diff --git a/10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb b/10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb
index fc044d9..0e171b1 100644
--- a/10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb
+++ b/10_Unscented_Kalman_Filters/Unscented_Kalman_Filter.ipynb
@@ -1,7 +1,7 @@
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@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fa311bd04e0>"
+        "<IPython.core.display.HTML at 0x7f578bd5d4e0>"
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@@ -271,7 +271,9 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "In the previous chapter we developed the Extended Kalman Filter to allow us to use the Kalman filter with nonlinear problems. It is by far the most commonly used Kalman filter. However, it comes with a lot of difficulties and limitations. For example, it requires that you be able to analytically derive the Jacobian. However, for many problems finding the Jacobian is either very difficult or impossible. Furthermore, being an approximation, the EKF can diverge. I particularly like this way of looking at the problem, which I am borrowing from Dan Simon's *Optimal State Estimation* [1]. Consider a tracking problem where we get the range and bearing to a target, and we want to track it's position. Suppose the distance is 50km, and the reported angle is 90$^\\circ$. Now, given that sensors are imperfect, assume that the errors in both range and angle are distributed in a Gaussian manner. That is, each time we take a reading the range will be $50\\pm\\sigma^2_{range}$ and the angle will be $90\\pm\\sigma^2_{angle}$. Given an infinite number of measurements what is the expected value of the position?\n",
+      "In the previous chapter we developed the Extended Kalman Filter (EKF) to allow us to use the Kalman filter with nonlinear problems. It is by far the most commonly used Kalman filter. However, it comes with a lot of difficulties and limitations. For example, it requires that you be able to analytically derive the Jacobian. However, for many problems this is either very difficult or impossible. Furthermore, being an approximation, the EKF can diverge. In this chapter we will use the unscented Kalman filter (UKF) to tackle these problems.\n",
+      "\n",
+      "I particularly like the following way of looking at the problem, which I am borrowing from Dan Simon's *Optimal State Estimation* [1]. Consider a tracking problem where we get the range and bearing to a target, and we want to track its position. Suppose the distance is 50km, and the reported angle is 90$^\\circ$. Now, given that sensors are imperfect, assume that the errors in both range and angle are distributed in a Gaussian manner. That is, each time we take a reading the range will be $50\\pm\\sigma^2_{range}$ and the angle will be $90\\pm\\sigma^2_{angle}$. Given an infinite number of measurements what is the expected value of the position?\n",
       "\n",
       "I have been recommending using intuition to solve problems in this book, so let's see how it fares for this problem (hint: nonlinear problems are *not* intuitive). We might reason that since the mean of the range will be 50km, and the mean of the angle will be 90$^\\circ$, that clearly the answer will be x=0 km, y=90 km.\n",
       "\n",
@@ -307,9 +309,9 @@
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GEQDD88+jIjERlSEhTfIenCUTgYGB2LNnT43lzsA1IyMDBQUFmD9/PsLDwyGRSPD888/D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\nG7X05ufnKyEhQTExMZo1a5Z27dolSerr61NhYaG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Sb54wxZuxTIKIiIiIvBaDYSIiIiLyWgyGiYiIiMhrMRgmIiIiIq/FYJiIiIiI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\ngg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f575f2e8>"
+        "<matplotlib.figure.Figure at 0x7f576f8e82e8>"
        ]
       }
      ],
@@ -345,9 +347,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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X8PuRSwhoVBfje/mhb1svyIw5lzBRKTbARDpMbmIMG4uSIR6Fueqd6RnYoQEG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\nj+q/Xtmg31431XROr5edna2cnBxJkt1ul8vl6tT9bF6v19u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PP3H37l0MHDiwaFlJ59SFh4ersxsiIlFVlWtwsmYTkT4oT81WqwE+e/YsTpw4oXA7vsjI\nSFy5cgXfffedwrp+fn4lbmP1wStYH3EdS99vj5qu4t5G0d7eHlu3bkVoaKioOcpDl7ICupVXl7IC\nupVXl7ICBXmVva6oNtNEzRZLRb93ElKfo/Mn2xCfnIFRnepi9oiWWpFLVcylHOZSjjbnKm/NVmsO\n8IQJEyCXy4u+QkNDsXz58mKFtDQnrj9CkK8znqRklr0yERGpTBM1u6pysDbFsgntITOQYuXBK9gS\nfUvsSERUgUS/FfLPH3TAlH5BWH/kOo5djkNyepbYkYiISA8F+Tpj1vAWAICPVhzD5dhEkRMRUUXR\naAMcERGBUaNGKf06B2tTTB/cFDcfJOOthYfxLDNHk7GUom1/9iuNLmUFdCuvLmUFdCuvLmWt6lSt\n2WKpjPfOsPZ+eD20FrJy8jFm4UEkpZU9KKOt72nmUg5zKUdbc5WX6CPAhdzsLdC0tgtkBlLEPk4V\nLYcu/UB1KSugW3l1KSugW3l1KStpl8p470gkEnw5shUCfRzxz9N0jF9yBPlyuei5VMFcymEu5Whr\nrvLSmgYYAOIS01HX0x47T8Tg9I14seMQEZEeMjEyxLKJHWBvZYLIS3GYu/mc2JGISMO0qgHu0tgL\n7/VsgGfPc2BmrPYliomIiFTiZm+Bn95vDwOpBD/svIBdp2LEjkREGqRVDTAALNt7CakZ2fB0shI7\nChER6bGW/q74bEgzAMCknyN5UhxRFaJ1DfCYLgFoVtsFczefxZ7Td8WOQ0REemxMlwAMDKmF59l5\nGPXdQSSkPhc7EhFpgNY1wPZWphjVOQBW5kZIeMZCQ0RE4pFIJPhmVGs0qumEuMR0vPX9IeTk8dbr\nRLpO7QZ46NChqFatGqytrdGgQQPs3LlTE7lgbixDZlYuHiama2R7RERUcTW7KjOWGWD5xI5wsTXH\n6RuP8enq4yXeQpqIdIfaDfBHH32E2NhYpKamYv78+RgwYAAyM9W/q9uw9n6oUc0G01ZGY83hq8jI\nylV7m0RE+q6ianZV52xrhpWTO8JEZoD1EdexbN8lsSMRkRrUboDr168PY2NjCIKAnJwcWFhYQCKR\nqB3M0swInYI88d3YUBhIJdgUdVPtbRIR6buKqtn6oIGPIxa8EwoAmL3hFPad4XkqRLpKI3OAx40b\nB1NTUwwdOhS7du2CqampJjYLoOAucUPb+eHY5Thk5eRpbLtERPqqImt2VdezeQ18PKgJBAEY/2ME\nzt54JHYkIlKBRhrgH3/8Eenp6fjiiy8wdOhQZGWVfetIZbXyd8XvkTdx7HIcjl2Ow+Nk/smOiEgV\nlVGzq7L3XmuAwW1qIysnH/3C/sA9Ee9eSkSqkQgansnv5+eH+fPno3v37kXLwsPD0bp1a7W2m/js\nOa7dTwAAnLvxCGYmMrzVvaFa23yZTCYDAOTmav98Y13KCuhWXl3KCuhWXl3KChTkjYiIQPv27cWO\nUmEqqmZrmra9d3Lz8tHz002IuHAPfh4OODz/Ddhbac9IurYdr0LMpRzmUo4yNVvjt1t7VT89e/bs\non+HhIQgNDRUqe3aW5midUB1AEB2Tj4u332iekgioleIjIxEVFQUAMDAwAAhISEiJ6pYFVWzqzqZ\noQF++7QPOvxvPS7ffYo+MzZj79evw8LUSOxoRHpF1Zqt1gjw48ePsXv3bgwYMABmZmZYsWIFPv74\nY9y+fRt2dnZF64WHh8PPz0/V3RSTnZuPOZvOwNyk4DeQS7EJWPRuW1iZqVd47O3tAQCJidp/tx9d\nygroVl5dygroVl5dygoU5I2Ojq4yI8Bi1WxN0Nb3TjaM0HbyOtx7nIqQADesntIZxjIDsWNp7fFi\nLuUwl3KUqdlqzQE2MDDAhg0bUKNGDdjb22P16tXYuXOnQiGtCMYyA8x4ozk+7BeED/sFYVKfRvjf\n8ijsOX0XUZfjKnTfRES6SqyaXZW52ltiz1eD4GBliqjLcZiw9Cjy5XKxYxFRGdSaAuHg4IDw8HBN\nZVFZAx9HTOzdCADw4+6/4elkCU8nK5FTERFpF22p2VVNTTc7rJ/aBf2/2I1dp2JgYSrD3NHBkEp5\neTkibaV1t0JWlZ+HHfw87DC2az2sOHAFqw5eETsSERHpiQAvB6z6sDNMZAb47egNTF1xDHI57xZH\npK00fhKc2Op5O6CetwNGzD+A5nWqFXve28UKJkZV7tsmIiKRtfCrhtVTOmPktwew4egNAMAcjgQT\naaUqMwL8so8HNUFMfKrC1++RN3D4r/tiRyMioioqOMANqz/sDBMjA2w4egMfcSSYSCtV2aHQOtXt\nUKe64okd7RpUx4SfjuLyvUR0a+KF+t6OIqUjIqKqKjjADWumdMaI+Qfw29EbeJ6dhwXvhMLIUPyr\nQxBRgSrbAJfE1NgQyyZ0wIOnaVi2/zIOnb+PvHw5qjtaol9rX2T/e6vl3Dw5ZIZVdnCciIgqWOu6\nblg7pQve/O4gtp+4g8S0LPwyoQMs1bxcJxFphl41wIXcHS0xa1gLAAXN7k97LmLZ3kswMzPDs8xs\nxD5KxPfvtBE3JBER6bRWdV2x5dMeGDZvP45djkP/L3dj3Udd4GhtJnY0Ir2nlw3wi2SGUrzfKxBA\nwQWUT19/iOm/HMZPey4qrOfvYYeQeu5iRCQiIh1Vz9sBO8J6Ysg3+3A5NhG9wnZi9YedUcvdVuxo\nRHpNrb/z5+XlYfjw4XB1dYWNjQ3atWuHq1evaiqbKIJ8XbBycicMa++n8LXlz9v4dss5fLvlHBZs\nOy92TCIipVXFmq0LPJ2ssOPznmjg44B7T9LQ4/Md2HfmrtixiPSaWg1wfn4+fH19cfbsWaSkpKBn\nz57o3bu3prKJwsBACnMTWbGv799pU3TnOXtLE4Vm+GlqZrGvrH/nExMRaYuqWLN1hYO1KbZ8+hp6\ntaiBjKxcjFl4GPP+OMsrRBCJRK0pEMbGxvjss8+KHo8cORKTJ09GYmJi0X2iq6LhHfyL/r3m8FXs\nPROr8Pzj5Ezk5OajaW2X/xZKgE6NPCspIRFRcfpas7WFqbEhlrzXFvW9HfDlb6excNtfuHQ3AQve\nDoW9lanY8Yj0ikbnAJ84cQJubm56VUhHvNAMF3qek4fbcSkKy349cg2X7iYoLBMEoEcz72KXayMi\nqgz6WLPFJpFI8E73+vD3sMO7i48g/MI/6PDxFix4OxRt6lcXOx6R3tBYA5yamoqJEyfiu+++K/F5\nXSiwMpkMgGayuldzVnjcpnHtYuvEPErBgj9OwckmvsRt5MsF1PVyxIBQvwrNWhl0Ka8uZQV0K68u\nZQX+y1sV6VrN1tb3jqq5+rSxR5C/F0bN243oS//gjTn78V6vIHw5uq1G7lZa1Y5XRWMu5Wh7rvKQ\nCIKg9gSk7OxsdO3aFcHBwZg5c2ax58PDwxEREVH0OCQkBKGhoeruVuMKD1xubq7ISQrk5OZj1q/H\nYCwrfvF0AwMDXL77BO+81gjOtubFnjczkaG6o1VlxCwXbTu2pdGlrIBu5dWFrJGRkYiKigJQ8P9Z\nSEgI2rdvL3IqzdLFmq2t7x11c+Xny/Ht5lOY9esx5OXLUcvdDj980AUh9T1EzVVRmEs5zFU2VWu2\n2g1wfn4+BgwYACcnJ/z0008lrhMeHg4/v+KjmNqm8DeZxMREkZOUzd7eHnceJiPy/K0Sn991Mgad\nG796znG3Jt4wN6m80S1dO7aAbmQFdCuvLmUFCvJGR0dXqQZYV2u2tr53NJXr75ineP/HCNx5lAoA\nGBRaC58ObgY7SxNRc2kacymHuZSjTM1W++8sb7/9NqRSKX788Ud1N0VKquFqCxvjGiU+17S2C3Ly\n8kt87tD5+5j/xzlYmJa/AXa0NlU4+Y+IdBNrtnZq4OOIQ1/3w5KdF7B45wVsjLyJg+fuYdqgJng9\ntDYMDXh3UiJNUqsBvnfvHlauXAkzMzNYW1sXLd+/fz9atWqldjhSXTW74tMiCo3pEqD09hZsPY9v\nt5wr8bnHyZkY91oDeDhavvL1hZf6KemSP1KpROk8RKQ81mztZiwzwOR+QejZogY+XhWN41cfYeqK\naKzYfxnTX2+KDg09IJGwXhJpgloNsKenJ+RyuaaykBab1LfRK5+78SAJ2/68XerrTc0KLvHzPPO5\nwvLbD1MwqnNdlTLVcLWBjbmxSq8l0kes2bqhpqsNNk3vjp0nY/DNxjO4GZeCkd8eRAu/apg6oDGa\nvHiJTSJSid7fCpnUV9vdDrXdS7+U26vmC5289gjPMnOU3uedR6nYGHUTtd1Uv51o7eq2aF3XTeXX\nExFVFIlEgl4taqBLYy/8Gn4NC7adx4lrj9B71i60quuKSX0aoYVfNbFjEuksNsAkquYqFvDgADek\nPVe+cX7RjLUncOp68UvQmZr+O1r9/Hmx5wr5uFijT6uaau2fiKgsxjIDjOkSgAHBvli27xJWHriC\nP688xJ9XHqJZbReMe60B2jWozqlkREpiA0w6ydBAClsL1c6OLrR4XNsSl5fn7NZZ608iJj5Vrf0D\nBR9ub7Sro9RrLEyMIDPkCTFE+sTa3Bj/698YY7vWw8oDV/DLvks4dSMep27Ew9fVBm93r4c+LWtq\n5BrCRPqA/6cQqWDGG801sp2VBy5ja3Tp86dfdPdxKlztLODn8d+UEyurgkb82bNnKueoZmfOOxIS\n6QBrc2NM6tsIY7oEYH3EdSzffxm3HqZgyi/H8M3GsxjW3g/DO/hp3Q0KiLQNG2AiEY3qrNwVOdIy\nc3AzLllhmXXhiYB5RirnmLX+JBr7Ope94r+a1nHh/GkiEVmaGeGd7vUxunMAdp2KwU97LuLKvUQs\n2HYeP+y8gP6hfhjfuzG87FWvC0RVGRtgIh1iaWaEoJca1f+mbKg+JWT91K5Krf/B0qMlzp9+WWpm\nDoa390NNVxtVoxFRKWSGUvRtVRN9WtbAyevxWLH/Mg6cu4ffjlzBb0euIMjXCaM7B6BbE29OnSJ6\nARtgIlL62qKvmj/9shsPkvBH9C3I/r2I/8snGMYnZSC4nhsa+Dgqtf9C7g4WMJDyQ51IIpGghV81\ntPCrhn+epuH3YzFYfeBvnLv1BOduHYGLrTne7OSPoe39ePlIIqjZAO/YsQPffPMN/vrrLwwePBir\nVq3SVC4iqgJqu9th2sD/5ha/fILhk5RMRF56gNM3yh5NflnE3w8wtms9BNZQrXnWV6zbVV91R0t8\n81Y7fDq0NZbtPIWVB67g9sMUfL3xDL7f/hdeD62NMV0D4OlkJXZUItGo1QDb2Njgo48+wuHDh5GZ\nmampTESkJ5xszDAguJZKr63n5YAvfzuNFZM7wsjQQMPJqi7Wbf1hYWqEER38MaydHyIvPcCyvZcQ\ndTkOKw9ewepDV9GjmTfe7xUIfw+eMEf6R60GODQ0FABw/vx5FlIiqlR1qtthQIgvFm77C3n5cjSu\n5YxOjTzFjqX1WLf1j1QqQdsG1dG2QXVcvZ+IZXsvYfvxO9h5MgY7T8agYyMPfNCrIRrVdBI7KlGl\n0cjkOUEQNLEZIiKlBHg5oEtjTySlZcHajGe7K4N1Wz/5e9hj4Ttt8OeCQRjVqS5MZAY4dP4+Xvt8\nB0Z+ewBX77/6+udEVYlGToIrzwk0unBNQplMBoBZK4Iu5dWlrIBu5VU3a36+HCv2XSh6fPj8XYzo\nVB/92wSgU5MaMDTQ7AlxhXmrorLqtra9n7T1fa6ruezt7fHjZE98/mY7LNp6Gkt3nseh8/dx+K/7\nGNjGHzOGBaOGq+q3mlc1l1iYSznanqs8NNIAl2ckYfbs2UX/DgkJKfozHBERAOTm5ePj5RH/Xde4\nBPlyARamRhjWsR4AoH+oH+wsTTWaIzIyElFRUQAAAwMDhISEaHT72qKsus2arR+cbc3x5ei2+KBv\nU8z9/Th+2XsBGyOuYuux63ivV2N8PKQlrM3Vu+smUUVStWZX2gjwuHHjFB6XdptZsZTnFrjaQpey\nArqVV5eyArqRNzdPjpy8fNjZFVwRIikpqfg6+XI8TkxFbo4JMrJy0TnIC+0Cq5e8QXkWAEDIARIT\nNTuPNSAgAAEBBTcosbe3R3R0tEa3ry3KqtvaVrO19X1eVXIZApg+sBGGt/XFt1vPY/Oxm1i45TR+\nPXQR0wZLbf1lAAAgAElEQVQ2waDQWhq55GBVOV6VhbnKpmrNVqsBlsvlyMnJQV5eHvLz85GdnQ1D\nQ0MYGPCMbCL6z9Y/b2NT1A1YWZjBxsIEtV0tS1zvxdsxeziVvA6ph3WbSuPuaIkFb4diVKe6mPHr\ncZy+8Rj/W34Mv4Zfw7wxwQjwchA7IpFGqNUAr127FqNGjSp6vG7dOoSFhWHGjBlqByMi3bbn9F3E\nPEoFADx99hxmxjJIJEBeXj7e6V5f5HT6i3WbyqOetwO2fvYadp6MwewNp3DxbgK6fbYdb3Wthw/7\nNoKZSdWdH0/6Qa0GeOTIkRg5cqSGohCRrtkQcR2PkjJKfC45PQufDWmusMzezg4SiQRpz1IqIx6V\ngHWbyksikaBXixro0NADczefxcoDV/DTnovYczoGc8eEICTATeyIRCrjrZCJqFxmbzgFM2PFkiEI\nwJT+QeXehrERSw6RrjE3kWHmsBbo07Im/rc8ClfvJ2Hw13sxqlNdTH+9KUyN+f816R6+a4moRAfO\nxiJP/t+VAu7Gp2Ll5E4iJiIiMQXWcMTe2X2wZNcFLNh2HisPXkHkpQdY9G5b3pKcdA4bYCIq0eKd\nf6OanRmAgpHezkG8yxqRvpMZSjGxTyO0D/TAB0sjcDMuBT3DdmBK/yCMfy0QUmnZV4Ui0gaavWo8\nEVUZu2f1wi8TO+KXiR3hZGMGmSGvEkBEBep5O2DfF33wVtcA5MsFzNl0Fm/M2Yenqby9NukGjgAT\n6bFHSRlISc8uevz99r/g62ZTbL3WAa7o1sS7MqMRkZYzMTJE2NAWCK3njgk/HUXU5Th0/HgrFo1r\nyxPkSOuxASbSMykZ2dh7+i4A4ND5++gf7Fv03Jud/NGsTjWxohGRDmrboDoOftUX45dE4MS1Rxjy\nzV5M6ReED3o15JQI0lpsgImqqMfJmVi+/xJMXrryQmpGNup62iM4wA3tAqvDxdZcpIREVFW42Jpj\n4/RuWLjtLyzYdh7z/jiHCzFP8f07bUq9vTmRWNSeA/zgwQO0adMG5ubmCAoKwpUrVzSRi4jUlCeX\nIztP/t/jfDnuPErF5H5BGBRaG672Fmx+9RBrNlUUA6kUH/YLwtopXWBtZoRD5++j22fbcf2f4rc+\nJxKb2g3w2LFjUb9+fSQlJWHQoEEYNGiQJnIRkQoeJWXg5LVHOHntEf55koZujb3Qyt8Vrfxd8Sgp\nA37V7WDMk9n0Gms2VbR2gdWx78s+8PewQ+zjZ+jx+Q7s+XfaFZG2UKsBfvbsGQ4dOoRp06bB2NgY\nEydOxL1793D58mVN5SMiJRw6fw9Pnz1Hnlxe7Kt/sC/e7xXIi9brMdZsqiyeTlbYGdYLfVvVxPPs\nPIz9/jC+3XIO8heuLU4kJrUa4Nu3b8PExATm5uYIDg7G3bt3UaNGDVy/fl1T+YhICSZGhqjv7YDW\ndd1K/CL9xppNlcnU2BCL3m2Dz4Y0g1QiwXdbz2PwF9uQ/jxH7GhE6p0El5GRAQsLC6SlpeHatWtI\nTk6GpaUlMjIyiq1rb2+vzq4qhUwmA8CsFUGX8mpz1ty8fCSlZSksS0rLxi97ziMnNw+ZWbno5eQA\ne3tLkRKWTpuPbUkK81YVytRsVzft/IXJVewAr8Bcrzbr3y8AwFEgam1dOEeHw8ul+CUXxaKttYm5\nlKNMzVarATY3N0d6ejrc3d2RkJAAAEhLS4OFhUWxdWfPnl3075CQEISGhqqzayK9dDHmCcYv2g9H\nG3PU93GCh5MVpAYGsLc2w5iuDcSOVyVERkYiKioKAGBgYICQkBCRE2mOMjWbqKKEPLgCpwlrsXFG\nX7Ss6y52HNJxqtZstRrgmjVr4vnz54iLi4ObmxtycnJw584d1K5du9i648aNU3icmJiozq4rROFv\nMtqY7WW6lBXQrbzanNXJXAIfF0s8SXkOVxsj9GvhqdV5X6YLWQMCAhAQEACgIG90dLTIiTRHmZr9\nMC5OhISvpq3vHeZSTuFfFp6mZqLL1A2YOyYYA4JriZxKe48Xc5VN1Zqt1hxgKysrdO7cGd988w2y\nsrKwYMECeHp6FgUhIs0yM5HBxsIETWo540FCOr7dcg6zfz2GXw9dEjsa6QDWbNIWb3byR06eHBN/\nisRXv5/myXFU6dQ+Hfznn3/G0KFDYWdnBz8/P2zcuFETuYjoJW8tPAQTI0N4OVsVe87JxkyERKSL\nWLNJG3wxohVqudni0zXHsWTX37jzKAWL3m0Lc5OqNe+etJfaDbC7uzuOHj2qgShE9Cr/PE2DkaEB\n4hLSsXhcW4XntOlPUaT9WLNJWwzv4A8vF2u88/1h7D97D71n7sTqDzvDzYFz0qniqX0jDCKqOLl5\ncnyx4RTmbj6L19vULtb8EhHpspAAN+yc2QveLla4ej8J3Wdsx7lbj8WORXqAV8Qn0kKnb8Rj18kY\nWJjKUN/HAT2b1xA7EhFRhajpaoNdM3vh7UXh+PPKQwz4cg/mjg5G/2BfsaNRFcYRYCIt5GBtCjtL\nEySmZeFhYgbO3Xqs8JWSkS12RCIijbG1MMH6j7piRAd/ZOfmY8JPR/HFhlPIl8vFjkZVFEeAibSQ\nj4s1JvVthGeZOTh36zF2nIxBzMMUAMCz5zl4v2cgOjbyFDklEZHmyAyl+OrNVqhT3RafrT2OpXsu\n4kZcMn4Y1xbW5sZix6MqhiPARFrMyswIbRtUh6WpDD7VrOFTzRqBPo6IS0gXOxoRUYUY3sEfv03r\nBlsLYxy58A+6z9iOGw+SxI5FVQxHgIl0wP/6N1Z4POPXE/hszXEAQFzyc+yYPVCMWEREFaKlvyv2\nzu6N0QsO4er9JPSYsQML3g5Fj2Y+YkejKkLlEeAbN26gS5cusLW1hbe3tyYzEVEZzt96jLvxqXC0\nMUWnIH4gUNlYs0nXeDhZYWdYL/RpWQOZ2Xl4e1E4vvr9NPLyOS+Y1KdyAyyTyTBkyBDMmzdPk3mI\nqBy+frM1BABdgrzwXu/GZa5PxJpNusjU2BCLx7VF2NDmMJBKsGTX3xj01R7EJ2eIHY10nMpTIHx8\nfODj44PDhw9rMg8RlUM9bwf4utlg58kYHL74CACQ+iwdLf1dEVrfXeR0pI1Ys0lXSSQSvNW1HgK8\nHPDeD0dw8no8Ok3fisXvtmW9I5XxJDgiHTWsvR/O3IzH3fgU3I1PQVxiOhLTspCUliV2NCIijWvh\nVw0HvuqD4AA3JD7Lwhtz9+GbTWeQm8cpEaQ8NsBEOsrByhT9g33RrqEX2jX0Qmh9d+TL5fj699Ni\nRyMiqhCO1mZYP7ULpvQPggQSLN5xAb1m7sDtfy8TSVRepU6BCAsLw6xZs4ot7927N7Zu3arUjuzt\n7ZVLJgKZTAaAWSuCLuXVlaz29sA7Hq5FeVPSMvD1huOQGRlrbXZdObaFCvPqiqpcs7X1vcNcqlE3\n1xdjOqJzs9oYNW83/o5JQJdPt+GbMe0wtkdDSCQSpbenrceLuZSjTM2WCIIgqLOzw4cP46233sLd\nu3dfuU54eDgiIiKKHoeEhCA0NFSd3VaIwgOXm5srcpKy6VJWQLfy6lJW4L+8iSnp+Hh5BFzszAEA\nKelZ+PbdjmJGK0YXjm1kZCSioqIAAAYGBggJCUH79u1FTqU5ulqztfW9w1zKMTYxAQBkZ2lmqlZq\nRhYm/XgIG8KvAADaN/LCDx90gbeLjVLb0dbjxVxlU7Vmq3Ud4KysLOTm5kIQBGRnZ0MikcDIyKjE\ndceNG6fwODExUZ1dV4jC32S0MdvLdCkroFt5dSkr8F/e3Kx0zBrapGj57lMx6PXJb5jYp2HZ27Ay\nhZu9RYVlLNqPDhzbgIAABAQEACjIGx0dLXIizdHlmq2t7x3mUo7rv//VZK55o1oipK4Lpq2MRvj5\nWDR6ezn+1z8IozsHwNCgfDM9tfV4MVfZVK3ZKjfAsbGx8PEpuP6oRCKBqakp2rRpgyNHjqi6SSLS\noB7NfODuYIn45Mxiz20/fgdpmTlFj2u52+KzIc0qMx5VMtZsqspea+aDFnWq4fNfT2D7iTuYtf4U\nth+/g29GtUYDH0ex45EWUrkB9vLyglzOMy+JtFlgjZIL/47jd+DmYIHs3HwMCPZFfW+HSk5GlY01\nm6o6B2tTLBnfDn1b18S0ldG4eDcB3Wdsx8CQWpg2sAmcbMzEjkhahLdCJtJDS8a3AwDcfJCMjVE3\nse9MLNo2qI6mtZ2L1pFIJDA30a2TwIiI2gd6IGJOfyzc9heW77+MjZE3sfvUXXzQKxCjuwTA1Iit\nD7EBJtJrhVMfnqRkYuuft3EzLrnouT+vPsTYrvUU1jcxMkSTWs4vb4aISKtYmBrh0yHN8Ea7Opi9\n4RQOnLuHrzeewcoDV/BBr0AMblsHxjIDsWOSiNgAExGcbMzwTvf6CsscrEzx856LCssysnOxbUbP\nyoxGRKQybxdrrJzcCVGX4/DVb6dxKTYBn6w5jh93X8SE3g3RP9iXjbCe4o0wiKhE+XI5/DzsUNfL\nAXW9HOBsa4aN07uLHYuISGkhAW7Y90VvLJ/YAXXcbRGXmI6PVhxDi4m/44edF5CSzjto6huOABNR\niQaF1lZ4fPDcPSzecUFhWb5cgMxQioEhteBkbQaZIX+nJiLtJJFI0LWJNzoHeWHXqRgs3nkB1+4n\n4euNZ7B45994s0t9DGjljRrVlLuGMOkmNsBEVC6dgjzRKchTYVm+XI5NUTexaPtfqOFqA09HS3Ru\n7CVOQCKicpBKJejVogZ6NvdB1KU4/Lj7b0RfeYjF285i8bazCA5ww4gOfujYyLPc1xEm3cMGmIiU\ntiX6FmIfPyt6bGJkiDM34rHzxB02wESkEyQSCULruyO0vjv+Sc7Fz7v/wm9HLuPY5TgcuxwHJxtT\nDAiuhYEhtVDTlaPCVY1aDfDcuXOxfPlyxMfHw9PTE19++SV69uQJMkS67KvfT5d5UkhWTh4+Gcwb\nZ+ga1myikgXWdMHSiV0xpW8D/HHsFtYevoo7j1KxZNffWLLrbzSp5YwBwbXwWnMfWJmVfPdE0i1q\nNcAymQzbtm1D3bp1cfz4cXTt2hUXLlyAt7e3pvIRUTlkZuXi2v2kcq0774+zqOtp/8rna7nZon+w\nr6aikRZhzSYqnY25McZ0CcDoznVx9tYTbIy8gZ0nY3Dm5mOcufkYM9YeR6cgT/QP9kVoPXdOkdBh\najXAkyZNKvp3y5Yt4ePjg/Pnz7OYEqkoL79gTq0yzM0tcO9xKjIyMlC/HLf8fL9XIBrWcFI1Iukw\n1myi8pFIJGhSyxlNajlj5rAW2HP6Lv6IvoXjVx9i58kY7DwZAycbU/Rr5YsBIb6o7W4ndmRSksbm\nACcnJ+PmzZsICAjQ1CaJtFJunhxf/X4aFqaav0taXr4cJkaGGKDECKytre2/L37OOxxRubFmE5WP\nuYkMA0MK5gLHJaRj65+3senYTcQ8SsXSPRexdM9FBPo4YkjbOujdsgbvoKkjJIIgCJrY0MCBA+Ho\n6IglS5YUey48PBytW7fWxG4qlExW8KbNzc0VOUnZdCkroFxeQRCQk5uv0n5SMrIx7Zcj8FHjMjYG\nBgXzX/PzS84gFwTU93FCn9Z1VN6HJunSe0GXsgIFeSMiItC+fXuxo2icrtVsbX3vMJdyjE1MAADZ\nWdp13V1lj5cgCDh17SF+PXQJmyOv4VlmNoCCO9C93tYfY3s0RH0f9e+aqa0/R23OVd6aXWYDHBYW\nhlmzZhVb3rt3b2zduhUAMH36dJw5cwb79u2DoWHxEajw8HBEREQUPQ4JCUFoaGiZ4Sqbtv5AS6It\nWc/fikdGVk6Z6xW+L/Ly8spc93FyBv6IvIZGvtVUytS+kReCaqn2WkB7jm156VJeXcgaGRmJqKgo\nAAW/DIWEhOhUA1xVa7a2vneYSzlVpQF+0fPsXGw9dgPL917AiasPipa3CfTExL5N0amxD6RSSaXn\nqkjalEvVmq32CPCCBQuwYcMGHD16FObm5iWuEx4eDj8/P3V2Uyns7QtODEpMTBQ5SdkqMmtSWla5\n56Gev/0EwzuU/bO1srICADx79qyMNQvUdLWBi23J76eKpkvvA0C38upSVqAgb3R0tE41wGXR1Zqt\nre8d5lKOq5sbAOBhXJzISRRp6njdeJCEdeHXsTHqJjKyCppDX1cbvNujAfq1rqn0SXPa+nPU5lzl\nrdlqTRhcs2YNfv75Zxw7duyVhZTEtWL/ZaRkZCv1mmeZOWjpVw2tA9zKXHdER/9yzTvV1v9ZiPQJ\nazZRxartbofZI1piSv8gbIi4jhUHruDWwxRMXhaJH3ZdwId9G6Fn8xoqjwiT5qjVAM+cOROPHj2C\nj49P0bJPPvkE06ZNUzuYvknJyC73vNef9lyEg23BiOrz589LXddYZoAP+wWpnY+IdB9rNlHlsDY3\nxrs9GmBMl3rYfuI2Fm77CzGPUvHekggs2nEBnw5uhnaB1cWOqdfUaoBjYmI0laPKe5KSiQt3nr7y\n+c3HbiE4wLVc26rv7YDRrxXchIAjqkRUXqzZRJVLZijFgOBa6N2iJv6IvokFW//CjQfJGDZvPzoH\neWLmsBao7mgpdky9xGsmaVDs42fYfOwmpJLif9q4G5+KdoEe8H3F7RQ/GhAEXzfbio5IRERElUxm\nKMXgNnXQt5UvVh28gu+2nseBc/cQefEBxvcMxLjXGpR5B07SLDbASpq64hicbMxKfC4lPRuDQmsj\nwOvVd9kiIiIi/WQsM8A73eujd8samL3+FLafuIP5W85hz5m7WPxuW/h58IYalYUNMAC5XEBMfCoS\nMgsep6SkFD23KeomDKSSojM32wd6oFOQpxgxiYiIqApwsTXHkvHt8Ea7Ovjf8mO4dj8J3T7bhqkD\nm2Bs13o8Sa4S6GUDHJeYjuNXHxY9zsmV4/jVh+gbWhcAkJ6eXvRcXU979GjmDQMp7/dNREREmtPS\n3xUHv+qLmetPYv2R65i94RQO/3UfS95rB2fbkv/aTJqhlw3w3zFPYWdpgpovzMft0cwbPh4FJ6Hx\nxDIiIiKqDOYmMswdHYyODT3wv+XHcOLaI3T7bBuWTeiATvacUllRqmwDLAgCBAHYefIOrv2TDCPD\n/0ZwU9Kz8U6P+nCztxAxIREREVGBjo08cehrR7z9fThO3YhH/y92Y+F7uRjVtYHY0aqkKtUAP07O\nxIOENABA1OU4JD57DntLU0zs07BcN2sgIiIiEoujtRk2Tu+OmetPYNXBqxj3/T78fecxpg9sqPRd\n5Kh0Kh/NBQsWwMfHB1ZWVvD09MRXX32lyVxKSUrLwvL9l/HJ6j/xMCkDzzJzEOjjiI8HNcWkvo3Y\n/BIRQbvqNhGVTGYoxRcjWuG7saEwlhng593n8faiw3iekyd2tCpF5c6wR48eePPNN2FjY4P79++j\nefPmaNKkCTp27KjJfGWa98dZpD3PRdNazpg/NgRWpkY8e5KIqATaUreJqGyDQmshsHZ19P18M/af\nvYehc/Zh5eROsDY3FjtalaDyCLCvry9sbApOIsvOzgYAWFpWzt1M0p/nIDk9C1/9fhruDhaYNawF\nejTzgY25MZtfIqJXELNuE5HyWtZ1x5H5Q+Fia46T1+PR74vdeJycKXasKkGtCSUbNmyAhYUF6tSp\ng48//hjNmzfXVK5XOnX9ESb9HIWt0bfh62aDwW3qVPg+iYiqCjHqNhGpzt/LETvDeqJGNWtcu5+E\nvrN34VFShtixdJ5EEARB3Y0cO3YM/fv3x8GDB9GgQfGzFcPDw9G6dWt1d4Nlu88j6uJ9TBvcEgHe\nTmpv72UymQwAkJubq/Fta5ouZQV0K68uZQV0K68uZQUK8kZERKB9+/ZiR9G40uq2pmq2Jmnre4e5\nlGNsYgIAyM7KEjmJIm09Xi/mSkjNxGufbMRftx/D180OB+cOQTWRrmalzcervDW71DnAYWFhmDVr\nVrHlvXv3xtatW4seBwcHo2/fvli3bl2JDTAAzJ49u+jfISEhCA0NLTNcoaycPHy49DDcHS3x68e9\nIJFwmgMRVYzIyEhERUUBAAwMDBASEiJyIuVoqm6rU7OJSPMcrM2w5+vX0XXab/j7zhN0mfYbDs4d\nAmdbc7GjiUrVmq2REWAAePvtt2FlZYV58+YVey48PBx+fn5Kbe/B0zTkyQVEXnqA23Ep6NjIAyH1\n3DUR9ZXs/73gtC7cCEOXsgK6lVeXsgK6lVeXsgIFeaOjo6vkCDDw6rqtSs2uaNr63mEu5bi6uQEA\nHsbFiZxEkbYer5JyJaVlYeCXe3DtnyTUcrPB5k96wMHaVPRc2kCZmq3yHOBFixYhLi4OgiDgxIkT\n2LhxI7p06aLq5hQcPHcPYetO4uzNxzA3luF/AxpXePNLRFTVVWTdJqLKYWdpgo3Tu6G2uy1uxqVg\n2Lz9yMjSrqkIukDlBvjixYto1qwZLC0tMWLECMybN09joyR/Xn2IL0e2Qv9gX/QP9oWVmZFGtktE\npM8qsm4TUeWxtzLFxund4OVshYt3E/DWwkPIycsXO5ZOUfk6wMuXL9dkDgVWZkZwtjWrsO0TEemj\niqzbRFS5HK3NsH5qV/QK24nIS3GY8ksUvn+nDc+TKietuq/eo6QMXLz7FKduxOPktUdixyEiIiLS\nWl7OVlj7v84wMzbElujb+HrjGbEj6QytaoBnrjuJ+ORMjOkSgFrutmLHISIiItJqDXwc8cvEDjA0\nkGDJrr+x5vBVsSPpBK1pgNccvgobC2N0auSJTo08YWdpInYkIiIiIq3Xpn51zBtTcPmvz9YcR/QV\n7brKhjbSigb4rYWHkZyWhW9GadeF14mIiIh0wcCQWhj/WgPkywW8/X04YuJTxY6k1URvgJPTs/Ao\nKQMT+zQSOwoRERGRzpo6sAk6NfJESkY2Rs4/gNSMbLEjaS3RG+A5m85iSNvaYscgIiIi0mlSqQSL\nx7WBX3U73HmUincXhyMvXy52LK0kegOckZWL10PZABMRERGpy8LUCKs+7AR7KxNEXorDnE28MkRJ\n1G6Ak5OT4ejoiGHDhin92vjkDKRmZCPteY66MYiIqBzUqdlEpBuqO1pi2QcdYCCV4MfdF7Hn9F2x\nI2kdtRvg6dOnw8fHR+kLLx+58A+mr/oTXZt4wcRI5ftxaNy1a9fEjlBuupQV0K28upQV0K28upS1\nKlK1ZmsDbX3vMFfVoK3HS9Vczf2q4bMhzQAAk36OxM0HyZqMpbXHq7zUaoDPnTuH2NhYdOvWDYIg\nKPXaZfsuwdLMCL1b1ISxzECdGBqlSz9QXcoK6FZeXcoK6FZeXcpa1ahTs7WBtr53mKtq0NbjpU6u\nMV0C0KtFDWRk5WL0wkNIy9TcX9y19XiVl8oNsCAImDBhAr799luVCuknrzdFZlYe8uWcnE1EVNHU\nrdlEpHskEgnmjwlGHXdbxDxKxaSfI/n//79UnnuwYsUK1K9fH/7+/uX6U5q9vb3C4x+XHMX8cZ3h\nWc1G1QgaJ5PJ0K5dO9jYaE+mV9GlrIBu5dWlrIBu5dWlrEBB3qpC3ZotNm197zCXavj+Kh9N5LIH\nsGXWQLR8fzX2nY3Fusg7mNivmei5KoIyNbvUBjgsLAyzZs0qtrxNmza4f/8+Tpw4AQDl+m0iOjpa\n4fEH7Zzx8M5lPLxT7qxERFSKiqzZRBpx+HDBf/n+qnRbJjX591+5/P8bgERQYSz877//RsOGDYst\nDwwMxPnz5zUSjIiINIM1m4hIkUoN8MtmzpyJO3fuYO3atZrIREREFYg1m4j0neg3wiAiIiIiqkwa\nGQEmIiIiItIVHAEmIiIiIr3CBpiIiIiI9Ir23IOYiIi0Snp6OiZMmIDAwEC8//77YsfB7t27sX//\nfqSlpcHc3BwdOnRA3759xY6FHTt24MiRI0hJSYGDgwMGDx6Mxo0bix0LDx8+xKpVq3D79m2YmZlh\nyZIlouZJTEzE4sWLcefOHbi6umL8+PGoXr26qJnOnDmD7du3IzY2Fq1atcK4ceNEzVMoPz8fS5cu\nxaVLl5CdnQ1vb2+MHj0a7u7uYkfDokWLcPnyZWRnZ8PJyQmDBg3Sivd7oWvXriEsLAxvv/022rVr\n98r1OAJMREQl+u233+Ds7FyuG2dUhqCgIMyZMwdr1qzBrFmzcODAAVy8eFHsWDAwMMCUKVOwZs0a\njB07FosXL8aTJ0/EjgUDAwO0bt0aQ4cOFTsKAGDZsmXw8PDAypUr0bJlSyxcuFDsSDA3N0evXr3Q\ntm1bsaMokMvlcHFxwddff43Vq1ejcePGmDdvntixAAC9evXCkiVLsGbNGgwbNgzfffcdsrOzxY4F\noOAXhw0bNsDNza3MddkAExFRMTExMXj69CkaNmyoNbdOrVatGszNzQEAubm5AAATExMxIwEAevTo\nUTSSWbt2bTg7OyMmJkbkVICzszNCQ0Ph6OgodhRkZmbi4sWL6N27N2QyGbp3746nT5/i/v37ouby\n9/dH06ZNYWFhIWqOl8lkMvTv3x92dnYACm5mEx8fj7S0NJGTAZ6enpDJZBAEAXl5eTAxMdGaX5L3\n7duHRo0awdrausx12QATEZECQRCwatUqDB8+XGua30LR0dEYNmwYJk6ciN69e6NWrVpiR1KQnp6O\nR48ewcPDQ+woWiU+Ph4ymQwmJiaYMWMGnjx5AmdnZzx8+FDsaDrh5s2bsLOzg6WlpdhRAADLly/H\n0KFDsXjxYkydOhVGRkZiR0JKSgoiIyPRo0ePcq3PBpiIiBQcOXIEnp6ecHd315qRnUKtW7fGr7/+\nirCwMGzduhWxsbFiR1KwbNkyhIaGwtXVVewoWiU7OxsmJiZ4/vw54uLikJ6eDlNTU2RlZYkdTetl\nZmZi9erVGD58uNhRiowZMwZr167FoEGDsHjxYuTk5IgdCWvXrkWfPn0gk8nKtT5PgiMi0kObNm3C\nli1bii339/dHQkICvvzySwCo9BHgV+Vq0qQJpkyZUvTYz88PTZs2xbFjx+Dl5aUVuTZs2ICMjAxM\nmG1wgiwAACAASURBVDChwvMok0sbGBsbIysrC/b29lixYgUA4Pnz51oxhUWb5ebmYt68eWjVqhVa\ntGghdhwFBgYG6NKlCw4cOIDLly+jUaNGomW5fv06nj59ipYtW5b7NWyAiYj00MCBAzFw4MBiy2Nj\nYzF16lS89dZbCssfPHiAOXPmiJarJJXZnJeVa/fu3bh06RI+//xzGBgYaE0ubeHi4oKcnBwkJSXB\nzs4OeXl5ePz4MUfKSyGXy/H999+jWrVqWv0z1oZpUjExMbh58yYGDRpUtOzq1av4559/MGLEiBJf\nwwaYiIiKeHl5YePGjUWPN2/ejMePH2P8+PEipiqwd+9eNG/eHLa2trh16xaOHz+ODz/8UOxYOHr0\nKA4fPoxZs2Zp3YhmTk4O8vPzARSMJkokEhgaVv5Hv5mZGRo0aIDt27dj6NCh2Lt3LxwdHUWfKy2X\ny5GXlwe5XA65XI7c3FwYGBhAKhV/huiyZcsgkUgwZswYsaMUSUlJwfnz59G8eXMYGxvjyJEjSE1N\nFX0ufrdu3dCtW7eixzNnzkRwcHCpl0FjA0xERDrh/v372LlzJzIyMmBnZ4ehQ4eiXr16YsfCH3/8\ngeTkZIVfEvr27YvevXuLmAp48uSJwvWbhw4dCn9/f3z++eei5Cm8RNyoUaPg5uaGiRMnipLjRVFR\nUVi6dGnR42PHjmHAgAHo37+/iKmAp0+fIiIiAkZGRhg5cmTR8unTp6NOnTqi5ZJKpYiOjsb69euR\nl5cHd3d3TJ06VeuuolEeEkEbxq6JiIiIiCqJ+GP8RERERESViA0wEREREekVNsBEREREpFfYABMR\nERGRXmEDTERERER6hQ0wEREREekVNsBEREREpFfYABMRERGRXmEDTERERER6hQ0wEREREekVNsBE\nREREpFfYABMRERGRXmEDTERERER6hQ0wEREREekVNsBEREREpFfYABMRERGRXmEDTERERER6hQ0w\nEREREekVNsBEREREpFfYABMRERGRXmEDTERERER6hQ0wEREREekVNsBEREREpFfYABMRERGRXmED\nTERERER6hQ0wEREREekVNsBEREREpFfYABMRERGRXmEDTERERER6hQ0wEREREekVNsBEREREpFfY\nABMREVGJVq9eDalUivv374sdhUij2AATERHRK0kkErEjvNLChQuxY8cOsWOQDpIIgiCIHYKIiIi0\nj1wuR15eHoyMjMSOUiIvLy+0a9cOK1euFDsK6RiOABMREVGJpFKp1ja/hTiOR6pgA0xEREQKAgIC\nIJVKi75KmgMslUoxc+ZMzJ8/H9WrV4eNjQ369OmDxMREhfVGjhwJb29v7N27F/7+/jA1NUVgYCD2\n7dunsN7Ro0chlUoRFRVV4usLFc5LLsy1Zs0ahawvv56oJIZiByAiIiLtMmfOHKSmpiIqKgrLli17\n5XobNmyAra0tpk2bhtjYWCxcuBBjx47Fli1bitaRSCRITEzEG2+8gXfffRcuLi745Zdf0KtXL0RG\nRqJFixZl5nlxHnJoaCjWrVsHQRAwadIk+Pv7Y+zYsUXP16lTR8XvmvQJG2AiIiJS0L17dwBATk5O\nqQ1wRkYGLl26VDRNIjk5GevWrYNcLodUWvBHZkEQkJ6ejl9++QWjR48GALzxxhvw9PTEzJkzsX//\n/jLzvDjNwdvbu2hE+NNPP4WPjw+GDBmi2jdKeotTIIiIiEgl3bt3V5gj3LBhQ+Tk5ODJkycK60ml\nUoUm1d7eHp07d0ZUVBTkcnml5SUqxAaYiIiIVFKtWjWFx+bm5gAKRo5fZG9vD1NTU4Vl1atXR1ZW\nFhISEio2JFEJ2AATERGRSgqnOZSltCs1GBsbl/ra/Px8pTIRlQcbYCIiIqpQif9v787Do6rvPY5/\nkslk3ycbSYAgCAmGRQRknYihXrR6oVZFexFtUavUVn2qpbVPq4LF4l6R2qutCl53QbEUxWvAxAgK\nQmVHlrAEQkIIJGRPJjP3DyXXGJAEk/zOZN6vvzLxTPKegzx888tvzikrU21tbYvPHThwQJGRkYqK\nipKk5q0UNTU1LY4rKio67c04rHyTDlgbAzAAAOhUHo9HL7/8cvPjo0ePasWKFZowYULz51JTUyVJ\na9eubf5cYWGh8vPzT/t1IyIiVFRU1AnF6O64CgQAAGi2adMmbdq0SZK0Zs0aSdLbb78th8MhSbrk\nkkuUkJDQrq8ZHh6ue+65RwUFBUpMTNRzzz2nxsZG3Xvvvc3H9OrVS4MHD9a8efNUX1+v8PBwPf/8\n8+rXr1+rVeGTxo4dq+eee05z587VkCFDZLPZdOGFFyomJuZsXjp8CAMwAABo9vbbb+uBBx5ofuzn\n56e77rqr+eNVq1Z95wB8qm0JDodDCxYs0N13362CggKlp6dryZIlGjFiRIvjlixZohkzZuipp55S\namqq5s6dq+XLlys3N/eU3+vBBx9UWVmZHn30UZWXlzf3OZ3Os3np8CF+Hu4hCAAAOsmNN96o3Nxc\n7d2713QK0Iw9wAAAoFPxZjVYDQMwAADoVPyyGVbDAAwAADqNn58fK8CwHPYAAwAAwKdwFQgAQAs5\nOTmmEwDgrGVnZ5/xGAZgAEArGRkZphNacDgcWrJkibKyskyntEBX+9DVPnS1j8Ph+M4bp3wTe4AB\nAADgUxiAAQAA4FMYgAEAXsFq2zJOoqt96GofujoHAzAAwCtY9R9cutqHrvahq3MwAAMAAMCnMAAD\nAADApzAAAwCADvfO6t2a+9paVdY0mE4BWmEABgAAHW7P4QqlxoWrtsFlOgVohRthAACALnG0okYN\nriYFmQ6Bz2MABgAAnWZ/yQnNX/qFSitqVefyKDgwQHNvGKV9JScUGOCvAJu/QoMC1Csh0nQqfAgD\nMAAA6BC19S79+Y116p8SI0lKjYvQsrV7NWVMX53X26H4uDi9kbtNT769QZlpcYoMDVRjk1srvyjU\nEz+31m110b0xAAMAgLP2+xc/0d7iCr0861I1NrmVEheu4uPVkqSLh/bUxUN7Nh8baLdp2sRBuvT8\n5BZfY+2OYj385uf61eShCg5kNEHn401wAADgrMVGBOvWy4fojr99pOdXbJEkuZrcqmvHm9/m3DBG\nUWGBanS5OysTaIEfswAAwPfizEzR0HPiVXC4QsmOMCVEh57V1ykpr9FruV9qd1G5hpwTr59MSO/g\nUuArrAADAIDvLTI0UEP7xp/18DsgNUav536pYf0SdP/1o7VlX5nu+u9c/fb5fBWVVXVwLXwdK8AA\nAOCsPL54vQ510HB60eCeumjw/+8XnvvTsZKkJZ/s1jur9+iykX2UlsiVItAxGIABAK04HA7TCS3Y\n7XZJdLVVV3UFh4Ro4e8uafPxZ9N17cRwrd9VrB1FlbpgYJ92N3ZWV1egq31OdrUFAzAAoJU5c+Y0\nf+x0OpWVxSWq8P9q6hq1ftdhNTZ1/pvWIkKD1DM+UodKKzv9e8H75ObmKi8vT5Jks9nkdDrb9Dw/\nj8fj6cwwAIB3ycnJUUZGhumMFk6uNJWVlRkuaclXu/YcLtebH+/S1ePPVd8e0Z3edayyTs+9t1ke\nSb+9ZkS7ntuZXZ2NrvZxOBzKz89Xdnb2GY/lTXAAAKDNPt9Vohc+2KrBfeLaNfx+H7ERwZp1zQhV\nVNfrodfWdsn3RPfGAAwAAM5o/5ET2l1Urr3FFbr50kG6bETn7Mf9Lg/9dJxqG1z606ufqcnNNYNx\n9hiAAQDAGS14d6MefvNzbSo4arRj9vQxCgu2a0fhcdXUNRptgffiTXAAAOCMEmNCddeVw3S8qk5J\nMWFGWy4b0UfvrNmjiwanauSAJKMt8E4MwAAAoE16xIapR6zZ4VeS+qfGaFRGD9MZ8GIMwAAA4JTW\nflmsV1btUM/4COOrvqdSW+9Sg6tJgQE20ynwMgzAAACghWOVdaptcKnJ7dGPx52r8ZkpppNaOScx\nUktW79b2wmO69YeDTefAyzAAAwCAFp5YskEl5TUKDPDX1KwBpnNOKTU+QjP+I1O3PpWjhOhQXTm2\nn+kkeBGuAgEAAFqIDg/Ss3dM1NO/uNiSq78nhQXb9dJvJunLwmO694VPTOfAizAAAwAAr/a7a0fK\nERlsOgNehAEYAAAAPoUBGAAAdAtL1+zRPc/ladXGQtMpsDjeBAcAAFRUVqU/LFott1uKCgs0nXNW\nqmobddnIPqqpd5lOgcUxAAMA4OOOV9Vp6/4yZQ/tpZ9MSDedc1bO6+3Qln1l6p8SzQCMM2ILBAAA\nPu6TrUU6dLRKWYNSTaectUnD03T3VRcoPMQ7V6/RtRiAAQCARmX0UEpcuOmMDvH5zhJt3V9mOgMW\nxhYIAEArDofDdEILdrtdEl1t1d6uiIhSRUdHd/rr6IrzNTwiSrEx0Xozb7ucw/pbputs0NU+J7va\nggEYANDKnDlzmj92Op3KysoyWIPO8tqqrSotr1FKXITplA4THBiggWnxKn7nc23dV6rz0uJNJ6ET\n5ebmKi8vT5Jks9nkdDrb9Dw/j8fj6cwwAIB3ycnJUUZGhumMFk6uNJWVWevX2t7e9fCbn6vJ7VFh\naaXumDJUA1JjLdHVET7eckgr1u/TgzeMPeOx3v7n2NWs3JWfn6/s7OwzHssKMAAAPsrm76ffXD3c\ndEanGJ+ZorVfFpvOgEXxJjgAANBtPfH2Bv3yr6u0ZZ+1VithFivAAAD4oKVr9qjsRJ3pjE7lanLL\n3ejRFReeI3Z84ptYAQYAwIc0ud2qqWvU2i+L9YsrhpjO6VQDezsUFxViOgMWxAowAAA+ZOv+Mr3w\nwTZdm9W/21z393SuuPAcSdIH6/cbLoHVsAIMAICPuXREmi5M72E6o0tt3ndUxcerTWfAIhiAAQDw\nEbUNLpVXN5jO6HIXnJuguKgQPb5kg/72r02mc2ABDMAAAPiI13N3auOeUqWnxphO6VKOyBBdMqy3\nHp4xXtV1jaZzYAHsAQYAwIdcd9EA3hgGn8cKMAAA3dyuQ8f1yFufa8u+owoI4J9+gBVgAAC6ub/9\na5P+c1RfZQ1ONZ0CWAI/BgIA0M0lO8IZfr9WWdugg6WVpjNgGAMwAADwGaPSe+jRxetNZ8AwBmAA\nAOAzJg1PU0RIoJ57b7PqGlymc2AIAzAAAN1UTV2j5rzymRoam0ynWMrvpo5QXUOT6jgvPos3wQEA\n0E3Vu5qUHBumGZMyTadYSmiwXSFBjEC+jD99AEArDofDdEILdrtdEl1tdbIrNjZWYWFhlumz0vka\nlt5LTy7drKuc6ZqYlCTJGl3fZKXz9U1W72oLBmAAQCtz5sxp/tjpdCorK8tgDdDxfnBBH8VGBOuv\n766Xyy1NGtnPdBLOQm5urvLy8iRJNptNTqezTc/z83g8ns4MAwB4l5ycHGVkZJjOaOHkSlNZWZnh\nkpas2rXk0wPaceCoqmtqNaJ/oqaMscZwZ9Xz9dfl2/SH68dbrsuq58vKXfn5+crOzj7jsawAAwDQ\nzRQfq9Jjt/3AcgMKYBVcBQIAAPi00ooa3fL4v3Tb/BzTKegirAADANCN/GHhakVHhpvO8CpZg3sr\nISZU763ZYToFXYQVYAAAupHo8CDNvWmC6Qyv8mNnusYP6iVXk1ub9pZygwwfwAAMAICXa3K7dbyq\nTtMfeV+OyBDTOV5r8ui+WrqmQAePVplOQSdjCwQAAF6u4HCFFvxzo2ZePkSjMnqYzvFa6T1jNbhP\nnOkMdAFWgAEA6AYmDOnJ8Au0EQMwAADA13olRGjRh9v07qd7TKegE7EFAgAA4Gvn901QXGSIVm87\nbDoFnYgVYAAAvNjSNXv0Us529U6INJ0CeA1WgAEA8GJ1DU26+dJB6hkfYTql2/D389OqjYUKCbLp\nP0f1NZ2DTsAKMAAAXmrBP7/Q1v1HFRLIelZHSokL199+la1dh8pNp6CTMAADAOCl6hqaNHv6GMVF\nce3fzlBR06DZL39qOgOdgAEYAADgFGZfP1phwXbTGegE/M4EAAAv4mpy65VVO7Rm+2H2/QJniQEY\nAAAv4mpyq+hYtR762ThFhwWZzun2qmob9fGWQxo7MFn+/n6mc9BBGIABAK04HA7TCS3Y7V/9Gpou\nqa7BpYTYKPXtlXzaYzhf7fNdXbdOHqmnl36uS0cPVFAXv9nQG8+XSSe72oIBGADQypw5c5o/djqd\nysrKMliDkzYXHNHCDzZpSN9E0yk+Y2BavNISozX3ldX66aTBSkuKNp2Eb8jNzVVeXp4kyWazyel0\ntul5fh6Px9OZYQAA75KTk6OMjAzTGS2cXGkqKyszXNJSV3et3lYkSRoz8PSrvxLnq73a0vXJ1iIV\nllZq4vm9uuyqG958vkxwOBzKz89Xdnb2GY/lKhAAAHiBXYeO65OvB2B0vfSeMQoJCtCji9fr/v9Z\nYzoH3xMDMAAAXuCDDft16fA0DT0n3nSKT3JEhmjy6L7688/GKSIk0HQOvif2AAMA4CX6Jkdz1zeg\nA7ACDAAA0A57iyv0xJINevfTPaqpazSdg7PAAAwAgMU9sWSD9h+plL8f16G1godnjNeV4/qp4HCF\njlfVm87BWeD3KAAAWNia7Yd1pKJGD88YbzoFXwsNtqt3sF1JMWGmU3CWWAEGAMDC3lm9W/81Id10\nBtCtMAADAGBhCdGhykyLM52B03jv8306WFppOgPtxAAMAIAFnahp0FNL/61q3mRlWZeN7KNzkqK0\nYc8R0yloJwZgAAAspuR4jd5bt1eOiBDde+1I0zk4jcjQQKXGhZvOwFngTXAAAFjMZ18eVnBggMad\nl6wAG2tVVmYP8NdHmw7K1eTRlWP7mc5BG/G3CgAAC8roGStHZIjpDJxBn6QoPX5LlvYWV5hOQTsw\nAAMAYCEbC0r12Y5i0xlAt8YADACARewuKtfLq3bo5ksHKS0p0nQO2mFjQakefWu9nnh7g+kUtAF7\ngAEAsIh/rNii/7igt9ISGX69zaJ7JkmSHn1rveEStAUrwAAAWMCNj63QgJQYZQ/tZToF3wN3q/YO\nDMAAAFjAoLQ43XjJeaYz8D25PR49+95mHSmvMZ2C78AWCABAKw6Hw3RCC3a7XVL37Co4XK5/LP+3\nEh3RHfb6uvP56gwd2XX/T7O17NNdqnYFfO+v5wvnqyOd7GoLBmAAQCtz5sxp/tjpdCorK8tgTfdW\ndqJG4wf10qSRfU2noAMEBwYoLKjtgxi+n9zcXOXl5UmSbDabnE5nm57HAAwAaGXmzJktHpeVlRkq\n+crJlSbTHd/2fbtKK2q0butBxUUFd+hr667nq7N0dFdVVZXe+miLrnb214j+iZbp6ihW6srMzFRm\nZqakr7ry8/Pb9Dz2AAMAYMg/Py1QkN1fQ/rEm05BB7p4aE/9/rqReilnm178YKvpHJwCAzAAAAZ8\nsGG/8rYcknNQquKiuONbdxMVFqSnbpugJrdHNz3xv6Zz8C1sgQAAwICNBaV65pfZCrbbTKegE82Y\nlKny6nrTGfgWVoABAOhi/7Nyu3YXlSskMEB+XDi22+sRG6aZT6/U8ao60yn4GgMwAABd7NDRKj35\n84tMZ6CL/GRCukalJ2n+0i+069Bx0zkQAzAAAF2mye3W3uIKeSSFBLEL0ZdMnzhQk0f31b6SE6ZT\nIPYAAwDQJT7aVKh/flqgFEe4sof0NJ0DQ9xuj9xuj/z92fpiEgMwAABdoNHl1vSJAzXkHC555qsS\nY0K1cmOh3srfpRt/cJ7GnpdsOslnsQUCAIBO9nruTq3aeFChbHvwaUkxYbrrR8M065oRWrWx0HSO\nT+NvIgAAnWjhh9uU8+8DWnTPJNMpsIh+ydHsATeMFWAAADrR0Ypahl+0UlFdr9XbilRZ06Amt9t0\njs9hAAYAoIPV1ru07sti/de89xTPXd5wCleNP1e7isr1+4WfaHdRuekcn8P6OwAAHexA6Qmt3Fio\n2dNHq2+PaNM5sKDBfeI1uE+8YiOCtb+kUkkxYYoKCzKd5TNYAQYAoBMM7O1g+MUZDeuXoKKyKi1f\nt9d0ik9hAAYAoIMs+nCb7vrvXP112SbTKfASKY5wTRnbT/uKT+jPb6wzneMz2AIBAEAHKa2o1eO3\nOE1nwMtEhwXpd9eO1GOL15tO8RkMwACAVhwOh+mEFux2uyRrdrma3Nqw74ReW7VVPWLDFRcXZzrL\n0udLout0io7X69anP1KAzV/P33O5Zbq+zepdbcEADABoZc6cOc0fO51OZWVlGayxtvoGl5at2aXf\nTB2tAT2tNRDAu7w46wpJ0mNvfiqPx3CMl8jNzVVeXp4kyWazyels229gGIABAK3MnDmzxeOysjJD\nJV85udJkuuPbHA6HGl2Nio8IUFyodfqsfL4kus4kzO7RPX9bodGZveUc3EthNpfppBasdL4yMzOV\nmZkp6auu/Pz8Nj2PARgAgLPw9Ltf6EBZrfwkDe0TYzoH3cjV4/vrkmH12nOkTvPfXqfpE85VsiPc\ndFa3wgAMAMBZcHs8euaOSxVot1liJQzdS1RYkC4+P0nHq+r0p1fXavrEDF2Y3sN0VrfBAAwAQDv8\n/f0t2rzvqGrqrPVraXQ/Npu/rrlooOLD/bXkk91a9tle/WrKUMVHhZpO83oMwAAAtMOxyjo9ctN4\nBQbYFGi3mc6BDzh517jF+bv06FvrNaxfokZlJKl3QqTpNK/FAAwAwBms31Wi//33Adlt/grw91Ng\nAIMvut6Px52rS4enad3OEr360Zf67TUjTCd5LQZgAABOY+5ra1VaUau0xEhdNe5c9Uvm1sYwKzTY\nrqzBqdq4t1S/emaVnrptgukkr8QADADANxQfr9aS/N1as/2wxgzsoSvH9lNhaaV6xIaZTgOa/Wry\n+Xps8XrtP3JCu4vKVd/YpAGpMerbgx/S2oIBGACAbzh0tErn9IjSTZdmNm91SO8Za7gKaK2uwaUH\nX/lMM68YoobGJq3aeJABuI0YgAEAkHSwtFK7D5friz2lGnJOPPt8YXm/v+7C5o+r6xr1r7V79dji\n9fr1jy8wWOUd/E0HAABg2p/fWKd5b36uiuoGTTy/l0b0TzSdBLRLWLBds6ePUUV1vW59Kkc3P/mh\n8jYfVHl1vek0S2IFGADgc5rcbhUfq9GGPUfk8XjU6HJr/kzeTATvN3v6GElSYWmlVm87rA+/2KCE\nqBBdmzVAcVEhhuusgwEYANDtNbndqqpt1MynVyrA5i9Xk1uD+8Rp7HnJSogK1cgBSaYTgQ7VMz5C\nU7MiJEmv536peW+sU3l1g267fLAyesYqJMi3R0DffvUAgG5rd1G5Vm4sVGVNg46eqFWjy607ppzP\nsAufMzVrgKZmDdDuonLlbzmkN/J2qr6xScGBNvVOiNStPxxsOrHLMQADALzC9u3blZCQcNr/fqyy\nTlv2l+mjjYXaVVSuc5OjNfa8ZGUP7WW0yxS62scXuvolR6tfcrTcbo8qauoVGGDTog+36bHF63Wk\nvEb+/n5K7xmrgb0c6hkfrqSY01/6z6rnq60YgAEAXmH79u064QpU/pZD2n24XPFRoSqtqFHJ8Rql\n94xVyfEaXZiepF9cMUSOyK7b62jVQYCu9vGlLn9/P8WEB0uSbrt8iCTJ7fao3tWkdTtLtP1AmZ57\nb7Pio0Lk7+enrMGpGpPRQ4F2m/z8JI/HY9nz1VYMwACALlVeXa9jlXUKtPkrKixIZZV1OnDkhIrK\nqlV4tFIpjnCt2X5YvRMiJUk19Y0KCgrW6n8f0siiYI3OSNZV48/V0RN1iosMVnhIoOFXBHg/f38/\nhQQGyJmZImdmiqZPHChJqqiu1+L8XXr/832SpNS4cFU1eLRzV4mW7czTeb0d2ltyQhEhgaqsbVCv\n+Aj1T42Rv5+fHJHB6p0QqbITtYoOD1J4SKA8Hk/z9/Tz8zPxUr/63p5vlgAAfF5OTo5W7T39Pw1V\ntQ2SdMbBc+OeEg3p+9XlxE5+tcNlVWpyuzUyPUX2AH+t33lYYUF2De6bqH4pMUqNi1TB4eNKig1X\n3+SY5q9lt9tVWlqq6GhrXeSfrvahq32s3FVSckRuW5BOTpHx0aGqqWvU5r1H1OBqUvGxau06eExV\ntQ2KCg/SoaOVio8K1ZeFZfJ4pLSkKAUFBuh0I/Cho5VKiYtoc5NHkj3ApvE93crOzj7j8QzAAIAW\ncnJyTCcAwFljAAYAAAC+hTvBAQAAwKcwAAMAAMCnMAADAADApzAAAwAAwKcwAAMAAMCncCMMAMAp\nVVVV6Y477tDQoUP1y1/+0nSOli1bpvfff1+VlZUKCwvTxIkTdeWVV5rO0tKlS7Vy5UqVl5crLi5O\n1113nYYPH246S0VFRXrhhRe0e/duhYaGasGCBUZ7ysrKNH/+fO3Zs0fJycm6/fbb1bNnT6NN69at\n0zvvvKN9+/Zp7NixmjlzptGek5qamvTMM89o8+bNqq+vV58+fTRjxgylpqaaTtNTTz2lLVu2qL6+\nXgkJCZo6daol/n8/afv27br//vv185//XBdffPFpj2MFGABwSq+++qoSExON3q3pmy644ALNmzdP\nCxcu1OzZs7VixQpt2rTJdJZsNpvuvvtuLVy4ULfccovmz5+vI0eOmM6SzWbTuHHjNG3aNNMpkqRn\nn31WvXr10vPPP68xY8boySefNJ2ksLAwTZ48WRMmTDCd0oLb7VZSUpIeeughvfjiixo+fLgeeeQR\n01mSpMmTJ2vBggVauHChrr/+ej3++OOqr683nSXpqx8cXnnlFaWkpJzxWAZgAEArBQUFKi0t1fnn\nny+rXC6+R48eCgsLkyQ1NjZKkoKDg00mSZIuv/zy5pXMAQMGKDExUQUFBYarpMTERGVlZSk+Pt50\nimpqarRp0yZNmTJFdrtdP/zhD1VaWqoDBw4Y7Ro4cKBGjhyp8PBwox3fZrfbddVVVyk2NlaSdNFF\nF6m4uFiVlZWGy6TevXvLbrfL4/HI5XIpODjYMj8kv/feexo2bJiioqLOeCwDMACgBY/HoxdexInT\n7wAAA9tJREFUeEHTp0+3zPB7Un5+vq6//nrdeeedmjJlivr37286qYWqqiodPnxYvXr1Mp1iKcXF\nxbLb7QoODtYf//hHHTlyRImJiSoqKjKd5hV27typ2NhYRUS0/dbAnenvf/+7pk2bpvnz52vWrFkK\nDPzu26J3hfLycuXm5uryyy9v0/EMwACAFlauXKnevXsrNTXVMis7J40bN04vvfSS7r//fi1ZskT7\n9u0zndTCs88+q6ysLCUnJ5tOsZT6+noFBwertrZWhw4dUlVVlUJCQlRXV2c6zfJqamr04osvavr0\n6aZTmt10001atGiRpk6dqvnz56uhocF0khYtWqQf/ehHstvtbTqeN8EBgA964403tHjx4lafHzhw\noI4ePao//elPktTlK8Cn6xoxYoTuvvvu5scZGRkaOXKkPv74Y6WlpVmi65VXXlF1dbXuuOOOTu9p\nT5cVBAUFqa6uTg6HQ//4xz8kSbW1tZbYwmJljY2NeuSRRzR27FiNHj3adE4LNptNkyZN0ooVK7Rl\nyxYNGzbMWMuOHTtUWlqqMWPGtPk5DMAA4IOuueYaXXPNNa0+v2/fPs2aNUs333xzi88fPHhQ8+bN\nM9Z1Kl05nJ+pa9myZdq8ebPuu+8+2Ww2y3RZRVJSkhoaGnTs2DHFxsbK5XKppKSElfLv4Ha79Ze/\n/EU9evSw9J+xFbZJFRQUaOfOnZo6dWrz57Zt26bCwkLdcMMNp3wOAzAAoFlaWppef/315sdvvvmm\nSkpKdPvttxus+sry5cs1atQoxcTEaNeuXVq9erV+/etfm87SRx99pA8//FCzZ8+23IpmQ0ODmpqa\nJH21mujn56eAgK7/pz80NFRDhgzRO++8o2nTpmn58uWKj483vlfa7XbL5XLJ7XbL7XarsbFRNptN\n/v7md4g+++yz8vPz00033WQ6pVl5ebk2bNigUaNGKSgoSCtXrlRFRYXxvfiXXXaZLrvssubHDzzw\ngMaPH/+dl0FjAAYAeIUDBw7o3XffVXV1tWJjYzVt2jQNGjTIdJbeeustHT9+vMUPCVdeeaWmTJli\nsEo6cuRIi+s3T5s2TQMHDtR9991npOfkJeJ+9rOfKSUlRXfeeaeRjm/Ky8vTM8880/z4448/1tVX\nX62rrrrKYJVUWlqqVatWKTAwUDfeeGPz5++9916lp6cb6/L391d+fr5efvlluVwupaamatasWZa7\nikZb+HmssHYNAAAAdBHza/wAAABAF2IABgAAgE9hAAYAAIBPYQAGAACAT2EABgAAgE9hAAYAAIBP\nYQAGAACAT2EABgAAgE9hAAYAAIBP+T90e3wwmEPQ2QAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f57440b8>"
+        "<matplotlib.figure.Figure at 0x7f576f8c9048>"
        ]
       }
      ],
@@ -359,9 +361,9 @@
      "source": [
       "I generated this by taking 500,000 samples from the input, passing it through the nonlinear transform, and building a histogram of the result. From that histogram we can then compute a mean and a variance that we compared to the output of the EKF.\n",
       "\n",
-      "It has perhaps occurred to you that this sampling process constitutes a solution to our problem. This is called a 'monte carlo' approach, and it used by some Kalman filter designs, such as the *Ensemble filter*. Sampling requires no specialized knowledge, and does not require a closed form solution. No matter how nonlinear or poorly behaved the transfer function is, as long as we sample with enough points we will build an accurate output distribution.\n",
+      "It has perhaps occurred to you that this sampling process constitutes a solution to our problem. Suppose for every update we generated 500,000 points, passed them through the function, and then computed the mean and variance of the result. This is called a 'monte carlo' approach, and it used by some Kalman filter designs, such as the *Ensemble filter*. Sampling requires no specialized knowledge, and does not require a closed form solution. No matter how nonlinear or poorly behaved the transfer function is, as long as we sample with enough points we will build an accurate output distribution.\n",
       "\n",
-      "\"Enough points\" is the rub. The graph above was created with 500,000 points, and the output is still not smooth. You wouldn't need to use that many points to get a reasonable estimate of the mean and variance, but it will require many points. What's worse, this is only for 1 dimension. In general, the number of points required increases by the power of the number of dimensions. If you need $50$ points for 1 dimension, you need $50^2$ for two dimensions, $50^3$ for three dimensions, and so on. So while this approach does work, it is very computationally expensive. The Unscented Kalman filter uses a somewhat similar technique but reduces the amount of computation needed by a drastic amount, and also uses a deterministic method of choosing the points."
+      "\"Enough points\" is the rub. The graph above was created with 500,000 points, and the output is still not smooth. You wouldn't need to use that many points to get a reasonable estimate of the mean and variance, but it will require many points. What's worse, this is only for 1 dimension. In general, the number of points required increases by the power of the number of dimensions. If you only needed $50$ points for 1 dimension, you'd need $50^2=2,500$ for two dimensions, $50^3=125,000$ for three dimensions, and so on. So while this approach does work, it is very computationally expensive. The Unscented Kalman filter uses a somewhat similar technique but reduces the amount of computation needed by a drastic amount by using a deterministic method of choosing the points."
      ]
     },
     {
@@ -394,7 +396,7 @@
        "output_type": "display_data",
        "png": 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ayWRCfUs3ksd5GSuWVPo0h2MICimg4jqsAUsiCIIw2pWbN29GZqY4k+THkj09\nF0l3PAdVZLzYUbxOcLkwcKoSvZU70Vu1C/buljHHqyITEJa9AGHZ86FLyYJEygLlaS67FU2v/wDz\nMpLx8m9+zV1XKGiV7t6LfmUEomJiL/u+WFJpLAN9ZqCnCUvz88SOQpehuroay5Yt+8zr/O43aVtb\nG4aGhqCMiBM7iigkMhn0adOgT5uGxKvugq39FHqrdsJUuROWpmPnjbd3N6N9+1to3/4W5CFhMGRe\ngbDs+QhNn8WtbD1EptIgef1TKPvrD/DIY9/BL599hke5Kej09PSgxTSI1JxLP+jBkkoXyma1IE7P\nDRsCmd8V1oqKChiSp7AAAJBIJNDEpkITm4q4pTdhyNyF3qrd6K3aif66gxBczhHjnYO96N63Ad37\nNkCiUMGQPgth2QtgyLwCCl2YSM8iMEkVKiTe+ENs/MOD+NULL+Bb3/ym2JGIvKryWC30MUkX/VrN\nkkqXYshqRVhCqNgxyIP8rrAePHQI0thJYsfwSUpDFIx5a2DMWwOXbRDm4/vQW7kT5pq9cFkHRowV\nHHb0Vu1Cb9UuQCKFLiXrzNSBrPlQRyeK9AwCi0ytRdItT+F3v3sASQkJuPbaa8WOROQVF3t0lSWV\nLpd7yIqQkOD85DVY+F1h3bv/ENQTP3t+A/2PTB2CiGkFiJhWALfLiYETFWcKauVODPV2jBwsuDHQ\ncBQDDUdx+sPfQW1MHi6vIUlTgmatW09QGqKQvP4pfPcHjyAmJgYLFy4UOxKRxx2pOY7Q2OQxj66y\npNJ4cg1ZuUJAgPOrwioIAqqOHsGEgrvFjuJXpDI5QtNnIjR9JpLW3gtraz16K8+UV0tL3XnjbR2N\naOtoRNvWv0Ohj4AhKw9hWQsQOikXUgW3vbtYmthUJF7/OL561z147+23fPJERqLx0t3djbZeC1Jz\njOddx5JKnuB2uyE4hrgGa4Dzq8La2toKNyRQhEaJHcVvSSQSaOMnQRs/CfEr1sNuaoe5ajdMlTvR\nf+Iw4HaNGO/o70HX3g/RtfdDSJVqGDLmICxrAQyZ8yDXcr7QhdJPnA7j6rvx5ZtuwYYP3kd8fPCt\ncEHB4eix2hFHV1lSydNsFgsMOi3PbQlwflVYu7q6oAmL4j/KcaQKj4FxwdUwLrgaTusAzDV70Vu5\nC+ZjZXDbLSPGuodsMB0phelIKSCVQp+aM7xklipIV224GBEzlsJp7sR1N96Mkv9+wKMBFHC6u7vR\nZrYiJTEPfDnmAAAgAElEQVQa9TWVLKnkFQP9ZiRG8sThQOdXhbWvrw9yNeeoeIpco0Nk7jJE5i6D\n2zmE/vrDw/NeHX3dIwe73eg/cRj9Jw6j6T8vQxOX9slmBQugTUjnm4pRRC+6Di2dp/DgI4/ilRdf\n4H8nChiCIOD9Dzdg5/5DOPDD77CkktcMDfYjmp9aBTy/Kqz9/f2QqnhUyhukciUMGXNgyJiD5HX3\nwdJ8/My816pdsLadPG+8tfUErK0n0Lr5dSgM0QjLykNY9gLo06ZDKleI8Ax8k0QiQeza+1D6yv14\n429/w8033SR2JKJLJggCjh49iv/85z/497//jdOnT486liWVPMVp7UdYGI+wBjq/K6wSFY+weptE\nKkVI0hSEJE1BwsrbYe9ugemT8jpw8ggguEeMd5g70bn7fXTufh9SlRZhU+ad2awgYy7kGi7sLFNq\nkHD943jiqYcwY/p0TJ06VexIRBfs3JL6wQcf4NSpU6OOZUklT3MMDUEBF1cICAJ+V1jBI6yiU0XG\nI3bRtYhddC0cg2aYq/egt2oX+o6Vw+2wjxjrtlvQc3greg5vhUQmhz5tOsKy5yMsaz6UYeefRRws\nNDEpMK7+Ou648y58vHkjNBr+IiffxZJKvmqg34yYyAixY5AX+FVh7evrg6BkYfUlihADomYXIWp2\nEdwOO/pqDwxvSOAc6B0xVnA50Ve7H321+9H43m+gTUg/M+916gJoYtOCbj5n5MwVaK7bj+99/4f4\n5S+eETsO0QgXVVJVKuTMzsPSq77AkkpeZenrw6R4TgcIBn5VWK1WGyDnOqC+SqpQnZm7mpUHwf0t\nDDZVo7dyJ3ord8HWef4JGJbmWliaa9Gy8TUow2OHj7zqJ0yDRCYT4Rl4X8yab+C/L96FouJiFBUV\niR2HgtzFlFSNRoPly5ejqKgIA4ICk2blQxYkP7fkO1zWAYSHJ4sdg7zArwpreHgYYGsXOwZdAIlU\nCl1KNnQp2UhcfSdsHU0wVZ0pr4ONVYAgjBg/ZGpDx4530LHjHcg0ehgy5yE8ewFCJ8+BTBW4R2vk\nGh3iv/QYHnj4EeycMwcREfxoi7zrUkrqmjVrsHTpUmg0GlTXHMOxLhvLKnmd2+2Ge2gQBoNB7Cjk\nBX5VWCMiIiCx9Ykdgy6B2piEOOP1iFt8PRz9Peit3oPeyl3oq90PwTk0YqzL2o+eA5vQc2ATJHIF\nQifNRFjWfBiy8qAMjRTpGXiOPnUqBrIX4cmf/BTPP/es2HEoCFxuST3L5XKh5mQjotJneCM20QiD\n/X2INOj5ZilI+F1hdVtYWP2dQh+B6LmrET13NVxDVvQd34/eyp0wV++B81P/fwWnA+aavTDX7AXe\neR4hyZmfrPc6H2pjSsDMe41efis+fP523Lr+ZkyfPl3sOBSAxquknqulpQUulR4qtdpTsYlGNdjf\nj4mR4WLHIC/xu8LqHDSLHYPGkUypQfjUfIRPzYfgcmHgVOWZea9Vu2Dvbjlv/GBjNQYbq9G84VWo\nIhOGd9rSpWRBIvXfd9lyjQ7Gwtvx4Le/g40ffQCpVCp2JAoAniip56qqPYnwmAnjGZnogjksfYhM\nTRU7BnmJ3xXWIRbWgCWRyaBPmwZ92jQkXnUXbO0Nn6z3uhOWpmPnjbd3N6N9+1to3/4W5CFhMGRe\ncWa91/RZkCn974hPxMxCNOz/CH//+99xEzcUoEvk6ZJ6Vnd3N8wOAakGnqFN3ud2u+Gy9HHefxDx\nu8Jq62dhDQYSiQSa2AnQxE5A/LKbMGTuQm/VbvRW7UR/3UEILueI8c7BXnTv24DufRsgUahgSJ+F\nsOz5MGTmQaHzj1+oEqkUMVd9A0/99LtYvXo1wsP5URddGG+V1HMdqz8JXVTcpUYmuiz95l7EhIdC\nqeTKQcHCrwpraGgoILjhtPRDrtWLHYe8SGmIgjFvDYx5a+CyDcJ8fN+Zea81e+GyDowYKzjsw2vB\nQiKBLiX7zNSBrPlQRyeK9AwujDYhHfrshXjqpz/Hc8/8TOw45MPEKKlnDQ4OoqnDhJRp6Zd1P0SX\nasDUjemJwbv5TDDyq8IqkUiQkTUVg6ePwTB5tthxSCQydQgiphUgYloB3C4nBk5UnCmolTsx1Nsx\ncrAgYKDhKAYajuL0h7+D2pg8XF5DkqZA4oNzRaOX34b/PH87vv7V2zF58mSx45APEbOknutkwymo\nImI415pE4xgwwWjk62Mw8avCCgDzZuXio0YWVjpDKpMjNH0mQtNnImntvbC21qO38kx5tbTUnTfe\n1tGIto5GtG39OxT6CBgy8xCWvQChk3IhVfjGR0tyrR4R87+A5194Ea+8+ILYcUhkvlJSz3K5XDh2\nqhkxGbnjft9EF8I6OIAQpQw6nU7sKORFfldYZ83MxQd7Xxc7BvkgiUQCbfwkaOMnIX7FethN7Z8c\ned2F/hOHAbdrxHhHfw+6yj5EV9mHkCrVMGTMQVjWAhgy50GuDRXpWZwROW8NNj73FTQ3NyMhIUHU\nLOR9vlZSz9Xe3g5BFQKlSuXRxyEajbmnB2lxnA4QbPyusObm5qLv1PcQLwgBswYneYYqPAYxC76A\nmAVfgNM6AHPNXvRW7oL5WBncdsuIse4hG0xHSmE6UgpIpdCn5gwvmaWK8P6JJXKtHhGzVuKl3/4O\nTz/5hNcfn7zPl0vquU42NUMXwbJA4hkaMCE2fYrYMcjL/K6wJiQkQCYBHOYuKMOixY5DfkKu0SEy\ndxkic5fB7RxCf/3h4Xmvjr7ukYPdbvSfOIz+E4fR9J+XoYlL+2SzggXQJqR77Y1S5IIv4J+/vhMP\nf+sBLt0SoPylpJ5ls9nQ3NmLpByebEXicDiGgCELXxODkN8VVolEguycaehoqmFhpUsilSthyJgD\nQ8YcJK+7D5bm42fmvVbtgrXt5Hnjra0nYG09gdbNr0NhiEZY1pl5r/q06ZDKFR7LqTREI2xqPv74\npz/j4Yce9NjjkHf5W0k9V2trK2T6cG6FSaLpM5mQGBPFE/6CkN8VVgAoWHAF/rL9IMJzFoodhfyc\nRCpFSNIUhCRNQcLK22Hrbhk+aWug4SgguEeMd5g70bn7fXTufh9SlRaGKXMRnr0AoRlzIdeM/wkA\nEfnX4Q9/eBD33H0XtFrtuN8/eYc/l9Rz1Z46jbDoVLFjUBCzmHswbTLn9Qcjvyysa666Ci+8/DvE\nXXUvJHynT+NIHRmP2EXXInbRtXAMmmGu3oPeql3oO1YOt8M+YqzbboHp8McwHf4YEpkc+rTpCMue\nj7Cs+VCGjc8cP7UxCSEp2Xj77bexfv36cblP8o5AKaln9fX1odcyxJ2tSDSCIMA5aEZk5DSxo5AI\n/LKwpqamIj4+Hv0nKxA6iUureFP/iQoIbhdkKi2kKg1kKs2Z75Uan1zT9HIoQgyIml2EqNlFcDvs\n6Ks9MLwhgXOgd8RYweVEX+1+9NXuR+N7v4E2If3MvNepC6CJTbusea8hM1bgH/96l4XVDwRaST3X\n6eYWKA1RYsegIGY29SAmTAe12v+23qbL55eFFQC+9IW1eO3jbSysXnbqnedh62j8zOukCvUnJVYL\nmUoD6fDXT12m/N915xbfEZf5WAGWKlRn5q5m5UFwfwuDTdXordyJ3spdsHU2nTfe0lwLS3MtWja+\nBmV47PCRV/2EaRf9qYBh8hxUvf0s2tvbERMTM15PicZJIJfUswRBQF1TC8JTs8SOQkGsr7sdV6Qn\niR2DROK3hXXd2rX49YsvI3bNNyCV+e3T8DuuTy0HdS63wwa3wwbngGlcHstXC7BEKoUuJRu6lGwk\nrr4Tto4mmKrOlNfBxipAEEaMHzK1oWPHO+jY8Q5kGj0MmfMQljUfhslzIFN//rxUqUKJiKw8fPDB\nB7jjjjvG5TnQ5QmGknqunp4e2KGARhsidhQKUi6nE4LFDKORB6mCld82vaSkJCQlp6C//hB3vfIi\nt93qvcfykwKsNiYhzng94hZfD0d/D3qr96C3cif6ag9AcA6NyOCy9qPnwCb0HNgEiUyB0PSZZ8pr\nVh6UoZGjZtdOLcBb777LwiqiYCup52poPA0NV2UhEfV0dSAlLhpKpW/sSEje57eFFQCu++I6vFqy\njYXVi6Y9/ibcditcdivcdgtcditcdss53//vcrfdAteQ9ZPxn33Zp49GepI3C7BCH4HouavgGOiF\nvbsVts5GuIdsI24vuBww1+yFuWYv8M7zCEnO/GS91/lQG1NGzHsNTZ+F6refQUtLC+Lj48clP32+\nYC6pZzmdTjS0diA2c6bYUSiIWXo6kTpjstgxSER+XVjXrV2LX/zyVzCuvBNyrV7sOEFBptRAptRA\nMQ7/uQVBOFMiWYABAION1RhsrEbzhlchkSkgDzFAGRYNRZgRcnUIpCHheOjhh7Fo4UKEhIRAp9NB\np9MhJCRk+O/nfuU6hZeGJXWk7u5uCEotFAoe2SJx2G02yF1WREXxpL9g5teFNS4uDstXLMehvf9B\nzJIbxY5DF0kikbAAj/ZcXA44+rrg6OsCGquHL9/e1oDt27Zd0H1oNJrzSuznFV2dTgetVjtizNnL\nAnmxeJbU0bV1dEIdyl2FSDymznZMSk7gm/Ag59eFFQC+dd83sOaLX0J0/jWQKlRixyERebcAf6rk\n2q1wD33GdZ+6zJsF2Gq1wmq1orOzc1zuL9AKMEvqhWloaUfExByxY1AQs/V2IIlTUoKe3xfWjIwM\n5M6YjqZ9xYjOWyt2HAogYhRgx4AZ1pY6WNtOwt7TCsHluPwHHieBUIBZUi+O2WyGXZBCpQ6+506+\nYbC/D3qVDGFh3LAi2Pl9YQWAhx+4H+vvvBdRc6/kzlfksy62ALtdTgycqDizWUHlTgz1dow5Pjk5\nGbNmzcLUqVMRExMDq9WKgYEBDA4ODn89+/2n/372e7fbPeZjjCdvFeCQkBA4HA60traioaEBZrN5\n1PtQq9VYsmQJ1q1bh+XLlwdlST1XR2cnlDpOByDx9Ha2IyclUewY5AMkgjD6Z5SbN29GZmamN/Nc\nsqIr18KStRIRM5aKHYVo3AmCAGtrPZo3/BFoOwZzb++Y441GI1asWIHCwkLk5+df0M4wgiDAarWO\nKLGjlVtfLMDj7dwCfKFHekc7ShwSEjLmEeCXX34Zf/jDHxAZGYno6GhERkYiKipq+Pvo6GhERUUh\nKioKkZGRUKm8M/1p8/adQGQy9NyOlUQgCAIaDu/F2mX5Qf/mMVhUV1dj2bJln3ldwBTWkpISPPTD\npzHh3lcuaxtMIl822HQMlo9+jbfe+AtKSkpQXFyMPXv2wOl0jnobrVaLxYsXo6ioCMuWLUN4eLhX\nsrIAjzRWAX7//fcv6r4MBsNwqT3757PKbVRUFPR6/SW9JtpsNvx7UylSp8/jayqJoqerA1prFxbl\nzRM7CnlJUBRWt9uNxcsLIcz6Io+yUsByOx048uOrUXX0CLTaM7tk9fb2YuvWrSguLsaWLVswODg4\n6u1lMhnmzp2LoqIiFBUVITk52VvRL5sgCLDZbCMKbX9/PyorK7Fz507s27cPPT09o95eKpVCq9VC\nLpfD6XTCYrH4dQG+UCqVatRy++miGxERAbn8zEyx5uZm7DnejKR0//gdQIHnVNVhLJqezi2pg0hQ\nFFYAKCsrwy13fB3pD7x6QVteEvmjhlfuxau/+jlmzz5/wwy73Y5du3ahuLgYGzduRFtb25j3lZmZ\nOVxec3Jy/OJImiAIOHLkCD744IPLOnHqswrwaF8/6yixxWI572iwvxdgiUSC8PBwREVFQalSQxMW\nBWNcPAwRUTCERyDs7NfIM1/VGr7OkmcMDvTD0nQMq5cv9ovXJRofQVNYAeCue+/DQbMSsau+JnYU\nIo9oeeOHePL+27By5coxx7ndblRUVKC4uBglJSWoqakZc3xcXBwKCwtRVFSEvLw8n9oCcbxKqqcz\nXmoBLikp8UrG8abWaGEIj4AhPBKGiDN/wiIiR5TbrNw5oi9hRv6nqa4GucmRSEtLEzsKeVFQFdaO\njg4sXLwUKV/7JTRG//m4k+hCtb77HB744hLceOPFbZbR0NAwPO+1rKxszKOBer0eS5YswcqVK7Fk\nyRKEhoZebuyL5g8ldbxcf/31KC0tFTuGR7y98yhk8oBYkIa8xDE0hNbqA1hXuBgKhULsOORFYxXW\ngHsVMRqNePCBb+KVt15C8q0/40cJFHAEdeiYczVHk5qaijvvvBN33nknenp6sGnTJpSUlODjjz+G\n1WodMba/vx/vv/8+3n//fSgUCuTl5aGoqAgrVqxAQkLCeD2V8wRTST3Xiy++CJvNBpfLdd4ft9sN\nl8sFp9M5/P2FjnG73XA6nRc9prOzE90WJzQhIXAPX+6E2+WG233m7+7h27uGL//036VSGcsqXbSu\nthZMTolnWaURAu4IKwA4nU4ULF0Bad71iJhWIHYconHVtu0tFMULeOJHPxyX+7NardixYwdKSkpQ\nUlKCrq6uMcfn5OSgqKgIhYWFyMrKuuw3hcFaUn3Z1h274QpPQKjBOytKEJ3ldrvReKQcqwuugE6n\nEzsOeVlQTQk4a8+ePbj1znsw6VuvQqbkLzUKHF37NmDqUD1+99Jvxv2+XS4XDhw4MDx1oL6+fszx\nSUlJw/Ne582bN3yG+edhSfVdLpcL73y0GYk5czn3lLyuu6MdoQ4TFsybI3YUEkFQFlYAuPu+b2J/\n+xDi1n1T7ChE46a3ajei6jfhX/94w+OPVVdXN1xe9+/fjzFeLmAwGLBs2TIUFRVh8eLF5x0dYUn1\nDyaTCZvKjyAlK1fsKBSEGioPYMnMLERHR4sdhUQQVHNYz/XM00+hYOlymCp3Ijx7gdhxiMaFTB2C\n/v5+rzzWpEmTMGnSJNxzzz3o7OzEpk2bsGHDBpSWlsJut48Yazab8c477+Cdd96BUqlEfn4+CgsL\nkZiYiN27d7Ok+gmz2Qy55gL2DiYaZwN9ZoQqgKioKLGjkA8K6MKq1+vxu5dfxE233oGQpClQhkaK\nHYnosrnsFoSJMLcrOjoaN9xwA2644QZYLBZs27YNxcXF2LRpE0wm04ixQ0ND2LJlC7Zs2TLmfbKk\n+p62rh5odN5fFYLI1N6C2RNTebI0faaALqwAMGfOHNxx63r8/e1nkHzr05BIOSeL/JvLZoFeL+4R\nMK1Wi1WrVmHVqlVwOp0oLy/H66+/ji1btqCvr2/M20okEkyYMAHXXHMNvvrVr/LECh/T3mVCZDqX\nBCTvsttskFjNSEiYKXYU8lFSsQN4w0PfegCJehk6tnh+zh+Rp7lsgwg3iH8ETBAEVFRU4JlnnsFD\nDz2E995773PL6tnbnThxAs8++yzmz5+PBx98EMXFxectrUXeZ7FYYHcDSpVK7CgUZDpbGpE9KfWC\nT9yk4BMU/zLkcjle/d0rWLqiCJqUbISmzxI7EtElc9kGEG4Up7Be7IlT+fn5MBqNaGpqwp49ezA0\nNDRiTHd3N9588028+eabUKvVWLRo0fB6r5GRnMLjbSaTifNXyevsNisw0IMJqTliRyEfFhSFFQBi\nYmLw+1dewm133g31PS9BGcYzEMk/CfZBhBmSvPd443R2f39/P7Zu3YqSkhJs3rz5vKOxNptteC1Y\niUSC2bNnY+XKlSgsLOT2jF7SbeqFIoSFlbyro7kRUyel+tR20OR7gqawAsCCBQtw/z134bd/fRyp\nX/slZOoQsSMRXTTJkNXjc1g9sQSVXq/H2rVrsXbtWjgcDuzZs2d4yazm5ubzHr+8vBzl5eV48skn\nkZ6ePrxZQW5uLqTSoJjN5HWtnT3QxU0UOwYFEbvNCsmgCWkTposdhXxcUBVWALj3nrtxqrEJG//2\nBJLWPwWpnFu/kX9xm9sRFxc37vfrzXVSFQoFFi5ciIULF+KJJ55AZWUliouLUVxcjMrKyvPG19bW\nora2Fi+++CKMRiNWrFiBwsJC5OfnQ61WX/RzpfO5XC6YByxICeFJcOQ9nc2NmJo+gduw0ucK6I0D\nRuNyuXDLrbfj+IAM8dc8wiU0yG8IgoDqp6/Dtk3FiI+PH5f787XF/E+fPj185HXPnj1wOp2jjtVq\ntVi8eDGKioqwbNkyhIdzK9FL1dfXh5LdB5GczbO0yTtsVgu6aiuwZsViFlYCEMQ7XY3FarVizdXX\nYCAuBzErbhM7DtEFsZvaceq396HqyOFLfqPliyV1NL29vdi6dSuKi4uxZcsWDA4OjjpWJpNh7ty5\nKCoqQlFREZKTuTTTxWhtbcXumiYkpgfmaz75nqa6GkxLjED6JE5DoTNYWEfR1dWFoivXQH3FtYia\ne6XYcYg+l+lIKYyntuHtv79+Ubfzp5I6Grvdjl27dqG4uBgbN25EW1vbmOMzMzOHy2tOTg4/Sfkc\ndXV1qOywIi45VewoFARsVgu66ypw1XIeXaX/YWEdw4kTJ3DVui8get2DCMucJ3YcojG1Fr+KL02L\nwSMPP/y5YwOhpI7G7XajoqICxcXFKCkpQU1NzZjj4+LiUFhYiKKiIuTl5fFs5M9QfuAQuiU6RETH\niB2FgkBTXQ2mJ0Vg0kQeXaX/GauwBt1JV5+WlpaGv/75j7jh5q9AesPjCJ04Q+xIRKNytdZixi2F\no14fyCX1XFKpFDNmzMCMGTPw6KOPoqGhYbi8lpWVwe12jxjf2tqK1157Da+99hr0ej2WLFmCoqIi\nLF26FKGh4m/C4AtMfQNQx7KskufZrBbIbGakpuSKHYX8SNAfYT1rx44duOPOuxB37aMwZMwROw7R\neQSXC5U/uRa7S7fBaDT+7/IgKakXqqenB5s2bUJJSQk+/vjjMXfQksvlmD9//vBmBQkJCV5M6jsE\nQcDbH5YgMWceZDJuX02e1VRbgxnJEZjIo6v0KZwScIHKy8tx8623IXbdgwjLni92HKIRzMf3Qbr7\nDWwp+Ygl9QJZrVbs2LFjeEOCrq6uMcfn5OQMr/ealZUVNPNerVYr/rN1F1KnzRU7CgW4wf4+DDQd\nw+plBXxzROdhYb0Ihw4dwvU33wLjld9A+LQCseMQDWt+5zmszo6HWq1iSb0ELpcLBw4cGF4yq76+\nfszxSUlJw/Ne582bF9B7nHd1dWHb4eNIyuDWmORZDVWHsCA7LWg/zaCxsbBepMrKSnzp+hsRWfhV\nRMxcIXYcCmKCIMDSXIueQ1vQUfovCIJ71LEsqRenrq5uuLzu378fY7wUwmAwYNmyZSgqKsLixYuh\n0wXW4vqnTp3CgcYeJEyYJHYUCmA9XR1Q9rdjSX5e0Hx6QReHhfUSHD9+HNdcdz0MBTchkktekRed\nLammim0wVWyDvad11LEsqeOjs7MTGzduRHFxMUpLS2G320cdq1QqkZ+fj8LCQhQWFiImxv9PVKo4\nWolGqwzGOB71Is9wu91oOLIPhXm5iIiIEDsO+SgW1kt08uRJXH3tddDOugrRi67jO0LyGJZU32Gx\nWLBt2zYUFxdj06ZNMJlMY47Pzc0dXu81PT3dL18ntu8uw5A+FqFh3CmMPKO9uQkxchvmzuJOajQ6\nFtbL0NzcjBtu+QoshmTEXf0ApHKu30jj42JKKgCsXr0aV199NUuqFzmdTuzbtw8bNmxASUnJmPOG\nASA1NXW4vM6ePdtvTiop3loKdfwkaEICa6oD+QbH0BCaqw/gqiULoNVqxY5DPoyF9TJZLBbc/Y37\ncbDuNBJv/AEUen6cQZfmYkqqVKGCITMPwmAPri9aiO9/77teTEqfJggCjh07Nrze66FDh8YcHxER\ngRUrVqCoqAiLFi3y6TcZ/96wGVGTZ0DBDRXIA5rqapAdZ8CUjMliRyEfx8I6DtxuN575xXP48xv/\nQNLNP4Y2nicn0IW5lJIaMb0AoRlzYe9sQvNfH0fZ7p0Bd6KPv2ttbR2e97pz5044HI5Rx6rVaixa\ntAhFRUVYvnw5oqKivJj08731/gak5M73y+kM5NsG+sywNNdi5ZKFAb3SBo0P7nQ1DqRSKR779iPI\nmpKBhx99DLHrvonwnIVixyIfdTklVaZUD1/XtfFVPPytB1hWfVBcXBzWr1+P9evXo7+/H1u3bkVJ\nSQk2b96Mvr6+EWNtNtvwWrASiQSzZ88enjqQlpYm0jM4Y2hoCIJEyrJK404QBHQ11mPh9Cksq3TZ\neIT1ElRUVODmW2+HNncljEtu4gs9ARi/knpWX+1+9H/0Enbv2AaFQuHJ6DSOHA4H9uzZM7xkVnNz\n85jj09PThzcryM3NhVQq9VLSMwYHB/Hh9r1IzeEOfzS+utpaoXeYsDBvnthRyE9wSoAHtLe34+av\n3I5uqR5xV38L8hCD2JFIBONdUofv1+3GyZfvxdPffRBr1qzxRHTyAkEQUFlZieLiYhQXF6OysnLM\n8UajEStWrEBhYSHy8/OhVo/+b2S8mEwmbNlXiaTM6R5/LAoeDscQTlcewOqCK6DX68WOQ36ChdVD\n7HY7nvzJT/HP9/6N+C8+gtB0LtcRDDxVUs/VXf4RVNUbsWnDhzyCH0BOnz49fOR1z549cDqdo47V\narVYvHgxioqKsGzZMoSHe2bJqY6ODuw4egKJk7M9cv8UnBprq5EdZ0DmlAyxo5AfYWH1sG3btuHe\n+x9ASM4SGFfcyqWvApA3SupZ1o5GNPz+Qfz7nX/y5y+A9fb2YuvWrdiwYQO2bt2KwcHBUcfKZDLM\nnTt3eN5rcnLyuOVobm7G3rpWJE5ksaDxYeruhNDdhBUF+X6ztBv5BhZWL+jp6cF9DzyIw7WnEP+l\nx6CJSRE7El0mb5bUs9wOO06+ch8eve9O3HLzzZcanfyM3W7Hrl27UFxcjI0bN6KtrW3M8ZmZmcPl\nNScn57KOwp88eRKHmvuQkCruyV8UGJxOB5qOHkDhglke+1SAAhcLq5cIgoC/vv46nnz6ZzAuvxWR\n867ix7l+RoySeq6Wd5/H9Egp/u+3L/PfTpByu92oqKgYXu+1pqZmzPFxcXEoLCxEUVER8vLyoLzI\nte3TBEsAABiXSURBVFSrqmtQ1+dGbELS5cQmAgCcrj+GjOgQZGexO9DFY2H1srq6Otzx9bvRrwxH\n7LoHuNGAjxO7pJ7Vc3ALbDvfwNaNxVzGioY1NDQMl9eysjK43e5Rx+r1eixZsgRFRUVYunQpQkND\nP/f+Dx+txGmbHNGx8eMZm4KQ2dQDZ8dJFC5eyKkAdElYWEUwNDSEnz/7C/zl9TcQvfQWRF2xBhIp\nf4B9ha+U1LNsHU048fsH8O4/38TUqVPH/f4pMPT09GDTpk0oKSnBxx9/DKvVOupYuVyO+fPno6io\nCCtWrEBCQsJnjjt4+AhanSpExcR5KjYFAZfTicbKA1iRl4uICB6koUvDwiqi48eP48FHHsOpzl7E\nrL0fIUlTxI4UtHytpJ5l727ByT88jB9/91Fcf/2XPfY4FFisVitKS0tRUlKCjRs3oqura8zxOTk5\nw+u9ZmVlDU85OXCoAm0uDaJiYr0RmwJU88laTAxXYdpUrjZBl46FVWSCIODtt9/GD554ErrMfBhX\n3Aa5luvSeYOvltSz7D1taPjDw3jsW/fh1lu/4vHHo8Dkcrlw4MCB4SWz6uvrxxyflJSEwsJCFBYW\nQqHWokcaikhjjJfSUqDpM5tgb6lHEbdfpcvEwuojent78cRPfooP/vsRjEVfQ8TM5TyxxgN8vaSe\nZTe149QfHsaD992Fr91xh9celwJfXV3dcHndv38/xniZh06nw9Q5eVi44krkXpEPTQjnT9OFc7lc\naKw8gKVzchAdHS12HPJzLKw+5sCBA/jWI4+i162E8cp7oI2bKHYkv+cvJfWsIXMnGv7vYdx/1x24\n++t3ev3xKXh0dnZi48aNKC4uxvbt2zE0NDTqWLlCgWmz8zB30VLMWbQUEVFGLyYlf9R8sg6poTLk\nTp8mdhQKACysPsjlcuG11/6CZ59/HiETZiBy6XqooxPFjuVX/K2knmU3taPxT4/hnttv+f/27jwo\nzvy+8/inm6s5m0sgQBwCCYSYkQAhrkYnMCMmW4ljbybZqk3FKXsTb5yrKqnaP7Ne17qSde1WdpPN\n2Jls7SZrJ/bu2E482cwoY81YQjMICXFJCAkd3Iibbq5u+tw/NDNlWScy8DwN79c/8MfzPP3RH01/\n9PTv+f7021/+LcNyYOdZXV3V+fPnP5336nQ6n3p8cdlhVR8/rerjjdqzt4hvhPAQ18K8vJP31HzC\nse5xasDjUFhNbGVlRX/55pt645tvyl7mUPqpf63oZO5qPEm4ltRPuAY6NPHWf9If/v7v6jf+zReN\njoMdzO/368qVK/rf3/q2PrrUrpnJiacen7Un/9PyWnKo4qGxRYuuBSXZGRK/k3jX1jRxs1tNdZVM\nBcCGobCGgYWFBf33N97QX//Nt5RS2az0E7+iqAQ+AKTwL6nSg3/DzPnvaLH9H/TmN/5C9fX1RkcC\nJEndvdc17o3S6sqyLl94X5cvnNOdG9eeek5ScoqqHCdVfaJJJS+X6zc/c1p7i0vlaGxRfeMZHuDa\n5kKhkIb6e3WkKEv7iljSho1DYQ0j09PT+i9/+l/11vd/oLSaf6H0Y68rMnbnPQSxHUrqJwKeFU18\n7+tKDy3rf/2Pv1R2NgPaYR6P2zhgbnpKVy5+oMvnf6TejksK+P3rumbp4SNqaG5R7alXWAe7DU2O\nDCk9Yk111UdYJoINRWENQ2NjY/rjr/9nnT37z0qpOqO0ul/Y9ksFtlNJ/YR7akhj3/6KWhpP6Gtf\n/YpiYmKMjgQ8pO9Gv+4tS5nZj19Dv7q8rK5Lrbp84Zw6Pjyv1eWl5762xWLRwYoqNTS9ptpTryg5\nNW2jYsMgi84FrU7c0asnHPw9w4ajsIax4eFhfePNv9Jbb70le3GV7HWfU0Le9tl8YDuWVEkK+rya\nufBdzbf9vb76lT/SL7/+utGRgMe6d++eeu8vKTu/8JnH+v0+3ejq0OUL59R+/pxmp578fv1pFqtV\nLx+pkaOpRbUnm5WUzJKncOPzejV2o0vN9axbxeagsG4Di4uL+tu//Tt9482/UighTUm1v6iUsgZZ\nwni/5rWFKQ188w+2TUn9hOvWFU3/45+r8lCZ/uQ/fvWJW2ICZjA+Pq72O/e1p6hkXeeFQiG9/Z2/\n1v/80z9e92taIyJ0+Gid6htbVHOiUYn25HVfA1tvsL9X5fkZKineb3QUbFNPK6xsSREmkpKS9KUv\n/aa++MUv6OzZs/pvf/FNDbz7ppJrf15pR18Ly3Wu0fZdCvoenQkZjiVVkrzOGU2/84ZC0/f0Z1//\n2hPfdICZREdHKxTwrfs8i8WilLRdSt2VofmZ6XWdGwwE1HXporouXdQ3/uSPVF7tkKO5RdXHGxWf\nwC6AZjQ1NqKcxEgV799ndBTsUNxhDWPd3d36sze+qR9/8IFSXmpQUuUrSih4OawWwY/8w59r+sMf\nhG1JlR58/T/b9veavfBdfeHXP6/f+53fVmxsrNGxgOficrn0XnuP8g5WvND5oVBII3cH1Nl2Ud3t\nF9XXdWXdD2l9IjIqShW1x+RoalH1sVPsumUSSy6nlkcH9OpJh2y28PnbjPDDkoBtbmpqSm9973v6\nm29/R0vuNSVWNCulolkxKeYfLeOeHJJnZiTsSqok+d3Lmmt/W/Mf/UCVFeX62n/49yosfPY6QMBM\n3G633v7gIxUcqt6Y662u6HrnZXW1taqzrVVT46MvdJ2o6GhV1p9QQ1OLqhpOyhYbtyH5sD4+n1ej\nN7rVVHNY6enpRsfBNkdh3SFCoZC6u7v1rb/7rt5++4eK312g2LJTSj50XFHxdqPjbRvexTnNffh9\nzXe8o8bTp/X7v/Nl3icIW8FgUP/nH8+qsLJhU65/f3RYXZcelNfrVy9rzeNe9zWiY2yqajgpR+MZ\nHXGcUIyNbzC2QjAY1PDNayovyGTdKrYEhXUHWltb0/nz5/Wdt76v8x98oOSiQ7IdcMh+oFpRiTzd\n+SI8M6Oav/h/5bzWqs997rP68r/9knJzc42OBfzMvv9P72n3wSOKjIza1Nfxeb3q77mqzrZWdV1q\n1cjd2+u+hi02VlUNp9TQ/Joqao8pmtFKm2b0zk3lJUXqaOWLLRcB1ovCusOtrKzo3Xff1Q//6V19\ndLFVcbtyFFN0VIkHahS/p1gWa/hOGthsvmWnFq61ytP3Y61ODevXP/9r+o0vfoGRLthW3nn/vOL2\nlCg2Ln5LX3d2alLd7RfV2daqnssfrWvGqyTFxsWr+nijHE1nVF7ToCj2s98wU+OjSvA6dby+5qFt\neIHNRGHFp3w+nzo6OnT2vR/p3ffOaW52RsklR2XbX62k4ipFxiUZHdFwAc+KFvo+lPv6j+Ua7NOJ\nU6f0y5/7RZ08eZJB2diWzn/ULp89S0l242ajBvx+DfT1fjw9oFV3+6/rKR9Pj4hLSFTNiUY5ml7T\noaO1ioqivL4o59ysfNNDajxWx0NW2FIUVjzR2NiYzp07p/939kfquNKupOy9itpzULG5pYrPO6ho\n+85YZO9bdmrpTpdWb1yQc6BTVTU1+lf/8rNqbm5WfPzW3nUCtlpXzzVN+KIf2p7VaK6FefVc/lBd\nbQ/GX7kW5p773IQku2pPNsvReEYvVdVs+lKH7WR1eVmz9/rU7Dgqu51nH7C1KKx4Lm63W1euXNHV\nq5262H5F13q6ZI2yKTH/oKzZB5SQX6q4nP2yRob/nQv/6qKWh/rkHuyWZ7BHK7P3VVl1VJ/9+Z/T\nmTNnlJLCLjzYOUZGRnR1eFY5e835YE0wGNTgQL+6LrWqq+2ibl7rUjAQeK5zE+3Jqjv1ihzNr6ms\n4ihfbz+Fz+vVaH+3Th4p0+7du42Ogx2IwooXEgqFNDg4qM7OTrVd7tDljqsaHbqn5D1FisoslDU1\nR7b0PbLtylV06m5ZI8y5D4V/xSXPzJjcU0Pyjd+Ue+SG3M4ZlR0q1+ljDh071qBDhw4pKoq7MNiZ\nFhYWdO7KdeUdLDc6ynNZWV7StY5L6mprVdeli5qZnHiu8+wpaao7/aocTWdUevgI5fUnBINBDff3\nqLIoR/v3FRkdBzsUhRUbZnV1VT09Pbp165Zu3b6j/oE7Ghoc1MLstBJ3Zcm2a48sydmKSs9VTFq2\nohJSFBmfpMg4+6ZuIxv0rWltflKemVGtzYwqtDAu39yYliZHFAoGlJu/VyX796uu+oiqqqpUWlrK\nhxXwMb/fr++9c04F5XVhtfGI9OA/1mND9z6++9qqvq4r8nkf3UHvp6Wk71L96VflaHpNJS+Xy2q1\nbkFa8xq53a/ClBhVlh82Ogp2MAorNp3H49Hw8LDu3r2ru3fv6sbAHd0bHNLC/LxczgWtLi0qOi5e\nsYnJikqwKzI+WZbYJIVsiQpFRD/4kLRaJVke/G6xyGKxShaLZLEq6FuTPEuyrC0r5FlWYHVJvtVF\neVeX5FlelEIhpe/OUmFhkUqL96lk/z4VFRWpqKhI6enpYfchDGy1d94/r7ic4rDfXWrN41ZfV8fH\nd19bNT48+Mxz0jJ2q77xVTkaW1T80uEd9/dicnRY9uCSGmqr+Y88DEVhheECgYBcLpfm5+c1Nzf3\n0E+fz6dAIKBgMKRgMKhgKKRAMPjg92BQgUBACXFxSk1NUXJyspKTk2W32z/9PTk5ma1QgZ9RR1eP\npoNxSs/cXmsXpybGHkweaGtVb0ebPKurTz1+1+5s1TeeUUNzi4oOvLTty+vc1H3JOaHTDXVMQYHh\nKKwAgKcaGhpS18iCcgr3GR1l0/h8Xt3q7fp4dNZFDQ70P/X4zJxcORrPyNHUor3FpduuvM5PT8s/\nO6zTDbWKi2PrWxiPwgoAeKq5uTl90NmvvNKds4ZxfnZa3e0fqqutVd3tH2p50fXEY7Ny8+VoalFD\nU4vyiorDvrw652blvn9PjQ01SkgI72Ug2D4orACAp/J6vfrB2R9rb0Wd0VEMEQgEdLf/uroutaqz\nrVW3b1xTKBh87LE5BYVqaGqRo7FFuWF4R3rRuaCl0QE1OqqVlMRmMTAPCisA4JnOvn9B0dn7FJ+Q\naHQUwy25nOq90qbOjx/eWpideexxeUX75WhskaPpjHLyC7c45fotuZxaGLmlxtojzJuG6VBYAQDP\ndPPWgG7OeZSdt9foKKYSCoU0fGfg07uv/T1XFfD7HzmuYP8BOZrOyNH0mrL25BmQ9OlWlpc0e/eG\nTtdWKC0tzeg4wCMorACAZ3I6nXqvvVv5ZUeMjmJq7tUVXb/ars62i+pqu6CpibFHjik6UCZHU4sc\njWeUkb3HgJQPc6+uaOr2dZ08ekgZGRlGxwEei8IKAHguP/zn95W8t0y2WJ4afx6hUEj3R4c/3Tb2\n2tV2edc8Dx2zv+yQHI1n9MpnXjdkzu2ax637t67pWOVBZWVlbfnrA8/raYXVnHtpAgAMUZCdqeH5\nedlyKKzPw2KxKDuvQNl5Bfq5139V3rU13ejuUPeli+q8dFGj927rdl+vBgf69cpnXt/yfGset8Zv\nXZPjUDFlFWGNwgoA+FT27kwNdN2Ucoz/GjscRcfEqLzGofIahz7/e/9Os1P31XXpopxzs1t+d9W9\nsqypOzdU//J+5ebmbulrAxuNwgoA+FRqaqqsPrd8Pq+ioqKNjhP20jOz1PwLv7Tlr7u86NLs4E0d\nqyzjziq2BavRAQAA5mG1WpWXtUvOuVmjo+AFuRbmNT98U6dryimr2DYorACAh+RmZ2ll/vFzR2Fu\nC7MzWhm/rcbaI0pPTzc6DrBhKKwAgIdkZGQo3urXytKi0VGwDnNT9+WdHlKjo4ZNAbDtUFgBAA+x\nWCx6af9ezU0+Ol8U5jQ1NiKLc0KNDbVKTGSnMmw/FFYAwCNycnIU4VnUmsdtdBQ8w8TQPcWuLeik\no1ZxcYwjw/ZEYQUAPCIyMlKlhfmavT9udBQ8QSAQ0Ojtm0qxrOp4XbVsNpvRkYBNQ2EFADxWQX6e\nAouz8vt9RkfBT1nzeDTS36O9KdFqqK1WdDQjyLC9UVgBAI9ls9m0L3e35ibvGx0FP2HJ5dTEzR5V\nFeeqsvywIiIijI4EbDoKKwDgifYV7pV77r4CgYDRUSBpdnJCS6O31FRXoaLCQqPjAFuGwgoAeKKE\nhAQV52ZqcmTQ6Cg7WjAY1Ni9AUWvzOiV4/VKS0szOhKwpSisAICnOnigRNbVeeayGsTn9Wqkv1d7\n4i1MAsCORWEFADxVdHS0qg8d1PTwbYVCIaPj7CgrS4sa6+9WRVG2qo9UKjIy0uhIgCEorACAZ8rK\nylJuSoKmJ9hMYKvMTIzJOdSvU0df1v59RUbHAQxFYQUAPJeKQ2VamxtnM4FNtubxaPBGjxL9Lr16\nol4ZGRlGRwIMR2EFADyX2NhYVZbu0+TQHaOjbFtzU1OavNWjqn3ZOlZXw3pV4GMUVgDAc9tbUKBd\ntgd712Pj+HxeDQ/0KWp5UmeO16iosFAWi8XoWIBpUFgBAM/NYrGotqpS1uUZLcxMGx1nW3DOz2ms\nr0svZafo9LF6JSYmGh0JMB0KKwBgXWw2m47XHNHq5KCWXE6j44StQCCgsXsDCswMqbm+UqUHSmS1\n8rEMPA7vDADAuiUmJup4dYVmB2/K4141Ok7Ycc7PaaSvU0UpMWo+0aDU1FSjIwGmRmEFALyQtLQ0\nOcpLNTHQJ5/Pa3ScsLDmcWv41nWF5kbUVHNYh19+idmqwHPgXQIAeGF79uxRxapbPQM3lFvykiIo\nX48VCAQ0PT4iv3NKlQeKtLeggK//gXXgLwsA4GdSUrxffr9f1250K6e4TDG2WKMjmcrc1JQWJ4e1\nLztdZZXHZLPZjI4EhB0KKwDgZ1Z2sFSJCfFq6+1R2t4SJdlTjI5kuEXXgubHBpWREK06xxElJycb\nHQkIWxRWAMCGyMvLU0JCgi5c7pTPk6e0zCyjIxliedGluYkRJVi8On74gHbv3m10JCDsUVgBABsm\nNTVVrxyv14ftHRpfXVF2QdGOGYDvnJuVc2pcCRF+1ZYUKScnh3WqwAahsAIANlRcXJxONtTpanev\nhvp7tbtgn2Lj4o2OtSmCwaDmZ6a0ND2u9IQYHT+0T5mZmTumpANbhcIKANhwUVFRqqmqVNbIiK72\nXVNEcqYycvIUERFhdLQNEfD7NTt1X+7Z+8pJT1JtzWFmqQKbiMIKANgUFotF+fn5yszM1PX+m7pz\n/arsWflKy8g0OtoLW1leknNmWj7XjIpyMrT/2FElJSUZHQvY9iisAIBNZbPZVFVRrsL8efX23dTg\n9TGl5uyVPSU87ki6V1e0MDstr2tW8VFWHczLUV5ViWJjGd8FbBUKKwBgS6SmpurksXpNTU2pt39A\ng6N3ZUvepdSM3Yox2WzSNY9HztlpuV2zirUEVJybrZyyI7Lb7UZHA3YkCisAYEtlZmaqOTNTLpdL\nY+MTunOnV15rjOLTMpSStsuQ3bICgYBWlha1srioteV5xQR9KszNUk7xy0pJSeEhKsBgFFYAgCHs\ndrvsdrtKD5RodnZWgyNjGrk+JGu8XVFxiYpLSFBcfIIiI6M2/LXXPG4tLy7KvexSwL0si39N6clJ\nKk5LUUZJmVJTUympgIlQWAEAhrJarcrIyFBGRoYqvF7NzMzI6VrUzPy4JoaWFLJGKsIWr0hbgmzx\n8YqKjpbVapXVGiFrxIOfPzl9IBQKyef1yuddk8/rlde7Jr/Pp6Dfq5DfK//qsmKjI5SVnqqM/HTZ\n7UVKSkpiZipgYhRWAIBpREdHKycnRzk5OZIelM/V1VUtLi7K6VrUnHNGngWvPAG//P6g/AG//IGg\nAoGALNYIyWKVggHF2qIVb7MpwRaj+Dib4lNiFROTrJiYGCUmJvLAFBBmKKwAANOyWCyKj49XfHy8\nsrKevNVrKBRSMPiguEZFRfF1PrDNUFgBAGHPYrEoIiJi22xMAOBhLNgBAACAqVFYAQAAYGoUVgAA\nAJgahRUAAACmRmEFAACAqVFYAQAAYGoUVgAAAJgahRUAAACmRmEFAACAqVFYAQAAYGoUVgAAAJga\nhRUAAACmRmEFAACAqVFYAQAAYGoUVgAAAJgahRUAAACmRmEFAACAqVFYAQAAYGoUVgAAAJgahRUA\nAACmRmEFAACAqVFYAQAAYGoUVgAAAJgahRUAAACmRmEFAACAqVFYAQAAYGoUVgAAAJgahRUAAACm\nRmEFAACAqVFYAQAAYGoUVgAAAJgahRUAAACmRmEFAACAqVFYAQAAYGoUVgAAAJgahRUAAACmRmEF\nAACAqVFYAQAAYGoUVgAAAJgahRUAAACmRmEFAACAqVFYAQAAYGoUVgAAAJgahRUAAACmRmEFAACA\nqVFYAQAAYGoUVgAAAJgahRUAAACmRmEFAACAqUU+64D+/v6tyAEAAAA8liUUCoWMDgEAAAA8CUsC\nAAAAYGoUVgAAAJgahRUAAACmRmEFAACAqVFYAQAAYGr/Hw5Nn3BoIahcAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa311bd54a8>"
+        "<matplotlib.figure.Figure at 0x7f576f8c3978>"
        ]
       }
      ],
@@ -404,13 +406,13 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "Here on the left we show an ellipse depicting the $1\\sigma$ distribution of two variables. The arrows show how three randomly sampled points might be transformed by some arbitrary nonlinear function to a new distribution. The ellipse on the right is drawn semi-transparently to indicate that it is an *estimate* of the mean and variance of this collection of points - if we were to sample, say, a million points the shape of the points might be very far from an ellipse. \n",
+      "Here on the left we show an ellipse depicting the $1\\sigma$ distribution of two state variables. The arrows show how three randomly sampled points might be transformed by some arbitrary nonlinear function to a new distribution. The ellipse on the right is drawn semi-transparently to indicate that it is an *estimate* of the mean and variance of this collection of points - if we were to sample, say, a million points the shape of the points might be very far from an ellipse. \n",
       "\n",
-      "Let's look at that. Let's run a bunch of points through a nonlinear function.\n",
+      "Let's look at that by running a bunch of points through a nonlinear function.\n",
       "\n",
       "###### Exercise: Plot 2D points\n",
       "\n",
-      "Write a function to pass points through state transition:\n",
+      "Write a function to pass points through the state transition function:\n",
       "\n",
       "$$\\mathbf{Fx} = \\begin{bmatrix}1 & 1 \\\\ .1x &y\\end{bmatrix}\\begin{bmatrix}x\\\\y\\end{bmatrix}$$ \n",
       "\n",
@@ -492,16 +494,16 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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pU6dw3333Wa999NFH1ntPnTqFOXPmjO7AibQkNtmY53D4/WLK5opUwQMq8QIK\n/HmA5iNi6JBwJgYknvhJ5eQzkuKqrU3sUw2Dw3zGEZEKIMr+0VeYi1HbckENAHl5LBIsy7DEMmC3\ngpgoKWFhjvfeY6HckhIRbrcWVac6Xn3n2M9n3bouVFV5cemSDsPoe172mwp+cYstiZfoxmMkMuLp\nAjX6/OAHP4AoiqipqYl6/v7778eOHTuwZs0aNDQ04N1338V///d/AwA2bNiAe+65B5WVlcjNzcWe\nPXvw1FNPjcHoiXQimUYodsbiZtl+TCBxAjRZx4hUQMKZSIqRmHxi23PzWsbJ7C+RmOed/NhrLjgc\nIkwzsozIt+EVOvx+MWrC7U84JrogqKqIpiYZRUV9OwTW1eVYDVhqajwoL++AYCuIweuJ2it1JPp8\n7BQU6GhtlfDYYz0AggN+ZskK4pG+SSJGnpaWFuzZswculwu5ubnW83/6059QWVmJjz76CIWFhZgw\nYQL27NmD/Px8AMCdd96Jxx9/HCtXroSqqti0aRNV1CD6YJ8jIuXq4lvFCCITIeFMjAnxohjxohoD\n+Z3tjUNiO/nt3Mm8wrzOsd2+sGOHFz09AgoKdMvPax9TeXlHVHWL/sYTDrPaoDz5D4hU1ODVOHhy\noSSZVu3ourocqCowdaqB8+dFmCa/EIl9bCV2EcttG4891oOpU51oa4tO4IqNvsQbd+zFz54QSRGa\n8U1RUREMI/H3tHv3buzevTvua1u2bMGWLVtGamjEOCTezbTdsgGM/dwQO0/Z57/YYAxfgaS5jBgq\nJJyJQTPSS3HhsIi6uhwrGpzoGKoqYu/eiPdXVVlVCjNOcJVPrMuWqfj0UwnhMPMfz56tW6/b4ZUt\nVq3qQSAgoLQ0GFVpw955audOD0pKVHg8ZlRk1+cz0NTE/ovxyhb8QrNpUyeamyWYJnDXXapl3wiH\nRdTWunqT//piL3fn9Ub++8bzKw8ET5B89lk2ph//OECtugmC6EMy80m6rUzFy2kZjOWEIBIxLOH8\n8ssv46mnnkJDQwMefPBB/Nd//RcAWMt8+/btw4QJE/DMM8/Qkl+GMdyJNJ745l5fnkzHO1ElIhSS\n0MMKZ1hR44ULVcgysyZs28Ybj7DoSHGxho8/luD3iygs1DF1qoH587tjOgm2W81VRNHEJ59IaGmR\nALhQUhKyKmJs3+6BKJooLVVx++0a6usdEATg5pt74PWqUecCsPGEwyJuvNHAuXMSjh7NgtPJRLbP\np6KgIFJT/zecAAAgAElEQVTCormZtfq2Nz3hn2dtrRvFxRpKS4MAPFZVjcFGhbnw13XB6kwYr0Mh\nJfcRBBGPZH3FYzUWbpcjiFQzLOF8ww034Cc/+QneeOMNBINB6/n+2roS1wex3eviLY3F/5uJbafT\njOq6Z98vZ9cuNyZPNnDhgogXXnCjvLzDilSvXh2pIRoKsX2XlIRQVeVFQYGOsrJg3Ogq9xM/+mi7\n1UpbkkxoGis/ZxgCpk/XkZ+v4+JFEYGAgNxcE4LAIt2HDmVh9WrW4MTe5IR3KdQ0Ju6bmuSoz+Xb\n3w7CMFg9aS6o7aKZ74OL6pISwaqjunlz/Jbc8T5jAAgEFDz3nBu6LmDr1g4sWaKirU20alnH+0wG\nQ7pFngiCGBli8yDGktix8KToeM2vaG4ihsOwhDMven/8+PEo4dxfW1fi+sLnMwblg7M37JBlM8qf\nFutBZu+HVW/Y6Yzf9pUnDXJ/sb2KBh+X/b2KYlivV1QEoKoiDhxwWe8xDGDhQg3f+laPJTbnzQvj\nyJEsnDghY+XKvkuEtbVuhMMCnE4TK1eG8E//FB1JPnDAhZYWEYrS90aDXwhk2bREtd17HduFMFEE\nWlVFywrCkygPHHDB7xetz3O4kCeaIK4/0mllio+Fz3Gx4+F5JGM9TmL8khKPsxljKu2vrSuR+XCh\nF2tXSHZbe41QgAnJ7Gzd8veGwwLq6nL6lJmLNxEGg7pV07mgQIeiAGVlnVGC3C42TRPYsoUdLxSS\nrLFzMc/H+PzzbvT0ODFjho5167ogyyYkCSgtVfuMAWDRD/7eWPuFpgnIyzPQ3Cxh8mQdui5Ywj1R\n0ouiGFFWlGQ+16qqXBQXa2htlVBYqGP9+iBeeMENn8+wEg5J7BIEkQnEBmxCIQn19S4cO6bA6TRp\nriOGTEqEsyBELxX319Y1HunaAz2TerTHYyTOLxjU8cwzvBWrAZdLwuOP8zanE5LaXhQFiL0rf+Gw\ngP373fjnfzaxc6eMu+/uwaefSrh0SYLLlWO1nY7dB8DOT5IECEIkujx5soHnnvNCEARrfABw6VIY\n4TD7HTc0ZGPJEuCll1gSYVaWiYkTPXC5WKe+3btNTJliwDSBmTN1yw89f76GN9904OhRBT/9KW+J\nPQHXrqmYP1+DaQJHj2ajqUmxRC//rDZvDkPTVJw4IaOmxoOf/lTHxIkKLl0KIz/fwMWLErxeb9S5\nKoqCzk4N2dk3RD1/7ZqK/HwDggBrG/65fvihgsce64HDIWLiRC8ef1xHd7eJp59m5x57jHjfD/su\nE79nMN93IjL9/x5BZAJ2i0bsStNYWbZ4MCL2uepqLyZPNiCKqa9VT1xfjEjEub+2rvF48sknrcfL\nly+3LCDE+MXnY4KM05/QsuNySdi2TQcgoLtbx//5PwL8fgmABp9Px5tvOrBkiYqyMg1PP+0EAGzb\nplv7DwZ1PPmkCNM0UVkZwg03KPj5z010dzMx19Nj4uTJ6GMGgzo0zcSCBWrvGEy8+aaIzz4TsWSJ\niq99zYTL5Yzaxu9nzU4eeEDDm28C+fkGJkxgXmdBgCXog0Ed/+t/sSRGHkVfvFhFIGDC4RDBPd2y\nLCAvj20fDgv47W8F3H9/GE895YAomvjJT3oAyAgGI+fa2anhF78w4POZ+Od/VjFxooJgkH1mn34q\nISvLBGDGfK6A/b+9yyVFvZboJoSfC/dV2z/zeN9hpnLo0CG8/fbb1t8rV64cw9EQxNgRzzoX77XR\nEtJcMPNIc3GxBgCWZYMnC1ZUdMatjU8QyTIiEef+2rrGo7y8POrvdOmJPt57tA9Ef+c3nElu82Zm\ncfjlLyNisbLyi6T3ZT/2+vVMhJmmjvXr2VLbxYsiamtFq2teIBCw7AqqKsIwcjF5soFf/lKEw6Hj\nRz8KRNkxfD7WfS8U0hAIsJJ2eXmsiYlpAiUlnXjlFQ8WLFBx5YqIvXuBjRu/sMa3di2ruqHrgK6H\nsXVrD3btcuPyZRFbt3ZAFE0EAgZCIbY8aJpeyDLzYhcVGTAM4Le/lTB1qoFNm5gFpKqK3Vh+4xs9\naGyUceGCiHC4C4ADhiEgHO7GE0+w95SXt0OSWBTc5zPR3CziqaeALVu+hCybaG3NxYwZOsrKOhEI\nmAgEohNnIhe0vt9JKBT9PdjfCwCGkdvnMx/oOxwK6fp/b+7cuZg7d6719+nTp8dwNASRHtTV5eC2\n2zRMnBg/mjvSuQ/xuqV6vSbq6xU0NXlRURGwrH88r4OsGsRQGZZwNgwDPT090DQNuq4jHA5DkqR+\n27oS6c9wJ7lEXep4CTR7Yl6swIqtxhHrMT55UrYal/AGJfZkD0UxUF7egRMnstDaKoFHXO1oGnDi\nRBbuvDNoVapoaRGxaJFmVZdwOk3IMvDZZ7wyh2QlCJaVdWLGDN3qBsgjGT6fgV273FYpvbKyTmt5\ncNIkAxMnmjhyRIGmARcvirh4UURbW07U2GbNCmPWrLDlg+YtwNlnw6LZH3/sxB//6MRjj/Vg7VoD\nVVVZCIeB+noXVq/uGrESUckmAFGCIEFkPjyRW9dZzklDgwJJMrFoEcvRiE1YHi3y8gwrZ8QwBMS7\nBhDEcBiWcP7Nb36D7373u9bfv/3tb/GLX/wCP/vZzxK2dSUyn3id6zRNwDPPcDEVsKpCxFaf0DSh\nT/1NXpnD52O+XVE0MXWqAUkyo2o+84oa9s56Xq8cFRn9/vc78dxzbpw7J2H+/G5IEqtU4fMZOHky\n8t+BLz02NclQVaCpyWl1BtR1AevWdfW+7rHKHum6gJ07PTBNJnJ5XWQm4IGJE9kNg2myyhyiyB6f\nOyeitFTFHXeEo5L0gMhNRigkoaDAgK6zm4cVK3pQVaUgP9/A9Ok6dB1Wd0IuVEMhKSrJMva7SabC\nSbyOYQRBXL/YRTCfv8vLO6y5iwdO7HNFqqpuJFrJslfSsCeX25O3B+qmShDJMizh/PDDD+Phhx+O\n+1p/bV2J9CYVk1zsduEws0GIoonDh7Nx7JgS1eAkdsLj8HJwu3a5rbJpkmTGjWprGmvm4fOxiIPD\nIaK720AgoEAUTSs6zH3IAKyGIkuXdmPHDtbU5K23XHj/fZZ5/W//1okjR7Jw5owEh4N5kO2tsu3e\nvpoaD6ZONTBjho5z56Te8nftCIUkXL4s4//+3yxMn25YXQF518KiIh2HDimor1fg8xm4cqVvdEaW\nTVy6JCIcFlBYqGPevDD+9jfWdGX9+iBE0Yyq1hEKSaitdVuiPvYiNtTvMdlt0qU0FUEQqcO+mrRp\nU6c1XzudA/+fH+5cMNBKFltxRFQ509jugTQfEamAWm4TcRnKBNOfr9XpNLBkiQqv17Qit/YC9QAs\nwev3M4FYX+9CU5OMcFjAggUqcnNNnDiRhfnzu636nDzKwLvqqSqzQTQ3SxAEFcePy9A0D6ZP161J\nfuvWiNjlDUWWLevG9Ok6Jk0y8PnnIkyTWTpeftmFCxeYjWPDBhZlrqvLgWkCly+z1uDNzaw19+TJ\nLCI8aZKBri42YVdUBPD886xRi2EAFy6IWLuWNTt54QU23nnzNOi6YEXSH3igb+MXgJXJU1URomjC\n7dbwH/+h4Ze/lLBrl7tPvWZ7JN5+kzFame50gSKIzIHPG5omQNPYalptrcsSqqNtx0gEn/uAyKod\ndRAkUg0JZyIl9Nd0A2BCavXqrl5PsRuLF6soLQ1G1Sv2+0UrqlxXl2NZDwBmQ7h6lQnPM2ckXLkS\nLbqrq1knvEWLVFy8yLbzeEwUFjKvW16egZKSbqthCY9u84Yi2dk6vvWtIA4dyoJpAosXM6H+l784\n4HCYWLas21qK3LSpE9u3e1BQoOOWW3SoKqvEwSPlLS0Sjh1TsHixCl0XkJ9voKcHmD9fQ0lJN7Zv\nZ4mH06YZOH9ehCiy45WUhPp0C4z9bCPJlu1wOGCV0OMtx/ljoG/3xVR5j6kzIEFcP8RWzygu1nD1\nKpuri4oMq1HV3r1sfty4sWNE5obBrmRpmoDaWrfVWIogUgUJ5zFivImPeOMd6BziC7WIQF69OhIJ\n1TTBEoGSZFp+ZQAIh0UYhoDnn3dbFgt7R0K+ZCgIwPLl3SgtZUt0O3e6AQDf+U4Iv/tdFk6fdlvC\ns7hYQ1OTjE2boqO7sgw4HMDKlUHIson587tRV5cT1TlQUZgdo7lZgiyzyPOf/+zAokVsn21tzFvX\n1CQDyAYAXLki4v77uyBJJgoKdDQ0MCvIPff04NVXmeWipCT+5x0Oi1BtfVV8PgOaJsDrFXHzzToM\nI+IrtDeQKSvrjNtWfDhQ4h9BXL9IkonTp2X4fAa2bu2wbvRDIclaSbTfxKeaweRlcChBkEg1JJzH\ngPEmPhLV5Ix9rr9oAJ9ME71Pls0oz7M9SsojvVu2RLr36bqAqiomnJuanLh4UYQkse2qq70oLtZ6\nJ0xg4kQNsu2XzgX6mjVhHDqUhYICdqxXXnFCEFjyIu8caBjM7hAICCgr67QE6tq1QVRXu6FpkX1+\n/nnEg83baB8/Llv74BeZDRu6rHOaMaMHgsCE8+HD2WhqkqMSbSoqApY3e/ZsHcXFYeza5UZ1tReP\nP27ikUcEBAIRDzNPruTR79gOifHaiydDOizDEgQx+sS2sLbPIeGwaD3PV+/GwhoWO17Oxo0dCVtv\nE8RQIeFMpIx4yRoVFQHU1jKhxwV2vAlMUViEtL7ehdpad9zlPl0XLPGpqqyRCAAEAiwp0D5hNzXJ\nqKzshiQJyM5Wo6p2HD+ejWvXBPz1rwr8fhGNjQIWLlQhiiZMU4BhCAiFJDz7rBemySK8Fy+KKClh\ngptHdNevZ90GnU4TZ89KaGhQYBiC5XtevFjtFbAm1q4NWhcZt1uLygBftEjD5csi3ntPwcyZOg4f\nzrb2G/l8gKNHFSuqw3G5pKga1vYs93hJMYluevqjv7bfA22XzPsIgkh/Yr3Db7yRg/feU6xgA7fO\npdoaNhx4wiBBpBISzmPAeKs6kGi8sRNlPGTZjKqSwYmNYCqKAV0XrAitfblv2TIVug5UVXnhdJpW\n1nRbm2glGJaWRrKoN23qBAC89VYWAGDlStkqfwcAn34q4fx5Zte4eFG0Ev3+5//sRmOjjAMHXFi7\nNgiAlYxjbVojZe6WLVPx979LqK3NwsKFKk6eZBVD7r03bLVzFQQTDQ0y/v3fO3H2rAM7d7qhqqwi\nxgMPdEaJ/LY2ERcuiJAkE2vWBPHCC26rDjSvh8pFtj2hsr+W1qxpSl//cypI5jebDhdNgiBSg91K\nxyO4nIICPWqFjIQqkemQcB4jxpuQiPU220VRLPZIYzzRHS/ZraIigLq6HBiG0Nt8hE3M4bCIQ4cU\nTJ1qQBRNaBpw/Hg2jhxREA4LvTWMWRS3s1PGjh0eaJqAe+8N48QJ9vO+7TYFv/99NgoKdFy+HCnp\ndvPNOm67TcPHH8u4elVER4dgifHnn3dj8+ZOvPKKC19+KWLdOiZofT4Dhw4p0HXWhMXrNSH1BoE/\n/FDG66878P3vd6KpyYn2dgG6DnR0CDBNJuwnTTJ6I8PMf80TIadPZ77pgwddKC/viJskyG0g/fmW\n+f527XJj1aoeBAKClbzT33cy0Hc/nm70CIJILXYrHQ+WrF7dhZISNq/U1HgQDgtRq4VjMW/QKhcx\nGpBwJqIY6sSjqqJVzcFuF4gVevb3sb+ZP1jXBatg/Zo1QasbYF1dDnRdwJUrIsrLIzWVATaB5+UZ\nqKvLwdq1QRw9yipiFBbqcDpNy+MMsKjIpEkGLl9mkd0pUwz87W8K8vIM3HyzjhtuMFFbm4U1a8I4\nd84JTQOuXpWhacD58xJ+/Ws3fvhDFsnevt0DQTCxcmUP9u7NwpQpBubP1/D66w4AwDvvZOHYMQWC\nYGLmTAXt7QKKizVIErOQ2JMUJSkSkb/jDg2qyi5C/ILDs9VbWkTLK80TJ69dU6O+M34zUlzMjNfN\nzRL8fhGlpUJUB8aBVgni/QYG+3sgsU0QmYOiGNa8Y7d98cfckuf3i1ErXKP5fz9RLs5oj4PIfEg4\nExbJLq/HiqJQSEJ9vQvHjilWF768PMNK3nv00XZkZ+t92mkDrE301KkGnE7mh66vd2HHDg8kyURF\nRSeamyUUFOgoKwta7axvuknHli2RVq8tLSK2b/egsFBHSYmKI0cUvPdepMFKXp6Gy5ezcOWKiB/+\nkEWDAwEBN95o4PhxGSdOyPjWt8IoKNARDLJoNAD4/RImTWICV9eB2loXrlwRUVkZgKYJeOedLOg6\n0NoqIi9PtCwihw5lQRBYJDocFnDihAzTZLWoNY35lXmiIfeB19e78Mc/OixxHAvvmAhEyiw1N0sQ\nRRNFRR6UlXVa7+VR80QdGPv7fkMhKWUNA+hiRRCZQ3//n7OzmbUsNp9ltIgNyPDnyC5GjAQknImk\nsU9OXPTxhDTmA2bCrqysE7ou4NixxGY3bsVgJduA0lK2X167mZed4yKST3qCYOKGG9hjt1tDWVln\nb7MPQJLYP9NkzVWWLVPR3i7ggw+cVrLdgQMuNDdLmDFDxz/9UxDHj7MSdy6XicuXxd7IdodVzs7n\nM/DDH3bi9Gkn3npLgSgy//DBg2w/a9aE0dgoo7FRQUGBgQ8/ZFHqwkLmi541KwxFccAw2PkWFRlW\nC1h+TvYGMFeuiJYIDoUkyLJpXZAAVs2Dt/KO/TztXmj+PcQ2iuGvJfp+a2vdfRITCYIgBstoRXtj\nAzL2axNBjAQknAmL/pbXuWVAVYHWVglOZ3RZoitXxKi6ngCLNAOI8tjGm9gmTzZw/Hg2jh5VUFys\n4ZvfDOLVV13Ytcsd9f5NmzrR2OjE2bORyhKyzJIFefT56FEFCxdqmDLFwKuvOgEApaU9mD5dx333\nBfHyyy4UFuq44w4N776bhXvu6UFXl4Df/z6rtyEJa5XtdBooKjIwfbqOXbvckCRgwQINpgk8/7wb\nPT0sMjxrVhivv+5AYaGO115zQNcFLF6soqEh8l+LR8d5gmKsbULTBOsGgj9XVZULVWVCu6ys06o9\n/dJLLpgma/c9c6aOJUsAIGh9nvZyUX2tFuKAVg2/X7SasQymZjdBEJlNf3OAvaIPX00ci2ivfW4j\nuxgxUpBwJqJIdoLhTTh41Yd428baM+xd7/j7y8s7sH27BxcvilixQsVf/uLAyZMy5s3T4PeLUSKb\nR4sdDtMS43zf3/9+Jy5cECEIwLVrItraRCiKCcNg0ev58zW8+qoLra2sU9/8+Ww83B7xla+o8PkM\nTJnCBT2LDNfXZ0PTWJKfJLGIuCCwrnzr1nVZpeVCIdb6WpZNfPWr3dA0Zg3Zvz/H6mRo73Robylu\nt1RwIR0Os4TCvDzDujnYscMLVQWWL1dx6BDzUy9Z0gMgftTFTqwFI9F3X1ERQHW1F01Nnn5rdhME\ncf0w0Bxgr7rB/7bXkx9J+hPINFcRIwEJZyKKRFEFnhzCRKSAmhpPSrxsLELNhCS3UDQ3S2hrE3tL\nG/XdtyBE2kpzP/Vzz7mRn29g/XrW8c8wBBw44EJeHnv9iy9ETJxo9NZBNhEMCjh7ljU4kSQTHo+J\nDz9kZeV+9SsPiopYhFqSgKIiHZIElJSErKhxfb0LdXU5WLeOdQPctYvZOtat60JdXQ5UFbhwQcJN\nN+nQtIgveePGDpSXd+Dw4WzLlmIvMccjw9yiUlrKosmaJqGnh53zvHlhvPsus8Hs2yehtTUX5eUd\n1oWLC3K78E3WghEruAmCIAYitoGVPfqcKvEae22KrRREEKMFCWfCYqCoAi8mb/eOhcN96zHHTmh8\n6S62o5T9dZ6RzcupxcKFezjMKm3w9/t8Bu66qxvvveeBaQK//jWzURQW6pg3T8PJkzLef19Gfj6r\nqFFa2oOzZyUEAgLOnxcxfbqOO+9UcfCg02rbXVioo6VFQl2dC5cuiZgyxcCSJaoVPd60qdOqmvGH\nP+Tg889F3H23GtWcRFGArVs7IIom3norG1euiFbGOa95ysvO2T837muOzWAHIn7vnBwW5Xc43Hj6\naRmACUmKXLi4ILfDK5aUlUW3GI+9GCWK3sQmGhIEcf0wkO3BXnXDTqpuxOOVQE3lKhhZ0YjBQMKZ\nGDR2sVtV5cXixWqURzfWwxtrEYi1FHChyKPI69Z1RUW07cfl5OWxx83NEl591YWVK3vQ3s7K1gEs\nKn3ihIzWVlaVQxAAXWcNTb79bRYlXrqUvVcUgfLyHjidzH7S3S2ipsYN0wSM3kPW1TmhaQKKinSc\nPeuAJJlWAuPtt2v45BMJly6JlujnNg4mhIFp0ww8+GCk8oWqAn//uxOzZoXhdLLKJLxShtNpRolq\n7h/0+Ywo4TtxooL/+A8VwWCXldUORC5WA4nhRDdK8S4eo7HkShBE+pJIVMYTnYnmm/72kwrsxwiF\nWCCjv7r3fBuyohGDgYQzYTFQVME+KSXqCKjrrMybYQB/+IMb69d3xY1Wco8vj45GqmMIWLWqx/LH\n2StB8LH5/Sx6+/3vswYln33GOgEuXKhhyxYW3e7uFvHCC+5eL3IQBw+ysndnzkgoLhZw+HC21S52\nwQIVjY0KKisDqKnxwOczsHChBl0HJk0y4PMZaG11WnWjT5xgvuQbb2TR7hdeYF0Bp0/XUVSk49Ah\nBTt3evDDH3Zi1y5WDcOe9Mc7Ib7yihOC4LRuPIqLNTQ3S1BVYN++HCgKoqI43PPNCQZ1PP20jKlT\n3dbNhv1Ysd/tUKEkG4Ig4pHszfdwxWm8Oai/xlrl5R19yqESRKog4ZzBxLvD5yXlEi2h9RdVqKrK\ntaKe9lbQsmxi9Wr2Pk1jjUwMAygoMHDiRBYuXBCt17KzdcvjC8CyN/ASawUFOurrFcgysHkzE42x\nSSY+H+uyV13twYIFKhwOlrBXWhpEdraOQEBBdbUHBQU6Fi1iSYEXL7KOgUykOuF2mxBFE6bZt7Rb\nXp6BkydlhMMswizLTFwXFBh4/XUHfD4Dug68/76Czz9n45JlEzffrOPTTyWYpgBVNdHY6LTGzpP+\nios1XLsm4IYbIhHrqVMNABpKSkJYtqw7qmSepglQVVZRo62NlwMUre/J59Nx9qxkCX6gr8e5P/qr\nskEeQoIggMTXklir3khGlQdK/Iut45zsuCgwQAwWEs4ZSqIuSlVVuQiHWSm1f/s3HS6XNMCeIvh8\nLLnOnhSo9CnVLGL6dBZxnjCBCUxNY8K4vt6F0tJglJ2BWzxYwxNWtm3nTg96elhUmL/Om4sALPJ6\n4YKIwkIdjY2K5SXmlTaamlgZutZWCXPm6FbyHd+3YTCxWlho4L77gr3CPwiAVeeoqXFj6lQWdZ41\nKwwAOHEiC5cuiVY3xAsXHAAcaG0VMW+ehqtXRbjdJj7/XMRNN7HIc329A06niS1bWMMU3iRGlk0U\nF2tYvFjF0qXdqK72WOfW1iZaCYcArCj8ihU9uPFGLSr6npcnYcOGMKqqROvzrKnxRIluu8ju7/cx\n0OsjcUEhXyFBpD+JriVvvJGDS5dE3Hgjq0YUuzoYm+8ykuKUW914grbbreHRR9utgMxAcxnNQcRg\nIOFMJIWiGJadYiD8fhGmyeozHz6swOczMGmSgbY2EbouRCXR+XwGTBNWsl9FRQDz5mm4dEm0bBya\nxrrxnT4to7y8A5Mns21mzWKVL+wR2rKyThw9quDee8O4+WYDBw86rBbUAHDjjQZaW1lHwMZGBU4n\ns52EwyKqqrwQRRMFBQZaWiQ4HMCcOd346CPW5ru1lY172bJu7N2bBQDYuLEbv/89i55v2NCFOXO6\noetCb2dBpddbzRICJ082IEks8mz2BvxFEdbjoiIdx4/LqKnxRNktJMnEJ59IuHBBivKTB4M6tm93\nwOfTo8Q2L5UXrwMgv5CNZrmoeJCvkCDGL5om4PhxGbrOLGrxEpJH6/84zwFRVXY9qavLsfI9uH1t\nLOc6IvMg4Zyh2LvFJX7OE3/jOCRqrpEoAUMQWPm2piYPrlwRsWFDl5XsxttyA0xk9/QA+fkG5s7V\nrKW/qVMNlJYGoaoiXnrJhYYGBTNn6jAMAYIAXLjAIh0vv+zC+fMsAZAnDPIExTfeAP7xH3vQ2Cij\nttaNCxdEqCqzXRQXa/iHf+gGwKIRpslsIq2tEtasCUIQAI9HR1eXhNdec0DTBBQUsNJyra2KFclo\nb2eNXwDWUZC3AJ83T8PXv94TlTA4ZYqBW27R8eWXQq9oN1Fayip2TJ1q4LPPJOTnsyYsvHRdZWU7\nurok7N/vQkGBjpKSEFav7mud4MewN1WJvVjEq6mdqFwULV8SBAHEnwuYvQtQFBPf/naX1fjKvnrV\nn3UildhrSM+erVurovbcGnsXVYIYLiSc04iRWLqOjTryOsFVVbkQRQHbtg2cNMGXwXjzEr4Ex6O0\nQCQBI3aSjZfAUVysoaGBRVa///1O1NW5cOedKv72NwU7dngwfTrzFQPA0aNZME0WjV27Nohdu1g9\n4ptu0jFhgomGBlaFoqyMvdbU5EV5eYclDFtbRZw7J2HJEtVqkOLzGaivd+D8+WzcdJOOG29kEez1\n64MwDOC559gNxebNHWhpcWDaNAOffy5i1aoenD4t44svRBQVsUodYq8ura1lAl6WTcyfzzoMNjbK\n8PtFFBUZWLZMxaFDTHCvWNFjRYcBdoNx+HC2ZeNYtarHisorioGcHFjVQuydGV0uCdu26bhypcv6\nHrigT7aOan/lokbyIkPCnCDGD/H8xfYcF3tSoD0fZtOmTojiyNaGt9eQXrQohEWL2PwXWx9fVcWo\nUqo07xBDhYRzmpCuS9d8GYz7ovlzfGIURdZsRNeFqNrM8fZjxzBYSbZ33snCnXeqqKtzQtdZVJfD\nRSAQsTMAgMNh4t57gxBFoLg43PteVqmjoEC3lueKizVcvChi0SLWQnrpUgFNTU5cuiRi8mQD585J\nMAxYyYzvvJPVm6gHLFyoYtcut7UU+Y//2INDhxy93QOBm2/WYRjAwYNOOBwOLF2qwjSZwF22rBs1\nNY7KYCkAACAASURBVG4YBksuvOMOZj0xTVgXkRUrVMyf3x0VGZEkE5IEBAKJIzWxvmWXS4LTaVhR\nfHuSIBAtjGMjQvy5sSJdfuMEQQyeeBWPOD6fgZYWEdu3syRtXiFopJIG7XXvVTW2oVTkmsWTrO1B\nIIIYLCScM5j+onoVFQG4XDkAkvN9sahuZ1TCh9/PbArcomBvp83hAptbBior21FSEsKlSzkQBMDr\nNfHFF6xahCSZWL68B59/zhqU1NXl4JvfDFoitKnJiaVLVeTlGXjpJRcuXJCwYkUPjhxRUFysWZUv\nPvyQ/ayZpUOCogA9PSwxMD9fx8WLLPL87W934+pVlmgoiiwxr61NxDe/GcaNN7KycwBrtd3VJcAw\ngHnzmF/61VdZy+uCAra/5mYJV64wi8arr7ogSYBpmrjpJh2vvcYE9/LlKr78UsChQw7cdJOO4mJW\nZePSJRGzZ+tYsyaIDz904i9/YfvWNKF3OZS1JjeM+Bcqe/SHR1r4BYKT6tUMSuwjiOuHeF377N1I\nY0V0pLzo6IyPX5cCAQUHDrisaHN0BSIDx4/LMAwhqsshQQwWEs5pwkgtXfdXT3P+fA1tbSY2bIhf\necE+LnsJu9jIpb1SB28tDbAqDz6fAVVlCX6FhYblYb58WURPjwBZZu8LBAR4vSZOn2Y/ycZGBQsX\nqjh6NAuGIcDhMOFymWhslFFf74AomsjP19HeLqCgwEBuronmZglXr4q4eFFEYaGBoiIdn38uQtOA\n//f/siAIrAQcT9ibNk1FbS2LiMybp+HWW9ksv3Mni1ivXKliwgTmPa6vd6CwUMeFCyLmzNEgisyX\nvWoV81A3NChWO+u8PHbsv/9dwtmzEiSJHbOlRcKECey1c+dYNKawkFlFrl0T8M47LOGQj5MTCkmW\nFcOeHBj7PSkKogR0vO88VV224mXZ83EQBJE52P+/25OW7d1IYy1f9nKlAEbcX6yqIvbu9UBV2erk\nbbdpfWyKZWWd1rUptoMqQQwGEs5pxGj/R2YeYMmKbPYnfuJ7pSPb8EoOzEfGhOLOnR4IArBsmYpJ\nk1ht5O3bPcjPN3DvvWEcP67A7xfhdBooKQnh7bezrSj0okUqrl5lFooFC1RMnGji5pt7cPKkbNk2\nvvpVFe+9p2DZsh7U1mZB14G771YhisBnn0loaRHxwAPdOHLEgYYGBd/4Rhi5uQYCARGdnSzJ0Ok0\nceONBrKz2U5PnMhCTw+zfEyYYFgWksJCFqEwTeDIEVap4+RJGb/9bRbmztWwZAmzg9TUeDBlioGc\nHB3TphlWXea8PANLl3Zj/34XJk0ycPEiS4o0DHYTIQiwfNZAlvW5h0JSlGXF6zWjrBjBoG616ebf\nCxfQ/LsMhSTLxhIOJ75JGirpajMiCCK12HNdYlcXYwM/8cuVDo2Bbsw1jVVrMk1ms5s4sa+v2t60\nK1WtwInrExLO45ihRPnsEeTnnvMiKytS/3go4se+v9paN1paRBiGgAULWDKeogB33BHGkSMscsya\njgBnz0rIyzOsskEnTmTh2DE2y1ZUdODUKSfef1+CILDI+KlTMu68U0dZWRfefjsbug7s3++EJAF+\nv4SeHlbize02MXEiqyMtScDhww6cPy+hqEhHdraJvXuzLCGck+PAI490oqbGg/ffV/A//oeJv/9d\nwoIFKk6elLF/vxP5+Swx8JZb7Et7Rq+oFwCYWLGi26oqMm0aq3V9/ryILVs6reePHVNw8qSMr3+9\nB8GggM2bmfXipZfYsuLChRqOHVNw8KDLahjT1ua2yuwxSwxw8KDLmvyDQR27d5s4ezYXTqeZsHOX\nLJtYvFi1St3FK083lN8QfxzrXycIInOItYLZn4+NRo9Eh75krk2ybMLpZNeWkpJuHDzospp10Y08\nkWpIOKchyYiZZIVu7L7s5eN+/nOz97n+xc9ANhJeqQOAZauQZaCoyIhqBc0blZw4kYU332TWB11n\nFpBAQLDsCYLAEuSYHcNAKCRg3bou63jXromYPl23bBeTJhmYPl2HaQKdnYLlW1YUVrdZFFlXP4eD\n1WhubRUtDzNPBhQEQJaB8+eZoOdjmTLFwKxZOs6ckTBvnoYPPmAR77vuYlH09vZIBFfTBMycyWwY\nQCQRsKysEzt2eHH77Ro6OgS0twtWreZZs3TMn6/h5pt70NTE9q33Xns0jdk2brpJx8svu6BpgKIk\nrpTR38rBsmXdaGpy9/ltDDVSbH8vVcggiMwm3kqWnXBYsEpoxnYXjPf+kRif3RoSL7IcazHkyewE\nMVhIOKcZQxEziTrExe5L0wQ88wz7+9FHWec5AAiFkhXHkf3GPsczm+O18+bWguxsvbftdBjNzRKa\nm1mr6PLyDixd2o2vfrUbp045UV3twYwZOjZv7sQLL7itSTAQEKBpwLlzTBhv2sRK2b37rgJFYUKz\nvV3AXXepeP991nxk7lwNt9wiIBAQcOgQs0AsWKDhxAkZCxZo+POfHViwQIUkAV98IaKggAny8vJO\niCKwaxfrRiVJwGuvOSAILKr80ktOLF+uoqQkaC1fFhdr0DSWQHjtmmh1PqysbMfmzR04d86B2lpW\nXm/xYhW6LuDMGQnnz0twOh34+teZX5p3Lbx4kVXi+PJLAZ99xr6rxYtV6/N1uSQ88oiOK1cCqKvL\nQXW116phzb9zANi71xPVIMZeTpDXP+Wie6jQBYggMptE835FRcCaA2PfnwoL10DXJnvQh899vMOr\npgnQNMmKhMdGyclaRgwFEs7jlHjLZwNNAroe6VjHfbPBoJ70nbeqilHJf7HRhXietlCIeY35Y16e\naP36IF54wY3iYg1vvZWNhgYmdFet6oEomlBV4NQp1jpbEIAvvhBw/Lhi1Un+4gvmC25tZc1PWHMT\nobf+s24d8ytfMXHggBPTp0c8ylevipAk4O67u/HSSy40NipwOExMmdKDSZMMXLki4pVXXJY/e86c\nME6dcuLcOVZ9Q5ZZ5HfBgjB27XJDVVl03TSBpqZINy2OpglwOg1MnaoCyIIgsAYv4TAbrz0RkNeC\n/vBDGTNm6DhxQsSkSYaVMLh0aXef70WSIt49XReixLD9JsbvF/vYchYvVq3qG8nWOKVEQIK4vugv\nITg7W7eCJomCOMMlXlCIY7eKcOrqcpCXZ1j2P95ngCBSAQnnNGMwy952i0Sy+1qyRAXAGmkEgzqe\nfFKEYeT2iVQmil4XF2tWc4541oBYUaXrgpX4BrDybefPS3jhBTcqKgLQdQFvvZWNggIdkycbuO22\nMObMCeP551mnv4ULNatUnCQx8Xj5sojLl0VkZRn49re70d0t4MIFET6fgVmzenD2rKPXfwxMmWLi\na19jDUW2bmWT+8GDLsybp0EUWd3k0tIenD0r4c9/duC++8KWiP/GN8L44x+dePNNBxYtUrFihWpZ\nStatC0IUTUuk3nGHBsNg5ylJJmbO1LFwYTdKSlhCzYwZOv7yFxbdzs838OqrTgiCA8uXq5g7V0Mw\nKODSJRFbtnRAlk3U1eXgzBkJCxZouOuubnz5pavX1+3ClSssOSfy/Xmjst15qSWeMGhfCYj1JJeW\nBgFEIjW8dGCimzCK1hAEYW+KxQVrbS3LybDnW4yEhcveR2DNmiBUFdZ8bw8m8U6yAPr0GSBrGTEc\nSDinIcNJ1OpvX4piYPXqrkEfA2Aiedo0A5cuiVajDiBSBgiAVbvT5zOwdi0TZEePZmHaNBaJVhQD\n69cH8etfR7y2TqeBlStZRY3GRhmNjR4sWKBi0SLWXfDKFRGyDKxZE8Qrr7hwww0GfD4D3d2CVVLu\nyJEsNDQo+PJLHbff3o05c7rhdLLydXV1Dly4IKGigi3dHT6cDVWNJOvdeKOBL78UIIqAqgo4e5ZN\n/LoOq9IGwARxYaGO//2/s7FggYqdO90QRRaxNQygo0PA228ruOceVvmjvt6B+fPDOHCAeZN5BLqh\nQUFBQRiSZMIwgE8+kXDxooiCAgMtLRLa2kRs3NiBtWuD2L7dg4sXRbjdTug6MGsWE9+8bmqi34Pd\nDx65WLCLHV+2pOQ+giCSxX6d0TQBO3Z40dMjYPFi1brptgdV7Nvxjn2pFKk+H0vC3rnTjXnzNMgy\nbJWFIja10lJ2neB5NrFVoQhiKJBwzgCGmtTFWzYHAoF+KyXwO3hVZf7dqVMNtLWxjoLl5R1obmZ2\nCVVlUd/PPhPx3HNuK3o5bRo7Jo9IzJih45vfDGLHDm+vn7gDn3/OSs8JgmmJ5fLyTkgSUF3txv79\nLsgys0I0NgpYuFDFjh0emCZrLLJyZQ/efluxfL7vv6/g/HlmLSks1HH2rANvvOGApgHr14d7rR3M\n73zypIxLl0QsXMhK4H396z0AWIfCf/mXEL74QsQHH8g4d05CYaGOq1fZfkXRxF13RboELl6sYubM\nHrz5pgMzZuiWjcPnM/DGGw584xth5OUZOHLEgX//905cuybjd7+LlJ7jlg1NE3DggMvy6H3yiYRb\nbtFxxx1hFBeHcfRoFmpr3fiXfzGwbRtw7VpHlLevqsoLUTRRVBSJHsf62+3LlrFJM/39pihaQxDX\nJ/y6UF/vQjjMGlbZk45LSkIoKQlBkvp27ANSt0LFazLzjrbt7WJUsMA+Vlk2oetCVBMughguJJyv\nQ+yi2OWSEApFlzCzL4UBrKEHt1rk5pr/n70vj6rqvNd+9rv3PudwOIACKsMRxSFNoggSNYOi6Of9\nGo01pMRr0uFbvbettaTEeFfTfv+0uXd13a6uJqvWGIek/e7QIb2pIRJiTNokBkVirAoIDokRkRlE\nVAbPtKfvjx/vewbA2Vab/ayVFTycs88+A7/3t5/3+T2P0PYCJEvgVmfbtnmwZs0gtm71wDDIoxgg\nb2LGgD17XLAsGS0tDA0NTuH3DFBU9aRJFCjy/vsOnDlDco7vfGcQhiGho4Nh2bIQWlockCQLjAGM\n0UDgZ5/JeOABDbNm6bh4kYbeTDOs8S0oCKGqyoFQiJpdh8PCwoUkWfnznx3C49jrNXHxIkNdHf1Z\npKaS8wZjwLJlNLh3//0aduxwIi9PQ06ODsuibUJJAiZPNiDL4cGUTZvowmD8eBOHD6tob2fieRmz\nkJqq4emntajPxumkN42/3tpaZcg7WkVlpQNz5mjo6qLI2J//HPjxj60RPUklCUhKIlaGh85cDrH2\ndNdyX7uBtmHj84PeXnI1olkPU4Rc8WFoIDoo5VYgNmAlVmYYuYbxeZd1625N5LeNzx/sxvkOxvU0\nLpHSim9/24DbLY963+xsA5WVKpqaZKxYEURdnYL9+1XMnesXzaGiWOjtDXsaK4qFWbN0nD1Lg3SL\nFmlwOk3BWP+v/xWC221h504nvF4DeXk6nE6azK6picN//ZcLBQUaOjooWXDnTje+9KUgnE56nowM\nE4wBCQkWJkyg152aGg4qycoysGOHG1OnGqLh7+wkOcTs2RruvltHdbUDLS0yJk8Oa4GnTTPQ1UWD\neAcPksZ56lQDjJE+rr5eQWoqpQguXKihqkpFba2KiRMNlJQM4OOPXSgvdyI9nYZRVq26hLVrB7Fj\nhxsJCfS+mCZFd69c6RNJgN//fh8UxRJFvqjoEpxOYnX9fhk+Hy0MnZ2Rg3v0nyTRex7LAq9fTy4b\n9fUK5szRIuLQ+yHL1qhDMldih2K17LbW2YaNzxc421tW5kFKihkVzgTQ7lpKihnVyN4qC7hICVos\ndF2KCooyTWLII8+DH8OGjWuF3TjfgeDuFldy0xipOPCEJQAIBPRhjTMvQiUlAygvjxe+zF1dlDKY\nnW0IfVt2toGioktR0asAUFOjiiG4d95xICcnKLb27r03CEkCZNmBtjYZigJMn0666ZMnZZimhKoq\nFcuXh3D0qIKmJvJVrq0l+ziAmOyLFyW0tcmQZWvIX5k0yMnJFE4yOChh7lwdu3Y5UFcn4+tf9+P3\nv3ehp4fCTIiRpqTB06dl7NunYvZsHfPnB1BbS38W994bxOCghO5uhrY2soazLKC3l9huDkUBLl4M\nx3xXVjqweTMlJI4fbyInJ4iWFnpf+TBeLDhr8/rr8SKGfOPGBDBmYd26AeTn+4eeK1z8ExMTxecc\n+Rk7nbQtqaoQaYb89utdKPx+eZjNnY3bE2+++SZ+9rOfoba2Fk8++ST+8z//EwDwr//6r/j3f/93\nuFwkDxo3bhxOnz4tHvfiiy/ipz/9KUKhENauXYuf/vSnf5Pzt3F7g9egQ4dU9PYyIYFYtYo8+zs6\nWJQ04lZdaEeug5FhJ5G1KtJpgxMG9kW/jRuF3TjfYYiVUVzpfkB420xVTZGwBAAuV/S2/OCggu3b\n4wEQo9nURJreCRMoEnv+/ACqq11oapJFuEd5ebwoWnFxpHNesiSEzz6TceSIgsxMCiCZM0eDrgMv\nvZSAyZMNFBRoOHVKhq4DoZCEhARiD+bM0fDAAwG88opnyM+YpAyGIcHjsVBXRzKMWbN0jBkTgsdj\nweeTkJtLw4Td3QyrVwdQWenAr37lgmURC93WJiM93RyyewO6uhjS0oiRbmkhxjwxkV5TejrdfuKE\nE2fPUlhKaqqJhgYFEycS67xiRRATJ4bgcIS9njs6mLiI6Oxkwg6poUGJ+gw0bTj7W1R0CRs2JMKy\nyPmEMyTcpWM0lpi7osSmBkY2t5HPfTmMpl/WNIayMg+CQUl8d+wwgdsXY8aMwQ9+8AO8//778PnC\nF2qSJOHJJ5/Eb37zm2GPOXDgAP7t3/4N+/btQ1JSEhYsWIDZs2dj1apVf81Tt3GbIpKEidQYj4bi\n4sGrqjk3cj58feO1l1ttRtaqyJpr1ygbNwt243yHgtsAxVqMxRaGjAwz6upbUSzR8LjdY8X9NI1h\ny5YEBAI0eHfiBPkIp6TQICBnLTMyTGRnG1i5kuzYysup0Y5kQqurVQSDYUba49FRUODH66/Hw7Lo\n3B95xIeLF104coS00U8/PYD2djY00OdCKCShtZWa0Jkzg9B14PRpWSQNdnQw1NcrQ1HUEnbscMOy\nqAmvqnJgyhRilbu7KZjkvfccePBBDd3d9D5lZpKLxdmzDPn5Orq7GS5elNDY6EBXFzEmlgW0tzN0\ndTHMn68hN1dHQUEADQ1OvPWWE5LkRGnpAADSgXMmf+ZMHY89FoRpSsIjmWM05tbpNIfcMsjjOdKN\nJFYrGPZPHf37MRLLczUYbWGJ3FWIbNBt9ub2w6JFiwAANTU1UY2zZVmwrJF1p6+//jqKi4txzz33\nAAC+9a1v4X/+53/sxtnGiH/jimJF6Zhjmd/YC/2bPVQc6SoU2aTz2jhnjiZ29/r7VVRXu6IIDHvA\n2caNwG6c7zCMVIBGKmwjBaRw383IBocHoHC4XBYWLQpg6VIqjrouobLSLfw5i4ouobw8Htu2UXhJ\nSwuFiXDjew6n0xIFTdMYPB4djz9+CS0tDjQ2ynj7bfeQ9EFCZqYBTQsHdyQlWcjONjBjBjle7Nvn\nQVFREEeOKPB6Ddx/vybiswFg61YPJkww8eUvB3H6tAzLAj76SIVlAatWBTAwwPCd7wzi2DEnZs3S\nkZhoIT3dQHs7pfjNnKlD1xUcPqwiPT2ImTPpeTkyM01UV6swTaCgAJgxI4jdux2wLGD/fhdKS/uh\naSTlIEkLw7Fj8UhKMpGcTPHdlZVuMTjDdwsi0/pU1URJyQA+/DAOb7/tQGZmWG4RicjP+rnnLDz7\nrI6BgZHlH6PhWvV9tpPGnYnYJlmSJLz11ltITU3FxIkT8ZOf/AQrVqwAAJw8eRILFy7Exo0b0dra\nigULFuDVV1/9W5y2jdsMXC/MpRexu5kjYbSUwRvFSPIMvtPJf8d3/goLKRwlEKDh75wcfcQ10IaN\na4XdON8hiN0qu9r7R04f82LDG7bIABQ+xBGrnVYUCw0NimAbOdsLAImJ1FgbRrgxjDwOMDzVaccO\nJ0yT5BPZ2fTfRx+p+NWvPMIOLi8viLlzTbz4ItkNzZ6t4dAhFbm5OpKSLJw5I6Ori5IDjx5VEAxK\naGtjyMmRUFdHLhiZmSba2xlOnlRw6JAKRXHgK18J4ORJB2pqSBudm6sjIcHCe+85wBh5LUsSJQty\ntnnt2kE0NzvQ1OSEJAF797pw/LiCkhKK0H7vPQcKCiSUlbkxbhxFZfPUv927HQCA2bM19PSELypW\nrvQJS7mioktCiuF0miJJsKeHYd26gWG6ZF0PX2D092vYvl1GY2NiVOgAx9VeZF0NbHu6Ow+SFD24\ntXr1apSWliIpKQkVFRV44oknUFtbi+nTp+PSpUvweDw4fvw4mpubsWzZMgwODo567JSUlFt9+rcc\n6tBV6Z3+Wm7l6/D5DLzwAoNlWfjhDw0kJ4+Fz2eAMQmWZWHvXjf+8hcVTqeF0tIQXnrJgZdeShy6\n8Jfx7LM6XC522SH0q30t/FwyMqhOMwa43fFwuRief55ScRcs0DBuHM25uN3x4rGSBBQWmti8WQVj\nNB9yted0vbC/X7cf1Fgm6jphN863EUZjAq/U7IzkwTycgQ4P/G3alIj16/suu9U/2rH5MQEgGGT4\n7DMZkkRMazAoCeuzLVsSkJFhCnaVT2FTJLaFlBQTp07J+MIXDGFJt3ixXzh1+P0yDIP0vffco+O1\n11xobpaRl6ehrk4VzahpQuig/X4JlkWNPJeZpKWZmDiRnqO3lw09P4Y8o+nntjbSci9dSh7MBQUh\nVFc7YJrArl0UYJKfr4kExBkzdGzd6oFlSVi/vh+aRixHa6uMCxcMPPoopVl98AE1zklJFo4fZygp\nIVlHeXk8Tp+m5v3DD+NQV6cKb+WlSy+hsJBObCSNoKJYIhnQ4bg667hbre+zG+bbE7GM89133y1+\nfuyxx1BYWIh3330X06dPR3x8PAYHB7Fx40YAwI4dO+DxeDAafvKTn4ifFy5cKOQhNv4+IUlS1EzM\ns8/SoPbvf0+36TpQWxvrsGHg5z+XIUkSfvSjyzs4XQs6OmQ880wIv/yliuefl7BuXQiBgAMTJxr4\n8ENqjH7wAw0ul4L16zUcPEiPo9wCU/xs4/OBPXv2YO/evQAAWZaxcOHCGz6m3TjfJrhRreiV7u/3\nyygvjxcBJEB0AAoQZodLSqKZzthBscjbHn/8EkxTQnW1C2lpJjZvToBhQISetLczGAYN8x06pArd\n844d8Whvl9HTwzBrlh51/Ndfd6OlheHhh0NISjJRVUUR2tw+LivLwAMPBLB/P2mku7oYvv3tQXR1\nqXjkkSBcLguNjTScWFtLjzNNoL5egSQBeXnUBD/wQAB+v4LmZgNnzsj47/92IS9PR2enjLY2arIz\nM6nZP3ZMESw2D13p65NEhLdpkvY6OdnEZ585hrYKaXjR75dQUjKALVsShDREkmhI8dy5kZvfWF16\nJDjzn5w8Ft/8poHz5/vEfWIb5JG+VzZD/PlALON8Odx111345JNPxL+PHz8e1WjHoqSkJOrfvb29\n136Cf2NwBu1OPPdI3OrXsW4d1Si/38T58zxqW0ZpaT9WrbKG4rfd+OADB9av7xde9Hz30rIs9Pf3\ni7yAG3kt/FwAwLKSYFkWdN2P7Gxa17q7aSdS1/34t39LBKAiJ4fWnn37wrtyfv8NvilXAfv7dXtg\n5syZmDlzJgB6Lfv27bvhY9qN8x2ASL3ytdyf/6xpTKQsxQ548QAUnvqnacSIrl49MOy4kU0YbwSB\nsGZ3xQofDh2i5vCuuwzk5gbEfRITLTBGMg/TlDBpkiGM6dPSqCGtrXVh714VhkFpfz6fhK4uBe3t\nDHl5Go4dU9DaSkmEb73lRm6ujiNHaCCwosKNlBQT588ztLTIyMoykJVlwjCAMWNMwRafO8fQ0KCg\nqCiIXbvcMAxg/vwQxoyheO/MTAo9yc3VcffdOk6coD+RM2dIO82DXfr6JJw7x7BjhxsdHQz5+cR6\nnztHLHhSkoWmJhktLTIcDgszZtCAo65LME2guDiA5mYZBQVhmzlagMIsX0aGKdIVc3Lo4oLrpMMD\nnjL8fmOYBdPlJtrthvnvG6ZpIhQKQdd1GIaBYDAIWZbx1ltvYfHixUhMTMQ777yDPXv24Be/+AUA\nYNWqVVi2bBnWr1+PpKQk/Md//Ad+9rOf/Y1fiY3bAZEyL76OeL0GKivdKCz0QVVN9PQwOJ1WDOGC\na1q3ruVcgLD8Ly7OEC4fmZmmIBY4KN3QbnVs3DzY36bbBFfDBF7Jt5ljNMkHY7TNbxjSiAlxHR0U\nQz1p0vBjk3QiXACrquIQCBDLym3Xdu50Y+JEYoVzcwNwOk2RrifLdNyiIvL6DAYl5OVpmDrVwF/+\noqKlRYaiWGII5f77NZSXO2FZwJe/HITDYeH4cQWyTIEntbUqzp5l+O53B1FRQYOGXi+xw21tFL7S\n2kqvkRNvnZ3EFhcVBXHhAhMuGNnZMurqVMgyWfW1tlLDa1nAmDEWPvzQgfx8DRcu0MXFlCkGGhvD\nW30ZGSYSEy1UVpI0IznZHJJiULOuaUB1tQuzZuk4d46JNMILF8i/mstY6uuVIVmILnTQXNKh6yQ1\n0TQy849cjLgF07hxJE2J1Jtf7qLLDgH4+8RvfvMb/PM//7P49+9+9zs899xzOH78OP7pn/4JhmFg\n+vTpeO2113DXXXcBAObNm4fnnnsOixcvhqZpWLt2re2oYWNEOJ0W7rqLZjgaGhKFJI8TMrF15WrX\nrWsBb+L5cXmjzOdv/H4Za9YMYudON7Zt81y1JacNG1cDu3G+jXCzpo5HctgoLe1HZaUblZUqdu92\nIDvbwNe/rsHlYlFNtKpiWAHUdQkvvMCP2S/ipGfP1pCcbCE3l67oOzrI+q23l8HppCHEQ4fI3WL2\nbA0rVvhEepPDQf9vbKQGlTe3X/qSD++848aZM+FGffx4DVu3JiAz08Djj/uGAlAYxo41ceIEOWX8\n6U8OnD1LbhQLF2pITzfQ0uKCLFPTDhBbDACHD6tITTVx332kk+7rCzeVfj/5J3MN9OTJBubOXyNL\njwAAIABJREFUJQ9qHoIyfbqBoiIfzpwhHfQ779BwIX9NFy8ynD1L8pGGBicAYGCA0geLioKwLBU1\nNSrWrRsYuogh2zxis4nJrq52oaZGEe9xU1PYOo9eiwSfj3SDui4hJcVEdzfDoUOUZsgZav75xU6T\n2zZyf7/4xje+gW984xvX/Linn34aTz/99M0/IRt/F4gkd3RdQnW1KpICGxoU4aAUmx9wI7iai3td\nl6KG4COHymfM0NHR4RhVymbDxvXAbpzvEFytNnW0bbG4OAOFhT40NCQiGCQW9N13LdTXS7CssKsG\nP35kAVy7Njxdzx0gSkv7xRX/vHm+qILKjxEMKpBlC+npxEi8+aYb3d0Ma9YMgrFwcMiECSamTjWQ\nmkoSjPZ2KpZZWaRNPnDABUmy0N4uwzSBqioXdB2oq1NgWaRhTk830dYmY8YMHW+95cTEiQZWrw5g\n714HNA147DEfjh93YsIEA/v3O1BTowh3j+RkC9/4hh9NTTKSkkykp9P5fPSRiiNHFJSWDqKx0YGM\njBAuXZLgdls4c8aBgQEJfX0SvvzlIA4cUDF+vIm77jLQ1CSjpIT8pfftI+nJ5MkGvvvdQcgyUF7u\nhCRZaGhwoqmJ4sAXLdIwfbqBkyfp35JEzDPXZfNtScOQUF4ejw0bEuFyWfjBDzTxOXB2v7WVmuyl\nSzFMpmPDhg0b14tIGQZfA3imAE/t464/lZVuLF166bqDki53cc/JoLIyjxh2V1UTuk5D5YxZ6Olh\nOHpUwcKFGvx+GRUV7mH5BzZsXA/sxvkOwuWG9PhtsfGjkeBX5cEgQ3l5PHp7iUEd6fhAWLtcUeHG\nnDka5s8PiCv3SCYhslnnTdyaNYPYv9+FnBwdFy4w7NrlhCyTFOONN9zo7GR46qlBNDU58M47xNw+\n8UQAqammOKe5czUcOKDi8GEVX/pSEFOnhqCqJpKSLPT0IOL5Sb6Rmmpi7Fhy0Whrk7F9uwsTJlBD\nbVnAvn0qdF3Fww+H0N7uAGAJvfJ//7cLjAE5OfRaBge5swUVYR7/zZiFwkINn30mDw0KSrh4kT6L\ntjYZaWn0nm3ZkoCcHB2yDKGz3rzZA1UFFi3SYBjAnj3UVGdmUsO8atUl5OdHMzQNDeF0Li7pAACv\n14DDEf39KCz0wTAkoSuPBPfVHild0F5AbNiwca2IXAMif/Z6jWHryq3Y4Yq0Ro28LSvLxD330C5k\nRoaJPXtol5V7Od8K6YiNzxduaePc1taGr33tazh48CDuvvtu/OY3v8GMGTNu5VN+LnClIsTjR/l9\nAUQ1TKpqYvXqATgcHvz+9yZMEyPelxeljAxKD9y2zRP1nJG+z8GghDlztKHjADt2uNHVxbBwoYaL\nFyEK6bx5GsrKXPB6Dbz8sgeaBuTm6sjMNPHHP5KrhapSM3z+PMPUqWQlN3VqCImJGjSNAlcmTCB2\nd9Ik0trV1yt4+OEQGhoULF4cwquvuob0zCGcOCGjuprSCL1eOt748aSHzs42cOqUDMYgtHq9vZRK\n+MQTAbS0yDhyxBX1/vL3iw8c6jqQl6fjscd8cDpNwQpzF5EVK3yoqnLBNCUANCi4cyfpsh0OC9Om\nGdizJ9wF8wHB1asHopj8piYZTU0y1q0bwLZtHkgSBdaMZhfIMVqDbC8aNmzYuF5EXnwDtH4oioW0\nNJr5yMkJ3rTjj1SrRvq9rktYudKHigo3srJMrFjhw8sve2AYxJIXFPjR0DCcWLBh41pwSxvnNWvW\nYNasWfjTn/6EjRs3YvXq1Th69OitfMq/a4w00BeL2Bjuy211uVxsyOXCGhZ8EoniYpJq8N+PBMao\nM167dhD79rlw6JAKxizce28Q+fkmamri8OmnMsrLnSguDiAYlLBzp1MMDk6eHEJODml0nU4LDz6o\nYfdu0qbl5OioqHCL8+jvJ7N70wT+67/i4PUaePjhEBgjV4vf/jYOWVnk47xrlwMzZugYO5ZYiD/+\n0YXOToeI3LYscvW4/34Nb77pRGurE14vpRb6fBL27HFAkizk5+u47z4NjY0yGhtlFBX58PHH1FCn\npZl45x0HDMOJyZNJcpKSYooEq/h4AwUFATz0UAAVFW5s3uxBfr4OXafGu6WFmmHOUnNmOxikxMWh\ndxhOpxX1XpPd2PAtR1vDbMOGjZuN0dIAYx196usVhEISTp6UoapEAHBpRezjr4Qr3Tfy94ODCjZu\nTIBpSlGzOJmZNJTOHT9uttOHjc8fblnj3N/fj/feew+//vWv4XQ68cwzz+AnP/kJjh49Kjz1bFw9\nYhuhKyXCxYJHYkcWGrdbxrPPavD5Lg0b4hjJ0q6kZACybIliWVnpRm8vpetVVLhRX6+gt9eN/HyS\nIvT0EDscF0fWdPv20ZX+uXMMqakmJk8m9jcpyUJFhRuMAXPmaDh8mEzsFcUCY0BysgVFMYe5Tzz8\ncAgAaXrHjzeRnW2ADV1btLczpKSYaGlhCIVocDEz0xRDf1OnUmPd08MwfbqOTz5RorYXe3vJmSMv\nj9IMu7sZJk82huQq9JoaGhRkZJgiaZA7aOg60NfHxELh98t48cUEzJ6tCdnIpEkGamsVGEZYezx/\nvob+fgkOh4VQSBpmC8gLflkZxZ0vXy4hEKDjx8Vdn37ZdtawYcPGlTDahbimMVRWuhEKUV0NBhl0\nPfqxfO25lRIJTSP5oWHQcDcP0gJo55R7S/N6Z8s1bNwIblnjfOrUKbhcLsTHx6OgoAC//vWvMXXq\nVHzyySfDGufbNcrxdoqa5DGnwMhxoZG/dzg8cLkYUlJkPPecgUDAxPPPU9H70Y9M8dhgUMLzzysA\nkvCjH5l47jkqNG73WHFMAAgETGzYQEMf8+ZpWLLEwp49Ejo7GTo7GdrbVWHtNmWKgdZWGX19DA4H\nEBfnRFxcPF54gQkm9+23HTAMmnpevNhAfT2l7lkWWbktXx7Eu+8SKzxliiGmt8+cYaIxNU0JaWl0\n344OsnhLSKAY7enTDZw/L4lGmDt2tLfTEJ4kARcvkstFejpJNmprVXi9lB7Y1ESs8pgx1tBwH0W5\n7tjhhCQ5MXmyiVWrDMyapSMxke6Tm0urBbl0MKxebQCIw/btMpKTTRHlXVwcwLlzDGfOkB562jQD\n+fkmNm70wDAkzJ2r4amngti+nb+nbmzc6BCfXSBgCsnGwoUh/OIXDgBJ+L//NwSHg4l4W5/PwA9/\naAz9e+yo36kXXmDDvhe3A26nvz0bNmyMDO6clJlpYNo0A/v3u5CfryM+3oIs0+7gpk2JgkS4lecB\n0PqzcqVP7LpGSt0iWXEbNm4Et6xxvnTpEjweDwYGBnDixAlcuHABCQkJuHTp0rD72vGtVwZP+eM/\nj/Z7apLpY73WmNPI+/p8Bn7yEwbLsjBrloVgkK7kOzsZPvjAxOHDxJZOmkSexnPmaEhKoiZSkoDv\nfS+E999n+N3vZHztazoABzo7ZfzjPxr485+B9HQDGRkmystVpKSYwgKut5ccJUxTQlsbscacwWCM\n2IOFCzXEx1v44AMHmptlLF4cwsMPh7BrlwOqCnzxiyFcvCihpoaCTMaOJR1xSgr5KzNG7DB34vif\n/3EJB48PPnCgtZW8k2fP1odiv4GEBEsEs8ybF8LBgzIOHiQ5yuLFxBQvWmQCMKEoErZvp+abMfK5\nXrIkhD17VLz5phPp6Sa6uhhyc3V8/LGKuXNDkCSSYHR1MWzZomDWLB1O5/BYbZeLweUip5LPPrOE\nld/Bg8AHHyjCaYO+A0xEzI6G2FhmG5dHZHwrACxevPhveDY2bPx1cDm9sdNJDhZf+IIhiARdj84D\nUJThsxjXg8sNxQMQu5+x1ptlZR4EgxKcTuumnYuNzy9uWeMcHx+PwcFBeL1enDt3DgAwMDAAj8cz\n7L63a3zr7Ro1OVJcaKT+2TSJXT5/fgB+PzXbkbGp/PFxcWPw7LMmQqHBqNs1jQ0l3FEx6ulhWLIk\nBLfbQl0dJewpCkTEdG2tihUrgqivVxAISHC5LGhaCEeO0GcdDF7C2rUBAICq6igtlfHii4nYtYts\n4+65R0d3NzXJXi/pkidPpqG6zk4VXq+JY8cUZGWZWL7ch7ffpkhusmwj3XF2toHMTNIVDwxIOHyY\nGOSMDBO7djnBmIWvfjWAmhpiMhcvDoExur9hACtX+rB/vwtnzzKhn/Z4yJrOsgCfT8JXvuLHa6+5\n8OqrcVixIoisLHpvP/xQhWlKeOihATFAOX8+nZeqAg8+GEBZmVvIRPjxAQxNeTvEIMsrr1CBtyzg\nsccGYVlG1GcHAN/7HmkKm5sdWLUqgPR0DTt3ugHQsQcGfEhLi0dHB0N/fz+GEtWHLRKaxpCeTvKZ\n/v6Bq4rE/Wvhdv3bi4xvBYATJ078Dc/Gho2/HkaqH9zFKTvbwJ49KtLTiejgeQCx7ks34ut8NXMb\nFRVuJCWZmDFDh65LQ4PmxEbHpubasHG9uGWN87Rp0+D3+9He3o7MzEyEQiE0NjbiC1/4wq16ys8t\nYgvKSP6WIxU92qZnWLcu3HgHgwxVVXFoaFDEoKGuS6ipicOuXQ5IEskWxowxwRhQW0vG911dDG1t\nDBMnGli16hJk2YJlSUPPJWHLFg9MU0JpKWl2OYtsWRQjnZpKzbGi6EhPJ1s53kQuXhwSzOrbb7sx\nZowpIrABkndUVVHQSm6ujosXyZ95/vwQUlKIybWsMJsNACkpJgYGZHz1qwG0t8s4ftw5JMkI4cQJ\nujjo6pKFp7QsA6dO0XMyZqGhgTTROTk6uroc0HUL1dWuofdQwunTNPDHB/lUFVi4UBOJgkVFQTQ2\nkqQFCOvweFBNb2/YGSX2s+O3m6aEjz9WkZYmo6jokgiX4exLScnAsKHPkRxYRvo+jfS8NmzYsMHB\nG9KODiZsMltb5SEJnl/MXfD1iXvJ82HBm4mcHJLN9fdLOHxYQW2thGPHFJFUC5BE43pnQWzYiMQt\na5wTExPxxS9+ET/72c/w/PPPY+PGjZg0aZI9GPhXwEj+lrHQdQmWZUGSJNFc5eToIulv7lxN6MSC\nQQWffkpJfk6nhcZGGampNKSXkWFi9mwd06cHUVAQDkjRNGKpx441UVHhhmmS1KO8nGzYFi3SkJxM\net2aGpI85OVR8evpCeuSFcXCqVMUCrJggYZ9+1SMHWsiK8uAYZBlXWFhCMeOKdA0CRcuUKKgZQF7\n9zqQnm4iN5eCQerrFeTlaWAMaG6mMJVXX3VBkoBVqwI4edKBjg4ZU6cSU93dzbBsWQhxcRYOHKD3\n5ZFHQpg8OYSKCjdaWqixfuopinZtaFCwdu0gysoo+bCx0YGdOyk5cP36fui6hJwcem0vvpgASQLW\nraNFhF/olJb2o6Hh8n+WfEL9jTc8CIWAmhoFDQ0Jw4ZCy8uJdeYx5iMd50pDplfyDrdhw8bnD1z+\nkJOjo7DQN3SrG7pO3vf8Ip4jI8OEpt3Yc45Wr4JBhq4uhq4uoKjIh9paj7AJraqKE48fzaLVho1r\nxS21o3v55Zfxta99DcnJybjnnnvw2muv3cqn+9xipIIykoYrUs6xaVMivF4TX/2qhVCIgkkSE7nN\nGfld8seYpgRJgmCTTVPCjh1uzJypIyOD9LrvvRe+qtc0Br9fxu7dDuTlUbX0eg2MH0+uFufOMZw+\nLcPttpCYSBPQhkHyBMMAWlsZ2tsZZs7Uce4cw/TpxBL09UlYuFCD12ugstKBceMo4e+3v3WJBjkx\nkfygLYuKNaVIkTRi6lRjyKXDhMNhoarKgbQ0Gtzr7+eDjqQ95hcFlgV84Qs6OjpIwvHlL4egKBbG\njzfR1kYMtqJYUYwvAGRmRjMbui7hjTdIe6eqECEtqmqOeqFDE+rSiCxJXJyBb31LQn+/jg0bIiPT\nzaiQGyC8ZTqaF+rVwLa3s2HDBkekr3xhIdXAhgYFOTk6GhoUNDQkRtUJXt94wuD1InY927AhKYoY\nePttN2bP1sVawp9zNIvWkY5rw8aVcEsbZ6/Xi8rKylv5FDYQfQUd2RxHWtBFFgw+VdzRIcPlMhEK\nQXgOcwZUUSxs2JAkWGjGLCxfHhLNYXOzjOZmGQsWDGDKFODcOWJZuczAMMjKrbZWhaJYKCggtvjJ\nJwP47W/jIEkWTFMRW3t3362jrMwlkvcsixpsANi7l3TJsgxcvEhN8Lx5GtxuiuGWZV6YFZw9y1BS\nMgjTBLq6VJSXO8VQX0aGPuS3LOH//B8/GKMmXZKA3Nxwgw2Q5OPIEQWdnUw05ceOKdi5042mJhlL\nloSwenUAAwMMW7YkYMIEE7NmEfM+ZAiBu+8OYPr0oNjSHDeOhhFDIaC42AdVNUVTHHmhs359Hy5d\nkrF9ezza2mThSRrb/LrdMtxuGevXXxCP5eDbkyUlA8MedznG5VoSBW3mxoaNzw8i/94VxRK+8pEO\nFrouRaWdxuJGNM4jISMj7JnP0dtLPzc1yXA6LSxdGq5/ui6JRvtKMjYbNkaDHbl9G+BGGpDYhpgX\ngowMkkFwTVksSkv7kZycALdbFsNjwaCE6mpXxNZbGIZBLhfHjnmGzOSpAHZ2qigrc0GSLCxZEkJ/\nvySs6ebPDwnZwcCAhJkzdbS0yKL5JjcNIDPTxN69DmgaWcYtW0YuE5Mn0/83b6att7FjyTO5r09C\nZSVJLIqLAyguJm/l3/3OhYULNbz8sgehkIS8PLKekyQLqakmEhPp/ZUkoL1dxty5Gjo7nTBNeq7M\nTEV4S58+LQtP0CNHFHR3M6xZM4itWz3IzDSG4rKJUX/oIQpraW1leOSR6KGYTZsSEQiQ5nrlSh8u\nXmTCQSNyEYndetyyxQNdp4a/qipOpBCOxB6P9r3JyDBFAx05YX4l5vhqGmqbgbZh4/ODkf7eR7rA\nVhQrKuiEr20328WCD7BzwoezygA1x4YhidoX+ZhIS7qb3cTb+PzAbpxHgaYx+HzXZud2vc9zvQ0I\nLx4chjF6GlJsRDYA4dusqiZKSgZw5IgLu3c70NvrQXHxoLh/QYEE05SEzU95eTwyM0088ogPR486\nh4b0JLjddDyHwxrSlzmwYAFZx3V3k9cyQDrhhASSVFDTKkGWgSeeCKCjg3TDx48rkCTH0LAjpRF6\nPBbOnw+/Jkmy0NQkC7eKL3+ZBu40LcxYf+c7Abz1FkVyX7zIkJVlYuZMHV1dDE1NKmbM0MEYcPSo\nE8nJJM04dUpGW5uMrCwDU6YYQ17O9PqXL6fQlT//2YGMDAP19QpkmaQXra0yfL7w5HgwyMRQo2UB\nBw648OijPjQ0OLF9e3xUqhZAQSY8btuyaBF67DFy2vB6DWRnG1GFn38/R4pVj/2sbwbs5tiGjb9/\njETkRDK1I91nJNlEJJFzMy+uI5+L66YNQxKzNbHN8UjPG+vzbNc2G9cCu3EeAfwPkzFJeCffbohs\nuEtKBlBeHo8tWxKinDBomG+4o8ZIx+Jxz5MnG2hqksWgWmQRKi4ehKYx4R6xdSuFdixZEkJamoFX\nX42DJAGlpQPYuZOGAPfvV4dkGzoyM80hrXQIf/mLC7ouCccLAPjDH+LAmIVZs8LRU7pOLHhdnQLG\naHo6IcHCN7/pR1+fhAMHVLS1kV9yZiYx0hkZJiZMIM12czPDlClkzs+DVP78Z3IHWbuWGOS0NBN1\ndaTlXrQohM5OSglctsyHEyec+OIXQzhyhGQlubkUxT1+vIlx42iRCIUk3H+/hsce8yE+nr4vGzYk\nQddpCPLee4MoL3ejq4th61ZyCvF6yVfa75eFTdOmTYkIBollLi0dFFIO7pLS1CSLhamszAPGLKxa\nFRzxwoucVIYzPTdrsbAXHRs2/v4wEpET24wCGKYTjiRwNI0PA15+QP1G0dHBUFio4eRJGVu2DB+O\njsVIsd927bJxPbAb578xbkYDIsvh4bJIiQAQLmyRBTEnRx/SgYWLXUaGiUOHVMyeraG1lY7FGWzL\nAsrKPOjoYBg3js7x0Ud9qKnxQJYtzJgRxPHjTsiyNSRDgJBWtLXRsTweC2fPkk75lVdIhjBnjoYH\nHgjg+HGneB7LovssWEAaZr+f4exZBsOQIEkWEhIs9PVJ+I//iMPs2Zp4nCwDly5JoulOSjLx6acK\nPvzQMRTIMoAZM4JoaHDi9Gl5aJjFCU0jyUhXF4Nh0LEnTjQxZoyJrVsTUFwcwMGD6pB0xMCRIwok\nKawJz82l99Lr1aCqplhAuBczD1BRVfIR7eoiqYYsA/HxFl580SPcNYidpsAULuXgiw+Xv3C9c1mZ\nB42NMv74x5G/E1fSMWsag6axy37nriQhshcdGzY+X4iVN4yUyFdZ6UZNjYJt2zyXZX2vF7GJgNXV\nieLnuDhj2G7bSBcAtrzMxo3AbpxHAP/D5NHWIwWO3Oznu57HxA6U8Z9HutLnW23t7Qy6To1YIKDD\n7SZnh+LiQWzalIjaWhVz5miYPz+Aqqo43HsvWcRVValQFBqaa29n+PhjF2bN0pGUZGHnTrIhys/X\nMX9+ABUVbmgakJRELPDYsSaqq1WYJjB7to6WFgbGKP2OMWDmzCD6+lxITjbR20ts75kzMk6elHHk\niIL2dorrnjaNZBOWRS4dPT0MjAGFhSE0NsrYs0dFXp6O2lqyhUtKkuH1Gmhvl3HmjAPvvEMuGo88\nEkRSkok//CEOeXka6usVMYnd3U3hLnwwsbGRpCD336/B76eY7tbWsEdzVVUckpNNNDQ4sXu3A4xZ\nKC0dxJw5JFGpqlLx8cfEdO/e7cDs2RqmTDFw8KCKxsZoGdCCBcSe1NcrWLwYUdp1rinnQ4LFxYN4\n6aVEdHbKKC3ti1qcriT/uRp5kK1htmHj84eRiJyRblu7dhDA8EZaUSwUFvrEgGCkm0Xk4290sDh8\nbhgxt0DXw7WVD8nbsHGzYDfOo0BVzVuub75RjDYgNlIjza+0Z83SUVOjYMmSECKLSeSVOgAhG8jK\nMpCeTrZzsgx0djLU1qqYNMkQdmw8XS8xMdy8dXaSt2ZODkk06urouImJ5LCRmmri4EEVb7/tEBKM\n8+fp/n6/hLo6BWlpJlJTTUyZYuD0aRmnT4dDT1au9OGTT8JMNQ8qIXs44OxZFZWVKhijAcIdO5xY\ntoyGFXftcsLhsHDffRoSEy1MnGiKphkAHnnEB4fDwpEjLlRWqlBVwOvV8NJLpD9esiSEuDgDqmpi\n/vwANm5MGPKHJlu96mqXCEiZNMkUVnaMWbhwgeHwYQZZBqZNM5CXp2Pq1BCcTrq4mD+fJB+R1nY8\nIjYYDCd1FRcP4sc/tgBYIh3yWpCRYTfCNmzYGI4rDQwPDir45S9p8O6ZZwYE0xzJ5paW9osdy5Es\n4K72ovxqGuxYO8/IRMOioktR52XLy2zcDNiN898pRisMvb0MkyaZ2LdPxUcfAT/+sRHzGDYUjgIh\nNfD7JZw9y6BpJL/gcgxJIvu2ceModvvcOYb8fD+Kii5h8+YEjBtHDPK5c8QYKwrQ3y/h0CEVWVnk\n69zcTM3w/PkBOBwWAgEJb73lRno6WbedPcvw+OMB5OQEceaMA4CC5GQT27Z5oKqk796yJQGTJlFk\ntWkC3d1uNDbKsCwJuk7s8fLlIbS1MSQnm2hpkYeGEg288YYTkgQsW+bD1q20GHz8sQsLF/qxe7cD\nXq+B4mKfaIoti14DQMN8FRVuyLKF+noFy5dTEAtA711mJhVu05SwZ48LhgHMnKlj504nnE6SZFBj\n7hCLzaZNidi924HsbAMlJQOQ5TBjU14ej3vuIY/pTZsS8cMfGnC52KjDgZf7HlwpIMfWMNuwYeNy\n8HoNbN3qGZpr6Re3cw957upUVHRp1BCmy+Fqd71ia5XfL49KDNi1zMbNgN04fw4QWVgAKmwbNtBV\neH+/JnyHIwvVd787iCNHnLh4UcKZMzK++91BvPGGG4wB992noaeHkvxOnSIfZS4lqKmhpKZZs3R4\nPFaUHCE52UR6uglVJYbg0Ud96OlhME2gosKNRx+lYbz2doZFizQxePfJJwp6ehyYMIGaXsMIJwvS\n65HQ2spQUeFGezv5LtfVqfj61/1oaZFRVaUiK8tEczMbem0DeOstNz7+WBUXASdOOMEYabS7u5nQ\nYKelUfKhZZFzBo+W1XUJlZUkSZk9m3TO773nEI38vfdSEMCWLQnIyDBx+rSMiRNJ47xkSQgzZgSh\nKBYOHUpEMAgxIMgnxVNSoq3kdF0SWmzTpNTH3/9eAmChrW3k4cCb8b2xYcOGjUh4PDr+5V/6hdMS\nEN4V49piPgCdnW1g8+YE0VxfLqDrRhDrTNTRQbuXVVVxKCkZEEPytve8jZsBu3G+Q3A9f/B+PzWt\nXFbAjyPLFrKzDWgasH27jFWrhg+JqaqJjz6ijvqpp6jwPP74JVRVxaG7m4mBvMcf9yEuzkBhoYRA\ngKG8nAppWpqJvXupYb3vPg2HDqmoqVFx5IiFBQs0NDbKQq7w6ad0ntx/efZsDffcE8TFixLOnWNC\nb3z+PMPEicSQr1kzCIcDcDpNzJmjQdeBujoVsmyhoCCE7m4ZH37owP33azBNCZpGDbIsU9Pd1kZM\nN/eerqx0ID+fGuX6egUffURyi0OHJGRmUuLgrFk63n3XAVkG5s8nKcq4cSYefDAANkSmOJ30Pp47\nxwRrzw36eerixo0J+OADB555ZgDZ2dEyC87KrFp1Cb29lPzHF6P0dBOdnQyZmQbS0kxcvHj1DE7k\n98dmk23YsHEj8Hh0+P2yqFfhhFP6t9NpobjYh+pqF4JBKYroAK6u7lxtnYpNxA0Gyelpzx4Vui6h\nt5ehuHgQuh62+4y0AbVh41phN853AK5nUMvvl/HCC/SY73+/T3hcvvYasZhFRZdQURGPjo4wIxxZ\nqCjumW4vL49HRwdDSckAenspllpRqLnjjWJlpRumCbS2ypAkoKiIXDfOnJHx6KM+XLjA0NLChkJF\nqLHeto2K2D/8QwimCfzpTw5MnGjA6zXx//6fB5oGLFsWwpQpIagqMdq1tS6kpJh4+WXVxMw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eoSTFPCiRM0eF1V5UJTk4zsbCOqjt0MxO648Ya8ulrFkiUhjB1rYscO2rlcsICGAwGqybyB5lZ2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\ntllElCTOQgghAPD7/dx666189tlnDBgwgJdeeonBgwdHelgiBpx3nsmAA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M5nMaDApRnTNk2YTLZeLKFeYDXV7uR329HJYaEnmt/HPkxY9OcMHMPw9i/PKD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\nssSmKT5jWQqqL6HvT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J56KsKKFc1s2pTK1q0plJZGvW1dHX3/3OxvoO5pbhWOgYJFi1qw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\ngg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f52ff080>"
+        "<matplotlib.figure.Figure at 0x7f576af89358>"
        ]
       },
       {
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "Difference in mean x=-0.091, y=44.968\n"
+        "Difference in mean x=-0.167, y=44.040\n"
        ]
       }
      ],
@@ -511,15 +513,17 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "So what would be fewest number of sampled points that we can use, and what kinds of constraints does this problem formulation put on the points? We will assume that we have no special knowledge about the nonlinear transform as we want to find a generalized algorithm. For reasons that come clear in the next section, we will call these points *sigma points*.\n",
+      "This plot shows the strong nonlinearity that occurs with this function, and the large error that would result if we linearized the function at (0,0). We would reasonably expect an EKF to diverge rapidly with this function.\n",
       "\n",
-      "Let's consider the simplest possible case, and see if it offers any insight. The simplest possible system is *identity* - the transformation does not alter the input. It should be clear that if our algorithm does not work for the identity transformation then the filter will never converge. In other words, if the input is  1 (for a one dimensional system), the output must also be 1. If the output was different, such as 1.1, then when we fed 1.1 into the transform at the next time step, we'd get out yet another number, maybe 1.23. The filter would run away (diverge). \n",
+      "In the chart above we used 5,000 points to generate this solution. While the answer is quite accurate, computing 5,000 points for every update would cause our filter to be very slow. So, what would be fewest number of sampled points that we can use, and what kinds of constraints does this problem formulation put on the points? We will assume that we have no special knowledge about the nonlinear function as we want to find a generalized algorithm that works for any function. For reasons that come clear in the next section, we will call these points *sigma points*.\n",
+      "\n",
+      "Let's consider the simplest possible case and see if it offers any insight. The simplest possible system is *identity* - the transformation does not alter the input. In mathematical notation this is just $f(x) = x$. It should be clear that if our algorithm does not work for the identity function then the filter will never converge. In other words, if the input is  1 (for a one dimensional system), the output must also be 1. If the output was different, such as 1.1, then when we fed 1.1 into the transform at the next time step, we'd get out yet another number, maybe 1.23. The filter would run away (diverge). \n",
       "\n",
       "The fewest number of points that we can use is one per dimension. This is the number that the linear Kalman filter uses. The input to a Kalman filter for the distribution $\\mathcal{N}(\\mu,\\sigma^2)$ is just $\\mu$ itself. So while this works for the linear case, it is not a good answer for the nonlinear case.\n",
       "\n",
-      "If we were to pass some value $\\mu+\\Delta$ instead, the identity system would not converge, so this is not a possible algorithm. Since we cannot set our one point sample to $\\mu$, or any value that is not $\\mu$, we must conclude that a one point sample will not work.\n",
+      "Perhaps we can use one point per dimension, but altered somehow. However, if we were to pass some value $\\mu+\\Delta$ into the identity function $f(x)=x$ it would not converge, so this is not a possible algorithm. We must conclude that a one point sample will not work.\n",
       "\n",
-      "So, what is the next lowest number we can choose? Consider the fact that Gaussians are symmetric, and that we probably want to always have one of our sample points be the mean of the input. Two points would require us to select the mean, and then one other point. That one other point would introduce an asymmetry in our input that we probably don't want. I recognize that this is rather vague, but I don't want to spend a lot of time on a scheme that doesn't work. \n",
+      "So, what is the next lowest number we can choose? Consider the fact that Gaussians are symmetric, and that we probably want to always have one of our sample points be the mean of the input for the identity function to work. Two points would require us to select the mean, and then one other point. That one other point would introduce an asymmetry in our input that we probably don't want. It would be very difficult to make this work for the identity function $f(x)=x$.\n",
       "\n",
       "The next lowest number is 3 points. 3 points allows us to select the mean, and then one point on each side of the mean, as depicted on the chart below."
      ]
@@ -538,7 +542,7 @@
        "output_type": "display_data",
        "png": 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1T8JCLAYnkqbmz3+8lOAAXzbvPMDn3514Bl4RkTOhIi0ibueD9b+wLbuQ0Fb+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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f5306a58>"
+        "<matplotlib.figure.Figure at 0x7f576f8c92e8>"
        ]
       }
      ],
@@ -559,7 +563,7 @@
       "\n",
       "If we look at this is should be clear that there is no one unique answer - the problem is unconstrained. For example, if you choose a smaller weight for the point at the mean for the input, you could compensate by choosing larger weights for the rest of the $\\mathcal{X}$, and vice versa if you chose a larger weight for it. Indeed, these equations do not require that any of the points be the mean of the input at all, though it seems 'nice' to do so, so to speak.\n",
       "\n",
-      "Methods for selecting these sigma points is it own topic. In the next section I will develop the most typically used method in practice. It has the virtue of requiring only 3 sigma points per dimension, which is far lower than we might expect to provide good results. Despite the low number of points, the computations for the weight selections are very easy and efficient, and the numerical performance of the filter is as good as, and usually better than the EKF.\n",
+      "Methods for selecting these sigma points is it own topic. In the next section I will develop the two most typically used methods. They have the virtue of requiring only 3 sigma points per dimension, which is far lower than we might expect to provide good results. Despite the low number of points, the computations for the weight selections are very easy and efficient, and the numerical performance of the filter is as good as, and usually better than the EKF.\n",
       "\n",
       "But before we go on I want to make sure the idea is clear. We are choosing 3 points for each dimension in our covariances. That choice is *entirely deterministic*. Below are three different examples for the same covariance ellipse."
      ]
@@ -578,7 +582,7 @@
        "output_type": "display_data",
        "png": 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DkrgrxApwhHUFZmdnEU6qYS4sEh1lXYTD4YfKKgAEgwF0dnYqZqRVpVLB4Kzg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\nivYrRVMNT18SlH8AAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f51cfb38>"
+        "<matplotlib.figure.Figure at 0x7f576aff1d68>"
        ]
       }
      ],
@@ -590,9 +594,15 @@
      "source": [
       "Note that while I chose the points to lie along the major and minor axis of the ellipse, nothing in the constraints above require me to do that; however, it is fairly typical to do this. Furthermore, in each case I show the points evenly spaced; again, the constraints above do not require that. However, the technique that we develop in the next section *does* do this. It is a reasonable choice, after all; if we want to accurately sample our input it makes sense to sample in a symmetric manner.\n",
       "\n",
-      "There are many published ways for selecting the sigma points. For now I will stick with the original implementation by Julier and Uhlmann. This method defines a constant kappa ($\\kappa$) which controls how spread out the sigma points are. Before we work through the derivation, let's just look at some examples. I will use the class UnscentedKalmanFilter from FilterPy to generate the sigma points for us. It is convention to import UnscentedKalmanFilter as UKF - UKF is the standarized shorthand used in the literature for this filter.\n",
+      "There are many published ways for selecting the sigma points. For now I will stick with the original implementation by Julier and Uhlmann [2]. This method defines a constant kappa ($\\kappa$) which controls how spread out the sigma points are. Before we work through the derivation, let's just look at some examples. I will use the class UnscentedKalmanFilter from FilterPy to generate the sigma points for us. It is convention to write\n",
       "\n",
-      "I will plot the sigma points on top of a covariance ellipse showing the first and second deviations, and size them based on the weights assigned to them."
+      "    import UnscentedKalmanFilter as UKF\n",
+      "    \n",
+      "because UKF is the standarized shorthand used in the literature for this filter.\n",
+      "\n",
+      "I will plot the sigma points on top of a covariance ellipse showing the first and second deviations, and size them based on the weights assigned to them. I am using the covariance ellipse\n",
+      "\n",
+      "$$\\mathbf{P} = \\begin{bmatrix} 4&2\\\\2&4\\end{bmatrix}$$"
      ]
     },
     {
@@ -605,8 +615,8 @@
       "from filterpy.common import plot_covariance_ellipse\n",
       "\n",
       "x = array([0, 0])\n",
-      "P = array([[4, 1],\n",
-      "           [1, 4]])\n",
+      "P = array([[4, 2],\n",
+      "           [2, 4]])\n",
       "\n",
       "sigmas_kp5 = UKF.sigma_points(x=x, P=P, kappa=.5)\n",
       "sigmas_k2 = UKF.sigma_points(x=x, P=P, kappa=2)\n",
@@ -625,9 +635,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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SJqw3yvk8F43vmiR5wiRKyMq8hq1RMA28Crgii0ahV0SA6Szv/tGYTthhvcRL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\nAHyP0AsAAADfI/QCAADA9wi9AAAA8D1CLwAAAHy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ldxALH0sLOuSCl2lMmyxLEBAQJSjrDeMMiqTD1g3W8xLNOIZeIpqoIEqx0XOx\nPdrG8oJe6DreNMsvB95EjBh46XVGQQZHs1FlaQPRzGPoJaKJyXOBCztDbPnbqDiAbRZ35ivLx4vW\ntv0uA++EZbmALCml+HuOwgy26qBqM/QSzTqGXiKamI2uj22vhxg+Fgvcj3e3D2971EeUs6RhkvJ8\nvHhNkSXIBd+5LssFolTA0Sx2biAqAIZeIpqI4SjGZt9FJ2xjtWkWeiveja6PtjeAlw6xzMA7UUII\nyJJc+MALjEsbLMWGY/EYISoChl4i2rM0y3Gx7WJztIlmTS10e7Ktvo+O72KY9LDU0CErDDOTlOWA\nJMmlCIlukKKiVVBjPS9RIRT3mYmIZsbFtodtvwNZTdCoFvdt3o4bou166IYdLNa58cR+yDIBRVKg\nFrzlW5YJBGGOql5Bo1LsDiVE86LYZx0imrquG2JnOMQg7mKlWdwZr2EQYXvgoR200ayp3Fp4nyRp\nDl3RoBf8BYUbpHBUBzXbKHyAJ5oXfKQS0U1L0vzyrmuLDb2wT/5RkmGr52Mn2EG1IsMqcNeJWZek\nApqkQy94T1vPT1E1qpzlJSqQYj5DEdFMWO96aAddaHqGql3MXdeyfBzc26MuDCMv7P0oijjNoSkq\nTK24f+ckzREnEqp6BXWboZeoKBh6ieimuKMYO0MP/Wi84KuoNnsjdMM+MinEQoHrkYtACIE0AzS5\n2PXS7ihFRa+g7hilWJBHNC+Ke9YhoqkRQuBSx8NOsIOFmlrYANMeBuj4LrxkiMWGVug2a0WQpAKq\nrMLQit2yzPVT1PQqGg5neYmKpJjPVEQ0VVv9EbqjAVIEaFSK+Ta1F8bYGfrohh206jqUgtYjF0kY\n5zAUA6ZezGMGAMI4g8gVVAybu7ARFQzP8kR0Q6Ikw3bfx07QxvKCUcjZ0TTLsdUP0Ak7qDlKofsK\nF0kU5zAVA5ZR3NDrjlJUjRrqnOUlKhye6YnohlzqeNgZdWBbgGUUcwX+Vn+EXtCDrKSoOsUNYEUi\nhECU5DAVE7Ze3Nppb5ShqlXRrJjTHgoR3SCGXiK6bn0/Qsd14aUDLNaL+dZu34vQH/nwUxetWnHD\nV9FEsYAm67AMrbCt7fwghSoZqJpmoWerieZVMc88RHTgdlt7bQc7aNU1KAXcnjdKM+wMfbSDNhaq\nGrcYPkBBlMJSTdhGcV9o9L0Udb2OZpWzvERFxNBLRNdlszdCZ9SHJMeoOcULLgICWz0f3aAH0wQ3\noDhgQZQQFI6mAAAgAElEQVTDUm1UrWK+QxDFOeJYQt2oMvQSFRRDLxFdUxCl2B546IRtLC0UcwFP\n143QDzxEYoRGpXihvciCKIMi6XB0HUZBd2LrewnqRh3NmgVF5lMnURHxkUtE17Te9dAJuqg6ciE7\nHcRphq47Qi/soVnTZ35DgVzkyEU+7WFMTBBkcFQbFauYL5iyTMAPcjSMOhZr1rSHQ0Q3iZX4RPSG\nvCBGzx/BT4c4tljMJ/ydQYB+NICpY2ZDeyIS7AQ7OD88j3bYBgAsmos4XjuOJWsJmlTM2ek8Fwji\nHA3bRtUq5n0YeAkqWhUNxyrsTDURMfQS0TVs9kboBB00qhqUGZ8hvRI3jNEfjTBKPaw2Z3Om8aJ/\nEd948Rv4Tvs7V/z5Wxffivfe8l4ccY4c8Mj2LoxyaLIB29Shq8ULjEIIDPwUh50GlurFfNFHRGMM\nvUR0VQM/Qs/3EOY+VpziPeHnQqDdD9CLeqg56kx2a3i+/zw+98znkIoUAGAoBtYqawCAS94lxFmM\n77S/g2c7z+JDb/sQbm3cOs3h3jBvlKKiLaBW0N3Lhn4KXbZQtyw4ZjFnqolojKGXiK5qs+ejHbax\nUNNmvg72SrpuiH7oAlKCij17s7wX/YuXA6+lWnhP8z8h757E+f93CQDwMz+6A3n5BXyz+zcI0gCf\ne+Zz+J2f+p3CzPjGcY40k1BxKoXt2tD3EiwZS1jkLC9R4e25uO3ixYt497vfDcdx8JM/+ZP43ve+\nN4lxEdGUdd0Q/cBHIkLUC7hrWZrl6HkBBtEACzO4CUUiEnzjxW9cDry/2Pxd/J8vvgv/82+P4nv/\nauJ7/2rif/7tUfyfL74Lv9j8XViqhVSkl3+nCIajFFW9ioajQy7gdtV+mELKddTNChrcdpio8PYc\nen/7t38bb33rW9HtdvH+978f73//+ycxLiKaIiEEtnojtIM2WjUdUgEDS9cNMYxdGAaga7O3eG0n\n2Llcw/ue5n/C1774ZoTB68cZBjK+9sU34z3N/wgA+E77O9gOtg90rDcjTXNEsUBFq6A+g7Ps16Pv\npqgbdbRqZiEfA0T0ant6JhgOh/j7v/97fOITn4BhGPjYxz6G8+fP47vf/e6kxkdEU9AZhuiHQwgp\nRrWAs7xxmqHnB3BjF/XKbI7//PA8gHENb949ecXAuysMZIjuKeiK/qrfnWVukMLRHNQdo5DbDodx\nhjiW0DBqaNW4GQVRGezp2eAHP/gBTNOE4zh417vehQcffBCnTp3Cv/3bv+H06dOvumyr1drTQGeR\npo3fMi3jfaPJK8rxkucClwbbiNQYt6y0ULFnrzTgWtbbLlI1Q6tZwUJj9mYZc5Ffbku2Vlm7XMP7\nRs49u4S1/7CGc4NzaIdt2I4NWZrNMJnlOXqejEMLazh2qAnTmM0XHm/E3Rrh+HILb1pdw/JSbdrD\neUNFObfQ9M37sbKnM5Hv+6hUKnBdF8899xx6vR6q1Sp833/dZR944IHLn995552466679nLTRLRP\ntno+uqM+ZDVFxXamPZwbFsUp+l4AL3FxuMEZumkYeCkcrYK6YxYy8I7CFHEk4VhzAYealWkPh4je\nwKOPPorHHnvs8td33333VS+7p7OR4zjwPA9HjhxBuz2etXBdF5XK608SH/7wh1/1dafT2ctNz4Td\nV0pluC+0/4pwvKRZjudf6uLF/otYWVQx6BdjwdQrbfZ8XOptQ9JCBMHs7mq2aC4CGLcl+5kf3cH3\n/vXoG17+xI/u4O+99cu/O/JH+z7Gm5GmOXa6CQ7ZFehSikF/MO0h3bCLWwFq6hJ0JOj3e9MezjUV\n4dxCs6GMx8rp06dfVV3w3HPPXfWye3pv7E1vehOCIMClS5cAAHEc4+zZs7jtttv2crVENCWdYYhe\nOICuC1hG8TYSSLIMg1EIP/VQs2d7hvF47TgAIM5iyM0XYFpXD+imlUNqnkWcxa/63Vk09NPx4jXH\nhFHAzSj8IEWeaWjadW5GQVQyewq9tVoNP/uzP4tPfepTCMMQn/nMZ3D8+PHX1fMS0ewTQqDjBuhH\nfTSqxavjBYC+F8FLfFi6DGXGF08tWUt46+JbAQDf7P4N7r3v+1cMvqaV4977vo9vdr8EYLw727K1\nfKBjvV5JmiOIBKp6Fc1qMUtL2oMYLauJ5bpdyN7URHR1e54K+fznP49f+7VfQ7PZxO23346//du/\nncS4iOiA9bwIg9CDJKewzeLNcOVCYDhK4MUuFhdme5YXADRJw3tveS+e7TyLIA3wP7r/N37mvv8I\n0T2Fc8+OF7ad+NEdSM2z+B/dLyFIA6iSivfe8l6o0mzev76XoqrXsOBYhdxy2PVTyMLAgsWODURl\ntOcz55EjR/DII49MYChENE3tQYB+1MNCQWd5h34ML/agqmIm+/JeyRHnCD70tg/hc898DkEa4KHt\nv4ShGDj0Hw4BAP7eW0e8PS5pUCUVH3rbh2Z2N7YgzJDGElaqtULO8goh0BnGWLEOY7lhsy8vUQnN\n5nQBER0oL4gxCEaI8xAVu3izvADQ90O4iYfqjPblvZpbG7fid37qd/CNF7+B77S/gyiLcG5w7lWX\neeviW/HeW947s4E3zwV6XoKmuYRW1SpkX96Bn0KTLCxYlUKGdiK6tmI9OxDRvtgZjGt56xW1kDNc\nYZzCjyNkIoZtFi+wHHGO4Dff8pvYCXZwfnj+cg/fRXMRx2vHsWwtz2xJAwC4oxQGLNQtG43K7PVF\nvpY8F+gNE6zZK1hZsKc9HCLaJ7N7FiWiAxHGKfp+AD/1cNwpXmAEgGEQI0hHsM3i1ZHu0iQNa/Ya\n1uw12M44eM1qW7JXStIc7ijDIbuB5XoxA2PfS2DJDhacCupO8UI7EV2f4r0HRUQT1RmG6McDVGwZ\nilK8WV4BATdI4Cd+oUPvK8mSPLO7rb1Wb5igoTfQrNgw9eLNo6RZjoGboWm1sMpZXqJSK8ZZlYj2\nRZrl6LoBBuEAjUoxF7CNwhRBEkKS88IsYCsLd5RC5BpqZg2tejHfJWj3Y9T0OharFVQsfdrDIaJ9\nxGcIojnWGYboR0OYBgobGN0ghp/4cEoyy1sUSZpj6GVomk2sNCwoBZmZfiU/TBGGEhatFtZaxdty\nm4huTPHOUkQ0EWXYjEJAwA9TBElQmtKGIhBCoNNPUNcbaFVsVMzizZAKIbDTi7FkL2O16RSyrzAR\n3RiGXqI55QYJ3GgEyElhA2MYZwjTELKSQ1V5OjsoAy+FAgMLVhVLBV281hsmMCQHLbuGxVox2/QR\n0Y3hswTRnOp7IdzYRc0p5iwvAPhhgiALYRo8lR2UKM4xCgRaVhMrCw7kAra4i9McfS/Dkr2Iw4uV\nQrbpI6Ibx2cKojmU5wIDP4Ibu6hYxZzlBYAgThGlIUy9uPehSPJMoDNMsGA2sVizYRWwWwMA7PRi\nNI0mlmsVOGZxX/QR0Y1h6CWaQ30/ght7MHRAK2hZQC4EwihFnMUwCroIr2jagxiOUsGCXdxdy9xR\niixR0bKbWG1y8RrRPOEzBdEc2g29VbuYM3UAEEQpojyGrkqQZb49vd96bgzkOprWAg4tOJBQvL95\nlgu0+zGW7SUcajqF3C6ZiG4eH/FEcyZJcwxHIYJshEqBQ2+cZoizCJpWvPBVNKMwQxAAi1ar0GGx\nO4jhqDU0nWphZ6qJ6OYV88xFRDet74dwYw+WIUEp8AxpnGZIsoRdG/ZZnOToDVMsWotYbVQKW8cb\nxhm8kcCi1cLhxcq0h0NEU8BnC6I50/ciuPGw0KUNwHgFfiJS6Ay9+ybPBNqDGAvGAharFdQdY9pD\nuilCCGx1I7TMRSzXncIGdyLaGz5bEM2RME7hhiESEcEpcNcGAIiTDEkWQ1OLO1s9y/JcYLu3Ww5Q\nw3KjmP14gfECPAMOlioNrC5w8RrRvGLoJZojuwvYbFMpdG/SJMsQZylkGVzEtg+EEGgPEuiyjSW7\ngbVmMReuAeN6ZM8Hlp1lHFuq8XghmmMMvURzZFza4KLmFPvt3SwTyEVW6JrkWdYbJJAyHUt2C4ea\nFShyMZ8qspdnq1fsFaw1q7CMYh/3RLQ3xTyTEdENC+MUfhQhQwzLKHZpQ5YLZCLnrN0+GHgJklTF\nirOEw60KdLW4x8p2N4Kj1LBYKXZ5BhFNBkMv0ZxwgxijxIdd8MALAJnIkec5mHkny/VTjEbAkrWI\n1QUHhlbcY8X1U8SxghVnCceWq9MeDhHNAIZeojnhBgn8dAS74AvYACDPgBwZCvqu+0xy/RSun2PZ\nXsbqQrG3503SHO1+glV7BWsFn60mosnhUwbRHMhzAW8UI8hGsM3iB4BMCOQQLG+YEHe0G3hXcKhZ\nRd0uZmuyXVudCA2jieV6jZtQENFlDL1Ec8ALE4zSAIZa7A0pdkkSIAEQYtojKT7XT+F648C7tlD8\nwNsdxpCEgSWniSPchIKIXoGhl2gOuEGMUeqXYpYXAGRIkCBBMPXuydD/4Qzv2kK1sJtP7ArjDH03\nw4q9gqOL1cJ2nSCi/cEzAtEc8IMEQRrCKknolWRAgsyZ3j0YeAl8X1xu51X0wJvnAludCEvWMlYa\nVVRtfdpDIqIZw6aFRCWXZjlGcYI4j2Dq1rSHMxGyLEGWJIbemyCEQG+w25ZsEasLlcKXNADAVjeC\npdSw5Iw30yAiei2GXqKS88IEQTqCqcuF3oXtlWRIkCUFWcbUeyPyXKAzSIBMx4rdwlqzWuguDbu6\nwxhpouFwbRnHV2qlOc6JaLIYeolKzg8TjJIAllmeaiZdk6HJKlKG3uuWZwI7/RiaZGOx0sKRVqXQ\nfXh3+WGKgZvjaPUwji3XSnGfiGh/lOdZkIiuyA8ShFlY+F3YXklTFGiKhjwfz17SG0vSHJu9CKZS\nxYrTwtHFcgTeJM2x3Y2x6hzC4WYdNdbxEtEb4EwvUYkJIRAlGeIsgqGVo553l67J0BQdSSpg6Hw7\n+2pGYYb+MEVdb6JVqWGt6ZSiq0GeC2y0IzT1RazU6lhZ4DbDRPTGGHqJSixMMoRpBFVB6TZy0FUZ\nmqwhTiIYevFD3H7ouwlGgcCitYTFagVLdRtySepdt3sRDMnBcrWJo0vsx0tE18ZnCqISi5IMSR5D\n08r3ULcMFZZqIoiyaQ9l5uS5wHYvRhwpWHVWcXihgZWGU5rA23cTxJGKQ5VVHF+ulWLmmoj2H2d6\niUosjFNEeQyjhKHXMTVYioVOKJDn3JJ4VxznaA9j2EoFrUoTh5oOLL08p/pRmKE7THG0cgxHl6ow\nS3TfiGh/8WxBVGJhnCFOY1Ts8oVeRZJhmxrMwEIYpbCt4i/M2gshxHhL4VGOBaOJplPDoQUHqlKe\n//s0y7HVjbBqr2GtWSv8hhpEdLAYeolKLIzTlxexTfahvtsHddrbAFdMDbZmwQt6cx16kzRHd5BA\nEjoOOS20qjZaNRMSyjP7LcR44VpDb2G5VschbkBBRDeIoZeopPJ83LkhRQpNncwGBG7q4uzgLJ5c\nfxIAcMfaHThVP4WqWp3I9d+oiq2holfQd/uI4xz6HC5o80YpBl6Gul5Hw6xhZcGGbRR/w4nX2uhE\n0OFgpdLC8eXpHG9EVGwMvUQltduqTFOkiexQ5aYuHvzug3j43MOXv/fQ2Ydw5sQZ3H/6/qkEX0WS\n0ajoGEZ1DPwBlvT56dOapDn6boosVbBkLaNVcbDUsKBI5Qv+O70IItWxVl3FiRUuXCOim8MzB1FJ\nBXGKMIsmtojt7ODsqwLvrofPPYwXBi9M5DZuRsMxUDMqSJLxIq6yE0Jg6CXY7iYwpCrWqodwdLGO\n1QWnlIG3O4wRBAoOV9ZwYrXOhWtEdNPKd4YkIgDjet5xu7K9z/JKknS5pOFKnlh/YiKzyTdDkWU0\nKgYaRgOdYVzqHdrCKMdmJ0IUalixV3Gk0cKJ5SpqVjkXdA39BENX4HB1DceX63DM8pVtENHB4Utm\nopJKsxxplsJRy//atlk14QVVhGmIvheiWStXmUOa5hh4KaIYWDBbqJsOlhvlakX2Wn6Yot1PcaRy\nFMcW6+zUQER7Vv5nQ6I5leYCGTJMovxRCIE71u646s/fufbOqXZykCBhtemgaS0gDMe9XMsgzwR6\nwxib3RgqKlirHMKRZhPHlmulDrxhnGG7k2DNWcPhZh2tWrm20Cai6WDoJSqpLMuR5TmUCW3acKp+\nCmdOnHnd98+cOIOT9ZMTuY29MFQFSzUHi9YiesMUUYHre3Mh0HNjbHRiILWx5qzhaGMRJ1bqaFbK\n1YrsteI0x0Y7wrK9gkONBluTEdHElHeqgGjOZblALjIo8mTqIKtqFfefvh/3HL0HT6w/AWA8w3uy\nfnJqLcteq1ExEKdV5EKg02+jWddgGsV5bZ9nAl6QoucFsFQHq7aDmm2hVTNhqOXvQ5xlAhs7IZrG\nElZqCziyWJn2kIioRBh6iUoqzQQykUGWJ1ffWlWreFvrbXj74tsBTH9ziitZbtgQEJAkCe3BDuoV\nBRV7tk91cZLDG6UIIgFLtXGoeQh1y4YhJXPTrSDPBdbbISpqA6vVJk4s16a2OJKIymk+zqZEc0YI\nMf4nYWLlDa+9/lm20nAgSRIkSOj6XQRxjFZVg6zMTogSQiCMcrhBhjQBKnoFh5wKqpaJ46st2KaG\nQX8w7WEeCCEENjsRdFRwqLqMW1brkPfhuCWi+ban9/3+7M/+DLfeeitqtRp+7Md+DH/3d383qXER\n0U26cMHEV75awR89UMGD/72KRx6xsblZrm4G12O5buPoYh1rlVVowsZmN8IomP4CtzjO0RvG2GhH\nGHoSHKmBI9XDOLqwhFtWGlhrOrDnrDXXZicCMhNr1VXcslqHqhSnJIWIimNPM72apuGrX/0q3vKW\nt+Cpp57Cz/3cz+Ff/uVfcMstt0xqfER0A555xsYHPlDD0MuBag9oaHjmnxw4To4/+VMft745nPYQ\nD1TNMmDpKvSegkFgo+/3MfBD1BwVtqkc2NvncZIjiDL4YQYpV+DoFSxbDmxdR83WUXP0Um4scT02\nOyHy1MDR6hpOrtZhaOWvXSai6dhT6P34xz9++fN3vOMdOHnyJJ5++mmGXqIpuHDBHAfeoQwoKSBn\ngBiHOt+X8fv/1cFnP5tjdTWe8kgPlqYoOLpYRW2ko+Ka8OIQA7+PgRfBMmRYhgpDn8xWzbuSNEcU\nj/+FcQZZ0mAqFhYNG7ZmomJpqDnGXCxOeyNbnQhZouNo9TBOrtZhGay4I6L9M7EzTK/Xw/PPP4/T\np09P6iqJ6AY8/bQ2DrwAgByQMuAVra18X8a//7s6d6F3V902ULcNDAMTPdeEF0UIswADN0Qmxts1\na6oEVR1/1K6xqYcQAnkukGXjNltJmiNJBZI0hwQFpmLCUAzUbROmqsExNVQsDbYxX6ULV7PVjZAm\nGo5Wj+Dkan3uSjqI6OBNLPR+8IMfxH333Yfbbrvtij9vtVqTuqmZoWnjk3QZ7xtN3n4eL1mW4aGH\nXvlwvvKs5WOP6Xjf+xTIc1wzWW8ARw8BQZTADRJ4owhBnCBMA0RZjDRPMAoTZCIDJECWAEkGdieC\n8wzIhIAkZEiSDFVWoMk6bEuDLuvQFR2aqsI2xwHXMTXoN/iWvaqqL4+1Pum7PxM22gFM3cGxpaO4\n9UgLFWv+as4nic9FdL3m/Vi5Zuj9oz/6I/zxH//x675/77334itf+QoA4Pd///fR6/XwpS996arX\n88ADD1z+/M4778Rdd911M+MlousiAUIBMNtdFqbJMjRYhoblho04yRBEKaIkRZxmiJMMUZJBiBwC\nQI4cQow3u5AlBbIkQ5FkKPJ4ZlhXFZi6CkNTYGgq1DnY+vlmbbQDJKGGY/WjePPhJgMvEe3Jo48+\niscee+zy13ffffdVLyuJPfYe+sxnPoMvfelLeOSRR+A4V94555vf/CZuv/32vdzMTNp9pdTpdKY8\nEiqC/T5evva1Kj7ykZc3iZAToPYSUH8JiH/Y4P8Tn/Bx112jfbn9shEQyIWAyMc7pInxNyHLgCLL\nkPdxEdzuDG/ZWpZtdSIksYoj1cM4udpg4J0QPhfR9ZqHY+W5557De97zniv+bE/lDX/1V3+Fz3/+\n83j88cevGniJ6GD8xE8kqNXyl+t6JUDIeOVMr+PkuO22dGrjKxoJEhRJAuZ7rdnEbHZCZImOY7Uj\nuGW1Doc1vER0wPb0HtwnP/lJnD9/HidPnkS1WkW1WsWnPvWpSY2NiG7AsWMhvvzlIWq1/OWuDRJ2\nQ+9uy7J5XcRG0yOEwEY7RJYYlxetMfAS0TTsaab3hRdemNQ4iGgC3va2Ef73/87x7f9Pwd98PUNg\npvi5e3zcdlvKwEsHLs8FNrsRkJqX+/CySwMRTQubIhKVzLFjIY4cETj1dhcvDlycPDz9Xcho/mTZ\neIZXg4O16ipOHmqwDy8RTRXPQEQlJEnj4oY8Z/cGOnhJmmN9J4Sj1nGosoJbVmswdT7dENF08SxE\nVEKSJEHCeJcxIcSBbbdLFMU51tshmvoiVmstnFipX3OjDyKig8DQS1RSqiJDkRQkmYCuMvTS/huF\nGbY6MZasFazWmzi+XIUiM/AS0Wxg6CUqKVNXoCk6kiSDzpk22meun2Knn2DNWcNqvY6jS1W+w0BE\nM4XPhEQlZWgKDEVHlOTTHgqVXM9N0O5nOOwcwdFWE8eWawy8RDRzONNLVFKmrkKTdYSJN+2hUInt\n9CIEgYJjtaM42qpjsW5Ne0hERFfE0EtUUqamwFQMDELO9NLkCSGw2YmQpzqO1dZwbLmOhmNMe1hE\nRFfF0EtUUsbLNb1xytBLk5VmOTbbEVRh42j1ELcVJqJCYOglKilFlmGoKhRJRZLmbBtFExFEGTY7\nEep6EyvOIk6s1mCxBy8RFQDPVEQlZugKdEVHnGQMvbRnfS9Bd5BixT6E5Wodx5drUBUeV0RUDAy9\nRCVm7nZwiH04XF9UGHk+WyUpQghsd2NEkYKjlWM4tFDFWqsy7WEREd0Qhl6iEqtYOmzVQSccolmf\n9mjoWtzUxdnBWTz1r08BAN6x9g6cqp9CVa1ObUxJmmOjHUGHjWO1FS5YI6LCYuglKrGqpaGi29jw\nx4uP+Fb07HJTFw9+90E8fO7hy9/7+tmv48yJM7j/9P1TCb6jMMNWN8KC3sJypYXjK6zfJaLi4jMg\nUYlJkoSKpaOiOfCCbNrDoTdwdnD2VYF318PnHsYLgxcOfDw9N8FWJ8GqfRhHF5bx5sMNBl4iKjSG\nXqKSq9k6HM2Bz9A7syRJwpPrT17150+sP3FgO5zlucBmJ4TnyThaPYrji03cslqHIvPpgoiKjWcx\nopLbDb1hlCPLxbSHQzMsinO8tB1ASh0crx3FqdUmVhecaQ+LiGgiGHqJSk5VZFQsHaZqYRRytncW\nCSFwx9odV/35O9feCSH29wVL301waSdCQ1vCscYhvGmtgToXrBFRiTD0Es2Bum2golXgB+m0h0JX\ncap+CmdOnHnd98+cOIOT9ZP7drtpluPSTgjXk3G0cgwnF1fw5rUFmKzfJaKS4VmNaA7UnPFito67\ngywXUOSDqQ+l61dVq7j/9P245+g9l+t771i7AyfrJ/etc4M7StHux6jrTSw3WjiyWEXN1vfltoiI\npo2hl2gO6KqCumPBCaoYuAGadQabWVT9/9u7/9i2znqP4x/7+Ng+xz9iO78aJ01pVioIiLagFrqN\njnVCgk1TNwRDIE2o6h9IaIh/ENxbdLdS4F4EAioYFHXsTgwJ7ZYfqwR02h8TY4NRFbVTK8Em2one\nLkmTpvnp+Nexfc79I0u4pUuXpGnsHL9fkuXjYzv+Jnn0+OPHz3lOKKEtrVt0+8bbJUm56dxNeZ2a\n6+nKhKNSKaBsrEftyaR62uIsaQfA1wi9QJPoTNkan0lrMJ9TKmEqyGhvwwrexJUSiuWahsfKioWS\neltLu7rbEsokojft9QCgURB6gSYRi5pKx2xNlOKanCkpk2S0t5l4nqexqYpyeVcddpfa4y1a355Q\nxDTqXRoArApCL9BE5kZ7h2ZeVyrOaG+zKDuuRsbLCslSb7JD2XRSHSlr1db+BYBGQOgFmkjcCisd\ni2m8FNN03lEqYda7JNxErutpfLqi6XxNrdE2tcfS6m1PyI7yfwfQfAi9QJPpSNmayKc1lBtQSzzE\naJ9PzRSqGp10ZBtxbUi2qaMlrq50jNF9AE2L0As0maQdVsqOaaxkayLnMLfXZypVV6OTjirloDrt\nrFpjCfW0JWRF6O4BNDd6QaAJrUvHlCu162LuouxoTdEwBzOtdZ7naSJX0WSuplQkrZ5URl2ZmFqT\nVr1LA4CGQOgFmlDCDqszlVCx2qGRsRGt77T42nsNK5Zrujwxe6Da+ni32pMxdWXiMkOsuwsAcwi9\nQJPKZmLKFysqVPK6MlVURzpS75KwRLWapyuTjgolqd3qVGusRd2tccUtpqwAwL8i9AJNKhAIaH1H\nQgWnXRenLyofrSpm0SU0ArfmXvd+z/M0NVPVRK6ieKhFG1ta1ZmKswwZAFwH73BAE7PCIXW3JlSu\nrdPwxKB6w4YMg9BUL8PDYb36akgvvjg7x/qDH7T1jndUtW6dM/+Y6XxFY1MVRYK2svY6tSbi6m6N\nc5IJAHgLhF6gybW32Mq9Mc1hZHxa2XZOSVsPfz8X1f5/jymf/+c83JdeMhWLufrP/8or2z2jsSlH\nhiLqsruVtuPqTMeUtJnKAACLwVEOALS+LaEOu01eLaKRsXK9y2k6w8PhawLvnHzB1b/9h6e/nzfV\nFu1SX3qD3t7Vrrd3pwm8ALAEjPQCkBkKauO6Frny9Pr0gEYnymrnwLZV8+qroWsDb6AqhUqSZ6g4\nmlV+wNTm7Z4yiSjzdgFgGRjpBSBJsqOmNna2qCfRrVIppNFJRnxXQyAY0Isv/r8R20BNMvNSqCwV\n2qXxTVKuW6df6FZbi03gBYBlYqQXwLy4FdbbOlNyPU+DM0O67JVZymy1BKqzQTfgSYVWqZiRnPjs\nRbpiUOgAAA2DSURBVEEFAs5b/ggAwMIY6QVwlaQdVt+6tNYnuuWUQhoZK8vzvHqX5Uue52l6pqL+\nrROSUZkNu+ObpOn1Ur5TcpKa66bvvZf/AwDcCEZ6AVwjaYd1S1dawUBQA7khDV4uqqM1ojBn+FoR\nNdfTdL6qyVxFZiCibW/vUMzpUn46IVVsybt6+bFk0tW2bZU6VQsA/sA7GIA3FYuauqUrpQ0tPYob\nGQ2OlDWdJ3jdiErV1ehEWf97qSinEFHW7tEtmQ3atTWt//lvS8lo7E0D71NPTau3t1SnqgHAHxjp\nBbAgKxLS5p6U7CshWVO2hqeGlS+W1JGJyAhyQNViFcs1TeYqKpY9JcNJ9SZSSsVstSctJd5Ydiyz\ntahnn/X08sumfvOb2XnU995b1rZtFQIvAKwAQi+A6zKCQW3oSCphhWWNRTSSH9XrI9PqSIdlRzkL\n2EKcqquZfFW5QlVyQ0pFMupqSSoTt9TaYskKX9v99vaW1Ntb0mc+M/t3nZzMrXbZAOBbhF4Ai5JJ\nRBW3TEUvh3RlJqaRsRHF7KoySVMhg5lSklSrecoVq8rlq6pUAkqEE1pnJRSLWMokompLWov6WxkG\nHyYAYKURegEsWjhkaFM2pcREWNGJiMaK47o4PK24HVQ6YcpswgPdXNdTvlhTrlBV0XEVD8WVCbcp\nEY8rFQsrFY8obnHmNACoN0IvgCXrTNtKxsIanbQ0NpPWZGlSF0cmFY8aSreYvl/lwfM8Fco1zeRr\nypdqihiWEmZKXcm4WmJRpeIRJa2wgsx7BoCGQegFsCxWOKTejqQ6UrZGp2Iay6U1UZ7UwMik7GhA\n6URYkbB/wq9TdVUo1lQo1VR0XEWCESXMtNoScSWsqNLxiFpiEaZ6AECDuuHQOzExoc2bN+sjH/mI\nfvazn61ETQDWkGg4pPXtCXWkbF2ZiunKdFqT5SkNjU4qGKwpZoUUswxZkbU1T7Vac1UsuSqUayqW\na/JcQ3bIVtK01ZmwFY+GlbQjSsUjiphr63cDgGZ0w6F3//796uvr43zwQJOLmIa62+LqSNm6PGVr\nciajglPUTDWv0bG8airIjhqKW4bsqNFQfYbrenKqrpyKq5LjqliqqVoLyApZskMJpSxbdjiqhGUq\nYYeVsMKM6ALAGnNDoffUqVO6cOGC7r77bp0/f36lagKwhpmhoLpb4+pujStfqmgqX9Z0wVG+XFa+\nMqOJyRkNu0VZ4aDCZlBhMzB7HQquyhxYp+KqXHFVeePaqbiq1iQzGFY4GFY0FFWLZckyLcWiIcUt\nU/FoWFaE2WAAsJYtuxf3PE9f+MIXdOTIER09enQlawLgE7GoqVjUVLZVKjpV5Qotmi44yhXLKtWK\nKlcdFcuOJmuOKm5RwaAUMYMyzYDCoaBCRkCBQEDBgBQIBBQIan577lqaXSqs5nlya55q7uzFdTW/\nXat5qlQ9VWqeQgrJNMKKGGHFjYjCVljR0OwUhWjYUDQcUjxqyoqEGmo0GgBwY5Ydeh9//HG95z3v\nUX9//6LeGFpbW5f7Ug3LNE1J/vzdsPJoL/9UqdaUL1VULFdVqlRVdqoqOVWVqxWVq2U5blnlmqOa\nW5PnefLkyvU8uZ4rz5vd9uTKkydJCiokI2goFDQUDBgyAkFFzDe2g4aMgKGwEVbEiCgaDs1frEhI\n1hvbjRRwaStYCtoLFqvZ28p1Q++BAwd08ODBa/Z/6EMf0sWLF/XnP/9Z0uyo71v52te+Nr+9a9cu\n3XHHHUutFYBPmCFDqbihVPzq/WVnNgQXy7MhuOZ68jxPruvNht656/ltKSDJMAIKBYMyjKBCRlBG\nMDC7HXzjthFQxDRkhU2WEQMAH/nDH/6gF154Yf72nXfeueBjA95iEuu/OHPmjLZt23bN/q1bt+r0\n6dPX7H/uuef0zne+c6kv0/DmPimNjY3VuRKsBbQXLBZtBUtBe8FiNUNbeeWVV3TXXXe96X3Lmt6w\nZcsWua47f/urX/2qXnvtNT355JPLqxAAAAC4iVhzBwAAAL63ImvwPPLIIyvxYwAAAICbgpFeAAAA\n+B6hFwAAAL5H6AUAAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H6AUAAIDvEXoBAADge4ReAAAA+B6h\nFwAAAL5H6AUAAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H6AUAAIDvEXoBAADge4ReAAAA+B6hFwAA\nAL5H6AUAAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H6AUAAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H\n6AUAAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H6AUAAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H6AUA\nAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H6AUAAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H6AUAAIDv\nEXoBAADge4ReAAAA+B6hFwAAAL53Q6H30KFD2rBhgxKJhN73vvfJ87yVqgsAAABYMcsOvU899ZS+\n+93v6tixY8rlcvrpT3+qQCCwkrWtCa+88kq9S8AaQnvBYtFWsBS0FyxWM7eVZYfeH//4x9q/f7+2\nbdsmSXr3u9+9YkWtJc3ceLB0tBcsFm0FS0F7wWI1c1tZdug9e/asRkZGtGnTJm3YsEEHDhxYwbIA\nAACAlRNa7hOnpqb0zDPP6MSJEyoWi9q1a5e2bt2q++67700f39rauuwiG5Vpmtq9e7dSqVS9S8Ea\nQHvBYtFWsBS0FyxWs7eV64beAwcO6ODBg9fs37Nnj2KxmPbu3au2tjZJ0sc+9jE9//zzC4beP/7x\njytQLgAAALB0bxl6F5q2MDeXd871Vm646667ll4ZAAAAsEKWPaf3/vvv109+8hONj49raGhIx44d\n05133rmStQEAAAArYtlzer/85S/rH//4h/r6+mTbtj772c9qz549K1kbAAAAsCICHmeUAAAAgM9x\nGmIAAAD4HqEXAAAAvrfsOb3NbGhoSE888YTOnz8v27b1wx/+8Kr7jx8/rqefflrValUf/vCH9elP\nf7pOlaLRHD16VE8//bRM05QkJZNJPfroo3WuCo1kbGxMP/jBD/Taa68pm83qoYce0vr16+tdFhrU\ngQMHdO7cORmGIUnasWOHHnrooTpXhUbwl7/8RceOHdOFCxd022236XOf+5wkqVqt6rHHHtOJEycU\ni8X04IMPaufOnXWudnUQepfBMAzdfvvt+sAHPqBf//rXV9137tw5/fKXv9TBgwdl27Yefvhhbdy4\nsWkaFK4vEAjotttu400JCzpy5Ih6e3v1la98RcePH9ehQ4f0ne98p95loUEFAgHt27dPu3fvrncp\naDCxWEx79uzR2bNn5TjO/P7f/e53GhgY0OHDh3XhwgV985vf1ObNm315ErF/xfSGZejs7NQdd9yh\n9vb2a+47ceKE3v/+96unp0eZTEa7d+/Wn/70pzpUiUbked5117RGcysUCjp79qzuu+8+maape+65\nR6Ojo7p48WK9SwOwxvT392vHjh2Kx+NX7T9x4oQ++tGPyrZt9ff3a/PmzTp58mSdqlxdhN4VdunS\nJWWzWR0/flxPPvmkenp6dOnSpXqXhQYRCAR06tQp7du3T1/60pd06tSpepeEBjI8PCzTNBWNRvXw\nww/r8uXL6uzs1NDQUL1LQwP7+c9/rn379unrX/+6BgcH610OGtzQ0JCy2ay+//3v66WXXlJPT0/T\n9DGE3hVWLpcVjUY1MjKi4eFhWZalUqlU77LQIG699VY9+uijeuyxx/Txj39chw4daprOBm9trv8o\nFosaHBzUzMwMfQiu68EHH9Thw4f1ox/9SH19ffrWt76lWq1W77LQwOb6mddff13j4+OKRqNN08cw\np3cBR48e1a9+9atr9m/fvl1f/OIXF3xeJBJRqVTS3r17JUknT55UNBq9aXWi8Sy27ezYsUPvete7\ndObMGWWz2dUsEQ1qrv9obW3V448/LkkqFov0IVhQX1/f/PanPvUpPfvssxocHFRvb28dq0Ijm+tn\nvv3tb0uSnnjiCVmWVeeqVgehdwEPPPCAHnjggSU/r6ur66qvlwYGBgg0TWa5bQdYt26dHMfR+Pi4\nMpmMqtWqRkZG6EMArJhsNqvBwcH5D0wDAwPavn17nataHUxvWCbHcea/QqpUKqpWq5KknTt36uTJ\nkxoYGND4+Lh+//vf69Zbb61nqWggJ0+eVD6fl+u6On36tP72t79py5Yt9S4LDcK2bW3ZskXHjh2T\n4zj67W9/q/b2dkbt8KYKhYJefvllVSoVVSoV/eIXv1AqlVJPT0+9S0MDcF1XjuPIdV25rqtKpaJa\nraadO3fqmWeeUaFQ0F//+ledO3dOO3bsqHe5q4LTEC/D5cuX9fnPf/6qff39/XrkkUcksU4vFva9\n731PZ86ckeu66urq0ic/+Um9973vrXdZaCBz6/SeP39e3d3drNOLBU1PT+sb3/iGLl26JMMwtGnT\nJu3du5dvBiBJev7553X48OGr9n3iE5/Q/fffryNHjjTlOr2EXgAAAPge0xsAAADge4ReAAAA+B6h\nFwAAAL5H6AUAAIDvEXoBAADge4ReAAAA+B6hFwAAAL5H6AUAAIDvEXoBAADge/8HqE4UwHbMyq0A\nAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f51c0550>"
+        "<matplotlib.figure.Figure at 0x7f576af206a0>"
        ]
       }
      ],
@@ -637,7 +647,42 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "We can see that the sigma points lie between the first and second deviation, and that the larger $\\kappa$ spreads the points out further. Furthermore, the larger $\\kappa$ weighs the meaan (center point) higher than the smaller $\\kappa$, and weighs the rest of the sigma points less. This should fit our intuition - the further a point is from the mean the less we should weight it. We don't know *how* these weights and sigma points are selected yet, but the choices look reasonable."
+      "We can see that the sigma points lie between the first and second deviation, and that the larger $\\kappa$ spreads the points out further. Furthermore, the larger $\\kappa$ weighs the mean (center point) higher than the smaller $\\kappa$, and weighs the rest of the sigma points less. This should fit our intuition - the further a point is from the mean the less we should weight it. We don't know *how* these weights and sigma points are selected yet, but the choices look reasonable."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Choosing Sigma Points\n",
+      "\n",
+      "As I already said there are several published algorithms for choosing the sigma points. I will present the method first chosen by the inventors of the UKF. \n",
+      "\n",
+      "Our first sigma point is always going to be the mean of our input. We will call this $\\mathcal{X}_0$. So,\n",
+      "\n",
+      "$$ \\mathcal{X}_0 = \\mu$$\n",
+      "\n",
+      "Tne corresponding weight for this sigma point is\n",
+      "$$\n",
+      "W_0 = \\frac{\\kappa}{n+\\kappa}\n",
+      "$$\n",
+      "where $n$ is the dimension of the problem, and $\\kappa$ is a scaling factor that will be discussed in a moment.\n",
+      "\n",
+      "The rest of the sigma points are defined to be\n",
+      "\n",
+      "\n",
+      "$$ \n",
+      "\\begin{aligned}\n",
+      "\\mathcal{X}_i &= \\mu + (\\sqrt{(n+\\kappa)\\Sigma})_i\\,\\,\\,\\, &\\text{for}\\text{ i=1 .. n} \\\\\n",
+      "\\mathcal{X}_i &= \\mu - (\\sqrt{(n+\\kappa)\\Sigma})_{i-n}\\,\\,\\,\\,\\, &\\text{for}\\text{ i=(n+1) .. 2n} \\\\\n",
+      "\\text{and the corresponding weights are} \\\\\n",
+      "W_i &= \\frac{1}{2(n+\\kappa)}\\,\\,\\,\\text{for i=1,2..2n}\n",
+      "\\end{aligned}\n",
+      "$$\n",
+      "\n",
+      "$\\kappa$ (kappa) is a scaling factor that controls how far away from the mean we want the points to be. A larger kappa will choose points further away from the mean, and a smaller kappa will choose points nearer the mean. Julier and Uhlmann suggest using $\\kappa + n = 3$ if the distribution is Gaussian, and perhaps choosing a different value if it is not Gaussian. \n",
+      "\n",
+      "It may not be obvious why this is 'correct', and indeed, it cannot be proven that this is ideal for all nonlinear problems. But you can see that we are choosing the sigma points proportional to the square root of the covariance matrix, and the square root of variance is standard deviation. So, the sigma points are spread roughly according to 1 standard deviation. However, there is an $n$ term in there - the more dimensions there are the more the points will be spread out and weighed less."
      ]
     },
     {
@@ -652,71 +697,92 @@
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "**author's note:** needs rewrite, we already explained what kappa is\n",
+      "The unscented transform is the core of the algorithm yet it is remarkably simple. Given a set of sigma points and a transfer function we need to compute the new mean and covariance of the points after they have passed through the function. In other words, loop through the sigma points and compute their new position after passing through your nonlinear function. Then just compute the mean and covariance of these points.\n",
       "\n",
-      "So our desire is to have an algorithm for selecting sigma points based on some criteria. Maybe we know something about our nonlinear problem, and we know we want our sigma points to be very close together, or very far apart. Or through experimentation we decide that a certain choice of basis vectors from our hyperellipse are the best axis to choose our sigma points from. But we want this to be an algorithm - we don't want to have to hard code in a specific selection algorithm for each different problem. So we are going to want to be able to set some parameters to tell the algorithm how to automatically select the points and weights for us. That may seem a bit abstract, so let's just launch into it, and try to develop an intuitive understanding as we go.\n",
-      "\n",
-      "Assume a n-dimensional state variable $\\mathbf{x}$ with mean $\\mu$ and covariance $\\Sigma$. We want to choose $2n+1$ sigma points to approximate the Gaussian distribution of $\\mathbf{x}$.\n",
-      "\n",
-      "Our first sigma point is always going to be the mean of our input. We will call this $\\mathcal{X}_0$. So,\n",
-      "\n",
-      "$$ \\mathcal{X}_0 = \\mu$$\n",
-      "\n",
-      "Tne corresponding weight for this sigma point is\n",
-      "$$\n",
-      "W_0 = \\frac{\\kappa}{n+\\kappa}\n",
-      "$$\n",
-      "where $n$ is the dimension of the problem, and $\\kappa$ is a scaling factor that will be discussed in a moment.\n",
-      "\n",
-      "So for each dimension we need to select 2 more points. We want them to be symmetric around the mean so that for the linear case they cancel out and we are just left with the mean as the result. Here is how we are going to do that:\n",
-      "\n",
-      "\n",
-      "$$ \n",
-      "\\begin{aligned}\n",
-      "\\mathcal{X}_i &= \\mu + (\\sqrt{(n+\\kappa)\\Sigma})_i\\,\\,\\,\\, &\\text{for}\\text{ i=1 .. n} \\\\\n",
-      "\\mathcal{X}_i &= \\mu - (\\sqrt{(n+\\kappa)\\Sigma})_{i-n}\\,\\,\\,\\,\\, &\\text{for}\\text{ i=(n+1) .. 2n} \\\\\n",
-      "\\text{and the corresponding weights are} \\\\\n",
-      "W_i &= \\frac{1}{2(n+\\kappa)}\\,\\,\\,\\text{for i=1,2..2n}\n",
-      "\\end{aligned}\n",
-      "$$\n",
-      "\n",
-      "$\\kappa$ (kappa) is a scaling factor that controls how far away from the mean we want the points to be. A larger kappa will choose points further away from the mean, and a smaller kappa will choose points nearer the mean. Julier and Uhlmann suggest using $\\kappa + n = 3$ if the distribution is Gaussian, and perhaps choosing a different value if it is not Gaussian. So in one dimension we get something like the following. Here I have plotted two different choices for kappa to show how kappa affects the distribution of the points."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "ukf_internal.show_sigmas_for_2_kappas()"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [
-      {
-       "metadata": {},
-       "output_type": "display_data",
-       "png": 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LTBN3qXVyaXqWEhGxwZ9L9LDwlsx8rJduNlIBnYMasOKlAQx7fS0b955SmRa5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\n/T3pElidJxpX06IpIiIiFqSMjXV+qX7nH605eDKRDfvOsul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-       "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f51e9a90>"
-       ]
-      }
-     ],
-     "prompt_number": 10
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "The remainder of the algorithm follows from our equations above:\n",
+      "In other words, we just compute the mean and covariance from the equations above:\n",
       "\n",
       "$$\\begin{aligned}\n",
       "\\mu &= \\sum_i w_i\\mathcal{X}_i\\;\\;\\;&(2) \\\\\n",
       "\\Sigma &= \\sum_i w_i{(\\mathcal{X}_i-\\mu)(\\mathcal{X}_i-\\mu)^\\mathsf{T}}\\;\\;\\;&(3)\n",
       "\\end{aligned}\n",
-      "$$\n",
-      " \n",
+      "$$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## The Unscented Filter\n",
       "\n",
-      "In other words, we generate sigma points from an existing state variable from its mean and covariance matrix. We pass those sigma points through the nonlinear function that we are trying to filter. Then we use equations (2) and (3) to regenerate an approximation for the mean and covariance of the output."
+      "Now we can present the entire Unscented Kalman filter algorithm. Assume that there is a function $f(x)$ that performs the state transition for our filer - it predicts the next state given the current state. Also assume there is a measurement function $h(x)$  - it takes the current state and computes what measurement that state corresponds to. These are just nonlinear forms of the $\\mathbf{F}$ and $\\mathbf{H}$ matrices used by the linear Kalman filter.\n",
+      "\n",
+      "### Predict Step\n",
+      "For the predict step, we will generate the weights and sigma points as specified above.\n",
+      "$$\\mathcal{X} = sigma\\_function(\\mu, \\Sigma) \\\\\n",
+      "W = weight\\_function(n, \\kappa)$$\n",
+      "\n",
+      "We pass each sigma point through the function f.\n",
+      "\n",
+      "$$\\mathcal{X_f} = f(\\mathcal{X})$$\n",
+      "\n",
+      "Then we compute the predicted mean and covariance using the unscented transform. I've dropped the subscript $i$ for readability, and switched to the roman letters x and P for the mean and covariance.\n",
+      "\n",
+      "$$\\begin{aligned}\n",
+      "\\mathbf{x}^- &= \\sum W\\mathcal{X_f} \\\\\n",
+      "\\mathbf{P}^- &= \\sum W{(\\mathcal{X_f}-x^-)(\\mathcal{X_f}-x^-)^\\mathsf{T}} + \\mathbf{Q}\n",
+      "\\end{aligned}\n",
+      "$$\n",
+      "\n",
+      "This corresponds with the linear Kalman filter equations of\n",
+      "$$ \\begin{aligned}\n",
+      "\\mathbf{x}^- &= \\mathbf{Fx}\\\\\n",
+      "\\mathbf{P}^- &= \\mathbf{FPF}^T+\\mathbf{Q}\n",
+      "\\end{aligned}$$"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Update Step\n",
+      "Now we can perform the update step of the filter. Recall that Kalman filters perform the update state in measurement space. So, the first thing we do is convert the sigma points into measurements using the $h(x)$ function.\n",
+      "\n",
+      "\n",
+      "$$\\mathcal{X_z} = h(\\mathcal{X})$$\n",
+      "\n",
+      "Now we can compute the mean and covariance of these points using the unscented transform.\n",
+      "\n",
+      "$$\\begin{aligned}\n",
+      "\\mathbf{x}_z &= \\sum w\\mathcal{X_z} \\\\\n",
+      "\\mathbf{P}_z &= \\sum w{(\\mathcal{X_z}-x)(\\mathcal{X_z}-x)^\\mathsf{T}} + \\mathbf{R}\n",
+      "\\end{aligned}\n",
+      "$$\n",
+      "\n",
+      "The $z$ subscript denotes that these are the mean and covariance for the measurements.\n",
+      "\n",
+      "All that is left is to compute the residual and Kalman gain. \n",
+      "\n",
+      "The residual is trivial to compute:\n",
+      "\n",
+      "$$ \\mathbf{y} = \\mathbf{z} - \\mathbf{x}_z$$\n",
+      "\n",
+      "\n",
+      "The Kalman gain is not much worse. We have to compute the cross variance of the state and the measurements, which we state without proof to be: \n",
+      "\n",
+      "$$\\mathbf{P}_{xz} =\\sum W(\\mathcal{X}-x)(\\mathcal{X_z}-\\mathbf{x}_z)^\\mathsf{T}$$\n",
+      "\n",
+      "And then the Kalman gain is defined as\n",
+      "$$K = \\mathbf{P}_{xz} \\mathbf{P}_z^{-1}$$\n",
+      "\n",
+      "Finally, we compute the new state estimate using the residual and Kalman gain:\n",
+      "\n",
+      "$$\\hat{\\mathbf{x}} = \\mathbf{x}^- + \\mathbf{Ky}$$\n",
+      "\n",
+      "and the new covariance is computed as:\n",
+      "\n",
+      "$$ \\mathbf{P} = \\mathbf{P}^- - \\mathbf{PKP}_z\\mathbf{K}^\\mathsf{T}$$\n",
+      "\n",
+      "This step contains a few equations you have to take on faith, but you should be able to see how they relate to the linear Kalman filter equations. We convert the mean and covariance into measurement space, add the measurement error into the measurement covariance, compute the residual and kalman gain, compute the new state estimate as the old estimate plus the residual times the Kalman gain, and then convert both back into state space. The linear algrebra is slightly different from the linear Kalman filter, but the algorithm is the same."
      ]
     },
     {
@@ -724,255 +790,14 @@
      "level": 2,
      "metadata": {},
      "source": [
-      "Implementation"
+      "Using the UKF"
      ]
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "So let's just implement this algorithm. First, let's write the code to compute the mean and covariance given the sigma points. \n",
-      "\n",
-      "So we will store the sigma points and weights in matrices, like so:\n",
-      "\n",
-      "$$ \n",
-      "\\begin{aligned}\n",
-      "weights &= \n",
-      "\\begin{bmatrix}\n",
-      "w_1&w_2& \\dots & w_{2n+1}\n",
-      "\\end{bmatrix} \n",
-      "\\\\\n",
-      "sigmas &= \n",
-      "\\begin{bmatrix}\n",
-      "\\mathcal{X}_{0,0} & \\mathcal{X}_{0,1} &  \\mathcal{X}_{0,2} \\\\\n",
-      "\\mathcal{X}_{1,0} & \\mathcal{X}_{1,1} &  \\mathcal{X}_{1,2} \\\\\n",
-      "\\vdots & \\vdots & \\vdots \\\\\n",
-      "\\mathcal{X}_{2n+1,0} & \\mathcal{X}_{2n+1,1} & \\mathcal{X}_{2n+1,2}\n",
-      "\\end{bmatrix}\n",
-      "\\end{aligned}\n",
-      "$$\n",
-      "\n",
-      "In other words, each column contains the $2n+1$ sigma points for one dimension in our problem. The $0th$ sigma point is always the mean, so first row of sigma's contains the mean of each of our dimensions. The second through nth row contains the $\\mu+\\sqrt{(n+\\lambda)\\Sigma}$ terms, and the $n+1$ to $2n$ rows contains the $\\mu-\\sqrt{(n+\\lambda)\\Sigma}$ terms."
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Computing the weights in numpy is extremely simple. Recall that\n",
-      "\n",
-      "$$\n",
-      "\\begin{aligned}\n",
-      "W_0 &= \\frac{\\kappa}{n+\\kappa} \\\\\n",
-      "W_i &= \\frac{1}{2(n+\\kappa)}\\,\\,\\,\\text{for i=1,2..2n}\n",
-      "\\end{aligned}\n",
-      "$$\n",
-      "\n",
-      "These two lines of code implenent these equations with the `np.full()` method, which creates and fills an array with the same value. Then the value for the mean($W_0$) is computed and overwrites the filled in value. We make $W$ a $(2n+1)\\times1$ dimension array simply because linear algebra with numpy proceeds much more smoothly when all arrays are 2 dimensional, so the one dimensional array `[1,2,3]` is better expressed in numpy as `[[1,2,3]]`.\n",
-      "\n",
-      "    W = np.full((2*n+1,1), .5 / (n+kappa))\n",
-      "    W[0] = kappa / (n+kappa)\n",
-      "    \n",
-      "\n",
-      "The equations for the sigma points are:\n",
-      "\n",
-      "$$\n",
-      "\\begin{aligned}\n",
-      "\\mathcal{X}_0 &= \\mu \\\\\n",
-      "\\mathcal{X}_i &= \\mu +  \\bigg[\\sqrt{(n+\\kappa)\\Sigma}  \\bigg]_i\\,\\,\\,\\, &\\text{for}\\text{ i=1 .. n} \\\\\n",
-      "\\mathcal{X}_i &= \\mu - \\bigg[\\sqrt{(n+\\kappa)\\Sigma}\\bigg]_{i-n}\\,\\,\\,\\,\\, &\\text{for}\\text{ i=(n+1) .. 2n}\n",
-      "\\end{aligned}\n",
-      "$$\n",
-      "\n",
-      "\n",
-      "The Python for this is not much more difficult once we wrap our heads around the $[\\sqrt{(n+\\kappa)\\Sigma}]_i$ term.\n",
-      "\n",
-      "The term $[\\sqrt{(n+\\kappa)\\Sigma}]_i$ has to be a matrix because $\\Sigma$ is a matrix. The subscript $i$ is choosing the column vector of the matrix. What is the 'square root of a matrix'? The usual definition is that the square root of a matrix $\\Sigma$ is just the matrix $S$ that, when multiplied by itself, yields $\\Sigma$.\n",
-      "\n",
-      "$$\n",
-      "\\begin{aligned}\n",
-      "\\text{if }\\Sigma = SS \\\\\n",
-      "\\\\\n",
-      "\\text{then }S = \\sqrt{\\Sigma}\n",
-      "\\end{aligned}\n",
-      "$$\n",
-      "\n",
-      "However there is an alternative definition, and we will chose that because it has numerical properties that makes it much easier for us to compute its value. We can alternatively define the square root as the matrix S, which when multiplied by its transpose, returns $\\Sigma$:\n",
-      "\n",
-      "$$\n",
-      "\\Sigma = SS^\\mathsf{T} \\\\\n",
-      "$$\n",
-      "\n",
-      "This latter method is typically chosen in computational linear algebra because this expression is easy to compute using something called the *Cholesky decomposition*. \n",
-      "Numpy provides this with the `numpy.linalg.cholesky()` method. If your language of choice is Fortran, C, C++, or the like standard libraries such as LAPACK also provide this routine. And, of course, matlab provides `chol()`, which does the same thing.\n",
-      "\n",
-      "This method returns a lower triangular matrix, so we will take the transpose of it so that in our for loop we can access it row-wise as `U[i]`, rather than the more cumbersome column-wise notation `U[i,:]`.\n",
-      "\n",
-      "    Xi = np.zeros((2*n+1, n))\n",
-      "    Xi[0] = X\n",
-      "    U = linalg.cholesky((n+kappa)*P).T\n",
-      "\n",
-      "    for k in range (n):\n",
-      "        Xi[k+1]   = X + U[k]\n",
-      "        Xi[n+k+1] = X - U[k]\n",
-      "\n",
-      "The full listing from the `filterpy.kalman` library follows.\n",
-      "\n",
-      "(**author's note: FilterPy has been updated since this was written; it now uses an class to implement the Unscented Kalman filter, not standalone classes. Until I update this, refer to the test_ukf.py file in that library to see how to use the class**)"
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def sigma_points (X, P, kappa):\n",
-      "    \"\"\" Computes the sigma points and weights for an unscented \n",
-      "    Kalman filter given the mean and covariance of the filter.\n",
-      "    \n",
-      "    kappa is an arbitraryconstant. \n",
-      "    \n",
-      "    Returns tuple of the sigma points and weights.\n",
-      "\n",
-      "    Works with both scalar and array inputs:\n",
-      "    sigma_points (5, 9, 2) # mean 5, covariance 9\n",
-      "    sigma_points ([5, 2], 9*eye(2), 2) # means 5 and 2, covariance 9I\n",
-      "\n",
-      "    Parameters\n",
-      "    ----------\n",
-      "    X An array of the means for each dimension in the problem space.\n",
-      "        Can be a scalar if 1D.\n",
-      "        examples: 1, [1,2], np.array([1,2])\n",
-      "\n",
-      "    P : scalar, or\n",
-      "\n",
-      "    Returns\n",
-      "    -------\n",
-      "    sigmas : np.array, of size (n, 2n+1)\n",
-      "        Two dimensional array of sigma points. Each column contains\n",
-      "        all of the sigmas for one dimension in the problem space.\n",
-      "\n",
-      "        Ordered by Xi_0, Xi_{1..n}, Xi_{n+1..2n}\n",
-      "\n",
-      "    weights : 1D np.array, of size (2n+1)\n",
-      "    \"\"\"\n",
-      "\n",
-      "    if np.isscalar(X):\n",
-      "        X = np.array([X])\n",
-      "\n",
-      "    if  np.isscalar(P):\n",
-      "        P = np.array([[P]])\n",
-      "\n",
-      "    \"\"\" Xi - sigma points\n",
-      "        W  - weights\n",
-      "    \"\"\"\n",
-      "\n",
-      "    n = np.size(X)  # dimension of problem\n",
-      "\n",
-      "    W = np.full((2*n+1,1), .5 / (n+kappa))\n",
-      "    Xi = np.zeros((2*n+1, n))\n",
-      "\n",
-      "    # handle values for the mean separately as special case\n",
-      "    Xi[0] = X\n",
-      "    W[0] = kappa / (n+kappa)\n",
-      "\n",
-      "    # implements U'*U = (n+kappa)*P. Returns lower triangular matrix.\n",
-      "    # Take transpose so we can access with U[i]\n",
-      "    U = linalg.cholesky((n+kappa)*P).T\n",
-      "\n",
-      "    for k in range (n):\n",
-      "        Xi[k+1]   = X + U[k]\n",
-      "        Xi[n+k+1] = X - U[k]\n",
-      "\n",
-      "    return (Xi, W)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 11
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "Now let's implement the unscented transform. Recall the equations\n",
-      "$$\\begin{aligned}\n",
-      "\\mu &= \\sum_i w_i\\mathcal{X}_i\\;\\;\\;&(2) \\\\\n",
-      "\\Sigma &= \\sum_i w_i{(\\mathcal{X}_i-\\mu)(\\mathcal{X}_i-\\mu)^\\mathsf{T}}\\;\\;\\;&(3)\n",
-      "\\end{aligned}\n",
-      "$$\n",
-      " \n",
-      "We implement the sum of the means with\n",
-      "\n",
-      "    X = np.sum (Xi*W, axis=0)\n",
-      "\n",
-      "If you are not a heavy user of numpy this may look foreign to you. Numpy is not just a library that make linear algebra possible; under the hood it is written in C to achieve much faster speeds than Python can reach. A typical speedup is 100x. To get that speedup we must avoid using for loops, and instead use numpy's built in functions to perform calculations. So, instead of writing a for loop to compute the sum, we call the built in `numpy.sum()` method which takes an entire array and computes the sum in C. The `axis` parameter tells sum over which axis to sum the array. For example, if \n",
-      "\n",
-      "$$Xi = \\begin{bmatrix}1&2&3\\\\1&2&3\\\\1&2&3\\end{bmatrix}$$\n",
-      "then \n",
-      "\n",
-      "    sum(Xi) == 18\n",
-      "    sum(Xi,axis=0) == [3,6,9]\n",
-      "    sum(Xi,axis=1) == [6,6,6]\n"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "All that is left is to compute $\\Sigma = \\sum_i w_i{(\\mathcal{X}_i-\\mu)(\\mathcal{X}_i-\\mu)^\\mathsf{T}}$\n",
-      "\n",
-      "    P = np.zeros((n,n))\n",
-      "    for k in range (2*n+1):\n",
-      "        s = (Xi[k]-X)[np.newaxis] # needs to be 2D to perform transform\n",
-      "        P += W[k]*s*s.T\n",
-      "\n",
-      "This introduces another new feature of numpy. The state variable $X$ is one dimensional, as is $Xi[k]$, so the difference $Xi[k]-X$ is also one dimensional. numpy will not compute the transpose of a 1-D array; it considers the transpose of `[1,2,3]` to be `[1,2,3]`. I consider that a deficiency of numpy, but you have to live with it. So we need to make the array two dimensional, with the second dimension of size 1. You do this in numpy by appending `[np.newaxis]` to the array.  For example, `np.array([1,2])[np.newaxis]` returns `array([[1, 2]])`.\n",
-      "\n",
-      "The following code is the implementation from the `filterpy.kalman` library. The function includes the ability to sum a noise covariance into the covariance matrix; this feature will be used to implement the full blown unscented Kalman filter."
-     ]
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "def unscented_transform (Xi, W, NoiseCov=None):\n",
-      "    \"\"\" computes the unscented transform of a set of signma \n",
-      "    points and weights.\n",
-      "    \n",
-      "    returns the mean and covariance in a tuple\n",
-      "    \"\"\"\n",
-      "    kmax,n = Xi.shape\n",
-      "\n",
-      "    X = np.sum (Xi*W, axis=0)\n",
-      "    P = np.zeros((n,n))\n",
-      "    \n",
-      "    for k in range (kmax):\n",
-      "        s = (Xi[k]-X)[np.newaxis] # needs to be 2D to perform transform\n",
-      "        P += W[k]*s*s.T\n",
-      "\n",
-      "    if NoiseCov is not None:\n",
-      "        P += NoiseCov\n",
-      "\n",
-      "    return (X, P)"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": [],
-     "prompt_number": 12
-    },
-    {
-     "cell_type": "heading",
-     "level": 2,
-     "metadata": {},
-     "source": [
-      "Unscented Kalman Filter"
-     ]
-    },
-    {
-     "cell_type": "markdown",
-     "metadata": {},
-     "source": [
-      "We are now ready to consider implementing a Kalman filter using the approximations for the mean and covariances afforded by the unscented transform. \n",
+      "We are now ready to consider implementing a Kalman filter. All of the math is above is already implemented in FilterPy, and you are perhaps a bit lost at this point, so lets just launch into solving some problems so you can gain confidence in how each the UKF actually is. Later we will revisit how the UKF is implemented in Python.\n",
       "\n",
       "Let's solve the 'bearing only' target tracking problem. Here the idea is that we have a ship or other platform tracking a moving target. Our sensor only provides the bearing to the target relative to the ship.\n",
       "\n",
@@ -999,7 +824,7 @@
       "As it turns out our state transition is linear. We model the target movement using the standard Newtonian equation\n",
       "$$x_k = x_{k-1} + \\dot{x}\\Delta t$$\n",
       "\n",
-      "However, the UKF code does not differentiate between linear and nonlinear versions, so we will write a function. The signature of the function required by `filterpy.UnscentedKalmanFilter` is\n",
+      "However, the UKF code in FilterPy does not differentiate between linear and nonlinear versions, so we will need to write a Python function to perform this computation. The signature of the function required by `filterpy.UnscentedKalmanFilter` is\n",
       "\n",
       "    def fx(x, dt):\n",
       "       return x\n",
@@ -1021,13 +846,20 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 13
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
-      "### Define Measurement Function\n",
+      "> FilterPy's KF and EKF code uses a 2D array to store the state. Due to implementation details the UKF uses a 1D array. I am not enamored of this inconistency, but for now this is how it is."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Define the Measurement Function\n",
       "\n",
       "Now we define our measurement function. In the linear KF this is implemented with the $\\mathbf{H}$ matrix, but because the UKF is nonlinear we need to provide a function. We have already given the computation of the bearing from position as\n",
       "\n",
@@ -1053,7 +885,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 14
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -1070,7 +902,7 @@
      "source": [
       "### Design Noise Parameters\n",
       "\n",
-      "We have spent a lot of time designing the $\\mathbf{R}$, $\\mathbf{Q}$, and $\\mathbf{P}$ matrices in past chapters, and nothing changes for the UKF filter, so I will just choose some arbitrary values without much discussion. Let's assume the variance in the bearing measurement is $\\sigma=0.35$ radians."
+      "We have spent a lot of time designing the $\\mathbf{R}$, $\\mathbf{Q}$, and $\\mathbf{P}$ matrices in past chapters, and nothing changes for the UKF filter, so I will just choose some arbitrary values without much discussion. Let's assume the standard deviation in the bearing measurement is $\\sigma=1^\\circ$."
      ]
     },
     {
@@ -1146,13 +978,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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HnO92sm1/LgAuziYmDOjEYwM6Vj223dezEXd2a2nPqCIOzao53cOHD8fd3d3W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\nIiIiIrWcg7ULqC4Xcwt5YsZatiSk4mS0Z/Zf+tC+iZYIFBE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Mm9tPc9Nqa2vZsWMHwcHBzZtQRETEBh07XUpxaRUhbTxwd6l77kfuiWKe+2Ab\nK3bk1B83sHs7/jHlOrqF+F7yZ3y65SBPv1tXyGdNvJqHbr5KK62I2KifLeX/PTVl0aJFDfbZ29sT\nFBTE/fff3yzBREREbM2BH8+wckcOK7bnsDP7ZP12N2cHHO3tKK2spqbWwNXZAT8PF46fKeXbfce5\n8ZlkHh3bl/tG9MTTzemi5y+vqiHx6z2s2J5LcVklOceLMQx4asLVzBjdpyW+oog0k58t5UuXLsUw\nDCZOnMiCBQvw8fFpqVwiIiI2wTAMPtuazZzlaezLP12/3dXZgXY+bhwtLKWssgYAkwluv64bT99x\nNYG+7hSXVTH7/S0sScnixY928OZnu5g4pDt/uHMgTg72DT5nTdphnkpM5Whhaf02kwkeHtOH/3fL\nVS3zZUWk2fziRLZzN4rohhEREZE6ldW17D9ymuxjRSz+Zh8b9xwBwMfdmev7hXLTgI7E9mqPq7MD\nhmFQUl5NrWHgYGfCw/WnK+Febk688ts4xkR3Ye6nO9mScZSFK/cQ4u/Ogzf1rj+urKKah95YS1ll\nDVEd/Xn8tv50CvTCz9MFfy/XFv/+ItL0GnV3iR4QJCIiUndVfPnmgzy3eBvHz5TVb/fxcGbWHQOZ\nEBuOo0PDhc1MJtPPTkkBiO3Vnthe7UnamMWj/0zhi22HGpTy1WmHKausoW+XAD778y2aNy7SCv2q\nW77NZjOFhYW0adOmqfKIiIhYjfKqGrZnHWf7vmPkHC8mI6+QjMOFAIQFeNKjgx8RoX5MuTEKP0+X\nX/158QM78/S7m/j+wAmOFJQQ4l+3isryzQcBuG1wFxVykVbqV5Xy4uJiZsyYoSvpIiLSqhw6VsSc\n5Wl8tjWbyuraBvv8vVzqr4o3dUF2dXZg2FUd+PLbQ3z17SEeGNWLM6WVfLMrDzuTifhrOjfp54mI\n9dAi4yIiIv+R/Z8y/knqAcyGgckEUR39GRwZTPf2voS08aBP57YN5oU3tZsHduLLbw/x5X9K+crt\nOVTXmonpGUyAj9svn0BEbNJFS/n8+fO57777cHV1vehTO6uqqpotmIiISEsoKq1k/Q/5rNiew5ff\nHsJsGNjbmZgYG87DY/rSsZ1Xi+a5vm8oLo72bM86ztHCUj7dUjd1ZUx0lxbNISIt66Kl/MSJE5jN\nZqDuIULh4eHnPb2zurq6edOJiIg0g7KKalanHeaTTQdY/0MeNbV1D8pzsDdxx3XdeXhMH8ICWraM\nn+Pu4siu23g0AAAgAElEQVTQqzqwYkcOsU98RFllDY72doy6uqNF8ohIy7hoKf/zn//c4PXjjz9+\n3jrlZ86c4cEHH2yWYCIiIk3JbDZITf+RpA1ZrNyRU792uJ3JRHREENf3DeXmgZ3o0NbTwklhQmw4\nK/6T0cXJnqk3RuHr8etvJBUR69WoOeW9e/fGyan55s+JiIg0l8KzFXy0IYt/r80g53hx/fZ+XQMY\ne20XRg/qTFtv65qrfUP/MFJeGo+rswNBvu5acUXkCtCoUv7MM89ccLuHhwd/+tOfmjSQiIhIUyit\nqOYP/7eZ5ZsPUFVTNx0z2N+dO+O6c1tMtxafK36pugbrKdoiV5JftfqKg4MDPXv2bKosIiIiTcIw\nDB57u+4hPCYTDO/TgbuHRzCsTwfs7ex++QQiIi3ssn8ylZSUNGUOACZNmkRQUBDe3t5cddVVfPbZ\nZ0DdDaVTpkzBy8uLsLAwkpKSGrxv7ty5BAYG4ufnx6xZs5o8l4iI2Ja3vviBL7YdwsPFkdV/H8d7\nvxvJiH5hKuQiYrUadaX8iy++wM3NjWHDhlFTU8Pf/vY30tPTadOmDU8++SShoaFNEub3v/89Cxcu\nxNnZmdWrVxMfH09hYSHz5s1j79695Ofnk5aWRnx8PNHR0bRv355t27Yxe/ZsUlNT8fb2JiYmhr59\n+zJ+/PgmySQiIrbDMAw+XL+P55duB2DuQ0OICPWzcCoRkV/WqEsGq1evJiAgAIAtW7aQk5PDzJkz\n6dixI//+97+bLEzv3r1xdnbGMAyqqqrw8PDAZDKRlJTEI488gpeXF3FxcURHR5OcnAzAsmXLGDdu\nHBEREQQHBzN16lSWLFnSZJlERMS6lVVUk5V/mrU7DzPpHyv53b82YjYMEsb248YBHS0dT0SkURp1\npfzUqVMEBgYCkJGRQVxcHNdeey2hoaH88Y9/bNJA06dPJzExEVdXV7788kvc3NzIysqie/fuTJo0\nidGjRxMZGcm+ffsAyMrKIjY2ljlz5pCXl0dMTAyLFy++4Ln9/f2bNKs0P0dHR0BjZ6s0frbNWsfP\nMAzWpuXw8kdb2XPoBKeKyhvs9/N04dXpI7hjSCQm05W5aom1jp00jsbPdp0bu8vRqFLu4eHBsWPH\naNOmDXv37uWuu+4CwGQy1T9gqKnMnz+fuXPnsmDBAiZNmkR6ejqlpaV4eHiwZ88e+vfvj6enJ3l5\neQD1+9LT08nNzWXUqFEXne/+3HPP1f86NjaWuLi4Js0uIiLNa2v6Ef60KIUNPxyu3+bkaE9ogBeh\nAd5EhrXhiQmDCPTzsGBKEbmSpKSksGHDBgDs7e2JjY29rPM0qpQPGjSIV155BV9fX4qLi+nVqxcA\n2dnZ9VfQm5KDgwMzZszgzTffZO3atbi7u1NaWsrOnTsBmDlzJp6edQ93cHd3p6SkhDlz5gCQnJyM\nh8eFfxhPnz69weuCgoImzy5N69xVAo2VbdL42TZrGb/qGjNbMo+S+PUeVn9fV8Z93J2ZPro342K6\nEeDt1nAdb6OSgoJKC6W1DtYydnJ5NH62JSoqiqioKKBu7FJTUy/rPI0q5XfffTdt27blxIkTxMbG\n4urqCkBxcTE33njjZX1wYxiGgWEYhIeHk5GRQb9+/QBIT09nzJgxAISHh5OZmVn/nvT0dHr06NFs\nmUREpPlUVteyLfMoKbuPkHfyLEWllezJKeBMaV3JdnN24IFRvXjwpl54uztbOK2ISNNpVCl3cHAg\nPj7+vO0333xzkwU5fvw4X3zxBePHj8fNzY2FCxdy4sQJrr32WiZMmMDcuXOJj48nLS2NrVu3smjR\nIgDGjx/PqFGjSEhIwNvbm8TERF544YUmyyUiIs3LMAx27D/Bh+sz+XxrNmWVNecdEx7iw00DOzF5\nRKTVPX1TRKQp/KqHBzUle3t7Fi9ezFNPPUVVVRWRkZF89tln+Pn5kZCQQGZmJh06dMDX15fExERC\nQkIAGDhwIM8++yxDhw6lurqaadOmaTlEEREbcLKojE82HeDDb/ax/8cz9dsjQ/0YdlUHIsP88fVw\npn1bTzoHelswqYhI8zMZhmE05sANGzawcuVKjh49CkBQUBA33nijzdwsuXbtWiIiIiwdQy6R5tXZ\nNo2fbWuO8auoqmFZ6n6Wbz7I1syjnPsTqK23K+Ov68Ydcd31ePkmoN97tk3jZ7vOzSkfPnz4Jb+3\nUVfKly9fTlJSEjExMVx33XUAHDp0iLfffpuCggJuu+22S/5gERG5cuSeKOazLdks/HoPJ/+zhKGT\ngx1xvdszMa47w/uE4uigp22KyJWrUaV81apVPPjgg+ct8dKzZ0+WLFmiUi4iIufJO3mWTzYd4LMt\nB8nMP12/vWeYPw+MiuLG/h3xcnOyYEIREevRqFJeVFREeHj4edu7detGUVFRk4cSERHbdfhEMX/5\nYBsrduTUb/N0dWToVR2YEBvOkN7tr9iH+oiIXEyjSnlgYCDffPMNd955Z4Pt69evJygoqFmCiYiI\nbSmvquGtz3cx7/NdVFTX4uJoz8gBHRk7uCuxvUJwcrC3dEQREavVqFI+ceJEXn31VbZt20ZoaCgA\nubm5nDhxgoSEhGYNKCIi1sswDI6dLmNLxlH+kbSdvJN1T1Qee20XnrnzGoL83C2cUETENjSqlF99\n9dW8+uqrrFmzpn71lQEDBjB8+HCCg4ObNaCIiFifzLxCFn+TyadbsjlVXF6/PSLUj7/ecy2DIvSv\nqCIil6LR65QHBQVx9913N2cWERGxAS8t28HryWn1r33cnYkI9SP+ms5MGtYDB3utoiIicqkaXcrN\nZjO7du3i2LFjGIZBYGAgffr0wc5OP3xFRK4UqXuP8HpyGvZ2Ju4a1oO7hvagZ5i/btwUEfmVGlXK\nc3Nzefnllzlx4gQeHh4AlJSU0LZtW5544gk6duzYnBlFRMQKFJVWkrAgBYCEsf1IuK2fhROJiLQe\njSrlCxYsICwsjOeeew4fn7onrZ0+fZqFCxfy9ttv8/e//71ZQ4qISMuqqTWz9Jt09uUXcPhoAQXF\nFWQdOc2PBaX07dKWh8f0sXREEZFWpdFXymfMmFFfyAF8fX2ZOHEiTz75ZLOFExERy/jz+1t4d1X6\nedt9PJyZ89AQzRsXEWlijSrlYWFh5OXlERIS0mB7fn4+HTp0aJZgIiJiGUkbs3h3VTpOjvbMvG0g\nPi4m2ni70tbble4d/PBxd7Z0RBGRVqdRpbx379689dZb7N69m9DQUEwmE7m5uWzevJkbbriBlJSU\n+mPj4uKaLayIiDSvnQdP8tTCVABefWgEU2/qQ0FBgYVTiYi0fo0q5cnJyQCsWbPmvH3Lly9v8Fql\nXETENq36LpcZ89ZRUV3Lb4Z0Z+pNmjcuItJSGlXKly5d2tw5RESkGRiGUb9cYU2tmZpaMy5ODvWv\nF3+Tye5Dpyg4W8Gq73MxDBgX05W/Th5sydgiIlecRq9TLiIitqPwbAVTX1vNt1nHcHa0x97OjtKK\nakwmuKFfGBOHdGf+57vYnnW8wfuenDCAh2/po3XHRURamEq5iEgrU1RayW9eWMHunFMAVFTVArWY\nTGDCxNff5fL1d7kABPq6MT3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i2KTZRUREWoNGlXI/Pz/+8Y9/sGnTJg4dqrtJ66abbiIm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\nAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f541ff60>"
+        "<matplotlib.figure.Figure at 0x7f576af20048>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -1178,13 +1010,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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la+LwBFXUNam0wqZ9xRXae6RcnxdVuseRGBMmfz8/NbU6NCo5Wier7Co+XX/e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\nyEgmTpzYZ+Psa8OGDcPFxaXHvt5motVqcXNz6/O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9qEyfHD2titpmFZ6q7XS/WHuwti+do9qGVp2qblRZdaPKqps+/7vRvaykskGl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\n/wc+KguJu337pwAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f51e9080>"
+        "<matplotlib.figure.Figure at 0x7f576b012fd0>"
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -1192,9 +1024,7 @@
      "source": [
       "As we can see the actual performance is abysmal. This is not an indictment of the UKF, but of the difficulty of the problem we are trying to solve. There are an infinite number of solutions for each measurement. For example, if our ship is at (0, 20), a bearing reading of 0 could reflect a target position of (0, 20) or (100, 20), or (1e300, 20). The Kalman filtering literature has many treatments of this problem; a common solution is to have the ship perform manuevers while tracking. This allows the filter to reduce the range of possibilities to something managable. This is rather outside the scope of this book so I will leave it to you to search the literature if you are interested in this specific problem.\n",
       "\n",
-      "Instead, I will make the problem tractable by introducing a second sensor. In the Designing Nonlinear Filters chapter we solved the two sensor version of this problem by linearizing the problem while assuming a nominal position for the target track. Now we will solve this by using the UKF.\n",
-      "\n",
-      "The problem is as follows. Assume we have two sensors, each which provides a bearing only measurement to the target, as in the chart below."
+      "Instead, I will make the problem tractable by introducing a second sensor. The problem is as follows. Assume we have two sensors, each which provides a bearing only measurement to the target, as in the chart below. In the chart the width of the circle is intended to denote a different amount of sensor noise."
      ]
     },
     {
@@ -1229,11 +1059,11 @@
        "output_type": "display_data",
        "png": 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9L3i7WGckRuO/f3Yz1i7OCNo1ie5mw9JM/MVnNyItIXjp67beYXz/jROY8Ny7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\no835jvgAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f5322fd0>"
+        "<matplotlib.figure.Figure at 0x7f576af6b438>"
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 14
     },
     {
      "cell_type": "markdown",
@@ -1271,9 +1101,11 @@
       "from filterpy.kalman import UnscentedKalmanFilter as UKF\n",
       "from filterpy.common import Q_discrete_white_noise\n",
       "\n",
+      "\n",
       "def bearing(sensor, target_pos):\n",
       "    return math.atan2(target_pos[1] - sensor[1], target_pos[0] - sensor[0])\n",
       "\n",
+      "\n",
       "def measurement(A_pos, B_pos, pos):\n",
       "    angle_a = bearing(A_pos, pos)\n",
       "    angle_b = bearing(B_pos, pos)\n",
@@ -1285,6 +1117,7 @@
       "    x[2] += x[3]\n",
       "    return x\n",
       "\n",
+      "\n",
       "def hx(x):\n",
       "    # measurement to A\n",
       "    pos = (x[0], x[2])\n",
@@ -1292,7 +1125,8 @@
       "\n",
       "\n",
       "def moving_target_filter(std_noise):\n",
-      "    f = UKF(dim_x=4, dim_z=2, dt=0.1, hx=hx, fx=fx, kappa=2.)\n",
+      "    dt = 0.1\n",
+      "    f = UKF(dim_x=4, dim_z=2, dt=dt, hx=hx, fx=fx, kappa=2.)\n",
       "    f.x = np.array([target_pos[0], 1., target_pos[1], 1.])\n",
       "\n",
       "    Q = Q_discrete_white_noise(2, dt, 1.1)\n",
@@ -1348,11 +1182,11 @@
        "output_type": "display_data",
        "png": 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MSJ3bSTn55Vq+Mcvo8YAq41QjAMBjrd68X28v2qTrr4jUA3dulincLt/yi5Ty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\nXbMoogsAPAxHvAAv9tvNUdf/eLVe/eR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AAPgdl2a8Bg8eLH9/f5lMJplMJt19992SJIfD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\nluiQgCnNwkMAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f53a0a20>"
+        "<matplotlib.figure.Figure at 0x7f576f32b2b0>"
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -1380,11 +1214,11 @@
        "output_type": "display_data",
        "png": 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5eYqOjlZMTIzOnj0ri8WiiRMnln/dESNGqFWrVkpPT9eAAQM0YMCAipwXAGCQ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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f0e09240>"
+        "<matplotlib.figure.Figure at 0x7f576f2db4a8>"
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -1411,7 +1245,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 20
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -1468,11 +1302,11 @@
        "output_type": "display_data",
        "png": 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le5m9bD/Ldybxwf296BbVqK6rV8WgDsFs+nACfZ5fZCrbd+IsgZO/Ie7b+3B2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2DBulgycovNEaqeQqGgX9hHhLh2ZWCjzyipyEGn05k1JiGE+C/SCo9x9NISAOIzV5ktjt93nyIj\nz7C8ULCPM63DTD+vTdw4Scpqsefvb4W7ky1/JaSxYs8pnh54F/WcryzLPpOez6bDqXRv3oCi0go+\n/vUvM0ZbxdHaizb1H+Fs3m7WJr3E6dxt5g5JCCGuS6vTsu3slQLYQyLmmSUOnU5X7Yp6qU1Wc0hS\nVou5OtgwZWQbAGb8tBetTsc7D3U06DP/j2N0a+4PwNxVh7iYVWjyOKuno1Jbyv4L33D80jJzByOE\nENeUVXwSBQqUCiu6BU1BaeIisX/bfuw88amGewkrFHB/dJhZ4hE3TpKyWm5E53DuCvEkLaeYWb8f\npm+bIKKj/Az6fLMulm7N/Cktr+S9X3abKVJDgS5XksfYjOUUlWeaMRohhKhehaYUT4dwugZNoXPA\n83g5NDZbLPPWHjVq6xjpR/16jtX0FpZIkrJaTqlU8Nb4DgDMX3uM5LQ83hrXwaDPmfR8tDodCgV8\n80cM5zIKzBGqAYVCwYBGs/THq08+Z8ZohBDCWKW2lM1n3uLghYXUsw/F2zHq3y+6Q+JTstl27LxR\nu0zwr1kkKasDWjT0YmSXRlRotLz5/R5C/Vx5vF8zgz7bjp0nwMuFikotn/5+2EyRGrK3cifKc4j+\nOL3QeEWREEKYg06n47f4CeSWniWt6DiVWvNuWTe/mrlkjnbW9G0TZPpgxE2TpKyOmDyiLc721mw5\neo4Nh1KYOKQF3q72Bn3OpucBsGT7CZLT8swRppEmXvfRxu9/NPMagZdDpLnDEUIIAI5dWqr/uLn3\nCKxV5tu+KD2nmCXbTxi139cpHHtbKzNEJG6WJGV1RD0XO168v6pY7JuL9qBSKfWV/6+m0er4ZNmh\nas+ZQ4hbFxp79ud4xjIWx46RbZiEEGal0+mIz6yqReZl3xh/5zZmjefbDYZPEXzcqn7hfrBnU3OE\nI26BJGV1yNiekTRu4E5KRgFfrjnK4A4Nad/Yt9q+y3cnkXDVKh5zKtcUE5fxO1C1DZNWpzVzREKI\nukqhUDAofDYuNg3oEvSKWWMpLq3g8xUx+uNgH2fScooJ9HYhukkDM0YmboYkZXWIWqVkxuVJ/rNX\nxHAmPZ9Zj3fF1dHGqK9OBx/9dtDUIV6Ttcqe6AZXJvtvPj3DjNEIIeqif47S26pd6B36jtnKX/zt\n6seWjRt4APBgzyYolVKbrKaRpKyOad/Yl/s6hlJaoeHpOVvwcrHnk8e6VNt37f4zHD2dYeIIr62+\nc0t8HKsWKGSVJJFbmmLmiIQQdYVWp2Fp/EMsT3gcrc4yplBotFpe/e5KGaPoKD82xVR9Xxwtjy5r\nJEnK6qAZ4zrg5+HA4VMZfLzsIPe2DOR/vZtU2/eDpZYzWgbQJfBF6ju1olPA87jYNKCw/JK5QxJC\n1AG/xo0HoFxTSEFZmnmDuWz9wbMGx3Y2asoqNAy8O4QQX1czRSVuhSRldZCrgw2zn+iGUqHg85Ux\n7Im/yKsPtKVVmI9R380xqew/kW6GKK8tOmAi3g5N2HdhPutPvU5eqXFtHiGEuF1S8/bqP27h8yAu\ntv5mjOaKR2dt0n8c7u/GxsMpWKmUvDzcvAsPxM2TpKyOahfhy9OD7kKng6fnbKGotILvJw/C2d54\nftn7v+43Q4TXp1AoqdCUUKEt5s9Tr5BfdtHcIQkhaiGtTktsxgoAPOxCaeTRy8wRVTl4Mh2tTmfQ\nptPBmB6NCfJ2NlNU4lZJUlaHPTekJS1DvbiYXcRLX+8kxNeVORN7G/XbHXeRnbGWNRqlVChp4/c/\n/fEfSS+h0ZabMSIhRG1TWpmPVldO16BXuMt7FD2C3zB3SHqj3/tD/7GDrRWJ53JwtLVi4pAWZoxK\n3CpJyuowK7WS2U92w9HWirX7T7Nw3VHu79yYMT2M9257+Zud6K76rczcbNSOdA96TX8cd/m3WSGE\nuFVllQXsTPmUjcnT0WjLCa/XB4XCMlYzJqflUVBSoT+u1FSVCHq8fzM8nO3MFZa4DSQpq+MCvZx5\n56Gqzb+fn7uRE6lZvPng3TQOcDfodyY9n00xqeYI8bo8HcL1KzJPZK+ntNIydiIQQtRc5Zpifk98\ngqySk5RpClApLacqvk6no9PzSwzayio0eLva82gfWXFZ00lSJhgaHcZ9HUMpLqtg3HsrUSkVfPl0\nD+xs1Ab9xn24Dq3WskbLoGpF5l3eo+gU8Dw2KmfO5R8wd0hCiBqqUlvO8oTH9Met/R7CVu1ixogM\nLduVVG37pKEtZUulWkCSMgHA2+M7EuTjwuGkdN5fcoBQP1dmXh5B+6dv11vmpuDh9frgaR/O0viH\n2ZU6i5i0n80dkhCiBlJw5RFlz+Cp1HdqacZoDOUWlfHM3K1G7aF+rozsEm76gMRtJ0mZAMDZ3pqF\nLw1EpVQwd81Rth8/z7BOjRjWKcyg3xuL9ujnL1gahUKBjcoJgMSstWw7876ZIxJC1DQqpRXDI7+j\nT+h7eNg3NHc4Bt5ctKfa9skj2qBWyY/z2kD+F4Xe3ZH1eXV0NAAT524lu6CUt8d3JNTPsAhhn9eW\nmyO8/2Rg+Gf6j9OKjnE0/VczRiOEqAl0Oh3bz35EWWUBUFVyx9nGz8xRGdqfmMbSHSeN2luHedOr\nVaAZIhJ3giRlwsDLI9vTLtyH9Nxinp+/HXsbNXOf7o7qH3uoxaVksy/RMipaV2d45HfYqd0ACHGr\nfgspIYT427pTr3KxMIbfE59Aq9OYOxwj5ZUanvhic7XnXhvVzmJWhYpbJ0mZMKBSKfn8iW4421uz\n/tBZvt8UT2SAh36F5t+GTF9FRaWlPsZUMqDRLIZEfIm1yoGtZ94luyTZ3GEJISzQoYuLyCurWlke\n7NoZpUJl5oiMzVtzjAtZRUbtvVsH0qaRtxkiEneKJGXCSP16jrz3SNVjzOk//EXiuWxGd4tgQLsQ\ng35B474xR3j/iUKhwFrlQELmGtKLYtmQ/CbHLy0zd1hCCAuSU3KWUzlXRqDa1p9gxmiqdyY9n3eX\nGO+qolQoeEW2U6p1JCkT1Rp4d0OGd25EaYWGJ7/YQlmFhvf/1wn/eo4G/RZtijdThP9NE6/79B/H\nZiznmMwxE6LO0+l0FFdk42Lrj69jc8Lc72V45PfmDsuITqdjyrc7qz33QNdwwuq7mTgicadJUiau\nacbY9gR5OxOfks07i/fjbG/NVxN7GvR5ZcFOMvKKzRThv1Mq1PQP+0R/HJe5koyiRDNGJIQwJ61O\ny4GL37Lu1KvklZ6jvf9TtPB50CLnZa3Yc4ptx4y3uLO1VjFpqOWU6hC3jyRl4poc7az54snuqFUK\nvvnzOJtjUmkW7Mkvk/sa9LvriR8tbgumf3KwrseARp/qjw9eXIhWZ5nz4YQQd45Wp+HXuHEk52yh\nXFNISWU2KqXaIhOynMJSXvx6R7XnHu3TFB83BxNHJExBkjJxXXc19OTF+1sD8Ny8bWTkFdOpSX1+\nermPQb/qChpaEnsrD4ZFfke4R186BUyiUlvKkbRfzB2WEMJEdDodv8aN1x83cG6Hn5Plbt4985f9\nFJdVGrW7O9nyRP/mZohImIIkZeJfPd6/GR0ifcnML+G5L7eh1ero0syfD/7XSd9n2a4kNhw6a8Yo\n/51SoeQunwews3JnecJjJGStYd2p1/79QiFEjadQKAhwaQ9AxwbP0qHBU2aO6Nr2Jabx45aEas9N\nHNwCJ3trE0ckTEWSMvGvVEols/6vK66ONmw5eo4Fl7daGtUtgj6tg/T9xn+0novZxsu2LY1SocLV\ntqrYYm7pWTYmT7Pox69CiNujvf8T9A/7GH/n1uYO5ZrKKzX832ebqj0X6OXEmJ6NTRyRMCVJysR/\n4ufhyIeXR8be/nkvcSlZAEYT/1s//RNlFZZXfPFqvRq+pf84qySJ45eWmjEaIcSdUKEpYXHsGLKK\nr2zi7WDtacaI/t2Xa46Snlv94qmXh7fBWm15ddTE7SNJmfjP+rQJZnT3CMortTw5ezMlZZUoFAqO\nzHnQoF/I+AVotZY/8jQiapF+s+EoryEUV2TLiJkQtcS5/P0sS3gUgI2np6GrAYt7Tp7P4b0lB6o9\n1yy4nlGtSFH7SFImbsjU0XcT6ufKifO5TP2hanPcei52fH3ViFmDMV/XiAQnOuA5hkd+T2F5ButP\nvc5f5+dSWH7J3GEJIW5BZvEJdqVe2Qc3usFzKBSW/eNOo9Xy4Pt/VnvOxd6az5/ohlJpeatExe1l\n2Z+lwuLY21rxxZPdsVYr+WFzAqv3Vm1f1KdNMEOjQw36Bo9bYI4Qb5hCoaC4IpMyTQEpeXtYc/J5\nzuUbV9AWQtQM2SWn9R93C5pCfWfLr+k1f+0xzmUWVntu3rM9CfVzNXFEwhwkKRM3rEmQB6+PagfA\ni1/vIDWjAIAZYzvgZGel71eh0dL1xZpRQd/HsSlt/f6nP96V+hlJ2dVvACyEsDxanYZDF38gKXsT\njTx60bHBswyLXIiXg+VPjE+6kMtbP++r9tx7j0TTqUl9E0ckzEWSMnFTHro3intbBpJfXM6TX2ym\nolKLi4MN854xfIx58kIuw99ZY6Yob0ywW2cGh8/RHx+8+C0XC46aMSIhxH9RXJHNr3HjOZm9jpi0\nnyitzMffubVFbi5+NY1Wy/1vra723KN9mvJgd8tPKsXtI0mZuCkKhYKPHu2Mj5sDB09e4qNlBwHo\n0syfsVct2d4Ve4EnZ9eMUScbtRPDIhfSzGs43g5ReDtGcqkongpNqblDE0JUI7c0hVUnntUfdwl8\nCVu1sxkjujHz1hwjI6/EqL1pUD1eG9XWDBEJc5KkTNw0dydbZj/ZDaVCweyVMew4XrVH2xuj76Zj\nlJ9B39/3nOK173aZI8wbplSoaOw5gM6BL1FUnsmWM++wLGECuaUp5g5NCHGVfy4oauHzIJ4O4WaM\n5sYkXcjl7V+qf2y59LV+qJTyI7qukf9xcUvaN/Zl4pAW6HTwzNwtZOaVYGet5rvnexHh72bQ99v1\ncbz/a/XLvS2RUqEkp/TKLgXrTr1KftlFM0YkhADQ6bT6ZMzNLpDWfo9wT8gMGnn0MnNk/51Gq2XA\nmyuqPbf7kxE42knV/rpIkjJxy54d3IJ24T5cyi3huXlV2zDZ2ahZOW2QUd9Zvx/myzU1Z55WgEs7\nugdd2Yrpj6SXKKssMGNEQtRtpZV5LIkbx/4LX+vbGrp1xd0uyHxB3YS5q4+SX1xu1P76qHYEetWc\nx6/i9pKkTNwytUrJ5092w9XRhs1HUpn/xzEAHGytSPhqnFH/GT/t5cfN1e/rZok8HcIZFD4bgHr2\njbBRO3E6dwdlldUvXxdC3BnxmatZkVi1Z+Xp3O1UaIznYtUESRdymbnYuOyOrZWK/+vXzAwRCUsh\nSZm4Lep7OPLxhM4AvLt4P0eSMwBwsrfm+LwxRv1f+mYHy3YlGbVbKlu1C8Mjv6Nb0BTO5R9g3/n5\n/J74ONvPfmDu0ISoE05lb+Fo+mL98T0h07FS2Zkxopuj0Wrp+cpv1Z77a9ZIE0cjLI0kZeK26dU6\niIfvjaJCo+WJ2ZspuDw07+ZoS+z8sUb9n56zRV98tiZQKJQoFSrcbIOwVbsAcLHwKItjx1CpNX4M\nIYS4dRWaUsoqC/B0CEettCXQpSNDIr7E3S7Y3KHdlM9WxFChMd7yacbY9ni62JshImFJJCkTt9Wr\nD7QlMsCdM+n5TP52p34yrquDDce+NB4xe+yzTaw/dNao3ZI5WNejb6jhCNmm09MoKs80U0RC1E6J\nmX+w7tQU9p2fj5O1L/3CPuRu///DWuVg7tBuStKFXD5cetCovVF9V8bdE2mGiISlkaRM3Fa21mrm\nPt0DOxs1y3efYsn2k/pz7k62HJ37oNE1D320nq1HU00Z5i2zUtkxImoRbrZBAOSWplJckWXeoISo\nJbS6SnanziYm/SeKKjIorsyhXFOkH6GuiTRaLV2uscPJhxM6S/kLAUhSJu6AUD9X3h7XEYBXv9tF\n0oVc/TkPZzsOfzEaZ3vD5d6j3/uTXbEXTBrn7XBvwxkMCp9Nu/qP4ukQzqoTE7o62ogAACAASURB\nVPk94ckaOwFZCEuw9/x8UvP36o/vCZmKjdrRjBHduqmL/qq2fVTXcFqFeZs4GmGpJCkTd8TwzmEM\n6dCQkrJKHv98E6XllfpzXq72bHn/fnzcDB9BDH9nDfsT00wd6i2zVbsQ5BpNbmkKxRVZlGnyWZbw\nKCezNhgUthRC/Dd/70Mb4dGXEVGLUCrUZo7o1iSey2bB+lijdldHGyaPlKr94gpJysQdoVAomPlQ\nNEHezsSlZPP2VZvt+rg5sGraQBp4Gv72O3j6Kg6cTDdlqLeNq20Abfwm6I8PpX3P9pQPzRiREDVD\ncs42FseO0dcAVCmtGR75Hc19HjBzZLdOo9XS/eXqV1tOGdEWdydbE0ckLJkkZeKOcbK3Zs5T3bFS\nKVmwPpZ1B84YnPfzcGTpq/3xr2eYmA2aupK/4mtm5fwQt84MCp+Nj2NTANr6TSCn9CxJ2RvR6YxX\nXAlRl1Vqy1gcO0ZfCHZD8hv6cwpF7fjxNP7D9dW239cxlAe61pwtoYRp1I7PemGxmod48sqINgBM\n+mo7F7IMC676ezrx66v98HU3fJQ59K3VbDxcM/eatFW70CXwJe5v/A22amcOXFjAwYvfsSRuHKdz\nd5g7PCEsQqW2lD+TpuiPXWwa0Dv0XTNGdPvtS0xj8xHjRUzPDm7BZ493RalUmCEqYckkKRN33KN9\nmtKtmT+5hWU89cUWKq+q0RPg5cyvr/bDx82wRs+4D9exZPsJU4Z6W6mU1oCCCI9++rZ95+ezONa4\nNIgQdUWFppTz+QdRK23xdoy6/Nj/EXqHvoNaaWPu8G6bguJyhkxfZdT+0YTOvDSsNQqFJGTCmCRl\n4o5TKhV8+n9d8XK1Y29iGtN//Aut1nACfLCPC4un9MPL1bBC93PztvHZisOmDPe2UigUNHBpa7B/\nJsCOsx9JwVlRp1Rqy1kcO4ZlCRPYlTqLzOIkWviM4p6Q6YS4dTV3eLfV+axCIiZ8Z9T+8+S+jJRH\nluI6JCkTJlHPxY7Pn+iGWqXgm3WxPPvlVsorNQZ9Qv1cWTy5Hx7OhhNf31tygJe+qdmP/TwdwhkW\n+R32VvVQKazRoUOttGb/2Z9Iy685+4AKcTNOZ+5h/s7B+mNnm/qolTaolbYoFSozRnb7HT2dQdtn\nfjZq3zhzKJ2b1DdDRKImkaRMmEx0VH0WPt8Lexs1y3YlMf7DdRSWGI4WNfJ3Y/Hkfrg5Gj7G+HFz\nAq8s2GnKcG87pULJgEaf0L/Rx7TyHUdBWRr7z/7AsphJLI4dI8VnRa3l7RxhcHxvwxm42jYwUzR3\nztr9p+nz2u9G7SunDqRxgLsZIhI1jSRlwqS6NW/A0tf64+Fsy7Zj57n/rTVk5BUb9Gkc4M4vk/vh\n6mCYmC3aFM+0H6ovwFiT2KpdcLD2RKurNGhfdWIix9Krr/gtRE1SWH6J5Qn/R6W2FAB7azf6N5nO\n0Mbf1Iq6Y1fT6XTMWXWECZ9uNDo3bUx7KQ4r/jNJyoTJNQ/xZMWbAwn0cuLYmUwGTV3J6bQ8gz5N\ngjxYPKWv0arM+X8c462f9lIbuNj680TntTT1G6hva+ByNwVlF7lQECOFZ0WNo9PpWBw7hjUnn6dc\nU8Tu1Nn6cwHurVErra9zdc1UXqnhxa938PYv+4zOdW5Sn0d6RZkhKlFTSVImzCLYx4UVUwfSLLge\nZy8VMGjaSmJOZRj0aRJUj3VvDyE6ys+gfe6aozw3b5spw72jOoX+H8Mjv6d70Gu42jbg+KVl7Ej5\niCVxY0nIXCPJmagRKrXlbDo9w6AtyLWjmaIxjdyiMka/9wc/b000Oudsb81Hj3aWVZbihkhSJszG\n08Wepa/1p0vT+mTll3L/26vZclVNHw9nO356pQ9PDbzLoH3J9hMMf2eNKcO9oxQKBZ4OVauy3O1C\n9O1H0n9hSdxYMotPXutSIcymXFPMiaz1JGSuQa20xkpli5XSHgcrL4ZFfkuAS3tzh3jHnE7LY+Cb\nK9gdV32h6+lj2+PnUbP36xSmJ0mZMCsHWysWvtCLodGhlJRVMv4j49pkKqWSySPasOC5ewzad8Ve\noNVTPxmV16jpwuv1MSqhsen0dNIKj6PVaWTkTJhdaWUe2868z/KExzictojjl5ZRWplHa9+HGNDo\nU/o3+qjWzRv7p70JF+n/5gpOXcyr9vw9LQO4PzrMxFGJ2kCSMmF21moVs/6vK08OaE6lRsdz87bx\n+QrjOVW9Wgex46PhONlZ6dvScopoMOZrikorTB32HeXpEM6IqEVEN3iOELduuNuF4OUQyb7zX7Ek\nbix7z883WigghKmsTHyatKJj+uP2/k9go3LGwdoTK5Xdda6s+ZbuOMmId9aSW1hW7XlXRxvef6ST\nPLYUN0WSMmERFAoFU0a2ZfqY9igU8O6S/bz+/W40WsPq/yE+LhyaPZq7I3wM2hs9spBzGQWmDNkk\n6ju3pI3fw/QMfhOlQsnZvF0AnMndwa9xD7H/wgIZORN3XIWmlOySM/rjexpWzR1r7j2SYZELqe/c\nqtYnIVqtjveW7OfZL7dSobn2PrbvjO+Il6v9Nc8LcT2SlAmL8kjvJsx5qjvWaiXfro/j8c83U1pu\nOCJkb2vF0tf6G80zazfxF/YlppkyXJP5e3PmIRFf4mR9JSFNztmCQqGgoOyiJGfitsssTtJX4d+Q\n/Lr+c8zNNpARUYuIqNev1hV/rU5JeSVPzN7MZytirtuvb5tgBt4dct0+QlyPJGXC4gy8uyE/vtwH\nJzsr1uw7zej3/iCvyPBRgUKhYPKINvzwUm+D9iHTV/FLNSuhagtrlQN9wz5gaONvcLHxp239xyjX\nFLEheSpL4sayOHYMmcU1d79QYTmOpP3MptPTDNpKK3PNFI35ZOQVM+ytNazam3zdfh7Otrz7cMda\nP2Io7ixJyoRF6hDpx7I3BuDjZs9fCWncN30VF7IKjfp1a96AfbMeMGh7/qvtTP1hj9HG57WJWmlN\n79CZBLtGU1B28fLm51U2nZ7B4tgxnM3bI/POxH+WXXKa/Re+Yf2p19Foy3G2qa+frO/v3IZhkd9h\nZ+Vm5ihNKyE1m/5vrODwqUvVno8K9NB/PPOhaDyca/d8OnHnSVImLFZkgAcr3hxIqJ8rCedyGDRt\nJSfO5Rj1q1/PkaRvHzJo++qP49w3YxX5xbV/028P+1D6h32Eh12oQfvhi4vQ6bSczz9IZnGSmaIT\nli6r+BTL4h9jQ/IbJOdsJaf0DBcKDhPg0oH+YZ8wImoRHRs8g1JRt35cbDmSyqCpKzmXafzLoFql\n4I3R7XCwrUpaB7dvSL+2waYOUdRCdeurTNQ4/p5OLH9jAK3DvLmQVcSQ6auqnTdmZ60meeHDBm0H\nT16i8YTvSE6rftl6baJSWtMz5E1GRC2ib+iHtPIdT5TnEFRKa3amfsqm09NYHDuGlLy9MvdMoNVp\n9B+Xa4qo0F7Z6qxz4Iv4O7dBpVRjZ+VqjvDMbuGGOMZ+sI7CalZ1+7g5sPS1ASgUCvYlpuPlaseM\ncR3MEKWojSQpExbP3cmWX6b0pVerQHKLynhg5lr+PHDGqJ+NlYqkBQ8ZtXd6fgk7jp83QaSWwcnG\nm1D3HoR53INGazhSuOfcbJbEjQWgqDxTErQ6pKQil8WxY1gcO4Zf48br230cm2KrdqFzwAuMiFqE\nr2Mz/cKSukan0/H2z3t5deEutNV8bXRuUp/17wzBzdGG9xbvB+C9Rzrh7mRr6lBFLVU3v/JEjWNn\nrWb+sz0Z3T2C0goNEz7dyMxf9hmtzLSzUXPim/HY2RgWrhw5cy3z/zhGXaNSWjMiahFt6z+qbwtx\n7YpGW8mG5Df0iwPO5O5Ep6u9c/DquqPpS1h54mmDtpKKqkn7CoWCQeGz8XVqbo7QLIZWq2Pyt7uY\ns/ooymom60+6ryU/vNwbV0cbnpu3jdIKDcM6hXFvy0AzRCtqq9pbclnUOmqVkvcejsbP3YEPfzvI\n7FVHWHfwLB892plWYd76fg62VhyaPZpeU5aR8o/aZdN++Itft59gzYzBWKtr/zL+fwp27USwaycq\ntVWrWIsq0g0qru89P4+95+cRWW8g4fX6Ya2SOks1lU6nZf+FBRRXZFKuKaJnyDRsVE6AAtBhpbSn\ne/CrdfbRZHUqNVomzd/GbzuTUCkVaK7aJeTHl3vTtVkDdDodMxfv51DSJXzcHJg2pvZuIyXMQ0bK\nRI2iUCiYOKQly98YSENfF05eyGXQtJVM++EvSsqujJo521uz/p376N3a8LfYuJRsgsct4OjpjKtf\nuk5QK21QK21wtqlP/0afGO1NeCJ7PSqFFYmZf7Dp9AxS8/bJCs4a5EjazyyJG8fp3G2kF8WSU3qG\nrJIkgt06M6DRp4yIWsR9jefhahtg7lAtRlmFhsc/38RvO5OwUimNErJ9nz2gT8im/fgXX6w6glKh\n4KNHO+HiYGOmqEVtJSNlokZq08ib9e/cx8fLDjF39VHm/3GM9YfO8vGjnWkX4QuAk701X0+8h8Xb\nTvD8V9sNru/z2u/0bRPM/Gd71Nm6QkqFivb+T9De/wk02nIuFSdQXJGNSmlFTPpPAAY1z+5v/A2g\nQKlQ19l7Zik02gqSc7ZyPOM3Iur1p3G9/gA4WHsa9OsZPBV3u2AUCiXWKgdzhGrRSsoq+d+nG9h6\n9ByAQaX+VmFeLH2tP9ZqFRqtlpe/2cnPWxOxUin54qnudG3WwFxhi1pMkjJRY9laq5kysi192wQz\naf42Es/lcN+M1Tx0bySTR7TFwdYKhULByK7htG7kTZcXfzW4fu3+0/g/+DXbPxxGQ9+6/ShHpbTG\n17GZ/jjYtTOnc7cb9Tl08QcuFBykqCKTYNcuNPceiY3a0dTh1llaXSX7zn+t324L4Gj6Yn1SFujS\nkQptKWHu96BWyijO9RQUlzPuw3XsrWY19zOD7uLl4W0AKK/U8Mycrazam4yttYqvJ95Dt+aSkIk7\nQ5IyUePd1dCTP94awmcrDjN7ZQzfro9j0+FUPpjQieio+gCE+rmSvPBh7pn8G6cuGpbI6PzCr4zu\nHsF7D0fLCNBlbetPoG39CWi0lWSWnKCovOpxb3ZJMkUVmQCczt3G6dxtBLlEE+zWGS+HxpRrimRE\n5jY5X3CIlLy/SMnbg7dDE+rZh9HE6z6ySk7p+9ionAjzuFd/bKWy0ydo4tqyC0oZ8/6fxCQbT2NY\n8Nw99GodBFRtr/TorI1sjknFyc6K717opR+JF+JOUOgseE38pk2baNy4sbnDqDM8PKqqU2dlZZk5\nkpt3/EwWk+ZvI/Zs1XsY3T2C1x9oh5P9lYr3n604zHtLDlR7/c6PhhPs42KSWKHm3XOtTktK3h72\nnv/SoL2t3wQauLTlt/gJ+jY/pxaEuHWlvlNLU4d5XZZ4z0sqckkviiXItSMAGm0lS+MNy7u42gbS\nq+FbpBfGYqN2wsXGv8aUrrCke34pt5gHZq4loZpC1BtnDqVxgDtQNZL20Mfr2RN/ETdHG356pQ/N\ngj2NrrFUlnTP6woPDw927txJjx49bvo1bvorOjExkd69e+Pm5kZwsHEl488++wwfHx/c3d2ZMmWK\nwbmtW7cSHh6Oo6MjQ4YMIT8//2bDEMJAkyAP1kwfzIv3t8JKpeTHzQl0e3kpW46k6vs8M6gFa6YP\nrvb66OeX8MaiPWi0Uh6iOkqFkiDXjoyIWsSwyO/oEfwmLX3G4uUQSWbxSYO+FwoOE5exEp1Oy9Yz\n77L+1BssiR3HhYIYyjVFZnoH5qXTafWlKLKKkziWvpTFsWNYeeJp9p7/Ul9XTqU0fIjRzGs4XQNf\nBsDbMQpX24Aak5BZkvOZhQyZvqrahGzfrAf0CVl2QSkjZ65lT/xFfNzsWfb6gBqVkIma66ZHypKT\nk9m5cyfl5eW8/fbbnD59Wn9u79699O3bl507d+Li4kJ0dDTvvfcew4YNo7i4mMDAQD7//HMGDRrE\n6NGj8fX15YsvvjD6N2SkzLRq229WCanZTJq/jSPJVY/bhnduxJsP3o3r5RVTeUVlRD76/TWv3/Tu\nUCIauN/RGGvbPS+rLCApexOp+fvIK0slusFEXG0DWX3yOaO+jtZeRDeYBOjYevZd/J1a424XQqBr\nB4NyHbebqe/5rpRZnCu4MjJrq3ZhUPhsjl36jbiM3w369g/7BAfreiaJy5Qs4fM8OS2Pke+s5Xw1\ne+ge/mI0Xq5VZWDSc4p54N21JJ7LIcDTicVT+hLg5WzqcG+ZJdzzuuZ2jJTd8uPLjRs3MmHCBIOk\n7MUXXyQvL4/58+cD8M4773Dw4EF+++031qxZw8SJEzl5suq36t27dzNw4EAyMzONXluSMtOqjV/E\nlRot89ce48PfDlJWocHb1Z53H4nWF3zUaLWM/3A9m/8xkvZP/+vdhCkj22JjdWfqmtXGe361Sm0Z\nmcUn2Xb2PaNzQyLmEZfxO4lZfxi0t/d/Cm+HSJJztpJeFEtxRQ4qpRUN3bri5RCJo7XPTe/FeKv3\nPLc0hQpNCRpdOV4OUeSWniU1by8JWWv0fXo3nImLrT8Ai2PHGFxvp3ajd+i75JSe4WJBDBpdBQ3d\nuuNqW3snj5v78zw+JZthb68mp7DMoD3I25lV0wbpK/KnZhQwcuZazqTn06i+Kz+90hdf95o5R9Lc\n97wuuh1J2R35dfTEiRN07tyZWbNmkZqaSnR0ND/9VLXEPjExkYiICHbt2sX06dNZtGgR2dnZZGVl\n6T+JhLhd1ColTwxozr2tApk0fxsHT17ioY/WM6RDQ6aP7YC7ky2LXurN+oNneejj9UbXf/3ncb7+\n8zi/vzGANuE+ZngHNZ9aaYOPYxNGRC0Cqray0eoqKCzPwFplj5ttEGqlLZXaUv01TjY+5JamcPTS\nEoPXOnjxO6CqPMfeC99yJnenwfkBjWah0ZaTnLPVIEkKcL4bT4fGhLp3Jz0/kbSCeOIvbMTR2pv8\nsgt0C5pCcUUmp3N3cCLrT/113YKm4OVQ9YvhxuTpZJUYPqIdFD6b/LLzBv8WQHpRrD4pa+P3CBlF\nidiqXQh264yzjR8A3g6ReDtE3vgNFTck5lQG/d743ai9ZagXP7zUW19rLOlCLiPeWUtaThHNguvx\n48t9ZPskYXJ3JCkrKirC0dGRuLg4zp49S58+fSgsLDQ4l5aWRnx8PDY2VV8QhYWF1SZlkqiZjpWV\nFVA777mHhwfbPw1mzsqDvLFwG8t3n2JX3EU+ffJe7usUwQP3etCzbQQNRn5W7fWDp6/if33v4p1H\nuuF8GwtG1uZ7/m+8qFrF5uExgJYNBwBVCVtxeTZ21q7kFKfSrHIQR8+vMLiunmNDvDx9KUo1LmXg\n5GxHbkm2UZKUkv8XFwoP0y5sGL/FvE16fgJQtZoUwM5JSWlRqUFCBpCvTaaxRzQAqhTDlbn+rnfh\n7OKEg3NrdOoiTmZsw0btSJhXV0I9O2Nn5XL5/Q29qftTm5jr83zHsZRqE7IB7cP49qUBONpVLQCK\nSUrj/rfWkJFXTMcm/iybdj8uDjU7IavL31vM5e97fiuum5RNnTqV6dOnG7UPHjyYZcuWXfM6BwcH\nCgsLmTVrFgDLly/H0dHR4NzQoUMZOnQoOTlVEy7/Pn+1GTNm6D/u3LkzXbp0+Ze3JET1VColTw9p\nQ992oTz2yVp2Hktl1Nu/M7hjI95+pBsN/dwoXvsyz8xex9drY4yu/3ptDF+vjWHZtPvp2y7UDO+g\n9lMoFDjYVP0Q8XAIIrrhY0Q3fKzavv2bzCC/NI29Z75DrbIlr+Q8dtZV9ebuDhrPX2cW6vu62wfi\n79YCAG+nMH1S9jdrlQPuDkF0CPkfMeeWodGWU1ZZSKB7W32fvlFTKasswNXO36h0SuvAUbQOHHXL\n71/cPusPJDPwtSVG7VNGd+S10dEolVX/h7tjzzHkjV/JKyrj3tYh/PLaEOxtb/2Hq6gbtm3bxvbt\nVTUdVSoVnTt3vqXXu2NzynJzc/nqq68AePvttzl8+DBLly5l9erVPPfcc/o5Zbt27WLQoEEyp8wC\n1KU5CFqtju83xfP2z3spLqvESqVkTI/GTBzSAg9nO/YnpjF4+qprXt+3TTBTH7yb+vVurXBqXbrn\nlkLuuemZ+p7/vDWBF77aYdBmY6Xis8e70r9diL5t+7FzPPzJBkrKKunXNpjZT3arNfviyue56Zm1\nJAZAaWkpFRUV6HQ6ysrKKC+vWs49bNgwli1bRlxcHOfPn2fBggWMGDECgO7du5OXl8fPP/9MUVER\nH374of6cEKaiVCoYf08kWz8YxrBOYVRqtSxYH0uH5xbzyfJDRAV6EP/VOJz/Ud/sn9buP03bZ3/m\n0+WHKC2XvSGFsAQarZan52wxSsj86zmycuogg4Tsj/2nGffhOkrKKhneuRFznupeaxIyUXPddFJ2\n5swZ7O3t6devH6mpqdjZ2dG7d28A2rZty5tvvkm3bt1o2rQpI0aMYNiwYQDY29vz66+/MnXqVLy8\nvAB49913b8NbEeLG1fdw5NP/68r6d+6je/MGFJZW8OHSg3R8fjEr9pzi6NwxvDy89TWv/2DpQdo/\n9wvrD53FguswC1HrxZ7NImDMN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O97rrNnz+b8+fMADA4OSrYBlpiYqDzeunUr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\nRK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f52d3048>"
+        "<matplotlib.figure.Figure at 0x7f576b0c5198>"
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -1513,11 +1347,11 @@
        "output_type": "display_data",
        "png": 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5fP7nfj7/cz99WjXkqUGt6d82WBa/FeIqtmzZwtatWwHQaDR069bthuqrlp6ykJAQoqOj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OjYEJstbjESyoRVmzwg7IpDlOiNJmZ+sY35q6Ixm2t/MlOpUHK7773YadxZdfoxTuesZ2P8q7WW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\nAEhAlAEAJPACFh/ejO9aHuAAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f525a6d8>"
+        "<matplotlib.figure.Figure at 0x7f576f8c94e0>"
        ]
       }
      ],
-     "prompt_number": 22
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -1539,7 +1373,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -1571,11 +1405,11 @@
        "output_type": "display_data",
        "png": 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jy/H+Z8k4cjoCPafYAICf/+UA3v06B5vWzsOYiADfV5yI+oXBjGiAHThbigde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\nRERMQsFMRERExCQUzERERERM4v8Di1vz8YTpEEoAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f5340780>"
+        "<matplotlib.figure.Figure at 0x7f576f33a358>"
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1598,7 +1432,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 25
+     "prompt_number": 22
     },
     {
      "cell_type": "markdown",
@@ -1634,7 +1468,7 @@
        ]
       }
      ],
-     "prompt_number": 26
+     "prompt_number": 23
     },
     {
      "cell_type": "markdown",
@@ -1670,11 +1504,11 @@
        "output_type": "display_data",
        "png": 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Sa6/ZdOKEWQsWnNILL9hVUeFQVZVV773HmSoASAQdKiBLDB5co0AgT5ddFtLN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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f5204c50>"
+        "<matplotlib.figure.Figure at 0x7f576aeed630>"
        ]
       }
      ],
-     "prompt_number": 27
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -1729,11 +1563,11 @@
        "output_type": "display_data",
        "png": 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6s8/WaPjwGjNLBPB/2PQNIOEYhqH7Vt6n17e8rsZgo9nlhHQwOFVX21VU5Gq+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\nAAAADP4HF1CTyUoO+XQAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f0cf3e10>"
+        "<matplotlib.figure.Figure at 0x7f576b0de198>"
        ]
       }
      ],
-     "prompt_number": 28
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1810,7 +1644,6 @@
       "        plt.scatter(xs_a, ys_a)\n",
       "        plt.scatter(xs_b, ys_b)\n",
       "\n",
-      "    #plt.plot([5.5, pos[0]], [6, pos[1]], c='g', linestyle='--')\n",
       "    plt.axis('equal')\n",
       "    plt.show()\n",
       "\n",
@@ -1830,11 +1663,11 @@
        "output_type": "display_data",
        "png": 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uLlb64sVSJKKt143VqrUZKv63ZNVo9uzaQ96TmekoL89WWVlYXbo4ys6WciKV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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f0dffac8>"
+        "<matplotlib.figure.Figure at 0x7f576f36d7f0>"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1865,11 +1698,700 @@
        "output_type": "display_data",
        "png": 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ccAGvvvoqb7/9NsOHD/9sIxYRkX/aPp2291s+cIJBsKzktnzDtnF9PlJeeAG7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\ngAGRBAAAYEAkAQAAGBBJAAAABkQSAACAAZEEAAB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        "text": [
-        "<matplotlib.figure.Figure at 0x7fa2f5302358>"
+        "<matplotlib.figure.Figure at 0x7f576aefbfd0>"
        ]
       }
      ],
-     "prompt_number": 30
+     "prompt_number": 27
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Implementation (Optional)\n",
+      "\n",
+      "Implementing the UKF is quite straightforward. First, let's write the code to compute the mean and covariance given the sigma points. \n",
+      "\n",
+      "We will store the sigma points and weights in matrices, like so:\n",
+      "\n",
+      "$$ \n",
+      "\\begin{aligned}\n",
+      "weights &= \n",
+      "\\begin{bmatrix}\n",
+      "w_1&w_2& \\dots & w_{2n+1}\n",
+      "\\end{bmatrix} \n",
+      "\\\\\n",
+      "sigmas &= \n",
+      "\\begin{bmatrix}\n",
+      "\\mathcal{X}_{0,0} & \\mathcal{X}_{0,1} &  \\mathcal{X}_{0,2} \\\\\n",
+      "\\mathcal{X}_{1,0} & \\mathcal{X}_{1,1} &  \\mathcal{X}_{1,2} \\\\\n",
+      "\\vdots & \\vdots & \\vdots \\\\\n",
+      "\\mathcal{X}_{2n+1,0} & \\mathcal{X}_{2n+1,1} & \\mathcal{X}_{2n+1,2}\n",
+      "\\end{bmatrix} \\\\\n",
+      "sigmas &= \n",
+      "\\begin{bmatrix}\n",
+      "\\mathcal{X}_{0,0} & \\mathcal{X}_{0,1} &  \\mathcal{X}_{0,2n+1} \\\\\n",
+      "\\mathcal{X}_{1,0} & \\mathcal{X}_{1,1} &  \\mathcal{X}_{1,2n+1} \\\\\n",
+      "\\vdots & \\vdots & \\vdots \\\\\n",
+      "\\mathcal{X}_{D-1,0} & \\mathcal{X}_{D-1,1} & \\mathcal{X}_{D-1,2n+1}\n",
+      "\\end{bmatrix}\n",
+      "\\end{aligned}\n",
+      "$$\n",
+      "\n",
+      "In other words, each column contains the $2n+1$ sigma points for one dimension in our problem. The $0th$ sigma point is always the mean, so first row of sigma's contains the mean of each of our dimensions. The second through nth row contains the $\\mu+\\sqrt{(n+\\lambda)\\Sigma}$ terms, and the $n+1$ to $2n$ rows contains the $\\mu-\\sqrt{(n+\\lambda)\\Sigma}$ terms. the choice to store the sigmas in row-column vs column row format is somewhat arbitrary; my choice makes the rest of the code a bit easier to code as I can refer to the ith sigma point for all dimensions as `sigmas[i]`."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Weights\n",
+      "\n",
+      "Computing the weights in numpy is extremely simple. Recall that for the Julier implementation\n",
+      "\n",
+      "$$\n",
+      "\\begin{aligned}\n",
+      "W_0 &= \\frac{\\kappa}{n+\\kappa} \\\\\n",
+      "W_i &= \\frac{1}{2(n+\\kappa)}\\,\\,\\,\\text{for i=1,2..2n}\n",
+      "\\end{aligned}\n",
+      "$$\n",
+      "\n",
+      "These two lines of code implement these equations with the `np.full()` method, which creates and fills an array with the same value. Then the value for the mean($W_0$) is computed and overwrites the filled in value. \n",
+      "\n",
+      "    W = np.full((2*n+1,1), .5 / (n+kappa))\n",
+      "    W[0] = kappa / (n+kappa)\n",
+      "    \n",
+      "Our final function will look something like this."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def weights(n, kappa):\n",
+      "    \"\"\" Computes the weights for an unscented Kalman filter. \"\"\"\n",
+      "\n",
+      "    k = .5 / (n+kappa)\n",
+      "    W = np.full(2*n+1, k)\n",
+      "    W[0] = kappa / (n+kappa)\n",
+      "    return W"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 42
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Sigma Points\n",
+      "The equations for the sigma points are:\n",
+      "\n",
+      "$$\n",
+      "\\begin{aligned}\n",
+      "\\mathcal{X}_0 &= \\mu \\\\\n",
+      "\\mathcal{X}_i &= \\mu +  \\bigg[\\sqrt{(n+\\kappa)\\Sigma}  \\bigg]_i\\,\\,\\,\\, &\\text{for}\\text{ i=1 .. n} \\\\\n",
+      "\\mathcal{X}_i &= \\mu - \\bigg[\\sqrt{(n+\\kappa)\\Sigma}\\bigg]_{i-n}\\,\\,\\,\\,\\, &\\text{for}\\text{ i=(n+1) .. 2n}\n",
+      "\\end{aligned}\n",
+      "$$\n",
+      "\n",
+      "\n",
+      "The Python for this is not much more difficult once we wrap our heads around the $[\\sqrt{(n+\\kappa)\\Sigma}]_i$ term.\n",
+      "\n",
+      "The term $[\\sqrt{(n+\\kappa)\\Sigma}]_i$ has to be a matrix because $\\Sigma$ is a matrix. The subscript $i$ is choosing the column vector of the matrix. What is the 'square root of a matrix'? The usual definition is that the square root of a matrix $\\Sigma$ is just the matrix $S$ that, when multiplied by itself, yields $\\Sigma$.\n",
+      "\n",
+      "$$\n",
+      "\\begin{aligned}\n",
+      "\\text{if }\\Sigma = SS \\\\\n",
+      "\\\\\n",
+      "\\text{then }S = \\sqrt{\\Sigma}\n",
+      "\\end{aligned}\n",
+      "$$\n",
+      "\n",
+      "However there is an alternative definition, and we will chose that because it has numerical properties that makes it much easier for us to compute its value. We can alternatively define the square root as the matrix S, which when multiplied by its transpose, returns $\\Sigma$:\n",
+      "\n",
+      "$$\n",
+      "\\Sigma = SS^\\mathsf{T} \\\\\n",
+      "$$\n",
+      "\n",
+      "This latter method is typically chosen in computational linear algebra because this expression is easy to compute using something called the *Cholesky decomposition*. \n",
+      "Numpy provides this with the `numpy.linalg.cholesky()` method. If your language of choice is Fortran, C, C++, or the like standard libraries such as LAPACK also provide this routine. And, of course, matlab provides `chol()`, which does the same thing.\n",
+      "\n",
+      "This method returns a lower triangular matrix, so we will take the transpose of it so that in our for loop we can access it row-wise as `U[i]`, rather than the more cumbersome column-wise notation `U[i,:]`.\n",
+      "\n",
+      "    Sigmas = np.zeros((2*n+1, n))\n",
+      "    U = linalg.cholesky((n+kappa)*P).T\n",
+      "\n",
+      "    for k in range (n):\n",
+      "        Sigmas[k+1]   = X + U[k]\n",
+      "        Sigmas[n+k+1] = X - U[k]\n",
+      "\n",
+      "    Sigmas[0] = X"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "Now let's implement the unscented transform. Recall the equations\n",
+      "$$\\begin{aligned}\n",
+      "\\mu &= \\sum_i w_i\\mathcal{X}_i\\;\\;\\;&(2) \\\\\n",
+      "\\Sigma &= \\sum_i w_i{(\\mathcal{X}_i-\\mu)(\\mathcal{X}_i-\\mu)^\\mathsf{T}}\\;\\;\\;&(3)\n",
+      "\\end{aligned}\n",
+      "$$\n",
+      "\n",
+      "The scaled UKF uses different weights for the means and covariance, so we will use the variable `Wm` for the mean weights and `Wc` for the covariance weights.\n",
+      "\n",
+      "We implement the sum of the means with\n",
+      "\n",
+      "    X = np.dot (Wm, Sigmas)\n",
+      "\n",
+      "If you are not a heavy user of numpy this may look foreign to you. Numpy is not just a library that make linear algebra possible; under the hood it is written in C to achieve much faster speeds than Python can reach. A typical speedup is 100x. To get that speedup we must avoid using for loops, and instead use numpy's built in functions to perform calculations. So, instead of writing a for loop to compute the sum, we call the built in `numpy.dot(x,y)` method. If passed a 1D array and a 2D array it will compute sum of inner products:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "a = np.array([10, 100])\n",
+      "b = np.array([[1, 2, 3],\n",
+      "              [4, 5, 6]])\n",
+      "\n",
+      "np.dot(a,b)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 28,
+       "text": [
+        "array([410, 520, 630])"
+       ]
+      }
+     ],
+     "prompt_number": 28
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "All that is left is to compute $\\mathbf{P} = \\sum_i w_i{(\\mathcal{X}_i-\\mu)(\\mathcal{X}_i-\\mu)^\\mathsf{T}} + \\mathbf{Q}$\n",
+      "\n",
+      "    kmax, n = Sigmas.shape\n",
+      "    P = zeros((n, n))\n",
+      "    for k in range(kmax):\n",
+      "        y = Sigmas[k] - x\n",
+      "        P += Wc[k] * np.outer(y, y) \n",
+      "    P += Q\n",
+      "\n",
+      "This introduces another feature of numpy. The state variable $X$ is one dimensional, as is $Xi[k]$, so the difference $Xi[k]-X$ is also one dimensional. numpy will not compute the transpose of a 1-D array; it considers the transpose of `[1,2,3]` to be `[1,2,3]`. So we call the function `np.outer(y,y)` which computes the value of $yy^\\mathsf{T}$ for a 1D array $y$.\n",
+      "\n",
+      "Our `sigma_points` function can be implemented as below"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def sigma_points(x, P, kappa):\n",
+      "    \"\"\" Computes the sigma pointsfor an unscented Kalman filter\n",
+      "    given the mean (x) and covariance(P) of the filter.\n",
+      "    kappa is an arbitrary constant. Returns tuple of the sigma points\n",
+      "    and weights.\n",
+      "\n",
+      "    Works with both scalar and array inputs:\n",
+      "    sigma_points (5, 9, 2) # mean 5, covariance 9\n",
+      "    sigma_points ([5, 2], 9*eye(2), 2) # means 5 and 2, covariance 9I\n",
+      "\n",
+      "    **Parameters**\n",
+      "\n",
+      "    X An array-like object of the means of length n\n",
+      "        Can be a scalar if 1D.\n",
+      "        examples: 1, [1,2], np.array([1,2])\n",
+      "\n",
+      "    P : scalar, or np.array\n",
+      "       Covariance of the filter. If scalar, is treated as eye(n)*P.\n",
+      "\n",
+      "    kappa : float\n",
+      "        Scaling factor.\n",
+      "\n",
+      "    **Returns**\n",
+      "\n",
+      "    sigmas : np.array, of size (n, 2n+1)\n",
+      "        2D array of sigma points. Each column contains all of\n",
+      "        the sigmas for one dimension in the problem space. They\n",
+      "        are ordered as:\n",
+      "\n",
+      "        .. math::\n",
+      "            sigmas[0]    = x \\n\n",
+      "            sigmas[1..n] = x + [\\sqrt{(n+\\kappa)P}]_k \\n\n",
+      "            sigmas[n+1..2n] = x - [\\sqrt{(n+\\kappa)P}]_k\n",
+      "    \"\"\"\n",
+      "\n",
+      "    if np.isscalar(x):\n",
+      "        x = asarray([x])\n",
+      "    n = np.size(x)  # dimension of problem\n",
+      "\n",
+      "    if  np.isscalar(P):\n",
+      "        P = eye(n)*P\n",
+      "\n",
+      "    Sigmas = zeros((2*n+1, n))\n",
+      "\n",
+      "    # implements U'*U = (n+kappa)*P. Returns lower triangular matrix.\n",
+      "    # Take transpose so we can access with U[i]\n",
+      "    U = cholesky((n+kappa)*P).T\n",
+      "    #U = sqrtm((n+kappa)*P).T\n",
+      "\n",
+      "    for k in range(n):\n",
+      "        Sigmas[k+1]   = x + U[k]\n",
+      "        Sigmas[n+k+1] = x - U[k]\n",
+      "\n",
+      "    # handle value for the mean separately as special case\n",
+      "    Sigmas[0] = x\n",
+      "\n",
+      "    return Sigmas"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 43
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Predict Step\n",
+      "\n",
+      "For the predict step, we will generate the weights and sigma points as specified above. We pass each sigma point through the function f.\n",
+      "\n",
+      "$$\\mathcal{X_f} = f(\\mathcal{X})$$\n",
+      "\n",
+      "Then we compute the predicted mean and covariance using the unscented transform. In the code below you can see that I am assuming that this is a method in a class that stores the various matrices and vectors needed by the filter."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def predict(self):\n",
+      "    \"\"\" Performs the predict step of the UKF. On return, self.xp and\n",
+      "    self.Pp contain the predicted state (xp) and covariance (Pp). 'p'\n",
+      "    stands for prediction.\n",
+      "\n",
+      "    Important: this MUST be called before update() is called for the first\n",
+      "    time.\n",
+      "    \"\"\"\n",
+      "\n",
+      "    # calculate sigma points for given mean and covariance\n",
+      "    sigmas = self.sigma_points(self.x, self.P, self.kappa)\n",
+      "\n",
+      "    for i in range(self._num_sigmas):\n",
+      "        self.sigmas_f[i] = self.fx(sigmas[i], self._dt)\n",
+      "\n",
+      "    self.xp, self.Pp = unscented_transform(\n",
+      "                       self.sigmas_f, self.W, self.W, self.Q)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 44
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Update Step\n",
+      "\n",
+      "The update step converts the sigmas into measurement space via the `h(x)` function.\n",
+      "\n",
+      "\n",
+      "$$\\mathcal{X_z} = h(\\mathcal{X_f})$$\n",
+      "\n",
+      "The mean an covariance of those points is computed with the  unscented transform. The residual and Kalman gain is then computed. The cross variance is computed as:\n",
+      "\n",
+      "\n",
+      "$$\\mathbf{P}_{xz} =\\sum W(\\mathcal{X}-x)(\\mathcal{X_z}-\\mathbf{x}_z)^\\mathsf{T}$$\n",
+      "\n",
+      "\n",
+      "Finally, we compute the new state estimate using the residual and Kalman gain:\n",
+      "\n",
+      "$$\\hat{\\mathbf{x}} = \\mathbf{x}^- + \\mathbf{Ky}$$\n",
+      "\n",
+      "and the new covariance is computed as:\n",
+      "\n",
+      "$$ \\mathbf{P} = \\mathbf{P}^- - \\mathbf{PKP}_z\\mathbf{K}^\\mathsf{T}$$\n",
+      "\n",
+      "This function can be implemented as follows, assuming it is a method of a class that stores the necessary matrices and data."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "def update(self, z):\n",
+      "    \"\"\" Update the UKF with the given measurements. On return,\n",
+      "    self.x and self.P contain the new mean and covariance of the filter.\n",
+      "\n",
+      "    **Parameters**\n",
+      "\n",
+      "    z : numpy.array of shape (dim_z)\n",
+      "        measurement vector\n",
+      "    \"\"\"\n",
+      "\n",
+      "    # rename for readability\n",
+      "    sigmas_f = self.sigmas_f\n",
+      "    sigmas_h = self.sigmas_h\n",
+      "\n",
+      "    # transform sigma points into measurement space\n",
+      "    for i in range(self._num_sigmas):\n",
+      "        sigmas_h[i] = self.hx(sigmas_f[i])\n",
+      "\n",
+      "    # mean and covariance of prediction passed through inscented transform\n",
+      "    zp, Pz = unscented_transform(sigmas_h, self.W, self.W, self.R)\n",
+      "\n",
+      "    # compute cross variance of the state and the measurements\n",
+      "    Pxz = zeros((self._dim_x, self._dim_z))\n",
+      "    for i in range(self._num_sigmas):\n",
+      "        Pxz += self.W[i] * np.outer(sigmas_f[i] - self.xp,\n",
+      "                                    sigmas_h[i] - zp))\n",
+      "\n",
+      "    K = dot(Pxz, inv(Pz)) # Kalman gain\n",
+      "\n",
+      "    self.x = self.xp + dot(K, z-zp)\n",
+      "    self.P = self.Pp - dot3(K, Pz, K.T)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 45
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### Full Source from FilterPy\n",
+      "\n",
+      "Without further explanation, here is the full source from FilterPy."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from __future__ import (absolute_import, division, print_function,\n",
+      "                        unicode_literals)\n",
+      "\n",
+      "from numpy.linalg import inv, cholesky\n",
+      "import numpy as np\n",
+      "from numpy import asarray, eye, zeros, dot\n",
+      "from filterpy.common import dot3\n",
+      "\n",
+      "\n",
+      "class UnscentedKalmanFilter(object):\n",
+      "    \"\"\" Implements the Unscented Kalman filter (UKF) as defined by Simon J.\n",
+      "    Julier and Jeffery K. Uhlmann [1]. Succintly, the UKF selects a set of\n",
+      "    sigma points and weights inside the covariance matrix of the filter's\n",
+      "    state. These points are transformed through the nonlinear process being\n",
+      "    filtered, and are rebuilt into a mean and covariance by computed the\n",
+      "    weighted mean and expected value of the transformed points. Read the paper;\n",
+      "    it is excellent. My book \"Kalman and Bayesian Filters in Python\" [2]\n",
+      "    explains the algorithm, develops this code, and provides examples of the\n",
+      "    filter in use.\n",
+      "\n",
+      "\n",
+      "    You will have to set the following attributes after constructing this\n",
+      "    object for the filter to perform properly.\n",
+      "\n",
+      "    **Attributes**\n",
+      "\n",
+      "    x : numpy.array(dim_x)\n",
+      "        state estimate vector\n",
+      "\n",
+      "    P : numpy.array(dim_x, dim_x)\n",
+      "        covariance estimate matrix\n",
+      "\n",
+      "    R : numpy.array(dim_z, dim_z)\n",
+      "        measurement noise matrix\n",
+      "\n",
+      "    Q : numpy.array(dim_x, dim_x)\n",
+      "        process noise matrix\n",
+      "\n",
+      "\n",
+      "    You may read the following attributes.\n",
+      "\n",
+      "    **Readable Attributes**\n",
+      "\n",
+      "    xp : numpy.array(dim_x)\n",
+      "        predicted state (result of predict())\n",
+      "\n",
+      "    Pp : numpy.array(dim_x, dim_x)\n",
+      "        predicted covariance matrix (result of predict())\n",
+      "\n",
+      "\n",
+      "    **References**\n",
+      "\n",
+      "    .. [1] Julier, Simon J. \"A New Extension of the Kalman Filter to Nonlinear\n",
+      "        Systems\". Proc. SPIE 3068, Signal Processing, Sensor Fusion, and\n",
+      "        Target Recognition VI, 182 (July 28, 1997)\n",
+      "\n",
+      "    .. [2] Labbe, Roger R. \"Kalman and Bayesian Filters in Python\"\n",
+      "\n",
+      "        https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python\n",
+      "    \"\"\"\n",
+      "\n",
+      "    def __init__(self, dim_x, dim_z, dt, hx, fx, kappa=0.):\n",
+      "        \"\"\" Create a Kalman filter. You are responsible for setting the\n",
+      "        various state variables to reasonable values; the defaults below will\n",
+      "        not give you a functional filter.\n",
+      "\n",
+      "        **Parameters**\n",
+      "\n",
+      "        dim_x : int\n",
+      "            Number of state variables for the filter. For example, if\n",
+      "            you are tracking the position and velocity of an object in two\n",
+      "            dimensions, dim_x would be 4.\n",
+      "\n",
+      "\n",
+      "        dim_z : int\n",
+      "            Number of of measurement inputs. For example, if the sensor\n",
+      "            provides you with position in (x,y), dim_z would be 2.\n",
+      "\n",
+      "        dt : float\n",
+      "            Time between steps in seconds.\n",
+      "\n",
+      "        hx : function(x)\n",
+      "            Measurement function. Converts state vector x into a measurement\n",
+      "            vector of shape (dim_z).\n",
+      "\n",
+      "        fx : function(x,dt)\n",
+      "            function that returns the state x transformed by the\n",
+      "            state transistion function. dt is the time step in seconds.\n",
+      "\n",
+      "        kappa : float, default=0.\n",
+      "            Scaling factor that can reduce high order errors. kappa=0 gives\n",
+      "            the standard unscented filter. According to [1], if you set\n",
+      "            kappa to 3-dim_x for a Gaussian x you will minimize the fourth\n",
+      "            order errors in x and P.\n",
+      "\n",
+      "        **References**\n",
+      "\n",
+      "        [1] S. Julier, J. Uhlmann, and H. Durrant-Whyte. \"A new method for\n",
+      "            the nonlinear transformation of means and covariances in filters\n",
+      "            and estimators,\" IEEE Transactions on Automatic Control, 45(3),\n",
+      "            pp. 477-482 (March 2000).\n",
+      "        \"\"\"\n",
+      "\n",
+      "        self.Q = eye(dim_x)\n",
+      "        self.R = eye(dim_z)\n",
+      "        self.x = zeros(dim_x)\n",
+      "        self.P = eye(dim_x)\n",
+      "        self.xp = None\n",
+      "        self.Pp = None\n",
+      "        self._dim_x = dim_x\n",
+      "        self._dim_z = dim_z\n",
+      "        self._dt = dt\n",
+      "        self._num_sigmas = 2*dim_x + 1\n",
+      "        self.kappa = kappa\n",
+      "        self.hx = hx\n",
+      "        self.fx = fx\n",
+      "\n",
+      "        # weights for the sigma points\n",
+      "        self.W = self.weights(dim_x, kappa)\n",
+      "\n",
+      "        # sigma points transformed through f(x) and h(x)\n",
+      "        # variables for efficiency so we don't recreate every update\n",
+      "        self.sigmas_f = zeros((2*self._dim_x+1, self._dim_x))\n",
+      "        self.sigmas_h = zeros((self._num_sigmas, self._dim_z))\n",
+      "\n",
+      "\n",
+      "    def update(self, z, residual=np.subtract, UT=None):\n",
+      "        \"\"\" Update the UKF with the given measurements. On return,\n",
+      "        self.x and self.P contain the new mean and covariance of the filter.\n",
+      "\n",
+      "        **Parameters**\n",
+      "\n",
+      "        z : numpy.array of shape (dim_z)\n",
+      "            measurement vector\n",
+      "\n",
+      "        residual : function (z, z2), optional\n",
+      "            Optional function that computes the residual (difference) between\n",
+      "            the two measurement vectors. If you do not provide this, then the\n",
+      "            built in minus operator will be used. You will normally want to use\n",
+      "            the built in unless your residual computation is nonlinear (for\n",
+      "            example, if they are angles)\n",
+      "\n",
+      "        UT : function(sigmas, Wm, Wc, noise_cov), optional\n",
+      "            Optional function to compute the unscented transform for the sigma\n",
+      "            points passed through hx. Typically the default function will\n",
+      "            work, but if for example you are using angles the default method\n",
+      "            of computing means and residuals will not work, and you will have\n",
+      "            to define how to compute it.\n",
+      "        \"\"\"\n",
+      "\n",
+      "        # rename for readability\n",
+      "        sigmas_f = self.sigmas_f\n",
+      "        sigmas_h = self.sigmas_h\n",
+      "        W = self.W\n",
+      "\n",
+      "        if UT is None:\n",
+      "            UT = unscented_transform\n",
+      "\n",
+      "        # transform sigma points into measurement space\n",
+      "        for i in range(self._num_sigmas):\n",
+      "            sigmas_h[i] = self.hx(sigmas_f[i])\n",
+      "\n",
+      "        # mean and covariance of prediction passed through inscented transform\n",
+      "        zp, Pz = UT(sigmas_h, self.W, self.W, self.R)\n",
+      "\n",
+      "        # compute cross variance of the state and the measurements\n",
+      "        Pxz = zeros((self._dim_x, self._dim_z))\n",
+      "        for i in range(self._num_sigmas):\n",
+      "            Pxz += self.W[i] * np.outer(sigmas_f[i] - self.xp,\n",
+      "                                        residual(sigmas_h[i], zp))\n",
+      "\n",
+      "        K = dot(Pxz, inv(Pz)) # Kalman gain\n",
+      "\n",
+      "        y = residual(z, zp)\n",
+      "\n",
+      "        self.x = self.xp + dot(K, y)\n",
+      "        self.P = self.Pp - dot3(K, Pz, K.T)\n",
+      "\n",
+      "\n",
+      "    def predict(self):\n",
+      "        \"\"\" Performs the predict step of the UKF. On return, self.xp and\n",
+      "        self.Pp contain the predicted state (xp) and covariance (Pp). 'p'\n",
+      "        stands for prediction.\n",
+      "\n",
+      "        Important: this MUST be called before update() is called for the first\n",
+      "        time.\n",
+      "        \"\"\"\n",
+      "\n",
+      "        # calculate sigma points for given mean and covariance\n",
+      "        sigmas = self.sigma_points(self.x, self.P, self.kappa)\n",
+      "\n",
+      "        for i in range(self._num_sigmas):\n",
+      "            self.sigmas_f[i] = self.fx(sigmas[i], self._dt)\n",
+      "\n",
+      "        self.xp, self.Pp = unscented_transform(\n",
+      "                           self.sigmas_f, self.W, self.W, self.Q)\n",
+      "\n",
+      "\n",
+      "\n",
+      "    @staticmethod\n",
+      "    def weights(n, kappa):\n",
+      "        \"\"\" Computes the weights for an unscented Kalman filter.  See\n",
+      "        __init__() for meaning of parameters.\n",
+      "        \"\"\"\n",
+      "\n",
+      "        k = .5 / (n+kappa)\n",
+      "        W = np.full(2*n+1, k)\n",
+      "        W[0] = kappa / (n+kappa)\n",
+      "        return W\n",
+      "\n",
+      "\n",
+      "    @staticmethod\n",
+      "    def sigma_points(x, P, kappa):\n",
+      "        \"\"\" Computes the sigma pointsfor an unscented Kalman filter\n",
+      "        given the mean (x) and covariance(P) of the filter.\n",
+      "        kappa is an arbitrary constant. Returns tuple of the sigma points\n",
+      "        and weights.\n",
+      "\n",
+      "        Works with both scalar and array inputs:\n",
+      "        sigma_points (5, 9, 2) # mean 5, covariance 9\n",
+      "        sigma_points ([5, 2], 9*eye(2), 2) # means 5 and 2, covariance 9I\n",
+      "\n",
+      "        **Parameters**\n",
+      "\n",
+      "        X An array-like object of the means of length n\n",
+      "            Can be a scalar if 1D.\n",
+      "            examples: 1, [1,2], np.array([1,2])\n",
+      "\n",
+      "        P : scalar, or np.array\n",
+      "           Covariance of the filter. If scalar, is treated as eye(n)*P.\n",
+      "\n",
+      "        kappa : float\n",
+      "            Scaling factor.\n",
+      "\n",
+      "        **Returns**\n",
+      "\n",
+      "        sigmas : np.array, of size (n, 2n+1)\n",
+      "            2D array of sigma points. Each column contains all of\n",
+      "            the sigmas for one dimension in the problem space. They\n",
+      "            are ordered as:\n",
+      "\n",
+      "            .. math::\n",
+      "                sigmas[0]    = x \\n\n",
+      "                sigmas[1..n] = x + [\\sqrt{(n+\\kappa)P}]_k \\n\n",
+      "                sigmas[n+1..2n] = x - [\\sqrt{(n+\\kappa)P}]_k\n",
+      "        \"\"\"\n",
+      "\n",
+      "        if np.isscalar(x):\n",
+      "            x = asarray([x])\n",
+      "        n = np.size(x)  # dimension of problem\n",
+      "\n",
+      "        if  np.isscalar(P):\n",
+      "            P = eye(n)*P\n",
+      "\n",
+      "        Sigmas = zeros((2*n+1, n))\n",
+      "\n",
+      "        # implements U'*U = (n+kappa)*P. Returns lower triangular matrix.\n",
+      "        # Take transpose so we can access with U[i]\n",
+      "        U = cholesky((n+kappa)*P).T\n",
+      "\n",
+      "        for k in range(n):\n",
+      "            Sigmas[k+1]   = x + U[k]\n",
+      "            Sigmas[n+k+1] = x - U[k]\n",
+      "\n",
+      "        # handle value for the mean separately as special case\n",
+      "        Sigmas[0] = x\n",
+      "\n",
+      "        return Sigmas\n",
+      "\n",
+      "\n",
+      "def unscented_transform(Sigmas, Wm, Wc, noise_cov):\n",
+      "    \"\"\" Computes unscented transform of a set of sigma points and weights.\n",
+      "    returns the mean and covariance in a tuple.\n",
+      "    \"\"\"\n",
+      "\n",
+      "    kmax, n = Sigmas.shape\n",
+      "\n",
+      "    # new mean is just the sum of the sigmas * weight\n",
+      "    x = dot(Wm, Sigmas)    # \\Sigma^n_1 (W[k]*Xi[k])\n",
+      "\n",
+      "    # new covariance is the sum of the outer product of the residuals\n",
+      "    # times the weights\n",
+      "    P = zeros((n, n))\n",
+      "    for k in range(kmax):\n",
+      "        y = Sigmas[k] - x\n",
+      "        P += Wc[k] * np.outer(y, y)\n",
+      "\n",
+      "    return (x, P + noise_cov)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 50
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "### The Scaled Unscented Transform\n",
+      "\n",
+      "todo"
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Nonlinear State Variables\n",
+      "\n",
+      "todo"
+     ]
     },
     {
      "cell_type": "markdown",
@@ -1877,7 +2399,9 @@
      "source": [
       "## References\n",
       "\n",
-      "[1] Simon, Dan. *Optimal State Estimation*, John Wiley & Sons, 2006."
+      "- [1] Simon, Dan. *Optimal State Estimation*, John Wiley & Sons, 2006.\n",
+      "\n",
+      "- [2] Julier, Simon J.; Uhlmann, Jeffrey \"A New Extension of the Kalman  Filter to Nonlinear Systems\". Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, 182 (July 28, 1997)"
      ]
     }
    ],