Fixed use of # in ```python block

If not indented, gets treated as a header by nbconvert.

04-One-Dimensional-Kalman-Filters.ipynb

@@ -443,7 +443,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAAEWCAYAAABlmwYuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7158748>"
+       "<matplotlib.figure.Figure at 0x796e080>"
       ]
      },
      "metadata": {},
@@ -483,9 +483,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8Ddf/P/DX3Cxu9lUkiESCBCFRYgkSS1G72HdJbbXV\nkoagKpSWRi1FW62WqMZaFT4ltZVYYq+d0BBLJUFkQfbk/P7IL/N1e29ISHKD1/Px8HjcmTlz5pw7\nc+O+75l5H0kIIUBERERERERapdB2A4iIiIiIiIjBGRERERERUbnA4IyIiIiIiKgcYHBGRERERERU\nDjA4IyIiIiIiKgcYnBEREREREZUDDM6Iyrng4GAoFAocOnRI200hohJw8OBBKBQKzJkzR9tNISKi\ncobBGVE5J0mS/I9KnhAC7dq1g0KhgEKhQFZWVrHr+OOPP9C6dWuYmZnBxMQETZs2xbp160qhtfQ2\n4We6ZEVFRaFz586wtLSEoaEh3N3dsWzZMuTl5RW7rqtXr6Jv376wsbGBgYEBXF1dERwcjIyMjFJo\nORHR/5E4CTVR+ZaYmIjExETY29vDwMBA28156yxduhSBgYHQ19dHRkYG0tPToa+vX+T9V65ciQkT\nJsDa2hr9+vWDvr4+tmzZgnv37iEgIAAhISGl2Hp6E6Wnp+Pu3buwtraGpaWltpvzVggPD0evXr1g\naGiIfv36wdLSEjt27EB0dDR69+6NzZs3F7mukydPok2bNsjJyUHv3r1hb2+P/fv34/Tp02jevDn2\n799frL8RRETFweCMiN5Zly9fhqenJyZPnoywsDDcuXOnWMFZbGwsXF1dYWJigjNnzqBatWoAgOTk\nZHh6eiImJgbHjh1D06ZNS7MbRO+01NRU1KhRA0+ePMHRo0fx3nvvAQAyMzPRpk0bREVFYcOGDejX\nr99L68rNzUW9evUQHR2N8PBwdOnSBUD+CHvfvn3x22+/4csvv8S0adNKtU9E9O7ibY1EhTh+/DgU\nCgW6detWaJlGjRpBR0cHt2/fBgBkZ2djxYoV6NSpExwcHKBUKmFpaYn3338ff/zxh8Y6HB0d5dvp\ngoODUbNmTVSoUAGTJ08G8H/PnEVGRqrst337dgwePBi1atWCsbExjI2N0bBhw0Jv4/Hz85OfXdu6\ndSsaN24MIyMjWFlZYcCAAbh//77G9iUlJWHWrFmoV68ejI2NYWZmhnr16iEwMBDJyckqZVNSUvDZ\nZ5/Bzc0NRkZGMDU1RcuWLbF169bC32gtycrKkt+/4OBgvMrvVD///DOysrIwfvx4OTADAHNzc8yY\nMQMA8P333792W5+/RmbMmAFHR0colUrUrFkT8+bNQ3Z2tto+CoUC1atXR2pqKiZNmgQHBwfo6elh\n2bJlcplt27bJt2MaGBigTp06mD17Np49e6axHaV5Laxbtw7NmzeXbyOzt7dH27ZtsXr1apVyN2/e\nxOjRo1GzZk0YGhrC0tISderUwfDhw3H37t0Xvo8ZGRmwsLCAtbW1xvcMAD755BMoFAqV21Jf57P2\nyy+/wNPTE0ZGRmjQoAGAwp85O3PmDCZOnAh3d3dYWVnBwMAAtWrVwpQpU5CUlKR2nLVr18r1nDt3\nDp07d4a5uTmMjIzQqlUrREVFaexjbm4ufvjhB7Rs2RLm5uYwNDSEs7Mz/Pz8cPnyZZWyeXl5WL16\nNZo3by5fJ/Xq1cOCBQsKfQ/L2tatW/Ho0SP0799fDswAoEKFCpg3bx4A4LvvvitSXYcOHcK1a9fg\n7e0tB2ZA/i2oX331FYCS+UwTERVKEFGhateuLfT09MSDBw/Utl26dElIkiRat24tr4uLixM6OjrC\n29tbjBw5UsyYMUP4+fkJKysrIUmSWLVqlVo9Dg4OQpIk0blzZ1G5cmUxfPhwMW3aNPH9998LIYSY\nPXu2kCRJHDp0SGU/V1dXUbduXTF06FAxffp0MXbsWOHi4iIkSRIDBw5UO86wYcOEJEmib9++QqlU\nin79+ompU6cKb29vIUmSqF27tsjMzFTZ5+bNm3L7GjZsKAICAkRAQIDo2rWrMDQ0FOfPn5fL3rt3\nT9SsWVNIkiR8fHzElClTxKhRo0SVKlWEJEkiODi4eG9+KZs2bZrQ19eX++Dg4CAUCoXae/AizZs3\nF5IkiePHj6tti4uLE5IkiWrVqr12WwvOQdeuXUWVKlXExIkTxSeffCKcnZ2FJEmiR48eavtIkiTs\n7OzEe++9J2rUqCHGjBkjAgICxG+//SaEEGLWrFlCkiRhbW0tPvroIxEUFCTee+89IUmS8PDwEE+e\nPFGprzSvhenTpwtJkoSTk5MYN26cmDlzphg+fLho2LCh8Pb2lsvdv39fWFpaCj09PdGtWzcxbdo0\nMXnyZNGzZ09hbm4u9u/f/9L38qOPPhKSJIlt27apbcvJyRG2trbCxMREPHv2TF7/qp+1Ll26CAMD\nA9G/f38xffp0ERAQIIQQ4q+//hKSJIk5c+ao7Dd69GhRqVIl0a9fP/HJJ5+IKVOmyJ9PV1dXtXOy\nZs0a+TiGhobi/fffF4GBgaJv375CR0dHGBgYiOjoaJV9MjMzRbt27eRrc8yYMWL69Oli4MCBwtbW\nVixbtkwum52dLbp27Soff8yYMWLy5MmiQYMGQpIk8f7774ucnJyXvuelbdCgQUKSJLFx40a1bTk5\nOcLQ0FDo6+uLrKysl9Y1c+ZMIUmSWLBggcbttWrVEgqFQsTExLx2u4mINGFwRvQCCxcuFJIkiSVL\nlqhtCwwMFJIkidDQUHldZmam+Pfff9XKpqSkCDc3N2FpaSnS09NVthV84XV3dxeJiYlq+xYWnN28\neVOtbF5envzF8L8BQ8F6MzMzcenSJZVtAwcOFJIkic2bN6usb9asmZAkScybN09jn54+fSovt23b\nVujo6Kh9QUpNTRXvvfeeUCgUKl/gX+T3338Xs2fPLvK/4gZ+kZGRQkdHR6VfrxKcWVtbC4VCIR4/\nfqxxu5GRkVAoFGrnvLgKrhEXFxeRnJwsr09PTxeNGzcWkiSJDRs2qOwjSZKQJEm0b99e7fhRUVFC\nkiRhb28v4uLiVLYVXCfjx49XWV+a14KlpaWoWrWqSEtLU6v70aNH8utvvvlGSJKkEkAUyMrKUmlD\nYU6cOCEkSRLdu3dX2/bHH38ISZKEv7+/yvpX/awZGxtrvOYLC85u374t8vLy1Mr/9NNPGgOGguDs\nv3+HhBBi1apVQpIkMXbsWJX1BYFwly5d1K71nJwckZCQIC9//vnnQpIkMWHCBJV25eXliVGjRglJ\nksTy5cvV2qvJ33//XazP9OzZs0VsbGyR6m7UqJGQJEmcPXtW4/a6desKhUIhrl279tK6evfuXWjw\nLoQQnTt3FpIkiYiIiCK1jYiouBicEb3A/fv3ha6urvDw8FBZn5OTI+zs7NR+YX+Rr7/+WkiSJCIj\nI1XWF3zx3rFjh8b9CgvOCnPmzBkhSZL4/PPPVdYXfGGcNWuW2j4FXxYDAwPldadPn5aDxpe5cOGC\nkCRJ9OrVS+P2HTt2CEmSxNSpU4vUBz8/P/lLZ1H+KRSKItUrRH4g4ejoKJo0aSJyc3Pl9a8SnOnp\n6QmFQqFSz/MqV64sFAqFiI+PL3KdmhRcI+vXr1fbtmfPHiFJkmjXrp3K+oL35cKFC2r7jBgxQkiS\nJL777ju1bQkJCcLAwECYmJiI7OxsIUTpXwtWVlbCycnppe/98uXLCx2BLo46deoIfX198fDhQ5X1\nffr0KdHP2pQpUzTuV1hwVpi8vDxhamoq2rZtq7K+IDhr2bKl2j7Z2dlCV1dXeHp6yutycnKEmZmZ\nMDQ0FPfu3XvhMXNzc4W1tbWwtbXVeH0nJSUJhUIhmjRpUqQ+rF27ttif6aKeh5o1a75wNMvLy6vQ\nEe7/KhhVLGwUtuCHLE2jdEREJUFX27dVEpVndnZ2aNeuHSIiInD+/Hm4u7sDAPbs2YP4+Hj4+fnB\n0NBQZZ/Lly8jJCQEkZGRiI+PV0u9rOnZLkmS0Lhx42K1LTExESEhIdi1axdu3ryJtLQ0le3//vuv\nxv0aNWqktq5q1aoAoPJcy/HjxwEA7du3f2lbjh49CiD/OaPg4GC17Q8fPgSQn566KNasWYM1a9YU\nqWxxTZgwAQ8ePEBERAQUijfnsVtJkuDj46O2vmXLlgCAc+fOqW1TKpWoV6+e2vqzZ88CANq0aaO2\nzcbGBvXq1cOpU6dw48YN1K5du9SvhUGDBmH58uWoXbs2+vTpgxYtWsDLy0stk2G3bt0wY8YMjB8/\nHhEREWjfvj2aNWuG+vXrFyst/bBhwxAUFIRff/0VEydOBJB/7e/YsQNOTk7w9vZWKf+qn7Xifqaz\ns7OxatUqbNy4EVeuXEFqaqrKM23F+Uzr6uqiUqVKKp/pa9euITU1FY0aNUKVKlVe2Jbr168jMTER\nzs7OmDt3rsYySqUSV65cKUrXMGzYMAwbNqxIZYmI3mUMzohewt/fHxEREQgNDcXixYsBAKGhoQDy\nH/x/3vHjx9GmTRvk5eWhbdu26NGjB0xNTaFQKPD3338jPDwcmZmZGo9TqVKlIrepIBtgbGwsmjRp\nAj8/P1haWkJXVxdJSUlYtmxZoccxNzdXW6erm/+nIDc3V+UYAF76JQ7I//IKAPv378f+/fs1lpEk\nqdBEE2Vl586d+OWXX/D111/DxcVFYxlRjMQgZmZmePz4MVJSUmBhYaG2PSUlRS5XEjRdI0qlEqam\npvKxnmdjY6OxnpSUFEiSBFtbW43b7ezsAPzfNVDa18KSJUtQo0YNrF27FiEhIfjqq6+gUCjQtm1b\nhISEoH79+gCAatWq4dSpU5gzZw4iIiKwfft2APnvy4QJExAUFFSkgHvIkCGYOXMmQLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71e4c50>"
+       "<matplotlib.figure.Figure at 0x7a29fd0>"
       ]
      },
      "metadata": {},
@@ -535,9 +535,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlYlNUXB/DvOwPDAAIJiIKKgiIo7koqmvuSC+aSmjuu\nWe6VC2ouqZlrrmVZKf1KszSX3NLMDdKU3HcNzBUXRAVl5/z+uMwrI8MyMAsw5/M8PC/vfoHXcc7c\ne8+RiIjAGGOMMcYYY8ysFOZuAGOMMcYYY4wxDs4YY4wxxhhjrFDg4IwxxhhjjDHGCgEOzhhjjDHG\nGGOsEODgjDHGGGOMMcYKAQ7OGGOMMcYYY6wQ4OCMMROaOXMmFAoFDh06ZO6mMMaKkObNm0Oh4P+y\nGWOsuONXesZMSJIk+YsV3O3btzF37lz06NEDlStXhkKhgEKhwNWrV3M8LyEhATNnzoSvry9sbW1R\nunRp9OrVC5cvX87xXkOGDIGHhwfUajW8vLwwfvx4PHnyRO92x8bGYvz48ahYsSLUajXKli2LIUOG\n4M6dO3pfi1kGft0wvPy8DmQnLS0Ny5YtQ82aNWFnZwcXFxd07NgRR48eNULLGWPFmcRFqBkznZiY\nGMTExKB8+fKwtbU1d3OKvK1bt6Jbt25QKBTw8vJCTEwMnj59ikuXLqFKlSo6z0lKSkLr1q0RHh6O\ngIAAtGzZEjdv3sQvv/wClUqFP//8E6+//rrWOZGRkQgMDMSDBw/QpUsX+Pn54e+//8aBAwfg6+uL\n8PBwODs756nNMTExaNy4Ma5evYpWrVohICAAly5dwrZt2+Dm5oajR4/Cy8urwL8bVrzcunULCQkJ\n2T7XTD/5eR3IDhGhV69e2LRpE/z8/BAUFISYmBhs3LgRiYmJ2Lx5Mzp37mzkn4gxVmwQY4wVUbdv\n36awsDCKi4sjIqJmzZqRJEl05cqVbM/59NNPSZIk6tmzp9b2bdu2kSRJ5O/vT+np6Vr72rZtS5Ik\n0cqVK7W2f/DBByRJEo0YMSLPbR4+fDhJkkQfffSR1vbly5eTJEn05ptv5vlajLH8yc/rQHbWr19P\nkiRRkyZNKCkpSd5+4sQJsrGxITc3N/k1ijHGcsPBGbMIR48eJUmSKCgoKNtj6tWrRwqFgm7cuEFE\nRMnJybRixQpq3749eXp6ko2NDZUsWZJatWpFO3bs0HmNChUqkCRJlJSURDNmzKDKlSuTSqWicePG\nERHRjBkzSJIkOnTokNZ5W7Zsob59+5KPjw/Z29uTvb091a1bl5YuXUppaWlZ7jNw4ECSJIkOHjxI\nv/zyCwUEBJCdnR05OzvTO++8Q3fu3NHZvsePH9O0adOoevXqZG9vT46OjlS9enX66KOPKDY2VuvY\nJ0+e0Mcff0z+/v5kZ2dHDg4O1KRJE/rll1+y/0WbWW7BWXp6Onl6emr9nTNr2rQpSZJEBw4ckLdd\nv36dJEkib2/vLMfHxcWRvb09lShRgp4/f55r++Li4sjW1pYcHBwoPj4+S9s0z09kZGSu18rJ2rVr\nSZIkmjlzJoWFhVHLli3J0dGRHB0dqX379nTy5Mks52iezXXr1tHOnTvpjTfeIAcHBypZsqR8THR0\nNI0ePZq8vLzIxsaGXFxcqFOnTnT48OFs27J3714KCgoiNzc3srGxoXLlylHHjh3pt99+y3Lsn3/+\nSUFBQeTq6koqlYoqVKhA77//PkVHR2c59t9//6Xhw4dT5cqVydbWlkqWLElVq1alwYMH082bN7WO\nDQ0NpcDAQCpVqhSp1WoqV64ctWzZktasWZPr73LevHkkSRItXrxY5/6YmBhSqVRaz8edO3do1qxZ\nFBgYSKVLlyaVSkUeHh7Uu3dvunDhQpZrREVFkSRJ1Lx5c7pz5w4NGjSIypQpQ0qlkrZt20ZEL5/t\nzAryGpWamkpz586lypUrk42NDZUvX54mTZpEycnJOs+7fPkyDR48mCpWrEg2NjZUqlQpCgwMpCVL\nlmQ59tq1azRkyBC5TaVKlaKuXbvqfO7MIT+vAzl544035NfjVw0YMIAkSaK1a9cWsNWMMUvBwRmz\nGFWrViVra2t68OBBln3nz58nSZKoRYsW8rZ79+6RUqmkpk2b0rBhw2jKlCkUHBxMLi4uJEkSffXV\nV1muo3nj07FjR/Lw8KAhQ4bQpEmTaPXq1USUfXDm5+dH/v7+NGDAAAoJCaH333+ffH19SZIk6tOn\nT5b7aIKznj17klqtpl69etHEiRPlNxVVq1bV+gSXiCgyMlJuX7169ejDDz+kDz/8kIKCgsjOzo7O\nnDkjH3v79m3y8fEhSZKoWbNm9MEHH9Dw4cOpbNmy8pv+wii34OzatWskSRL5+fnp3K95I/7xxx/L\n29asWZNj75imV23//v25tm/fvn059o69++67JEkSffvtt7leKyea4Kx9+/ZkY2NDQUFBNHXqVOrR\nowcplUqys7Ojv/76S+sczbPZsWNHsrKyoi5dutDkyZNp0KBBRER048YNKleunPzvRPPvwc7OjpRK\nJa1bty5LO6ZPn06SJJGDgwP179+fpk6dSoMHD6Zq1apR165dtY797LPPSJIkcnV1pYEDB9KkSZPo\nzTffJEmSqFy5cnT79m352Lt375KzszNZW1tT586dadKkSTR+/Hjq1q0bvfbaa1p/i5CQEDm4Hjly\nJE2dOpWGDBlC9erVo6ZNm+b6u7xz5w4plUqqWbOmzv0rV64kSZJo1qxZ8rYNGzaQnZ0dderUiUaN\nGkWTJ0+mbt26kUqlInt7ezp16pTWNTTBWY0aNahChQpUq1YtGjt2LI0ePZqOHDlCROLZVigUWucV\n5DWqR48e5O7uTkOGDKHx48dTlSpVSJIk+e+d2Y4dO8jW1pYUCgV16NCBpkyZQiNHjqSmTZuSi4uL\n1rH79++nEiVKkEqlom7dutGkSZOob9++ZGtrS2q1mn7//fdcf+fGlp/XgewkJCSQUqmkEiVK6Pwg\nbcOGDSRJEg0YMKDA7WaMWQYrcw+rZMxUgoODMXnyZPz4448YN26c1r7Q0FD5GA1nZ2fcvHkTHh4e\nWsc+e/YMjRs3RkhICAYMGAC1Wp3lXrdv38a5c+fyPA9p165dWeYZEREGDRqE77//HmPGjEGDBg2y\nnPf7778jIiIC/v7+8ra+fftiw4YN2LZtG3r06KG1/ebNm5g9ezamTp2a5WdSKpXy+sCBAxEZGYkN\nGzagV69e8va4uDg0b94cn3zyCbp27YqaNWvm+rNt3boVp0+fzv2XkEGSJMyYMSPPx+vjypUrAJDt\nvJ3KlSsDAK5du5bnc3x8fLBv3z5cu3YNLVu2NPj9C2LPnj1YtWoV3nvvPXnbr7/+irfffhuDBw/G\npUuXdJ6za9cutG3bVmv7iBEjcOfOHcyaNQsff/yxvP3DDz9EgwYNMGLECLRu3Rply5YFAOzduxez\nZ89GxYoVceTIEXm7RubkJ4cPH0ZISAgaNWqE3bt3w9HRUd73ww8/YMCAARg7diw2bdoEANi0aRNi\nY2OxdOlSjBkzRuu6KSkpSE5Olte/+uorlC1bFufPn88yz/Px48c5/wIBeHh4oG3bttizZw9Onz6N\n2rVra+0PDQ2FQqHAwIED5W2tWrXCgwcPYG9vr3Xs2bNn5deO3bt3Z7nX+fPnMWDAAHz33Xd5ysxY\nkNeoqKgoXLx4Ea+99hoAYM6cOahVqxa+//57zJs3D6VLlwYAPHr0CH369EFaWhr279+P5s2ba10n\n89/x6dOn6NmzJ9RqNU6cOAE/Pz953+XLl/H6669j0KBBiIqKgkqlyvXnW7p0qV4Jd7y8vLT+Dtkx\n5L/Df//9F+np6fD29tb5N9NcK7ckRYwxJjN3dMiYqdy9e5esrKyodu3aWttTU1PJ3d2dHBwc8jQ0\njYho8eLFJElSluFcmk+lt2/frvO87HrOsvPPP/+QJEk0e/Zsre2anjNdn+weOHCAJEmiCRMmyNsi\nIiJIkiSqVatWrvc8e/YsSZJE3bt317l/+/btJEkSTZw4MU8/Q3BwMEmSlOevV3sH9JFbz9mPP/5I\nkiRR//79de7fu3dvlp6tYcOG5dibNWXKFJIkiT777LNc2zd37twcP5H/+uuv9Z7Dpoum56xKlSo6\n9wcGBpIkSRQeHi5v0zyb3bp1y3L87du3SZIk8vT0pJSUlCz7P/zwQ5IkiebNmydv69SpE0mSRJs2\nbcq1vd26dSNJkujcuXM699epU4esrKzkoaCa+Xm6eoZe5eLiQt7e3ll6kvWxceNGkiSJxo4dq7X9\nwoULWXrccxMUFERqtZpSU1PlbZqeM7VaTQ8fPtR5nq6es5zk9hqlq6dX8wzs3LlT3rZo0SKSJIlG\njRqV6z01f5cVK1bo3D9+/HiSJIl27dqVp5+hYsWKer125PXvkJ/XgeyEh4eTJEn0xhtv6Nx/9erV\nHHvpGGPsVdxzxiyGu7s72rRpgz179uDMmTOoVasWAPEJf3R0NIKDg2FnZ6d1zoULF7Bw4UIcPnwY\n0dHRSExM1Np/9+7dLPeRJCnPWb40YmJisHDhQuzatQuRkZF48eKF1v7sUqzXr18/y7Zy5coBEOna\nNY4dOwYAWXpDdAkPDwcgPgWfOXNmlv0PHz4EAJ29LrqsXbsWa9euzdOxzLDeeOMNndubNm2Ko0eP\n4vTp0wgMDNTap+vZPXnyJACgcePGsLLK+t9G69atsWTJEpw6dUreduzYMUiShPbt2+fazvDwcFhZ\nWWHz5s1y71hmSUlJSEtLw5UrV1C3bl289dZbmDp1KkaNGoU9e/agbdu2aNSoEWrWrJkl3Xzfvn2x\nYsUKVK1aFT169ECTJk0QGBiY515tAOjSpQtee+01rF+/HosWLZJ/B7p63DV27tyJ1Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFNf7NvB7FliWqoIiWBEbioo1Kipgb2CwdwGNmMSC\nJRbUiCYaEzV2E40mSL5GojFGYtdYgqCoxN4bagCxICIqnfP+wbvzcwUUDTAI9+e6vOScac/u7M7u\ns+fMOZIQQoCIiIiIiIgUpVI6ACIiIiIiImJyRkREREREVCQwOSMiIiIiIioCmJwREREREREVAUzO\niIiIiIiIigAmZ0REREREREUAkzOiQjR79myoVCr8/fffSodCRO8RV1dXqFT8yCYiKu54pScqRJIk\nyf/ov4uKisK8efPQt29f1KhRAyqVCiqVCteuXXvtdklJSZg9ezZq164NIyMjlC9fHv3798eVK1de\ne6wRI0agQoUK0Gg0qFatGiZMmIAnT568ddzx8fGYMGECbG1todFoULFiRYwYMQLR0dFvvS8qGXjd\nyH/vch3ITUZGBpYtW4YGDRrA2NgYlpaW6N69O44dO1YAkRNRcSZxEmqiwhMXF4e4uDhUrlwZRkZG\nSofz3tu2bRt69eoFlUqFatWqIS4uDgkJCbh8+TJq1aqV4zYpKSno0KEDwsLC0KxZM7Rr1w53797F\nb7/9BrVajYMHD+KDDz7Q2ebWrVtwcnLCgwcP4OHhAXt7exw/fhyHDh1C7dq1ERYWBgsLizzFHBcX\nh1atWuHatWto3749mjVrhsuXLyM4OBhWVlY4duwYqlWr9p+fGype/v33XyQlJeX6uqa38y7XgdwI\nIdC/f39s2bIF9vb2cHd3R1xcHDZt2oTk5GT8/vvv6NGjRwE/IiIqNgQR0XsqKipKhIaGisTERCGE\nEC4uLkKSJHH16tVct/nqq6+EJEmiX79+OvXBwcFCkiTh4OAgMjMzdZZ16tRJSJIkVq5cqVM/ceJE\nIUmS+Pjjj/Mcs4+Pj5AkSXz22Wc69cuXLxeSJIkuXbrkeV9E9G7e5TqQm40bNwpJkkTr1q1FSkqK\nXH/y5ElhaGgorKys5GsUEdGbMDmjEuHYsWNCkiTh7u6e6zpNmjQRKpVK3L59WwghRGpqqlixYoXo\n2rWrqFKlijA0NBRlypQR7du3Fzt27MhxH1WrVhWSJImUlBTh7+8vatSoIdRqtRg/frwQQgh/f38h\nSZL4+++/dbb7448/xODBg0XNmjWFiYmJMDExEY0bNxZLly4VGRkZ2Y7j6ekpJEkShw8fFr/99pto\n1qyZMDY2FhYWFmLAgAEiOjo6x/geP34sZs6cKerVqydMTEyEubm5qFevnvjss89EfHy8zrpPnjwR\nn3/+uXBwcBDGxsbCzMxMtG7dWvz222+5P9EKe1NylpmZKapUqaJznl/m7OwsJEkShw4dkutu3Lgh\nJEkSdnZ22dZPTEwUJiYmwtTUVDx//vyN8SUmJgojIyNhZmYmnj17li027evn1q1bb9zX6wQEBAhJ\nksTs2bNFaGioaNeunTA3Nxfm5uaia9eu4tSpU9m20b42169fL3bu3CnatGkjzMzMRJkyZeR1YmNj\nxdixY0W1atWEoaGhsLS0FG5ubiIkJCTXWPbt2yfc3d2FlZWVMDQ0FJUqVRLdu3cX27dvz7buwYMH\nhbu7uyhbtqxQq9WiatWq4tNPPxWxsbHZ1r1586bw8fERNWrUEEZGRqJMmTKiTp06Yvjw4eLu3bs6\n6wYGBgonJydRrlw5odFoRKVKlUS7du3E2rVr3/hczp8/X0iSJL799tscl8fFxQm1Wq3z+oiOjhZz\n5swRTk5Oonz58kKtVosKFSqIgQMHiosXL2bbR2RkpJAkSbi6uoro6Gjh7e0trK2thZ6enggODhZC\n/N9r+2X/5RqVnp4u5s2bJ2rUqCEMDQ1F5cqVxdSpU0VqamqO2125ckUMHz5c2NraCkNDQ1GuXDnh\n5OQkFi9enG3d69evixEjRsgxlStXTvTs2TPH150S3uU68Dpt2rSRr8evGjZsmJAkSQQEBPzHqImo\npGByRiVGnTp1hIGBgXjw4EG2ZRcuXBCSJIm2bdvKdffu3RN6enrC2dlZjBw5UkyfPl14eXkJS0tL\nIUmSWLNmTbb9aL/4dO/eXVSoUEGMGDFCTJ06VaxevVoIkXtyZm9vLxwcHMSwYcOEn5+f+PTTT0Xt\n2rWFJEli0KBB2Y6jTc769esnNBqN6N+/v5gyZYr8paJOnTo6v+AKIcStW7fk+Jo0aSImTZokJk2a\nJNzd3YWxsbE4e/asvG5UVJSoWbOmkCRJuLi4iIkTJwofHx9RsWJF+Ut/UfSm5Oz69etCkiRhb2+f\n43LtF/HPP/9crlu7du1rW8e0rWoHDhx4Y3z79+9/bevYqFGjhCRJ4scff3zjvl5Hm5x17dpVGBoa\nCnd3dzFjxgzRt29foaenJ4yNjcXRo0d1ttG+Nrt37y709fWFh4eHmDZtmvD29hZCCHH79m1RqVIl\n+X2ifT8YGxsLPT09sX79+mxxzJo1S0iSJMzMzMTQoUPFjBkzxPDhw0XdunVFz549ddb9+uuvhSRJ\nomzZssLT01NMnTpVdOnSRUiSJCpVqiSioqLkdWNiYoSFhYUwMDAQPXr0EFOnThUTJkwQvXr1EqVL\nl9Y5F35+fnJyPXr0aDFjxgwxYsQI0aRJE+Hs7PzG5zI6Olro6emJBg0a5Lh85cqVQpIkMWfOHLku\nKChIGBsbCzc3NzFmzBgxbdo00atXL6FWq4WJiYk4ffq0zj60yVn9+vVF1apVhaOjo/D19RVjx44V\nR44cEUJkvbZVKpXOdv/lGtW3b19hY2MjRowYISZMmCBq1aolJEmSz/fLduzYIYyMjIRKpRLdunUT\n06dPF6NHjxbOzs7C0tJSZ90DBw4IU1NToVarRa9evcTUqVPF4MGDhZGRkdBoNGLv3r1vfM4L2rtc\nB3KTlJQk9PT0hKmpaY4/pAUFBQlJksSwYcP+c9xEVDLoK92tkqiweHl5Ydq0afjll18wfvx4nWWB\ngYHyOloWFha4e/cuKlSooLPu06dP0apVK/j5+WHYsGHQaDTZjhUVFYXz58/n+T6kXbt2ZbvPSAgB\nb29v/Pzzzxg3bhyaN2+ebbu9e/ciIiICDg4Oct3gwYMRFBSE4OBg9O3bV6f+7t27+PLLLzFjxoxs\nj0lPT08ue3p64tatWwgKCkL//v3l+sTERLi6uuKLL75Az5490aBBgzc+tm3btuHMmTNvfhL+P0mS\n4O/vn+f138bVq1cBINf7dmrUqAEAuH79ep63qVmzJvbv34/r16+jXbt2+X78/2LPnj1YtWoVPvnk\nE7lu69at6NOnD4YPH47Lly/nuM2uXbvQqVMnnfqPP/4Y0dHRmDNnDj7//HO5ftKkSWjevDk+/vhj\ndOjQARUrVgQA7Nu3D19++SVsbW1x5MgRuV7r5cFPQkJC4Ofnh5YtW2L37t0wNzeXl23YsAHDhg2D\nr68vtmzZAgDYsmUL4uPjsXTpUowbN05nv2lpaUhNTZXLa9asQcWKFXHhwoVs93k+fvz49U8ggAoV\nKqBTp07Ys2cPzpw5g4YNG+osDwwMhEqlgqenp1zXvn17PHjwACYmJjrrnjt3Tr527N69O9uxLly4\ngGHDhuGnn37K08iM/+UaFRkZiUuXLqF06dIAgLlz58LR0RE///wz5s+fj/LlywMAHj16hEGDBiEj\nIwMHDhyAq6urzn5ePo8JCQno168fNBoNTp48CXt7e3nZlStX8MEHH8Db2xuRkZFQq9VvfHxLly59\nqwF3qlWrpnMecpOf78ObN28iMzMTdnZ2OZ4z7b7eNEgREZFM6eyQqLDExMQIfX190bBhQ5369PR0\nYWNjI8zMzPLUNU0IIb799lshSVK27lzaX6X//PPPHLfLreUsN//884+QJEl8+eWXOvXalrOcftk9\ndOiQkCRJTJ48Wa6LiIgQkiQJR0fHNx7z3LlzQpIk0bt37xyX//nnn0KSJDFlypQ8PQYvLy8hSVKe\n/73aOvA23tRy9ssvvwhJksTQoUNzXL5v375sLVsjR458bWvW9OnThSRJ4uuvv35jfPPmzXvtL/I/\n/PDDW9/DlhNty1mtWrVyXO7k5CQkSRJhYWFynfa12atXr2zrR0VFCUmSRJUqVURaWlq25ZMmTRKS\nJIn58+fLdW5ubkKSJLFly5Y3xturVy8hSZI4f/58jssbNWok9PX15a6g2vvzcmoZepWlpaWws7PL\n1pL8NjZt2iQkSRK+vr469RcvXszW4v4m7u7uQqPRiPT0dLlO23Km0WjEw4cPc9wup5az13nTNSqn\nll7ta2Dnzp1y3aJFi4QkSWLMmDFvPKb2vKxYsSLH5RMmTBCSJIldu3bl6THY2tq+1bUjr+fhXa4D\nuQkLCxOSJIk2bdrkuPzatWuvbaUjInoVW86oxLCxsUHHjh2xZ88enD17Fo6OjgCyfuGPjY2Fl5cX\njI2Ndba5ePEiFi5ciJCQEMTGxiI5OVlneUxMTLbjSJKU51G+tOLi4rBw4ULs2rULt27dwosXL3SW\n5zbEetOmTbPVVapUCUDWcO1a4eHhAJCtNSQnYWFhALJ+BZ89e3a25Q8fPgSAHFtdchIQEICAgIA8\nrUv5q02bNjnWOzs749ixYzhz5gycnJx0luX02j116hQAoFWrVtDXz/6x0aFDByxevBinT5+W68LD\nwyFJErp27frGOMPCwqCvr4/ff/9dbh17WUpKCjIyMnD16lU0btwYH374IWbMmIExY8Zgz5496NSp\nE1q2bIkGDRpkG25+8ODBWLFiBerUqYO+ffuidevWcHJyynOrNgB4eHigdOnS2LhxILine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7158400>"
+       "<matplotlib.figure.Figure at 0x79716a0>"
       ]
      },
      "metadata": {},
@@ -558,9 +558,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77a5160>"
+       "<matplotlib.figure.Figure at 0x8180be0>"
       ]
      },
      "metadata": {},
@@ -589,9 +589,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79182e8>"
+       "<matplotlib.figure.Figure at 0x84885f8>"
       ]
      },
      "metadata": {},
@@ -621,9 +621,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79189e8>"
+       "<matplotlib.figure.Figure at 0x8625cc0>"
       ]
      },
      "metadata": {},
@@ -659,7 +659,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a69f98>"
+       "<matplotlib.figure.Figure at 0x8780a58>"
       ]
      },
      "metadata": {},
@@ -697,7 +697,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x777d278>"
+       "<matplotlib.figure.Figure at 0x8490c50>"
       ]
      },
      "metadata": {},
@@ -790,7 +790,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71af860>"
+       "<matplotlib.figure.Figure at 0x83a7fd0>"
       ]
      },
      "metadata": {},
@@ -847,7 +847,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77b5b00>"
+       "<matplotlib.figure.Figure at 0x84b1908>"
       ]
      },
      "metadata": {},
@@ -900,7 +900,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78370f0>"
+       "<matplotlib.figure.Figure at 0x84fa6d8>"
       ]
      },
      "metadata": {},
@@ -990,7 +990,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7987dd8>"
+       "<matplotlib.figure.Figure at 0x86b0f98>"
       ]
      },
      "metadata": {},
@@ -1241,9 +1241,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7866dd8>"
+       "<matplotlib.figure.Figure at 0x8556160>"
       ]
      },
      "metadata": {},
@@ -1302,7 +1302,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77dbe48>"
+       "<matplotlib.figure.Figure at 0x86635f8>"
       ]
      },
      "metadata": {},
@@ -1439,9 +1439,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x799c5c0>"
+       "<matplotlib.figure.Figure at 0x83bbe80>"
       ]
      },
      "metadata": {},
@@ -1541,7 +1541,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a024a8>"
+       "<matplotlib.figure.Figure at 0x8673828>"
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      },
      "metadata": {},
@@ -1706,7 +1706,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77470f0>"
+       "<matplotlib.figure.Figure at 0x9f1a7f0>"
       ]
      },
      "metadata": {},
@@ -1736,7 +1736,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c57048>"
+       "<matplotlib.figure.Figure at 0x83aaa58>"
       ]
      },
      "metadata": {},
@@ -1860,9 +1860,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6YAAAEPCAYAAABRDtnkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd8Tff/wPHXOTfjJkQSRIyQ2Dtm1VYtmtpUqVn6pYqf\nokOrdlv6rdFSo+VbSq3SKi2lNlFba8YKCSFkIYlE5j3n98eJW6mVRJIbvJ+Ph0fuufeM90mOc8/7\nMxVd13WEEEIIIYQQQggbUW0dgBBCCCGEEEKIZ5skpkIIIYQQQgghbEoSUyGEEEIIIYQQNiWJqRBC\nCCGEEEIIm5LEVAghhBBCCCGETdnZOoDHFRMTY+sQhBBCCCGEyDJXV1dbhyCEzUmNqRBCCCGEEEII\nm5LEVAghhBBCCCGETT3xTXnvVqBAARRFsXUYIg87fPgwAHXr1rVxJOJJIdeMyAy5XkRmyTXz7JLu\naEKk91TVmM6dO9fWIQghhBBCCCGEyKSnKjHt16+frUMQQgghhBBCCJFJT1ViajZbbB2CEEIIIYQQ\nQohMeqoSU4ji6NGj6Lpu60CEEEIIIYQQQmTQQxNTf39/2rdvj5eXF6qqsnjx4nvWOXfuHJ07d8bd\n3Z18+fJRp04dzpw5k6GD//nnn9jZ2VG9evV7Plu9ejVVqlTBbDZTtWpV1q5d+8j9des2gI4dOxAW\nFpah4wshhBBCCCGEsL2HJqbx8fH4+voyc+ZMnJyc7hnxNjg4mEaNGlG2bFl27NhBQEAAkyZNIn/+\n/I888M2bN+nTpw8tWrS4Z7/79u3j9ddfp3fv3hw7doyePXvy2muvcfDgwYfuc+LEgZw7t4NixYo9\n8vhCCCGEEEIIIfIGRc9gu1cXFxfmzJlDnz59rO/16NEDk8nEkiVLMn3gzp07U6tWLTRN4+eff+bE\niRPWz7p160Z0dDSbNm2yvteyZUs8PDxYvnx5uv3cPdS2q+t5dN0EVEVR7DMdk3j6ybD8IrPkmhGZ\nIdeLyCy5Zp5d6Z9hXW0YiRB5Q5b7mGqaxvr166lcuTJ+fn4UKVKEevXqsWrVqkduO3fuXCIjIxkz\nZsx9+4Pu37+fVq1apXuvVatW7N27NwORpbJp08/MmDEjo6cihBBCCCGEEMKGspyYRkREEBcXx+TJ\nk/Hz82Pr1q10796dnj17smHDhgdud+LECT755BOWLl16TxPeO8LCwvD09Ez3nqenZ4b6jl66dI2P\nPppI4cKFMndCQgghhBBCCCFswi6rG2qaBkDHjh0ZPnw4AL6+vhw+fJjZs2fTunXre7ZJSkqiW7du\nTJs2DW9v76we+oFOnToFwLJlH6NpJa3NY4T4N7k2RGbJNSMyQ64XkVlyzTx7ypcvb+sQhMhTslxj\nWrhwYezs7KhSpUq69ytVqkRISMh9t7l27RpnzpyhX79+2NvbY29vz6effkpAQAD29vZs3boVgKJF\ni95TOxoeHk7RokUzFJuiKJhM1wGwWGRuUyGEEEIIIYTIy7JcY+rg4MBzzz13z9Qw586dw8fH577b\neHl5cfLkyXTvzZkzhy1btrB27VprLWqDBg3YsmUL77//vnW9LVu20KhRo4fGdHeSvH37YUaNmkz/\n/m8zYMCAzJyaeIrJIBMis+SaEZkh14vILLlmnl13D34khHhEYhofH09gYCBgNN29dOkSR48epVCh\nQpQsWZKRI0fStWtXmjRpQvPmzdmxYwcrV67k119/te6jT58+KIrC4sWL71vD6uHhgaOjY7r3hw0b\nRtOmTfniiy/o0KEDa9asYefOnezZsyfDJ2Zvb+LDD/9D+/b9MryNEEIIIYQQQojc99CmvIcOHaJ2\n7drUrl2bxMRExo8fT+3atRk/fjwAHTp0YP78+UybNg1fX1/mzJnDkiVLeOWVV6z7uHz5MpcvX37g\nMRRFuWcQpAYNGvDjjz+yaNEiatSowdKlS1m1ahXPPfdchk+sSZNadOr0PKqakOFthBBCCCGEEELk\nvgzPY5pX/Xse039LTXVj585gXnrppQeOAiyeHdJkSmSWXDMiM+R6EZkl18yzS+YxFSK9LPcxfVK0\naNGNW7csbN68lUKFZAoZIYQQQgghhMhrnvrEdMmSiRQvXg2TSZJSIYQQQgghhMiLsjxdzJOiZMmi\nqOpNdD3F1qEIIYQQQgghhLiPpz4xBUhJSeSHH75l0qRJtg5FCCGEEEIIIcS/PBOJaUhIGEuXLqdu\n3dq2DkUIIYQQQgghxL889X1MAcqVK8mWLbPRtFK2DkUIIYQQQgghxL88EzWm/4gkJSWFpKQkWwci\nhBBCCCGEECLNM5WYrl27gYoVy7N69WpbhyKEEDYXHx/PN998Y+swhBBCCCGercS0ePHCLFnyBd27\nd7d1KEIIYRO7d+8mNTUVXddxcFAYN24sQUGH0HULAFFRUTaOUAghhBDPomcqMa1fvzoNG5ZD1+Ns\nHYoQQuSK27dvk5CQAICu6wwbNpQ//1yHrp/Fzu40U6f+H3AVCODUqd34+vqSkiLTawkhhBAidz1T\niSmAokBi4hVWrVqFruu2DkcIIbLd3fe2vn37smrVEjTtCrp+irfeas2tWxdR1XgUBfr2bUeZMl4o\nSgp7927m//6vCyZTFLqeSmBgICdPnrThmQghhBDiWfFMjMp7N13XadCgAyVKlMHPz48CBQrYOiQh\nhMiwlJQU4uPjcXNzAyA2NpawsDAqVKgAwMyZM4mPj+Ojj4YAMbRpU4OTJ/9EVesA8Pbbrz5w3/37\nd0x7dRVdj+Djj8dQv35TqlWrlpOnJIQQQgjxLNaYKuzaNZ/167/FxcUFTdOk5lQIkWdMnz6dfv36\noes6uq6zY8cOWrdunbassXPndl59tTOaloSmJXLgwG4GDXoLTbuFpkVRvboHv/76I6p6AVWNok+f\nl5k6dVgWIkmhbNlCDBzYGE27gqYlM3HiREJDQ7P9nIUQQgghnrnEFMDNzQVFuY6uBzJr1nRGjhxp\n65CEEAJd1+nYsQlhYRfQ9dNAAHZ2IcTGXgOOAkfJl+8aZnMyinISRQmgUKFovL1dUdVzqOolmjYt\ny+bNs637VBQlS7EoisJ//zuU/PkdUNVwjhxZzfz53+Dunt9amCeFekIIIYTILs9kYgpGX1NNu8m8\ned/Qr18b64iUQgiRmzRN45133iEyMhRdD6RMGRMLF45GVRNQlCQaNKjE1q1zUBQNRdFp2LA6v/8+\nE0Ux7mO1a1di4cJx1v3Z2dnh6po/2+P08SnCTz9NxskpCF0PYffu7Xz66afW5HT16tUsXbrUuv72\n7dvZuHGjdfnIkSMcPHgw2+MSQognkQwyJ8S9ntnEFIwHuKNHl1Oligtwmhs3LtGsWTPi4+NtHZoQ\nj23r1q2EhYVZl//44w+uXbtmXV63bl26ZpkbN27kxo0buRqjMGomTaZk3ntvIKp6C0WBYsUKWz+3\ns7PDbHa0YYSGQoXcaNiwBoqioapRzJ79XyyWCHT9PJp2gePH/Tl79iCadglNu4y//+/s3bsZTQtD\n08L57bcf+e23VWjaTTQtjhUrVjBjxgxbn5YQQuSa8PBw6+vbt2/bMBIh8qZnOjEFcHCwB0BRkpg1\n63MqViyOk9MzNyaUeAp06tSJU6dOoGkxaNpNJk+eyMmT+9G0G2jadaZMmURAwH407TqaFsVXX03h\n9OmDaFokmhbJ1KmfM2/ePFufxjMhPj6ejRs3omnJ6HoQkyb1Zty4/rYOK1O+/348gwe3R1VjUdVo\nunVrTI8eTVHVKFQ1gpdeqswrr1RHVUNR1SvUrFmE+vW9UNUgFOUsK1bMp2BBBU2LQdc1fvzxRw4c\nOGDr0xJCiGxjsViwWIwWeZqmUatWLc6d+wtNu0SBAiE2jk6IvMc0YcKECbYO4nEkJSVZX5vNj1fb\n07ChLy1a1MHBIRZdV5g+/Vtu3bpFuXLlHjdMYWPh4eGEhYWRmJiIoig4OjoSFRWFq6urrUPLsmnT\nppGcnEypUp7o+g127dpGVFQgjRt7oSg3CQ29Sp06Xnh6qihKNOHh4dSpU5IiRRQUJYbr16OoXduL\nIkVUIJqYmGiGD++HorhmuV/i0+jq1asAFC9ePNv2GRkZySuv+NG8eSm8vPJjb29HwYJP1rVob29H\n/vzO1mUPD3c8PNyty97exShZsqh1uVIlHypU8AaMJsjPP1+FunXL4OBwC4iiV6+htGzZBG/vMoDC\n1q1bcXd3x8nJKbdOKVvkxPUinm5yzTy9WrduTbFihShdugAQxsWLQRQokELlykVQFAtJSQWt65rN\nZtsFKkQeIYnpXUwmEw4O9iiKzpUr5+jdewSjR7+Lq2vhTD+op6SkYDKZAKOU7L333sPT05OiRYs+\nYkuRXXRdt/7dli5dwvz5c2nZsiL29teZN28JW7fuok2bNjaOMuO2bNnC2bNnKVeuLLp+i927t7Bv\n3zbatCmPqsZSr14lGjeugb29UePfpEktihT550uvUaOa6RKHBg18rcuKolC/fjVUNQFdv8327X+x\nefMW6tatm7snmQGrV68mLi6OEiVKAOn/zjkhux4ak5OTiYmJwWy2I3/+aKpWLUKhQgXw9Cz46I2f\nQu7uBdLutwAWHBxMtG9fC0W5TkrKLRo2fIU33uiNq6s7iqIQEhLyRBQkSZIhMutpv2YmT56Mp6cn\nhQoVAoyCOTs7O+zsnvzWaZqmkZiYiL290fpu3LhxXLwYTI0aZdD1SIKDzxAScormzSugKEm0bt2I\nSpV8rNtLYipEes98U94H8fLy5MCBRZQqlYCuX+batSuMHTv2gevPmzePhIQEwHhQLlasGFFR19C0\nGOA6P/+8Ek9PJ3RdA+DQoUPW5h1PstTUVOvrPXv2MHHiROtyaGiozZrmHT9+HD8/vLine truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7744c50>"
+       "<matplotlib.figure.Figure at 0x8407048>"
       ]
      },
      "metadata": {},
@@ -1872,7 +1872,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a029e8>"
+       "<matplotlib.figure.Figure at 0x8d8a668>"
       ]
      },
      "metadata": {},
@@ -2032,7 +2032,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x799eeb8>"
+       "<matplotlib.figure.Figure at 0x8530ac8>"
       ]
      },
      "metadata": {},
@@ -2094,9 +2094,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEPCAYAAAAKz9G5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEPCAYAAADh+Gq6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x799c780>"
+       "<matplotlib.figure.Figure at 0x83bbfd0>"
       ]
      },
      "metadata": {},
@@ -2152,9 +2152,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAEPCAYAAABx6xN1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAEPCAYAAABx6xN1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xlcjtn/x/HX3Z5qWlVMyVoja8ieLTKW0QxjGsYXQ4yx\nf83ILH5jG3zHWJJ1BmPPbjAYQ5SQpaLssu8lIVuluq/fH/d0j6ZN2iyf5+NxP9K5z3Wuc3WFd6dz\nnaNSFEVBCCGEEEIIUSR0SroDQgghhBBCvMkkcAshhBBCCFGEJHALIYQQQghRhCRwCyGEEEIIUYQk\ncAshhBBCCFGE9Eq6A89LTEws6S4IIYQQQgjx0szNzbOUyQi3EEIIIYQQRUgCtxBCCCGEEEXolZpS\n8rzshuOLUkREBAD16tUr1vOKkif3/u0k9/3tJff+7ST3/e1VHPc+r2nRMsIthBBCCCFEEZLALYQQ\nQgghRBGSwC2EEEIIIUQRksAthBBCCCFEEXplH5oUQgghhBAiQ2xsLOvWrePhw4c0atSIVq1aAfD0\n6VN++eUXzp8/j5OTE1988QUWFhYl3NvMJHALIYQQotCp1WqePXtW0t3QcnJyAiA5ObmEeyJexpMn\nTzh9+jRVqlQB4NmzZ5w5c4Zy5coRFRVF1apVqVq1KgDR0dG4ublhYGAAFPzeGxgYoKNTsEkhEriF\nEEIIUagURSElJQUjIyNUKlVJdwcAIyOjku6CKAAjIyPtiPa/NWnSJM9jX5aiKCQnJxf4e1nmcAsh\nhBCiUD179gwDA4NXJmwL8bJUKhUGBgYF/m2NBG4hhBBCFCpFUdDV1S3pbghRKHR1dVEUpUBtSOAW\nQgghhBCiCEngFkIIIYQQoghJ4BZCCCGEEKIISeAWQgghhCigK1euoKOjw9KlS7VlS5YsQUdHh2vX\nrpVgz8SrQAK3EEIIIcQLyAjQ2b2GDBmCSqXKc2WWwMBAZs6cWUw9Fq8KWYdbCCGEECIfxo0bR6VK\nlTKVubi4sGHDBvT0co9WgYGBnDp1imHDhhVlF8UrRgK3EEIIIUQ+tG3blvr167/08UWxPnlSUhLG\nxsaF3q4oHDKlRAghhBCigLKbw/1vLVq0YPv27dq6Ga8MiqIwa9YsatSogbGxMXZ2dvj6+pKQkJCp\nnfLly9OuXTt2795NgwYNMDY2ZsqUKUV2baLgZIRbCCGEECIfHjx4wN27d7N9L7fR69GjR+Pn58eN\nGzfw9/fP8v6XX37Jb7/9Ru/evRk6dCjXrl1j1qxZHDlyhPDwcAwNDbXnuHDhAl27dqV///7069eP\ncuXKFc7FiSIhgVsIIYQQIh/ef//9TJ+rVCqOHz+e53GtW7embNmyPHjwgO7du2d6LywsjF9//ZXl\ny5fz2WefZTqXh4cHy5Yto1+/foBmJPzixYts2bKFjh07FsIViaImgVsIIYQQJWrsIoXxvxVN2z/0\ngbF9C3fO9KxZs6hatWqmMiMjowK1uXbtWkxNTfHy8so0eu7i4oKtrS3BwcHawA3g6OgoYfs1IoFb\nCCGEECIf3N3dszw0eeXKlQK1GRMTw+PHj7Gzs8v2/fj4+EyfV6xYsUDnE8Ur14cmJ0+ejLu7O+bm\n5tja2tKpUydOnTqVpd7YsWN59913KVWqFC1btuT06dOZ3k9JSWHIkCGULl0aU1NTvL29uXnzZuFe\niRBCCCHEa0qtVmNtbU1QUFC2r38/FCkrkrxech3h3rt3L4MHD8bd3R21Ws0PP/xA69atOX36NJaW\nlgD89NNPTJ8+naVLl+Ls7Mz48eNp06YN586dw9TUFIDhw4ezZcsWVq9ejZWVFSNGjKBjx45ERkZm\nejpXCCGEEG+fsX1VjO1b0r0oHjk9VFmpUiWCgoJo0KABJiYmxdwrUdRyTbs7duygV69euLq6Ur16\ndZYvX058fDxhYWGAZtK+v78/3377LR999BHVqlVj6dKlPHr0iMDAQAASExP57bffmDp1Kp6enri5\nubF8+XKOHz9OUFBQ0V+hEEIIIcQrwsTEhPv372cp//TTT1Gr1YwfPz7Le+np6Tx48KA4uieKSL6G\nlx8+fIhardaObl++fJm4uDi8vLy0dYyMjGjWrJk2lEdGRpKampqpjoODA1WrVtXWEUIIIYR4G7i7\nu5OYmMjw4cMJDAxk9erVAHh4eDBo0CB+/vln2rVrx4wZM5g7dy4jRoygYsWKbNmypYR7LgoiXw9N\nDhs2DDc3Nxo1agRAbGwsQJYJ/ra2tty6dUtbR1dXF2tr60x17OzsiIuLe+mOCyGEEEIUt/zuEvnv\n+gMHDuTEiROsWLGCWbNmAZrRbdCsflKnTh3mz5/P6NGj0dPTw8nJCR8fH1q1avXSfRAlT6UoivIi\nFUeMGMHatWvZv38/5cuXBzRrRjZt2pRr167h4OCgrdunTx9u377Nn3/+SWBgIL169SI1NTVTe56e\nnjg7OzNv3jxtWWJiovbP58+fL8h1CSGEEKKEODk5Ubp06ZLuhhCFJj4+nqtXr+b4fpUqVbR/Njc3\nz/L+C00p+e9//8uaNWvYs2ePNmwD2NvbA2QZqY6Li9O+Z29vT3p6epZtSWNjY7V1hBBCCCGEeFPl\nOaVk2LBhrFu3juDgYJydnTO9V6FCBezt7dm5cyd169YFIDk5mf379zN16lQA6tati76+Pjt37qRb\nt24A3Lhxg7Nnz9K4ceMcz1uvXr2XvqiXERERUSLnFSVP7v3bSe7720vufdFLTk4u6S4IUajMzMxy\n/Tfj+Vka2ck1cA8aNIgVK1awadMmzM3NtXO2zczMMDExQaVSMXz4cCZNmsR7771HlSpV+PHHHzEz\nM9NuWWpubk7fvn3x8/PD1tZWuyxgrVq1aN26dX6vVwghhBBCiNdKroF73rx5qFQqPD09M5WPHTuW\nH374AQA/Pz+SkpIYNGgQ9+/fp2HDhuzcuTPTGpL+/v7o6enh4+NDUlISrVu3ZsWKFTLpXwghhBBC\nvPFyDdxqtfqFGhkzZgxjxozJ8X0DAwMCAgIICAjIX++EEEIIIYR4zck2j0IIIYQQQhQhCdxCCCGE\nEEIUIQncQgghhBBCFCEJ3EIIIYQQQhQhCdxCCCGEEEIUIQncQgghhBBCFCEJ3EIIIYQQQhQhCdxC\nCCGEEEIUIQncQgghhBAvYMmSJejo6KCjo8P+/fuzrVO5cmV0dHRo2bJlMfdOPC8sLIxx48aRmJhY\n0l0BJHALIYQQQuSLsbExgYGBWcoPHTrEpUuXMDIyQqVSlUDPRAYJ3EIIIYQQr7F27dqxbt060tLS\nMpUHBgby3nvvUalSpRLqWeF48uRJSXeh0CiKUtJdACRwCyGEEELkS7du3bh37x5//fWXtiw9PZ21\na9fy2WefZamvKAqzZs2iRo0aGBsbY2dnh6+vLwkJCZnqbdmyhQ8++ABHR0eMjIwoX748fn5+pKSk\nZKoXFxeHr6+vtp69vT3t27fn9OnT2jo6OjqMGzcuS1/Kly/P559/rv08Y5pMcHAwQ4cOxc7ODjMz\nM+374eHhtG/fHgsLC0qVKoWHhwchISGZ2hw7diw6OjqcPXuWHj16YGFhQenSpfn+++8BuH79Ot7e\n3pibm2Nvb8/UqVOz9CslJYVx48ZRpUoVjIyMcHBwYMSIESQlJWWqp6Ojw5dffsmmTZuoXr06RkZG\nVK9ePdO9GDt2LH5+fgBUqFBBOw0oNDQUgKNHj9K+fXtsbW0xNjamfPny9OzZk+Tk5Cz9Kix6Rday\nEEIIIUQhURSFo0ePcuHCBZycnGjQoEGJTdtwcHDAw8ODwMBAOnToAEBQUBB37tyhW7durFq1KlP9\nL7/8kt9++43evXszdOhQrl27xqxZszhy5Ajh4eEYGhoCmvBrbGzMsGHDMDc35+DBg8yYMYPr169n\navPjjz/m5MmTDBkyhAoVKnDnzh1CQ0M5f/48rq6u2nrZfX1UKlW25UOGDMHKyor/+7//007D2Lt3\nL23btqVOnTqMGTMGPT09li9fjpeXF7t27aJ58+aZ2ujWrRtVq1blp59+Ytu2bUyePBlzc3MWLlxI\n69atmTJlCitWrMDPz4+6detq57krisJHH31EaGgo/fv3x9XVldOnTzN37lxOnTqVKUwDHDx4kD/+\n+IOBAwdiampKQEAAXbp04dq1a1hZWdGlSxfOnz/PqlWr8Pf3x8bGBoCqVasSHx9PmzZtsLW1ZdSo\nUVhaWnLt2jX++OMPnj59ipGR0Yt9E+SX8gp58OCB9lXcwsPDlfDw8GI/ryh5cu/fTnLf315y74te\nUlJSobaXnp6u+Pr6KoD29fHHHyupqamFep68LF68WFGpVMrhw4eVX375RTExMVGePn2qKIqi/Oc/\n/1EaNWqkKIqiVKtWTWnZsqWiKIpy4MABRaVSKStWrMjU1v79+xWVSqX8+uuv2rKMtp43adIkRUdH\nR7l+/bqiKIpy//59RaVSKdOmTcu1ryqVShk3blyW8vLlyyuff/55lmtq2LChkp6eri1Xq9WKi4uL\n0qZNm0zHP3v2TKlWrZrSuHFjbdmYMWMUlUql+Pr6asvS09MVR0dHRaVSKZMmTdKWP3jwQClVqpTS\no0cPbdnKlSsVHR0dJTQ0NNO5Vq5cqahUKmXnzp2ZrsvQ0FC5ePGituz48eOKSqVSZs+erS37+eef\nFZVKpVy9ejVTm5s2bVJUKpUSGRmZzVctZ3l9T+eVYWVKiRBCCCFeaQsXLmThwoUYGRnxwQcfYGJi\nwvr167OdmlBcunbtSmpqKps2bSIpKYlNmzZlO51k7dq1mJqa4uXlxd27d7UvFxcXbG1tCQ4O1tY1\nNjYGQK1Wk5iYyN27d2nSpAmKonDs2DFtHQMDA4KDg7l//36hXU+/fv3Q0fknFkZHRLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e54358>"
+       "<matplotlib.figure.Figure at 0xa00a0b8>"
       ]
      },
      "metadata": {},
@@ -2215,9 +2215,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEPCAYAAACA4Ej4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEPCAYAAACA4Ej4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x719bdd8>"
+       "<matplotlib.figure.Figure at 0x9ecce48>"
       ]
      },
      "metadata": {},
@@ -2274,9 +2274,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEPCAYAAAAKz9G5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FFUXxt/Z3Wyy6YWEGgIECF0JhA6C0otIE0FAugIf\noKjYQKqggIogKCBIbyLSSygRpIcOUkILIZQAgfS+O98fJzczszu72YRgKPf3PHmSnZmduVM2+95z\n33uOIIqiCA6Hw+FwOBwOh1MoaAq7ARwOh8PhcDgczssMF+QcDofD4XA4HE4hwgU5h8PhcDgcDodT\niHBBzuFwOBwOh8PhFCJckHM4HA6Hw+FwOIWIrrAbkBfi4+MLuwkcDofD4XA4HE6+8fDwsFjGI+Qc\nDofD4XA4HE4hwgU5h8PhcDgcDodTiDxXlhU5auF+DgcAjh8/DgCoXbt2IbeE8yzDnxOOPfDnhGMP\n/Dl5iRFFQKMB4uIAG9o0N9s1j5BzOBwOh8PhcDj5QRCALVsAg+GJdvPcRsg5HA6Hw+FwOJxCp127\nJ94Fj5BzOBwOh8PhcDiFCBfkHA6Hw+FwOBxOIcIFOYfD4XA4HA6HU4i8sB5yk8mEjIyMwm4GpxAI\nCAgAAKSlpRVySwoGvV4PjYb3nTkcDofD+c9xcgISEgC9Xn393bvA4MHA5s1PdJgXUpCLooj09HQ4\nOTlBEITCbg7nP8bJyamwm1BgiKKItLQ0/ixzOBwOh/NfI4pAejqJ7uxgnwUpKZRl5dYtwN/fcl1I\nCPDvv7ke6oUMu2VkZECv13MBw3nuEQQBer2ej/ZwOBwOh/NfYzQqf6uRlUW/1fKMp6YCFy7YdagX\nUpCLogitVlvYzeBwCgStVgtRFAu7GRwOh8PhvFwwIc5EtxpsnZpoj421+1AvpCDncDgcDofD4XCe\nCCa27RHkatvkwanBBTmHw+FwOBwO58Vgzx7ybhcE8gh5QgLw+++W29iKkDNMplwPxQU5h8PhcDgc\nDufFYMQI4MqVgtmXqytQtSr9vXo10L+/5TaVKgHu7uoRcnssL9lwQc7hcDgcDofDeTEoXZoynthD\nUhJw9Kj19RoNcP48UKOG9ai7iwtlWQkKslxnMgGOjoCDQ65N4YL8OWHx4sXQaDTQaDQ4cOCA6jbl\ny5eHRqNBs2bN/uPWceQcOnQIEyZMQLzajGsOh8PhcDhPD39/+wX5zZtAv372bVumjPV1jRsDPj6W\ny11dgffft8tLzgX5c4bBYMDKlSstlh85cgTXr1/n+aqfAbgg53A4HA6nkMiLIHdyAuwtItisGbBx\nY97aUro08NNPdm3KBflzRps2bfDHH38gy8yPtHLlSlSqVAmBgYGF1LKCITk5ubCbUGDwVIUcDofD\n4fzH5EWQA0BMjPJ1RgagpkU8PIA333yyttmAC/LnjB49euDRo0fYuXNnzjKj0Yi1a9fi3Xfftdhe\nFEXMnj0b1atXh8FgQNGiRTFw4EDEmuXG3LRpEzp06AB/f384OTmhTJkyGD16NNLT0xXbxcTEYODA\ngTnbFStWDG3btsUFWeJ7jUaDCRMmWLSlTJky6CcbGmI2nLCwMIwYMQJFixaFm5tbzvrw8HC0bdsW\nnp6ecHZ2RuPGjfH3338r9jl+/HhoNBpcunQJvXr1gqenJ3x9ffHVV18BAG7duoWOHTvCw8MDxYoV\nw4wZMyzalZ6ejgkTJqBChQpwcnJCqVKlMGrUKKSmpiq202g0GDJkCDZs2IBq1arByckJ1apVU9yL\n8ePHY/To0QCAsmXL5tiM9u/fDwA4efIk2rZtCz8/PxgMBpQpUwZ9+vRBmr09dA6Hw+FwCpvz54HL\nlwu7FZZkZpIFRc3Prca+fZbe8FWrgKFDC75tuaD7z4/IeSJKlSqFxo0bY+XKlWjXrh0AYPfu3bh/\n/z569OiBVatWKbYfMmQIFi1ahL59+2LEiBGIiorC7NmzcezYMYSHh8PR0REAiWODwYCRI0fCw8MD\nhw8fxo8//ohbt24p9tm1a1ecP38ew4cPR9myZXH//n3s378fV65cQZUqVXK2U7PNCIKgunz48OHw\n9vbG2LFjc2we+/btQ6tWrRAcHIxx48ZBp9Nh2bJlaNmyJXbt2oXXXntNsY8ePXqgcuXK+O6777B1\n61ZMnToVHh4e+O2339C8eXNMmzYNy5cvx+jRo1GrVq0cn70oiujUqRP279+PwYMHo0qVKrhw4QLm\nzp2Lf//9VyG2AeDw4cPYvHkzhg4dCldXV8yaNQtdunRBVFQUvL290aVLF1y5cgWrVq3CzJkzUaRI\nEQBA5cqV8eDBA7Ro0QJ+fn747LPP4OXlhaioKGzevBkpKSlwcnKy7yHgcDgcDqcw6dULqFIFULHQ\nFipGI6DXA2PG2Le9TkUG6/UUJQeA69eBDh2AkydpcqYaf/8NbN4MfP99vpqcg/gcERcXl/Nji9TU\n1P+oRf8dv//+uygIgnj06FFx3rx5oouLi5iSkiKKoij27t1brF+/viiKoli1alWxWbNmoiiK4sGD\nB0VBEMTly5cr9nXgwAFREARx/vz5OcvYvuRMmTJF1Gg04q1bt0RRFMXHjx+LgiCI33//vc22CoIg\nTpgwwWJ5mTJlxH79+lmcU7169USj0Ziz3GQyiUFBQWKLFi0U78/IyBCrVq0qNmjQIGfZuHHjREEQ\nxIEDB+YsMxqNor+/vygIgjhlypSc5XFxcaKzs7PYq1evnGUrVqwQNRqNuH//fsWxVqxYIQqCIIaG\nhirOy9HRUbx27VrOsrNnz4qCIIg///xzzrLp06eLgiCIN2/eVOxzw4YNoiAI4okTJ1Summ1exGe6\nsAkPDxfDw8MLuxmcZxz+nHDs4aV7TgBRLF++8I5/86Yoqn0vJieLosFg/35WrKBzkbNunSh27kx/\nX7hA6zdssL6PdetomwMH1Nc3aCCKt2/nqmG5ZeU5pFu3bsjMzMSGDRuQmpqKDRs2qNpV1q5dC1dX\nV7Rs2RIPHz7M+QkKCoKfnx/CwsJytjUYDAAAk8mE+Ph4PHz4EA0bNoQoijh16lTONnq9HmFhYXj8\n+HGBnc+gQYOg0UiP4pkzZxAREYEePXoo2h0fH4/mzZvj6NGjFhaPgQMH5vyt0WhQq1YtCIKAAQMG\n5Cz38PBAUFAQbty4obhGFStWRJUqVRTHatKkCQRBUFwjAGjWrBnKlSuX87p69epwd3dX7NManp6e\nAIDNmzdbzAHgcDgczkvMhg0Flzv7ZaBzZ+DsWcvlRiOlKrQXjQZ4+23lMnmEXJ5HfNMm4PRpy32w\n7/OpU4HDh5XrHj4EDh0CzOy/qk2xv9UvKOPHUzoa85/x4wtm+6eAl5cXWrVqheXLl2PTpk1ITU1F\n9+7dLbaLiIhAUlISihYtCj8/P8XP/fv38eDBg5xtz58/j7Zt28LNzQ1eXl7w8/ND06ZNASDHRuLo\n6IjvvvsOO3bsQNGiRdG4cWNMnToV0dHRT3Q+5hNRIyIiAAADBgywaPesWbMgiqKFB7506dKK1x4e\nHnBwcICfn59iubu7u6IzERERgcuXL8PX11dxHLY/+TVSOw5A98OeDsprr72Grl27YsKECfDx8cGb\nb76J3377DSkFVVGMw+FwOM8nK1YA2cGvQiE6mmwXzwseHlQ50xyjEdBq7d9PVpalbSU+XproKRfk\nCxZQekO1fQDAtm3A1avKdWx+na0qntlwD/n48XkT03nd/inRs2dP9OnTBwkJCWjRokWOV1mOyWSC\nj48P1qxZo7oPLy8vACS4mzVrBjc3N0yZMgXly5eHwWBAdHQ0+vbtC5Os5OvIkSPRsWNHbNy4Ebt2\n7cKkSZMwZcoUbNmyxcLXbY61qDCLzsvbDQDfffcdatWqpfoe8/PVqnwAraV/FGXZT0wmE6pWrYqf\nrKQlKlGiRK7HMd+nLdauXYvw8HBs2bIFu3btwuDBgzF16lQcOXIEvr6+du2Dw+FwOC8Yzs4FV+7d\nHu7fB1JTgYAAej1pEjB/PvBfZQebOBFo0QKoXz9v7+vVi9rp7k7C2RyTKW+CXK8HsrVQDnPmAOHh\n9DfTLZmZwOPHVEjIHLaNKFr6zJl+smNUnAvy55SOHTvC0dERhw4dwpIlS1S3CQwMxO7du1G3bl24\nuLhY3VdYWBhiY2Oxfv16NJb1/nbt2qW6fZkyZTBy5EiMHDkSt2/fxquvvopvvvkmR5B7eXkhLi5O\n8Z6MjAzcvXvXrnNjEXNXV1e8/vrrdr0nv5QvXx4nTpwo0OPklgc+JCQEISEhmDBhAnbs2IG2bdti\nwYIF+PLLLwusDRwOh/PckpFBP66uhd0SJf37UyGZPXsKft8uLv+tIK9XD4iNVRe19lK9ev7fO24c\nCWt7Rtjj4wFPT7J/rFhBEzatRcg9PSkDzKZN9qUofPttS8tKvXrSMnmE3CzzGpKSADc34LffpGV6\nvXIbJsjtiJBzy8pzisFgwC+//IJx48bhrbfeUt3mnXfegclkwsSJEy3WGY3GHNHMor7ySLjJZMIP\nP/ygeE9qaqpFKsCSJUvC19dXUQQnMDAQ+/btU2w3f/58xf5tUbt2bZQvXx4//PADklR6o+Y2EmvY\nUyCpe/fuiImJwS+//GKxLj09XfX4ucE6P48ePVIsj4uLs4ik16xZEwB4ESEOh8NhLFoE9OhR2K2w\nZPNmYO/ep7NvZ2f13NdPixs3qBx8flm0iKLq+aVECeD2bfu2Zd/57Du1bFnrEXKNhqLU8uqbMTHq\nVTStkZEhCeuaNSkqr9OpC3KAhH/z5vS3eYRcLuhzgUfIn2N69eqlupyJvsaNG2PYsGGYPn06zp49\ni5YtW8LR0RFXr17Fn3/+iUmTJqFPnz5o1KgRfHx88N5772H48OHQ6XRYt26dRZGeyLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEPCAYAAADh+Gq6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7cce630>"
+       "<matplotlib.figure.Figure at 0x853b160>"
       ]
      },
      "metadata": {},
@@ -2320,9 +2320,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEPCAYAAADh+Gq6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEPCAYAAAAKz9G5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXl8Ddf7xz+TPSEJURIJomInrWpiDUEtUURbYgulNKh9\nL2ptS0vQWkpKbUVQGvvO11LEWkqo0lA7kUjShOzz+f1xf3OacUMSItdy3q/XvF65M2fOPDNn7s1n\nnnnO8ygkCYlEIpFIJBKJRGISzExtgEQikUgkEolE8jojBblEIpFIJBKJRGJCpCCXSCQSiUQikUhM\niBTkEolEIpFIJBKJCZGCXCKRSCQSiUQiMSEWpjYgN8THx5vaBIlEIpFIJBKJ5KlxdHQ0Wic95BKJ\nRCKRSCQSiQmRglwikUgkEolEIjEhL1XISmaycvc/T06cOAEA8PLyytfjSkyLHPfXDznmrydy3F8/\n5Ji/fphyzLMLu5YecolEIpFIJBKJxIRIQS6RSCQSiUQikZgQKcglEolEIpFIJBITIgW5RCKRSCQS\niURiQl7aSZ0SiUQikUgkEsmTSElJwYIFC3D+/HmQxEcffWRqk7LklRXkqqoiNTU1z/pzd3cHACQn\nJ+dZn5IXnxdh3K2srGBmJl9mSSQSiUSSG5KTk9G4cWMcOnRIrAsLC8OJEydQsmRJE1pmzCspyEki\nJSUFNjY2UBQlT/q0sbHJk34kLxemHneSSE5OztN7WSKRSCSS14GQkBAcOnQIrq6uGDRoEBYvXow/\n//wTo0aNwvLly01tno5X0u2WmpoKKysrKWAkLz2KosDKyipP3/ZIJJIXg/T0dCQlJZnaDInklSUi\nIgIAMGLECAwfPhxDhw7VrX+ReCUFOUmYm5ub2gyJJE8wNzcHSVObIZFI8oikpCR8+umnsLW1hZ2d\nHWrXro3IyEhTmyWRvHK4uroCAFavXo3jx49j3bp1uvUvEq+kIJdIJBKJ5EWlb9++WLhwIdLT06Eo\nCo4cOYKmTZviwYMHpjZNInml6N27N5ydnREeHo4aNWpgy5YtMDc3x+jRo01tmhFSkEskEolEkk+k\npqZi6dKlAICTJ08iNjYWZcuWxeXLl7F3714TWyeRvFq4urri8OHDaNOmDSpXrowaNWpg7ty58PHx\nMbVpRrySkzolEolEInkRSUlJgaqqsLCwQIUKFVCgQAGUKlUKf//9t/SQSyTPgTJlymDt2rUAgBMn\nTpjYmscjPeSvAP/88w/MzMyE1wUAlixZAjMzM1y7ds2ElkkkEokkM/b29qhWrRrS09NRo0YNNGvW\nDP/73/9gZWWFmjVrmto8iURiIqQgf0nQBHZWS//+/aEoSrZZZUJDQzFz5sx8slgikUgkWbFy5UqU\nLFkS58+fx86dO2FlZYVFixahdOnSpjZNIpGYCBmy8pIxceJEeHh46NZVqFABv/76KywsnjycoaGh\nOHfuHAYOHPg8TZRIJBLJE6hYsSIiIiKwe/duJCUloXbt2ihTpoypzZJIJCZECvKXjGbNmqFGjRpP\nvf/zyM2elJQEW1vbPO9XIpFIXlUcHBxe2BLeEokk/5EhK68AWcWQP0qDBg2wdetW0VZbNEhi9uzZ\n8PT0hK2tLZydnfHpp58iJiZG10/p0qXRvHlz7NmzBzVr1oStrS2mTp363M5NIpFIJBLJy8vatWsR\nGBiIwMBA/Prrr6Y254VFeshfMuLi4hAdHZ3ltid5v8eMGYMRI0bgxo0b+P777422f/bZZ1i0aBG6\ndeuGAQMG4Nq1a5g9ezaOHTuG48ePw9raWhzj77//RkBAAHr27ImgoCCUKlUqb05OIpFIJBJJnpGe\nno709HTY2NiY5PiTJk3CmDFjxOfQ0FBMmjTphcwDbmqkIH/J8PPz031WFAVnzpzJdr/GjRvD1dUV\ncXFx6NSpk27b4cOHMX/+fCxbtgyBgYG6Y9WrVw8///wzgoKCABg86ZGRkdi4cSNatmyZB2ckkUgk\nEokkL0lKSkL//v2xdOlSpKeno06dOli2bFm+zlWIiorCuHHjAABfffUVAGDs2LEYO3YsgoKCULRo\n0Xyz5WXgtRfkExYSXy56fv2P6w5M6JF3cduzZ89GpUqVdOue9cn3l19+QcGCBdG0aVOd971ChQoo\nVqwY9u7dKwQ5AJQsWVKKcYlEIpFIXlD69OmDJUuWiM+HDx9Gs2bN8Mcff8DOzi5fbLhy5QpUVUXV\nqlWFl3zVqlU4d+4c/vnnHynIH+G1F+QvG97e3kaTOv/5559n6vPixYtITEyEs7Nzltvv3bun+yyz\nAUgkEolE8nyIi4tDTEwMSpYsCSsrq1zvn5ycjJ9//hmAoRpsmTJl4OXlhb///hv79u3D+++/n9cm\nZ0nJkiUBAOfOncO8efNAEufPn4eiKGKb5D+kIJdAVVUUKVIEq1evznJ74cKFdZ9lRhWJRCKRSPKW\n9PR0DB48GD/88ANIwsnJCYsXL4a/v3+u+tGqwVpaWuqqwUZGRiIxMfE5WW+Mq6srhg8fjuDgYPTp\n00esHzFiBFxcXPLNjpeF116QT+ihYEIPU1uRPzxu0qeHhwd2796NmjVrokCBAvlslUQikUgkksmT\nJ2POnDkAgGLFiiEqKgoBAQE4ceIEPD09c9yPo6Mj3nrrLZw5cwY1a9aEm5sb9u7dCwsLCzg5OYFk\nnqRATk5OxoIFC/Dnn3/Czc0NPXv2NApDmTJlCsqWLYt169ZBURR8+OGH+PTTT/Pk2GPGjMGWLVtg\nYWGB9u3bY9SoUTA3N3/mvk2FTHv4GlGgQAHExsYare/QoQNUVcWXX35ptC0jIwNxcXH5YZ5EIpFI\nJK8tK1euBACEhYXhzp07aNeuHVJTUxEWFvZUfZUoUQLnzp3Dzp07ARg88E2aNEHbtm2RkpLyTLYm\nJyfjvffew4ABAzBv3jyMGTMG7777Lm7evKlrpygKevbsiW3btmHr1q0ICgp65ocBkujQoQOmT5+O\nCxcuICIiAmPHjsWgQYOeqV9TIwX5a4S3tzfi4+MxaNAghIaGYtWqVQCAevXqoW/fvggODkbz5s3x\n3XffYe7cuRgyZAjKlCmDjRs3mthyiUQikUhebdLS0gAARYoUgaIoKFKkCACDkM4tlStXRkREBCZO\nnCjWeXt7w9bWFmFhYSL7ydMyd+5cHD58GG5ubggODsY777yD69evY9SoUc/Ub06IiIjAhg0bYGdn\nh507d2LNmjUAgB9++AH3799/7sd/Xrz2ISsvE7l9qny0fZ8+fXD27FksX74cs2fPBmDwjgOG7C3V\nq1dHSEgIxowZAwsLC7i7u6N9+/Zo1KjRU9sgkUgkEokke5o3b445c+agZcuW8PT0xOHDhwEATZs2\nfar+HB0dRcx4//79MWvWLOzYsQN+fn7YvHkzpkyZ8tS2RkREAAA+//xz9O/fHzVq1ICvry/OnTv3\n1H0+jtOnT2P27NmIjo5G9erV8e677wIAPD090aRJE5CEq6srbt26hXv37sHJySnPbcgPpCB/SejW\nrRu6deuW5bbSpUtDVdVs29va2urSID3KJ598gk8++eSJdly5ciUn5kokEolEIskF3377Lf7++29s\n374dhw8fhrm5Ob777jvUq1fvqfvUYqoTEhJAEgkJCbr1T0vx4sUBGNIY1qpVCwsWLNCtzysOHz6M\nRo0aiRCbjRs3wsvLC+bm5jh69Cj69++P+Ph43Lp1Cw4ODi91oUIZsiKRSCQSiURiYgoUKICtW7fi\n5MmT2LZtG65cuYL+/fs/U59t2rSBubk5lixZglKlSqFdu3YAgPbt2z9Tv3369EHRokVx+PBh1KhR\nA8uXL4elpWWeV+AcNmwYUlJS0LZtW/z8889wdXXFiRMnhP1z5szBsmXLYGFhgZ9++umlzgInPeQS\niUQikUgkT2D79u3YsGEDAODDDz986jCS7FAUBdWrV8+z/ry8vLB8+XL07NkTN27cgKIo6Nu3L0aO\nHPlM/bq5uSE8PBwjRozAhQsX4ObmhnHjxqFOnTp5ZLmByMhIAMDUqVPx5ptv4vz58/j2229RpkwZ\n7Nu3D9u2bYO5uTnatGmTp9fNFEhBLpFIJBKJRPIYvv/+ewwePFh8DgkJwezZs9GvXz8TWpVzOnTo\nAH9/f1y9ehXFihUTk0WfFQ8PD/z666950tfjePPNNxEVFYUxY8bggw8+wIoVK8R6X19f+Pr6Ptfj\n5ycyZEUikUgkEokkC+7du4fhw4cDMBS0GTZsGABgyJAhL1VGDzs7O1SqVCnPxHh+MWXKFFhaWiI0\nNBTt2rXD9evX4e3tjU6dOpnatDxHesglEolEIpFIsiAyMhLp6emoWrWqyEqyefNmXLhwAVeuXHlp\nM3q8LPj6+uLQoUP4/vvvERMTg3feeQdffPEFbGxsTG1aniMFuUQikUgkEkkWuLm5AQD+/PNPrFix\nAqqq4uLFiwAMpeFfVQ4cOIAFCxYgMTER9erVw4ABA2BhYRrJ6O3tLUJVcgJJAC9fmmYpyCUSiUQi\nkeg4deoUzp49C2dnZzRq1AiWlpamNskklCxZEr1790ZISAg6d+4s1vfr1y/PU/y9KISFhSEgIECk\nU16/fj0OHTqEtWvX4ubNm0hISECZMmVgbW1tYkv1xMbGolevXti8eTPMzMxQv359dO/eHY0bN0ah\nQoVMbV62yBhyiUQikUgkgs8//xzVq1dH165d4efnhwYNGuDff/81tVkmY86cOZg6dSrq1KmDunXr\nYtq0aZg5c2aO9t2xYwdq1aqFEiVKoHHjxjh79uxztvbZGThwIFRVRe/evbFgwQI4ODggLCwMdevW\nRcmSJVG5cmW4u7tj//79pjZVoKoqWrdujTVr1iApKQkPHjzAtm3bEBAQACcnJ3z11Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c53a90>"
+       "<matplotlib.figure.Figure at 0x84dcc50>"
       ]
      },
      "metadata": {},
@@ -2418,9 +2418,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ab8f98>"
+       "<matplotlib.figure.Figure at 0x8365240>"
       ]
      },
      "metadata": {},
@@ -2515,7 +2515,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e3feb8>"
+       "<matplotlib.figure.Figure at 0x8574630>"
       ]
      },
      "metadata": {},
@@ -2604,7 +2604,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7183320>"
+       "<matplotlib.figure.Figure at 0x838e438>"
       ]
      },
      "metadata": {},
@@ -2757,7 +2757,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

12-Particle-Filters.ipynb

@@ -859,14 +859,14 @@
     "Most of this code is devoted to initialization and plotting. The entirety of the particle filter processing consists of these lines:\n",
     "\n",
     "```python\n",
-    "# distance from robot to each landmark\n",
-    "zs = norm(landmarks - pos, axis=1) + randn(NL)*sensor_std_err\n",
+    "    # distance from robot to each landmark\n",
+    "    zs = norm(landmarks - pos, axis=1) + randn(NL)*sensor_std_err\n",
     "\n",
-    "# move diagonally forward to (x+1, x+1)\n",
-    "pf.predict(u=(0.00, 1.414), std=(.2, .05))\n",
-    "pf.update(z=zs)\n",
-    "if pf.neff() < N/2:\n",
-    "    pf.resample()```\n",
+    "    # move diagonally forward to (x+1, x+1)\n",
+    "    pf.predict(u=(0.00, 1.414), std=(.2, .05))\n",
+    "    pf.update(z=zs)\n",
+    "    if pf.neff() < N/2:\n",
+    "        pf.resample()```\n",
     "\n",
     "The first line takes advantage of `numpy.linalg.norm`, which computes the Euclidian distance of a vector. In other words, we compute the distance of the robot to each landmark, and add in sensor noise so that this is a realistic simulation. Uou wouldn't add noise if this was real sensor data, of course!\n",
     "\n",