minor wording changes.

2018-04-28 14:22:29 -07:00
1 parent 49673c6bc2
commit 7963e3f15f
@@ -337,7 +337,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138c3414a8>"
+       "<matplotlib.figure.Figure at 0x1c9dae4b828>"
      ]
     },
     "metadata": {},
@@ -565,7 +565,7 @@
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ce062e8>"
+       "<matplotlib.figure.Figure at 0x1c9dbbb94a8>"
      ]
     },
     "metadata": {},
@@ -603,9 +603,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138b3a6e80>"
+       "<matplotlib.figure.Figure at 0x1c9dae4b208>"
      ]
     },
     "metadata": {},
@@ -615,8 +615,8 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "[[11.007  3.032]\n",
-      " [ 3.032  2.311]]\n"
+      "[[11.54   3.224]\n",
+      " [ 3.224  2.378]]\n"
     ]
    }
   ],
@@ -644,9 +644,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138b3d85f8>"
+       "<matplotlib.figure.Figure at 0x1c9da2f95f8>"
      ]
     },
     "metadata": {},
@@ -656,8 +656,8 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "[[ 0.996 -0.004]\n",
-      " [-0.004  1.004]]\n"
+      "[[ 1.002 -0.003]\n",
+      " [-0.003  1.   ]]\n"
     ]
    }
   ],
@@ -681,19 +681,20 @@
   "source": [
    "## Multivariate Normal Distribution Equation\n",
    "\n",
+    "Recall the equation for the normal distribution from the **Gaussians** chapter:\n",
+    "\n",
+    "$$ \n",
+    "f(x, \\mu, \\sigma) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}} \\exp \\Big [{-\\frac{1}{2}}{(x-\\mu)^2}/\\sigma^2 \\Big ]\n",
+    "$$\n",
+    "\n",
+    "\n",
    "Here is the multivariate normal distribution in $n$ dimensions.\n",
    "\n",
    "$$\n",
    "f(\\mathbf{x},\\, \\mu,\\,\\Sigma) = \\frac{1}{\\sqrt{(2\\pi)^n|\\Sigma|}}\\, \\exp  \\Big [{ -\\frac{1}{2}(\\mathbf{x}-\\mu)^\\mathsf{T}\\Sigma^{-1}(\\mathbf{x}-\\mu) \\Big ]}\n",
    "$$\n",
    "\n",
-    "I urge you not to try to remember this equation. We will program it in a Python function and then call it if we need to compute a specific value. However, note that it has the same form as the univariate normal distribution:\n",
-    "\n",
-    "$$ \n",
-    "f(x, \\mu, \\sigma) = \\frac{1}{\\sqrt{2\\pi\\sigma^2}} \\exp \\Big [{-\\frac{1}{2}}{(x-\\mu)^2}/\\sigma^2 \\Big ]\n",
-    "$$\n",
-    "\n",
-    "The multivariate version merely replaces the scalars of the univariate equations with matrices. If you are reasonably well-versed in linear algebra this equation should look quite manageable; if not, don't worry, we have code to compute it for us! Let's plot it and see what it looks like."
+    "The multivariate version merely replaces the scalars of the univariate equations with matrices. If you are reasonably well-versed in linear algebra this equation should look quite manageable. If not, don't worry, both FilterPy and SciPy provide functions to compute it for you. Let's ignore the computation for a moment and plot it to see what it looks like."
   ]
  },
  {
@@ -707,7 +708,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138cedc6d8>"
+       "<matplotlib.figure.Figure at 0x1c9dbf425f8>"
      ]
     },
     "metadata": {},
@@ -832,6 +833,34 @@
    "multivariate_gaussian(x, mu, P)"
   ]
  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can get the same result from the `scipy.stats` module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0315\n"
+     ]
+    }
+   ],
+   "source": [
+    "import scipy\n",
+    "try:\n",
+    "    print('{:.4f}'.format(scipy.stats.multivariate_normal(mu, P).pdf(x)))\n",
+    "except:\n",
+    "    print('you have an old version of scipy, upgrade it now!')"
+   ]
+  },
  {
   "cell_type": "markdown",
   "metadata": {},
@@ -850,16 +879,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138a368518>"
+       "<matplotlib.figure.Figure at 0x1c9dbce6b70>"
      ]
     },
     "metadata": {},
@@ -888,7 +917,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
@@ -897,7 +926,7 @@
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138b3d8208>"
+       "<matplotlib.figure.Figure at 0x1c9dc01d518>"
      ]
     },
     "metadata": {},
@@ -928,14 +957,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzt3XuMZVte2Pfv2u+9z/vUsx/33r4zXIYYEjBBhATFJgbsMcEMTmQJMBaJLcWOTRJHiRyQhUD+y7IdO46DSRzPBBxeQgTCBIPNBAuNkGJCGF4zDDAD99Gv6nqcOs/93nvlj1N1b8+de29XV63zqFO/j9TqrtNVu1ZXn7N/57d+a/2W0lojhBBCmGKtegBCCCE2iwQWIYQQRklgEUIIYZQEFiGEEEZJYBFCCGGUBBYhhBBGSWARQghhlAQWId6FUur7lFLfd92uLcSqSWAR4gKUUr5S6sNKqdeVUhOl1K8rpf70M77m284+f6aU+j+VUv1ljVeIVZLAIsTFOMB94I8DHeB7gJ9QSt17p09WSn0x8L8AfwHYA2LgHy9joEKsmrPqAQhxHWitZ8D3PfXQzyqlXgX+beC1d/iSPw/8X1rrjwMopb4H+LRSqqW1nix4uEKslGQsQlyCUmoP+ELgU+/yKV8M/Ob5B1rrPwDys68RYqNJYBHiOSmlXOBHgB/SWv/uu3xaExi97bER0Frk2IRYBxJYhHgOSikL+N+ZZx/f+R6fOgXab3usDcg0mNh4UmMR4oKUUgr4MPNi/DdorYv3+PRPAV/61Ne+D/CB31/oIIVYA5KxCHFxPwD8G8Cf0Vonz/jcHwH+jFLq31dKNYC/BfyUFO7FTSCBRYgLUEq9BPxl4MuAA6XU9OzXn3+nz9dafwr4K8wDzCHz2spfXdZ4hVglmQoT4gK01q8D6jm/5keBH13MiIRYX5KxCCGEMEoyFiHe3S9d02sLsVJKa73qMQghhNggMhUmhBDCKAksQrwLaZsvxOVIYBHigpRSfaXUT5+1wX9dKfVtz/j89ymlfvaszf6xUurvLGusQqySBBYhLu77mbdy2WPevfgHztrjfx6llAd8DPhXwD5wF/jhJY1TiJWSwCLEBZztnv+Pge/RWk+11r8MfJT5eSvv5D8BHmmt/77Weqa1TrXWv7Wk4QqxUhJYhLiYLwQqrfXTvb5+k3l7/HfyVcBrSqmfP5sG+yWl1L+58FEKsQYksAhxMc/bBv8u8C3A/wjcBv458DNnU2RCbDQJLEJczPO2wU+AX9Za/7zWOgf+HrDFvImlEBtNAosQF/P7gKOUeuWpx76Udz9B8rcA2X0sbiQJLEJcwNmZ9z8F/C2lVEMp9dXAh5gf+vVOfhj4KqXU1ymlbOCvA8fAp5cyYCFWSHqFCXFxfxX4CPM2+CfAf37WHv/zaK1/Tyn17cD/DOwCnwC+6WxaTIiNJoFFiAvSWg+Ab36Oz/8p5lmOEDeKTIUJIYQwSjIWId7dL13TawuxUtI2XwghhFELy1hGo5FELCGE2HCdTufzjuyWGosQQgijJLAIIYQwainF+06ns4xvI4QQYglGo7e3zftckrEIIYQwSgKLEEIIoySwCCGEMEoCixBCCKMksAghhDBKWroIcUl1rcnLiqqq0YDWGqUUllLYlsJzbZT6vL1jQmw8CSxCXEBeVMzSnDQvSbKSJCtI85KsytG6pka/FVhQ2JaN73gEnkPou4RnvzcCF9uWiQKx2SSwCPEuirLidJIymCSM4oSkiMnKjLTKyKucvMxwnHl2opRCKdB6nrmUlaauFb7t4Ts+vhPg2z4NN6LbDOi3QzqNAMuSjEZsnmcGFqXUR4BvBA611l/ytr/7b4G/C+xorY8XM0QhlqeuNYNJwmCcMJwlTLIJ42xMUsVEvo3vWbQaNr7r4rn+ewaGqtZkeUWWZ2R5wjiteDDWtKYt2qdtWl6DXiuk3wrpNIMl/iuFWKyLZCw/CPxPwD97+kGl1AvA1wNvmB+WEMulteZ4FPP4ZMogHjJKR8yKGY3QpttzuRu2nju7sC1FFDhEwVsvs7KsGc8yjmePeDSG9qRFN+jSb7a4s92m3fBN/9OEWLpnBhat9ceVUvfe4a/+AfA3gJ8xPCYhlkZrzWCc8OhkwmA24ig+Aqug1/a51WhhG56qchyLfsen3/HJi4rxLOH+eMRx0uR0tsNWs8mdnTbN0DP6fYVYpkvVWJRS3wQ81Fr/pqx6EdfVeJbx4GjMyWzM4fSQSmXs9gNajeVMS3muzXbXpt/2OZ3kvDF6lZOkzelsm+12i7s7bQJPyqDi+nnuZ61SKgL+JvAnzQ9HiMWra82DozGPBkMOpgdkVcx2L6DTbK5kebBlKbY6Pt2Wx2CU8ofDP2SQ9BjNdnhpr8d2J1r6mIS4isu8HXo/8DJwnq3cBT6hlPpKrfWBycEJYVqSFbz6eMjB+IQnswP6HZe7ndZa7DexLcVOL6DX9nhyMuEPBlPSImc86/LSXkeWKYtr47kDi9b6t4Hd84+VUq8BXyGrwsS6Ozyd8cbhkMfTA5Jqwov7EYFvr3pYn8exLe7sRoymOW+cvEZc7jJLc+7td2lFUtwX6+8iy41/DPgaYFsp9QD4Xq31hxc9MCFMqWvNq49PeTw85eHkEY0QXt5rrv0ekk7TI/RtHh0dMz2eEme3eHG3x62t1qqHJsR7usiqsG99xt/fMzYaIQwrq5rPPhzw8PSIw/iA/e2AduP6rLjyXJuXbjU4Hma8OnyVsi4pq5oXduXwPLG+ZMmJ2FhFWfGZBwMeDJ9wmh7x0q0Gvrd+U1/PotS89hJ4BW8cv0GNpqxq7u1316I2JMTbSWARGynLSz7zcMD908dMylNeut3Eda538bvVcLEsxYPD+9R1RVVr3nert/ZTeuLmkcAiNk6SFXzmwYA3hg/I9JSXbjVwNmRFVSN0eGEv5MHhQ8p63ln5C+70ZcWYWCsSWMRGyfKS37t/zOvDB1Qq5oX9hvHd86sWBg4v7je4f3BATQ3AK3e3JHMRa0MCi9gYZVXzmYcDHoweoa2EF/caG1uD8D2bl243ef3RIbaycR2b993urXpYQgASWMSGqGvNHzwc8HD0hLSe8NKt1eyiXybXsbi73+CNx49xbRfftbmz0171sISQwCI2w2sHQx4Ojxmmx9y7vf57VEwJPJvbOyEPjx5gKxvPtdnpNlY9LHHDSWAR197DozEPTwc8mT3mxVsNnGu++ut5NSOXnb7m/ul9HMvGdx1pvy9W6ma9AsXGORnFvHF0ysPxA27vhATXcJ+KCd2WR7upuD+6z2ceHpPm5aqHJG4wCSzi2srykteenHJ/fJ+dvkczclc9pJXa7Yc4fsnjyQGvPj5Fa73qIYkbSgKLuJa01rx6MORg8oQg0HRb16dNyyLd2o6YlWOejAc8PJ6sejjihpLAIq6lxydTnowGTIsR+9vhqoezNmxLcWcn4vHkgIfHQyZxtuohiRtIAou4dqZJzv3jIY+nj7m1E23cBsirCgOHXsfh0eQxrx0Mqap61UMSN4wEFnGtVFXNawdDDiaP6bRsGqEsbHwn212f2ko5mBzz+pPRqocjbhgJLOJaeXA05sn4mIKYnd5yzqa/jpRS3N4JOY6POBiOGIyTVQ9J3CASWMS1MYkzHp+OOIwPub0TbfzO+qvyXJvdvs+jyUPuH45kSkwsjQQWcS1orbl/OObJ7JB+272W56qsQrfloeySo9mJrBITSyOBRVwLR8OYo+mQtJrS78iu8uexvx1yHB/z+HRMnBarHo64AZ4ZWJRSH1FKHSqlPvnUY39XKfW7SqnfUkr9tFKqu9hhipusrGoeHo95Mn3C/lZ4Y/qAmRJ4Nq2mzdHskPuHUsgXi3eRjOUHgQ++7bGPAV+itf63gN8HvtvwuIR406PjCcfxANerbvzu+sva6QVM8jHHkwnDabrq4YgN98zAorX+ODB422O/oLU+b0b0r4G7CxibEKR5yZPTCcezY3b7shHysmxLsdPzOZwd8uBoLO1exEKZqLH8ReDnDVxHiM/z4GjMUXxMq2GtZYPJsqqYpTnTJGOSZEyTnCQrqOr1W4HVbXkUJJxMhxwN41UPR2ywK+0uU0r9TaAEfsTMcIR4S5wWnIynDLNT3n+nuerhABBnBeM4I81KsqKkKEsKXVLXFRqNUha2snBtD9+Zn48S+S6tyMd3V7uZUynFbi/gcHBEb9BhuxNJvUosxKWf6Uqp7wC+EfhaLXm1WICDwZSTZEC36a70jJW0KBlNU8azjFmeMstnZFVGXuVAjetaWAoUCo2mqjVFqecHb9kegRvSdBo0Ap9Ww6fbDHCs1fx7Wg2X42HKaTziZNyUQ8HEQlwqsCilPgj8d8Af11pLTi2MS/OSo/GUYXrK+7dXk63kRcnRKOZ0GjPJJkzzGZqSRuTQadj4boBtv3OA0GjKsiYvNXE65sFkgBcHNKcN2sMWW+2IfjvEXkGA2er6HA9OeHLaZbsjG02Fec8MLEqpHwO+BthWSj0Avpf5KjAf+NjZk/Jfa63/ygLHKW6YJ4Mpp8mAdsNZerZS1jXHw5iT8YxRNmKcjWmGDjt9B9+72B4ahcJ1bFwHGoFD3dEkWcV0NmQ4GjLOepxOWmx1IvqtcKk391bkcjiYcBpPOJ206LdlUYQw65mBRWv9re/w8IcXMBYhAMiLiqPRjNNkyL07y73pzdKcR8cTTpIhw2RIFFrc2Q1x3iUzuShLKRqBQyNwSPOKwXjAaDhikveZxG3ubLdwneUsTlBKsd0NOBkfczBoS2ARxklrWLF2DoczBskpUaTw3OXcbLXWHA1nHA6nHMXH1FbGrR0fbwE3+8Czub0dEaclx8NDkiIhLUpu9Zt0GstprNlpuhydTjidTRhNW3Sa0tBTmCMtXcRaKauaw9Mpg+SE7c5ybnZlXfP6kxH3T054MHlIEJbc2g4XElSeFgUOd3ZDSjXjwegLine truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f6a7048>"
+       "<matplotlib.figure.Figure at 0x1c9dbe87080>"
      ]
     },
     "metadata": {},
@@ -962,14 +991,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f6d5940>"
+       "<matplotlib.figure.Figure at 0x1c9dc0b55f8>"
      ]
     },
     "metadata": {},
@@ -995,14 +1024,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138cdc66a0>"
+       "<matplotlib.figure.Figure at 0x1c9dc20cd30>"
      ]
     },
     "metadata": {},
@@ -1038,14 +1067,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138d53fb38>"
+       "<matplotlib.figure.Figure at 0x1c9dc010b38>"
      ]
     },
     "metadata": {},
@@ -1081,14 +1110,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138d531668>"
+       "<matplotlib.figure.Figure at 0x1c9dc0ce278>"
      ]
     },
     "metadata": {},
@@ -1113,7 +1142,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
@@ -1123,7 +1152,7 @@
       "       [ 135.701 , 1092.29  ]])"
      ]
     },
-     "execution_count": 22,
+     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -1135,7 +1164,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
@@ -1144,7 +1173,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f12eb00>"
+       "<matplotlib.figure.Figure at 0x1c9dc160470>"
      ]
     },
     "metadata": {},
@@ -1168,14 +1197,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f011390>"
+       "<matplotlib.figure.Figure at 0x1c9de1eb320>"
      ]
     },
     "metadata": {},
@@ -1203,13 +1232,13 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "72e55a1b5ba944dc89bacb0771e873e6",
+       "model_id": "c5f05c3a9276482983846d668e17c601",
       "version_major": 2,
       "version_minor": 0
      },
@@ -1282,7 +1311,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
@@ -1291,7 +1320,7 @@
       "0.9540"
      ]
     },
-     "execution_count": 26,
+     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -1310,7 +1339,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
@@ -1319,7 +1348,7 @@
       "-0.9178"
      ]
     },
-     "execution_count": 27,
+     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
@@ -1339,14 +1368,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f048b38>"
+       "<matplotlib.figure.Figure at 0x1c9dc21fd68>"
      ]
     },
     "metadata": {},
@@ -1370,14 +1399,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ebae438>"
+       "<matplotlib.figure.Figure at 0x1c9dc1d34e0>"
      ]
     },
     "metadata": {},
@@ -1413,14 +1442,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ea2ecf8>"
+       "<matplotlib.figure.Figure at 0x1c9ddcd4780>"
      ]
     },
     "metadata": {},
@@ -1470,7 +1499,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
@@ -1479,7 +1508,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ea15240>"
+       "<matplotlib.figure.Figure at 0x1c9de1e1978>"
      ]
     },
     "metadata": {},
@@ -1500,7 +1529,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
@@ -1509,7 +1538,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ea1eeb8>"
+       "<matplotlib.figure.Figure at 0x1c9de1e1c88>"
      ]
     },
     "metadata": {},
@@ -1533,7 +1562,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
@@ -1542,7 +1571,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ebff9b0>"
+       "<matplotlib.figure.Figure at 0x1c9dc117978>"
      ]
     },
     "metadata": {},
@@ -1571,14 +1600,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ebaecc0>"
+       "<matplotlib.figure.Figure at 0x1c9ddca8390>"
      ]
     },
     "metadata": {},
@@ -1600,14 +1629,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ea15a90>"
+       "<matplotlib.figure.Figure at 0x1c9dbce6b38>"
      ]
     },
     "metadata": {},
@@ -1632,14 +1661,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ce8f7f0>"
+       "<matplotlib.figure.Figure at 0x1c9ddc9eb70>"
      ]
     },
     "metadata": {},
@@ -1663,14 +1692,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f6c49b0>"
+       "<matplotlib.figure.Figure at 0x1c9dbe58518>"
      ]
     },
     "metadata": {},
@@ -1694,14 +1723,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ec27828>"
+       "<matplotlib.figure.Figure at 0x1c9dbe6dcc0>"
      ]
     },
     "metadata": {},
@@ -1727,14 +1756,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f019c50>"
+       "<matplotlib.figure.Figure at 0x1c9ddcf9898>"
      ]
     },
     "metadata": {},
@@ -1766,14 +1795,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ4AAAEDCAYAAAAFhGKSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAD8tJREFUeJzt3X9o3HWex/HXu8bchiRmbVk4TdSCKdoh20yPVBpCLUg9qlY4JEc5XCtVF4KH3bVnoSAIin8EUiNKaerS1dWyXfzDvXKNcj0rLmWl3d2sjDWm0nZYdptGtKHNOGmnl+p97o9Ms/2RphPN9/1N5vt8QGAy83HmzVB4+v3OZ76xEIIAAPAyL+4BAADJQngAAK4IDwDAFeEBALgiPAAAVxVxvXAul2M7HQCUubq6Orv8Po54AACuCA8AwFVsp9ouVldXF/cIidHX1ydJamlpiXmSZOF9jwfvezxyudyUj3PEAwBwRXgAAK4IDwDAFeEBALgiPAAAV4QHAOCK8AAAXBEeAIArwgMAcEV4AACuCA8AwBXhAQC4IjwAAFeEBwDgivAAAFwRHgCAK8IDAHBFeAAArggPAMAV4QEAuCI8AABXhAcA4IrwAABcER4AgCvCAwBwRXgAAK4IDwDAFeEBALgiPAAAV4QHAOCK8AAAXBEeAIArwgMAcEV4AACuCA8AwBXhAQC4IjwAAFeEBwDgivAAAFwRHgCAK8IDAHBFeAAArggPAMAV4QEAuCI8AABXFXEPAABRGC2MaU/fcZ0cOaf+4Qq1r0yppqoy7rEgwgOgzJwpjKmju1cHBgaVHTotSerZe0Qv7tyv1lSDtm9co2oCFCvCA6BsnCmMadUzb+ngwIkrHssOnVZ26LSODZ3Svi3riE+MIvuMx8x+YGZ/NLNPzOwzM3s+qtcCAEnq6O6dNDoXOzhwQh3dvU4TYTJRbi74X0n3hBCaJaUlrTaz5RG+HoAEGy2M6cDAYElrDx4+odHCWMQT4WoiO9UWQgiSRou/Xl/8CZOt7evri2oMXAXveTx436Ozp+/4xGc613LsxCl1vfmuHmy5JeKpkmnRokVTPh7pdmozu87MMpK+kvR+COEPUb4egOQ6OXJuWuuHc9Nbj5kT6eaCEMK3ktJm9kNJ/2lmTSGE/svXtbS0RDkGLnLh/7h5z33xvkevf7hCPXuPlLx+WfNitbSkI5wouXK53JSPu3yBNIQwIul3klZ7vB6A5GlfmdLtN99Y0trG+vlqX5mKeCJcTZS72n5UPNKRmVVJWiXp86heD0Cy1VRVqjXVUNLa1lQDXyaNUZRHPDdJ+tDMDkn6k8Y/42EPI4DIbN+4RstT9VOuWZ6qV8/TDzhNhMlEuavtkKSlUT0/AFyuuqpS+7asu+LKBdL46bXWVIN6nn6AL4/GjCsXACgr1VWV2vnsQxotjKnrzXc1nDunZc2LuVbbLEJ4AJSlmqrKie/psHttduHPIgAAXBEeAIArwgMAcEV4AMxZIyMj2rZtW8nrH3/8cTU3N2vJkiVqb2/X6Ojotf8jzDjCA2DOmm54Xn75ZX3yySc6dOiQbr31Vm3dujXC6XA1hAfAnLV582Zls1ml02lt2rTpmutvuOEGSVIIQYVCQWYW9YiYBNupAcxZnZ2d6u/vVyaTUT6fVzp96bbps2fPSpJ2796tVGr82mzr16/Xe++9p1QqpZdeesl9ZhAeAGWitrZWmUzmkvsuXBX8QnQk6Y033tC3336rp556Sm+//bbWr1/vOicID4Aykc/ntWLFikvum+yIR5Kuu+46rV27Vl1dXYQnBoQHwJxVW1urfD4/cXuqI54QgrLZrBobGxVC0J49e3TnnXe6zwzCA2AOW7Bggdra2tTU1KT77rtPXV1dV10bQtCjjz6qr7/+WiEENTc3q6enx3FaXEB4AMxpu3btKmndvHnz9NFHH0U8DUrBdmoAgCvCAwBwRXgAAK4IDwDAFeEBALgiPAAAV4QHAOCK8AAAXBEeAIArwgMAcEV4AACuCA8AwBXhAQC4IjwAAFeEBwDgivAAAFwRHgCAK8IDAHBFeAAArggPAMAV4QEAuCI8AABXhAcA4IrwAABcER4AgCvCAwBwRXgAAK4IDwDAFeEBALiqiHsAoNyNFsa0p++4To6cU/9whdpXplRTVRn3WEBsCA8QkTOFMXV09+rAwKCyQ6clST17j+jFnfvVmmrQ9o1rVE2AkECRhcfMbpH0lqR/lPR/kn4RQnglqtcDZpMzhTGteuYtHRw4ccVj2aHTyg6d1rGhU9q3ZR3xQeJc9TMeM3vPzBZ+j+f+RtJ/hBAWS1ou6d/NLPU9ng+YMzq6eyeNzsUODpxQR3ev00TA7DHV5oJfSfofM3vWzK6f7hOHEL4IIXxcvJ2XdFhS/XeaEphDRgtjOjAwWNLag4dPaLQwFvFEwOxiIYSrP2hWLek5Sasl7dT4KTNJUgihu+QXGT9y2i+pKYTwtSTlcrmJFz569Og0xwZmrz19x/XC24dKXv/c2iV6sOWWCCcCfC1atGjidl1dnV3++LU+4zkv6Yykf5BUq4vCUyozq5H0jqSfX4gOUM5Ojpyb1vrh3PTWA3PdVcNjZqsldUv6L0n/FEI4O90nL56ie0fSr0MIv73aupaWluk+Nb6jvr4+SbznUeofrlDP3iMlr1/WvFgtLekIJ0ou/r3HI5fLTfn4VJ/xPCvpX0MIm79jdEzSLyUdns5pOWCua1+Z0u0331jS2sb6+WpfyZ4bJMtVwxNCWBFC+Ox7PHebpEck3WNmmeLP/d/j+YA5oaaqUq2phpLWtqYa+DIpEiey7/GEEH4v6YoPlYAk2L5xjY4NnZpyS/XyVL16nn7AcSpgduBabUAEqqsqtW/LOv1k1Y+vOO3WWD9fj9y7hC+PIrG4ZA4QkeqqSu189iGNFsbU9ea7Gs6d07LmxVyrDYlHeICI1VRVTnxPh91rAKfaAADOCA8AwBXhAWbAyMiItm3bVvL6hx9+WHfccYeampr02GOP6fz58xFOB8wuhAeYAd8lPJ9//rk+/fRTFQoF7dixI8LpgNmF8AAzYPPmzcpms0qn09q0adM1199///0yM5mZ7rrrLg0OlnY1a6AcsKsNmAGdnZ3q7+9XJpNRPp9XOn3p7rWzZ8evOrV7926lUn+/RM758+e1c+dOvfIKfyMRyUF4gBlWW1urTCZzyX0XLlZ5cXQk6cknn9Tdd9+tFStWuM0HxI3wADMsn89fEZLJjnief/55nTx5Uq+99pr7jECcCA8wA2pra5XP5yduX+uIZ8eOHdq7d68++OADzZvHR61IFv7FAzNgwYIFamtrU1NTU0mbCzo6OvTll1+qtbVV6XRaL7zwgsOUwOzAEQ8wQ3bt2lXy2m+++SbCSYDZjSMeAIArwgMAcEV4AACuCA8AwBXhAQC4IjwAAFeEBwDgivAAAFwRHgCAK8IDAHBFeAAArggPAMAV4QEAuCI8AABXhAcA4IrwAABcER4AgCvCAwBwRXgAAK4IDwDAFeEBALgiPAAAV4QHAOCK8AAAXBEeAIArwgMAcEV4AACuCA8AwBXhAQC4qoh7APgZLYxpT99xnRw5p/7hCrWvTKmmqjLusQAkDOFJgDOFMXV09+rAwKCyQ6clST17j+jFnfvVmmrQ9o1rVE2AADiJLDxm9rqkNZK+CiE0RfU6mNqZwphWPfOWDg6cuOKx7NBpZYdO69jQKe3bso74AHAR5Wc8v5K0OsLnRwk6unsnjc7FDg6cUEd3r9NEAJIusvCEEPZLOhXV8+PaRgtjOjAwWNLag4dPaLQwFvFEACBZCCG6JzdbKKl3slNtuVxu4oWPHj0a2QxJtqfvuF54+1DJ659bu0QPttwS4UQAkmDRokUTt+vq6uzyx9lOXcZOjpyb1vrh3PTWA8B3MSt2tbW0tMQ9QlnqH65Qz94jJa9f1rxYLS3pCCdKrr6+Pkn8W/fG+x6PXC435eMc8ZSx9pUp3X7zjSWtbayfr/aVqYgnAoAIw2Nmv5F0QNIdZjZoZo9H9VqYXE1VpVpTDSWtbU018GVSAC4iO9UWQvi3qJ4bpdu+cY2ODZ2ackv18lS9ep5+wHEqAEnGqbYyV11VqX1b1uknq358xWm3xvr5euTeJXx5FICrWbG5ANGqrqrUzmcf0mhhTF1vvqvh3Dkta17MtdoAxILwJEhNVeXE93TYvQYgLpxqAwC4IjwAAFeEp8yMjIxo27ZtJa/funWrGhsbZWYaHh6OcDIAGEd4ysx0w9PW1qZ9+/bptttui3AqAPg7wlNmNm/erGw2q3Q6rU2bNl1z/dKlS7Vw4cLoBwOAIna1lZnOzk719/crk8kon88rnb5099rZs2clSbt371YqxSVyAPgjPGWstrZWmUzmkvsuXDSR6ACIC+EpY/l8XitWrLjkPo54AMSN8JSZ2tpa5fP5idsc8QCYbdhcUGYWLFigtrY2NTU1lbS54NVXX1VDQ4MGBwe1ZMkSPfHEEw5TAkgyjnjK0K5du0peu2HDBm3YsCHCaQDgUhzxAABcER4AgCvCAwBwRXgAAK4IDwDAFeEBALgiPAAAV4QHAOCK8AAAXBEeAIArwgMAcEV4AACuCA8AwBXhAQC4IjwAAFeEBwDgivAAAFwRHgCAK8IDAHBFeAAArggPAMAV4QEAuCI8AABXhAcA4IrwAABcER4AgCvCAwBwRXgAAK4IDwDAFeEBALgiPAAAV4QHAOCK8AAAXBEeAIArCyHE8sK5XC6eFwYAuKmrq7PL7+OIBwDgivAAAFzFdqoNAJBMHPEAAFwRngQxs9fN7Csz6497liQxs1vM7EMzO2xmn5nZz+KeKQnM7Adm9kcz+6T4vj8f90wYx6m2BDGzuyWNSnorhNAU9zxJYWY3SbophPCxmdVK+rOkfwkhDMQ8WlkzM5NUHUIYNbPrJf1e0s9CCAdjHi3xOOJJkBDCfkmn4p4jaUILine truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f05f278>"
+       "<matplotlib.figure.Figure at 0x1c9ddc9e4a8>"
      ]
     },
     "metadata": {},
@@ -1793,14 +1822,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f05f3c8>"
+       "<matplotlib.figure.Figure at 0x1c9ddd3e0b8>"
      ]
     },
     "metadata": {},
@@ -1827,14 +1856,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138f1240f0>"
+       "<matplotlib.figure.Figure at 0x1c9ddcf9da0>"
      ]
     },
     "metadata": {},
@@ -1854,14 +1883,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138d53f198>"
+       "<matplotlib.figure.Figure at 0x1c9ddcf9278>"
      ]
     },
     "metadata": {},
@@ -1881,7 +1910,7 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
@@ -1890,7 +1919,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAEDCAYAAAB+oKBeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXd4lGXWh+8nk15JCCUEkI69otgr9oYd17Wvva4r7C66utZV18+KYldQUWwgKlhQsa8IighKDTUEQnqfSWae748z78wkJDEgycwk576uXHnLM8mZYSY/TnnOMdZaFEVRFCVSiAm3AYqiKIoSigqToiiKElGoMCmKoigRhQqToiiKElGoMCmKoigRRWy4DWiJ8vJyLRdUFEXp5GRkZJim19RjUhRFUSKKdhcmY8wLxphCY8yikGv/NsbkG2MW+L9OaG87FEVRlOigI0J5LwETgMlNrj9srX2wLT8gIyOj0fm8efMAGDFixB+3roOIRpshOu1WmzuOqLK7sBCuuQbeekvOhw2DZcvk+PvvYb/9wmfb7xBVr3MILdldXl7e6uNMR3R+MMYMAN631u7qP/83UNWaMIXmmJYvX97OFiqK0plJWbSIwePGEb95M97kZNZfey3pP/xA5uefU77//ix/7DEwW6Q6lHZi6NChgeNIyzFda4xZ6A/1ZYbRDkVROjHZ777L8MsvJ37zZir32ovFr79OfffuZH7+Od7kZNaMH6+iFGGEy2PqBRQBFrgLyLHWXhL6mFCPSUN54SMa7VabO46IttvjgRtvhIkT5fzaa+Ghh1gwZw67jBlDXEkJTJgg4b0IJ6Jf51ZoSyivOY8pLOXi1tpNzrEx5lng/XDYoShKJ2XjRjjrLPj6a4iPh6eegosvhvp6Bv3znyJKhxwCV10VbkuVZgiLMBljcqy1Bf7T04BFra1XFEVpM3PnwumnQ34+5ObCO+8ECxvGjiV9/nw83bsT//rrEKM7ZiKRdhcmY8xrwOFAtjFmPXA7cLgxZk8klLcauKK97VAUpQvwwgviBXk8cPDBUoHXq5fce/llePRRfLGxrLz/fnbq0ye8tiot0u7CZK09t5nLz7f371UUpQvh88HNN8PDD8v51VfLcXy8nM+fD5dfDsDasWOp3mOPMBmqtIWIbUmkKIrSJmpr4c9/lpBdXJwUO1x6afB+YSGcdhrU1cFll1F0+unhs1VpExpgVRQletm8GY48UkQpIwM++qixKNXXw9lnw7p1cMAB8Pjj4bNVaTPqMSmKEp0sWwYnnAArV0L//jBzJuyyS+M1N98MX3wBvXtLvikhITy2KluFekyKokQf33wDBx4oorT33vC//20pSs8/D489JuG9t98GLXaIGlSYFEWJLt58E446CoqL4cQTxSPKyWm85uOP4Qp/se8TT4iIKVGDCpOiKNGBtfDgg5IzcrulLHz6dEhNbbzu55/hzDPB64Vx4+Cyy8Jjr7LNaI5JUZTIp6EBbrgBnnxSzu+/H8aO3bLH3fr14kVVVsI558B//tPxtip/GPWYFCUCmPT5Si5+/Bs+mrsi3KZEHtXVUu795JNSvDB1qnhCTUWpokJEKT9fNte+9JJ2dohS9F9NUcLMwpWbmDBzCYvWlnHpf2fgqfeG26TIYdMmOOwweP99yMqC2bMllNeU+nrpjbdwocxZmj4dEhM73l5lu6DCpChh5s0vFgeO84sq+W7xujBaE0GsXClFC/Pnw6BB8O234gk1xVq48kopeOjRA2bNgu7dO95eZbuhwqQoYebDuSsBGNAzRc5/0HAeP/4oopSXByNGwHffwfDhza+95x7pkZeUBO+9JyKmRDUqTIoSRmrd9cxftgFXjOGq4+QP71cL14bZqjDz6acSvisshKOPhs8+g549m1/7yivwr39JvmnKFBg5smNtVdoFFSZFCSOrCsqwFnIyk9iprwzEzCsoDbNVYeSNN6SbQ1UVnHuu5JbS0ppf+/nncIl/vugjj8Do0R1np9KuqDApShhZuaEEgL7dk+mZkURcbAwFxVXU1NWH2bIw8MQTMGaMjKy4/nrxhpzu4E1ZvFgq9errZUrt9dd3rK1Ku6LCpChhJG+DeEe53ZNxxRgG5mQCsKoreU3Wwm23yehza2Xv0SOPtFzqnZ8vXlV5uYjTgw92rL1Ku6PCpChhZKUjTFnJAAzyC5NzvdPj9UoHh7vuApdLihj+8Y8t9yg5lJbCccfB2rWw//7iVblcHWuz0u5o5wdFCSOOAPXtLhV5g/s4wlQSNps6DI8Hzj9f8koJCfL9lFNaXl9bK/cXLYIdd5T8U3Jyx9mrdBgqTIoSRpxCh9zujT2mTl8AUVUFZ5whe4/S02HGDKnEa4mGBsk/ff019O0rc5d0r1KnRYVJUcKEz2cDuSQnlBf0mDqxMDldwb//XsrAP/wQ9tqr5fXWSrhvxgzIzJT1/ft3nL1Kh6PCpChhYkNxJe56Lz0zU0hJlI/i4NwsIFgU0enIz4djjoFff4UddoBPPoGhQ1t/zG23wXPPyQba99/fcu6S0unQ4gdFCROOtzSwd7fANed41cYyrLVhsavdWL4cDjpIRGnnnWXY3++J0oQJcPfdUuAwdarOVeoiqDApSpjYXFYDQO+s4DyhlKR4UpPi8dR7qazxhMu07c9PP4korVkj3Rm++gpycyWcN3o0XH01+HyNH/Paa8H9Sc8+Cyef3PF2K2FBhUlRwkRJZS0AWWlJja5npSc1uh/1fPklHH44bN4sLYZmzxYP6NprpeT73Xdh4kQRLYdZs+CCC4L7mi6+OGzmKx2PCpOihImSCr8wpTcRJr9QOfejmhkz4NhjZVbS2WdLk9Uff5RQ3hNPBNf16ROssvv2W6nYa2iAm2+Gv/89PLYrYUOFSVHCRIseU1on8ZgmT4bTT4e6OrjiCmmy+uKLcNRRsGED9OsXXDthgpSN//KLVOzV1kofvAceaHmzrdJpUWFSlDBRWtmCx5TeCTymhx+GCy+Uzg633AKPPQbXXSdl3w0N0t9u2DBZe+qp0looL0+8q7IyyTs9/bSKUhdFhUlRwkRJZR3QnMeU6L8fhcJkLdx6K9x0k5w/9BDccIPkliZOlA4PkyZBbKyMt8jOFm9p40YpIy8ogCOOkMKHWN3N0lXRf3lFCRONckw2KEJR6zF5vXDNNeLpOH3v9tgD9t1XChtycmDaNFi6VBqvxsbCW29Baqp0fVi5EvbZR8eiK+oxKUq46FQ5pvp6OO88EaWEBHjnHUhJkX1Ha9bAfvvBvHkSmrv8cnnM44+LaJ18MixcKKG9WbMk16R0aVSYFCVMlLSUY4qyqjzj8cBZZ8kG2LQ0EZf58+HMM6GmRhq1fvGFLB49GtxuuPJKuPRSqdRz+t998gn06BHeJ6NEBBrKU5Qw4QhPZmoiRSHXM6PIY4qpq2Pw2LHwv/9JH7sZM6TQ4c03ZZ7SAw9IvsntlgKHggI49FApjrj4YvjgAykT//hj7X+nBFBhUpQw4PX6qKqVzg5pyQmN7mWkyHlFjbvD7doqqqoYcuONpM+fL0UM06bBv/4Fc+ZIOG7qVJmdZK2Ui8+dK/3x3nwTxo2DV1+VcN+sWbDTTuF+NkoEocKkKGEgKErxxMQYGrw+VhdWMQJId4SpOoKFqbwcTjiB9Pnz8WRnE//mm1L4sHChFDnMmiWFDyDe0eTJMjvp3XfhqackvxQfL4UO++4b3ueiRBwqTIoSBhxvKN3vLV0x8X8sXFPKr5sN543ardGaiKO4WPYbzZ+Pu3dvVo8fz/CLLpIih2HDZFbSgAGydtYsGDtWjidNknzS7bdLmG/KFBg1KlzPQolgtPhBUcKA4w2lpySwbF0xC9dIp/GXPlpAWnI8QGQ2cS0slH1G8+fDoEGs/dvfGPSvfwUr7775JihKc+dKAYTPJyG++nrZZAtSvXfGGWF7Gkpkox6TooSBSieUl5TA+98tC1xfV1jB6o3lQAR6TPn54uEsWSKjzcePZ9Dll+Oqq4Pjj5fcUYqMiGfZMmktVFMjzVhHjpSKPGvhvvvgL38J73NRIpp295iMMS8YYwqNMYtCrmUZYz4xxiz3f89sbzsUJZII9Zh+Xrmp0b3l64sxBmrq6mnw+pp7eMezZo1U0y1ZArvtJmMqLr4YV10dRSedJLkjR5Q2bpRQX1GRCNaf/tS4Keu4ceF9LkrE0xGhvJeA45pc+wfwqbV2KPCp/1xRugyhOaaVG0oA2H0H+f9ZXkFpIPfkFEmElRUr4JBDpJfdPvuI53P99eD1UnDRRay+7TaIi5O1FRUiRqtXS1HDX/8qjVydvUvalFVpA+0uTNbaL4GSJpdPBSb5jycBo9vbDkWJJByPKS05npX+Meojh2UDkFdQFighD3tl3q+/iqe0bp10cRg5Eu66S8TlscfIv+aaoNA4e5UWLJDJtHffLZ5STY00dH3iCRUlpU2EK8fUy1pbAGCtLTDG9Gxt8bx587bqeiQTjTZDdNodyTYvXroKgKqKUjaWVBHrMuw1KAuAn5etJS5GQnjf/vAjhb3TwmJj0rJlDLv2WuJKS6nYe288aWlkP/kkvthYVt1xB6UHHBBYO2/uXAbdcgtZn32Gp3t3Vl99NYPOOovYykpKjj6avKuukjlMEUQkvz9aIhpthi3tHjp0aKvrtfhBUcJAdV0DAF6fBaBPVjL9siVHk19cS26WdH+o8a/raJIXL2bY9dcTW1FB+ciR+GJjyf7oI7yJiaz873+p2H//4GJr6ffQQ2TNno03JYW148Yx8I47iK2ooPSww1h1553S1FVR2ki4hGmTMSbH7y3lAIWtLR4xYkSjc0d9m16PZKLRZohOu6PB5qnzJLqdnpEJrCM3K5ke6YnEx7koqXKz17A+sLaM3P4DGTFiSMca9/XXUtZdWQknnEBGZSV89RVkZuKaOZNhIaI0b948ek+eTK+pUyE+HtdTTzFk3DiZqXTccWROn84Line truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138ebc7668>"
+       "<matplotlib.figure.Figure at 0x1c9ddcf49e8>"
      ]
     },
     "metadata": {},
@@ -1912,14 +1941,14 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x2138c341b70>"
+       "<matplotlib.figure.Figure at 0x1c9ddcf4a20>"
      ]
     },
     "metadata": {},