Fixed notebooks for FilterPy code reorg.

I made a separate filterpy.stats module becuase it made
little sense to import filterpy.common for stats
functions. This required a lot of changes in the notebooks
and supporting code.

03_Gaussians.ipynb

@@ -458,7 +458,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7769668>"
+       "<matplotlib.figure.Figure at 0x6b6a6a0>"
       ]
      },
      "metadata": {},
@@ -493,7 +493,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80a8ba8>"
+       "<matplotlib.figure.Figure at 0x7bb3048>"
       ]
      },
      "metadata": {},
@@ -680,7 +680,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAACxCAYAAAALSUEOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEhkAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87ab7f0>"
+       "<matplotlib.figure.Figure at 0x7be3ba8>"
       ]
      },
      "metadata": {},
@@ -775,7 +775,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAEtCAYAAADDb0y/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlUVeXixvHvOYdBQEBFQQVnUXMWwXlMxdlMy8rK7JYN\nV8u06VrdbNBKm27dypuaZl0zpwbnWXHCARU1Z0VxQHBCUJDpnPP7g/vbReaEyGZ4Pmuxlu+7N/Cw\nF8rjZp/3tTidTiciIiIiInLbrGYHEBEREREpKlSuRURERETyiMq1iIiIiEgeUbkWEREREckjKtci\nIiIiInlE5VpEREREJI+oXIuIiIiI5JHrlusvv/ySRo0a4evri6+vL61atWLRokXX/YC7d++mffv2\neHp6EhQUxLvvvpungUVERERECiqX6x2sVKkS48ePJzg4GIfDwbfffkvfvn3ZunUrjRo1uur85ORk\nunTpQocOHYiKimLfvn08/vjjeHl5MXLkyDv2RYiIiIiIFASWW92h0c/Pjw8++IAhQ4ZcdWzChAmM\nGjWKhIQE3N3dARg7diwTJkzg5MmTeZNYRERERKSAuulnru12Oz/++CNpaWm0a9fuL8+JjIykbdu2\nRrEGCA8PJy4ujtjY2NtPKyIiIiJSgF33sRDIfoa6ZcuWpKen4+HhwaxZs6hdu/ZfnhsfH0/lypVz\nzAUEBBjHqlSpkgeRRUREREQKphuW6zp16rBr1y6SkpKYPXs2Dz74IKtXryY0NPSqcy0Wyy198qSk\npFs6X0RERESkIPH19c0xvmG5dnV1pXr16gA0adKErVu38uWXXzJ16tSrzi1fvjzx8fE55hISEoxj\nIiIiIiJF2S2vc22323E4HH95rGXLlqxbt4709HRjbvny5QQGBuqREBEREREp8q575/of//gHvXr1\nIigoiEuXLvHDDz8QERHBkiVLABg1ahRbt25lxYoVAAwcOJC3336bwYMH88Ybb3DgwAHGjRvHW2+9\ndcMgf76lfqdERUUB/OVjLXJ9una5o+uWe7p2uadrl3u6drmna5d7una5Y9Z1u96jzdct1wkJCTzy\nyCPEx8fj6+tLo0aNWLJkCV26dAGyX6QYExNjnO/j48Py5csZOnQooaGhlClThpdeeokRI0bk0Zci\nIiIiIlJwXbdc/9Vz1Tc6Xr9+fSIiIm4vlYiIiIhIIXTLz1yLiIiIiMhfU7kWEREREckjKtciIiIi\nInlE5VpEREREJI+oXIuIiIiI5BGVaxERERGRPKJyLSIiIiKSR667zrWIiBRcdoed5JQLJKdcJDk1\nkUupSWRmpZOZlUHs8WMAJHIcF5sLbi7ulPTwwduzND5epfD18sPVxdXcL0BEpAhSuRYRKQSSUxI5\nFn+Q4wmHiL9wkoTEk5y7GI/dkXXd99t5Yu1fzlssVsr4lCOgdBABpQOpHFCTKuVr4ecTgMViuRNf\ngohIsaByLSJSAF1KTeLA8Wj2xe7gyKk9XLh0Nk8/vtPp4HxSAueTEth7bJsxX9LDl+oV76JO5cbc\nVaUJfr4Befp5RUSKOpVrEZEC4nxSAjsObSD6cCQnEg7jxHnD9/H2LIWvVxl8PEvh7VkKdzcPXF1c\nOZtwDoByAeXIsmeSnpnG5dQkklMvciklkYspF3A6HVd9vMtXkth1ZBO7jmwCwL9URRrWbEmT4NYE\nlaumu9oiIjegci0iYqLU9MtE7Y9gy741HE84dM3zXG1uVPKvQdUKtahYtirly1TCv3QgJdw8/vL8\nqKgoAEJDQ//yeGZWBmcvniYh8RRx544RG3+Q2PiDXMlIzXHemYtxrIiay4qouZQrVZGmtdvSom5n\nyviUy+VXLCJStKlci4jkM6fTybH4g2zcvZTth9aTmZVx1TlWi5WqFWpzV5Um1KncmKBy1bHZ8u6f\nbFcXNyqWrULFslVoEtwKAIfTQcKFUxw4Hs3+2B0cOvVbjmxnL8axZPNMlm6exV1VQ2hVP5z61UKx\nWm15lktEpLBTuRYRyScOh52dRzazImouJ84cueq4zepCncqNaVKrNfWrh+HpXjJf81ktVir4VaKC\nXyU6NOlNZlYG+49Hs+PgBnbHbCY9Mw0AJ072HtvG3mPbKOtbnrtD+tKsbkfcXNzzNa+ISEGkci0i\ncodl2TPZuj+ClVE/ceZi3FXHA8tWpVWDrjSt1RbPEvlbqK/H1cWNBtWb0aB6MzKy0vktZiuRvy3n\nwImdxjnnkuKZtfo/LNo0g3aNetCucc98/0+BiEhBonItInKHOBx2tu6PYNGmGST+abUPV5sbIbXb\n0rpBV6oEBBf4Fwq6ubgTUqsNIbXacPbiaSL3rGDj7qWkpl8Gsl8IuWjTDFbvmEfn0P60b9QTN1fd\nyRaR4kflWkQkjzmd2Y9NzNvwHafPH89xzMPNk7aNetC+cS+8PUuZlPD2lCtVgT6tH6Vr2H1s3LOc\nNdvnkXg5e3WSK+kpzN/wHRHR8+nabACt6nXJ02fFRUQKOv2LJyKSh06dPcqciMkcObUnx3xJD186\nhtxDmwZd8XD3Mild3nJ386Bjkz60a9iDbQfXsWTzTM4lxQPZm97MXv01a6MXcl+HIdSu3MjktCIi\n+UPlWkQkD1xJT2XRph9Yt3MRjj+sH+3mWoJOIX3pGHLPNZfNK+xsNhea3dWRprXasmnvShZv/pHk\nlEQAEhJP8uXPo2lUsyX3tv2blvATkSJP5VpE5DY4nU62HVjLL+u+JTk10Zi3Wm20adCV8LAB+HgV\nzsc/bpXN5kLrBl0Jq9OBiJ0LWbZ1NukZVwDYeTiSvce20bXZADqF9NWjIiJSZOlfNxGRXLp4+Tw/\nrvwqx/bhALWCGnB/x6cJKBNkUjJzubm60yW0H83u6sD8Dd+zZd9qIHvjmgUb/0v0oY0M7DKMoHLV\nTU4qIpL3VK5FRG6R0+lky75V/BTxTY4dDX28SnNv278RUqtNgV/9Iz/4epXhkfDhtKofzuw1Ezl1\n9igAJ8/G8NGPL9MltD/hYffj6uJqclIRkbyjci0icguSLl9gxsovr7pb3a5RT3q2fBgPd0+TkhVc\n1SvexUsPfsSaHfNYGPkDWfZMHA47S7fMYveRzQzqNpKKZauYHVNEJE+oXIuI3KQ9R6P47/LPSbmS\nbMyV9S3PwC7PUTOwnonJCj6b1UanpvdSv3ozZiz/gpjT+wCIOx/LRz++RN+2g2nbsIfu+ItIoady\nLSJyA5lZmczbMI2I6AU55ts37kWvVo/g7lrCpGSFT0DpQJ6/fyzrdi5i3obvyMzKIMueyZw1k9gX\nu4OBnZ/D29PX7JgiIrlmNTuAiEhBdibxFJ/MeiVHsfb1KsOwfu/Sv/2TKta5YLVYad+4Fy89+DGB\nZasa83uORjFu+gsc/tMa4SIihYnKtYjINew6sokPf3zJeCEeQP3qzXj14X9Rq1IDE5MVDRX8KjHy\ngQ/p0KSPMZecmsgXc//J6u3zcDqdJqYTEckdPRYiIvInDoedRZt+ZNnW2caci82Vvm0fp23D7nou\nOA+5urjSr93fqFO5Mf9d9hmXryThcDr4ed0UjsUfYGDnYbgX0c13RKRo0p1rEZE/SEm7xH/mjclR\nrMv4+DNiwDjaNdIL7u6UulVDePmhj6hSvpYxt+PQBj6a+TIJF06amExE5NbcsFy///77hIWF4evr\ni7+/P3369GHPnus/D3fs2DGsVutVb8uWLcuz4CIieS3u3DE+mvES+2N3GHN1qjTh5Yc+ppK/Njy5\n00p7l+P5/mNp07C7MZdw4SQfzXyZvce2m5hMROTm3bBcR0REMGzYMCIjI1m1ahUuLi507tyZxMTE\nG70rS5cuJT4+3njr2LFjnoQWEclre45G8emsf3A+OcGYCw+7n2f6vIFXCW8TkxUvri6uDOj4NI+E\nD8fV5gZAesYVvp43hrU7F5qcTkTkxm74zPWSJUtyjL///nt8fX3ZuHEjPXv2vO77lilTBn9//9tL\nKCJyh63duYi5EZNxOh0AuLt58EiX4TSq2cLkZMVXs7s6UrFsFSbNe4/Ey+dwOh3MWTOJhAun6Nf+\nCWxWm9kRRUT+0i0/c52cnIzD4aB06dI3PLdfv34EBATQpk0b5s6dm6uAIiJ3isNhZ27EZOasmWgU\n69Le5Rhx/wcq1gVAULnqvPjgh1QJCDbm1u1axNfzxnAlPcXEZCIi13bL5Xr48OE0adKEli1bXvMc\nb29vPv74Y2bPns3ixYvp1KkTDzzwANOnT7+tsCIieSU94wqT5r+fY/3qKgHBvPjAeG3FXYD4eJXm\nufvGEFKrjTG3P3YH/5o9isRL50xMJiLy1yzOW1hIdOTIkcyaNYv169dTtWrVW/pEw4YNY926dezc\nudOYS0pKMv586NChW/p4IiK5lZaZwsq9Mzl/Oc6Yq+J3F62D++BiczUxmVyL0+lk54m17Dqxzpjz\ndPOhc72BlPIsa2IyESmOgoN//42ar2/OXWVv+s71iBEjmDlzJqtWrbrlYg0QFhamAi0iprucdpEl\nu6blKNb1A1vRrnY/FesCzGKx0Lhye9oE34PFkv2jKzUjmSW7v+VM8gmT04mI/O6mNpEZPnw4s2fP\nZvXq1dSqVevG7/AXoqOjqVix4jWPh4aG5urj3qqoqKh8/XxFia5d7ui65V5eX7u4c7FM+OUrktMu\nAGDBwv0dn6ZNw2558vELkqL6fRdKKA1iG/PNwnFkZKaRkZXGyn0zGNz9JRpUb5Ynn6OoXrv8oGuX\ne7p2uWPWdfvj0xd/dsM710OHDuXbb79l+vTp+Pr6GsvqpaT8/mKSUaNG0blzZ2M8bdo0ZsyYwb59\n+zhw4AAfffQRX331Fc8999xtfikiIrlz5NRePpvzGkkp2cXaZnNhcI+Xi2SxLuruqLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87b4828>"
+       "<matplotlib.figure.Figure at 0x7be7588>"
       ]
      },
      "metadata": {},
@@ -783,8 +783,7 @@
     }
    ],
    "source": [
-    "from stats import plot_gaussian\n",
-    "\n",
+    "from filterpy.stats import plot_gaussian\n",
     "plot_gaussian(mean=1.8, variance=0.1414**2, xlabel='Student Height')"
    ]
   },
@@ -813,7 +812,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88f4b00>"
+       "<matplotlib.figure.Figure at 0x7d2deb8>"
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      },
      "metadata": {},
@@ -851,7 +850,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x88189b0>"
+       "<matplotlib.figure.Figure at 0x7cc4b38>"
       ]
      },
      "metadata": {},
@@ -931,7 +930,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8065080>"
+       "<matplotlib.figure.Figure at 0x7b9ee80>"
       ]
      },
      "metadata": {},
@@ -939,7 +938,7 @@
     }
    ],
    "source": [
-    "from stats import gaussian, norm_cdf\n",
+    "from filterpy.stats import gaussian, norm_cdf\n",
     "plot_gaussian(22, 4, mean_line=True, xlabel='$^{\\circ}C$')"
    ]
   },
@@ -964,12 +963,12 @@
     "\n",
     "$$ \\int^{x_1}_{x_0}  \\frac{1}{\\sigma\\sqrt{2\\pi}} e^{-\\frac{1}{2}{(x-\\mu)^2}/\\sigma^2 } dx$$\n",
     "\n",
-    "I wrote `stats.norm_cdf` which computes the integral for you. So, for example, we can compute"
+    "I wrote `filterpy.stats.norm_cdf` which computes the integral for you. So, for example, we can compute"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 16,
    "metadata": {
     "collapsed": false
    },
@@ -1059,7 +1058,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x883e240>"
+       "<matplotlib.figure.Figure at 0x7c7a080>"
       ]
      },
      "metadata": {},
@@ -1107,7 +1106,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x87f5d30>"
+       "<matplotlib.figure.Figure at 0x7cdb630>"
       ]
      },
      "metadata": {},
@@ -1152,7 +1151,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a1d5c0>"
+       "<matplotlib.figure.Figure at 0x8e50940>"
       ]
      },
      "metadata": {},
@@ -1292,9 +1291,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.50612396 -2.0611277  -0.34801741  0.7494353  -0.27528631  3.44204351\n",
-      "  2.96201651  5.94330752  4.28180156 -1.45836258  3.32654708  5.5268677\n",
-      " -0.84579705 -2.56359567 -2.45896645]\n"
+      "[-0.42731687  6.28321669  2.92563648  1.95575684  4.66942384  2.42647457\n",
+      " -0.48271159  3.8215393   5.59190768 -1.88855789  2.04227006  0.49550218\n",
+      "  1.22948499  2.59064287  2.36829778]\n"
      ]
     }
    ],
@@ -1395,7 +1394,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x99f4940>"
+       "<matplotlib.figure.Figure at 0x8dfa080>"
       ]
      },
      "metadata": {},
@@ -1452,9 +1451,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x997e9b0>"
+       "<matplotlib.figure.Figure at 0x8edec18>"
       ]
      },
      "metadata": {},
@@ -1505,9 +1504,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAADaCAYAAADqvoesAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAADaCAYAAADqvoesAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9aea0b8>"
+       "<matplotlib.figure.Figure at 0x8ee5a90>"
       ]
      },
      "metadata": {},
@@ -1533,9 +1532,9 @@
     "\n",
     "This is not an idle concern. The Kalman filter equations assume the noise is normally distributed, and perform sub-optimally if this is not true. Designers for mission critical filters, such as the filters on spacecraft, need to master a lot of theory and emperical knowledge about the performance of the sensors on their spacecraft. \n",
     "\n",
-    "The code for rand_student_t is included in `filterpy.common`. You may use it with\n",
+    "The code for rand_student_t is included in `filterpy.stats`. You may use it with\n",
     "\n",
-    "    from filterpy.common import rand_student_t"
+    "    from filterpy.stats import rand_student_t"
    ]
   },
   {

04_One_Dimensional_Kalman_Filters.ipynb

@@ -433,7 +433,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3wAAAEWCAYAAAA95JU3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6c5ae80>"
+       "<matplotlib.figure.Figure at 0x6f97ba8>"
       ]
      },
      "metadata": {},
@@ -474,9 +474,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ce98d0>"
+       "<matplotlib.figure.Figure at 0x70286d8>"
       ]
      },
      "metadata": {},
@@ -530,9 +530,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6c5a978>"
+       "<matplotlib.figure.Figure at 0x6f97710>"
       ]
      },
      "metadata": {},
@@ -553,9 +553,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7485748>"
+       "<matplotlib.figure.Figure at 0x77b25c0>"
       ]
      },
      "metadata": {},
@@ -586,7 +586,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7734f98>"
+       "<matplotlib.figure.Figure at 0x792ff60>"
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@@ -619,7 +619,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x773e4a8>"
+       "<matplotlib.figure.Figure at 0x7a5e6d8>"
       ]
      },
      "metadata": {},
@@ -655,7 +655,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a6c518>"
+       "<matplotlib.figure.Figure at 0x7c4db70>"
       ]
      },
      "metadata": {},
@@ -663,7 +663,7 @@
     }
    ],
    "source": [
-    "import stats\n",
+    "import filterpy.stats as stats\n",
     "with figsize(y=3):\n",
     "    stats.plot_gaussian(mean=23, variance=5)"
    ]
@@ -692,7 +692,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75e7c50>"
+       "<matplotlib.figure.Figure at 0x6f97e48>"
       ]
      },
      "metadata": {},
@@ -781,7 +781,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76a2a90>"
+       "<matplotlib.figure.Figure at 0x79815f8>"
       ]
      },
      "metadata": {},
@@ -842,7 +842,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x76a2c18>"
+       "<matplotlib.figure.Figure at 0x7983390>"
       ]
      },
      "metadata": {},
@@ -894,7 +894,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7883898>"
+       "<matplotlib.figure.Figure at 0x7b6ac88>"
       ]
      },
      "metadata": {},
@@ -1168,7 +1168,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x798ac50>"
+       "<matplotlib.figure.Figure at 0x7c8ab00>"
       ]
      },
      "metadata": {},
@@ -1229,7 +1229,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7947ef0>"
+       "<matplotlib.figure.Figure at 0x794f4e0>"
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      "metadata": {},
@@ -1347,7 +1347,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79f4c88>"
+       "<matplotlib.figure.Figure at 0x7e00c18>"
       ]
      },
      "metadata": {},
@@ -1539,7 +1539,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a21d68>"
+       "<matplotlib.figure.Figure at 0x7cb3208>"
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      },
      "metadata": {},
@@ -1569,7 +1569,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x786fb00>"
+       "<matplotlib.figure.Figure at 0x7c21a58>"
       ]
      },
      "metadata": {},
@@ -1726,7 +1726,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x783e8d0>"
+       "<matplotlib.figure.Figure at 0x7b516d8>"
       ]
      },
      "metadata": {},
@@ -1736,7 +1736,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x770fe10>"
+       "<matplotlib.figure.Figure at 0x79d4eb8>"
       ]
      },
      "metadata": {},
@@ -1872,7 +1872,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77c8208>"
+       "<matplotlib.figure.Figure at 0x7b73588>"
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      },
      "metadata": {},
@@ -1938,7 +1938,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79cbfd0>"
+       "<matplotlib.figure.Figure at 0x8563c18>"
       ]
      },
      "metadata": {},
@@ -1948,7 +1948,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b41f60>"
+       "<matplotlib.figure.Figure at 0x7b5e320>"
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      },
      "metadata": {},
@@ -2030,7 +2030,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81de208>"
+       "<matplotlib.figure.Figure at 0x8563160>"
       ]
      },
      "metadata": {},
@@ -2096,7 +2096,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81fb320>"
+       "<matplotlib.figure.Figure at 0x82fd4a8>"
       ]
      },
      "metadata": {},
@@ -2162,7 +2162,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81fb240>"
+       "<matplotlib.figure.Figure at 0x8381710>"
       ]
      },
      "metadata": {},
@@ -2215,7 +2215,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9346390>"
+       "<matplotlib.figure.Figure at 0x83817f0>"
       ]
      },
      "metadata": {},
@@ -2335,7 +2335,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9352470>"
+       "<matplotlib.figure.Figure at 0x8646390>"
       ]
      },
      "metadata": {},
@@ -2441,7 +2441,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x934f438>"
+       "<matplotlib.figure.Figure at 0x8643940>"
       ]
      },
      "metadata": {},
@@ -2531,7 +2531,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuEAAAEWCAYAAADIP0muAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x935a9e8>"
+       "<matplotlib.figure.Figure at 0x7b617f0>"
       ]
      },
      "metadata": {},

05_Multivariate_Kalman_Filters.ipynb

@@ -390,9 +390,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x442f400>"
+       "<matplotlib.figure.Figure at 0x560a4a8>"
       ]
      },
      "metadata": {},
@@ -449,7 +449,7 @@
    },
    "outputs": [],
    "source": [
-    "from filterpy.common import gaussian, multivariate_gaussian"
+    "from filterpy.stats import gaussian, multivariate_gaussian"
    ]
   },
   {
@@ -559,9 +559,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x442f2e8>"
+       "<matplotlib.figure.Figure at 0x560a2e8>"
       ]
      },
      "metadata": {},
@@ -596,9 +596,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8562898>"
+       "<matplotlib.figure.Figure at 0x560a320>"
       ]
      },
      "metadata": {},
@@ -635,7 +635,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b5fa58>"
+       "<matplotlib.figure.Figure at 0x85b4d68>"
       ]
      },
      "metadata": {},
@@ -673,7 +673,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b6a208>"
+       "<matplotlib.figure.Figure at 0x9b13a58>"
       ]
      },
      "metadata": {},
@@ -681,7 +681,7 @@
     }
    ],
    "source": [
-    "from filterpy.common import plot_covariance_ellipse\n",
+    "from filterpy.stats import plot_covariance_ellipse\n",
     "P = [[2, 0], [0, 9]]\n",
     "plot_covariance_ellipse((2, 7), P, facecolor='g', alpha=0.2, \n",
     "                        variance=[1, 2**2, 3**2],\n",
@@ -706,7 +706,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8bc9c50>"
+       "<matplotlib.figure.Figure at 0x9471fd0>"
       ]
      },
      "metadata": {},
@@ -784,7 +784,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f82f60>"
+       "<matplotlib.figure.Figure at 0x864f7b8>"
       ]
      },
      "metadata": {},
@@ -820,7 +820,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c587f0>"
+       "<matplotlib.figure.Figure at 0x9471470>"
       ]
      },
      "metadata": {},
@@ -863,7 +863,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8599e80>"
+       "<matplotlib.figure.Figure at 0x86af860>"
       ]
      },
      "metadata": {},
@@ -880,7 +880,7 @@
    "source": [
     "The combination of measurement 1 and 2 yields more certainty, so the new Gaussian is taller and narrower - the variance became smaller. The same thing happens in multiple dimensions with multivariate Gaussians.\n",
     "\n",
-    "Here are the equations for multiplying multivariate Gaussians. You will not need to remember these equations, as they are computed by Kalman filter equations that will be presented shortly. This computation is also available in FilterPy using the `multivariate_multiply()` method, which you can import from `filterpy.common`. \n",
+    "Here are the equations for multiplying multivariate Gaussians. You will not need to remember these equations, as they are computed by Kalman filter equations that will be presented shortly. This computation is also available in FilterPy using the `multivariate_multiply()` method, which you can import from `filterpy.stats`. \n",
     "\n",
     "$$\\begin{aligned}\n",
     "\\mu &= \\Sigma_2(\\Sigma_1 + \\Sigma_2)^{-1}\\mu_1 + \\Sigma_1(\\Sigma_1 + \\Sigma_2)^{-1}\\mu_2 \\\\\n",
@@ -917,7 +917,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x865fc88>"
+       "<matplotlib.figure.Figure at 0x9b8a048>"
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      },
      "metadata": {},
@@ -948,7 +948,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81d7748>"
+       "<matplotlib.figure.Figure at 0x9b527b8>"
       ]
      },
      "metadata": {},
@@ -979,7 +979,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ad7390>"
+       "<matplotlib.figure.Figure at 0x9420be0>"
       ]
      },
      "metadata": {},
@@ -987,7 +987,7 @@
     }
    ],
    "source": [
-    "from filterpy.common import multivariate_multiply\n",
+    "from filterpy.stats import multivariate_multiply\n",
     "\n",
     "P2 = multivariate_multiply((10, 10), P0, (10, 10), P1)[1]\n",
     "plot_covariance_ellipse((10, 10), P0, facecolor='y', alpha=0.2)\n",
@@ -1017,7 +1017,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95c8860>"
+       "<matplotlib.figure.Figure at 0xac83438>"
       ]
      },
      "metadata": {},
@@ -1048,7 +1048,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x962bc88>"
+       "<matplotlib.figure.Figure at 0xace8828>"
       ]
      },
      "metadata": {},
@@ -1088,7 +1088,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90fa320>"
+       "<matplotlib.figure.Figure at 0xac7ae80>"
       ]
      },
      "metadata": {},
@@ -1122,7 +1122,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x985eb00>"
+       "<matplotlib.figure.Figure at 0xabe21d0>"
       ]
      },
      "metadata": {},
@@ -1155,7 +1155,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b03f60>"
+       "<matplotlib.figure.Figure at 0x8586780>"
       ]
      },
      "metadata": {},
@@ -1204,7 +1204,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9890a90>"
+       "<matplotlib.figure.Figure at 0x9b8aa58>"
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      "metadata": {},
@@ -1234,7 +1234,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9890e48>"
+       "<matplotlib.figure.Figure at 0x9bb2630>"
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      },
      "metadata": {},
@@ -1269,7 +1269,7 @@
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b92550>"
+       "<matplotlib.figure.Figure at 0x858a470>"
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      "metadata": {},
@@ -1298,7 +1298,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b5fb38>"
+       "<matplotlib.figure.Figure at 0x80c56a0>"
       ]
      },
      "metadata": {},
@@ -1327,7 +1327,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90afa90>"
+       "<matplotlib.figure.Figure at 0x9b8dba8>"
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      },
      "metadata": {},
@@ -1356,7 +1356,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c8cb70>"
+       "<matplotlib.figure.Figure at 0x86b9518>"
       ]
      },
      "metadata": {},
@@ -1443,7 +1443,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8665c50>"
+       "<matplotlib.figure.Figure at 0x94cc7f0>"
       ]
      },
      "metadata": {},
@@ -1688,7 +1688,9 @@
     "$$\n",
     "\\mathbf{x} = \\begin{bmatrix}1 \\\\ 2.3\\end{bmatrix},\\, \\,\n",
     "\\mathbf{P} = \\begin{bmatrix}500 & 0 \\\\ 0&400\\end{bmatrix}\n",
-    "$$"
+    "$$\n",
+    "\n",
+    "There are better initialization schemes than this - typically we use the first measurement to determine the values for $\\mathbf{x}$ and $\\mathbf{P}$, but we will need to know a bit more before I can explain it. Within limits the Kalman filter can recover from poor initialization, so for now this scheme is fine."
    ]
   },
   {
@@ -1921,7 +1923,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95ea588>"
+       "<matplotlib.figure.Figure at 0x8688828>"
       ]
      },
      "metadata": {},
@@ -2234,9 +2236,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b48390>"
+       "<matplotlib.figure.Figure at 0xacfd780>"
       ]
      },
      "metadata": {},
@@ -2246,7 +2248,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c53160>"
+       "<matplotlib.figure.Figure at 0x947fba8>"
       ]
      },
      "metadata": {},
@@ -2293,9 +2295,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAE5CAYAAACZGEi2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x909a438>"
+       "<matplotlib.figure.Figure at 0x9b8d7b8>"
       ]
      },
      "metadata": {},
@@ -2348,9 +2350,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEjCAYAAAB+VMTJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x85857b8>"
+       "<matplotlib.figure.Figure at 0x9b6c5c0>"
       ]
      },
      "metadata": {},
@@ -2459,7 +2461,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b35278>"
+       "<matplotlib.figure.Figure at 0x9b6c7f0>"
       ]
      },
      "metadata": {},
@@ -2469,7 +2471,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b6bc50>"
+       "<matplotlib.figure.Figure at 0x86b96a0>"
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      },
      "metadata": {},
@@ -2528,7 +2530,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86a6470>"
+       "<matplotlib.figure.Figure at 0x942afd0>"
       ]
      },
      "metadata": {},
@@ -2558,7 +2560,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90c7438>"
+       "<matplotlib.figure.Figure at 0x943fd68>"
       ]
      },
      "metadata": {},
@@ -2589,7 +2591,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f970b8>"
+       "<matplotlib.figure.Figure at 0x9b6c780>"
       ]
      },
      "metadata": {},
@@ -2659,7 +2661,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x86adb38>"
+       "<matplotlib.figure.Figure at 0x9412278>"
       ]
      },
      "metadata": {},
@@ -2667,7 +2669,7 @@
     }
    ],
    "source": [
-    "from filterpy.common import stats\n",
+    "import filterpy.stats as stats\n",
     "\n",
     "P = np.array([[2., 0.], \n",
     "              [0., 6.]])\n",
@@ -2693,7 +2695,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9615eb8>"
+       "<matplotlib.figure.Figure at 0x9b2f0f0>"
       ]
      },
      "metadata": {},
@@ -2756,7 +2758,7 @@
      "data": {
       "image/png": 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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9604860>"
+       "<matplotlib.figure.Figure at 0x93fb0f0>"
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      "metadata": {},
@@ -2766,7 +2768,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x964cda0>"
+       "<matplotlib.figure.Figure at 0xbb784a8>"
       ]
      },
      "metadata": {},
@@ -2810,7 +2812,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x860d5c0>"
+       "<matplotlib.figure.Figure at 0x86f1160>"
       ]
      },
      "metadata": {},
@@ -2820,7 +2822,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x958bdd8>"
+       "<matplotlib.figure.Figure at 0xabf0fd0>"
       ]
      },
      "metadata": {},
@@ -2854,7 +2856,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8558908>"
+       "<matplotlib.figure.Figure at 0x943b588>"
       ]
      },
      "metadata": {},
@@ -2888,7 +2890,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x858e860>"
+       "<matplotlib.figure.Figure at 0x944c2e8>"
       ]
      },
      "metadata": {},
@@ -2918,16 +2920,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 54,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x910f0f0>"
+       "<matplotlib.figure.Figure at 0x9446e80>"
       ]
      },
      "metadata": {},
@@ -2935,9 +2937,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x96025f8>"
+       "<matplotlib.figure.Figure at 0xac2f320>"
       ]
      },
      "metadata": {},
@@ -3037,16 +3039,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 55,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95c6f98>"
+       "<matplotlib.figure.Figure at 0x744ae10>"
       ]
      },
      "metadata": {},
@@ -3073,6 +3075,70 @@
     "> **sidebar**: when plotting covariance ellipses, make sure to always use *plt.axis('equal')* in your code. If the axis use different scales the ellipses will be drawn distorted. For example, the ellipse may be drawn as being taller than it is wide, but it may actually be wider than tall."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Filter Initialization\n",
+    "\n",
+    "\n",
+    "There are many schemes for initializing the filter (i.e. choosing the initial values for $\\mathbf{x}$ and $\\mathbf{P}$. I will share a common approach that performs well in most situations. In this scheme you do not initialize the filter until you get the first measurement, $\\mathbf{z}_0$. From this you can compute the initial value for $\\mathbf{x}$ with $\\mathbf{x}_0 = \\mathbf{z}_0$. If  $\\mathbf{z}$ is not of the same size, type, and units as $\\mathbf{x}$, which is usually the case, we can use our measurement function as follow.\n",
+    "\n",
+    "We know\n",
+    "\n",
+    "$$\\mathbf{z} = \\mathbf{Hx}$$\n",
+    "\n",
+    "Hence,\n",
+    "\n",
+    "$$\\begin{aligned}\n",
+    "\\mathbf{H}^{-1}\\mathbf{Hx} &= \\mathbf{H}^{-1}\\mathbf{z} \\\\\n",
+    "\\mathbf{x} &= \\mathbf{H}^{-1}\\mathbf{z}\\end{aligned}$$\n",
+    "\n",
+    "Matrix inversion requires a square matrix, but $\\mathbf{H}$ is rarely square. SciPy will compute the Moore-Penrose pseudo-inverse of a matrix with `scipy.linalg.pinv`, so your code might look like"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3.2],\n",
+       "       [ 0. ]])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import scipy.linalg as la\n",
+    "\n",
+    "H = np.array([[1, 0.]]) \n",
+    "z0 = 3.2\n",
+    "\n",
+    "x = np.dot(la.pinv(H), z0)\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Specialized knowledge of your problem domain may lead you to a different computation, but this is one way to do it. \n",
+    "\n",
+    "Now we need to compute a value for $\\mathbf{P}$. This will vary by problem, but in general you will use the measurement error $\\mathbf{R}$ for identical terms, and maximum values for the rest of the terms. Maybe that isn't clear. In this chapter we have been tracking and object using position and velocity as the state, and the measurements have been positions. In that case we would initialize $\\mathbf{P}$ with\n",
+    "\n",
+    "$$\\mathbf{P} = \\begin{bmatrix}\\mathbf{R}_0 & 0 \\\\0 & vel_{max}^2\\end{bmatrix}$$\n",
+    "\n",
+    "The diagonal of $\\mathbf{P}$ contains the variance of each state variable, so we populate it with reasonable values. $\\mathbf{R}$ is a reasonable variance for the position, and the maximum velocity squared is a reasonable variance for the velocity. It is squared because variance is squared: $\\sigma^2$."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3188,7 +3254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "collapsed": false
    },
@@ -3197,7 +3263,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90c1a20>"
+       "<matplotlib.figure.Figure at 0x9ba16a0>"
       ]
      },
      "metadata": {},
@@ -3271,16 +3337,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAESCAYAAAAsfxlJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8b4d358>"
+       "<matplotlib.figure.Figure at 0x868ab70>"
       ]
      },
      "metadata": {},
@@ -3323,16 +3389,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x869fcf8>"
+       "<matplotlib.figure.Figure at 0xbb19d30>"
       ]
      },
      "metadata": {},
@@ -3365,16 +3431,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEWCAYAAAC68CsYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEWCAYAAAC68CsYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x869f160>"
+       "<matplotlib.figure.Figure at 0x93b4390>"
       ]
      },
      "metadata": {},
@@ -3414,7 +3480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "collapsed": true
    },
@@ -3432,16 +3498,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEWCAYAAAC68CsYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEWCAYAAAC68CsYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x85850b8>"
+       "<matplotlib.figure.Figure at 0x943fda0>"
       ]
      },
      "metadata": {},

06_Kalman_Filter_Math.ipynb

@@ -579,7 +579,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAEWCAYAAACpJ2vsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6efa518>"
+       "<matplotlib.figure.Figure at 0x6c98710>"
       ]
      },
      "metadata": {},
@@ -587,7 +587,7 @@
     }
    ],
    "source": [
-    "from filterpy.common import plot_covariance_ellipse\n",
+    "from filterpy.stats import plot_covariance_ellipse\n",
     "\n",
     "x = [[1, 2, 3, 4], [2, 4, 6, 8]]\n",
     "cov = np.cov(x, bias=1)\n",
@@ -613,7 +613,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6efa198>"
+       "<matplotlib.figure.Figure at 0x6c98588>"
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      },
      "metadata": {},
@@ -643,7 +643,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fdeb38>"
+       "<matplotlib.figure.Figure at 0x7d15a20>"
       ]
      },
      "metadata": {},
@@ -776,7 +776,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fe74e0>"
+       "<matplotlib.figure.Figure at 0x4e18748>"
       ]
      },
      "metadata": {},
@@ -1163,9 +1163,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEjCAYAAAB+VMTJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fffd68>"
+       "<matplotlib.figure.Figure at 0x248b5c0>"
       ]
      },
      "metadata": {},
@@ -1175,7 +1175,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a26518>"
+       "<matplotlib.figure.Figure at 0x7ec5390>"
       ]
      },
      "metadata": {},
@@ -1636,7 +1636,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fde278>"
+       "<matplotlib.figure.Figure at 0x783c978>"
       ]
      },
      "metadata": {},
@@ -1679,7 +1679,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x823ff28>"
+       "<matplotlib.figure.Figure at 0x8028128>"
       ]
      },
      "metadata": {},
@@ -1725,7 +1725,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x835b978>"
+       "<matplotlib.figure.Figure at 0x803c630>"
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      },
      "metadata": {},
@@ -2410,7 +2410,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fd2b38>"
+       "<matplotlib.figure.Figure at 0x8035f98>"
       ]
      },
      "metadata": {},
@@ -2444,7 +2444,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8355b70>"
+       "<matplotlib.figure.Figure at 0xa304048>"
       ]
      },
      "metadata": {},
@@ -2526,7 +2526,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa6410f0>"
+       "<matplotlib.figure.Figure at 0xa338710>"
       ]
      },
      "metadata": {},
@@ -2675,7 +2675,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa64a208>"
+       "<matplotlib.figure.Figure at 0xa36a550>"
       ]
      },
      "metadata": {},
@@ -2746,7 +2746,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp8AAAGNCAYAAABXHhoFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa639828>"
+       "<matplotlib.figure.Figure at 0xa3780b8>"
       ]
      },
      "metadata": {},
@@ -3228,7 +3228,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

08_Nonlinear_Filtering.ipynb

@@ -370,9 +370,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ef38d0>"
+       "<matplotlib.figure.Figure at 0x70b6b38>"
       ]
      },
      "metadata": {},
@@ -430,9 +430,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6eeaac8>"
+       "<matplotlib.figure.Figure at 0x7098ba8>"
       ]
      },
      "metadata": {},
@@ -468,9 +468,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x790c518>"
+       "<matplotlib.figure.Figure at 0x7c83710>"
       ]
      },
      "metadata": {},
@@ -509,9 +509,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa4c2c18>"
+       "<matplotlib.figure.Figure at 0xa775eb8>"
       ]
      },
      "metadata": {},
@@ -551,9 +551,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa4c7630>"
+       "<matplotlib.figure.Figure at 0xa8c9e10>"
       ]
      },
      "metadata": {},
@@ -585,8 +585,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: 0.0002, 0.9975\n",
-      "output mean, variance: -0.1252, 2.3994\n"
+      "input  mean, variance: -0.0017, 0.9984\n",
+      "output mean, variance: -0.1220, 2.4009\n"
      ]
     }
    ],
@@ -613,9 +613,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa49e940>"
+       "<matplotlib.figure.Figure at 0xa7980b8>"
       ]
      },
      "metadata": {},
@@ -625,7 +625,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: -0.0003, 2.2445\n"
+      "output mean, variance: 0.0025, 2.2465\n"
      ]
     }
    ],
@@ -657,8 +657,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: -0.0149, 7463.4375\n",
-      "nonlinear output mean, variance: -9.2169, 30408.5116\n"
+      "linear    output mean, variance: 0.1453, 7470.2044\n",
+      "nonlinear output mean, variance: -8.8565, 30504.9480\n"
      ]
     }
    ],
@@ -693,9 +693,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a02cf8>"
+       "<matplotlib.figure.Figure at 0x7e97978>"
       ]
      },
      "metadata": {},
@@ -744,7 +744,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a02400>"
+       "<matplotlib.figure.Figure at 0x7f6bf60>"
       ]
      },
      "metadata": {},
@@ -752,7 +752,7 @@
     }
    ],
    "source": [
-    "import stats\n",
+    "import filterpy.stats as stats\n",
     "P = np.array([[6, 2.5], [2.5, .6]])\n",
     "stats.plot_covariance_ellipse((10, 2), P, facecolor='g', alpha=0.2)"
    ]
@@ -775,7 +775,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAEWCAYAAACt0rvRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c0ddd8>"
+       "<matplotlib.figure.Figure at 0x8446780>"
       ]
      },
      "metadata": {},
@@ -811,7 +811,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80b1048>"
+       "<matplotlib.figure.Figure at 0x8dae8d0>"
       ]
      },
      "metadata": {},
@@ -847,7 +847,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74b3390>"
+       "<matplotlib.figure.Figure at 0x7ee2828>"
       ]
      },
      "metadata": {},
@@ -951,7 +951,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

09_Unscented_Kalman_Filter.ipynb

@@ -3916,7 +3916,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

10_Extended_Kalman_Filters.ipynb

@@ -310,9 +310,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ed8eb8>"
+       "<matplotlib.figure.Figure at 0x59fce80>"
       ]
      },
      "metadata": {},
@@ -359,9 +359,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ed8e48>"
+       "<matplotlib.figure.Figure at 0x59fcda0>"
       ]
      },
      "metadata": {},
@@ -436,7 +436,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ed8668>"
+       "<matplotlib.figure.Figure at 0x5a07198>"
       ]
      },
      "metadata": {},
@@ -772,9 +772,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwYAAAEtCAYAAACyMncIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8126f60>"
+       "<matplotlib.figure.Figure at 0x81bb048>"
       ]
      },
      "metadata": {},
@@ -782,9 +782,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x815ee10>"
+       "<matplotlib.figure.Figure at 0x824f898>"
       ]
      },
      "metadata": {},
@@ -792,9 +792,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAEtCAYAAABUG7xMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtcVHX+P/DXGe4XYRQZUPGeIGmg4CVI1xsiFIqVN1DI\nYpXUTHJd2/pWaK21VtuiXWU3gyi/1rdoy91S0ixN2Z+gsWVqXjIKFUXlIiPXmfP748iBDzdHAWdg\nXs/HgwfzOZ/PnPOeM4dh3ufz+ZwjybIsg4iIiIiIrI7G3AEQEREREZF5MBkgIiIiIrJSTAaIiIiI\niKwUkwEiIiIiIivFZICIiIiIyEoxGSAiIiIislJMBoiIiIiIrJTZkoE9e/ZgxowZ8PHxgUajQXp6\nepM2x48fx3333Yfu3bvDxcUFwcHBOHbsmFpfVVWF5cuXw9PTE66uroiOjsaZM2eEdRQXFyMuLg5a\nrRZarRbx8fEoLS3t8NdHRERERGTpzJYM6PV6BAQEYMOGDXBycoIkSUL96dOncdddd2Hw4MHYvXs3\nfvzxR6xbtw6urq5qm6SkJGRmZmLr1q3Yu3cvysrKEBUVBaPRqLaJjY1FXl4eduzYge3bt+PQoUOI\ni4u7Za+TiIiIiMhSSZZwB+Ju3brh9ddfR3x8vLosNjYWNjY2yMjIaPY5paWl0Ol0SEtLQ0xMDACg\noKAA/fv3xxdffIHw8HAcPXoUw4YNw759+xASEgIA2LdvH8aPH49jx47B19e3418cEREREZGFssg5\nA0ajEf/617/g7++PiIgI6HQ6jBkzBh9++KHa5uDBg6ipqUF4eLi6zMfHB/7+/sjOzgYAZGdnw9XV\nVU0EACA0NBQuLi5qGyIiIiIia2WRycCFCxdQXl6O559/HhEREdi5cydiYmIwf/58fP755wCAwsJC\n2NjYwMPDQ3iul5cXCgsL1Taenp5CvSRJ0Ol0ahsiIiIiImtla+4AmlM35n/mzJlISkoCAAQEBCA3\nNxevvfYa7r777hafe7OjnjipmIiIiIg6M3d39xt+jkX2DPTs2RO2tra4/fbbheVDhw7Fr7/+CgDw\n9vaGwWDApUuXhDbnz5+Ht7e32qaoqEiol2UZFy5cUNsQEREREVkri0wG7O3tMXr0aOEyooByqdEB\nAwYAAIKDg2FnZ4esrCy1vqCgAMeOHUNoaCgAICQkBOXl5cL8gOzsbOj1erUNEREREZG1MtswIb1e\njxMnTgBQhgXl5+cjLy8PHh4e6Nu3L1avXo05c+Zg/PjxmDRpEnbv3o0PPvgAn376KQClGyQhIQGr\nV6+GTqdDjx49sHLlSgQGBiIsLAwA1AnIiYmJSE1NhSzLSExMxPTp0zFkyJAWY7uZLhaiWyk3NxcA\nMGrUKDNHQmQaHrPUmfB4pc6krUPdzdYzkJOTg6CgIAQFBaGyshLJyckICgpCcnIyACA6Ohqpqal4\n+eWXERAQgNdffx0ZGRmIjIxU15GSkoJ7770Xc+fOxbhx4+Dm5oZt27YJ9yzYsmULAgMDMW3aNERE\nRGDkyJEtXq6UiIiIiMiaWMR9BixBw6yKPQNk6XjWijobHrPUmfB4pc6krd9hLXLOABERERERdTwm\nA0REREREVorJABERERGRlWIyQERERERkpZgMEBERERFZKSYDRERERERWiskAEREREZGVYjJARERE\nRGSlmAwQEREREVkpJgNERERERFaKyQARERERkZViMkBEREREZKWYDBARERERWSkmA0REREREVorJ\nABERERGRlWIyQERERERkpZgMEBERERFZKSYDRERERERWiskAEREREZGVYjJARERERGSlmAwQERER\nEVkpJgNERERERFaKyQARERERkZUyWzKwZ88ezJgxAz4+PtBoNEhPTxfqFy5cCI1GI/yEhoYKbaqq\nqrB8+XJ4enrC1dUV0dHROHPmjNCmuLgYcXFx0Gq10Gq1iI+PR2lpaYe/PiIiIiIiS2e2ZECv1yMg\nIAAbNmyAk5MTJEkS6iVJwtSpU1FYWKj+fP7550KbpKQkZGZmYuvWrdi7dy/KysoQFRUFo9GotomN\njUVeXh527NiB7du349ChQ4iLi7slr5GIiIiIyJLZmmvDkZGRiIyMBKD0AjQmyzLs7e2h0+mafX5p\naSk2b96MtLQ0TJkyBQCQkZGB/v37Y+fOnQgPD8fRo0exY8cO7Nu3D2PHjgUAbNq0CePHj8fx48fh\n6+vbMS+OiIiIiKgTsNg5A5Ik4dtvv4WXlxf8/PywePFiFBUVqfUHDx5ETU0NwsPD1WU+Pj7w9/dH\ndnY2ACA7Oxuurq4ICQlR24SGhsLFxUVtQ0RERERkrczWM3A9ERERuP/++zFw4ECcPn0aTz31FCZP\nnoyDBw/C3t4ehYWFsLGxgYeHh/A8Ly8vFBYWAgAKCwvh6ekp1EuSBJ1Op7YhIiIiIrJWFpsMzJ07\nV308bNgwBAcHo3///vj3v/+Ne++9t8XnybLc5m3n5ua2eR1EtwKPVepseMxSZ8LjlTqDIUOGtOn5\nFjtMqLFevXrBx8cHJ0+eBAB4e3vDYDDg0qVLQrvz58/D29tbbdNwaBGgJAsXLlxQ2xARERERWSuL\n7RlorKioCGfOnEGvXr0AAMHBwbCzs0NWVhZiYmIAAAUFBTh27Jh6CdKQkBCUl5cjOztbnTeQnZ0N\nvV7f5DKlDY0aNaqDXw1R29SdreKxSp0Fj1nqTHi8UmfS1kvmmy0Z0Ov1OHHiBADAaDQiPz8feXl5\n8PDwQI8ePZCcnIxZs2bB29sbv/zyC5544gl4eXmpQ4Tc3d2RkJCA1atXQ6fToUePHli5ciUCAwMR\nFhYGAPD390dERAQSExORmpoKWZaRmJiI6dOnt7lLhYiIiIioszPbMKGcnBwEBQUhKCgIlZWVSE5O\nRlBQEJKTk2FjY4PDhw8jOjoafn5+WLhwoXqVIBcXF3UdKSkpuPfeezF37lyMGzcObm5u2LZtm3DP\ngi1btiAwMBDTpk1DREQERo4ciYyMDHO8ZCIiIiIiiyLJ7THjtgto2MXi7u5uxkiIro9d2NTZ8Jil\nzoTHK3Umbf0O22kmEBMRERERUftiMkBEREREZKWYDBARERERWSkmA0REREREVorJABERERGRlWIy\nQERERERkpZgMEBERERFZKSYDzTAYeOsFIiIiIur6mAw0o6rG3BEQEREREXU8JgPNqKw2dwRERERE\nRB2PyUAzKqvMHQERERERUcdjMtAM9gwQERERkTVgMtAMzhkgIiIiImvAZKAZ7BkgIiIiImvAZKAZ\nTAaIiIiIyBowGWgGJxATERERkTVgMtAMzhkgIiIiImvAZKAZHCZERERERNaAyUAzmAwQERERkTVg\nMtCMKiYDRERERGQFmAw0gz0DRERERGQNmAw0g8kAEREREVkDJgPNYDJARERERNbAbMnAnj17MGPG\nDPj4+ECj0SA9Pb3FtomJidBoNPjrX/8qLK+qqsLy5cvh6ekJV1dXREdH48yZM0Kb4uJixMXFQavV\nQqvVIj4+HqWlpa3GxjkDRERERGQNzJYM6PV6BAQEYMOGDXBycoIkSc22++ijj5CTk4PevXs3aZOU\nlITMzExs3boVe/fuRVlZGaKiomA0GtU2sbGxyMvLw44dO7B9+3YcOnQIcXFxrcbGngEiIiIisga2\n5tpwZGQkIiMjAQALFy5stk1+fj6SkpKwa9cuRERECHWlpaXYvHkz0tLSMGXKFABARkYG+vfvj507\ndyI8PBxHjx7Fjh07sG/fPowdOxYAsGnTJowfPx7Hjx+Hr69vs9tlMkBERERE1sBi5wzU1tYiJiYG\nTz/9NPz8/JrUHzx4EDU1NQgPD1eX+fj4wN/fH9nZ2QCA7OxsuLq6IiQkRG0TGhoKFxcXtU1zmAwQ\nERERkTWw2GQgOTkZOp0OiYmJzdYXFhbCxsYGHh4ewnIvLy8UFhaqbTw9PYV6SZKg0+nUNs2pqmlj\n8EREREREnYDZhgm15uuvv0Z6ejry8vKE5bIsX/e5prS5nrPnLiE395c2r4eoo+Xm5po7BKIbwmOW\nOhMer9QZDBkypE3Pt8iegW+++Qbnzp1Dr169YGdnBzs7O+Tn5+Pxxx9Hv379AADe3t4wGAy4dOmS\n8Nzz58/D29tbbVNUVCTUy7KMCxcuqG2aU11jkbuFiIiIiKhdWWTPwNKlSzF79my1LMsypk2bhtjY\nWCxatAgAEBwcDDs7O2RlZSEmJgYAUFBQgGPHjiE0NBQAEBISgvLycmRnZ6vzBrKzs6HX69U2zXFy\n6Y5Ro0Z11MsjarO6s1U8Tqmz4DFLnQmPV+pMrnfJ/OsxWzKg1+tx4sQJAIDRaER+fj7y8vLg4eGB\nvn37Nhnrb2dnB29vb7UrxN3dHQkJCVi9ejV0Oh169OiBlStXIjAwEGFhYQAAf39/REREIDExEamp\nqZBlGYmJiZg+fXqrXSqcM0BERERE1sBs42FycnIQFBSEoKAgVFZWIjk5GUFBQUhOTjZ5HSkpKbj3\n3nsxd+5cjBs3Dm5ubti2bZtwP4ItW7YgMDAQ06ZNQ0REBEaOHImMjIxW18urCRERERGRNTC5Z+Di\nxYvYt28fjh49iosXL0KSJPTs2RP+/v4IDQ1Fz549b2jDEydOFG4Odj2nT59ussze3h4bN27Exo0b\nW3yeVqu97pf/xiqrbqg5EREREVGn1GoyUFVVhffffx/vvPMO9u3b1+qKQkND8eCDD2LBggVwcHBo\n1yBvNfYMEBEREZE1aHGY0JtvvonBgwdj6dKl6N69O1JSUrB3716cOXMGV69ehV6vR0FBAfbu3YuU\nlBR0794dy5Ytw+DBg/HWW2/dytfQ7jhngIiIiIisQYs9A+vWrcMf/vAHPPTQQ3B3dLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8044400>"
+       "<matplotlib.figure.Figure at 0x829c860>"
       ]
      },
      "metadata": {},
@@ -897,7 +897,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAAmBAMAAAA2HDP0AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nUqOHbKQnT/0tv+59MpOT+AmTXbj1s0kQCQAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAAmBAMAAAA2HDP0AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ngg==\n",
       "text/latex": [
        "$$\\left[\\begin{matrix}\\frac{x_{pos}}{\\sqrt{x_{alt}^{2} + x_{pos}^{2}}} & 0 & \\frac{x_{alt}}{\\sqrt{x_{alt}^{2} + x_{pos}^{2}}}\\end{matrix}\\right]$$"
       ],
@@ -1046,7 +1046,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80c57f0>"
+       "<matplotlib.figure.Figure at 0x6024d68>"
       ]
      },
      "metadata": {},
@@ -1589,7 +1589,7 @@
    },
    "outputs": [],
    "source": [
-    "from filterpy.common import plot_covariance_ellipse\n",
+    "from filterpy.stats import plot_covariance_ellipse\n",
     "from math import sqrt, tan, cos, sin, atan2\n",
     "dt = 1.0\n",
     "\n",
@@ -1640,9 +1640,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9ed1a20>"
+       "<matplotlib.figure.Figure at 0x8177c88>"
       ]
      },
      "metadata": {},
@@ -1652,7 +1652,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.02404007  0.04230372  0.002213  ]\n"
+      "[ 0.02426139  0.0425823   0.00221613]\n"
      ]
     }
    ],
@@ -1674,9 +1674,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x803f160>"
+       "<matplotlib.figure.Figure at 0xac02f98>"
       ]
      },
      "metadata": {},
@@ -1686,7 +1686,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.01886792  0.02163098  0.00153879]\n"
+      "[ 0.02089027  0.01976026  0.00153243]\n"
      ]
     }
    ],
@@ -1719,9 +1719,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAEWCAYAAABL+3CVAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x803f080>"
+       "<matplotlib.figure.Figure at 0xabc2be0>"
       ]
      },
      "metadata": {},
@@ -1731,7 +1731,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.0198982   0.04518314  0.00022389]\n"
+      "[ 0.01961672  0.04581986  0.0002252 ]\n"
      ]
     }
    ],
@@ -1758,9 +1758,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa07ff28>"
+       "<matplotlib.figure.Figure at 0xa979978>"
       ]
      },
      "metadata": {},
@@ -1770,7 +1770,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.28419616  0.80806564  0.00361808]\n"
+      "[ 0.27794637  0.75174993  0.00348487]\n"
      ]
     }
    ],
@@ -1797,9 +1797,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa233748>"
+       "<matplotlib.figure.Figure at 0xab75be0>"
       ]
      },
      "metadata": {},
@@ -1809,7 +1809,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.00893561  0.0085525   0.00077497]\n"
+      "[ 0.00894682  0.00859964  0.00077197]\n"
      ]
     }
    ],
@@ -1859,9 +1859,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa233908>"
+       "<matplotlib.figure.Figure at 0xab754a8>"
       ]
      },
      "metadata": {},
@@ -1871,7 +1871,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.30, std=1.14\n",
+      "actual mean=1.30, std=1.13\n",
       "EKF    mean=1.00, std=0.95\n"
      ]
     }
@@ -1899,9 +1899,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAEWCAYAAAC+BfslAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x74fff60>"
+       "<matplotlib.figure.Figure at 0xad2feb8>"
       ]
      },
      "metadata": {},
@@ -1911,7 +1911,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.30, std=1.13\n",
+      "actual mean=1.31, std=1.13\n",
       "UKF    mean=1.30, std=1.08\n"
      ]
     }
@@ -1946,7 +1946,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

11_Particle_Filters.ipynb

@@ -16,11 +16,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 72,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -250,7 +258,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -310,16 +318,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 73,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x57ea550>"
+       "<matplotlib.figure.Figure at 0x5c64c18>"
       ]
      },
      "metadata": {},
@@ -384,7 +392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {
     "collapsed": false
    },
@@ -393,7 +401,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x96c09b0>"
+       "<matplotlib.figure.Figure at 0xaad32b0>"
       ]
      },
      "metadata": {},
@@ -451,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 75,
    "metadata": {
     "collapsed": false
    },
@@ -468,7 +476,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x966bb00>"
+       "<matplotlib.figure.Figure at 0xaacd400>"
       ]
      },
      "metadata": {},
@@ -511,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 76,
    "metadata": {
     "collapsed": false
    },
@@ -520,7 +528,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f05160>"
+       "<matplotlib.figure.Figure at 0x951cac8>"
       ]
      },
      "metadata": {},
@@ -545,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 77,
    "metadata": {
     "collapsed": false
    },
@@ -554,7 +562,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f2ca58>"
+       "<matplotlib.figure.Figure at 0xa858b38>"
       ]
      },
      "metadata": {},
@@ -599,7 +607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 78,
    "metadata": {
     "collapsed": true
    },
@@ -758,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 79,
    "metadata": {
     "collapsed": false
    },
@@ -770,7 +778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 80,
    "metadata": {
     "collapsed": false
    },
@@ -786,7 +794,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb19cf28>"
+       "<matplotlib.figure.Figure at 0x8f12be0>"
       ]
      },
      "metadata": {},
@@ -873,7 +881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 81,
    "metadata": {
     "collapsed": false
    },
@@ -889,7 +897,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xaada240>"
+       "<matplotlib.figure.Figure at 0xb19f7f0>"
       ]
      },
      "metadata": {},
@@ -925,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 82,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -940,9 +948,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8e885c0>"
+       "<matplotlib.figure.Figure at 0xb19f9b0>"
       ]
      },
      "metadata": {},
@@ -977,7 +985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 83,
    "metadata": {
     "collapsed": false
    },
@@ -993,7 +1001,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9517ac8>"
+       "<matplotlib.figure.Figure at 0xb19fd30>"
       ]
      },
      "metadata": {},
@@ -1016,7 +1024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 84,
    "metadata": {
     "collapsed": false
    },
@@ -1030,9 +1038,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9684e10>"
+       "<matplotlib.figure.Figure at 0x8ea4eb8>"
       ]
      },
      "metadata": {},
@@ -1140,7 +1148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 85,
    "metadata": {
     "collapsed": false
    },
@@ -1156,7 +1164,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0xb70f240>"
+       "<matplotlib.figure.Figure at 0x8ede7b8>"
       ]
      },
      "metadata": {},
@@ -1188,7 +1196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 86,
    "metadata": {
     "collapsed": false
    },
@@ -1197,7 +1205,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa85a438>"
+       "<matplotlib.figure.Figure at 0xb6ea390>"
       ]
      },
      "metadata": {},
@@ -1261,7 +1269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 87,
    "metadata": {
     "collapsed": false
    },
@@ -1270,7 +1278,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f27470>"
+       "<matplotlib.figure.Figure at 0x5f413c8>"
       ]
      },
      "metadata": {},
@@ -1304,7 +1312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 88,
    "metadata": {
     "collapsed": false
    },
@@ -1313,7 +1321,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ec2dd8>"
+       "<matplotlib.figure.Figure at 0xaaaf438>"
       ]
      },
      "metadata": {},
@@ -1363,7 +1371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 89,
    "metadata": {
     "collapsed": false
    },
@@ -1372,7 +1380,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9550710>"
+       "<matplotlib.figure.Figure at 0x5f83e48>"
       ]
      },
      "metadata": {},
@@ -1423,7 +1431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 90,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1433,7 +1441,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x96a4cf8>"
+       "<matplotlib.figure.Figure at 0x5d80c18>"
       ]
      },
      "metadata": {},
@@ -1443,7 +1451,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8e805f8>"
+       "<matplotlib.figure.Figure at 0x5f87668>"
       ]
      },
      "metadata": {},
@@ -1453,7 +1461,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95bb940>"
+       "<matplotlib.figure.Figure at 0xaaa4ba8>"
       ]
      },
      "metadata": {},
@@ -1463,7 +1471,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAABCCAYAAACSCTtfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9611828>"
+       "<matplotlib.figure.Figure at 0x955cbe0>"
       ]
      },
      "metadata": {},

12_Smoothing.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -250,7 +250,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,16 +281,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEWCAYAAADFITnpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4516320>"
+       "<matplotlib.figure.Figure at 0x6e18550>"
       ]
      },
      "metadata": {},
@@ -326,16 +326,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAEWCAYAAABYLDBhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7002320>"
+       "<matplotlib.figure.Figure at 0x4f48630>"
       ]
      },
      "metadata": {},
@@ -359,16 +359,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7591b00>"
+       "<matplotlib.figure.Figure at 0x75d0eb8>"
       ]
      },
      "metadata": {},
@@ -502,16 +502,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEPCAYAAAAOOgGEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9734438>"
+       "<matplotlib.figure.Figure at 0x76605c0>"
       ]
      },
      "metadata": {},
@@ -575,16 +575,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x703a0b8>"
+       "<matplotlib.figure.Figure at 0x760f160>"
       ]
      },
      "metadata": {},
@@ -604,16 +604,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEPCAYAAAAOOgGEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x820dcc0>"
+       "<matplotlib.figure.Figure at 0x77ef160>"
       ]
      },
      "metadata": {},
@@ -635,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
@@ -649,9 +649,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt0AAAEWCAYAAAC68CsYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9440588>"
+       "<matplotlib.figure.Figure at 0x7844358>"
       ]
      },
      "metadata": {},
@@ -682,16 +682,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9790908>"
+       "<matplotlib.figure.Figure at 0x77e4fd0>"
       ]
      },
      "metadata": {},
@@ -721,16 +721,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9a148d0>"
+       "<matplotlib.figure.Figure at 0x75f5e10>"
       ]
      },
      "metadata": {},
@@ -740,8 +740,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "standard deviation fixed lag: 0.682200657746\n",
-      "standard deviation kalman: 0.915729357528\n"
+      "standard deviation fixed lag: 2.61624417392\n",
+      "standard deviation kalman: 3.56221928461\n"
      ]
     }
    ],
@@ -836,7 +836,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

13_Adaptive_Filtering.ipynb

@@ -401,7 +401,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e6cc50>"
+       "<matplotlib.figure.Figure at 0x6eaba20>"
       ]
      },
      "metadata": {},
@@ -464,9 +464,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvsAAAE6CAYAAACI3Z3CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvUAAAE6CAYAAACWFK1xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6e7cb70>"
+       "<matplotlib.figure.Figure at 0x6e8dd30>"
       ]
      },
      "metadata": {},
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
@@ -549,16 +549,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu8AAAE6CAYAAACmmLxQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAu8AAAE6CAYAAACmmLxQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8098748>"
+       "<matplotlib.figure.Figure at 0x7704be0>"
       ]
      },
      "metadata": {},
@@ -597,16 +597,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAE6CAYAAAB0yLYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x850d278>"
+       "<matplotlib.figure.Figure at 0x763e6d8>"
       ]
      },
      "metadata": {},
@@ -642,16 +642,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAE6CAYAAAB0yLYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAE6CAYAAAB0yLYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x808df28>"
+       "<matplotlib.figure.Figure at 0x767a390>"
       ]
      },
      "metadata": {},
@@ -684,16 +684,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAE6CAYAAAB0yLYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xt8z+X/x/HH+7OZHcwwZpnDTJNDhJyK5qwSaio5JIfK\nt5BjJUokh5QoOql+X3wrhEQlmhBmKSLkMOczc5ph7fy5fn8sm08b5rB9dnjebze323Zd1/v9fn3e\nY3u6dn2ut2WMMYiIiIiISJ5ic3YBIiIiIiJy/RTkRURERETyIAV5EREREZE8SEFeRERERCQPUpAX\nEREREcmDFORFRERERPIgBXkRkQLu2WefpUSJEly4cMHZpRR4hw4donDhwrz++uvOLkVE8gAFeRHJ\nc2w2Gzbb1b99NW3aFJvNxqpVq3Koquy3aNEiOnToQEBAAO7u7vj6+tKwYUPGjh1LTEzMDZ1z69at\nTJ8+nSFDhuDt7X2LK3a+n376if79+1OnTh18fX1xd3fn9ttv5/nnn+fAgQNXPC46OppBgwYRGBiI\nu7s7AQEBPP300xw9evS6a4iLi2PUqFHccccdeHh4ULp0aZ544gl27tyZYWz58uV55plnmDRpEseO\nHbvua4lIwWLpgVAiktfYbDYsyyIlJeWKY5o2bcqaNWtYuXIlISEhOVjdrXf+/Hk6derE0qVLKVq0\nKG3atKFixYpcuHCBZcuWERkZiZ+fHwsWLODee++9rnOHhoaybNkyjh8/nu+CfHx8PJ6enri5udG4\ncWNq1aqFZVmEh4fz22+/UaRIEZYtW0aDBg0cjjtz5gyNGjVi165dtGjRgnr16rFjxw4WLVqEn58f\nv/76KxUrVsxSDQkJCbRs2ZK1a9dSr149mjdvzqFDh5g3bx5ubm6sWLGC+vXrOxxz8OBBgoKCeP75\n5/nggw9u2f0QkXzIiIjkMZZlGZvNdtUxTZo0MZZlmVWrVuVQVdkjJSXFtG7d2liWZVq1amVOnz6d\nYcx7771nbDabKVq0qNm5c2eWz33o0CHj4uJiOnfufCtLzjWSkpLM2LFjzdmzZzP0vfnmm8ayLFOz\nZs0Mfb179zaWZZkXX3zRoX3KlCnGsizzwAMPZLmGcePGGcuyTMeOHR3aFy1aZCzLMtWrVzd2uz3D\ncU2aNDFFixY1Fy9ezPK1RKTgUZAXkTznZoK8ZVmmadOmmR4zcuTIK4b/L7/80tSuXdt4eHgYPz8/\n061bN3P06NG062RmxYoVpl27dqZkyZLGzc3NVKhQwfTp08ecOHEii6809bqWZZlKlSqZ2NjYK457\n6aWXjGVZ5v7778/yuUePHm0syzKLFi1yaJ89e7axLMu88MILmR6XlJRk/P39TZEiRdJqiomJMW+/\n/bZp1qyZCQgIMG5ubqZUqVKmXbt2JiIiItPzWJZlAgMDTUxMjBkwYIApX768cXV1Ne+9916WX8ON\nSk5ONh4eHsZmszkE/QsXLhgPDw/j7e2dIUTb7XZToUIFY1mW2bdv3zWvYbfbTfny5Y3NZjMHDhzI\n0B8SEmIsyzIrV67M0Pfxxx8by7LM9OnTr/u1iUjBoTXyIlLgWJZ1XePffvttunXrxsGDB+nevTu9\nevVi27ZtNG7cmJiYmEzPN2HCBFq0aMGvv/7KQw89xKBBg6hatSoff/wxdevWzfJa608//RSAF198\nEU9PzyuOGzZsGG5ubixbtoyDBw9m6dxhYWFYlkWjRo0c2kNDQylWrBhz5swhKSkpw3E//fQTUVFR\nPP7442k1bd++nddeew1XV1fatWvHkCFDaNWqVdrSpiVLlmRaQ0JCAs2aNWPx4sU89NBDDBgwgHLl\nymWp/pthWRaurq4AuLi4pLWvW7eO+Ph4GjVqhJeXV4ZjHnjgAQBWrlx5zWvs3buXw4cPU7lyZSpU\nqJCh/8EHHwRgxYoVGfoaN24MpH6NRESuxNXZBYiI3AhjDG+88QbmCm/zudobGa/Hvn37ePXVV/H1\n9WXjxo1pIXP8+PF06dKFOXPmZAjyq1evZtiwYdxzzz0sWbKEokWLpvV9+eWXPPXUUwwYMID58+df\n9drJycmsW7cOy7Jo1arVVccWL16cOnXqsG7dOsLDwzMNjpdLSEhg/fr1VKxYEV9fX4e+woUL06lT\nJz755BMWL17MI4884tA/c+ZMAHr06JHWVq1aNY4fP06JEiUcxh49epT69eszePDgtOB6uRMnTlCj\nRg3Wrl2Lu7u7Q9+ff/7JwoULr/o6/q1nz57XfO0Ac+fO5eLFi9xzzz0OX5/IyEgAKleunOlxt99+\nOwC7d+++5jVu5lzVqlXD09OTNWvWXPM6IlJwKciLSJ71xhtvZPs1Zs2aRUpKCn379s0wU/zWW28x\nd+7cDP+ZeP/99wGYNm2aQ0gEePLJJ5k0aRKLFi0iNjY2w6zv5c6ePUtSUhKWZWVplrpcuXKsW7eO\nEydOXHPssWPHSExMpGzZspn29+jRg08++YQZM2Y4BPno6Gi+++47goKCHN5E/O/XeUlAQACPPvoo\nH3zwAUeOHMlwPcuymDhxYoYQD7B582ZGjx59zddy+bmaN29+zSC/Z88eXnjhBQoVKsTkyZMd+i7t\n/uPj45PpsZfaz507d816buZcNpuN2267jX379pGUlEShQoWueT0RKXgU5EUkT8rKrjWrV6++6ets\n2rQJSF/qcLny5ctTrlw5Dh065NC+du1aXF1d+eabbzKddU9ISCAlJYXIyEjq1Klz0zX+29XuyyWn\nTp0CyDCDfkn9+vWpWrUqS5Ys4fTp05QsWRKAOXPmkJiYyFNPPZXhmLVr1/L+++/z66+/curUKRIT\nEx36jx49miHIu7u7U6NGjUxr6N69O927d7/ma7keR44c4f777+fs2bNMmzYtw44xuYmvry/79u3j\n5MmTBAQEOLscEcmFFORFRK7i0qxq6dKlM+0vXbp0hiB/5swZUlJSrvobA8uyiI2Nveq1S5QoQaFC\nhUhOTubQoUNpSzGu5PDhwwBZCn2XlgNdaWkSpM7KDx06lFmzZtG/f38gdVmNzWbLELC//fZbHnvs\nMTw9PWnVqhWVKlXCy8sLm83GypUrWbVqFQkJCRmu4efnd81ab5VDhw7RvHlzDh48yEcffcQzzzyT\nYcylWfIr7ct/qb1YsWLXvN7NnstutwPX/54OESk4FORFpMBJTk7OtD2zJQ6XloxERUVlOnMcFRWV\noc3Hx4fk5OQsLb+4GldXV+655x5Wr17NsmXLrhrkz549y8aNGzN982pmLs2wnz179opjunXrxvDh\nw5kxYwb9+/dn586d/P777zRt2jTD8pURI0bg7u7Ohg0buOOOOxz6jh49esUHc10tpN7KNfJ79+6l\nRYsWHD16lM8//9xhff/lqlSpAsCuXbsy7b+0nv1K695v5bkufW1KlSp1zWuJSMGkIC8iBUrx4sXT\nZq7/bf369Rna6tSpw8KFC1mzZg0tW7Z06Dt48GCm57r33nv54Ycf2LJlCzVr1rypenv37s3q1at5\n99136dGjBx4eHpmOmzBhAomJibRo0YLAwMBrnjcgIAA3NzeOHDlyxTH+/v60bt2aJUuWsHXrVmbN\nmgWQaQjes2cPNWrUyBDi7XY74eHh16wnM7dqjfyOHTto2bIlp06d4osvvqBTp05XPEfDhg1xd3dn\n7dq1XLx4kSJFiqT12e32tJ1+mjVrds16KlWqRPny5YmMjOTAgQMZvi6XdvJp3rx5hmPtdjsnTpyg\nTJkyWh8vIlfm3N0vRUSu383sI9+mTRtjWZb58ccfHdo//fTTtPNefszevXtNoUKFjK+vrzl48GBa\nu91uN507d860lpUrVxrLskyDBg3MkSNHMtQWFxdn1qxZk6XXarfbzf33328syzKtW7fO8EAou91u\npkyZYmw2m/Hz8zN79+7N0nmNMea+++4zlmVl+pCpS+bNm2csyzIDBw40ZcuWNd7e3pnuZ1+lShVT\ntGhRc+zYMYfaRowYkel9NSb161ixYsUs13sjNm/ebEqVKmUKFy5sFixYkKVj/vOf/xjLssyQIUMc\n2t9//31jWZZ58MEHMxyzY8cOs3PnTpOUlOTQPn78eGNZlnn88ccdHvy0cOFCY1mWufPOOzOtYcuW\nLcayLNO1a9cs1SwiBZNm5EWkQHnppZf46aefCA0NpWPHjpQqVYo//viDP/74g7Zt2/LDDz84jA8K\nCmL06NEMHz6cWrVq8cQTT+Dj48OyZcuIjo7mrrvuYuvWrQ7HNG3alHfeeYehQ4cSHBxMmzZtqFix\nInFxcRw6dIjVq1cTFBTExo0br1mvZVnMmzePJ554gqVLl1KpUiXatGlDYGAgFy5cYPny5ezcuZNK\nlSoxe/ZsgoKCsnwvWrduTXh4OOHh4Tz88MOZjmnfvj0lSpTgo48+IikpiR49emS6n/2gQYN47rnn\nqF27Nh06dKBQoUKsXbuWHTt20K5dO77//vss13WrREdH06xZM6Kjo2ncuDGbN29m8+bNGcb9eznO\nuHHjWLVqFZMmTeLPP/+kXr167Nixg++++47SpUvz4YcfZjhHtWrVgNRtT8uXL5/WPnjwYBYvXsz8\n+fNp0KABzZs359ChQ8ybNw8vLy/++9//Zlr7pd9iXGvbUREp4Jz9PwkRkeuVlRn5pk2bZjoLbIwx\nixcvNg0aNDAeHh6mePHi5uGHHzbbtm0zo0aNuuIxX3zxhaldu7Zxd3dPe7LrsWPHTPXq1U3x4sUz\nrWHdunWmc+fOpmzZssbNzc2ULFnS1KxZ0/Tr1y/LM/KX+/bbb80jjzxiypQpYwoVKmQsyzKWZZmQ\nkBCTmJh43ec7fPiwcXV1NZ06dbrquH79+l1xVv1yM2bMMLVq1TJeXl6mVKlSpkOHDLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x830ca58>"
+       "<matplotlib.figure.Figure at 0x78782b0>"
       ]
      },
      "metadata": {},
@@ -727,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -752,16 +752,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAE6CAYAAAB0yLYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8447c88>"
+       "<matplotlib.figure.Figure at 0x77d80b8>"
       ]
      },
      "metadata": {},
@@ -822,7 +822,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6f3e630>"
+       "<matplotlib.figure.Figure at 0x77f0b38>"
       ]
      },
      "metadata": {},
@@ -843,16 +843,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84650f0>"
+       "<matplotlib.figure.Figure at 0x815a1d0>"
       ]
      },
      "metadata": {},
@@ -958,9 +958,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81dc828>"
+       "<matplotlib.figure.Figure at 0x815a2e8>"
       ]
      },
      "metadata": {},
@@ -1012,9 +1012,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x838c438>"
+       "<matplotlib.figure.Figure at 0x812b160>"
       ]
      },
      "metadata": {},
@@ -1100,9 +1100,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x81c26d8>"
+       "<matplotlib.figure.Figure at 0x8240358>"
       ]
      },
      "metadata": {},
@@ -1202,9 +1202,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83e7588>"
+       "<matplotlib.figure.Figure at 0x822c8d0>"
       ]
      },
      "metadata": {},
@@ -1231,9 +1231,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x748c160>"
+       "<matplotlib.figure.Figure at 0x7691668>"
       ]
      },
      "metadata": {},
@@ -1260,9 +1260,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83ca550>"
+       "<matplotlib.figure.Figure at 0x9470ac8>"
       ]
      },
      "metadata": {},
@@ -1270,9 +1270,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x950f518>"
+       "<matplotlib.figure.Figure at 0x93ccb00>"
       ]
      },
      "metadata": {},
@@ -1302,9 +1302,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82cd860>"
+       "<matplotlib.figure.Figure at 0x7809550>"
       ]
      },
      "metadata": {},
@@ -1312,9 +1312,9 @@
     },
     {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x84417f0>"
+       "<matplotlib.figure.Figure at 0x7809470>"
       ]
      },
      "metadata": {},
@@ -1322,9 +1322,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x75060b8>"
+       "<matplotlib.figure.Figure at 0x7853eb8>"
       ]
      },
      "metadata": {},
@@ -1332,9 +1332,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6edd240>"
+       "<matplotlib.figure.Figure at 0x80100f0>"
       ]
      },
      "metadata": {},
@@ -1342,9 +1342,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8465e10>"
+       "<matplotlib.figure.Figure at 0x950f9b0>"
       ]
      },
      "metadata": {},
@@ -1415,9 +1415,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95d9fd0>"
+       "<matplotlib.figure.Figure at 0x7663cc0>"
       ]
      },
      "metadata": {},
@@ -1571,7 +1571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false,
     "scrolled": true
@@ -1579,9 +1579,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ed7940>"
+       "<matplotlib.figure.Figure at 0x81d6c50>"
       ]
      },
      "metadata": {},
@@ -1603,16 +1603,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x80ea898>"
+       "<matplotlib.figure.Figure at 0x82b6be0>"
       ]
      },
      "metadata": {},
@@ -1620,20 +1620,9 @@
     },
     {
      "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEWCAYAAAAq41LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x744ee10>,\n",
-       " <matplotlib.lines.Line2D at 0x6ed7d68>]"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEWCAYAAAAq41LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x80c3c88>"
+       "<matplotlib.figure.Figure at 0x82b69e8>"
       ]
      },
      "metadata": {},
@@ -1667,7 +1656,8 @@
     "    plot_track_and_residuals(t, xs, z_xs, res)\n",
     "\n",
     "run_filter_bank(threshold=1.4)\n",
-    "plt.plot(zs)"
+    "plt.plot(zs)\n",
+    "plt.show()"
    ]
   },
   {
@@ -1712,7 +1702,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
   }
  },
  "nbformat": 4,

code/gaussian_internal.py

@@ -9,7 +9,7 @@ import math
 import matplotlib.pylab as pylab
 import matplotlib.pyplot as plt
 import numpy as np
-import stats
+import filterpy.stats as stats
 
 def plot_height_std(x, lw=10):
     m = np.mean(x)
@@ -20,13 +20,13 @@ def plot_height_std(x, lw=10):
     plt.xlim(0,len(x)+1)
     plt.axhline(m-s, ls='--')
     plt.axhline(m+s, ls='--')
-    plt.fill_between((0, len(x)+1), m-s, m+s, 
+    plt.fill_between((0, len(x)+1), m-s, m+s,
                      facecolor='yellow', alpha=0.4)
     plt.xlabel('student')
     plt.ylabel('height (m)')
     plt.show()
 
-    
+
 def plot_gaussian (mu, variance,
                    mu_line=False,
                    xlim=None,

code/mkf_internal.py

@@ -4,14 +4,14 @@ Created on Thu May  1 16:56:49 2014
 
 @author: rlabbe
 """
+import filterpy.stats as stats
+from filterpy.stats import plot_covariance_ellipse
 import numpy as np
 from matplotlib.patches import Ellipse
 import matplotlib.pyplot as plt
 from matplotlib import cm
 from mpl_toolkits.mplot3d import Axes3D
 from numpy.random import multivariate_normal
-import stats
-
 
 def show_residual_chart():
     est_y = ((164.2-158)*.8 + 158)
@@ -312,7 +312,6 @@ def plot_3d_sampled_covariance(mean, cov):
     ax.contour(xv, yv, zv, zdir='y', offset=maxy, cmap=cm.BuGn)
 
 
-from filterpy.common import plot_covariance_ellipse
 def plot_3_covariances():
 
     P = [[2, 0], [0, 2]]

code/nonlinear_plots.py

@@ -7,7 +7,7 @@ Created on Sun May 18 11:09:23 2014
 
 from __future__ import division
 
-from filterpy.common import multivariate_gaussian
+from filterpy.stats import multivariate_gaussian
 from matplotlib import cm
 import matplotlib.pyplot as plt
 from mpl_toolkits.mplot3d import Axes3D

code/smoothing_internal.py

@@ -4,14 +4,7 @@ Created on Tue May 27 21:21:19 2014
 
 @author: rlabbe
 """
-from filterpy.kalman import UnscentedKalmanFilter as UKF
-from filterpy.kalman import MerweScaledSigmaPoints
 import matplotlib.pyplot as plt
-from matplotlib.patches import Ellipse,Arrow
-import math
-import numpy as np
-import stats
-from stats import plot_covariance_ellipse
 
 
 def show_fixed_lag_numberline():

code/stats.py

@@ -1,449 +0,0 @@
-"""
-Author: Roger Labbe
-Copyright: 2014
-
-This code performs various basic statistics functions for the
-Kalman and Bayesian Filters in Python book. Much of this code
-is non-optimal; production code should call the equivalent scipy.stats
-functions. I wrote the code in this form to make explicit how the
-computations are done. The scipy.stats module has many more useful functions
-than what I have written here. In some cases, however, my code is significantly
-faster, at least on my machine. For example, gaussian average 794 ns, whereas
-stats.norm(), using the frozen form, averages 116 microseconds per call.
-"""
-
-from __future__ import (absolute_import, division, print_function,
-                        unicode_literals)
-
-import math
-import numpy as np
-import numpy.linalg as linalg
-import matplotlib.pyplot as plt
-import scipy.sparse as sp
-import scipy.sparse.linalg as spln
-import scipy.stats
-from scipy.stats import norm
-from matplotlib.patches import Ellipse
-
-_two_pi = 2*math.pi
-
-
-def gaussian(x, mean, var):
-    """returns normal distribution (pdf) for x given a Gaussian with the
-    specified mean and variance. x can either be a scalar or an 
-    array-like. 
-
-    gaussian (1,2,3) is equivalent to scipy.stats.norm(2,math.sqrt(3)).pdf(1)
-    It is quite a bit faster albeit much less flexible than the latter.
-
-    @param x test
-
-    Parameters
-    ----------
-    x : scalar or array-like
-        The value for which we compute the probability
-
-    mean : scalar
-        Mean of the Gaussian
-
-    var : scalar
-        Variance of the Gaussian
-
-    Returns
-    -------
-    probability : float, or array-like
-        probability of x for the Gaussian (mean, var). E.g. 0.101 denotes
-        10.1%.
-        
-    Examples
-    --------
-    gaussian(3, 1, 2)
-    gaussian([3,4,3,2,1], 1, 2)
-    """
-    
-    return (np.exp((-0.5*(np.asarray(x)-mean)**2)/var) / 
-            np.sqrt(_two_pi*var))
-
-
-def mul (mean1, var1, mean2, var2):
-    """ multiply Gaussians (mean1, var1) with (mean2, var2) and return the
-    results as a tuple (mean,var).
-
-    var1 and var2 are variances - sigma squared in the usual parlance.
-    """
-
-    mean = (var1*mean2 + var2*mean1) / (var1 + var2)
-    var = 1 / (1/var1 + 1/var2)
-    return (mean, var)
-
-
-def add (mean1, var1, mean2, var2):
-    """ add the Gaussians (mean1, var1) with (mean2, var2) and return the
-    results as a tuple (mean,var).
-
-    var1 and var2 are variances - sigma squared in the usual parlance.
-    """
-
-    return (mean1+mean2, var1+var2)
-
-
-def multivariate_gaussian(x, mu, cov):
-    """ This is designed to replace scipy.stats.multivariate_normal
-    which is not available before version 0.14. You may either pass in a
-    multivariate set of data:
-
-       multivariate_gaussian (array([1,1]), array([3,4]), eye(2)*1.4)
-       multivariate_gaussian (array([1,1,1]), array([3,4,5]), 1.4)
-
-    or unidimensional data:
-       multivariate_gaussian(1, 3, 1.4)
-
-    In the multivariate case if cov is a scalar it is interpreted as eye(n)*cov
-
-    The function gaussian() implements the 1D (univariate)case, and is much
-    faster than this function.
-
-    equivalent calls:
-       multivariate_gaussian(1, 2, 3)
-       scipy.stats.multivariate_normal(2,3).pdf(1)
-
-
-
-    Parameters
-    ----------
-    x : scalar, or np.array-like
-       Value to compute the probability for. May be a scalar if univariate,
-       or any type that can be converted to an np.array (list, tuple, etc).
-       np.array is best for speed.
-
-    mu :  scalar, or np.array-like
-       mean for the Gaussian . May be a scalar if univariate,  or any type
-       that can be converted to an np.array (list, tuple, etc).np.array is
-       best for speed.
-
-    cov :  scalar, or np.array-like
-       Covariance for the Gaussian . May be a scalar if univariate,  or any
-       type that can be converted to an np.array (list, tuple, etc).np.array is
-       best for speed.
-
-
-
-    Returns
-    -------
-    probability : float
-        probability for x for the Gaussian (mu,cov)
-
-
-    """
-    scipy.stats.multivariate_normal
-    # force all to numpy.array type
-    x   = np.array(x, copy=False, ndmin=1)
-    mu  = np.array(mu,copy=False, ndmin=1)
-
-    nx = len(mu)
-    cov = _to_cov(cov, nx)
-
-    norm_coeff = nx*math.log(2*math.pi) + np.linalg.slogdet(cov)[1]
-
-    err = x - mu
-    if (sp.issparse(cov)):
-        numerator = spln.spsolve(cov, err).T.dot(err)
-    else:
-        numerator = np.linalg.solve(cov, err).T.dot(err)
-
-    return math.exp(-0.5*(norm_coeff + numerator))
-
-
-def plot_gaussian(mean, variance,
-                  mean_line=False,
-                  xlim=None,
-                  ylim=None,
-                  xlabel=None,
-                  ylabel=None):
-    """ plots the normal distribution with the given mean and variance. x-axis
-    contains the mean, the y-axis shows the probability.
-
-    mean_line : draws a line at x=mean
-    xlim: optionally specify the limits for the x axis as tuple (low,high).
-          If not specified, limits will be automatically chosen to be 'nice'
-    xlabel : optional label for the x-axis
-    ylabel : optional label for the y-axis
-    """
-
-    sigma = math.sqrt(variance)
-    n = scipy.stats.norm(mean, sigma)
-
-    if xlim is None:
-        min_x = n.ppf(0.001)
-        max_x = n.ppf(0.999)
-    else:
-        min_x = xlim[0]
-        max_x = xlim[1]
-    xs = np.arange(min_x, max_x, (max_x - min_x) / 1000)
-    plt.plot(xs,n.pdf(xs))
-    plt.xlim((min_x, max_x))
-
-    if ylim is not None:
-        plt.ylim(ylim)
-
-    if mean_line:
-        plt.axvline(mean)
-    if xlabel:
-       plt.xlabel(xlabel)
-    if ylabel:
-       plt.ylabel(ylabel)
-
-
-def covariance_ellipse(P, deviations=1):
-    """ returns a tuple defining the ellipse representing the 2 dimensional
-    covariance matrix P.
-
-    Parameters
-    ----------
-    P : nd.array shape (2,2)
-       covariance matrix
-
-    deviations : int (optional, default = 1)
-       # of standard deviations. Default is 1.
-
-    Returns (angle_radians, width_radius, height_radius)
-    """
-    U,s,v = linalg.svd(P)
-    orientation = math.atan2(U[1,0],U[0,0])
-    width  = deviations*math.sqrt(s[0])
-    height = deviations*math.sqrt(s[1])
-
-    assert width >= height
-
-    return (orientation, width, height)
-
-
-def is_inside_ellipse(x,y, ex, ey, orientation, width, height):
-
-    co = np.cos(orientation)
-    so = np.sin(orientation)
-
-    xx = x*co + y*so
-    yy = y*co - x*so
-
-    return (xx / width)**2 + (yy / height)**2 <= 1.
-
-
-    return ((x-ex)*co - (y-ey)*so)**2/width**2 + \
-           ((x-ex)*so + (y-ey)*co)**2/height**2 <= 1
-
-
-
-def plot_covariance_ellipse(mean, cov=None, variance = 1.0,
-             ellipse=None, title=None, axis_equal=True,
-             facecolor='none', edgecolor='#004080',
-             alpha=1.0, xlim=None, ylim=None):
-    """ plots the covariance ellipse where
-
-    mean is a (x,y) tuple for the mean of the covariance (center of ellipse)
-
-    cov is a 2x2 covariance matrix.
-
-    variance is the normal sigma^2 that we want to plot. If list-like,
-    ellipses for all ellipses will be ploted. E.g. [1,2] will plot the
-    sigma^2 = 1 and sigma^2 = 2 ellipses.
-
-    ellipse is a (angle,width,height) tuple containing the angle in radians,
-    and width and height radii.
-
-    You may provide either cov or ellipse, but not both.
-
-    plt.show() is not called, allowing you to plot multiple things on the
-    same figure.
-    """
-
-    assert cov is None or ellipse is None
-    assert not (cov is None and ellipse is None)
-
-    if cov is not None:
-        ellipse = covariance_ellipse(cov)
-
-    if axis_equal:
-        plt.axis('equal')
-
-    if title is not None:
-        plt.title (title)
-
-
-    if np.isscalar(variance):
-        variance = [variance]
-
-    ax = plt.gca()
-
-    angle = np.degrees(ellipse[0])
-    width = ellipse[1] * 2.
-    height = ellipse[2] * 2.
-
-    for var in variance:
-        sd = np.sqrt(var)
-        e = Ellipse(xy=mean, width=sd*width, height=sd*height, angle=angle,
-                    facecolor=facecolor,
-                    edgecolor=edgecolor,
-                    alpha=alpha,
-                    lw=2)
-        ax.add_patch(e)
-    plt.scatter(mean[0], mean[1], marker='+') # mark the center
-    if xlim is not None:
-        ax.set_xlim(xlim)
-
-    if ylim is not None:
-        ax.set_ylim(ylim)
-
-
-def _to_cov(x,n):
-    """ If x is a scalar, returns a covariance matrix generated from it
-    as the identity matrix multiplied by x. The dimension will be nxn.
-    If x is already a numpy array then it is returned unchanged.
-    """
-    try:
-        x.shape
-        if type(x) != np.ndarray:
-            x = np.asarray(x)[0]
-        return x
-    except:
-        return np.eye(n) * x
-
-
-
-def do_plot_test():
-
-    from numpy.random import multivariate_normal
-    p = np.array([[32, 15],[15., 40.]])
-
-    x,y = multivariate_normal(mean=(0,0), cov=p, size=5000).T
-    sd = 2
-    a,w,h = covariance_ellipse(p,sd)
-    print (np.degrees(a), w, h)
-
-    count = 0
-    color=[]
-    for i in range(len(x)):
-        if is_inside_ellipse(x[i], y[i], 0, 0, a, w, h):
-            color.append('b')
-            count += 1
-        else:
-            color.append('r')
-    plt.scatter(x,y,alpha=0.2, c=color)
-
-
-    plt.axis('equal')
-
-    plot_covariance_ellipse(mean=(0., 0.),
-                            cov = p,
-                            variance=sd*sd,
-                            facecolor='none')
-
-    print (count / len(x))
-
-
-from numpy.linalg import inv
-from numpy import asarray, dot
-
-
-def multivariate_multiply(m1, c1, m2, c2):
-
-    C1 = asarray(c1)
-    C2 = asarray(c2)
-    M1 = asarray(m1)
-    M2 = asarray(m2)
-
-    sum_inv = inv(C1+C2)
-    C3 = dot(C1, sum_inv).dot(C2)
-
-    M3 = (dot(C2, sum_inv).dot(M1) +
-          dot(C1, sum_inv).dot(M2))
-
-    return M3, C3
-
-def norm_cdf (x_range, mu, var=1, std=None):
-    """ computes the probability that a Gaussian distribution lies
-    within a range of values.
-
-    Paramateters
-    ------------
-
-    x_range : (float, float)
-        tuple of range to compute probability for
-
-    mu : float
-        mean of the Gaussian
-
-    var : float, optional
-        variance of the Gaussian. Ignored if std is provided
-
-    std : float, optional
-       standard deviation of the Gaussian. This overrides the var parameter
-
-
-    Returns
-    -------
-    probability : float
-        probability that Gaussian is within x_range. E.g. .1 means 10%.
-
-    """
-
-    if std is None:
-        std = math.sqrt(var)
-    return abs(norm.cdf(x_range[0], loc=mu, scale=std) -
-               norm.cdf(x_range[1], loc=mu, scale=std))
-
-
-
-def test_norm_cdf():
-
-    # test using the 68-95-99.7 rule
-
-    mu = 5
-    std = 3
-    var = std*std
-
-    std_1 = (norm_cdf((mu-std, mu+std), mu, var))
-    assert abs(std_1 - .6827) < .0001
-
-    std_1 = (norm_cdf((mu+std, mu-std), mu, std=std))
-    assert abs(std_1 - .6827) < .0001
-
-    std_1half = (norm_cdf((mu+std, mu), mu, var))
-    assert abs(std_1half - .6827/2) < .0001
-
-    std_2 = (norm_cdf((mu-2*std, mu+2*std), mu, var))
-    assert abs(std_2 - .9545) < .0001
-
-    std_3 = (norm_cdf((mu-3*std, mu+3*std), mu, var))
-    assert abs(std_3 - .9973) < .0001
-
-
-if __name__ == '__main__':
-    test_norm_cdf ()
-
-    do_plot_test()
-
-    #test_gaussian()
-
-    # test conversion of scalar to covariance matrix
-    x  = multivariate_gaussian(np.array([1,1]), np.array([3,4]), np.eye(2)*1.4)
-    x2 = multivariate_gaussian(np.array([1,1]), np.array([3,4]), 1.4)
-    assert x == x2
-
-    # test univarate case
-    rv = norm(loc = 1., scale = np.sqrt(2.3))
-    x2 = multivariate_gaussian(1.2, 1., 2.3)
-    x3 = gaussian(1.2, 1., 2.3)
-
-    assert rv.pdf(1.2) == x2
-    assert abs(x2- x3) < 0.00000001
-
-    cov = np.array([[1.0, 1.0],
-                    [1.0, 1.1]])
-
-    plt.figure()
-    P = np.array([[2,0],[0,2]])
-    plot_covariance_ellipse((2,7), cov=cov, variance=[1,2], facecolor='g', title='my title', alpha=.2)
-    plt.show()
-
-    print("all tests passed")

code/ukf_internal.py

@@ -6,12 +6,12 @@ Created on Tue May 27 21:21:19 2014
 """
 from filterpy.kalman import UnscentedKalmanFilter as UKF
 from filterpy.kalman import MerweScaledSigmaPoints
+import filterpy.stats as stats
+from filterpy.stats import plot_covariance_ellipse
 import matplotlib.pyplot as plt
 from matplotlib.patches import Ellipse,Arrow
 import math
 import numpy as np
-import stats
-from stats import plot_covariance_ellipse
 
 def _sigma_points(mean, sigma, kappa):
     sigma1 = mean + math.sqrt((1+kappa)*sigma)