Reran without debugging print out

I had a print statement in kinematic.py, causing a spurious output
when importing from the common submodule.

08-Designing-Kalman-Filters.ipynb

@@ -239,11 +239,14 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
+       "                    scale: 95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                },\n",
+       "                 \"fast-preview\": {\n",
+       "                    \"disabled\": false\n",
        "                }\n",
        "        });\n",
        "</script>\n"
@@ -341,9 +344,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16118003f60>"
+       "<matplotlib.figure.Figure at 0x281e24e70f0>"
       ]
      },
      "metadata": {},
@@ -650,9 +653,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvwAAAEaCAYAAABtrYONAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvwAAAEaCAYAAABtrYONAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16119129128>"
+       "<matplotlib.figure.Figure at 0x281e361a0b8>"
       ]
      },
      "metadata": {},
@@ -766,7 +769,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAEdCAYAAABQYbvhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x161190f9898>"
+       "<matplotlib.figure.Figure at 0x281e3604710>"
       ]
      },
      "metadata": {},
@@ -874,7 +877,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3WdYFMn6+P3vEESiiogiroE1sUbM\nWQyromIOsGZ/5hUXczyY4+ox55xzXFExYTyuCXNWUBQxkBRUYGDmecFD/5kddEURUO7Pde213VXV\n3VVNg/fUVFeptFqtFiGEEEIIIUS6MkjvCgghhBBCCCEkMBdCCCGEECJDkMBcCCGEEEKIDEACcyGE\nEEIIITIACcyFEEIIIYTIAIzSuwIAGo0GjUajk6ZSqVCpVOlUIyGEEEIIIb6cVqvln5MfGhgYYGDw\n8X7xDBOYv3v3Lr2rIYQQQgghxDdjbm7+ycBchrIIIYQQQgiRAUhgLoQQQgghRAYggbkQQgghhBAZ\nQIYYY57cS57Pnj0jNjYWIyMjSpQokQ61Et+bW7duERcXJ8+M+GzyzIiUkmdGpIQ8L5nDxo0bGThw\nIABjx46lb9++PH78mIIFC+q9Q/lvE5tk2MBco9EQHx//r2+vCpFInhmRUvLMiJSSZ0akhDwvmUNM\nTAyhoaEABAcHU7FiRV69esXdu3f1yv5bYC5PiRBCCCGEEF/IyclJ2d67dy8vXrzg+fPnzJs3L8Xn\nksBcCCGEEEKIZCR+6/Ep5cqVo3z58gDcvn0bS0tLChUqRM6cOVN8PQnMhRBCCCGE+P/FxcWxZs0a\nKleujJGREUZGRpQuXZoFCxYQHR0NJCwetHHjRqpXr86HDx+YP38+JiYmADx48ICAgADGjBmT4mtL\nYC6EEEIIIQQQHR1N06ZN6datGxcuXFBW7rxx4wYeHh7UqlWL8PBwhg0bRseOHfnf//7HzJkzqVq1\nKocPH6ZAgQJfdf0M8fJnSsXHxxMREcH79+/TuyoiAzE2NsbIyAiVSsXTp0/TuzopYmBggLW1Naam\npuldFSGEECLT8vT0xMfHR9kvWrQoWbJk4ebNmwBcvHiRTp068eeffzJnzhzi4uK4desWWq2WWrVq\n8fDhQw4cOMDp06cxNjZO8fW/u8A8Pj6eoKAgcuTIgbW19b++3Soyj/fv36PValGpVJiZmaV3dVJE\nrVYTHByMvb09hoaG6V0dIYQQItN58eIFq1atAsDU1JRdu3bRsGFDYmNjOXbsGF26dCEkJARvb2+m\nTp3KtGnTKFGiBI0aNVLOYWRkRLNmzWjWrBkajYbIyMgU1eG7C8wjIiLIkSMHlpaW6V0VIVKNsbEx\nNjY2hIaGYmtrm97VEUIIITKU+Ph4jh49yq1btzAyMqJ69erKC5epZc+ePajVagA8PDxo2LAhe/fu\nZciQITg4ODBmzBg8PT0B2LZtGxMnTkzV68N3GJi/f/8ea2vr9K6GEKnO1NRUmQdVCCGEEAn27NnD\nwIEDefz4sU565cqVWb58OaVKlUqV67x69UrZrl69OnFxcQwZMoRHjx7x6NEjXFxclPzXr1+nyjX/\n6bt8+VOGr4gfkTzXQgghhK4tW7bQqlUrvaAc4Pz589SoUYMbN26kyrVsbGyU7QsXLmBsbMzkyZMB\ncHZ2JiwsTMn/kqkQP8d312MuhBBCCCF+fJGRkfTu3VuZGaVOnTp07tyZyMhIFi5cyL1793j79i19\n+/blzJkzX3Wt4OBg/Pz8MDAwQKPRMHfuXBo1akTbtm2xtrYmd+7cNGzYUCnfunXrr7rex0hgLoQQ\nQgghMpyNGzfy9u1bANq2bcvWrVuVb5e7d++Ok5MTDx484OzZs1y7do0yZcp80XV2795Nx44def/+\nPSVLluTmzZtERUVRs2ZNnJycMDEx4e+//1bK169fn3Llyn19A5PxXQ5lEUIIIYQQP7Zz584p2wMH\nDtQZ8mlubk6fPn2SLZtS5cuXV1b3fPnyJdWqVVPyrly5ohOUlyxZko0bN37xtf6NBOYZhEql+qz/\nTpw4kWZ1io6ORqVSMWTIkDS7phBCCCEEJKzAmcjc3FwvP+nUyEnLJvXkyRO8vLxwdXWlWbNmjB8/\nXm9Mev78+Rk8eDADBgzg1q1bHD9+nPnz51OiRAmlTKFChZg6dSrnzp37prOnyVCWDOKfn/QmTpyI\nr68vx48f10n/5Zdf0rJaQgghhBDpIulsK6tXr2b27NnKvkajYe3atcmWBdBqtUyYMIEJEyag0WiU\n9L/++otx48bx559/6nQ8Jr7kmah///7079+fd+/eodFosLCwSJNJGiQwzyCqVKmis58rVy4MDAz0\n0j8mNjYWQ0NDWZxGCCGEED+Erl274uXlhVqtZs6cOcTHx9OlSxfevn3LrFmzlCEmxYoVo1atWjrH\nzpgxg3Hjxn303EOHDsXc3Jy+fft+sg7J9dR/SzKU5Tt06NAhVCoVW7duZcCAAdjZ2ZE1a1aePn1K\ncHAwffr0wdHREXNzc3Lnzk39+vWTHXv14cMHvLy8KFasGCYmJtjY2FCvXj0uXrz40WtrNBoGDx5M\nlixZWLdu3bdsphBCCCEysTx58uDl5aXsz58/nwoVKlC3bl28vb0BMDQ0ZN68eTq92REREYwfP17Z\nHz58OI8ePWLZsmU65x8zZgwfPnz4xq1ImR8qMP/vf/9Lvnz5yJcvn95Y7ICAACXPw8ND79hmzZop\n+f+0Zs0aJW/Xrl06eZGRkUpehw4dUrU9/2bw4MGEhISwYsUK9u7dS44cOQgJCcHY2Jjx48dz4MAB\nVq5cib29PTVr1tQJzmNjY/n111+ZPn06rVq1Yu/evaxatYqKFSsSGBiY7PU+fPhA27ZtWblyJYcO\nHaJz585p1VQhhBBCZEKjR49m8uTJZMmSRS8vZ86c7Ny5kwYNGuikL1++XAm4e/fuzbRp03BwcKBH\njx54e3vTrl07AMLCwti9e/e3b0QK/FBDWd6+fUtQUBAAMTExOnnx8fFKXnh4uN6xr1+/VvL/6d27\nd0re+/fvdfK0Wq2SFxIS8nUNSKESJUqwadMmnbRSpUoxf/58ZT8+Pp5GjRrx6NEj5s+fT9WqVQFY\nu3YtZ8+eZf369XTs2FEp36xZs2Sv9fr1a1xdXQkODubs2bM6L0QIIYQQQnwLKpWKUaNG0aNHD9at\nW8etW7cwMjKievXqtG/fHlNTU53yR44cYcyYMcq+m5ubzrkaN25MXFwc27ZtA+Du3btp05DP9EMF\n5lZWVtjb2wNgYmKik2doaKjk5ciRQ+/YXLlyKfn/ZG5uruQlfQMYEn7IiXlJV4xKC8lNbq/Valmw\nYAGrVq3izp07Oh9Q3r17p2wfPHiQbNmyfVYv/4MHD6hSpQrZs2fn77//xs7OLnUaIIQQQgjxGWxt\nbT9rlrgyZcooCxJBwqws//T69Wtl+5/xYnr7oQLzQYMGMWjQoGTzChUqxLNnzz567L59+z6a17Vr\nV7p27ZpsnqWl5SfP+y0lFyBPnTqV0aNH4+HhweTJk7GxscHAwIDhw4frfCPw+vVr7O3tP+sN4//9\n73+EhIQwa9YsCcqFEEIIkSFotVpev36tM32hra0tbm5urF+/HoDFixfj5uamBODv3r1jzpw5Svna\ntWunbaX/xQ8VmGc2yQXVGzZsoFGjRsybN08n/c2bNzr7uXLl0pvH82O6dOlC1qxZGTx4MBqNRuY1\nF0IIIUS6OnPmDMOGDePt27dcu3ZNZ1a61atXc/bsWfz9/Tl//jylS5emS5cuxMXFsXr1ah4/fgwk\n9K5Xr149nVqQvB/q5U+REKz/82uZS5cu4efnp5Pm4uLCmzdv9Maof8ykSZOYNm0aQ4cO/eT0Q0II\nIYQQ35JWq2Xo0KGcO3eOW7duKb3jiQwNDdmwYYMy/Pj+/fuMHj2asWPHKkF5tmzZWLNmTZrMTZ4S\n0mP+g2natCkzZ85k0qRJVKtWjdu3bzNx4kQKFiyoU65Lly6sXbuW7t27c/PmTWrXrk1cXBznzp2j\nXLlytGrVSu/cw4cPx9LSUplw/88//0yjVgkhhBBCJFCpVEybNg1nZ2ccHR3JmzevXpmqVaty6tQp\nPD09OXPmjE5evXr1mDt3boacyEIC8x/MuHHjiI2NZdGiRUyePJmSJUuyevVq1q1bx9WrV5VyWbJk\n4ciRI0yaNImtW7cyc+ZMrKyscHJyokWLFh89f79+/bCwsKB79+5ERUWxaNGiDPdpUwghhBA/hoCA\nACZNmsSoUaP4+eeflfTatWtz8OBB6tevj5FR8uFs+fLlOX36NLdv38bPzw+VSkXFihUpWrRoWlU/\nxVTapK+uphONRkNkZKROWmBgIDExMRgbG1OmTBkl/enTp/z0009pXUXxHXj//j1arRaVSqU3e873\nQp7vtHXt2jXUarXe3xkhPkaeGZES8rzoU6vV+Pj4cP/+fUxMTKhTpw6//PJLsmW9vb1p0aIFcXFx\ndO7cmbVr16Zxbb9OcvGtpaUlBgYfH0kuPeZCCCGEEOKbW79+PSNGjOD58+c66XXr1mX58uU4ODjo\npNesWRMrKyvCwsLw9vbm7du3WFlZpWWV05y8/CmEEEIIIb6phQsX0rlzZ72gHOD48eNUrlxZbxVO\nKysrxo0bh5eXFw8fPvzhg3KQHnMhhBBCCPENBQcHM3DgQGW/SZMmtGvXjpcvX7JgwQICAwMJCQmh\nQ4cOREREkCVLFqWsh4dHelQ53UiPuRBCCCGE+GZWrFiBWq0GoE+fPuzfv5/OnTszdOhQrly5ogTi\nHz58YObMmelZ1XQngbkQQgghhPhmzp07p2z37NlTJ8/a2ho3NzdlP+kqnpmRBOZCCLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x161191e5e10>"
+       "<matplotlib.figure.Figure at 0x281e372f1d0>"
       ]
      },
      "metadata": {},
@@ -1186,7 +1189,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAEvCAYAAACJwGSIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611920af28>"
+       "<matplotlib.figure.Figure at 0x281e2795cf8>"
       ]
      },
      "metadata": {},
@@ -1238,7 +1241,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAFICAYAAAAS6xIWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a2bd748>"
+       "<matplotlib.figure.Figure at 0x281e47793c8>"
       ]
      },
      "metadata": {},
@@ -1269,7 +1272,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAFICAYAAAAS6xIWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a333400>"
+       "<matplotlib.figure.Figure at 0x281e48b5f60>"
       ]
      },
      "metadata": {},
@@ -1300,7 +1303,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAEvCAYAAACJwGSIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a25f898>"
+       "<matplotlib.figure.Figure at 0x281e48eac88>"
       ]
      },
      "metadata": {},
@@ -1333,7 +1336,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAv4AAAFICAYAAAAoHvpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a3e62e8>"
+       "<matplotlib.figure.Figure at 0x281e49512e8>"
       ]
      },
      "metadata": {},
@@ -1367,7 +1370,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAEvCAYAAACJwGSIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a3e6a90>"
+       "<matplotlib.figure.Figure at 0x281e48645c0>"
       ]
      },
      "metadata": {},
@@ -1402,7 +1405,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAFGCAYAAAAo4XNmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a543d68>"
+       "<matplotlib.figure.Figure at 0x281e4a3b160>"
       ]
      },
      "metadata": {},
@@ -1439,7 +1442,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAFGCAYAAAAo4XNmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a52dcf8>"
+       "<matplotlib.figure.Figure at 0x281e49ce1d0>"
       ]
      },
      "metadata": {},
@@ -1481,7 +1484,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzgAAAEvCAYAAACJwGSIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a4d2320>"
+       "<matplotlib.figure.Figure at 0x281e4a3b8d0>"
       ]
      },
      "metadata": {},
@@ -1512,7 +1515,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvoAAAEvCAYAAAA0BGXUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a532240>"
+       "<matplotlib.figure.Figure at 0x281e49a4518>"
       ]
      },
      "metadata": {},
@@ -1522,7 +1525,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAFGCAYAAADOyLgiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a532a90>"
+       "<matplotlib.figure.Figure at 0x281e49a4a90>"
       ]
      },
      "metadata": {},
@@ -1562,7 +1565,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvQAAAEzCAYAAABe2ZeHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611800cc18>"
+       "<matplotlib.figure.Figure at 0x281e36e0a58>"
       ]
      },
      "metadata": {},
@@ -1572,7 +1575,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAFGCAYAAAAo4XNmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a473b00>"
+       "<matplotlib.figure.Figure at 0x281e36b6cc0>"
       ]
      },
      "metadata": {},
@@ -1612,7 +1615,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAEaCAYAAACRuKhHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a2f0748>"
+       "<matplotlib.figure.Figure at 0x281e49b8438>"
       ]
      },
      "metadata": {},
@@ -1646,7 +1649,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvAAAAEaCAYAAAB3kWMDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xl4lOW9//H3TCaZSTJZmExCCJAQ\n1rAGjIBYaeNSigpHRG21Vi0WbRGL9LhRWYQWOdqDvxaptnrUoqVi3dBKVSoKtm6ILCGgKBBCICzZ\n90wyk3l+fwRGYgIkTJLJ8nldV66E57kz850vCfnk5n7ux2QYhoGIiIiIiHQK5kAXICIiIiIizacA\nLyIiIiLSiSjAi4iIiIh0IgrwIiIiIiKdiAK8iIiIiEgnYgl0AS3l9Xrxer0NjplMJkwmU4AqEhER\nERE5d4Zh8O2NIc1mM2Zz03PtnTLAV1ZWBroMEREREZE2Ex4eftoAryU0IiIiIiKdiAK8iIiIiEgn\nogAvIiIiItKJdLo18E1drHqmNUJtYffu3Xg8HiwWC8OHD2+35+0q1D//qYf+Uf/8o/75R/3zj/rn\nH/XPP23Vv6au8TzTBi1dIsCf6SrdtuD1eqmrq2v35+0q1D//qYf+Uf/8o/75R/3zj/rnH/XPP+3Z\nvzMFeP3NiYiIiIh0In4H+KeffhqTyYTdbm90btu2bVx22WXY7Xaio6OZPn06WVlZTT7OypUrSUlJ\nwWq1kpyczJIlS3C73f6WJyIiIiLSpfgV4HNzc7nnnntISEhodG7Pnj2kp6dTW1vLSy+9xLPPPsvX\nX3/NxIkTyc/PbzD2oYce4q677mL69OmsX7+eO+64g2XLljF79mx/yhMRERER6XL8WgP/i1/8gu9+\n97s4HA5eeeWVBucWLVqE1Wpl3bp1REZGApCWlsagQYNYvnw5jzzyCACFhYUsXbqU2267jWXLlgGQ\nnp6O2+1mwYIFzJ07l2HDhvlTpoiIiIhIl3HOM/CrV6/mgw8+4Iknnmh0zuPxsG7dOq655hpfeAdI\nSkri4osvZu3atb5j77zzDi6XixkzZjR4jBkzZmAYBq+//vq5ligiIiIi0uWc0wx8Xl4ec+fO5eGH\nH6ZPnz6Nzu/fv5/q6mpGjRrV6NyoUaN49913cblc2Gw2du3aBcDIkSMbjOvVqxdOp9N3/kx2796N\n1+s9l5dyTk6uzXe73WRkZLTb83YV6p//1EP/qH/+Uf/8o/75R/3zj/rnn7bqn9lsJjExsdnjzynA\n33HHHQwZMoRZs2Y1eb6wsBAAh8PR6JzD4cAwDIqLi+nVqxeFhYVYrVbCw8ObHHvysc7E4/FQV1fX\nwlfROnShrX/UP/+ph/5R//yj/vlH/fOP+ucf9a+h2jqDbUdruKCPrVnjW7N/QUFBLRrf4gD/6quv\n8uabb7J9+/Yz7k8JZ96/8tRzzR13OhaLpV33Mj31Lyw4OLjdnrerUP/8px76R/3zj/rnH/XPP+qf\nf9S/plXUevmfjwrYlVfLnePMfH9A44llaLv+tTTHtijAV1RUMHv2bH75y1+SkJBASUkJALW1tQCU\nlJQQHBxMTEwMQJOz50VFRZhMJqKjowGIiYnB5XJRVVVFWFhYo7FpaWlnrWv48OHtGuAzMjJwu90E\nBweTmprabs/bVah//lMP/aP++Uf984/65x/1zz/qX2PHy1zc8uxn7MmrJcJq4cLUwaQOcDY5tq36\n5/V6KS8vb/b4FqXegoICjh8/zqOPPkqPHj18b2vWrKGyspIePXpw4403MmDAAEJDQ8nMzGz0GJmZ\nmQwcOBCbrf6/J06uff/22GPHjlFQUMCIESNaUqKIiIiISLPsz69g+hMfs+dYObERVl78+QVceJrw\n3pG0aAY+Pj6ejRs3Njr+8MMP88EHH/D222/jdDqxWCxMnTqV1157jd/97ndEREQAkJOTw8aNG/nV\nr37l+9zJkydjs9lYtWoV48eP9x1ftWoVJpOJadOmnetrExERERFp0vacYm5dtYXiKjfJznCev3Uc\nfR1hZ//EDqBFAd5ms5Gent7o+KpVqwgKCmpwbsmSJYwdO5YpU6Ywb948XC4XixYtwul0cvfdd/vG\nORwOFixYwMKFC3E4HEyaNIktW7awePFiZs6cqT3gRURERKRVbfwqjztWb6PaXUdqnyie/elYYuzW\nQJfVbH7dyOlMUlJS2LRpE/fffz/XXnstFouFSy65hOXLlxMbG9tg7Pz584mIiODxxx9n+fLlxMfH\nM2/ePObPn99W5YmIiIhIN/TK1sPc/+pO6rwG3x0cy59uPI9wa5tF4jbRKtWuWrWKVatWNTqelpbG\nhg0bmvUYc+bMYc6cOa1RjoiIiIhIA4Zh8OcPsnjknT0ATB/Tm0euHUVwUPtthNJaOtevGyIiIiIi\nLVTnNVjy5m6e/+QgAD//bn/un5yC2Xz27co7IgV4EREREemyXO465qzZzr++OI7JBPOvGMrMif0D\nXZZfFOBFREREpEsqqqxl5nNb2JZTQojFzO9/OJorR/UKdFl+U4AXERERkS4np7CKn/7lM7IKKom0\nWXj6lrGMS3YEuqxWoQAvIiIiIl3KzsMl3LpqCwUVtfSODmXVjLEM6hkR6LJajQK8iIiIiHQZG/fk\nMfuFbVTV1jG0VySrZoylZ6Qt0GW1KgV4EREREekSXvwsh/mv76LOazBxkJMnbjyPCFtwoMtqdQrw\nIiIiItKpGYbBo//6mj9u3AfA9PN68/D0UYRYOt8e782hAC8iIiIinVaNp477XtnJGzuOAHDnxQO5\ne9JgTKbOucd7cyjAi4iIiEinVFJVy+3Pb+Wz7CIsZhPLrh7JD8f2DXRZbU4BXkREREQ6nYOFlcxY\ntYWs/EoirBb+9JM0LhrkDHRZ7UIBXkREREQ6lW05xcx87nOKKmtJiLLxlxnjGBLfdbaJPBsFeBER\nERHpNN7OPMrcv++gxuNlRO9Inrml620TeTYK8CIiIiLS4RmGwdP/OcCyt7/EMOCSlDhW3jCGcGv3\ni7Pd7xWLiIiISKfirvPy4D9288LmHABuuiCJB6cOwxLUNbeJPBsFeBERERHpsEqr3dz5wjb+s7cA\nkwnmXzGUn12U3KW3iTwbBXgRERER6ZAOFVUxY9UW9uVVEBYSxIrrx/D9YT0DXVbAKcCLiIiISIez\n9WARtz+/lcLKWuIjbTx9y/mM6B0V6LI6BAV4EREREelQ3tiRy72v7KTW42V4Qv1OM/FR3WunmTNR\ngBcRERGRDsEwDB57bx+/3/A1AJcN7cljN4wmLESR9VTqhoiIiIgEnMtdx69fy2Tt9lwAbpuYzLzL\nhxJk7r4Xq56OAryIiIiIBFR+eQ23//VztueUEGQ28durRvDj8YmBLqvDUoAXERERkYD54kgZM5/b\nwpFSF5E2C0/cmMZFg5yBLqtDU4AXERERkYD41+5jzP37Dqpq60h2hvPMLefTP9Ye6LI6PAV4ERER\nEWlXhmHw5w+y+N36PRgGfGdgDE/8OI2osOBAl9YpKMCLiIiISLup8dRfrPratvqLVW+6IIlFU4cR\nHGQOcGWdhwK8iIiIiLSLgooafvHXrXx+sJggs4kHpw7j5gn9Al1Wp6MALyIiIiJt7osjZdz2/Ofk\nllQTYbPwxI3nMXFQbKDL6pQU4EVERESkTb2deZT/fimDancd/WLCePqWsQyM08Wq50oBXkRERETa\nhNdrsOK9vax4by8AEwc5+eMN5+liVT8pwIuIiIhIq6us8fDfL+1g/e7jAPzsomR+fXkKFl2s6jcF\neBERERFpVYeKqrjt+c/Zc6yckCAzS68ewQ/P7xvosroMBXgRERERaTWf7C/kjr9tpbjKjdNu5cmb\n0khL6hHosroUBXgRERER8ZthGKzenMOSf+zG4zUY2TuKp25Oo1dUaKBL63IU4EVERETELzWeOh58\nYzcvbjkEwNTUBH53zShCQ4ICXFnXpAAvIiIiIufseJmLX6zeyvacEkwmuPcHQ5j1vQGYTKZAl9Zl\nKcCLiIiIyDnZerCIX6zeRn55DZE2C4/dMIb0IXGBLqvLU4AXERERkRZ7YXMOD/5jF+46g8E97Tx1\n0/n0c4YHuqxuQQFeRERERJqt1uNl8Zu7eWFzDgCXj4jnf69LxW5VrGwv6rSIiIiINEtemYtZf9vG\n1oPFmExwz6Qh3JGu9e7tTQFeRERERM5q68Ei7vjbNo6X1RBhs/DY9WO4OEXr3QNBAV5ERERETssw\nDP766UF+u+4L3HUGA+Ps/N/N55Os9e4BY27J4B07dnDllVeSmJhIaGgoDoeDCRMmsHr16kZjt23b\nxmWXXYbdbic6Oprp06eTlZXV5OOuXLmSlJQUrFYrycnJLFmyBLfbfW6vSERERERaRXVtHXe/lMGi\nN3bjrjO4cmQv3pj9HYX3AGvRDHxJSQl9+/blhhtuoHfv3lRWVvK3v/2Nm266iezsbBYsWADAnj17\nSE9PZ/To0bz00ku4XC4WLVrExIkT2bFjB7Gxsb7HfOihh1i4cCHz5s1j0qRJbNmyhQULFpCbm8tT\nTz3Vuq9WRERERJolp7CKn6/eypdHywgym5g3OYWZE5O13r0DaFGAT09PJz09vcGxKVOmcODAAZ56\n6ilfgF+0aBFWq5V169YRGRkJQFpaGoMGDWL58uU88sgjABQWFrJ06VJuu+02li1b5nsOt9vNggUL\nmDt3LsOGDfP3NYqIiIhIC2z8Ko+5L+6gtNpNTHgIK388hgsHOANdlpzQoiU0p+N0OrFY6n8X8Hg8\nrFu3jmuuucYX3gGSkpK4+OKLWbt2re/YO++8g8vlYsaMGQ0eb8aMGRiGweuvv94a5Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a397470>"
+       "<matplotlib.figure.Figure at 0x281e47b2630>"
       ]
      },
      "metadata": {},
@@ -1700,7 +1703,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8UAAAEvCAYAAABscmOhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a39a198>"
+       "<matplotlib.figure.Figure at 0x281e4808128>"
       ]
      },
      "metadata": {},
@@ -1710,7 +1713,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAFGCAYAAAAo4XNmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a39a908>"
+       "<matplotlib.figure.Figure at 0x281e2745080>"
       ]
      },
      "metadata": {},
@@ -1748,7 +1751,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8UAAAEvCAYAAABscmOhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a709940>"
+       "<matplotlib.figure.Figure at 0x281e3728080>"
       ]
      },
      "metadata": {},
@@ -1758,7 +1761,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAw0AAAFGCAYAAAAo4XNmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611915a828>"
+       "<matplotlib.figure.Figure at 0x281e366bfd0>"
       ]
      },
      "metadata": {},
@@ -1791,7 +1794,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8UAAAEvCAYAAABscmOhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a392400>"
+       "<matplotlib.figure.Figure at 0x281e365f7f0>"
       ]
      },
      "metadata": {},
@@ -1801,7 +1804,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAFGCAYAAADOyLgiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a365d68>"
+       "<matplotlib.figure.Figure at 0x281e365ff60>"
       ]
      },
      "metadata": {},
@@ -1964,9 +1967,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEbCAYAAABjg8q5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEbCAYAAABjg8q5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x161191b3198>"
+       "<matplotlib.figure.Figure at 0x281e372f0b8>"
       ]
      },
      "metadata": {},
@@ -1983,7 +1986,7 @@
     "for z in zs:\n",
     "    kf.predict() \n",
     "    kf.update(z)\n",
-    "    likelihoods.append(kf.likelihood)\n",
+    "    likelihoods.append(kf.likelihood(z))\n",
     "plt.plot(likelihoods);"
    ]
   },
@@ -2003,9 +2006,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAx4AAAEaCAYAAACfNIEBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3X1YlGXeP/73PMEMjAgMyNOmlIis\nyENfM6Xyt6DhjalrsHmXpSXlbqmZ7m9FXTXEArPVtmW5tV1dXVwprUz0l+uzBvd+czHWBwJKbXXV\nsggcUHlmhrl+f+BccDnDkw4zwLxfx+EBXPOZ87y4ODuO+XSen/OUCYIggIiIiIiIqAfJHX0DRERE\nRETU/zHxICIiIiKiHsfEg4iIiIiIehwTDyIiIiIi6nFMPIiIiIiIqMcpHX0DzsJkMsFkMkmuyWQy\nyGQyB90REREREdHdEwQBd26QK5fLIZdbn9tg4mEnJpMJtbW1jr4NIiIiIqIe4+7u3m7iwaVWRERE\nRETU45h4EBERERFRj2PiQUREREREPY41HnZirYi8ozVwPaG0tBRGoxFKpRLh4eF265d6D44BAjgO\niGOAOAbINmPAWg1zRxsnMfGwE2t/hI6q/nuCyWRCc3Oz3ful3oNjgACOA+IYII4B6rkx0FHiwZFG\nREREREQ9jokHERERERH1OCYe96CmpgaLFi1CYGAg1Go1oqOjsXPnTkffFhERERFRr8Maj3uQlJSE\nwsJCrF27FqGhofjggw8wY8YMmEwmPPvss46+PSIiIiKiXoOJx13av38/jhw5IiYbABAXF4crV64g\nJSUFTz/9NBQKhYPvkoiIiIiod+BSq7uUm5sLrVaL6dOnS64nJyfj+++/x8mTJx10Z0REREREvY9M\nEATB0TfRF8XExKC5uRlffPGF5HppaSlGjhyJP//5z/jVr34lXjeZTKiurpbEXr16FSaTyS73CwAG\ngwF/OHkT/64yQIb2tzqj/ktA63/uHAPOi+OAOAaov40BD7Ucv/w/nhimc3H0rdhdbZMJFyubEOmv\n7tb7DAaD+L1KpbqrvuVyOQYPHiy5NmDAgHa35+VSq7uk1+vxwAMPWFz39vYWX++M0WhEc3Ozze+t\nI5X1zSirsW+fRERERD3phxog43+v43cTvOGlcZ6l7l9ca8DmM9WoM5jw7kQfDHK/u9+9bRLSHd0t\nK2DicQ86OiClo9fMlEqlXQ/tMRgMmPOgB+oMJiiV/NM7I6PRKH7PMeC8OA6IY4D60xgQALxXWIWr\nN4145+QtpI/3hUrR92dxOlJZ34xNp27gn9/WAwACByjRYJJ3a+bCVjMe3dG3R5oD6XQ6q7MalZWV\nAFpnPjoSHh5u18SjqKgIgwe2DK6oqCi79Uu9R1FREQwGA8eAk+M4II4B6m9jYGx0LX7+P/8X5643\nYe9VJd58cqSjb6lHCIKAj/71LTIOfo1bDUYo5DK8/P88gNcmDINa1b3ZB1uMAWulBB1hcfldioiI\nwNdffy35PwYAUFxcDAAYObJ/DngiIiKi3uZ+H3dkPhMNmQzYXnAFHxV+6+hbsrnL12vx7OaTWPpJ\nMW41GBERNBCfvvoYliSEdTvpcBQmHncpMTERNTU1+OSTTyTXt23bhsDAQIwZM8ZBd0ZERETkfMaH\n+eHXj4cCAFbuKUHRtzccfEe2YWw24b28i/ivP/wv/nlJD7VKjpWTf4rceY9gRKCHo2+vW7jU6i5N\nmjQJ8fHxmDt3Lm7duoWQkBDs2LEDBw8eRE5ODs/wICIiIrKzV+NC8OV3N3H06x/xSs4pfLrgMfho\nXR19W3et5NpNLP3kS5R+fwsA8FiID9YkRmCwzs3Bd3Z3OONxD3bv3o1Zs2YhNTUVCQkJOHnyJHbs\n2IHnnnvO0bdGRERE5HTkchl+/3QUHvB1xw83GzD//dMwNNvv6AJbqW9qxlsHvsa0DZ+j9PtbGKhR\nYf30KGx/6eE+m3QATDzuiVarRWZmJn744Qc0NjaiqKgIzzzzjKNvi4iIiMhpeahV2DRrFNxdFDj5\nn0q8tf+co2+pW05dqURC5v/iz/mX0GwSMCUyAEf/35/hqVE/6dKuqb0ZEw8iIiIi6ldCBg3AO/8d\nDQDY+vl/kHvmOwffUdcIgoC5OadxRV8Hfw81Nj//EP7n2f8D3wF9d7lYW0w8iIiIiKjfSRjpjwXj\nQwAAv91djJJrNx18R52rbjSivLoRAHBg4TjEj/Bz8B3ZFhMPIiIiIuqXFj0eitjhvmgwmPDy9lOo\nrG1y9C11qLKm5f7cXBTwcndx8N3YHhMPIiIiIuqXFHIZMp9+EEN0brh2ox6v7TgDYy8uNtffToy8\n+2HSATDxICIiIqJ+bKCbCptmPQSNSoH/++/rWHf4vKNvqV3mGRkdEw8iIiIior5nuP8ArJseCQD4\nc/4l7PvyewffkXWVtS31HZzxICIiIiLqo6ZEBuLlnz0AAEj5+EucK7vl4Duy1LrUqn/sYnUnJh5E\nRERE5BRSJg7HYyE+qDc04+Xtp1DdYHD0LUmYi8t1Ws54EBERERH1WUqFHFkzHkTAQDWu6Otw7Oty\nR9+SRCWLy4mIiIiI+gcvdxc8FOwNAL1ue13uakVERERE1I94qJUAgFu9bakVd7UiIiIiIuo/PDQq\nAMCteqOD70RKX8NdrYiIiIiI+g0PdUvicbO+98x4CIIgLrXScVcrIiIiIqK+z0PT+5Za1TU1o9HY\ncqq6N3e1IiIiIiLq+8wzHrd60YyHub7DRSmHu4vCwXfTM5h4EBEREZFTEWs8GnpPjYe+TWG5TCZz\n8N30DCYeRERERORUBmp644xH/y4sB5h4EBEREZGT6Y3b6epr+vcZHgATDyIiIiJyMualVjWNRphM\ngoPvpkV/P8MDYOJBRERERE5mwO0ZD0EAqntJnYeYeGj751a6ABMPIiIiInIyrkoF1KqWj8G9ZbmV\nubicS62IiIiIiPqR3naIIJdaERERERH1Q61b6vaOxIMzHkRERERE/VDrlrq9pcajZTtdXT89tRxg\n4kFERERETqi3balbKW6ny+Jyu8jLy4NMJrP6r6CgwCL+6NGjiImJgZubG3x8fDB79myUl5dbxBkM\nBqxevRrBwcFwdXVFWFgYsrKyrN7DpUuXkJSUBE9PT2i1WsTHx+P06dNWY3fu3Ino6Gio1WoEBgZi\n0aJFqKmpubeHQEREREQ9zqMXHSLYYGhGbVMzgP691Erp6BuwZs2aNYiLi5NcGzlypOTn/Px8TJo0\nCZMnT8bevXtRXl6OpUuXYsKECfjXv/4FV9fWbHHevHnYvn073nzzTYwePRqHDh3CwoULUV1djeXL\nl4txFRUVGDduHLy8vLB161ao1Wq89dZbiI2NRWFhIYYPHy7Gvv/++5g5cybmzJmDd999FxcuXMDS\npUvx1Vdf4fDhwz30ZIiIiIjIFszF5b0h8TAXlqsUMnEmpj/qlb/ZsGHDMHbs2A5jUlJSEBoail27\ndkGpbPk17r//fjz66KPYunUr5s6dCwAoLS3Fli1bkJGRgZSUFABAbGws9Ho90tPT8corr8Db2xsA\nsG7dOlRUVODEiRMYMmQIAOCxxx7D0KFDkZqaig8//BAA0NzcjJSUFEycOBGbN28GAMTFxWHAgAF4\n7rnncODAAUyaNMn2D4aIiIiIbMJDY15q5fgaD3Pi4eXmAplM5uC76Tm9aqlVV127dg2FhYWYNWuW\nmHQAwCOPPILQ0FDk5uaK1/bs2QNBEJCcnCxpIzk5GfX19Th48KB4LTc3F+PHjxeTDgDw8PBAUlIS\nPv30UxiNLQOzoKAAP/zwg0Wb06dPh1arlfRPRERERL1Pb5rxcIYdrYBeOuMxf/58PPPMM3Bzc0NM\nTAxef/11PPbYY+LrJSUlAIDIyEiL90ZGRuLzzz+XxPr6+sLf398irm1b9fX1uHjxIhITE622WV9f\nj0uXLiE0NLTd/lUqFcLCwsTXO1NaWgqTydSlWFswGAzi16KiIrv1S70HxwABHAfEMUAcAwBwS18L\nAPi2XO/wZ3D2Py334iI02e1ebDEG5HI5Bg8e3OX4XpV4DBw4EAsXLkRsbCx0Oh3+/e9/Y926dYiN\njcXf//53/Nd//RcAQK/XA4C4RKotb29v8XVzrLU4d3d3uLi4iLFVVVUQBKHdNtv221n/ly9f7tLv\nazQa0dzc3KVYWzMPNnJeHAMEcBwQxwA57xhQy1v+529NY7PDn0FlXUv/A1wc8/e42z4VCkW34nss\n8cjLy7MoEG/PmTNnEB0djQcffBAPPvigeH3cuHFITExEREQElixZIiYeZu2tgbvzekdr5Xoitqtr\n85RKJeRy+612azuoVCqV3fql3oNjgACOA+IYII4BAPDQtPzP3zqD459BrbHls6OnRmW3e7HFGOju\n59geSzyGDx8uFl53pqMpGk9PT0yZMgV/+tOfUF9fD41GA51OBwCSmQ2zyspKyUyETqfD2bNnLeJq\na2vR1NQkxnp5eUEmk7XbJtA6w9G2fz8/vw7770h4eLhdE4+ioiIYDAaoVCpERUXZrV/qPTgGCOA4\nII4B4hgAAJnuBvDZdTRB4fBnsOPfXwKoxvAhgYiKGmaXPm0xBkwmE6qrq7sc32OJR0BAAObMmWOT\ntgRBANA6k2DeWre4uBhPPPGEJLa4uFiy9W5ERAR27tyJsrIySZ1HcXGxpC2NRoOQkBDx+p1tajQa\nPPDAA2Kb5usjRowQ44xGI86dO4cZM2bc2y9MRERERD2KxeX21+t3taqqqsK+ffvEg/oAICgoCA8/\n/DBycnIkNRIFBQU4f/48kpKSxGvTpk2DTCbDtm3bJO1mZ2dDo9EgISFBvJaYmIjjxLine truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwkAAAEaCAYAAABaRhuQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzt3Xt41OWd///XTGaSyZEwCUKCAhaU\nLIeAjQi6pSQKiBZWiLLbkxVc1AoK+PWUFoTEAoUuXl9YVrqe+GGXekSxPymHigLWrgcQyAYqoiLi\nohiYcMhpkpnM5/vHZAaGBMhpTpnn47q4hM/nnpl7Ju/2mnfu+/2+TYZhGAIAAACARuZwTwAAAABA\nZCFJAAAAABCAJAEAAABAAJIEAAAAAAFIEgAAAAAEsIR7ApHG4/HI4/EEXDOZTDKZTGGaEQAAANB2\nhmHo3IamZrNZZvP51wtIEs7h8XhUXV0d7mkAAAAAQZOcnHzBJIHtRgAAAAACkCQAAAAACECSAAAA\nACAANQnnaK5A+WJ7toJh3759crvdslgsGjhwYEhfG5GBGAAxAGIAEnGA9sdAczW3F2vKQ5JwjuY+\nsItVfweDx+NRQ0NDWF4bkYEYADEAYgAScYDgxMDFkgQiDQAAAEAAkgQAAAAAAWImSaiqqtLs2bOV\nnZ0tm82moUOH6qWXXgr3tAAAAICIEzM1CYWFhdqxY4cWL16sK6+8Ui+88IJ+8pOfyOPx6Kc//Wm4\npwcAAABEjJhIEjZs2KC33nrLnxhIUkFBgb766is9/PDD+pd/+RfFxcWFeZYAAABAZIiJ7Ubr1q1T\nSkqKJk+eHHB96tSp+uabb/Thhx+GaWYAAABA5DEZhmGEexLBdu2116qhoUEfffRRwPV9+/Zp0KBB\neuqpp3T33XdL8raYqqysDBh3+PBheTyekM1Xkurr6/Xnz2skSWZz01WOs39qxln/NQxDDYbk8UgN\nhqEG338NyePx/rfBkBo8hjyN/20wJI9vzFnjPAHXjcZ73r8bjXM4N3jOdw2tZ5z1SZp04TZl6JyI\nARADkDpfHPTqYtF9w7sqLSH2dnEcOe2S2yP1Tre26nEul8v/d6u1dY+VvO38e/XqFXAtNTX1gu1U\nY2K7kcPh0Pe+970m1+12u//+hbjdbjU0NARlbheyurQq5K8JAAAQTN9WuVXznkNzf5CuOHP0Jz0t\n4fYY+v8P1OjVv1epZ6pFi2+wy9LG9352wtBSbdlWHxNJgnThAyMueuKcxRLyw0vq6+v1g8tskiRz\nYxCd/duD803ZbJLizCbFmaQ4k/ex3r+bFGc++57332aTZPb/3dT4b+9939/N54w1m87ap2aSf1Ym\nU+DvN0xn3UPrud1u/98tlpj5nyrOQgyAGIDUueKgst6jJ/67QmXl9Xrp7zWaclV6uKcUdJ856vUf\nH53QoZPeL/ddE+PkUpwSrS3/4t4RKwmtFd2R1kIZGRnNrhZUVFRIOrOicD4DBw4MeZJQWlqq2cO7\nyGq1asiQISF9bUSG0tJSuVwuYiCGEQMgBiB1vjjIvvRbzXhhl9btr9IN379C43Ozwz2loKipd+v/\nvnVAz733v/IYUtckq+ZNGKCJQ3te9BfU52pvDDS3nf5iYqJwefDgwfrkk08CMnFJKisrkyQNGjQo\nHNMCAACIOT/KzdI9o7zbwB9+9X+0/+jpMM+o4/31s2O6cdm7euavX8pjSLcMzdZb/2eUJl11aasT\nhHCJiSRh0qRJqqqq0muvvRZw/fnnn1d2draGDx8eppkBAADEnkduzNHIKzJV62rQ3X/4WKdqWr/P\nPhKdqK7Xg6+U6vbnPtLXFbXK7mLT/zdlmJb/+CplpiSEe3qtEhPbjW666SaNGTNG9957r06fPq1+\n/frpxRdf1KZNm7RmzRrOSAAAAAihOLNJ//7jqzThP97T4YoazXp5t567Y1jUFjIbhqH1//OtSt7c\np+NV9TKZpDuu7aOHbuyvlITo/LodEysJkvT666/r9ttv17x58zRu3Dh9+OGHevHFF/Wzn/0s3FMD\nAACIOV2T4/XU7XmyWc3a9ukx/d+3DoR7Sm3y7ala3fWHnbr/xd06XlWvKy5J0dpfXqfifxoYtQmC\nFCMrCZKUkpKi5cuXa/ny5eGeCgAAACQNzO6ixYW5mv3yHv3H1s81qGcXjRvUI9zTarGXPjqsBX/+\nRFV1blnjTJpR0E/35vdVgiX6d6nEzEoCAAAAIs/Eq3rqzn+8XJL04Ct79Hl567rwhMvn5VX61boy\nVdW5dVWvdP155kjNHn1lp0gQJJIEAAAAhNmvbs7RiO/ZVV3foLv/62OddkZ+IfPBY1UyDCmnR6rW\n/vI6Xdk9NdxT6lAkCQAAAAgra5xZ//HT7yuri00Hj1Xr/7xcKo/HCPe0Lqiiul6SlJ2eGLUF1xdC\nkgAAAICwy0xJ0H/+PE/xFrO2fPKdVrzzebindEGOxiTBnhwf5pkEB0kCAAAAIsKQy9K1YKL3kNtl\nbx/Q2598F+YZnZ9vJSGDJAEAAAAIrn+++jLdPqK3DEOa/fIefXm8OtxTalYFKwkAAABA6Dw2foCu\n7t1VlU637v7DTlXVucM9pSbYbgQAAACEULzFrJU//74uSU3QZ+VVenTt/4R7Sk1UVNdJkjJSSBIA\nAACAkLgk1abf/zxPZpP057JvdfSUM9xTClBR5VtJSAjzTIKDJAEAAAARKa93V2WkeL+E+2oAIoFh\nGP7tRhQuAwAAACGWZrNIUkQdsFZT36A6t0cSNQkAAABAyKUlWiVJp2sjJ0lwNG41SrCYlRQfF+bZ\nBAdJAgAAACJWms2bJJyKpCTBV7ScHC+TqfOdtiyRJAAAACCC+VcSnJHTBtV/RkIn7WwkkSQAAAAg\ngvlrEiJqJaFzdzaSSBIAAAAQwc6sJEROklDRyTsbSSQJAAAAiGBd/IXLEbjdiCQBAAAACD1f4XIk\nrSQ4qkgSAAAAgLBJS4y8moSKs7obdVYkCQAAAIhYkdgC1V+TkELhMgAAABByvsLlyghqgeqgJgEA\nAAAIn0hsgUp3IwAAACCM/CsJdW41eIwwz0ZyuhpUU98gicPUAAAAgLDw1SRIUlUEbDnybTWyxpmU\nmmAJ82yChyQBAAAAESveYlaiNU5SZLRBrTir/anJZArzbIKHJAEAAAARzdcGNRI6HDka25/akztv\nZyOJJAEAAAARzn+gWgQkCbFQtCyRJAAAACDC+YqXI2K7UQy0P5VIEgAAABDhzrRBjZzCZZIEAAAA\nIIy6RNJKQhXbjULunXfe0Z133qmcnBwlJyerZ8+euuWWW/Txxx83O37Xrl0aPXq0UlJSlJ6ersLC\nQh08eLDZsStWrFBOTo4SEhJ0+eWXq6SkRC5X+AMNAAAAF+bfbhQBNQn+lYROfEaCFGFJwu9//3sd\nOnRIs2bN0oYNG7R8+XKVl5drxIgReueddwLG7t+/X/n5+aqvr9crr7yiVatW6cCBAxo5cqSOHTsW\nMHbhwoWaNWuWCgsLtXnzZk2fPl2LFi3SjBkzQvn2AAAA0Ab+wuUIOCehorG7UWdfSYioEyCefPJJ\nXXLJJQHXxo0bp379+mnRokW6/vrr/dfnzZunhIQErV+/XmlpaZKkvLw8XXHFFVq6dKmWLFkiSXI4\nHFqwYIHuuusuLVq0SJKUn58vl8uluXPnavbs2RowYECI3iEAAABaK5JaoJ4pXKYFasicmyBIUkpK\nigYMGKCvv/7af83tdmv9+vW69dZb/QmCJPXu3VsFBQVat26d/9qmTZvkdDo1derUgOedOnWqDMPQ\nG2+8EYR3AgAAgI4SSS1QY6VwOaJWEppz6tQp7dq1K2AV4YsvvlBtba1yc3ObjM/NzdVbb70lp9Mp\nm82mvXv3SpIGDx4cMC4rK0uZmZn++xeyb98+eTyedr6T1vHVS7hcLpWWlob0tREZiAEQAyAGIBEH\nklTxXY0k6VvHybB+Bq4GQ5WNW56+PXRA1d/GheZ12xkDZrNZvXr1atVjIj5JmDFjhqqrqzVnzhz/\nNYfDIUmy2+1NxtvtdhmGoRMnTigrK0sOh0MJCQlKTk5udqzvuS7E7XaroaGhHe+ifSiwBjEAYgDE\nAKTYjQOb2fvL2qo6T1g/g4pa7/dBs0lKMDXI5QrtL5GltsVAXFzrk5mgJQnbtm1TQUFBi8bu3r1b\nQ4cObXL9scce0x//+EetWLFCeXl5Te6bTKbzPufZ91o67nwsFovM5tDuzDo7AKxWa0hfG5GBGAAx\nAGIAEnEgSV2SDElStdsI62dQXeWdR2q8WQnxodtu1N4YaMv32KAlCf3799czzzzTorHNLX+UlJRo\nwYIFWrhwoe67776AexkZGZLU7CpARUWFTCaT0tPT/WOdTqdqamqUlJTUZGxzyce5Bg4cGPIkobS0\nVC6XS1arVUOGDAnpayMyEAMgBkAMQCIOJCndUS1tLpfTrbB+Bqc/OyapXN3Tk0I6j/bGgMfjUWVl\nZaseE7QkISsrS9OmTWvTY0tKSlRcXKzi4mL9+te/bnK/b9++SkxMVFlZWZN7ZWVl6tevn2w2m6Qz\ntQhlZWUaPny4f9zRo0d1/PhxDRo0qE1zBAAAQGj4Cper6xvkavDIGhee3jsVMVK0LEVYdyNJ+s1v\nfqPi4mLNnTtX8+fPb3aMxWLRhAkT9PrrrwdkRYcPH9bWrVtVWFjovzZu3DjZbDatXr064DlWr14t\nk8mkiRMnBuV9AAAAoGOk2s78XrsyjGclOPynLXfu9qdShBUuP/HEE5o3b57GjRunH/3oR/rggw8C\n7o8YMcL/95KSEg0bNkzjx49XUVGRnE6n5s2bp8zMTD344IP+cXa7XXPnztVjjz0muLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611839b5f8>"
+       "<matplotlib.figure.Figure at 0x281e372b5c0>"
       ]
      },
      "metadata": {},
@@ -2018,7 +2021,7 @@
     "for z in zs:\n",
     "    kf.predict() \n",
     "    kf.update(z)\n",
-    "    log_likelihoods.append(kf.log_likelihood)\n",
+    "    log_likelihoods.append(kf.log_likelihood(z))\n",
     "plt.plot(log_likelihoods);"
    ]
   },
@@ -2088,7 +2091,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwUAAAEvCAYAAAAUzW0AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611839b8d0>"
+       "<matplotlib.figure.Figure at 0x281e3629dd8>"
       ]
      },
      "metadata": {},
@@ -2185,7 +2188,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwUAAAE1CAYAAAC2gEz9AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x161183c8588>"
+       "<matplotlib.figure.Figure at 0x281e370cc18>"
       ]
      },
      "metadata": {},
@@ -2267,7 +2270,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvAAAAEaCAYAAAB3kWMDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611839b780>"
+       "<matplotlib.figure.Figure at 0x281e47fdb38>"
       ]
      },
      "metadata": {},
@@ -2302,7 +2305,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwAAAAEaCAYAAACmUzBaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xl4FGW6NvC7el+TTjqBhCUJhLAv\njgguCKIoChhEkDPq5bhzjspxxhkdxQ1BHUccnKPj6DfjwrgcVxQcAQHHA+go7mhkkUUgKwkkne6k\n96Wqvj9iItm37q6k+/5dVy6gu+p9nwrV1e9T9S6CLMsyiIiIiIgoKaiUDoCIiIiIiOKHCQARERER\nURJhAkBERERElESYABARERERJREmAERERERESUSjdADxJkkSJElq9pogCBAEQaGIiIiIiIh6TpZl\ntJzYU6VSQaVq+15/UiYAXq9X6TCIiIiIiGLGbDa3mwCwCxARERERURJhAkBERERElESYABARERER\nJZGkGwPQ1mDfjvpI9Rd79+5FJBKBRqPBuHHjlA6HEhTPM4oXnmsUDzzPKB7icZ61Nca1owlumACg\n41HS/YUkSRBFMSGOhfounmcULzzXKB54nlE8KHWedZQA8GwnIiIiIkoiTACIiIiIiJIIEwAiIiIi\noiTCBICIiIiIKIkk3SBgIiIiongRRREulws+n0/pUEghWq0WGo0GgiCgrKysR2WoVCqkp6fDaDRG\nJSYmAEREREQxIIoiKioqkJaWhvT09A5nZaHE5fP5IMsyBEGAyWTqURnhcBiVlZUYPHgw1Gp1r2Ni\nAkBEREQUAy6XC2lpabBarVErU5ZF+HwH4PMdhCwHIQh6mEwjYTKNgiD0vmFIfZNWq0VGRgYcDgcG\nDBjQ6/KYABARUcxJUgSiWIdI5OQf10+v1UOWQ5BlEbIsApDg9x+DJMmIRDQoLR0CQVBDELRQq1Og\n0dig0aQ2/ajVqdBobFCp+JVGfYvP50N6enqvy5FlETU1m1Ba+ghCoWOQpCAiERdkOQRB0P10/uuh\n0w1CTs49yMiYx2QgARmNRjgcjqiUxaslERFFjSj6EAiUIhgsQSBQilCo8qeGvrfznU8iSQ5IkgRA\nBb8/2KV91GoTNBobdLos6PU5MBhyYTDkQq029+BIiKKjt91+nM4dOHBgCcLh4xBFd6v3ZTmEcPgE\nACAYLMP+/b+CVjsAo0Y9h7S0mb2qm/qWaHYhYwJAREQ90rKxHwiUIByuVjQeUfQhGDwGt3tX0+ta\nrR0GQy6TAupXZFnEgQNL4HBsQDhc0+X9RLEeoliPffsWw24vxKhRz/FpALXCBICIiLpElmUEAkfg\n8RTB4/keoVCl0iF1STjsQDjsaJYU6HQDYTZPgMUyCUbjCAgCZ8WmvkOWRezeXQincwdk2d+jMsLh\nGhw//gZCoSpMmLCBSQA1wwSAiIjaJUlBeL374PV+D49nd5tdEPqjUOg4QqHjcDo/hFpthtk8Hmbz\nRJjN46FWG5QOj5LcgQNLetX4byTL/p+6EP0nRo9+IUrRUSLgLQ8iImomEqmHy/Uxysufwo8//g7H\njv0NdXU7E6bx35IoelFf/wUqK5/D4cO/Q1nZE3A6tyMcdiodGiUhp3MHHI4NvW78N5JlPxyO9+B0\n7ohKeY1efPFFCIKAr7/+utv7CoKAFStWNP173759WLFiBYqLi1tte+211yIvL69HMfZm30THJwBE\nRAQA8Hr3w+XaAa+3CLIsKR2OIhqmWPwBPt8POHHiTVgsE5Caeg7M5nGcw51irrHff3f6/HdFOFyD\nAweW4PTT9/eJrkCfffYZhgwZ0vTvffv2YeXKlZg5c2arBvv999+P3/zmN3GOMPExASAiSmKi6Ed9\n/U64XB8jFKpSOpw+RobH8z08nu+h1WbCZpuB1NRpHEBMMVNTswnh8PGYlB0OH4fD8T4yMgpjUn53\nnHHGGV3eNj8/P4aRJC92ASIiSkKBQBmqql7BkSN34cSJt9j470Q4XI3q6ndw+PBdqKz8B/z+o0qH\nRAmotPSRmHW1E0U3SksfiUnZQEN3G4vFgh9//BFz586FxWLB0KFDcfvttyMYbD6V78ldgF588UUs\nXrwYAHDuuedCEAQIgoAXX3yxqdyWTwWefvppzJgxAwMGDIDZbMaECRPw2GOPIRwOx+z4Eg2fABAR\nJRG3+1s4nR/A7z+idCj9kiyHUV//OerrP4fBkIu0tAtgtZ7G7kHUa7IsIhQ6FtM6gsEKyLIYs25A\n4XAY8+fPxw033IDbb78dH3/8MR566CGkpqZi+fLlbe4zb948PPLII7jnnnvw9NNP49RTTwXQ8Z3/\nw4cP48orr8SwYcOg0+lQVFSEP/zhD9i/fz/WrFkTk2NLNEwAiIiSgM93ENXV6xAI8M51tAQCJais\nfB61tVuRmXkpzOZxSodE/ZjPdwCS1LVF73pKkoLw+Q7AbB4bk/JDoRBWrlzZdEd/1qxZ+Prrr/Ha\na6+1mwBkZmaioKAAADB27NgudQ/685//3PR3SZIwffp02O12XHfddXj88ceRlpYWhaNJbOwCRESU\nwAKBMpSX/wVlZY+z8R8jweDPv2N2DaKe8vkOIhJxxbSOSMQFn+9gzMoXBAGFhc3HGEycOBElJSVR\nrefbb7/F/PnzYbfboVarodVqcfXVV0MURRw8GLvjSyR8AkBElIBCoWrU1PwTbvfXAGSlw0kKPt9B\nlJY+CovlF8jIWAC9PkvpkKgfkeUgZDkU4zrCkOXYPWUwmUwwGJqvo6HX6xEIBKJWR2lpKaZPn45R\no0bhySefRF5eHgwGA7788kssXboUfn90pk9NdEwAiIgSSCRSD4djE+rq/g1ZFpUOJyl5PN/C6y1C\nSsqZsNsLodWyOwJ1ThD0EARdTJMAQdBCEPQxKz8e3n33XXi9Xqxbtw65ublNr3/33XcKRtX/MAEg\nIkoQLtcnqK5eC0mK3t026hlZllBX9ync7q+QkbEQNttMDhSmDplMI6HR2BAOn4hZHRqNDSbTyJiV\n31N6fUNS0pW7942fo8Z9AECWZTz33HOxCS5BdXsMgNvtxp133onZs2cjMzOz1WpuJ9u1axfOP/98\nWCwW2Gw2LFy4EEeOtJ55onHKp5Y/jz76aJdi8ng8uO222zBo0CAYDAaccsopeOONN7p7aERE/VI4\n7ER5+V9w/PgrbPz3MZIUwokTb6Cs7HGEQtVKh0N9mMk0CipVbO/Oq1R6mEyjYlpHT4wfPx4A8Oyz\nz+KTTz7B119/DYfD0ea2F1xwAXQ6Ha644gps3rwZ69evx4UXXginkyt3d0e3EwCHw4Fnn30WwWAQ\nCxYsaHe7/fv3Y+bMmQiFQnjrrbewZs0aHDx4ENOnT0d1deuL4GWXXYbPPvus2c/VV1/dpZgWLlyI\nl156CQ888AA2b96MKVOm4IorrsBrr73W3cMjIupXXK5PUFy8Al7vXqVDoQ74/YdQUvIgnM7tkGWO\nyaDWBEENnW5QTOvQ6wf3iZWAWxo2bBieeOIJFBUVYebMmZgyZQo2bNjQ5rajR4/GO++8A6fTiYUL\nF+LWW2/FKaecgr/85S9xjrp/E+RuXokaNxcEATU1NcjMzMQDDzzQ6inAf/zHf2D79u04fPgwUlJS\nAAAlJSUoKCjAb3/7W6xaternIAQBS5cuxV//+tduH8D777+PefPm4bXXXsMVV1zR9Prs2bOxd+9e\nlJaWQq3++WSXJAlud/NFNqxWK1Sq/j0hUlFREcLhMLRaLSZNmqR0OJSgeJ71HeGwE8ePv5KwDX+H\nwwFJkqBSqWC325UOJ6qMxgJkZV0DnS5T6VCSXqyvaWVlZRg6dGiXt6+ufg/7918Vk8XA1Gorxox5\ntU+sBJxsfD4fZFmGIAgwmUy9Kqu9c6q77dtut3obu+d0JBKJYOPGjVi0aFFT4x8AcnNzce6552L9\n+vXdrbZd69evh8ViaZpzttF1112HY8eO4YsvvohaXUREfQHv+vdvfBpA7cnImAetdmBMytZqB8Ju\nnxuTsqn/iclt78OHD8Pv92PixImt3ps4cSJ+/PHHVlNCvfbaazAajdDr9Zg8eTL+8Y9/dKmuPXv2\nYMyYMdBomo9nbqx7z549PTwKIqK+RRT9KC//K/v6J4DGsQHl5f+DSMSjdDjURwiCGqNGPQetNiOq\n5Wq1GRg9+vk+2f2HlBGTWYAaB26kp6e3ei89PR2yLMPpdCI7OxsAcOWVV2LevHkYOnQoTpw4gRde\neAHXX389jhw5goceeqjTuoYPH95mPSfH0pG9e/dCkqROt+vLwuFw059FRUUKR0OJiueZciTJgWDw\nTUhS59e0RNB4TZYkqUvX8f7K4diJioq90Ot/CZUqNnd+qX2xvqZptVr4fL5u7aPXT0Vq6hw4HG9D\nlns/p70gmJCaOgc63ZRux0LR0fikT5blXv8fuN3uNs9VlUqFnJycLpcT02lAO+oqdPJ7r776arP3\nFi1ahMLCQjz66KP49a9/jczMjvtJdrWe9kQiEYhi4syX3XhBI4olnmfxI4qHEYn8E7KcnHf9+/sN\nms5IkhOiuAYaTSHU6r43Q0uyiMU1TaPR9KibV17eXxEOV6G+/pNeJQGCYEJKyjTk5f2V3c36iN7+\nP8iy3Oa5evJ4166ISQLQOGCrrbs2tbW1EAQBNputwzKuuuoqbNy4EV9//TXmzJnTYV3t1QO0/RSi\nJY1G0+8HAZ98Mmi1WgUjoUTG8yz+wuHPIIrbIAgSBKF/X6e64+RGf3+/PndNBKL4LlSqGdBqp3PN\ngDiJ9TWtK+Mm295Pg5Ej38HRo0vhcr2PSOTndo4oA2U+oNwPhCRApwKGGIGhJkB9ULine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611839bb70>"
+       "<matplotlib.figure.Figure at 0x281e370c668>"
       ]
      },
      "metadata": {},
@@ -2353,7 +2356,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwAAAAEaCAYAAACmUzBaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a271198>"
+       "<matplotlib.figure.Figure at 0x281e3761f28>"
       ]
      },
      "metadata": {},
@@ -2440,7 +2443,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAFGCAYAAABEy+MmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x161191b3ef0>"
+       "<matplotlib.figure.Figure at 0x281e48eaf28>"
       ]
      },
      "metadata": {},
@@ -2450,7 +2453,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAFGCAYAAABEy+MmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a408a58>"
+       "<matplotlib.figure.Figure at 0x281e48d9588>"
       ]
      },
      "metadata": {},
@@ -2460,7 +2463,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAFGCAYAAABEy+MmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a408080>"
+       "<matplotlib.figure.Figure at 0x281e48a8470>"
       ]
      },
      "metadata": {},
@@ -2517,7 +2520,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAFGCAYAAABEy+MmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611915bef0>"
+       "<matplotlib.figure.Figure at 0x281e4a3f048>"
       ]
      },
      "metadata": {},
@@ -2527,7 +2530,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAFGCAYAAABEy+MmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611915b6d8>"
+       "<matplotlib.figure.Figure at 0x281e496c9b0>"
       ]
      },
      "metadata": {},
@@ -2537,7 +2540,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA7oAAAFGCAYAAABEy+MmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a4f78d0>"
+       "<matplotlib.figure.Figure at 0x281e4a46c18>"
       ]
      },
      "metadata": {},
@@ -2643,7 +2646,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvoAAAEvCAYAAAA0BGXUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611839bd68>"
+       "<matplotlib.figure.Figure at 0x281e36de940>"
       ]
      },
      "metadata": {},
@@ -2790,7 +2793,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwgAAAFGCAYAAADOyLgiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xd8ldX9wPHPc3dyc2/2IhMSIOyt\noCBLFHexWrVaraPV2uH6qThYrlZF66zWaqVaW0cdWESwTEGGIDPsEEL2Hnfl7uf3R+BKTIAANzLy\nfb9evkyecc557pOQ53uec75HUVVVRQghhBBCCCEAzclugBBCCCGEEOLUIQGCEEIIIYQQIkQCBCGE\nEEIIIUSIBAhCCCGEEEKIEAkQhBBCCCGEECESIAghhBBCCCFCJEAQQgghhBBChEiAIIQQQgghhAiR\nAEEIIYQQQggRIgGCEEKIsJk5cyaKorBs2bIOn1NUVISiKPzyl7/stHYJIYToOAkQhBDiDKAoSqv/\ntFotCQkJTJw4kffff/9kNw9FURg3btzJboYQQogOUFRVVU92I4QQQpwYRVEAmDFjBgA+n49du3bx\n2WefEQgE+L//+z+effbZTm9HbW0ttbW1ZGZmEhkZ2ap9Y8eObffNgs/nY+/evURHR5OamtrpbRRC\nCHFkEiAIIcQZ4GCA8MN/0hcvXsykSZNQFIXCwkKysrJORvOOGCAIIYQ4tcgQIyGEOINNnDiRvLw8\ngsEg69ata7Xvgw8+YMyYMURHRxMREUH//v156qmncLvdbcrZuHEj11xzDVlZWRiNRuLj4xk4cCB3\n3XUXPp8vdNwP5yDMmTMnFLwsX7681TComTNnAkeeg1BeXs6dd95JdnY2BoOBxMREpkyZ0uZaDq1r\nzpw5LF26lHHjxmGxWLBarVx88cVs27btOD9FIYToWnQnuwFCCCE6V3svih988EGeeeYZEhMTuf76\n6zGbzcyfP59HHnmEBQsWsGjRIgwGAwCbNm1i1KhRaDQaLr/8crp3747NZqOgoIDXXnuNJ598Er1e\n327dgwcPZsaMGcyaNYusrKxWQcDR5iQUFhYyevRoKioqmDhxItdddx0lJSV89NFHfPHFF3z00Udc\nccUVbc6bN28ec+fO5aKLLuKOO+5g+/btzJ8/n3Xr1rF9+3YSExM7/uEJIURXpAohhDjtAWp7/6Qv\nWbJE1Wg0qqIo6r59+1RVVdWVK1eqgJqVlaVWVVWFjvX5fOrFF1+sAuoTTzwR2n7PPfeogPrpp5+2\nKb++vl4NBAKh72fMmKEC6tKlS9u0b+zYse22fd++fSqg3nTTTa22T5o0SQXUP/3pT622r1ixQtVo\nNGpsbKxqs9lC299++20VULVarbpo0aJW50ydOrXdsoQQQrQlQ4yEEOIMMnPmTGbOnMkjjzzC1Vdf\nzQUXXEAwGOTuu+8mOzsbgLfffhuARx99lKSkpNC5Op2O559/Ho1Gw1tvvRXafnCI0KGTjg+KjY1F\nown/n5LS0lL+97//kZWVxX333ddq3+jRo7n22mtpaGjg008/bXPuddddx8SJE1tt+/Wvfw3Q7tAk\nIYQQrUmAIIQQZ5BZs2Yxa9Ys/vjHP7J48WJGjx7Nu+++y/PPPx86ZuPGjQCMHz++zfm9e/cmPT2d\nffv20djYCMC1116LVqvlJz/5CTfddBPvvPMOe/fu7dTrONjGMWPGoNO1HQ17/vnnA7Bhw4Y2+4YP\nH95mW0ZGBgANDQ3hbKYQQpyRJEAQQogziKqqqKpKMBikvr6epUuXcsMNN7Q6pqmpCYCUlJR2yziY\navTgcSNGjGDFihVMmDCBjz76iJtuuonc3Fz69OnDBx980CnXcaxtPFR0dHSbbQeDjEAgEK4mCiHE\nGUsCBCGE6GIOPkBXVla2u7+ioqLVcQCjRo1i3rx5NDQ08M033zBt2jQqKyu57rrrWLJkySnRRiGE\nEOEhAYIQQnQxQ4YMAWh3TYKCggJKS0vp3r07MTExbfYbjUbOOeccHnvsMV566SVUVeWzzz47ap0a\njeaYeu8PtnHlypX4/f42+5cuXQrA0KFDO1ymEEKIjpEAQQghuphbbrkFgCeeeIKamprQ9oMrLgeD\nQW699dbQ9hUrVrQ7lKeqqgoAk8l01Drj4+MpKSnpcBvT09OZNGkSRUVFvPDCC632rV27ln/961/E\nxsYyZcqUDpcphBCiY2QdBCGE6GLOOeccHnjgAZ555hn69+/PVVddhdls5ssvvyQ/P5/Ro0dz//33\nh45/7rnn+Oqrrxg3bhw9evQgKiqKbdu28eWXXxITExPKEHQkEydO5P333+eKK65gyJAh6HQ6zjvv\nPM4777zDnvP6669z7rnncv/99/PVV18xfPjw0DoIGo2Gt99+G4vFEpbPRAghxPckQBBCiC7o6aef\nZsiQIbzyyiu88847+Hw+cnJyeOKJJ7jvvvtCi6QB3HnnncTGxrJ27Vq++eYb/H4/6enp3Hnnndx3\n331kZWUdtb4XX3wRRVFYvHgx8+bNIxgMMmPGjCMGCD169GD9+vU88cQTzJ8/n2XLlmG1Wpk8eTKP\nPPIII0aMCMtnIYQQojVFVdtZYlMIIYQQQgjRJckcBCGEEEIIIUSIBAhCCCGEEEKIEAkQhBBCCCGE\nECESIAghhBBCCCFCukQWo2AwSDAYbLVNURQURTlJLRJCCCGEEOL4qarKD3MNaTQaNJoT7//vMgGC\n0+k82c0QQgghhBCi05jN5rAECDLESAghhBBCCBEiAYIQQgghhBAiRAIEIYQQQgghREiXmIPQ3mTk\ncI3ROpJt27bh9/vR6XT069evU+sSpxa5912b3P+uS+591yX3vms7Gfe/vTm24UrA02UDhHDN8j6S\nYDBIIBD4UeoSpxa5912b3P+uS+591yX3vmtSVZWP1pfSXRtACZ78+x+uAEF+goUQQgghhDgO760t\n5oGPt/Dw4moCQfXoJ5wmJEAQQgghhBDiGOWXNfHYf7cDMDojEq3mzFlfSwIEIYQQQgjRYVtKG7nr\n/Y0UVDtOdlNOmqZmH3e+twFvIMikvslckRd1spsUVhIgCCGEEEKIDstNiqLe6eWmv39Ltd19sptz\nUjz8yVaK612kx0Yw+6pBYRv7f6qQAEEIIYQQQnSYxxeksMZJWWMzv/rHepq9gZPdpB/dDSOzSIuJ\n4C/XDyU6Un+ymxN2EiAIIYQQQogjWryjimcX7kRVVWLNBv5529nEROrZXNrEPR9sIngGTdDtiFE5\n8Sy7fxwD02NOdlM6hQQIQgghhBDisBbvqOKOf37Hq0v3MndTOQDdE8y88YvhGLQaFmyr5OkFO09y\nKztfg9PL3prv513otWfuY/SZe2VCCCGEEOKELN1ZzW/+uQFfQOXiASlcMjA1tO+s7nE8e/VAAP76\ndSHvrd1/sprZ6YJBlXs+3MTlL69k0faqk92cTicBghBCCCGEaGPpzmpuf/c7vIEgF/VP4cVrh7Tp\nNb9icBr3TuoVOl5Vz8yhRq8t38uyXTX4gyppsRGh7YU1Dl5fvhe3P3gSWxd+XWIlZSGEEEII0XFL\nd7UODl66rm1wcNDvJ+SSHhvB5YO6nfbZfPyBIEV1TnZU2NldZaeozkl+WRP7al0AnJsbzwfrSrC7\n/WwubQylen1oTDzDks+cx+oz50qEEEIIIcQJq7a5+c0/W4KDyf2OHBwAKIrClUPTQ9+rqorLG8Bs\nPPJj5q5KOxuLG0iJNpESbSLVGoE1QheWIKOp2UdRrZOiOicub4Bmb4DyxmYqmtw0+wIEVZVAUEVV\nIRBUsXt8NDh91Dg8eI/wNmDJzhqgJvS9TqMwKieeyDPsifoMuxwhhBBCCHGs9lTZyYyPxKjTkmQ1\n8dgV/Vm2q7rdYUVH4gsEmT43n23lNv7+yxHsr3OxvcLG9nIb2ytsvHDNYLonmAH4encNT87f0ep8\nk15DirUlYHj0kr70T4sO7VNVlYJqB6sL6yiucxETqcds1LGxuJE91Q7cvgAurx+XJ4Dd4z/uzyLS\noKVHgpn8chsAyVYjY3ITiIsy4vYFsJh0RBn1pMdGcF6vRKIj9GzevBmfz3fcdZ5qJEAQQgghhOiC\n3L4AX+ZX8K+1xawrauDl64Zw2aBuAPxseAZXD0s/5t78aruHhduqqHd6Gf7Eojb7t5U3hQKEbjER\njO+dSKXNQ2VTMw0uH25fkKI6F0V1LvyBloBg7b46Vu+tY01hPbUOT4fbkmgx0i3ahEGnIclioluM\niegIAwAefwCvP4jbF8Tp8bOlrJFIg46XrxtCWkwEGo3CnG/2MTgzlkHp0af90KljJQGCEEIIIUQX\nUlzn4h+ri/h4QymNrpZeb61GaZXCEziuh+K0mAj+duMwrn9zLW5fkESLkX7drPRNtdK3m5Wzu8eH\njr1kYCoT+ySxqaSRdfvq2VrWRK3DQ6PLh83t47q/raHZ13oRNqNOw4jsOPJSLNjcPhpdPnolWxiW\nFYvFpMOk1xJh0JJsNaEA6/c3MLZXYuj8G95cy8qC2nbbrtMomI06NJqW6/7lud2P+frPFBIgCCGE\nEEJ0AV5/kJcW7+GNrwvxBlrG2afFRHDtiAx+NiKDZKspLPUMy4pj5QPjCagqydaWjD+qquLxB6my\nuZm7qZaNxY1sKmlkW3kTvsDhMx8ZdRrSYiIorHUC8IcJudw5PveowcvSXdU8+mk+1XY38/8whp7J\nFgCiI/VYTDqsptb/H5IZw5VD04kzG8LyGZzuJEAQQgghhOgCdBqFFQW1eANBzs2N57bRPTivVyJa\nzfEPn1FVlVqHl8IaB3trnBTWOMgvb2JzSRO+QBBrhB6PL3BgYnD7ZSRZjIzoHseQjLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16119201cc0>"
+       "<matplotlib.figure.Figure at 0x281deee4a90>"
       ]
      },
      "metadata": {},
@@ -2988,7 +2991,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvAAAAEaCAYAAAB3kWMDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a469a20>"
+       "<matplotlib.figure.Figure at 0x281e372be80>"
       ]
      },
      "metadata": {},
@@ -3252,7 +3255,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvAAAAEaCAYAAAB3kWMDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a58be10>"
+       "<matplotlib.figure.Figure at 0x281e361a160>"
       ]
      },
      "metadata": {},
@@ -3374,7 +3377,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a70cef0>"
+       "<matplotlib.figure.Figure at 0x281e4c0b438>"
       ]
      },
      "metadata": {},
@@ -3519,7 +3522,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a6daf28>"
+       "<matplotlib.figure.Figure at 0x281e4866630>"
       ]
      },
      "metadata": {},
@@ -3588,7 +3591,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEgCAYAAAD4wLZnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a2cc160>"
+       "<matplotlib.figure.Figure at 0x281e4826ba8>"
       ]
      },
      "metadata": {},
@@ -3598,7 +3601,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3XlYVNUbwPHvsK8qiyhi7msm7uZG\n4lq5ZLmllgu22M8KK3NNcS2XtNRyyXJLsTLNSrLcMS01zTWX3FNRVEARQRCY+f1xnBmGfZ8B3s/z\nzAP33DuXd+ayvJx7zns0Op1OhxBCCCGEEMKsrMwdgBBCCCGEEEIScyGEEEIIISyCJOZCCCGEEEJY\nAEnMhRBCCCGEsACSmAshhBBCCGEBbMwdAIBWq0Wr1Zq0aTQaNBqNmSISQgghhBAi93Q6HamLH1pZ\nWWFllXG/uMUk5rGxseYOQwghhBBCiALj7OycaWIuQ1mEEEIIIYSwAJKYCyGEEEIIYQEkMRdCCCGE\nEMICWMQY8/QmeWY1Bic7Tp48SVJSEjY2NtSrVy9P5xL5S66NZZLrYpnkulguuTaWSa6LZSpp1yW9\nOZRZFTax2MQ8q1mr2aHVaklOTs6Xc4n8JdfGMsl1sUxyXSyXXBvLJNfFMsl1yToxL5nvihBCCCGE\nEBZGEnMhhBBCCCEsgCTmQgghhBBCWABJzIUQQgghhLAAFjH5M6eSk5O5e/cucXFxmR5na2uLjY0N\nGo2Gq1evFlJ0Ijvk2piysrLC3d0dR0dHc4cihBBCCDMpcol5cnIyYWFhuLm54e7ununs1ri4OHQ6\nHRqNBicnp0KMUmRFro2pxMREbty4gY+PD9bW1uYOR4hiJSEBIiLUQ6MBJydwdFQfnZzAzk61CyGE\nuRW5xPzu3bu4ubnh6upq7lCEyDe2trZ4enoSGRmJl5eXucMRosDFxUF4ONy8aXzcvQvW1mBrq5Jl\nW1vjI+W2/nMbG/WciAi4fTv9R0QE3LuXeSxWVsYkPXXSXrkytG6tHvXqqWOFEKKgFLnEPC4uDnd3\nd3OHIUS+c3R0JDIy0txhCJEnkZFw/jzcuJE28U65ff++uSM10mpVPOnFtGcPrFmjPi9TBlq2VEl6\nmzbQrFnhximEKP6KXGIOWRdnF6Ioku9rUVTcuwfnzsHZs+pjys/v3DF3dEbu7uDpCWXLqoenp+rx\njouII+78deJidcThyAMXL+KS7IiLw+SRnGx6vrt34ddf1QNUj32dOjXw9Y2hSZN4ypeHcuUK/3UK\nIYqPIpmYCyGEKFhaLZw+DWfOpE3Ab93K/XnLlFHJa7lyGBJZ/cPdXX3dxETj4+HDTD4/cJhSSVGU\nLZVAWec4yjrFUtb2LmWrOOMx9jVs0vsLFxYGM2ZALVewt1cD0GNiYNw48PExHKbTQXw8HD8Of/wB\ne/eqjylfe1IS/POPM//848zatTBypBr6UqmSOlV6jwoV1FAcIYRIjyTmQgghePAADh5UCejevfDn\nnxAdnbNzVKoENWuqR8WKqZJvXThev6/H4d4t8PCA3r1NEuFcCfox/UHfD6My/uu2fj24PkrKwfhx\nwwYIDDQcptGoseZPPqke772nkvULF4xJ+t696h+XlP77Tz0yU7aseumVKkHTpmp4TPPmUKpU9l62\nEKL4ksRcCCFKoIgIlXzrE/FDh1RPdFa8vY3Jd82aUKuW+li9ukpk06XvpdYnxOHhajtVL3WOeXio\nc+mTa1A94N7eGT8nMtL0eFDbERFZfjmNBmrUUI8hQ1Tb7t3/8Pff9pw4UZpzJ0tz8ngydxMyrzSl\nn5h69Cj8/LPx3PXqQYsW6tGyJdSpI5NNhShpJDG3ICtXriQgIACAXbt24e/vb7Jfp9NRs2ZNLly4\nQNu2bQkNDS38IAUA169fZ+nSpTz//PM0bNjQ3OEIkSmdDi5eNCbhf/yhhqlkxstLTXBs1MiYfNeo\nAa73wlSvc2QkOHhA82z0fGezlxpQSbz+/Fn1rPfurRJ8/Tn1w1LeeivjWHKTzGeiTJlknnoqmg4d\n4mgQ+g10CCfOyoWwe66ExZQiLMqRMI0PYVXaEBaG4XHjhhoKo6fTwT//qMdXX6m20qVVb70+WX/y\nSTXcRwhRfEliboFcXV1ZtmxZmsR89+7dXLhwQUpFWoDr168zZcoUqlSpIom5sFjXr8Pq1bByZdoh\nF6nVrq0S8TZtVNWRGjXSqe2d257v7PZS5/T8Pj5q34YN6lze3iopzyyW3CTz2fXodTqRSE2PKGp6\nREEVQHsSprYxOVSrVf8sHTgA+/bB/v2qBz3lhNPoaNi6VT30fH2hUyfo3Bn8/DK5SyGEKJIkMbdA\nL774IsHBwSxcuJBSKQYdLlu2jJYtW3Ivq6K8Fu7BgwfodDpzhyFE0ZdO73K8hw+bNsGKFbBli0oA\nU7Ox0dHEJ5w23hdp83gUrQKb4tUgGz3GOen5Tim7vdS5Ob+PT+ZfO73jc5rM66XXm59SDnrjrayM\nw2Jeekm1xcXB338bE/V9+9TpUjp+XD3mzlVfxs9PJeqdOkGDBjL0RYiiTn6ELVD//v0B+Oabbwxt\n0dHRbNiwgaFDh6Y5/uHDh0yfPp06depgb29P2bJlCQgI4Pbt2ybHfffdd3Tu3Blvb28cHR2pW7cu\nY8eOJTY21uS4ixcv0q9fPypUqIC9vT3lypWjQ4cOHD161HCMRqNh8uTJaWKpUqUKQ/SDL1HDczQa\nDVu3bmXo0KGULVsWJycnEhISADh//jwDBgzAy8sLe3t76taty8KFC03OGRoaikajYe3atYwZMwZv\nb29cXFzo3r07N2/eJCYmhtdffx1PT088PT0JCAjgfqqCxDqdjkWLFtGwYUMcHR1xc3Ojd+/eXLx4\n0eQ4f39/nnjiCQ4ePIifnx9OTk5Uq1aNmTNnon2U4YSGhtLsUQHjgIAANBqNyfuRnfdPiDzT9y6H\nh6PTWHHoiDVvPnOBCuW19O2rSvqlTMr9/GDaNNi17jbRQ99jf/8FzHl6G897/YnXlx+q82Ult+Oz\ne/dWvdKPfu4NvdS9euXP+XNKn8xPnao+Zjcpf/R+Y2Vl6M23uXnTeEx2X2cGnO6E4Xd4PqPvB/FD\n2/lcPxjG5cvwzTcwYgQ0aWJ6FyMhAbZvhzFjoHFjNdF2wAB1hyQ7l1MIYXmkx1wvJ+MaC1ipUqXo\n3bs3y5cvZ9iwYYBK0q2srHjxxReZN2+e4VitVkuPHj3Ys2cPo0ePplWrVvz3339MmjQJf39/Dh06\nhOOje53nzp2jS5cuvPPOOzg7O3PmzBlmzZrFX3/9xc6dOw3n7NKlC8nJycyePZtKlSoRERHBn3/+\nyd27d3P9moYOHUrXrl1ZvXo1sbGx2Nracvr0aTp27EilSpWYO3cu5cuXZ8uWLQQGBhIREcGkSZNM\nzjF+/HjatWvHypUruXz5Mu+//z79+/fHxsaGBg0a8M0333DkyBHGjx+Pq6srCxYsMDx32LBhrFy5\nksDAQGbNmkVUVBRTp06lVatWHDt2jHIpig+Hh4fz0ksvMXLkSCZNmsTGjRsZN24cFSpUYNCgQTRu\n3JgVK1YQEBDAhAkT6Nq1KwAVK1YssPdPiDTWrydc403w301Zeawh/9xKW0C7cmUYPBgGDVKTMwGY\nvxbcHXLe6w25H5+d3V7qfB7/na8y6M0vvWMHD/r0UW157Y1PNYxHM3MGlceNo3I/H/r1U4dFRsKO\nHbBtmxricuWK8RS3b6skXt+nU7cudO0Kr72m5gkIISyfJOZQcBUD8mDo0KG0a9eOkydPUq9ePZYv\nX06fPn3SjC9ft24dv/32Gxs2bKBnz56G9gYNGtCsWTNWrlzJ//73PwAmTJhg2K/T6WjdujV169al\nbdu2HD9+HF9fXyIjI/n333+ZN28eL7/8suH4lOfOjQ4dOvDFF18YtuPi4hg3bhwuLi7s3bvXMGSn\nU6dOJCQkMHPmTAIDA3FzczM8x9fXlxUrVhi2z5w5w7x58wgMDOTjjz82PH/fvn0EBwcbEvP9+/fz\n5ZdfMnfuXN577z3D8/38/KhVqxaffPIJs2bNMrRHRkayefNmmjdvDkDHjh0JDQ1l7dq1DBo0iFKl\nSvHEE08AUL16dVq0aGHy3IJ4/4TQe/gQQkJg5cKn2Xy+Fsk60xufjjYP6dXPjoAA8PdPZ2hDXnql\n8zI+OztDTgpy/HdeZfC+2Vy/btqW06E1etkcxuPhAX37qodOp2rL65P0XbvU26V3+rR6zJkDTz+t\n3sZnnwVr65yHJ4QoHDKUBdL/hejqqn4hmknbtm2pXr06y5cv58SJExw8eDDdYSwhISGUKVOG7t27\nk5SUZHg0bNiQ8uXLm1RuuXjxIgMGDKB8+fJYW1tja2tL27ZtATj9qESDu7s71atX5+OPP+aTTz7h\nyJEjhiEcedEr1a3c+Ph4QkNDee6553BycjKJvUuXLsTHx7N//36T53Tr1s1ku27dugCGHuuU7VFR\nUYbhLCEhIWg0Gl5++WWTr1O+fHkaNGiQprpN+fLlDUm5nq+vL/9lVZyYgnv/RMn24IGG3btLM2VK\nRXx81MiITefqmCTlrR+7wpfPbiD8w+WsXg3t22cw3tjDwzjUQi8hQS2LmRV9j7C3txon4+2dvx0Y\nBX3+vMjgfUtK0XmQJ7n4h0mjgVrOYbyZNJ+fGgQROfkz9m68TVCQKreY8vpv2QLdu6sx7R9/rL6c\nEMLySI85FN64xhzQaDQEBASwYMEC4uPjqVWrFn5+fmmOu3nzJnfv3sUug6XkIh69hvv37+Pn54eD\ngwPTp0+nVq1aODk5cfXqVXr27MmDBw8MX3fHjh1MnTqV2bNnM3LkSNzd3XnppZf48Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a2218d0>"
+       "<matplotlib.figure.Figure at 0x281e4834e10>"
       ]
      },
      "metadata": {},
@@ -3660,7 +3663,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611920a400>"
+       "<matplotlib.figure.Figure at 0x281e48ff1d0>"
       ]
      },
      "metadata": {},
@@ -3670,7 +3673,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEaCAYAAABdIZKsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xd4U2X7wPFv0pTuQgejlF02CAiI\nTClDQKZAUURBQF8noq/IlKkgoICCgqA/hiCoCMKrRUA2IkOQIYKIioAUygirtHSkye+PhzRNB3Qk\nPWl6f67rXM05PTm5k9Mmd55zP8+js1gsFoQQQgghhBCa0msdgBBCCCGEEEIScyGEEEIIIVyCJOZC\nCCGEEEK4AEnMhRBCCCGEcAGSmAshhBBCCOECDFoHAGA2mzGbzXbbdDodOp1Oo4iEEEIIIYTIO4vF\nQsbBD/V6PXp99u3iLpOYx8fHax2GEEIIIYQQTuPn53fXxFxKWYQQQgghhHABkpgLIYQQQgjhAiQx\nF0IIIYQQwgW4RI15Vp0871WD4yjHjh3DZDJhMBioU6eO0x9P5IycF9ck58V1yblxTXJeXJOcF9fk\nbuclqz6U9xrYxGUT83v1WnUUs9lMampqgT2eyBk5L65JzovrknPjmuS8uCY5L66pKJyXeyXm7vms\nhRBCCCGEKGQkMRdCCCGEEMIFSGIuhBBCCCGEC5DEXAghhBBCCBfgEp0/cys1NZXr16+TkJCQ72N5\nenpiMBjQ6XT8+++/DohOOEJROy96vZ7g4GB8fHy0DkUIIYQQGil0iXlqaioxMTEEBQURHBx8z96t\n95KQkIDFYkGn0+Hr6+ugKEV+FbXzkpKSwoULFwgPD8fDw0PrcIQQQgihgUKXmF+/fp2goCACAgK0\nDkUIh/H09CQ0NBSj0UipUqW0DkcIIWxiYmDVKjAaISQEoqIgPFzrqIRwS4WuxjwhIQF/f3+twxDC\n4Xx8fEhKStI6DCGEsImJgalTITYW9Hr1c+pUtV0I4XCFLjGHew/OLkRhJH/XQgiXs2oVBASAl5da\n9/JS66tXaxuXEG6qUCbmQgghhCgARqMtKbfy8oIrV7SJRwg3V+hqzIUQQghRQEJC4ORJ+PtvSEgA\nX1+IiIAaNbSOTAi3JC3mQgghhMhaixawcydcv65qzK9fV+vNm2sdmRBuSRJzF7JkyRJ0Oh06nY7t\n27dn+r3FYqFq1arodDoiIyMLPD5hc/78eSZOnMjhw4e1DkUIIZznp5+gdWsICgKLRf1s3Rp279Y6\nMiHckpSyuKCAgAAWLlyYKfnesWMHf//9twwV6QLOnz/PpEmTqFSpEg0aNNA6HCGEq7kzxGDpY8dI\nCgjgVseOWkeUN0YjhIaqJT2pMRfCKaTF3AU9/vjjrF69mps3b9ptX7hwIc2aNaNChQoaReYYt2/f\nxmKxaB2GEEI4R4YhBosZjZRatKhwDjEYEgIZh3FNSsqcqAshHEIScxf0xBNPAPDFF1+kbbtx4war\nV69m8ODBmfZPTk5m8uTJ1KxZEy8vL0qWLMmgQYO4fPmy3X5fffUVHTp0ICwsDB8fH2rVqsWoUaOI\nj4+32+/UqVP07duXsmXL4uXlRenSpWnXrp1d2YZOp2PixImZYqlUqRIDBw5MW7eW5/zwww8MHjyY\nkiVL4uvrmzZe959//km/fv0oVaoUXl5e1KpVi7lz59odc/v27eh0OlasWMHIkSMJCwvD39+fbt26\ncfHiReLi4njuuecIDQ0lNDSUQYMGcevWLbtjWCwW5s2bR4MGDfDx8SEoKIioqChOnTplt19kZCR1\n69Zl//79tGrVCl9fX6pUqcK0adMwm81p8TzwwAMADBo0KK38yPp65OT1E0K4sQxDDFqKFcPs6+v8\nIQZjYmD2bBg/Xv10xBeBqCiIi7Ml50lJar137/wfWwiRiZSy3KE7fx7WrXOJmc0CAwOJiopi0aJF\nPP/884BK0vV6PY8//jgffPBB2r5ms5kePXrw448/MmLECJo3b86ZM2eYMGECkZGRHDhwAB8fH0Al\nwZ07d+a1117Dz8+PEydOMH36dH7++We2bt2adszOnTuTmprKu+++S4UKFbhy5Qq7d+/m+vXreX5O\ngwcPpkuXLixbtoz4+Hg8PT05fvw4zZs3p0KFCsycOZMyZcqwceNGhg4dyoULFxg9erTdMcaMGUOb\nNm1YsmQJp0+f5o033uCJJ57AYDBQv359vvjiCw4dOsSYMWMICAhgzpw5afd9/vnnWbJkCUOHDmX6\n9OlcvXqVt956i+bNm3PkyBFKly6dtm9sbCxPPvkkw4YNY8KECaxZs4bRo0dTtmxZBgwYQMOGDVm8\neDGDBg1i7NixdOnSBYBy5co57fUTQjMy62PuZTHEoKVYMeeWf1hb6a1fCKwTAY0enb/zFR6ujrF6\ntYo/LAyGDJG/ASGcRBJzVFJumDEDgoMd+4aWD4MHD6ZNmzYcO3aMOnXqsGjRIvr06ZOpvnzlypVs\n2LCB1atX06tXr7Tt9evX54EHHmDJkiW8+OKLAIwdOzbt9xaLhRYtWlCrVi1at27Nr7/+Sr169TAa\njfzxxx988MEHPPXUU2n7pz92XrRr144FCxbYbXv99dcJCAhg165dBAYGAvDwww+TlJTEzJkzeeGF\nFwgODk7bv169eixevDht/cSJE3zwwQcMHTqU9957L+3+e/bsYfny5WmJ+d69e/n000+ZOXMmr7/+\netr9W7VqRfXq1Zk1axbTp09P2240Gvn+++9p0qQJAO3bt2f79u2sWLGCAQMGEBgYSN26dQGIiIig\nadOmdvd1xusnCgl3S2Kdley5u5AQ9VqlS851ycnOLf/IaiIgUAn10KH5O3Z4eP6PIYTIESllATzW\nrnW5mc1at25NREQEixYt4ujRo+zfvz/LMpbo6GhKlChBt27dMJlMaUuDBg0oU6aM3egup06dol+/\nfpQpUwYPDw88PT1p3bo1AL///jsAwcHBRERE8N577zFr1iwOHTqUVsKRH70zXPZMTExky5Yt9OzZ\nE19fX7vYO3fuTGJiIvv377e7T9euXe3Wa9WqBZDWYp1++9WrV9PKWaKjo9HpdDz11FN2j1OmTBnq\n16+faQScMmXKpCXlVvXq1ePMmTP3fJ7Oev1EIeCOU5drOeujM8oyCkqG8g9dcjL6hATnln84cyKg\nwnwuhChkJDEHdC44s5lOp2PQoEF8/vnnzJ8/n+rVq9OqVatM+128eJHr169TrFgxPD097ZbY2Fiu\n3HkOt27dolWrVuzbt4/Jkyezfft29u/fzzfffAOoDpnWx92yZQsdO3bk3XffpWHDhpQsWZKhQ4cS\nFxeX5+cTFhZmt240GjGZTHz44YeZ4u7cuXPaPumlbz0HKFas2F23JyYmpr1GFouF0qVLZ3qsvXv3\npr1GViEhIZni9/LySnuN7sZZr58oBNxx6nKt3hsL+5cca/lHWBiYzSSHhnJp8GDnXmVwVifNwn4u\nhChkpJQFsISEoLt4ETw9bRuTktSbqoYGDhzI+PHjmT9/PlOmTMlyn9DQUEJCQtiwYUOWv7eWvmzd\nupXz58+zffv2tFZyIMu654oVK7Jw4UIATp48ycqVK5k4cSLJycnMnz8fUIlqUsYPATIn01Y6nc5u\nPSgoCA8PD/r378/LL7+caf/ExESHjT4TGhqKTqfjxx9/xCtjkgFZbsuPnLx+wg254Bf8fMuiJCMv\n742pqRAdDYcOgbe3mjzS1xf8/Gy30y9+X27A11CKoGJmdODYsoyCcqf84+KRI6SkpOCZ/vPFGaKi\nVMIM6vWydtIcMiR/x71LiUzS80O5eFH9iWS13LoFlSqpSUKrV1dL5cpwp+1ECJEFScyB1EcfRT9j\nhkrMHfmGlk/h4eEMHz6cEydO8PTTT2e5T9euXfnyyy9JTU3lwQcfzPZY1sQ4YxKase47o+rVqzN2\n7FhWr17NwYMH07ZXqlSJX3/91W7frVu3ZhoNJTu+vr60adOGQ4cOUa9evbRWbquEhASHDanYtWtX\npk2bRkxMDI899phDjml9He/Vip7d6yfckIOSWJeSz2TPZIIvvoB33oETJ3LzwM8AEOyTQJPwGB4M\nj6FJeAwPJP9Lydw9gzy5dUs9TT8/tXh4FMCD5peTOmleOpvIwYt1OHghjGOXS3IhLoDYW/7E3vLj\n2qu5P56Hh0rOrYl6zZrQvj1EROQrTCHchiTmgKVsWUzDh+P5/fcu1+t82rRpd/193759Wb58OZ07\nd+bVV1+lSZMmeHp6cu7cObZt20aPHj3o2bMnzZs3JygoiBdeeIEJEybg6enJ8uXLOXLkiN3xfv31\nV4YMGUKfPn2oVq0axYoVY+vWrfz666+MGjUqbb/+/fszbtw4xo8fT+vWrTl+/DgfffQRxYsXz/Fz\nmz17Ni1btqRVq1a8+OKLVKpUibi4OP766y/Wrl3L999/n7sXKxstWrTgueeeY9CgQRw4cICHHnoI\nPz8/Lly4wK5du7jvvvvSOsjmVEREBD4+PixfvpxatWrh7+9P2bJluXLlSo5eP+GGnNViqaU8JntJ\nSfDZZzBtGvzzT94f/uptXzb8VY0Nf1VL21Z5DTRpAg8+qH7ef79qZc8vkwnWr4dFi1Trvslk+52X\nly1Jz2opVQqefx7q1ct/HPmSj06aFouqTjl40H6JiRnp0BBTU+Gvv9SS/i2+Zk3o2lUtzZvbX8AW\noiiRxPwOS9myhecSaToeHh58++23zJ49m2XLljF16lQMBgPlypWjdevW3HfffYCqm163bh3Dhg3j\nqaeews/Pjx49evDVV1/RsGHDtOOVKVOGiIgI5s2bx7///otOp6NKlSrMnDmTV155JLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1611a737898>"
+       "<matplotlib.figure.Figure at 0x281e3650d68>"
       ]
      },
      "metadata": {},