abhigyan/Jupyter/Contour Plots.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting contours and Quivers\n",
    "\n",
    "In this lecture, we will learn how to make contour plots and quiver plots.\n",
    "\n",
    "Now, we will make a list of numbers which will represent our values of x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(map(lambda a: a*0.5, list(range(0,25,1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=x[:]\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gauss(x,y,x0,y0,sd):\n",
    "    r=(x-x0)**2+(y-y0)**2\n",
    "    return math.exp(-r/(sd**2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Numerical Python to be able to deal with 2 dimensional matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zeros function to generate an empty 2D space with no values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.zeros([25,25])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filling the space with a Gaussian distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(25):\n",
    "    for j in range(25):\n",
    "        z[i,j]=gauss(x[i],y[j],3.0,3.0,3.0) - gauss(x[i],y[j],9.0,9.0,3.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, plotting this surface as a contour plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting with contourf gives us a filled contour. Colorbar gives a legend of the colors used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A Hill and a Valley')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AFeWeRlaXtojKKHenfCSpsf+jpMuB3wHrAnuY2cHArsAne23QzJ4AvgHMAxYAL5jZ1W3anSZplqRZa5YO3vXMGZoQ5B5aOQaykXvogs8ixrSOY5nKMEWRJGN/L/AtM9vFzL5uZk8DmNkK4EO9NihpHNGdV9sAWwKjJR3dup2ZTTezKWY2ZdhY//qWF1WTe1oSyKKEEJrgG/FkEZNLPV+S1NiPMbMbO7x2XR9tHgA8amaLzGwV8HPgrX3sx8mIqtXdQ5Y7FC/4rNv3unr+FNGNcR6wp6RRinrk70/UDcgJjCLlHupY7VlKKm/B59FemsfLs/Xk5C52M7sVuBS4E7gvjmF61zc5hVEluadF1hlo1sLN6wTimXpxFDKkgJl9AfhCEW07vRPyODO9ENJQA0lolW+/QxMUUeZJW+qerffGkGKXdB9Ng9bEvADMAv7TzJ7NIjAnLIqSe9oDhKUl97QGCuuFToLWo08EdRHWpV48STL23wBrgJ/Gy42biZYA5wH/mH5YTojUZRCxpBQh93ZUWepOfyQR+95mtnfT8n2S/sfM9m7XTdGpPnln76Fm7RCO3EMgC6l7tt4fSS6ejpH0lsaCpD2AMfHi6kyicoIn74uqofaSgUhodc9UXephkSRjPx44R9IYokHjlwDHSxoN/J8sg3PCxjP3V9OQW50y+KxOaC71wRhS7GZ2O/AGSRsCMrPnm16+JLPInFKQd909dLlDfcozLvVwSdIrZiTwHmASMDye5QMz+3KmkTmlIs/svSxyh2pm71mWnVzq6ZCkxv5LorFdVgPLmx6O8yryrLuHXHNvpkr196w/i0s9PZLU2Cea2UGZR+JUAs/c21Pm8kweJyaXerokEfsfJb3BzO7LPBqnEpS5v3vWcm9QFsnXVeprVq3D4oV9zSUUBEnE/jbgWEmPAiuJesaYme2SaWRO6ckje087a4d8hh4IWfJ5lo5ClHoVSCL2gzOPwqksZZY7kMvYMiFIvojrAC717EjS3fEvknYF9olX3WRm92QbllMlyip3yH/gsLwkX/QFXZd6tiTp7ngy8GGiCTEAfiJpupl9L9PInEpRdrlDPtl7M0PJt5v4ixZ3N1zq2ZOkFHMc8BYzWw4g6avAzYCL3emJPC6qNrpBViF7H4qQ5d0OF3p+JOnHLqLRHRusidc5Tl/k0d89q0k6lm05zAXVI37M8idJxn4ucKuky+Plw4EfZxdStRk3YWkm+y1b16wyl2YgvOw9VFzoxZDk4uk3Jd1A1O1RwAfN7K6sAyszWcm7nzZDFn4V5A75197LgAu9WDqKXdLGTYuPxY+/vWZmz2UXVrkoQuRJaY4tRMnnVXfPSu7ggm+lSKmHOk9u3nTL2O8gmhKvUU9vTI+n+PnrMowreEKWeSdaYw5J9Fln71nLHVzwRWfpLvVX6Ch2M9smz0DKQBll3o3QsvkqyB3qJ/iihQ4u9VaSXDxF0jhgMrBeY52Z3ZhVUCFRNZl3IhTJ5yF3cMEPSggyBxd6J5LcoHQ8cDIwEbgb2JOoH/s7sg2tOOoi8040Pn9Rgq9C3b2ZKgk+FKGDS70bSTL2k4HdgVvM7O8l7QB8KduwiqHuQm8lBMFXRe6wthTLIvqQZN7Apd6dJGJ/0cxelISkkWb2oKTtM48sJ1zmQ1NkmaZKpZlWmoUZmuRDlHkDl/rQJBH7fEkbAb8ArpG0GHgy27Cyx4XeH0Vk8WXv756EIrP5kCXejAs9OUluUHp3/PSLkq4HNgR+m2lUGeJCT4e8BZ+X3KFYwTcYSra9ir8s8u6ES703EvWKaWBmv0+j0fgbwNnAzkR94j9kZjense9uuNTTZ9yEpbnKHbKfmano7D0JZRd1L5Rd6pIOAr4DDAPONrMzWl4fCZwPvBl4FnifmT02SJtJBgHLgu8AvzWzHYBdgTlZNjZuwlKXeobkfXzzGkSs7EIpO1X4HUgaBpxFNGHRTsCRknZq2ew4YLGZbQt8C/jqoO3mLnZJGwBvJx5IzMxeMrPns2jLhZ4veR7vPOQO5RdLWanQcd8DmGtmj5jZS8DFwGEt2xwGzIifXwrsL2mgEXQTiV3SayUdED9fX9Ig371fBywCzpV0l6SzJa01sLSkaZJmSZq1ZmnvM8m40IsjL8HnKfcKiSZoSnisxzc8FT+mtby+FfB40/L8eF3bbcxsNfACsMkgQSW5QenDwDRgY+D1RDcq/QDYf4A23wScaGa3SvoOcDrw780bmdl0YDrAyEkTba29dMCFHg551N/zqrtDWBdXq0ZoMtcqJf2besbMpnTbVZt1rT5Lsk1PJLl4+nGirxO3ApjZw5I2G6DN+cB8M7s1Xr6USOwDUyWp77rp0D1K71m0ZQ6RDEZevWfy6DXToAwXV8tEaFJPmfnA1k3LE1m7u3hjm/mShhP1PBxo9NwkYl9pZi81Sj5xw32fTcxsoaTHJW1vZn8iyvwf6Hd/Dcok9STSTms/ocg/r+w9T7mDC34QKi70BrcDkyVtAzwBHAEc1bLNTGAq0VAt/wz8zswyz9h/L+kzwPqSDgQ+BvxqkEaBE4ELJY0AHgE+2O+OQhd6WhJPq/0iRZ9H9p6n3MEF3w81EToQ1cwlnQBcRdTd8Rwzu1/Sl4FZZjaTqCPJBZLmEmXqRwzabhKxn07UHec+4CPAlUR90PvGzO4GutWlEhGi1IsW+VA0x1eU5LPO3vOsuzdolpVLvj11EnozZnYlkTeb132+6fmLwHvTbDOJ2NcnOsv8CP7WL3N9YEWagfRKaFIPXejtKDKbr1ppphnP4l+hrjIvmiRivw44AFgWL68PXA28NaughiIUqZdR5t1ofJ68BF/F0kwzdRW8y7x4koh9PTNrSB0zWyZpVIYxdcSFng9FCL6qcof6lGlc6OGQROzLJb3JzO4EkPRm4K/ZhrU2RUu96jJvR56Cr7rcG1RN8i7zMEki9lOAn0lqmG0L4H3ZhbQ2w9Z9Oc/m1qKOUm8mrwuuVbyo2o1WKZZB9C7ycpBk2N7b41mTtie6Q+pBM8vnXu6CqbvQ25F1Fl/1uns3Qs3mXeblI+mwvbsDk+Ltd5OEmZ2fWVQF40Ifml03fbL02XuIcm/QSaZZCt8FXh2SjBVzAdEYMXcDjdH9jWj84MrhUk9OHtl7neXeDpevk4QkGfsUYKdBb3ENHRd6/2SZvdet7u44aZBE7LOBCcCCjGMpjNClvs9GD6217qbntysgks5kmb1X+WYmx8mCJGIfDzwg6TZgZWOlmR2aWVQ5EYLQ20k7rfcVIf+ssneXu+MkJ4nYv5h1EEVQlNT7FfmgbeUp+ayyd5e74yQjSXfHVCawDom8pZ6nzJPEkJfks8jeqzZ5h+NkQZJeMXsC3wN2BEYQDT253Mw2yDi2TMhL6iHIvBN5Sr6scgfP3p3ykqQUcybR+MA/I+ohcwwwOcugsiIPqYcs9HbkIXmXu+PkS6LJrM1sLjDMzNaY2bnAfplGlQFZS32fjR4qndRbyfIzZHH88xo/KK9Jsx0nLZJk7CvimY7ulvQ1om6Po7MNK12ylHrZZd6OfTZ6KJPsveyZO3jdvS6ssyqsYR16JUnG/oF4uxOA5USTrv5TlkGliUu9P7LK3sucuYNn7045SCL2w83sRTNbYmZfMrPTgEOyDiwNspJ6FcouScnis7rcHSdbkoh9apt1x6YcR+pkIY86Cb2VMsg9T1zuTsh0rLFLOhI4CthG0symlzYAns06sEHISup1p3EM0qq/p11zz6ve3sB7zDih0u3i6R+JLpSOB/67af1S4N4sgxoEl3r2pHlxtQpyB7+o6oRFx1KMmf3FzG4gmsj6pvgO1AXARKIJN2qBS709aR6XtE/GRUyj6KUZJySSdHe8EdhH0jjgOmAW0dR4788ysH5IWxBFSf2AUXN7fs+1K7bNIJLuhJy5F4GXZpxQSCJ2mdkKSccB3zOzr0m6K+vAeqWsUu9H4r3sJ2vhZ9XnfVDyLsk0cLk7IZCkV4wk7UWUof86Xpd0Sr1SkofUDxg1NzWpF91OWserCiUZiOTupRmnSJKI/RTg08DlZna/pNcB12cbVm+kKYSspZ6X0PNuN1S5F4nL3SmKpMP2/r5p+RHgpEEbljSMqF7/hJkFccNTllIvQubtaMSRRYkmxLJMUSWZBl6acYqgWz/2b5vZKZJ+RTR59atIYQalk4E5RP3i+yatDC8rqYci9FayEnwacq/ChdRmXO5O3nTL2C+If34j7UYlTQTeBXwFOK3f/YT8tT1UobdywKi5hfSoGYo05V501g7e393Jl45iN7M74p+/l7Rp/HxRSu1+G/g3oON/m6RpwDSAdTfNdk6PtLP1ski9QdrZu5dkOuPZu5MHHS+eKuKLkp4BHgQekrRI0ucHaVDSIcDTjRNHJ8xsuplNMbMpwzfMbpTguku9mTRjT+O4hvyNbBD8oqqTNd16xZwC7A3sbmabmNk44C3A3pJOHaDNvYFDJT0GXAy8Q9JPet1JiP/0ZZZ6HSiq+2M7XO5OlnQT+zHAkWb2aGNF3CPm6Pi1vjCzT5vZRDObRDTl3u/M7Oh+9zcIaWbrVZF6aFl7lXG5O1nRTezrmtkzrSvjOrsXCStMSCepEL+ZpYnfzFRvJG0s6RpJD8c/x3XZdgNJT0g6c6j9dhP7S32+lhgzu6GfPuxp/LN7tt6dtD5TaFl7SOWYZlzuteV04Dozm0w0FtfpXbb9D5ruKepGN7HvKmlJm8dS4A2Jw644VZR6aFQ9a2/gcq8lhwEz4uczgMPbbSTpzcDmwNVJdtpt2N5hZrZBm8dYM/NSDNWXumft+eNyrx2bm9kCgPjnZq0bSFqHaE6Mf02609IN5hVaGabqhHoDU5Xxm5mKZ51VMObJNUk2HS9pVtPydDOb3ryBpGuBCW3e+9mE4XwMuNLMHpeSTYVROrGHQtWz9bQZ9Kalss+01A9+M1MpeMbMpnTbwMwO6PSapKckbWFmCyRtATzdZrO9iObE+BgwBhghaZmZdazHJxnd0ak5fhIrDi/NVJ6ZwNT4+VTgl60bmNn7zew1cRfxTwLnd5M6uNgdJ3hc7pXmDOBASQ8DB8bLSJoi6ex+d1q7Ukwa9XXPYJ288bp7NTGzZ4H926yfBRzfZv15wHlD7dczdicRfjILA8/enSS42J3SUJWp8wbF5e4MRanEXpcbVaqKdzNND5e7041SiT0EvCThhILL3emEi91JjJ/UwsMHEXPa4WJ3nArgcneacbE7TkVwuTsNXOxOYnzMmPBxuTvgYu8Zl5sTOl53d0ol9jQHgXKcquNyry+lErtTbgYZ3dHpD5d7PXGxO4nwElR5cbnXj9qJPY2s0SXnlA2vu9eL2ondceqMy70euNid0uAXz9PB5V59XOx9UqdyTJ0+a11wuVeb0ok9jazNe2ckJy2p+zEPD5d7dandDEppcu2KbX1gLKfU+MxM7Rn20hpGz1tedBh9U7qMPTSqXKYIKVv3+nq2ePZeLUop9tDKMVWUexU/UyuLF44tOoSgcLlXh9zFLmlrSddLmiPpfkkn5x2D0500pe619XLhcq8GRWTsq4FPmNmOwJ7AxyXtVEAcnrWXBC/D5IvfzFR+che7mS0wszvj50uBOcBWve4nxH/2Ksjds3Wngcu9vBRaY5c0CdgNuLWoGNKWT5nlXubYe8Xr68lwuZeTwsQuaQxwGXCKmS1p8/o0SbMkzVr9QvtuR2ll7S739GNO65iG+M2sbrjcy0chYpe0LpHULzSzn7fbxsymm9kUM5syfMPRmceUhdzLIPgs4vQSTPXwunu5KKJXjIAfA3PM7JuD7i/0jC5UwWcVV5pSz+p362WY/nG5l4MiMva9gQ8A75B0d/x4ZwFxrEWWmWYogs8yjjJI3Rkcl3v45D6kgJn9AVCa+7xn0ZbsuumTqezrpue3Y5+NHkplX+1oSDXvoQhCOKmEgGfr6bBywiofhiBgSnnnaTvSzPDyqBHnlcHn1Y5n6/XDM/dw8UHAOpB15t6gk3R7zeiLzMjLcrHUs/X08UHEwqRSYk+zJAP5yb0dZSmdpC11z9bLiZdmwqIypZisKEs2WgQudacZL82EQ+XEnoUcXO5rU7Zj4mWYfHC5h0HlxA4u9yy56fntMjkWWWbrLm85KsoAAAsDSURBVPV88ZuZiqeSYofs5F5nwWf12b0EU01c7kMjaWNJ10h6OP45rsN2X4uHOZ8j6bvxjZ4dqazYITth1E3wWX7erKXu2XqxuNyH5HTgOjObDFwXL78KSW8lurFzF2BnYHdg3247rbTYIVtxVF3wWX8+z9Trgcu9K4cBM+LnM4DD22xjwHrACGAksC7wVLedVl7skL1Aqij3rD9THlL3bD0cKlx3H98YhTZ+TOvx/Zub2QKI5qoANmvdwMxuBq4HFsSPq8xsTredVqofezfS7uPeSkOERfV7T4s8TlIu9fpSmv7uK1ehR59IsuUzZjal2waSrgUmtHnps0kakLQtsCMwMV51jaS3m9mNnd5TG7FD9nKH8go+r28dXn5xSiP3lDCzAzq9JukpSVuY2QJJWwBPt9ns3cAtZrYsfs9viKYV7Sj2WpRimslLLI36dMhlmrxjzOvYe7YePhUty/TDTGBq/Hwq8Ms228wD9pU0PJ7LYl+iKUU7UquMvUEemXszzeIsOpMv6kTjmbrTSt0y9w6cAVwi6Tgigb8XQNIU4KNmdjxwKfAO4D6iC6m/NbNfddtpLcUO+cu9QatYsxZ9CN8Y8pS6Z+vlou6DiJnZs8D+bdbPAo6Pn68BPtLLfmsrdnhFOEUIvkES8XaSfwjS7kbeWbpLvbx49p4utRZ7g6Ky96SELvBWiii7uNTLj8s9PWp38bQT9yza0uvAKeBSdwbBL6qmg2fsLYRQnikjRZ0UXerVo+519zTwjL0Dnr0nx6XuZIFn7/1Tiox9zapizj+evXenyJOfS70eeN29P0qTsRf5j+zZ+6sp+nqES71eeObeO6XI2BssXjiWcROWFtJ2s8jqmsGHcIJzqdcTr7v3RqnEDsXKvUGdSjQhyLyBS93x0kwySid2CEPuUO0sPiShg0vdeQWX+9CUUuwQjtwbVCGLD03m4EJ32uNy705pxQ6v/NOHKPgGoYs+RJk3cKk73fC6e2dKLfYGoWXvzbQTZ1GyD1nirbjUnaR49r42hYhd0kHAd4BhwNlmdsag+wxZ7q10Emwawi+TvDvhUnd6xeX+anIXu6RhwFnAgcB84HZJM83sgUH3XSa5t6MKUh4EF7ozCC73VyjiBqU9gLlm9oiZvQRcTDRTdyosXjjWBVFC/HfmpIHfzBRRRClmK+DxpuX5wFtaN4pn+27M+L1y3vGfmt1LI/P6Di81xgPPFB1ED5QtXvCY86Bs8QJsP+gOlqx55qqrnv3R+ASbBnlsihC72qyztVaYTQemA0iaNdRM4KFRtpjLFi94zHlQtnghinnQfZjZQWnEUhRFlGLmA1s3LU8Ewu4T6DiOUyKKEPvtwGRJ20gaARxBNFO34ziOkwK5l2LMbLWkE4CriLo7nmNm9w/xtunZR5Y6ZYu5bPGCx5wHZYsXyhlzqshsrfK24ziOU2JKMx674ziOkwwXu+M4TsUIWuySDpL0J0lzJZ1edDxDIWlrSddLmiPpfkknFx1TUiQNk3SXpCuKjmUoJG0k6VJJD8bHeq+iYxoKSafGfxOzJV0kab2iY2pF0jmSnpY0u2ndxpKukfRw/HNckTG20iHmr8d/G/dKulzSRkXGWATBir1p6IGDgZ2AIyXtVGxUQ7Ia+ISZ7QjsCXy8BDE3OBmYU3QQCfkO8Fsz2wHYlcDjlrQVcBIwxcx2Juo0cESxUbXlPKC1//bpwHVmNhm4Ll4OifNYO+ZrgJ3NbBfgIeDTeQdVNMGKnYyHHsgCM1tgZnfGz5cSCWerYqMaGkkTgXcBZxcdy1BI2gB4O/BjADN7ycyeLzaqRAwH1pc0HBhFgPdumNmNwHMtqw8DZsTPZwCH5xrUELSL2cyuNrPV8eItRPfK1IqQxd5u6IHgJdlA0iRgN+DWYiNJxLeBfwNeLjqQBLwOWAScG5eOzpY0uuigumFmTwDfIBrpYgHwgpldXWxUidnczBZAlLgAmxUcT698CPhN0UHkTchiTzT0QIhIGgNcBpxiZkuKjqcbkg4BnjazO4qOJSHDgTcB3zez3YDlhFceeBVxXfowYBtgS2C0pKOLjar6SPosUXn0wqJjyZuQxV7KoQckrUsk9QvN7OdFx5OAvYFDJT1GVO56h6SfFBtSV+YD882s8U3oUiLRh8wBwKNmtsjMVgE/B95acExJeUrSFgDxz6cLjicRkqYChwDvtxrerBOy2Es39IAkEdV+55jZN4uOJwlm9mkzm2hmk4iO8e/MLNhs0swWAo9Laozgtz8w8Fj+GTMP2FPSqPhvZH8Cv+DbxExgavx8KvDLAmNJRDyRz6eAQ81sRdHxFEGwYo8vfjSGHpgDXJJg6IGi2Rv4AFHWe3f8eGfRQVWQE4ELJd0LvBH4r4Lj6Ur87eJS4E7gPqL/u+Bue5d0EXAzsL2k+ZKOA84ADpT0MNHkOAPPdpYmHWI+ExgLXBP/D/6g0CALwIcUcBzHqRjBZuyO4zhOf7jYHcdxKoaL3XEcp2K42B3HcSqGi91xHKdiuNidtkhaE3cVu1/SPZJOk7RO/NoUSd/t8t5Jko7KL9pkSHpMUpKZ5x2n1OQ+NZ5TGv5qZm8EkLQZ8FNgQ+ALZjYL6DYT/CTgqPg9juPkjGfszpCY2dPANOAERezXGLdd0r5NN2PdJWks0U0s+8TrTo0z+Jsk3Rk/3hq/dz9JNzSNrX5hfGcmknaX9Mf428JtksbGY8Z/XdLt8VjbH2kXr6RfSLoj/rYxrcM2p8Vjo8+WdEq8blI8vvuP4vdeLWn9pnjulXRzHMPsNvt8t6Rr42O0haSHJE0Y/DfgOD1iZv7wx1oPYFmbdYuBzYH9gCvidb8C9o6fjyH6Fvi31+P1o4D14ueTgVnx8/2AF4jGAVqH6A7CtwEjgEeA3ePtNoj3Ow34XLxuJNG3hm3axLlx/HN9YDawSbz8GDAeeDPRHaCj45jvJxqJcxLRoFFvjLe/BDg6fj4beGv8/Axgdofj9hOiO6avAI4s+vfoj3o+PGN3eqHdiJv/A3xT0knARvbKONjNrAv8SNJ9wM+IJk5pcJuZzTezl4G7ieS6PbDAzG4HMLMl8X7/AThG0t1EwyFvQnSiaOUkSfcQjcW9dZtt3gZcbmbLzWwZ0aBc+8SvPWpmd8fP7wAmxTPwjDWzP8bru5WYTiSa2GGlmV3UZTvHyQyvsTuJkPQ6YA3R6H47Ntab2RmSfg28E7hF0gFt3n4q8BTRbEfrAC82vbay6fkaor9J0X6IZgEnmtlVXeLcj2g0xb3MbIWkG4DWaejanaA6xbP+ENu3shXRuPabS1onPmE5Tq54xu4MiaRNgR8AZ5qZtbz2ejO7z8y+SlQa2QFYSjQIU4MNiTLwl4kGSRs2RJMPAltK2j1uY6yimYeuAv4lHhoZSdtp7Uk2NgQWx1LfgWiKwlZuBA6PR1scDbwbuKlTMGa2GFgqqbGvttPaxTGeS3TheA5w2hCf03EywTN2pxPrxyWPdYnqzhcA7YYiPkXS3xNltw8QzVbzMrA6LoecB/xf4DJJ7wWuJ5ocoyNm9pKk9wHfiy9e/pUoCz+bqFRzZ3yRdRFrT9X2W+Cj8ciPfyIqx7Tu/05J5wG3xavONrO7FM161YnjiMpJy4EbiK4NtPIZ4CYzuyk+drdL+rWZlWWIXqci+OiOjpMASWPiejySTge2MLOTCw7LcdriGbvjJONdkj5N9D/zF+DYYsNxnM54xu44jlMx/OKp4zhOxXCxO47jVAwXu+M4TsVwsTuO41QMF7vjOE7F+P9Uw6j8fXS+KgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(x,y,z,10)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance along x')\n",
    "plt.ylabel('Distance along y')\n",
    "plt.title('A Hill and a Valley')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting with contour gives us a line contour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A Hill and a Valley, but without fill')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contour(x,y,z,10)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance along x')\n",
    "plt.ylabel('Distance along y')\n",
    "plt.title('A Hill and a Valley, but without fill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A clearer hill and valley with more contour intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'A Hill and a Valley, but clearer')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3YeWGeY5Sy0/XJ7X2qqsC1922Vb/NK4BGJO+J/FOjfpfd0yXiXyap6oaIPAk4D1gDXqmql4jI84CLVfUc4A+AfxSRp1F8Q05TVRWRlwKvAj5DYRO9SlU/1bVPOxb6sL3BP0iU34etEymTBPauwE+0c/wAb25LgX0M9F5o93GTlpnGabRjDwBQDAL2ANAG/namj8/rd9o9jgneeYJfEQ715BWp6rkUaZjmtmcZv18K3N9x3A0UaZu9akdDH0afvw8NYeuk3Hwzb+C7ovtc2CeBPgD5SUZGz3TdfCCK47iJNNp3XQXUMmcC8DdTPnOi/hD4i22LAf921Y6HfqXtFPWvSpRfK5/YZ1e9obROaA/81Og+F/YpoE+CuwfkKXVM16V+fJWx47kKSIF/NeFbRf1mmqcJfqAW9aeAv5IP/Ob+PjRF2K+7+qlsyTRC39B2Av+81FeU79uXE/279rv6NwTwW8E+Bvo2tk7KMROptRUbACr4m96/6fmbaZ81G8cDfmjaPSlWj8vjrzRG+ekaoW9pBL9DSxLlu8o74e+qz5UtUpX3AD82WZsK/BrsW4JeMid1bem6QUQL8LEBYLIxzYr6bfAX5bZ8fnAMEC3BvwpLMSybRug7tNPAP/jqhD1H+aH9IR8/lKWTAvxO0b05aERAHwO8TNMGAJ1sEdBVp65PkgcAO+oHN/xd4C/6rL2Df3ZuFvj7kKpwcAmT/vvQCH2PVnWCd563obdZNrnPbJ1Qm/MCfmvYR0CfAnYvyBPq0MlkdvzsmKpPM3unTKWs4G9M/Lrgb4Mf3D6/b4LXB/6ZLPDH/P1Rbo3Qj2i7R/19TuDmLqjWh60T9PE7AD/FzjFvpJqVdVg5PtjHQN/G0kk5Rtcns7ZM+Ff7fNH/dN3c1vT7Z3+fiN3jm+D1gb+2Xk/VBQf4+5SOE7k7W9sd/G2UtVZ+qSFsHXubK1PHLjME8PuCvRfaiZaOVw6rx4Q/RAaAEv41zz/g94fsnk7gD2T0jNF+mkboJ2oVwJ8dOS9JlO/rR+y2e9vHT8nUSQV+KB3TC3yXleOBfTCqjwA+9QqgPnlrHeMAvG8AmMG/FvX7LZ95gd/n7/chhd5uzlo2jdDP0Kr6/H2r7yi/Vi4xGydYn2XrdAW+a8LWB3xXdO+DfQPebeydDeuDWN8Cle/4AuLmjHfT37ftn9yoPxf8qfKBf8zcSdcI/RZahah/4ero5fdp6/SlLsBPhb0T1DbYQ/KVdQwGWxO41Yfojv5zo/424E+N9kPqE/zFzVm7+6lsyTSOjS21U1bqTFGXS+poZk5ulF+q7yg/GfhT9QJfNqZNG8e3b2Nz62WrOi72MmXWV9YZ6o+r/+a2kJVVfTZi/w2MVTsh42/RsPGq9qiVG5WmMdLvoG0d8WfekFVTRl6+S12j/MYzbecJ/KpNB/BnCkX2JuRTJ283yhNZt77OvuMnk6121tfSIv+y6iS7JyPi71u9LcOgwv7pmL0zyqFV9fnbRuddo6rcRdUax2e032oNoo7Ad/n3W9FxB9hvbDS3pZbxDQYe+IPl+xugN+2eWn5/C/ADrW2elEndUW4NZu+IyCtF5GoR+Yyx7dYicr6IfKH8ecRQ7c9bo93Tv1wZO/Y+X5Tf1tYp9g0HfK+NU5WpQLuxUX/Z2pyGX6Z8ddXaq1tJtX5a5+K0qwJXPT7l2jwx9WnzVEsrx16rqCE9/VdTPgLM0JnABap6HHBB+X7baCeCP/aQlKQ19T3Rf7StlkpKz2wJfJlOi1cAmkAc9pVSoW4rdFwm/F3nEPL5zc8m198PKebtL6tE5GQR+ZyIXC4iTuaJyMNF5FIRuURE3mBsv5OIvFdELiv336Vrfwazd1T1g44OngI8qPz9bOBC4OlD9WERWpTP3+vyC5lPx0pVKtztsr59baN8U6H0zFmZDOCb7512js/GsUHvk8/CSbV2qrrXJs25gIDtM7N7LKsHHD6/afVAlr/f2uYptWyTuiKyBrwUOIniebkXicg55YNTqjLHAc8A7q+q14nIUUYVrwGer6rni8jN6WGIm7enfztVvQpAVa+yTm7baFtP8A6o0ARu1zmI0F23KcsrtAV+J9jHfPzQRG2ojvX1PPjb4Gfrc3T6/DXw09nfT1Lp7felHpdhOB64XFWvABCRN1EEv5caZX4TeKmqXgegqleXZe8BrKvq+eX2G/ro0NKmbIrIGSJysYhcvHnD9xbdnWzpms7V7snJTx5sVc1Ea6fNBK5dPjXKd9fhtxRCyyvMDfguHz+WkplT1qw/ZvsY/XdlIAWzlGZl3amcpsJ/k8QUzsXoyIpT5esMa/8dgSuN9/vKbabuBtxNRP5dRD4iIicb278tIu8QkU+IyIvKK4dOmnek/w0RObqM8o8GrvYVVNWzgLMADrvTsStrli9j1K9r/YB/qEya0ARuG7lWz2zui9s67rr9lk5Dpm9vyga+Xd4jPdQ8Idnl+Upbd+DO2qqi+81pEfXb20tLpxHxx+TJ6Kn11RHtb+1bbLSf8Yzca1R1b2C/68tv/0OtA8dRWN/HAB8SkXuW2x8A3Af4KvBm4DTgFSkd82nekf45wKnl76cC755z+wvRTpzg7VNt8/xD/m5KlF8rH8jUcfbBjvJdwLcnZSPA10MbtZdLKWWyrzZ8EX9OtF8qJdp3KTeTZ4m0DzjWeH8M8HVHmXer6iFV/RLwOYpBYB/wCVW9QlU3gHcBP9a1Q0OmbL4R+DDwgyKyT0SeALwAOElEvkAxsfGCodpfNq06+If+suVk+dh9cT3+sHGsw8vf2pcX5WfbOj7gV7IBay58FgN4KK2TwCAQAn9oIrlUSlZPShqn628WmnifHWdZPFtthvudqqkKB6a7oq8EXQQcJyJ3FZHdwCMpgl9T7wJ+BkBEjqSwda4ojz1CRG5blnsw9bmAVhoye+dRnl0nDNXmsmtVb+QaSp3tm4TjU6LH2raEKL9WPpSW6VNCdO+FvH1MaLuduWPUK7vWrQlbazK3snsqq8eyeWKyJ3VNVZO69fL+O3VTLB7XmvvLIFXdEJEnAecBa8ArVfUSEXkecLGqnlPu+58icimwCfyRql4LICJ/CFwgIgJ8DPjHrn0a78hdgIby+XXSc+pmB6VM4jr3d+x/V2sH8qJ8r3xRfhvgp9yN6+yDfxDQQxtb/n8F9OqYRPCHUjnNRzUCTm/fzuSplXfcYet6DONQUvpbWllVzwXOtbY9y/hdgd8vX/ax5wP36qUjpZY2e2e7a9XtnqGVm6qZa+04l03uK8rvC/gh22ZzM/hyyqov2e4xtyX6+/V9AfvMkMtma2XxjApqjPQXqGXM7GmtwBgW+1K28vMT0vVCN2NF+5ca5ecCPxX2DnlhHikra1bEakTzSRF/C+VG+/Vj/RZPaKG2Pi0eZfsuuDZG+gvWvPP5t7NC1k72BC6e1TNzvPtKLYEfi97tSVpvRo+rHiPqrx3rivjnEO3byzNAvkU3Kk0j9JdEfYF/VZ4eNK9L8lBufm2b4+7bej2um44ysnUcx0EY+D6lpmx6jzfrdvWxC/gNueY9Ji4LJ3JzVYrFMypdo72zROrL7lmmCd15aKecazCjx1Pee7NWrLxp9eTIcVzyDV3mMZlZPH0/LrFI2dyeeFyRuHDnaF5Wz4quCtu7ogNGatTvk8vaKZUT5ecC3zyu7bGDKmHp5TZa8JIMK6ER+kuoPsC/KjaPqT4j9tRJ3Nr+HJhX7XSwdlIVg3bKBK9dh8/icWb0uLKPIgqtweM9JrH6kN3TlxRhY7oWfa2iVhANO0MLneCdNNuN/n9vkySkkKK5+T3IBngq8IOpmol1Jcvj67dRbKAdJ2z71/Y0rbaRuvj8MW+/r4XXFqUul/KuzJ1a3ZH9XZWUotkiwtfNzWaKplVn5dvXynZM0QS3d+9K3VwFKaOnP2qB2ukpnb4cfZfaRIbR7JFA5k5QvoyXHhTM7snI53ceP/AcQGxAjQ3Is3pWOGBZpLbnULYNta1u5EpUb49HzPGA2/jFqX6+81j3BG4o5z5FoYg/K6tntvTCxtbDV9YisWKLDB7XTVq1/YGbsoaQKhxaxYmxBG3Ps9qmauPzx/5vlzGLZ5lTMLMydzxqnYmTGcEn+fwpE7ou9eHr9zQhu8oW5SI0RvorqNyov1Xe/kQbz8qdrtUvqVPnBFzth7bNflbPRC3f2+3vNMnaWjb4Q/5+8LjUK4Eqok9YebN5bE9LJgwQuCjCwdHTH7VM6tPnH+RLM+QVRPlf61pnxZVlFLMFarZCrKzLoqiANwNgGBY1oBplUwCdA3FfWbP9WplQv6t9a2nnaCt2c1bI2hnVr7bnULZDlBPxt8rkcUT7QQnBhde87SxAui7JE4a6PmnaOpNJfGJ2bVJ44OvrhR2Sckwp2bXutVhiEX/b6N6pNpk3A2TrzNPPh+LfeFXz8GMaI/0VV47P38e8lP096COi71qH67xckMj5Dk/Xh4VMzvIIzuPX1pxwjwE/Jcrv2jdTrnTN2GdrPy/Xp1VhsoicLCKfE5HLReTMQLlfFREVkb3W9juJyA3lA1U6a4R+G0nmaw7q5S7enr9EyzhJHJL9NCdbLoDlrinjlMfikV3rUQDXymcAv1eV9labzyL2ma8K2H0SkTXgpcBDgHsAjxKRezjK3QJ4MvBRRzV/A7ynrz6N0PepT4jPaVBIAX92tJ97d67nfNzReGZfInINMrHH7EX7kGorpPr6LayPFPB3Ar4vyrfPxfbzA+fiHAD6mridg9WjKhycrkVfCToeuLx8uPlB4E3AKY5yfwq8ENhvbhSRh1E8L/eSbme0pRH6sLDovNF+DxoE/HNU1bfZz7X6+9n3rMM5RKHRZjLXp8DEZ86Ebpco3XVsr75/T1rm/8uAjhSRi43XGdb+OwJXGu/3ldtmEpH7AMeq6j9b228GPB14bp8d3rkTucuYLGD2qYNbk/IAdt/EbpsJ3dzJWVf5Pid4XamdOpHgTVq6JsE7c1tP5vao0OSur/zclWqBxQZWx/7QVdtWUNDXk7NgY5o0Cl2jqnsD+10dmv2jiciEwr45zVHuucDfqOoNxXPR+9HOgf4yQj6kHgaAIe/iTc2ZHzpbZ7omrdfgiWXwTNclaQ0e54AQkjFY1EBeZfngz9CZrZsTgX8I+EkTuKk2VAc/v9I2T9fcBxxrvD8G+Lrx/hbAPYELS7DfHjhHRH4RuC/wqyLyQuBWwFRE9qvqS7p0aHtDf7v8L9nnkcG4EPir6Khxk1TXaN+Tupl6k1YbuesuontdZ/YELfdVwNax0/VJc+XHiczuHtXJpFhEzAX69bXiztQK6hXE7dRNSAY/uO/EbRPFNyydFOB38PNrE99l5G5m7rgmcV2ZO6FMrKGSBSpPvwddBBwnIncFvgY8Enj0Vjv6HeDI6r2IXAj8oapeDDzA2P4c4IauwIft5ukv0pefpzLPL+bzOydZXf/v1qRu6oSuc4I1sK2Lr1+Vjd2kVf1uQsZ1k5YJKTfMJrV93gldl7dv1OPz92FrojZlwjb5uD6Ab0f5xrFqfy6mXNaNo1jujXZ9WTt9SlU3gCcB5wGXAW9R1UtE5HllND93bY9If/n+1vNRdd4JkX/M6nFGypkRf4qVM0S0X1k8oWi/Xj4x2jci/Op3M8Kf/V5G7bP3KRE/bN2wBTCd1q0bs4wlE+DmVUDSgJADe8gGfgjyrvx8c6CtBmD34Ow5H4aZAJ4iHNzs5zJCVc8FzrW2PctT9kGe7c/ppTOscqS/EyL6VCV+DrEbuZIjfkMLj/YdCqVuuoBSP9ZhJTgth8hXx474K6154BqK+s2XpegVgOf4TsCPyBXlzz7D2QBgXhU06wj9fYe2dra7Vi/SX1LId705qpcJ18TIv/ME7zJF+xNg6p7QrSL6WLRfTej2Gu3bMv38KuK3t/t8frueSq4lnBPWxMkCvksJUf7WvtA8UJp/7xqw56HNFc0hjSl6ViLy0DKtaLFacFRfRcm+1xD1t1bCVZCv/jb+fpubtYJ1d5B9ZVDb5+BhdrRfu1KwYJfq70NyxB+8K9eO4gPAd9aVAvxMWye47EIgyk+dwG2WKduo/maLJ9XSK+UjeiTwBRF5oYjcvY9GReRpInKJiHxGRN4oInv6qLdv9Qn1ruP+WxwAACAASURBVO237seQ4Ddkgt9XNlaHz8rpcqNW6mSgCzrTQLRqQ64X8JuAtR9CYkA7JXMnWN6s3x4scoDvUdcofxmsHVXh0OZa9LWKikJfVR8D3Af4IvAqEfmwiJxRrhWRLRG5I8UaE3tV9Z7AGsXAsnD1Hb0PoVZ9i4DfVV8S+B1LNDjLOtofZPJtrZoIdLXXnBCcAaQWvZc/E6P9JMXAH4r6PT66DfWkQcGuL6UfIR+/Y5RfKbTA2qKsne2spK+eql4PvJ1i3YijgV8CPi4iv9ey3XXgJiKyDtyU+s0Kc9MqQN6n7H5HbZaW4DeUktacOqFr96HLhO7Co31TORG2eYz5SlXouJQrjlr5NFsnJ8oPTeCO1s5wSvH0f0FE3gn8K7ALOF5VHwLcG8he6lNVvwb8JfBV4CrgO6r6Xke7Z1TrWWze8L3cZvztryjkQ0o+pxY+fxT8gdz9nGi/YenkXjlbAGgb7eu6NKJ9E/x25Jpl85gRvwv+ZrQd8uptmPteplz1udqz26j6Hjg3H/BdUb4N/JQ0Tdv2mUfWjgKb00n0tYpK6fWvUaz/cC9VfZGqXg2gqjcCj89tUESOoFhl7q7AHYCbichj7HKqepaq7lXVvWs3v1luM/W65gn6SU+vlkq6egnAfx7gN7eFVt9s6+2bN+nY0b4JlAZMAjZPLvh1fQu+s/dQf6xgCvwruSZtnRF5Qjkf7O0Boxyoav3vAHxbNvBDV2Q28H1R/qovxTwPRWeEVPWxgX0XtGjzROBLqvpNABF5B/BTwOta1OXVoICfxwAfaiMjtTG4+JonxdN1TPTmrcw0TnOb/UxcZx1VGats6Pm5dtnQQmyhFM5qMTZziYbZujxWGmcFwdpSDa6bt2DrYeLGDVq1m7JcaZOb1h8hlqLpS730ZAwV+4ybv0JpmZnAd9k6voE3tiT20FJNXnBt5bSIs/oqcD8RuakUKwydQHF7cmcNEtH3GIX3phb9CX4mnsjfPkYn7sh8q18JaZyJN2z1bfPUry7c9kF9n328ATAP3MyIOGT3OC2fqowd+fui9NSXrVCWkNGfWHTfFfihFM2YrTNG+d009/FUVT8qIm8DPg5sAJ8AzupUZ99R/TKAPUdmf4PPwY0suSw4o367vBn1N6JzI+KvRc9mOVc7risJT1QfjfYnJN+wVS3GphNhilr7IhH/VOsRPzSi/kbED7OoH4hH/uCP5l03Z4XKV0qJ7K2yrgydrsBPSdG0Nb/7pYTNnOdDr5AWchGlqs8Gnt25nr5gP6d/pNT+drtb1nrvXDM/YvnMA/w0t/Vh8wSPsWye0J26MfAX5yl18BvbvOCHfPjPOpdp7VTyZfwkwN7c51xErSfg507ejlF+e0X/a0Tk0zQwwHeAi4E/U9Vrh+hYSL3AvmfQ93m1Easra1CozjMH/g6vvy/w19pQz7E0YZ66Lr8v2g/5+2a0b/r7IfBPqS/TUEEv5PNX37Zk+MPWAGCWb5z01L/PpUzYQ7/An3U7E/gpD0rp60pAFTZW9OarmFJChfcAm8AbyvfVjVTXA68GfqH/brm1TLBfZMqn3XbSINAW/ppQbrY/Yd0dj82TFLF3tHlS1tyPgd9UbX0eaNo9JfiBRtRfbQP88If6AGAqNhj4jqvOOxP2te3GHEjOpO1WnT3ZJlaUv02XyuldKdC/v6re33j/aRH5d1W9vyvVchBJR8h2/GdY9pz+rEEgAv8Y+O1yNkh9GT1t/P3QQOADf4p8No8P/O6+NTN6GnaP+fAVT3YPhOFvqraQWwTqjXMO3SjmKdMW+O72u9k6i7gRa7pNPf2Uj+7mInLf6o2IHA/cvHyb/sDORWhC63+OvjOBZE07vdr23dt/z2fjPMaR3WOWsbN6fDn8bfL3c7N5Yrn7dj0p8NGJFK91md28pZMC/LomTNcnjTt3p+tlPv9EvNk9dl5/LVvGcYOVWS73VZNRt12m6lfzngMJnpsZ4eua8RmVn5n9mcY+c/NvFLJ1ViHKF5GTReRzInK5iJzp2P/7InKpiHxKRC4QkTuX23+0XPbmknLfI/roT0qkfzrwShG5OcXX83rg9PJJ7X/eRyd6VQfId1EumPusXyP2TtCWMT+vWsTuOMby+u0rA6/HnxHx5/j7XW2e3Ig/JasH2Ir8A9k9Mxlhk1gRfi3jxyd7cjd1zXuPhWPvK+q0vPvaNqOejCydXODX+0O9rC/g6CBV6eWOWxFZA14KnETxvNyLROQcVb3UKPYJirXIbhSR3wZeCDwCuBF4rKp+QUTuAHxMRM5T1W936VPKzVkXAT8iIrcExGrwLV0a71WZf5+2kB8a7m3k6pNrIDDPOTgAWPAPWT724GCDv9hHK/C7BpFlBn9xrtrw+qdVWrwFf9gCbG3Cl+YAUPv4TZsnAPnoapgeH3+rbr+VU2yr+/dFnVV9/QLfl60zBPB71vHA5ap6BYCIvIliRYIZ9FX1/Ub5jwCPKbd/3ijzdRG5GrgtMCz0ReQw4FeAuwDr5RPbUdXndWm4Nw0M+94gH1iRMlsJXqPZ7+wBoASkXbYR9Xuye7w+f0fwz+pbQvBDHf5TjKjfl+FTyYr+7QFgtn269eCWXPme9OWL6mddC8Ae8oBfuwmuB+DXzmNxwD9SRC423p+lquZ9R3cErjTe7wPui19PoEieqam01XdTrHbcSSn2zrspUjQ/Bhzo2mBvGhD2rUHfJ9hz2wkMBKkDQA3qnqi/Vi5g95ggLPZlgJ/6Ntdk7SLAX3wcWn485cnP0jCb8A9ZPrOPOWEAmH0eOWmZASWD3tofg31Rd3fg1/oSAL77butmHW01TUuNvkZV9wb2uypxgqJMjNkLPNDafjTwWuBUVW3zfLmaUqB/jKqe3LWhXpXxhx0U9vOCfIp8fbEGg9AAkAN/X9Sf5PPHwF+rbznAD/GovyiTZvkUJ583ALhkP5ox+SogEfTFvvawN7cV5XCWc4E7lKkTWqhvybQPONZ4fwyOpeRF5ETgmcADVfWAsf1w4P8Cf6KqH+mjQynQ/w8R+RFV/XQfDXZS4h91END3Afg5Wzy1Nh3lfQOAF/6xqD/R5+8T/Fv9GQ78QNTu2SoTt3yA/AHAljUn4FQkHz4E+q0yft++aN/cHvfvXeWCwDf7MkcfXxW0nwXXLgKOE5G7Al+juM/p0WYBEbkP8A/AydUqxuX23cA7gdeo6lv76AykQf9/AKeJyJco7B0BVFXv1VcnkpTw+Scvc5AT0eeCepEWD/gHA7N8YACw4Z8V9Sf6/G3AP6vTA3NnmR7AD3Gfv1mmHvWDO/KffayRAWCrnPFhZN7g5KrPV4+db+9cHdMBexgA+Ik+/pJG+ajqhog8CTiP4imBr1TVS0TkecDFqnoO8CKKNPi3lnOmX1XVXwQeDvw0cBsROa2s8jRV/WSXPqVA/yFdGpiXUoA/SFS/TBYPNPvjGgQCA4CsaVrUDzP4N8APTrunC/iTbB3XFUAH8BfnHff5Q1E/hOEPRAeA2a+BxwpmKQP0s/cZwA/ZObVyLYAf8/H7g7+kevpRqeq5wLnWtmcZv5/oOe519LzkPKSlbH5FRO4NPKDc9CFV/X99d6SteoN9CrxbAF7mNChoSoQPzUHAMQC4bJ+Y5ROze6LgL9vvC/w1yLYA/+xYj93Denm+rkneTPhXil4B+FQNDBnRv/OhJgHQQ3/RfdFWsx2Xh78Y4G9vpaRsPgX4TeAd5abXichZqvp3g/Ysol5g3yPo5wX33PYbg0HI5nH4/7bt04B7YtQfBX/VfgL4i/bD4G+UCYC/fhpFH11Rf+UihSZ5i3Lp8J99ztYgYA8ALtWWf7AUOxaakId2oLf3haL7ot1mW0sHfA0EUiuuFHvnCcB9VfV7ACLyF8CHgYVAP7oCZR9RfQLAcyE/GfimLt+lqN3P2j+y7yqgLfwjUb+5vQv4zW3BCD8wuVv7jDpG/W3hP2vfGgRcVwGzfcbzAVLAXnxWfng5s2Cs/uXC3twei+6LMnXgN9sIH78CN2gtlVKgLxSrbFaqvn5z1eCRfQTiqZAfGu657dqDgXke3quATPgnR/2W3WMDFeo+f22StOd0TtfxLvAX7cej/uI80uFflYc6ZF1XAdVnVPS5+1cvBfRFubCFs3Vs+BgbxLn+vV3H3IC/gyP9VwEfFZF3lu8fBrxiuC45JB2jex+wewB9LuRl0vneCqd86WV2/8xBwDsAJMI/O+rP9fmtCd5U8BfthcEPCT4/JEX9kAd/u/zsM7WuAsylnPu2L2KQr9QF9pAQ3cNyAn8bK2Ui969F5EKK1E0BHqeqnxi6YylaBOxTID8U2Nu0aQ8GZv+jA4AP/uW2VlF/js8fA/+snjq4zTpCKZ5Rnz8x6s+Fv5nqWcl1FeACs0u1wSEjwycGeVc5+4Hldh2hzJxifxP2drsj8IeVF/oicmvj7ZfL12yfqn5ruG7FFQR+C9h3AX0u5Pu2gEKpZWbfcgcAL/yrbVa2j25KNOrP9vlD4K/Vkwd+aPr89r6cqL84hzz4zz5vaxBwXQXYMh/zGAJ9rB7fYwZDUb1rf9vovnFsR+D3dkWk7Eh752MUp27EVMDWxfX3Ddgvr5YF9jHQz9PbD7VVh7l1y74xCFR1ZMHfem9bPqngN7f3BX7wWDuRzB57X2rUX/W3Oj4GfygGAGhG+D4Qmw90afv0qVTIz7Z7LBxXXX3BPlTXXIC/zeWFvqredZ4dSZEX+C0maH2wbwv6XMiv9WgBhdb99kXz4L4KcJVPgn8s6jei5ZjP74K2K5e/NvlrgR8i1k6iz19tS4n6zeOrfrngX5TTRvTsugqoyoIf2G2UAnlf2VTYF2XbR/eNfXMGfuw5FauqlIlcROQI4DhgT7VNVT84VKecfcgFfk+w7wL6PsHepZ1NB9DBfxVgDwBJ8O8S9Ud8/ja5/CHwQ5rdA+lRf7E9bvtUx7sncY33xsNVenumrCUX4H1thkBflK+/T4G9fVxo8EguN/r7UaXcnHU68BSK1eE+CdyPIk//wcN2LaIeYJ8L+hDkUwE/mcNNXPazPe2+xQaB6vyz4O8YCIJRf6bPn5vS6cvicW1rDBCeKwKw4D/74Moflu1Tla/1neYAULQdvgpwSSIPK02pw27b1rxh39iXaPu46ukklfrd59tIKf8WTwF+AviIqv6MiPwQ8NxhuxWRC5wDwb4N6HPAvr62GS8U0MZm8z/d1b45EMQGgRj8qzI1+Ecsn0bUn2n3DJ3ZU7Tvt3vM/dX2huVTnk8M/pXsK4DiGD+EzclbSIe6LV/9Xr8/wUrxwd6uNwviiwL+NlfKv81+Vd0vIojIYar6WRH5wcF75lJGdJ9q4+RE9aFoPgb6rnBvW281KNj98w0Cm9OJA+5u68cZ+XssHzvqz7V7cnz+3Ane4LYA/BuWT3U+4IV/VVdxvsY2x1VArV76t3lCcwQuiAajesiCvd1GanTfKDsk7OefeT0XpUB/n4jcCngXcL6IXIfjIQCDKzG6nyfsfaBPAfzaADbPpiPFzO5LbBCozjUl+nfZPl6/34r6Q5O8Lrsnx+f3TfBW22J2j7kN8iwfcE/4Ftt1dg6mfFcBkDZ5O8ko61IInFHQQ2+wd77PjO7HDJ64Um7O+qXy1+eIyPuBWwL/MmivbLkCnETgp8A+FfShaD4E+raAXw9cWWw4MnZ87ZiDgdlP0xqqzq0r/L1+vxX1hyZ5+wB/UXcd/Oa21Ki/1l6C5QOOCV9oTPoW+5uDgOsqwCVzCekU2KdExD5gpoDe1Y+siD2jbMr+zlJ2tKc/k6p+oI9GyyuHlwP3pPg6Pl5VP5xcwcQGeZpvbwO/K+xdoI8BPgTyHKXUUw0Mdp+qQcB1FWCe63QqXuvHhL9rwjdo+SRM8rp8/s4TvFT1OICeGfXPfneUqfY54Q+Nid9Ktg1kqvGgmI62RgySbUHvqjsK8GUDfs8SkZOBF1M8ROXlqvoCa/9hwGuAHweuBR6hql8u9z2DYtHLTeDJqnpe1/60nArqrBcD/6Kqv1o+EuymSUf1FN13gX0O6FPA3Edapy9P324/dRCwLSBX9F8HfHPC12v5pET9Pp+/6wQvBO0es56UqD6UoZNk/VQKDAKVhgKbE+61TriOaW5rY710hb2rTK/qwYEVkTXgpcBJFM/LvUhEzlHVS41iTwCuU9UfEJFHAn8BPEJE7kHxeMUfBu4AvE9E7qaqnSYI5w798kG/Pw2cBqCqB4GD0QNbRPcpVo4N3S6gj0F+qLz91Dx9s3+mPWSey+ZUeoF/MOq3BgHnJK/L5+84wQt5dg+4B4TGdk+GTsj6AcfkbyXPIOCSOTDMDm+7EmcAoCmgh3awdx3XahBZzgye44HLVfUKABF5E3AKYEL/FOA55e9vA14ixXMTTwHeVD4o/UsicnlZX7or4lDqzVl3Bo5T1feJyE2AdVX9bss2vw/4JvCq8olcHwOeUq3Xb7R5BnAGwNptblXvTw/RfRvYp4I+Be67BsrmqXTI8OttiwbiVwEm/CFs/cTg34j6EyZ55+LzU9UTt3sg7uU33gesHwhk6vjg6/i3GgLwW3U3t/ki66Fg37bMHHWkiFxsvD9LVc8y3t8RuNJ4vw+4r1XHrEz5TN3vALcpt3/EOvaOXTuccnPWb1LA99bA91PcpPUy4IQObf4Y8Huq+lEReTFwJvC/zULlB3cWwGF3PWZGmxjwc6P7NrDPAX0M7mu2WdtSm9Y3wdXuoc01b46+fRVgwh/c1o8Z+W86rZ2pM+pPsXvm4vOD1+6BfPjP9gXgD01oRQeB2QYGUWwiOAf0rvJ9gnxuwE+fyL1GVfcG9rsqsaHjK5NybLZSIv3fpbik+CiAqn5BRI7q0OY+YJ+qfrR8/zYK6EdlA79LdJ8Le591Y0M0BPkcwO9OuBI4aEbzgbqrAcHum28QqM7VhH+xr279mLZPTtSfYvfMw+cHv90DidZOwMtvDAaOvHzzGLNs1deQGoNCRKkpnSGfPBX03m0dbJoVsXNs7QOONd4fQzPlvSqzT0TWKTIkv5V4bLZSoH9AVQ8WFhOUnWo92qjqf4nIlSLyg6r6OYorhkuDB0ke8GMTtTbwc2GfAvoQhFOAnqLUgcHuS8ogYMMf3NZPDvy9UX9Cdk/fPj+Es3sg09oJwN/53pOXb5at7XdaO81tuUqZCM0BvW97n7APle3zmX49XYRfBBwnIncFvkYxMftoq8w5wKkUXv2vAv+qqioi5wBvEJG/ppjIPQ74z64dSoH+B0Tkj4GbiMhJwO8A/9Sx3d8DXl9m7lwBPC7n4JCdkxPd59g4LvvGhqYL9CEw784N1RJ10KCB3X7KIOCDPzStHxv+IcsnOer32D19+vyQH/VDf/CvlDIIuI4bSrHouSvoc8ouCvZ9qvTonwScR5Gy+UpVvUREngdcrKrnUDyJ8LXlRO23KAYGynJvoQiKN4Df7Zq5A2nQP5MipejTwBOBcyly7FtLVT8JhHwwr/oAfhfYx0DfFvJd8/fNbBxXO9VAYPavsofMc9jUiRP+4Ld+KvjHon6X1++c5PXYPX37/EDSJO/sOMKWT6VQFk+1v1LqIFAr00OskGqNBK2ejl6/r2xuvUMAX7SfzxlAVc+l4Ka57VnG7/uBX/Mc+3zg+f30pFAK9G9CMTr9I1Dlnd4EuLHPjsSUY+eEMnNM4KfCPsW+sWHfF+B99Ry0ru1jd+/a9RycrgWvAmz4A17rZ22iUcunD7und5+/bCM4yQtJlk+1D9Kj/9lxCYOAqaG87NjVRHR/z9k+obLA0kb3y64U6F8AnAjcUL6/CfBe4KeG6lRT7YDfFfZQB34b0Ptg3NXaSTm+Ghhc+fnm8fZVgA/+4LZ+UiwfV9SfZfcM4fND3O6BhuVT7cu2djwWTs4g4FLIe+5qC/U1qRs6pk0bPuD3OiDuwMclVtqjqhXwUdUbRCTtDtqeFbJz2kT3Q8A+F/LrPfv6G6af74juXfn59gDggj+4rZ82lk8o6vfaPQPk84M76jftnlkZYyCp9qXAPWjtJGbzuI41NSTYa+V6Tu1sdUyAwyuSzbNwpUD/eyLyY6r6cQAR+XHgv4ftVlOpwE/17lOsHBN4sajehmlbyO+eRJ6M4dDB6daf0Vf/xnQtOgiYA4ANf6AR/YeyfXyWjwl+qE/yBu2eASZ4wR3128slp1g+Vb3gBrvP+7fLzfY5/oyLWGOmLehjxy497DU/JXZVlAL9pwJvFZEqP/Ro4BHDdakpsf7gucD32Tkh394H/BDs3fZOc1sO2HcFrt0PaWXVhOs7OF2v9aO6GrAjfBPaLviDFf2XXzYz6ge8lk+K3WOv35M6wZsKfiDb7oF0ywfS4G+WqxQaBGZlBgBRDjTb+Popxwb7MEb3vSplaeWLyqdl/SDFx/9ZVT00eM886gL8LtF9yMKp70uHfAjoKUo5/pBOau3bAwDUrwJC8Iem758b9ftSO2M+f1+ZPZBv90A86q/2zerAHdX7LB1feVuLgFyXXP7Y8csM+yEG2GVQ6oJrPwHcpSx/HxFBVV8zWK886hv4Q8E+B/KHtbBzYjpg2D1mm/YAAO5BoNjuhn+1b/faZjDqh+ZEb4rP70vrHCqzB+J2DyRE/eCFf9UGpHn6ZnlbPd0w5FSOfdTF9okeP0b2gypl7Z3XUqy580mKNZ2h+PeeK/T7AH7b6D5k4YRgnwP5PiZ0K9vG1caB6XpjAACiVwE+39814WtG/UBtojfV5w9N8HrX7ckFP+T5/BCP+iEL/pDv6dvHz1O95PXH6ogky8SO7+MO5Z2glEh/L3APVW299EKfigE/x7/Pie5TYW+D3gXgFMCnXgEcSJzENeuzBwCoXwWkRP/mNjPqD93cVaigQq7Pnwp+oJnSaYMfgjdyFWXLYp6oHzzpnVXdEIQ/hO0cF+DmYTfkRtKdIvpKywr7pSBe/0qB/meA2wNXDdyXqHKA38bOicHeBqEP9qmgTwX7nklzCmX/dFdSHQcck7j2MeYg4IO/y/d3R/1FnaH0zjY+fyr4wZHS6crlB++NXBCP+iFi+VT1QwP+s92Jk7qzMksQyfaV3gksL+y3uVKgfyRwqYj8J3Cg2qiqvzhYrxwaAvipVk4b2KdC3gX0FKUct3+6qxHhu7J4qjI58E+N+oHaRG9bn98FfqB1Lj+kTfAW2ynLlyfqifrNsrXyFvwr5Xr6Q/r5LuVaSX2APrWewYHf4zIMy6YU6D9n6E7EJLL1bUkFfqqdkxPdt4G9DXoXrHf1+N91yPjGmG3ZA0BIKfCvlBP1x8APfp+/75u4IM3nh/SoH+q53d4J37ItU0Pm6Tesox7mBbKuOnoCPYzRfR9KSdn8wDw6kqJFAT8X9jHQxyB/WIvo/0Bp97jqPqRrjQHA7Gc1L7A+2azZPzH421G/K7/fl9rpAj/g9PnnAn5w+vyQHvVDIvyrtmYFGn+y5IndFM0d8pAE+px65w17Yf5XVvNSSvbO/YC/A+4O7KZIzvueqh4+cN9qCt1pC+2BH7NzKtDlwj4F9ClwDw0QZlTvq+vAdFetDtcA4LoCiMHfB/7i2HrUnwv+SosEv73dBH+xzx/1gx/+zj9nZACojm0cNoD90GneIGOpmkFgP/F8eKNqSrF3XkKxvvNbKTJ5HkuxmP9C5ErLdK13MwTwc2GfAvoca2ePFMfuV39Ub+qQrtXaSx0ADlgevg1/H/jBb/fkgL+K9k2lgH+mAcEPHrunahca8HdF/RAZABL4tfCJ3QzIQ15/Fw77He7po6qXi8hauYD/q0TkPwbuV1Suh5S7Jm0r+YCfaudU4GsD+xTQV0BPUaysa1DIHQCKMk3419I9rSsEn93TBfxmVk+l3rJ6IBv8EMjpn3UwbvlUx1Zq1OEC6qID2UzIw4Cgh20R2YvIrYE3U9z8+mXg4ap6nVXmR4G/Bw6nuFfq+ar65nLf6ymC8UMUT9V6YmzFhBS378byCVefFJEXisjTgJtlnFdvSvHxQ1k6OcDfJVN2yZTDJhtB4O+ZHJrBcpdszkB62OTQ7GXuq/bvkUO1lymzbOrLlKtuu6yvf9U5Vedon7u5rYL/bquM73PebV19za7GynJbf8vyCq7cX1l7tQyuyncvt229N0BQ/W5sm+Xym0tyG9+C2napbzf3ae0YB9wm2oDSdM0PtqqO6J2qrlff6thO0rkYCn0uTjk+2yEk0/irB50JXKCqx1EsY+96XviNwGNV9YeBk4G/FZFblfteD/wQ8CMUy96fHmswJdL/DYqvxZOAp1E8qPeXE47rVSk+fhvgp9g5sejehO5hgYjfBfeYYqmZ+wMTuFBG8Ea7+7UZ4Vd9rkX/k636fZZPzO6x0zr7iPjNhd0qBSP+Sj1E/PY+l88PaZbPbLcrS8dzM5dXQ4A/U22spp0Y2Tt0CvCg8vezgQuBp5sFVPXzxu9fF5GrgdsC3y6fygVAmVZ/TKzBFOg/TFVfDOwHnltW/hTgxQnH9iJzlU3bx58X8GNWjg/2MdCn5unvkYOz3/fr7qTj97ssHM8A4IJ/yPJJBT/U7+TtE/zJa/XYlg/9gh8S4e94KEdoAJjnSpup6jqP0CoLZxGwT/f0jxSRi433Z6nqWRkt3U5VrwJQ1atE5KhQYRE5niKh5ovW9l0UAfpTYg2mQP9UmoA/zbFtcPmAXyuTCfwU/960crbK+mHfBvQm1GOKla0GBXuS1jcAuOBvRv0V/IO5/tVyEBGfH4YDf6XBwA9JPv9sm2+iF4IDQGyVjnlk8vQ1Sdwp1XI1IvtrVDX4vG8ReR/Fqga2npnTkIgcDbwWOFVVbfj9f8AHVfVDsXq80BeRRwGPBu4qIucYuw4Hrs3pbB/KydTJBX4oO8cGvsvK8UX2te0W6EPgbnOzVu2mLKvu/brbOwD44F/ZO66o37Z82tg9Q4A/dz1+yAQ/RCd4ISHqr/oA0egf0h7msfBMnlKd8+k7gn5ZPgdTqnqib5+IfENEji6jLLmZfgAAIABJREFU/KOBqz3lDgf+L/AnqvoRa9+zKeyeJ6b0JxTp/wfFejtHAn9lbP8u8KmUyoeUbevME/ix6D4V9kmefiBTp8rSCdVVQP1gWb5+BRCDf0rU3wb8QG9ZPSHwN+QBf6W24AeS4O/8E0Wif2g3CMxLvd00tYSwn9PNWedQuCkvKH++u9GPIpHmncBrVPWt1r7TgZ8FTnBE/055s3dU9SuqeiHFQ9E/VN6ZexXFRMGcp47itk5f6gJ8OwPGBP4eOTiDr51xY2fb+LJ6bKUcU88YOlgbdHyZR+b5VOdsX9WYGT6uz83O7DGVmtXTVkkZPWZ5MysnJasHGt+Axn6ad8JGM1oSs1KqbJdFLElgtt1LVN8xEycnS2hJ9QLgJBH5AnBS+R4R2SsiLy/LPBz4aeA0Eflk+frRct/LgNsBHy63PyvWYIqn/0HgASJyBEVK0cUUj0v89YwTG0R9R/mpwHfZOTYMi311myWWzVNpdwt752DN3qnXa3v29qSwHflXUX/I68+J+AGnzePK4zc1iL9f29m0eRpKjPid+/F7/RDw4T2Tvi7FwNvmqmDQwaQnn35o0IvOJ9JX1WuBExzbL6ZMv1TV1wGv8xyf+iCsmVLy9EVVb6RI0/w7Vf0l4B65DXWVL1tnGYFvR9S+yB4KwNsvWykRvaue3Ubk7roasftqn7PLsmoT8cfy+E11zeG38/dNzSL+SOTfiNp7jvi3ygbg1UMUDM3IPOXVu3o6F9gWkf3ClTJKiIj8JEVk/4SM45ZOfQHfBUMT+PY++xhwR/M5d+X6yps+v9nOwUC2TtXvKuq3I37zmJSI3yXb36/vC/v7bZW0VINZ3vX0rVl5siJ+aPr8sefeBiN/U4lXAQtVz5k3iwD9dl2GISXSfyrwDOCdqnqJiHwf8P5hu1VXlaffNco31RX4rjtYXZ69HZW7IvCud+Wa8tWZEvmbEf+eyaGoz1/7XAxbK8Xfj921a6pttO9S6h274I7avfscHLbL6CS84mVyFNtj5NyLzP703K8xsu9fqUsrf8B4fwXw5K4Ni8gaxfzA11T1oV3rq5Rq65j72gDfLF/sO+gsW8mM7H2ZPiHtscrtN74JKXfjVlcAscgftjJ9XFF/LOKH+A1cEF6nB4hm89hqtSJnrQK3v+/L6Gnsg0bE7yyDO7unfky5P+VfIwTYvq8I5jDILA3g5+TpL0KhPP2/VdWnisg/4VjqqYcnZz0FuIwi7z+q1CjfpVB6ZqW2wLdhb5aFfNjbcPfJV841GOTCv5rsHQr80JzYNZVi84RW5PQpavN4JlCHAD+kwx9aWg3LciUQ0dKAfocoFOm/tvz5l303KiLHAD8PPB/4/bb15Ng6KcsrdAV+LuxjgN8l8S/tIa3DxHVFEIP/wZmvf6gX8APejB5w+/tDZvMEb9qCqL8PLcAPUZ9/ti8C/+J4o/pt4DWvAuiX6X6IPuWFvqp+rPz5ARG5bfn7N3tq92+B/wXcwldARM4AzgDYfVRxMeBaI98n1zIL9j7X8gp9Aj+0HIML+CmQTznGHAiqdkLwt6P+LuAHvKmcRX/rN24BDZvHlM/myZXrpq3WaZwQBz9kRf0Qnuyt12E0sSJgWgXI7xR5vz1S6Dkicg3wWeDzIvLNlOT/kETkocDV1aDik6qepap7VXXvrlveNFqva7nkSjm2jkuhDB1bLuA30yTNKxCdvVzaIxp8ufvbrLPeZtiKagx0CSmddt2u9YVcn79rkj00qbt1XPtJXedEr8cKSZ3YTdmfUi422dusa3mBmrvE8qj5KPTv9VTg/sBPqOptVPUI4L7A/cs19dvq/sAvisiXgTcBDxYR540HLrXx8k25bJ1KodRMl+xyPuBX2iObDeDbSoG6r7y7j3ng97fjBr/5u+uRja7lqVOyeUz1fadufVv6ANAsl9Koe3N0AMm8mDEBuyjILkUfengecFERxZVc7LWCCn1EjwUepapfqjaUmTuPKfe1kqo+Q1WPUdW7UDyG8V9V9TGx41K+8CkpmqZCUX6urZMC/K3tW1F4CuT3yMT5apZz15UDfl+075OrXJ/Rvkt9pnDWNFS03wH8bSFmA7gvCLvqXYZovstntdMU+ph2qeo19sbS19/lKD93haLDSrscJmlKlG8qxcdvtusH/tb2NMj7FCtjwr8+0Gxdcbhy8XNtHlOhaN+U6+/iAr29Lk9fig4EgWjftz6Pc3+lAPiHhH+9re6vZVL1uQwBewEmmxp9raJCH1do0fb0xd8DUtUL2+Toh73e5gSuy2LIifKj/QlEx6nAjwF8F5PZy6XQQJET9eeC31WmVj5w0xakLchmam4Wj10mBOaO4PeWt8uM0Swwfg5dFfro7i0i1zte36V4HuPcJCWoQtFeaALXVN9Rvh+Ofv9+T+33fNCb22ODgKvNVPD75FpqwjzG9XnGon17XR5TQ92lG53QTY32Ic3fh87gh50JvSGjeneDc3tG7twVWlp5TVUPd7xuoaoLt3dC1k5oAtdUapTfBvhbbTT9++L3JuxTYO5S6Jhc8JuK+fsuzz40cEJ+tG9q7hO64F2iIUVeiPcAftj+8J876HeIVu7jTLF2TB3msHlCefnm7ynPr40Bf6tcPbqflYlA/jBZD75suQYOc4Cxff6tPm15/OZ5xWwe1zHQLdo3NfSEbk76pq2UaL8t+NvAf9UBuWznIZvx1ypqCT7aNPVh7dT3+6P8+rZwlO+vPw34Pvmg3racTyngD6lLtO9TqsXTRYNF+z2BP3hcRMsEzphWqa9DSERuLSLni8gXyp9HBMoeLiJfE5GXOPadIyKfSWlzpT/qUNZOvdzqDMltIe47xhxYUqwel1LSOFMnvb1tZFo8y6K2YJ5pIPDPjl8yqC5bNO+TaLEMQ+zVg84ELlDV4ygeUnVmoOyfYix+OeuryC8DN6Q2uMQfezu54BFTLDJNPd7ljedE+V0i9j6Oz1XozmRwp2/W9+f/rbqqa85+2rHNTV3g3XlgqepZAGhXBfIL1CnA2eXvZwMPcxUSkR+neCzie63tN6dYv+zPUhvccX+GHJhXSrkRq14+/0saA/YuWau9QvXYdaVE+30pZgm1Af0QqZumkgeCUrkTuvEK40Vyff5gXRP/q+96V1ky1egLOFJELjZeZ2Q2cztVvQqg/HlUox8iE+CvgD9yHP+n5b4bUxtc0SdgpX3hY4BxRaJdbYo2SgG+a9sh9ff1MFnngKYDdpcoh1TYI5uzxdnMhdXM1ThtVQus+eveWn0zJteTtUKKrbzZmwLPrXUuomYtyBaVY3G25LZ61KqDegG6RlX3hgqIyPuA2zt2PTOxjd8BzlXVK0W2/vblw9F/QFWfJiJ3SaxrNaHvUsy3bxNhxuyL+PFxa6cN8M19qeDfxYRDPSwWUg0CMcVA71p501Ronf0c2cstm4quvJnxgPIU9QXsocE/ql+p6om+fSLyDRE5WlWvEpGjgasdxX4SeICI/A5wc2C3iNwAfAX48XIds3XgKBG5UFUfFOrPSkG/7wwOU7nPp61k+/ltrB1/3XHAxcDv0h6ZsF+n5e/Kfs0HiAvqMdDHrghWScFll1tXSlK0DyP4B5eCzGfK6RzgVOAF5c93N7qi+uvV7yJyGrBXVasJ378vt98F+OcY8GEbePp9pWuacvn+qX7+Vvn2UX7Mt3eV9ylnctdcmydVXSdzTc0rgyc5X79RKOMOXWg3oZvB8b48/lEL1QuAk0TkC8BJ5XtEZK+IvHyIBlcq0m+jmO2zCA8/pBzYu44LRf2mxZMS7ef4+qZSLSDYesLWTlI0Ss+M+MH9RK5RHaRUE7XDNqN6LXCCY/vFwOmO7a8GXu3Y/mXgniltrnykP7TmOSi0Bf681dYKq7SIVM3WykjdTI32h9AY9Y9K1c4KsVrokPGYweHb2lwJ8KdG8T7ttMjepSE8+dHn70/F0sqL7sUw2vaRfiz9LzWVMFdtJkchbM/Ejosda2bvVNZO8bu7r9VnU0E+xdoxy6dolQeA3vP1a5W3O2yM+EfFtPLQt9P6gEb6X3N/GF6uDBMbfNX7lEHDBKwJXl8efQrA7fI+5eTqVw9U358xEO7X3cH9B7ZJts4qqc8buXasNH5j1jw8/yG0UtDPzdHOUVfLogLloZYRvksp4G9zZZAS5bdR16umg46of2O6Uv+iWeozk6dV/aN2pLbNNypm47SxEWJRbPx4MX7Pi/ZnZQNQjwHfrLuPG7PqbecDvq8c/c3xttFkjeBvKQXZ0OhrFbWS3x6XpeNSDPQu68EFs6F8/1k/WoA/B/ht5PPzXVdELpjHPrOcpRZWXl2+ZT1ciI3gH2VqJaHfRV2izVRf/1Atwk/71qaA33zl1JUygdvGz+9LrjmY1IF92TQIYHsC/wj/PO24xyWuqly+cEyuqLTN4BADps/iqdQ1Oj+gG53ryJHL/nJdCQx9pbTKmjeIR/CPWmnop07yrZKV0BbcKcfEovx5qEuK5pAT+UuvHv9EI/h3tlYG+qHJu9S0TRM4zuje5VeX0WzM497a38ziSZnQbfQ1Ef6xcjkTuFt9TzvXWjuObbF0TdcA4BqgV9XqWWaN4I9IQTY1+lpFrdydMQc317yrbW5M1xpr7RyYrjdu+98/3TVbVO3AdBeHTQ7V7rytfjfLtdEhldkiZvtVZouw7dfpbBE2E8qup2nlRv0uyKdE+LnArwZD0wZzDQqz8kY5N9jz0jUPlVF/tVxyVXYzYSnk6XjXKjDewbtTtTKRvq0q+nOBwQ2QtOjUBa420b4Z8W9NksoMuvt1WoMxFMA2X6kKHZMD/FxVIE+N8l2Rfe4kbmq6ZvUAlWk5CNhr6dvyDgQ9rqe/jBoneN0SYLIxjb5WUSsBfS0B1dbiMWHjAs+BALwqsOWCv9hmRrx+u8eG/+wYzyCQMjjY9aYAPzfKr7eXF+Wbf4dqkA5ZO6vi569q5DyCf+doJaBvqwKAC/QVOGLRvh2lHojYFKb6Bn/xflp7OdtNgLzr+CGA74ryXZ9lpdQov1LKJH0seo/JfmrW0mvg8WQEvyFVmCa8VlBz/68XkWNF5P0icpmIXCIiT+laZ9D7jUzoNss3b0Kyo/0UtQG/qdgAkFLGtJPqNtNaFvB9cn1WjX2JA8DWYB23dg5ZUf+i/Pzen5y1BBrBP1+JyK1F5HwR+UL58whPuTuJyHtLbl5aPRNXCj1fRD5f7ntyrM1FhDobwB+o6t2B+wG/KyL3iB00m7hLsHhcNsEBh4UQivZn7VrwSrV5zDLF9rDPbwLalh3Fp4A+NbpPyaEPTd7aZUIZO6kTuFv72lk7tp+fI93mHn6KRp8fUJhsaPTVg84ELlDV44ALyvcuvQZ4UcnN49l6lu5pwLHAD5X73hRrcO7QV9WrVPXj5e/fBS4D7phbT8jimZUJACU32jeVC/6UqH9rf3wQyCkfs3Oq/ra1dUJXA1XZ3AncvhdZaz2J2yi4swaEHQ/++egU4Ozy97OBh9kFyqB4XVXPB1DVG1T1xnL3bwPPUy2iQFV1PVi9poWamuUlyn2Aj3atqwJF6oRupTbRvksuaIai/q3tUov8m/VK8OWSq84Q8Cu1WTbaLBeaEHdN4Nr7ktrypGrmKsnP32GQd2lHgz/N0z9SRC42XmdktnI7Vb0KioAYOMpR5m7At0XkHSLyCRF5kcjsaUvfDzyibPs9InJcrMGF5emLyM2BtwNPVdXrHfvPAM4A2H3U4UDxhd+1tsmmTliTaVLO/sHpeuOB29W+KofflY8/y9XXXeyRrTz+qux+3c0eOVjL76/KHtS12QPUq21mncX24m9mPoTcBv8uiX/hYnfT+mBf9a1SeDI6Pcq3B8hQlB/KzbetnViqZuXnbwSsoDE/v53GfP6grlHVvaECIvI+4PaOXc9MbGMdeABFgPxV4M0Uts4rgMOA/aq6V0R+GXhlWTZY2dwlIrsogP96VX2Hq4yqngWcBXCzux0dpN/B6Rq7J5tsTCesT6bG+62btQ7phF0ynYHeBX4mxTNx7Ru2UsAP1MpWEN1dbgNmdVRloZkjHxoEUuTKuU+Bvbk95OHbwDejfB/wU9I0U+66zYnyU/PzTXX1871gXM107prGB7C3l6qe6NsnIt8QkaNV9SoROZotr97UPuATqnpFecy7KOZDX1Hue3tZ7p3Aq2L9WUT2jlB09jJV/evU46ovrz2ha3v7oZu1bJvHBSOfVdEA4nRX8dLdTo/ftHtclk/l9TeBvOZ82Uop15xPSOtXsa0Z3YeAP2sjAnzTy7eBP68o37R2km/Kst5vx8ydFO0Yu0dBNqbRVw86Bzi1/P1U4N2OMhcBR4jIbcv3DwYuLX9/V/ke4IHA52MNLsLTvz/wG8CDReST5evn+m7EzuSxwe+Sz993wS6W1VOVj0HWrM9+1fqWAHff8Tn9MM/FlYvvA/4hXUsGfspKqDbw20T5o4bRjgH/fPQC4CQR+QJwUvkeEdkrIi8HUNVN4A+BC0Tk0xR3bPyjcfyvlNv/HDg91uDc7R1V/TcybzPR8n9sczphbTL1evsxm8f090M2zx7D2vFZPdBco8dl98AWKH22T6Wq3kptlyR2ee0uG8fVTop/nzJxa8sGfizKT1UoyndZO74ofzBrZ6DjFq3t7vMLikyH9+VU9VrgBMf2izEAXmbu3MtR7tvAz+e0uXILrtnyTepW4Hcpxd+PgR/w+vzFvnT4V/KlP9qDQay8qVzYQzvgd/HxY7ZO1yg/ewK3T2tnG/j5Pm138G9XrQz0K4jb0b6tKrq339vRflfww1ZmjmtVzlz420oZDGrnHYi2c2FvlnFl8+QCP2SpdVGXKH9UP9q2E7ylp78dtTLQd6mrzdMF/EAjsweYRXY58C/216P5EMRTFAK92a/Z+wT/HtoDf9mi/D6tnVFj1L9KWhHolxGdFe33qVzwQzOXv7athGEI/uAeAEz5rJ1KqevjbJV3w94sG7JzauUGBn5IuVF+siLWjksj7AptL/ArzMHTX4RWBPpN9TWpW0X7kAd+IOjzAzX4Aw3PH9yToLsyrZ1KoQlVn41jH5c6Ydtnpo5POQ9KiUX5SWmaCerTz88G5AokzWwv8G9PrQz0N6fC2kSdE7RdwA9AmdGTavUUZf12D9ThP9tnRP92xF0NAtA+a8eUc817R7S+VT4vui/2pwN/CFunDy+/Zu2MUX4v2hY+/+jpL4ds8Js2T1vwA1keP1CDfxXNheAPBKN/cEPalDkopJSflUuM6u19oei+2J9n6RTb2gHflMvWcd19O4SX3+cNWSsNxESNUf9yaiWgr8ZlbQj8szIZ4AeSJ3cBJ/xdlg/U4Q/h6N+n2KAQOx7CUb29Pxf25rY+gW9qLrZOiyg/qO0ZJGZrZcGvChvudb1WXSsBfSgiu3UrRTOUxpkK/qKeeFYP4IV/dV+zD/7VvtAAYKrm6SdAvVLY0w9n86RaOZBn5xTb8yduQz6+a7383MnbPqL8ucJsBfx8n1YW/NtUKwN92AJ/Fe1Dd/ADQZ8fA/Ih+Lv8/kqxAQDqWTr9ePr+FTAr2ctIhyZqIS26N8uZd9emAL8PHz8lym8Af85R/k4E4Lbw+beJVgr6pkzwb23LBz80b+AqtrmjfqBWLuT3VxF7bACAtCydamDoktETAj2kw744dj7AD/n4pnzA76qFR/nbSCsV9Y8pm8sh0+ZxZfSE7thNBT/4fX6IR/1gTfaSNgC4FEvfTLkqiIEe4laOvT3Hvze3pQDfVMzHT7F1Vj7KX2Frx6WVAv821MpAfzoVJpWlE/D3TdkZPeAGP5Dk8wPZ8IfmAFDUs9mAcaXq5i/Is3pCz6eNgb44Ph32MBzwQz6+qVxbp4126hLKQ2vpwa8KGxvxciuolYE+uMHv8/cB581b0AS/eWzI58+Ff7XPlaLpugqoFIJ3qnyDRQj0kAd7s3zIzoH+gJ+Tnmmrryi/7cNSxii/rqUH/zbVSkBfjSdIpYIf3HftAg2PH/BG/bbdU5RNgz8QHADAfRXg0y7ZzJ7kdWX/hEBv74/Bvtjuj+6hvYcPceDb6nPydvTyh9fSTvAqsDl6+guVmY+fAn4gKasHcMLfFfUXdebBv5J9BVCVS0nJdF0dQHo6Z+rDyNvCvtjuj+7BnYffB/CHytbJtnXGKL+TdmrULyK3pnjm7V2ALwMPV9XrHOVeSLFu/gQ4H3iKqqqIPAr4Y4r/lq8Dj1HVa0JtrtRas7VLegccNqdSh8bMKtgCjv24RajDyYxaXSDbmK7VnsZlPpHLfByj+TKPs+0T18vW7NGM1suUry5X+2aevV3OPA/z/Oy+hz6ng5trSwP8NvIBfzs/A3cZtFRP5Ko8/diru84ELlDV44ALyvc1ichPUTxx8F7APYGfAB4oIuvAi4GfUdV7AZ8CnhRrcGUi/UqxiL8o48/jB7x2D8Sj/q3t4cgfqC3kVsl1FVDVU8kF/ly52vDVba93nxLZF/vSonsI+/fF9mGAP7iPP/CxO007MOI/BXhQ+fvZwIXA060yCuwBdlMsObwL+Eb5uwA3E5FrgcOBy2MNrgz0p5vCpIwEuoAfcNo94Ia/7fUDSbYPNGFqWkCVzNz/IZQCeWiuhpkCe3OfD/awWsDv29ZprSUKeuetpfH50/L0jxSRi433Z6nqWRmt3E5VrwJQ1atE5Ci7gKp+WETeD1xFAfmXqOplACLy28Cnge8BXwB+N9bgykAf+gG/eWwFI9vrB6ITvRCHf6WcQcCnanColHM1kAJ5aA48Ls/etS8W3UPczoHlBf7C4bNDtSJR/zWqujdUQETeB9zeseuZKQ2IyA8AdweOKTedLyI/DXwY+G3gPsAVwN8BzwD+LFTfSkDfXHCtDfiB2U1cgNPuAbIsn0q+Cd+t/emDgK3KHoIw5FMfR5gL+mJ/N9hDO//ePG62L/GO2yjwMxSETkIgOE7gdtPCwK+KHuonT19VT/TtE5FviMjRZZR/NHC1o9gvAR9R1RvKY94D3A/477L+L5bb34JjTsDWykzk+qK60OTuRi3SdE/wmscf8kDMnui1I2DT227ub06gVhOk9stWNakae9kK1e/qT+55mRO1mzoJRvfzAH7nB58z3oS1zFqqCd7+dQ5wavn7qcC7HWW+SjlxKyK7gAcClwFfA+4hIrcty51Ubg9qJSL9SjqdIFVkH4j4gWy7B2hYPq6oH+qRP4Sjf7NM0W7zSqBSl6dM+eSbK7D7V5T1R/VQH/wgbuW46hwa+HOzdYaK8kc5tTQ+f/96AfAWEXkCBdx/DUBE9gK/paqnA28DHkzh3SvwL6r6T2W55wIfFJFDwFeA02INrhT0IQx+oLXdU9URs3yAmu0DdW8faAwAZplKvkEgpPUWx1RyQb6oy3Wl4LZwKqXA3q47tKTCCHyzQ+0O2ymam90zp2UYVPVa4ATH9ouB08vfN4Eneo5/GfCynDZXBPr2l7sOfiAa9Veg8UX9QCPDB9zwh7To36y7km8QsLV7stnYFoJ9rD5XX0LHh6J68Pv2rnZSonu7jibQw3n4I/B3jlZkgndptSLQb8LdBH+1v0vUD+nwB5Kj/0q+QaBqz1YKxGPyQd5Xdwz00A/soV//fpmAP2o+Gh78im6OT85aCplwr4DgsnsgL+qHPPhDPPoH/yAA4YGgT4UGkJh9A+5HGKbCHtKje+jZzvFt86gPiIxR/vy0jX3+QbUa0Le+EDbcY3YP+KN+yIO/WZcv+ofmFYCp0EDgksvqyTl+Vt7Rl0q5oIc82EN6dA/DAr9Vps5o6yythoj6VektZXPZtBDoi8jJFGtGrAEvV9UXxI7RqSDGk7JC4Hftj2X4QBr8q7oqmRk/4B4AZmUDAwFsDQa2cuAeAnslV98qheybSl1g76qzd//et40l9PFH9aLR50/X3KEvImvASylySvcBF4nIOap6aexYF/gh7POb+11RP+TDH5oDgMv+AWpP77JhuyZ1mqQAO1chwENaRA9uC2pI2Lv2F2W2GfDHKL839Qp+HT39PnU8cLmqXgEgIm+iWHQoCn1ogh/CPr+9H+pRP+TDH+LRfyUXVENXA0PK1ZdKqaAvynaDPWwf4HfSCPzetc1v5OpFi4D+HYErjff7gPvahUTkDOCM8u2Brzz2GZ+ZQ9/61JFAcF3rJdOq9RfGPs9Dq9ZfgB/sWsF3ue68903fcmRC0VX7bBYCfVdo1Riey5XqzgIQkYtjixotm1atz6vWXxj7PA+tWn+h6HPXOlT15D76soxaxNo7+4BjjffHUDzxZdSoUaNGDaxFQP8i4DgRuauI7AYeSbHo0KhRo0aNGlhzt3dUdUNEngScR5Gy+UpVvSRyWM5DCZZFq9bnVesvjH2eh1atv7CafZ6bRHWc7R41atSonaKVWU9/1KhRo0Z11wj9UaNGjdpBWmroi8jJIvI5EblcRKKPAVu0RORYEXm/iFwmIpeIyFMW3adUiciaiHxCRP550X2JSURuJSJvE5HPlp/1Ty66TzGJyNPK/4nPiMgbRWTPovtkS0ReKSJXi8hnjG23FpHz5f9v795j5CrLOI5/f7VVt+1SFKSWYrJgkEKIFqWJtoCLoIlAuPxhFERLIKkQbCk1UUAT/yMlGAJKhIQaSqSSIBcvEKWINjQC9rKUdrEKCVStqbSJFUpBCu3PP953dJjO5VTIvmeZ55OczNlz22dnZ955z3vmPI/0bH58X8kYW3WI+fr82tgo6X5JB5eMsW5q2+g3pWv4PHAccL6k48pG1dMbwDdsH0uqYXn5OIi54QoqlFqriZtI1YNmAR+j5nFLmgksAk60fTzpCwxfKhtVW8uB1u+nXwU8Yvto4BEq1GAdY8vZP+aHgeNtfxR4hlQsPGS1bfRpStdgew/QSNdQW7a32R7J87tIjdHMslH1JukI4ExgWelYepF0EHAK8CMA23ts/6tsVJVMBAYkTQQmU8N7U2w/CvyzZfE5wB15/g7g3DENqodJO6NtAAAFYElEQVR2MdteabuRIvMJ0r1AIatzo98uXUPtG9AGSUPACcAfykZSyY3ANxkfZUKOAnYAt+fhqGWSppQOqhvbfwe+R6qBug140fbKslFVNt32NkidGuCwwvEcqIuBX5UOok7q3OhXStdQR5KmAvcCi22/VDqebiSdBWy3vb50LBVNBD4O3GL7BGA39RtyeJM8Dn4OcCRwODBF0oVlo3rnk/Rt0pDritKx1EmdG/1xma5B0iRSg7/C9n2l46lgHnC2pC2kIbTPSLqzbEhdbQW22m6cQd1D+hCos9OB523vsP06cB8wt3BMVb0gaQZAftxeOJ5KJM0HzgK+7LgZ6U3q3OiPu3QNkkQaa95s+4bS8VRh+2rbR9geIj3Hv7Vd216o7X8Af5PUyKR4GhXTchf0V+CTkibn18hp1Pzic5NfAPPz/Hzg5wVjqSQXafoWcLbtV0rHUze1bfTzhZhGuobNwN0V0jWUNg/4Cqm3vCFPZ5QO6h1oIbBC0kZgNnBt4Xi6ymcl9wAjwCbS+652qQIk3QU8DhwjaaukS4ClwGclPUsqfNSzyt1Y6hDzzcAg8HB+D95aNMiaiTQMIYTQR2rb0w8hhPD2i0Y/hBD6SDT6IYTQR6LRDyGEPhKNfggh9JFo9ENbkvbmr7s9LekpSUskTcjrTpT0/S77Dkm6YOyirUbSFkmHlo4jhJLGvFxiGDdetT0bQNJhwE+AacB3ba8D1nXZdwi4IO8TQqiR6OmHnmxvBxYAX1cy3Mi7L+nTTTeiPSlpkHQDz8l52ZW5579a0kie5uZ9hyWtasqNvyLfsYqkOZIey2cZayQN5pz/10tam3Olf61dvJJ+Jml9PktZ0GGbJTm3/aikxXnZUM7Pf1ved6WkgaZ4Nkp6PMcw2uaY50n6TX6OZkh6RtIH3/p/IIS3ke2YYtpvAl5us2wnMB0YBh7Iy34JzMvzU0lnj/9dn5dPBt6b548G1uX5YeBFUl6lCaQ7K08C3g08B8zJ2x2Uj7sA+E5e9h7S2caRbeJ8f34cAEaBQ/LPW4BDgU+Q7oydkmN+mpQRdYiUoGt23v5u4MI8PwrMzfNLgdEOz9udpDvJHwDOL/1/jCmm1il6+uFAtMt8+nvgBkmLgIP9vzzmzSYBt0naBPyUVBSnYY3trbb3ARtIDe8xwDbbawFsv5SP+zngq5I2kFJWH0L6EGm1SNJTpFzqH2qzzUnA/bZ3236ZlADt5Lzuedsb8vx6YChXXhq0/Vhe3m3YaiGpaMdrtu/qsl0IRcSYfqhE0lHAXlKWxWMby20vlfQgcAbwhKTT2+x+JfACqcrVBODfTetea5rfS3pNivZptAUstP1QlziHSVktP2X7FUmrgNbShO0+vDrFM9Bj+1YzSXUJpkuakD/MQqiN6OmHniR9ALgVuNm2W9Z92PYm29eRhltmAbtICa8appF67vtICene1eNX/gk4XNKc/DsGlSpOPQRcltNXI+kj2r+AyjRgZ27wZ5HKVrZ6FDg3Z72cApwHrO4UjO2dwC5JjWO1LXWYY7yddBF7M7Ckx98ZwpiLnn7oZCAPo0wijXP/GGiXLnqxpFNJveI/kqoU7QPeyEMsy4EfAvdK+gLwO1Lhk45s75H0ReAH+ULqq6Te+zLS8M9IvuC7g/3L9/0auDRn4PwzaYin9fgjkpYDa/KiZbafVKp21sklpCGq3cAq0rWIVtcAq22vzs/dWkkP2h4vaZRDH4gsmyFUIGlqHv9H0lXADNtXFA4rhAMWPf0QqjlT0tWk98xfgIvKhhPC/yd6+iGE0EfiQm4IIfSRaPRDCKGPRKMfQgh9JBr9EELoI9HohxBCH/kPoI+g2XDHSjwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(x,y,z,100)\n",
    "plt.colorbar()\n",
    "plt.xlabel('Distance along x')\n",
    "plt.ylabel('Distance along y')\n",
    "plt.title('A Hill and a Valley, but clearer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we have plotted a scalar field.\n",
    "\n",
    "Next we shall plot a vector field, as a quiver plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "gx = np.zeros([24,24])\n",
    "gy = np.zeros([24,24])\n",
    "for i in range(1,24):\n",
    "    for j in range(1,24):\n",
    "        gx[i,j] = (z[i+1,j]-z[i-1,j])/2\n",
    "        gy[i,j] = (z[i,j+1]-z[i,j-1])/2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Various HSV color maps are feasible. E.g. hot, hsv, rgb, cool, and many more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Colormap hs is not recognized. Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-f8b281f684fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontourf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'hs'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquiver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mcontourf\u001b[0;34m(data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2528\u001b[0m     __ret = gca().contourf(\n\u001b[1;32m   2529\u001b[0m         \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2530\u001b[0;31m         **kwargs)\n\u001b[0m\u001b[1;32m   2531\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0msci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m__ret\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2532\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m__ret\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1599\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1601\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1603\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mcontourf\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   6420\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcontourf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6421\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'filled'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6422\u001b[0;31m         \u001b[0mcontours\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmcontour\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mQuadContourSet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6423\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6424\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcontours\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/contour.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, ax, levels, filled, linewidths, linestyles, alpha, origin, extent, cmap, colors, norm, vmin, vmax, extend, antialiased, *args, **kwargs)\u001b[0m\n\u001b[1;32m    897\u001b[0m             \u001b[0mkw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'norm'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    898\u001b[0m         \u001b[0;31m# sets self.cmap, norm if needed;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 899\u001b[0;31m         \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mScalarMappable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvmin\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    901\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvmin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/cm.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, norm, cmap)\u001b[0m\n\u001b[1;32m    216\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0;31m#: The Colormap instance of this ScalarMappable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 218\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_cmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    219\u001b[0m         \u001b[0;31m#: The last colorbar associated with this ScalarMappable. May be None.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolorbar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/cm.py\u001b[0m in \u001b[0;36mget_cmap\u001b[0;34m(name, lut)\u001b[0m\n\u001b[1;32m    181\u001b[0m         raise ValueError(\n\u001b[1;32m    182\u001b[0m             \u001b[0;34m\"Colormap %s is not recognized. Possible values are: %s\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 183\u001b[0;31m             % (name, ', '.join(sorted(cmap_d))))\n\u001b[0m\u001b[1;32m    184\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    185\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Colormap hs is not recognized. Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.contourf(z,150,cmap='hs')\n",
    "plt.colorbar()\n",
    "plt.quiver(gx,gy)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}