nbconvert treated comment as chapter title!

A comment in a markdown cell inside a ```python ...``` block was treated as a chapter header by nbconvert. I filled a bug report with IPython and removed the comment.
2015-07-18 18:10:03 -07:00
1 parent 43070a4439
commit f6d191353d
@@ -413,7 +413,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "The constructor `__init()__` initializes the DogSimulation class with an initial position `x0`, velocity `vel`, and the variance in the measurement and noise. The `move()` function has the dog move based on its velocity and the process noise. The `sense_position()` function compute and returns a measurement of the dog's position based on the current position and the sensor noise.\n",
+    "The constructor `__init()__` initializes the DogSimulation class with an initial position `x0`, velocity `vel`, and the variance in the measurement and noise. The `move()` method has the dog move based on its velocity and the process noise. The `sense_position()` method computes and returns a measurement of the dog's position based on the current position and the sensor noise.\n",
    "\n",
    "We need to convert the variances that are passed into `__init__()` into standard deviations because `randn()` is scaled by the standard deviation. Variance is the standard deviation square, so we take the square root of the variance in `__init__()`."
   ]
@@ -443,7 +443,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1YAAAEWCAYAAABlmwYuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b940b8>"
+       "<matplotlib.figure.Figure at 0x6bd4748>"
      ]
     },
     "metadata": {},
@@ -483,9 +483,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6c4a4a8>"
+       "<matplotlib.figure.Figure at 0x6c40518>"
      ]
     },
     "metadata": {},
@@ -535,9 +535,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVNX7B/DPnYFhAAFZRBYFQVlURHOXDBW3NDGw1DQX\n1DTLXUtFK7U0c8ndNK2v4s9cSlMrFTVFBdIUzX1NcINARcSNnfP743CvjAwwMDPMgM/79eJ15+6H\nmcvlPnPOeY7AGGMghBBCCCGEEGJQMkMXgBBCCCGEEEIIBWeEEEIIIYQQYhQoOCOEEEIIIYQQI0DB\nGSGEEEIIIYQYAQrOCCGEEEIIIcQIUHBGCCGEEEIIIUaAgjNCKtDMmTMhk8lw5MgRQxeFEFKJtG/f\nHjIZ/csmhJCqju70hFQgQRCkH6K9u3fvYs6cOejduzfq1asHmUwGmUyGa9eulbhfRkYGZs6cCR8f\nH5ibm6NmzZro27cvrly5UuK5hg0bBhcXFyiVSnh4eGDChAl49OhRmcudlpaGCRMmoE6dOlAqlXB1\ndcWwYcOQmJhY5mORVwPdN3SvPPeB4uTl5WHp0qXw9/eHhYUF7O3t8dZbb+HYsWN6KDkhpCoTaBBq\nQipOamoqUlNTUbt2bZibmxu6OJXezp070atXL8hkMnh4eCA1NRXp6em4fPkyvL291e6TlZWFTp06\nITY2Fi1atEBQUBBu376NX375BQqFAocOHULLli1V9omPj0dAQADu3buHkJAQ+Pr64u+//0ZUVBR8\nfHwQGxsLOzs7jcqcmpqK119/HdeuXUPHjh3RokULXL58Gbt27YKjoyOOHTsGDw8Prd8bUrXcuXMH\nGRkZxV7XpGzKcx8oDmMMffv2xbZt2+Dr64vg4GCkpqZi69atyMzMxPbt29GzZ089/0aEkCqDEUJI\nJXX37l0WExPDnjx5whhjrF27dkwQBHb16tVi9/n666+ZIAisT58+Kst37drFBEFgDRs2ZPn5+Srr\nunTpwgRBYCtWrFBZPnHiRCYIAhs5cqTGZR4xYgQTBIF98sknKsuXLVvGBEFgb775psbHIoSUT3nu\nA8XZtGkTEwSBtW3blmVlZUnLT548yczMzJijo6N0jyKEkNJQcEZeCceOHWOCILDg4OBit2nWrBmT\nyWTs5s2bjDHGsrOz2fLly1m3bt2Ym5sbMzMzY7a2tqxjx47sjz/+UHsMd3d3JggCy8rKYjNmzGD1\n6tVjCoWCjR8/njHG2IwZM5ggCOzIkSMq++3YsYO9//77zMvLi1laWjJLS0vWtGlTtmTJEpaXl1fk\nPIMHD2aCILDDhw+zX375hbVo0YJZWFgwOzs79t5777HExES15Xv48CH77LPPmJ+fH7O0tGTW1tbM\nz8+PffLJJywtLU1l20ePHrHPP/+cNWzYkFlYWDArKyvWtm1b9ssvvxT/RhtYacFZfn4+c3NzU/mc\nCwsMDGSCILCoqChp2b///ssEQWCenp5Ftn/y5AmztLRk1apVY8+ePSu1fE+ePGHm5ubMysqKPX36\ntEjZxOsnPj6+1GOVZN26dUwQBDZz5kwWExPDgoKCmLW1NbO2tmbdunVjp0+fLrKPeG2uX7+e7d69\nm73xxhvMysqK2draStskJyezMWPGMA8PD2ZmZsbs7e1Zjx492NGjR4sty/79+1lwcDBzdHRkZmZm\nrFatWuytt95iv//+e5FtDx06xIKDg5mDgwNTKBTM3d2dffzxxyw5ObnItjdu3GAjRoxg9erVY+bm\n5szW1pbVr1+fDR06lN2+fVtl24iICBYQEMBq1KjBlEolq1WrFgsKCmJr164t9b2cO3cuEwSBffvt\nt2rXp6amMoVCoXJ9JCYmslmzZrGAgABWs2ZNplAomIuLC+vXrx+7ePFikWMkJCQwQRBY+/btWWJi\nIhsyZAhzcnJicrmc7dq1izH24touTJt7VG5uLpszZw6rV68eMzMzY7Vr12ZTpkxh2dnZave7cuUK\nGzp0KKtTpw4zMzNjNWrUYAEBAWzRokVFtr1+/TobNmyYVKYaNWqw0NBQtdedIZTnPlCSN954Q7of\nv2zQoEFMEAS2bt06LUtNCHlVUHBGXhn169dnpqam7N69e0XWXbhwgQmCwDp06CAt+++//5hcLmeB\ngYFs+PDhbNq0aSwsLIzZ29szQRDY999/X+Q44oPPW2+9xVxcXNiwYcPYlClT2OrVqxljxQdnvr6+\nrGHDhmzQoEEsPDycffzxx8zHx4cJgsD69+9f5DxicNanTx+mVCpZ37592eTJk6WHivr166t8g8sY\nY/Hx8VL5mjVrxiZNmsQmTZrEgoODmYWFBTt79qy07d27d5mXlxcTBIG1a9eOTZw4kY0YMYK5urpK\nD/3GqLTg7Pr160wQBObr66t2vfgg/vnnn0vL1q5dW2LtmFirdvDgwVLLd+DAgRJrxz788EMmCAL7\n8ccfSz1WScTgrFu3bszMzIwFBwez6dOns969ezO5XM4sLCzYX3/9pbKPeG2+9dZbzMTEhIWEhLCp\nU6eyIUOGMMYYu3nzJqtVq5b0dyL+PVhYWDC5XM7Wr19fpBxffPEFEwSBWVlZsYEDB7Lp06ezoUOH\nsgYNGrDQ0FCVbb/55hsmCAJzcHBggwcPZlOmTGFvvvkmEwSB1apVi929e1faNikpidnZ2TFTU1PW\ns2dPNmXKFDZhwgTWq1cvVr16dZXPIjw8XAquR40axaZPn86GDRvGmjVrxgIDA0t9LxMTE5lcLmf+\n/v5q169YsYIJgsBmzZolLdu8eTOzsLBgPXr0YKNHj2ZTp05lvXr1YgqFgllaWrJ//vlH5RhicNao\nUSPm7u7OGjduzMaNG8fGjBnDoqOjGWP82pbJZCr7aXOP6t27N3N2dmbDhg1jEyZMYN7e3kwQBOnz\nLuyPP/5g5ubmTCaTse7du7Np06axUaNGscDAQGZvb6+y7cGDB1m1atWYQqFgvXr1YlOmTGHvv/8+\nMzc3Z0qlku3bt6/U91zfynMfKE5GRgaTy+WsWrVqar9I27x5MxMEgQ0aNEjrchNCXg0mhm5WSUhF\nCQsLw9SpU/HTTz9h/PjxKusiIiKkbUR2dna4ffs2XFxcVLZ9/PgxXn/9dYSHh2PQoEFQKpVFznX3\n7l2cP39e435Ie/bsKdLPiDGGIUOGYMOGDRg7dixatWpVZL99+/YhLi4ODRs2lJa9//772Lx5M3bt\n2oXevXurLL99+za++uorTJ8+vcjvJJfLpfnBgwcjPj4emzdvRt++faXlT548Qfv27fHll18iNDQU\n/v7+pf5uO3fuxJkzZ0p/EwoIgoAZM2ZovH1ZXL16FQCK7bdTr149AMD169c13sfLywsHDhzA9evX\nERQUpPPzayMyMhIrV67ERx99JC379ddf8e6772Lo0KG4fPmy2n327NmDLl26qCwfOXIkEhMTMWvW\nLHz++efS8kmTJqFVq1YYOXIkOnXqBFdXVwDA/v378dVXX6FOnTqIjo6WlosKJz85evQowsPD0aZN\nG+zduxfW1tbSuo0bN2LQoEEYN24ctm3bBgDYtm0b0tLSsGTJEowdO1bluDk5OcjOzpbmv//+e7i6\nuuLChQtF+nk+fPiw5DcQgIuLC7p06YLIyEicOXMGTZo0UVkfEREBmUyGwYMHS8s6duyIe/fuwdLS\nUmXbc+fOSfeOvXv3FjnXhQsXMGjQIPzvf//TKDOjNveohIQEXLp0CdWrVwcAzJ49G40bN8aGDRsw\nd+5c1KxZEwDw4MED9O/fH3l5eTh48CDat2+vcpzCn2N6ejr69OkDpVKJkydPwtfXV1p35coVtGzZ\nEkOGDEFCQgIUCkWpv9+SJUvKlHDHw8ND5XMoji7/Dm/cuIH8/Hx4enqq/czEY5WWpIgQQiSGjg4J\nqShJSUnMxMSENWnSRGV5bm4uc3Z2ZlZWVho1TWOMsW+//ZYJglCkOZf4rfRvv/2mdr/ias6Kc+rU\nKSYIAvvqq69Ulos1Z+q+2Y2KimKCILBPP/1UWhYXF8cEQWCNGzcu9Zznzp1jgiCwd955R+363377\njQmCwCZPnqzR7xAWFsYEQdD45+XagbIorebsp59+YoIgsIEDB6pdv3///iI1W8OHDy+xNmvatGlM\nEAT2zTfflFq+OXPmlPiN/Jo1a8rch00dsebM29tb7fqAgAAmCAKLjY2VlonXZq9evYpsf/fuXSYI\nAnNzc2M5OTlF1k+aNIkJgsDmzp0rLevRowcTBIFt27at1PL26tWLCYLAzp8/r3b9a6+9xkxMTKSm\noGL/PHU1Qy+zt7dnnp6eRWqSy2Lr1q1MEAQ2btw4leUXL14sUuNemuDgYKZUKllubq60TKw5UyqV\n7P79+2r3U1dzVpLS7lHqanrFa2D37t3SsoULFzJBENjo0aNLPaf4uSxfvlzt+gkTJjBBENiePXs0\n+h3q1KlTpnuHpp9Dee4DxYmNjWWCILA33nhD7fpr166VWEtHCCEvo5oz8spwdnZG586dERkZibNn\nz6Jx48YA+Df8ycnJCAsLg4WFhco+Fy9exIIFC3D06FEkJycjMzNTZX1SUlKR8wiCoHGWL1FqaioW\nLFiAPXv2ID4+Hs+fP1dZX1yK9ebNmxdZVqtWLQA8Xbvo+PHjAFCkNkSd2NhYAPxb8JkzZxZZf//+\nfQBQW+uizrp167Bu3TqNtiW69cYbb6hdHhgYiGPHjuHMmTMICAhQWafu2j19+jQA4PXXX4eJSdF/\nG506dcKiRYvwzz//SMuOHz8OQRDQrVu3UssZGxsLExMTbN++XaodKywrKwt5eXm4evUqmjZtirff\nfhvTp0/H6NGjERkZiS5duqBNmzbw9/cvkm7+/fffx/Lly1G/fn307t0bbdu2RUBAgLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlczPkfB/DXd6ZjuhyVdKCiFFl3i9jctwjrvsI6dq3b\nImsXi7XOZbEsu0v722Wte3eRLKFaIbklR2JdIUlF1/T5/fFpvhpN6ZjpO9X7+Xj0+Pa93zXTNO/5\nfD7vj8AYYyCEEEIIIYQQIimZ1AEQQgghhBBCCKHkjBBCCCGEEEL0AiVnhBBCCCGEEKIHKDkjhBBC\nCCGEED1AyRkhhBBCCCGE6AFKzgghhBBCCCFED1ByRkgJmj9/PmQyGU6cOCF1KISQUqRNmzaQyehf\nNiGElHX0Sk9ICRIEQfwixXf//n0sXrwY/fr1g4uLC2QyGWQyGW7cuJHvea9fv8b8+fPh5uYGExMT\nVK1aFQMGDMD169fzvdfo0aNhb28PhUIBZ2dnTJ06FS9evCh03AkJCZg6dSqcnJygUCjg4OCA0aNH\n48GDB4W+Fikf6HVD+4ryOpAXpVKJNWvWoH79+jA1NYWVlRW6d++OU6dO6SByQkhZJtAk1ISUnPj4\neMTHx6N69eowMTGROpxSb9++fejTpw9kMhmcnZ0RHx+PxMREREVFoXbt2hrPSUtLQ4cOHRAWFgZP\nT0+0a9cO9+7dw86dO2FkZIRjx47h/fffVzsnJiYGXl5eePLkCXx9feHu7o7Tp08jODgYbm5uCAsL\ng6WlZYFijo+PR8uWLXHjxg20b98enp6eiIqKwv79+2FjY4NTp07B2dm52L8bUrb8999/eP36dZ7P\na1I4RXkdyAtjDAMGDMCuXbvg7u4OHx8fxMfHY8eOHUhNTcXu3bvRs2dPHf9EhJAygxFCSCl1//59\nFhoaypKSkhhjjLVu3ZoJgsCio6PzPOfrr79mgiCw/v37q23fv38/EwSBeXh4sKysLLV9nTp1YoIg\nsHXr1qltnzZtGhMEgY0fP77AMY8dO5YJgsBmzJihtv27775jgiCwLl26FPhahJCiKcrrQF62bdvG\nBEFgrVq1YmlpaeL2s2fPMmNjY2ZjYyO+RhFCyLtQckbKhVOnTjFBEJiPj0+exzRp0oTJZDIWGxvL\nGGMsPT2drV27lnXt2pXVqFGDGRsbs8qVK7P27duzv//+W+M1HB0dmSAILC0tjc2bN4+5uLgwIyMj\nNmXKFMYYY/PmzWOCILATJ06onbd37142ZMgQ5urqyszMzJiZmRlr3LgxW716NVMqlbnuM2LECCYI\nAjt+/DjbuXMn8/T0ZKampszS0pINHDiQPXjwQGN8z58/Z3PnzmX16tVjZmZmrEKFCqxevXpsxowZ\nLCEhQe3YFy9esC+++IJ5eHgwU1NTZmFhwVq1asV27tyZ9y9aYu9KzrKysliNGjXUHuecvL29mSAI\nLDg4WNx269YtJggCq1mzZq7jk5KSmJmZGTM3N2cpKSnvjC8pKYmZmJgwCwsLlpycnCs21fMnJibm\nndfKz5YtW5ggCGz+/PksNDSUtWvXjlWoUIFVqFCBde3alUVGRuY6R/Xc3Lp1Kztw4AD74IMPmIWF\nBatcubJ4zOPHj9nEiROZs7MzMzY2ZlZWVqxHjx7s5MmTecYSFBTEfHx8mI2NDTM2NmbVqlVj3bt3\nZ3/99VeuY48dO8Z8fHyYtbU1MzIyYo6OjuyTTz5hjx8/znXs7du32dixY5mLiwszMTFhlStXZnXq\n1GGjRo1i9+7dUzs2ICCAeXl5sSpVqjCFQsGqVavG2rVrxzZv3vzO3+WSJUuYIAhs5cqVGvfHx8cz\nIyMjtefHgwcP2IIFC5iXlxerWrUqMzIyYvb29mzQoEHs6tWrua5x584dJggCa9OmDXvw4AEbOXIk\ns7W1ZXK5nO3fv58x9ua5nVNxXqMyMzPZ4sWLmYuLCzM2NmbVq1dns2bNYunp6RrPu379Ohs1ahRz\ncnJixsbGrEqVKszLy4utWrUq17E3b95ko0ePFmOqUqUK6927t8bnnRSK8jqQnw8++EB8PX7b8OHD\nmSAIbMuWLcWMmhBSXlByRsqNOnXqMENDQ/bkyZNc+65cucIEQWBt27YVtz169IjJ5XLm7e3NxowZ\nw+bMmcP8/PyYlZUVEwSB/fDDD7muo3rj0717d2Zvb89Gjx7NZs2axTZu3MgYyzs5c3d3Zx4eHmz4\n8OHM39+fffLJJ8zNzY0JgsAGDx6c6z6q5Kx///5MoVCwAQMGsJkzZ4pvKurUqaP2CS5jjMXExIjx\nNWnShE2fPp1Nnz6d+fj4MFNTU3bx4kXx2Pv37zNXV1cmCAJr3bo1mzZtGhs7dixzcHAQ3/Tro3cl\nZzdv3mSCIDB3d3eN+1VvxL/44gtx2+bNm/NtHVO1qh09evSd8R05ciTf1rFx48YxQRDYTz/99M5r\n5UeVnHXt2pUZGxszHx8f9vnnn7N+/foxuVzOTE1N2b///qt2juq52b17d2ZgYMB8fX3Z7Nmz2ciR\nIxljjMXGxrJq1aqJfyeqvwdTU1Mml8vZ1q1bc8Xx5ZdfMkEQmIWFBRs2bBj7/PPP2ahRo1jdunVZ\n79691Y795ptvmCAIzNramo0YMYLNmjWLdenShQmCwKpVq8bu378vHvvw4UNmaWnJDA0NWc+ePdms\nWbPY1KlTWZ8+fVilSpXUHgt/f38xuZ4wYQL7/PPP2ejRo1mTJk2Yt7f3O3+XDx48YHK5nNWvX1/j\n/nXr1jFBENiCBQvEbdu3b2empqasR48e7NNPP2WzZ89mffr0YUZGRszMzIydP39e7Rqq5Oy9995j\njo6OrEGDBmzy5Mls4sSJLCQkhDHGn9symUztvOK8RvXr14/Z2dmx0aNHs6lTp7LatWszQRDExzun\nv//+m5mYmDCZTMa6devG5syZwyZMmMC8vb2ZlZWV2rFHjx5l5ubmzMjIiPXp04fNmjWLDRkyhJmY\nmDCFQsEOHz78zt+5rhXldSAvr1+/ZnK5nJmbm2v8IG379u1MEAQ2fPjwYsdNCCkfDKTuVklISfHz\n88Ps2bPx22+/YcqUKWr7AgICxGNULC0tce/ePdjb26sd+/LlS7Rs2RL+/v4YPnw4FApFrnvdv38f\nly9fLvA4pIMHD+YaZ8QYw8iRI/HLL79g0qRJaNasWa7zDh8+jIiICHh4eIjbhgwZgu3bt2P//v3o\n16+f2vZ79+5h4cKF+Pzzz3P9THK5XFwfMWIEYmJisH37dgwYMEDcnpSUhDZt2uCrr75C7969Ub9+\n/Xf+bPv27cOFCxfe/UvIJggC5s2bV+DjCyM6OhoA8hy34+LiAgC4efNmgc9xdXXFkSNHcPPmTbRr\n107r9y+OwMBArF+/Hh9//LG4bc+ePfjwww8xatQoREVFaTzn4MGD6NSpk9r28ePH48GDB1iwYAG+\n+OILcfv06dPRrFkzjB8/Hh06dICDgwMAICgoCAsXLoSTkxNCQkLE7So5i5+cPHkS/v7+aNGiBQ4d\nOoQKFSqI+3799VcMHz4ckydPxq5duwAAu3btQkJCAlavXo1JkyapXTcjIwPp6eni+g8//AAHBwdc\nuXIl1zjP58+f5/8LBGBvb49OnTohMDAQFy5cQMOGDdX2BwQEQCaTYcSIEeK29u3b48mTJzAzM1M7\n9tKlS+Jrx6FDh3Ld68qVKxg+fDh+/vnnAlVmLM5r1J07d3Dt2jVUqlQJALBo0SI0aNAAv/zyC5Ys\nWYKqVasCAJ49e4bBgwdDqVTi6NGjaNOmjdp1cj6OiYmJ6N+/PxQKBc6ePQt3d3dx3/Xr1/H+++9j\n5MiRuHPnDoyMjN75861evbpQBXecnZ3VHoe8aPPv8Pbt28jKykLNmjU1Pmaqa72rSBEhhIikzg4J\nKSkPHz5kBgYGrGHDhmrbMzMzmZ2dHbOwsChQ1zTGGFu5ciUTBCFXdy7Vp9J//vmnxvPyajnLy7lz\n55ggCGzhwoVq21UtZ5o+2Q0ODmaCILDPPvtM3BYREcEEQWANGjR45z0vXbrEBEFgffv21bj/zz//\nZIIgsJkzZxboZ/Dz82OCIBT46+3WgcJ4V8vZb7/9xgRBYMOGDdO4PygoKFfL1pgxY/JtzZozZw4T\nBIF9880374xv8eLF+X4iv2nTpkKPYdNE1XJWu3Ztjfu9vLyYIAgsLCxM3KZ6bvbp0yfX8ffv32eC\nILAaNWqwjIyMXPunT5/OBEFgS5YsEbf16NGDCYLAdu3a9c54+/TpwwRBYJcvX9a4v1GjRszAwEDs\nCqoan6epZehtVlZWrGbNmrlakgtjx44dTBAENnnyZLXtV69ezdXi/i4+Pj5MoVCwzMxMcZuq5Uyh\nULCnT59qPE9Ty1l+3vUapamlV/UcOHDggLhtxYoVTBAE9umnn77znqrHZe3atRr3T506lQmCwA4e\nPFign8HJyalQrx0FfRyK8jqQl7CwMCYIAvvggw807r9x40a+rXSEEPI2ajkj5YadnR06duyIwMBA\nXLx4EQ0aNADAP+F//Pgx/Pz8YGpqqnbO1atXsXz5cpw8eRKPHz9Gamqq2v6HDx/muo8gCAWu8qUS\nHx+P5cuX4+DBg4iJicGrV6/U9udVYr1p06a5tlWrVg0AL9euEh4eDgC5WkM0CQsLA8A/BZ8/f36u\n/U+fPgUAja0ummzZsgVbtmwp0LFEuz744AON2729vXHq1ClcuHABXl5eavs0PXcjIyMBAC1btoSB\nQe5/Gx06dMCqVatw/vx5cVt4eDgEQUDXrl3fGWdYWBgMDAywe/dusXUsp7S0NCiVSkRHR6Nx48bo\n1asXPv/8c3z66acIDAxEp06d0KJFC9SvXz9XufkhQ4Zg7dq1qFOnDvr164dWrVrByLine truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b923c8>"
+       "<matplotlib.figure.Figure at 0x6bd4390>"
      ]
     },
     "metadata": {},
@@ -558,9 +558,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmcjeX/x/HXfWYxqzEzkrGOwdj3LSpE+BKihCRGhKif\nfW1BliKl5asSpVGpRFHJRLLvoqyJLF9ZRsYwttmv3x/HnJxmhpkycwbv5+NxHua+r+u+z+c+5z7H\n/TnXdV+XZYwxiIiIiIiIiEvZXB2AiIiIiIiIKDkTERERERHJE5SciYiIiIiI5AFKzkRERERERPIA\nJWciIiIiIiJ5gJIzERERERGRPEDJmUgeN3bsWGw2G6tWrXJ1KCJyA6xcuRKbzca4ceNcHYqIiOQx\nSs5E8jjLshwPuXHmzJlDvXr18Pf3p0CBAtx3330sXrw4W/tIu8jO7DFq1Kgcil5uBfpM31gbNmzg\ngQceICgoCB8fH6pVq8Ybb7xBampqtve1d+9eOnbsSKFChfD29qZ8+fKMHTuW+Pj4HIhcROQvliah\nFsnbYmJiiImJoXjx4nh7e7s6nFvCsGHDePXVVylevDgdOnQgISGBzz77jDNnzvDWW2/Rv3//LO1n\n5cqVNGnShOrVq9OuXbt05ffccw9NmjS50eHLTe7y5cscPXqUggULEhQU5OpwbgmLFi3i4YcfxsfH\nh06dOhEUFMTXX3/Nvn376NChA/PmzcvyvjZv3kyTJk1ITk6mQ4cOFC9enOXLl7N161buvvtuli9f\njqenZw4ejYjczpScichtZf369dxzzz2UKVOGLVu2EBAQAMCRI0eoVasWFy9e5Ndff6VkyZLX3Vda\nchYREcEHH3yQ06GLSAbi4uIoU6YM58+fZ926ddSsWROAhIQEmjRpwoYNG/j000/p1KnTdfeVkpJC\nlSpV2LdvH4sWLaJ169YAGGPo2LEjCxYs4KWXXmLEiBE5ekwicvtSt0aRTGzcuBGbzUbbtm0zrVO7\ndm3c3Nw4cuQIAElJSfz3v/+lVatWlCxZEi8vL4KCgrj//vsz7TIXGhqKzWYjMTGRsWPHUrZsWfLl\ny8egQYOAv+45W716tdN2CxcupGvXroSHh+Pn54efnx+1atXKtBtPRESE4961+fPnU7duXXx9fQkO\nDubRRx/l+PHjGcYXGxvL888/T5UqVfDz8yMgIIAqVaowbNgwzp4961T33LlzvPDCC1SuXBlfX1/y\n58/Pvffey/z58zN/oXPZu+++C8Czzz7rSMwASpYsSf/+/UlISGD27NmuCi9DV58jo0ePJjQ0FC8v\nL8qWLcuECRNISkpKt43NZqNUqVLExcUxcOBASpYsiYeHB2+88Yajzpdffsl9991HQEAA3t7eVKxY\nkTFjxnDx4sUM48jJc2HOnDncfffdjm5kxYsXp2nTpsyaNcup3sGDB+nTpw9ly5bFx8eHoKAgKlas\nSM+ePTl69Og1X8f4+HgCAwMpWLBghq8ZwNChQ7HZbMyZM8ex7t981j766CPq1KmDr68vNWrUADK/\n5+ynn35iwIABVKtWjeDgYLy9vQkPD2fw4MHExsame54PP/zQsZ+ff/6ZBx54gAIFCuDr60vjxo3Z\nsGFDhseYkpLCe++9x7333kuBAgXw8fGhdOnSREREsHv3bqe6qampzJo1i7vvvttxnlSpUoWXX345\n09cwt82fP5/Tp0/TuXNnR2IGkC9fPiZMmADAO++8k6V9rVq1il9//ZWGDRs6EjOwd0GdMmUK8Nd3\niIhIjjAikqkKFSoYDw8Pc+rUqXRlu3btMpZlmfvuu8+x7sSJE8bNzc00bNjQPPnkk2b06NEmIiLC\nBAcHG8uyzIwZM9Ltp2TJksayLPPAAw+YIkWKmJ49e5oRI0aYd9991xhjzJgxY4xlWWbVqlVO25Uv\nX95UqlTJdOvWzYwaNcr069fPlCtXzliWZbp06ZLuebp3724syzIdO3Y0Xl5eplOnTmb48OGmYcOG\nxrIsU6FCBZOQkOC0zcGDBx3x1apVywwZMsQMGTLEtGnTxvj4+JhffvnFUfePP/4wZcuWNZZlmUaN\nGpnBgweb3r17m6JFixrLsszYsWOz9+LnkKJFixqbzWZOnjyZrmzDhg3GsizTsGHDLO1rxYoVxrIs\n07x5c/POO++YiRMnmvfee8/s3bv3hsac9h60adPGFC1a1AwYMMAMHTrUlC5d2liWZdq1a5duG8uy\nTEhIiKlZs6YpU6aMeeqpp8yQIUPMggULjDHGPP/888ayLFOwYEHTt29fM3LkSFOzZk1jWZapXr26\nOX/+vNP+cvJcGDVqlLEsy4SFhZn+/fubZ5991vTs2dPUqlXL6b04fvy4CQoKMh4eHqZt27ZmxIgR\nZtCgQeahhx4yBQoUMMuXL7/ua9m3b19jWZb58ssv05UlJyebwoULG39/f3Px4kXH+n/6WWvdurXx\n9vY2nTt3NqNGjTJDhgwxxvx13owbN85puz59+pg777zTdOrUyQwdOtQMHjzY8fksX758uvdk9uzZ\njufx8fEx999/vxk2bJjp2LGjcXNzM97e3mbfvn1O2yQkJJhmzZoZy7JMiRIlzFNPPWVGjRplunTp\nYgoXLmzeeOMNR92kpCTTpk0bx/M/9dRTZtCgQaZGjRrGsixz//33m+Tk5Ou+5jntscceM5Zlmc8+\n+yxdWXJysvHx8TGenp4mMTHxuvt69tlnjWVZ5uWXX86wPDw83NhsNvP777//67hFRDKi5EzkGiZP\nnmwsyzLTpk1LVzZs2DBjWZaJjIx0rEtISDDHjh1LV/fcuXOmcuXKJigoyFy+fNmpLO2Ct1q1aiYm\nJibdtpklZwcPHkxXNzU11XFhuHHjRqeytPUBAQFm165dTmVdunQxlmWZefPmOa2vX7++sSzLTJgw\nIcNjunDhgmO5adOmxs3NLd0FUlxcnKlZs6ax2WxOF/DX8tVXX5kxY8Zk+ZHVxO/ChQvGsiyTP3/+\nDMv//PNPY1mWKVy4cJb2l3aRndGjbdu25vTp01naz/WknSPlypUzZ8+eday/fPmyqVu3rrEsy3z6\n6adO26TF0bx583TnXFoSWrx4cXPixAmnsrTz5Omnn3Zan5PnQlBQkClWrJi5dOlSun1f/Rq++eab\nxrIspwQiTWJiolMMmdm0aZOxLMs8+OCD6coWL15sLMsyPXr0cFr/Tz9rfn5+GZ7zmSVnR44cMamp\nqenqv//++xkmDGnJ2d+/h4wxZsaMGcayLNOvXz+n9WmJcOvWrdP9GJOcnGyio6Mdy+PHjzeWZZln\nnnnGKa7U1FTTu3dvY1mWeeutt9LFm5Ht27dn6zM9ZswYc/jw4Sztu3bt2sayLLNt27YMyytVqmRs\nNpv59ddfr7uvDh06ZJq8G2PMAw88YCzLMlFRUVmKTUQku5SciVzD8ePHjbu7u6levbrT+uTkZBMS\nEpLuF/ZrefXVV41lWWb16tVO69MuvL/++usMt8ssOcvMTz/9ZCzLMuPHj3dan3bB+Pzzz6fbJu1i\ncdiwYY51W7dudSSN17Njxw5jWZZ5+OGHMyz/+uuvjWVZZvjw4Vk6hoiIiEyTnoweNpstS/s9duyY\nIynJSGJiorEsy3h5eWVpf7t37zZTpkwxu3fvNhcvXjQxMTEmKirK0QJ11113ZXixnV1p58jHH3+c\nrmzp0qXGsizTrFkzp/Vpr8uOHTvSbdOrVy9jWZZ555130pVFR0cbb29v4+/vb5KSkowxOX8uBAcH\nm7CwsHTJwt+99dZbmbZAZ0fFihWNp6en+fPPP53WP/LIIzf0szZ48OAMt8ssOctMamqqyZ8/v2na\ntKnT+rTk7N577023TVJSknF3dzd16tRxrEtOTjYBAQHGx8fH/PHHH9d8zpSUFFOwYEFTuHBhk5KS\nkq48NjbW2Gw2U69evSwdw4cffpjtz3RW34eyZcteszWrQYMGGSbRGUlrVcysFTbth6yMWulERG4E\nd1d3qxTJy0JCQmjWrBlRUVH88ssvVKtWDYClS5dy8uRJIiIi8PHxcdpm9+7dvPLKK6xevZqTJ0+m\nG3o5o3u7LMuibt262YotJiaGV155he+++46DBw9y6dIlp/Jjx45luF3t2rXTrStWrBiA030tGzdu\nBKB58+bXjWXdunWA/T6jsWPHpiv/888/Afvw1Fkxe/bsPHffV0YqVqxIxYoVHcs+Pj60aNGCBg0a\nUL16dTZt2sSiRYsyHMkxuyzLolGjRunW33vvvQD8/PPP6cq8vLyoUqVKuvXbtm0DyHAkyUKFClGl\nShW2bNnC/v37qVChQo6fC4899hhvvfUWFSpU4JFHHuGee+6hQYMG6UYybNu2LaNHj+bpp58mKiqK\n5s2bU79+fapWrZqtYem7d+/OyJEj+eSTTxgwYABgP/e//vprwsLCaNiwoVP9f/pZy+5nOikpiRkz\nZvDZZ5+xZ88e4uLinO5py85n2t3dnTvvvNPpM/3rr78SFxdH7dq1KVq06DVj+e2334iJiaF06dK8\n+OKLGdbx8vJiz549WTk0unfvTvfu3bNUV0TkdqbkTOQ6evToQVRUFJGRkbz22msAREZGAvYb/6+2\nceNGmjRpQmpqKk2bNqVdu3bkz58fm83G9u3bWbRoEQkJCRk+z5133pnlmM6ePUudOnU4fPgw9erV\nIyIigqCgINzd3YmNjeWNN97I9HkKFCiQbp27u/2rICUlxek5gOtexIH94hVg+fLlLF++PMM6lmVl\nOtBEbkkbAOTcuXMZlqetz+g1yg5/f3+6dOnCxIkTWbdu3Q1JziDjc8TLy4v8+fNneEyFChXKcD/n\nzp3DsiwKFy6cYXlISAjw1zmQ0+fCtGnTKFOmDB9++CGvvPIKU6ZMwWaz0bRpU1555Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x73ace80>"
+       "<matplotlib.figure.Figure at 0x7221a58>"
      ]
     },
     "metadata": {},
@@ -591,7 +591,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7648748>"
+       "<matplotlib.figure.Figure at 0x7393438>"
      ]
     },
     "metadata": {},
@@ -623,7 +623,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2cAAAE6CAYAAACSxCplAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x764c390>"
+       "<matplotlib.figure.Figure at 0x750e470>"
      ]
     },
     "metadata": {},
@@ -659,7 +659,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAADaCAYAAADnoPfrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x79a4208>"
+       "<matplotlib.figure.Figure at 0x766d8d0>"
      ]
     },
     "metadata": {},
@@ -697,7 +697,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b94080>"
+       "<matplotlib.figure.Figure at 0x6bd4710>"
      ]
     },
     "metadata": {},
@@ -790,7 +790,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA2MAAAEWCAYAAADrQU9SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x75b1ef0>"
+       "<matplotlib.figure.Figure at 0x73beef0>"
      ]
     },
     "metadata": {},
@@ -847,7 +847,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x75b3ef0>"
+       "<matplotlib.figure.Figure at 0x73a2160>"
      ]
     },
     "metadata": {},
@@ -900,7 +900,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x77472b0>"
+       "<matplotlib.figure.Figure at 0x72c1438>"
      ]
     },
     "metadata": {},
@@ -990,7 +990,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEPCAYAAACa3Kh2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x78f7f28>"
+       "<matplotlib.figure.Figure at 0x7406e10>"
      ]
     },
     "metadata": {},
@@ -1243,7 +1243,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5AAAAESCAYAAACLqzreAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd0VMXDxvHvbnpIIUAChAChCQRBDSC2UFSqAjY6AioW\nQDpiQyOi8FKkCioqRYqComCXIsgPEU2oKlJFeocECAkpe98/LgSWDSFLsqnP55w9ZGfm3js7BM2T\nuXfGYhiGgYiIiIiIiMh1WPO6AyIiIiIiIlIwKECKiIiIiIhIlihAioiIiIiISJYoQIqIiIiIiEiW\nKECKiIiIiIhIlihAioiIiIiISJYoQIqIiIiIiEiWZBogV69eTZs2bQgLC8NqtTJ79myHNjt27OCR\nRx4hKCiIYsWKUbduXbZt2+ayDouIiIiIiEjecM+sMiEhgTp16tC9e3e6deuGxWKxq9+zZw933303\nPXr04PXXX6d48eJs27YNPz8/h3PFx8fnbM9FRERERHJRYGBgXndBJM9ZDMMwstLQ39+fqVOn0q1b\nt/Syzp074+bmxpw5c657vAKkiIiIiBRkCpAi2XgG0maz8e2331KzZk1atGhBSEgIt99+OwsXLszJ\n/omIiIiIiEg+kektrJk5duwY586dY+TIkbz11luMGTOGFStW0KVLF/z8/GjVqtU1j9Vvb64vNjYW\ngHr16uVxTwoOjZlzNF7O05g5R+PlPI2ZczRe17FmDbRvD4cPQ9mysHgxsVZz7iB9zAwDNm+GJUvg\n669hwwbw9YUTJ8DHJw87n3/oLjoRezd8C+uhQ4cICwujc+fOzJ07N71dly5dOH36NN9//73d8Vf+\n49u5c2dO9F1ERERErmYYhHz2GWGTJmFNS+Psbbexe+RIUkuVuu6hHkeO4LtrF/H33ONQ53b2LGU+\n+YS4Ro1IiIgAa9FYzL9atWrpX2sSRCQbM5ClSpXC3d2diIgIu/IaNWqwYMGCbHdMRERERJzn/d9/\n6eHxSJcuHHz+eQz3rP3Il1KmDPFlymRYF7h2LWVnzaLsrFkklypFfFQUcY0acaZePQwvr5z8CCKS\nj91wgPT09KR+/foOW3bs2LGD8PDwTI/VrSbXp9tynKcxc47Gy3kaM+dovJynMXOOxusa6tUzb0EN\nDqbMY49xZRzM1ph5eJi3w379NZ779xP81VcEf/UVPPUUfPRRzvQ9H9ItrCL2rruNx6XbTW02G3v3\n7mXTpk2ULFmS8uXLM3ToUNq3b09UVBRNmjRh5cqVLFiwgCVLluRK50VEREQkA7165fw5b7kF3n0X\npkyBTZvM5yaXLIEHH8y4fWKinqMUKYQyvXk9JiaGyMhIIiMjSUpKIjo6msjISKKjowFo27Yt06dP\nZ9y4cdSpU4epU6cyZ84cWrZsmSudFxERESnSsraURc6yWOC22+CNN2DjRmjbNuN2jzwCtWvDsGEQ\nEwM2W652U0RcI9MZyMaNG2O7zj/27t2707179xztlIiIiIhcx5Ej0KkTDBhw7RCXGywWx7LkZFi/\nHo4fh7/+grffhtBQaNPG/LpEidzvp4jkiKKxfJaIiIhIYfLrrxAZCatWwUsvQVpaXvfInqcnHDgA\nP/0EvXtDWBgcOgSffQb+/nndOxHJBgVIERERkYLCMGDyZGjc2FzQJioKfv4Z3NzyumeOPD2hWTOY\nOhX27YPYWJg+3VyM52rHjpmf67//cr2bIuIcBUgRERGRgmLwYOjfH1JTYeBAWLECypbN615dn8UC\ndetCu3YZ13/9tfm5KlUyF+t5/XXzFti8eMZTRDKlACkiIiJSULRvbz4/uHAhjB+f8WxeQVS1qhku\n/fxgyxYYMcLcjuTll/O6ZyJylRveB1JEREREctkdd5i3eRa25wgbNzZfFy6Yz3UuWWLOSt57b8bt\n09Ly5227IkWAAqSIiIhIQVLYwuOVvLygeXPzNXXqtW9hbdoUrFZz9dk2baBixdztp0gRpltYRURE\nRPKbo0fho4/yuhd5y2IxQ+LVzpwxV6FdsQL69YPw8Mv7Up4/n9u9FClyFCBFRERE8pO1a80tOp5+\nGhYvzuve5D8BAeaWIJ98Ao8+CsWKwaZN5gqv3t553TuRQk+3sIqIiIjkB4Zh3rY5cKC5ympUFDRo\nkNe9yp9KloTHHzdfSUmwciWcOpXxjOXBg7B6NbRqBYGBTl3m3HmtAityNQVIERERkbyWkADPPAPz\n55vvBw6E0aMLzyqrruTtDS1bXrv+iy9gwABwdzcX6rn03GSFCgAYhsGhE7Bt7+XX9r2wbR8cOAan\nv8+djyFSUChAioiIiOS1pCRYs8a8HXPGDHO7DskZYWHQqBHG//6HZflyWL4c+vZlfvNxTCw3iG17\n4VxiXndSpOBQgBQRERHJayVLms87enlBRERe96ZAOxlv2M8m7nuEbaUfIa7+SVqe/I42p76medxP\nTDvYgNizmZ/LXTuFiDhQgBQRERHJD267La97UGCkpRnsPXIxJO674rbTvXA87hoHuZVkTkg35oR0\nw8uWRLLFM72quD/UrAjVK8Lr85viXjYE60NtCO7UEq3rKmJPAVJEREQkNx0/DsWL6/nGLDh33mDH\n/itnE80/d+yHC8nOnctigYploEZFqF7BmxoVSX+FBIHFYoHDh+HVFfA3sPxTGOhh/n2JSDoFSBER\nEZHc8ttv0K4dPPYYTJyY173JFwzD4PCJyzOJVy5is/+o8+fz8YLqFS4GxYpQ4+LX1cqDr7cl84PL\nloWdO2HJEvP166839qFECjEFSBERERFXu7RFx6BBkJIC69fDhQvmM49FRHKKwe6D9qudXnqdvYH7\nRMuUNMNh9StmEmtUhPIhYLVeJyhmpmpVGDzYfGn2UcSBAqSIiIiIKyUkwLPPwrx55vsBA2DMmEJ7\nC+vpM4bjbOJe2H0I0tKcO5ebG1Qtd3k2sWbFS7egQnH/bITErAoOhvh4119HpABRgBQRERFxpVde\nMcNjsWLw0UfQsWNe9yjbbLYrFrG5eLvppaB47LTz5wv0u3yr6ZUzipVDwdMjF4KiiGSZAqSIiIiI\nK0VHw44dMG4c1KqV171xyvkkI33hmiufTdyxD5KcXMQGrlzExv6209IlLi5iIyL5ngKkiIiIiCuV\nKAE//JDXvbgmwzA4eoqr9k40/9x7xPnzeXtetYjNxYVsqpWHYj4KiSIFnQKkiIiISBFxPN6Dv/f6\nsvwfI/2W0237IP6c8+cKCXJc6bRGRXOWMVuL2IhIvpZpgFy9ejXjxo1jw4YNHDp0iJkzZ9K9e/cM\n2z777LN8+OGHjB07lsGDB7uksyIiIiL51rp15m2q8+blu9VVExIN3vgYJi6oTZot6+HOzQ2qhDo+\nm1i9ApQIUEgUKYoyDZAJCQnUqVOH7t27061bt2vem/7FF18QExNDaGio7l8XERGRosUwYNo0GDjQ\n3KLjzjvNLSDyiW/WGPQdD/uOAmT8c5q/L9QMd9wWo0o5LWIjIvYyDZAtW7akZcuWAPTo0SPDNnv3\n7mXAgAGsWLGCFi1a5HgHRURERPKthAR47jmYO9d8368f9O2bt3266MAxg/4T4KvV9uU1yidwf4Ni\nlxexqQBlS2kRGxHJmmw9A5mamkqnTp147bXXqF69ek71SURERCT/O3kSmjSBP/8EX19zi45OnfK6\nV6SmGry7CF7/EM4lXi4vGQh9HtzDA/VPUb9+vbzroIgUaNkKkNHR0YSEhPDss8/mVH9ERERECoYS\nJaByZbhwARYtgptvzuseEfOPwXNjYOMO+/InHoQxvWHPzlN50zERKTQshmEYWWno7+/P1KlT6dat\nGwCrVq2ia9eubNq0iVKlSgFQqVIlnn/++QwX0YmPj0//eufOnTnRdxEREZE85XbOXL40zc8vT/tx\nLtHKe9+V44s1wRjG5VtRK5VO5MX2+4isegPLrAoA1apVS/86MDAwD3sikj/c8AzkL7/8wuHDhylb\ntmx6WVpaGi+++CKTJk1i3759OdJBERERkfwqr4OjYcCKTcUZ/2V5TpzxTC/38rDxZLPDdL33KB7u\nWZorEBHJkhsOkL1796Zdu3bp7w3DoHnz5nTu3Jmnn34602Pr1dN999cTGxsLaKycoTFzjsbLeRoz\n52i8nKcxc06ujtfvv0Pp0hAe7vprZdGeQwbPvwM/rLMvb94A3h1kpUpYGBBmV6fvMeddeRediGRh\nG49Lt5vabDb27t3Lpk2bKFmyJOXLlyc4ONiuvYeHB2XKlLGb6hcREREpsAwD3n8f+veHOnVgzRrw\n9s7TLqWkGrzzKYyYCYkXLpeXKQkT+kH7+7Siqoi4jjWzypiYGCIjI4mMjCQpKYno6GgiIyOJjo7O\nrf6JiIiI5I3z56F7d+jd29zf8a67wJrpj04ut2azQWQPeOX9y+HRYoFej8DWedDhfovCo4i4VKYz\nkI0bN8Zms2X5ZHv27Ml2h0RERETy3K5d8OijsGWLuUXHhx9C58551p1TZwyGToUZ39qX31IV3h8K\nDWopNIpI7sjWNh4iIiIihdJ335nh8aab8nSLDsMwmPMjDHkXTsRdLi/mA8N7Qr/HwN1d4VFEco8C\npIiIiMjV+vUDmw2eegoCAvKkC9v3GvR5B35eb1/eNgomDYAKZRQcRST3KUCKiIiIXM1igYED8+TS\nSRcM/m8u/N8cSE65XF6+NEweCG2jFBxFJO8oQIqIiEjRlpAAxYrldS8AWBFr0Hsc7Nx/uczNDfq1\ng+FPgZ+vwqOI5K28XUpMREREJK9c2qKjShX499887cqx0waPDzdo2t8+PN4eATEfwTt9LQqPIpIv\nKECKiIhI0XP+PDzxBPTqBUePwldf5Uk3bDaD6UsManSCeUsvlwcUg3cHw6/vw603KLine truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x78aab70>"
+       "<matplotlib.figure.Figure at 0x738cc18>"
      ]
     },
     "metadata": {},
@@ -1251,7 +1251,7 @@
    }
   ],
   "source": [
-    "##### assume dog is always moving 1m to the right\n",
+    "# assume dog is always moving 1m to the right\n",
    "np.random.seed(13)\n",
    "pos = (0., 400.)   # gaussian N(0, 400)\n",
    "velocity = 1.\n",
@@ -1263,6 +1263,7 @@
    "dog = DogSimulation(pos[0], velocity=velocity, \n",
    "                    measurement_variance=sensor_variance, \n",
    "                    process_variance=process_variance)\n",
+    "\n",
    "# simulate dog and get measurements\n",
    "zs = [dog.move_and_sense() for _ in range(N)]\n",
    "\n",
@@ -1301,7 +1302,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtYAAAEgCAYAAACQMzRfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x78f7eb8>"
+       "<matplotlib.figure.Figure at 0x72e4c50>"
      ]
     },
     "metadata": {},
@@ -1353,8 +1354,6 @@
    "dog = DogSimulation(pos[0], velocity=velocity, \n",
    "                    measurement_variance=sensor_variance, \n",
    "                    process_variance=process_variance)\n",
-    "\n",
-    "# simulate dog and get measurements\n",
    "zs = [dog.move_and_sense() for _ in range(N)]```\n",
    "    \n",
    "It may seem very 'convenient' to set the simulator to the same position as our guess, and it is. Do not fret. In the next example we will see the effect of a wildly inaccurate guess for the dog's initial position. We move the dog for 10 steps forward and store the measurements in `zs` via a list comprehension.\n",
@@ -1442,7 +1441,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5wAAAEPCAYAAAAu38sVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x75b1860>"
+       "<matplotlib.figure.Figure at 0x747ada0>"
      ]
     },
     "metadata": {},
@@ -1542,7 +1541,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvwAAAEtCAYAAAB00rSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7709ba8>"
+       "<matplotlib.figure.Figure at 0x7427320>"
      ]
     },
     "metadata": {},
@@ -1641,14 +1640,14 @@
    "        self.R = R\n",
    "        self.Q = Q```\n",
    "            \n",
-    "Now we can implement the `update()` and `predict()` function. In the literature the measurement is usually named either `z` or `y`; I find `y` is too easy to confuse with the y axis of a plot, so I like `z`. I like to think I can hear a `z` in *measurement*, which helps me remember what `z` stands for. So for the update method we might write:\n",
+    "Now we can implement the `update()` and `predict()` methods. In the literature the measurement is usually named either `z` or `y`; I find `y` is too easy to confuse with the y axis of a plot, so I like `z`. I like to think I can hear a `z` in *measurement*, which helps me remember what `z` stands for. So for the update method we might write:\n",
    "\n",
    "```python\n",
    "def update(z):\n",
    "    self.x = (self.P * z + self.x * self.R) / (self.P + self.R)\n",
    "    self.P = 1 / (1/self.P + 1/self.R)```\n",
    "\n",
-    "Finally, the movement is usually called `u`, and so we will use that. So for the predict function we might write:\n",
+    "Finally, the movement is usually called `u`, and so we will use that. So for the predict method we might write:\n",
    "\n",
    "```python\n",
    "def predict(self, u):\n",
@@ -1707,7 +1706,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgAAAAFoCAYAAAAhN0NQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7aba278>"
+       "<matplotlib.figure.Figure at 0x6c19c18>"
      ]
     },
     "metadata": {},
@@ -1737,7 +1736,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvwAAAEtCAYAAAB00rSYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9312390>"
+       "<matplotlib.figure.Figure at 0x764c8d0>"
      ]
     },
     "metadata": {},
@@ -1844,7 +1843,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Now we need to write the Kalman filter processing loop. As with our previous problem, we need to perform a cycle of predicting and updating. The sensing step probably seems clear - call `volt()` to get the measurement, pass the result into `update()` function, but what about the predict step? We do not have a sensor to detect 'movement' in the voltage, and for any small duration we expect the voltage to remain constant. How shall we handle this?\n",
+    "Now we need to write the Kalman filter processing loop. As with our previous problem, we need to perform a cycle of predicting and updating. The sensing step probably seems clear - call `volt()` to get the measurement, pass the result into `update()` method, but what about the predict step? We do not have a sensor to detect 'movement' in the voltage, and for any small duration we expect the voltage to remain constant. How shall we handle this?\n",
    "\n",
    "As always, we will trust in the math. We have no known movement, so we will set that to zero. However, that means that we are predicting that the temperature will never change. If that is true, then over time we should become extremely confident in our results. Once the filter has enough measurements it will become very confident that it can predict the subsequent temperatures, and this will lead it to ignoring measurements that result due to an actual temperature change. This is called a *smug* filter, and is something you want to avoid. So we will add a bit of error to our prediction step to tell the filter not to discount changes in voltage over time. In the code below I set `process_variance = .05**2`. This is the expected variance in the change of voltage over each time step. I chose this value merely to be able to show how the variance changes through the update and predict steps. For an real sensor you would set this value for the actual amount of change you expect. For example, this would be an extremely small number if it is a thermometer for ambient air temperature in a house, and a high number if this is a thermocouple in a chemical reaction chamber. We will say more about selecting the actual value in the next chapter. \n",
    "\n",
@@ -1863,7 +1862,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6YAAAEPCAYAAABRDtnkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x77891d0>"
+       "<matplotlib.figure.Figure at 0x7477e10>"
      ]
     },
     "metadata": {},
@@ -1873,7 +1872,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvEAAAEjCAYAAAC7Jl5YAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9cleXh//H34accFRSRHwKBmuIvKgU1KdI1pVlf+zGn\nK/tYMsuPZqUwH3OUpaW1nBsL++SPzJQ2m+ayNj/js6ClJulSZpghZYn5Iz0YpJAYvw739w/HyRMH\nOCp2PIfXcw8f3ee6r+s6123X7O3NdV+3yTAMQwAAAADchperBwAAAADgwhDiAQAAADdDiAcAAADc\nDCEeAAAAcDOEeAAAAMDNEOIBAAAAN0OIB4B26osvvpCXl5dSU1NdPRQAwAUixAOAC9x7773y8vLS\nCy+80GrdCRMmyMvLS6tXr74sYzGZTJelXwDA5WPiZU8A8MPbvn27Ro4cqfj4eO3du7fZeidPnlRU\nVJTMZrOOHz8us9ncZmOor69XSUmJgoKCFBYW1mb9AgAuP+7EA4ALJCcnq1+/ftq3b5927drVbL3s\n7GzV19frnnvuadMAL0k+Pj7q27cvAR4A3BAhHgBcZNq0aZKkVatWNVvn5Zdflslk0rRp07RlyxZN\nmzZNAwYMUFBQkMxmswYNGqQFCxaourq6SdsFCxbIy8tL2dnZysnJ0U033aTAwEAFBwdLan5N/IED\nB/TrX/9aiYmJ6t69uzp06KDY2Fg9+OCDOnr0aJPv2bp1q62fL774QnfffbdCQkIUEBCgoUOH6u9/\n/3uz1/f6669rzJgx6tatm+17Jk6cqPfff79J3TfeeEOjR49WcHCwOnTooL59+yojI0PffPNNs/0D\ngKcixAOAi9x///3y8/PThg0bdObMmSbnt23bps8++0xDhgzR4MGDtXjxYr3zzjsaMmSIpk+frgcf\nfFD+/v56+umndcstt8hqtTr8no0bN+qOO+5Qt27dNHPmTN15551257+/Jn7Tpk1auXKlYmJidO+9\n9+rRRx/VgAED9Morr2jo0KH68ssvHX7P4cOHNXz4cB05ckT333+/fv7zn+vjjz/WHXfcoa1bt9rV\nNQxDU6ZM0d133629e/fqrrvu0i9/+UuNHDlSu3fv1htvvGFX/6GHHtKECRP02Wefafz48Xr00UcV\nFhamxYsX68Ybb3T4+wcAHs0AALjMf/3Xfxkmk8l46aWXmpybNGmS3bmSkhKHfTzxxBOGyWQy1q9f\nb1c+f/58w2QyGd7e3sbbb7/dpN2hQ4cMk8lkpKam2pV/+eWXRm1tbZP6ubm5hre3tzF9+nS78i1b\nthgmk8kwmUzG008/bXfu7bffNkwmk3Hrrbfala9cudIwmUzG0KFDjdOnT9uda2hoML788kvb5z/+\n8Y+GyWQyxo8fb1RXV9vVXbRokWEymYxf/vKXTcYLAJ6MB1sBwIXy8/N10003KTEx0W5t/Ndff60e\nPXrI399fx48fV8eOHZvt4+uvv1ZISIh+8Ytf6OWXX7aVL1iwQE8//bTuuuuuJne2pXPLaXr16qUp\nU6bolVdecWq811xzjaqqqnTw4EFb2datW3XzzTcrNjZWBw8ebHJnPyYmRt9++61OnjxpK4uPj9f+\n/fu1a9cuJSQktPidCQkJ2rdvn06ePKkuXbrYnbNarQoPD5ePj49OnDjh1DUAgCfwcfUAAKA9u/HG\nG9W/f38VFBToo48+0jXXXCNJ+uMf/6ja2lrdf//9tgBfVVWlrKwsvfnmmzpw4IDOnDmj8+/DNLfM\nZdiwYRc8rj/96U9au3at9u7dq9OnT9st1fH393fY5rrrrnO4XWV0dLQ++OAD2+eqqioVFRUpJCSk\n1QB/9uxZffjhh+rWrZuef/55h3X8/Px04sQJnTp1Sl27dnXm8gDA7RHiAcDFpk2bprS0NK1atcq2\nb/yqVatsD7RKUl1dnW6++Wbt3r1b8fHxuueee9S9e3f5+vrKMAw99dRTqqmpcdh/eHj4BY0nLS1N\nWVlZ6tGjh8aOHavIyEgFBARIktasWaMjR444bPf9u+SNfHx81NDQYPt8+vRpSVJkZGSrYzl16pQk\nqby8XE8//XSz9Uwmk86cOUOIB9BuEOIBwMXuu+8+/frXv9Zrr72mJUuW6MMPP9T+/fs1ePBg253q\nv/71r9q9e7dSU1ObvPTpxIkTeuqpp5rt/0Je5nTy5EktXbpU8fHx2rFjR5NlPOvWrbuAK3OsMew3\n95OD8wUFBUk6t4ynsLDwkr8bADwFu9MAgIt17dpVEyZM0KlTp7Rx40bblpONd+El6fPPP5ck/fSn\nP23Sftu2bW02lpKSEhmGoZSUlCYB/tixYyopKbnk7+jYsaMGDRqksrIyFRQUtFi3U6dOGjRokIqL\ni1VeXn7J3w0AnoIQDwBXgMbAnpWVpddff12dOnXSvffeazvfs2dPSdKWLVvs2pWUlGju3LltNo7G\n79m+fbvdEpgzZ87owQcfbHYbywv16KOPSpJmzJihiooKu3OGYdg9pPrLX/5SdXV1Sk1NtS2vOd83\n33zT4guzAMATsZwGAK4AjQ+47tmzR5I0depUderUyXZ+3Lhxuvrqq5WZmal9+/bpuuuu05EjR/T3\nv/9d/+///T+tX7++TcYRFhamu+++W+vXr9d1112nMWPGqKKiQnl5eTKbzbruuusualnL9zdCe+CB\nB5Sfn69XX31VV199te688051795dJ06c0LZt23THHXfoD3/4g6Rz++nv2bNHL7zwgnr37q1bbrlF\nMTExOn36tL744gtt375dt9xyizZt2tQmvwcA4A6cuhO/bNky9ezZUwEBAUpMTFR+fn6L9fft26eR\nI0fKbDYrKipKCxcutDtvsVg0adIk9e/fXz4+Pk3eFtjo97//veLi4mQ2mxUdHa2HH35YVVVVTl4a\nALiX//7v/5YkuwdaG5nNZr377ruaNGmSioqK9MILL+jjjz/Wk08+qT/+8Y8O+zOZTBe0Hr7R6tWr\n9dhjj+nbb7/VsmXLlJeXp9tvv13vv/++goKCLrjP5saxdu1arVu3ToMGDdJf/vIX/eEPf9DWrVs1\nbNgw/fznP7erm5WVpf/7v//TTTfdpK1bt+oPf/iD3nrrLZWVlenhhx/W/PnzL/g6AcCdtbpP/IYN\nGzR58mQtX75cN954o1588UWtWbNG+/fvV3R0dJP6lZWV6tu3r0aNGqUnn3xSxcXFSk1N1YIFC5Se\nni7p3Fv9MjMzlZCQoJUrVyouLq7JHsWvvvqqpk2bptWrVys5OVkHDx7U1KlTdfPNN9vtgwwAAAC0\nN62G+OHDh+u6667TypUrbWV9+/bVz372Mz377LNN6i9fvlwZGRkqLS217SX8zDPPaPny5Tp27FiT\n+uPGjVP37t2bhPiHH35YH3/8sd2ruufPn69NmzZp3759F3SRAAAAgCdpcTlNbW2t9uzZo5SUFLvy\nlJQU7dixw2GbnTt3Kjk52e5lICkpKTp+/LgOHz7s9MDGjh2rvXv32l4QcuTIEf3tb3/Tbbfd5nQf\nAAAAgCdqMcSXlZXJarUqLCzMrjw0NFQWi8VhG4vF0qR+4+fm2jhy22236ZlnnlFycrL8/PwUGxur\na6+9Vs8995zTfQAAAACeqM13p7mYh6gcefPNN/XYY49pxYoVGj58uD777DPNmjVL8+fPt3upyfe3\nJgMAAADcTePL7ZzVYogPCQmRt7e3SktL7cpLS0sVERHhsE14eHiTO+6N7S/k1d/PPfecpk6dql/8\n4heSpIEDB6qqqkoPPPCA5s+fLy8vtrgHAABA+9RiEvbz81NCQoJyc3PtyvPy8pSUlOSwzYgRI7R9\n+3bV1NTY1Y+MjFRMTIzTAzMMo0lQ9/LyarLXMAAAANDetLqcJj09XZMnT9awYcOUlJSkFStWyGKx\naPr06ZKkjIwM7d69W++8844kadKkSXrqqac0ZcoUzZs3T59++qkWL16sBQsW2PXb+LKQiooKeXl5\nqbCwUH5+fhowYIAk6c4779TixYuVmJioYcOG6fPPP9cTTzyhcePGNXsX/kJ/DIH2o/HV7omJiS4e\nCa5kzBM4g3kCZzBP4IxLWRbeaoifOHGiysvLtWjRIp04cULx8fHKycmx7RFvsVhUUlJiqx8YGKi8\nvDzNnDlTiYmJCg4O1pw5c5SWlmbX75AhQySdW0NvGIY2b96s2NhYW19z586VYRh64okndOzYMXXv\n3l3jxo3TM888c9EXCwAAAHiCVveJv9Kd/zcY7sSjOdwRgTOYJ3AG8wTOYJ7AGZeSY3k6FAAAAHAz\nhHgAAADAzRDiAQAAADdDiAcAAADcDCEeAAAAcDOEeAAAAMDNEOIBAAAAN0OIBwAAANwMIR4AAABw\nM4R4AAAAwM0Q4gEAAAA3Q4gHAAAA3AwhHgAAAHAzHhXiG4wGVw8BAAAAuOw8KsRXfVvp6iEAAAAA\nl51HhfiKqq9dPQQAAADgsvOsEH+GEA8AAADP51khvuqUq4cAAAAAXHaeFeLPlLt6CAAAAMBl51kh\nnjXxAAAAaAcI8QAAAICbIcQDAAAAbsazQjy70wAAAKAdcCrEL1u2TD179lRAQIASExOVn5/fYv19\n+/Zp5MiRMpvNioqK0sKFC+3OWywWTZo0Sf3795ePj49SU1Md9lNZWalHH31UkZGR6tChg/r06aON\nGzc2+71nzlbIaq135pIAAAAAt+XTWoUNGzZo9uzZWr58uW688Ua9+OKLGjt2rPbv36/o6Ogm9Ssr\nKzVmzBiNGjVKBQUFKi4uVmpqqjp27Kj09HRJUk1Njbp3766MjAytXLlSJpOpST91dXUaM2aMQkJC\ntHHjRkVFRenYsWPy8/NrdqyGDFWePa2unUMu5PcAAAAAcCuthvjMzEylpqZq6tSpkLine truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x813be10>"
+       "<matplotlib.figure.Figure at 0x7550e10>"
      ]
     },
     "metadata": {},
@@ -2033,7 +2032,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEWCAYAAAAuFoLqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x776ecf8>"
+       "<matplotlib.figure.Figure at 0x7561c88>"
      ]
     },
     "metadata": {},
@@ -2097,7 +2096,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEPCAYAAAAKz9G5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x760a9e8>"
+       "<matplotlib.figure.Figure at 0x747a5f8>"
      ]
     },
     "metadata": {},
@@ -2155,7 +2154,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAEPCAYAAABx6xN1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9254278>"
+       "<matplotlib.figure.Figure at 0x773a320>"
      ]
     },
     "metadata": {},
@@ -2218,7 +2217,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEPCAYAAACA4Ej4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x812ff28>"
+       "<matplotlib.figure.Figure at 0x78eed30>"
      ]
     },
     "metadata": {},
@@ -2277,7 +2276,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuQAAAEPCAYAAAAKz9G5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x777ffd0>"
+       "<matplotlib.figure.Figure at 0x78c0080>"
      ]
     },
     "metadata": {},
@@ -2323,7 +2322,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEPCAYAAADh+Gq6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a8a278>"
+       "<matplotlib.figure.Figure at 0x7824470>"
      ]
     },
     "metadata": {},
@@ -2419,9 +2418,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x76df6a0>"
+       "<matplotlib.figure.Figure at 0x7523d68>"
      ]
     },
     "metadata": {},
@@ -2514,9 +2513,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x77139b0>"
+       "<matplotlib.figure.Figure at 0x776cd30>"
      ]
     },
     "metadata": {},
@@ -2603,9 +2602,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXVUVGsXxp+ZoVNAQkKRUAREUOzGLixsLFTU67X76md3\nF3YnCiqiGIiKgiAIUjYhoJR058x8fwyemZGGSTi/tVzrvDMn9siJffa797MpTCaTCRISEhISEhIS\nEhISgUEVtgEkJCQkJCQkJCQkTQ3SCSchISEhISEhISERMKQTTkJCQkJCQkJCQiJgSCechISEhISE\nhISERMCQTjgJCQkJCQkJCQmJgCGdcBISEhISEhISEhIBQzrhJCQkJCQkJCQkJAKGp074li1bQKVS\nuf5pa2vz8hAkJCQkJCQkJCQkYo8Er3doYmICb29vYkyj0Xh9CBISEhISEhISEhKxhudOOI1Gg4aG\nBq93S0JCQkJCQkJCQtJo4HlOeExMDHR0dGBgYIApU6bgx48fvD4ECQkJCQkJCQkJiVhDYTKZTF7t\n7OnTp8jLy4OJiQlSUlKwY8cOfP36FZ8+fYKqqiqvDkNCQkJCQkJCQkIi1vDUCf+bgoICtG7dGuvW\nrcPy5csBANnZ2fw6HAkJCQkJCQkJCYlAUFZWbtD2fJUolJOTg5mZGaKiovh5GBISEhISEhISEhKx\ngq9OeFFREb58+YIWLVrw8zAkJCQkJCQkJCQkYgVP1VFWrVoFW1tb6Onp4ffv39i+fTsKCwsxc+bM\nStdvaBifpPESFBQEALC2thayJSSiDHmekNSGv88T34h49F5yCQBAo1Lw5coiJKXnYfdNXzwNrDhz\nK0Gjoqe5HoZ0NsSQzkawNNIClUoBg8FE8PdEPPT7Dne/bwiLTuHarrmyHByGWWL+KGsYaKvw+VeS\nNBTyfkJSG3iZVs1TJzwhIQFTpkxBWloa1NXV0b17d7x79w56enq8PAzPCIlMAgBYGmmBQqEI2RoS\nEhISEkGw5bI3sWw/yALGumow1lVDnw6tEBqVjD03feHy+jMYDFbJVBmdgddhcXgdFof/zr+Ehoo8\nurbTQdC3RCSl51V5nLTsAuxz9sP+234Y0tkIC22tMaKbMWg0slm1OBH4JQHOLz9iVI826G/VWtjm\nkDQieOqE37p1i5e74yt3X3+G3RYXAIB5aw04juyI6YM7oJmCjJAtIyEhISHhFz7hcXjxgSWdS6NS\nsHF6H67vLY204LzJDjsSMnDh8Qc8DYxGaFQy1zq/M/Px0O97hX1LSdJgY9Ua7Vtr4Lb3J8SnsCJm\nTCbwNDAKTwOjoKehhLMrR2FoFyM+/UISXlFGZ2Dn9TfYfvUN6Awmjt4LwIuDM9DPUl/YppE0Eviq\njlIZnGF8YaajDF1zHc/eR3N9JistgYn9zDB/VCd0M9Ulo+NChJwWJKkN5HlCUhs4z5MBK67iZQjL\nCZ811BKX1o6ucfvkjDx4vo/Gs/fR8AyKRlp2AfGdmpIsRnZvg1Hd22BwZ0MoykkDAOh0Bh4HROKU\nexCeBkaB80krKy0BvxNzYGmkxcNfSdJQOM+T2OQs2O+8h7cff3Kt00JNAWHnF0C9mbwwTCQRAXjp\nxwrVCadJyUJBVkqQhwcA5BYUo/mY/SgppVe5TnsDDaye1AP2gyxIZ1wIkM4VSW0gzxOS2vDnPCmQ\nVEffZZcBsKLg367+C0OduvWwYDCYCIlMQlh0Cox1VdHDTK/G9JKYxEycfRSM8x4fkJ5TCADQ12qG\noNPzoKYsV/cfRMIX/pwnkdnSWHDYAzn5xZWuN6yrER7tmgoqlfQNmiK8dMKFmpjmEx4nlOM+D4oh\nHHAzfXWcWj6iQkQiIuY3Zux2w4n7gcIwkYSERMxgMpnIKyxBXHIWgr8lwvN9NG69iMDxewE47OKP\nhNQcYZvY5NlyxZtYnjG4Q50dcACgUino1FYbDsOt0NuiVa3yuw20VbDHcSB8jztAUY4VeIpNzsLk\n7XdRRmfU2QYS/pBXVIrNt0Ixdcc9wgGnUSnY7tAfD3dNIdZ7EhCFg3f8hGUmSSOCpznhdcUrOAbD\nuhoL/LiP/Nm5fGN6mWCBrTXmj+qEoG+JOPMwGLdefkRBUSkAYLnTM7RrpY6BnQwEbicJCYl4cOC2\nH7ZeeY28wpIq1znh9h5h5xcIZfaPBAiOTserkFgAleeCCwKTls1xbf1YjPnfbQCsZ+CG8y+wd/4g\ngdtCwk1YVDLsD/siIYOdatS6RTPc3Dge3Ux1AQCrJ/XA/tss5/u/8y/R26IV8R1J4yEsKhkWhpoC\nyYIQaiTcK/iHwI/JYDDhERBJjEd1bwMAoFAo6Gyig/OrbZHosgJdTHQAAHQGExO3uiAqIUPgtpKQ\nkIg+z4Oisfr082odcICVkrDh/AsBWUXyN+ees4MvM4d0EJpk4OheJvgfxwvAPmc/3Hn1SSi2kLAI\nj06BzcqrXA749EEWCD23gMvJ3jnXhhiX0RmYvM0VmbmFAreXhH/EJmeh4/yzMJt9Emfcg8DvjG2h\nOuHhMSlIyaha3okfvP+agN+Z+QAADRV5dC53tjlRVpDB/e2T0EJNAQCQmVuE0Rucq8wP+wOdzsDH\nH79RXFLGe8NJSEhEjrTsAszc40aMpSVp0FVXQgdDTdhYtcaEvqaw62tKfH/8fiDeRsQLw9QmTXB0\nOoKjWYEUCRpVKFFwTrbM6ofhHLPADvse4OOP30K0qOnyJS4VA1ddRUZ5rr68jARubhyHq/+NhZK8\nNNe6khI03PrfeEJFLS4lG3P2u1fqqDEYTLz/moC7rz8TM+skoo+TWyAYDCa+xKXB9c0XvkfDhS5W\n+qdKXVA85EhFGdHVuMrCCu3minDbPhnSkjQAwOe4VEzbeQ/0KvL3fMLj0NHxLNo7nELnheeqLfok\nISERXfIKS6q8zjlhMpmYs8+d0InWUJFHnPMy/LyzHKHnF+DFoRm4s2UC7my2I+TomExgzn53FJEv\n6gKDyWTijCf7vj9raAe0biHcxjlUKgU3No6DUXlOen5RKcZsdCajqrWEzqDjScBtPPC9jKKS+v+f\nRf5Kx4CVV5GaxYqAy8tI4KRjV0wZ0L7KbfS1muHiGltifN/nK5zc3gNg3Tvu+3zBnH0PoG13EF0W\nnofdFhcMXXu9VvcUEuGSX1iC8x4hxHjp+K58P6bQnXCv4BiBHo9T23VUjzbVrtulHSs95Q+P/L/j\nfxdfca2TkJqDaTvuoc/SywiPYXVLi4j5jQdvv/LQahISEkFw/Xk4lEbsRpsZJ/Dhe1K16555GAx3\nv2/E+NKa0dBUVaiwHoVCwZkVI4lc8G8/07HtymveGk5SJd6hsQiJYUfBN9gLNwr+h2YKMnDbPgny\nMpIAgOjETNjvvE80CCKpmvDoADx5dwsvgt3wIvhevfbxIykTNiuuEi/RCrJSODanC0z1mtW47dje\n7fDv2M7EeOUpTwxadQ1qo/dh3KY7uPgkFCnlM+4A4BMej3MeH+plJ4nguOoZhqy8IgCAkY4q12wV\nvxC6E/48OIbvOTd/iE/JJhxlKUkaBlkb1riN/SALrJncgxjvvumLWy8iUFJKx75bb9F2xgncfBFR\nYTvygiMhES+y8oqw5PgTMJms/O3eSy/Bzbfyl+kvcalYcfIZMV48tguGd6v6ht1SUxl7HQcS433O\nb2t08kl4w/Zrb4jl2UMtoa9Vs5MlKMxaa+DyujHE+HFAJIzsj2HREQ888v+O/BrqDJoqBUW5xHJG\nTmqdt//5Oxs2K67iV7likay0BDx2T4WFfu1nSPYvGAwrY5aqWkkpHV7BMRVmwGWk2NoX68+9IFJh\nSUQPBoOJY/fYaniLx3YRiASl0J3wn79zBFb0yKmK0t9Sv9YqBbvmDvgrf88d5g4nsfasF/I5cr1G\n92yLP+lDz4Ni8CMpkzeGk5CQ8J0Dt/2QmVtEjAuKSjFu023sveXLFSgoLinDlO13UVjMSikxb62B\nfQtqVrdYYGuNPhatALAKvufsd0dpGZm2xk/eRsRzKaKsn9ZLuAZVgl1fU6yb2pMY/0jKwskHQRj1\n3y2ojd6Hwauv4bCLPykOwIGsNLtRTklZ9bVaf5OYlgubFVcRm5wFgFXH4b5zCvp0aFWn/chISeD2\nJrsKfoSFgSb+m9YLficckOa2GoblBcBZeUVYffp5nY5BIjieB0Xja3waAEBRTgqzhloK5LhCd8IB\nwaWkcOaD/1FFqQ00GhU3N46DScvmAICikjJE/mLfEM301fHy0Ay47ZiMIZ3ZrYgvPA6psC8SEhLR\nIzkjD4dd3xFjFUVW4RWTCaw7+wIO+9yJKNd/518gLJo1oyYtySrU4ox4VQWVSsH51aOIdUOjkrHP\n+S2vfwoJBztv+BDLwzrqCD0XvCp2ONhg1aTuhIb4H4pL6XgeFIMVJz3RZvpxXH0WJiQLRQs5aXba\nV2FR7cUdUjLyMGDlVeKFRlKCivvbJ9VbgthYVw3eh2di8dguOLlsOOKclyHswgLsnDsA3c30IC8r\nhRNLhxPrX/UMw+vQ2Hodi4S/HL0XQCzPHmpZoSiXX4iIE87/4sy8whKuItDh3QyRW5CN5IyfyMip\nuSpdWUEG7jsnE1XRAKAsL40ji4Yg5Nx89LdqDQCYN6Ij8f2lp6FkIwYSEjFg5/U3hIJBB0NNfLv6\nLxG1BoDLT0MxcNVV3HoRgUMubGd9/4JBMG+tUevjGOuqYdvsfsR429U3+Bxb9+l0kpoJ/paIJwFR\nAAAKBZjZv+b0Q2FBo1Gxf8FgpLmtwctDM7B6Uo8K5xWTCaw585xU2gAgJ8N2wguKa5fiEZ2QgZ6L\nLxLRTgkaFS6bJzS4V0mntto4tmQYFo7ujJaaFbsnDu1ixKWQ9M+Rx6Rwg4jx/Wc61Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXWYFtUXxz+zXbAsXUt3C0gpICX5owRpQSQFREIBQelW\nkVSkle4GCZFG6e5mqaW38/7+uLs788L2vrk7n+fx8d55Jy47debcc75HEUIIdHR0dHR0dHR0dHTM\nhp2lB6Cjo6Ojo6Ojo6OT1tCNcB0dHR0dHR0dHR0zoxvhOjo6Ojo6Ojo6OmZGN8J1dHR0dHR0dHR0\nzIxuhOvo6Ojo6Ojo6OiYGd0I19HR0dHR0dHR0TEzuhGuo6Ojo6Ojo6OjY2aMaoSPGjUKOzs7g/9y\n5sxpzEPo6Ojo6Ojo6Ojo2DwOxt5hsWLF+Oeff2L69vb2xj6Ejo6Ojo6Ojo6Ojk1jdCPc3t6erFmz\nGnu3Ojo6Ojo6Ojo6OqkGo8eE37p1i1y5clGgQAHatWvH7du3jX0IHR0dHR0dHR0dHZtGEUIIY+1s\n586d+Pv7U6xYMZ48ecK4ceO4cuUKFy9eJGPGjMY6jI6Ojo6Ojo6Ojo5NY1Qj/G0CAwPJnz8/Q4cO\nZcCAAQC8fv3aVIfT0dHR0dHR0dHRMQuenp4p2t6kEoVubm6ULFmSGzdumPIwOjo6Ojo6Ojo6OjaF\nSY3w4OBgLl++TI4cOUx5GB0dHR0dHR0dHR2bwqjqKIMHD6Zp06Z4e3vz9OlTxo4dS1BQEJ07d451\n/ZS68XVSLydOnACgYsWKFh6JjjWjXyc6ieHt6+TQWUGNL+Vv9vZwaRk8eg6T/oSdx97d3sEePigD\nH1eC+pWhXGGws1OIjBScvApbDsn/zr416Zs5A3zeGHo2gwK5FFP+E3WMgP480UkMxgyrNqoR7uPj\nQ7t27Xj27BlZsmShatWqHDt2DG9vb2MexmicvibD4csVBkXRH5A6Ojo6aYHRC9V2x4+hsLdCYW+o\nUQ7OXBNMXgpr9kFkpFwnPAL2n5b/DZ8LWb2gcgnBiSvSeI+LZ69g6jL4cTnUryzo1RwaVwN7e/19\nY0v8d0mwcg/87wOoVUE/dzrGw6hG+IoVK4y5O5Oybp+g9QjZLlUAujcVdGoAGdLpN5iOjo5OauXg\nGcFe6fDE3h6GdzH8vVwRhRVjYOwDwYIt8Ne/cOa64TpPX8KWw+/u28kRaleQ75TVe+HeE7lcCOlh\n33kMvLPB3G8FDaro7xprJzxcMP4PGLcYIiJgxhrYM13wUXn93OkYB5PGhFsz87eo7Qu3oP8vkKsZ\nfD5OcPSCwISiMTo6Ojo6FkLrBe9UHwrljt2gKpRbYWJvhVOLFR5uhsUjoH09GWKiJZMndG4Ia8aB\n7zbY/pPClD4KN9fApsnQsApoJ1rvP4FPvpMedx3r5c4jQa1+MHqBNMBBzox0GAW+L/Vzp2McTCpR\nGBvaWBp7x/R4uJn/i9IvQJClMYSGxb1O6YIwuD10rK+HqlgCPTZPJzHo14lOYoi+TgIdKvBRH7nM\n3h6uLIeCcRjhcREZKTh9TcZ/F84N1UonHF5yy0fw+2ZYsAWeR70C8+WA4wsgk6f+frEWoq+T6y8r\n0HsqvAmIfb2GVWDLVJkXoJP20NqxVi1RmBAHz1rmuLuPqwZ4yfwwZ7CMC9dy/iZ0Hguz1pp/fDo6\nOraHEAL/QMHdx4KTVwS7/hWs2C2YuUYwbaXAx1f3nlma0QvUdqcGSTfAQRpeFYopdG2iUL2ckqj4\n7gK5FCb1Vjj4K6Rzk8vuPIJ2P8iQBx3rwD/YjpFL89FhlGqA29vDmO6weYq63o5j8JPtRN/qWDFG\njQlPKntOQMOq5j/u1iNqu1kN6NVCoWdzmWQzdyOs3AOBwfL3gTOheD5B3ff1L14dHZ3Y+XG5YMxC\n8A+Ke53Z6+DMEmGR2T8dOHXDg32nZNveHkbELtplUorlVfjjB0GLobK/5wQM/x0mf2n+segYcva6\noNOUEvg8d45Zlj8nLBsJVUrJe3Zwe8GPy+Vvw+dC9bIi5jed1MPZ64IyhcwTBWFRT3h0cow5iYwU\nbNcY4f/7QP5fURTeL64wf5iCzyaoVEIuj4iANt/DjQe6t0JHR+dddv8n+HZ2/AY4wK2H0uDSsQzz\nduSMaX/W0HKSgc2qK4zoovanLoPVe/X3iyU5d0NQ5ysMDPBODeD0YgyM7PE9oUpJ2Q6PgHYj4eUb\n/dylJu48ElToCqU6wtyNps8PtKgRfu4GPHlh3gv4+GWZ2Q5SZur94u+u4+mhsH4C5Mgk+y/9oPkQ\neBMQ/1gjIgQXbglCQvWbUkcnLfDslaDLOLXv7AS5s0LZQlIlo3VtaFVL/X3WWjh8Tn8+mJuT1z04\neSMdIDW/LeEF1zLqC2ikmQX+YiJcuKVfF5bg8h1Bvf7w4o3su7tEsGwULPleIb274Yeao4PC8tGQ\nQV5K3H0M3SYSq6EWGSk4flmwbp8gMFg/t7bC7HUyAffyHVi3z/TecIuro/x90rzH08pKNaoWd2JF\nziwKGybJlyrApTvQcbQ0tGPj4Bn59VSmE1TqBqFh+k2no2OL+AeKOO9zLUIIuk1UdaKzesGddXBv\ng8LpJQp7ZiisGquwaiw0qBK9jXxpB4fozwdzIYTgd40XvHMjyJ/TsiEEdnYKS0dCodyyHxAELYbq\nXtXEEhEZwY5/V7Hp0GKCQxOYgoqH6/cFdb8C31ey7+4Swew+12hXL+7rI18OhQXD1P6GA9JwA/ns\n2LBf8MVEQa5mULkbtB4BDQfGbTvoWA8BQYIFW9X+V5+a/pgWN8L3mDkkZavGCI8ORYmLSiUU5g01\n3Pb7eYbr+PgKOo4S1OwjPfsgkzo3HTTOeHV0dMzH0r8Enh9D0bZw6mr8L825G2HzIbW/8DvIlvHd\nl7eiKPz2DXi4yv7VezBmkTFHrRMf/5yC0zdVL/hwC3vBo8mQTmHDRHCPui5u+kCnMdKDqhM/527+\ny45jK9h7ciN7T65P1j5uP5QhKNEf0R6uML3XdUrkCUxw2xY1Ffp8ovYHz4KP+wsyN5Lyk4u2wpMX\n6u8Hz8K8zckapo4Z+WMnvPKT7UK5DWerTIXljfDjsU/lmIJ7j0WMoezkCPXeT3ibjvUVvumg9if9\nCSt2C0LDBFOWCYq1g+W7391uvn7D6ejYFK/8BP2nSW/1rYdQ40vYeCD2Z9PlO4JBM9V+31bQqFrc\n3rM82RUmaZLvpi5P2MjXMQ7jFqvtLo2lJ9NaKFlAYdFwtb/9KBRuA31+Emw9LAgI0q+R2AgM9otp\nv3jjm+Tt7z+RBviDp7Lv6gxbf4Qy+ePQJIyFqX3gvSKyHRomHYpvyx67OKnt7+bCU11f3GqJjBTM\nXKP2+7YyjwSlxY3w+0/gxgPzHEurilKrPIlWKZjQ8634vQlQuhMMnSOnEaNpVl0tyrD7uPzS1tHR\nsQ1+XCHzP6IJDJZerclLDZNzQkIF7UdCUIjslyoAUxKhbtGruSyLDjLhu9tECNPl6UzK4XNCVUSx\nEwzrZNnxxEarWgpDOqr92w/h1/XQ9FvI3Ajqfy0lLnVxABVXZ/eYdmh4SJK2fegrDfA7j2Tf2UkW\nVapRLmkGl4uzwsox6gxXNGUKwbDP4PBc8N0OBXPJ5a/84NtZSTqEjhnZfRyu3JXtdG7QpZF5jmtx\nIxzMF5KiDUVpkkAoihZ7e4Vlo6BYXtkPDoXr99XfS+aHvTNgwySF+pXV5drYIh0dHevl8XPBL6vU\nvldU4pUQMOxX+eEdnefx3VxZqAXkC3z5aPlCTgg7OxneFu0dO3Mdpiwz5r9C520mLFHbDSs+t3gs\neFyM6wGD2qka4tGEhErjYNBMGSL1xw7dEAdwc/aIaQcF+yd6uycvBHX7q44/RwdYP4FkSxAX9lbY\nN0t6TWcPljkhZ5YojO+pULWUgrurwsyB6vp/7IT9p/VzaI3MWK22uzTmnaRcU2EVRvje46Y/hn+g\nMEgCbVQ1Er/A1zx+cZ8Xb54muL2nh8KmyWpWtFwG0/rDqcVQq4I8Yd3+p/6+eJteiEFHxxYYv0St\nDVC2EFxZoXqtARZvh3r9ZSjatJXq8ilfQqkCiX9YF/ZWGN1N7Y9dBJdu688IU3DyimDHMdlWFEHn\nuo8tO6B4sLdXmNpXwXe7dOgMbi9nWLQIAUPmoCttAG4uqhEeGJK4EJKbDwQf9lK9nQ72sHocNKya\nMmOrQjGFGQMUerdQyJP93X01qKIYKCT1+VEXbrA2rt3TPiugXyvzHdsqjPC/T5k+c1gbr1W6INz3\n/Yvh8zoz4c9+7D6+LlH7KOytsPNnqF8Z+rWGqyuh/6cKjg7qjfe/DyFbRtl++EzG+Ono6Fgvt3wE\nv29S++N7QhYvhV2/SI9INAfPQodRar9RVekBSyoD2kDFYrIdGibDUnTlBOMz8Q+1XbfcS/JmS1rY\ngiVwclSoVUFhSh+Fc38q3F0Pc4dA9ii53CcvVCWOtIw2HCUwJGFP+Kmrgg96yeRXADs7WDZKarab\ng5+/UsNWLt2BX1bHu7qOmZmpqYzeuBoUSkYl3eRiUSM8Wof7lR+cumbaY215KxTFwzV9TN8/6HWi\n91OphMKOnxWmf62Q1evdE+XooNBZE0s0f0uyhqujo2MmRi2AsHDZrl5WreLr5CilyCZ9qeZ6RJPV\nCxYOT56GrIODwoLv5FQ4wLGLhi8BnZRz8ZZg/X6136We9XrB48M7m0L3pgo/fK4um7IM/BKoWZHa\nESKS4nnLU63Ux1QtWTfedfccF3zUR60P4uIE6yZA69rmM7RyZ1UYpZkBG7MQ7j5O2+fQWnjtL1iy\nXe1/1dq8x7eoEV5Xo05iyuqZb1fJfNcIf2PU42lDUrYfhQdP9ZvNmvHxFbT9XtBjsLine truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x79d1d68>"
+       "<matplotlib.figure.Figure at 0x74f80b8>"
      ]
     },
     "metadata": {},