phobson/gs_example.ipynb

## gs_example.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gs\n",
    "\n",
    "SIZE = (6, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ljifJV5MsJXkqyQ39x5Tmn7MkTabLFdRDwN63Ob4P2D38c5Dp/hPL0jx7CGdJ6mzVgqqq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\nBSVJatL/AJjTVWm1VnCAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8a104c710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, f1_axes = plt.subplots(ncols=2, nrows=2, figsize=SIZE)\n",
    "fig1.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ljifJV5MsJXkqyQ39x5Tmn7MkTabLFdRDwN63Ob4P2D38c5Dp/hPL0jx7CGdJ6mzVgqqq\nR4HX3mbJAeAbteJx4MokH+groLRROEvSZPr4J9+3A+dGts8P9/1kfGGSg6z8ZsgVV1xx47XXXtvD\n6aXpOHXq1CtVtbCOp3SWtCGtdZb6KKjOquoIcARgMBjU4uLiep5emkiS/5h1hstxljRP1jpLfXyL\n7wKwc2R7x3CfpMk4S9KIPgrqOHDn8BtINwM/rapfektC0qqcJWnEqm/xJfkmcAtwdZLzwN8AvwJQ\nVYeBE8B+YAn4GXDXtMJK88xZkiazakFV1e2rHC/g870lkjYoZ0majHeSkCQ1yYKSJDXJgpIkNcmC\nkiQ1yYKSJDXJgpIkNcmCkiQ1yYKSJDXJgpIkNcmCkiQ1yYKSJDXJgpIkNcmCkiQ1yYKSJDXJgpIk\nNcmCkiQ1yYKSJDXJgpIkNcmCkiQ1yYKSJDXJgpIkNcmCkiQ1yYKSJDXJgpIkNcmCkiQ1yYKSJDXJ\ngpIkNalTQSXZm+TZJEtJ7rvE8fcm+U6SJ5OcSXJX/1Gl+eYcSZNZtaCSbAHuB/YBe4Dbk+wZW/Z5\n4Jmquh64Bfj7JNt6zirNLedImlyXK6ibgKWqOltVbwLHgANjawp4T5IA7wZeA5Z7TSrNN+dImlCX\ngtoOnBvZPj/cN+prwHXAS8DTwBer6q3xB0pyMMliksWLFy+uMbI0l3qbI3CWtDn09SWJTwNPAB8E\nfg/4WpJfG19UVUeqalBVg4WFhZ5OLW0YneYInCVtDl0K6gKwc2R7x3DfqLuAh2vFEvACcG0/EaUN\nwTmSJtSloE4Cu5PsGn5gextwfGzNi8CnAJK8H/gwcLbPoNKcc46kCW1dbUFVLSe5B3gE2AIcraoz\nSQ4Njx8GvgQ8lORpIMC9VfXKFHNLc8U5kia3akEBVNUJ4MTYvsMjP78E/HG/0aSNxTmSJuOdJCRJ\nTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU2y\noCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAk\nSU2yoCRJTbKgJElN6lRQSfYmeTbJUpL7LrPmliRPJDmT5Af9xpTmn3MkTWbraguSbAHuB/4IOA+c\nTHK8qp4ZWXMl8HVgb1W9mOR90woszSPnSJpclyuom4ClqjpbVW8Cx4ADY2vuAB6uqhcBqurlfmNK\nc885kibUpaC2A+dGts8P9436EHBVku8nOZXkzr4CShuEcyRNaNW3+CZ4nBuBTwG/CvwwyeNV9dzo\noiQHgYMA11xzTU+nljaMTnMEzpI2hy5XUBeAnSPbO4b7Rp0HHqmqN6rqFeBR4PrxB6qqI1U1qKrB\nwsLCWjNL86i3OQJnSZtDl4I6CexOsivJNuA24PjYmn8APpFka5J3AR8DftxvVGmuOUfShFZ9i6+q\nlpPcAzwCbAGOVtWZJIeGxw9X1Y+T/BPwFPAW8GBVnZ5mcGmeOEfS5FJVMznxYDCoxcXFmZxb6iLJ\nqaoazDrHapwltW6ts+SdJCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAk\nSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElN\nsqAkSU2yoCRJTbKgJElNsqAkSU2yoCRJTbKgJElNsqAkSU3qVFBJ9iZ5NslSkvveZt1HkywnubW/\niNLG4BxJk1m1oJJsAe4H9gF7gNuT7LnMui8D3+s7pDTvnCNpcl2uoG4ClqrqbFW9CRwDDlxi3ReA\nbwEv95hP2iicI2lCXQpqO3BuZPv8cN//SbId+CzwwNs9UJKDSRaTLF68eHHSrNI8622OhmudJW14\nfX1J4ivAvVX11tstqqojVTWoqsHCwkJPp5Y2jE5zBM6SNoetHdZcAHaObO8Y7hs1AI4lAbga2J9k\nuaq+3UtKaf45R9KEuhTUSWB3kl2sDNRtwB2jC6pq1//+nOQh4B8dKukXOEfShFYtqKpaTnIP8Aiw\nBThaVWeSHBoePzzljNLcc46kyXW5gqKqTgAnxvZdcqCq6s/eeSxp43GOpMl4JwlJUpMsKElSkywo\nSVKTLChJUpMsKElSkywoSVKTLChJUpMsKElSkywoSVKTLChJUpMsKElSkywoSVKTLChJUpMsKElS\nkywoSVKTLChJUpMsKElSkywoSVKTLChJUpMsKElSkywoSVKTLChJUpMsKElSkywoSVKTLChJUpMs\nKElSkywoSVKTOhVUkr1Jnk2ylOS+Sxz/XJKnkjyd5LEk1/cfVZpvzpE0mVULKskW4H5gH7AHuD3J\nnrFlLwB/UFW/C3wJONJ3UGmeOUfS5LpcQd0ELFXV2ap6EzgGHBhdUFWPVdXrw83HgR39xpTmnnMk\nTahLQW0Hzo1snx/uu5y7ge9e6kCSg0kWkyxevHixe0pp/vU2R+AsaXPo9UsSST7JymDde6njVXWk\nqgZVNVhYWOjz1NKGsdocgbOkzWFrhzUXgJ0j2zuG+35Bko8ADwL7qurVfuJJG4ZzJE2oyxXUSWB3\nkl1JtgG3AcdHFyS5BngY+NOqeq7/mNLcc46kCa16BVVVy0nuAR4BtgBHq+pMkkPD44eBvwZ+Hfh6\nEoDlqhpML7Y0X5wjaXKpqpmceDAY1OLi4kzOLXWR5NQ8FISzpNatdZa8k4QkqUkWlCSpSRaUJKlJ\nFpQkqUkWlCSpSRaUJKlJFpQkqUkWlCSpSRaUJKlJFpQkqUkWlCSpSRaUJKlJFpQkqUkWlCSpSRaU\nJKlJFpQkqUkWlCSpSRaUJKlJFpQkqUkWlCSpSRaUJKlJFpQkqUkWlCSpSRaUJKlJFpQkqUkWlCSp\nSRaUJKlJnQoqyd4kzyZZSnLfJY4nyVeHx59KckP/UaX55hxJk1m1oJJsAe4H9gF7gNuT7Blbtg/Y\nPfxzEHig55zSXHOOpMl1uYK6CViqqrNV9SZwDDgwtuYA8I1a8ThwZZIP9JxVmmfOkTShrR3WbAfO\njWyfBz7WYc124Ceji5IcZOU3Q4D/SnJ6orTTcTXwyqxD0EaOFjJAOzk+3ONj9TZH4Cw1ngHMMW5N\ns9SloHpTVUeAIwBJFqtqsJ7nvxRztJWhtRyzznA5zlK7Gcxx6Rxr+e+6vMV3Adg5sr1juG/SNdJm\n5hxJE+pSUCeB3Ul2JdkG3AYcH1tzHLhz+C2km4GfVtUvvS0hbWLOkTShVd/iq6rlJPcAjwBbgKNV\ndSbJoeHxw8AJYD+wBPwMuKvDuY+sOXW/zPFzLWSADZhjinPUa853qIUcLWQAc4xbU45UVd9BJEl6\nx7yThCSpSRaUJKlJUy+oVm7v0iHH54bnfzrJY0muX+8MI+s+mmQ5ya19Z+iaI8ktSZ5IcibJD2aR\nI8l7k3wnyZPDHF0/k5kkw9EkL1/u7xG1cvsh52iyHCPrNvwstTBHw/P0P0tVNbU/rHwY/Dzwm8A2\n4Elgz9ia/cB3gQA3A/8+oxwfB64a/ryv7xxdMoys+1dWPjC/dUbPxZXAM8A1w+33zSjHXwJfHv68\nALwGbOs5x+8DNwCnL3N86q/Pnp6rTTFHXXOMrNvQs9TKHA0fu/dZmvYVVCu3d1k1R1U9VlWvDzcf\nZ+XvoKxrhqEvAN8CXu75/JPkuAN4uKpeBKiqaWTpkqOA9yQJ8G5WBmu5zxBV9ejwcS+nhdsPOUcT\n5hjaDLPUxBzBdGZp2gV1uVu3TLpmPXKMupuVpl/XDEm2A59lujcJ7fJcfAi4Ksn3k5xKcueMcnwN\nuA54CXga+GJVvTWFLG9nPV6ffWTYLHPUKccmmqV5mSNYw2t0XW91NA+SfJKVwfrEDE7/FeDeqnpr\n5ZedmdkK3Ah8CvhV4IdJHq+q59Y5x6eBJ4A/BH4L+Ock/1ZV/7nOOTShGc8ROEuj5naOpl1Qrdze\npdM5knwEeBDYV1WvziDDADg2HKirgf1Jlqvq2+uc4zzwalW9AbyR5FHgeqDPoeqS4y7g72rlDeyl\nJC8A1wI/6jHHalq4/ZBzNHmOzTJL8zJHsJbXaN8flI19KLYVOAvs4ucf4P322JrP8IsfnP1oRjmu\nYeVv8H98Vs/F2PqHmM4Hu12ei+uAfxmufRdwGvidGeR4APjb4c/vH76Yr57Cc/IbXP6D3am/Pnt6\nrjbFHHXNMbZ+w85SS3M0fPxeZ2kqL6CxUPtZ+W3heeCvhvsOAYeGP4eVf8jteVbeHx3MKMeDwOus\nXAo/ASyud4axtVMZqq45gL9g5dtHp4E/n9H/kw8C3xu+Lk4DfzKFDN9k5Z+z+G9Wftu9exavzx6e\nq00zR11fwyNrN/QstTBHw/P0Pkve6kiS1CTvJCFJapIFJUlqkgUlSWqSBSVJapIFJUlqkgUlSWqS\nBSVJatL/AJjTVWm1VnCAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8a1621da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = plt.figure(figsize=SIZE)\n",
    "spec2 = gs.GridSpec(ncols=2, nrows=2)\n",
    "f2ax1 = fig2.add_subplot(spec2[0, 0])\n",
    "f2ax2 = fig2.add_subplot(spec2[0, 1])\n",
    "f2ax3 = fig2.add_subplot(spec2[1, 0])\n",
    "f2ax4 = fig2.add_subplot(spec2[1, 1])\n",
    "fig2.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JK+VCkkz5jY+shcjMSoD7gZnAeGCBmY3voN3/Bp6JepASP8qFJJnyGy+5vCK6\nFGhx9zfd/R/AeuDqdtr9P8BjgH6ytGdQLiTJlN8YyaUQDQP2ZSzvT61LM7NhwFxgzbl2ZGY1ZtZo\nZo2HDh3q7FglXiLLRaqtsiHFpPzGSFR/R7QKuMPdT52rkbuvdfdKd68cMmRIRF1LjOWUC1A2JJaU\n3yLJ5VtzB4ARGcvDU+syVQLrU2drHQzMMrMT7v54JKOUOFIuJMmU3xjJpRBtBcaY2WhaD9R84JrM\nBu4++vR1M6sDntTB6vaUC0ky5TdGshYidz9hZkuBp4ESYJ27N5vZotT2s89DId2eciFJpvzGS05/\n0Orum4BNbda1e6Dc/dv5D0uSQLmQJFN+4yPRJz0VEZHkUyESEZGgVIhERCQoFSIREQlKhUhERIJS\nIRIRkaBUiEREJCgVIhERCUqFSEREglIhEhGRoFSIREQkKBUiEREJSoVIRESCUiESEZGgVIhERCQo\nFSIREQlKhUhERIJSIRIRkaBUiEREJCgVIhERCUqFSEREglIhEhGRoFSIREQkKBUiEREJKqdCZGYz\nzOx1M2sxs+XtbF9oZjvMbKeZ1ZvZpOiHKnGjXEiSKb/xkbUQmVkJcD8wExgPLDCz8W2avQX8D3cv\nBe4B1kY9UIkX5UKSTPmNl1xeEV0KtLj7m+7+D2A9cHVmA3evd/cjqcXNwPBohykxpFxIkim/MZJL\nIRoG7MtY3p9a15Ebgd+3t8HMasys0cwaDx06lPsoJY4iywUoG1J0ym+MRPplBTObTusBu6O97e6+\n1t0r3b1yyJAhUXYtMZYtF6BsSHwpv4XXO4c2B4ARGcvDU+vOYGZlwIPATHd/L5rhSYwpF5Jkym+M\n5PKKaCswxsxGm9n5wHxgY2YDMxsJ/Ba41t3/Ev0wJYaUC0ky5TdGsr4icvcTZrYUeBooAda5e7OZ\nLUptrwXuBv4b8ICZAZxw98rCDVtCUy4kyZTfeDF3D9JxZWWlNzY2Bum7uzGzbd3pP4iy0bMov91D\nPsdRZ1YQEZGgVIhERCQoFSIREQlKhUhERIJSIRIRkaBUiEREJCgVIhERCUqFSEREglIhEhGRoFSI\nREQkKBUiEREJSoVIRESCUiESEZGgVIhERCQoFSIREQlKhUhERIJSIRIRkaBUiEREJCgVIhERCUqF\nSEREglIhEhGRoFSIREQkKBUiEREJKqdCZGYzzOx1M2sxs+XtbDczW53avsPMJkc/VIkb5UKSTPmN\nj6yFyMzzsnFbAAAYVUlEQVRKgPuBmcB4YIGZjW/TbCYwJnWpAdZEPE6JGeVCkkz5jZdcXhFdCrS4\n+5vu/g9gPXB1mzZXAw97q83AQDP7fMRjlXhRLiTJlN8Y6Z1Dm2HAvozl/cBlObQZBryT2cjMamh9\nZgFw3Mx2dWq0YQwGDoceRBZfDtBnZLmAWGUj5PHuqX0rv9FJ5HHMpRBFxt3XAmsBzKzR3SuL2X9X\nJGGcZtYYegz5iks21HeYvkP0GyXlN7/jmMtbcweAERnLw1PrOttGuhflQpJM+Y2RXArRVmCMmY02\ns/OB+cDGNm02AtelvmVSBXzo7me9fJVuRbmQJFN+YyTrW3PufsLMlgJPAyXAOndvNrNFqe21wCZg\nFtACfALckEPfa7s86uJKwjiLPsYC5gLC3ufquwf0rfzGq29z9ygHIiIi0ik6s4KIiASlQiQiIkEV\nvBAl4TQaOYxxmpl9aGZNqcvdAca4zsze7ejvE+JwP2aTTxay3TaCvhem+txpZvVmNilj257U+qau\nfkU1n4wVYe7fz+h3l5mdNLNBqW1dnns+mc13zoUW+nEt1GNWwR6H3L1gF1o/BHwD+CJwPrAdGN+m\nzSzg94ABVcCfCjmmLo5xGvBkMcfVzjivACYDuzrYHvR+LGQWcrltBH1XA59NXZ+Zef8Be4DBITJW\njLm3af8N4Pko5t7VzOY75zhnuYj9F+Qxq1CPQ4V+RZSE02jkMsbg3P0l4P1zNAl9P2aTTxbyPUZZ\nb+/u9e5+JLW4mda/GYlKPuMv+NzbWAA82on9dyiPzMb9/2Tox7Vg90+hHocKXYg6OkVGZ9sUUq79\nV6deav7ezCYUZ2idEvp+zCafLOQ7t87e/kZan9Wd5sBzZrbNWk/l0ln5ZKxoczezPsAM4LGM1fnO\nvStj685ZLlb/EOYxq0vzLuopfhLsv4CR7n7UzGYBj9N6Rl7pZsxsOq2FaGrG6qnufsDMPgc8a2av\npZ4ZRikOGfsG8Iq7Zz7jLcbcJXpxyFPOCv2KKAmn0cjav7v/zd2Ppq5vAs4zs8HFG2JOQt+P2eST\nhXznltPtzawMeBC42t3fO73e3Q+k/n0X2EDrWyOdkU/GijL3lPm0eVsugrl3ZWzdOctF6T/gY1bX\n5h31h1ltPrjqDbwJjOafH6pNaNNmNmd+uLWlkGPq4hj/hX/+8e+lwNunl4s81lF0/CFh0PuxkFnI\n5bYR9D2S1r+gr26zvi/QP+N6PTCjWBkrxtxT7QbQ+t5/34jn3unM5jvnOGe5iP0X7DGrK8c06z6L\ncNBmAX+h9Vse/55atwhYlLputP5A1RvATqAyQLCyjXEp0Jw64JvbPlgVaYyP0nr6+U9pfd/1xrjd\nj4XMQnu3jbjvB4EjQFPq0pha/8XUcd+eykCn+843Y4Wee2r528D6NrfLa+75ZDbfOcc5y0XqvyCP\nWfkc03NddIofEREJSmdWEBGRoFSIREQkKBUiEREJSoVIRESCUiESEZGgVIhERCQoFSIREQlKhUhE\nRIJSIRIRkaBUiEREJCgVIhERCUqFSEREgspaiMxsnZm9a2a7OthuZrbazFpSvwY4OfphShwpG5Jk\nym985PKKqI7Wnw/uyExaf/lvDFADrMl/WJIQdSgbklx1KL+xkLUQeevPAr9/jiZXAw97q83AQDP7\nfFQDlPhSNiTJlN/46B3BPoYB+zKW96fWvdO2oZnV0PrMgr59+1aMHTs2gu5l27Zth919SOhxtEPZ\nkKyU3+4hn+MYRSHKmbuvBdYCVFZWemNjYzG777bMbG/oMeRL2ei5lN/uIZ/jGMW35g4AIzKWh6fW\niSgbkmTKb5FEUYg2AtelvmFSBXzo7me9dJUeSdmQJFN+iyTrW3Nm9igwDRhsZvuBHwDnAbh7LbAJ\nmAW0AJ8ANxRqsBIvyoYkmfIbH1kLkbsvyLLdgSWRjUgSQ9mQJFN+40NnVhARkaBUiEREJCgVIhER\nCUqFSEREglIhEhGRoFSIREQkKBUiEREJSoVIRESCUiESEZGgVIhERCQoFSIREQlKhUhERIJSIRIR\nkaByKkRmNsPMXjezFjNb3s72AWb2hJltN7NmM9Pp0nsA5UKSTPmNj6yFyMxKgPuBmcB4YIGZjW/T\nbAnwqrtPovX3PX5mZudHPFaJEeVCkkz5jZdcXhFdCrS4+5vu/g9gPXB1mzYO9DczA/oB7wMnIh2p\nxI1yIUmm/MZILoVoGLAvY3l/al2m+4BxwEFgJ/C/3P1U2x2ZWY2ZNZpZ46FDh7o4ZImJyHIByoYU\nnfIbI1F9WeEqoAkYCpQD95nZRW0buftad69098ohQ4ZE1LXEWE65AGVDYkn5LZJcCtEBYETG8vDU\nukw3AL/1Vi3AW8DYaIYoMaVcSJIpvzGSSyHaCowxs9GpD+rmAxvbtHkb+BqAmV0MfBl4M8qBSuwo\nF5Jkym+M9M7WwN1PmNlS4GmgBFjn7s1mtii1vRa4B6gzs52AAXe4++ECjlsCUy4kyZTfeMlaiADc\nfROwqc262ozrB4Erox2axJ1yIUmm/MaHzqwgIiJBqRCJiEhQKkQiIhKUCpGIiASlQiQiIkGpEImI\nSFAqRCIiEpQKkYiIBKVCJCIiQakQiYhIUCpEIiISlAqRiIgElVMhMrMZZva6mbWY2fIO2kwzsyYz\nazazP0Q7TIkj5UKSTPmNj6xn3zazEuB+4H/S+nO6W81so7u/mtFmIPAAMMPd3zazzxVqwBIPyoUk\nmfIbL7m8IroUaHH3N939H8B64Oo2ba6h9ZcM3wZw93ejHabEkHIhSab8xkguhWgYsC9jeX9qXab/\nDnzWzF40s21mdl1UA5TYUi4kyZTfGMnph/Fy3E8FrT+reyHQYGab3f0vmY3MrAaoARg5cmREXUuM\n5ZQLUDYklpTfIsnlFdEBYETG8vDUukz7gafd/ePUT+m+BExquyN3X+vule5eOWTIkKwd33rrraxa\ntSq9fNVVV3HTTTell2+77TZWrlzJwYMHmTdvXrv7mDZtGo2NjQD85Cc/Sa/fs2cPEydOzDqGTM8+\n+ywVFRWUlpZSUVHB888/36nbdzOR5QI6n41MccvJli1bKC8vp7y8nEmTJrFhw4ZO3V6Kouj5jVtO\n33vvPaZPn06/fv1YunRph+1++MMfMmzYsHSmN23a1GHbrsqlEG0FxpjZaDM7H5gPbGzT5v8CU82s\nt5n1AS4Dduc7uMsvv5z6+noATp06xeHDh2lubk5vr6+vp7q6mqFDh/Kb3/wm6/4yD1xXDB48mCee\neIKdO3fy0EMPce211+a1v4QLlou24paTiRMn0tjYSFNTE0899RS33HILJ06cyGufErmi5zduOb3g\nggu45557+OlPf5q17a233kpTUxNNTU3MmjUrr37bk7UQufsJYCnwNK0H4f+4e7OZLTKzRak2u4Gn\ngB3AFuBBd9+V7+Cqq6tpaGgAoLm5mYkTJ9K/f3+OHDnC8ePH2b17N5MnTz7j2cCxY8eYP38+48aN\nY+7cuRw7dgyA5cuXc+zYMcrLy1m4cCEAJ0+e5Oabb2bChAlceeWV6bYdueSSSxg6dCgAEyZM4Nix\nYxw/fjzfaSZSyFy0Fbec9OnTh969W9/1/vvf/46ZRT1lyVOI/MYtp3379mXq1KlccMEFXZ1SdNw9\nyKWiosJzMWrUKN+7d6/X1tb6mjVr/M477/Tf/e53/vLLL/vUqVPd3f2tt97yCRMmuLv7z372M7/h\nhhvc3X379u1eUlLiW7dudXf3vn37pvf71ltveUlJif/5z392d/dvfetb/stf/tLd3desWeNr1qw5\n57h+/etf+9e+9rWc5lBoQKMHOo6FuOSajUxxy8nmzZt9/Pjx3rdvX//tb3/b6fn0JD0pv3HLqbv7\nL37xC1+yZEmH23/wgx/4yJEjvbS01G+44QZ///33222Xz3GM6ssKBVNdXU19fT319fUsW7aMAwcO\nUF9fz4ABA7j88svPav/SSy/x3e9+F4CysjLKyso63Pfo0aMpLy8HoKKigj179gCwaNGic46pubmZ\nO+64g2eeeaaLs5KoxS0nl112Gc3NzezevZvrr7+emTNnxuOZpwQVt5zmYvHixdx1112YGXfddRe3\n3XYb69aty2ufbcX+FD+n31fduXMnEydOpKqqioaGhvT7qfn4zGc+k75eUlKS0/v4+/fvZ+7cuTz8\n8MN86Utfyqt/iU7ccnLauHHj6NevH7t2Rf6OpCRQXHN6LhdffDElJSX06tWLm2++mS1btkSy30yx\nL0TV1dU8+eSTDBo0iJKSEgYNGsQHH3xAQ0NDuwfuiiuu4JFHHgFg165d7NixI73tvPPO49NPP+3y\nWD744ANmz57NihUr2n32IuHEKSdvvfVW+kFg7969vPbaa4waNarL+5PuI045zdU777yTvr5hw4ZO\nfzsvF7EvRKWlpRw+fJiqqqoz1g0YMIDBgwef1X7x4sUcPXqUcePGcffdd1NRUZHeVlNTQ1lZWfrD\nvY7U1tZSW1t71vr77ruPlpYWfvzjH6e/yvjuu/pj6ziIU05efvllJk2aRHl5OXPnzuWBBx5odwzS\n88QppwCjRo1i2bJl1NXVMXz4cF59tfUMRzfddFP6a+K33347paWllJWV8cILL/Dzn/+80/POxlo/\nYyq+yspKPz1RyY+ZbXP3ytDjiIqy0bMov91DPscx9q+IRESke1MhEhGRoGJfiPr163fGcl1d3TlP\nRwGwceNGVqxYcc42L774Il//+tfb3bZq1So++eSTrGNbuXIl48ePp6ysjK997Wvs3bs3622kMJQT\nSQLltH2xL0RdMWfOHJYvb/d3rnKS64G75JJLaGxsZMeOHcybN4/bb7+9y31K8SknkgQ9IaeJLkSH\nDh3im9/8JlOmTGHKlCm88sorwJnPMt544w2qqqooLS3lzjvvPOMZydGjR5k3bx5jx45l4cKFuDur\nV6/m4MGDTJ8+nenTp5+z/+nTp9OnTx8Aqqqq2L9/f4FmKvlQTiQJenROu3pKhnwvuZ7GpVevXj5p\n0qT0ZcSIEenTUSxYsMD/+Mc/urv73r17fezYse5+5ikrZs+e7Y888oi7t57q4vRpMV544QW/6KKL\nfN++fX7y5EmvqqpK7+sLX/iCHzp0KD2GG2+8MX1ajY4sWbLE77nnnpzmFDV60ClSOqKcJFdPym93\nzmk+xzH2p/i58MILaWpqSi/X1dWlv9/+3HPPpb/3DvC3v/2No0ePnnH7hoYGHn/8cQCuueYavve9\n76W3XXrppQwfPhyA8vJy9uzZw9SpU88aw4MPPnjOMf7nf/4njY2N/OEP+kn7UJQTSQLltH2xL0Tn\ncurUKTZv3tzlc3hFcUqM5557jv/4j//gD3/4wxn7k/hQTiQJenJOc/qMyMxmmNnrZtZiZh1+amZm\nU8zshJm1/6tOEbvyyiu5995708uZzzROq6qq4rHHHgNg/fr1Oe23f//+fPTRR1nb/fnPf+aWW25h\n48aNfO5zn8tx1N1HXHPRlnIi7YlbfntyTrMWIjMrAe4HZgLjgQVmNr6Ddv8bKNopqVevXk1jYyNl\nZWWMHz++3dNYrFq1ipUrV1JWVkZLSwsDBgzIut+amhpmzJiR/nAv83QXmb7//e9z9OhRvvWtb1Fe\nXs6cOXPyn1RCxDkXbSkn0lYc89ujc5rtQyTgK7T+XO7p5X8D/q2ddv8vsASoA+Zl229XPpDuio8/\n/thPnTrl7u6PPvqoz5kzpyj9FhMBPuwtVC68iNnI1BNyElfKb+7inNN8jmMunxENA/ZlLO+n9Sdz\n08xsGDAXmA5M6WhHZlYD1ACMHDkyh67zt23bNpYuXYq7M3DgwMh/R6MHiywXqbZFz0Ym5aTHSWR+\nu2tOo/qywirgDnc/da6fRXb3tcBaaD0xYER9n9NXv/pVtm/fXoyu5Gw55QLCZCOTciLtiF1+u2tO\ncylEB4ARGcvDU+syVQLrUwdrMDDLzE64++ORjFLiSLmQJFN+YySXQrQVGGNmo2k9UPOBazIbuPvo\n09fNrA54Uger21MuJMmU3xjJWojc/YSZLQWeBkqAde7ebGaLUtvb/8Ul6daUC0ky5TdecvqMyN03\nAZvarGv3QLn7t/MfliSBciFJpvzGR6JPeioiIsmnQiQiIkGpEImISFAqRCIiEpQKkYiIBKVCJCIi\nQakQiYhIUCpEIiISlAqRiIgEpUIkIiJBqRCJiEhQKkQiIhKUCpGIiASVUyEysxlm9rqZtZjZ8na2\nLzSzHWa208zqzWxS9EOVuFEuJMmU3/jIWojMrAS4H5gJjAcWmNn4Ns3eAv6Hu5cC95D6yVzpvpQL\nSTLlN15yeUV0KdDi7m+6+z+A9cDVmQ3cvd7dj6QWN9P6s7vSvSkXkmTKb4zkUoiGAfsylven1nXk\nRuD37W0wsxozazSzxkOHDuU+SomjyHIByoYUnfIbI5F+WcHMptN6wO5ob7u7r3X3SnevHDJkSJRd\nS4xlywUoGxJfym/h5fJT4QeAERnLw1PrzmBmZcCDwEx3fy+a4UmMKReSZMpvjOTyimgrMMbMRpvZ\n+cB8YGNmAzMbCfwWuNbd/xL9MCWGlAtJMuU3RrK+InL3E2a2FHgaKAHWuXuzmS1Kba8F7gb+G/CA\nmQGccPfKwg1bQlMuJMmU33gxdw/ScWVlpTc2Ngbpu7sxs23d6T+IstGzKL/dQz7HUWdWEBGRoFSI\nREQkKBUiEREJSoVIRESCUiESEZGgVIhERCQoFSIREQlKhUhERIJSIRIRkaBUiEREJCgVIhERCUqF\nSEREglIhEhGRoHIqRGY2w8xeN7MWM1veznYzs9Wp7TvMbHL0Q5W4US4kyZTf+MhaiMysBLgfmAmM\nBxaY2fg2zWYCY1KXGmBNxOOUmFEuJMmU33jJ5RXRpUCLu7/p7v8A1gNXt2lzNfCwt9oMDDSzz0c8\nVokX5UKSTPmNkay/0AoMA/ZlLO8HLsuhzTDgncxGZlZD6zMLgONmtqtTow1jMHA49CCy+HKAPiPL\nBcQqGyGPd0/tW/mNTiKPYy6FKDLuvhZYC2BmjUn4VcYkjNPMEv9zkHHJhvoO03eIfqOk/OZ3HHN5\na+4AMCJjeXhqXWfbSPeiXEiSKb8xkksh2gqMMbPRZnY+MB/Y2KbNRuC61LdMqoAP3f2sl6/SrSgX\nkmTKb4xkfWvO3U+Y2VLgaaAEWOfuzWa2KLW9FtgEzAJagE+AG3Loe22XR11cSRhn0cdYwFxA2Ptc\nffeAvpXfePVt7h7lQERERDpFZ1YQEZGgVIhERCSogheiJJxGI4cxTjOzD82sKXW5O8AY15nZux39\nfUIc7sds8slCtttG0PfCVJ87zazezCZlbNuTWt/U1a+o5pOxIsz9+xn97jKzk2Y2KLWty3PPJ7P5\nzrnQQj+uhXrMKtjjkLsX7ELrh4BvAF8Ezge2A+PbtJkF/B4woAr4UyHH1MUxTgOeLOa42hnnFcBk\nYFcH24Pej4XMQi63jaDvauCzqeszM+8/YA8wOETGijH3Nu2/ATwfxdy7mtl85xznLBex/4I8ZhXq\ncajQr4iScBqNXMYYnLu/BLx/jiah78ds8slCvsco6+3dvd7dj6QWN9P6NyNRyWf8BZ97GwuARzux\n/w7lkdm4/58M/bgW7P4p1ONQoQtRR6fI6GybQsq1/+rUS83fm9mE4gytU0Lfj9nkk4V859bZ299I\n67O60xx4zsy2WeupXDorn4wVbe5m1geYATyWsTrfuXdlbN05y8XqH8I8ZnVp3kU9xU+C/Rcw0t2P\nmtks4HFaz8gr3YyZTae1EE3NWD3V3Q+Y2eeAZ83stdQzwyjFIWPfAF5x98xnvMWYu0QvDnnKWaFf\nESXhNBpZ+3f3v7n70dT1TcB5Zja4eEPMSej7MZt8spDv3HK6vZmVAQ8CV7v7e6fXu/uB1L/vAhto\nfWukM/LJWFHmnjKfNm/LRTD3roytO2e5KP0HfMzq2ryj/jCrzQdXvYE3gdH880O1CW3azObMD7e2\nFHJMXRzjv/DPP/69FHj79HKRxzqKjj8kDHo/FjILudw2gr5H0voX9NVt1vcF+mdcrwdmFCtjxZh7\nqt0AWt/77xvx3Dud2XznHOcsF7H/gj1mdeWYZt1nEQ7aLOAvtH7L499T6xYBi1LXjdYfqHoD2AlU\nBghWtjEuBZpTB3xz2werIo3xUVpPP/8pre+73hi3+7GQWWjvthH3/SBwBGhKXRpT67+YOu7bUxno\ndN/5ZqzQc08tfxtY3+Z2ec09n8zmO+c4Z7lI/RfkMSufY3qui07xIyIiQenMCiIiEpQKkYiIBKVC\nJCIiQakQiYhIUCpEIiISlAqRiIgEpUIkIiJB/f/q5WZ/mA1rXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8a171d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig3 = plt.figure(figsize=SIZE)\n",
    "widths = [2, 3, 1.5]\n",
    "heights = [1, 3, 2]\n",
    "spec3 = gs.GridSpec(ncols=3, nrows=3, width_ratios=widths, height_ratios=heights)\n",
    "for row in range(3):\n",
    "    for col in range(3):\n",
    "        ax = fig3.add_subplot(spec3[row, col])\n",
    "        label = 'Width: {}\\nHeight: {}'.format(widths[col], heights[row])\n",
    "        ax.annotate(label, (0.1, 0.5), xycoords='axes fraction', va='center')\n",
    "        \n",
    "fig3.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vPH7wgx8AsG7dOtauXQt0XRX0wgsvdI9bs2YNp556avf0I488AsCvfvUrjjvu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f8a1c1ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4 = plt.figure(figsize=SIZE)\n",
    "spec4 = gs.GridSpec(ncols=3, nrows=3)\n",
    "anno_opts = dict(xy=(0.5, 0.5), xycoords='axes fraction', va='center', ha='center')\n",
    "\n",
    "fig4.add_subplot(spec4[0, 0]).annotate('GridSpec[0, 0]', **anno_opts)\n",
    "fig4.add_subplot(spec4[0, 1:]).annotate('GridSpec[0, 1:]', **anno_opts)\n",
    "fig4.add_subplot(spec4[1:, 0]).annotate('GridSpec[1:, 0]', **anno_opts)\n",
    "fig4.add_subplot(spec4[1:, 1:]).annotate('GridSpec[1:, 1:]', **anno_opts)\n",
    "\n",
    "fig4.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "wqio",
   "language": "python",
   "name": "wqio"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}