmqgxvsL+Bdfc4hSVoM53gNlXd4liRJTTH8SJKkphh+JElSUww/kiSpKYYfSZLUFMOPJElqiuFHkiQ1xfAjSZKaYviRJElNMfxIkqSmGH4kSVJTDD+SJKkphh9JktQUw48kSWqK4UeSJDXF8CNJkppi+JEkSU0x/EiSpKYYfiRJUlMMP5IkqSmGH0mS1BTDjyRJaorhR5IkNcXwI0mSmmL4kSRJTTH8SJKkphh+JElSUww/kiSpKYYfSZLUFMOPJElqiuFHkiQ1pVf4SbIvyQNJjie5Zo39FyS5I8mXk9yb5LI+55MkzY9zvIZq0+EnyTbgOuBSYC9wMMneVc3+N3BTVV0MHAD+erPnkyTNj3O8hqzPOz+XAMer6kRVPQbcCOxf1aaAn+2Wnwec7HE+SdL8OMdrsPqEn/OAB8fWV7pt494PvDXJCnAL8O61nijJoSTLSZZPnTrVoyRJ0pQ4x2uw+oSfrLGtVq0fBD5eVecDlwF/l+Qp56yqw1W1VFVLO3fu7FGSJGlKnOM1WH3Czwqwa2z9fJ76ludVwE0AVfUF4NnAuT3OKUmaD+d4DVaf8HMXsCfJ7iRnMbrY7ciqNv8G/AZAkpcxemH4nqckbX3O8RqsTYefqnocuBq4Ffgaoyv+jyW5NsnlXbM/At6e5CvADcCVVbX6bVNJ0hbjHK8h297n4Kq6hdFFbuPb/mRs+X7gNX3OIUlaDOd4DZV3eJYkSU0x/EiSpKYYfiRJUlMMP5IkqSmGH0mS1BTDjyRJaorhR5IkNcXwI0mSmmL4kSRJTTH8SJKkphh+JElSUww/kiSpKYYfSZLUFMOPJElqiuFHkiQ1xfAjSZKaYviRJElNMfxIkqSmGH4kSVJTDD+SJKkphh9JktQUw48kSWqK4UeSJDXF8CNJkppi+JEkSU0x/EiSpKYYfiRJUlMMP5IkqSmGH0mS1BTDjyRJaorhR5IkNaVX+EmyL8kDSY4nuWadNr+T5P4kx5L8fZ/zSZLmxzleQ7V9swcm2QZcB7wBWAHuSnKkqu4fa7MH+GPgNVX1SJKf61uwJGn2nOM1ZH3e+bkEOF5VJ6rqMeBGYP+qNm8HrquqRwCq6qEe55MkzY9zvAarT/g5D3hwbH2l2zbuJcBLknw+ydEk+9Z6oiSHkiwnWT516lSPkiRJU+Icr8HqE36yxrZatb4d2AO8DjgI/J8kz3/KQVWHq2qpqpZ27tzZoyRJ0pQ4x2uw+oSfFWDX2Pr5wMk12vxDVf2oqr4FPMDohSJJ2tqc4zVYfcLPXcCeJLuTnAUcAI6savP/gF8DSHIuo7dIT/Q4pyRpPpzjNVibDj9V9ThwNXAr8DXgpqo6luTaJJd3zW4FvpfkfuAO4H9V1ff6Fi1Jmi3neA1ZqlZ/hLtYS0tLtby8vOgyJOkZIcndVbW06Dom5RyvPqY13r3DsyRJaorhR5IkNcXwI0mSmmL4kSRJTTH8SJKkphh+JElSUww/kiSpKYYfSZLUFMOPJElqiuFHkiQ1xfAjSZKaYviRJElNMfxIkqSmGH4kSVJTDD+SJKkphh9JktQUw48kSWqK4UeSJDXF8CNJkppi+JEkSU0x/EiSpKYYfiRJUlMMP5IkqSmGH0mS1BTDjyRJaorhR5IkNcXwI0mSmmL4kSRJTTH8SJKkphh+JElSUww/kiSpKb3CT5J9SR5IcjzJNWdo95YklWSpz/kkSfPjHK+h2nT4SbINuA64FNgLHEyyd412zwX+EPjiZs8lSZov53gNWZ93fi4BjlfViap6DLgR2L9Guw8AHwL+q8e5JEnz5RyvweoTfs4DHhxbX+m2/bckFwO7quqfzvRESQ4lWU6yfOrUqR4lSZKmxDleg9Un/GSNbfXfO5NnAR8G/mijJ6qqw1W1VFVLO3fu7FGSJGlKnOM1WH3Czwqwa2z9fODk2PpzgVcAdyb5NvBq4IgXxEnSM4JzvAarT/i5C9iTZHeSs4ADwJEndlbVD6rq3Kp6cVW9GDgKXF5Vy70qliTNg3O8BmvT4aeqHgeuBm4FvgbcVFXHklyb5PJpFShJmj/neA3Z9j4HV9UtwC2rtv3JOm1f1+dckqT5co7XUHmHZ0mS1BTDjyRJaorhR5IkNcXwI0mSmmL4kSRJTTH8SJKkphh+JElSUww/kiSpKYYfSZLUFMOPJElqiuFHkiQ1xfAjSZKaYviRJElNMfxIkqSmGH4kSVJTDD+SJKkphh9JktQUw48kSWqK4UeSJDXF8CNJkppi+JEkSU0x/EiSpKYYfiRJUlMMP5IkqSmGH0mS1BTDjyRJaorhR5IkNcXwI0mSmmL4kSRJTTH8SJKkphh+JElSU3qFnyT7kjyQ5HiSa9bY/54k9ye5N8lnk7yoz/kkSfPjHK+h2nT4SbINuA64FNgLHEyyd1WzLwNLVfULwM3AhzZ7PknS/DjHa8j6vPNzCXC8qk5U1WPAjcD+8QZVdUdV/bBbPQqc3+N8kqT5cY7XYPUJP+cBD46tr3Tb1nMV8Jke55MkzY9zvAZre49js8a2WrNh8lZgCXjtOvsPAYcALrjggh4lSZKmxDleg9XnnZ8VYNfY+vnAydWNkrweeC9weVU9utYTVdXhqlqqqqWdO3f2KEmSNCXO8RqsPuHnLmBPkt1JzgIOAEfGGyS5GPgYoxfFQz3OJUmaL+d4Ddamw09VPQ5cDdwKfA24qaqOJbk2yeVdsz8Hfgb4dJJ7khxZ5+kkSVuIc7yGrM81P1TVLcAtq7b9ydjy6/s8vyRpcZzjNVTe4VmSJDXF8CNJkppi+JEkSU0x/EiSpKYYfiRJUlMMP5IkqSmGH0mS1BTDjyRJaorhR5IkNcXwI0mSmmL4kSRJTTH8SJKkphh+JElSUww/kiSpKYYfSZLUFMOPJElqiuFHkiQ1xfAjSZKaYviRJElNMfxIkqSmGH4kSVJTDD+SJKkphh9JktQUw48kSWqK4UeSJDXF8CNJkppi+JEkSU0x/EiSpKYYfiRJUlMMP5IkqSmGH0mS1BTDjyRJakqv8JNkX5IHkhxPcs0a+89O8qlu/xeTvLjP+SRJ8+Mcr6HadPhJsg24DrgU2AscTLJ3VbOrgEeq6ueBDwN/ttnzSZLmxzleQ9bnnZ9LgONVdaKqHgNuBPavarMf+ES3fDPwG0nS45ySpPlwjtdgbe9x7HnAg2PrK8Avrdemqh5P8gPgBcB3xxslOQQc6lYfTfLVHnUNybms6qtG2Q+n2Rcj9sNpL53R87Yyx2+1sWQ9ZzaV8d4n/KyV7msTbaiqw8BhgCTLVbXUo67BsC9G7IfT7IsR++G0JMuzeuo1tg1ujreeM9uK9Uzjefp87LUC7BpbPx84uV6bJNuB5wEP9zinJGk+nOM1WH3Cz13AniS7k5wFHACOrGpzBLiiW34LcHtVPeWnAknSluMcr8Ha9Mde3ee7VwO3AtuA66vqWJJrgeWqOgL8X+Dvkhxn9NPAgQme+vBmaxog+2LEfjjNvhixH06bSV80NMdbz5kNsp4Y0iVJUku8w7MkSWqK4UeSJDVlYeHH26afNkFfvCfJ/UnuTfLZJC9aRJ2ztlE/jLV7S5JKsmV+/XKaJumHJL/TjYljSf5+3jXOywSvjQuS3JHky93r47JF1DlrSa5P8tB698fJyEe6fro3ySvnXWNXx6bn9SR/3G1/IMmb5lTPunNrkh8nuad7rL7Qe1b1XJnk1Nh5f39s3xVJvtE9rlh97Axq+fBYHV9P8v2xfbPom02P8U31TVXN/cHo4rlvAhcCZwFfAfauavMHwEe75QPApxZR6xbpi18DfrpbfucQ+2KSfujaPRf4HHAUWFp03QsaD3uALwM7uvWfW3TdC+yLw8A7u+W9wLcXXfeM+uJXgVcCX11n/2XAZxjdd+fVwBe36L/XmvN692/3FeBsYHf3PNvmUM+6cyvwHwvonyuBv1rj2HOAE92fO7rlHbOsZVX7dzO64H0mfdNnjG+2bxb1zo+3TT9tw76oqjuq6ofd6lFG99sYmknGBMAHgA8B/zXP4uZokn54O3BdVT0CUFUPzbnGeZmkLwr42W75eTz1PjSDUFWf48z3z9kPfLJGjgLPT/LC+VT33/rM6/uBG6vq0ar6FnC8e76Z1jPnuXXSOW4tbwJuq6qHu9f9bcC+OdZyELihx/k21GOMb6pvFhV+1rpt+nnrtamqx4Enbps+NJP0xbirGKXfodmwH5JcDOyqqn+aZ2FzNsl4eAnwkiSfT3I0SZ9JcCubpC/eD7w1yQpwC6OfUFv0dOeRRdWw3rw+i/r7zq3PTrLcvcbe3LOWp1PPb3cf69yc5ImbTE67fyZ+vu6jwN3A7WObp903k1iv5k31TZ+vt+hjardNH4CJ/55J3gosAa+daUWLccZ+SPIsRt8afeW8ClqQScbDdkYffb2O0U+q/5LkFVX1/dUHPsNN0hcHgY9X1V8k+WVG95x5RVX9ZPblbSlbYb7sM6/Pov6+c+sFVXUyyYXA7Unuq6pvzriefwRuqKpHk7yD0btkvz7hsdOu5QkHgJur6sdj26bdN5OY6thZ1Ds/3jb9tEn6giSvB94LXF5Vj86ptnnaqB+eC7wCuDPJtxl95nskw7voedLXxj9U1Y+6jwgeYBSGhmaSvrgKuAmgqr4APJvRFzG2ZqJ5ZAvUsN68Pov6e82tVXWy+/MEcCdw8azrqarvjdXwt8CrJj122rWMOcCqj7xm0DeTWK/mzfXNtC9amvDCpu2MLkrazemLrV6+qs27ePKFcTctotYt0hcXM7o4bc+i611kP6xqfyfDvOB5kvGwD/hEt3wuo7d8X7Do2hfUF58BruyWX9ZNell07TPqjxez/sWgv8mTLwb90hb991pzXgdezpMveD5B/wueNz23Mrpw9uxu+VzgG5zhguAp1vPCseXfAo52y+cA3+rq2tEtnzPLWrp2LwW+Pf6amkXf9Bnjm+2bub44Vv1FLgO+3g2893bbrmWUvmH0E9ynGV349iXgwkXVugX64p+Bfwfu6R5HFl3zIvphVds7GWD4mXA8BPhL4H7gPuDAomteYF/sBT7fTd73AG9cdM0z6ocbgO8AP2L0k+5VwDuAd4yNieu6frpvUa+NPvM6o3dfvsnoncxL51TPmnMr8CtdP36l+/OqOdXzp8Cx7rx3ABeNHft7Xb8dB94261q69fcDH1x13Kz6ZtNjfDN949dbSJKkpniHZ0mS1BTDjyRJaorhR5IkNcXwI0mSmmL4kSRJTTH8SJKkphh+JElSU/4/EngtYlYS71oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,5))\n",
    "\n",
    "ax1 = plt.subplot2grid((3, 3), (0, 0))\n",
    "ax2 = plt.subplot2grid((3, 3), (0, 1), colspan=2)\n",
    "ax3 = plt.subplot2grid((3, 3), (1, 0), colspan=2, rowspan=2)\n",
    "ax4 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the tight_layout automatically adjusts the spacing between plots so that things don't overlap."
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}