Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy.random import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(1, 10, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.47368421,  1.94736842,  2.42105263,  2.89473684,\n",
       "        3.36842105,  3.84210526,  4.31578947,  4.78947368,  5.26315789,\n",
       "        5.73684211,  6.21052632,  6.68421053,  7.15789474,  7.63157895,\n",
       "        8.10526316,  8.57894737,  9.05263158,  9.52631579, 10.        ])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10,  9,  7, 10, 15,  9, 48, 41, 23,  2, 40, 29, 40, 30,  4, 47, 41,\n",
       "        4, 14, 35])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.random.randint(1,50,20)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Annotation',\n",
       " 'Arrow',\n",
       " 'Artist',\n",
       " 'AutoLocator',\n",
       " 'Axes',\n",
       " 'Button',\n",
       " 'Circle',\n",
       " 'Figure',\n",
       " 'FigureCanvasBase',\n",
       " 'FixedFormatter',\n",
       " 'FixedLocator',\n",
       " 'FormatStrFormatter',\n",
       " 'Formatter',\n",
       " 'FuncFormatter',\n",
       " 'GridSpec',\n",
       " 'IndexLocator',\n",
       " 'Line2D',\n",
       " 'LinearLocator',\n",
       " 'Locator',\n",
       " 'LogFormatter',\n",
       " 'LogFormatterExponent',\n",
       " 'LogFormatterMathtext',\n",
       " 'LogLocator',\n",
       " 'MaxNLocator',\n",
       " 'MultipleLocator',\n",
       " 'Normalize',\n",
       " 'NullFormatter',\n",
       " 'NullLocator',\n",
       " 'Number',\n",
       " 'PolarAxes',\n",
       " 'Polygon',\n",
       " 'Rectangle',\n",
       " 'ScalarFormatter',\n",
       " 'Slider',\n",
       " 'Subplot',\n",
       " 'SubplotTool',\n",
       " 'Text',\n",
       " 'TickHelper',\n",
       " 'Widget',\n",
       " '_INSTALL_FIG_OBSERVER',\n",
       " '_IP_REGISTERED',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_auto_draw_if_interactive',\n",
       " '_autogen_docstring',\n",
       " '_backend_mod',\n",
       " '_get_running_interactive_framework',\n",
       " '_interactive_bk',\n",
       " '_log',\n",
       " '_pylab_helpers',\n",
       " '_setp',\n",
       " '_setup_pyplot_info_docstrings',\n",
       " '_show',\n",
       " '_string_to_bool',\n",
       " 'acorr',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'arrow',\n",
       " 'autoscale',\n",
       " 'autumn',\n",
       " 'axes',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bone',\n",
       " 'box',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clf',\n",
       " 'clim',\n",
       " 'close',\n",
       " 'cm',\n",
       " 'cohere',\n",
       " 'colorbar',\n",
       " 'colormaps',\n",
       " 'connect',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'cool',\n",
       " 'copper',\n",
       " 'csd',\n",
       " 'cycler',\n",
       " 'dedent',\n",
       " 'delaxes',\n",
       " 'deprecated',\n",
       " 'disconnect',\n",
       " 'docstring',\n",
       " 'draw',\n",
       " 'draw_all',\n",
       " 'draw_if_interactive',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'figaspect',\n",
       " 'figimage',\n",
       " 'figlegend',\n",
       " 'fignum_exists',\n",
       " 'figtext',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'flag',\n",
       " 'gca',\n",
       " 'gcf',\n",
       " 'gci',\n",
       " 'get',\n",
       " 'get_backend',\n",
       " 'get_cmap',\n",
       " 'get_current_fig_manager',\n",
       " 'get_figlabels',\n",
       " 'get_fignums',\n",
       " 'get_plot_commands',\n",
       " 'get_scale_docs',\n",
       " 'get_scale_names',\n",
       " 'getp',\n",
       " 'ginput',\n",
       " 'gray',\n",
       " 'grid',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hlines',\n",
       " 'hot',\n",
       " 'hsv',\n",
       " 'importlib',\n",
       " 'imread',\n",
       " 'imsave',\n",
       " 'imshow',\n",
       " 'inferno',\n",
       " 'inspect',\n",
       " 'install_repl_displayhook',\n",
       " 'interactive',\n",
       " 'ioff',\n",
       " 'ion',\n",
       " 'isinteractive',\n",
       " 'jet',\n",
       " 'legend',\n",
       " 'locator_params',\n",
       " 'logging',\n",
       " 'loglog',\n",
       " 'magma',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matplotlib',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mlab',\n",
       " 'new_figure_manager',\n",
       " 'nipy_spectral',\n",
       " 'np',\n",
       " 'pause',\n",
       " 'pcolor',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pie',\n",
       " 'pink',\n",
       " 'plasma',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'plotfile',\n",
       " 'plotting',\n",
       " 'polar',\n",
       " 'prism',\n",
       " 'psd',\n",
       " 'pylab_setup',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'rc',\n",
       " 'rcParams',\n",
       " 'rcParamsDefault',\n",
       " 'rcParamsOrig',\n",
       " 'rc_context',\n",
       " 'rcdefaults',\n",
       " 'rcsetup',\n",
       " 're',\n",
       " 'register_cmap',\n",
       " 'rgrids',\n",
       " 'savefig',\n",
       " 'sca',\n",
       " 'scatter',\n",
       " 'sci',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set_cmap',\n",
       " 'setp',\n",
       " 'show',\n",
       " 'silent_list',\n",
       " 'specgram',\n",
       " 'spring',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'streamplot',\n",
       " 'style',\n",
       " 'subplot',\n",
       " 'subplot2grid',\n",
       " 'subplot_tool',\n",
       " 'subplots',\n",
       " 'subplots_adjust',\n",
       " 'summer',\n",
       " 'suptitle',\n",
       " 'switch_backend',\n",
       " 'sys',\n",
       " 'table',\n",
       " 'text',\n",
       " 'thetagrids',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'tight_layout',\n",
       " 'time',\n",
       " 'title',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'uninstall_repl_displayhook',\n",
       " 'violinplot',\n",
       " 'viridis',\n",
       " 'vlines',\n",
       " 'waitforbuttonpress',\n",
       " 'warn_deprecated',\n",
       " 'warnings',\n",
       " 'winter',\n",
       " 'xcorr',\n",
       " 'xkcd',\n",
       " 'xlabel',\n",
       " 'xlim',\n",
       " 'xscale',\n",
       " 'xticks',\n",
       " 'ylabel',\n",
       " 'ylim',\n",
       " 'yscale',\n",
       " 'yticks']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26f3a21f208>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  4  4  7  9  9 10 10 14 15 23 29 30 35 40 40 41 41 47 48]\n"
     ]
    }
   ],
   "source": [
    "print(np.sort(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.sort(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26f3aefda90>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.47368421,  1.94736842,  2.42105263,  2.89473684,\n",
       "        3.36842105,  3.84210526,  4.31578947,  4.78947368,  5.26315789,\n",
       "        5.73684211,  6.21052632,  6.68421053,  7.15789474,  7.63157895,\n",
       "        8.10526316,  8.57894737,  9.05263158,  9.52631579, 10.        ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  4,  7,  9,  9, 10, 10, 14, 15, 23, 29, 30, 35, 40, 40, 41,\n",
       "       41, 47, 48])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "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.plot(x, y, color = 'g')\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Random plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2 = y*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.        ,   5.89473684,   7.78947368,  16.94736842,\n",
       "        26.05263158,  30.31578947,  38.42105263,  43.15789474,\n",
       "        67.05263158,  78.94736842, 131.94736842, 180.10526316,\n",
       "       200.52631579, 250.52631579, 305.26315789, 324.21052632,\n",
       "       351.73684211, 371.15789474, 447.73684211, 480.        ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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.subplot(1, 2, 1)\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Y')\n",
    "plt.plot(x, y, 'ro', markersize = 5)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, y2, 'b*')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "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.subplot(1, 2, 1)\n",
    "plt.xlabel('X axis')\n",
    "plt.ylabel('Y')\n",
    "plt.plot(x, y, 'ro-', markersize = 10)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(x, y2, 'b*--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# matlab vs matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#objet oriented method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Set Title of Random')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeMAAAFdCAYAAAAwtwU9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xm4XeP9///nWwShNIZITTEX1QGNCsrHkKI1tl9Cq6JotcYKKVGKKmqeaqj4GGKuIh9TSZCoOSXS1hBUlQxCEGliqAzu3x/3zi+nZJSz972H5+O6cp2z195n7Vf2RV7nXute94qUEpIkqZyFSgeQJKnVWcaSJBVmGUuSVJhlLElSYZaxJEmFWcaSJBVmGUt1JiLei4g15vD8qxHRswrvu3lE/KPy/ru19/7nM8tWETGmZAaplixjaRYi4psR8VhE/DsiJkTEoxGx8Tz+bIqItWbz3C8rZfdeRPwnIqa3efwcQErpcymlVyqvvzoiTmm/v9kcnQxcVHn//5tF9lcj4sNK1jcq2T5Xo2xSU7OMpU+IiKWAu4DfAcsAKwG/Bj5a0H2nlE6rlN3ngJ8Bj894nFJaf0H3v4BWBZ6by2t2rmTfANgQOLbqqaQWYBlLn/ZFgJTSjSml6SmlD1NKg1NKf5/xgojYPyJGRsS7ETEoIlatbH+o8pK/VUaQe87vm88YWUfEgcDewNGVfd05i9cuFBH9IuKfEfFORNwcEcvMYd8/iYiXK6P9OyJixcr2fwJrAHdW3mvROWVMKb0BDCKX8ox97xgRIyJiUkSMjoiT2jy3WuXvtW9EjIqItyPiuDbPd6qMtN+NiOeB/zoKERHrRcSDETExIp6LiF3aPHd1RFwSEfdUsj8aEV+IiPMr+3shIjac099HKs0ylj7tJWB6RAyIiG9HxNJtn6ycT/0l8D2gC/AwcCNASmnLysu+Vhnt/uGzhkgp9QeuB86s7GvnWbzscGA34H+AFYF3gYtntb+I2Ab4LdALWAF4Dbip8l5rAqOojHxTSnM8ChARKwPfBl5us/l9oDfQGdgROGgW556/CawDbAucEBHrVbafCKxZ+bM9sG+b9+oI3AkMBpYHDgOuj4h12uy3F3A8sBz5CMbjwNOVx7cA587p7yOVZhlLn5BSmkQujQRcDrxVGUV2rbzkp8BvU0ojU0rTgNOADWaMjmvsp8BxKaUxlQI9Cdg9IhaexWv3Bq5MKT1dee2xwKYRsdp8vN//RcRkYDQwnlyiAKSUHkwpPZNS+rhyFOFG8i8Jbf26cqThb8DfgK9VtvcCTk0pTUgpjQYubPMzPYDPAaenlKaklIaQTyN8v81rBqaUhqeU/gMMBP6TUrompTQd+AP5kLpUtyxjaRYqRfujlNLKwJfJo87zK0+vClxQOWQ6EZgABPnccq2tCgxsk2UkMB3oOovXrkgeDQOQUnoPeIf5y71bSmlJYCtgXfLIE4CI2CQihkbEWxHxb/I58eU+8fNvtPn+A3LJzsg2us1zr7X5fkVgdErp40883zb3m22+/3AWj51oprpmGUtzkVJ6AbiaXMqQS+OnKaXObf50Sik9Vo23n8vzo4FvfyLLYimlsbN47evk8gYgIpYAlgVm9do5h0rpz+TP5Ow2m28A7gBWSSl9Hvg9+ZeUeTEOWKXN426fyL1KRCz0iefnO7dUryxj6RMiYt2IOKpyXpSIWIV8SPSJykt+DxwbEetXnv98ROzRZhdvkidDtYe57ev3wKltJpB1iYhdZ/PaG4D9ImKDygSt04BhKaVXP2O284FvRcSMSVxLAhNSSv+JiG8AP5iPfd1M/kyXrnzuh7V5bhj5fPTREdExIrYCdqZyvltqBpax9GmTgU2AYRHxPrmEnwWOAkgpDQTOAG6KiEmV577d5udPAgZUDh33WsAsVwBfquzrU9f+AheQR6ODK+dyn6hk/5SU0gPAr4BbySPRNYG9PmuwlNJbwDWVfQIcDJxcyXECuWDn1a/Jh57/RZ6odW2b95kC7EL+jN8GLgF6V45YSE0hUprbUTBJklRNjowlSSrMMpYkqTDLWJKkwma1MEC7iYhXyZNhpgPTUkrdK0v1/QFYDXgV6JVSereaOSRJqme1GBlvnVLaIKXUvfK4H/BASmlt4IHKY0mSWlZVZ1NXRsbdU0pvt9n2IrBVSmlcRKwAPJhSWmd2+wBYbrnl0mqrrVa1nJIkVcPw4cPfTil1mdvrqnqYmrx60OCISMBllYXvu6aUxgFUCnn5ue1ktdVW46mnnqpyVEmS2ldEvDb3V1W/jDdPKb1eKdz7ImKeL9Kv3D7uQIBu3brN5dWSJDWuqp4zTim9Xvk6nnwnlW8Ab1YOT1P5On42P9s/pdQ9pdS9S5e5jvAlSWpYVSvjiFgiIpac8T2wHXnZwDuYea/SfYHbq5VBkqRGUM3D1F3Jt3ab8T43pJTujYgngZsj4gDyzcz3mMM+JElqelUr45TSK8y8cXjb7e8A21brfSVJajSuwCVJUmGWsSRJhVnGkiQVZhlLklSYZSxJ0gxVXCJ6Tqq9ApckSfUrJXjpJRg8GAYNgrFjYcSImsewjCVJreXdd2HIkFy+gwfDa5Xlo9daC7bbDqZMgUUWqWkky1iS1NymTYMnn5xZvsOGwccfw1JLwTbbQL9+uYTXWKNYRMtYktR8Xntt5qHnBx6AiRMhAjbeGI47LpfvJptAx46lkwKWsSSpGbz3Hjz44MwCfumlvH3lleH//b9cvj17wjLLFI05O5axJKkxPfMM3H13Lt9HH4WpU6FTJ9hqKzjoINh+e1h33TwirnOWsSSp8dx5J+yyS/7+a1+DI47I5bv55rDYYmWzfQaWsSSpsXzwARx2GHz5y/mw9AorlE60wCxjSVJjOfXUPEHroYeaoojBFbgkSY3kxRfhrLOgd2/YYovSadqNZSxJagwpwaGHwuKLw5lnlk7TrjxMLUlqDH/8I9x/P1x0EXTtWjpNu3JkLEmqf5MnQ58+sNFG8LOflU7T7hwZS5Lq369/DePGwW23QYcOpdO0O0fGkqT69uyzcP758OMf5yUsm5BlLEmqXynBIYdA587w29+WTlM1HqaWJNWv667L1xNffjksu2zpNFXjyFiSVJ8mToS+faFHD9h//9JpqsqRsSSpPv3qV/D223DPPbBQc48dm/tvJ0lqTE8/DZdcku++tNFGpdNUnWUsSaovH38MBx8Myy0Hp5xSOk1NeJhaklRfrrwShg2DAQPyLOoW4MhYklQ/3nkH+vXLN4HYZ5/SaWrGMpYk1Y9jj82zqC++GCJKp6kZy1iSVB+GDYP//V/4+c/hK18pnaamLGNJUnnTp+dJWyusACedVDpNzTmBS5JU3mWX5cuZbroJllyydJqac2QsSSpr/Hg47jjo2RN69SqdpgjLWJJU1tFHw/vvw0UXtdSkrbYsY0lSOY88kq8n7tsX1lmndJpiLGNJUhnTpuVJW9265cPULcwJXJKkMn73O3jmGRg4EJZYonSaohwZS5Jq7/XX4cQT4dvfhl13LZ2mOMtYklR7Rx0FU6bk0XGLTtpqyzKWJNXWAw/k64n79YM11yydpi5YxpKk2pkyBQ49FNZYA445pnSauuEELklS7Zx7LrzwAtx9N3TqVDpN3XBkLEmqjVGj4De/gd12g+98p3SaumIZS5Jq44gjICU4//zSSeqOh6klSZ82bVr77m/QoHw98Wmnwaqrtu++m4BlLEmCSZNgyBAYPDgX5yuvtP97rLNOvqRJn2IZS1Irmj4dhg+fWb6PP563LbEEbL019O4NHTq03/tFwPe/D4ss0n77bCKWsSS1ijFjZpbv/ffDhAl5+0Yb5TsnbbcdbLaZhVlA1cs4IjoATwFjU0o7RcTqwE3AMsDTwD4ppSnVziFJLeeDD+Chh3L5Dh4Mzz+ft6+wAuy8M2y/fb6HcJcuZXOqJiPjnwMjgaUqj88Azksp3RQRvwcOAC6tQQ5Jam4p5RsvzCjfhx+Gjz6CRReFLbeE/ffPo98vf9klKOtMVcs4IlYGdgROBY6MiAC2AX5QeckA4CQsY0mt4oUX4Lnn2nefkyfD0KG5gN94I29bf3045JBcvltu6QIbda7aI+PzgaOBJSuPlwUmppRmzJkfA6w0qx+MiAOBAwG6detW5ZiSVAPPPAMbb5xHq+1t2WXhW9/K5bvddrDSLP9pVZ2qWhlHxE7A+JTS8IjYasbmWbw0zernU0r9gf4A3bt3n+VrJKlhfPAB7LUXLL003H57+45UO3aEtddu39nPqqlqjow3B3aJiO8Ai5HPGZ8PdI6IhSuj45WB16uYQZLqw5FHwsiR+VDyN75ROo3qTNWWw0wpHZtSWjmltBqwFzAkpbQ3MBTYvfKyfYHbq5VBkurCrbfCZZfly4d69iydRnWoxNrUx5Anc71MPod8RYEMklQbo0bBj3+cR8O/+U3pNKpTNVn0I6X0IPBg5ftXAI/RSGp+06bB3nvnla1uuCGf25VmwRW4JKlaTjkFHnkErrsO1lyzdBrVMW+hKEnV8NBD+bB07955dCzNgWUsSe1twoRcwGusARddVDqNGoCHqSWpPaWUJ2y9+Wa+E9KSS879Z9TyLGNJak+XXQYDB8LZZ8PXv146jRqEh6klqb08+yz06ZPvhtSnT+k0aiCWsSS1hw8/zMtdLrUUDBgAC/nPq+adh6klqT0cdVS+G9O990LXrqXTqMH4q5skLaiBA+HSS6Fv33yIWppPlrEkLYjRo+GAA/JkrVNPLZ1GDcoylqTPavr0fD3x1Klw442wyCKlE6lBec5Ykj6rU0+Fhx+Ga67J9xOWPiNHxpL0WTzyCPz61/DDH8I++5ROowZnGUvS/Hr3XfjBD2D11eHii0unURPwMLUkzY+U4Cc/gXHj4LHH8nXF0gKyjCVpflx+Odx6K5x5Jmy8cek0ahIeppakefXcc/Dzn8O3vpUX+ZDaiWUsSfPiww/h+9/Pd2G65hqXu1S78jC1JM2LX/wCnnkG7r4bvvCF0mnUZPzVTpLm5vbb86zpPn3gO98pnUZNyDKWpDkZOxb23x823BB++9vSadSkLGNJmp3p0/OiHh99BDfdBIsuWjqRmpTnjCVpdk4/HR58EK66Cr74xdJp1MQcGUvSrDz2GJx4Iuy1F+y7b+k0anKWsSR90sSJebnLbt3g97+HiNKJ1OQ8TC1JbaUEBx6YJ2498gh8/vOlE6kFWMaS1NaVV8If/5hnTm+ySek0ahEeppakGUaOhMMPh223haOPLp1GLcQyliSA//wnL3e5+OIud6ma8zC1JAEccwz87W9w112w4oql06jF+KufJN15J1x4Yb4j0447lk6jFmQZS2ptr78O++0HG2wAZ5xROo1alGUsqXXNWO7yww/hxhtd7lLFeM5YUus680wYOhSuuALWXbd0GrUwR8aSWtMTT8CvfgV77pkPU0sFWcaSWs+//50vY1plFZe7VF3wMLWk1pIS/OxnMHo0PPwwdO5cOpFkGUtqMQMG5HsTn3oqbLpp6TQS4GFqSa3kxRfh0ENh663zIh9SnbCMJbWGjz7K54kXWwyuvRY6dCidSPr/eZhaUms49lgYMQLuuANWWql0Gum/ODKW1Pz+9Cc47zw47DDYeefSaaRPsYwlNbdx4+BHP4KvfjUv8iHVIctYUvP6+GPo3Rveey8vd7nYYqUTSbPkOWNJzevss+H++6F/f/jSl0qnkWbLkbGk5vSXv8Bxx8Huu8OPf1w6jTRHlrGk5jNpUr6MacUV86jY5S5V5zxMLam5pAQHHQSvvgoPPQRLL106kTRXVRsZR8RiEfGXiPhbRDwXEb+ubF89IoZFxD8i4g8RsUi1MkhqQddeCzfcACedBJtvXjqNNE+qeZj6I2CblNLXgA2AHSKiB3AGcF5KaW3gXeCAKmaQ1EpeegkOPhi23BJ++cvSaaR5VrUyTtl7lYcdK38SsA1wS2X7AGC3amWQ1CJSguefz+eJF10Urr/e5S7VUKp6zjgiOgDDgbWAi4F/AhNTStMqLxkDzHJduog4EDgQoFu3btWMKakRvfNOvmxp8OD8Z8wYWGghuO02WHnl0umk+VLVMk4pTQc2iIjOwEBgvVm9bDY/2x/oD9C9e/dZvkZSC5k6FZ54IhfvoEHw1FN5RNy5M2y7LZxwAmy/PfjLuxpQTWZTp5QmRsSDQA+gc0QsXBkdrwy8XosMkhrQP/+Zi3fwYBgyBCZPzqPfHj3gxBNz+XbvDgt7YYgaW9X+C46ILsDUShF3AnqSJ28NBXYHbgL2BW6vVgZJDWbSpFy6M0a/r7ySt6+6aj4fvP32sM02eTQsNZFq/jq5AjCgct54IeDmlNJdEfE8cFNEnAKMAK6oYgZJ9Wz6dBg+fGb5Pv543rbEErl0+/SB7baDtdd24Q41taqVcUrp78CGs9j+CvCNar2vpAaQEvziF3DVVTBhQt729a/D0Ufn0e+mm8IiLkGg1uGJFkm1d8stcM45sNtu0KsX9OwJXbqUTiUVYxlLqq3Jk/Ph5w03zKXs9cCSZSypxk4+GcaOtYilNrxrk6Taee45OP/8fEvDHj1Kp5HqhmUsqTZSyutGL7UU/Pa3pdNIdcXD1JJq4/rr8y0NL7sMlluudBqprjgyllR9EydC377wjW/kQ9SS/osjY0nVd8IJMH483H13Xs5S0n/x/wpJ1TViBFx8MRx0UF7YQ9KnWMaSqufjj/OkrWWXhVNOKZ1GqlseppZUPVddlW97ePXVsPTSpdNIdcuRsaTqeOcdOOYY+OY3oXfv0mmkumYZS6qOX/4yz6K+5BLvuCTNhWUsqf395S9w+eVw+OHwla+UTiPVPctYUvuaPj1P2vrCF+Ckk0qnkRqCE7gkta/+/WH4cLjxxrz0paS5cmQsqf2MH5/PFW+zDey5Z+k0UsOwjCW1n2OOgfffz4t8OGlLmmeWsaT28eij+Xrio46CddctnUZqKJaxpAU3bVqetLXKKnD88aXTSA1ntmUcEX+KiNVqF0VSw7roIvj73+H882GJJUqnkRrOnEbGVwODI+K4iOhYozySGs3rr+e7Mu2wA3z3u6XTSA1ptpc2pZRujoi7gROApyLiWuDjNs+fW4N8kupd374wZQr87ndO2pI+o7ldZzwVeB9YFFiSNmUsSQwZkq8nPuEEWGut0mmkhjXbMo6IHYBzgTuAjVJKH9QslaT6N2UKHHIIrL469OtXOo3U0OY0Mj4O2COl9FytwkhqIOedBy+8AHfdBZ06lU4jNbQ5nTPeopZBJDWQUaPg5JNh111hxx1Lp5EantcZS5p/Rx4JKcEFF5ROIjUFbxQhaf4MGgS33gqnngqrrlo6jdQUHBlLmnf/+Q8ceih88Yt52UtJ7cKRsaR5d9ZZ8PLLcN99sOiipdNITcORsaR5869/wWmnQa9e0LNn6TRSU7GMJc2bww+HhReGc118T2pvHqaWNHd33pmvJz77bFhppdJppKbjyFjSnKWUl7tcZ508OpbU7hwZS5qzIUPgr3+FK66Ajt7ATaoGR8aS5uyss6BrV9h779JJpKZlGUuavb//PS/ycfjhXsokVZFlLGn2zj0XFl8cfvaz0kmkpmYZS5q1sWPhhhvggANgmWVKp5GammUsadYuvBCmT4c+fUonkZqeZSzp0yZNgt//HnbfHVZfvXQaqelZxpI+7YorciH37Vs6idQSLGNJ/23qVDj/fNhyS9h449JppJbgoh+S/tstt8CoUXDRRaWTSC3DkbGkmVLKi3ysuy7suGPpNFLLcGQsaaahQ2HECLj8cljI39WlWvH/NkkznX02LL88/PCHpZNILaVqZRwRq0TE0IgYGRHPRcTPK9uXiYj7IuIfla9LVyuDpPnw7LNwzz1w2GGw2GKl00gtpZoj42nAUSml9YAewCER8SWgH/BASmlt4IHKY0mlzVj68qCDSieRWk7VyjilNC6l9HTl+8nASGAlYFdgQOVlA4DdqpVB0jx6/XW47jrYf39YdtnSaaSWU5NzxhGxGrAhMAzomlIaB7mwgeVn8zMHRsRTEfHUW2+9VYuYUuv63e/y0pdHHFE6idSSql7GEfE54FbgiJTSpHn9uZRS/5RS95RS9y5dulQvoNTqJk/OS19+73uw5pql00gtqaplHBEdyUV8fUrptsrmNyNihcrzKwDjq5lB0lxceSVMnOjSl1JB1ZxNHcAVwMiU0rltnroD2Lfy/b7A7dXKIGkupk2D886DLbaATTYpnUZqWdVc9GNzYB/gmYj4a2XbL4HTgZsj4gBgFLBHFTNImpNbboHXXsu3S5RUTNXKOKX0CBCzeXrbar2vpHk0Y+nLL34RdtqpdBqppbkcptSq/vxnePppuOwyl76UCvP/QKlVzVj6snfv0kmklmcZS63o+efh7rvh0ENd+lKqA5ax1IrOOQc6dXLpS6lOWMZSqxk3Li99ud9+sNxypdNIwjKWWs9FF8HUqdCnT+kkkiosY6mVvPceXHppXvpyrbVKp5FUYRlLreTKK+Hdd136UqozlrHUKmYsfbn55tCjR+k0ktpw0Q+pVdx2G7z6ai5kSXXFkbHUClLKi3ysvTbsvHPpNJI+wZGx1AoefhiefDLft7hDh9JpJH2CI2OpFZx1Vr6m2KUvpbpkGUvNbuRIuOuuvPRlp06l00iaBctYanbnnpvXnz744NJJJM2GZSw1szfegGuuyUtfdulSOo2k2bCMpWZ28cUufSk1AMtYalbvvw+XXAK77ZYvaZJUtyxjqVlddRVMmODSl1IDsIylZjR9ep64temmsNlmpdNImgsX/ZCa0cCB8K9/wTnnlE4iaR44MpaaTUp5kY+11oJddimdRtI8cGQsNYOpU+Hxx2HwYBg0CJ56Kk/eculLqSFYxlKjevnlmeU7ZAi8914u3x494PTT4cc/Lp1Q0jyyjKVG8e9/w9ChuXwHD4ZXXsnbV1sN9t4btt8ett4aOncuGlPS/LOMpXo1fToMHz6zfB9/PG/73Ody6R55JGy3XT43HFE6raQFYBlL9WTMmJmHnu+/P18nHAEbbQTHHJPLd9NNYZFFSieV1I4sY6m0YcPgpptyCT//fN624op5JvR220HPnq4rLTU5y1gq6a678nKVHTvCllvC/vvnc7/rr++hZ6mFWMZSKY89Br16wYYb5kPSn/986USSCnHRD6mE556DnXaClVeGu++2iKUWZxlLtTZ6NOywAyy6aJ6otfzypRNJKszD1FItvfNOnpQ1aRI8/DCsvnrpRJLqgGUs1cr778OOO+YbOAwaBF/9aulEkuqEZSzVwtSpsMce8OSTcMst8D//UzqRpDpiGUvV9vHHcMABcM89cNll8N3vlk4kqc44gUuqtn794Npr4eST4cADS6eRVIcsY6mazjkn31v4kEPg+ONLp5FUpyxjqVquvRb69s3nii+4wBW1JM2WZSxVwz335KUtt9kml3KHDqUTSapjlrHU3p54AnbfHb7yFRg4MC/uIUlzYBlL7WnkyHwt8Qor5NHxUkuVTiSpAVjGUnsZMybfcaljx7yoR9eupRNJahBeZyy1hwkTchFPnAh//jOsuWbpRJIaiGUsLagPPoCdd4aXX4Z77823RJSk+WAZSwti6lTYc094/HG4+WbYeuvSiSQ1IMtY+qxSyitq3XUXXHJJnkEtSZ+BE7ikz+rYY+Hqq+HEE+Ggg0qnkdTAqlbGEXFlRIyPiGfbbFsmIu6LiH9Uvi5drfeXquq88+CMM+CnP81lLEkLoJoj46uBHT6xrR/wQEppbeCBymOpsVx/PRx5JHzve3DxxS5zKWmBVe2ccUrpoYhY7RObdwW2qnw/AHgQOKZaGdTCJkyA++/Pq2FNndp++506Fa64ArbaKpeyy1xKage1nsDVNaU0DiClNC4ilp/dCyPiQOBAgG7dutUonhrW1KkwbBgMHpwX3HjyyTzBqlOn/Kc9bbkl3HYbLLZY++5XUsuq29nUKaX+QH+A7t27p8JxVI9eeWVm+Q4ZApMmwUILwSab5PO4220HG28MC9ftf+aSBNS+jN+MiBUqo+IVgPE1fn81ssmTYejQXL6DB+dFNgBWXRX22iuX77bbQufOZXNK0nyqdRnfAewLnF75enuN31+N5OOP4emnZ5bvY4/BtGmwxBJ5cY3DD89LUK69tpOoJDW0qpVxRNxInqy1XESMAU4kl/DNEXEAMArYo1rvrwY1duzMQ8/33w/vvJO3b7QR9O2by3fTTb0toaSmUs3Z1N+fzVPbVus9VSOvvQa/+hU8+GD77nfaNBg3Ln//hS/ATjvlQ889e8Lys53rJ0kNz5ktmncTJ8Jpp8GFF+bDwt/9bvvPKF5vvTz6/cpXPPQsqWVYxpq7KVPy2su/+Q28+y707p2/X2WV0skkqSm4NrVmL6V8J6L11oM+feDrX88Tqq6+2iKWpHZkGWvWHnkkT5Tac888e/nee/PEqg02KJ1MkpqOZaz/9uKL+VzwFlvA6NFw5ZUwYkQ+jytJqgrLWNn48XDIIbD++vmSolNOgX/8A/bbz/WXJanKnMDV6j74YObtAD/4YOYtAb2USJJqxjJuVdOnwzXX5OuFx46F3XaD00+HddYpnUySWo6HqVvRoEGw4Yaw//6w0krw0EMwcKBFLEmFWMat5K9/zSta7bADvP8+/OEP+X6/W2xROpkktTTLuBVMmAA/+lFe33n48HyO+PnnoVcvV7mSpDrgOeNmN3Ik7LJLXk+6b1849lhYeunSqSRJbVjGzeyee/J9fhdbLN/UYbPNSieSJM2Ch6mbUUpwzjn5rkerrw5PPmkRS1Ids4ybzUcf5VnSffvmlbQefRS6dSudSpI0B5ZxM3nzTdhmm3wjhxNOyDd5WGKJ0qkkSXPhOeNmMWIE7LorvP12LuE99iidSJI0jxwZN4Nbb4VvfjOfK37kEYtYkhqMZdzIUoLf/AZ23x2++tU8UWujjUqnkiTNJw9TN6oPPsh3VLr5ZthnH+jfP1/CJElqOJZxIxozJp8fHjECzjwzz5x2JS1JaliWcaN54ol8ydL778Mdd+RriSVJDc1zxo3k2mthq61g8cXh8cctYklqEpZxI5g+HY45Bnr3hk03hb/8BdZfv3QqSVI78TB1vZuojXM9AAAGeklEQVQ0CX7wA7j7bjjoILjgAujYsXQqSVI7sozr2SuvwM47w4svwsUXw8EHl04kSaoCy7heDR2arx9OCQYPzstcSpKakmW8oP76V3j44fbd5xtv5EuW1l47z5hea6323b8kqa5Yxp/Va6/B8cfDdddVZ/877ZT3/fnPV2f/kqS6YRnPr4kT4bTT4MIL80Ib/frBoYe27+pXEbDMMu23P0lSXbOM59WUKXDppXDyyfDuu3kJylNOgVVWKZ1MktTgvM54blKCP/4RvvQlOOKIfCOGp5+GAQMsYklSu7CM5+TRR2GzzaBXL+jUCe65J89s3mCD0skkSU3EMp6Vl16C730v3yN41Ci44oo8a3qHHbwhgySp3VnGbY0fD4cckg9J33dfvlfwSy/B/vtDhw6l00mSmpQTuCDfG/i88+CMM/L3Bx4IJ54IXbuWTiZJagGtXcbTp+c7IR1/PIwdm+8RfPrpsO66pZNJklpI6x6mHjw4z4zebz9YaSX485/h//7PIpYk1VzrlfHf/gbbb5//TJ4MN90ETzwBW25ZOpkkqUW11mHq227LN1/o3BnOPTffBWnRRUunkiS1uNYq4299C447Do48EpZeunQaSZKAVivjJZfMlytJklRHWu+csSRJdcYyliSpMMtYkqTCLGNJkgqzjCVJKqxIGUfEDhHxYkS8HBH9SmSQJKle1LyMI6IDcDHwbeBLwPcj4ku1ziFJUr0oMTL+BvBySumVlNIU4CZg1wI5JEmqCyXKeCVgdJvHYyrbJElqSSXKOGaxLX3qRREHRsRTEfHUW2+9VYNYkiSVUaKMxwCrtHm8MvD6J1+UUuqfUuqeUurepUuXmoWTJKnWIqVPDUqr+4YRCwMvAdsCY4EngR+klJ6bw8+8BbxWm4R1Zzng7dIhmpSfbXX5+VaPn231tPdnu2pKaa4jyprfKCKlNC0iDgUGAR2AK+dUxJWfadmhcUQ8lVLqXjpHM/KzrS4/3+rxs62eUp9tkbs2pZT+BPypxHtLklRvXIFLkqTCLOP61790gCbmZ1tdfr7V42dbPUU+25pP4JIkSf/NkbEkSYVZxnUqIlaJiKERMTIinouIn5fO1GwiokNEjIiIu0pnaSYR0TkibomIFyr//W5aOlOziIg+lX8Pno2IGyNisdKZGllEXBkR4yPi2TbblomI+yLiH5WvS9cii2Vcv6YBR6WU1gN6AId4Q41293NgZOkQTegC4N6U0rrA1/AzbhcRsRJwONA9pfRl8qWhe5VN1fCuBnb4xLZ+wAMppbWBByqPq84yrlMppXEppacr308m/4PmGt7tJCJWBnYE/rd0lmYSEUsBWwJXAKSUpqSUJpZN1VQWBjpVFk9anFmsXqh5l1J6CJjwic27AgMq3w8AdqtFFsu4AUTEasCGwLCySZrK+cDRwMelgzSZNYC3gKsqpwD+NyKWKB2qGaSUxgJnA6OAccC/U0qDy6ZqSl1TSuMgD4qA5WvxppZxnYuIzwG3AkeklCaVztMMImInYHxKaXjpLE1oYWAj4NKU0obA+9ToMF+zq5y73BVYHVgRWCIiflg2ldqLZVzHIqIjuYivTyndVjpPE9kc2CUiXiXfT3ubiLiubKSmMQYYk1KacRTnFnI5a8H1BP6VUnorpTQVuA3YrHCmZvRmRKwAUPk6vhZvahnXqYgI8nm3kSmlc0vnaSYppWNTSiunlFYjT4AZklJyhNEOUkpvAKMjYp3Kpm2B5wtGaiajgB4RsXjl34dtcXJcNdwB7Fv5fl/g9lq8aZG1qTVPNgf2AZ6JiL9Wtv2ysq63VM8OA66PiEWAV4D9CudpCimlYRFxC/A0+WqLEbgS1wKJiBuBrYDlImIMcCJwOnBzRBxA/gVoj5pkcQUuSZLK8jC1JEmFWcaSJBVmGUuSVJhlLElSYZaxJEmFWcZSC6rcFexfEbFM5fHSlcerls4mtSLLWGpBKaXRwKXkayqpfO2fUnqtXCqpdXmdsdSiKsutDgeuBH4CbJhSmlI2ldSaXIFLalEppakR8QvgXmA7i1gqx8PUUmv7Nvl2fF8uHURqZZax1KIiYgPgW0APoM+MO9VIqj3LWGpBlbv+XEq+T/Yo4CzyjeslFWAZS63pJ8ColNJ9lceXAOtGxP8UzCS1LGdTS5JUmCNjSZIKs4wlSSrMMpYkqTDLWJKkwixjSZIKs4wlSSrMMpYkqTDLWJKkwv4/jrpiiMiuehAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1, 1])\n",
    "axes.plot(x, y, 'r')\n",
    "axes.set_xlabel('X')\n",
    "axes.set_ylabel('Y')\n",
    "axes.set_title('Set Title of Random')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_add_text',\n",
       " '_adjustable',\n",
       " '_agg_filter',\n",
       " '_alpha',\n",
       " '_anchor',\n",
       " '_animated',\n",
       " '_aspect',\n",
       " '_autoscaleXon',\n",
       " '_autoscaleYon',\n",
       " '_autotitlepos',\n",
       " '_axes',\n",
       " '_axes_locator',\n",
       " '_axisbelow',\n",
       " '_cachedRenderer',\n",
       " '_clipon',\n",
       " '_clippath',\n",
       " '_connected',\n",
       " '_contains',\n",
       " '_current_image',\n",
       " '_facecolor',\n",
       " '_frameon',\n",
       " '_gci',\n",
       " '_gen_axes_patch',\n",
       " '_gen_axes_spines',\n",
       " '_get_axis_list',\n",
       " '_get_lines',\n",
       " '_get_patches_for_fill',\n",
       " '_get_view',\n",
       " '_gid',\n",
       " '_gridOn',\n",
       " '_hold',\n",
       " '_in_layout',\n",
       " '_init_axis',\n",
       " '_label',\n",
       " '_layoutbox',\n",
       " '_left_title',\n",
       " '_make_twin_axes',\n",
       " '_mouseover',\n",
       " '_mouseover_set',\n",
       " '_navigate',\n",
       " '_navigate_mode',\n",
       " '_oid',\n",
       " '_on_units_changed',\n",
       " '_originalPosition',\n",
       " '_path_effects',\n",
       " '_pcolorargs',\n",
       " '_picker',\n",
       " '_position',\n",
       " '_poslayoutbox',\n",
       " '_process_unit_info',\n",
       " '_prop_order',\n",
       " '_propobservers',\n",
       " '_quiver_units',\n",
       " '_rasterization_zorder',\n",
       " '_rasterized',\n",
       " '_remove_legend',\n",
       " '_remove_method',\n",
       " '_right_title',\n",
       " '_sci',\n",
       " '_set_artist_props',\n",
       " '_set_gc_clip',\n",
       " '_set_lim_and_transforms',\n",
       " '_set_position',\n",
       " '_set_title_offset_trans',\n",
       " '_set_view',\n",
       " '_set_view_from_bbox',\n",
       " '_shared_x_axes',\n",
       " '_shared_y_axes',\n",
       " '_sharex',\n",
       " '_sharey',\n",
       " '_sketch',\n",
       " '_snap',\n",
       " '_stale',\n",
       " '_sticky_edges',\n",
       " '_tight',\n",
       " '_transform',\n",
       " '_transformSet',\n",
       " '_twinned_axes',\n",
       " '_update_image_limits',\n",
       " '_update_line_limits',\n",
       " '_update_patch_limits',\n",
       " '_update_title_position',\n",
       " '_update_transScale',\n",
       " '_url',\n",
       " '_use_sticky_edges',\n",
       " '_validate_converted_limits',\n",
       " '_visible',\n",
       " '_xaxis_transform',\n",
       " '_xcid',\n",
       " '_xmargin',\n",
       " '_yaxis_transform',\n",
       " '_ycid',\n",
       " '_ymargin',\n",
       " 'acorr',\n",
       " 'add_artist',\n",
       " 'add_callback',\n",
       " 'add_child_axes',\n",
       " 'add_collection',\n",
       " 'add_container',\n",
       " 'add_image',\n",
       " 'add_line',\n",
       " 'add_patch',\n",
       " 'add_table',\n",
       " 'aname',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'apply_aspect',\n",
       " 'arrow',\n",
       " 'artists',\n",
       " 'autoscale',\n",
       " 'autoscale_view',\n",
       " 'axes',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axison',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bbox',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'bxp',\n",
       " 'callbacks',\n",
       " 'can_pan',\n",
       " 'can_zoom',\n",
       " 'child_axes',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clear',\n",
       " 'clipbox',\n",
       " 'cohere',\n",
       " 'collections',\n",
       " 'containers',\n",
       " 'contains',\n",
       " 'contains_point',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'convert_xunits',\n",
       " 'convert_yunits',\n",
       " 'csd',\n",
       " 'dataLim',\n",
       " 'drag_pan',\n",
       " 'draw',\n",
       " 'draw_artist',\n",
       " 'end_pan',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'eventson',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'fmt_xdata',\n",
       " 'fmt_ydata',\n",
       " 'format_coord',\n",
       " 'format_cursor_data',\n",
       " 'format_xdata',\n",
       " 'format_ydata',\n",
       " 'get_adjustable',\n",
       " 'get_agg_filter',\n",
       " 'get_alpha',\n",
       " 'get_anchor',\n",
       " 'get_animated',\n",
       " 'get_aspect',\n",
       " 'get_autoscale_on',\n",
       " 'get_autoscalex_on',\n",
       " 'get_autoscaley_on',\n",
       " 'get_axes_locator',\n",
       " 'get_axisbelow',\n",
       " 'get_children',\n",
       " 'get_clip_box',\n",
       " 'get_clip_on',\n",
       " 'get_clip_path',\n",
       " 'get_contains',\n",
       " 'get_cursor_data',\n",
       " 'get_data_ratio',\n",
       " 'get_data_ratio_log',\n",
       " 'get_default_bbox_extra_artists',\n",
       " 'get_facecolor',\n",
       " 'get_fc',\n",
       " 'get_figure',\n",
       " 'get_frame_on',\n",
       " 'get_gid',\n",
       " 'get_images',\n",
       " 'get_in_layout',\n",
       " 'get_label',\n",
       " 'get_legend',\n",
       " 'get_legend_handles_labels',\n",
       " 'get_lines',\n",
       " 'get_navigate',\n",
       " 'get_navigate_mode',\n",
       " 'get_path_effects',\n",
       " 'get_picker',\n",
       " 'get_position',\n",
       " 'get_rasterization_zorder',\n",
       " 'get_rasterized',\n",
       " 'get_renderer_cache',\n",
       " 'get_shared_x_axes',\n",
       " 'get_shared_y_axes',\n",
       " 'get_sketch_params',\n",
       " 'get_snap',\n",
       " 'get_tightbbox',\n",
       " 'get_title',\n",
       " 'get_transform',\n",
       " 'get_transformed_clip_path_and_affine',\n",
       " 'get_url',\n",
       " 'get_visible',\n",
       " 'get_window_extent',\n",
       " 'get_xaxis',\n",
       " 'get_xaxis_text1_transform',\n",
       " 'get_xaxis_text2_transform',\n",
       " 'get_xaxis_transform',\n",
       " 'get_xbound',\n",
       " 'get_xgridlines',\n",
       " 'get_xlabel',\n",
       " 'get_xlim',\n",
       " 'get_xmajorticklabels',\n",
       " 'get_xminorticklabels',\n",
       " 'get_xscale',\n",
       " 'get_xticklabels',\n",
       " 'get_xticklines',\n",
       " 'get_xticks',\n",
       " 'get_yaxis',\n",
       " 'get_yaxis_text1_transform',\n",
       " 'get_yaxis_text2_transform',\n",
       " 'get_yaxis_transform',\n",
       " 'get_ybound',\n",
       " 'get_ygridlines',\n",
       " 'get_ylabel',\n",
       " 'get_ylim',\n",
       " 'get_ymajorticklabels',\n",
       " 'get_yminorticklabels',\n",
       " 'get_yscale',\n",
       " 'get_yticklabels',\n",
       " 'get_yticklines',\n",
       " 'get_yticks',\n",
       " 'get_zorder',\n",
       " 'grid',\n",
       " 'has_data',\n",
       " 'have_units',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hitlist',\n",
       " 'hlines',\n",
       " 'ignore_existing_data_limits',\n",
       " 'images',\n",
       " 'imshow',\n",
       " 'in_axes',\n",
       " 'indicate_inset',\n",
       " 'indicate_inset_zoom',\n",
       " 'inset_axes',\n",
       " 'invert_xaxis',\n",
       " 'invert_yaxis',\n",
       " 'is_figure_set',\n",
       " 'is_transform_set',\n",
       " 'legend',\n",
       " 'legend_',\n",
       " 'lines',\n",
       " 'locator_params',\n",
       " 'loglog',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mouseover',\n",
       " 'mouseover_set',\n",
       " 'name',\n",
       " 'patch',\n",
       " 'patches',\n",
       " 'pchanged',\n",
       " 'pcolor',\n",
       " 'pcolorfast',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pick',\n",
       " 'pickable',\n",
       " 'pie',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'properties',\n",
       " 'psd',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'redraw_in_frame',\n",
       " 'relim',\n",
       " 'remove',\n",
       " 'remove_callback',\n",
       " 'reset_position',\n",
       " 'scatter',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set',\n",
       " 'set_adjustable',\n",
       " 'set_agg_filter',\n",
       " 'set_alpha',\n",
       " 'set_anchor',\n",
       " 'set_animated',\n",
       " 'set_aspect',\n",
       " 'set_autoscale_on',\n",
       " 'set_autoscalex_on',\n",
       " 'set_autoscaley_on',\n",
       " 'set_axes_locator',\n",
       " 'set_axis_off',\n",
       " 'set_axis_on',\n",
       " 'set_axisbelow',\n",
       " 'set_clip_box',\n",
       " 'set_clip_on',\n",
       " 'set_clip_path',\n",
       " 'set_contains',\n",
       " 'set_facecolor',\n",
       " 'set_fc',\n",
       " 'set_figure',\n",
       " 'set_frame_on',\n",
       " 'set_gid',\n",
       " 'set_in_layout',\n",
       " 'set_label',\n",
       " 'set_navigate',\n",
       " 'set_navigate_mode',\n",
       " 'set_path_effects',\n",
       " 'set_picker',\n",
       " 'set_position',\n",
       " 'set_prop_cycle',\n",
       " 'set_rasterization_zorder',\n",
       " 'set_rasterized',\n",
       " 'set_sketch_params',\n",
       " 'set_snap',\n",
       " 'set_title',\n",
       " 'set_transform',\n",
       " 'set_url',\n",
       " 'set_visible',\n",
       " 'set_xbound',\n",
       " 'set_xlabel',\n",
       " 'set_xlim',\n",
       " 'set_xmargin',\n",
       " 'set_xscale',\n",
       " 'set_xticklabels',\n",
       " 'set_xticks',\n",
       " 'set_ybound',\n",
       " 'set_ylabel',\n",
       " 'set_ylim',\n",
       " 'set_ymargin',\n",
       " 'set_yscale',\n",
       " 'set_yticklabels',\n",
       " 'set_yticks',\n",
       " 'set_zorder',\n",
       " 'specgram',\n",
       " 'spines',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stale',\n",
       " 'stale_callback',\n",
       " 'start_pan',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'sticky_edges',\n",
       " 'streamplot',\n",
       " 'table',\n",
       " 'tables',\n",
       " 'text',\n",
       " 'texts',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'title',\n",
       " 'titleOffsetTrans',\n",
       " 'transAxes',\n",
       " 'transData',\n",
       " 'transLimits',\n",
       " 'transScale',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'update',\n",
       " 'update_datalim',\n",
       " 'update_datalim_bounds',\n",
       " 'update_from',\n",
       " 'use_sticky_edges',\n",
       " 'viewLim',\n",
       " 'violin',\n",
       " 'violinplot',\n",
       " 'vlines',\n",
       " 'xaxis',\n",
       " 'xaxis_date',\n",
       " 'xaxis_inverted',\n",
       " 'xcorr',\n",
       " 'yaxis',\n",
       " 'yaxis_date',\n",
       " 'yaxis_inverted',\n",
       " 'zorder']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(axes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Y2 Plot')"
      ]
     },
     "execution_count": 29,
     "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": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_axes([0, 0, 1, 1])\n",
    "ax2 = fig.add_axes([0.1, 0.6, 0.4, 0.3])\n",
    "ax1.plot(x, y, 'r')\n",
    "ax1.set_xlabel('X')\n",
    "ax1.set_ylabel('Y')\n",
    "ax1.set_title('Y Plot')\n",
    "ax2.plot(x, y2, 'g')\n",
    "ax2.set_xlabel('X')\n",
    "ax2.set_ylabel('Y')\n",
    "ax2.set_title('Y2 Plot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plt.subplot\n",
    "#plt.subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26f3c296e48>]"
      ]
     },
     "execution_count": 34,
     "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": [
    "fig, ax = plt.subplots(1,2)\n",
    "ax[0].plot(x, y, 'r')\n",
    "ax[1].plot(x , y, 'bo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots(1,2)\n",
    "col = ['r','g']\n",
    "data = [y, y2]\n",
    "for i, axes in enumerate(ax):\n",
    "    axes.plot(x, data[i], col[i])\n",
    "    \n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,4), dpi = 100)\n",
    "ax.plot(x, y, 'r', label = 'y')\n",
    "ax.plot(x, y2, 'b', label = 'y*x')\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_title('Random Number Plot')\n",
    "ax.legend(loc = 0)\n",
    "fig.savefig('random file.png', dpi = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,4), dpi = 100)\n",
    "ax.plot(x, y, 'r', label = 'y')\n",
    "ax.plot(x, y2, 'b', label = 'y*x')\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_title('Random Number Plot')\n",
    "ax.legend(loc = 4)\n",
    "fig.savefig('random file.png', dpi = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, markersize = 10, linewidth = 2, color = '#005425')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y, 'b--' ,markersize = 10, linewidth = 2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 4)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 3, figsize = (12, 4))\n",
    "ax[0].plot(x, y, x, y2)\n",
    "ax[1].plot(x, y**2, 'k')\n",
    "ax[1].set_ylim([0, 500])\n",
    "ax[2].plot(x, y, x, y2)\n",
    "ax[2].set_ylim([0, 100])\n",
    "ax[2].set_xlim([1 ,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_add_text',\n",
       " '_adjustable',\n",
       " '_agg_filter',\n",
       " '_alpha',\n",
       " '_anchor',\n",
       " '_animated',\n",
       " '_aspect',\n",
       " '_autoscaleXon',\n",
       " '_autoscaleYon',\n",
       " '_autotitlepos',\n",
       " '_axes',\n",
       " '_axes_class',\n",
       " '_axes_locator',\n",
       " '_axisbelow',\n",
       " '_cachedRenderer',\n",
       " '_clipon',\n",
       " '_clippath',\n",
       " '_connected',\n",
       " '_contains',\n",
       " '_current_image',\n",
       " '_facecolor',\n",
       " '_frameon',\n",
       " '_gci',\n",
       " '_gen_axes_patch',\n",
       " '_gen_axes_spines',\n",
       " '_get_axis_list',\n",
       " '_get_lines',\n",
       " '_get_patches_for_fill',\n",
       " '_get_view',\n",
       " '_gid',\n",
       " '_gridOn',\n",
       " '_hold',\n",
       " '_in_layout',\n",
       " '_init_axis',\n",
       " '_label',\n",
       " '_layoutbox',\n",
       " '_left_title',\n",
       " '_make_twin_axes',\n",
       " '_mouseover',\n",
       " '_mouseover_set',\n",
       " '_navigate',\n",
       " '_navigate_mode',\n",
       " '_oid',\n",
       " '_on_units_changed',\n",
       " '_originalPosition',\n",
       " '_path_effects',\n",
       " '_pcolorargs',\n",
       " '_picker',\n",
       " '_position',\n",
       " '_poslayoutbox',\n",
       " '_process_unit_info',\n",
       " '_prop_order',\n",
       " '_propobservers',\n",
       " '_quiver_units',\n",
       " '_rasterization_zorder',\n",
       " '_rasterized',\n",
       " '_remove_legend',\n",
       " '_remove_method',\n",
       " '_right_title',\n",
       " '_sci',\n",
       " '_set_artist_props',\n",
       " '_set_gc_clip',\n",
       " '_set_lim_and_transforms',\n",
       " '_set_position',\n",
       " '_set_title_offset_trans',\n",
       " '_set_view',\n",
       " '_set_view_from_bbox',\n",
       " '_shared_x_axes',\n",
       " '_shared_y_axes',\n",
       " '_sharex',\n",
       " '_sharey',\n",
       " '_sketch',\n",
       " '_snap',\n",
       " '_stale',\n",
       " '_sticky_edges',\n",
       " '_subplotspec',\n",
       " '_tight',\n",
       " '_transform',\n",
       " '_transformSet',\n",
       " '_twinned_axes',\n",
       " '_update_image_limits',\n",
       " '_update_line_limits',\n",
       " '_update_patch_limits',\n",
       " '_update_title_position',\n",
       " '_update_transScale',\n",
       " '_url',\n",
       " '_use_sticky_edges',\n",
       " '_validate_converted_limits',\n",
       " '_visible',\n",
       " '_xaxis_transform',\n",
       " '_xcid',\n",
       " '_xmargin',\n",
       " '_yaxis_transform',\n",
       " '_ycid',\n",
       " '_ymargin',\n",
       " 'acorr',\n",
       " 'add_artist',\n",
       " 'add_callback',\n",
       " 'add_child_axes',\n",
       " 'add_collection',\n",
       " 'add_container',\n",
       " 'add_image',\n",
       " 'add_line',\n",
       " 'add_patch',\n",
       " 'add_table',\n",
       " 'aname',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'apply_aspect',\n",
       " 'arrow',\n",
       " 'artists',\n",
       " 'autoscale',\n",
       " 'autoscale_view',\n",
       " 'axes',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axison',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bbox',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'bxp',\n",
       " 'callbacks',\n",
       " 'can_pan',\n",
       " 'can_zoom',\n",
       " 'change_geometry',\n",
       " 'child_axes',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clear',\n",
       " 'clipbox',\n",
       " 'cohere',\n",
       " 'colNum',\n",
       " 'collections',\n",
       " 'containers',\n",
       " 'contains',\n",
       " 'contains_point',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'convert_xunits',\n",
       " 'convert_yunits',\n",
       " 'csd',\n",
       " 'dataLim',\n",
       " 'drag_pan',\n",
       " 'draw',\n",
       " 'draw_artist',\n",
       " 'end_pan',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'eventson',\n",
       " 'figbox',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'fmt_xdata',\n",
       " 'fmt_ydata',\n",
       " 'format_coord',\n",
       " 'format_cursor_data',\n",
       " 'format_xdata',\n",
       " 'format_ydata',\n",
       " 'get_adjustable',\n",
       " 'get_agg_filter',\n",
       " 'get_alpha',\n",
       " 'get_anchor',\n",
       " 'get_animated',\n",
       " 'get_aspect',\n",
       " 'get_autoscale_on',\n",
       " 'get_autoscalex_on',\n",
       " 'get_autoscaley_on',\n",
       " 'get_axes_locator',\n",
       " 'get_axisbelow',\n",
       " 'get_children',\n",
       " 'get_clip_box',\n",
       " 'get_clip_on',\n",
       " 'get_clip_path',\n",
       " 'get_contains',\n",
       " 'get_cursor_data',\n",
       " 'get_data_ratio',\n",
       " 'get_data_ratio_log',\n",
       " 'get_default_bbox_extra_artists',\n",
       " 'get_facecolor',\n",
       " 'get_fc',\n",
       " 'get_figure',\n",
       " 'get_frame_on',\n",
       " 'get_geometry',\n",
       " 'get_gid',\n",
       " 'get_gridspec',\n",
       " 'get_images',\n",
       " 'get_in_layout',\n",
       " 'get_label',\n",
       " 'get_legend',\n",
       " 'get_legend_handles_labels',\n",
       " 'get_lines',\n",
       " 'get_navigate',\n",
       " 'get_navigate_mode',\n",
       " 'get_path_effects',\n",
       " 'get_picker',\n",
       " 'get_position',\n",
       " 'get_rasterization_zorder',\n",
       " 'get_rasterized',\n",
       " 'get_renderer_cache',\n",
       " 'get_shared_x_axes',\n",
       " 'get_shared_y_axes',\n",
       " 'get_sketch_params',\n",
       " 'get_snap',\n",
       " 'get_subplotspec',\n",
       " 'get_tightbbox',\n",
       " 'get_title',\n",
       " 'get_transform',\n",
       " 'get_transformed_clip_path_and_affine',\n",
       " 'get_url',\n",
       " 'get_visible',\n",
       " 'get_window_extent',\n",
       " 'get_xaxis',\n",
       " 'get_xaxis_text1_transform',\n",
       " 'get_xaxis_text2_transform',\n",
       " 'get_xaxis_transform',\n",
       " 'get_xbound',\n",
       " 'get_xgridlines',\n",
       " 'get_xlabel',\n",
       " 'get_xlim',\n",
       " 'get_xmajorticklabels',\n",
       " 'get_xminorticklabels',\n",
       " 'get_xscale',\n",
       " 'get_xticklabels',\n",
       " 'get_xticklines',\n",
       " 'get_xticks',\n",
       " 'get_yaxis',\n",
       " 'get_yaxis_text1_transform',\n",
       " 'get_yaxis_text2_transform',\n",
       " 'get_yaxis_transform',\n",
       " 'get_ybound',\n",
       " 'get_ygridlines',\n",
       " 'get_ylabel',\n",
       " 'get_ylim',\n",
       " 'get_ymajorticklabels',\n",
       " 'get_yminorticklabels',\n",
       " 'get_yscale',\n",
       " 'get_yticklabels',\n",
       " 'get_yticklines',\n",
       " 'get_yticks',\n",
       " 'get_zorder',\n",
       " 'grid',\n",
       " 'has_data',\n",
       " 'have_units',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hitlist',\n",
       " 'hlines',\n",
       " 'ignore_existing_data_limits',\n",
       " 'images',\n",
       " 'imshow',\n",
       " 'in_axes',\n",
       " 'indicate_inset',\n",
       " 'indicate_inset_zoom',\n",
       " 'inset_axes',\n",
       " 'invert_xaxis',\n",
       " 'invert_yaxis',\n",
       " 'is_figure_set',\n",
       " 'is_first_col',\n",
       " 'is_first_row',\n",
       " 'is_last_col',\n",
       " 'is_last_row',\n",
       " 'is_transform_set',\n",
       " 'label_outer',\n",
       " 'legend',\n",
       " 'legend_',\n",
       " 'lines',\n",
       " 'locator_params',\n",
       " 'loglog',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mouseover',\n",
       " 'mouseover_set',\n",
       " 'name',\n",
       " 'numCols',\n",
       " 'numRows',\n",
       " 'patch',\n",
       " 'patches',\n",
       " 'pchanged',\n",
       " 'pcolor',\n",
       " 'pcolorfast',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pick',\n",
       " 'pickable',\n",
       " 'pie',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'properties',\n",
       " 'psd',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'redraw_in_frame',\n",
       " 'relim',\n",
       " 'remove',\n",
       " 'remove_callback',\n",
       " 'reset_position',\n",
       " 'rowNum',\n",
       " 'scatter',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set',\n",
       " 'set_adjustable',\n",
       " 'set_agg_filter',\n",
       " 'set_alpha',\n",
       " 'set_anchor',\n",
       " 'set_animated',\n",
       " 'set_aspect',\n",
       " 'set_autoscale_on',\n",
       " 'set_autoscalex_on',\n",
       " 'set_autoscaley_on',\n",
       " 'set_axes_locator',\n",
       " 'set_axis_off',\n",
       " 'set_axis_on',\n",
       " 'set_axisbelow',\n",
       " 'set_clip_box',\n",
       " 'set_clip_on',\n",
       " 'set_clip_path',\n",
       " 'set_contains',\n",
       " 'set_facecolor',\n",
       " 'set_fc',\n",
       " 'set_figure',\n",
       " 'set_frame_on',\n",
       " 'set_gid',\n",
       " 'set_in_layout',\n",
       " 'set_label',\n",
       " 'set_navigate',\n",
       " 'set_navigate_mode',\n",
       " 'set_path_effects',\n",
       " 'set_picker',\n",
       " 'set_position',\n",
       " 'set_prop_cycle',\n",
       " 'set_rasterization_zorder',\n",
       " 'set_rasterized',\n",
       " 'set_sketch_params',\n",
       " 'set_snap',\n",
       " 'set_subplotspec',\n",
       " 'set_title',\n",
       " 'set_transform',\n",
       " 'set_url',\n",
       " 'set_visible',\n",
       " 'set_xbound',\n",
       " 'set_xlabel',\n",
       " 'set_xlim',\n",
       " 'set_xmargin',\n",
       " 'set_xscale',\n",
       " 'set_xticklabels',\n",
       " 'set_xticks',\n",
       " 'set_ybound',\n",
       " 'set_ylabel',\n",
       " 'set_ylim',\n",
       " 'set_ymargin',\n",
       " 'set_yscale',\n",
       " 'set_yticklabels',\n",
       " 'set_yticks',\n",
       " 'set_zorder',\n",
       " 'specgram',\n",
       " 'spines',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stale',\n",
       " 'stale_callback',\n",
       " 'start_pan',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'sticky_edges',\n",
       " 'streamplot',\n",
       " 'table',\n",
       " 'tables',\n",
       " 'text',\n",
       " 'texts',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'title',\n",
       " 'titleOffsetTrans',\n",
       " 'transAxes',\n",
       " 'transData',\n",
       " 'transLimits',\n",
       " 'transScale',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'update',\n",
       " 'update_datalim',\n",
       " 'update_datalim_bounds',\n",
       " 'update_from',\n",
       " 'update_params',\n",
       " 'use_sticky_edges',\n",
       " 'viewLim',\n",
       " 'violin',\n",
       " 'violinplot',\n",
       " 'vlines',\n",
       " 'xaxis',\n",
       " 'xaxis_date',\n",
       " 'xaxis_inverted',\n",
       " 'xcorr',\n",
       " 'yaxis',\n",
       " 'yaxis_date',\n",
       " 'yaxis_inverted',\n",
       " 'zorder']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ax[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Annotation',\n",
       " 'Arrow',\n",
       " 'Artist',\n",
       " 'AutoLocator',\n",
       " 'Axes',\n",
       " 'Button',\n",
       " 'Circle',\n",
       " 'Figure',\n",
       " 'FigureCanvasBase',\n",
       " 'FixedFormatter',\n",
       " 'FixedLocator',\n",
       " 'FormatStrFormatter',\n",
       " 'Formatter',\n",
       " 'FuncFormatter',\n",
       " 'GridSpec',\n",
       " 'IndexLocator',\n",
       " 'Line2D',\n",
       " 'LinearLocator',\n",
       " 'Locator',\n",
       " 'LogFormatter',\n",
       " 'LogFormatterExponent',\n",
       " 'LogFormatterMathtext',\n",
       " 'LogLocator',\n",
       " 'MaxNLocator',\n",
       " 'MultipleLocator',\n",
       " 'Normalize',\n",
       " 'NullFormatter',\n",
       " 'NullLocator',\n",
       " 'Number',\n",
       " 'PolarAxes',\n",
       " 'Polygon',\n",
       " 'Rectangle',\n",
       " 'ScalarFormatter',\n",
       " 'Slider',\n",
       " 'Subplot',\n",
       " 'SubplotTool',\n",
       " 'Text',\n",
       " 'TickHelper',\n",
       " 'Widget',\n",
       " '_INSTALL_FIG_OBSERVER',\n",
       " '_IP_REGISTERED',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__spec__',\n",
       " '_auto_draw_if_interactive',\n",
       " '_autogen_docstring',\n",
       " '_backend_mod',\n",
       " '_get_running_interactive_framework',\n",
       " '_interactive_bk',\n",
       " '_log',\n",
       " '_pylab_helpers',\n",
       " '_setp',\n",
       " '_setup_pyplot_info_docstrings',\n",
       " '_show',\n",
       " '_string_to_bool',\n",
       " 'acorr',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'arrow',\n",
       " 'autoscale',\n",
       " 'autumn',\n",
       " 'axes',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bone',\n",
       " 'box',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clf',\n",
       " 'clim',\n",
       " 'close',\n",
       " 'cm',\n",
       " 'cohere',\n",
       " 'colorbar',\n",
       " 'colormaps',\n",
       " 'connect',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'cool',\n",
       " 'copper',\n",
       " 'csd',\n",
       " 'cycler',\n",
       " 'dedent',\n",
       " 'delaxes',\n",
       " 'deprecated',\n",
       " 'disconnect',\n",
       " 'docstring',\n",
       " 'draw',\n",
       " 'draw_all',\n",
       " 'draw_if_interactive',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'figaspect',\n",
       " 'figimage',\n",
       " 'figlegend',\n",
       " 'fignum_exists',\n",
       " 'figtext',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'flag',\n",
       " 'gca',\n",
       " 'gcf',\n",
       " 'gci',\n",
       " 'get',\n",
       " 'get_backend',\n",
       " 'get_cmap',\n",
       " 'get_current_fig_manager',\n",
       " 'get_figlabels',\n",
       " 'get_fignums',\n",
       " 'get_plot_commands',\n",
       " 'get_scale_docs',\n",
       " 'get_scale_names',\n",
       " 'getp',\n",
       " 'ginput',\n",
       " 'gray',\n",
       " 'grid',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hlines',\n",
       " 'hot',\n",
       " 'hsv',\n",
       " 'importlib',\n",
       " 'imread',\n",
       " 'imsave',\n",
       " 'imshow',\n",
       " 'inferno',\n",
       " 'inspect',\n",
       " 'install_repl_displayhook',\n",
       " 'interactive',\n",
       " 'ioff',\n",
       " 'ion',\n",
       " 'isinteractive',\n",
       " 'jet',\n",
       " 'legend',\n",
       " 'locator_params',\n",
       " 'logging',\n",
       " 'loglog',\n",
       " 'magma',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matplotlib',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mlab',\n",
       " 'new_figure_manager',\n",
       " 'nipy_spectral',\n",
       " 'np',\n",
       " 'pause',\n",
       " 'pcolor',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pie',\n",
       " 'pink',\n",
       " 'plasma',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'plotfile',\n",
       " 'plotting',\n",
       " 'polar',\n",
       " 'prism',\n",
       " 'psd',\n",
       " 'pylab_setup',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'rc',\n",
       " 'rcParams',\n",
       " 'rcParamsDefault',\n",
       " 'rcParamsOrig',\n",
       " 'rc_context',\n",
       " 'rcdefaults',\n",
       " 'rcsetup',\n",
       " 're',\n",
       " 'register_cmap',\n",
       " 'rgrids',\n",
       " 'savefig',\n",
       " 'sca',\n",
       " 'scatter',\n",
       " 'sci',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set_cmap',\n",
       " 'setp',\n",
       " 'show',\n",
       " 'silent_list',\n",
       " 'specgram',\n",
       " 'spring',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'streamplot',\n",
       " 'style',\n",
       " 'subplot',\n",
       " 'subplot2grid',\n",
       " 'subplot_tool',\n",
       " 'subplots',\n",
       " 'subplots_adjust',\n",
       " 'summer',\n",
       " 'suptitle',\n",
       " 'switch_backend',\n",
       " 'sys',\n",
       " 'table',\n",
       " 'text',\n",
       " 'thetagrids',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'tight_layout',\n",
       " 'time',\n",
       " 'title',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'uninstall_repl_displayhook',\n",
       " 'violinplot',\n",
       " 'viridis',\n",
       " 'vlines',\n",
       " 'waitforbuttonpress',\n",
       " 'warn_deprecated',\n",
       " 'warnings',\n",
       " 'winter',\n",
       " 'xcorr',\n",
       " 'xkcd',\n",
       " 'xlabel',\n",
       " 'xlim',\n",
       " 'xscale',\n",
       " 'xticks',\n",
       " 'ylabel',\n",
       " 'ylim',\n",
       " 'yscale',\n",
       " 'yticks']"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAENlJREFUeJzt3W9sXXd9x/H3d24Qlz+TKXWrxqFLkKoMRAWeLNTN0sRatHQCUauiCLahaKqUJ2wrAwUSnqA9alAmYA+maRFlRBqDViW4VYswVVKEtgcdDmaELnjtulJynTXuqAebrJFm3z3wdfMHOz7X98+5/vn9kqp778/n5Hx11Xxy/P2dc36RmUiSNr9fqbsASVJ3GOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpENdU2SgingN+DlwAXs7M8Yi4FngA2Ak8B3wgM1/qTZmSpPW0c4b+O5n5jswcb30+ABzPzJuB463PkqSaRJU7RVtn6OOZ+eIlY3PAuzLzbETcCHw7M3df7c+57rrrcufOnZ1VLElbzMmTJ1/MzJH1tqvUcgES+FZEJPA3mXkEuCEzzwK0Qv369f6QnTt3MjMzU/GQkiSAiPhxle2qBvpEZs63QvvxiPhRG4XsA/YB3HTTTVV3kyS1qVIPPTPnW6/ngK8D7wReaLVaaL2eW2PfI5k5npnjIyPr/sYgSdqgdQM9Il4bEa9feQ/8LvBD4BFgb2uzvcDDvSpSkrS+Ki2XG4CvR8TK9n+fmd+MiO8CD0bEPcDzwN29K1OStJ51Az0znwXevsr4fwK396IoSVL7qk6KSpLaNDXb5PD0HPOLS2wfbrB/z24mx0Z7djwDXZJ6YGq2ycFjp1g6fwGA5uISB4+dAuhZqPssF0nqgcPTc6+E+Yql8xc4PD3Xs2Ma6JLUA/OLS22Nd4OBLkk9sH240dZ4NxjokrSGqdkmE4dOsOvAY0wcOsHUbLPyvvv37Kaxbeiysca2IfbvueojrzripKgkraLTSc2VbbzKRZJqdrVJzaqhPDk22tMAv5ItF0laRR2Tmp0y0CVpFXVManbKQJekVdQxqdkpe+iStIo6JjU7ZaBL0hr6PanZKVsuklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAhXLJI0sKZmmx0tAdfp/puNgS5pIE3NNjl47BRL5y8A0Fxc4uCxUwCVQrnT/TcjWy6SBtLh6blXwnjF0vkLHJ6e68v+m5GBLmkgzS8utTXe7f03o8qBHhFDETEbEY+2Pu+KiCcj4umIeCAiXtW7MiVtNduHG22Nd3v/zaidM/R7gdOXfP4M8LnMvBl4Cbinm4VJqt/UbJOJQyfYdeAxJg6dYGq22bf99+/ZTWPb0GVjjW1D7N+zuy/7b0aVAj0idgDvAb7Q+hzAbcBDrU2OApO9KFBSPVYmFZuLSyQXJxWrhnKn+0+OjXLfXbcwOtwggNHhBvfddUvlCc1O99+Mql7l8nngE8DrW5/fCCxm5sutz2eAcr8laQu62qRilVDsdH9YDuVOArjT/Tebdc/QI+K9wLnMPHnp8Cqb5hr774uImYiYWVhY2GCZkvrNScnNp0rLZQJ4X0Q8B3yV5VbL54HhiFg5w98BzK+2c2YeyczxzBwfGRnpQsmS+sFJyc1n3UDPzIOZuSMzdwIfBE5k5h8ATwDvb222F3i4Z1VK6jsnJTefTq5D/yTwsYh4huWe+v3dKUnSIHBScvOJzFVb3z0xPj6eMzMzfTueJJUgIk5m5vh623mnqCQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiGqLkEnaROamm1yeHqO+cUltg832L9nt4+vLZiBLhVqZZHmlXU9VxZpBgz1QtlykQp1tUWaVSYDXSqUizRvPQa6VCgXad56DHSpUC7SvPU4KSoVamXi06tctg4DXSrY5NioAb6F2HKRpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqxLqBHhGvjoh/ioh/joinIuLPW+O7IuLJiHg6Ih6IiFf1vlxJ0lqqnKH/L3BbZr4deAdwR0TcCnwG+Fxm3gy8BNzTuzKlrWlqtsnEoRPsOvAYE4dOMDXbrLskDbB1Az2X/Xfr47bWfwncBjzUGj8KTPakQmmLmpptcvDYKZqLSyTQXFzi4LFThrrWVKmHHhFDEfF94BzwOPBvwGJmvtza5AzgU/SlLjo8PcfS+QuXjS2dv8Dh6bmaKtKgqxTomXkhM98B7ADeCbxltc1W2zci9kXETETMLCwsbLxSaYuZX1xqa1xqawm6zFyMiG8DtwLDEXFN6yx9BzC/xj5HgCMA4+Pjq4a+VKqp2eaG1/TcPtyguUp4bx9udLtMFaLKVS4jETHcet8A3g2cBp4A3t/abC/wcK+KlDajTnvg+/fsprFt6LKxxrYh9u/Z3YNqVYIqLZcbgSci4gfAd4HHM/NR4JPAxyLiGeCNwP29K1PafDrtgU+OjXLfXbcwOtwggNHhBvfddYuLPmtN67ZcMvMHwNgq48+y3E+XtIpu9MAnx0YNcFXmnaJSj6zV67YHrl4x0KUesQeufmvrKhdJ1a20SjZ6lYvULgNd6iF74OonWy6SVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgrhEnTSVUzNNl0TVJuGgS6tYWq2ycFjp1g6fwGA5uISB4+dAjDUNZBsuUhrODw990qYr1g6f4HD03M1VSRdnYEurWF+camtcaluBrq0hu3DjbbGpboZ6NIa9u/ZTWPb0GVjjW1D7N+zu6aKpKtzUlRaw8rEp1e5aLMw0KWrmBwbNcC1adhykaRCGOiSVAgDXZIKYaBLUiHWDfSIeFNEPBERpyPiqYi4tzV+bUQ8HhFPt17f0PtyJUlrqXKG/jLw8cx8C3Ar8JGIeCtwADiemTcDx1ufJUk1WTfQM/NsZn6v9f7nwGlgFLgTONra7Cgw2asiJUnra6uHHhE7gTHgSeCGzDwLy6EPXN/t4iRJ1VUO9Ih4HfA14KOZ+bM29tsXETMRMbOwsLCRGiVJFVQK9IjYxnKYfzkzj7WGX4iIG1s/vxE4t9q+mXkkM8czc3xkZKQbNUuSVlHlKpcA7gdOZ+ZnL/nRI8De1vu9wMPdL0+SVFWVZ7lMAB8GTkXE91tjnwIOAQ9GxD3A88DdvSlRklTFuoGemf8AxBo/vr275UiSNso7RSWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgpR5fG50qY1Ndvk8PQc84tLbB9usH/PbibHRusuS+oJA13FmpptcvDYKZbOXwCgubjEwWOnAAx1FcmWi4p1eHrulTBfsXT+Aoen52qqSOotA13Fml9camtc2uxsuWigddID3z7coLlKeG8fbnS7TGkgeIaugbXSA28uLpFc7IFPzTYr7b9/z24a24YuG2tsG2L/nt09qFaqn4GugdVpD3xybJT77rqF0eEGAYwON7jvrlucEFWxbLloYHWjBz45NmqAa8vwDF0Da61etz1waXUGugaWPXCpPbZcNLBWWiXe6SlVY6BroNkDl6qz5SJJhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYVY907RiPgi8F7gXGa+rTV2LfAAsBN4DvhAZr7UuzJVl04XWXaRZql/qpyhfwm444qxA8DxzLwZON76rMJ0usBEp/tLas+6gZ6Z3wF+esXwncDR1vujwGSX69IA6HSBCRdplvproz30GzLzLEDr9fq1NoyIfRExExEzCwsLGzyc6tDpAhMu0iz1V88nRTPzSGaOZ+b4yMhIrw+nLup0gQkXqJD6a6OB/kJE3AjQej3XvZJ0qanZJhOHTrDrwGNMHDrRdv+5k/07XWDCBSqk/tro89AfAfYCh1qvD3etIr1iZVJxpQ+9MqkIVLpSpNP9O11gwgUqpP6KzLz6BhFfAd4FXAe8AHwamAIeBG4CngfuzswrJ05/yfj4eM7MzHRY8tYxcegEzVX6zaPDDf7xwG0931/SYIiIk5k5vt52656hZ+aH1vjR7W1XpbY4KSmpHd4pOsCclJTUDgN9gDkpKakdLhI9wJyUlNSOdSdFu8lJUUlqX9cmRdUZH04lqV8M9B7q9DpwSWqHk6I95MOpJPWTgd5DXgcuqZ8M9B7yOnBJ/WSg95DXgUvqJydFe8jrwCX1k4HeY5Njowa4pL6w5SJJhTDQJakQBrokFcJAl6RCGOiSVIjir3Lp9OFYPlxL0mZRdKDXvciyJPVT0S2XTh+O5cO1JG0mRQe6iyxL2kqKDnQXWZa0lRQd6C6yLGkrKXpS1EWWJW0lLhItSQOu6iLRRbdcJGkrGfiWizf2SFI1Ax3o3tgjSdUNdMvFG3skqbqBDnRv7JGk6gY60L2xR5KqG+hA98YeSapuoCdFvbFHkqob6ECH5VA3wCVpfR21XCLijoiYi4hnIuJAt4qSJLVvw4EeEUPAXwG/B7wV+FBEvLVbhUmS2tPJGfo7gWcy89nM/AXwVeDO7pQlSWpXJ4E+Cvzkks9nWmOXiYh9ETETETMLCwsdHE6SdDWdBHqsMvZLj27MzCOZOZ6Z4yMjIx0cTpJ0NZ1c5XIGeNMln3cA81fb4eTJky9GxI87OOYguA54se4iBojfx0V+F5fz+7io0+/i16pstOHnoUfENcC/ArcDTeC7wO9n5lMb+gM3iYiYqfJc4q3C7+Miv4vL+X1c1K/vYsNn6Jn5ckT8MTANDAFfLD3MJWmQdXRjUWZ+A/hGl2qRJHVgoJ/lMqCO1F3AgPH7uMjv4nJ+Hxf15bvo65qikqTe8QxdkgphoFcUEW+KiCci4nREPBUR99ZdU90iYigiZiPi0bprqVtEDEfEQxHxo9b/I79Zd011iYg/a/0d+WFEfCUiXl13Tf0UEV+MiHMR8cNLxq6NiMcj4unW6xt6cWwDvbqXgY9n5luAW4GP+Owa7gVO113EgPhL4JuZ+evA29mi30tEjAJ/Coxn5ttYvgLug/VW1XdfAu64YuwAcDwzbwaOtz53nYFeUWaezczvtd7/nOW/sFv2ub4RsQN4D/CFumupW0T8KvDbwP0AmfmLzFyst6paXQM0WveqvIZ1bjgsTWZ+B/jpFcN3Akdb748Ck704toG+ARGxExgDnqy3klp9HvgE8H91FzIA3gwsAH/bakF9ISJeW3dRdcjMJvAXwPPAWeC/MvNb9VY1EG7IzLOwfHIIXN+LgxjobYqI1wFfAz6amT+ru546RMR7gXOZebLuWgbENcBvAH+dmWPA/9CjX6kHXas3fCewC9gOvDYi/rDeqrYOA70NEbGN5TD/cmYeq7ueGk0A74uI51h+bPJtEfF39ZZUqzPAmcxc+Y3tIZYDfit6N/DvmbmQmeeBY8Bv1VzTIHghIm4EaL2e68VBDPSKIiJY7pGezszP1l1PnTLzYGbuyMydLE94ncjMLXsWlpn/AfwkIlZWL78d+JcaS6rT88CtEfGa1t+Z29miE8RXeATY23q/F3i4FwcZ+DVFB8gE8GHgVER8vzX2qdbjD6Q/Ab4cEa8CngX+qOZ6apGZT0bEQ8D3WL4ybJYtdsdoRHwFeBdwXUScAT4NHAIejIh7WP5H7+6eHNs7RSWpDLZcJKkQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYX4f7Oaoiul9gR5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  4,  7,  9,  9, 10, 10, 14, 15, 23, 29, 30, 35, 40, 40, 41,\n",
       "       41, 47, 48])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 20 artists>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.bar(y, height=10, width=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9616, 4369, 6839, 7021, 8366, 7784, 2521, 8010, 6596, 491]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = sample(range(1, 10000), 10)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "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.hist(data, rwidth=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = [np.random.normal(0, std, 100) for std in range(1,3)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-0.15026254, -0.91303282,  1.39050564, -0.89146729,  0.09503126,\n",
       "         2.98923768, -0.00535862,  0.10627922,  0.52065107,  0.99575005,\n",
       "         0.36528997,  0.50578285,  1.4612474 ,  0.31475321, -0.61748661,\n",
       "         0.99331767,  1.41906681, -1.01445401,  1.69967059,  0.4015416 ,\n",
       "        -0.37954989,  0.44111855,  1.31691407, -1.23720567, -2.21497486,\n",
       "        -0.55066684,  1.12538851, -1.87066005, -1.39583442, -0.36645314,\n",
       "        -0.69075495,  1.48093853,  0.19813355, -0.58002476, -0.43237686,\n",
       "         0.68634407, -0.66627185,  0.23418729, -1.17486275,  0.34201776,\n",
       "         0.76477748,  1.17223311,  0.32890807,  0.81629706,  0.78105465,\n",
       "         0.25619567, -0.22852119, -0.69565346,  0.23096463,  0.69518152,\n",
       "        -0.90314225, -0.98804774,  0.61858964,  1.14251455,  0.20645582,\n",
       "        -1.31059822,  0.53484522,  0.36368647,  0.46845127,  0.59017814,\n",
       "         1.38338324,  0.62989708, -0.91352915, -0.7065873 , -0.04745956,\n",
       "        -1.07164304, -1.59163364,  0.16494412, -0.67164224,  1.04112844,\n",
       "         2.63443638,  0.96877076,  0.18020279,  0.39523754,  0.00438327,\n",
       "         0.06342923, -0.80558575,  0.12887839, -0.17154149,  0.02092808,\n",
       "         1.95117564,  0.41100062, -0.54402853,  0.03596272, -0.22248689,\n",
       "         0.40453835,  0.67567175, -0.87196947, -0.50538701, -0.30704379,\n",
       "        -1.25612116, -0.07617727, -0.55085423, -0.64928879, -0.37238256,\n",
       "         0.59016592,  0.53423818, -0.29239499, -0.25093703, -1.73079529]),\n",
       " array([ 0.40220804, -1.77893893,  2.12536222,  2.86115957, -1.12864547,\n",
       "         1.19494212,  0.13411758,  0.85318661,  0.24759522, -1.43384075,\n",
       "         2.92991237,  1.67091169,  3.10214368,  1.43498435,  3.62920406,\n",
       "         1.63764253,  1.17733398,  2.16827263,  0.45423012,  1.38375638,\n",
       "         1.61221697, -1.27102529, -0.1739158 ,  0.73021681, -0.4551833 ,\n",
       "        -2.90426799,  1.45955548,  0.24535449,  0.9911229 ,  1.72447949,\n",
       "        -4.69118605, -2.18994192, -1.28590193, -0.37189137, -1.71704039,\n",
       "         0.58120434,  1.43312501,  1.84241806,  0.83951116, -0.83859305,\n",
       "         0.50163869,  0.09901592, -0.59861849,  1.10359912,  1.52838981,\n",
       "         1.18499399, -0.61096509, -1.51586423, -0.88894584, -0.44754289,\n",
       "         0.92491372, -0.21820684,  5.89410179, -0.28803201,  0.66854822,\n",
       "        -1.69891004,  4.79838512,  0.1193721 , -1.38646812, -1.49053858,\n",
       "         2.98510565, -1.03614752,  0.19742215, -1.60593554, -0.12613841,\n",
       "         4.5759156 ,  3.1526547 ,  2.14390793,  1.8761106 ,  2.01737764,\n",
       "        -0.49893499,  2.86911201, -1.03689837,  0.33109748,  3.42732719,\n",
       "        -0.19272833, -0.689505  ,  2.93923565, -0.21449239, -2.10798507,\n",
       "        -0.51506969, -0.07320276,  2.14557824, -2.62062068, -1.18006692,\n",
       "         1.80479043, -0.69643083, -0.19471433, -3.03827894,  0.55969295,\n",
       "        -0.46567874,  2.35829489, -0.89856564,  1.92902341, -2.30897985,\n",
       "        -1.18993621,  0.25313199, -1.15154662,  1.30626114,  1.52567446])]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "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.boxplot(data, vert = True, patch_artist= True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize= (10, 4))\n",
    "ax[0].plot(x, y, x, y2)\n",
    "ax[1].plot(x, np.exp(x))\n",
    "ax[1].set_yscale('log')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize= (10, 4))\n",
    "ax[0].plot(x, y, x, y2)\n",
    "ax[1].plot(x, np.exp(x))\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,5))\n",
    "ax.plot(x, y2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAEyCAYAAADJI8VDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl4VdWh/vF3ZR4hM4QECIRZkCkEp7YqWpU6Kw7ViorFtrbW2tqq19p6a28dbmv1d62WqoiiIiIW56EqzgIJMxJkCiQQyBwykOmc9fsjG0SJJEBO9jk538/z8Jx99tlJ3jQ+ydu11lnbWGsFAACArhXidgAAAICeiJIFAADgA5QsAAAAH6BkAQAA+AAlCwAAwAcoWQAAAD5AyQIAAPABShYAAIAPdKpkGWMKjTFrjDErjTF5zrkkY8w7xpiNzmOic94YYx4yxmwyxqw2xkzw5TcAAADgj0xndnw3xhRKyrHWlh9w7j5Jldbae4wxt0pKtNb+zhgzVdIvJE2VNFnSg9bayYf6/CkpKTYrK+vIvwsAAIBukp+fX26tTe3ourCj+BrnSTrZOZ4jabGk3znnn7Jt7e1zY0yCMSbdWlvybZ8oKytLeXl5RxEFAACgexhjtnXmus6uybKS3jbG5BtjZjrn+uwrTs5jmnM+Q1LRAR9b7Jz7ZsCZxpg8Y0xeWVlZJ2MAAAAEhs6OZJ1ord1pjEmT9I4xpuAQ15p2zh00J2mtnSVpliTl5ORwl2oAANCjdGoky1q703kslfSSpFxJu40x6ZLkPJY6lxdL6n/Ah2dK2tlVgQEAAAJBhyXLGBNrjInfdyzp+5LWSnpZ0nTnsumSFjnHL0u6ynmX4XGSag61HgsAAKAn6sx0YR9JLxlj9l3/rLX2TWPMMknzjTEzJG2XNM25/nW1vbNwk6QGSdd0eWoAAAA/12HJstZukTS2nfMVkqa0c95KuqFL0gEAAAQodnwHAADwAUoWAACAD1CyAAAAfICSBQAAAp7Xa/Xsku3aWb3X7Sj7UbIAAEBAK9i1Rxc/+qluf2mN5i0r6vgDusnR3LsQAADANXubPXrovY3614dbFB8Vpr9OG6sLJxx0Jz/XULIAAEDAWbyhVL9ftFZFlXs1bWKmbps6UkmxEW7H+hpKFgAACBiltY3606vr9cqqnRqcGqvnfnycjs9OdjtWuyhZAADA73m9Vs8u3a573yxQU4tXvzptmH5y8mBFhoW6He1bUbIAAIBfK9i1R7cvXKPl26t1/OBk3X3BaGWnxrkdq0OULAAA4Jf2Nnv04Lsb9dhHX1/Y7txP2e9RsgAAgN9ZvKFUd/x7rYqr/Hdhe0coWQAAwG+U7mnUf7/6hV5dXaLs1FjNm3mcjhvsnwvbO0LJAgAArvvawvZWr24+fZiu/55/L2zvCCULAAC4qmDXHt22cI1WbK/WCdnJuvv80RocAAvbO0LJAgAArmhobnUWtm9V7+hw/e2SsbpgfOAsbO8IJQsAAHS79zeU6vfOwvZLcjJ121kjlRhgC9s7QskCAADdpnRPo+569Qu91gMWtneEkgUAAHzO67V6Zul23fdGgZo8Xv369GGaGeAL2ztCyQIAAD61vmSPbn+pbWH7iUOSdff5YzQoJdbtWD5HyQIAAD7z2Edb9Jc3CtQ7OlwPXDpW54/rOQvbO0LJAgAAPpG/rUr/8/p6TRnZR/dddGyPW9jeEUoWAADocnubPfrNC6uU3jtaD1w6TnGRwVc5gu87BgAAPnf/Wxu0tbxez143OSgLliSFuB0AAAD0LEu2VGj2p1t11fEDdcKQFLfjuIaSBQAAukx9U6t+s2CV+ifG6NazRrgdx1XBOX4HAAB84p43ClRctVfPzzxeMRHBXTMYyQIAAF3ik03levrzbbr2xEHKHZTkdhzXUbIAAMBRq21s0W8XrNbglFjdcsZwt+P4heAexwMAAF3iz6+tV0nNXi346QmKCu+5t8o5HIxkAQCAo/L+hlLNW1akmd/N1oQBiW7H8RuULAAAcMRqGlp064urNTQtTjedNtTtOH6F6UIAAHDE7np1ncrrmvXYVZOYJvwGRrIAAMAReeeL3Vq4fIduODlbYzJ7ux3H71CyAADAYauqb9ZtC9doZHov/fxUpgnbw3QhAAA4bHe+vE41e5v11LW5ighjzKY9/K8CAAAOy+trSvTKqp268dShGtWvl9tx/BYlCwAAdFp5XZPu+PdajcnorZ+cnO12HL9GyQIAAJ1irdUdL61VXWOr/nrJWIWHUiMOhf91AABAp7y8aqfeXLdLN39/mIb1iXc7jt+jZAEAgA6V7mnUnYvWafyABP34O4PdjhMQKFkAAOCQrLW6beEaNbZ49L/Txio0xLgdKSB0umQZY0KNMSuMMa86zwcZY5YYYzYaY543xkQ45yOd55uc17N8Ex0AAHSHBfnFeregVL89c4SyU+PcjhMwDmck65eS1h/w/F5JD1hrh0qqkjTDOT9DUpW1doikB5zrAABAACqp2av/fuUL5WYl6ZoTstyOE1A6VbKMMZmSfiDpMee5kXSqpAXOJXMkne8cn+c8l/P6FOd6AAAQQKy1+u2C1Wr1Wt0/7ViFME14WDo7kvV3Sb+V5HWeJ0uqtta2Os+LJWU4xxmSiiTJeb3GuR4AAASQecuK9NHGct02dYQGJse6HSfgdFiyjDFnSyq11uYfeLqdS20nXjvw8840xuQZY/LKyso6FRYAAHSPosoG3f3qFzohO1lXTh7odpyA1JmRrBMlnWuMKZQ0T23ThH+XlGCM2Xfvw0xJO53jYkn9Jcl5vbekym9+UmvtLGttjrU2JzU19ai+CQAA0HW83rZpQkm672KmCY9UhyXLWnubtTbTWpsl6TJJ71lrr5D0vqSLncumS1rkHL/sPJfz+nvW2oNGsgAAgH+au2SbPttSoTvOHqXMxBi34wSso9kn63eSbjbGbFLbmqvHnfOPS0p2zt8s6dajiwgAALpLYXm9/vJ6gb43LFWXTervdpyAFtbxJV+x1i6WtNg53iIpt51rGiVN64JsAACgG3m8VrcsWKWwUKN7LhojNgc4Ouz4DgAAJEmzP9mqZYVV+sM5xyi9d7TbcQIeJQsAAGhzWZ3uf2uDThuZposmZHT8AegQJQsAgCDX6vHq1/NXKToiVP9zIdOEXeWw1mQBAICe518fbdXKomo9eNk4pcVHuR2nx2AkCwCAILZhV60eeOdLnTW6r84d28/tOD0KJQsAgCDV4vHq1y+sVHxUmO4+fzTThF2M6UIAAILUI4s3a+2OPXrkiglKjot0O06Pw0gWAABBaN3OGj307kadO7afzhqT7nacHomSBQBAkGlubXs3YWJshO469xi34/RYTBcCABAktlc0aMHyYr2YX6wd1Xv12FU5SoyNcDtWj0XJAgCgB6tvatXra0q0IL9YS7ZWyhjppCEpuvOcUTptVB+34/VolCwAAHoYr9dqaWGlXsgr1htrS9TQ7NGglFjdcsZwXTA+Q/0SuGVOd6BkAQDQQxRVNujF5cV6cXmxiir3Ki4yTOeN66eLJ2ZqwoBEtmjoZpQsAAACWENzq95Ys0sL8ov12ZYKGSOdkJ2sX58+XGcc01fREaFuRwxalCwAAAKMtVbLCqu0IL9Ir60uUX2zRwOTY/Tr04fpggkZykyMcTsiRMkCACBg7Kjeqxfz26YDt1U0KDYiVD84Nl0XT+yvSVlMB/obShYAAH5sb7NHb65re3fgp5srZK10/OBk/XLKUJ05uq9iIvhT7q/4yQAA4GestcrfVqUF+cV6dXWJ6ppa1T8pWjdNGaYLJ2SofxLTgYGAkgUAgJ/YWb1XL63YoQX5xdpaXq+YiFBNHZOuiydmKjcrSSEhTAcGEkoWAAAu21XTqAff/VLz84rl8VpNHpSkG04ZorNG91VsJH+qAxU/OQAAXFLd0KxHPtisJz8plNdaXTl5gGacNFgDkpkO7AkoWQAAdLO9zR7N/nSrHl28WbVNrbpgXIZ+dfow1lr1MJQsAAC6SYvHq+eXFemhdzeqtLZJU0ak6TdnDNfI9F5uR4MPULIAAPAxr9fqtTUl+uvbG1RY0aCcgYl6+IoJmpSV5HY0+BAlCwAAH7HW6qON5brvrQKt3bFHw/vE6/HpOTp1RBobhwYBShYAAD6wsqha975RoM+2VCgjIVp/u2SszhuXoVC2YQgalCwAALrQptI6/fXtDXpj7S4lx0boD+eM0g8nD1BkGDdqDjaULAAAukBJzV79/Z2NeiG/SNHhobrptKG67juDFcc+V0GLnzwAAEehuqFZ/1i8WU9+WihZafoJWfr5KUOUHBfpdjS4jJIFAMARaGhu1exPCvXoB5tV19SqC8dn6qbThrLXFfajZAEAcBhaPF7Nc/a6Kqtt0mkj++iWM4ZreN94t6PBz1CyAADoBK/X6lVnr6ttFQ2alJWoR66YoBz2usK3oGQBAHAI1lp9uLFc971ZoHU792hE33jNvnqSTh6eyl5XOCRKFgAA7ajZ26LX15Rofl6RVmyvVv+kaP390nE6d2w/hbDXFTqBkgUAgKPF49WHX5Zp4fIdemf9bjW3ejUkLU53nXuMLs8doIiwELcjIoBQsgAAQc1aq3U79+jF5cV6eeVOVdQ3Kyk2Qj/MHaALJ2RoTEZvpgVxRChZAICgtKumUf9euUMLlxfry911iggN0ZSRabpwQqa+NyyVUSscNUoWACBoNDS36q11u7Rw+Q59vKlc1koTBybqzxeM1tlj+ql3TLjbEdGDULIAAD2ax2v1+ZYKLVy+Q2+sLVFDs0eZidH6xalDdcH4DA1KiXU7InooShYAoEfaVFqrF5fv0L9X7FBJTaPiI8N07th+unBCpnIGJvIOQfgcJQsA0GNU1DXplVU7tXDFDq0urlFoiNF3h6bo9qkjdfqoPooKD3U7IoIIJQsAENCaWj16b32pXly+Q4s3lKrVa3VMv1664wcjde64fkqLj3I7IoIUJQsAEHCstVq+vVoLlxfr1dUlqtnborT4SM04aZAumJChEX17uR0R6LhkGWOiJH0oKdK5foG19g/GmEGS5klKkrRc0o+stc3GmEhJT0maKKlC0qXW2kIf5QcABBmv1+o3L6zSwhU7FBUeojOP6asLJ2TqxCEpCmWdFfxIZ0aymiSdaq2tM8aES/rYGPOGpJslPWCtnWeMeVTSDEmPOI9V1tohxpjLJN0r6VIf5QcABBFrrX6/aK0Wrtihn52crZ+dMkRxkUzKwD91uNOabVPnPA13/llJp0pa4JyfI+l85/g857mc16cYtsoFABwla63uebNAzyzZruu/N1i3nDGcggW/1qntbI0xocaYlZJKJb0jabOkamttq3NJsaQM5zhDUpEkOa/XSEpu53PONMbkGWPyysrKju67AAD0eP9YvFn//GCLrpg8QLeeOYJb3cDvdapkWWs91tpxkjIl5Uoa2d5lzmN7/9Xbg05YO8tam2OtzUlNTe1sXgBAEHryk626/60NOn9cP/3pvNEULASEw7oxk7W2WtJiScdJSjDG7BunzZS00zkultRfkpzXe0uq7IqwAIDg80Jekf74yhc6fVQf3T9tLJuIImB0WLKMManGmATnOFrSaZLWS3pf0sXOZdMlLXKOX3aey3n9PWvtQSNZAAB05I01Jfrdi6t10pAU/b/Lxys8lJs2I3B0ZsVguqQ5xphQtZWy+dbaV40xX0iaZ4y5W9IKSY871z8u6WljzCa1jWBd5oPcAIAebvGGUt04b4XG9U/QrKsmsls7Ak6HJctau1rS+HbOb1Hb+qxvnm+UNK1L0gEAgtLSrZX6ydx8DU2L1+xrchUTwbsIEXgYdwUA+JU1xTW69sll6pcQradm5Kp3dLjbkYAjQskCAPiNjbtrddUTS9Q7OlxzZ0xWSlyk25GAI0bJAgD4he0VDbrisSUKCw3RM9dNVr+EaLcjAUeFkgUAcN2umkZd8fjnavZ4NXfGZGWlxLodCThqlCwAgKsq6pp05eNLVFnXrDnX5Gp433i3IwFdgrdrAABcs6exRdNnL1VRZYOevCZXY/snuB0J6DKMZAEAXNHQ3KprZy9TQUmtHr1yoo7PPug2t0BAo2QBALpdU6tH1z+dr+Xbq/T3y8bplBFpbkcCuhzThQCAbtXq8eqXz63URxvLdd9Fx+rsY/u5HQnwCUayAADdxuu1+u2Lq/Xmul36/dmjdMmk/m5HAnyGkgUA6BbWWt31yjotXL5DvzptmGacNMjtSIBPUbIAAN3if9/eoDmfbdN1Jw3SjVOGuB0H8DlKFgDA5x5ZvFkPv79Zl03qr//6wUgZY9yOBPgcJQsA4FNPf75N975ZoLOPTdefLxhDwULQoGQBAHzmpRXFunPRWk0ZkaYHLh2n0BAKFoIHJQsA4BNvrdul37ywWscNStbDV0xQeCh/chBc+C8eANDlPt5Yrl88u0JjMnrrX9NzFBUe6nYkoNtRsgAAXSp/W6V+/FSeBqfG6slrJikukn2vEZwoWQCALrNuZ42unr1MfXpF6qkZuUqIiXA7EuAaShYAoEt8ubtWVz2+VPGRYZp73WSlxUe5HQlwFSULAHDU1u2s0WWzPldoiNHc6yYrMzHG7UiA6yhZAICjsqqoWpfP+lxRYSGaf/3xGpwa53YkwC+wGhEAcMTyCit19exlSowN17PXHaf+SYxgAftQsgAAR+SzzRWaMWeZ+vSK0rM/nqz03tFuRwL8CtOFAIDD9sGXZbp69lJlJETr+ZnHUbCAdjCSBQA4LP/5Yrd+9sxyZafFae6MXCXHRbodCfBLjGQBADrtjTUl+sncfI1Mj9dzP55MwQIOgZEsAECnLFq5QzfPX6Vx/RM0+5pJ6hUV7nYkwK8xkgUA6ND8ZUW66fmVmpSVqKeuzaVgAZ3ASBYA4JDmfr5Nd/x7rb4zNEWzfpSj6Ahu9gx0BiULAPCtHv94q/706heaMiJND18xQVHhFCygsyhZAIB2/WPxJt335gadNbqvHrxsvCLCWGECHA5KFgDga6y1+vt/NurBdzfqvHH99NdpYxUWSsECDhclCwCwn7VW9765QY9+sFnTJmbqnouOVWiIcTsWEJAoWQAASW0F665XvtCTnxbqiskD9KfzRiuEggUcMUoWAEBer9Udi9bq2SXbde2Jg/T7s0fKGAoWcDQoWQAQ5Dxeq9+9uFoL8ov105Oz9dszhlOwgC5AyQKAINbi8erm+av0yqqd+tVpw3TjlCEULKCLULIAIEg1t3p143Mr9Oa6XfrdmSP005Oz3Y4E9CiULAAIQo0tHv3smeV6r6BUd549SteeNMjtSECPQ8kCgCCzt9mjmU/n6aON5frzBaN1xeSBbkcCeiRKFgAEkbqmVs14cpmWFVbq/ouP1bSc/m5HAnosShYABIk9jS26+omlWlVcowcuHafzxmW4HQno0Tq8T4Ixpr8x5n1jzHpjzDpjzC+d80nGmHeMMRudx0TnvDHGPGSM2WSMWW2MmeDrbwIAcGjVDc268rElWrOjRg//cDwFC+gGnbkZVaukX1trR0o6TtINxphRkm6V9K61dqikd53nknSWpKHOv5mSHuny1ACATquoa9Jlsz5Xwa5a/fNHE3Xm6HS3IwFBocPpQmttiaQS57jWGLNeUoak8ySd7Fw2R9JiSb9zzj9lrbWSPjfGJBhj0p3PAwDoJmW1TVqQX6y5n29TRX2THrsqR98dlup2LCBoHNaaLGNMlqTxkpZI6rOvOFlrS4wxac5lGZKKDviwYufc10qWMWam2ka6NGDAgCOIDgD4Jo/X6qONZZq3tEj/Wb9brV6r3EFJevCyccrJSnI7HhBUOl2yjDFxkl6UdJO1ds8hdgRu7wV70AlrZ0maJUk5OTkHvQ4A6LySmr16Ia9Yzy8r0o7qvUqKjdA1J2bp0kkDNCQtzu14QFDqVMkyxoSrrWA9Y61d6JzevW8a0BiTLqnUOV8s6cD3BGdK2tlVgQEAbVo9Xr2/oUzzlm7X+xtK5bXSSUNSdNvUETp9VB9FhoW6HREIah2WLNM2ZPW4pPXW2r8d8NLLkqZLusd5XHTA+Z8bY+ZJmiyphvVYANB1iiobND+vSPPzirR7T5NS4yP1k+9l69JJ/TUwOdbteAAcnRnJOlHSjyStMcasdM7drrZyNd8YM0PSdknTnNdelzRV0iZJDZKu6dLEABCEmlu9+s/63Xpu6XZ9vKlcknTysFT993kDdOqINIWHdubN4gC6U2feXfix2l9nJUlT2rneSrrhKHMBACRtLa/XvGXb9WJ+scrrmpXeO0o3njpUl0zqr4yEaLfjATgEdnwHAD/T2OLRW+t26bml2/X5lkqFhhidOiJNP8wdoO8OS1VoyLe+8QiAH6FkAYCf2Li7Vs8tLdLCFcWqbmhR/6Ro3XLGcF08MVN9ekW5HQ/AYaJkAYCL9jZ79NqaEj23dLvyt1UpPNTo+6P66vLcATohO1khjFoBAYuSBQAuKKps0KwPt+jfK3eotrFVg1NidfvUEbpwQqZS4iLdjgegC1CyAKCbrd1Ro6ueWKq6plZNHd02apU7KEmH2OQZQACiZAFAN1qypUIz5uSpd3S4XvzpCRqUwr5WQE9FyQKAbvJewW79dO5yZSZGa+51k5Xemy0YgJ6MkgUA3WDRyh369fxVGpneS3OuzVVSbITbkQD4GCULAHzs6c+36c5Fa5WblaTHpucoPirc7UgAugElCwB8xFqrfyzerPvf2qDTRqbp/344QVHh3LQZCBaULADwAWut/vJGgWZ9uEXnj+un+6eN5f6CQJChZAFAF/N4rW5fuEbP5xXpquMH6o/nHMOmokAQomQBQBdqavXoV8+v1OtrdunGU4foV6cPY/8rIEhRsgCgizQ0t+r6p/P10cZy3fGDkbruO4PdjgTARZQsAOgCNQ0tuubJpVpZVK37LjpWl0zq73YkAC6jZAHAUSqtbdRVjy/VlrJ6/eOKCTpzdLrbkQD4AUoWAByFosoGXfn4EpXVNunxq3P0naGpbkcC4CcoWQBwhDburtWVjy/R3maP5l43WRMGJLodCYAfoWQBwBFYVVSt6bOXKjw0RPN/crxG9O3ldiQAfoaSBQCH6dPN5frxnDwlxUVo7ozJGpgc63YkAH6IkgUAh+Htdbv08+dWaGBSjJ6eMVl9e0e5HQmAn6JkAUAnLVxerFsWrNbojN568upJSoyNcDsSAD9GyQKATnjyk6364ytf6ITsZM26Kkdxkfz6BHBo/JYAgEOw1uqhdzfpgf98qe+P6qOHLh+vqPBQt2MBCACULAD4Fl6v1d2vrdcTn2zVhRMydN9FxyosNMTtWAACBCULANrR6vHq1oVrtCC/WFefkKU7zx6lkBBu9Ayg8yhZAPANjS0e3fjcCr39xW7ddNpQ/XLKUBlDwQJweChZAHCAuqZWXf90nj7ZVKE7zx6la08a5HYkAAGKkgUAjuqGZk2fvUxrd9Tor9PG6qKJmW5HAhDAKFkAIGlPY4uuemKpCkpq9Y8rJuiMY/q6HQlAgKNkAQh6dU2tmv7EUq0v2aNHr5yoKSP7uB0JQA9AyQIQ1BqaW3XN7KVaXVyjh384gYIFoMuw4QuAoNXY4tF1c/KUv61Kf790nM4czRQhgK7DSBaAoNTY4tGPn8rTZ1sq9LdLxuqcsf3cjgSgh2EkC0DQaW716mfPLNdHG8t174XH6oLxvIsQQNejZAEIKi0er37x3HK9V1Cqu88frUsm9Xc7EoAeipIFIGi0ery66fmVemvdbv3hnFG68riBbkcC0INRsgAEBY/X6pYFq/Xa6hLdPnWErjmRndwB+BYlC0CP5/Va3friar20YoduOWO4Zn432+1IAIIAJQtAj2at1R2L1uqF/GLdOGWobjhliNuRAAQJShaAHstaq7te+ULPLtmun56crV+dNtTtSACCCCULQI9krdVf3ijQk58WasZJg/TbM4bLGON2LABBpMOSZYx5whhTaoxZe8C5JGPMO8aYjc5jonPeGGMeMsZsMsasNsZM8GV4AGiPtVb/+/YGzfpwi646fqDu+MFIChaAbteZkawnJZ35jXO3SnrXWjtU0rvOc0k6S9JQ599MSY90TUwA6LyH3t2kh9/frMtz++uP5xxDwQLgig5LlrX2Q0mV3zh9nqQ5zvEcSecfcP4p2+ZzSQnGmPSuCgsAHfnH4k164D9f6uKJmfrz+WMUEkLBAuCOI12T1cdaWyJJzmOacz5DUtEB1xU75wDA5x77aIvue3ODzhvXT/dedCwFC4Crunrhe3u/0Wy7Fxoz0xiTZ4zJKysr6+IYAILNU58V6u7X1mvqmL7667SxCqVgAXDZkZas3fumAZ3HUud8saQDbwSWKWlne5/AWjvLWptjrc1JTU09whgAID27ZLvuXLROp4/qowcvG6+wUN44DcB9R/qb6GVJ053j6ZIWHXD+KuddhsdJqtk3rQgAvrAgv1j/9e81OmV4qv7vh+MVTsEC4CfCOrrAGPOcpJMlpRhjiiX9QdI9kuYbY2ZI2i5pmnP565KmStokqUHSNT7IDACSpEUrd+iWBat00pAUPXLlREWGhbodCQD267BkWWsv/5aXprRzrZV0w9GGAoCOvL6mRDfPX6XJg5I060c5igqnYAHwL4yrAwg4b6/bpRufW6Hx/RP0+PRJio6gYAHwP5QsAAHl/YJS3fDsco3O6K3Z10xSbGSHA/IA4ApKFoCA8dHGMl0/N18j+vbSnGtzFR8V7nYkAPhWlCwAAeGzzRW6bk6eslPj9PSMXPWOpmAB8G+ULAB+L6+wUjPmLNPA5BjNnZGrhJgItyMBQIdYzADAr1hrVVy1V3nbKpVXWKX8bVXasLtWg1JiNfe6yUqOi3Q7IgB0CiULgKtaPF6t27lHeYWVyt/WVqpKa5skSfGRYRo/MFFTx6Tr8twBSo2nYAEIHJQsAN2qpqFFy7dX7R+pWlVcrcYWryQpMzFaJ2Qna2JWknIGJmpYn3juQQggYFGyAPiMtVbbKxuUV1ilvG1Vyt9WqS9310mSQkOMjunXS5fnDlDOwCTlZCWqT68olxMDQNehZAHoMs2tXq3dWaPl26r2F6vyOmfqLypMEwYk6pxj+2lQOBBrAAAK0UlEQVRiVqLG9U9QTAS/ggD0XPyGA3DEqhualb/NGaVypv6aWtum/gYkxeg7Q1M0cWCicrISNSwtXiFM/QEIIpQsAIfF67V6t6BUsz7crGWFVZKksBCjYzJ668rjBipnYKImDkxUGlN/AIIcJQtApzS1erRoxU7988PN2lxWr8zEaN18+jDlDkrS2MwE7h8IAN9AyQJwSLWNLXp2yXY98clW7d7TpJHpvfTgZeP0gzHpCgtlP2MA+DaULADtKt3TqCc+KdQzn29TbVOrTshO1v0Xj9V3hqbIGNZWAUBHKFkAvmZzWZ3+9eEWLVy+Q61er84ak67rvztYx2YmuB0NAAIKJQuAJGn59ir984PNevuL3YoIDdElkzJ13UmDlZUS63Y0AAhIlCwgiFlr9f6GUj36wRYt3Vqp3tHh+vkpQzT9hCylcI9AADgqlCwgCLV4vHp55U7N+nCLNuyuVb/eUfr92aN02aT+io3k1wIAdAV+mwJBpK6pVfOWbtcTH2/VzppGDe8Tr79dMlbnjO2ncN4pCABdipIFBIGy2ibN+bRQT31WqD2NrZo8KEl/vmCMTh6eyjsFAcBHKFlAD1ZYXq9/fbRFL+QXq8Xj1Rmj+ur67w3W+AGJbkcDgB6PkgX0QKuLq/XPD7bojbUlCgsJ0UUTM3TddwYrOzXO7WgAEDQoWUAAstaqvtmjstomldc1fe0xr7BKn22pUHxkmK7/XrauOSGL+wgCgAsoWYAfaWhuVXlts8rqGlVW26yyuiaV1zZ9/dEpU40t3oM+PsRIGYnRun3qCF2eO0DxUeEufBcAAImSBfic12u1a0+jdu9pdEacmg8egXJKVH2z56CPN0ZKiolQSlykUuIjNHFAolLiIpUaH3nQY1JshEJDWMgOAP6AkgV0kcYWj7aW12tzWZ02l9ZrU1mdNpfWaUt5XbujTgkx4UqNaytIYzMTDihMEfuLU5pTnLgRMwAEHkoWcBistaqsb9bmsn1lqq6tTJXVqbhqr6xtu84YKTMxWtmpcTo+O1mDU2OV3jtKqXFRSomPUHJspCLCKE4A0JNRsoB2tHq8Kq7a21akDhyZKqtTdUPL/uuiwkM0OCVO4/on6qIJmcpOjdOQtDgNSolVVHioi98BAMBtlCwEtfqmVm3ZNypVVqdNpW2PheUNavZ8NcWXEhep7NRYTR2TruzUOGWnxmpIWpz69Y5WCGugAADtoGShR/F4raoamlVZ36zyuiZV1rcdV9Q1f3Vc37T/eUV98/6PDQ0xGpgUo8GpcTplRJpTptoKVUJMhIvfFQAgEFGy4NdaPV5VNjR/rRRVOuWpov7A823nqve27F8X9U0JMeFKio1QcmyEBqfGKicrSRkJURqS1lamBiTHKDKMKT4AQNegZMFV1lqV1TapYFetNuyqVcGuWm2vrFdFXVuJqtnb0u7HGSMlxkQoKbbt37A+8UqOi1BSbKSSnXPJsRFKims7ToyJ4AbIAIBuRclCt6lvatWXu78qUxt21WrD7lpVHjBllxofqcEpsRrZr9fXy1JspJLjIvafS4hhPygAgH+jZKHLtXq8KqxoaCtRu/aoYP8IVcP+a6LDQzWsb7xOH9lHw/vGa0R6vIb3iVdyXKSLyQEA6DqULByxA6f6CpwytWFXrTaW1qm5te2deSFGykqJ1ZiM3rp4YmZboeobr/6JMbwrDwDQo1Gy0Cn7pvq+Wju1Rxt21arqgD2j0uIjNbxvvKYfP1DD+/bSiL7xGpIWx35RAICgRMnC13i8VoUV9SooaZvqW++UqgOn+mIiQjWsT7zOOKavMzLVS8P7xisplm0OAADYh5IVxMpqm/aPSu0bofpyd62aDpjqG3TAVN8Ip1BlJrIBJwAAHaFkBYG9zR5tLG2b6isoqdWG3W1TfeV1X39X34i+8frRcQM1Ip2pPgAAjhYlqwfxeq2Kqhq0vmTf9gh7VFBSq8KKenmdDTqjwkM0vE+8Th2RphHOuqnhfXlXHwAAXY2S5cestWpq9aqpxau9LR41tnjU2OrR3maPGlu8amzxqLCifv++U1/urlVDs0dS22adA5NiNKJvL50ztp9GpsdreN9eGpAUw/5SAAB0A0rWUbLWqr7Zo6r6ZlU3tKiqoVl7Glv2l6B9/9pKknf/cdMBx22PXjV989pWz7feIuZASbERGt4nXpdO6u+MTPXSsD5xiongxwsAgFt88lfYGHOmpAclhUp6zFp7jy++Tlfzeq32NLaoyilLVfXNqmpoUXVDs3PT4a+Oq+rbrqluaFGzx9vh5w4NMYoKC1F0RKgiw0IVHRGqqPAQRYWFKjYyTEmxbc+jw0MVFf7VcWR46EHnosJDFRkeoqjwUGUmRis1LlLGMDoFAIA/6fKSZYwJlfSwpNMlFUtaZox52Vr7RVd/rc4q3dOo5durnYJ0cIGqdMpSdUPz/rVL3xQaYpQYE66EmAglxURoYHKMxvVPUGJshBJjwpUYE7H/uFd0uFOQ2opQdHgo980DACDI+GIkK1fSJmvtFkkyxsyTdJ4k10rWiqJq/WRu/v7nEWEhSoqJUEJMuJJiIzSyb6/9xwkxTmlybiq87zg+MozRIgAA0Gm+KFkZkooOeF4safI3LzLGzJQ0U5IGDBjggxhfOW5Qsl79xUn7R5qiw0MpTAAAwKd8UbLaay8HTcJZa2dJmiVJOTk5nVjefeR6x4Srd0xvX34JAACAr/HFQqFiSf0PeJ4paacPvg4AAIDf8kXJWiZpqDFmkDEmQtJlkl72wdcBAADwW10+XWitbTXG/FzSW2rbwuEJa+26rv46AAAA/swn+2RZa1+X9LovPjcAAEAgYPMmAAAAH6BkAQAA+AAlCwAAwAcoWQAAAD5AyQIAAPABShYAAIAPGGt9ekebzoUwpkzSNh9/mRRJ5T7+GvAtfoaBj58hAF/rjt8zA621qR1d5BclqzsYY/KstTlu58CR42cY+PgZAvA1f/o9w3QhAACAD1CyAAAAfCCYStYstwPgqPEzDHz8DAH4mt/8ngmaNVkAAADdKZhGsgAAALoNJQsAAMAHenzJMsY8YYwpNcasdTsLjowxJsoYs9QYs8oYs84Yc5fbmXD4jDGFxpg1xpiVxpg8t/MACHzt/Y03xiQZY94xxmx0HhPdytfjS5akJyWd6XYIHJUmSadaa8dKGifpTGPMcS5nwpE5xVo7zl/2sAEQ8J7UwX/jb5X0rrV2qKR3neeu6PEly1r7oaRKt3PgyNk2dc7TcOcf79gAgCD3LX/jz5M0xzmeI+n8bg11gB5fstAzGGNCjTErJZVKesdau8TtTDhsVtLbxph8Y8xMt8MA6LH6WGtLJMl5THMrSJhbXxg4HNZaj6RxxpgESS8ZY0Zba1lnF1hOtNbuNMakSXrHGFPg/L9QAOiRGMlCQLHWVktaLNbZBRxr7U7nsVTSS5Jy3U0EoIfabYxJlyTnsdStIJQs+D1jTKozgiVjTLSk0yQVuJsKh8MYE2uMid93LOn7khiJBOALL0ua7hxPl7TIrSA9vmQZY56T9Jmk4caYYmPMDLcz4bClS3rfGLNa0jK1rcl61eVMODx9JH1sjFklaamk16y1b7qcCUCA+5a/8fdIOt0Ys1HS6c5zd/JxWx0AAICu1+NHsgAAANxAyQIAAPABShYAAIAPULIAAAB8gJIFAADgA5QsAAAAH6BkAQAA+MD/B0CEsWEUm6raAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,5))\n",
    "ax.plot(x, y2)\n",
    "ax.set_xticks([1 , 3, 5, 10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (10,5))\n",
    "ax.plot(x, y2)\n",
    "ax.set_xticks([1 , 3, 5, 10])\n",
    "ax.set_xticklabels([r'a', r'b', r'$\\gamma$', r'$\\delta$'], fontsize=18)\n",
    "ax.set_yticks([0, 100, 500])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scientific Notation')"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAEICAYAAABRSj9aAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzt3Xl8VOW9x/HPD0IWtiRsYQkQEEQRZREBt4pi61qhi1VrLaV4sa0Xl6q1++1ie21vq9JFe71apVXBtYrWqoC7VmVHJEDYiYQkkJUlCUl+94850QgRApnJSSbf9+uV18w858yc3wzkO0+ec85zzN0REZH41S7sAkREJLYU9CIicU5BLyIS5xT0IiJxTkEvIhLnFPQiInFOQS+hM7MPzGxijF77X2Y2td7j28xsp5ntMLMBZrbbzNrHYtuxZGZnmtnasOuQ1kFBL1FjZmeY2dtmVmpmRWb2lpmdcrjnufsJ7v5qFLb/MzN76IDXvsDdZwfL+wM3AcPdvbe7b3X3zu5ec4Tb+YaZuZndckB7bmO+sMxsopnlHuE23cyG1D129zfcfdiRvIa0XQp6iQoz6wo8B/wR6Ab0A34OVIZZ1wEGArvcvSAKr1UE3Bq8b5EWTUEv0XIsgLvPcfcad9/n7i+5+8q6FczsP8ws28zKzWy1mY0J2jeb2bnB/XZm9n0z22Bmu8zsMTPrFizLCnq2U81sazAE86Ng2fnAD4HLguGYFUH7q2Z2dfD684G+wfIH671eQrBuNzN7wMy2m1mxmT19iPebDfwbuLGhhWaWZGZ3Ba+1PbifZGadgH/Vq2O3mfU1s3Fm9m8zKzGzPDP7k5klBq/1evCyK4L1LzvwrwIzOz54ryXBUNgl9ZY9aGZ/NrN/Bp/9u2Z2TGP/YaX1U9BLtKwDasxstpldYGbp9Rea2aXAz4CvA12BS4BdDbzOdcAU4CygL1AM/PmAdc4AhgGTgJ+a2fHu/gLwa+DRYDhmZP0nuPsC4AJge7D8Gw1s++9AR+AEoBdw52He80+AG+u+iA7wI2ACMAoYCYwDfuzuew6oo7O7bwdqiHxp9ABODd7bd4LaPxO85shg/Ufrb8jMOgDPAi8Fdc8EHjaz+kM7VxD5CysdWA/86jDvTeKIgl6iwt3LiASwA/8HFJrZPDPLCFa5Gvituy/yiPXuvqWBl7oG+JG757p7JZEvhy/X9boDPw/+YlgBrCASpE1iZn2IBPC33L3Y3fe7+2uHeo67LycSrrc2sPhK4BfuXuDuhURC9qpDvNYSd3/H3avdfTPwv0S+7BpjAtAZuN3dq9z9ZSLDaFfUW+cpd3/P3auBh4l8AUkboaCXqHH3bHf/hrtnAiOI9MjvChb3BzY04mUGAv8IhiBKiAyR1AAZ9dbZUe/+XiIh11T9gSJ3Lz7C5/0U+LaZ9T6gvS9Q/4tsS9DWIDM71syeC44GKiPy10mPRtbQF9jm7rUHbK9fvcex+MyklVDQS0y4+xrgQSKBD7ANaMy48DbgAndPq/eT7O4fNmazR1ftR9vtZmZpR/Kk4H0+RWT/QH3biXxp1RkQtEHDdd4DrAGGunvX4PWskWVsB/qbWf3f5wFAYz4zaQMU9BIVZnacmd1kZpnB4/5Ehg7eCVa5D7jZzE62iCFmNrCBl/oL8Ku6ZWbW08wmN7KMfCDrgMBrFHfPI7KT9G4zSzezDmb2mcM9L/BzYBpQ/0tiDvDjoP4eRHr+dYd+5gPdzSy13vpdgDJgt5kdB3z7gG3kA4M/ZfvvAnuA7wV1TwQ+D8xtZP0S5xT0Ei3lwHjgXTPbQyTgVxE5bh13f5zIDsBHgnWfJnIY5oFmAfOAl8ysPHid8Y2s4fHgdpeZLT2K93AVsJ9Iz7oAuKExT3L3TUR25Haq13wbsBhYCbwPLA3a6v4KmANsDIao+gI3A18l8tn8H/CJHa5E9lXMDtb/ygHbryKyc/sCYCdwN/D1YDsimC48IiIS39SjFxGJcwp6EZE4p6AXEYlzjQr64BT1981suZktDtq6mdl8M8sJbtODdjOzP5jZejNbacFp7iIiEo5G7Yw1s83AWHffWa/tt0ROMLndzL4PpLv7rWZ2IZFTsC8kcrTELHc/5FETPXr08KysrKN/FyIibdCSJUt2unvPw62XcLgVDmEyMDG4Pxt4lcip4JOBv3nkG+QdM0szsz7BccoNysrKYvHixU0oRUSk7TGzhqYROUhjx+idyHHNS8xsRtCWURfewW2voL0fkbMM6+TyyVOx6wqcYWaLzWxxYWFhI8sQEZEj1dge/enuvt3MegHzzexQJ2I0dNr2QeND7n4vcC/A2LFjdTC/iEiMNKpHH0yjSnDBhn8QmXI1P5jxr27mv7qLOeQSmSCqTiYfz/EhIiLN7LBBb2adzKxL3X3gc0RObZ8H1F2LcyrwTHB/HvD14OibCUDpocbnRUQkthozdJNBZNrYuvUfcfcXzGwR8JiZTQe2ApcG6z9P5Iib9USmQ50W9apFRKTRDhv07r6RBi7s4O67iFwF58B2B66NSnUiItJkOjNWRCTOKehFREJQVV3Lr5/PZnvJvphvS0EvItLMivZUcdX973Lv6xtZuKbg8E9ooqacGSsiIkcoJ7+c6bMXs6OsgrsuG8WU0QedTxp1CnoRkWbyypoCZs5ZRnKH9jw6YwKjB6Q3y3YV9CIiMebu3P/mJn79fDbH9e7KfVPH0jctpdm2r6AXEYmhqupafvL0Kh5dvI3zT+jNHZeNpGNi80avgl5EJEaK9lTxrYeW8N6mIv7z7CF897PH0q5dQ9OBxZaCXkQkBtbllzN99iLyyyqZdfkoJo+K/U7XT6OgFxGJspfX5HPdnOWkJDbvTtdPo6AXEYmSup2uv3o+m+ND2On6aRT0IiJRUFVdy4+ffp/HFueGttP107SMKkREWrFduyv59kNLeW9zETPPGcKN54az0/XTKOhFRJpg7Y7ITteC8vB3un4aBb2IyFF6eU0+Mx9ZRsekBB675lRG9U8Lu6QGKehFRI7C2h3l/MfflnBc7y7cN3UsfVLD3+n6aRT0IiJHYdbCdaR0aM9D08eT3ikx7HIOSdMUi4gcodXby3j+/R1MOz2rxYc8KOhFRI7YrIXr6JKUwNVnDA67lEZR0IuIHIFVH5by4gf5fPOMQaR27BB2OY2ioBcROQJ3Lciha3IC3zxjUNilNJqCXkSkkVbmlrAgO5+rzxxMakrr6M2Dgl5EpNHuWpBDakoHpp2eFXYpR0RBLyLSCMu3lfDymgJmfGYwXZJbT28eFPQiIo1y5/x1pHfswNTTssIu5Ygp6EVEDmPJlmJeW1fIjM8cQ+ek1neeqYJeROQw7lqwju6dEvn6qQPDLuWoKOhFRA5h0eYi3sjZyTVnDaZTK+zNg4JeROSQ7py/jh6dk7hqQlbYpRw1Bb2IyKd4Z+Mu3t6wi2+dNZiUxPZhl3PUFPQiIp/izvnr6Nklia9NaJ1j83UU9CIiDXh7w07e3VTEdyYeQ3KH1tubhyMIejNrb2bLzOy54PEgM3vXzHLM7FEzSwzak4LH64PlWbEpXUQkNtydO+evI6NrEleMGxB2OU12JD3664Hseo9/A9zp7kOBYmB60D4dKHb3IcCdwXoiIq3GW+t3sWhzMdeePaTV9+ahkUFvZpnARcB9wWMDzgGeCFaZDUwJ7k8OHhMsnxSsLyLS4rk7d8xfS5/UZC47pX/Y5URFY3v0dwHfA2qDx92BEnevDh7nAnWXPu8HbAMIlpcG63+Cmc0ws8VmtriwsPAoyxcRia7Xc3aydGsJ1549hKSE1t+bh0YEvZldDBS4+5L6zQ2s6o1Y9nGD+73uPtbdx/bs2bNRxYqIxFKkN7+OfmkpfGVsfPTmoXEXBz8duMTMLgSSga5EevhpZpYQ9Nozge3B+rlAfyDXzBKAVKAo6pWLiETZq2sLWbGthP/+4okkJsTPQYmHfSfu/gN3z3T3LOBy4GV3vxJ4BfhysNpU4Jng/rzgMcHyl939oB69iEhL4u7cuWAd/bul8OWTM8MuJ6qa8pV1K/BdM1tPZAz+/qD9fqB70P5d4PtNK1FEJPYWZhewMreUmWcPpUP7+OnNQ+OGbj7i7q8Crwb3NwLjGlinArg0CrWJiDSLut78wO4d+cKYfod/QisTX19bIiJH4aXV+XywvYyZ58Rfbx4U9CLSxtXWRs6CHdSjE1NG9Q27nJhQ0ItIm/biBztYs6Oc6yYNISEOe/OgoBeRNqy21rlrQQ6De3bikpHxNzZfR0EvIm3W86vyWJtfzvWThtK+XfzO1KKgF5E2qWhPFXctyGFor85cfFJ8js3XaZ0XQBQROUpFe6q4742NzH57M3v313DvVWPjujcPCnoRaSOK91Txf/UC/uKT+nLdOUMYmtEl7NJiTkEvInGteE8V9725kQffansBX0dBLyJxqWRvFfe9sYkH397MnqpqLjqxD9dNGsqxbSjg6yjoRSSuHBjwF57Yh+vOGcqw3m0v4Oso6EUkLpTsreL+NzfxwFub2V1ZzUUnKeDrKOhFpFUr3buf+9/cyANvbaa88uMhGgX8xxT0ItIqVeyv4Z5XN/DXNzdRXlnNhSf25rpJQzmud9ewS2txFPQi0uqs2VHG9XOWsza/nAtG9Ob6cxXwh6KgF5FWo7bWefDtzdz+whq6JnfgwWmnMHFYr7DLavEU9CLSKhSUVXDzEyt5fV0h5x7fi9986SS6d04Ku6xWQUEvIi3e/NX53PrkSvZWVXPblBFcOX4AZvE9bUE0KehFpMXaW1XNbf/M5pF3t3JC367MunwUQ3rpaJojpaAXkRZp1YelXDd3GZt27uGaswZz02eHkZigCXePhoJeRFqU2lrn3jc28vuX1tK9UxIPTx/PaUN6hF1Wq6agF5EWI690H999dAX/3riLC0b05tdfOJH0Tolhl9XqKehFpEX458o8fviP99lfU8tvv3QSl47N1A7XKFHQi0iodldW8/N5H/D4klxG9k9j1mWjyOrRKeyy4oqCXkRCs2xrMTc8upxtRXuZec4Qrps0lA7ttcM12hT0ItLs1heU86eX1zNvxXb6pKYwd8apjBvULeyy4paCXkSaTXZeGX96eT3Pr8ojOaE9V585mGvPHkJqSoewS4trCnoRiblVH5byh4U5vLQ6n85JCXxn4jFMP2Mw3XRETbNQ0ItIzCzdWswfF+bwytpCuiYncP2koXzz9EGkdlQPvjkp6EUk6t7bVMQfX87hjZydpHfswC3nDeOqUwfSNVkBHwYFvYhEhbvz9oZd/GFhDu9uKqJH50R+cMFxfG3CQDolKWrCpE9fRJrE3XltXSF/WJjD0q0lZHRN4qcXD+eKcQNISWwfdnmCgl5EmuDlNfnctSCHlbml9E1N5pdTRnDpyZkkd1DAtySHDXozSwZeB5KC9Z9w9/8ys0HAXKAbsBS4yt2rzCwJ+BtwMrALuMzdN8eofhEJydPLPuSGR5czoFtHbv/iiXxxTKZml2yhGvOvUgmc4+4jgVHA+WY2AfgNcKe7DwWKgenB+tOBYncfAtwZrCcicWRb0V5+8vQqTslKZ+FNZ3H5uAEK+RbssP8yHrE7eNgh+HHgHOCJoH02MCW4Pzl4TLB8kmlmIpG4UVPrfPex5QDc8ZVRmrKgFWjUv5CZtTez5UABMB/YAJS4e3WwSi7QL7jfD9gGECwvBbo38JozzGyxmS0uLCxs2rsQkWZzz6vrWbS5mF9MOYH+3TqGXY40QqOC3t1r3H0UkAmMA45vaLXgtqHeux/U4H6vu49197E9e/ZsbL0iEqLl20q4a0EOnx/Zlymj+h3+CdIiHNHfXO5eArwKTADSzKxuZ24msD24nwv0BwiWpwJF0ShWRMKzp7KaG+Yuo1eXJG6bMkJzxbcihw16M+tpZmnB/RTgXCAbeAX4crDaVOCZ4P684DHB8pfd/aAevYi0Lr98bjVbivZyx2WjNAlZK9OY4+j7ALPNrD2RL4bH3P05M1sNzDWz24BlwP3B+vcDfzez9UR68pfHoG4RaUYvrNrB3EXb+PbEY5gw+KBdbtLCHTbo3X0lMLqB9o1ExusPbK8ALo1KdSISuvyyCn7w1EpG9OvKjeceG3Y5chR0XJSIfKraWufmx1ewb38Nd102WsfKt1L6VxORT/Xg25t5I2cnP75oOEN6dQ67HDlKCnoRadCaHWXc/sIazj2+F1eOHxB2OdIECnoROUjF/hqun7OcrskduP1LJ+lQylZOs1eKyEF++8Ja1uaX88C0U+jROSnscqSJ1KMXkU94fV0hf31rE1NPHcjZw3qFXY5EgYJeRD5StKeKmx9fwdBenfnBhQ3NdCKtkYZuRASIXCnq+0+upHhvFQ9MO0UXD4kj6tGLCACPLtrGS6vzueW8YZzQNzXsciSKFPQiwqade/j5s6s57ZjuXH3G4LDLkShT0Iu0cftrarlh7jISE9rx+6+MpF07HUoZbzRGL9LG/WFhDityS7n7yjH0SU0JuxyJAfXoRdqwRZuL+PMr6/nyyZlceGKfsMuRGFHQi7RRZRX7uWHucjLTO/KzS04IuxyJIQ3diLRB7s5Nj60gv6yCR685lc5JioJ4ph69SBv0l9c2Mn91Pj+88HhOHpgedjkSYwp6kTbm7Q07+Z8X13DRSX2YdnpW2OVIM1DQi7QhO0oruG7OMgb16MRvNCtlm6GBOZE2Yn9NLdc+spS9VTXMnTFB4/JtiP6lRdqIXz+fzZItxfzxitEM6dUl7HKkGWnoRqQNeHbFdh54azPTTs/i8yP7hl2ONDMFvUicW19Qzq1PruTkgen84AJNPdwWKehF4tjuymqu+fsSOia2589fHUNign7l2yKN0YvEKXfn1idXsmnnHh66ejy9U5PDLklCoq93kTj1wFub+efKPG457zhOO6ZH2OVIiBT0InFo8eYifv18Np8bnsG3ztL88m2dgl4kzhSWV3LtI0vJTE/hd18ZqZOiRGP0IvGkuqaWmXOWUrpvPw9OG0fX5A5hlyQtgIJeJI787qV1vLOxiN9fOpLj+3QNuxxpITR0IxInXvxgB395bQNXjh/Al07ODLscaUEU9CJxYNPOPdz82ApOykzlp58fHnY50sIo6EVauX1VNXz7oSW0b2/cfeUYkhLah12StDCHDXoz629mr5hZtpl9YGbXB+3dzGy+meUEt+lBu5nZH8xsvZmtNLMxsX4TIm2Vu/Ojf7zP2vxyZl0+msz0jmGXJC1QY3r01cBN7n48MAG41syGA98HFrr7UGBh8BjgAmBo8DMDuCfqVYsIAA+/u5Wnln3IDZOO5axje4ZdjrRQhw16d89z96XB/XIgG+gHTAZmB6vNBqYE9ycDf/OId4A0M9Pl5UWiaHdlNbMW5PCLZ1czcVhPZp4zJOySpAU7osMrzSwLGA28C2S4ex5EvgzMrFewWj9gW72n5QZteQe81gwiPX4GDBhwFKWLtD0V+2t46J0t3P3qBor2VHH+Cb25/Usn0q6dToqST9fooDezzsCTwA3uXnaIs+0aWuAHNbjfC9wLMHbs2IOWi8jHqmtqeXJpLrMW5LC9tIIzhvTglvOGMbJ/WtilSSvQqKA3sw5EQv5hd38qaM43sz5Bb74PUBC05wL96z09E9gerYJF2pLaWudfq3bw+/lr2Vi4h5H90/jdpSM5bYgmKZPGO2zQW6Trfj+Q7e531Fs0D5gK3B7cPlOv/T/NbC4wHiitG+IRkcZxd15bV8jvXlrLqg/LGNqrM/971cl8bniG5q6RI9aYHv3pwFXA+2a2PGj7IZGAf8zMpgNbgUuDZc8DFwLrgb3AtKhWLBLnlmwp4jcvrOW9TUVkpqfw+0tHMmV0P9prHF6O0mGD3t3fpOFxd4BJDazvwLVNrEukzcnOK+P3L61lQXYBPTon8fNLTuDycf11ApQ0mSY1EwnZll17uGP+Ouat2E7npARuOW8Y007PomOifj0lOvQ/SSQk+6pq+NXzq5n73jYS2hvXfOYYvnXWYNI6JoZdmsQZBb1ICNyd7z25kudWbufK8QOYec5QMrrqmq4SGwp6kRDc+/pGnl2xnVvOG8a1Z+usVoktzV4p0sxeW1fIb15Yw4Un9uY7E48JuxxpAxT0Is1o8849zHxkKcdmdOF/vqzruUrzUNCLNJM9ldXM+PtizIx7rxpLpySNnErzUNCLNAN35+bHV7C+YDd/+upoBnTXvPHSfBT0Is3g7lc38K9VO/jBBcdz5lDNGy/NS0EvEmMvr8nndy+tZfKovlx95qCwy5E2SEEvEkMbCndz/ZzlDO/Tldu/eJJ2vkooFPQiMVJesZ8Zf1tMh4R2/O9VJ5OSqDlrJBza7S8SA7W1zo2PrmDzrr08NH28LtotoVKPXiQGZi3MYUF2Pj+56HhOPaZ72OVIG6egF4myFz/YwayFOXz55EymnpYVdjkiCnqRaMrJL+e7jy5nZGYqt00ZoZ2v0iIo6EWipHTffmb8fQkpiQn85aqTSe6gna/SMijoRaKgpta5fu4ycov38pevjaFPakrYJYl8REfdiETBHfPX8uraQn71hRGMzeoWdjkin6AevUgT/XNlHn9+ZQNXjBvAleMHhl2OyEEU9CJNkJ1Xxs2Pr+Dkgen87JLhYZcj0iAFvchRKt27n2v+voSuKQncc+UYkhK081VaJo3RixyFyDVfV5BXuo/HrjmVXrreq7Rg6tGLHIWH3tnCix/kc+v5xzF6QHrY5YgckoJe5Ahl55Xxy39mM3FYT755uqYdlpZPQS9yBPZWVTNzzjJSUzrwu0tH0q6dznyVlk9j9CJH4BfPrmZD4W4emj6eHp2Twi5HpFHUoxdppGdXbGfuom18Z+IxnD6kR9jliDSagl6kEbYV7eWHT73PmAFp3HDusWGXI3JEFPQih7G/ppaZc5aBwazLR9OhvX5tpHXRGL3IYdwxfx3Lt5Vw95Vj6N9NV4qS1kddE5FDeDNnJ395LTKPzYUn9gm7HJGjctigN7O/mlmBma2q19bNzOabWU5wmx60m5n9wczWm9lKMxsTy+JFYqmwvJIbH1vO0F6d+enFmsdGWq/G9OgfBM4/oO37wEJ3HwosDB4DXAAMDX5mAPdEp0yR5lVb69z0+ArK9u3nj1eMISVR89hI63XYoHf314GiA5onA7OD+7OBKfXa/+YR7wBpZqa/d6XVue/Njby+rpCfXDycYb27hF2OSJMc7Rh9hrvnAQS3vYL2fsC2euvlBm0HMbMZZrbYzBYXFhYeZRki0bdiWwm/fWEtF4zozZXjB4RdjkiTRXtnbEPng3tDK7r7ve4+1t3H9uzZM8pliByd8or9zJyzjIyuydz+xZN0cW+JC0cb9Pl1QzLBbUHQngv0r7deJrD96MsTaT7uzo+fXsWHJfuYdfkoUjt2CLskkag42qCfB0wN7k8FnqnX/vXg6JsJQGndEI9IS/fEklyeWb6dGyYN1XVfJa4c9oQpM5sDTAR6mFku8F/A7cBjZjYd2ApcGqz+PHAhsB7YC0yLQc0iUbehcDc/feYDJgzuxnfOHhJ2OSJRddigd/crPmXRpAbWdeDaphYl0pwqq2uY+cgykju0467LRtNeUw9LnNEUCNLm/ffza1idV8ZfvzGW3qm6JKDEH02BIG3a/NX5PPj2Zr55+iDOOS4j7HJEYkI9emmTCsorWJhdwG9eWMMJfbty6wXDwi5JJGYU9NImuDs5BbuZvzqf+avzWb6tBIBBPTrxxytGk5SgKQ4kfinoJW5V19SyeEsx81fnsyA7ny279gIwMjOVmz93LOcOz2BYRhedFCVxT0EvcWV3ZTVvrCtk/up8Xl5bQMne/SS2b8dpQ7oz4zODmXRchna4SpujoJdWL7+s4qNe+9vrd1FVU0taxw6cM6wXnx2ewZnH9qRzkv6rS9ul//3Sar2ypoC7FuawIhhvH9CtI1edOpDPDs9g7MB0EnTJPxFAQS+tUF7pPn7x7Gr+tWoHg3t04pbzhvHZ4RkM7dVZ4+0iDVDQS6tRXVPL7H9v4Y6X1lJd69xy3jD+48zBJCao5y5yKAp6aRWWbS3mR/9Yxeq8MiYO68kvLhnBgO66ULdIYyjopUUr3bef/3lxDQ+/u5VeXZK458oxnD+it4ZoRI6Agl5aJHdn3ort/PK51RTtqWLaaYO48bND6ZKsOeJFjpSCXlqcjYW7+ckzq3hr/S5GZqby4LRxjOiXGnZZIq2Wgl5ajIr9Ndzz6gbueXUDSR3a8cspI/jquAGaNlikiRT00iK8kVPIT55exeZde5k8qi8/uuh4enXRGawi0aCgl1AVlFdw23PZzFuxnUE9OvHQ9PGcMbRH2GWJxBUFvcRcVXUteaX7yC3ex4fF+8gt2Udu8V4+LN7HB9vLqKqu5YZzh/Kts44huYNmkRSJNgW9NNm+qho+LNlLbnEQ5iVBoBfv5cOSfRSUV+L+8fpm0LtrMv3SUrjwxN5866xjGNyzc3hvQCTOKejliBSUVbB8Wwkrc0tZkVvC6u1l7NpT9Yl1OrQ3+qSm0C8thTOH9iQzPXK/X3oK/dM70js1mQ6ah0ak2Sjo5VOVVezn/SDQVwThnldaAUD7dsawjC6ce3wGA7p3/ESY9+qSrCNlRFoQBb0AkUMbs/PKPgr05bklbCzc89HyrO4dGTeoGydlpjGqfyrD+6SSkqjxdJHWQEHfhlRV11JQXkFeaeRnR+k+tuzay8rcUtbsKGN/TWQgvUfnJEb1T+MLo/oxsn8aJ2WmktYxMeTqReRoKejjRGV1DfmlleSV7mNHWQXbSyJBnldawY6ySLDv3P3JnaIAXZITOLFfKtPPGMyo/qmclJlGn9RkzSUjEkcU9K2Mu5NbvI9Fm4tYtLmI9z8sJa+k4qAdohAJ8T6pyfROTeH43l3pnZpM37TI40h7Ml2SEhTqInFOQd/C1dQ62XllLN5cxKItxSzeXER+WSUQCfJR/dM4sV/aR8HdJ/jpnZqiy+eJCKCgb3H2VdWwfFvJR8G+dEsxuyurAeiTmsz4Qd05JSudsVndGJbRhXY6ukVEDkNBH7KiPVUs3lzE4i3FLNpcxKoPSz/aKTosowuTR/Vl3KBujM3qRr+0lJCrFZHWSEHfjOrG198Z8DdOAAAFV0lEQVTbVMTiLUW8t6mIDcEhjInt2zGyfypXnzmYU7LSOXlAN1I7au51EWk6BX0M1dQ6a3eUfxTqiw4YXx87MJ0vjslk3KBunNgvVfO8iEhMKOijqGJ/DStzSz86ImbJlmLKKyLj6727JjNuUHfGaXxdRJqZgv4IuDv79tewu7KavZWR24LyChZtLmbRpiJW5pZSVVMLwJBenbn4pL6ckpXOKVndyExP0WGMIhKKNhX07k7ZvmryyysoKKskv6yCXXsq2V1Zw57KavZWVbO7soa9ldWRMK+KtH90v6r6oBOOABLaGSP6pfKN07MYOzDSY+/WSWeSikjLEJOgN7PzgVlAe+A+d789Ftup4+6UVVRTWF5BfhDg+WWVFNQL9ILyyG1ldW2Dr9EpsT2dkhKCn/Z0TEygZ5ckBnbvSOe69mCdjkkJdA7W6dYpkRF9Ne+LiLRcUQ96M2sP/Bn4LJALLDKzee6+OtrbenTRVu5+dQP5ZRVU7D84wDsnJdCraxIZXZIZPSCNjK7J9OqS9Inb7p0T6ZSYoPFyEYlbsejRjwPWu/tGADObC0wGoh703TslMTIzjYyukdDuGYR3XZB30pmhIiIxCfp+wLZ6j3OB8QeuZGYzgBkAAwYMOKoNnTs8g3OHZxzVc0VE2opYXOanoTGQg3Zhuvu97j7W3cf27NkzBmWIiAjEJuhzgf71HmcC22OwHRERaYRYBP0iYKiZDTKzROByYF4MtiMiIo0Q9TF6d682s/8EXiRyeOVf3f2DaG9HREQaJyaHpbj788DzsXhtERE5MrEYuhERkRZEQS8iEucU9CIicc68oVm6mrsIs0JgS9h1NFEPYGfYRbQg+jw+ps/ik/R5fFJTPo+B7n7YE5FaRNDHAzNb7O5jw66jpdDn8TF9Fp+kz+OTmuPz0NCNiEicU9CLiMQ5BX303Bt2AS2MPo+P6bP4JH0enxTzz0Nj9CIicU49ehGROKegFxGJcwr6JjKz/mb2ipllm9kHZnZ92DWFzczam9kyM3su7FrCZmZpZvaEma0J/o+cGnZNYTKzG4Pfk1VmNsfMksOuqbmY2V/NrMDMVtVr62Zm880sJ7hNj8W2FfRNVw3c5O7HAxOAa81seMg1he16IDvsIlqIWcAL7n4cMJI2/LmYWT/gOmCsu48gMrvt5eFW1aweBM4/oO37wEJ3HwosDB5HnYK+idw9z92XBvfLifwi9wu3qvCYWSZwEXBf2LWEzcy6Ap8B7gdw9yp3Lwm3qtAlAClmlgB0pA1dlMjdXweKDmieDMwO7s8GpsRi2wr6KDKzLGA08G64lYTqLuB7QG3YhbQAg4FC4IFgKOs+M+sUdlFhcfcPgd8BW4E8oNTdXwq3qtBluHseRDqNQK9YbERBHyVm1hl4ErjB3cvCricMZnYxUODuS8KupYVIAMYA97j7aGAPMfrTvDUIxp8nA4OAvkAnM/tauFW1DQr6KDCzDkRC/mF3fyrsekJ0OnCJmW0G5gLnmNlD4ZYUqlwg193r/sJ7gkjwt1XnApvcvdDd9wNPAaeFXFPY8s2sD0BwWxCLjSjom8jMjMgYbLa73xF2PWFy9x+4e6a7ZxHZyfayu7fZHpu77wC2mdmwoGkSsDrEksK2FZhgZh2D35tJtOGd04F5wNTg/lTgmVhsJCaXEmxjTgeuAt43s+VB2w+DyymKzAQeNrNEYCMwLeR6QuPu75rZE8BSIkerLaMNTYdgZnOAiUAPM8sF/gu4HXjMzKYT+SK8NCbb1hQIIiLxTUM3IiJxTkEvIhLnFPQiInFOQS8iEucU9CIicU5BLyIS5xT0IiJx7v8B3W4tGpEfI/IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y2)\n",
    "ax.set_title('Scientific Notation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(x, y2)\n",
    "ax.set_title('Scientific Notation')\n",
    "formatter = ticker.ScalarFormatter(useMathText=True)\n",
    "formatter.set_scientific(True)\n",
    "formatter.set_powerlimits((-1, 2))\n",
    "ax.yaxis.set_major_formatter(formatter)"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}