{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking for cross basin tilt along different wind scenerios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import netCDF4 as nc\n",
    "import matplotlib.pyplot as plt\n",
    "from salishsea_tools import (nc_tools, viz_tools, gsw_calls)\n",
    "import numpy.ma as ma\n",
    "from matplotlib import animation, rc\n",
    "from scipy.optimize import curve_fit\n",
    "from visualisations import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rc('animation', html='html5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def calc_rho(Sal, TempC, P):\n",
    "    sqrSal = np.sqrt(Sal)\n",
    "    R1 = ((((6.536332e-9 * TempC - 1.120083e-6) * TempC + 1.001685e-4)\n",
    "           * TempC - 9.095290e-3) * TempC + 6.793952e-2) * TempC - 28.263737\n",
    "    R2 = (((5.3875e-9 * TempC - 8.2467e-7) * TempC + 7.6438e-5)\n",
    "          * TempC - 4.0899e-3) * TempC + 8.24493e-1\n",
    "    R3 = (-1.6546e-6 * TempC + 1.0227e-4) * TempC - 5.72466e-3\n",
    "    SIG = (4.8314e-4 * Sal + R3 * sqrSal + R2) * Sal + R1\n",
    "    V350P = 1.0 / 1028.1063\n",
    "    SVA   = -SIG * V350P / (1028.1063 + SIG)\n",
    "    rho = 28.106331 - SVA / (V350P * (V350P + SVA)) + 1000\n",
    "    return rho\n",
    "def func(x,m,b):\n",
    "    return m*x + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lake_meshmask = nc.Dataset('/home/vdo/MEOPAR/NEMO-forcing/grid/lake_meshmask201702.nc')\n",
    "U24W = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24UW/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U24S = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24US/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U48W = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48UW/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U48S = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48US/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U72W = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72UW/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U72S = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72US/SalishSea_1h_20160601_20160605_grid_T.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f29637719e8>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeoAAAHVCAYAAAA+QbhCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAA4RlADAOAYQQ0AgGMENQAAjv1/R1ep1KO0DBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29637abc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,8))\n",
    "ax.pcolormesh(lake_meshmask.variables['tmask'][0,0,...])\n",
    "ax.plot([0,200], [200,200], 'r-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "file2 = U24W\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.2299156189\n",
      "11.509311676\n",
      "0.204864947553\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29640276a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 30\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = U48W\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.2299156189\n",
      "10.5047655106\n",
      "0.213468427451\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIhIT/x8/fImJ3EyXHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2963187198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 24\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = U72W\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.2299156189\n",
      "11.509311676\n",
      "0.228863143153\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nA9NqxVtcVL75AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2963fd6400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 17\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24UW0.1/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.2299156189\n",
      "7.50062322617\n",
      "0.197451324029\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2963fd6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 47\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48UW0.1/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.2299156189\n",
      "6.50031042099\n",
      "0.215303846287\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLiISE/8fG2f5WHEJXDAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2963105e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 40\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72UW0.1/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.2299156189\n",
      "7.50062322617\n",
      "0.198712917308\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAHMRJREFUeJzt3XmQVfWd9/H3tze2BlmUtgMoMCDK1n27ibFjJjbgQtRS\nKxqjqTjGmIdKVTJPqGfyPKPzVMWamkolqanHmBmNmigRExSMSyTqjAvTHU1AR+gGZBFxIW4ILqCC\n0g3d3+ePc6733qaX2913Pf15VZ3qPtvt77nVfPrwu+d8j7k7IiJS/EryXYCIiGSGAl1EJCIU6CIi\nEaFAFxGJCAW6iEhEKNBFRCJiUIFuZkvMbKeZvWxm12WqKBER6T8b6HXoZlYKvAScA7wJPA9c6e7b\nM1eeiIikazBn6KcDL7v7q+7eDqwCLs5MWSIi0l9lg9h3EvBG0vybwBe6bmRmS4GlACNGjKifMmXK\ngH5YZ2cnJSXFNeSvmrOv2OoF1ZwrxVZzb/W+9NJL77n7CX2+iLsPaAIuA+5Imr8KuLm3ferr632g\nmpqaBrxvvqjm7Cu2et1Vc64UW8291Qts8DRyeTB/vt4Ckk+3J4fLREQkDwYT6M8DM81smplVAFcA\nazJTloiI9NeAx9Dd/aiZfR94HCgFlrv7toxVJiIi/TKYD0Vx98eAxzJUi4iIDELxfAQsIiK9UqCL\niESEAl1EJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCJCgS4iEhEKdBGRiFCgi4hEhAJdRCQiFOgiIhGh\nQBcRiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIhToIiIRoUAXEYkIBbqISEQo0EVE\nIkKBLiISEQp0EZGIKI5AX7+ek1auhPXr812JiEjBKvxAX78eFi9m2vLlsHixQl1EpAeFH+jNzdDe\njnV2Qnt7MC8iIsco/EBvbISKCjpLSqCiIpgXEZFjFH6gNzTA2rXs/va3Ye3aYF5ERI5Rlu8C0tLQ\nwOttbUxXmIuI9Kjwz9BFRCQtCnQRkYhQoIuIRETxBvr69fCTnySuS+86LyIyxPT5oaiZLQcuBPa5\n+9xw2XhgNTAV2A1c7u77s1dmF+HNRrS3B5cy3nQTLFuWmNfVMCIyBKVzhn4XsKTLsuuAte4+E1gb\nzudOeLMRHR3B1wceSJ3XzUciMgT1Geju/jTwQZfFFwMrwu9XAJdkuK7ehTcbUVoafL300tR53Xwk\nIkOQuXvfG5lNBR5JGnI54O5jw+8N2B+f72bfpcBSgKqqqvpVq1YNqNCDBw9SWVn52fyYbdsYu2kT\nB2pr+WjOnGPm090mm7rWXAyKreZiqxdUc64UW8291btw4cKN7r6gzxdx9z4ngrHyrUnzB7qs35/O\n69TX1/tANTU19W+HdevcR4xwLy0Nvt5+e+r8unUDriVd/a65ABRbzcVWr7tqzpViq7m3eoENnkbG\nDvQql71mVg0Qft03wNfJHo2zi8gQM9BAXwNcHX5/NfBwZsrJII2zi8gQk85li/cCjcDxZvYmcAPw\nU+A+M7sW+CtweTaLHJCwqRfNzUF4NzTAvHmp8yIiEdJnoLv7lT2sWpzhWjKvoSE1uLvOi4hESPHe\nKSoiIikU6CIiEaFAFxGJCAW6mnqJSEQUxxOLsqVrky819RKRIja0z9C73nykm41EpIgN7UDvevOR\nbjYSkSI2tIdcurv5SESkSA3tQAfdbCQikTG0h1xERCJEgS4iEhEKdBGRiFCgd5XOjUZdt9HNSSJS\nAIriQ9HVq+Ghh6bzzjsQi8HMmVCSjT9F6dxo1HWbm26CZct0c5KI5F1RBHpLCzz44GRWrw7mKyuh\npgbq6oKAj8Vg9uwgTweluxuNuoZzOk9CUqCLSB4URaD/7Gdw9tnPMHHiWbS0QGtrEPLLl8OhQ8E2\nFRUwd24Q7vGgnz8fRo3qxw+K32gUP9vu7kajrttceik880zv+4iI5EBRBDpAeblTUxOcmV9zTbCs\nsxN27UoEfGsr/OEPcOedwfqSEpg1KzXkYzEYN66HH9LdjUbr16fO60lIIlKgiibQuxMP7Fmz4Ior\ngmXu8MYbqSH/9NNwzz2J/aZOTQR8/Gt1dbgy+UajnsbU9SQkESlARR3o3TGDk04KposvTix/990g\n3JOD/sEHE+tPPDFxBh8P+WlNzZjGx0WkSEQu0Htywglw7rnBFPfRR7B5cyLgW1vhiSeC/AY4rvJ/\nE/MvErNW6kq2EDvpK8w6CmVD5l0TkWIypKNpzBj4278NprjDh2Hr1njIl9H6TB237vwih4+Uwzdh\n+HeCD1uTh2zmzoXhw/N3HCIiMMQDvTvDh8OCBcEUGM3Ro7BzZxDy8bP5e++F224LtigrCy6bTA75\nmsPPcdLqlTBsWPcfrkL3y0REBkiBnoayMpgzJ5iuuipY5g6vvZY6XPMf/wF33RWsNz7PDMZR95vN\nxC56jrpHf0zs6PMcP+xfgg9XQU9LEpGMUqAPkBlMnx5Ml12WWL5nD7Rcdx8tv91Gq9fwXMcCVj80\nDXgEgMmfvkHdtZ8Sq95LrO086jo3MLltD6YPXEVkkBToGVZdDRd8dwoX/P5bdLa1UTJsGB/8+FY2\nXbeK1iNzaSmpp+WTi/hj00zcg8H7CZ3vUfdQGbEDiSGbGTOy1N5ARCJLgZ4N4c1Hu5cvZ/q3v834\nhgYWnXEKi5qbofFkaBjJoUOwZeULtDzyNq3U0vrW8fz853DkSPASlZVQW5s6Ln/aaVBentcjE5EC\npkDPloYGXm9rY3p8GKXLzUejRkHD0nk0LJ332bL2dti+PXGtfEtLcNdrvL3BsGHBFTXJIT9vHowc\nmcsDE5FCpUAvIBUb11Pb3ExtYyPXXBOEf0fHse0N7r8ffv3rYJ+SEjj11NTWBrEYjB2bxwMRkbxQ\noBeKHtoMlJYGgX3qqXDllcGm7vD666kh39QEv/td4uWmTUucxcfD/sQT83NoIpIbCvRCkU7r3pAZ\nnHxyMF1ySWL5vn2pId+1vUF1dWprg7q64DXMsnpkIpIjCvRCkU7r3j5MnAjnnRdMcR9+CJs2pfax\nefzxRHuDceNSP3w9cmQkHR1QWpqRoxKRHFKgF4ru2vJmwHHHwVlnBVPcp5/CCy+khvzNN0NbG8Dp\nfO97QXuD5LP5uXODD2VFpHAp0AtJjtrwjhgBp58eTHFHjsCLL8I99+zg8OHTaGmBlSvh1luD9fG7\nZZPH5WtqgssrRaQwKNALXdd+L1nq/1JeHlwCed55e2lsPA0IHiDy6qupZ/KPPprU3sDglFOOfYDI\nhAkZK0tE+qHPQDezKcDdQBXgwK/c/RdmNh5YDUwFdgOXu/v+7JU6BOX5gdQlJcEdqzNmwNe+Fixz\nh7ffTr1Wft06WLUqsd9JJx0b8pMm6cNXkWxL5wz9KPAP7t5iZqOBjWb2JPAtYK27/9TMrgOuA/4x\ne6UOQQX4QGqzIJwnTYILL0wsf//9Yx8gsmZN8AcAgn70XR8g8jd/o/YGIpnUZ6C7+x5gT/j9x2a2\nA5gEXAw0hputAJpRoGdWET2QesIEOPvsYIo7eBC2bCHlwd433phobzB6dHCFTXLIq72ByMCZx0+h\n0tnYbCrwNDAXeN3dx4bLDdgfn++yz1JgKUBVVVX9quT/m/fDwYMHqSyyT+AyUfOYbdsYu2kTB2pr\n+WjOnGPmMy3b73N7u7F79yh27ark5ZdHs2tXJa+8Usnhw8F1kuXlnUyffpCZM+PTx0yffohhwzrz\nUm82qObcKLaae6t34cKFG919Qbcrk7l7WhNQCWwEvhrOH+iyfn9fr1FfX+8D1dTUNOB980U1p+fo\nUfft291XrnT/4Q/dFy1yHzvWPRiwcS8tdZ871/2qq9xvvNG9udn9wIH81TtYqjk3iq3m3uoFNnga\nOZ3WVS5mVg48AKx09/i9h3vNrNrd95hZNbAvndcS6aq0NBhqOe00+MY3gmXusHt3Yly+tRWeegp+\n+9vEftOnw5Qpszn33MSQTVVVXg5BpCCkc5WLAXcCO9z9xqRVa4CrgZ+GXx/OSoUyJJkF/WimTYOv\nfjWxfO/e1A9e160bzZ/+lFhfXZ3av6auLrjqRlfYyFCQzhn6mcBVwAtmtilc9k8EQX6fmV0L/BW4\nPDslSp/SuVY9zW1OWtnHc1BzVXMP9VU1N7OksZEl/xQsa25+jliskU2bjn0cYGc47D5+xKfE5rQR\naxwbhLy1MvPVxylddNag369c3CMgkrZ0xmUyNWkMPQvWrXMfMSIYaB4xwv3221Pn163r1zadJSU9\nb5OrmtM5hrCent7jQ4fcn/31Fr+1/O/9f9ivfIFt8Iryjs/G5Udy0L9Yss6/17jV7yj/rreU1Hvb\n8DH9fr963aYH+l3OjWKrOWdj6FLA0rlWHdLexjo7s3/Ne181p3MMfdQzciR84d1H+ELnL8GDbmNH\nfvRjduybQMvN62j1Glo761jxzDRu6Qj6G5QfbmfOpR9Q97m9xA5/h5hvpKZtK5UDfU91li45pkAv\nduleq57mNp1tbZRk+5r3vmpO5xjSqafLzylf/GXmA/Pv+J98q/3u4HhvvIlXlv07Le1zaS2tp3XK\nd1jz0myW+78BYJ2dnLL5I+p4nJi1UFf6ArG6sxk/5mjR3CMgQ4cCvdh116Vx3rxjx3LT3Cb+HNQe\nt8lVzekcw0B+TpfXKWloYGbNPGY2N/P1xjOhYSzu8NaajbQ+8CotFWfQ+t4U/vzsJdy79+vQDiwJ\n+sjHPv8mdRVbiZ13AnUXnkb13HnYn7LwfomkK51xmUxNGkMvfMVWcy7rffdd9yeecP/Zz9y//nX3\nmTMT18qD+8SJ7ued53799e733ef+8svunZ35rTlTVHP2aQxdJIeOPx7OOSeY4j7+GDZvTr2U8l//\nFY4eDdaPGZP6AJFYDDo6dA2lZIcCXWQQRo+GL30pmOLa2mDr1tSQv/324MEiABUVX6KmJvV6+Xnz\nYPjw/ByDRIcCXSTDhg2D+vpgiuvogJ07g3B/+OG3ee+9KaxaFQQ9BHfLzp6d2qistjY4wxdJlwJd\nJAfigT17Nkya9AqNjVM+a2+Q3I3yiSfg7rsT+82YcWxv+YkT83YYUuAU6CJ5ktze4NJLE8v37Ent\nYbNhA/z+94n1kyalhnxdHUyZovYGokAXKTjV1cF0/vmJZfv3w6ZNqQ8ReeyxRHuDCROOfYDIzJl6\ngMhQo0CX/imkHidde89097PzrZv3os+auzmGcS+uZ+GzzSxsbIT/FWzzyRN/ZsvnltB6dF7wOMBn\nDvKLpuG0dwT/rEeN6KB24tvEPl9G3fnVxGIw+6NnqfhLU7976PTZ4yeD/YRy1pcoir140rm2MVOT\nrkMvfL3WnOUeJ/3StfdML/1e8qaH96LXmgfZi6etZLhvGna6L/+7Jv9+6S/9TP7so/j4s2vlKzjs\ndWzwa0t/4zdf8YyvqzjLD5aMHlyPnwwfQ076EuXq97QfdB265Fam+sZk4uwnrOWz3jMD6PeSdT28\nX73WPMhePBWdHdQc3UjNOz/hGtYCHXSWlPHysptpeauK1vt20eo1/KHjQu5cdTzQTAkdzPp0J7Eb\n3iF2+PvU+QZibVsYl26PnwwfQ076EqXzOkV4lq5Al/Rlsm9Mhmr5rPfMQPu9ZFMP71evNQ+yF093\n25RUlHPKZfM5BbhizTegvR0vr+DNG35Nyw0P03pkLq1Wx9Ntjdzji4LX6oSp/36YulmHiFk7tfY8\n9eXbqc5Cfdnaps++RBHsxaNAl/Rlsm9MhmpJ6T3T3c/Opx7erz5rHkQvnnS3scZGpjQ0MOWs6Vzc\n3AyNE6Chkncfe57W+1+hdXgDrR+cTEvLcB48ekOw72Go+hHU1X6N2LDt1J1zPLGqWUybBpaHYxh0\nX6Jc/J7mWL8eEj1YCxYs8A0bNgxo3+bmZhqL7K+mas6+YqsXiq/mjz6Cu+5qxT322VU227YFoxMA\nxx2XuMImfpXNrFlQlufTxWJ7n3ur18zSeki0ztBFpFdjxsD8+R+mjEIcPhy0N0i+KerWW4PlELQx\nqKlJvYxy7ly1N8g2BbqI9Nvw4bBgQTDFHT0atDdIDvl774XbbgvWl5UFd8om3/VaWxv0w5HMUKCL\nSEaUlcGcOcF01VXBMnd47bUg3ONB/9hjcNddwXqzoL1BcsjHYnDCCXk7jKKmQBeRrDGD6dOD6bLL\ngmXuifYG8ZB/7jlYvTqx3+TJqSFfVxcsU3uD3inQRSSnzOBznwumCy5ILP/gg0R7g3jQ//GPwR8A\nSLQ3SO5hM2OG2hskU6CLSEEYPx4WLQqmuEOHYMuW1JC/6abgcnGAyspjHyAyezaUl+fnGPJNgS4i\nBWvUqOCS8OTLwtvbYfv2RMi3tMAdd8AnnwTrhw0Lrqg58cRT2LEjCPn582HkyPwcQy4p0EWkqFRU\nBGfltbVwzTXBso4O2LUrtRvlM8+cwKOPButLSuDUU4/tLT92bP6OIxsU6CJS9EpLg8A+9VS48spg\nWVPTX5g+vTEl5JubYeXKxH7TpqWOydfVQVVVPo4gMxToIhJJZnDyycF0ySWJ5fv2pY7Jt7YGvbni\nqquPPZOfOrU4rrBRoIvIkDJxIpx3XjDFffghbN6celPU448n2huMHXvsA0RmzQr+Z1BIFOgiMuQd\ndxx8+cvBFPfpp/DCC6nj8rfcAm1twfoRI7pvbzBsWH6OARToIiLdGjECTu9Yz+kfNMPVjXBbA0ee\neZad979Ay3GNtH48k5YWWHn3UW69NYjSsjKYM+0QsdGvUHfWaGJfnUZNDYzempunISnQRUS6s349\nLF6c6JF+002UL1vG3PZ25lZU8Hdr18Ll0LnobF4tmUxr2QJaFv2Qlife49HOGu5qmQg/BzNnJhO4\n3Z6icdi/BG17sxTqCnQRke6k+YSukiNtzOh8iRkdr/C1zvfA1uJ08HbJFFq/+f+CJ0Wt/YCJvifr\nT0NSoIuIdGcQT+iy9nYmVbzHpO9O5kKAdd/IydOQFOgiIt3J5BO6cvQkLQW6iEhPuvYd6Do/0G2y\nRH3KREQios9AN7PhZvbfZrbZzLaZ2T+Hy8eb2ZNmtiv8Oi775YqISE/SOUNvAxa5ew1QCywxszOA\n64C17j4TWBvOi4hInvQZ6B44GM6Wh5MDFwMrwuUrgEu62V1ERHLEPP44kN42MisFNgIzgFvc/R/N\n7IC7jw3XG7A/Pt9l36XAUoCqqqr6VatWDajQgwcPUllZOaB980U1Z1+x1QuqOVeKrebe6l24cOFG\nd1/Q7cpk7p72BIwFmoC5wIEu6/b3tX99fb0PVFNT04D3zRfVnH3FVq+7as6VYqu5t3qBDZ5GRvfr\nKhd3PxAG+hJgr5lVA4Rf9/XntUREJLPSucrlBDOLD62MAM4BXgTWAFeHm10NPJytIkVEpG/p3FhU\nDawIx9FLgPvc/REzWw/cZ2bXAn8FLs9inSIi0oc+A93dtwCxbpa/DyzORlEiItJ/ulNURCQiFOgi\nIhGhQBcRiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIhToIiIRoUAXEYkIBbqISEQo\n0EVEIkKBLiISEQp0EZGIUKCLiESEAl1EJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCJCgS4iEhEKdBGR\niFCgi4hEhAJdRCQiFOgiIhGhQBcRiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIhTo\nIiIRkXagm1mpmbWa2SPh/Hgze9LMdoVfx2WvTBER6Ut/ztB/AOxImr8OWOvuM4G14byIiORJWoFu\nZpOBC4A7khZfDKwIv18BXJLZ0kREpD/M3fveyOx+4CfAaOCH7n6hmR1w97HhegP2x+e77LsUWApQ\nVVVVv2rVqgEVevDgQSorKwe0b76o5uwrtnpBNedKsdXcW70LFy7c6O4L+nwRd+91Ai4Efhl+3wg8\nEn5/oMt2+/t6rfr6eh+opqamAe+bL6o5+4qtXnfVnCvFVnNv9QIbvI98dXfK0vjDcSZwkZmdDwwH\nxpjZ74C9Zlbt7nvMrBrYl8ZriYhIlvQ5hu7u17v7ZHefClwB/Je7fxNYA1wdbnY18HDWqhQRkT4N\n5jr0nwLnmNku4OxwXkRE8iSdIZfPuHsz0Bx+/z6wOPMliYjIQOhOURGRiFCgi4hEhAJdRCQiFOgi\nIhGhQBcRiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIhToIiIRoUAXEYkIBbqISEQo\n0EVEIkKBLiISEQp0EZGIUKCLiESEAl1EJCIU6CIiEaFAFxGJCAW6iEhEKNBFRCJCgS4iEhEKdBGR\niFCgi4hEhAJdRCQiFOgiIhGhQBcRiQgFuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRIQCXUQkIsrS\n2cjMdgMfAx3AUXdfYGbjgdXAVGA3cLm7789OmSIi0pf+nKEvdPdad18Qzl8HrHX3mcDacF5ERPJk\nMEMuFwMrwu9XAJcMvhwRERmotIZcAAeeMrMO4HZ3/xVQ5e57wvXvAFXd7WhmS4Gl4exBM9s5wFqP\nB94b4L75opqzr9jqBdWcK8VWc2/1npzOC5i7972R2SR3f8vMJgJPAn8PrHH3sUnb7Hf3cen80IEw\nsw1Jwz1FQTVnX7HVC6o5V4qt5kzUm9aQi7u/FX7dBzwEnA7sNbPqsJBqYN9gChERkcHpM9DNbJSZ\njY5/D5wLbAXWAFeHm10NPJytIkVEpG/pjKFXAQ+ZWXz7e9z9P83seeA+M7sW+CtwefbKBOBXWX79\nbFDN2Vds9YJqzpViq3nQ9aY1hi4iIoVPd4qKiESEAl1EJCKKItDNbImZ7TSzl82sIO9INbPlZrbP\nzLYmLRtvZk+a2a7wa9Yu6+wvM5tiZk1mtt3MtpnZD8LlhVzzcDP7bzPbHNb8z+Hygq0ZwMxKzazV\nzB4J5wu93t1m9oKZbTKzDeGyQq95rJndb2YvmtkOM2so5JrNbFb4/sanj8xs2WBrLvhAN7NS4Bbg\nK8Bs4Eozm53fqrp1F7Cky7JCbo9wFPgHd58NnAF8L3xfC7nmNmCRu9cAtcASMzuDwq4Z4AfAjqT5\nQq8Xiq/Vxy+A/3T3U4Eagve7YGt2953h+1sL1AOfEFwSPria3b2gJ6ABeDxp/nrg+nzX1UOtU4Gt\nSfM7gerw+2pgZ75r7KX2h4FziqVmYCTQAnyhkGsGJof/MBcBjxTD7wVBs73juywr2JqB44DXCC/y\nKIaau9R5LvCXTNRc8GfowCTgjaT5N8NlxSCt9gj5ZmZTgRjwHAVeczh8sYngRrYn3b3Qa74J+D9A\nZ9KyQq4XEq0+NoatO6Cwa54GvAv8JhzauiO8Z6aQa052BXBv+P2gai6GQI8ED/7kFtw1omZWCTwA\nLHP3j5LXFWLN7t7hwX9TJwOnm9ncLusLpmYzuxDY5+4be9qmkOpN8qXwPf4KwVDcl5NXFmDNZUAd\ncKu7x4BDdBmqKMCaATCzCuAi4Pdd1w2k5mII9LeAKUnzk8NlxaCg2yOYWTlBmK909wfDxQVdc5y7\nHwCaCD63KNSazwQuCp8nsApYZGa/o3DrBYqy1cebwJvh/9YA7icI+EKuOe4rQIu77w3nB1VzMQT6\n88BMM5sW/jW7gqDtQDEo2PYIFtz6eyeww91vTFpVyDWfYGZjw+9HEIz5v0iB1uzu17v7ZHefSvB7\n+1/u/k0KtF4ozlYf7v4O8IaZzQoXLQa2U8A1J7mSxHALDLbmfH8gkOaHBucDLwGvAP833/X0UOO9\nwB7gCMEZw7XABIIPxHYBTwHj811nUr1fIvjv3BZgUzidX+A1zwdaw5q3Aj8KlxdszUm1N5L4ULRg\n6wWmA5vDaVv831sh1xzWVwtsCH83/gCMK4KaRwHvA8clLRtUzbr1X0QkIophyEVERNKgQBcRiQgF\nuohIRCjQRUQiQoEuIhIRCnQRkYhQoIuIRMT/B5y1vmX7ZJepAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2962f28e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 34\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24UW0.3/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.2299156189\n",
      "11.509311676\n",
      "0.213558681545\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVLgiwdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2964036630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 26\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48UW0.3/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.6857566833\n",
      "12.5181665421\n",
      "0.243318403763\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEhMKdBGRmFCgi4jExP8H3vwJkXzRH+8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2963366358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 20\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72UW0.3/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34.6857566833\n",
      "10.5047655106\n",
      "0.289675441743\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQRcRiQkVdBGRmPh/BXmLntEd5MYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2962fc2518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 15\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1023.3\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24US/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.6342878342\n",
      "6.50031042099\n",
      "0.0967518569326\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2963fb2668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 25\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48US/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.6342878342\n",
      "6.50031042099\n",
      "0.11439483872\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29630d1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 19\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72US/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.5689821243\n",
      "4.5000705719\n",
      "0.0993047011578\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f296326b898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24US0.1/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.5689821243\n",
      "3.50003051758\n",
      "0.106664186399\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRUQi4v8DbibNRuGhZIMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f296324c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 32\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48US0.1/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.5181665421\n",
      "3.50003051758\n",
      "0.0733817162875\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUKCLiARCgS4iEggFuohIIBToIiKB+P+lVQsOMqoegwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29636b6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 23\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72US0.1/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.5689821243\n",
      "4.5000705719\n",
      "0.114811523367\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f296303af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 17\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24US0.3/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.6342878342\n",
      "6.50031042099\n",
      "0.0858465881976\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f29646c6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 23\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48US0.3/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.0071353912\n",
      "7.50062322617\n",
      "0.112669013666\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAl1EJBAKdBGRQCjQRUQCoUAXEQnE/weOPEOgqa9geQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2964033b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 17\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "file2 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72US0.3/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "yslice = 200\n",
    "deptht = file2.variables['deptht'][:]\n",
    "yslicemask = 1 - lake_meshmask.variables['tmask'][0,:,yslice,:]\n",
    "pressure1 = gsw_calls.generic_gsw_caller('gsw_p_from_z.m',\n",
    "                                        [-np.expand_dims(file2.variables['deptht'][:], 1) * np.ones(284),\n",
    "                                         (np.ones([284,40]) *np.expand_dims(file2.variables['nav_lat'][yslice,:][:],1)).T])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.4817848206\n",
      "7.50062322617\n",
      "0.121091605361\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2963373d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 11\n",
    "density1 = calc_rho(file2.variables['vosaline'][i,:,yslice,68:137],\n",
    "                    file2.variables['votemper'][i,:,yslice,68:137],\n",
    "                    pressure1[:, 68:137])\n",
    "rho = 1022\n",
    "index1 = (np.abs(density1 - rho)).argmin(axis=0)\n",
    "list_of_depths1 = np.zeros((69))\n",
    "for x in range(69):\n",
    "    list_of_depths1[x] = deptht[index1[x]]\n",
    "popt, pcov = curve_fit(func, np.arange(0,69), list_of_depths1)\n",
    "plt.plot(np.arange(0,69), list_of_depths1, 'r.')\n",
    "plt.grid('on')\n",
    "plt.ylim(50,0)\n",
    "plt.plot(np.arange(0,69), func(np.arange(0,69), *popt), 'b-')\n",
    "print(list_of_depths1.max())\n",
    "print(list_of_depths1.min())\n",
    "print(popt[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f29601d1da0>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApAAAAHwCAYAAAARqV+XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSVJZLCAlSZJUFgtISZIkleX/B9lXPRr0LclTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f296026ab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.set_ylabel('Displacement (m)')\n",
    "ax.set_xlabel('vtau')\n",
    "ax.grid('on')\n",
    "ax.plot(0.2,4.129659653,'o', color='purple', markersize = 25 )\n",
    "ax.plot(0.2,4.129659653,'o', color='green', markersize = 19)\n",
    "ax.plot(0.2,4.129659653,'o', color='blue', markersize = 15)\n",
    "ax.plot(0.1,4.129659653,'o', color='purple', markersize = 25)\n",
    "ax.plot(0.1,4.129659653,'o', color='green', markersize = 19)\n",
    "ax.plot(0.1,4.129659653,'o', color='blue', markersize = 14)\n",
    "ax.plot(0.3,4.129659653,'o', color='purple', markersize = 15)\n",
    "ax.plot(0.3,10.58550072,'o', color='green', markersize = 15)\n",
    "ax.plot(0.3,10.58550072,'o', color='blue', label = '40 seconds, winter')\n",
    "ax.plot(0.2,5.129522324,'o', color='orange', markersize = 15 )\n",
    "ax.plot(0.2,5.129522324,'o', color='yellow')\n",
    "ax.plot(0.2,4.129659653,'o', color='red', label = '40 seconds, summer')\n",
    "ax.plot(0.1,4.129659653,'o', color='yellow', label = '24 hours, summer')\n",
    "ax.plot(0.1,2.013401032,'o', color='orange', label = '12 hours, summer')\n",
    "ax.plot(0.1,4.129659653,'.', color='red')\n",
    "ax.plot(0.3,5.129522324,'o', color='yellow')\n",
    "ax.plot(0.3,7.502369881,'o', color='orange')\n",
    "ax.plot(0.3,8.97701931,'o', color='red')\n",
    "ax.plot(0.2,12.59094429,'*', color='purple', markersize = 15)\n",
    "ax.plot(0.2,13.59549046,'*', color='green')\n",
    "ax.plot(0.2,12.59094429,'*', color='blue')\n",
    "ax.plot(0.1,16.59963274,'*', color='purple', markersize = 15)\n",
    "ax.plot(0.1,17.59994555,'*', color='green')\n",
    "ax.plot(0.1,16.59963274,'*', color='blue')\n",
    "ax.plot(0.3,12.59094429,'*', color='purple', label = '24 hours, winter')\n",
    "ax.plot(0.3,11.58208942,'*', color='green', label = '12 hours, winter')\n",
    "ax.plot(0.3,13.59549046,'*', color='blue')\n",
    "ax.plot(0.2,4.129659653,'*', color='yellow')\n",
    "ax.plot(0.2,4.00445509,'*', color='orange')\n",
    "ax.plot(0.2,6.004694939,'*', color='red')\n",
    "ax.plot(0.1,7.004734993,'*', color='orange', markersize = 15)\n",
    "ax.plot(0.1,7.004734993,'*', color='yellow')\n",
    "ax.plot(0.1,6.004694939,'*', color='red')\n",
    "ax.plot(0.3,4.00445509,'*', color='yellow')\n",
    "ax.plot(0.3,3.004142284,'*', color='red', markersize=15)\n",
    "ax.plot(0.3,3.004142284,'*', color='orange')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Vtau vs Displacement')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A plot of vtau vs displacement. The colours indicate the type of wind event (specifically the ramp up time), stars = max upward displacement, circles = max downward displacement, warm colours indicate summer stratification, cool colours show winter stratification."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notes:\n",
    "* tracking 1023.3. in winter and 1022 in summer\n",
    "* upward displacement is generally more than downward (maybe because of chosen density to track?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f295fb722b0>"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApAAAAHwCAYAAAARqV+XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f295fb9dc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.set_ylabel('Time of Upwelling (h)')\n",
    "ax.set_xlabel('vtau')\n",
    "ax.grid('on')\n",
    "ax.plot(0.2,31,'o', color='purple' )\n",
    "ax.plot(0.2,25,'o', color='green')\n",
    "ax.plot(0.2,18,'o', color='blue')\n",
    "ax.plot(0.1,48,'o', color='purple')\n",
    "ax.plot(0.1,41,'o', color='green')\n",
    "ax.plot(0.1,35,'o', color='blue')\n",
    "ax.plot(0.3,27,'o', color='purple', label = '24 hours, winter')\n",
    "ax.plot(0.3,21,'o', color='green', label = '12 hours, winter')\n",
    "ax.plot(0.3,16,'o', color='blue', label = '40 seconds, winter')\n",
    "ax.plot(0.2,26,'o', color='yellow' )\n",
    "ax.plot(0.2,20,'o', color='orange')\n",
    "ax.plot(0.2,13,'o', color='red')\n",
    "ax.plot(0.1,33,'o', color='yellow', label = '24 hours, summer')\n",
    "ax.plot(0.1,24,'o', color='orange', label = '12 hours, summer')\n",
    "ax.plot(0.1,18,'o', color='red', label = '40 seconds, summer')\n",
    "ax.plot(0.3,24,'o', color='yellow')\n",
    "ax.plot(0.3,18,'o', color='orange')\n",
    "ax.plot(0.3,12,'o', color='red')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Vtau vs Time of Upwelling')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 24 hours ramp up has longest time for each season\n",
    "* 40 seconds has shortest\n",
    "* could fit quadratic curve? not enough points...\n",
    "* spacing between constant vtau seems pretty linear if we seperate by season "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f295f625828>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo0AAAHwCAYAAADD+VXPAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAWZI5RMbu9gSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f295f750358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.set_ylabel('Time of Upwelling (h)')\n",
    "ax.set_xlabel('ramp up time (h)')\n",
    "ax.grid('on')\n",
    "ax.plot(24,31,'o', color='purple', label = 'vtau = 0.2, winter' )\n",
    "ax.plot(12,25,'o', color='purple')\n",
    "ax.plot(40/3600,18,'o', color='purple', markersize = 18)\n",
    "ax.plot(24,48,'o', color='green', label = 'vtau = 0.1, winter')\n",
    "ax.plot(12,41,'o', color='green')\n",
    "ax.plot(4/360,35,'o', color='green')\n",
    "ax.plot(24,27,'o', color='blue')\n",
    "ax.plot(12,21,'o', color='blue')\n",
    "ax.plot(4/360,16,'o', color='blue', label = 'vtau = 0.3, winter')\n",
    "ax.plot(24,26,'o', color='yellow' , label = 'vtau = 0.2, summer')\n",
    "ax.plot(12,20,'o', color='yellow')\n",
    "ax.plot(4/360,13,'o', color='yellow')\n",
    "ax.plot(24,33,'o', color='orange')\n",
    "ax.plot(12,24,'o', color='orange')\n",
    "ax.plot(4/360,18,'o', color='orange', label = 'vtau = 0.1, summer')\n",
    "ax.plot(24,24,'o', color='red', label = 'vtau = 0.3, summer')\n",
    "ax.plot(12,18,'o', color='red')\n",
    "ax.plot(4/360,12,'o', color='red')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('ramp up time vs Time of Upwelling')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* So. Linear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from salishsea_tools import grid_tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "e3t_0 = lake_meshmask.variables['e3t_0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "222893.004147\n"
     ]
    }
   ],
   "source": [
    "U24W = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72UW0.3/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "t=53\n",
    "ssh = U24W.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "den1 = calc_rho(U24W.variables['vosaline'][t,:,200,68], \n",
    "                U24W.variables['votemper'][t,:,200,68],\n",
    "                U24W.variables['deptht'][:])\n",
    "A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "den1 = calc_rho(U24W.variables['vosaline'][t,:,200,136], \n",
    "                U24W.variables['votemper'][t,:,200,136],\n",
    "                U24W.variables['deptht'][:])\n",
    "print(A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.array([225522.0146,\n",
    "225500.8622,\n",
    "225689.2462,\n",
    "225743.8751,\n",
    "225741.8879,\n",
    "225715.2862,\n",
    "225372.2134,\n",
    "225338.683,\n",
    "225269.177,\n",
    "224942.7154,\n",
    "224821.6334,\n",
    "224972.7665,\n",
    "225429.1101,\n",
    "225258.0937,\n",
    "224736.07,\n",
    "224704.8263,\n",
    "224469.4481,\n",
    "224806.5727])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f295e0abf28>"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAHwCAYAAABueE1mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJnAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f295e3834e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.set_ylabel('delta PE')\n",
    "ax.set_xlabel('vtau')\n",
    "ax.grid('on')\n",
    "ax.plot(0.2,a[0],'o', color='purple' )\n",
    "ax.plot(0.2,a[1],'o', color='green')\n",
    "ax.plot(0.2,a[2],'o', color='blue')\n",
    "ax.plot(0.1,a[3],'o', color='purple')\n",
    "ax.plot(0.1,a[4],'o', color='green')\n",
    "ax.plot(0.1,a[5],'o', color='blue')\n",
    "ax.plot(0.3,a[6],'o', color='purple', label = '24 hours, winter')\n",
    "ax.plot(0.3,a[7],'o', color='green', label = '12 hours, winter')\n",
    "ax.plot(0.3,a[8],'o', color='blue', label = '40 seconds, winter')\n",
    "ax.plot(0.2,a[9],'o', color='yellow' )\n",
    "ax.plot(0.2,a[10],'o', color='orange')\n",
    "ax.plot(0.2,a[11],'o', color='red')\n",
    "ax.plot(0.1,a[12],'o', color='yellow', label = '24 hours, summer')\n",
    "ax.plot(0.1,a[13],'o', color='orange', label = '12 hours, summer')\n",
    "ax.plot(0.1,a[14],'o', color='red', label = '40 seconds, summer')\n",
    "ax.plot(0.3,a[15],'o', color='yellow')\n",
    "ax.plot(0.3,a[16],'o', color='orange')\n",
    "ax.plot(0.3,a[17],'o', color='red')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Vtau vs delta PE at time = set up - 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 398,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f295dfa5e10>"
      ]
     },
     "execution_count": 398,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAHwCAYAAABueE1mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+sdRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f295e0270f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b=([224299.227,\n",
    "224028.1897,\n",
    "223853.4521,\n",
    "225743.8751,\n",
    "225520.7612,\n",
    "225348.8909,\n",
    "223215.3118,\n",
    "222990.9758,\n",
    "222916.4572,\n",
    "222517.5889,\n",
    "222264.2071,\n",
    "222108.014,\n",
    "223974.5521,\n",
    "223787.6499,\n",
    "223643.1389,\n",
    "221289.3857,\n",
    "220982.896,\n",
    "220863.7496])\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.set_ylabel('delta PE')\n",
    "ax.set_xlabel('vtau')\n",
    "ax.grid('on')\n",
    "ax.plot(0.2,b[0],'o', color='purple' )\n",
    "ax.plot(0.2,b[1],'o', color='green')\n",
    "ax.plot(0.2,b[2],'o', color='blue')\n",
    "ax.plot(0.1,b[3],'o', color='purple')\n",
    "ax.plot(0.1,b[4],'o', color='green')\n",
    "ax.plot(0.1,b[5],'o', color='blue')\n",
    "ax.plot(0.3,b[6],'o', color='purple', label = '24 hours, winter')\n",
    "ax.plot(0.3,b[7],'o', color='green', label = '12 hours, winter')\n",
    "ax.plot(0.3,b[8],'o', color='blue', label = '40 seconds, winter')\n",
    "ax.plot(0.2,b[9],'o', color='yellow' )\n",
    "ax.plot(0.2,b[10],'o', color='orange')\n",
    "ax.plot(0.2,b[11],'o', color='red')\n",
    "ax.plot(0.1,b[12],'o', color='yellow', label = '24 hours, summer')\n",
    "ax.plot(0.1,b[13],'o', color='orange', label = '12 hours, summer')\n",
    "ax.plot(0.1,b[14],'o', color='red', label = '40 seconds, summer')\n",
    "ax.plot(0.3,b[15],'o', color='yellow')\n",
    "ax.plot(0.3,b[16],'o', color='orange')\n",
    "ax.plot(0.3,b[17],'o', color='red')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Vtau vs delta PE at time = 47')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f295deceef0>"
      ]
     },
     "execution_count": 399,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAHwCAYAAABueE1mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f295def0630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c = np.array([224068.1142,\n",
    "223902.8055,\n",
    "223792.8788,\n",
    "225468.7472,\n",
    "225315.2021,\n",
    "225170.8361,\n",
    "223058.5226,\n",
    "222962.1262,\n",
    "222893.0041,\n",
    "222313.7705,\n",
    "222109.604,\n",
    "221866.0211,\n",
    "223708.1778,\n",
    "223551.8488,\n",
    "223384.2918,\n",
    "221115.8315,\n",
    "220908.8443,\n",
    "220718.9864])\n",
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.set_ylabel('delta PE')\n",
    "ax.set_xlabel('vtau')\n",
    "ax.grid('on')\n",
    "ax.plot(0.2,c[0],'o', color='purple' )\n",
    "ax.plot(0.2,c[1],'o', color='green')\n",
    "ax.plot(0.2,c[2],'o', color='blue')\n",
    "ax.plot(0.1,c[3],'o', color='purple')\n",
    "ax.plot(0.1,c[4],'o', color='green')\n",
    "ax.plot(0.1,c[5],'o', color='blue')\n",
    "ax.plot(0.3,c[6],'o', color='purple', label = '24 hours, winter')\n",
    "ax.plot(0.3,c[7],'o', color='green', label = '12 hours, winter')\n",
    "ax.plot(0.3,c[8],'o', color='blue', label = '40 seconds, winter')\n",
    "ax.plot(0.2,c[9],'o', color='yellow' )\n",
    "ax.plot(0.2,c[10],'o', color='orange')\n",
    "ax.plot(0.2,c[11],'o', color='red')\n",
    "ax.plot(0.1,c[12],'o', color='yellow', label = '24 hours, summer')\n",
    "ax.plot(0.1,c[13],'o', color='orange', label = '12 hours, summer')\n",
    "ax.plot(0.1,c[14],'o', color='red', label = '40 seconds, summer')\n",
    "ax.plot(0.3,c[15],'o', color='yellow')\n",
    "ax.plot(0.3,c[16],'o', color='orange')\n",
    "ax.plot(0.3,c[17],'o', color='red')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Vtau vs delta PE at time = 53')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f295e1df048>"
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqoAAAHwCAYAAABueE1mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlYlNehP/DvGbZhBBdwBWRAYICBgShLRDESbRWbpYlL\n4w23muu9FWKT/KJNk19rgo1Ga7PcJCa24E1N4hXT+Evdm7pUI0RJU9GA6IAsIkSCK4jCsAxwfn/M\njAFkcRmcCX4/z8PDO+d93/OeM+TJ8/Wc876vkFKCiIiIiMjeKGzdACIiIiKirjCoEhEREZFdYlAl\nIiIiIrvEoEpEREREdolBlYiIiIjsEoMqEREREdklBlWie4AQIkEIcfYmj/2dEGJjX7fJHggh0oQQ\nr9i6HX1BCOEnhJBCCEdbt4WI6HYxqBK1I4TYLYRY3kX5T4UQ54QQjkKIj4QQr9mifXebNcKO+fx6\nIUSdEKJSCPHfQggH874zQogG8z7Lz/t3cJ3AHvY/JYQ41L5MSpkipVxxO9e7G4QQDwoh8oUQV4QQ\nl4UQW4UQ3u32uwgh1gshrpr/+1xiy/YSEVkbgypRRx8D+HchhOhU/nMAGVLKFhu0qT+IlFK6AZgK\n4EkAv2i37xEppVu7n2ds00S7pAfwEwBDAHgBKAbwp3b7fwcgCIAawIMAXhRCJN7lNhIR9RkGVaKO\ntgHwBDDJUiCEGALgYQAbhBALASTBFAjqhBA7zcf8XyFEqRDimhBCL4R4vN35HabSexqlFEK8JIT4\nrFPZu0KINebtp4QQp83XKRNCJHXVCSGEq3nkt0YIoQcQ02m/lxDir0KIi+Z6nuvm+8gy/75i7m+c\nECJACHHAPMJ3SQiRIYQY3M35HUgpCwF8CSD8Zo7v1OZYIcRX5tHFKiHE+0IIZ/M+SzvzzO18otO5\noQDSAMSZ918xl18fHbcsjxBCvCiEuGC+xmNCiJ8IIYqEENVCiN+2q1PR7u9+WQixWQjhcav96omU\n8ryU8lv5/SsEWwG0HzWeD2CFlLJGSlkAYB2ApzpVkySEqDD/rZZas31ERH2NQZWoHSllA4DNAOa1\nK/4ZgEIpZZ6Uch2ADACvm0f/HjEfUwpTuB0E4FUAG4UQo26jCX8B8BMhhDsAmKfIfwZgkxBiAIA1\nAGZIKd0BTACQ2009ywAEmH+mwxRoYK5TAWAngDwA3jCNcj4vhJjeRT0PmH8PNvf3KwACwO9hGuEL\nBTAappG9XgkhtDB9T9/czPGdtAJYDGAogDhzuxcBgJTS0s5Iczs/bX+iOcSlAPjKvL+7YD0SgBKm\n7yUVwP8A+HcAUeZ2vyKE8Dcf+yyAxwBMhum7qAGwtqtKhRC+5oDd3c+T3XXaci6ABgAvAHjdXD4E\nwCiY/o4WeQDCOlURDyAYpu8r1RzaiYh+EBhUiW70MYDZQgil+fM8c1m3pJT/T0r5nZSyzRySigHE\n3uqFpZTlAI4BsIzITgFgkFL+0/y5DUC4EMJVSlklpTzZTVU/A7BSSlktpfwWpoBrEQNgmJRyuZSy\nWUp5GqZANvcm21gipdwnpWySUl4E8N8whbWeHBNC1MAUkD8A8GG7fds6hbZfdFWBlPKolPKfUsoW\nKeUZAOk3cd1bZYTpezPC9I+GoQDelVJeM3/XegCR5mNTACyVUp6VUjbBFNZndzVSLqWskFIO7uFn\nU3cNspxrbsvLAArNu9zMv2vbHX4VgHunKl6VUjZIKfNgCrKRICL6geDdoESdSCkPCSEuAXhMCHEE\npsA5s6dzhBDzACwB4GcucoMpWNyOTQD+DcAGmNZzbjK3q948pf0CgD8LIQ4D+JV5Or0zLwDftvtc\n3m5bDcDLMv1t5gDTlHyvhBAjALwL0wijO0z/4K3p5bRxUsqSbvY9JqX8x01cVwNTKI4GoILp/19H\nb6bNt+CylLLVvN1g/n2+3f4GfB8Q1QC2CiHa2u1vBTACQKWV2wUpZbUQ4mOYljd4A6gz7xoIoNG8\nPQjAtU6nnmu3bcD37ScisnscUSXq2gaYRlL/HcAeKWX7sCLbHyiEUMM0IvkMAE/z6NcJmKbIAaAe\npmBlMbKXa/8/AAlCCB+YRlavj7ZJKfdIKX8M05Rvofm6XamCaUrewrfd9rcAyjqN6LlLKX/SRT2y\ni7JV5nKdlHIgTN9R55vP+sKfYOpzkPm6v73F63bVlzvxLUzLMNp/j0op5Q0h1Tx9X9fDT5drjbvg\nCGA4gIFSyhqY/s7tR0gjAXQ3yk5E9IPDoErUtQ0AfgTT3emdp/3PAxjT7vMAmELQRQAQQvwHOt4s\nlAvgAXNYGQTgNz1d2DydfhCm6fEy8/pKCCFGCNNjsgYAaIJpRK2tm2o2A/iNEGKIOfA+227fvwBc\nM9+45SqEcBBChAshYrqo56L5Gu37626+dq15ZO/XPfXHitxhmtquE0KEAHi60/7Of5fOzgPwsdyA\nZQVpAFaa/6ECIcQwIcRPuzrQPH3v1sNPRlfnCSFmCiGCzTduDYNpRPkbKWW1+ZANAF42/51DYfrv\n9SMr9Y+IyOYYVIm6YF4DmQ1TCN3RafefAWjN6ym3SSn1AN4C8BVMYUgH4HC7uvYB+BTAcZimqnfd\nRBM2wRSU269dVMC0vOA7ANUwrc/sHNYsXoVpur8MwF4A/9uuPa0wPcXgPvP+SzCtGx3UuRIppQHA\nSgCHzf0db657HExrI/8GYMtN9KcnOzuNLm7t5rgXYFoKcQ2mkeRPO+3/HYCPze38WRfnH4BptPGc\neWnHnXoXpv829gohrgH4J4D7rVBve94AdsPU53yY/tHweLv9y2C6ka8cpn/cvC6l3G3lNhAR2Yz4\n/qknRERERET2gyOqRERERGSXGFSJiIiIyC4xqBIRERGRXWJQJSIiIiK7xKBKRERERHaJb6YyGzp0\nqPTz87NqnfX19RgwYIBV67SF/tIPgH2xV/2lL/2lHwD7Yq+s3ZejR49eklIOs1qF39c73NHR8QOY\nninNQTHqThuAEy0tLf8VFRV1oasDGFTN/Pz8kJOTY9U6Dx48iISEBKvWaQv9pR8A+2Kv+ktf+ks/\nAPbFXlm7L0KI8t6PunWOjo4fjBw5MnTYsGE1CoWCz8GkLrW1tYmLFy9qz5079wGAR7s6hv/KISIi\nImsLHzZs2FWGVOqJQqGQw4YNq0XHtzl2POYutoeIiIjuDQqGVLoZ5v9Ous2jDKpERETUr5SUlDjd\nf//9moCAgLDAwMCwFStWDO98zLJly0YIIaKqqqpuWAa5a9cu9wcffDDw7rT2zjz//PNe27Ztc+/p\nmF27drnv27fvB7lQm2tUiYiIyKaOpB3xyFqe5V13rs7ZbaRb8wOpD1TGpMRU3259Tk5OeOutt87G\nx8cbampqFGPHjtX+5Cc/uRoVFdUImILs/v37B44aNarZer3ondFohJOTk1XrfOedd77r7ZgDBw64\nu7m5tf74xz+uv9l6+6Ktt4MjqkRERGQzR9KOeOxdvFddV1XnDAnUVdU57128V30k7YjH7dapVquN\n8fHxBgAYMmRIW0BAQENFRYWzZf8zzzwz+o033jgrhOi2jvr6eofExMQx/v7+YY8++qh/W1sbAGD7\n9u3uoaGhWo1Go50zZ45fQ0ODAABvb2+dZXQ2KytLFRsbGwwAS5Ys8Xrsscf8x40bFzJz5kz/nJwc\npU6nCw0JCdFqNBptfn6+S3dtyMzMVE2bNi0AADZu3DhYqVSOa2xsFAaDQfj4+OgAYNasWX4ffvjh\nEEsbFi9e7KXVakM1Go32m2++UZ46dcp5w4YNw9LS0kaEhIRod+/e7fbdd985Tp8+PSA8PDw0PDw8\ndO/evQO6auvtfv/WxBFVIiIispms5VneLY0tHQbOWhpbFFnLs7zvZFTV4tSpU856vV41efLkOsAU\n+EaNGmWMi4tr6Om8goIC19zc3NN+fn7GqKiokH379rlNmjSpPjk52X/v3r2nIiIimh5//HG/N954\nY1hqamqXj1ayKC4uVn799deFbm5ucv78+aMXLVp0/umnn65ubGwULS0t3Z43YcIEg16vVwFAVlaW\nW2BgYENWVpbKaDSKsWPH1nV1ztChQ1v0en3B6tWrh61evXrEp59+Wj5v3ryLbm5urcuXLz8PAI88\n8oj/kiVLzk+fPr2uuLjYefr06UGnT58+2bmtPX6xdwmDKhEREdlM3bk651spvxW1tbWKmTNnBqxe\nvfpbDw+PtmvXrilef/31kV988UVxb+fqdLr6gIAAIwCEhYUZSktLnQcOHNjq4+PTFBER0QQATz31\n1OW1a9cOB9BjUE1MTLxiCX5xcXH1b7755qizZ886z507t0an0zV1d56TkxN8fX0bjx07pjx27NiA\nZ5999vwXX3zh3traKiZOnNhlUH3yySdrACA2NtawY8eOIV0dc/jw4YHFxcWuls91dXUOtbW1is5t\ntQec+iciIiKbcRvp1uU60e7Kb1ZTU5N46KGHAubMmVM9f/78KwBQUFDgcvbsWZeIiAitt7e37vz5\n887jxo0LraiouGHgzsXF5XpYc3BwQEtLS/frBEzHSMvygIaGhg75asCAAW2W7ZSUlOrt27eXuLq6\ntj388MNBO3bs6PFGqIkTJ9bt2LFjkJOTk3zkkUeufvXVV25fffWV25QpU7oMqkqlUgKAo6Oj7K7N\nUkocO3asoLCwUF9YWKi/cOHC8UGDBrV1bqs9YFAlIiIim3kg9YFKR6Vjh3DkqHRseyD1gcrbrbOt\nrQ1z585VazSaxt/97nfnLeWxsbEN1dXVeZWVlfmVlZX5I0aMaD527FiBr69v9/Pv7URGRjZWVlY6\nnzhxwgUANmzY4Dlp0qRrAODj49N8+PBhFQBs3ry5y5FMANDr9c6hoaFNL7/88oXp06dfyc3NdQWA\nuLg4TVlZ2Q13L02ePLkuPT19eExMTJ2Xl1dLTU2N4+nTp5XR0dE9Ll1oz93dvfXatWsOls/x8fFX\nf//7319/EkJ2drZr12faHoMqERER2UxMSkz1tLenlbuNcmuGANxGuTVPe3ta+Z2sT923b5/btm3b\nPA8dOuQeEhKiDQkJ0X766aeD7rStKpVKpqWlnZkzZ06ARqPRKhQKvPDCCxcBIDU19bsXX3zRNzw8\nPNTBwaHbqfONGzd6aDSasJCQEG1BQYFrcnLy5dbWVpSXl7sMGzbshsCckJBQd/nyZaeEhIQ6ANBq\ntQ3BwcENCsXNR7hZs2Zd+dvf/jbYcjPVunXrvj127NgAjUajDQgICHv//fet/hpdaxFS2s0yBJuK\njo6WfIVq1/pLPwD2xV71l770l34A7Iu96oNXqB6VUkZbrUKzvLy8M5GRkZesXW9/deTIEWV6evrQ\nDz744Kyt22ILeXl5QyMjI/262sebqYiIiIhsKCYmpjEmJuaeDKm94dQ/EREREdklBlUiIiIisksM\nqkRERERkl7hGtQ/kZ+Rj/9L9qK2oRa5vLqaunApdks7WzSIiIiL6QWFQtbL8jHzsXLgTRoMRAFBb\nXoudC3cCAMMqERER0S3g1L+V7V+6/3pItTAajNi/dL+NWkRERHTvmTNnjp+Hh0dkUFBQWPvy5ORk\nH39//zCNRqP98Y9/HHDp0iWHzufu2rXL/cEHHwy8e629fc8//7zXtm3beny71a5du9z37ds34G61\nyZoYVK2stqL2lsqJiIjudWlH0jy83vLSKV5VRHm95aVLO5Lmcad1Lliw4NKOHTuKO5dPnz79alFR\n0cmioiJ9YGBg4yuvvDLyTq91s4xGY+8H3aJ33nnnu8cee+xaT8ccOHDA/csvv3S7lXr7oq23g0HV\nygb5dv3ii+7KiYiI7mVpR9I8Fu9drK6qq3KWkKiqq3JevHex+k7D6owZM+q6etPTzJkzrzo5md5U\nGhcXV19ZWenc1fn19fUOiYmJY/z9/cMeffRR/7Y201tet2/f7h4aGqrVaDTaOXPm+DU0NAgA8Pb2\n1lVVVTkCQFZWlio2NjYYAJYsWeL12GOP+Y8bNy5k5syZ/jk5OUqdThcaEhKi1Wg02vz8fJfu+pCZ\nmamaNm1aAABs3LhxsFKpHNfY2CgMBoPw8fHRAcCsWbP8PvzwwyGWNixevNhLq9WGajQa7TfffKM8\ndeqU84YNG4alpaWNsLyZ6rvvvnOcPn16QHh4eGh4eHjo3r17B3TV1tv97q2JQdXKpq6cCidVx1f1\nOqmcMHXlVBu1iIiIyH4tz1ru3djS2CGPNLY0KpZnLffu62t/9NFHQxMTE7uc8iwoKHBdu3bttyUl\nJScrKipc9u3b52YwGERycrL/p59+WlpUVKRvaWnBG2+80evrR4uLi5VZWVmndu7cWfbee+8NW7Ro\n0fnCwkL98ePHC/z9/Zu7O2/ChAkGvV6vAoCsrCy3wMDAhqysLNUXX3wxYOzYsXVdnTN06NAWvV5f\nsGDBgourV68eERwc3Dxv3ryLKSkp5wsLC/WJiYl1ycnJo5csWXL+xIkTBVu3bi1NSUnx66qtvX6B\ndwFvprIyyw1Tlrv+B/kO4l3/RERE3ThXd67LEc3uyq3lpZdeGung4CBTUlKqu9qv0+nqAwICjAAQ\nFhZmKC0tdR44cGCrj49PU0RERBMAPPXUU5fXrl07HMCFnq6VmJh4xc3NTQKmUdw333xz1NmzZ53n\nzp1bo9Ppmro7z8nJCb6+vo3Hjh1THjt2bMCzzz57/osvvnBvbW0VEydO7DKoPvnkkzUAEBsba9ix\nY8eQro45fPjwwOLiYlfL57q6Oofa2lpF57baAwbVPqBL0kGXpOtX75cmIiLqCyPdRjZX1VXdEEpH\nuo3sdqTxTq1Zs8Zzz549g7/88ssihaLryWUXF5frYc3BwQEtLS2ipzodHBykZXlAQ0NDh0oHDBjQ\nZtlOSUmpnjRpUv3WrVsHPfzww0Hvvfde+aOPPtrtGtOJEyfW7dixY5CTk5N85JFHrj755JN+ra2t\n4q233urylatKpVICgKOjo+yuzVJKHDt2rEClUt0QSNu31R5w6p+IiIhsJvWB1Eqlo7JDOFI6KttS\nH0it7IvrffbZZwPffffdkZ9//nmJu7v7LYWyyMjIxsrKSucTJ064AMCGDRs8J02adA0AfHx8mg8f\nPqwCgM2bN3c5kgkAer3eOTQ0tOnll1++MH369Cu5ubmuABAXF6cpKytz6nz85MmT69LT04fHxMTU\neXl5tdTU1DiePn1aGR0d3XCz7XZ3d2+9du3a9acbxMfHX/39738/3PI5OzvbteszbY9BlYiIiGwm\nJSal+u1pb5ePchvVLCAwym1U89vT3i5Piel6Sv5mPfLII/7x8fEhZWVlLiNGjIh4++23hwLAkiVL\nfOvr6x2mTJmiCQkJ0T755JO+N1unSqWSaWlpZ+bMmROg0Wi0CoUCL7zwwkUASE1N/e7FF1/0DQ8P\nD3VwcOh26nzjxo0eGo0mLCQkRFtQUOCanJx8ubW1FeXl5S5d3fyVkJBQd/nyZaeEhIQ6ANBqtQ3B\nwcEN3Y0Ed2XWrFlX/va3vw223Ey1bt26b48dOzZAo9FoAwICwt5///1e19naipDSbpYh2FR0dLTM\nycmxap39Zeq/v/QDYF/sVX/pS3/pB8C+2Ctr90UIcVRKGW21Cs3y8vLOREZGXrJ2vf3VkSNHlOnp\n6UM/+OCDLqfz+7u8vLyhkZGRfl3t4xpVIiIiIhuKiYlpjImJuSdDam849U9EREREdolBlYiIiIjs\nEoMqEREREdklBlUiIiIisksMqkRERERklxhUiYiIqF9qaWlBaGio9sEHHwy0lJ0/f95hwoQJQWq1\nOnzChAlBFy9edOipDltZs2aN57x58276Ga+3YvLkyYGXLl3qsd9r1qzxPHPmzA0vILjbGFSJiIjI\nptLS4OHlBZ1CgSgvL+jS0uBhjXpfe+21EYGBgR3e4LRs2bJRCQkJ18rLy08kJCRcS01NHWmNa/2Q\nZGZmlgwdOrS1p2M2btw4tKKi4paCqtFovLOGdYFBlYiIiGwmLQ0eixdDXVUFZymBqio4L14M9Z2G\n1dLSUqc9e/YM+sUvftHhxQO7d+8enJycfBkAkpOTL//973+/4XWn5eXlTtHR0cEhISHaoKCgsN27\nd7sBwJYtWwbed999IVqtNnTGjBljamtrFQCQmZmpGjt2bEhwcLBWp9OF1tTUKAwGg5g9e7afRqPR\nhoaGanfu3OkOmEYqp02bFjBp0qQgtVodnpKS4mO57rvvvuvp5+cXrtPpQrOzs90s5evXrx8SFBQU\nFhwcrI2Ojg7uqd+vvPLKiNdee204APznf/7n6PHjx2sAYMeOHe6PPvqoPwB4e3vrqqqqHE+dOuU8\nZsyYsLlz56oDAwPDJk6cGFRXVyc+/PDDISdOnFDNmzdvTEhIiLaurk58+eWXqpiYmOCwsLDQ+Pj4\noPLycicAiI2NDV6wYMHo8PDw0Ndee23E7fytesKgSkRERDazfDm8Gxs75pHGRiiWL4f3ndT7y1/+\ncvTrr79+tvOrRi9fvuyoVquNADB69Gjj5cuXb3j50fr16z2mTp1aW1hYqC8oKDh5//33G6qqqhxX\nrVo1Kisrq0iv1xeMGzfOsGLFihGNjY0iKSkp4J133qk4deqUPjMz85Sbm1vbH/7wh+FCCBQVFek3\nbdp0euHChX4Gg0EAgF6vV23btu10QUHByR07dgwpKSlxKi8vd1q9erVXdnZ24ZEjRwqLiopcLe1Z\nvXr1qL179xadOnVKv3v37pKe+p2QkFB3+PBhNwDIzc1V1dfXOzQ1NYnMzEy3SZMmXet8fEVFhfK5\n5567UFJScnLQoEGtGzZsGPIf//EfNeHh4YYNGzacLiws1Ds5OeG5557z3b59e+nJkycL5s+ff+mF\nF164/vdpbm4WJ06cKHj11VfP3+KfqVd8MxURERHZzLlzcL6V8pvxySefDBo6dGjLpEmTDLt27XLv\n7jiFQgEhxA3l48ePr09OTvYzGo2K2bNn10yYMKHhk08+cS8tLVXGxsaGAIDRaBRRUVF1x48fVw4f\nPtw4efJkAwB4eHi0AUB2drbbs88+ewEAxo4d2+jl5dWcn5+vBID4+Pirnp6erQAQGBjYWFpa6nLh\nwgXH8ePHX/Py8moBgJkzZ1YXFRUpASA6OrouKSnJb9asWTVJSUk1PfU9Pj7eMH/+/AHV1dUKFxcX\nGRERUffll1+qvvrqK/f33nuvovPx3t7eTRMmTGgwt9Nw5swZl87HHD9+3KW4uNh1ypQpGgBoa2vD\nsGHDrs/z/9u//Vt1T226EwyqREREZDMjR6K5qurGUDpyJJpvt85Dhw657du3b7C3t/egpqYmRX19\nveKnP/2p//bt28s8PT1bysvLndRqtbG8vNzJw8OjpfP5M2bMqMvKyjr117/+ddCCBQv8n3nmmfMe\nHh4t8fHxV3fu3FnW/th//etfrp3P742zs7O0bDs4OEij0XhjWm5n06ZNFQcOHBiwY8eOQVFRUdqj\nR4/qR44c2eUaUxcXFzl69OimP/7xj0NjY2PrIiMjG/7xj3+4l5eXu4wdO7axt7Y0NDTcMNsupRSB\ngYENubm5hV1d093dva2n9t8JTv0TERGRzaSmolKpRIego1SiLTUVlbdb59q1ayvPnz9/vLKyMv+j\njz46PX78+Gvbt28vA4Dp06dfSU9P9wSA9PR0z8TExCudzy8qKnL28fEx/upXv7o0b968i8eOHVMl\nJCTU5+TkuJ04ccIFAK5evao4fvy4S0REROOFCxecMjMzVQBQU1OjMBqNmDhxYt3GjRs9ANOIZFVV\nlXNERMQNQdHigQceqP/666/dz50759DU1CS2bt16fe3syZMnXaZMmVL/zjvvfDdkyJCW06dPO5eV\nlTnFxcVpuqorLi6ubu3atSMSEhKu/ehHP7r28ccfD9NqtYbOyyB64ubm1lpbW+sAABEREY3V1dWO\n//jHPwYAQFNTk8jJyVHedGV3gCOqREREZDMpKagGTGtVz52D88iRaE5NRaWl3NpeffXVqscffzxA\nrVYP9fb2bt66dWtp52P27NnjvmbNmpGOjo5SpVK1ZmRklHl5ebWkp6efmTt37pjm5mYBAMuWLauM\niIhoysjIKH3uued8GxsbFUqlsi0rK6voxRdfvDBv3jy1RqPROjg4ID09/Yyrq6u8sUUmarXa+NJL\nL303fvz4UHd399bw8HCDZd/ixYt9zpw54yKlFPHx8VfHjx/fcOjQIZWDg0OX9U2ePPnamjVrRk6Z\nMqV+4MCBbS4uLnLixIl1t/I9zZs379Kzzz6r/vWvf92Wk5NT8Je//KX0ueee87127ZpDa2urePrp\np89HR0d3G7ytRUjZ7Xd2T4mOjpY5OTlWrfPgwYNISEiwap220F/6AbAv9qq/9KW/9ANgX+yVtfsi\nhDgqpYy2WoVmeXl5ZyIjIy/1fiTdrlWrVg1Tq9XNSUlJtbZuy53Ky8sbGhkZ6dfVPo6oEhEREf3A\n/Pa3v71o6zbcDVyjSkRERER2iUGViIiIiOwSgyoRERER2SUGVSIiIiKySwyqRERERGSXGFSJiIio\nXykpKXG6//77NQEBAWGBgYFhK1asGN75mGXLlo0QQkRVVVXd8ASkXbt2uT/44IOBd6e11BMG1T6Q\nkZ8Bv3f8MCVzCvze8UNGfoatm0RERGTH0jwALx2giDL9TvO4k9qcnJzw1ltvnS0tLT155MiRgj//\n+c/Djx49ev1NSiUlJU779+8fOGrUqNt+TevtMBqNd/Nyd0VLyw1voLUqBlUry8jPwMKdC1FeWw4J\nifLacizcuZBhlYiIqEtpHsBiNVDlDEiYfi9W30lYVavVxvj4eAMADBkypC0gIKChoqLC2bL/mWee\nGf3GG2+cFUJ0W0d9fb1DYmLiGH9//7BHH33Uv63N9JbX7du3u4eGhmo1Go12zpw5fg0NDQIAvL29\ndZbR2aysLFVsbGwwACxZssTrscce8x83blzIzJkz/XNycpQ6nS40JCREq9FotPn5+S499WXRokXe\nAQEBYRqNRrtw4UIfAJg1a5bfhx9+eP0VqyqVaixgGgmOiYkJnjp1aoCPj49u0aJF3n/60588dDpd\nqEaj0Z7w6w5QAAAgAElEQVQ8edLFcn5SUpJvZGRkiI+Pj27Xrl3uc+bM8RszZkzYrFmz/Cz1btmy\nZeB9990XotVqQ2fMmDGmtrZWYenr008/7a3VakPXr18/BH2IQdXKlu5fCoPR0KHMYDRg6f6lNmoR\nERGRPVvuDTR2yiONClP5nTt16pSzXq9XTZ48uQ4ANm7cOHjUqFHGuLi4hp7OKygocF27du23JSUl\nJysqKlz27dvnZjAYRHJysv+nn35aWlRUpG9pacEbb7wxrLc2FBcXK7Oysk7t3Lmz7L333hu2aNGi\n84WFhfrjx48X+Pv7dzuqe+7cOYfPP/98SHFx8cmioiL9qlWrqnq7VmFhoev69esriouLT3z22Wee\nRUVFyvz8/IKf//znl956663rSyBqa2sdv/nmm8LVq1d/O3fu3MBf//rX54uLi08WFha6Zmdnu1ZV\nVTmuWrVqVFZWVpFery8YN26cYcWKFSMs53t6erbo9fqChQsX1vTWpjvBN1NZWUVtxS2VExER3dvO\nOd9a+c2rra1VzJw5M2D16tXfenh4tF27dk3x+uuvj/ziiy+KeztXp9PVBwQEGAEgLCzMUFpa6jxw\n4MBWHx+fpoiIiCYAeOqppy6vXbt2OIALPdWVmJh4xc3NTQJAXFxc/Ztvvjnq7NmzznPnzq3R6XRN\n3Z3n6enZ6uLi0vbEE0/4Pfzww1eeeOKJXl+XqtPp6tVqtREAfH19m2bMmFELAJGRkQ2ZmZnuluMe\neuihKwqFAuPGjTN4enoaY2NjGwBAo9E0lJaWupSXlzuXlpYqY2NjQwDAaDSKqKioOsv58+bN69OA\nasERVSvzHeR7S+VERET3tpHdjCh2V35zmpqaxEMPPRQwZ86c6vnz518BgIKCApezZ8+6REREaL29\nvXXnz593HjduXGhFRcUNA3cuLi7Ssu3g4ICWlpbu1wmYjpGW5QENDQ0d8tWAAQPaLNspKSnV27dv\nL3F1dW17+OGHg3bs2OGObjg5OSE3N7dg9uzZNbt27RqckJAQBACOjo6ytbUVANDa2gqj0Xi9be3b\nrVAooFQqpWW7tbX1+nGWcgcHBzg7O3c4p6WlRUgpER8ff7WwsFBfWFioLy0tPbl58+Zyy3Hu7u7X\n+9SXGFStbOXUlVA5qTqUqZxUWDl1pY1aREREZM9SKwFlp9CjbDOV3562tjbMnTtXrdFoGn/3u9+d\nt5THxsY2VFdX51VWVuZXVlbmjxgxovnYsWMFvr6+N3VHUGRkZGNlZaXziRMnXABgw4YNnpMmTboG\nAD4+Ps2HDx9WAcDmzZu7Xbep1+udQ0NDm15++eUL06dPv5Kbm+sKAHFxcZqysjKn9sfW1tYqqqur\nHZ544onatLS0bwsLC1UAoFarm48ePaoCgE2bNg3uLUTfjoSEhPqcnBw3S1+vXr2qOH78eI/rafsC\ng6qVJemSsO6RdVAPUkNAQD1IjXWPrEOSLsnWTSMiIrJDKdXA2+XAqGZAwPT77XJT+e3Zt2+f27Zt\n2zwPHTrkHhISog0JCdF++umng+60pSqVSqalpZ2ZM2dOgEaj0SoUCrzwwgsXASA1NfW7F1980Tc8\nPDzUwcFBdlfHxo0bPTQaTVhISIi2oKDANTk5+XJrayvKy8tdhg0b1iEwX7lyxSExMTFIo9Fo4+Li\nglesWPEtADz77LMXs7Oz3YODg7XZ2dkDXF1drT666eXl1ZKenn5m7ty5YzQajTY6OjokPz9f2fuZ\n1iWk7Pa7vKdER0fLnJwcq9Z58OBBJCQkWLVOW+gv/QDYF3vVX/rSX/oBsC/2ytp9EUIclVJGW61C\ns7y8vDORkZGXrF1vf3XkyBFlenr60A8++OCsrdtiC3l5eUMjIyP9utrHm6mIiIiIbCgmJqYxJibm\nngypvemzqX8hxGghxBdCCL0Q4qQQ4v+Yy98QQhQKIY4LIbYKIQa3OydCCPGV+fh8IYTSXB5l/lwi\nhFgjzA8+E0K4CCE+NZd/LYTwa1fXfCFEsflnfl/1k4iIiIj6Rl+uUW0B8CsppRbAeAC/FEJoAewD\nEC6ljABQBOA3ACCEcASwEUCKlDIMQAIAyysc/gTgFwCCzD+J5vL/BFAjpQwE8DaAP5jr8gCwDMD9\nAGIBLBNC9OkDaYmIiIjIuvosqEopq6SUx8zb1wAUAPCWUu6VUloWC/8TgI95exqA41LKPPM5l6WU\nrUKIUQAGSin/KU0LajcAeMx8zk8BfGze/gzAVPNo63QA+6SU1VLKGpjCsSXcEhEREdEPwF1Zo2qe\nkh8L4OtOuxYA+NS8rQEghRB7AAwD8Bcp5esAvAG0X7dx1lwG8+9vAUBK2SKEqAXg2b68i3Pat2sh\ngIUAMGLECBw8ePC2+teduro6q9dpC/2lHwD7Yq/6S1/6Sz8A9sVe9ae+EN2MPg+qQgg3AH8F8LyU\n8mq78qUwLQ/IaNeWeAAxAAwA9gshjgLo9S0Mt0tKuQ7AOsB017+17wrtL3ea9pd+AOyLveovfekv\n/QDYF3vVn/pCdDP69DmqQggnmEJqhpRyS7vypwA8DCBJfv98rLMAsqSUl6SUBgCfAxgHoBLfLw+A\nedvyEOBKAKPNdToCGATgcvvyLs4hIiKifm7OnDl+Hh4ekUFBQWHty5OTk338/f3DNBqN9sc//nHA\npUuXHDqfu2vXLvcHH3ww8O61lrrTl3f9CwB/BlAgpfzvduWJAF4E8Kg5kFrsAaATQqjMoXMyAL2U\nsgrAVSHEeHOd8wBsN5+zA4Dljv7ZAA6Yg+8eANOEEEPMN1FNM5fdJRkA/DB58hQAfvh+0JiIiIhu\nUJzmgS1eOmxSRGGLlw7FaR53WuWCBQsu7dixo7hz+fTp068WFRWdLCoq0gcGBja+8sorI+/0WjfL\naDT2ftAPTEvLTb3U67b15YjqRAA/BzBFCJFr/vkJgPcBuAPYZy5LAwDzTU//DeAIgFwAx6SUfzPX\ntQjABwBKAJQC+Lu5/M8APIUQJQCWAPi/5rqqAaww13UEwHJz2V2QAdOy13IIIQGUmz8zrBIREd2g\nOM0DRxer0VjlDEigscoZRxer7zSszpgxo67zm54AYObMmVednExvKo2Li6uvrKx07ur8+vp6h8TE\nxDH+/v5hjz76qH9bm+nlT9u3b3cPDQ3VajQa7Zw5c/waGhoEAHh7e+uqqqocASArK0sVGxsbDABL\nlizxeuyxx/zHjRsXMnPmTP+cnBylTqcLDQkJ0Wo0Gm1+fn6PryVdtGiRd0BAQJhGo9EuXLjQBwBm\nzZrl9+GHH15/mpFKpRoLmEaCY2JigqdOnRrg4+OjW7Rokfef/vQnD51OF6rRaLQnT550sZyflJTk\nGxkZGeLj46PbtWuX+5w5c/zGjBkTNmvWLD9LvVu2bBl43333hWi12tAZM2aMqa2tVVj6+vTTT3tr\ntdrQ9evX9+lTlfpsjaqU8hBM70Lr7PMeztkI0yOqOpfnAAjvorwRwJxu6loPYP3Nttd6lsK0xLY9\ng7mcr1ElIiLqIH+5N9oaOw6ctTUqkL/cG0G3/xrVm/HRRx8NnT17dpfXKCgocM3NzT3t5+dnjIqK\nCtm3b5/bpEmT6pOTk/337t17KiIiounxxx/3e+ONN4alpqZe6Ok6xcXFyq+//rrQzc1Nzp8/f/Si\nRYvOP/3009WNjY2ipxHJc+fOOXz++edDTp8+fUKhUKCrZQqdFRYWup44ceLk8OHDW9Rqtc7FxeVS\nfn5+wYoVK4a/9dZbw9evX/8tANTW1jp+8803hZs2bRo8d+7cwAMHDhRGRUU1REREhGZnZ7v6+/sb\nV61aNSorK6to4MCBbUuXLh25YsWKEW+++WYVAHh6erbo9fqC3tpzp/hmKquruMVyIiKie1jjuS5H\nNLstt5KXXnpppIODg0xJ6ToM63S6+oCAACMAhIWFGUpLS50HDhzY6uPj0xQREdEEAE899dTltWvX\nDgfQY1BNTEy84ubmJgHTKO6bb7456uzZs85z586t0el0Td2d5+np2eri4tL2xBNP+D388MNXnnji\niV5vMNfpdPVqtdoIAL6+vk0zZsyoBYDIyMiGzMxMd8txDz300BWFQoFx48YZPD09jbGxsQ0AoNFo\nGkpLS13Ky8udS0tLlbGxsSEAYDQaRVRUVJ3l/Hnz5tX01hZr6NObqe5NvrdYTkREdA9Tjmy+pXIr\nWLNmjeeePXsGb9mypUyh6DoKubi4WG72hoODA1paWrqaJUa7Y6RleUBDQ0OHSgcMGNBm2U5JSane\nvn17iaura9vDDz8ctGPHDnd0w8nJCbm5uQWzZ8+u2bVr1+CEhIQgAHB0dJStra0AgNbWVhiNxutt\na99uhUIBpVIpLdutra3Xj7OUOzg4wNnZucM5LS0tQkqJ+Pj4q4WFhfrCwkJ9aWnpyc2bN5dbjnN3\nd7/ep77EoGp1KwGoOpWpzOVERETUgS61Egplx9CjULZBl9onT+v57LPPBr777rsjP//885JbDVuR\nkZGNlZWVzidOnHABgA0bNnhOmjTpGgD4+Pg0Hz58WAUAmzdv7nbdpl6vdw4NDW16+eWXL0yfPv1K\nbm6uKwDExcVpysrKnNofW1tbq6iurnZ44oknatPS0r4tLCxUAYBarW4+evSoCgA2bdo0uLcQfTsS\nEhLqc3Jy3Cx9vXr1quL48eM9rqftCwyqVpcE06NZ1ZBSAFCbP3N9KhER0Q2CUqoR9XY5lKOaAQEo\nRzUj6u3yO12f+sgjj/jHx8eHlJWVuYwYMSLi7bffHgoAS5Ys8a2vr3eYMmWKJiQkRPvkk0/e9JSn\nSqWSaWlpZ+bMmROg0Wi0CoUCL7zwwkUASE1N/e7FF1/0DQ8PD3VwcJDd1bFx40YPjUYTFhISoi0o\nKHBNTk6+3NraivLycpfON39duXLFITExMUij0Wjj4uKCV6xY8S0APPvssxezs7Pdg4ODtdnZ2QNc\nXV2tPrrp5eXVkp6efmbu3LljNBqNNjo6OiQ/P19p7ev0Rnz/GNN7W3R0tMzJybFqnf3lwcz9pR8A\n+2Kv+ktf+ks/APbFXlm7L0KIo1LKaKtVaJaXl3cmMjLykrXr7a+OHDmiTE9PH/rBBx+c7f3o/icv\nL29oZGSkX1f7eDMVERERkQ3FxMQ0xsTE3JMhtTec+u8DGRmAnx8wZcpk+PmZPhMRERHRreGIqpVl\nZAALFwIGAwAIlJebPgNAEpepEhEREd00jqha2dKllpD6PYPBVE5EREREN49B1coqunmuf3flRERE\nRNQ1BlUr8+3mIRfdlRMRERFR1xhUrWzlSkDV6Xn/KpWpnIiIiO6elpYWhIaGah988MFAS9n58+cd\nJkyYEKRWq8MnTJgQdPHiRQdbtrE7a9as8Zw3b949P8zFoGplSUnAunWAWg0IIaFWmz7zRioiIqJu\npKV5wMtLB4UiCl5eOqSleVij2tdee21EYGBgQ/uyZcuWjUpISLhWXl5+IiEh4VpqaupIa1yLbk5b\nWxssr3+9GQyqfSApCThzBjhwIBNnzjCkEhERdSstzQOLF6tRVeUMKYGqKmcsXqy+07BaWlrqtGfP\nnkG/+MUvOrx4YPfu3YOTk5MvA0BycvLlv//97ze87rS8vNwpOjo6OCQkRBsUFBS2e/duNwDYsmXL\nwPvuuy9Eq9WGzpgxY0xtba0CADIzM1Vjx44NCQ4O1up0utCamhqFwWAQs2fP9tNoNNrQ0FDtzp07\n3QHTSOm0adMCJk2aFKRWq8NTUlJ8LNd99913Pf38/MJ1Ol1odna2m6V8/fr1Q4KCgsKCg4O10dHR\nwT31++rVq4qEhITA4OBgbVBQUNj//M//DAEAb29vXVVVlSMAZGVlqWJjY4MBYMmSJV4zZ870i4qK\nCvby8tJ9/PHHg1NSUnw0Go120qRJQU1NTcJy/i9/+UvvkJAQbXh4eOihQ4dU8fHxQaNHjw5//fXX\nh1mu/8orr4wIDw8P1Wg02sWLF3sBwKlTp5z9/PzCH3/8cT+NRhNWWlrqfLN/RwZVIiIisp3ly73R\n2NgxjzQ2KrB8ufedVPvLX/5y9Ouvv35WoehY9eXLlx3VarURAEaPHm28fPnyDY/qXL9+vcfUqVNr\nCwsL9QUFBSfvv/9+Q1VVleOqVatGZWVlFen1+oJx48YZVqxYMaKxsVEkJSUFvPPOOxWnTp3SZ2Zm\nnnJzc2v7wx/+MFwIgaKiIv2mTZtOL1y40M9gMAgA0Ov1qm3btp0uKCg4uWPHjiElJSVO5eXlTqtX\nr/bKzs4uPHLkSGFRUZGrpT2rV68etXfv3qJTp07pd+/eXdJTv7ds2TJw5MiRxlOnTumLi4tPzpw5\n82pv31V5eblLdnZ20V//+teSlJQU/ylTplwtKirSK5XKts2bNw+yHOfr69tcWFiov//+++sWLFjg\nt3PnztKvv/668A9/+IOX5dolJSXK48ePFxQUFOhzc3NVf//7390AoKKiwuWZZ565WFJSclKj0TT3\n1iYLBlUiIiKynXPnuh5d6678JnzyySeDhg4d2jJp0iRDT8cpFAoIIW4oHz9+fP0nn3wydMmSJV7/\n+te/XIcMGdJ28ODBAaWlpcrY2NiQkJAQ7V/+8hfPiooK5+PHjyuHDx9unDx5sgEAPDw82pycnJCd\nne3285///DIAjB07ttHLy6s5Pz9fCQDx8fFXPT09W1UqlQwMDGwsLS11ycrKGjB+/PhrXl5eLUql\nUs6cObPa0p7o6Oi6pKQkv7feemtoS0tLj30fN25cw5dffjnw6aef9t69e7ebp6dnr/PsP/rRj2pd\nXFxkbGxsQ2trq5g9e/ZVAAgLC2soKyu7/nf42c9+dgUAdDqdYdy4cfVDhgxp8/LyanF2dm67dOmS\nw+7duwdmZWUN1Gq12rCwMG1paamysLBQCQCjRo1qnjp1an1vbemMQZWIiIhsZ+TIrkfXuiu/CYcO\nHXLbt2/fYG9vb91TTz015p///Kf7T3/6U38A8PT0bCkvL3cCTFP8Hh4eNyS/GTNm1GVlZZ3y9vZu\nXrBggf/777/vKaVEfHz81cLCQn1hYaG+tLT05ObNm8tvp33Ozs7Ssu3g4CCNRuONabmdTZs2Vbz2\n2mvfffvtt85RUVHac+fOdXsDWERERNOxY8f0Op2u4ZVXXvF+4YUXRlmu09bWBgBoaGjokP9cXFyk\n+Rg4OjpKyyi0QqFAS0vL9bYplUppKW/fB4VCAaPRKKSUeP7556ss31FFRcWJxYsXXwIAlUrVdrPf\nT3sMqkRERGQ7qamVUCo7hhilsg2pqZW3W+XatWsrz58/f7yysjL/o48+Oj1+/Phr27dvLwOA6dOn\nX0lPT/cEgPT0dM/ExMQrnc8vKipy9vHxMf7qV7+6NG/evIvHjh1TJSQk1Ofk5LidOHHCBTCtBT1+\n/LhLRERE44ULF5wyMzNVAFBTU6MwGo2YOHFi3caNGz0A4Pjx4y5VVVXOERERjd21+YEHHqj/+uuv\n3c+dO+fQ1NQktm7den3t7MmTJ12mTJlS/84773w3ZMiQltOnTzuXlZU5xcXFaTrXc+bMGSd3d/e2\nRYsWVS9ZsuRcbm6uCgB8fHyaDx8+rAKAzZs337Au1xpmzJhx9X//93+HWtbulpWVOVVWVt7RW1D5\nClUiIiKynZQU0xT38uXeOHfOGSNHNiM1tfJ6uZW9+uqrVY8//niAWq0e6u3t3bx169bSzsfs2bPH\nfc2aNSMdHR2lSqVqzcjIKPPy8mpJT08/M3fu3DHNzc0CAJYtW1YZERHRlJGRUfrcc8/5NjY2KpRK\nZVtWVlbRiy++eGHevHlqjUajdXBwQHp6+hlXV1d5Y4tM1Gq18aWXXvpu/Pjxoe7u7q3h4eHXly0s\nXrzY58yZMy5SShEfH391/PjxDYcOHVI5ODjcUN/Ro0ddf/Ob3/goFAo4OjrKP/7xj+UAkJqa+l1K\nSorf8uXLWydMmHDNOt9mRzNnzrx68uRJZUxMTAhgGkXNyMgoc3R07LbfvRFS3va5/Up0dLTMycmx\nap0HDx5EQkKCVeu0hf7SD4B9sVf9pS/9pR8A+2KvrN0XIcRRKWW01So0y8vLOxMZGXmp9yPpdq1a\ntWqYWq1uTkpKqrV1W+5UXl7e0MjISL+u9nFElYiIiOgH5re//e1FW7fhbuAaVSIiIiKySwyqRERE\nRGSXGFSJiIjI2tra2tp6fOQSEQCY/zvp9tFVDKpERERkbScuXrw4iGGVetLW1iYuXrw4CMCJ7o7h\nzVRERERkVS0tLf917ty5D86dOxcODopR99oAnGhpafmv7g5gUCUiIiKrioqKugDgUVu3g374GFSJ\nyGbyM/Kxf+l+1FbUItc3F1NXToUuSWfrZhERkZ1gUCUim8jPyMfOhTthNBgBALXltdi5cCcAMKwS\nEREArhshIhvZv3T/9ZBqYTQYsX/pfhu1iIiI7A2DKhHZRG1F12/9666ciIjuPQyqRGQTg3wH3VI5\nERHdexhUicgmpq6cCieVU4cyJ5UTpq6caqMWERGRveHNVERkE5Ybpix3/Q/yHcS7/omIqAMGVSKy\nGV2SDrokHQ4ePIiEhARbN4eIiOwMp/6JiIiIyC4xqBIRERGRXWJQJSIiIiK7xKBKRERERHaJQZWI\niIiI7BKDKhERERHZJQZVIiIiIrJLDKpEREREZJcYVImIiIjILjGoEhEREZFdYlAlIiIiIrvEoEpE\nREREdolBlYiIiIjsEoMqEREREdklBlUiIiIisksMqkRERERklxhUiYiIiMguMagSERERkV1iUCUi\nIiIiu8SgSkRERER2iUGViIiIiOwSgyoRERER2SUGVSIiIiKySwyqRERERGSXGFSJiIiIyC4xqBIR\nERGRXWJQJSIiIiK7xKBKRERERHaJQZWIiIiI7FKfBVUhxGghxBdCCL0Q4qQQ4v+Yy98QQhQKIY4L\nIbYKIQaby/2EEA1CiFzzT1q7uqKEEPlCiBIhxBohhDCXuwghPjWXfy2E8Gt3znwhRLH5Z35f9ZOI\niIiI+kZfjqi2APiVlFILYDyAXwohtAD2AQiXUkYAKALwm3bnlEop7zP/pLQr/xOAXwAIMv8kmsv/\nE0CNlDIQwNsA/gAAQggPAMsA3A8gFsAyIcSQPurnjcoygG1+mPzdFGCbn+kzEREREd2SPguqUsoq\nKeUx8/Y1AAUAvKWUe6WULebD/gnAp6d6hBCjAAyUUv5TSikBbADwmHn3TwF8bN7+DMBU82jrdAD7\npJTVUsoamMJxIu6GsgzgXwsBQzkEJGAoN31mWCUiIiK6JXdljap5Sn4sgK877VoA4O/tPvubp/0z\nhRCTzGXeAM62O+asucyy71sAMIffWgCe7cu7OKdv5S0FWg0dy1oNpnIiIiIiummOfX0BIYQbgL8C\neF5KebVd+VKYlgdYhhqrAPhKKS8LIaIAbBNChPVx2xYCWAgAI0aMwMGDB++4zsmGCoguyqWhAplW\nqN8W6urqrPLd2AP2xT71l770l34A7Iu96k99IboZfRpUhRBOMIXUDCnllnblTwF4GMBU83Q+pJRN\nAJrM20eFEKUANAAq0XF5gI+5DObfowGcFUI4AhgE4LK5PKHTOQc7t09KuQ7AOgCIjo6WCQkJnQ+5\nddt8TdP9nQiVL6xSvw0cPHjwB9v2ztgX+9Rf+tJf+gGwL/aqP/WF6Gb05V3/AsCfARRIKf+7XXki\ngBcBPCqlNLQrHyaEcDBvj4HppqnTUsoqAFeFEOPNdc4DsN182g4Aljv6ZwM4YA6+ewBME0IMMd9E\nNc1c1vciVwIOqo5lDipTORERERHdtL4cUZ0I4OcA8oUQueay3wJYA8AFwD7zU6b+ab7D/wEAy4UQ\nRgBtAFKklNXm8xYB+AiAK0xrWi3rWv8M4H+FECUAqgHMBQApZbUQYgWAI+bjlrerq2/5J5l+5y2F\nNFRAqHxNIdVSTkREREQ3pc+CqpTyENDlcs3Puzn+rzAtE+hqXw6A8C7KGwHM6eac9QDW32x7rco/\nCfBPQianaIiIiIhuG99MRURERER2iUGViIiIiOwSgyoRERER2SUGVSIiIiKySwyqRGQzGfkZ8HvH\nD1Myp8DvHT9k5PNVw0RE9L0+fzMVEVFXMvIzsHDnQhiMpscpl9eWY+HOhQCAJB0f50ZERBxRJSIb\nWbp/6fWQamEwGrB0/1IbtYiIiOwNgyoR2URFbcUtlRMR0b2HQZWIbMJ3kO8tlRMR0b2HQZWIbGLl\n1JVQOak6lKmcVFg5daWNWkRERPaGQZWIbCJJl4R1j6yDepAaAgLqQWqse2Qdb6QiIqLreNc/EdlM\nki4JSbokHDx4EAkJCbZuDhER2RmOqBIRERGRXWJQJSIiIiK7xKBKRERERHaJQZWIiIiI7BKDKhER\nERHZJQZVIiIiIrJLDKpEREREZJf4HFUiIiI7l5+Rj/1L96O2oha5vrmYunIqdEk6WzeLqM8xqBIR\nEdmx/Ix87Fy4E0aDEQBQW16LnQt3AgDDKvV7nPonIiKyY/uX7r8eUi2MBiP2L91voxYR3T0MqkRE\nRHastqL2lsqJ+hMGVSIiIjs2yHfQLZUT9ScMqkRERHZs6sqpcFI5dShzUjlh6sqpNmoR0d3Dm6mI\niIjsmOWGKctd/4N8B/Guf7pnMKgSERHZOV2SDrokHQ4ePIiEhARbN4foruHUPxERERHZJQZVIiIi\nIrJLDKpEREREZJcYVImIiIjILjGoEhEREZFdYlAlIiIiIrvEoEpEREREdolBlYiIiIjsEoMqERER\nEdklBlUiIiIisksMqn0hIwPw88PkKVMAPz/TZyIiIiK6JY62bkC/k5EBLFwIGAwQAFBebvoMAElJ\ntmwZERER0Q8KR1StbelSwGDoWGYwmMqJqAPz5AOmTJnMyQciIroBR1StraLi1sqJ7lHtJh8ACE4+\nEBHRDTiiam2+vrdWTnSP4uQDERH1hkHV2lauBFSqjmUqlamciK7j5AMREfWGQdXakpKAdesAtRpS\nCDTWLLAAABxySURBVECtNn3mXCZRB5x8ICKi3jCo9oWkJODMGWQeOACcOcOQStQFTj4QEVFvGFSJ\nyCbaTT5ACMnJByIiugGDKhHZjHnyAQcOZHLygYiIbsCgSkRERER2iUGViIiIiOwSgyoRERER2SUG\nVSIiIiKySwyqRGRDGQD8MHnyFAB+5s9EREQmjrZuABHdqzIALARggBAAUG7+DAC8/Z+IiDiiSkQ2\nsxSAoVOZwVxORETEoEpENlNxi+VERHSvYVAlIhvxvcVyIiK61zCoEpGNrASg6lSmMpcTERH1EFSF\nEAN72MchDyK6Q0kA1gFQQ0oBQG3+zBupiIjIpKcR1YOWDSHE/k77tvVJa4joHpME4AwyMw8AOAOG\nVCIiaq+noCrabXv0sI+IiIiIyOp6Cqqym+2uPhMRERERWVVPD/wfLoRYAtPoqWUb5s/D+rxlRERE\nRHRP6ymo/g8A9y62AeCDPmsRERERERF6CKpSylcBQAgxVEp56e41iYiIiIio58dTPSyEuAjguBDi\nrBBiwl1sFxERERHd43q6mWoVgElSSi8AswD8/lYqFkKMFkJ8IYTQCyFOCiH+j7n8DSFEoRDiuBBi\nqxBicKfzfP9/e/ceZGdd53n8/V3CAC1ydYwIpBsWHSci3jLAelmyyUzIuI7EGpxht1fC4NpFyVoy\nq+UtuwNeMrVoLViUte5GYUGqa5YdAcVRVqOxGd0xUQaBiGEBpROCWVkJEmIvavC7f5xfy8nZvpxM\n+vT5ndPvV9WpPs/3ufTvmydP5ZPnck5E7I2I9zTVXh0RWyPioYi4JiKi1A+LiJtKfUtEDDWtszYi\nHiyvtQcydkmSJHXfTEF1X2beD5CZW9j/HtV27APenZlLgbOBSyNiKbAROD0zzwAeAD7Qst5VwO0t\ntU8BbwdeVF6rS/1twBOZeRpwNXAlQEQcB1wOnAWcCVweEcce4PglSZLURe089T/ldGZeNdOGM3MX\nsKu8fyoitgEnZuZXmxbbDJw/ORERa4CHgZ831U4AjsrMzWX6s8AaGmH2POCKsujngE+Ws63nAhsz\nc3dZZyONcPtXM41ZkiRJ9ZjpjOrkk/6Tr9bptpVL8q8EtrTMuphy9jQijgTeB3yoZZkTgZ1N0ztL\nbXLeIwCZuQ94Eji+uT7FOpIkSeoBsz71f7BKAL0ZuCwz9zTV19G4PWC0lK4Ars7MveUW1I6LiBFg\nBGDx4sWMjY3N6fb37t0759vshn7pA+ylVv3SS7/0AfZSq37qRWrHTJf+D1pEHEojpI5m5i1N9YuA\nNwIrM3PyW67OAs6PiI8BxwC/joiny/onNW32JODR8v5R4GRgZ0QsAo4GHi/15S3rjLWOLzM3ABsA\nli1blsuXL29d5KCMjY0x19vshn7pA+ylVv3SS7/0AfZSq37qRWrHTJf+D0q5V/RaYFvz/awRsRp4\nL/CmzJyYrGfm6zNzKDOHgE8Af5mZnyz3uu6JiLPLNi8EvlBWuw2YfKL/fGBTCb5fAVZFxLHlIapV\npSZJkqQe0ckzqq8F3gpsjYi7S+2DwDXAYcDGcol/c2ZeMsu23gFcDxxB457WyU8FuBa4MSIeAnYD\nFwBk5u6I+Ajw3bLchycfrJIkSVJvaCuoRsQ/B14KHD5Zy8wPz7ROZn4LmOpm0y/P9vsy84qW6TuB\n06dY7mngLdNs4zrgutl+lyRJkuo066X/iPjPwJ8C76QRPN8CDHZ4XJIkSVrg2rlH9TWZeSGND9b/\nEPBPgBd3dliSJEla6NoJqv+3/JyIiBcCvwJO6NyQJEmSpPbuUf2biDgG+DhwF5DAZzo6KkmSJC14\n7ZxR/Vhm/iwzb6Zxb+pLgI92dliSJGnS6NZRhj4xxIo7VjD0iSFGt47OvpLUB9oJqt+efJOZv8jM\nJ5trkiSpc0a3jjLyxRG2P7mdJNn+5HZGvjhiWNWCMG1QjYgXRMSrgSMi4pUR8aryWg4MzNsIJUla\nwNZ9fR0Tv5rYrzbxqwnWfX1dl0YkzZ+Z7lE9F7iIxtePXtVUf4rGB/dLkqQO2/HkjgOqS/1k2qCa\nmTcAN0TEH5f7UyVJ0jxbcvQStj+5fcq61O+mDaoR8W+nej8pM69qrUmSpLm1fuV6Rr44st/l/4FD\nB1i/cn0XRyXNj5ku/T933kYhSZKmNPyyYaBxr+qOJ3ew5OglrF+5/jd1qZ/NdOn/Q/M5EEmSNLXh\nlw0z/LJhxsbGWL58ebeHI82bWT+eKiJeHBFfj4jvl+kzIuLfdX5okiRJWsja+RzVTwMfoPHVqWTm\nvcAFnRyUJEmS1E5QHcjM77TU9nViMJIkSdKkdoLqTyPiHwMJEBHnA7s6OipJkiQteDM99T/pUmAD\n8JKIeBR4GPhXHR2VJEmSFrxZg2pm/gj4/Yh4DvCPMvOpzg9LkiRJC11bH/jfUgf8wH9JkiR1Vjsf\n+P87wO8Bt5XpPwJaH66SJEmS5tSsH/gfEX8LvGrykn9EXAF8aV5GJ0mSpAWrnaf+FwO/bJr+ZalJ\nkiRJHdPOU/+fBb4TEbeW6TXA9R0bkSRJkkR7T/2vj4jbgdeX0p9l5vc6OyxJkiQtdO2cUSUz7wLu\n6vBYJEmSpN9o5x5VSZIkad4ZVCVJklQlg6okSZKqZFCVJElSlQyqkiRJqpJBVZIkSVUyqEqSJKlK\nBlVJkiRVyaAqSZKkKhlUJUmSVCWDqiRJkqpkUJUkSVKVDKqSJEmqkkFVkiRJVTKoSpIkqUoGVUmS\nJFXJoCpJkqQqGVQlSarc6CgMDcGKFecwNNSYlhaCRd0egCRJmt7oKIyMwMQEQLB9e2MaYHi4myOT\nOs8zqpIkVWzdusmQ+qyJiUZd6ncGVUmSKrZjx4HVpX5iUJUkqWJLlhxYXeonBlVJkiq2fj0MDOxf\nGxho1KV+Z1CVJKliw8OwYQMMDkJEMjjYmPZBKi0EBlVJkio3PAzj47Bp0x2MjxtStXAYVCVJklQl\ng6okSZKqZFCV1D0Pj8Lnhzjnxyvg80ONaUmSCr+ZSlJ3PDwK3xmBZyYIgIntjWmAU7wBT5LkGVVJ\n3XLPOnim5et2nplo1CVJwqAqqVsmpvlanenqkqQFx6AqqTsGpvlanenqkqQFx6AqqTtevh4Oafm6\nnUMGGnVJkjCoSuqWU4bhzA0wMEgSMDDYmPZBKklSYVCV1D2nDMOace544SZYM25IlSTtx6AqSZKk\nKhlUJUmSVCWDqiRJkqpkUJUkSVKVDKqSJEmqUseCakScHBHfiIgfRMR9EfGuUv94RNwfEfdGxK0R\ncUypnxkRd5fXPRHx5qZtvToitkbEQxFxTUREqR8WETeV+paIGGpaZ21EPFheazvVpyRJkjqjk2dU\n9wHvzsylwNnApRGxFNgInJ6ZZwAPAB8oy38fWJaZrwBWA/8lIhaVeZ8C3g68qLxWl/rbgCcy8zTg\nauBKgIg4DrgcOAs4E7g8Io7tYK+SJEmaYx0Lqpm5KzPvKu+fArYBJ2bmVzNzX1lsM3BSWWaiqX44\nkAARcQJwVGZuzswEPgusKcudB9xQ3n8OWFnOtp4LbMzM3Zn5BI1wPBluJUmS1APm5R7Vckn+lcCW\nllkXA7c3LXdWRNwHbAUuKcH1RGBn0zo7S43y8xGAsuyTwPHN9SnWkSRJUg9YNPsiBycijgRuBi7L\nzD1N9XU0bg8Ynaxl5hbgpRHxu8ANEXF76/bmeGwjwAjA4sWLGRsbm9Pt7927d8632Q390gfYS636\npZd+6QPspTbPf/7XOPXUz3DOOY/x9NPP50c/+tc89tjvd3tYUsd1NKhGxKE0QupoZt7SVL8IeCOw\nslzO309mbouIvcDpwKOU2wOKk0qN8vNkYGe5n/Vo4PFSX96yztgUv2cDsAFg2bJluXz58tZFDsrY\n2Bhzvc1u6Jc+wF5q1S+99EsfYC91GaXxGMYEAIcf/hOWLr2apUt/F/Brh9XfOvnUfwDXAtsy86qm\n+mrgvcCbMnOiqX7K5MNTETEIvAQYz8xdwJ6IOLts80LgC2W124DJJ/rPBzaV4PsVYFVEHFseolpV\napIk9Zh1TIbUZ02UutTfOnlG9bXAW4GtEXF3qX0QuAY4DNhYPmVqc2ZeArwOeH9E/Ar4NfCOzPxp\nWe8dwPXAETTuaZ28JeBa4MaIeAjYDVwAkJm7I+IjwHfLch/OzN2dalSSpM7ZcYB1qX90LKhm5reA\nmGLWl6dZ/kbgxmnm3UnjNoDW+tPAW6ZZ5zrgunbHK0lSnZYA26epS/3Nb6aSJKlq64GBltpAqUv9\nzaAqSVLVhmk89ztIZgCDZdoHqdT/DKqSJFVvGBjnjjs2AeMYUrVQGFQlSZJUJYOqJEmSqmRQlSRJ\nUpUMqpIkSaqSQVWSJElVMqhKkiSpSgZVSZIkVcmgKkmSpCoZVCVJklQlg6okSZKqZFCVJElSlQyq\nkiRJqpJBVZIkSVUyqEqSJKlKBlVJkiRVyaAqSZKkKhlUJUmSVCWDqiRJkqpkUJUkSVKVDKqSJEmq\nkkFVUveMjsLQEOesWAFDQ41pSZKKRd0egKQFanQURkZgYoIA2L69MQ0wPNzNkUmSKuEZVUndsW4d\nTEzsX5uYaNQlScKgKqlbduw4sLokacExqErqjiVLDqwuSVpwDKqSumP9ehgY2L82MNCoS5KEQVVS\ntwwPw4YNMDhIRsDgYGPaB6kkSYVBVVL3DA/D+Dh3bNoE4+OGVEnSfgyqkiRJqpJBVZIkSVUyqEqS\nJKlKBlVJkiRVyaAqSZKkKhlUJUmSVCWDqiRJkqpkUJUkSVKVDKqSJEmqkkFVkiRJVTKoSpIkqUoG\nVUmSJFXJoCpJkqQqGVQlSZJUJYOqJEmSqmRQlSRJUpUMqpIkSaqSQVWSJElVMqhKkiSpSgZVSZIk\nVcmgKkmSpCoZVCVJklQlg6okSZKqZFCVJElSlQyqkiRJqpJBVZIkSVUyqEqSJKlKBlVJkiRVyaAq\nSZKkKhlUJUmSVCWDqiRJkqpkUJUkSVKVDKqSJEmqkkFVkiRJVepYUI2IkyPiGxHxg4i4LyLeVeof\nj4j7I+LeiLg1Io4p9T+IiL+PiK3l54qmbb261B+KiGsiIkr9sIi4qdS3RMRQ0zprI+LB8lrbqT4l\nSZLUGZ08o7oPeHdmLgXOBi6NiKXARuD0zDwDeAD4QFn+p8AfZebLgLXAjU3b+hTwduBF5bW61N8G\nPJGZpwFXA1cCRMRxwOXAWcCZwOURcWynGpUkSdLc61hQzcxdmXlXef8UsA04MTO/mpn7ymKbgZPK\nMt/LzB+X+n3AEeWM6QnAUZm5OTMT+Cywpix3HnBDef85YGU523ousDEzd2fmEzTC8WS4lSRJUg+Y\nl3tUyyX5VwJbWmZdDNw+xSp/DNyVmb8ATgR2Ns3bWWqUn48AlPD7JHB8c32KdSRJktQDFnX6F0TE\nkcDNwGWZuaepvo7G7QGjLcu/lMYl/FXzMLYRYARg8eLFjI2Nzen29+7dO+fb7IZ+6QPspVb90ku/\n9AH2Uqt+6kVqR0eDakQcSiOkjmbmLU31i4A3AivL5fzJ+knArcCFmfnDUn6UcntAcVKpTc47GdgZ\nEYuAo4HHS315yzpjrePLzA3ABoBly5bl8uXLWxc5KGNjY8z1NruhX/oAe6lVv/TSL32AvdSqn3qR\n2tHJp/4DuBbYlplXNdVXA+8F3pSZE031Y4AvAe/PzP85Wc/MXcCeiDi7bPNC4Atl9m00HrwCOB/Y\nVILvV4BVEXFseYhqValJkiSpR3TyjOprgbcCWyPi7lL7IHANcBiwsXzK1ObMvAT4N8BpwF9ExF+U\n5Vdl5mPAO4DrgSNo3NM6eV/rtcCNEfEQsBu4ACAzd0fER4DvluU+nJm7O9WoJEmS5l7HgmpmfguI\nKWZ9eZrlPwp8dJp5dwKnT1F/GnjLNOtcB1zX7nglSZJUF7+ZSpIkSVUyqEqSJKlKBlVJkiRVyaAq\nSZKkKhlUJUmSVCWDqiRJkqpkUJUkSVKVDKqSJNXu4VH4/BDn/HgFfH6oMS0tAJ38ZipJknSwHh6F\n74zAMxONb9GZ2N6YBjhluJsjkzrOM6qSJNXsnnXwzMT+tWcmGnWpzxlUJUmq2cSOA6tLfcSgKklS\nzQaWHFhd6iMGVUmSavby9XDIwP61QwYadanPGVQlSarZKcNw5gYYGCQJGBhsTPsglRYAg6okSbU7\nZRjWjHPHCzfBmnFDqhYMg6okSZKqZFCVJElSlQyqkiRJqpJBVZIkSVUyqEqSJKlKBlVJkiRVyaAq\nSZKkKhlUJUmSVCWDqiRJkqpkUJUkSVKVDKqSJEmqkkFVkiRJVTKoSpIkqUoGVUmSJFXJoCpJkqQq\nGVQlSZJUJYOqJEmSqmRQlSRJUpUMqpIkSaqSQVWSJElVMqhKkiSpSgZVSZIkVcmgKkmSpCoZVCVJ\nklQlg6okSZKqZFCVJElSlQyqkiRJqpJBVZIkSVUyqEqSJKlKBlVJkiRVyaAqSZKkKhlUJUmSVCWD\nqiRJkqpkUJUkSVKVDKqSJNVudBSGhjhnxQoYGmpMSwvAom4PQJIkzWB0FEZGYGKCANi+vTENMDzc\nzZFJHecZVUmSarZuHUxM7F+bmGjUpT5nUJUkqWY7dhxYXeojBlVJkmq2ZMmB1aU+YlCVJKlm69fD\nwMD+tYGBRl3qcwZVSZJqNjwMGzbA4CAZAYODjWkfpNICYFCVJKl2w8MwPs4dmzbB+LghVQuGQVWS\nJElVMqhKkiSpSgZVSZIkVcmgKkmSpCoZVCVJklQlg6okSZKqZFCVJElSlQyqkiRJqlLHgmpEnBwR\n34iIH0TEfRHxrlL/eETcHxH3RsStEXFMqR9flt8bEZ9s2darI2JrRDwUEddERJT6YRFxU6lviYih\npnXWRsSD5bW2U31KkiSpMzp5RnUf8O7MXAqcDVwaEUuBjcDpmXkG8ADwgbL808C/B94zxbY+Bbwd\neFF5rS71twFPZOZpwNXAlQARcRxwOXAWcCZweUQcO+cdSpIkqWM6FlQzc1dm3lXePwVsA07MzK9m\n5r6y2GbgpLLMzzPzWzQC629ExAnAUZm5OTMT+Cywpsw+D7ihvP8csLKcbT0X2JiZuzPzCRrheDWS\nJEnqGYvm45eUS/KvBLa0zLoYuGmW1U8EdjZN7yy1yXmPAGTmvoh4Eji+uT7FOs3jGgFGABYvXszY\n2NisvRyIvXv3zvk2u6Ff+gB7qVW/9NIvfYC91KqfepHa0fGgGhFHAjcDl2Xmnqb6Ohq3B4x2egzT\nycwNwAaAZcuW5fLly+d0+2NjY8z1NruhX/oAe6lVv/TSL32AvdSqn3qR2tHRp/4j4lAaIXU0M29p\nql8EvBEYLpfzZ/Io5faA4qRSm5x3ctnmIuBo4PHm+hTrSJIkqQd08qn/AK4FtmXmVU311cB7gTdl\n5sRs28nMXcCeiDi7bPNC4Atl9m3A5BP95wObSvD9CrAqIo4tD1GtKjVJkiT1iJj9hOY/cMMRrwO+\nCWwFfl3KHwSuAQ6jceYTYHNmXlLWGQeOAn4L+BmwKjN/EBHLgOuBI4DbgXdmZkbE4cCNNO5/3Q1c\nkJk/Ktu6uPw+gPWZ+V9nGe//AbYfZNutngf8dI632Q390gfYS636pZd+6QPspVZz3ctgZv72HG5P\nmlMdC6qCiLgzM5d1exwHq1/6AHupVb/00i99gL3Uqp96kdrhN1NJkiSpSgZVSZIkVcmg2lkbuj2A\nOdIvfYC91KpfeumXPsBeatVPvUiz8h5VSZIkVckzqpIkSaqSQbVNEbE6Iv5XRDwUEe+fYv5LIuLb\nEfGLiHhPO+tGxHERsTEiHiw/j625l4g4OSK+ERE/iIj7IuJdTfOuiIhHI+Lu8npDrX2UeeMRsbWM\n9c6meq/tk99p+jO/OyL2RMRlZd6875M2exmOiHvLn//fRcTLZ1u34v0yZS89eKzMtE967ViZbp/0\n4rFyXunl7oi4Mxof+zjjut3aL1LHZKavWV7AIcAPgVNpfMbrPcDSlmWeD/wesB54TzvrAh8D3l/e\nvx+4svJeTgBeVd4/F3igqZcrmpetuY8ybxx43hTb7al9MsV2/jeNz0Wc931yAL28Bji2vP9DYMts\n61a8X6brpdeOlSn7KNO9dqxM20vLdnrhWDmSZ2/ROwO4f7Z1u7FffPnq5Mszqu05E3goM3+Umb8E\n/htwXvMCmflYZn4X+NUBrHsecEN5fwOwplMNtDkeYPpeMnNXZt5V3j8FbANOnIcxT+Vg9slMemqf\ntFgJ/DAz5/qLKw5EO738XWY+USY38+xXJPfisTJlLz14rEy3T2bSU/ukRa8cK3szc/JBkucA2ca6\n3dgvUscYVNtzIvBI0/RO2v9HZ6Z1F2fjK2Kh8b/7xQczyDkYT9siYojGN4JtaSq/s1ymum4eLjcd\nbB8JfC0i/j4iRprqPbtPgAuAv2qpzec+gQPv5W00vm1utnV7Yb809/IbPXistPbRy8fKlPuEHjpW\nIuLNEXE/8CXg4jbW7cZ+kTrGoFqJ8r/mnvgIhog4ErgZuCwz95Typ2hchnoFsAv4j10aXrtel5mv\noHFp8NKI+KetC/TYPvkt4E3AXzeVq94nEfHPaASJ9x3IejXul+l66bVjZZo+evJYmWGf9NSxkpm3\nZuZLaJwZ/cgBrlvdfpEOlEG1PY8CJzdNn1RqB7vuTyLiBIDy87GDHOfBjmdWEXEojX94RzPzlsl6\nZv4kM5/JzF8Dn6ZxaaqTDqqPzHy0/HwMuJVnx9tz+6T4Q+CuzPzJZKEL+wTa7CUizgA+A5yXmY+3\nsW61+2WaXnruWJmuj148VqbrpeipY2VSZv4tcGpEPG+WdbuxX6SOMai257vAiyLilPK/8QuA2+Zg\n3duAteX9WuALczjmf8h4ZhQRAVwLbMvMq1rmndA0+Wbg+3M03ukcTB/PiYjnTr4HVvHseHtqnzT5\nF7RcyuzCPoE2eomIJcAtwFsz84E2161yv0zXS68dKzP00XPHygx/vyb10rFyWvm7RES8CjgMeHyW\ndbuxX6TOmY8ntvrhBbyBxpO7PwTWldolwCXl/Qto3Ce0B/hZeX/UdOuW+vHA14EHga8Bx9XcC/A6\nGpeR7gXuLq83lHVuBLaWebcBJ1Tcx6k0npK9B7ivl/dJmfccGv94Hd2yzXnfJ2328hngiaa/Q3fO\ntG7l+2XKXnrwWJmuj148Vmb6+9Vrx8r7yp/73cC3adyGUeWx4stXp15+M5UkSZKq5KV/SZIkVcmg\nKkmSpCoZVCVJklQlg6okSZKqZFCVJElSlQyqkuZNRAxFxL/s9jgkSb3BoCppPg0BBlVJUlsMqpI6\nIiL+Q0Rc2jR9BY0PIH99RNwdEX9ezrB+MyLuKq/XlGWXR8TfNK37yYi4aL57kCR1l0FVUqfcBPxJ\n0/SfAH8GfDMzX5GZV9P4HvI/yMxXAX8KXDP/w5Qk1WpRtwcgqT9l5vci4vkR8ULgt2l8reUjLYsd\nCnwyIl4BPAO8eJ6HKUmqmEFVUif9NXA+8AIaZ1hb/TnwE+DlNK7wPF3q+9j/is/hHRyjJKlSBlVJ\nnXQT8GngecA5wAuB5zbNPxrYmZm/joi1wCGlvh1YGhGHAUcAK4FvzduoJUlVMKhK6pjMvC8ings8\nmpm7IuKnwDMRcQ9wPfCfgJsj4kLgfwA/L+s9EhH/Hfg+8DDwva40IEnqqsjMbo9BkiRJ+v/41L8k\nSZKqZFCVJElSlQyqkiRJqpJBVZIkSVUyqEqSJKlKBlVJkiRVyaAqSZKkKhlUJUmSVKX/B3oJ0ebG\nx2LBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f295e378c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.set_ylabel('delta PE')\n",
    "ax.set_xlabel('vtau')\n",
    "ax.grid('on')\n",
    "ax.plot(0.2,225522.0146,'o', color='purple' )\n",
    "ax.plot(0.2,224845.4879,'o', color='green')\n",
    "ax.plot(0.2,224359.3601,'o', color='blue')\n",
    "ax.plot(0.1,226577.6333,'o', color='purple')\n",
    "ax.plot(0.1,226122.6376,'o', color='green')\n",
    "ax.plot(0.1,225803.4395,'o', color='blue')\n",
    "ax.plot(0.3,224637.2859,'o', color='purple', label = '24 hours, winter')\n",
    "ax.plot(0.3,223840.4802,'o', color='green', label = '12 hours, winter')\n",
    "ax.plot(0.3,223317.1638,'o', color='blue', label = '40 seconds, winter')\n",
    "ax.plot(0.2,224202.8902,'o', color='yellow' )\n",
    "ax.plot(0.2,223192.9619,'o', color='orange')\n",
    "ax.plot(0.2,222580.9966,'o', color='red')\n",
    "ax.plot(0.1,225925.6627,'o', color='yellow', label = '24 hours, summer')\n",
    "ax.plot(0.1,225000.7925,'o', color='orange', label = '12 hours, summer')\n",
    "ax.plot(0.1,224393.5779,'o', color='red', label = '40 seconds, summer')\n",
    "ax.plot(0.3,222960.0706,'o', color='yellow')\n",
    "ax.plot(0.3,221791.5272,'o', color='orange')\n",
    "ax.plot(0.3,221221.6667,'o', color='red')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Vtau vs delta PE at time = 30h')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "U24W = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24UW/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U48W = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48UW/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U72W = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72UW/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U24W1 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24UW0.1/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U48W1 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48UW0.1/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U72W1 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72UW0.1/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U24W3 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24UW0.3/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U48W3 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48UW0.3/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U72W3 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72UW0.3/SalishSea_1h_20170101_20170105_grid_T.nc')\n",
    "U24S = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24US/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U48S = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48US/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U72S = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72US/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U24S1 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24US0.1/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U48S1 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48US0.1/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U72S1 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72US0.1/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U24S3 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/24US0.3/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U48S3 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/48US0.3/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "U72S3 = nc.Dataset('/ocean/vdo/MEOPAR/completed-runs/SalishSeaLake/72US0.3/SalishSea_1h_20160601_20160605_grid_T.nc')\n",
    "runs = [U24W,U48W,U72W, U24W1, U48W1, U72W1, U24W3, U48W3, U72W3,\n",
    "       U24S, U48S, U72S,U24S1, U48S1, U72S1, U24S3, U48S3, U72S3 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U24W.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_for_U24W = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U24W.variables['vosaline'][t,:,200,68], \n",
    "                U24W.variables['votemper'][t,:,200,68],\n",
    "                U24W.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U24W.variables['vosaline'][t,:,200,136], \n",
    "                U24W.variables['votemper'][t,:,200,136],\n",
    "                U24W.variables['deptht'][:])\n",
    "    list_for_U24W[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U48W.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "list_for_U48W = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U48W.variables['vosaline'][t,:,200,68], \n",
    "                U48W.variables['votemper'][t,:,200,68],\n",
    "                U48W.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U48W.variables['vosaline'][t,:,200,136], \n",
    "                U48W.variables['votemper'][t,:,200,136],\n",
    "                U48W.variables['deptht'][:])\n",
    "    list_for_U48W[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U72W.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U72W = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U72W.variables['vosaline'][t,:,200,68], \n",
    "                U72W.variables['votemper'][t,:,200,68],\n",
    "                U72W.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U72W.variables['vosaline'][t,:,200,136], \n",
    "                U72W.variables['votemper'][t,:,200,136],\n",
    "                U72W.variables['deptht'][:])\n",
    "    list_for_U72W[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U24W1.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U24W1 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U24W1.variables['vosaline'][t,:,200,68], \n",
    "                U24W1.variables['votemper'][t,:,200,68],\n",
    "                U24W1.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U24W1.variables['vosaline'][t,:,200,136], \n",
    "                U24W1.variables['votemper'][t,:,200,136],\n",
    "                U24W1.variables['deptht'][:])\n",
    "    list_for_U24W1[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U24W3.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U24W3 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U24W3.variables['vosaline'][t,:,200,68], \n",
    "                U24W3.variables['votemper'][t,:,200,68],\n",
    "                U24W3.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U24W3.variables['vosaline'][t,:,200,136], \n",
    "                U24W3.variables['votemper'][t,:,200,136],\n",
    "                U24W3.variables['deptht'][:])\n",
    "    list_for_U24W3[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U48W1.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U48W1 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U48W1.variables['vosaline'][t,:,200,68], \n",
    "                U48W1.variables['votemper'][t,:,200,68],\n",
    "                U48W1.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U48W1.variables['vosaline'][t,:,200,136], \n",
    "                U48W1.variables['votemper'][t,:,200,136],\n",
    "                U48W1.variables['deptht'][:])\n",
    "    list_for_U48W1[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U48W3.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U48W3 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U48W3.variables['vosaline'][t,:,200,68], \n",
    "                U48W3.variables['votemper'][t,:,200,68],\n",
    "                U48W3.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U48W3.variables['vosaline'][t,:,200,136], \n",
    "                U48W3.variables['votemper'][t,:,200,136],\n",
    "                U48W3.variables['deptht'][:])\n",
    "    list_for_U48W3[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U72W3.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U72W3 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U72W3.variables['vosaline'][t,:,200,68], \n",
    "                U72W3.variables['votemper'][t,:,200,68],\n",
    "                U72W3.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U72W3.variables['vosaline'][t,:,200,136], \n",
    "                U72W3.variables['votemper'][t,:,200,136],\n",
    "                U72W3.variables['deptht'][:])\n",
    "    list_for_U72W3[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U72W1.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U72W1 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U72W1.variables['vosaline'][t,:,200,68], \n",
    "                U72W1.variables['votemper'][t,:,200,68],\n",
    "                U72W1.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U72W1.variables['vosaline'][t,:,200,136], \n",
    "                U72W1.variables['votemper'][t,:,200,136],\n",
    "                U72W1.variables['deptht'][:])\n",
    "    list_for_U72W1[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U72S1.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U72S1 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U72S1.variables['vosaline'][t,:,200,68], \n",
    "                U72S1.variables['votemper'][t,:,200,68],\n",
    "                U72S1.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U72S1.variables['vosaline'][t,:,200,136], \n",
    "                U72S1.variables['votemper'][t,:,200,136],\n",
    "                U72S1.variables['deptht'][:])\n",
    "    list_for_U72S1[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U72S3.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U72S3 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U72S3.variables['vosaline'][t,:,200,68], \n",
    "                U72S3.variables['votemper'][t,:,200,68],\n",
    "                U72S3.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U72S3.variables['vosaline'][t,:,200,136], \n",
    "                U72S3.variables['votemper'][t,:,200,136],\n",
    "                U72S3.variables['deptht'][:])\n",
    "    list_for_U72S3[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U72S.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U72S = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U72S.variables['vosaline'][t,:,200,68], \n",
    "                U72S.variables['votemper'][t,:,200,68],\n",
    "                U72S.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U72S.variables['vosaline'][t,:,200,136], \n",
    "                U72S.variables['votemper'][t,:,200,136],\n",
    "                U72S.variables['deptht'][:])\n",
    "    list_for_U72S[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U48S3.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U48S3 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U48S3.variables['vosaline'][t,:,200,68], \n",
    "                U48S3.variables['votemper'][t,:,200,68],\n",
    "                U48S3.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U48S3.variables['vosaline'][t,:,200,136], \n",
    "                U48S3.variables['votemper'][t,:,200,136],\n",
    "                U48S3.variables['deptht'][:])\n",
    "    list_for_U48S3[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U48S1.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U48S1 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U48S1.variables['vosaline'][t,:,200,68], \n",
    "                U48S1.variables['votemper'][t,:,200,68],\n",
    "                U48S1.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U48S1.variables['vosaline'][t,:,200,136], \n",
    "                U48S1.variables['votemper'][t,:,200,136],\n",
    "                U48S1.variables['deptht'][:])\n",
    "    list_for_U48S1[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U48S.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U48S = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U48S.variables['vosaline'][t,:,200,68], \n",
    "                U48S.variables['votemper'][t,:,200,68],\n",
    "                U48S.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U48S.variables['vosaline'][t,:,200,136], \n",
    "                U48S.variables['votemper'][t,:,200,136],\n",
    "                U48S.variables['deptht'][:])\n",
    "    list_for_U48S[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U24S.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U24S = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U24S.variables['vosaline'][t,:,200,68], \n",
    "                U24S.variables['votemper'][t,:,200,68],\n",
    "                U24S.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U24S.variables['vosaline'][t,:,200,136], \n",
    "                U24S.variables['votemper'][t,:,200,136],\n",
    "                U24S.variables['deptht'][:])\n",
    "    list_for_U24S[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ssh = U24S1.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U24S1 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U24S1.variables['vosaline'][t,:,200,68], \n",
    "                U24S1.variables['votemper'][t,:,200,68],\n",
    "                U24S1.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U24S1.variables['vosaline'][t,:,200,136], \n",
    "                U24S1.variables['votemper'][t,:,200,136],\n",
    "                U24S1.variables['deptht'][:])\n",
    "    list_for_U24S1[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ssh = U24S3.variables['sossheig']\n",
    "input_vars = {'e3t_0': e3t_0,}\n",
    "newgrid = grid_tools.calculate_time_dependent_grid(e3t_0[0,...], \n",
    "                                                   lake_meshmask.variables['tmask'][0,...],\n",
    "         ssh, input_vars)\n",
    "list_for_U24S3 = np.zeros(102)\n",
    "for t in range(102):\n",
    "    den1 = calc_rho(U24S3.variables['vosaline'][t,:,200,68], \n",
    "                U24S3.variables['votemper'][t,:,200,68],\n",
    "                U24S3.variables['deptht'][:])\n",
    "    A = (newgrid['e3t_t'][t,:,200,68] * den1 * 9.8).sum()\n",
    "    den1 = calc_rho(U24S3.variables['vosaline'][t,:,200,136], \n",
    "                U24S3.variables['votemper'][t,:,200,136],\n",
    "                U24S3.variables['deptht'][:])\n",
    "    list_for_U24S3[t] = A - (newgrid['e3t_t'][t,:,200,136] * den1 * 9.8).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f295d94c390>"
      ]
     },
     "execution_count": 433,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApAAAAHwCAYAAAARqV+XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f295deac710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "ax.set_ylabel('Time of Upwelling (h)')\n",
    "ax.set_xlabel('vtau')\n",
    "ax.grid('on')\n",
    "ax.plot(0.2,31,'o', color='purple' )\n",
    "ax.plot(0.2,25,'o', color='green')\n",
    "ax.plot(0.2,18,'o', color='blue')\n",
    "ax.plot(0.1,48,'o', color='purple')\n",
    "ax.plot(0.1,41,'o', color='green')\n",
    "ax.plot(0.1,35,'o', color='blue')\n",
    "ax.plot(0.3,27,'o', color='purple', label = '24 hours, winter')\n",
    "ax.plot(0.3,21,'o', color='green', label = '12 hours, winter')\n",
    "ax.plot(0.3,16,'o', color='blue', label = '40 seconds, winter')\n",
    "ax.plot(0.2,26,'o', color='yellow' )\n",
    "ax.plot(0.2,20,'o', color='orange')\n",
    "ax.plot(0.2,13,'o', color='red')\n",
    "ax.plot(0.1,33,'o', color='yellow', label = '24 hours, summer')\n",
    "ax.plot(0.1,24,'o', color='orange', label = '12 hours, summer')\n",
    "ax.plot(0.1,18,'o', color='red', label = '40 seconds, summer')\n",
    "ax.plot(0.3,24,'o', color='yellow')\n",
    "ax.plot(0.3,18,'o', color='orange')\n",
    "ax.plot(0.3,12,'o', color='red')\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.title('Vtau vs Time of Upwelling')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video width=\"576\" height=\"576\" controls autoplay>\n",
       "  <source type=\"video/mp4\" src=\"data:video/mp4;base64,AAAAHGZ0eXBNNFYgAAACAGlzb21pc28yYXZjMQAAAAhmcmVlAADxPW1kYXQAAAKsBgX//6jcRem9\n",
       "5tlIt5Ys2CDZI+7veDI2NCAtIGNvcmUgMTM4IHIyMCA0MGU3MmRhIC0gSC4yNjQvTVBFRy00IEFW\n",
       "QyBjb2RlYyAtIENvcHlsZWZ0IDIwMDMtMjAxMyAtIGh0dHA6Ly93d3cudmlkZW9sYW4ub3JnL3gy\n",
       "NjQuaHRtbCAtIG9wdGlvbnM6IGNhYmFjPTEgcmVmPTMgZGVibG9jaz0xOjA6MCBhbmFseXNlPTB4\n",
       "MzoweDExMyBtZT1oZXggc3VibWU9NyBwc3k9MSBwc3lfcmQ9MS4wMDowLjAwIG1peGVkX3JlZj0x\n",
       "IG1lX3JhbmdlPTE2IGNocm9tYV9tZT0xIHRyZWxsaXM9MSA4eDhkY3Q9MSBjcW09MCBkZWFkem9u\n",
       "ZT0yMSwxMSBmYXN0X3Bza2lwPTEgY2hyb21hX3FwX29mZnNldD0tMiB0aHJlYWRzPTQ4IGxvb2th\n",
       "aGVhZF90aHJlYWRzPTQgc2xpY2VkX3RocmVhZHM9MCBucj0wIGRlY2ltYXRlPTEgaW50ZXJsYWNl\n",
       "ZD0wIGJsdXJheV9jb21wYXQ9MCBjb25zdHJhaW5lZF9pbnRyYT0wIGJmcmFtZXM9MyBiX3B5cmFt\n",
       "aWQ9MiBiX2FkYXB0PTEgYl9iaWFzPTAgZGlyZWN0PTEgd2VpZ2h0Yj0xIG9wZW5fZ29wPTAgd2Vp\n",
       "Z2h0cD0yIGtleWludD0yNTAga2V5aW50X21pbj00IHNjZW5lY3V0PTQwIGludHJhX3JlZnJlc2g9\n",
       "MCByY19sb29rYWhlYWQ9NDAgcmM9Y3JmIG1idHJlZT0xIGNyZj0yMy4wIHFjb21wPTAuNjAgcXBt\n",
       "aW49MCBxcG1heD02OSBxcHN0ZXA9NCBpcF9yYXRpbz0xLjQwIGFxPTE6MS4wMACAAAAdi2WIhAAU\n",
       "//73x0/Apuj5ZsudRT0hMIrY+kgovVjs0DJ1AAADAAADAAAnC1TyJ9+uWgRrAAADA9AATRTD0mzG\n",
       "gJj4ADoCTKy1DNHi31iMDJmxzw+kUN3eENqRzcf1U1wqryo6EnJCtZ/ojBnPwbyDTXwqFovWuJjM\n",
       "lntNh06zFDZuaR8lHOwnkuxmbpFrmeJAYm9bZWosv5RT28M0I+w5x11KHqZg6QneLX/FDPV9dYkf\n",
       "4q9xqLaoXarY20nxB/YyvwkGYC8bAZmWP8T1SIaoaUk/q2ZXPdE8cVZAT3m+Cdrcd0Ne6saDiQRF\n",
       "0/CjKM5U0Lv3McUWG8DW1NGgJm3bETm5zTUISsY7Mzdr28yDXTXWE9AQXxzM8WQvAM6Ab69zgXXq\n",
       "0ThL0MZ+Nv2BG0D6VYko4wxxXlgMak8hBx80LdSezXBCnL2/cXj0a/OyUmqAXIU4MAUAIls5rxbW\n",
       "V+eb2NCNEhm0qWE6BAsmfRcp6XvBEcB8WxxIExb0SDn/vYYgSslBHqL/Le2yj+ewxo8mRIYsWejv\n",
       "95SCRVv+PNS+yZ54xt7ONqBJabx75Fvl+0eLxPm1shvt6IoDOutRVltAfsDJbrK2/DWysv7M2w84\n",
       "gm8vSi/8WL/oGmbCmNfu6dR3F3aQWbPeZWOTC7pmlsfS1UVwAP5bb45WIewjYXtuztFTEKflnMox\n",
       "uivAkr2TwsSxdfEgg1STIbJdc3iG7CahwAhPbKD/OsruggVc8dUBxuh767M4o21T2oCrcc68IPrv\n",
       "wgLnAa+kEphmCCfngGRb7vkvrOcrTqkyJGIW7ClEYdLBynyHH2UlzS1hVWc6xe3rNqsBRpppgg5u\n",
       "an5jGnH2tPk+jUenTexrnEzMz34Uz6e3rDw0KucS2DYnyzrvaWUcBaF5DwflHH5EcMvNNfw/eXYV\n",
       "xPERWAEz3vY/JWLRXq+zrR8hH4zdg5e8ms0IaJRpMr5nE+/VruXXjtAae714cY1PHJci6LpuO200\n",
       "YeCp3lIE+Vzio3onL03MeVLWGmgSOvvKD5DAJjto8Pa55TdBD6ONjMTvwLXYmH2lApFinqTFjwT6\n",
       "5OCuA3PnOhkJEW56QFKaIemp+jX6imdewjd+EraMuHJvrQ1xb8lebpcnzRABowtCXQDkkjJ1dSYQ\n",
       "BbuCnV57Nyzd1HN/zmsJ39lyedDsFaHQ3RkdPf2ybDhwSDy1mlFygDQTSNVykMwtV60ADHkKCEl7\n",
       "DYw28JGh+k1uj6aag427lguhtvi++w4k38iRUWtcRR3SL4gJ5nhR//94cpDIs4n83c/oav/cQCaX\n",
       "/0wRP1oN99XU/K1Ilym6txvddOTmxJmkg0ugEmTsFqmtFMe//lQsPpW5D28dVVyL7H4/RX0rpVDx\n",
       "Z0sdiP3h7MfBAKyTqWHyYcxxFDoPO4ZoQRF/zBqp6lKIGhvedbIaIBiYF4a4DUzlnHV2aozKIhPn\n",
       "uTAxXOeOKNIhkkqSAIKoAACc/r+UFVbFNAYvDvMjpYSMqinnt5TuborLc2h+xkBy+5m28XuYLj+R\n",
       "VPhmNVSJPNF3FsZAYwlk6Xt/a/5pTkeYEOU2t1ZZWsuC/r21Y81vEU89E1G32OULb9pMM0FAHJln\n",
       "h2xX0BrACRpFyFw8q3U/35hlLBv/PIM8TQpC20Vdv7WGaGm5FWertdPvGit6U1FGcIy3zooZIP6C\n",
       "vDq9SCv0pS6IAU+IdqAvzerIOgsVS/Db2vJbJUw0QcnAu9B2lgY4JvRbZN4LLRkrQYLw5kavFq8i\n",
       "vqJdLjwt/5yOmMIr38bmWCkXfxtV5wbuJR7Hg4I1dl1N5eSpD+U+nBka9oBbO4uDktdvzKYbdfxY\n",
       "rWOumWmxtpXuWTimSFbblx75k4b809NouHnpcUjCF9qck8oFEqxdGCPOS25uIw0AbZvaE+c3hdG+\n",
       "URcYH23wB6JdNy0rZ4B4YllY7reuSnu5PH0HGbglRwVsHb0ZilvtV7gIUMpdQ9AEj1lYoPuOcdup\n",
       "sJjiDoCu+mSadMjFEheyyVU9zhV2z5h5ZT/OhcFofjhc46l0pGNl9bYh8824VxGP7btfAbgbvcBE\n",
       "QHhONjdJGnCsdEsBr8tLH9hkyuU9PT4WW+C3ZPfOxxf0dExeU8/fgzqNyEeZ3FhxrXEHatsSkko4\n",
       "5lTNVuYI1nW6YwAA5yFEKxHtRj5e0rYn9a4fhlHYfsST94/KLOgQrcg91xSIHOPeR8QD55xegJHb\n",
       "1dYIZd4mHUryfAAQ28AhKrKykv/OuBufwCeODXQesge2el6sdiMAA5DWhHGu9FHr4ROwz9A2FtE3\n",
       "IFneBAPexl/caTIG0mBiaUw3bqhkrc5h6ZvirFz75RJSYeqIA02aRfZ9DSdYpYvYLOvT3pjONNNt\n",
       "YfkI2ioEor8qI8Uf+YTIL7Dh4Qw8Exy94jJxLKyNHntivwP+oJ2ogccOz5gpmVIdjFs18nkUMFbw\n",
       "QZ/DVpRDo8wZNyh20D4AFah511RNuQCvucqv5Fl9CbL7XlX/2wXCi+bCOOk79ScaUku0QeI/d/WT\n",
       "1i9y7+AJdFCPYldbqC/h1E5p9LKv2sRD+rL0uIkA9jEf+GKG/TddSd/3n8c8/13LSl21+x6cq1L/\n",
       "a0AelzT93QzH+NePRgj2ePb9BK3ez/TdZj+OIzQ7rHhJyeLWVMpGe+oZet24drO+YV9CtyEm72fT\n",
       "V1a78V6MwAFbv270WnPPapBCsXzALZc6qAOQChQyUt/Cw6EDqY737wgBYRabU6AAAEZPLpGkCP4R\n",
       "AriTx0AgFKPcOR5K/TFHKaS4L4T8deRyL93bJ1pQjHqEcmy5kOGh4VwUEpVvS6SvavfRRB7QyzxF\n",
       "oyOeuhOfWlbud26P2UtT6n/o+PUYK4i6JFgZ5mI6xee4COXZVtk2Sw6mKGEmUBUwQAuTTi1vo0DO\n",
       "/N1Xp0EClRhfLtCFr+zamW+QnngFwyxNNly9vF0VrSCGTWVaYCRbaAk4Ma5FY+jT/icaktO1yIVS\n",
       "OW1pGSZ9R6zVmMJ2Z8DHQKc5qjZSXLKeYqoV3S9bI4QgfX3dTn09qMRVYV6qrMhZG5reKP4TkDk1\n",
       "IUO9Sk/d+DspPQQ95K+n8Z4jVwqRCcHqtCnTmGA9G56MKbeON9yaSmZK4svG3S4mJ8CYTdtQHfhF\n",
       "dnc/2NLGu9r/dAJ+esJc1IHs6WYUQNjaT+cItHyhvZuUUSGCD0iA01fBv7eTp/Clxli+Im6GXHEP\n",
       "ftHzTSc0RNYdrrzT5DmNP9ypS7OSY/ieaP/xQ+ZSlDx/xXke/c6UQxWn6dUn9VQ+po2inU8lAXXd\n",
       "BJUpeA7vDEP+XEF2gwLIscLW9iD+UDPiuC6u/GxvT9vG42P7/vmYusuzcObl6q5LpAR5fQI5tGH2\n",
       "s8uhu7FZS0nTBcJY0/0VSTxkNQ7dN3+sOxZxDCTbZWatz/j5HDe2SG5fQLeLJxsl6sqiIFKHBfnG\n",
       "MUrpDKQDFxz5CeDsmg3Ymc+jriIVvx32ockFn3qL/fYr7gQcwVIeXl05gN0tI/O/znNC7Cx9HYnP\n",
       "TcgLXXSWsZvt9QP1l8F0KthlOC4xWMr1uJv9jNYO7IPCjh9JpoF64usxVjOVQM9jj+25eVIln1yg\n",
       "nBQdr+0zlSxEOTzdYa9IWVqJ7S9WEMCQ8vjxGAHmmrwr/2+CM44r6b8an72SkpN4egXwVRUEpVlG\n",
       "1/biuSICq6MWSloOAaCKBKDgB1oehLNLSTLaEJMsSs+MP+od5Q/u2HCiIAAAAwAEZinQszKiCd1f\n",
       "GI45N2TldWm9h0x0RUW/KE5ED3x6b5k0p08Q2J3XsZp/NgXPY5N3DhHExVXx7X2XdQwsw6qXmg66\n",
       "SZLSQMS9sKrk80OhTKdpmdrrbf+cSpJcj5RiyA9Qy6TP+mZuW2PfKgDufpk9N7xIFAIp+Sr4v4XR\n",
       "mnCZaoDkhNL/k4LKhTArC7lR1xfrGgwCg8L1dZNOjdRB3Zf2b+g6h2TyBEgm0g14lOwZaFyawQ5X\n",
       "VRrYE6oRHCFPaTfUtaBJg7RFlYPlpPXN1F3/wC67nxftJBR3WhT4XdYAYwoSuo+oKsZJWQ8qLBm0\n",
       "D2wyoctwcfWzeywMnVhnIPwpoZroYbE7x7P0wE0aohJ24aQlrToHGJFY1Sef4UXibssWqP4G5Nvl\n",
       "TN1r8D6hr2hIRQgiVaQRp7NXhBIMbnFZfICcEpz9DzagnpdVY52L3J0XsVGQzMF7cqokO+JV4rMJ\n",
       "9UNT3HW7JjBMzOqDz7flriq85fBsmMK60oUgviRYqwJvqmd2UXOYhqfwdWTi5H+gjaZCkGerfl59\n",
       "v6BCeplVhMhnvfgmVPk5nh5MS5bX4+3tQ2p6FGXGv2oPBYgLZKe5U/ISPz+arjbL0MkotCF2aYye\n",
       "tBKg6nXyugLbLIA90rkjh/PSJJ8h3R2l06fYv3esyQ36LUBIc+63jsZeqYNSuq7GiSorAzmEiX/U\n",
       "W3rVdMYaYxiuMZX6dYqXQ3qSZNK0vJHUHTRDPxJt5VUhu779N8E8y+KYnCc+1jn3BtJnHyz/eby3\n",
       "s8Fr4Yqo4W4t6Y2JPjyvleB6zksE9+yOpNN8QZXPcjwHZTcAEI4LnncE+/shJuLSvBYYVwjT5f/p\n",
       "HcPbcS2OhfCNiXllbH/ZUn5QQoEAdmfQekfZkITSkHsO+Nk4C0Ha/NkNxQQlNg3PSGyGIQ7LQ7/2\n",
       "B7EqWYrWrI+jndlo5yzh5hI2dcwh6dUa8+xLi8Evpz4WlCXqxEIHNXZSJj72avKixL5e4F4oV3+x\n",
       "F5Gt5KmPaaPsXglKwjr8SgqbCejfvBfHlTdP62Um6LYIpcEdEO1n78TfgNZiieTDSC9df7Kx6d8t\n",
       "v26zMMudbDq9vX3Y6evRGGtsngrQxUIDRLGF1AleXc9P5u9r+dKkTjbyAAADAB689/+zXlnseAAA\n",
       "FM5Qwf/V9TVQOzzOfAOHpyfsyMnNnb/5RQtAkcGZ1qNeUfjj49L4HYsVUTMfsIsTi56cSe5wLK1r\n",
       "6CsjwqXO4skW9v6uFmRLn2ANElWxq5E9Par4cBxlsXfzWFEJc/ADukLlpq8YVGpMc2o3jFeBpCSz\n",
       "8W+MiZKQxgrnNOjEi6KlflMm0ixSpVwsGk6InPJ28+IvGOKAZ6SKAhhQQjAP4DzVPPkM6SuM7CZa\n",
       "UDuhvtImw4KYzWKkrDNBJNRCUF6HD0e1UJ0IiqNaAtRtHDUZpXqUe1jy9Uq+We6mulRglaeB3MDj\n",
       "FAg8Ox/I1GYx378ricUFO8OtzKTPP4dJyzD4b5y5AuO9qQFZXzyGLjM8UBym85HfMlJlH7kXfC3h\n",
       "JFGGAZuCwlOOrvZJdZ8cxA3wXiL5UpoOzpvXk52i/lr+7hpsYXtjnMERGrwo6MQSDKlfCgvjvOPd\n",
       "jgPljFsDbF1u6B4opT3r83PlL+mG4ZU7DCZw4QlAhaC7eJ7ztlNxBiYBFo4ruSw38lBH+Eso3p72\n",
       "jkxEcnSJjnCUXIMpSy+K/AXVXbWe8tB+bgc2RM7kVFlOHVo4GX/YEKBTJyU2+ITCgNufSsOrxiLu\n",
       "zeWz/89UCUppjS17Tm7OUB9AWSZqVoAJMQu/3B8dkoeK3acEL7f2Zv9adGL9sUQVVndJvRuYtHbF\n",
       "3fxeE/6CjpkUcII6lRhsswGAKEVln6n1Pob7JRZwAV9hgV96VlhzUe4pv+nD8YhnZik2Sg2klToW\n",
       "Gq9zekSVTP2yd0+43fN+qDSTdA9wwBMYIu9xpnl1gAuzHc8DO/LjjnKUoAAAAwABxWbpHUpsxZrj\n",
       "pUHJ+hcn+TNye4IVxJTbOgLb0/F401Be8QTvfGZvsfIcPaiOZHM3buByU/Z18y5jq9fHXXbUOOR3\n",
       "rkTiIAalkVAZmlpJGhd+HlxtYAtuNy0FFU3Yr2ilC2qgrOWnT2yEhk/0J4M3OOOqYnRMF+hMRhwp\n",
       "JHQjqcVZcZrIJOzvdlOzwZvUDp92e9mKJOvoQz8wL6q4+TVERIfQsfgqsO9mlbYpO4Jc7kNsJtGt\n",
       "XHMR3ryoljZxkCOY0NUz83Ydd3SQ6PnYzAgM4/AELLOSstk//W4vXUNWjAn5P8klXIV8Ar3d1cYl\n",
       "PeaQVS6kyGHPGVEYwXmI8DhMnU56CnU6qviWcRde6DDqw+93UxGfwf5z/Ub1EqamGHVG2qmFtfmR\n",
       "juNhMuX4z+YssbiqHk2bmTDawyz0q+dmxFmG7qU2sw7SLi8lWXDlFFBaB9Ye9ljfXuLTb53Ep7E7\n",
       "By65bavKtS1cQw83CTc0ZQnrkYQM/JKlOcN1uk0Y9+dmFWb/x5ErJiRE7p/qBawhVbLsMcnVPhMb\n",
       "XCHz/85rVz9ma3/km+bEydmJi2YmciPLOeafnQM1pSJOwFp2khYcSOuTsRI/ntdCsGJ9gZobfcRO\n",
       "LZ8ZX/+sSwBOGzRL35WPpS+LNuAKuSVjM8N+WWSSNBr+DVA1uCWgx6Q9QIu070SS34dN7kCLCFAx\n",
       "hqgE5Mc2bZes2idN9l/716VVn0nsonJQZhGoBbtWFoX4oDjdR+rzxgr3F3Ap7t/PtL6+7z6h2Peq\n",
       "gQZewqIA9dfy2TTs4uoBEDkQHYvgCrCgo5C31X5NqPjSp+QqmhRK0XA7P8Liea1bd0LqNSOJt7dO\n",
       "4LHhGXlSxG0C0U77AfUcWJeoehCANXkGG7rklI41WUOJigwR0f9j6lPtxGnTbwRcB+/NhaFcvbBw\n",
       "ykPc1ov2nM3xBDZbiyckTQoyffEllXGim/yPVIQxLXlqW3NVWDHVZNOGNzIhTnq0uWsAitlTDgBO\n",
       "SKxnR28Fl7PVHzxZXo+37OGyIiU22nVl2lap5XDjeI6aa6L7QpC2///gLkyxp7eYqCAJf9Qmzdb7\n",
       "SPGl+xQPwcT/yhCGeKjb/FDgCHX+5tun6S4/EpRAPcSvJy9PDLJcLOGjGoyfwJae9ruolg15P/2V\n",
       "Mu+VCzodogtbQOeimpXKwEknLB3e2/Fa9/2xhgOC9spLALIeI82yLMZ53A6+5PZatqdnXpOyaEQV\n",
       "su6LsZVNRs4g78TvNmCdAh9q5K2icEuCpKarat0So5n3b55PS9xUWy1ME7eZ3B1oS7h1BTd9Gjwe\n",
       "yzWizzYdkpVM54kZexW6L5J0oj9sHQlkI8SIDhi7ByAhUMZt/9WwfilZ1E91y5l34aufbWmYLBdu\n",
       "iCAA03cf+NuDziR/RBloEDfJwSRmFDZqqCEyPyK6u2GPJM/dwPULX3HfGZsMYPW6qQvgnpaK3Eq7\n",
       "a0oEV1LAPtUnHk8UsCoirzk3xRbf5YVq4///4hB+5ujjjQ8zwPlqPuxd0IuFoAs0irUIfRcNqbw7\n",
       "O0boFT9LHkWBsa1bSQUUD0y9U6itv8DZ+YQQQP7HDo9HaFQ+/nZI+ahErUvL/Zp5JPtuiNHrtcmZ\n",
       "DHRxl/E4Mr1Vurpr4osDAkCk3cVQtksNJPGU35miYxQY2Mhp+j7+4UO75L5MoqQQbWvtySfQ9fVD\n",
       "q6S5HyY0xajZmqz4h/sZm1ajCneJcdSoPrGY4s34gq2U2tpykPaLXpezDgHUSDFkdJlXASvX9DJI\n",
       "a1r/75oxGXyH/xPQlpwxOuWQl9o56SpHA/fq8k5bQPb42exWB5Z7dVpTw+gIDeeDdQofpNzFdVM5\n",
       "s6WXIMjpalrAEUYA3Xx0JsYa2NvX74uRmpbhvIhRb1H5TDhIFyGnwnvqHfG8LimKdkjheErl6D4X\n",
       "bFxMA7BmLffFH9oWp3jh34zDyX1IMzIYq4Q9IljcRRzwKt9lajoRpsOyx81URBNFoGAalrNT+hRF\n",
       "IxawwKw3aIcfbiijmWZ31aLl2XzA6Uecy6kKHOUpxRL83/THAORkDRTi9dGIGf/RijQPdzoZ3M0n\n",
       "6rO9hBvFFEaXQaNDRhrJzfvE60uoQgs6vbfrZsLBiop5MtJmzHlIZOBvXr1A2jN1HKnfMZZDYmhu\n",
       "hNPYSPROJ2LMjl2LO8vKyRfv1dBWkwq5SeWebvhj5FCRNRNcxppsv+cRsGSyoK8bC/9RZbNFfoCx\n",
       "lgIWgmeAEERAD6MzhVBeZJmWVJTez+MRHqC5MChPPJApAPhcM70lUkljgetpEtoHcRnvLdAurFr/\n",
       "tTD8YRTqLbMBC1LqdNuZhOcfo6kiVeTQVqyS6cT/AObB/msOmaluCOFxoOGJ9aPjLkVLV/0+AjSP\n",
       "2ZI2YzU1QwBqrm5wbFGdhEnnDtBaBKCoBaMwHqIJRaX2M9w9qSH1VRbo6K2zPs/yy2nD5znIbJe8\n",
       "LCH9umYr8EbBUfWz+zb4ooywWz2AiiwhlQ/hhW0zwTAhxyHPrM8NLuwlqGNMAhX+YrCTpJvxZRJd\n",
       "p1IhD7H7cTO8nnmLkcHuBoywsFq7GmV1k6zpT9W4fvyUHCK37zhU8/An5Vw4JYCrYLkWsApDuWE5\n",
       "GsT68I1lXzPYufMFZzVDtvZM75CDi14kMzr8UrlqX2IPHBk7prbgSYvz9U5HieGS6+O8nHVPDoTy\n",
       "PR1xknQH00XIWN/lHRalVOzLnXiMKTNG0Iun0PfRZMRch6JyNUNaU1RGjLu/7v1v0DjxhFQhwjw8\n",
       "kxZYSQWwW2VxcfyA7hjsfkzqa0IT6ILC3PPpZZV35HcaMb7GvNUHCrf0V/TuQGt9dNVVaMSXGV6o\n",
       "Oymb4ioxn0dYm9ZGmnG3CfOIYtu4ySUeBIR/qnIffXK58eHr1U7vroyKlaM0M8s3i/lCUYAXA2xt\n",
       "kCq/ZxA3oAqV72bVk5Fl7pePM2ojgssSTl3Rafo+fc9my1x6xUVwhdsZ1xJiw0vtd0+GDOSS80P7\n",
       "wAF8hipgaXhHGd6KYlv/hrliWAPPvXNkt5CJcG2VrRrR4VH29Ked890ubRkJK08IAqKXLfKLBwfv\n",
       "xKkoqguxCLB+BXRVW8MdFilmnRj20PwoaCM635s56/46b5DiKfXYbtA7V0bmlFr8aR2H6Izr7N/U\n",
       "UVKAc2ahXzu37di8pF6tpadPij0Nz4OmCf2iazZMeweglKxbogpdvjRBttJin8EIqh1u7WdHw/Ei\n",
       "cZI7Y55ueJvgRy5I4s40ZQdPJU3edyPsSIK6+I/3W/cyODiN6lhY3LQ3fYQGZGYsBPrZXeFauPQe\n",
       "u/Zw92xK9M9+zRHA+Ew3rHdau1v2Smtz0ljDzdL7yzlWmiw44vGqWDP23CR9M9grbWjGSIDM3kkM\n",
       "pA1249gsOxx783qKJeFCYpTRi+rz+tOGU6TNPT43Mmv/cf/fNE8+/b/KJildtOG6FMAQe0OayTMX\n",
       "0+8E937g+sNcL3b4GtwvNy43ngYj04cMFmcEHMn+JWNqMqrmM3avgaxrzItAP4rw1nuUxjniF8oz\n",
       "kJD82XmHw3A/lzlhJahFZv4ylfL77iawnSi70gEFJw3uR0zzIo8zzCEu/Kv4ycyfx8DxpGmLRv/t\n",
       "qxJdRLPxZRUBNqwPNUmkpgXn0Ri2WB+O85Kny/d1CLXlEAjaZ3rP2s1KZULqdWJZnfVc8yj4ON3V\n",
       "XpmVNdCNs2YV35tlOsFR/fiFHg7n72uFs4kgcmDCm+ePG3wwFI236EpWYhRRBpCne7jyvBxJYouU\n",
       "bByfEAZm1jzU6fQlP+yA0fw38LyjR+OgYrC5uEcUv557FGr5qFbrvs74NdrRcxXJjyWmYOpKNwOu\n",
       "IzvQ56GGNy78VLhWYOhlx0y//0v4o4H8CipcitHU/73to0hds5v+IW3lkd6dnMayXhqR2OOXIIJT\n",
       "+bRC27SD/7nPC9B38jf89adMvTdGZEfu88zXHELLARR1nWCQCBT/RGJHNQ9O15k8z+my+lWrcne2\n",
       "E/RIxSyo4pb3lRXiOon0czkXImdHw3WXbNAqaBxt6VNGP6VxVRgKyrgBmA73LQNklSph4iYLZ6ZU\n",
       "1MiQChmSrFAUvUdMsyxjDoiTeLi81AR3IaeC5o0Lz4tNvx+ZgMt3BJqV2eTE7HcmTkblx8XWcgqT\n",
       "DMHcE9wbSyRNlWq2c4bH6ky/PCtoyq0MIZtsm0ovdiQXF+uxtWFhPndjZgAYEKUAUqZguGQ7VlGt\n",
       "YzsYkecv9XQ2CAzyqDe0Hww12lwHedOBMOPQj+XrA4faAWdSG9HBRRas2q0dM2kTjk4uKUCO8yPz\n",
       "mrLULVIsmCxFRvF0Gw9jhIs0buoit/dKN85VTvqmAaPGpAlOdLJnacwgcTrthb9jpFQcvicYt9+R\n",
       "YHU8v9j4wko8bKXoX4FzCOX4ezP21oGbPG/kAAADAAAEhQAAAalBmiRsQU/+1oywAUEjT4AhKlwp\n",
       "0uD3LlDq8z1NU/MlhenB7Vdj6NO1PdRFKoids1Z+vOCTLI+udLhJx4yr+y44qM5XH8/U4s5txCUj\n",
       "0N2sSEzRqxvyXX12g38kQobXdTsvXFUaU33rdciRCbHFnkLgWFGRR8aZKim44uQDRqLDNjgX29IW\n",
       "8YIIklbLsgO1sCR1mdEMopWTV19Vi62W2jYd5uwHggtlS4W/b5RhoHj3l3QlyMYN07KFe4lKcu0O\n",
       "92aiYs/nuxDbU3aL53JFFiZ7XY8z1I40x4eZb/4T/pcKznUdPP/OjEcdRejc29V6Ordp7j2gu+hL\n",
       "AZ5pOI/+bHaTC3UnZQXdJeW8xAkZFnzjcgxr72VN/5TPGcE0QkUtUUxtXW7lPNj45QSBcG/oaJp2\n",
       "+JF5ihVEckylonaxFvkBpqMsruTz4IL86ixTVjMxU0LHZgF/6y3GUALsSx6ICgfmUAahO/4sZIyr\n",
       "FH83gnaI9GgY+7ri7QiLEBAHPn69fcea+5f7YpgpkmwCa4MAMfx8LTZPNNQpRO/qOEHk9ij/BxkJ\n",
       "I4HDWLIO6AAAAkNBnkJ4gj8AAzbWzDTyLcU4csYACaKDFvvZO2KO84SquLZnBiOcVB0r0IlUZdVC\n",
       "KUZWehBdrC1kInbEcxanj0scME5/IaIrDBGrVfcBeSDrjiF4057PvkP2165dGVXP3njcvrlPp+Zk\n",
       "Z9ej6aNrSuBjt30TFgjJM9qHPJuAb4DVzYDcdFzZCCGxIjDw6c5N67cGQfe21Rk1dACVY7f8gP/X\n",
       "FCJAjbVR45KwGaeBAqCC6c/khhEqQG6UypFYe0IeVcwWrdzPfJARMwbSdSMqnZwrivAbnPJ3II3V\n",
       "N3RQ21m213vb0Fv0ytwk7K/hMsiiUlqDjd9eSAajOjHvwKfXHYeqSrK8jPUZIcx+rdD/kWPVkHex\n",
       "qhthnrZO98pY5oYCSjgNv6z+W9uCT488Qpq4qz6RlcSvf/YJDK3M2YmwdmrLTQMI7ZSEO3yRRsIg\n",
       "SBkAttHRmxEE7dAIf+fCtTZw/2HhpHPnHclgrCl3yll9UQTGtt/0IpEAXZ7qau7dJds5s85fZoHo\n",
       "bwcHOo84XOmnks80F07pCOIqEvUp7a0GTxNivmLXG3KBUkan6SYmPW0QEbq09S0u3gZfH+byi3Oq\n",
       "d8hNvxSvuY/ijMy9JqEwzL6+FlAn+szCK9T8SKJpEo4B0ibhGkx6veAFX/n4ADcQqaMyanoLvXjj\n",
       "asXUSpOBgyAfh3QcCmyicTet+fYVAtso5vipWNxBgm1uutrcVgSvMHz4q0EOG32Hhx7Gq7yVotfV\n",
       "sG7G1k/oDu4AcRW6XPdi976JRB0AAAELAZ5hdEEPAAYeUJ82N5egA/hHtDK/fkgpZhXsgKkR9FRR\n",
       "1NM14FIRQzfmM7T6YKjgN4cl1sLmDu7sO9Y88awhrNeQqgNJuFooUrdU97+ad108YobSX//vSXEf\n",
       "G04opjwRov4PYxJS5S06mCzgctx/kAxMDBSvJNinl6CFG7yafyQSl4xDO9GK9seMpEUDeHB+rKNV\n",
       "0kz0Gx594G9kDqravvay0GHBiHeVQIhHzlJ7ecjAhbWzjSHn66OzvmjkmqfWPiobi1I6T2ixt2po\n",
       "AXVJZ/xmk82S1JSVOxG0IvQUTngPq39jHellL876PxpsfpHjk8SCI4J1lFsEoqBgdgxSWvgQ0AAD\n",
       "QJ+eYBvQAAAAdgGeY2pBDwAFzy16tgAB/CPf0fW4nLU5LTyncio2shkfoW6IvYCfjj85vJLCaITI\n",
       "yoIhna8elB+cWgvxRaYJxqG3UJ49YYwfl2uSxrQRPfBA+2C17/edWlSP4ynO0lMV84FKAKOr004J\n",
       "YVvrcUXMBzsgAAADAz8AAAQPQZpoSahBaJlMCCn//taMsAExauCKAADsYmgZFh+qUTpf85NOkZUM\n",
       "5YIEL/yS3BI6xvv5B7g/sKonBC3Ngd3v7ULmHERhigNJF/iuqiQxxge1GdVOpf2OZp/uIL4tr+XP\n",
       "7ZvP9yHE1xqirAmQ5hfMA1FNNi1aYHCEnlMTjDYGeGS9/B05C+AgIUsbj+7+0wJ2cbghQnuF3NaS\n",
       "OG3kvp/OaqDg1UfvBfKVGBvBOc5YnOVSoVXBhH9i3n2Fr4zLLhHr14EDbtX4II4o+SHz+ytG+8v6\n",
       "v82nF5BMdA/5OBD8ctWrHIvnb7EOFZMElgPvK0jqh0frnBI6SVzkDt6z2ekYVIoEZspcGLxH//Nw\n",
       "QnPAbGiuPblnrkEy1WgfHM6ZgmrmKWD20S1hwlPKmK059ahbHrsuxoz6I4B+JmhaesJ6Q6Q7S3by\n",
       "umZT3EBrlBxI7Nykuv2N8X6KdwilC40EkH5L2AhxkmJbXHWEHgOwaVRxneYxen7S6O5A13cpiapv\n",
       "VLBvInY2bfFpqdxoNWpBOf8EWMvLfUFzuZRHFtT6/5HNI0V6mILSpfY3HJlOrBpYgT1yBmZpdyb/\n",
       "US11JlOJE2tOXHLBvFtrjyisCFQYQQ+5X3Xbbe8HDH6mZ7TFhH1dmJkzapxPERjmRBxP+aOaBAuw\n",
       "XdRknYPf2/h0+JyD3edJHg7lsuw/V1icXuJ93EQ+bUXiMAbKrC2Xa93U+gm3n7dj28ZbDhkyxZNU\n",
       "VGTG2qtFvf86P0LYoKanZLVl9V3p4I8v6SEb94VDz07gRsWIOyqbZFAK2qiO3hcvUBZf8m80wows\n",
       "KSx8C7nRKgDveJutToAtlJ31EjGspcPrYiSPPfKXPaR95LPHQ23DC/6VoQP3ij5uIRryTlsqdeJR\n",
       "xJLL7+5C6FrjKc5/FuQlj9djlM6D5NHqTN3rkKa/bPU4IjZB0Z7VJTXjQH31GmcQAKC8tDpRTkS1\n",
       "E729TMnhUdhRPQA+K2oGIE8tc3zeEc//QRsI8UiXiySzGOcc4wNljaRuDAPNXN8HPonnYv+kLKhs\n",
       "l6O4SRba5nxqqguj7js12U3ry6NVszQlNkM6OGqn53J04t2mhU//U9jPEByErroqf4885FkU39T5\n",
       "pnvNLJ4UNDnHS0R6Fi5DDR17IWYkNRlFW0xm3idxH6vKybzpC/vU8ILP/8P4Mo9XGGLXqPtCXQ93\n",
       "W2eV+RSIeHKmVGS38itUvTrrbzpeiOAb2ZaZd3p7wPmH4AM13s0dXHLCwCs+0Xrs1T/7WJnm3b6S\n",
       "fNsGfhutcG5cm7urxBG8o6EG7SHRpLran8h3h55Z7MBYen1SQYR15zXrY8LmDWp75w4KTAk5MkAm\n",
       "ZiRUj4XL0O8YKMVujBezxHxTQrMaYU0vQKyUMJQDawAAAqtBnoZFESwR/wADBbIbQ84VVt4AE01s\n",
       "UW3uJnQ9l+nI0nGJxJMyfm6rEYZQ3VJsoYbGp9g3C36FZnAlmBS8wE6S5HwA525IILGHVhQlavwE\n",
       "VIMQ5faBA9Tj/+CvhZpHzievBIJpoDTIQli8syCwmkvnJ2/N1EtfplgjF7WzKqqbrMX5tn+mAG8+\n",
       "dqfPmFAGM+1ge8HE5rnCRg8DPF9AkCEbGWcsMQWuq4W8kQ80vzwG2aTUvAerXpn6BvoRb0oXumgY\n",
       "KaPOmxFzVeQi8pnEpL4R5nD5ISPkk/PBgrSSlS6yrJwGdKDfqKuQgk+StnHK2NTEG0sbtPpfbLgz\n",
       "9S+cANKw9w0DMT8PfV0uWW9l4KPPhmxZRPloQf1HH5A1XytRrI1wJ38xo9zSJcd+XkGbQgAj05IL\n",
       "6JE2r3vo3RTIpMo8tVTduBGKP8vJ0voM3+NTSiA9QoLOLMZekA2UX1pzyIXrnJ+qkVG+aaRBhgh9\n",
       "BLl6zs15UL+DFEe0jQHDwhAqkBpOebCKQ/sjg0zc84u1NBth2c3oF3QwW1i1l2l31hQjnWSUB5aa\n",
       "3NMTPyjsIdKNLund9r5LMW/aAytWdUXRdacrV2OWAzshPTBcU+jZskmyxl1jVkQrgv5oM73n2SDl\n",
       "I828HuL6waoKbau7LYdAMheWxMBM0Elfn050hl1FEGGhhoHmrwp8T7ha0LOIFOXfi2Tnrp4pe3U0\n",
       "KULDy6a1t+55F16ODMfhDAes4YBnMXIViRz4U4aKOYepZhB0Qam4WugEmaf/rOclMAAqQue9OO8A\n",
       "hYwsp5EVwmfvFRHB0JQx0Hj4b4uo4MS7j/IwPz7+0IwCoArfk6qNuPcwrh2WvlcrwX44DpMsUbx0\n",
       "Yek0Ncbu7IkWDnihydv9rVbQP0IbUvEYDCmiR2coAADjgQAAAZUBnqV0QQ8ABb/cEY1gAB/CPfwr\n",
       "px+x4THm86pS1h17mck6My+N8x3mFDs34KEY32ibeNgAvGJgIBXkMrUQg6zq9Jmja+pMFtTvwkTh\n",
       "8MXTciXJav8W6z9Y3r/57mUKAEvA8BTizhJmbkfb6/TljF9lL0Y2ZjuZlVFtjGUy7LVOLTa16/nU\n",
       "a84AngTk+vWNbvc7uUQbAEbhv+ft/fd4aUoYQFX7b3fz9s0CGUEJGsbGoT/kVZXVVMDAqc3r0j+d\n",
       "RU0J+wBnsKu2kNdU9MhjXg9TA1f/y7ieFfJba3CV/iw2AJMcqTZMGSSC6FF6ppj7TGgFHLT/FnVe\n",
       "jFmngeq4GnkfUOhWkP/69BkyF6r+JAJe//bdCY77aScVC5tS+3rYI5kfqEwbN1RARaK4uUJHj/YL\n",
       "5aQ10rCN/EQ26QH2BkHMkdlWcyyNFo88ZHL66stozSxyvZTQlcc4JyEJUwec8+N9xanP9HOpc95M\n",
       "RBuIUojEsCPSHkzSGXUbQh6FCLruDg2lHGCQU9bThXBfmqNQmDZOogAAdMEAAANLAZ6nakEPAAXD\n",
       "z7U4rv/3QgBNNsvFnkEe4ddceKG8QWTfWeOLU6qDjSXKSkxe+Z1SCN5XTqtXKYNU95eatm0SYGdk\n",
       "iNB25QCYp8PyMK4ZG5YQzAP9SPiY10FmfKTrGkxGlnpoi5ttycY4yEeMCz+/NyZY5y8MNd6rgQ2u\n",
       "PxDcblPWfP7iIIyeaW3XvdpPb3XTd4k778mNWbj49D12RJDecTYfE9edqFKgwealR7Z0i6C3ne+C\n",
       "7JfDnCpRKQNGtKoUomZJ9YrjshAjFVWXR7NVwBG0UOE3iaFkcVOzkbsUR2vphM6SE3rYa9ogOP9H\n",
       "fNFs0x8WSHbb+/zDk5iLgrC4NGBIuJzVqg6OBu4wdmQNXydoPgeemWIiSBt9g0576mJXginy1Fy/\n",
       "QfXuh8GNVkIByommI4BZYVoT/ny3U3tuIn9D3GHIkZgQfhVj9NvlVnkf40vVXL7HYuspVkNe+RZg\n",
       "AFO6Vf1b0vMKKWV2fxVujpBxe8vuaej4s5iOPHP2ufyhqJ6NnodhsNqb11DpNgkXDr++DdZZIMhO\n",
       "HArthx/VEd9fvv82Na+JrnaZS/6XmULGhHGhRu6vfzn1y2iVVWQZ6KjW0643d0iFMMH7F2S0Bccc\n",
       "M0iE2ijzYjGF7aNlBiNFyWfcbIs450bOcQ2qvgJRXdQdwAb1u/W2GoyTbklls42eJtChnUVWA3xI\n",
       "2vZ1I10GnHWOaMz/djmi+rd00ne58qxA6hGkll5ewYMfARKnx9VkXnRzUYi093W2WqH0aisxMTO0\n",
       "SCns/fA+5Fg+zXrs0RbxbJl6I0Vm9POyLGfO4w5P4ggmh9AYRvNMotg0s/dly8eq2VmAi5Pa9ber\n",
       "K46cGhxGUvsZViaZAQ3a1ei5FNeiZDBSQJ3t16LjuYr+7fd06ITH96JpsAuC0gGJeERQmzzMPbXA\n",
       "NI+Kay3L5mY47WxJjvXEn4xGqeDwP4065BR+T1TK/D4UzcVVTbGBp+eTgtUT8b7/AnvlnWX0b7Rn\n",
       "ofosHAEGMYOkrzs1mMjKoTOrLerlWwGpVKqIkvpQPyk5No0XWLHtNadREoSNdhyBQInU2VGxlVVE\n",
       "B7TTiawkNbmYwmW1bC4EAbzt7h55CiuXI/0OPJpthYkAAAXcAAAF+EGarEmoQWyZTAgp//7WjLAB\n",
       "OEnGBL2cpABWzsFcwfvYwdYN9m0ndNCAxICDydyCZM5Gt4qiizwMxGH1IEYW0SBrkLJ4TsFazxjN\n",
       "DAMyYbMt0JOf/I+DH1Gik8Hxifsz6JPgpsZY50XhtD5XW3XHDT/HvKP4jcdfPVRZ3jow5C5fnXe4\n",
       "iPoS+n/6jPNJRuD96KFWJ/uiVZuHIPpM44/TJJt/5DSML2ntDvPS/P5TIck2iIX4+XI+b8lhQAmO\n",
       "w0kA85f4Ug3vYLYrjpWa9/T9Oimi3ujoNUOJdzi4+srSOnLUNDCDjvSm9Xkw95ShEr1RhK3FrJgS\n",
       "/cB6igsiCIWs8Cg+mOpDedIQb5HR8M4YjbJjrCYMz+KH0X8nqpFlzadkX6lkGLqc9jrHyQATuJ7g\n",
       "loqASMaYX+uL6cACpfHNt+x9sC39Ndo2KCltI+VtT/k3Ej1Hz+RvwYxuQ78Tir0qQres6Oo942e0\n",
       "mNNUXPYt712nPOnnB3do4jQkrpfX19199r1F+dOnCYcePx7wk6bsx51rbmj3yqeUth5E0YNEbcYN\n",
       "i6xQYNEHkUtItxBobigc8wKdpVcENL5yBwivVfXSoB22xryG6E6EYAGZ+2tnyRls1fQ62MQyOyI6\n",
       "27PnFlt1f32FenJIWB9s1wg/cK3O6MNcun+7CCeUz2EWoQFv57YjOQQMpyJ0FQsrKhXaadOfs43V\n",
       "jYTw6AlrjDyHFMTC7rfYZsCPw/hI2KNbBUIAnBEn4zXVThyoiUvxJSh6jcoNx9a6s3m8EEaBZsHn\n",
       "IA2uIIGZqm5cktXykqvrCRJLhhQu3wxxccWQWu49WxV8FflyiAUGxY0e+lI4AQI+W3nk5wAqhEdp\n",
       "dposhp+6yfEtqrOOm1SeOtv1lRr6On/fTX4P6MOlud8Ps/eXS9cgsF+jO96xp1+JgeYJh9udoVL6\n",
       "Awv0mf+G9CKfHtmy1GeGL0avTacOguufPyfGqjiiWcGb6+Glh8SKrv5pUr7L7pUpnb4t1mm2FGln\n",
       "Sy6te/SjBpXacX/Sizz4yCTE/iMzO8V+vAYa3hGWBZLbv3R05aV3C6IlLvmuiQ9wYHrQPnAfc4FR\n",
       "WpsLox33DpueNHvw0Z490H2dOPoeagNNUN1sRBQQYHq3zKjKk8j4WL8jSlM/+9LYm1UV7oX7lPaY\n",
       "6aS6SO8vxP3Kl4z5UHFIfnIi5G9/ho+BUglmll7nV3eFZyHiNAfUIUyGND1O1jhQwg9e0C2vUsLA\n",
       "kmKdFkWj4xb3pG1o8TD8+VowzokDou9bdZD4ahLrn598BKUOyzXWwRNRuCoLvdKay3Kx4CIIE7VJ\n",
       "wcVQe9cPns6tsP+r7oB1IbF+abhtEdaPW1tUP5hIKoexDKfKgymOlAXYg7DJBy2zC8QMaD20BRS5\n",
       "UAWyb0r2kDdj7eqAXJUfrk3mQMx1h8aAzIEzR2StiRhhKL6gjEAFeq+38w1XitI2ZYg681kTm2z7\n",
       "2sxDK5KuuUs0CVUm+5iSVL38tbOArbxoMvXu3c8Aq3NFqz+YVn7QHv2LvWhx7jq6mwTHEimjxZJm\n",
       "A8KJLM8m00MM5pCiB6uOYWMnNIxEWg560iS9FE8Zz1C7lI0Tmkxz9hX5X06uZQVDDjRrioxVn9rH\n",
       "YkiKqny4iIRRRs4y6sZJYtHmrjGq3WFO61Ed1W1nkf232wq7XJicxfAO83M+cma0jsGL5yP7oeLT\n",
       "emLyxLuE3XEvxAKBKyC43umG5I3nk4qZ1OTw9DP86UAhGdWYXIRtYA51V3fjC/gHBw5fHYEicLBC\n",
       "j2IncsYr6Oin+PFKpGxADI0NfnVQm36mCJYWwF4Av6xOrpnHKvsEqJvnPPr62Oo8KtlEsPwLyZA7\n",
       "uZ6z4ckztCOg+NTW3GF8H1aERP/QDdCIJjAdCYYf7VR5IvZnZmGwK0vw65lTgTKE5hyN9whwvDdy\n",
       "SnbHQFysiNNZTgDb2D78nwXP99KaQ/LR+Xj2MfxmV6k8R1lcNcmellvmXDnPcD5e7o3IxeuHSbCX\n",
       "WQOy4DOFFw6oXZafLuGlDd4AumK/zW8AnNroD0gAAATHQZ7KRRUsEf8AAyLWJ8/tWL7TWACda3YX\n",
       "ncjdZ9G1m/+F15F4BY8TrXQYEluoahmd8qAg2xw4eg5956ZPj3g49eesyx+yf6b1W05UYY/1nyPA\n",
       "vLC6eS01/HU1hzuQsDdf7i4nYOqLFZHrrpNv5bpzzJz7LUCOaDaDilGEPoAVQ2xbEBiFfHUg3xZn\n",
       "13F76W2GqeE/w57EqRwt8F9OkEa1vpNNhRpKn4X8VkevTUbLNgW8S9A1PGFfPGLNZhVfENdm/LV/\n",
       "d4672PLpr/ZnTCDBw1o0+JcS54uZRsMHyPOQVSyOdb1c7+8N3RfjG8YhN9io6RPT3dWiZqsy4MBI\n",
       "gvGznbX4XFMQRKtc6vECtnMpYeJwQ92/IaziimJZ1thQ9CljbrIUOtbnrOMiUtT1pJWZ5laG2tab\n",
       "1tQMJsgYGU4Tup0UF9FUgf6NAPsKmxdD/QfrilrdksN1esPO1Fgjw/syckVosUzeFpA1Lt/jLgBL\n",
       "70EC1Ufgf3bXUffw0D1vXtC8kkjJjGg6NbF9pFClPsFHi2TXmFlVcU6R4nIEzEwmQ04ulCSA9XMI\n",
       "p3mY3/dMLgkmn29Ql3UpxsY1k6Za6YhOhxTey6UWP06XKsOFjTtIv/ydxG3KJRAF48XoKbhmDAi4\n",
       "O8R8VB1EBAFMRLnTW3YiYF9nMcf4jT2C5FWg+Wv59u5A3EhUpxOXgO4nfYaSiOpAp3nYVpXvm5/p\n",
       "NYSYsOOZmMPzq81WT58odSkaow6LvjBDH+4Lbs2MP5iZhcr9hGsyUv9DJwHfVTLKvGhxF7PbeWIO\n",
       "hJ1R6TnM+aRaGH408nSMRArVVup0CxNdAth9y62KiE/najUjJcsfv9W1fHe+YdalzItfUKqCPEM1\n",
       "uGoPCHvhtzyLrgb21ktFqjOECMaYBgunHU46bf3yZIZxpF76XsQOddTJXi+dpdIVHKqu5CR6Bt7m\n",
       "tVuEkOQLaA25NLicyUQZVNuCmDU3r1qlDX3BZX3pwmKNskazTO8VoFsq0IJfOVSc/euCkxW+J1Yx\n",
       "MQ5XvE0sSW0i8Yvo84YWPlPMF3Uzzuxkxl8yy9JvZp/J2O3ogkOfC6/3e1C/ASt7zy0ZzkqPLYMn\n",
       "uxJHz2olZgmcr2yMGbjUrpXOkZmJGHb5ZZ4tGpU3KpAxcrV87AE4wKrXbbTHo/CX1zJjy/xmNZpw\n",
       "MmpN9QnOVbgsV6hNO7aIbgPCU88rG9jcpUG+YyPBQwnUDpjiLseAGUUjZxFuNzOfMncHBt6XBE30\n",
       "UJqBW2xmiOg27roZnYiWUitFu4PVlblPhVg/hbAD4DJ+g0QUCNZdY4q2h8/7jfVCp+7KtHGaQDsT\n",
       "eprJ2pSM1vApLBwCv//9FVMa4mi5+oS8OZ+jcD+5KHEL22WiiXX68CzNVAXxgOsYOXkce6B+YVgF\n",
       "RXc23nud/SdYGWxZnMjwd6DzL4ujf1Wi2Odv14l5ilD02+5Ka/CRipT68oHHlIsEeMCEQ4r7XVwm\n",
       "jTxWNYfjkfDXseUJBodle4EfkhUcxeJURpB3lnV4Kg6hllVhx1el35mlE53mQ1bhe7+86ZxxFf+Q\n",
       "Fq1anfjoo/PLiuOFGE2/mxqO1yMHUHL6uzrSzEV7zLEjJVPAtdG8u2V/8n0NvaPvlBhFbkAAGs1A\n",
       "PmEAAALxAZ7pdEEPAAX3KH4asIAAJo3kqJ4vrnqUE6NdUS39HpvoxnVw4r4tGS4rJkLdzLQ33EQa\n",
       "RCJ1sfNyXwJdAfw89Y1UxhhysYyTQjXgvltA7AsnliomiTqrfwI6YlW99nG82h4ZOleI3FBd2ie7\n",
       "e55KCt+vVWZJnE9u/nQ6RKi8piixmp6NtUWlab0TxQKk1O2qZMzaWsuR6Q28ev5sWqdNRdzLrBii\n",
       "/ZmR3xkWusP9NGUP0suVW39tRWp9+2kaH8CLIfhNJxfXk0t+geiB9C2C11je6iK+BTj4kAyU9K+T\n",
       "8ZRF4uZxjPS75yyWCdPCvs2ud+yCpmWkJ4L7ILxzTwfrwlTXdvyseIGoNixvWDYRtxjWBnyQnnYG\n",
       "BD2MqID2EmNSyMjoVfoyXSBY6anDpUbnY4VPIqaKVWaOIUiRdMeBnvf+5eyFbCyEixAW3G/wwzam\n",
       "/aqbky14bXxsflT3yqLy9abOzGHF93J5NQ4iWdmYtVMef6zbGTHqlbZzP/mI/WsEYyxpE8n6Wqr+\n",
       "hy5k3WwLnbrJrOJ40oVB8FLfQiAFmoms//jK1rYFs1UPjEdyfLllVMuJzaDteVZizirQeyVFLdU3\n",
       "Stvde6IAH0pngE5y5W8F7DXdOTaEM+VXNqn1RUayZMQ3Cplrp4nG/6i5V2VoC3Ll2WJ18Lf+3t+s\n",
       "midYuDGuPFJ7ug8a+ABz++SFx6xcZXnLnoVbeQO4MCCqpQ1FvF5osxV3xWIyqCttbth1zTlTlD33\n",
       "KGnqUFYXdFo2IHuPiHksUotmdJipvdQGfbMhMLDXI/QUejUxDPJdeERxTbXLbSuYNC/WqIPVAEh5\n",
       "M8l71Yk/kEX8Ribpm4di9yR50c043uIhiYW9JFPwp5g58Ep/T0w/wbYebCSjtdPAtzapcIngvp5Z\n",
       "dzy5rsusl714kCpRP+N07ISqqTxuKHllUlSHnpM8CUcAXC6avt2S/l5SVcekmxHGBKbY9MLeW63r\n",
       "PQT3MzIYlWwQup/wAC4bwGLAAAADVwGe62pBDwAFw8+1V98q/uhACabbJUhq/3fQKrAicC/1ErEM\n",
       "WiUuzVbZQDgxT/qgvhunjRCFj3kK3F53rH6GBefipN0UerqUMVx1WnGvsHt4EBENbF++tOzDjKID\n",
       "LXwAaAWN1vvNYC2rrm9EBsNMzyYW9IMmDncgKmvgowji7tNFgCnfSbO1aunthe8OdYGIzxKp3Q6Z\n",
       "0LPYFhQUJETd2S6otanNKIg0pM8rYzwQcT4SxjRx/V2ArcKbANVxV7r9fbwlBFq0XGWt2Qs3nUr9\n",
       "UeDaW7Yy4Sp6zI3iQIK0DU0HfC1gWdu/wmVl5FjjJQxjeRRr5F61vwaGWxUIj+8D5cWvBG4tSNYt\n",
       "m4+nH9Y/l6+r1CwKWAUMxUlXwPLZWrEbXPzxrQKTFVZRzHjzmKRqFsQ5FXqFYCjRSC/ja3vPj40M\n",
       "rzpcVwGaK4BSImZfPic8eJeocIEn5Rwrges1LxdVAmKMMKrYVYlctk7VRNdbQVNutZKIBZZbdf81\n",
       "B3uVqd2ouWh7e9wG40hTNTm//eroAIOcx5RklJfRSrT3Z+TL1AVIzaBohPDI2reAXuRMjXCIPaj0\n",
       "D04iXJGJxE0lLbNnwO7N6iIkrzx/pLkI9Mv58vFTur/FXH/FsdrZUhoY7bEjFXU3P+9G5C1uVQR9\n",
       "saKrvwdIQuQI+O2ORViSkpT3kT/l8iaSmgw9xnalOr5a7v5wXNkZL3hvkey5O8iXcwUzMEMChDjI\n",
       "/zOn42GN+x3bhzgyxbOSDlsDrJ/nJcPDUf8mqGrIA6Ictm3WmscFhIx6v7KKjVwQ5+m8NFyX3N7b\n",
       "tOntHlMj0HIUIRLhQ4dbJnLnK4d5BKy8/7SRKYhfEsTEVcaqaG47KM83w6NrQAcHNH/MtWmNOhBZ\n",
       "nOYGZlQAycKhF6J9eVgrDe73UOnDEHSftEuGsPkxwKKsjhxSbC2tWU+mhOn/+uTHqaZmEhDRNBZI\n",
       "yxXjfjmW+ThRYC1euK9VKL6lQ4Cx20N4eIjZx4dUfZ/hw9704QQCAx0VUkjtVHDE5+eNz2z2c+7L\n",
       "VWD5TVJnfllOMoUfEQnGnoxeaRIAPdcbOhjSPstPsWV7V+T6W5ZPnOa9kB4bJZcASAGyBhqo49Q+\n",
       "sIAxAK56VBq+J/b3sBL28pRMAAAyoAAABTlBmvBJqEFsmUwIKf/+1oywATBCLojba4OABtWBYbO8\n",
       "NRsYUsfNO/Yh3t2t6PN92jk765Gvx1LmUYU+CYIIuXYZqL2ARn5AeYIp/vAlnGeKFz5qv/x28DvT\n",
       "drMfEtqcSWwIBdOMOCs1PsBe3Si08mgJJUNiSg3HScdzjTLDtaRBKDamaI6KrGF1L9lRGDNzCCF3\n",
       "c6ZjDvBDiJLGVZU7py6DsbkhAZqz646BgDYvD3q7u+HRQ4oR850BPcWb/whnrOmNc54kUceYiRRb\n",
       "jbbPoRH4GLCrreLWy3TG205v8vMlUbHjyTGvL4yYT4ExIfh51hPdCnOFzy15wRQDzROVP0k5QiIT\n",
       "XxbxDcNLxZQQ/X6Yf+ulpZEEU23lu8ISjZO4bX5Ct0Xgblm9lJe6srTOrj63BMgwW7TROU7hUul/\n",
       "UkMscGBZ8j9zC6QO5IjuHvUPOrZSF9ZV/4ct5WAZm0V7goSUn8gdD44LxEI8lyCcNUSWUSK7WXw0\n",
       "QzAOTVXchq+bz+ZNg/J43R4ucRpwOMUT9etN8PGu3KAaqKzJBk5ZkOBEzGKZoho3VaqWnETJe9sA\n",
       "kb17xYenWQjgaYna816dlQIoSi+Rb6u3D6b0wi//95uIRkVCxYEm5Vu79VdvfC5aCubpDt4LlZpz\n",
       "F679Ty19tpBYcgnu36wVcXCEzIFq0jM4J5PNHacb+6RiVl1FR8cItOsBwDEEyyYyAPqQS8KL3xEa\n",
       "RE6aoCEcANXVafNV81Dz5S7g5oHjfeuiaYvTpPD3fQaZy+y/8pfAgU7RsIEu1S44ItCcb6u9thM2\n",
       "sduItCyq0sQwRkDg5rzm8foYKTV2FFfdExTqnMZF52aTeC7J1R1waJ5SBEws0MwCHFNVpCtFY6MR\n",
       "jpc6dig7vJWgFIcN1Rra+PfXebFeRD5rSwU2tE2fpl2+PART9icrF1PekRQM7uspq9mBPWrCAEHI\n",
       "mvTM7z9sFvkWfgOHSH4xAXzeMvOoMdPbISqdVXK6r4TQQanS6fC4VVDsq0gUKmXigEMe2SZ7tURr\n",
       "2NdQzOLbVZtzjdULDlMhzchTfM7TjrhNSFV+mfCV1KPvs2i27ad4oxASCs2u7nJJTyYxy5xUjcDF\n",
       "gU/iMmAoFp7D8jMpXYuhYJ1GMBJ0HqZaAWlWZ5CEZD0D2lAq1h8jiRMH0H5BNvLbpzj48QFEmKQz\n",
       "Pq1wwI7TcwfTLKBPeKQYBO0WOvxlRlGKdwhC6LW1WxYwS5JKsWkFd5+cziDMtGjJQ/CQLZ18cZ7Q\n",
       "viCvgYFkQ/fTwUWXIxrN6PQv5919zCwAIaMa5zp7ajJuPwlNxRGO8+9BYpgyoHxkEqeDJ1BW9xVr\n",
       "bmQPr21pwJJfhytm9OdM5stwOuF3Cbx+Vy8+tR3zkZLt85Qs1qjgYWlj7q4ZiUM9SxFP2ORs5qZZ\n",
       "ENSq+XOuXOiv7Js8bvocZT1q4nEPsgvJIimvOA4eWCwE7cBIH3HKMI93f61SZE5sInybxOFXTHuT\n",
       "PcabSKHRnX7LTQ/AuR8JdgppG2vYWWChuR/9YyAc6gHd2oWuxrze+7cqWeU/jaPDLuOsUQzAcUcp\n",
       "CzeEAhy6+4BUdxoS0aR6dR4oeH8p4bSqY8kzacdKkdobqjsIILlYiJnYuMSNFRGerYb7xKRuNpG1\n",
       "g9RuxyDYzCXRVTKcr6wqpucHxgYw/aDpodmg+7ULCvwSTa8gGXC9nt2OzcZN4aVMuOf9Qeus7ffb\n",
       "/V8H9b625WU/hzeaD/IRQU2vKCXftu+PP0fbPSblCSTMtadZWWC3sJ/ra9UBS/L0AADFgQAABIpB\n",
       "nw5FFSwR/wADCLGzG1vB9UgA2q9MfUzM9JmtrkEIrjKzQlZ200m1XjEeGYwAq5el4vxP72RyGncZ\n",
       "kvPotg58pWSuhgt+tC/+wIHdRHw2yijIavhQxBWxhPDsWCiLaASx/3ByFkJwmwmWSxuLyICTQd3q\n",
       "3fUfx5M6AtG+bWRcs5UCdgezobgKe9sFvs0Vd8GynhX+IXHBIgulDwraTh0x5A64elrwAzgIrmN5\n",
       "kEP0gBAAMZMpXlCSc9FUinNUmDBDkRe0+vwX6VeNb+B/Q1B4SMZ4G4dn7pvWZLveJF6ssBGprak8\n",
       "9Q3dyTGO9Wgar+5rqlrpWZXUJFELCCeSMj+WRIYMxm71Ct5m10krLzDrLolsYGBwV1QjT2+H+4Ia\n",
       "X5ZimQ3kPheJWl7zOe8MckPRcfKk4Ogx/j4jTnkDTpd+/EoaatDN1YqjkpIfeXK+D/yq/uff057A\n",
       "vs+kHHrVRumOGDkiC7DnTB1l0lH8XytLmHiicvL5i+iWCzawHgivm9Ju4m7v24fyRG99RITsuwDd\n",
       "aVaKHaUGl0vlU0mSLURoGgcQ6fLY4YoOeDXuhYtN+MczLukSQ5JKAn3BfYFX+ucnjEVON+BDModM\n",
       "pw1RK8j5Z1NFGikuH3YihTU4b1XukW2msHGIx/4SKQBM47xQxTy77vCU4JWCw1HLtFWQL4XyCG9G\n",
       "qZuLXQXoT0NKJ5QN/ktPruF6dveaC8Cken26NuqkRh6mQZKSInEmdQQfCHFXgVIjImgPprXWHnlZ\n",
       "nJzlCiv9MtEwXfoaG06LvMT9t02P9dLUjQ4eU6Ywo1CwF8zR+7ooFF6lfktGdEFyWUwsQXgd7eqa\n",
       "/qNk2KUXeerKsSifRS/EUDZkuseCl8Qvvh6iM2XNOXmHyhuVLvivkkqnVUKjKIoADYbiqkb7jfAC\n",
       "GTeAz3ZFNfahO8OzU8HnCbjzjmObdYWjHs6fFs3ya5JE3ylbLibWNuTPTxeTMYrRwWqbsMT6/lLp\n",
       "atgSp02EIstCKOmEdvPh5Ar9G4xQrKjcTzutBPGBWjyZNKjFfiWz6g7Iu1a0aOFpON9chEMWOYTV\n",
       "2jhBL0chw79y+cF8mPkDby3AOtr87jTkZvN7WRogcZF8IbrQemsQdRNuYSXb562qCWALZf2AiFgt\n",
       "8jwsGMf3t8oAlxv310b9zcWDjaXmgho+QguTdUOw77fYy022i0HPgVn735NejnFd/X0bed/Lvizq\n",
       "nlJgp3gfsfQRc9feVdVnAph0HDxm+ZEVfg/fN02tPFPkVz6RYeQqgGAdZojauIE/dHvyz9UXlvJG\n",
       "bmVlpLfxu60PrS5iCw42hnk+IQHjs3iQ2RedauvPVspy9naQDhBMMqXJ8KqoloXvgQQHQ2XM+QUI\n",
       "vfmtw+lcg5nAjNS/VSBOqPD5CG0MpDjRSOGeo34S0MVkPBVb4xrlzPSSKbjzCGBbhBnFu/M5Mkl6\n",
       "AdV9WnT3CSQHO9z1ahR9qzjw1v7JOItxtfv/6FjuMYAQNP6h4t/1q07HpiXuNJcWyQ69QI27e1ey\n",
       "HoJ/l86F3J1u3JXNPergI8J+AAQdAAADMQGfLXRBDwAFzad97qAAJptX0f7I9rYNL1UkTJh42vlR\n",
       "/rXVJv8UsXK2XAXFThv5oZN51g5NlzVQn9EzWP0tQbZdEuNQEIQYzV+rA2IOgOcACwEdwBj/IzzG\n",
       "dxylzYtHGQYHfduW5RQAf70mEU69L9f7DxSMDxzjIvDHdr9giBfEvZKOzceR849C4ugBcr4Huq9h\n",
       "IiGZKiesBPp4WZkwnVGa/VI3NedyXzWE0E0GN5t/6lJX6JWEA/0chxnwK3nSiGlMggcbot/iMOem\n",
       "Iu8rSzCnD6WORjdvv95Ls8Zx29JrjyPilyUyDdsYBOfJvc0MTths7u23iSculMTr3InRUxVncZKl\n",
       "2jyTeiIYOhcbUJwoZ0bsnAmi0g8oXeR8lmlZxVeFS0KSorI86cnNsARm2CQhhIc11M3T6Miw/Ffn\n",
       "01cq/03sweIE8s+vFPOzBgcD0Rf7g47gICvHWXk75IRbtqKvdxwTkWQpfSg0NamxYlliSnErT3hJ\n",
       "0lT3uQu0Mnzi+7qm5x8bRt6MXml1NkMIA7xF+gsmDIJAYEXjmpJXnOIFf1L/Fzg1zxj+pnCoPIAK\n",
       "PQ5Lm9OnxbHoRTWmtkXGxdC6xJ9OaWTfHa6+y/muDUFVEPBE4EDWCSLJIon1qTNIb+bbf1O+RiEw\n",
       "6xJeZJlpWg4OpF97EReKr5cFzOqKGnDV+ZfN9nqhHnPzSINmvVIRoiwO9lkpyXhgqiXvGhF9B+Ab\n",
       "X9mvRsDykU81rfdOzDAG1dNCzgYAdofMVrYu3iF0gSrNzjDHLlfRV035i2pHDXOMk+EfPKQVnBmU\n",
       "HWjErTXVAQxCZ8NyiPgvvbyH76897BarPb5CRxfJJUeKCY3O35Oo7z5YxUEtdM85pZryRRh91TpA\n",
       "aim24Mex9T1Tng34y/Q1ps1Fl3WELDaNZxXZ/Uas93rbJfg+Br1mIbT2uEj2MMMFDzyMRQEoHndO\n",
       "P7zU4cGhKsBejYDqeu2ZfmdDVYW9q4imtYOICrhI0lbvTBz8hPWcRJ3iUr/JGugisruDuzhqFSC9\n",
       "l3hT0MCTgj4XP+iNheFg3aa55W4ugfiVGt3WgvGOsujuECky2f0oX1HgAScAAAMPAZ8vakEPAAXP\n",
       "eylZMAAE622RCM6jnFLcRvdVuiUhrke9JQmwdeq/jAiYMsmny9in7Ic9XRjIElqcbPn0uoz9EpbY\n",
       "HNWLnHyAvusjMQv5Dhg88VunZQjtwBEIWUZHZZkFMCPjo1ZrexgdO1rKoy624JnPf8qAPL9kk2MH\n",
       "vELEcK7z7pDctgK+Zq0So4tkyz1p6jJ2aox7fkK9sdLtyjYVY4CtbswEp45jWg2ck7F4figBouTZ\n",
       "RJtpvIO2fBuyNAyomWHWLPe+QwxrUtCqO/M5rLOqh/ufWRd63N2mmprLweWUulRz7dERKPK7d6ZL\n",
       "MdVbiJIG8uHbJGPsZdkE1OTLNemuKvuSVbteM6LG1MA5jhNlQT+dj7Ieuq/wgObl6/6W2rjFLBt9\n",
       "Dl/3GzHtcXOQMStdale6jN9WKw6ueyytPIxkC6Mw+Pvn4SrCfIdpqdZewaP9PFyvHdEU4XWeTQg9\n",
       "wVN+Cz4+I4W5G8u6p7QSksWoek6Q9Ldo3XXHQpjFBp1/xbwqZ3YMd1ZwlvGnAcp0AqPtwMvbix99\n",
       "rCGut3sELXM7n3ZUxxeDWfO5z1mMnIFjtPUqRupJpgtwBFm3I88ZsIYs38p7ZUKAo6jT/guBMTp4\n",
       "p16ee3PMNUbJS+yyxM9ybIEibRQrfcBP4BqwoSNgKoTc5LZHYtPqjsf/nWpYRDhdrxNrjDugYINL\n",
       "GgpJMe4mwkbKlzc34Nh8tY7kUNHYZ7Oa+31bNVPHoX2T0Ujg3fq8pCuXc6+EvK7rZ6r1Kc4DIQHS\n",
       "d+eZs6O68r6adarPAOC1B8NgET4n6BIu10Zn9eENc1Xy3HLE6OEFGJ4SYJKh0PhOPg87qQv2KUe8\n",
       "ejjcgpeq5H/nU7U9MXpYOaO8lajD3mFlfW/nG3akGx9MZCEqd9gTh9sljuTtbEfqWw0RCzMbSvit\n",
       "qZwK4gqXePNS8K8N+TFErA4DIdGxbSQQdFgv018qxJOAkuRSe/ttf/zhiXZRaOqiwmg7+EPSIhu5\n",
       "Ujy/mGszyV3AcWoURW2HTXVyUsk9ZLy6nbjM+ivBAAGzAAAGIUGbNEmoQWyZTAgn//61KoAEwcr8\n",
       "ADlsiMttMQV3WuiI5EiVOr0fvMsSg/wOGevMwFv3dktOu0neh3gHKo7BR02MYjt9vFBbIOBXHAkZ\n",
       "r4ugWAxDvBVSoNxY45D6OAJD6p27ZJ1c74WFZ784bkx1I48t5/qLYgNL1krELZA+o3wk+muEcfyK\n",
       "fGt025/rgu+9C7RSxoEs+WjhpTad0XXfpZ4V2PYNbjr2tA5y3TIj7N0wieTWMx/fCwJDVfhfGoH/\n",
       "F0OaJMbp8Cdp9REjJYA49zsjNvZQAlCNQ4tCqPjC/C6U6mq5qXzW+8Tja3abcFyxAoWCVe/ixP/z\n",
       "RdC+dMLbVhmJ+MKmDGaXjPuOf2uJ63qryPaJzq4A/zg1J//1+dK/0vmQdUqq03zbfGFp5C38J7ac\n",
       "QR9v0xDXajqxOXIrpuDoUotJjJVPxgrc/v90eV5HBHSQPlGC/Ugk8bx+4+EEBdmzKwOPX52IG3DZ\n",
       "BbEBn4tM8sIqzHr4lTpxjq4N3w5X/Bt8bOJkayuczJn3HAXQS7+5j9pueL41AYvtvPG/Pv3PeWRT\n",
       "qnI/Rwm+jx+A8874gweuFCkvUan3Ds5fGUqFZYLwnOudI85HMWU3RYnk5gjk7xl14xq7l5/XfEHR\n",
       "XuatmWn2mWFpnHXju8pl26p0HIKRzO9KIebwraOHAe77UXJGzOlLOVCpULyjeDCjWnvEiKS1hbRy\n",
       "CpYFnZrGIAaGEx1CHBWc4rK2102oOMHEcGNaEZ368F38lObFac5AjfKvCFcDCuYcq8JvBaNkuAhv\n",
       "fq7BN/ma68eZ8Bc9UpqwPsOSrLyN8c6mWl1H+vQfLjwM+NubT5uI/kkIzvqIQfQ3/qWGz53nE4rS\n",
       "TZF95PWkzjim+5LeAJTXQlgX2DiwTPVvt08MNzLdgMhNRaSbcPI7qYW6TeFuTY3gKW1F9VU9pRc2\n",
       "/TjRAVN2VQdcNumUeEltfVE8jqc2r4ixs8GRZN2rrtsSxgFFqV3500XoqBQCecuti9dAKLj6S07Y\n",
       "XO8mMHTIDWM/W7vIsMPTDVbdiaTcXsWfPNcRWKqw/ATpe0O6WSD9f+JSMkK0+dYBwaEaT0SQuPQR\n",
       "FDSZd8DUWCTUmkPfecfJtOsLNgO8kJQAsb3Nqx2CV4C2LwzkSU8p5O44QhEXY1mXbYjddrm+MxqQ\n",
       "HxfzqnUcn4PXJEIUz5315RXN+59e/2TnMPOLVH1B3XGhtGEHCk0vjcgjIo6v9KJbFiPWxe702gYK\n",
       "S0IAO/sCkVJR1kxUcWJGPBhMwLJKeSGzFX766na8utjbGr2MMuMQ1gz/1YvOkoAKfFsndc8Ah8Dm\n",
       "DWpE/H073D/uk+DZuk4YyOQfLwOF1xznUo82ZgzCyeYvzocpbuafipCcQy9to6euSzsYqDQ94W43\n",
       "Iv91skcd8Elyu+xGEnJqzWZJawYIaMOX4fEnRDu4bRdcxxBbnesqnw8Wc0CnxKI43yNCr5OqAquQ\n",
       "wc4e0Z00dhhzj000jL2DtTNiEAlFBxL4BX6AzL++lKQZ66jg405kvXRca0a9MJOO6vXhbZUxrQNQ\n",
       "yZqytjUSJBgDArPSrMHFdX321M9EkLTy1kaUbHthpNmAFYh8dx6Ie6QXRH6kMiKFZRWoHMYHSi11\n",
       "b29606qyH2o3SodCFwq2/v0DjYIN/acp3wWDV8ZpU4y9CM+vTrYxWrlFiD9OvYYP/GBArVLLSVM4\n",
       "NUjc3oEr6QHM1byCajXYhg262L0pV2njmm3sPOMWmKbMWMTMnB2EeKDaJm6OnoY0m5twC288OcUz\n",
       "xa0ai0YvqOw9ow+fOo6/iOq6KEG3hDS2tQwnng9OG8H2Kco1IumcyzPw+8pnayQMDNBKmV5/loPJ\n",
       "915G0n0RlYSZqzn08OSeO6VtTtYpAmQDxPMPh8vgkx3MN6C56Y0tYEUY3iGnDrhlMRXqlGLeZjWr\n",
       "eT3xJYAOXBDEz9LnPC8Gn1Rn51toReQmOtyutDa6TsJX90w0++Rj7ruxFrBlcAvasl0kFJVI3o8K\n",
       "yr35Jm/M/7j3oTS4cGIyB92nQgg2dRA0OG7TTawvHRYZWlUUO9tY4cZfh1qQt2FwaG+KyAWKyNYH\n",
       "gEqYVo0upMADZgAABIRBn1JFFSwR/wADIyRol5iAAH7UTEHxbA73hFtp1Hm7TaJFChw+tiiy3ZOP\n",
       "nVOitD0sRHfyBPOUhnTNEI5qL21jf9AauxmVXwFh3NtJya+EPZqYD89lDc9ZVosPl6rlqzMO/JHv\n",
       "VRm0P52RD4j7TP68KwZCyfm4+lviZ7dbuo/w3itDvrXN+Nvmaff+7Ey+IsqZay4sbjIhb6c8deDy\n",
       "3SUZWWvbo9YvRQ/SrGAaoARJnbYWDP9hAkQEgd5QsZTqYGclH8ldnEt9G6OXXkjTDfBA0bSd91Om\n",
       "Q0MmWQOcSZ76aOpeeDi7A2GZYqUpS/2JTKVe+IYdhAkp8Z3fNi6+Zjk9jpdQDLTtheqvATZABhVP\n",
       "C0MA81VGjp9hjZ/wcb9PMGsENRkwcdhkEk5y3cK8evPrqi1OgoQmk61HyCJLJGLbHpFVkxhJ4L39\n",
       "nt53evXfW2ZeKfPewXt6en+OqfR/hevXoA/hgCWsrecf5aE39j1kcn8ZCm17pwMRKfgCREflYABf\n",
       "hq2f9PCj5deOLwV0wTtnAbCsvgCEoLldNnZxoRFN6GJ87gzRMk+lgq4YB5rXloCptomcjzxes8yO\n",
       "6Vs7bB/AnR+jhhIk0zaQRqOyshQaPKDS025xMPnMiD4s8nVsTSmtt0zTF7SpL3N3WduD0b3jvmE5\n",
       "LkcZrWTPSYeciyiNY5cAtBmDN9TWrnfYtI2qDzh5W9NBRIvHYJpL3cc8cnnIrqM/+5ISQKZv4rWJ\n",
       "E28TSOndDnNyYfII0op+CkAgngTbKkHWhX8bl8HyMuVkBcUXIebbvJZ0MExDVFdHsC8pohDyxI9t\n",
       "eNB5zhDoMnL7Wk2FbqwDrHaQhFCIf3yJULNBsAudOUYJQjGD3uYs8lGO3a9sGE9Z/PiyGSybc69Y\n",
       "tcBMIifjbtPWhC+OZYUfKtlM7b2vFEXj1DZwtwOxIUePh4wbFYQTdwNFclQp/5eUWWZvRVX1Z8h6\n",
       "wDsFOJMbQbI2gs1ySYbxckdERuDu9A2gmjr8mZzwwQDK1Js7X3Hkbc2XUv2siSTzeoggg3J3D2Bb\n",
       "pO2sN2CkcQrmPqFv7b5QZrBUD8cAx0aTH1SECGbQtZFL/yTWRtigO/sAAETwIF0zK+t1BtD+eUQ7\n",
       "+DjW6ewQ9dWDRb9icDDasse6sTQRaibFrYgXOO/85hz/10KYfa/i3hlObdfK8lkJDEVMJfGhyAOY\n",
       "4jzBXyJCZGt6c+yByTCTaFkrw5FeuEYR/umf00yE00sMjVo99WtXtBK1fntOUMFP8nqlnjY8tTzY\n",
       "rZBgUKr3ilTaF12sUaDLYIV6ARGJw0VjCQO/ZhRtUAx9hIOwu3j72ppRJWVoruKuX395wvlWl6sS\n",
       "uHeWpXHATw1dfT2dQU8mZZ823dn/+nQmhJf47T7soTZ6LsuFnQzUIwEDeFQaJEIqHjZqjphbns3h\n",
       "R8gHVQ8i5iaHkEKHfWSIxlchbql/Ywt4YGOQp6/zJ7Hku7BS3hyd4QZGeU+JMV/TD6EV5BqVvF3l\n",
       "qf15lrgoG6X1d0atTnCcaFl7NxAGdJsjPtIAAGpBAAADSwGfcXRBDwAFw8+1YeQP90IATTbLxZ5V\n",
       "a76fzgd6HxBZKTHxjP1yC9cMSym0e5F86O8BTfKsOlq+Lt6wqKZbzKGVbZBjYGfQrUeuFkdVRkvu\n",
       "gRyx9NaHdB47DLA4Y19NpY+CoxuIE6Kur3EP/Z0xx34k8wd59vX/vGK4dOKU7V2OmqoMwUDI7w0P\n",
       "W0k8UMeEJ799Al3t9ohxjSQVPZHH7EC6V+XqRj4+QyCOmnAp84ha/gPc85wPqKzCsN+EAbwYKjTl\n",
       "mDwtnDN3Wk1NJxZa96iFfk0MYNMQOy1AUpuG5h3YlhqJsxD8Ny9Q7UD9R+/7oFZy8xzZxRm4ddfE\n",
       "PBZEgK1xlraFPCbafMkYd0Vu3d3avGyCxzWwFVVzjAel40aDjWeLybus50RqILtLGydtxnwrOD9y\n",
       "VzY07Ksfl2dywEpc+4e+NxVo1eBPfO/9OiGGomfEZsk8VRXIOr+M5elJ9FkUVNGkjhjj6nHHl2ll\n",
       "tebHQrs2hckt7enC2/cerqcqIA/AdWcrKAWhZu/+yYDB7zAIO+ZJRAsPKXtb0imfdR3m0OGgFdir\n",
       "jiYDR3382ljVZ9AEiDs/PXE5A3JNfq5LpvTUwzn2LpolSXFjWzcetI0zbnVSdjH1dn313os7vPi6\n",
       "IgLgya2vD+Jj0uIOfzgNqBZbgUVKF1uRQzV/P5fv/Kkzxjf+BA1A7Zd0fnX7gXOuwdyAAk5guwgW\n",
       "0U79HtQQDaHG/likdDqqVgysP2uS+HIEJ0c8v/ez53GXEz8ozNPr2J0EhpEM3uHRcXF5tVKIFd5J\n",
       "Hn7BitEiTmi7xkI/dy5TcluthcyurKWkivHA+j4h2YRk7nWkiYlx8dYo+dZrAS5QRnX+N9xObN8Q\n",
       "UFy0fgTFibfinr7IY/pnni3bAR7Qqm7/qJIawASi86moAE0K/HeyLamIWzrVxTpL0gtZBpFA1lab\n",
       "ccDupIdX8UBa1ucDcWw4GRYfc7AjFOlG9dysZLxOtlCmxzgecErCfPw5azyj4Q0LlAQ2SD9rIj24\n",
       "MG58f4J06IISCpTTEpKoD4lUDROyY//Ml4zEuElxJWVLtsvEgQMplCSOYn3zMRQ6YcRAaEvupznZ\n",
       "Di42cx7MAnQ2T+MtL+8dnXf2QAA24AAAAcABn3NqQQ8ABfdUhTm0AAAH8MrMAaQhwu+uW07PBbk3\n",
       "hL4YQgeMFHAAwMfhoE33IoC4DwX5ITekI6hXbSlh7O6tzwWAB11enr5WCLn6LdzEObxLIa4OSFkO\n",
       "N1doqbyfYPBp9wyskwWvrREmMCu0C39CPxDb22lG6QY4LBnZNmXh5urLmOpxACQf+aiUa5wdPc81\n",
       "yEIHf3U5rll1QrEoXohZgvobOqvdUqxeLnf5tmgFjMdToKNgZUGSZMJ8wKAyylzbR0GeXWiKsHg2\n",
       "r5FG/mRPjaCz/wjZDsc2ptDAOdSqJQLunL7COjAzdGMsSyuhfVKxH03tmdbf3F6/A5TOvlUtuxu4\n",
       "bKjPf9MYV9+jExPvzoHaPaFVQbm3B5pN9Eo/txQQ5ohfCDU2d419N0HmvQz95hJg0cKL6sUcMhgB\n",
       "x0CwkcZG3TWGkLBlnLvB/PvRZzgSuG8XZDhTGPImdT+J+GVJd4GOUUtC1gv+0+WLeAHTnkaaSfD7\n",
       "1fNc1kijga0CT3Qk73gLzlvsn32d1lGokvolir5apnVU7eA71DdIH8wbfRmNBPTrPxw14afRVXGE\n",
       "+rojcqx0FC2Yk8avAS43AJOAAAADWEGbdUmoQWyZTAgp//7WjLABMeffvtUl/rRABzbl84N/V4A3\n",
       "kBEj5qzAgA+zKvvCb04Xx79ZouQAMPY6+uzd9GS7WcGU9NGKwJ4WgowSqXRr6M1sE1pwZ0elzeBS\n",
       "AFQZ8/9R4lnSuRWqZjsA7CZe0jFwDi34apYPOC9z4lQgDnJFIkM1m/DapshUA0/wqhExleJvDdGQ\n",
       "cyaHlv86QJReLX0Q5llq44OpAqOu/EQwOtxqxePg6gQkq40DBmNdJHWLbpRrfz3sv5FB0zGfJwEv\n",
       "0Fftk0/+Qg/mp+eTOaBlFRetS9tVrtLH/kENC6ekTz2VT3aUMvXE9kJHpxPSxkhHTGTPsub/flTZ\n",
       "Y2Py+lYIcvKLVJep8JJ79NlzADe0HelEairwcwyfopbMb8WlLXzcbapkq0e/r4SEzQTDxWW0NsTn\n",
       "d88aEmLjtWdyRK9htSl7Ug27GQ+AYxLVGiOm/dAQ/Un0iW682TwujMHQIGgmUKxaPTmw3fMYehHz\n",
       "j+HcjW3VvNlYxjC8MJ4i8dk7iKT8Qbf0vgZa86H+BZWXk/g0IIEfxAWo6iITkfMN2gsFDsuzbc9c\n",
       "j6on63DWRJs2wppfGYyjn1ObGy0hinRIvqw1P9MqHQOs0yuM3Hqc3V4ct3odJ36UCnBgqtoD+ag6\n",
       "BScKZ4F7aQVnUjokUksAmvr05t0L6YS+YclZqBwjZDkmtoTUy4444OL/OlNrKAbKi//0Yabu8KNG\n",
       "AA0UkqWZwd8NreRBOmlKNIbVQDJIrs4riWORfOoY27eTK29lBqiAa1jExDwhyiZWSBK7guL/08Gf\n",
       "b+SnEIu8ojrn6RN90aAg3t5baiIGx+4AXgovFu0gDHwEWDstACmC54OuzvRdyh0lNabOYkCMHvyj\n",
       "vDGD3MCvGs7WMHSx48TD0UnOWj0mvwthEqxq9RcATRfc1YVb+LDVPPdftJ84/K09pCs7hUBsl3k3\n",
       "8KVVkT9YDGJV6w/5jaPiatO3ghobwWo3eXrpBwZ08i/VjpBCtBBUTqm1egQSeCjwZhFv7KENfVcu\n",
       "5NwxGpyCKhvG1rBTeb9CDL6NYy8j2PnigUmPsn5bSwgU5MD9uMxidnjHxMABb/QtlpiSd+6/0X4E\n",
       "6mbT3C51FEk2fhbE4qQKAuxFKcD6T/EAAAUNQZuZSeEKUmUwIJ///rUqgASiw6AhUAE7ExXTJkQf\n",
       "Xj7Ql4EeztsAFVgmjSrUd775JvAXP1VTgqHlGGLr8P1BqFgyur6DDgePTpVfsa9BJIHUSFa5kRo2\n",
       "aawEtGkeU2F7kYCP/jo/e+fU6i/FgnUidqvE1ITxcEYor8OaD6gbSfvf4ZKHhtByNYkmAWV+j6Vg\n",
       "FhPV7m8pf3xn/jBw0s1gCHlojvG3Bxs/eIvL7tJzMf/CPegy9Q7puMvcaE+YRwh+pu2TuGXk9JRs\n",
       "eKgeKP5pGaJSPNrS1HdqWMcFVNDYgZZzkCYLzKR5sYK1/VdgLjKwkrS8fPEr0e08e1yjfikpxoav\n",
       "RNMVR+/7F+B4d81ZrAQZqXm3zqnde0uLZ5D6vbHqjV1SLURhS04WsCyszBDaHxX6GCsyv68SR2Y0\n",
       "AZGA9NAaxmfg5tJb2uWrIS75l8SDk4FsVuv8y4FN3fE5DfgsjiPPmPlPuSHZjuiGg+bOiH1ew7Gv\n",
       "tkZaP1EydxqQ9lgM2+5urodv3FO9OjcpQPG1fij8D4kWHKDPz00hDht7KpNVCFYa7YttgoWYQEw1\n",
       "L4qVMQmE2aO4IIRGoS3DE3KZonxyMwaLktiCzIwJC5uLRguehb2aWqrW/YltP3CIKZ+4cMIYpI1A\n",
       "Gp/ZOT3OUIxA0XIsGGzTcMgdSBR0kaLs1kl9lgnRinuH8Uiw8cNRsqiWpbkWRRIr2A4Cc23j9lhG\n",
       "zej3dOdlxnpuI033Fl3dDa5S9cVLM1wnQ1NvmyJ8LXrCguutPXRdNWvPGAsliYPViL8jOAixy3JK\n",
       "SKePigPuLWiyqTfsJLzyqR8pOtSNzl9zYYLzBy0O+RFOZCG6x8MX3qGE8IUeutv8ILS4c6rSiSOK\n",
       "rOD8hF9S/p1heQQsEMlgeB9upy4tdfrRAnODnp4v9YLIpzWJmANQA2yWGI/v+JtU86zyLVuUKmjW\n",
       "P8X5+/DFkjSTxWorUAA7UzmnMDCAglRtgt9Wsuu77FGpqtXSHnOseVpL1zg6Xdca2KbEjAUyDbMW\n",
       "u8A/nmy7kCiq28iV7uQenjgytpxxDd9AyLjvekylW2ujfDCOzwnHX90nYAf3Hk0XqWW6dp3O8daU\n",
       "3ebdnAT4AUN+m/w9D5c5hW4wTVj13s6Gh0sBT448YUR1RJPPJ+IiBqxfzpTdriBYn6OBz67wAkRv\n",
       "4gp1KW1czdxRpkpwjOyjfPSv/nOlroW7Y3X2sv1mME9IQvpgbJrzwDlpQtMG0YGci414nl8iPe3E\n",
       "OB0c2SnrG+t/9TsSgM7bnjykh5+AMHP1iv/dmFLlezwG+MDi5nHKOu+NGMrWUakE3xV79JYY5oyw\n",
       "sESQBTMrysu7NM0XxFH2FIR/Ac5mtAZLt9tBAMrQ8uox76aHDk1nmzXjzNafeRM+07jKQkb2VlDL\n",
       "mtnJUmqYBBi9F8C5Gf2M9E9S7oqgLv6nak6De8CFRDvEJ+pXGOH5RK46XRg5RrlsD+aqRC2R8YI7\n",
       "Rp3s02v2T/ArRW/kEr9cVJSqJ8ZsIeOc1HfhijyLZaqzPdyifE1De9X1diMNGas0i/N2ZnCfsKqx\n",
       "D2cOtduHqfG+7DKwoM96zFDHazwig/wL6ceMG03lYOSP5aa6eb4O6T2aBdC3qiXbTqpJil6n4Rpl\n",
       "JXy7KobFYQT+7OwuCxschdRiK/2iSTNDNQLv79Q2vKuFry8DXINhTwNK7sY64wJZVTKIPwS9IrYw\n",
       "ncFHMNGmAGLAAAADLkGft0U0TBH/AAMN84LcQAH8I18RiFNNZPxCh8j1x6N0uuByMXhD/dQYIMiy\n",
       "KVd3hvvhqHS4oG8wCb1AzRHuZzlmfFTdNbQu9wy25JYXNa+kscES2iwNdQnUIIPTEgMIyzQanwxa\n",
       "Sg9KBRZZeZTfAEdJZE7eeVXeI9i2zn9IpihLGXfsgfL/zgk2v1IzErqPsIC3r7UYORDzI2Zb+DNi\n",
       "xYj7neECRpQptjEWg1w9PddUlbXlft9KVExc1s9LKxulgTnHQwDs2sWvEIxuhG3FyyDJ58lUih0R\n",
       "nP89c95Ei2iX4a9Dhvdu3M63DzSB2m6BjRR1sYv1BLZSmGmEOT0UZBkH24JbyMfziaSq6vyMswt/\n",
       "DfCgX8E/QlmK5rCDsSj51yUu+7FKDcc5LbP/WI5dxctHYKAoSC2RdXFXZzJerA4IUnfnGijv7wV1\n",
       "QAPURTlfLce+Lp9ptNloPGNlKX2p3Ii9SQrnKrjLE9sHuE3K9K7jBw2/33U522z7mn7TprEq/ZwL\n",
       "DeCIdCPafOV83vTsLHlFI8JfJ/y98IQHXsCCeN3IZmiLD5qAu5+y45TBJVmXAjz+jiR2lZwgW8QY\n",
       "24PK/jGXWfs+zhFi9T8GHcZe57sdGEODvv5gHsXTPONOdL9fHQmObjDLLN/k+wC0bAcwghHJVyCj\n",
       "8s+wA0e84PeAkjlUSIqXc0lT1HwWZQLkAtmJE6/b3CrEAGIATE4eqEmn1VYZbvyinepSOTcQzvSS\n",
       "SQjXHTh3l8+AouB7rENScek1TLbCftoC7E20ljcd9MSmmzfadinR5Ghe/wnmYOvUhASVbNCyDqj9\n",
       "om94eXN/B7hslqB8ePs7D0bhRG30V5aT+vuzjcjWvmMxyFWoxbHbbKur/nxo5XSxcJ9EFuB//iDq\n",
       "dgvKd2SaqnBZnyYT4xyEzfGQj/TWRQAdmNj+s/6Gpk7X7YSTJmvvBkjZE64LT/93Oi/Afdh5xIfC\n",
       "ZthdSHfUEwKRF6x+D3ISFnd6NbQ5s7g/XRhwpKxi0gMB+M4yoiCAPU/4+GQUQa2jb8hnQb5sSfg+\n",
       "k+UjZyASDdkAhfBFv05nL6DFbvCfpgRUQkGwA9MAAAI0AZ/WdEEPAAXLTq96gACaV7aRMbEOD5xL\n",
       "Zy7R0zccs6lUDVSAR49XFRt4h3NRosYTP0l5mtP39mVUCAFAlJwW6cUcwW7b4iHYfNAt2OIK1Ftz\n",
       "7HCRpvIOU6v0e4WdG1cUXvBKV+O778P9XwToQpEyqtqDSEbTbTihRxBqI7gLfDSYCKWJuPshy7TK\n",
       "BI0FlNefwf7bmzv7/+z5YLAnZMGw4VLslDt6TA4QPf7NeCcEZrDRyBTEQhlSoPeU3CemkLiPHxRE\n",
       "QUVbI6d08J+utdtFnR5IqJ6//0l2Y+tm24zEgf9yM5uoKLmmqU7D/in2W4RP4KTjsbFSiX9hDkoq\n",
       "oMhJsgaB+gJnibGUlKSOmFPUNUH9l6lrGhz35f1ITtNLh/x7A5ggcVym8bDhGenwp8A++hJcE8a5\n",
       "c+G/wl5Vs64adhRiNDT3Ipm+Uscj6kwdvoGirBdB0DKLJELLWzweN8OVLJIzH8uZcXF7YDBhCir7\n",
       "iRPZ3tEp5rwrA6jAYnPGejRw4EkjKJDyPY6v/gCdxcfWXyzBDPsVEmaC5JLUtkYaNxUNblNaEtIa\n",
       "5A/vhBrgfSdrlgBpiSnOy8TByIN56PDX50ZuUztdi2uKIlEzYZlAVvBJAAAbwvsc7HchCOzd5CCH\n",
       "YuPetTPGLGU+mFsNbBOjSf1pDx6agIIVVLZFiZvHshVVxsj3hS3E5HDuYZmC8y6RBLAEyRPmxmUa\n",
       "ToXeha9zMG2om8fX+NgjpESIhrr8Hfa/TgE3AAACAgGf2GpBDwAFw8fg/cUn7LAB/SLyG4GZ6Skv\n",
       "mguIgRV9eZ5YQtnsavF0UsOGjt2Z2ck3XAARLq4umXHfumm+Us+pNbjOHq5tD8k2CqGbzPs+o1Ey\n",
       "EulA8TQ9amtJcAcTuyVTcWGk/svgt8zSE0D/hGS1JP3GpLHEyvCp/yT5z8TmZj2u+A7qFzKrQpTn\n",
       "jvd9RExd5Dyb1Slq7eJNpvZZO31RPNqvV3IaPRvqZetAdS7IQYdxHPuCVon296ZaHcCKHAS0LTHF\n",
       "ruTH+fNaAHYMWoPaY5dP7+sS9KRVoXMt/D7H4cTgVDkewJcC6NvJvdX9D0Kp/+/QtcDzaRZHIjhW\n",
       "cYp4QHRAyoAHl61UfWr2jQPUkcqS9n/TAaguLXByuDvu83s6NVln0/+ga7c5IiCq7BFGNEAaq5dM\n",
       "QwL8cxUp7zaPnyF6f13tAErwx63Zg2n6K1JPX1tnQj1zg2vEfnSLZmShUjC0MTXfgwUPe7gvveqe\n",
       "iG7Om7sooK8BBwnONOwkdKS1qBjKfgAibp+pvidcC+yl6P9J+yHlV3Lz4EEOWJEDB07nDv7ut0kd\n",
       "wPZDvPrgA/86FJg5cRAYAM0Fk+CElAbw8+Aosmg76U/2SIL6EMpvDq8+rxOp9Zx7a0jaDsaOO2Dv\n",
       "Y75Wq3J64Ofpgk/eTIZxviCNjS2Z+LIVoOADU96cDAgAAASqQZvdSahBaJlMCCX//rUqgAShFaaA\n",
       "AhQ32X+Xpa+/si45yFjDC6stD3fWTdpchkL7ky0VNjfJZUvlv9YccRlojidoJmFVslzOt9KY6Keh\n",
       "1geW/6ZHn+db/VmLBZDuB7KXI3GS9mayBpYRFMH7am8WixYBYF3aa4RcAUvL4ApORrD9u7uhbvDp\n",
       "JaV5fCczdLiuJusHBegVW0iY/hTQK1Y5jho44GBvKqUkhWV5cyaV+EnTdhsvyAvoKy/p9U3lcL00\n",
       "Xs33w0sU54yg06UY7lZIOW8Ycnrxxe/Lu7trYvGzYmKcDDffmCZH91fAB4zV5O2qo22IUOiqk6vZ\n",
       "Uq923Odj4/YTVR7T3VvcZ2IVTddWO2ZMHkUPVvpWYY/thm/rHzJXncrtK+SAOcRR57h/PWHypXiq\n",
       "XGCNTZQj048YMVQBqzQsCfI/G/ZRp+uGEYFjdOZL8N5JMf7Cu09+cWMVcoT2+20YDHW+UIMajuwg\n",
       "+ey4mOq8HCiYjrbpOuFmRsqH7vPtlBoMqGy0U9FP3yBeZ7dkVlOOc7u+eUBXTVuLtonwNtwkwCU1\n",
       "vKr+NtnOJ4BpO3SObZ+FPEtoTQM2HlvbLG+PuYIpV19/ll2Ku/F808pHcUEqKTjaskAED5v9lMej\n",
       "kFB/ZOk/3921+lHlJ4iMlevlMH+8GIsmvcYlK7QtXcJFh+iYI+MIRGoqSDJvAEGi+0PVW5k5mLPn\n",
       "V/J1V3lVovJXgv1gLu4jv95x1OUIHgoEdSAM0HCUBYTF84PRXY/CvtSU1OtKvQVzrIBqYlb7P+3H\n",
       "TzB04IjUFF89qmyxZCL2MSjM8dPRMVOzNitVwVywc052CmhcpxVHnOeENKqlRzxOWsNbQfeNAulz\n",
       "Ha5e609ITBOTAAMNlEFn+9YkNKWzDKkWHc+uu492tbo/ffjyEam8bRZexniO2bMZ895/uxlu4K8i\n",
       "5cpce1K5SaEoxE7i2y+NCgdIyLtg91frf0UhJJQKZoxl6f7iIyqThjfSU7OLQjrx1eKD6O1cfBpH\n",
       "dxZv/UcXmrSjHKkuSC0qtQPYgu1HHPcab10GhfLTkdDkcbYNsJgp7K9uF0J0yDQqeABOTTQ0yRwR\n",
       "cUVP64VLdZalE5NmrjXvBZVyTgEjaNpqstXY66wMJpwFXo4G1lrKhJbAdfo7FQFXDNjcHY9PhFK2\n",
       "vTUy9CMEp9TEHnfwfI+bWf5qs5y6127pP6okHOGp6dEBqPPMwzK7DDcoQUxYLKvgfWQAgpJOcTko\n",
       "Fu+gxZfzp8x8EyE6t8IHIi12o6tWZ3I6QJ5OxXAGvVmoDCMVwUJfFce7rGh1zELkH7JbgLBEonFj\n",
       "3Er4eQFC2DjVNbcFgOnyTxzD9QXof97EIaObc12qyDnAruO5w52YOTZ2c3jcb3tMp7+8BVz0m4TH\n",
       "tIUKl/0qZX4gqm+VzPbiCTIH9UbqSzx7ttjzfd6g4Vkdd55p0fgqR2zvynr6YiAMqhdpBhOThB65\n",
       "XXlos1qJpFmQUbCRgOueKepPjqbbIgRabJooeY1iV8aPXHl3FNdJk2TN5UnNX8e3SNCq9RXGa5mh\n",
       "Lmy5Ft14DY56/P+8Z0ETT0ty4BzlKyQzmHkLrSobQMCBAAACIkGf+0URLBH/AAMFm7TiZ4RxAAbN\n",
       "B1hAk5ivxi6YUXLV8sQ3n6z8zFCBLHJLaJo6Sn2gXEd52cxST7R6Q5NPiUhcjsiAKAnuiDTQ4ipq\n",
       "7vo+kEW8q+2R/R0S5nn1i51nhDpGvP2Bs0SsC/LVq695lhimLLh7LhjHxi7r8lhFXMSDIjUf5UZj\n",
       "/PVMR9WkGPSu9VUcgC2+I7Wpe9ccDj4IJhTn4RbNpPxXzhREzJwf2ZmuGYQ5n2gVA1V/kVfRsbga\n",
       "scnpSiOjkAxsqJiq+Np2Z0+WLrbFGCC9aLI5HggbroL1yhbbO2M4sqadoixZRfJNkU7qEM+iW89U\n",
       "4oEMF7jzcSzVhNdUmEClj/EqDoNXJVWzSyrXE4yXucEFpeCFt6yLhdPpoY8Xkx+miSXl1b8/y/lY\n",
       "0f8vxzAxfoAjeEcBnE/5XpdeGQ1vGYSTi4SkRSJXJ8kjGjfOTD8GchdmmNFl7TIzKydD2JlWUPWX\n",
       "myvZroSwNji+YfqdIOvvMf5c1os93OwvKYC4uxk6utBC6ED3kx7+Y779uvtPYt1Euu8qcAU75hj9\n",
       "pxJTlJ4pZ+DVtnkmzD2iaXdgh4tqvFW6mUqy540uELRRQsnqxqmVgYrAVhLLNoBBMLdr1OiZJJ6B\n",
       "gsAVY5bqMhK28FPqHTRG56ASsLzP4aPxDy5YUBcQshONhoB/pPHfLlu/wvfI7anhqlx4f4Pxbq/w\n",
       "A4odzKR7CReG41sC7gAAAeoBnhp0QQ8ABclcV22MAwATqmMRPk1vS1pcnmuvhhu9kQAj3mczvho1\n",
       "tMQp6G43p6HlvKchPWpalTur+vJwFWGCbKuARyH0EgzUGILxTKHFho8NMK6BBBNV0eNfr2C9ktib\n",
       "csGIXF2gu6c1l9hIrcgW//QsGJrCQpH88Fud9S+DHBRb1FdxF7l5S+1zXKXGDDTmYq5g/n2iubeG\n",
       "n4TKhlUoD2BehS7jHzCARUCUiVfJSwT7VTlN8PlqFB9qwWt6XRxUVkvQb9Y8oYmLUQloJk+bDywN\n",
       "ZNhmrPW1vcxHmRYDH/D7iO/bj8hTmtKYzQKfmx9U346z52+cGJPik83fYTnS20xFPEAYzLmB8B3B\n",
       "SIAfE9fwctBRpRiW1cBrVz8TVrL5NFbVgmPzqbR+6KABqWGkQhovS8risMQHgt+GM2M2eIeTA+zh\n",
       "UvHx+y3L1xPlCaAAHk75rTLJ7asmRMin69VxFkkEBz33jItcXeHYddES44b1yKE8IkizaWzF+XFj\n",
       "OM7fTT84YCjeOUfVVOLfgMnDH9nL9eV5UeWr2mklZipqBYIHCfD0Ad06BT/2tnJ0czCLjW0CMn+8\n",
       "PfmDlxLsBUfM1JtW2QOAiEcB0M1xSxMXbdymPvKMB8TzA+/t2F6Nhu+6MAdWRSkY4AJPAAABgwGe\n",
       "HGpBDwAFz3ogY0AATQZgFTkdFULEY9E9gyiwJ42akLpcFqPFH0wbC9O8wj1GJaFBOsmsVHxscAlc\n",
       "xs4v3yl5j4tH7A5MZXm8z+g8yKteS2I181FoQNQfkUR5gai3XTzN+cXp0lZh4SrGY5D65tAnyklZ\n",
       "2FewgEoy2O5uHUeZWYwz4+WkjOBeRfkvxdJMdkix/fy7jQafjUFEKmm0Aa3to+NEACzorhipgjkm\n",
       "SX8sDXk335v5fjwsR4d2e+0wPgAvssnms1eoUGYkUqBIPFY9wsiEI7fmN8db5S5waw8yZGmWjiZL\n",
       "ZUD/aZSmeozRXupViU5IuLsRkh7FDD2BPIsnLA9PPS5zRMFRAGQw+DsugpID9mnqhUp0XxbTNsnY\n",
       "KNvG7oUaK8XyDQW3vwOgHWZ6WarJZoc1EHXjswQxP0LJ98irVgg+lmE9gncZ0LP2f6hTd83Nt4j9\n",
       "7DGqDh5e+oJc781xdmyeaO6hkohJhMNtMVnecenARZwUg+E6o8IxSeAEjQAAAkZBmh5JqEFsmUwI\n",
       "Jf/+tSqABMHF2wCEHAnzuX9uZCMKKlPC1ryYny9tJwM15b8lWXbcmt8Y90CJPSgvY7gxWdAL6oZ7\n",
       "uUK1mH9DFH0AuR2KK7/yLCzLwS+5RepVb4s6BOzcIcwG2ks+IczFBAxnjy2MafP2R9pdwmNYyCIE\n",
       "Sgx/lt9Fe0xqilP5MX262NfnI7/UWO8jFI34F+jHxoMuRFJu9JUH450jAOmHdahfQ33tE8HACOe7\n",
       "eiRlnfdfuH/7bAUuQJkNutY3gIbC2xY+ga2AzrEqJMIBphwDn4uGS/KRI3MxMI15ex+yGLRCpr79\n",
       "sod1F/Nn8lH2dQtcf4uhqgkSPkYToPWvKhIX0uxLm8tm7UYWnijdn/D2FSK/ze7JU3PJR1/739C/\n",
       "zVhkwYbNkWYjVosVFL9AIp6GJLY5ZkC+R9CHanL5AIE0OgKXordXfBUirc/+JX1il8gWILA8iGf1\n",
       "PWZ2qhTaAlWUtdZipz3fOjtbJMWtGdedUkuC5Kw3ov1906/36es6OmjL6K3pdzQGHv+FAHwbKORv\n",
       "6h6DLCjiVk0+W6S49iF7AoM/3//lZL6l636byYWAlnLFSZWWPMyPhS4ov5wPPKdQ5Ham+e0/YQsc\n",
       "BwKPkLTjbNoPETcJthj9C+cMSt/jeTlA5nGdsW+IDjpdVVKjqwX/PxTPUAkVke1HlZ2RCztSnrXF\n",
       "jAoVby0i/t/H31fkoAWs4Vcv01OBI2c9ZKqOUrxk/jJbbZl4AJZyQj9E9rFdYazILV0lNUa3NNYk\n",
       "CXgAAAIAQZo/SeEKUmUwIJf//rUqgASnmhrcgOf2IgBChwa45dv9/SpIPWoXPG7ZSF+GUrox9mTY\n",
       "+4qZQ6Qlb3i2ZKffz5Q6XAsszq6lq9Z9jKKnqOGzAcEBDma0u+Y8vwnKR6D0USsAUpme0RTXU/EB\n",
       "AXCUbimOWRuxLcnRP0ov8nPXsaeTfSqr9Mi+fBDjDrX/Ae/+Dbvnwo44C76G9+ZRfXCK8xoXSc7U\n",
       "VzqRCAxvUD4IdZSdv47coPA7nV0y/RPkpPg7PAU0PNd2zFxQdihFZqMks4v6LpqZiOsmyRV2AK1F\n",
       "VWwz/wpPgjmOZKfPAdVpgI+yGnhBtf9F3sTOThmLUTc4JXFqngYL3E0/IY0ZEULPXX7FBDk5Tpru\n",
       "MMm0EDHd1KXJKkJwWavFvmhwpzJWz36dPX+7C+kCUoXyiSCuowyvv2OP8rAH6cR+lBZx9NzMmvnR\n",
       "xpmM6+8uD5Zsj6E2FqrP38vvBIatpkT54p4JuGk9C/BFTaHrpHZ/dPHbeJh9MkWu/7wsmrQkVVnI\n",
       "A7w0llnmXUaorbQIrHxhRjYztJQDiTSxJnm54lMh2qEoIb1GZ64uYw2DGnvhVzhbm6IxcTrlQiJE\n",
       "DdIqS61ZkM07jDo5PNeiomB4GJkIFU33vRn4GAhL2sI5Rsj7GH87kbgIxpwnU41tUYZULaFH/U84\n",
       "n9U+SO0AAAHnQZpASeEOiZTAgn/+tSqABKaO/2SQADmwrhqnJsLRRxPPP/sQZmDSNYLeB0j7j9CV\n",
       "jjybrnvIcQ5VfUq5k+RWXB92K6UE5fu1xhnYhsNI+YMCAIXxr9rwB6sYWvvQZDejtLXrbpGzDeOm\n",
       "5/hSn1ioglwoKD+OZHZKEzGT7WdO60XdOFgpBNdNO+kgRUaO59VfM7VhuG4QNZVh0AXwNb8pFJtM\n",
       "14nEFrHRoAyw5G9zEAWnKQQ/+iJ/e2KgdaewU/r/ACh/PKgp9W+a8IwHgoyS7QLEW43OQVtLivhZ\n",
       "Pb2avETNeBAL8mUgvc03pX7gISurNSROEi6u1Ow4XJgnat6uwjcJbefVsFfM7DMUsjySNR3DmzNC\n",
       "u0uu3ziHhRG5LBk1sUbnRzEabEIsxYdwoN5q3oryUc04WQigyMIZiQxEeD1dlP2S/7j4ULXfj1EU\n",
       "AgnoTomlFaq087SQkQJppUjm9r16FnkGogNTWed1IpKF3Hp9BCD1SuDVYolSP1q1q0F7yFidw6aK\n",
       "NyxBOMKC+kKoaDq2G2rT9eBwvnVbMxzs4w8LGf/ivvCPONqS10St0YUmRLFUfA1v4RP3RAvvshl7\n",
       "dDq98x0NAEsX+dyY+h4QnbjIBrmZdNYqrsFqcRFI2BZ4HJDzbsAMWQAAAcpBmmFJ4Q8mUwIJ//61\n",
       "KoAEpySCFQAOzcDpcLTgLY9AgGZRRSwbR8ymJnWUXqMjaUxPKDN7bLasifYHTLau/pJ94T2g56zR\n",
       "jyR1gAUbUYR8NZvSeEYWeBpRkmBxM1Pbq+LxyuMlCYwkjI5xb0xdzl8Owvmd01bH8W9muU30UyRS\n",
       "EU/rUDkNoy8gonw6lcHtHE7DNLkQroDFSbDgw+avgXA/ujqfYfkztXWrd1J67hyMbtYViHzUDi5Z\n",
       "nq4pHZBKVWuGhhIbJ+TMMhkocW/gWOPDhBCXCWzlMKfdHNx9FbftVPgO3s2dL0jfzEynfGBIUa7a\n",
       "qixSPRclCYwTTj8bpWspBVkGGb9t9s+g/NApVHB695jW+z3vP7CMHYiYMrcSXfyX4M2zcBSdbhlD\n",
       "DoCwmWcpeq//Fiy8y1VO0rwc3EYhPtdW6rbOb+IGtt5o2oZC+Wa8rmCzbGLIbWSj2G+FzvenNoNe\n",
       "rFQGQPx76IvP6l/oj8BGLpHCOQG5w6Ioj+PGYjhS6ttc9JTOPaGB2fOZEnBTgtO8zNOmeAAeZ46L\n",
       "gozGB30gDbmp3+qcdxe/3uGlX00aiBPPW8CeAl/s7urWwTT52iowe4NOb/QUEAAAAVJBmoJJ4Q8m\n",
       "UwIJ//61KoAEp5eA5UR3/TIAMvUsxijsXXuuq5BCMXyuYusnExISt+iNM1vZilLno5e6QIvDwzJa\n",
       "tc4iU0mZs+Y1FI3TStben/F8jeHL9AIGJ/HL6iu2f8cSZ1Lr8+BqLT30rWEAp9vTcWNu0rXbwgS7\n",
       "SkhZlttZtUggW3/Oo2IiWQmcg8TdX56CbPs+E5+HXbYMKPo0wFQ0bDoARo+yNkTiwKLRRc6gcSek\n",
       "0Po1IoP+F0gvXl5ZmXFXhbhM99Jy7O84qu6QMVwcHhJarDPqts6/5YjOlXYz1lhXnxTBjsjzzeUq\n",
       "bfHwwcmNBJEVPz5JCFMvfKsQhb8tfRPbDMvHTmqmmcfjnP8kDRmt47vOABoQlqF03+de8tkiY6aq\n",
       "5UlQZfMWZWKXPWkNzCQjXaYpIcGyee6kJuMFEfcmMgibP4ofyFkLmigLaVxOwQAAAVtBmqNJ4Q8m\n",
       "UwIKf/7WjLABMVZcf0oAL6Ov07VAVVSK7SCkZTdMbhXgCulIoWlI8bu7071r7KaAwzdG6HGvoS3y\n",
       "Iwu0/E0vs8KVfhgx6UbLArTeIde1v4Bc6M/81ghucT46tszr3rWRNzc8snrKcYKLepIPixNw5V+u\n",
       "YmMx1RX7F8fyQ08xFWzv/HJlFArxU07aMujs5NWBJdiW+avtfPYTci17s7Fd5Rw3UAzRjnTWAhfw\n",
       "a6Lv51bx3fhEQthBRtB2MWI4DBzvX3q0gtKvdSoRiLVlvOWYsuPKUiayXU4pPn1Zt9CEVX7UtbTF\n",
       "5pvKhjDq6fBAK2KY7GPK1SFlWY3OceQenqf7orTWbUlSYbsIvDR1G07ikEf1ELEqP35rPwkUATdJ\n",
       "gV7LXMtGzYs43C/c1E/A+SVgaMLIqayqr+W7+Uoapk/lPouUfRH0Nd1Wt0cP+xtbYyG0tYAkXAAA\n",
       "AcFBmsRJ4Q8mUwIKf/7WjLABMS4u/pQAbS0DpUhIUfnltPWLmh/4FoBntVSFaYzn1nfI292K61JE\n",
       "U0vBR/3YpfJ0UI9AamXnswK09rbPi8VJSrNxmvMpwzt2JoBNTM7juipQZln4ONiH6PRjA/VlRqvR\n",
       "mjfWsyJGGAPWPYTABw8mdO24FNKKPeFWAO86uReHvmKgmbhMHYK5xdmitJ1T0P28Ya3IcoCGTesK\n",
       "lEcy2qpZFWY/3HjGJ1UV8ExkSBEXviO/vwrsf405ZVqm1p20DxTAJaGjQKE+aIwyePRDRMU8KNiu\n",
       "bRLYnwd+npli0GYd6Thp0qHzxV1aIUsZahD9IWv2stXb0IjOYWTvfT9JkvX3OBYDgmjG11v7KG2S\n",
       "ka0tZwOE5sI0uLn4Y+MQ33y3lgllbyRGZAqit8f8lAhoT/P7yNMEgF38nOaM3nKtM8n3EcQQANSw\n",
       "GckN1cuQZpvnlOf0glpzJ9Z0oN4hIMrEfyGt3GPt6swJQTFK6326VMIh1YTj2gL+WXTmuXDYBKfc\n",
       "mQyi2sp7M2x955/47VQikAEFVSVEgcnUHD1PmcJ68e+2OrgKfYwLfBByF09lWboWs+pEfQAAAqdB\n",
       "muhJ4Q8mUwIKf/7WjLABORWHakAFbM4jTIYrKJE2V1gb5bDTHhtfwh53to/Bsey8qzcVQrHjt8tj\n",
       "0JVhxWwnMDl+8jYW55aJCJhVLwz6DZP1JwBMUsIfEBEpe6OSSzgmQm6XFlOY+jou3lbSvwALM9xx\n",
       "/mv5LCJV/PhQSarRDyqe+TQ5CsnVTyvLtP+QiVIBpvquRWC6SamuLm2/MLsOLh/aILq3/mLMCMLL\n",
       "QtF4u5k0Y9pYqqf+q2x0/z+4RT+nSaeMKiyysageTT4Ohm23ccwjlPemE7cPD14QEWrIUrkKcF4T\n",
       "lrar5cO6sWnLTXxgSd8aC12IHc5CIJnjDiTKY3LdljZXTJAOgEFk6fPxuf0k5BZ6q5UONaFqbwYi\n",
       "dIWXZomOWFY5i61yTPxmjdTJkvI8T0uOg9mNnFJjSyLMGFE62AzXCiyUCIXMsPcEWsbpLuZGiqV5\n",
       "/Bj7/yEdo4HniMwU9/FyiN0wso+BB2o2rNXGeFBL8xPi53SVJsDzNm/atR0N0jBOWg6P7kfbaggx\n",
       "s2BnhtZlRk8YvEN1FBtb8TNowa966Y6CoPoCk3QmmoEDEJaeoP7lL3CmdEcTpnzNR5FfCIRhf6X7\n",
       "WL0wqbedfT1lKrWi4AcoBZwSJJupCX02W9G7zDTJweyZuFz5L35VugNpUtCDNziYysYPJ1ZdKcOo\n",
       "jMw8xI5BfKiV84vYfbIeWiPIAsMkhZCZnDvUAef/ONG4NyhaeGiZalAU22MPaCtzvt+Xsd+B/hcA\n",
       "1Tlp7qy6dgAYUOhzuamJd+hcsI/4VvX79H85ALMUK+dIGIHwqKxngr99VYjg9aTypiSmd6yLA2Q5\n",
       "IxSl3J/oizPtLLCXXtr3DU3NsyouNBEDadA8etQ4OtqoFcnFIjEuccFPICQ+df5w4EvBAAABLUGf\n",
       "BkURPBH/AAMjJCnq2F+lheABM9jHPSOAWHrCKDBqsbU/Q9vWtl8HX+LxfNZ29nSkpfbhBWy4Y76A\n",
       "7K44niPBwszHm3v/AVLERv2TL4n41y0qrnKBXN2biHSFy5hy43Fy1lIIZPaF/bzfseqxakRi/ahD\n",
       "lg+8TVkmIckJTLW/4e3FselwHzCa8G6XF62WjKnL8fr1O19JMIA6hXbtlHr/MnTFjAt+7zqELuLb\n",
       "7kAPhxEtaOam6UVjc3K6eq/E7xVU7sRa/BeQVdpg9kXbcYhD+QoeBVVPr53VbM+qQTj0rsz58CK8\n",
       "s2WolfcL8G0ykNdvu86hqEeGBmSOGmI/qQf00G2mjZwvgu20BXI278Txzd4Onu3COWU/vryuowGp\n",
       "feLmKMpBuOkNVs1V0V8AAAEaAZ8ldEEPAAXOtWzeKAwATrbZB071jTX1vbkdKr7HSDHozTwPCMex\n",
       "epUhvAnjuQ5fPhC8/RIU/Y0ftnNzXkYOUYYFcPX3Xb99MukOaQ6UFe8GJVa7Xri1YpAvT27rfyqY\n",
       "VguvGj6POSw5qDsTijm6t77T8PAqWEfEmT8xP2QQAZxoUmbJm0FgGPzvTXmAaycGpFVqNg8rroqu\n",
       "3pO5xJxyazcf02upxu7TE6Qn6EMrM9nqDaArPTUfsDVCmUBYdm2E5XOzZHVNHQcCzzMTs+641Ahv\n",
       "DXf7+kzyxVX85tmPgKKzXLqFi9MwQBZAcSKhiPHCyiJ/Eh7Fa/TuuwIigrUOw6yvIX8D3vLltPBU\n",
       "wExnuud8TBufJ15vwgVtAAAAxQGfJ2pBDwAF9v4MHHEAB4xkiFRrO5orao5YglOiNFcaCGJg9fDU\n",
       "toiHHcJMaQbryVhzY4tit/JNLb3LiW/1Eg2mLpXobaun3Ty9G+OZ4n30B2mrpnXd9D0I2c4euY6e\n",
       "Nch7yJmZnXUviaZVS/YJ/awJVtz3WwdC8yon8kMMxDXn953/lRE/g7yN3oSIFiAZLYFRSuZ0kFQi\n",
       "cez/IR7R8lPtv9V1ND924umwaXJIEDOgGJanOcLQyl1B7Pi2AsJe/q7WJBUwAAACUUGbLEmoQWiZ\n",
       "TAgp//7WjLABMecnEDRB/6tABtW/BWGbTrNGmgpB8l+NTlCA0oFAAnac+9IJ3aDe0bcSh88RhIQX\n",
       "WEe3hqyT0+4XXkQKk/12hdWRnkja6Ad0FYyP/EJniEH1gAH98aHLS9Qn0Cbyl9fof07QTSrPCV4p\n",
       "7smNg5k6/8b0Ri8pqk1lqj6xguPrcOlDCVpyr1uddD6ylRo1W855fKneF7nm8bDU1oesmS6HvnY8\n",
       "FVDpOBI6R7L3C4S87h8fdPEqoEUeI7yeQtAGk2WlzTIEbpB01rDMrtQFiIBWkL3TjQwZvCe3032h\n",
       "1zQK8xv9JCHnH6EwGHbhyDoFvQfpswzRo+fgH1lDFLMrZsWnQ/oMwe+m+lKhze/rYEoYz1BAyy3V\n",
       "7iltOTxtmMlTo7oGw/9SFU2cOAnnhd/Pb91A3LiA+mmiIpbkL2meqjOFGQZ8RwBJF+q3WA7+EG+6\n",
       "rTlwzZ3utNCWIqMPG1WwdrTCdGNiaXlcH0MtQGUuM2yASNcZ5i+hq/1lwmD8Q74XJfU01uv6zpfr\n",
       "t78iRYkUcQD5bx+M2rVI3XsLqh/pVVTsiDiXEYKz5huPjeJOmF8QkRQV5XPLCDAtQ5R4YqGbTVWB\n",
       "3sq5K+W4dpN1n3quHvl5XoeU+Lw2InKOUw6n6BNiz7bld+6t9zMnJxHl74V3sRnuRaZSByct6g9s\n",
       "Ve9L2iH8iGwcQGqq2NQgAJDhJWcG3ZPNPmjSUyH7Emm+h/nP7Rs9wAoiSlCl1aEMgz+y9+cmay0J\n",
       "sD5MMWPzliVl6Y6UKAZUAAABdEGfSkURLBH/AAMFnVA7o4tPCSRF4gAP4RgzFQqfHD8b3vQMgkxc\n",
       "Re6phdeiec/TALtPB2Cz1Bmwm0Ex11Me7I+KKV+ZH+Omx73ywKmE4Idg+3eK9TvUF6TsSr/ISCKa\n",
       "Cu2AACdc961AqYX9S5pQBJQAtagNUlulyADStXqGWX3ujcxZM2M/NHfRtzMqCRv50p8XCP6oyqNt\n",
       "9POQrpXWMWKEkvwTFX6CKYI2b7lMvBTznmSNaAI82Wlvs35QX2J4QMc6cv69SJd0JyOi8pUKn0ed\n",
       "GqIY9GLWT6h1mW6/Hki5gNsHQ0y18tGNnv+BzB9Q2rQUvrEb17MTxYUhZBS1ICLxUrOuxIetQRB2\n",
       "JmrwLQBA8/SggV5YNKKZI/MgF4XQmBU6XvnKNFwHGWe1kbNgTmkBFNsj9a1s+Yq1G6Jc8SmQo5q5\n",
       "1jFre7zi1flND9aeFQr+TPkg2qdnQ2pcrpla6UUP1q7vJwDxGB6iuqSms0P261xBOwAAAOUBn2l0\n",
       "QQ8ABcPPtVvqq+6EAJptslSGr/d9AqsCQR3+olYhiaV3RIHJaN//htrQg4ysdMKEgmaHYomSdhCw\n",
       "MTtF5m3sypZu0dUhbAQYkdRrsisHf9+PW9pAnlVF1MGZMyP4mi+8YxUi4fsDwslxy8R/JI2vbvKa\n",
       "0QBV3dk3mchXFCpFIYBywW6tqN/2O+pAR0bXv5SnfDWylJwJEB94rfhjJ898HB8GHRpJcFyZrcd7\n",
       "zNdszQeRvlxGmsixMp+HxxUpuAJvNT6hOGp3gdujBkiSraGVQRMe+VB1wgBUnPZKsrDykGpAAAAB\n",
       "AAGfa2pBDwAFvrXLmwbmKeYAAJ1WpUCi/Q4M1sob0kA5xGZ2U/a4UoaBkIdYpwdtIQ72cVSfcisN\n",
       "kB/NxnsLBLwKmbqtWeK1vb93lLm/TgrP6ZBsvEjPUGmy6xZnXFzNyZA6KXm06n1Gb3t/Wf2Sn/IR\n",
       "jCJKPBxlhmCt0dxGzuRVgOiZHUCUD//r9GeItkaHgz1vezS/5ocokEHFDEPY1CtxV+9SYi7PkhfL\n",
       "tUxbYxNgR7EtjVAJfa6BSGdZVnT3EHNAlwky3P/0BgxYYZHfSgZAzYLTrUs+uaPvtvfh00nFhWBX\n",
       "2Jq/G7QhregI2oe0faSnsll3wVzoOokPVc8oJeAAAALjQZtwSahBbJlMCCn//taMsAExauCKAADt\n",
       "N97P4sD3kGveOuP5P+RC3Eft+N47XNO9zg12M83Pib5i6IS929qyBDRdB8w2Lrl+kH1c9XknkQad\n",
       "y+ZVin0+eLPMoA8nJ1Mu1A2zVfZcSVPy0qWryghuEe+ovLn+i3ljGY4UdQwk5aVvM122yLFIJ5lR\n",
       "jgCkCeOHlEji85v6gLBkk0Q93bFhOIRNgllyBwPduPoPhXPFuQew9dPKhfYvUKFgj8bdAkjB5i/F\n",
       "kejdUEoXovuPNVGBZeWl+PIe5ddWWTn9KeuRINv+y4q9UfTsl4G7YqdsLxkIlGUUwkXO7AjjqT2y\n",
       "uk/igTliebqcdiaGFAAUExV5FXU0PvPP8nWwt8+yiR0BiT7HFFch3fuA/fl6av4ZMKYr3pG3xIaF\n",
       "D6vRsxteLnLK0sXJom24EjYxAd3DgNuSdIyJgTuavmUJ3YQ8oFG3MVj5VWsL98gPfjMM3uTK5PLN\n",
       "HXWKWm/eP/qXHArWD8xBteDXBEGSlyVYILYsNOri/qJon6wpE+tHrHkdNkmBaLh6EYXe2hngUEgD\n",
       "m2+93RwTwJGGQ+AfrZl/mMCVUuCsw5zXqNE6feHxMi7Z3LIRxfQOS2uTw66G9DYf5Pch6V4nbdQt\n",
       "I5CID2xxl8GdJf6lMPOyNcjo5a7eVHKlvRVMSOsW6OkFwByzJH9ApeYCyLICcQjEHr96Wm6gfXqj\n",
       "7XsVvgCOySJg40oPkF0JCxycuENFBRuJ8cjDA/JSLDfTOhdfynT4h3+WDkMHmowx2DOsXWhklU3E\n",
       "nHWAYy1NyqIC//o21u0sctT/MLR9OIVNpxGRbDtICf3bwkN/OCym4rNl2MmbFikqFKxTF81NSrOZ\n",
       "0SrzPP3Sec2ZOmEvWsoJTrk3CH4gB6ct55HIul4+JMDgDtn+RAYNRN/iF3+PTEAwPROuCXBL/nn3\n",
       "+t5EudXPZJIX8wEKNEQ4MZLdKCi8P1/qwPIf9Jau4QAAAU9Bn45FFSwR/wADBbDJOgw9TvAAmmA4\n",
       "KbfONRz99RpQx4tnvEA0NOB0KAvnyknebizB591E2ziKl+DEs0xvcTs6JySr+ZupaE7B37qYfKoJ\n",
       "Ul3DZ4OkxLwOXAWumvY+d9vucny7aNHHhDG8pK6AlhrQdsz+7HIgTbhF/nKCGLmET6Aqon32z0HG\n",
       "qudeOe7auOVrgyTFeLRzI8uK3XX423MbLEDcib+KB73InCQJ2AkFLCJpRzkVQ5SzFrwdZAgx9bss\n",
       "Xl8Ke3doegBHK4p4VXhC4OSlaJjyXcn+U9Dw9OetrohMTjYYky/OAFQDNVbHHQD3HszPyQX2onq5\n",
       "C7gXny+GLpTrOgchGMGrgnjFEfZixfz9a5dZShKvCU55QqMk9rFIc30T8YCaW0Ji3xGh05VuUyUl\n",
       "h2LT0kPQDfUFhk7O7LbqF0BGyxSgrl2h65CvgQAAARIBn610QQ8ABb7ZH126yDAABOttkqkSHIX1\n",
       "Rik6fRAqETYu3sg8GwRUoSho+oJsIWoR7C5X50uz2QYYVXfMLlkmBruTS39+Zu1SVZAiUf6S5Cpy\n",
       "Oa2D5TGfbzrJ/8faBgCZSF/3a5L8bUL18z5Q6ArdH6u+l9tbkejRfQrCsvH2n+lP97a18lYIEXTZ\n",
       "JwENp7HIbr471Go0MOjoJf12k2B6ih/JSKVJVBu7yfR47ZPMiM5qWMmBy0K7CgyniR57nymv4RAp\n",
       "Hn8Ij/bNuQWlc2vDymVdLku/xGYNmbzN6DF6FktDP74n/srBJfeLYDtofwOUrgG9qmDvaZzT3Ptk\n",
       "UL+qO99qiiOutNrkzJAHR2FZjlbBAAAA1gGfr2pBDwAFw8+1ONk/90IATTbLxZ5Va76fzgtB/EFk\n",
       "pMfD+sN1/9s6Uwfwv5+JTrJF04iVAoRqJxKtEtJsvTQ7+vQqzgEUXq0I0LU3Hw2nf8g/8QfoGLOy\n",
       "ZwAYCmTRX1PR6MtmVoRGed/kyCuznP/tlRy84/ato39BOwvJNB+beBjri6lrZnKPd+nwwyb6gjx2\n",
       "9xIGXpvHZ9YPGvw5LFIFjKl6lddko9LnuPxNLMPwql50cgwMiOYhN1PfIOrtKj0+AXP16FOE//W5\n",
       "4tuH7eRPtQHQB0wAAANlQZu0SahBbJlMCCn//taMsAE5PHisAI9DGoH5feNPeN9ovec7pIST2SJu\n",
       "jGXLhXv/4EGo7klRX7WrHzxXs7KV8d5yr46DnLOKI9kkI+g9Y3kSPiVQT8cakBKYeVfzn/YDNeNk\n",
       "nto8evyopk9z9xuvp5V3iFqCWiSy87D5d4rlcvJmQXZt0V1pB5Zrn0VVQbDX611LQyKGtHhbpQgf\n",
       "AwlEUjK+ZZjtA/n6oyqanp1wUGAwCChtXwaO0CDZWjd0kPPj9Pz63p9FMFRsTVUCG4x9zQigsNTO\n",
       "86wZ1stmyNp1V1Z9ipSXmTHlhXrGHu9AwhyG4j4elj+BvVHZoZbTv28XXQpfRozTACyEJxQlCAUW\n",
       "0eYeqr9CVWmbp/2W3plq7WimqPluQE0eCsthuxMcv6lSRgCZv0Turd7viVpDaPqa0TTQbrG5jeB1\n",
       "uDWEKieTeRE7kSFZvAYxZv9hhgpgh3Mw1n/CHrWyl78GsmBYBHvLkzCCMDchhgX3bqUAnXs6rsJs\n",
       "ysKqDeWFZFn4Si42fvZwsne87272xa7P8VujdjRK4yLCDMlZI6JfpewDlx0BrB77gDP9iC8yU2T7\n",
       "7BAVerW1RodeJUNR45ySAoi9Znb9l4jo2wL8gB6g83bMiHqZJnbD2hV8U9ldp0tLZVdUGJzCWaKz\n",
       "MuZ5SosWNQ4SMAipmSWtIz3o/Uw/CPzfDFRrbKnjkgLseprOdKCzdpqDPfbs4wfrfTHZFO7iZiji\n",
       "hVnCRFxsfXppkh2JFGBzitYhsqzeg3lYVB9N3CJtQavm1N1s1BLkeKdHvlwXzn3z7NpMpYkJV3eO\n",
       "gKgZ3QT4SwR/cLFbOAGP1QHS0yjx7QRBj0U5BTjyK0iS8dbAoWk973gLSyWBXtCn0HEhaMZ36ylZ\n",
       "NIazf0HioDbH8KAYYpbqz79SAwPBXUKVyIG59FM6bE0kBo6uD/lglkHhf1glifX3PfTWx5fXq51/\n",
       "/MRsA7vRjb0pMVOgP0pUlhJn/TG5wYsZctNiBpu7E1QNDGWQtu7EniXIJudbXk0dOFKRdb5A4h0g\n",
       "q/4so3OLADMHwmNwhbs3VIGemVDZ6eFIQMONh2GiNzR4wqJPC4JDaRaBLXqtuDyFaWw3IsTwQHWB\n",
       "DY7Mr3eirtu4FVkijyuUTNJxFkDy8m/+sKwIr4AAAAGXQZ/SRRUsEf8AAyLWJy2uESvprABO0NYM\n",
       "foXR4HLqq32sWZaAgkCqbEkOuI9vD6KuwHivpGibbtgHCmeFjxmYaHv7ag18/MGH/7ZRVHsK+L8y\n",
       "p04qfvh7YvkQ2dcFUNdDF4ysbWCq+y20xW1GQsVyxv98zk24foAsK3S3kpP9XiUl4EdZD/4j0KM4\n",
       "Pg7IPZ1KX1MJ7RqdQv/xOWbMaNSzlZI79563O14talmlN3Xhc3DObEr+4xC86TibH63YZ1sI4gsU\n",
       "vjP0Hgl0kK8ZEkB3JFxVVQvDxyGL1KB4NuGKpZH0BTLacwGUuTMthMgW/5Kc8gypON4KWjM9Ibqw\n",
       "OZSPuNGWDNIuvU0pTABVHx6w3wUkxz+w1eWLPQju+lKv/JgI/Xs/44HztD/wte+PGQR8wfeuxXJj\n",
       "8qjSNC5vmR52rYIkALe3e5MkC7LQFUsjyzMgmkHl0VdlUkheSmoCTqT3bvRkL0s1Gv7Fq+sJ1MVK\n",
       "xWZn7JjkvVGC+N+L4F9IjxbzMIL3m932s4mhoDSWQvpkfFktt4x1TpkAAADuAZ/xdEEPAAX3J7Ty\n",
       "uvIuIAAJmFtELmbPS3PEmvfU8paNN9iHWcjV7/EvNcmk1oDBCCVJf0P6hsM5xUsmubHzG+yQrk41\n",
       "P8kUH/UcJ10pR6luA/ixalhkOGhdGkQUIJS3uQ7o7X8rnIcKTxa+T8UxW3YB8GV3xhmAW1SyCbvn\n",
       "yYl1f8fC3yCRbvXPiowPWsdG3P+WU4yNuTWcUk/wIW1iH7XGBxoNOKwPWZKkzV9IpmyV2B/FV/Eh\n",
       "buW9sTOO/6V/98WlC0lCpYekPkGrBItHlpR0LBoHWjeTM72VOM1nWmSd60rEWdXB0qenTQIDUgAA\n",
       "APQBn/NqQQ8ABcPPtT6/Ev3QgBNE/JUhq/3fQKrAr0f6iTSGOwQwDTuzU639WMh+7AZ/JRxKIiV4\n",
       "BkkELqwM5B2y+hCJbNQ7+vQqzgDRYOfbZ+FgTCx9F+tM8NMnTZRrN12E4chmRBMdNMDrbtnGYIO/\n",
       "+6205Fid/PqxpL1rVfdeH9DHa+Fh/jyy3h3fsIpyTv3VCVY9T5Nag0sZKZuNGwmloWcgN1ok0k4B\n",
       "BydS4BWA1gCLITfJd5cGejyWnU+mbXsUk+fHVnpfsi8VGbrwwUDyGDJrAhTrf0+1qDni3BlH+ELb\n",
       "gueKodiAtBs5AjEDLfKYkiTgAAAB2EGb+EmoQWyZTAgp//7WjLABMEL9GEsmH0AG0nhNSjvvwIhx\n",
       "ic2kOkejoHMzfjK4cVsg98zEtZaIQVFUlyjSWdel/Cvp8T4HpmMihYR7aA0EImgAw+mftlsmTr3V\n",
       "lS9rBF9dxp96VQxEHy/5SCHkiHsi0YoO5Fhqvsm8YqsxINR4XqHZIj0bt8Z2xLHOeYRMneclGPV/\n",
       "if2yR2r4Dh85VesttjZbrgKxPvp5yeClCWAEFu77naQ7VYpvx9CqcB4WnefaLGXgF7YySRO4TZKd\n",
       "5e6A9HDrDug/w6mXAVKVrm271nPvnPsqTAClmK+bCXk6W919h68Aka6R4+RY+dX3TsvIIRsfUJ77\n",
       "dboA1FjRpNln20R6Gk95c8pA7s+aIzrIeeiovcbcNw0YfE6h25niT2eb8UXhZ+D/0I4OTKJ55zpa\n",
       "uTl+39SacllnGhULMjbqfovMgHJDMkikQy0rr4+BzY+VlfQUeju2+ldjgpQBEjEkVFlBAZXCzqkh\n",
       "4sSNUE29keHxGQL0V8PfOzoBGgldZLGaDksopAYk4/cEDxG3KrfdGXTQQBKwqqHNxhwkeQKdD2Fi\n",
       "tt2Ozu8GKkt+oPeFToRu5wwk47JQrRSaC03GqX0PxJQu+t1Qb0EAAAEfQZ4WRRUsEf8AAwitNsHI\n",
       "H/BAARB9Kv67g7+J7VyCEP26zQlSRbIoHf5GOocLMD3/gM1os0dllQUJB6GdzIYHaZ5g33kSSsX1\n",
       "3dt07UGfJm7R1O33AWQ4w10cz4/l6lYivWqndQqJv+8TPKWsSN7mlKbkKOwBIgvSW8s7L/Uop/lw\n",
       "gPzJ8EjnjW7DOpRdqTVbt95XFcqhDqYHs1huySD7vTIqeVxK8rbApM9nxiA3vLwHGg/NldQTrGV9\n",
       "805yno5hmRSKnklaiQGcxxmV9zzK+9MNLHPSG3m/ZqD1YRjpQ3fwl2SlJ4fg8bIBmQR6km97+HEh\n",
       "f0k6GJBwdjBoAeut9iWwOA7wlxs1QGcQg4bZnjadIquacd6F6z3ZjwyQwpIAAAD3AZ41dEEPAAXN\n",
       "kbRfsAAP33yHU088qogNayRVaIP0INxMgusbpbl4oTOdenrKf+5ZmcTpshJ95rmC2pkGBU0DkOvF\n",
       "bcrnmPMOdBMAK19xf+GYtlyFKoG1k2+/552Atkbxd9swjwwOHUaUjXhYHUkRrFIJXk/f57f6mKkW\n",
       "Lb07+1L+LzoTvICSy4ocFFO7/RFTm/rKc61Mmu5yV6bXZodAOTZGuebDtKE/r38sWtLbSf2e4mhW\n",
       "GLtCsJHQ6ubO2frhlw8EARzs2dGRtn+kXVv0weeSIAoLi81BBbp4bAAxmQihOSaVCz22jt7haz5Y\n",
       "zKguIvW6lTIb0QAAAQsBnjdqQQ8ABcwPQjDFQABNNtkqkSHIVt4VahJmAPAPSxR281Oj8OThjuHk\n",
       "+wi++GxMiEd5XcDAPDz5UgaYfTXnhNuYQffUO/0NAdygGLEZuf+UOsxtgJroyRmCNFx0f/ZCLygh\n",
       "B/WowqxOeFgIp0p2Mmvf6rK1/+h81baHGS0H1dWWWSbcW3wK1j4aQ600fxJ+313eLUNMCSUW1n0x\n",
       "XoRlg0HpGDE44rtEpSQEp4rNvVHyxTlWDLDYVjJCn7paY7tqdEGHEp1rRw5gdlRZhnikKX6WuJ5Y\n",
       "f7Lz6dumG/YskxSbjjGin8BRJ0pBxtD2x2uzfJdu2vMWZuu+21u8y47ZVm+sTe/3wr/sYRcAAAME\n",
       "QZo8SahBbJlMCCn//taMsAE4SoxR77IAK2dg0HEyxPqjRNEJohmI9wrluqBD6qSCNVn/X2Di+Osy\n",
       "UMgTEGxtkHlo04gJ4ZKarhA8ZYonv3PZ9EsZpubiTSvhXjXvPWq1cYdFYVsVaVMoK2btXbffPrnH\n",
       "f1IkcX14/17A/Eu8l+4L+WWRQ96JGRBLiJ3EP0Dra0BobQR1GVniCDcUWShQdR9f5izAFlTtRveA\n",
       "GT9w8cmg20Nggj8gXzCrzLIUDNSt02Aocrpvgt/sILcV9wsn5DJ/iYW0isRdhufcawCIgFc++QgJ\n",
       "gInmHctQPwCO2g6LhrR1yKPSv7M2ym4bcvkCgl3/IYMhnPZ2OM4A8FzCjvNcyU7wxq4nhQ4bxTgl\n",
       "5OMFkZKRvvttm/ONs6iKqnbUJBjPTG9oFL4svOlSC9Dw3KDCQ8gTGUqaVIuQjYQICMMlsETS2v65\n",
       "mUhCJ+HRpb2ED6FemqP/0SUW6uKYGhDBtaZ/AAmwCP5EZlT28BDLB3h5FXHs/6DwyxLm8+4Ho2hJ\n",
       "nbI1ED22TDzFKF4fBu2zLQ3MwuwY/a4LYXz2DO9hgFQb0axRH5CbvKJOnulyNEh4sfUnuod0ClB3\n",
       "OMfNQSt17r1ZHZGAEz1aT113HHRGbCQZcLmg2M3Wsuebtc0cNqGpfES7zs4X/zGQabWjQlybZLJS\n",
       "q4+1kSaDQ8PGKwIKsTkfaU1DWXWn06RwPJ4InD020y3F3aN0wdJ4PUA8pNoVxYvGEu0kGsRikYdM\n",
       "OKE9LtWiG83aRghu6KbbfWICoNM1iIWB++im8vxSRb+Z0X1ipDtgcTvBWpQKQOFIPrwjayW7l8Nm\n",
       "L2T4DdSc+AkBgSB0Yg9l3O1ZLWfewLspWatmOHn0kN9TzR9lQvEMvGeGmBPhJ16t/TW8AVdKDuvK\n",
       "jY/S7woFke2ZTKPJPcwxTkiOBZEWTwzlKuvNYNpYpWjzrp9eoFDkTVhj75hz3EqDIbOdVoTRSOJs\n",
       "nQn0Y11MLPP6qCL0XJBRDyo4cC2gsa83kRYAq/ddwAAAATxBnlpFFSwR/wADIyQp6sKb3yBnABOs\n",
       "EfutXLGimH9svf6F2sTGd4x9E15mvhnc3MA3UUYSYBO2SPxjpqW9RPIsPGkHlnFS5oz92jPOWq/s\n",
       "6L6HIg4KmhdMLZrmQfoz+7duufuVJ1v3w/rmsBvZQlMwOLOkn5gcST/WjesTjwe8WFZE+q0xOg2f\n",
       "1FiomnqianpTHY400sKcznN+aps4wm4XNwjU/9c5et3tzQaTiOancPL6R/1reSCJD8g02lUVd+TM\n",
       "1yZx69RbTi83uyQ74MaCXb8oPyWAwvnTIBWbMI5pmD276Zv+O6syFzG9SmFhQv4RFQDZ3zlbnWlD\n",
       "fpd7Jv7FMAGV1lYj4JyIloO9zenQ+ZtpFnUHaBAyP+ym5WFrS+VCdN+1zf8HKjRS2t/H9T6UepPi\n",
       "GZI7Is77GK0TAAAAwQGeeXRBDwAFw8+1QE3v/rwACdJ68WeVWu+n84PU8QWSknHYGNn7KvTpS1WZ\n",
       "jXNbfn/xp4LY+bqJHCgoI2b+ZuxoVY2/OVkuQdurLIYElZdFZfOxGv6hc6FiY1HX+WfwX/Du2vUI\n",
       "FErmptH3HzBb0kkMcfhZdzEp+aAW3nmntmZV3HBmmuMJ/96cu7aup3ozZt0LaBcrDsFJzhBTpefB\n",
       "yEbRMPRiPxGKotg+Ug7ONv+3XQtGRcfm9tpGFESJ3BQ3GLAAAADIAZ57akEPAAX2/YCEZzbSWkgA\n",
       "Pi8Koni+2l0JGyX2LJRdz8eLuQ1q4LiWmosG9JawElczsgb4qhTxUGvy7PEUAgo35Z75BEmkK1Bl\n",
       "3Ovr/rfxGPzM4bYoju/7V4TIdmR8kinNoLf2nY/8LKn1bOqvK4Fu6WLH4phiwfC2S9Ca5dFmhE4j\n",
       "c0qUdhUaHZOhMVyHljJI2zkgq0BQvm3POrZeuoJsea/D5byGuW94jDxNTNQwKevkLQqtLK0IHQB1\n",
       "/Dk2pJZc+nTeLiEAAAL+QZpgSahBbJlMCCn//taMsAEx5ycQNEH/q0AF9GbhSWdP9OHZm6JQpfay\n",
       "h0oEKLQUwNHrCquQFIbZ8cr3Uvld0qQsersYiYXfpWmcvyd8JVcEQB7RBUtyOdldapM5OYD1tTaw\n",
       "AAo7QW8bcz082sZHruVuY0wgwE7nuINHermUA6pvg2HIWWG1y0mkBI67B5K7HXhRmtOa4F7qhMjN\n",
       "Ia9y1VRbFAU+lCUgCI5FcymULa7/O/6xPaZ6yu3FHH3IpZGP7/f32+G5OJP23XbotDTgR5KI48bq\n",
       "9t9C/Tzvr8h08GUXR4QSql3AwSf0NwcrWjDNFNJf20X7EbjOVuQ4uCUCOjtTNwkdItOL+YLXK7Mn\n",
       "d6dFYNk8t89mT4kQVliqtEJDmvkYcqBdfZ5Ldvj1jwqYr636Sa57WOA7APaaYSkPYEPQe3bcFjKL\n",
       "c6K2HfoCteJUhBq2OzscrJ/GwoG0FhWHfIzLAHUteOVkREsfakNDe5WDkK2Z9WagrkHqKlykjORO\n",
       "SVt6xzW/xpVkx+TAEGO2hyV3eGxY5ZrLnCXdHJiH11wECIGk6WPyw8QLn67vHTwr+GqNNkBTRRwv\n",
       "LQRl6t/1raBjNprvQHN4q+Bm/ztNbBQ3Wuex72IZe0gCIf4GHlcTKJCF4MI59dxAznVwWqZG23O3\n",
       "3xFP4oB99esOUFTuxMlbzYr4G0GPilQsVFVedVnGITTMQHk6Y6iEM6lboVxK0VL6SHHLT/F7sPSD\n",
       "yYvpg5k+RoORUARyAryzwz4+2JPxN4gKGnEsnlO1bKtz1X5r2LH4YUOLKkbnBEGo6yAtAGtymlgZ\n",
       "2kNzNvDrelB/m7AvFDQ6DdYMGAHw8N3HVt4M4f4fCdCQeMnvmDts8K8NDk+gn/SLk4bOmEX+94Rj\n",
       "/HmjHWPXSrQxwp9la5/oIwsGGBPUwWuKoFi9wrBjtLUm/q6axRLvhmLA0P6I7Jvf6ZjwEFnYsgr4\n",
       "9oX2dlgHwrTxbeHS14XXTCapwjB6jAUsa3yiJZaNNJnJF6Vx8VXCPwAAAXFBnp5FFSwR/wADDW34\n",
       "OFvAAmigxYdStTwGh0Qak9kG5R+j2rGC88eJdIxnn1gl1WPuf/xqEBUXmXSxg8LbRXXG+aZEU+Z+\n",
       "c4oktcv9n2/mblXDwEkOMWBhAorapgM1LRn3DfgMtHTxCc7dml1KWf0XMJeHOf/gmfaj6//FjjH3\n",
       "LEtVi0zLbcHIDm47m3ak5tcsgcl2/vqa+Okqhk3u4INu9G1zjUp+/k4TsN+HIIXEd/J58h2U6b2g\n",
       "ZJsJvQ14Ko1sN74fxGR5RpsacanJU8G0s0sh4a9PjSc4bqnqIuVzmSoy8iwVtAzUauTl8BM+NJx4\n",
       "YW7SHSdkKzZdhegBehDKS8yeuwedDBSrC5gDIQc1wtRuO+sTYHwkHhqZYSNzlObFdM3uAuaOQgbM\n",
       "qNuRft1wpqBAcz2bTHGlRdogxtnai9NXtVOawEnf1aZ7XZgGyaNl81OTjomaByKCUo0cMLE+8YGo\n",
       "U6z7aDTMpwq3hWI+EXAAAADUAZ69dEEPAAXDz7U+vxL90IATTbZKKc8q5e96b3tf6yD6mTKOVcJN\n",
       "Vi/kPhmxECwsbpJZbgOKkqdsHd59XeqhVuHWiwcvPF2YFTCaFWNvzmJJlobS/kTDWyRPO6fd6FRR\n",
       "uty4bTCadY7oXFKrIGkFzmoq8E4e3PyBuL0TVULRBP1TuWkzt+nFcFG6jSRtLAF7ciZY/Cg2zZPl\n",
       "mZdPcEymtyxkg71Pcmwum+tVprizkJGLAgS/PIQzURzFLeZ/bQQHXOLZDvGprwn1zPYJKP32QdTE\n",
       "xYAAAADWAZ6/akEPAAW+tfBW2fdsAAP33oK5e201RfUXE0UrgeNKUfYl96g1ng4GnlxmB77n11M5\n",
       "7MrcZIxf/O1Dbeu1Q7+z8lZ4eXLC8gZpsTYgIBb4OcLcIqnfYo0IgDQHroCjMlXGhmvjG+rRuz60\n",
       "Zs6e0On7cJSzENv0TerFQF7yKwQUmqtuoJuJca9k7g2xFt09NjFTNCraGjEl6U4Hb73wsqwM7dgh\n",
       "1vhJPQIs1Li2Gd3DvBPASWsPaCefBYc9jsvkAjJ+4FAFkVNIdEg5YmZMgnnt5bko2QAABHZBmqRJ\n",
       "qEFsmUwIJ//+tSqABKWhqvlgAQjQF6QmLCjo0URoOI3RAHVqDAlit2YhiWZpnH1qitVdJLLsFsCs\n",
       "Ykpr0MedPqx/ILo4u5aWm0nxRxSPjQKUT3/99EAN07sK21CM3XtggGqFadaKJ8FPQH78dSZuDFXy\n",
       "xIIbSKHdXankStgJhBMjpH0FZzQ8rSJ9B2l3qQRvdRNcpC/OdWpzpRNCDltm4LoYT7JWeDXAP4FS\n",
       "mltvzE3IB5Q1qkH/YfG8tgjevYQQvJYBqcCgXnRG4SQ8Uw6wgmLRAqTpTfdX42VKnu/vrHIsuxNL\n",
       "cndg02TblAtTuGyeT1/3CrAaAVDfocNwhi/vBi+34u4+x8Ml94+AEMxjEoXGKvAz/vJp+EH0dz8d\n",
       "PfsG6YD7z+ib+MQnMfiXQ7KSvXH6L19/oaUp6+LBsHJNjXD2HbaeBagkS32OBdW85qqgAQbD3GBP\n",
       "j8cIunrRFTPHJHiW9/w4p657wVqTT4B0BFe7dgvMbAHOsd1sB/mkIhDp82qgf6FMmUPfyOsO9gA+\n",
       "cmhVnCCeCEGp6xLi3sbE/bLb3EYeYovyv3rYr673ObxjijUSYHCztofQhO6w/R4DMAeLb5sKrlqG\n",
       "cGfhBx18wWVatEQpqiDyzvYLISx4eF76F9GI+2iWoZF/LXFPgVdZhrjnGBsshM0nmDx4KVpXFkLn\n",
       "rN1ARa7QItM8OHrNv4C8MTr73YnrjSQU1LSpIjpKrgm9WrdFbeChNnOr1IbxTeL2gqwMlHHhVzk7\n",
       "Q6dSj9Fbc/PneInh3/oj1mNQ8oQ/ZJNIBEP73LVMxom5KPTb6++EIv8CO7M/1alLFGEBiNbjgqIW\n",
       "iYwpkTUez10uh1JdUFT2FIiZxvs2gFfX+c06yw0GAPRL2A8/wQ2Iz6qQ2uspxLWiebGpp/seIX2S\n",
       "13rAdAT+PEezKuYkd0xQTjb5y20ZyYblOSPLKM3cjsAqCESCEFxUo9oEpzn515XMX/hDOskSBsv3\n",
       "9+3FdjoR7ioHQs1lCtjh4wDAYV2PUWoEhYN1FdHQJfVi+xaRin4HGQoaNSvgFCbgRlbltWvUJaxX\n",
       "6gv0IH9GyzVPcqQl7lxd2kwVogoJZ1FnCEJNM1Ns2I7HYS2yQcUOCkBJMMvsk0gRbpwQNjpok+pn\n",
       "H2OOTGh6xqqsXxCMwsRVX35ePa6GhENvAUC3Z3Zad7pIqd15BpZBIRkDmRh/1mUaCKCIJFv0lQLZ\n",
       "EUVyzs3swvttU8ktkytO16sIAzggnllyGz7yKhBr2nhFY/NKGKHI8Pls2mvznDrvjWatNDJoQidN\n",
       "trbkmeodIOMqF3pF3xsjuwHH8Ol8aiZbtmkLqiN8/ymuj9o6IF9ccofraJtF9dRfwNhZgEcdJWeM\n",
       "A3PzkHrrLjjfJyrkT6y4KPKLFH9dBzNQhuNZJXTEkWO6TMt2FxPb+kQwj8wWH16vAPUD4+cp5QmK\n",
       "COL3DG8sAP5t/9znuTjcRJdLwt1w8hAfqwIauEBI4ur5yVX5H0GwRF9c9CeEXel9biVWZ6hrQAAA\n",
       "AZVBnsJFFSwR/wADBbDJOgw9TvAAml/gUKOIBS305GlDHjNEYD0p8KK0W4vVPUbcAMAoNMLBfHyO\n",
       "+Spw4V5NP7odRUSAfzN1LQnYO/Tl7jtzg+OZ87TSd4nttUPki7ZoihX3UX3sdLaviBxvQZNBZm04\n",
       "9Ccvo7eJ58bifjDwD7rJvAQREirh1i+inuyWLv+hoMqIu5htOT/vqxfkcotfyiEKTfUlb58G9aVr\n",
       "Yv5W23cJc+R9n7yxDLK6kX5VL1ZtwXd6itE2O4cuoRlochGof6MqZjM5b6pmf7fPhWXlkIF4OQA2\n",
       "5maUdcTUgqmjRhg7hXL1ZBoL0pWVRV44lyjWRpfvfd8NIHIHWttAH7jihMojdzECDd3ksh4cUJeq\n",
       "68yF82nt6ApViOIJ6J4AR42fydjt2+4fusd7E0JzzSX9mPKpHJc6xTHzkU02nkFFeBtjXdP7EEk3\n",
       "Lq2ApdQA6C8PpcwTPA+AL87h8cZr1sQc15LUtwUk8t43+gXuHKpkGMwPnGB3iApq7G482Y5+dzGW\n",
       "03FPMazomzEAAAFZAZ7hdEEPAAW/3BGNYAAfwj4GrvEU+PEOWb5n6JdCzgbBa3BQ9nSY3VlK4XWm\n",
       "UZ369zSk7wNVDOg4ghfLMWa0fl1m7R1VdcBEjotx4rmIaYMtk+Rwh0ovbTQQvvfH4I5B0yRCcOQx\n",
       "ZiQ0BMElpqlamz63OpDCLcFw/2waZfxrw9rWlamcZkjByG0nhXU2JHOsFH5ehGr5/NVYwnUx6lsk\n",
       "6ojvlCzhQvYtFdXENn5PqFGEeEqJxsnXEtNZ8+jWOtOysrDzPV1zPqKnnt4wYASzB77zTZbCjcBM\n",
       "iyCRWV/V6BR4JBneEuAPGQQLIBIj6hhCpoVOSAKVngEZkk1DQI/6fguLaxTUBN3LtJ4MQaKY/j7p\n",
       "VNf6VcPIJZeFBKIJS+2O4kCJgVBz1yL28oJbZ07T1KJjkNoPBhcoVcj9d41rz44kuAgdBUg7mzoI\n",
       "4IB2rhmws8/xKRVqHzUgAAABBwGe42pBDwAFw8+1QGgf90IATTbLxexVTGO58NJybxBZMzv+OQNO\n",
       "GsYkSIv6p6mWUFxRUVUYIbzIot50zW//nxsguUtH8zeyLYDVh/3kpLNkFCyRDtBjuaX9J07aFC55\n",
       "G5K6Xc8AMHYeWg8FTKpsNb9zL8Y4u+HmVheWPLLXx2JXlAdcCKIdYntjpaIlllgS/ChucSOKIVCr\n",
       "lO/pqETzGyXgrdnmASdE/vq0IdlxUpIF279/QeLIJ9yDCADVanrHZibFikX6bFp6r7S3vLYiA5Yl\n",
       "ditCzt9Wv4RVuPULCztQpqVMQ0pDPNDuZfd/H7PwJJGz/WAaTz8b6DsIReMHQaDHzTdFoCqZAAAB\n",
       "uUGa5UmoQWyZTAgp//7WjLABMGYKQABzOYK3ty3QZjleR5tiXQpSfHcn626e49O6g88NKXvKgxfP\n",
       "2GGY/SBQe3q+pvxjgUjGNN/J0hIdCQ98iLXYTtmgAEX4MAIBNoAXOgObh717vD43ue+dW1TKv+KD\n",
       "Af/uoHD7HKHOvYzBZO77bc8u56sbo8maxSjV4FTJ2ZOwjhpMkJMP1lflh5q36OOKuWIF4P7RLD6K\n",
       "dLFQs7C4uX8rtp/PTO+PIt/3T3QEsCAr9Kkvnl4lydpYZav1vffYYb7zU+ZXk4J/gfeqCMz5ca6P\n",
       "OCkheEGZnGmghNHBUoO6UbVXyuGTQhtOqsXIoidcy/boxoN1JrAUxnlqh5o3u3yNYT6MP0WUe+n7\n",
       "qGOMVmOcwDe6+jNgyUaK02YjGuEEIMBQt9n0N15lNKAe/7aiktgA2w0Ce0VWtlNHp7Yh6GB1Wmj5\n",
       "KbVJozmPqu3/+EROcnQZ9hxBA6gAI6Pi96fRutuvlZJPJDWqKjHb8vLg3BlLW5t+Q3KvEgu5RHh7\n",
       "UJn0XqiP0w8oyXLU+AUf38a1oO+xjMS+nFeMoCpNcbTHjf5//57f95WCtwAAA11BmwlJ4QpSZTAg\n",
       "p//+1oywATk8eKwAcZwuYs+Yx0t31mSZLi8uQinAmMwGdmIM0ub1L+sfl9Tadzro+yo3Kc22L0Mr\n",
       "Zh6WWLZCw9tk+SO07CbbHXcMQwb5j7RciDOmluYmo+OfmAOIsE/nZMXp5LESsHkMYEAPKWsh9mAf\n",
       "hm5rJT/IijFTHfx1PSyfuaNCzlajuAVi2iBLOZaXxSIsdhT5yddmSKUHHn3cDK0WYAs1U6HJc7c9\n",
       "zdymeR8wMieczKuqn4qDRuDx/3mEvBhQBkhVKlfjpKvnJzCEGF+i9gMMBqKRm2IiD+i6vKt4IW93\n",
       "u5aCvL79NM4s4g6RfZQ/qRBVFJGq8NHvMQp/gFYIT/E4xqpbG9V606rBIeRPLYHkHK3mFG06b4md\n",
       "W9eLyduIIxu3jaXGFXMfCipJo9sprl2zyrp8aQLOOpZa4SmLyrMO5H2URwQMsqAkpbfktZ2TcnMm\n",
       "b7ITRWJs9X/tIJ1E7X9WcnG8GmP/RxG5VjpxWqh7iEzaGHUYAuKj5QZ0W0panxqetXSseck9HRZn\n",
       "6hln2WPCBrBRQgjA/uuaQoSN4SP+FxD/BkA6PO08fa8zq+rJal48uLKJga7Z0GbuxAeTSh2Xp/fd\n",
       "/ltSCrTgdM9V4vaYg0wUpUVzCB74Pmd+j8Q9vpvKeWcHfKL52qmsaMb3BwAJDY+n6aytw49Z9I/l\n",
       "c2TVBfspOuTTHs5+BOQa2R3fF587UV9CsXPwj8NzeAA3VTO/m11Y7/mlwbH0S1ohZOmb/Hvk5o2S\n",
       "sXeo1J8MR3ORP+tw7SiPfo1DVADF0TAkTCMQbZP1XUr5OBPQE40uS/YnW8viPso+z4/pHzWR/IKG\n",
       "mWeACOLgcXJ1e8tIyd9jQ1lcno3Yg8Xa7uXJ97PkIh+SsnUUmY2OCZm56pR6yAHmdnKKMpv6KvgV\n",
       "5TYOCnxVWDKXfrTKF4TX2MsLjLPKdWTVUUrGqhRW9CktwccvXl0CwI+RHGtj35DWrqvyX7oHewSJ\n",
       "EZUt35kImXOKa3bEfKF6MCkHfajyOSmsW/cdprtGmEv1tmOTXMP8BCLwJq/s5Cl/99e6IgK7z6/a\n",
       "vUc4JFSiYHmnbZEXNz0FOsKRZ4bH4E0bCVQRrvgYSIUr7vG3VKGVuhY33h2AoIuMDeVZN6EAAAG/\n",
       "QZ8nRTRMEf8AAyLWWckT3NKP8AQAhO3vhn2eEXxg+C0xWeWILgd7DkSXRv8/8HVcEiR3tgrISlxU\n",
       "/xPYSGyil9g4M0ElHmjP3B985NyizGojV+ilRnIPLJ/Hg5z3lqHPYoiY84V8NVYHCBxY3eUrAwy3\n",
       "bUDY4xzxKFv/P49EixqVHd44gEU91F1cheuelvFE83Pb5+2elk8IgC5Pmxom3ruN21Ymwnvierkm\n",
       "lLgTdniqk9npA9UNiwq8cP/Mm9QjxdcuVaZGqw3YhQtKS1iSYqOGeUgYJPe9khX5J5rQpRGDouma\n",
       "/SD4uRElotURtXVLzWTNadj+5lpKvWADcYAC2fU7lHc56xIjjzSbZcwRBzcBa4G2bTBIBXiQcUGD\n",
       "Mw3tjAu6JymAKSf5B0Iv1oxNGbC2X9gLl959/J+PMPUMp1273fdzaxsfwe/OkE/I8vclZ+zH949n\n",
       "z42qAmUrk1fW3yGdG2A5rWyUqn5QD/CIHUO6auHJcp96+AgHjJPmAMP8kOzEn7G+qL5Agxe5uXXT\n",
       "rQVHxwlNu2SHDEBKbV4IOEvcS1N7335PfqV5SXTklnrIXbG0TlWopqUQxei+ZQH/AAABBAGfRnRB\n",
       "DwAF9rvzzCvc6izAABOp6GEhojA6ECf9jeSjDOr191QfPhZkHwYugNJFTAuTcRswJO1mmVb978RY\n",
       "6fZkVLKCbwFd0aD41SbjYLLSOZDbfqFhym+kicCWc3AULlaROO0JRc23nSywY3zGYoBcCEQWEflc\n",
       "QlTZvs6kKH1tEx3xubMSjHcSjV6bGGFoxMNRBvfKNJ2mJN9tBvCyrkMIAEcwTI6H1H5kzcDG/h6m\n",
       "CHnBhZbvGgpS1smaLhL+mvtHm8lAO+YoEyfYMRCG2yHVABxWjLeHf1mZC0BtvPUty2F0B/iW+Xf2\n",
       "u34GWZe0NdVOppTu9KXkG1aZ7kc7o8h/vwoOAAAA8QGfSGpBDwAF1/AMAE61fVPLDsW5KqHm+YeH\n",
       "HpOIJfLTVfILOcn/8GrXTtOV7qkfAYi9NHw2HbUe+7Xo4EcCpZ54ddI85U/6L9/GYqPi0uDNCTCR\n",
       "sty1MJvLS2aDyHtdvZjmr3GbsQu25xG01VYMJqfxEUFKvDtIF/EkdURpjMVYUq3ExUuMO0yUrh0n\n",
       "ljDCfgddV0a2LIZBADg3CI5wN+9xAlzSUi45bA4CrqqArQCJaiOwgPAc0LBM5Umbq8MPHiCGgp8k\n",
       "btdEwsSARQ6QnEjHJDwQAFUea8n0ONodNUcOC7oo0KaE2aN27qFSkMRwJOAAAAJ2QZtNSahBaJlM\n",
       "CCf//rUqgASnmhrbE//+IoAL6OOxOPnKsu0C2wZEOI4a4hhsy50SfHMVbP/DN7s08idr2bVIabgo\n",
       "oPoykbfDYAAzbVTBKIA37gdWg6SQseZxge1CfGbp0eq6MljvcdzrfbSb+BIrQ0Fu2b1G9aUSapTw\n",
       "s668j4BzIpdJxDHnGhc+eBDv4uocQFNr7Z2Na3BGz3lokWTgcl6BnJpmwKsLy4SJaIWzHRXcdS3e\n",
       "b/rBvTgMaevMY4XH7+HAJnkqQxSD1yFiivxDIg79Z+MvxUnaZS3+3pKkaTGqfLGSc2yN6DwWI0/F\n",
       "f3wfr5QByanNvjFP1mHgBbgVEgB20CmUonJfaNNqQYedHuZcCYh/SMtB0e9WyNNaprLU4hZSOpBm\n",
       "K4LnGMkGn2GMZK/KU1TsSbXgph2OpGPqmp5RfG3cd8Un3WTYjftJFOxw8zRWUaslpeI4YFEIa18C\n",
       "YIfNPIoJivn0sTKXNBujG8I0NO5HTCk1jOHnB/bvS5bdXAtyKf47JbuZFGuy/ILfl9JJeQl9Fipp\n",
       "C2+lSoddpqKmWsbGgLFICu33Dl3yTXp1iFcr3n7xKwnYZyC/BST+9OB7i1V5qOhoYcXdwtHAqbIt\n",
       "A/8oGHwiUNZcYdaU+/VtKM/Ma84PPMUIzAUZ4EZ8cCRx/rh9WCLSbfJmQoHYgH5HgyjmCPQ+pO8G\n",
       "htUKd9pN/g42dVHRs17+TKwOEmf3wv0Tsv+KiUXaVno7Dibd8pBaPnBQyUguwPwUXW4rFx7yRVqa\n",
       "Dcn6hFox3tPXWyoi4aUd6f5C4uLVfX7fggnEnWgmbMFYGU1TekbTBAmqYFH5lRfadCpJAAABfkGf\n",
       "a0URLBH/AAMN70fiAA/hJWVr22wAOdW8cAbgQ/dF08UsWXhuMPFq7tslWRekulqLyD/pYK9fOAG6\n",
       "pFFN/M3azrgLFy0rxgTQiT4bnsWcD+QhgHiZQmSUZ3/QZqm6LeX/8FlrfvvEmGMX+TPEzW2PxEsy\n",
       "RWZ0lc1Jjcu7eWSsHNqC6LZRuhMl5S66GbfqJDG6gasAOxQ4Rp+0QravBUiXjz0W+Qo3RBQKPBFF\n",
       "kGxajRc7pP6TxL9zgtPL5a41YmsWMlxLgkRuEYLawOuANLKYteZPMDJx2y9Xds0ixyTjWLr2QB7I\n",
       "O00+khfFNEGdM+rfAwVSECMBmbq9AOKiSFe8KOTRr2fMFAeOymAdQh3b95CXI9U8fCeMogbjtEI6\n",
       "mo6sKdgUo43czUK4He0N0oPbrBk9KegqygErjFJROGqSfUBsroG5pa0AwqTeCRnqkWgPDEyN0Ido\n",
       "LgMrQZ+foAE74fmLIHjJv9ssg4Eb/LJnmzHIp02V8xMDjmMwDmgAAAEhAZ+KdEEPAAXDxuc7mi9l\n",
       "gA/hHwNXWwViZSspHvrumEKHo/ijEPw3QYn1Nwi2bpjcJuTkeBeI7DS1YXAJz/HFNNssZZyXlc6v\n",
       "V3XlDHfvFrRDgXrUO/TEKsbfnLBURbFfZO8KkP/t3nlQ2Cvbj0mSP5omsv0/M29WBNH6It5fvZbn\n",
       "UfEjVTUE9CI1N1OtqNV2mLblfvQdPQDFzuFjojYFivcaAMTfDFpYwqC5GIRfsT573+V8RkqKJgjV\n",
       "JOrxXeHVMi3mxTjQ0QhSZlzKwVl/0NaG16qUtvUDNWxvxFN8OUdue8E08OGu3+NDZZw/qkfMbQRj\n",
       "k+hADSP26S3SLh7zwv0zl9+WyjvikHf/MAmT04q4/73M0jmTjjc3WPDrjYgz4AAAAPwBn4xqQQ8A\n",
       "Bc8nBFWAAH8GLob/ItRacfHKUoapL/OimAnTnd7+XPyzCko/yR6Ep7+wyG3fb2QSp0Tn2T3vxNHB\n",
       "s7Jc9v8CpmOBgNtpVcbRvIZhkeq06LS8VKbr1n7KLZNeU6n7GLk0+8qnDHgQygsDFqVeJS5DyHwr\n",
       "wGD/gCLdAnsiN6Z7KVkWWENej90cIFcLBnef31jE3I1FH3T1PlbZunPHOX6ZfeQaX0CgdEhiTT3G\n",
       "/iwFTpHLrZgpSStNFmIwwqp5xe67ktx0mstUl2Mo9+6fTcYmxIJHpv5dbaokasiRYK1b8Tol5Wql\n",
       "rtYLaCk/F+3mjTpBCJgAC2kAAAJpQZuOSahBbJlMCCf//rUqgAShQw64ANn8hN1XPnObRnsinw8b\n",
       "PS4KF4w4yQUtVr7dCDy1VaDBWRPL1glLNTybtLKvge27UdL33ErSfN2FE6ziBV+MrWUtnGXbPbog\n",
       "gHZ6IALEblJ0ujiPpyoaLTN4A9sKQNkGvWMPoy6uLTn/GsnxsM0CtXqQdCsoQIq28qtDCULYLcgv\n",
       "JN0tDQ2ziHAy9zO2vqxV5TSVKBELwdKCJva4hST5R7HPVVb40vt9n5RnhP7SzgT5IRSKq5CtYfuf\n",
       "7ysSc58QIS0WajCiY3d7Ysy517GVS1FRZNXIb/Q2bKt+gW2IMSCDiY3HFkXePuC/R4KsDXpfPlou\n",
       "xsEwIAMlFd6IKWwYN6gam69tvR0XuUGjfCK6Ova0QUp3ZUspbfxLDvEqRwu5/OQOp1y+fkIncDxv\n",
       "z+4aFP7X3tn72J+Kp8/YZSuHZoI75nXg89zNcRz7cGO2XHF8//zDXIewoSbNzL/Pw58JtLWmlkah\n",
       "D4VWv375BLhdMk/ZxsNgOfduWmmo+QfNG44Q7WsQg9LXFe9EZnIFIESFjlR0Zob8S4srMA9aobun\n",
       "5XRkAEYsUUsLxr847bFrR4+EKwAkyLu/N1ZZVYwsdhygmbTrG7ZEE3or6KOjBe+XnaeXHTFRJ5RW\n",
       "r66aBefW/RstMFQbxBkAzF7/uKwdsiVkjAfuNkQ2o7KRHdLx+5MPbarHbIhjhlQl6fAi6FDPxfSM\n",
       "bG+NsLUp9WTpd3XBdSQg5FLWZg+jkq/dH37QShUVqCs+CBTqgA168eX+3I1D3Ru85Fp3QwVO+gs4\n",
       "uDKpZZrE62mADjkAAAMNQZuvSeEKUmUwIJ///rUqgASmx6iYAAQmhg1iJiRpWGXRROSP+8Z1Bg62\n",
       "GMht1AeUrI+9OFlavK5FKLE8cwOp8WBhJ1zvz0AsaX7BlVSWCVlB+7Fgl3/fRAIskXQrj7Ri51rs\n",
       "grvW4lxmOGoNBaUY/oOnF/nLzyKz1jz5gm2WbsfPNpazNQBPbVrtT97HUn4zXtjJhQ/wWrULiixF\n",
       "bOM6u9LFyGoxKXdmuQKZL/DBi7X+J91z8RyBMdHq1FxzTAJjeqA/nHkeOz3YYrxh6fn9ZvN5UF6k\n",
       "TcEz/belCH8NMPnFjfUBMZgoc09YVnfODDEQjRRw1I9cL3rkBAvxZMlv7ZDCf2wpREW3s7ripKa4\n",
       "i3d5ZQ3la4VIA1Wuhuf1rLrHbr+GnuU8ZNZhI3hdzuUlQ8y+nowGAm0unajOQYHack1TaMzXyJUg\n",
       "jfj3s0DgcY3fC1K3GGczh//PdMEFfCERBFOAqUzfNa2CxpMIqo92cb0Y6qbrew1ShHNnszFFEggG\n",
       "v7muqFyzwTmuSKPbAnKo8OvsTc9wBGs0O+ztlzWYDSG61KwRat5t/N+57sSHJXlccKOhxIioJmv2\n",
       "g1i4qfEO6TU/7mMFpU1rL9u3+LpQUgwlk0ByF/OIzv1eY9gVisN0uXdP1T1su6beujLFEfngxDnA\n",
       "i1yxUbdPmdEtJjqa90cqBE5MkbO762X/nxYcrOEaN9TWNDFUmn7PnuI2LQimEusmZMd59cKmT6RT\n",
       "ssppU3eWFAeeWQvNjQRLOZbzrrkHkNKTHK+lSeKbHiguzFPumL5Rl9VAb2Kycyu4kTyRne6qS5RZ\n",
       "VvBgAQsTUGQqjTvpBohHjHvgPQsml0HmoebGyjQttlCvfL2UgzM8WWh2Mk1jVoYMZWQVOPkSPAV0\n",
       "OLac29EVXLZLqxMB7lAoYFY5GN34vkrxi3eU5BlbZyh6p8xhWhFgrBfpsOlpzMv+UogtIp3lvnLB\n",
       "tQ5KArXwsyeXp53hV8CREsO/UKeAe7ME2xeyVnZ5EbIw64qtsfiGwmmrGoThF6edjjXO/sQxYQAA\n",
       "AeBBm9BJ4Q6JlMCCn/7WjLABOEqIz9IgA4qaJxZ1BUIDwpQsHeTycrRAhrRbapRrbI59BUwmwlCT\n",
       "lcpj5w87Bu90P7W6aov7xvCLQrsHRcH0yCRu5DLk7RG/wF1CtweW4u6gXXgAVdgICxjkdjkR4qH2\n",
       "uQtv6Rl9hYWthJ4QjaAUbtRdF1ejQC6lZDizgy6eFHd4yMdwGwU4tZEikWubYokWjlaHZ2g/3PGA\n",
       "UNJywybR9GGGXaR4feV9/p9Ddu3J99wOOZrvzIvTy23GnsdlAIZLk0gosUM5KBEI8pa6Af+XfD2B\n",
       "ivqe6zQ5JizEyQUdipzMTvSgZDKmt03H+skJUta/VBFGkNp4U5OiK13gG1J6cFIe/otk+ZbJdWzG\n",
       "jQtg+QtkxmAjRYOKFvUxsbCaJW2eIzGEVIV9Oep84QF7Xt2RYfA3rCTDW3VLuUUncRFCi/TS6S8T\n",
       "6EXUUj+NfHOD0KNNPedpCWsLuiM73LzTLuDKmWpXgbg0+3NLLG66L9BOAEI7Ld/MLB9AkJXbWtsH\n",
       "POGxJLyEFcVhrdmq7axsXuDtEm0QPFemd4ZCnVyuWkp4bxXeTHCpNMpF9NtqUcScTqRpzQb11evY\n",
       "ejAn6MDPHIp1EyFghRJquMkC2UR0ANVeDUgAAAN8QZv0SeEPJlMCCn/+1oywATHnQDlRHf9WgAvo\n",
       "6/OrKYz6LAj9XRKFL7WUOlAVI6hC9DVGQ83r2nZsW6Nzb2PPgc6LYd71jvA/tg/1nLjBwwNBdD/N\n",
       "9Rxqv1llvCEP1yRfrAAGcrTngUsB8JYT03Qjd39YQxHqAMMfNzXGO8EgPY2H8pR+2WwrUqEzjUPR\n",
       "/7/AiG/K3S5QtHc+Uh2mUMPS9jmXTKKo6Glm/ryPzsvW+SkcW1mryrV5FpcRhezXHpp6CS9wPnO2\n",
       "mN7UgW89W/PzMFlt/1cMOYOlgibVjKZxqnSf5N++j9KjRrOtzxRb0FvP79tSQQN75BFBdJoiE2Xe\n",
       "lbCBxeQnze2AVenv1uNfmJEuhzzTXe3F5FGyqEIhIVBIlo1+V+af0VyFN0zjgL/nrXLknv1ifHet\n",
       "iOQTQqMzOjKD8ze8BbXAJToHA7Zjz251fupit8Ye9n/t0GBECVuN1JNdpzTyg3ZytUU6ZhpsEP14\n",
       "fHfe8QwtAGIvBu6InrrKsOomMJwDu8gaRbb3zis53tVpr/FIbcUfi+jm4dodyOWv/OG7IZpfgJ/f\n",
       "lLIE9KbdBSJMjYXx9QMOJhtnnL+VqMZHSFe+epVl/+bxN4Qp+RD141YhIoUK7tH+aDNMTil7ZlLT\n",
       "AAAk4o8+x1S5Q9LCvep+loVDhDLR+h0eHhhnnrfcFPDs3Dheby7ACamrdMFCKF/l6ljFPgXad2W1\n",
       "0O+ZEvccgHVKAmCMATTWW4ctqJ1JgBUaJPiL6wCi7wauQsNWKYJXovHStDorXB2lgrsI8KwwhlaQ\n",
       "LfIvt8ThY13iBcBA2dQojZIQgibWQzbQjk5L87nLZaD29QqDwkXUrxUL7FQne4K8mfl0N+bS3EkL\n",
       "4+X6gzz801KE8VZn060wWgo1YivM7N0cYSaABBR2YRn0wbnxDNbPW1r0UhZeI5k+1RY6DtUxzSYN\n",
       "+30JgXg0whaiZKuDIrRypUeTK+coCEzdRvWxctXnOT8y+qdUpt9OWGE9G19Hbzy1xJQ7QWAAAQi8\n",
       "UjN3v6MooGVgsiZg6ZJ9jJxByIK0CkAGr0UmvoTbfrnu22cRlE4CKdjwLhq1IxXKalyMZ6+q7edw\n",
       "Vok5DT5lQeLnalpzQ6R5VPUXbQMi9gO5VZF3Ulzc9VX/cDDn2eTVjaYogjUZOvJjJjJH6nK2rlcT\n",
       "PjOew3WsWtBdwAAAAWVBnhJFETwR/wADDWwTOHRuAE6UGLCCG8/ztvQAWfW9gIXT0o6EQxSe5GWT\n",
       "MlAObyv5JI4C4ghLzKatzsNeVDexAptI9STdpktah3tf9euAI+H2kfql6EQwKhN0kk9eyWPwNOsB\n",
       "77ZMMk7FMY1oJ9+1eQhjoLgJqTfmkr/mS7RnwJ2HRplTU3EXGiwwOyyLTgL8EW8NWVBA0VwmSjYZ\n",
       "ksInv2yO8P9av2UIHBk8SocEmS/g/yj2ZpVuYVqoPHdiRaglZhWbt/h1CKLh1dD079FMEwPzAtLg\n",
       "RQP5hW2uHLfjDTJa8jrh5Ipuw74zbFYDXE9RFGmCHptJVaQxNe4dr7qAaFFK9LZbAbE9oY56oCSt\n",
       "I4FHGtlZ28Nv2CTcG+KGDQ1Gwy+fwoeWeNYTwUySmb+aI3r9zA5rHiJPmDl6vDGhsMojLRakvl84\n",
       "md2ruTdN5AnNDNo72lm24K2SCIVVHd8wVCIQHpEAAAEAAZ4xdEEPAAXDz7U8WlfrwACdavwQp3gu\n",
       "w6zW+Hd6zj8ZgFdZUi82Sdbn9ivXb4yNdGBWYYaxaIA/5jX/cOrGLKTgrOWd5uMTUv5peIPvmOz2\n",
       "NQJzJ3ZH/I0PDo9/XLsC9UXLPeA78iLgRknmuk0mlOX5PSQuncTCYjByCQeH8kXzk1Iv88gwZVvg\n",
       "U0wzZn4oH8mqc/WU/f4o8OsJNT8wp1B0XFlyzeF1RR5MJvUJjtGGjq/zp8DBOqLFrQN+LUGcEFBx\n",
       "Ec5uJPeE5mEIFD4u6KibCoOzDuUsbet5q9DBUhF6zqfMztNz8BPxYgSENpcjj+OMsv0gqA/P+6A0\n",
       "yZOxDwAAAQUBnjNqQQ8ABdBx9QABNNtQItIodHcQnISUKrNxlwnzexZOAKAEkgPEdP3FzqZU3gJ/\n",
       "iXMA6XTdjQpbKAlb+aWMV5JaqngGIdtOeCjUrxaYlPqhYPhQssr7z03SHBwQ4AK2cC1MOtdEurkI\n",
       "+Bmk9sAbimfYd6kP3xZof5St38gTpRtR30uAI2mD1k6Ta2+CpueSft+VZyteJFwZ9nvsh3QwdGaV\n",
       "GqVfukaM7kvYRVXELm02tkHFcS9wnRa/5UZNxJkgQL81BSW7dgo+3OO9IvHr6t64r5Y6i+YQsa3d\n",
       "pT4iIsZ0eMTfPW6gBRMCd2WsAlc3752ZjhSt05Bu9WBeP99vVRb5A9IAAALYQZo4SahBaJlMCCf/\n",
       "/rUqgASloZ2XwAbO9Dk3Y2MLX+KOlKJSCJLVwZP9IhRPswEjR4YR3F7ZW99Ctjz+dnAgeZipevWt\n",
       "bNoa0F1qwFIq/NzCKgyWaN3+sAAZuiR9VZct/CWJakBeemsMMmV2s8mLQ9gTUJSKYQB6sicujOp9\n",
       "htEeWJVNJkQIkHVdNfHnkVQThLgdmzYmU+4WxNMg2O9/+qIUg3mp5R/q+qactUCJGj0sxAbiAAt0\n",
       "nzNdgMm/MH9vVqq8pYr4sNE+lZttCcJOoRyZMqTnzobjnpvr0n6jOf/5adFFfuIuS5bhIKZEPlUE\n",
       "3Km2B0EsWPL/DnnH7acHr3JPtB9EQ+Y9z/o7VOO1WikryyRN53S3Bjmir3PcDxeZ05pUacCzbnjy\n",
       "9pgsnRqWZixKVb5Js5D6WnSySdqZdejDt1VwWaxosbTbvs1KiTwFbFUPqdeURhbdD0JRnqIlkdRz\n",
       "nsvrrzaJUIvh+23ZoEKxRN3crREnZNVL6+2WGSbgeqsC5hioSDSP7mwuYZHcOJdJPT+5YYLKk+et\n",
       "uWEQyOqudXw76ytyjwaO4oVLOSruSwyz4tP04mgAeo71cw8N3ob0LYINiUV+p9ZueD+NcfEMJHLX\n",
       "XoO6TJNFtOKjNCIEyiQ5M6ZUoUZIB9WpISq3v9sJsnqheD4zPREuGqt5ONAl+TbB+jMAZ0rJDviq\n",
       "UrC7Su/YB2dfBZiuNysw3isUDe8MGO+EcYHwN7PT9OeImyQGejx1otkM905YRTIGErjREnREGcjk\n",
       "hVWsS6tW6LAr25tjYN/rmALaPQcpA+G7wkJuiKI400/JLSC+an42s1/aJMTtwSIvdP4uhX/RVTBw\n",
       "44DzQayElmgsxKqJif+KqEFWw+HZoynzEUY5bdTo10NLchNzaADKDuAs2Gni6n0leFRygyxg+Bu2\n",
       "2Q5FQDg4oAnymZIyAPWKzSd25goB2sifkSnX34dmwTMAAAEmQZ5WRREsEf8AAwWwyToMPU7wAJpr\n",
       "KX5/2uXufvqNJx5U1uFE9iZRmeWC2jaUzy933zkcsju5/b/c2AGk8DrKkLOXibtHVJoTsHfupoj8\n",
       "8PjCfQ89DDI8zpjTPYnYztk7zzor2FvmyENKfqVjkG2sH75SxhcLo5Dpj2Y4l3xSsh45NYMr6RO3\n",
       "cEfB9LrcjcPQ/ugXxKARwiCwenOjN8k4SLMo/6xJ208KcCMT1p26qIFMGAfun9oP91UZzBSoJF8H\n",
       "7ZYAiogQC/0t0cF6qGq+KIDG4K5yaVI84WaNWhpyBm7k+zj1zrQf5OoPHzFUuKro9aKsEv1pKxUk\n",
       "LF97hJSYhBOTJjnQ4LtdfJlftuog+x9x+gZO93ns4f3ovmlzAu3fNpQEgE3AAAABQQGedXRBDwAF\n",
       "v9wRjWAAH8IVOFdOQL3djiAOIpawhDWGIvFuk+CpbCKNdBUL1aqmZwL+38IFx0Mey0PcSeBjmrVk\n",
       "hip//M3agqsdUqm+PCndRZitgvmOwjNfyuLenNKnTaE7WaCjkmv+xRUxd8NsUN2ppDbKQLLhIvxR\n",
       "dsTW5BvldAQrqNDnXm7x3YoHzYo9qncH3DS/RCCgeJI12gXJxGa0PVwoDVuDeL0zzLb4IbrxGidJ\n",
       "juftLD/eQLEgGGBeJCEsj95D6/9S1+yaZa/qgG5OzmlQlHiIxlBh4qZnQjLSBQ/kH9RYU+42dJ2d\n",
       "piKRMB/Gx/WKb5Dlc8mhTUxUgW/q2IEqno8hWl/mrO9gIzkPGhIx/537pJFQYNINhP2jv7FK1SUp\n",
       "fdoqNPs581eXRNRGpkxJ86UzdBjqOvu5loBvQQAAAJkBnndqQQ8ABcPPtTjZP/dCAE022SovKSp7\n",
       "Wzh7OD+tiVlVrHp37zNCUiyBgbFMmEgUeyS3vP3ajkKvzd5HIa7HKHznPGJaHf16FWcAijT/ias5\n",
       "h5NM9b4T7qR4uKdScMc6+7snla6+nYdPqsBHbIUho+WUVKA3s2ii+zyAkhJFetwck+2HUfZWMoui\n",
       "vIDIFUxUcJ1fdn+gAV8AAAGfQZp5SahBbJlMCCf//rUqgAShA0E91BfABs9/gGw79gUHNl/fEEnD\n",
       "2DNYhN8mPKcH0hcJsDpypJE5HuCVV5CbfZInfmp1oBsZhjA2GVIw4MpU7BC0ey/7gwBSo2HTA3af\n",
       "8CnMYJuX/FXLXb4yPlnmX2uWOPvEta4denssAajZ6f9s5K6ilLHMcSJcNl4zkq6GXXhGqNrhAhxQ\n",
       "ekmn3F0nZ91nPQcpJRqqh1x1XzLlCgviPdJSqZpWONd6b++y4ZiNJ2yCrBcSXw4XEh/EEESPbqV4\n",
       "cCDAzONfTI+R4Jw+ybjGnPs3WP9V9H18ryW/msdXpsYINSbeVMf3t4hSnTr8qpSd2tcKAKxEAm6O\n",
       "C5RJJelZLwRhE2orlNyEcvGb4pbADxiYjvMvl+01Z+TGKISwxkTWa20dx/UuCu6jj9akC5QMBvV7\n",
       "P1+o2E1X5PSXjF9l09r83iS7Snghqqvpq3FBsbl9PQfUF8bcREuGJSODhX6vmZEqCl/mTn8WANDc\n",
       "MzCIWC30gmPqGuBVj99FxDDSnNzkDgN3FHIh/oXwjL4BJwAAAp5Bmp1J4QpSZTAgl//+tSqABMDY\n",
       "dD97ADifVgkexwisYhGeDRadkEblfh8ydH9wMcNFyMTbJ6dNAfN07xa9jLbHSXE3Omkk9Fpk+H5r\n",
       "KBcU8agM4pV+ZNRE8ZfhVSSDsjsnBUoqGpDjKqusAHTztbIwNSWh0bi1dLl2QqpM/KgZLYo95K7A\n",
       "YPCfWFi/5gFC6MZNwV0W0R0dO+3hSc1A7x2KLJkexgBJf2txoeSdU0OZQh0DoXCuY2h+9ALSttXC\n",
       "LoojzM8BYwJNmaUt5DmjZxunuaElJ1WH4tloksYe8Uf6Bdr7F52UWn+ikDe8mKfupwyDJ5Tv+x5l\n",
       "j2Wbb7qjTtpy7TYIXagNkMDc7qVFk+oQNeaSa92neRswydOXGwoBJOl/e31fqr7RN/7jRMzlwpxt\n",
       "qC8a2SQ344zrwIi4IPAqdWMyHygkg+s9+UOSd/Q13rcdBv3sjsgK7R5XzSDnnDFGf0rBhwqcn/gY\n",
       "n9aYawzV9eJls3fSXJacOZhZMO8d6XyVzhY8mYf0j075FWX5aGdGTWfgaD6Kb626YN+tndoe4r3n\n",
       "tKVC2I2HMo4PGVrBm78cwoP7COn+1+8gziu/lQ/7d0L04r1ktqC1sP3RtDMMHSf8H7OnPPlGaauA\n",
       "clupI7pRd5xQKXeMwSdA25Hm3VMlg7//8vX55+K9gR0QKb71p3RSPUV7LOEBkH2+GEmZ35jS5rtx\n",
       "fc4lJAeBf6fDe+S61fD+7HN1hU9w6L9yNZM+i94RD7gjn47D5XG/dWt0l9b/ItPAEtUQuiWbm/gu\n",
       "dVrTcMI58bzv/gIaU7NbXFk126M9ghCMqboGCWjtjNZjhfl6YkpGNaJLw0i/ya2CnzTaJME5iOfn\n",
       "le6Qx/WamBxtN6jYjnI1Q4XlDDgjAMaBAAABE0Geu0U0TBH/AAMi1tCA8QAGy9dEHjKc4My2pVvg\n",
       "XUE6u7+Rcd+c1nQcD/04RX7vAYuBqesSJFKYEWNyxyDdG/Dvoj5W/mjP2yn5yS+RXqF5d8l/iPVf\n",
       "/QLaHL6faYPol/+uugcIrXtxiuf060r9wkX3hWAx60l7q8pLuVDetVRbRYsfjnBQUXKWcNiCXZri\n",
       "b0jxJBHozQgtfCiF5qj9Lgl33TJetLS67C1NErkVVypnymrV+9WkLt0oSTjCgOuFf94zK13piYK0\n",
       "I8Qy7IT8R1PV1RAn4bLotnCFjVJPmowbNG5DH+Mfyf+PnFKAx/YPgYxtettKGTC5rxLE8LS4dRbQ\n",
       "E4Xqo3uiQ6P0oN03h3WaABMwAAABAQGe2nRBDwAF9k+jnFhf+LMAAE61ISNIusuL+vznE3izcNNc\n",
       "wzDAbqmKglP2Ijyjfj271g0+doOJ20i8ubRtwIfCaWTWkgJXvKxsF4vukOaQUs+LmjACeuPQ6DU6\n",
       "oLvUt61KceEnz7TtZ9kJ9EdjS2poYuV0Z3vSZ74ZIwns3t8+NaOg8YOSPnP2QD66LjVAhzeWJ6uM\n",
       "BWVLUI1qoB2rBglHPj9om4AXeQ6FU00jLHrSTT5LoHR/NAm4cxmlx997k9n+6Pwt2sekUuBqmVsC\n",
       "1IUus59/J6aXYmpzcv8YP9f4tj1KqTepJbgNuxblNd3jtjrrqT9yppcF2HtGjfJ+AAbNAAAA1gGe\n",
       "3GpBDwAFvtbGmdV9I5WAAH8IxhuVu/jKkyklh84KhrtU0TWqbCAUXlUDmcY3Jo51acR3Pq3/LKKh\n",
       "auR1Dv7LAdjlomeeN/NX7x3/4z3tjEO+XA/i+QW5prT0wcXILWSQJotzH4IBP/f3arMDo8lV+wSj\n",
       "U5eShsGfHBU9pSPFLsA6Ba5mktqFQAo/rzstCwZT/0OF3jMIircJmbHq82GUd+FmJDhvyYumUQjv\n",
       "anhoReALVwCzwMwER4pk/BEs2BkWI9+1jh5EDrrg39rcp5IwbbIADUkAAAFoQZreSahBaJlMCCf/\n",
       "/rUqgASnl4DkP/B/ggAGjqWYxR2Lr3XVcghGgjXMYQSu/bjaFv5sQxEBHAq0FNM7k+meGI3ZLh53\n",
       "9zjQFuvuc30K8NfGThMUibl1376IBFfMqPksX9aoTi2yewSlTvbfOR9HyA7B5JqpXszmZNmMdpCq\n",
       "tLx8kG89G1glwfr+pxTKwJKyah+h/vONa8mxpK7Jzg684/3PlRfA81j9WN0nSHY/6OjmyubOTrsq\n",
       "2iGhhSVD2OJyiPfd0Lpfx76L0wkxlyVPkc3WbNRdYWQ/i8rW0a7eC1+ymOspOkpq5I//gJVUlcpP\n",
       "hLoPg026He66arH/2wjS9Kmd/rpOrwCPGTv21ZpHxBaHIVIbgMMwiqqwO+wiNuA9z0+7ss2VX+Q5\n",
       "vHpxAhPtUXxkK1zXM56X9sRBA/MJ52J2IrIosAcB9V5rCJ9fPU56BK1Y9UbZLvN0NXP0xRMR05g2\n",
       "r45D0Az4AAABekGa/0nhClJlMCCf//61KoAEoOJt4VV8CoAIgMU0/i9d2H9s1vc4rkRXo1b6Gnp9\n",
       "paZyz9piTP4ePCrvppNA3HYgfh2rOmqHesAAZ2AiH2fpZ8qmNhx9MlWkSwFHZn7mgHwrM0Qvl/8d\n",
       "afbrs976BkbQkS4JhqFMvjH+GK1zCGE+V82clW5/kqQJs2C+INXn62PyzvuUBOhmUJ7FoOcDDYRF\n",
       "wImH96qB54RWcr6cGTraoPiBMLDf7Cr0ieAWlKWmmzbsJQQcy7s9eG2wJdWMrfsAKR8S3xOxyIzq\n",
       "UsPpsTfj6fZX2yA1lIrgMYO3Zdk+x/uCDNeKhI14F17MZnUCp/O9x5rblfDex54v04VkHHC4RuqB\n",
       "DG+IijP//2AOfs/FNqk58WZIJA3af19iQKmqrdGLpi7D3GAo4F53lvpzcwQE3A316kStq86FvG2O\n",
       "K/kxm/BEyj4sOscMGOB/novcjRk0Dg94voyqsGkFiyW461k/dgfC8vAsaXAtoAAAAVlBmwBJ4Q6J\n",
       "lMCCf/61KoAEoQ+3kt0kABCfmZu9DbqzybiH+V9IJkeFQpdcroCEn9wLtSKfUnCzQwCN6WxmlUQ+\n",
       "uuhgnB8WBVpQRP/WSD8QEe1GWxaAQE2/uYBjzY3KlIKapwdNGq1b3NMKUFFAeMWCj/jI16Q+vcmt\n",
       "0EBtg0F97hXqLsSLKYsjVoctpb3MrvllJos1X/eHXuxdqHYGD+VndmkN45JYeOG7ttQZERav1aqv\n",
       "f/vQgfnrj5D6sHgHk40jE8GaRXL9gFW7ucKjB0cAie9Q5PjeM8Fr2LGamYFHmRhPbLRTic2jaWGE\n",
       "1sfwtoNWm66J7g8r/pCnTULaIp7kSvCfgYGd2heIElhUPGcFm+J76Ugv5C6B4nqujUC+aka/BVkH\n",
       "TRWLD6d7GPW/e4lg6nsIV5PmD78oIrBR8NCzjWuuVDNt/Yvy1dxpXDDPL67mwktN6s1wLaEAAAJi\n",
       "QZskSeEPJlMCCP/+tSqABMC3HpHTHRGgAfwkrK9hV1E0xSBdX4NhhDHhH3e63l8SBpxlVyKJe4UK\n",
       "zxAhKjJ4psUU2gn/CxmBTLc3yp6E7AR6nVM1YGoIxwB2YSW0Ak/SJU0ra2G43d8vFN2YP0ZVMRkh\n",
       "WCN88Wg994U+zOf0noNe7vSFoDWXqJQroIWYUmuDJZQIgcXi9Yh5NDAqP2+ZKY7xAReWzWTUDp/j\n",
       "AICxKkpDBOdhnk62cs2JSP18ChVw/Ksd0miCaOf+HOWP7IOkjKli9gB3YqVfe3bq4lDV9rs0RK6G\n",
       "vJIGvQjkRmMoZkenOdWR+VA7L++EvRgSSc5zPCZ7JiA/v2gzSuPSULRTEagttp3f/aW4R0Iemc+J\n",
       "md2yjkR8NteZi7yqtST0OMcKltyEmncgHQLJTVLnYnBNePsl3KIplwoJBW1SkDPJyifgimRTVlhW\n",
       "rTke3Vbo3GF68ojEP2Bpg442ZbWNYRZ8LwRubBdHXmLPNGH3jD/96auKVomQrk3ETht2qvwEafSp\n",
       "8FyRGNuShJSxZD2TOb4Q3EIr2psP9x4xMkiCdlzvRmlO6YHL8n/ucjjJ8yrSu6b9WgFK/Qz8TrMR\n",
       "THEWwSfuqdag8AkSynIQpOGaQm1IpwIqUTGgBNY3PBP9wsLgn2hL8XMF8a2pej8l0RhpEQ8DoLvi\n",
       "DOn84PW9tXRNNR9WbuScrP4nnLdNTM7aQN4XLx1TyDgQAmNuw6vYu5xipv7txeXwcFpd1eZ/91W0\n",
       "6OGyxWu2unf5my7h0uCAKdO+hAuYprrNGIuGMK6M39aukKBMVAAk4AAAAXtBn0JFETwR/wADIyRd\n",
       "/RLIAAfwmNKQrxagNo5fSfPgsHx4cZni1gc8CEqm91bO3gvTtDIsSHrHnLIgbFOAy37Ssg1Fusl1\n",
       "ZNLDZ/smXtfrPPPq2fuiddr6pkuAYZr7UokFDkNJ8t0lEFb7EJOm1FqTxWPHQIIJYLDaNdRlp/Mu\n",
       "aH9yH8cKfRdHi7D3jwkeGJYElWQ8QnnDcOvbMDVa0yAs8RgjjaYLtkXuwp28AFfDqWRuqNENoPy7\n",
       "99nRsiZbsxvpEsRGnC+cVfXY5iYPBC3Y5fmoE5j1nXYYD52BcN+ptCWj/H4VZhPejkVJgE2F+F06\n",
       "sOPeQeXt5kzhiyuFKrzqwjJVLLm87WVfQE2dEQz5MiRT3QB70Rvg2GJ8piYcw4tIqWyRj/oXE4rS\n",
       "TyK2N+Fb/sxuZbD9wxPsyUDz65jXk/ucyNHhH2mg8bexUUODviZ4TVRJv+e7bkcKBgs3wdYDk2in\n",
       "JHawvnlFjZi141JvUitkRRyGewHAAGhBAAABDQGfYXRBDwAFw8+1OLJ7/uhACabZeLPKrXfYJx3h\n",
       "D4gslJj44tSelP0RlrwM/xSydmw4eOvOxVZnHC7JzXhS4dS8XvtzgwK4dB98x2expevibXdYL6x+\n",
       "2jilVjyLNo4RksJkI6e6RpO3GDqnjRjbmTUO5piPlumVlNy+nd1c83NVi0GTBvSEgLWHGUeX4m1p\n",
       "v+P0QapGnEUe06ujY6oEcGueAVfJLgdyt3xTZp+3mIOpNSQisxMnVmvt4lWJ+Rdx/Z8O4NCtwgOd\n",
       "MTpJ/GydrR2pdzwM3C/fEkGtcJerQHeFjP9nKA6bsvfZ9uyrh2WhGUWzI/sUd9wMbtGDYeuuwiRC\n",
       "bmyylx6TIvnkAAd0AAABLgGfY2pBDwAF91SMFhxAAeMZVB41k+U7/Z2DgYhvr0QlYSAF98GfhH0s\n",
       "yr3qj/TcKUMpNzUQk6v8BUtvc1gj/URM9xhWvM6Qd2tF/ugBVvwA9KSEPSx2KYgoh8BdKaxWFqer\n",
       "DLZoBRD3M6TF/0HbbHyA/DtogNaChmPJ+CU9/gMAbgl0gQdIqm8PRflGE5Uavh5PB3Dd06KEk3HT\n",
       "DA45hPv3kY5vBpxdj7WW0MM96YrOw8EINUecDCm1XMOTqb8+vq80ZyglFZOVxurijG4WQ3kPYXUJ\n",
       "ZJCWTX2ZZZ9nKrfU1nT14gQrr+jEyudEw6uHbi4EV/xZsNc1DXgo/I3UTdao129HBUvujW11kf/Y\n",
       "XdGdtB1tWunQouJOjozm55HlGXP3UGUPf2s7NnvsAAGBAAABNkGbZUmoQWiZTAgh//6qVQAJTy1r\n",
       "ZudH7oQAmm2yVIawD+nSFu3LBf6iVmE+mAEDzxvQYjqXWqIGPU4HfTp0F/xrd9FEAHa+wToxBA3n\n",
       "JyudjSZAMPgPiruWyHgegjwSKtWD+9fXFbm1iEteUIdj+R/HiStzLXMueylbZItZPoko1dllY8za\n",
       "A6hGkI9jiZum/XBR5kJ1hhL5BBtqv4zB0DUm5PyPgH0ouASSZgtrT2Unc1bSGylI6k6wqIrM+IB4\n",
       "++ojxQ6GHB69wXafoXilAPYY7jizq9epHr+7KQqSxGT7uoKNMh1rqWIBwXo0XFzqNQi9hMZnZQER\n",
       "DN/glvl3UzN0RljvVD/y7B7Qp/OOYu4ddiDWV/OUdntLwtbsPeCY223Y2B7osRY45J5RE7BmxQIw\n",
       "MgAAhYEAAAenbW9vdgAAAGxtdmhkAAAAAAAAAAAAAAAAAAAD6AAAY5wAAQAAAQAAAAAAAAAAAAAA\n",
       "AAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
       "AAAAAAAAAgAABtF0cmFrAAAAXHRraGQAAAADAAAAAAAAAAAAAAABAAAAAAAAY5wAAAAAAAAAAAAA\n",
       "AAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAAAkAAAAJAAAAAAAAkZWR0\n",
       "cwAAABxlbHN0AAAAAAAAAAEAAGOcAAAgAAABAAAAAAZJbWRpYQAAACBtZGhkAAAAAAAAAAAAAAAA\n",
       "AABAAAAGYABVxAAAAAAALWhkbHIAAAAAAAAAAHZpZGUAAAAAAAAAAAAAAABWaWRlb0hhbmRsZXIA\n",
       "AAAF9G1pbmYAAAAUdm1oZAAAAAEAAAAAAAAAAAAAACRkaW5mAAAAHGRyZWYAAAAAAAAAAQAAAAx1\n",
       "cmwgAAAAAQAABbRzdGJsAAAAtHN0c2QAAAAAAAAAAQAAAKRhdmMxAAAAAAAAAAEAAAAAAAAAAAAA\n",
       "AAAAAAAAAkACQABIAAAASAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA\n",
       "GP//AAAAMmF2Y0MBZAAW/+EAGWdkABas2UCQEmhAAAADAEAAAAMCA8WLZYABAAZo6+PLIsAAAAAc\n",
       "dXVpZGtoQPJfJE/FujmlG88DI/MAAAAAAAAAGHN0dHMAAAAAAAAAAQAAAGYAABAAAAAAFHN0c3MA\n",
       "AAAAAAAAAQAAAAEAAALwY3R0cwAAAAAAAABcAAAAAQAAIAAAAAABAABQAAAAAAEAACAAAAAAAQAA\n",
       "AAAAAAABAAAQAAAAAAEAAFAAAAAAAQAAIAAAAAABAAAAAAAAAAEAABAAAAAAAQAAUAAAAAABAAAg\n",
       "AAAAAAEAAAAAAAAAAQAAEAAAAAABAABQAAAAAAEAACAAAAAAAQAAAAAAAAABAAAQAAAAAAEAAFAA\n",
       "AAAAAQAAIAAAAAABAAAAAAAAAAEAABAAAAAAAQAAIAAAAAABAABQAAAAAAEAACAAAAAAAQAAAAAA\n",
       "AAABAAAQAAAAAAEAAFAAAAAAAQAAIAAAAAABAAAAAAAAAAEAABAAAAAABwAAIAAAAAABAABQAAAA\n",
       "AAEAACAAAAAAAQAAAAAAAAABAAAQAAAAAAEAAFAAAAAAAQAAIAAAAAABAAAAAAAAAAEAABAAAAAA\n",
       "AQAAUAAAAAABAAAgAAAAAAEAAAAAAAAAAQAAEAAAAAABAABQAAAAAAEAACAAAAAAAQAAAAAAAAAB\n",
       "AAAQAAAAAAEAAFAAAAAAAQAAIAAAAAABAAAAAAAAAAEAABAAAAAAAQAAUAAAAAABAAAgAAAAAAEA\n",
       "AAAAAAAAAQAAEAAAAAABAABQAAAAAAEAACAAAAAAAQAAAAAAAAABAAAQAAAAAAEAAFAAAAAAAQAA\n",
       "IAAAAAABAAAAAAAAAAEAABAAAAAAAQAAIAAAAAABAABQAAAAAAEAACAAAAAAAQAAAAAAAAABAAAQ\n",
       "AAAAAAEAAFAAAAAAAQAAIAAAAAABAAAAAAAAAAEAABAAAAAAAwAAIAAAAAABAABQAAAAAAEAACAA\n",
       "AAAAAQAAAAAAAAABAAAQAAAAAAEAAFAAAAAAAQAAIAAAAAABAAAAAAAAAAEAABAAAAAAAQAAIAAA\n",
       "AAABAABQAAAAAAEAACAAAAAAAQAAAAAAAAABAAAQAAAAAAMAACAAAAAAAQAAUAAAAAABAAAgAAAA\n",
       "AAEAAAAAAAAAAQAAEAAAAAABAAAgAAAAABxzdHNjAAAAAAAAAAEAAAABAAAAZgAAAAEAAAGsc3Rz\n",
       "egAAAAAAAAAAAAAAZgAAID8AAAGtAAACRwAAAQ8AAAB6AAAEEwAAAq8AAAGZAAADTwAABfwAAATL\n",
       "AAAC9QAAA1sAAAU9AAAEjgAAAzUAAAMTAAAGJQAABIgAAANPAAABxAAAA1wAAAURAAADMgAAAjgA\n",
       "AAIGAAAErgAAAiYAAAHuAAABhwAAAkoAAAIEAAAB6wAAAc4AAAFWAAABXwAAAcUAAAKrAAABMQAA\n",
       "AR4AAADJAAACVQAAAXgAAADpAAABBAAAAucAAAFTAAABFgAAANoAAANpAAABmwAAAPIAAAD4AAAB\n",
       "3AAAASMAAAD7AAABDwAAAwgAAAFAAAAAxQAAAMwAAAMCAAABdQAAANgAAADaAAAEegAAAZkAAAFd\n",
       "AAABCwAAAb0AAANhAAABwwAAAQgAAAD1AAACegAAAYIAAAElAAABAAAAAm0AAAMRAAAB5AAAA4AA\n",
       "AAFpAAABBAAAAQkAAALcAAABKgAAAUUAAACdAAABowAAAqIAAAEXAAABBQAAANoAAAFsAAABfgAA\n",
       "AV0AAAJmAAABfwAAAREAAAEyAAABOgAAABRzdGNvAAAAAAAAAAEAAAAsAAAAYnVkdGEAAABabWV0\n",
       "YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAAAAAAAAAAAAAtaWxzdAAAACWpdG9vAAAA\n",
       "HWRhdGEAAAABAAAAAExhdmY1Ny41Ni4xMDE=\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x7f295db1d240>"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAHwCAYAAAAo4iyJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQZIkVRkWJElSlWFBkiRV/X93vc6CFbp/qQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f295bcaf3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (8,8))\n",
    "def animate30(i):\n",
    "    ax.clear()\n",
    "    ax.plot(0.2,list_for_U24W[i],'o', color='purple' )\n",
    "    ax.plot(0.2,list_for_U48W[i],'o', color='green')\n",
    "    ax.plot(0.2,list_for_U72W[i],'o', color='blue')\n",
    "    ax.plot(0.1,list_for_U24W1[i],'o', color='purple')\n",
    "    ax.plot(0.1,list_for_U48W1[i],'o', color='green')\n",
    "    ax.plot(0.1,list_for_U72W1[i],'o', color='blue')\n",
    "    ax.plot(0.3,list_for_U24W3[i],'o', color='purple', label = '24 hours, winter')\n",
    "    ax.plot(0.3,list_for_U48W3[i],'o', color='green', label = '12 hours, winter')\n",
    "    ax.plot(0.3,list_for_U72W3[i],'o', color='blue', label = '40 seconds, winter')\n",
    "    ax.plot(0.2,list_for_U24S[i],'o', color='yellow' )\n",
    "    ax.plot(0.2,list_for_U48S[i],'o', color='orange')\n",
    "    ax.plot(0.2,list_for_U72S[i],'o', color='red')\n",
    "    ax.plot(0.1,list_for_U24S1[i],'o', color='yellow', label = '24 hours, summer')\n",
    "    ax.plot(0.1,list_for_U48S1[i],'o', color='orange', label = '12 hours, summer')\n",
    "    ax.plot(0.1,list_for_U72S1[i],'o', color='red', label = '40 seconds, summer')\n",
    "    ax.plot(0.3,list_for_U24S3[i],'o', color='yellow')\n",
    "    ax.plot(0.3,list_for_U48S3[i],'o', color='orange')\n",
    "    ax.plot(0.3,list_for_U72S3[i],'o', color='red')\n",
    "    ax.set_title('hour = %03d'%(i)) \n",
    "    ax.set_ylabel('delta PE')\n",
    "    ax.set_xlabel('vtau')\n",
    "    ax.grid('on')\n",
    "    ax.set_ylim(220000, 230000)\n",
    "    return ax\n",
    "interval = 0.25#in seconds\n",
    "ani40 = animation.FuncAnimation(fig,animate30,frames=102,interval=interval*1e+3, repeat=False)\n",
    "ani40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "222.5"
      ]
     },
     "execution_count": 436,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc.Dataset('/data/vdo/MEOPAR/NEMO-forcing/grid/lake_bathy201702.nc').variables['Bathymetry'][200,136]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}