Edit to fit on 6x9 book.

Somewhat arbitrary, but I want the code to be readable on
narrow paper than 8.5x11.

01-g-h-filter.ipynb

@@ -1011,11 +1011,11 @@
     "\n",
     "    1. Initialize the state of the filter\n",
     "    2. Initialize our belief in the state\n",
-    "    \n",
+    "\n",
     "**Predict**\n",
     "\n",
-    "    1. Based on the system behavior, predict state at the next time step\n",
-    "    2. Adjust beliefto account for the uncertainty in prediction\n",
+    "    1.Use system behavior to predict state at the next time step\n",
+    "    2. Adjust belief to account for the uncertainty in prediction\n",
     "    \n",
     "**Update**\n",
     "\n",

02-Discrete-Bayes.ipynb

@@ -314,20 +314,18 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([ 0.1,  0.1,  0.1,  0.1,  0.1,  0.1,  0.1,  0.1,  0.1,  0.1])"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1]\n"
+     ]
     }
    ],
    "source": [
     "import numpy as np\n",
     "belief = np.array([1/10]*10)\n",
-    "belief"
+    "np.set_printoptions(precision=3, linewidth=50)\n",
+    "print(belief)"
    ]
   },
   {
@@ -372,7 +370,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ab97b8>"
+       "<matplotlib.figure.Figure at 0x6e60cc0>"
       ]
      },
      "metadata": {},
@@ -409,13 +407,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.333  0.333  0.     0.     0.     0.     0.     0.     0.333  0.   ]\n"
+      "[ 0.333  0.333  0.     0.     0.     0.     0.     0.\n",
+      "  0.333  0.   ]\n"
      ]
     }
    ],
    "source": [
     "pbelief = hallway * (1/3)\n",
-    "np.set_printoptions(precision=3)\n",
     "print(pbelief)"
    ]
   },
@@ -437,7 +435,7 @@
     "  * door\n",
     "  \n",
     "\n",
-    "Can we deduce where Simon is at the end of that sequence? Of course! Given the hallway's layout there is only one place where you can be in front of a door, move once to the right, and be in front of another door, and that is at the left end. Therefore we can confidently state that Simon is in front of the second doorway. If this is not clear, suppose Simon had started at the second or third door. After moving to the right, his sensor would have returned 'wall'. That doesn't match the sensor readings, so we know he didn't start there. We can continue with that logic for all the remaining starting positions. Therefore the only possibility is that he is now in front of the second door. We denote this in Python with:"
+    "Can we deduce where Simon is at the end of that sequence? Of course! Given the hallway's layout there is only one place where you can be in front of a door, move once to the right, and be in front of another door, and that is at the left end. Therefore we can confidently state that Simon is in front of the second doorway. If this is not clear, suppose Simon had started at the second or third door. After moving to the right, his sensor would have returned 'wall'. That doesn't match the sensor readings, so we know he didn't start there. We can continue with that logic for all the remaining starting positions. Therefore the only possibility is that he is now in front of the second door. We implement this in Python with:"
    ]
   },
   {
@@ -446,18 +444,9 @@
    "metadata": {
     "collapsed": false
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.  1.  0.  0.  0.  0.  0.  0.  0.  0.]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "belief = np.array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.])\n",
-    "print(belief)"
+    "belief = np.array([0., 1., 0., 0., 0., 0., 0., 0., 0., 0.])"
    ]
   },
   {
@@ -503,7 +492,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.3  0.3  0.1  0.1  0.1  0.1  0.1  0.1  0.3  0.1]\n",
       "sum = 1.6\n"
      ]
     },
@@ -511,7 +499,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b0c860>"
+       "<matplotlib.figure.Figure at 0x6ea77f0>"
       ]
      },
      "metadata": {},
@@ -527,8 +515,6 @@
     "belief = np.array([0.1] * 10)\n",
     "reading = 1 # 1 is 'door'\n",
     "update(hallway, belief, z=1, correct_scale=3.)\n",
-    "\n",
-    "print(belief)\n",
     "print('sum =', sum(belief))\n",
     "bp.bar_plot(belief)"
    ]
@@ -563,7 +549,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7126b38>"
+       "<matplotlib.figure.Figure at 0x6ea7e48>"
       ]
      },
      "metadata": {},
@@ -573,7 +559,7 @@
    "source": [
     "def normalize(distribution):\n",
     "    assert distribution.dtype.kind == 'f'\n",
-    "    \"\"\" Normalize a probability distribution so it sums to 1.0\"\"\"\n",
+    "    \"\"\" Normalize distribution so it sums to 1.0\"\"\"\n",
     "    distribution /= sum(distribution.astype(float))   \n",
     "\n",
     "def update(map_, belief, z, prob_correct):\n",
@@ -635,18 +621,20 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "belief before predict = [ 0.35  0.1   0.2   0.3   0.    0.    0.    0.    0.    0.05]\n",
-      "belief after predict = [ 0.05  0.35  0.1   0.2   0.3   0.    0.    0.    0.    0.  ]\n"
-     ]
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7435ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7106a58>"
+       "<matplotlib.figure.Figure at 0x7537048>"
       ]
      },
      "metadata": {},
@@ -655,8 +643,8 @@
    ],
    "source": [
     "def perfect_predict(belief, move):\n",
-    "    \"\"\" move the position by 'move' spaces, where positive is to the right, \n",
-    "    and negative is to the left\n",
+    "    \"\"\" move the position by 'move' spaces, where positive is \n",
+    "    to the right, and negative is to the left\n",
     "    \"\"\"\n",
     "    n = len(belief)\n",
     "    result = np.zeros(n)\n",
@@ -665,11 +653,10 @@
     "    belief[:] = result # copy back to original array\n",
     "        \n",
     "belief = np.array([.35, .1, .2, .3, 0, 0, 0, 0, 0, .05])\n",
-    "print('belief before predict =', belief)\n",
     "bp.bar_plot(belief, title='Before prediction')\n",
+    "plt.show()\n",
     "\n",
     "perfect_predict(belief, 1)\n",
-    "print('belief after predict =', belief)\n",
     "bp.bar_plot(belief, title='After prediction')"
    ]
   },
@@ -709,18 +696,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.   0.   0.   0.   0.1  0.8  0.1  0.   0.   0. ]\n"
-     ]
-    },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7099fd0>"
+       "<matplotlib.figure.Figure at 0x75b60b8>"
       ]
      },
      "metadata": {},
@@ -740,7 +720,6 @@
     "\n",
     "belief = np.array([0., 0., 0., 1., 0., 0., 0., 0., 0., 0.])\n",
     "predict(belief, move=2, p_correct=.8, p_under=.1, p_over=.1)\n",
-    "print(belief)\n",
     "bp.bar_plot(belief)"
    ]
   },
@@ -759,18 +738,11 @@
     "scrolled": true
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.    0.    0.    0.04  0.38  0.52  0.06  0.    0.    0.  ]\n"
-     ]
-    },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71e9d30>"
+       "<matplotlib.figure.Figure at 0x75ca400>"
       ]
      },
      "metadata": {},
@@ -780,7 +752,6 @@
    "source": [
     "belief = np.array([0, 0, .4, .6, 0, 0, 0, 0, 0, 0])\n",
     "predict(belief, move=2, p_correct=.8, p_under=.1, p_over=.1)\n",
-    "print(belief)\n",
     "bp.bar_plot(belief)"
    ]
   },
@@ -804,7 +775,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x45106a0>"
+       "<matplotlib.figure.Figure at 0x7575080>"
       ]
      },
      "metadata": {},
@@ -829,18 +800,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1  0.1]\n"
-     ]
-    },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6af37f0>"
+       "<matplotlib.figure.Figure at 0x74cb860>"
       ]
      },
      "metadata": {},
@@ -851,7 +815,6 @@
     "belief = np.array([1.0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
     "for i in range(500):\n",
     "    predict(belief, move=1, p_correct=.8, p_under=.1, p_over=.1)\n",
-    "print(belief)\n",
     "bp.bar_plot(belief)"
    ]
   },
@@ -931,19 +894,20 @@
    "outputs": [
     {
      "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "array([ 0.05,  0.05,  0.05,  0.05,  0.1 ,  0.45,  0.1 ,  0.05,  0.05,  0.05])"
+       "<matplotlib.figure.Figure at 0x76447b8>"
       ]
      },
-     "execution_count": 15,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "belief = np.array([.05, .05, .05, .05, .55, .05, .05, .05, .05, .05])\n",
+    "belief = np.array([.05] * 10)\n",
+    "belief[4] = .55\n",
     "predict(belief, offset=1, kernel=[.1, .8, .1])\n",
-    "belief"
+    "bp.bar_plot(belief)"
    ]
   },
   {
@@ -964,19 +928,20 @@
    "outputs": [
     {
      "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "array([ 0.05,  0.05,  0.05,  0.05,  0.05,  0.05,  0.1 ,  0.45,  0.1 ,  0.05])"
+       "<matplotlib.figure.Figure at 0x76c4b00>"
       ]
      },
-     "execution_count": 16,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "belief = np.array([.05, .05, .05, .05, .55, .05, .05, .05, .05, .05])\n",
+    "belief = np.array([.05] * 10)\n",
+    "belief[4] = .55\n",
     "predict(belief, offset=3, kernel=[.1, .8, .1])\n",
-    "belief"
+    "bp.bar_plot(belief)"
    ]
   },
   {
@@ -1006,20 +971,17 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([ 0.083,  0.167,  0.5  ,  0.167,  0.083])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 0.083  0.167  0.5    0.167  0.083]\n"
+     ]
     }
    ],
    "source": [
     "kernel = np.array([1, 2, 6, 2, 1], dtype=float)\n",
     "normalize(kernel)\n",
-    "kernel"
+    "print(kernel)"
    ]
   },
   {
@@ -1051,18 +1013,21 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.188  0.188  0.062  0.062  0.062  0.062  0.062  0.062  0.188  0.062]\n"
-     ]
+     "data": {
+      "image/png": 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truncated
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x74a7160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "hallway = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 0])\n",
     "belief = np.array([.1] * 10)\n",
     "update(hallway, belief, z=1, prob_correct=.75)\n",
-    "print(belief)"
+    "bp.bar_plot(belief)"
    ]
   },
   {
@@ -1072,18 +1037,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.088  0.175  0.175  0.075  0.062  0.062  0.062  0.062  0.075  0.163]\n"
-     ]
-    },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGYNJREFUeJzt3X9UlvX9x/EXYOpNISdnEAgJlMNUIuWOxS1lZ4fuM8uZ\nW0naZoE2BytTmTuOYsepqJU7bGqCzjzIahbu1HGdZB7oqBGDziS8m5k6GpvZgftuOkNzoUe4vn/0\n9T7d3ciPvPC6gefjHM+5+VyfD9f7fh/FFxcXnyvIMAxDAAAAAK5YsNUFAAAAAAMF4RoAAAAwCeEa\nAAAAMAnhGgAAADAJ4RoAAAAwCeEaAAAAMAnhGgAAADBJt+G6urpaM2bMUExMjIKDg1VWVtbtJz10\n6JCmTp2q0NBQxcTEaNWqVaYUCwAAAASybsP1uXPndNttt2n9+vWy2WwKCgrqcv6ZM2d07733Kioq\nSvX19Vq/fr3WrVunoqIi04oGAAAAAlFQb57QGBYWpk2bNunRRx+97JySkhLl5+fL4/Fo2LBhkqTV\nq1erpKREn3zyyZVXDAAAAAQo0++5rqur01133eUN1pLkdDrV3Nys48ePm306AAAAIGAMMfsTut1u\n3XTTTT5jkZGR3mNjxozxjre2tpp9egAAAOCqCQ8P9/nY9CvX3d2TDQAAAAxUpofrG2+8UW6322fM\n4/F4jwEAAAADlem3haSlpWnZsmU6f/68977rqqoqjR492ueWkK/7+iV1s9XX10uS7HZ7n56nP6En\n/uiJP3rij574oyf+6Ik/euKPnvjqL/3o6tbmHm3F53K55HK51NHRoePHj8vlcunEiROSpPz8fGVk\nZHjnP/LIIwoNDVVWVpYOHz6s119/Xc8995zy8vJMeCsAAABA4Oo2XB84cECTJ0/W5MmT1dbWpuXL\nl2vy5Mlavny5pC9/SbGpqck7f8SIEaqqqlJzc7PsdrsWLlyopUuXasmSJX33LgAAAIAA0O1tIffc\nc486Ojoue7y0tNRvbOLEiXr77bevrDIAAACgnzH9FxoBAACAwYpwDQAAAJiEcA0AAACYhHANAAAA\nmIRwDQAAAJiEcA0AAACYhHANAAAAmIRwDQAAAJiEcA0AAACYhHANAAAAmIRwDQAAAJiEcA0AAACY\nhHANAAAAmKRH4bq4uFjx8fGy2Wyy2+2qqanpcn5FRYXuvPNOjRgxQjfccINmzpypxsZGUwoGAAAA\nAlW34bq8vFyLFy9WQUGBXC6XHA6Hpk2bphMnTnQ6/6OPPtLMmTN1zz33yOVy6a233lJbW5vuu+8+\n04sHAAAAAkm34bqoqEjZ2dmaP3++EhMTtWHDBkVFRamkpKTT+S6XSx0dHVq7dq0SEhKUnJysZcuW\n6Z///Kf++9//mv4GAAAAgEDRZbi+cOGCGhoa5HQ6fcadTqdqa2s7XTNlyhRdd9112rp1q9rb23X2\n7Flt375dqampGjlypHmVAwAAAAEmyDAM43IHm5ubFRMTo+rqaqWnp3vHV65cqR07dujo0aOdrqut\nrdXMmTN1+vRpdXR0aNKkSfrLX/6iG264wWdea2ur9zX3ZAMAAKA/GDt2rPd1eHi4zzHTdwtpamrS\nzJkzlZ2drfr6eu3fv19hYWHKzMxUFzkeAAAA6PeGdHVw1KhRCgkJkcfj8Rn3eDyKiorqdM2WLVsU\nGxur5557zjv28ssvKzY2VnV1dXI4HJ2us9vtva29V+rr66/KefoTeuKPnvijJ/7oiT964o+e+KMn\n/uiJr/7Sj6/effF1XV65Hjp0qFJSUlRZWekzXlVVddmQbBiGgoN9P+2ljzs6OnpUMAAAANAfdXtb\nSF5enrZv365t27bpyJEjWrRokdxut3JyciRJ+fn5ysjI8M6fMWOGGhoatGrVKjU2NqqhoUHZ2dm6\n6aablJKS0nfvBAAAALBYl7eFSFJmZqZOnTqlwsJCtbS0KCkpSRUVFYqNjZUkud1uNTU1eeenp6er\nvLxczz77rJ5//nmFhoYqLS1Ne/bskc1m67t3AgAAAFis23AtSbm5ucrNze30WGlpqd/YQw89pIce\neujKKgMAAAD6GdN3CwEAAAAGK8I1AAAAYBLCNQAAAGASwjUAAABgEsI1AAAAYBLCNQAAAGASwjUA\nAABgEsI1AAAAYBLCNQAAAGASwjUAAABgEsI1AAAAYBLCNQAAAGASwjUAAABgkh6F6+LiYsXHx8tm\ns8lut6umpqbbNb/73e80btw4DR8+XNHR0crPz7/iYgEAAIBANqS7CeXl5Vq8eLFKSkqUnp6uTZs2\nadq0afrwww8VGxvb6Zq8vDzt3r1bv/nNb5SUlKTW1la1tLSYXjwAAAAQSLoN10VFRcrOztb8+fMl\nSRs2bNCePXtUUlKiNWvW+M0/duyYXnjhBR06dEiJiYne8eTkZBPLBgAAAAJPl7eFXLhwQQ0NDXI6\nnT7jTqdTtbW1na7585//rISEBFVUVCghIUHx8fHKysrSf/7zH/OqBgAAAAJQkGEYxuUONjc3KyYm\nRtXV1UpPT/eOr1y5Ujt27NDRo0f91uTk5KisrEy333671q1bJ0launSpJKmurk5BQUHeua2trd7X\njY2NV/5uAAAAgD42duxY7+vw8HCfY93eFtJbHR0dOn/+vF566SXdcsstkqSXXnpJiYmJqq+v1x13\n3GH2KQEAAICA0GW4HjVqlEJCQuTxeHzGPR6PoqKiOl0TFRWlIUOGeIO1JN1yyy0KCQnRxx9/fNlw\nbbfbe1t7r9TX11+V8/Qn9MQfPfFHT/zRE3/0xB898UdP/NETX/2lH1+9++LrurzneujQoUpJSVFl\nZaXPeFVVlRwOR6dr0tPTdfHiRTU1NXnHmpqa1N7erjFjxvSmbgAAAKBf6Xaf67y8PG3fvl3btm3T\nkSNHtGjRIrndbuXk5EiS8vPzlZGR4Z2fkZGhyZMna968eXK5XDp48KDmzZunO++8M+C/CwEAAACu\nRLf3XGdmZurUqVMqLCxUS0uLkpKSVFFR4d3j2u12+1ylDgoK0ptvvqmnnnpKd999t2w2m5xOp4qK\nivruXQAAAAABoEe/0Jibm6vc3NxOj5WWlvqN3Xjjjdq5c+eVVQYAAAD0Mz16/DkAAACA7hGuAQAA\nAJMQrgEAAACTEK4BAAAAkxCuAQAAAJMQrgEAAACTEK4BAAAAkxCuAQAAAJMQrgEAAACTEK4BAAAA\nkxCuAQAAAJMQrgEAAACTEK4BAAAAk/QoXBcXFys+Pl42m012u101NTU9+uSNjY0KCwtTWFjYFRUJ\nAAAA9Afdhuvy8nItXrxYBQUFcrlccjgcmjZtmk6cONHlugsXLmj27NmaOnWqgoKCTCsYAAAACFTd\nhuuioiJlZ2dr/vz5SkxM1IYNGxQVFaWSkpIu1y1btky33367Zs2aJcMwTCsYAAAACFRdhusLFy6o\noaFBTqfTZ9zpdKq2tvay63bv3q3du3dr48aNBGsAAAAMGkO6Onjy5Em1t7crMjLSZzwiIkJut7vT\nNc3NzVqwYIF27dql0NDQHhdSX1/f47lX4mqdpz+hJ/7oiT964o+e+KMn/uiJP3rij574CvR+jB07\n9rLHTN8tZO7cucrNzdUdd9xh9qcGAAAAAlqXV65HjRqlkJAQeTwen3GPx6OoqKhO1+zbt0/V1dVa\nsWKFJMkwDHV0dOiaa65RSUmJHn/88U7X2e32b1J/j136Dqivz9Of0BN/9MQfPfFHT/zRE3/0xB89\n8UdPfPWXfrS2tl72WJfheujQoUpJSVFlZaUefPBB73hVVZVmzZrV6ZoPPvjA5+Ndu3Zp9erVOnDg\ngKKjo3tTNwAAANCvdBmuJSkvL09z585VamqqHA6HNm/eLLfbrZycHElSfn6+Dhw4oLfeekuSNH78\neJ/1f/vb3xQcHOw3DgAAAAw03YbrzMxMnTp1SoWFhWppaVFSUpIqKioUGxsrSXK73Wpqauryc7DP\nNQAAAAaDbsO1JOXm5io3N7fTY6WlpV2uzcrKUlZWVq8LAwAAAPob03cLAQAAAAYrwjUAAABgEsI1\nAAAAYBLCNQAAAGASwjUAAABgEsI1AAAAYBLCNQAAAGASwjUAAABgEsI1AAAAYBLCNQAAAGASwjUA\nAABgEsI1AAAAYBLCNQAAAGCSHofr4uJixcfHy2azyW63q6am5rJz9+/frwceeEDR0dG69tprlZyc\nrNLSUlMKBgAAAAJVj8J1eXm5Fi9erIKCArlcLjkcDk2bNk0nTpzodH5dXZ2Sk5P12muv6fDhw8rN\nzdWCBQv0yiuvmFo8AAAAEEiG9GRSUVGRsrOzNX/+fEnShg0btGfPHpWUlGjNmjV+8/Pz830+zsnJ\n0b59+/Taa69pzpw5JpQNAAAABJ5ur1xfuHBBDQ0NcjqdPuNOp1O1tbU9PlFra6tGjhzZ+woBAACA\nfiLIMAyjqwnNzc2KiYlRdXW10tPTveMrV67Ujh07dPTo0W5P8uabb+qHP/yhamtrZbfbveOtra3e\n142Njd+kfgAAAOCqGjt2rPd1eHi4z7E+3y3kr3/9q370ox9p48aNPsEaAAAAGGi6ved61KhRCgkJ\nkcfj8Rn3eDyKiorqcm1NTY3uv/9+rVq1Sj/96U+7nNvXwbu+vv6qnKc/oSf+6Ik/euKPnvijJ/7o\niT964o+e+Oov/fjq3Rdf1+2V66FDhyolJUWVlZU+41VVVXI4HJddV11drfvuu08rVqzQU0891Yty\nAQAAgP6pR7uF5OXlae7cuUpNTZXD4dDmzZvldruVk5Mj6cvdQQ4cOKC33npL0pf7XN9///168skn\nNWfOHLndbklSSEiIbrjhhj56KwNH08mzaj53sc/Pc3Z4hCSp5vjpPj/XJdHXDlHCqLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b4bb38>"
+       "<matplotlib.figure.Figure at 0x7603550>"
       ]
      },
      "metadata": {},
@@ -1093,7 +1051,6 @@
    "source": [
     "kernel = (.1, .8, .1)\n",
     "predict(belief, 1, kernel)\n",
-    "print(belief)\n",
     "bp.bar_plot(belief)"
    ]
   },
@@ -1111,18 +1068,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.157  0.313  0.104  0.045  0.037  0.037  0.037  0.037  0.134  0.097]\n"
-     ]
-    },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAGhpJREFUeJzt3X9QVXX+x/EXYOrFiMklCIQEysVUIuXGxpXK2aE7S7Xm\nTknargXaGmyZyrrjUjRtiVq5w6YG1JaDbK2FOzVuk6wDjRqx0CyEtzVT15Zds4F7W11Dc0NHON8/\n+nqn20V+5IFzkedjxhn4nM+5533fgzOve/jwOUGGYRgCAAAAcMGCrS4AAAAAuFgQrgEAAACTEK4B\nAAAAkxCuAQAAAJMQrgEAAACTEK4BAAAAkxCuAQAAAJP0Ga7r6uo0e/ZsxcbGKjg4WJWVlX2+6N69\ne3XLLbcoNDRUsbGxWrVqlSnFAgAAAIGsz3B96tQpXXfddVq/fr1sNpuCgoJ6nX/ixAndeuutio6O\nVnNzs9avX69169appKTEtKIBAACAQBQ0kCc0hoWFqbS0VPfdd99555SXl6uwsFAej0djxoyRJK1e\nvVrl5eX67LPPLrxiAAAAIECZvua6sbFRN910kzdYS5LT6VRbW5sOHz5s9uUAAACAgDHK7Bd0u926\n6qqrfMaioqK8xyZOnOgd7+joMPvyAAAAwJAJDw/3+d70O9d9rckGAAAALlamh+srr7xSbrfbZ8zj\n8XiPAQAAABcr05eFpKena+XKlTp9+rR33XVtba0mTJjgsyTk2759S91szc3NkiS73T6o1xlO6Ik/\neuKPnvijJ/7oiT964o+e+KMnvoZLP3pb2tyvrfhcLpdcLpe6u7t1+PBhuVwuHTlyRJJUWFiozMxM\n7/x7771XoaGhysnJ0b59+/Tmm2/qmWeeUUFBgQlvBQAAAAhcfYbrpqYmzZgxQzNmzFBnZ6eeeOIJ\nzZgxQ0888YSkr/9IsbW11Tv/sssuU21trdra2mS327VkyRKtWLFCy5cvH7x3AQAAAASAPpeFzJo1\nS93d3ec9XlFR4Tc2bdo0vfvuuxdWGQAAADDMmP4HjQAAAMBIRbgGAAAATEK4BgAAAExCuAYAAABM\nQrgGAAAATEK4BgAAAExCuAYAAABMQrgGAAAATEK4BgAAAExCuAYAAABMQrgGAAAATEK4BgAAAExC\nuAYAAABM0q9wXVZWpoSEBNlsNtntdtXX1/c6v7q6WjfeeKMuu+wyXXHFFZozZ44OHTpkSsEAAABA\noOozXFdVVWnZsmUqKiqSy+WSw+FQVlaWjhw50uP8Tz75RHPmzNGsWbPkcrn0zjvvqLOzU7fddpvp\nxQMAAACBpM9wXVJSotzcXC1atEhJSUnasGGDoqOjVV5e3uN8l8ul7u5urV27VomJiUpJSdHKlSv1\nz3/+U//9739NfwMAAABAoOg1XJ85c0YtLS1yOp0+406nUw0NDT2eM3PmTF166aV66aWX1NXVpZMn\nT2rz5s1KS0vT+PHjzascAAAACDBBhmEY5zvY1tam2NhY1dXVKSMjwzv+1FNPacuWLTpw4ECP5zU0\nNGjOnDk6fvy4uru7NX36dP3lL3/RFVdc4TOvo6PD+zVrsgEAADAcTJo0yft1eHi4zzHTdwtpbW3V\nnDlzlJubq+bmZu3evVthYWHKzs5WLzkeAAAAGPZG9XYwIiJCISEh8ng8PuMej0fR0dE9nvPiiy8q\nLi5OzzzzjHfs1VdfVVxcnBobG+VwOHo8z263D7T2AWlubh6S6wwn9MQfPfFHT/zRE3/0xB898UdP\n/NETX8OlH99cffFtvd65Hj16tFJTU1VTU+MzXltbe96QbBiGgoN9X/bc993d3f0qGAAAABiO+lwW\nUlBQoM2bN2vTpk3av3+/li5dKrfbrby8PElSYWGhMjMzvfNnz56tlpYWrVq1SocOHVJLS4tyc3N1\n1VVXKTU1dfDeCQAAAGCxXpeFSFJ2draOHTum4uJitbe3Kzk5WdXV1YqLi5Mkud1utba2eudnZGSo\nqqpKTz/9tJ599lmFhoYqPT1dO3bskM1mG7x3AgAAAFisz3AtSfn5+crPz+/xWEVFhd/Y3Xffrbvv\nvvvCKgMAAACGGdN3CwEAAABGKsI1AAAAYBLCNQAAAGASwjUAAABgEsI1AAAAYBLCNQAAAGASwjUA\nAABgEsI1AAAAYBLCNQAAAGASwjUAAABgEsI1AAAAYBLCNQAAAGASwjUAAABgkn6F67KyMiUkJMhm\ns8lut6u+vr7Pc5577jlNnjxZY8eOVUxMjAoLCy+4WAAAACCQjeprQlVVlZYtW6by8nJlZGSotLRU\nWVlZ+vjjjxUXF9fjOQUFBdq+fbt++9vfKjk5WR0dHWpvbze9eAAAACCQ9BmuS0pKlJubq0WLFkmS\nNmzYoB07dqi8vFxr1qzxm3/w4EE9//zz2rt3r5KSkrzjKSkpJpYNAAAABJ5el4WcOXNGLS0tcjqd\nPuNOp1MNDQ09nvPnP/9ZiYmJqq6uVmJiohISEpSTk6P//Oc/5lUNAAAABKAgwzCM8x1sa2tTbGys\n6urqlJGR4R1/6qmntGXLFh04cMDvnLy8PFVWVur666/XunXrJEkrVqyQJDU2NiooKMg7t6Ojw/v1\noUOHLvzdAAAAAINs0qRJ3q/Dw8N9jvW5LGSguru7dfr0ab3yyiu65pprJEmvvPKKkpKS1NzcrBtu\nuMHsSwIAAAABoddwHRERoZCQEHk8Hp9xj8ej6OjoHs+Jjo7WqFGjvMFakq655hqFhITo008/PW+4\nttvtA619QJqbm4fkOsMJPfFHT/zRE3/0xB898UdP/NETf/TE13DpxzdXX3xbr2uuR48erdTUVNXU\n1PiM19bWyuFw9HhORkaGzp49q9bWVu9Ya2ururq6NHHixIHUDQAAAAwrfe5zXVBQoM2bN2vTpk3a\nv3+/li5dKrfbrby8PElSYWGhMjMzvfMzMzM1Y8YMLVy4UC6XS3v27NHChQt14403BvynEAAAAOBC\n9LnmOjs7W8eOHVNxcbHa29uVnJys6upq7x7Xbrfb5y51UFCQ3n77bT3yyCO6+eabZbPZ5HQ6VVJS\nMnjvAgAAAAgA/fqDxvz8fOXn5/d4rKKiwm/syiuv1NatWy+sMgAAAGCY6dfjzwEAAAD0jXANAAAA\nmIRwDQAAAJiEcA0AAACYhHANAAAAmIRwDQAAAJiEcA0AAACYhHANAAAAmIRwDQAAAJiEcA0AAACY\nhHANAAAAmIRwDQAAAJiEcA0AAACYpF/huqysTAkJCbLZbLLb7aqvr+/Xix86dEhhYWEKCwu7oCIB\nAACA4aDPcF1VVaVly5apqKhILpdLDodDWVlZOnLkSK/nnTlzRvPmzdMtt9yioKAg0woGAAAAAlWf\n4bqkpES5ublatGiRkpKStGHDBkVHR6u8vLzX81auXKnrr79ec+fOlWEYphUMAAAABKpew/WZM2fU\n0tIip9PpM+50OtXQ0HDe87Zv367t27dr48aNBGsAAACMGKN6O3j06FF1dXUpKirKZzwyMlJut7vH\nc9ra2rR48WJt27ZNoaGh/S6kubm533MvxFBdZzihJ/7oiT964o+e+KMn/uiJP3rij574CvR+TJo0\n6bzHTN8tZMGCBcrPz9cNN9xg9ksDAAAAAa3XO9cREREKCQmRx+PxGfd4PIqOju7xnF27dqmurk5P\nPvmkJMkwDHV3d+uSSy5ReXm5HnjggR7Ps9vt36X+fjv3CWiwrzOc0BN/9MQfPfFHT/zRE3/0xB89\n8UdPfA2XfnR0dJz3WK/hevTo0UpNTVVNTY3uuusu73htba3mzp3b4zkfffSRz/fbtm3T6tWr1dTU\npJiYmIHUDQAAAAwrvYZrSSooKNCCBQuUlpYmh8OhF154QW63W3l5eZKkwsJCNTU16Z133pEkTZky\nxef8v/3tbwoODvYbBwAAAC42fYbr7OxsHTt2TMXFxWpvb1dycrKqq6sVFxcnSXK73Wptbe31Ndjn\nGgAAACNBn+FakvLz85Wfn9/jsYqKil7PzcnJUU5OzoALAwAAAIYb03cLAQAAAEYqwjUAAABgEsI1\nAAAAYBLCNQAAAGCSfv1BI4ZW69GTajt1dtCvc3JspCSp/vDxQb/WOTHjRikxImzIrgcAADCUCNcB\nqO3UWS2pOzqEVzw9ZFfaeHOEEiOG7HIAAABDimUhAAAAgEkI1wAAAIBJCNcAAACASQjXAAAAgEkI\n1wAAAIBJCNcAAACASQjXAAAAgEn6Ha7LysqUkJAgm80mu92u+vr6887dvXu37rzzTsXExGjcuHFK\nSUlRRUWFKQUDAAAAgapf4bqqqkrLli1TUVGRXC6XHA6HsrKydOTIkR7nNzY2KiUlRW+88Yb27dun\n/Px8LV68WK+99pqpxQMAAACBpF9PaCwpKVFubq4WLVokSdqwYYN27Nih8vJyrVmzxm9+YWGhz/d5\neXnatWuX3njjDc2fP9+EsgEAAIDA0+ed6zNnzqilpUVOp9Nn3Ol0qqGhod8X6ujo0Pjx4wdeIQAA\nADBM9Hnn+ujRo+rq6lJUVJTPeGRkpNxud78u8vbbb2vnzp29hvHm5uZ+vdaFGqrrXIiTYyOtLmHQ\nnDx5Us3N/7S6jD4Nh5+ToUZP/NETf/TEHz3xR0/80RNfgd6PSZMmnffYoO8W8te//lU//elPtXHj\nRtnt9sG+HAAAAGCZPu9cR0REKCQkRB6Px2fc4/EoOjq613Pr6+t1++23a9WqVXrwwQd7nTvYwfvc\nJ6DhEPDrDx+XdNrqMgZFWFiY7NOusrqM8xpOPydDhZ74oyf+6Ik/euKPnvijJ76GSz86OjrOe6zP\nO9ejR49WamqqampqfMZra2vlcDjOe15dXZ1uu+02Pfnkk3rkkUcGUC4AAAAwPPVrtLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b4b860>"
+       "<matplotlib.figure.Figure at 0x76c8f28>"
       ]
      },
      "metadata": {},
@@ -1131,7 +1081,6 @@
    ],
    "source": [
     "update(hallway, belief, z=1, prob_correct=.75)\n",
-    "print(belief)\n",
     "bp.bar_plot(belief)"
    ]
   },
@@ -1153,7 +1102,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72c7f28>"
+       "<matplotlib.figure.Figure at 0x6eb74a8>"
       ]
      },
      "metadata": {},
@@ -1184,7 +1133,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7253ba8>"
+       "<matplotlib.figure.Figure at 0x750c7f0>"
       ]
      },
      "metadata": {},
@@ -1217,7 +1166,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x731c7b8>"
+       "<matplotlib.figure.Figure at 0x7589160>"
       ]
      },
      "metadata": {},
@@ -1235,7 +1184,7 @@
    "source": [
     "This filter is a form of the g-h filter. Here we are using the percentages for the errors to implicitly compute the *g* and *h* parameters. We could express the discrete Bayes algorithm as a g-h filter, but that would obscure the logic of this filter.\n",
     "\n",
-    "We can express this in pseudocode. I am going to u that won't immediately strike you as being identical to what we have done in this chapter.\n",
+    "We can express this in pseudocode.\n",
     "\n",
     "**Initialization**\n",
     "\n",
@@ -1244,12 +1193,12 @@
     "**Predict**\n",
     "\n",
     "    1. Based on the system behavior, predict state at the next time step\n",
-    "    2. Adjust belief in that state to account for the uncertainty in prediction\n",
+    "    2.  Adjust belief to account for the uncertainty in prediction\n",
     "    \n",
     "**Update**\n",
     "\n",
     "    1. Get a measurement and associated belief about its accuracy\n",
-    "    2. Compute difference (residual) between our estimated state and measurement\n",
+    "    2. Compute residual between estimated state and measurement\n",
     "    3. Determine whether whether the measurement matches each state\n",
     "    4. Update state belief if it matches the measurement\n",
     "\n",
@@ -1287,18 +1236,11 @@
     "collapsed": false
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[ 0.225  0.063  0.061  0.058  0.093  0.225  0.063  0.061  0.058  0.093]\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x724dcc0>"
+       "<matplotlib.figure.Figure at 0x6eab6a0>"
       ]
      },
      "metadata": {},
@@ -1314,8 +1256,7 @@
     "for m in measurements:\n",
     "    update(hallway, belief, z=m, prob_correct=.75)\n",
     "    predict(belief, 1, kernel)\n",
-    "bp.bar_plot(belief)\n",
-    "print(belief)"
+    "bp.bar_plot(belief)"
    ]
   },
   {
@@ -1336,7 +1277,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x717af98>"
+       "<matplotlib.figure.Figure at 0x7522dd8>"
       ]
      },
      "metadata": {},
@@ -1367,7 +1308,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAF9CAYAAADP4URIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x722c0b8>"
+       "<matplotlib.figure.Figure at 0x764f860>"
       ]
      },
      "metadata": {},
@@ -1427,7 +1368,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAACeCAYAAAD5RibQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x717d400>"
+       "<matplotlib.figure.Figure at 0x750db00>"
       ]
      },
      "metadata": {},
@@ -1435,8 +1376,9 @@
     }
    ],
    "source": [
-    "belief = [0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062, \n",
-    "          0.2245871, 0.06288015, 0.06109133, 0.0581008, 0.09334062]\n",
+    "belief = [0.2245871, 0.06288015, 0.06109133, \n",
+    "          0.0581008, 0.09334062, 0.2245871,\n",
+    "          0.06288015, 0.06109133, 0.0581008, 0.09334062]\n",
     "bp.bar_plot(belief)  "
    ]
   },
@@ -1490,10 +1432,10 @@
     "        self.sensor_accuracy = sensor_accuracy\n",
     "\n",
     "    def move(self, distance=1):\n",
-    "        \"\"\" move in the specified direction with some small chance of error\"\"\"\n",
+    "        \"\"\" move in the specified direction\n",
+    "        with some small chance of error\"\"\"\n",
     "\n",
     "        self.pos += distance\n",
-    "\n",
     "        # insert random movement error according to kernel\n",
     "        r = random.random()\n",
     "        s = 0\n",
@@ -1503,13 +1445,11 @@
     "            if r <= s:\n",
     "                break\n",
     "            offset += 1\n",
-    "\n",
     "        self.pos = (self.pos + offset) % len(self.track)\n",
     "        return self.pos\n",
     "\n",
     "    def sense(self):\n",
     "        pos = self.pos\n",
-    "\n",
     "         # insert random sensor error\n",
     "        r = random.random()\n",
     "        if r > self.sensor_accuracy:\n",
@@ -1548,30 +1488,27 @@
     "        robot.move(distance=move_distance)\n",
     "        m = robot.sense()\n",
     "        if do_print:\n",
-    "            print('time {}: pos {}, sensed {}, at position {}'.format(\n",
+    "            print('''time {}: pos {}, sensed {}, '''\n",
+    "                  '''at position {}'''.format(\n",
     "                    i, robot.pos, m, track[robot.pos]))\n",
     "\n",
-    "\n",
     "        update(track, belief, m, sensor_accuracy)\n",
+    "        index = np.argmax(belief)\n",
     "        if do_print:\n",
-    "            print('    update ', belief)\n",
-    "        ind = np.argmax(belief)\n",
-    "        if do_print:\n",
-    "            print('    predicted position is {} with confidence {:.4}%:'.format(\n",
-    "                  ind, belief[ind]*100))\n",
-    "            \n",
+    "            print('''        predicted position is {}'''\n",
+    "                  ''' with confidence {:.4}%:'''.format(\n",
+    "                  index, belief[index]*100))            \n",
     "        if i < iterations - 1:\n",
     "            predict(belief, move_distance, kernel)       \n",
-    "            if do_print:\n",
-    "                print('    predict', belief)\n",
     "\n",
     "    bp.bar_plot(belief)\n",
     "    if do_print:\n",
     "        print()\n",
     "        print('final position is', robot.pos)\n",
-    "        i = np.argmax(belief)\n",
-    "        print('predicted position is {} with confidence {:.4}%:'.format(\n",
-    "                i, belief[i]*100))"
+    "        iindex = np.argmax(belief)\n",
+    "        print('''predicted position is {} with'''\n",
+    "              '''confidence {:.4}%:'''.format(\n",
+    "                index, belief[index]*100))"
    ]
   },
   {
@@ -1594,30 +1531,23 @@
      "output_type": "stream",
      "text": [
       "time 0: pos 4.0, sensed 4.0, at position 4\n",
-      "    update  [ 0.08  0.    0.    0.    0.91  0.    0.    0.    0.    0.  ]\n",
-      "    predicted position is 4 with confidence 91.07%:\n",
-      "    predict [ 0.    0.    0.    0.    0.08  0.    0.    0.    0.91  0.  ]\n",
+      "        predicted position is 4 with confidence 91.07%:\n",
       "time 1: pos 8.0, sensed 8.0, at position 8\n",
-      "    update  [ 0.  0.  0.  0.  0.  0.  0.  0.  1.  0.]\n",
-      "    predicted position is 8 with confidence 99.99%:\n",
-      "    predict [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]\n",
+      "        predicted position is 8 with confidence 99.99%:\n",
       "time 2: pos 2.0, sensed 2.0, at position 2\n",
-      "    update  [ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]\n",
-      "    predicted position is 2 with confidence 100.0%:\n",
-      "    predict [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n",
+      "        predicted position is 2 with confidence 100.0%:\n",
       "time 3: pos 6.0, sensed 6.0, at position 6\n",
-      "    update  [ 0.  0.  0.  0.  0.  0.  1.  0.  0.  0.]\n",
-      "    predicted position is 6 with confidence 100.0%:\n",
+      "        predicted position is 6 with confidence 100.0%:\n",
       "\n",
       "final position is 6.0\n",
-      "predicted position is 6 with confidence 100.0%:\n"
+      "predicted position is 6 withconfidence 100.0%:\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x73eda90>"
+       "<matplotlib.figure.Figure at 0x756d1d0>"
       ]
      },
      "metadata": {},
@@ -1628,7 +1558,7 @@
     "import random\n",
     "\n",
     "random.seed(3)\n",
-    "np.set_printoptions(precision=2, suppress=True)\n",
+    "np.set_printoptions(precision=2, suppress=True, linewidth=60)\n",
     "simulate(4, kernel=[1.], sensor_accuracy=.999,\n",
     "         move_distance=4, do_print=True)"
    ]
@@ -1652,30 +1582,23 @@
      "output_type": "stream",
      "text": [
       "time 0: pos 4.0, sensed 4.0, at position 4\n",
-      "    update  [ 0.84  0.01  0.01  0.01  0.08  0.01  0.01  0.01  0.01  0.01]\n",
-      "    predicted position is 0 with confidence 84.11%:\n",
-      "    predict [ 0.01  0.01  0.01  0.09  0.67  0.09  0.01  0.02  0.07  0.02]\n",
+      "        predicted position is 0 with confidence 84.11%:\n",
       "time 1: pos 8.0, sensed 8.0, at position 8\n",
-      "    update  [ 0.01  0.01  0.01  0.06  0.43  0.06  0.01  0.01  0.4   0.01]\n",
-      "    predicted position is 4 with confidence 43.44%:\n",
-      "    predict [ 0.01  0.05  0.32  0.05  0.01  0.01  0.01  0.09  0.36  0.09]\n",
+      "        predicted position is 4 with confidence 43.44%:\n",
       "time 2: pos 2.0, sensed 2.0, at position 2\n",
-      "    update  [ 0.    0.01  0.81  0.01  0.    0.    0.    0.03  0.1   0.03]\n",
-      "    predicted position is 2 with confidence 81.09%:\n",
-      "    predict [ 0.01  0.03  0.09  0.03  0.01  0.09  0.65  0.09  0.    0.  ]\n",
+      "        predicted position is 2 with confidence 81.09%:\n",
       "time 3: pos 5.0, sensed 5.0, at position 5\n",
-      "    update  [ 0.    0.02  0.05  0.02  0.    0.48  0.37  0.05  0.    0.  ]\n",
-      "    predicted position is 5 with confidence 47.83%:\n",
+      "        predicted position is 5 with confidence 47.83%:\n",
       "\n",
       "final position is 5.0\n",
-      "predicted position is 5 with confidence 47.83%:\n"
+      "predicted position is 5 withconfidence 47.83%:\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x73c61d0>"
+       "<matplotlib.figure.Figure at 0x756d0b8>"
       ]
      },
      "metadata": {},
@@ -1709,7 +1632,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b02588>"
+       "<matplotlib.figure.Figure at 0x77f9e48>"
       ]
      },
      "metadata": {},
@@ -1721,7 +1644,8 @@
     "    for i in range (4):\n",
     "        random.seed(3)\n",
     "        plt.subplot(321+i)\n",
-    "        simulate(148+i, kernel=[.1, .8, .1], sensor_accuracy=.8,\n",
+    "        simulate(148+i, kernel=[.1, .8, .1], \n",
+    "                 sensor_accuracy=.8,\n",
     "                 move_distance=4, do_print=False)\n",
     "        plt.title ('iteration {}'.format(148+i))"
    ]
@@ -1855,18 +1779,31 @@
     " \n",
     " \n",
     " * [4] Khan Acadamy. \"Introduction to the Convolution\"\n",
+    " \n",
     " https://www.khanacademy.org/math/differential-equations/laplace-transform/convolution-integral/v/introduction-to-the-convolution\n",
     " \n",
     " \n",
     "* [5] Wikipedia. \"Convolution\"\n",
-    "http://en.wikipedia.org/wiki/Convolution\n",
+    "\n",
+    " http://en.wikipedia.org/wiki/Convolution\n",
     "\n",
     "* [6] Wikipedia. \"Law of total probability\"\n",
+    "\n",
     "  http://en.wikipedia.org/wiki/Law_of_total_probability\n",
     "  \n",
     "* [7] Wikipedia. \"Time Evolution\"\n",
-    "https://en.wikipedia.org/wiki/Time_evolution"
+    "\n",
+    " https://en.wikipedia.org/wiki/Time_evolution"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

03-Gaussians.ipynb

@@ -333,20 +333,17 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "1.8"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.8\n"
+     ]
     }
    ],
    "source": [
     "import numpy as np\n",
     "x = [1.8, 2.0, 1.7, 1.9, 1.6]\n",
-    "np.mean(x)"
+    "print(np.mean(x))"
    ]
   },
   {
@@ -368,19 +365,16 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "1.8"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.8\n"
+     ]
     }
    ],
    "source": [
     "y = [2.2, 1.5, 2.3, 1.7, 1.3]\n",
-    "np.mean(y)"
+    "print(np.mean(y))"
    ]
   },
   {
@@ -430,18 +424,15 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.14142135623730948"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.141421356237\n"
+     ]
     }
    ],
    "source": [
-    "np.std(x)"
+    "print(np.std(x))"
    ]
   },
   {
@@ -464,7 +455,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4170710>"
+       "<matplotlib.figure.Figure at 0x440c9b0>"
       ]
      },
      "metadata": {},
@@ -500,7 +491,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAADxCAYAAACpkFPLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83190f0>"
+       "<matplotlib.figure.Figure at 0x7ed1208>"
       ]
      },
      "metadata": {},
@@ -599,18 +590,15 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0\n"
+     ]
     }
    ],
    "source": [
-    "np.std([1.8, 1.8, 1.8, 1.8, 1.8])"
+    "print(np.std([1.8, 1.8, 1.8, 1.8, 1.8]))"
    ]
   },
   {
@@ -687,7 +675,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAACxCAYAAAALSUEOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAADGFJREFUeJzt3X9o1dX/B/DXXZbNipvMDMMZltN+UMacQv4hRiotTYJI\nDOwXoWQZlv/UHwuLgkQpKPAH/WOSBGmBFKgVJBuilCukkiKRfmITmrbmyH7s3s8fHxz5Tbd9O2ef\n3enjARffe7/vzusMDofnzj07FsrlcjkAAIB/rWqwOwAAAEOdUA0AAImEagAASCRUAwBAIqEaAAAS\nCdUAAJBIqAYAgERZQ/XatWtj8uTJUSwWo1gsxvTp02P79u05SwAAQMUp5PzPX955550YPnx41NXV\nRalUitdeey1Wr14d+/bti8mTJ+cqAwAAFSVrqD6dmpqaWLVqVSxevHggywAAwKAZNlANd3d3x9at\nW+PEiRMxY8aMgSoDAACDLnuo/vzzz+Pmm2+O33//Paqrq2PLli0xadKknucdHR25SwIAwP9MsVj8\nx73sp39cc8018dlnn8XHH38cy5Yti4ULF0Zra2vuMgAAUDEGfE/17NmzY+zYsbFx48aIsFINAMDQ\ndrqV6gHbU31Sd3d3lEqlfncIIiIKhULP9QD/3scQdvJTsIaGhkHuCZXMfEJfzCX0R18Lw1lD9VNP\nPRXz5s2LsWPHRmdnZ7zxxhvR3NwcO3fuzFkGAAAqStZQfeTIkVi0aFG0tbVFsViMyZMnx86dO2P2\n7Nk5ywAAQEXJGqpP7psGAIBzSfbTPwAA4FwjVAMAQCKhGgAAEgnVAACQSKgGAIBEQjUAACQSqgEA\nIJFQDQAAiYRqAABIJFQDAEAioRoAABIJ1QAAkEioBgCAREI1AAAkyhqqX3jhhZg6dWoUi8UYPXp0\nzJ8/Pw4cOJCzBAAAVJysobq5uTmWLVsWe/fujQ8//DCGDRsWs2bNimPHjuUsAwAAFWVYzsZ27tx5\nytevv/56FIvF2LNnT8ydOzdnKQAAqBgDuqf6119/jVKpFCNHjhzIMgAAMKgK5XK5PFCNL1iwIA4d\nOhStra1RKBQiIqKjo6Pn+cGDBweqNEPc1KlTe6737ds3iD0BhjrzCZBDXV1dz3WxWPzH86zbP/5u\nxYoVsWfPnti9e3dPoAYAgLPRgKxUP/HEE7Fly5bYtWtXTJw48ZRnf1+pPl3Kh4g45RexAfwwhSGu\ntbU1IiIaGhoGuSdUMvMJfTGX0B99ZdjsK9XLly+PrVu3njZQAwDA2ShrqH700Udj8+bNsW3btigW\ni9HW1hYREZdccklcdNFFOUsBAEDFyHr6x/r16+P48eNx6623xhVXXNHzevHFF3OWAQCAipJ1pbpU\nKuVsDgAAhoQBPacaAADOBUI1AAAkEqoBACCRUA0AAImEagAASCRUAwBAIqEaAAASCdUAAJBIqAYA\ngERCNQAAJBKqAQAgkVANAACJhGoAAEgkVAMAQKKsobqlpSXmz58fY8eOjaqqqti0aVPO5gEAoCJl\nDdVdXV1x4403xssvvxzV1dVRKBRyNg8AABVpWM7GGhsbo7GxMSIiHnjggZxNAwBAxbKnGgAAEmVd\nqf7/am1tHczyDBHGCX0xRugvY4XeGB/0pq6urtfnVqoBACDRoK5UNzQ0DGZ5hgjjhDM5uapkjNBf\nxgqnYy6hPzo6Onp9bqUaAAASZV2p7urqioMHD0ZERKlUiu+++y72798fNTU1UVtbm7MUAABUjKwr\n1fv27Yv6+vqor6+PEydOxMqVK6O+vj5WrlyZswwAAFSUrCvVM2fOjFKplLNJAACoePZUAwBAIqEa\nAAASCdUAAJBIqAYAgERCNQAAJBKqAQAgkVANAACJhGoAAEgkVAMAQCKhGgAAEgnVAACQSKgGAIBE\nQjUAACQSqgEAIFH2UL1u3boYP358VFdXR0NDQ+zevTt3CQAAqChZQ/Wbb74Zjz/+eDQ1NcX+/ftj\n+vTp0djYGD/88EPOMgAAUFGyhuqXXnopHnzwwXjooYdi0qRJ8corr8SYMWNi/fr1OcsAAEBFyRaq\n//jjj/j0009jzpw5p9yfM2dO7NmzJ1cZAACoOMNyNfTzzz9Hd3d3XH755afcHz16dLS1tZ32ewqF\nXNU5+5R7rowTzqxhsDvAkGA+oS/mEvr2yy+9P3f6BwAAJMoWqkeNGhXnnXdeHDly5JT7R44ciTFj\nxuQqAwAAFSfb9o8LLrggpkyZEu+//37cddddPfc/+OCDuPvuu0/7PeXyaW9DtLa2RkREQ4OP5Diz\nwt8+yy+bUDgD8wl9MZfQHx0dvT/PFqojIlasWBH33ntvTJs2LaZPnx4bNmyItra2ePjhh3OWAQCA\nipI1VC9YsCDa29vj+eefj59++iluuOGG2L59e9TW1uYsAwAAFSVrqI6IWLp0aSxdujR3swAAULGc\n/gEAAImEagAASCRUAwBAIqEaAAASCdUAAJBIqAYAgERCNQAAJBKqAQAgkVANAACJhGoAAEgkVAMA\nQCKhGgAAEgnVAACQSKgGAIBE2UL1q6++GrfccktceumlUVVVFd9//32upgEAoKJlC9W//fZb3Hbb\nbfHss8/mahIAAIaEYbkaWr58eUREtLa25moSAACGBHuqAQAgUbaV6n/DqjZ9MUboL2OFvhgj9Idx\nwpnU1dX1+rzXleqmpqaoqqrq9dXS0pK1wwAAMNQUyuVy+UwP29vbo729vdcGamtro7q6uufr1tbW\nmDZtWnz77bcxbty4f7y/o6Oj57pYLP6bPnMOOLlS0NDQMMg9oZIVCoWe616mMs5x5hP6Yi6hP/rK\nsL1u/6ipqYmampr8vQIAgLNItj3VbW1t0dbWFl9//XVERBw4cCCOHj0aV155ZYwcOTJXGQAAqDjZ\nTv/YsGFD1NfXx6JFi6JQKMTcuXNjypQp8e677+YqAQAAFSlbqH7mmWeiVCpFqVSK7u7unn/vu+++\nXCUAAKAiOacaAAASCdUAAJBIqAYAgERCNQAAJBKqAQAgkVANAACJhGoAAEgkVAMAQCKhGgAAEgnV\nAACQSKgGAIBEQjUAACQSqgEAIJFQDQAAibKE6mPHjsVjjz0W1157bYwYMSLGjRsXjzzySBw9ejRH\n8wAAUNGyhOrDhw/H4cOHY82aNfHFF1/E5s2bo6WlJe65554czQMAQEUblqOR66+/Pt5+++2er6+6\n6qpYs2ZNzJs3L44fPx4XX3xxjjIAAFCRBmxPdUdHRwwfPjxGjBgxUCUAAKAiZFmp/r9++eWXePrp\np2PJkiVRVXXm3N7a2joQ5TmLGCP0l7FCX4wR+sM44Uzq6up6fd7rSnVTU1NUVVX1+mppaTnle44f\nPx533HFH1NbWxurVq9N/AgAAqHCFcrlcPtPD9vb2aG9v77WB2traqK6ujoj/Burbb789CoVC7Nix\n47RbPzo6Onqui8Xiv+03Z7mTKwUNDQ2D3BMqWaFQ6LnuZSrjHGc+oS/mEvqjrwzb6/aPmpqaqKmp\n6Vehzs7OaGxs7DVQAwDA2SjLnurOzs6YM2dOdHZ2xrZt26KzszM6Ozsj4r/B/Pzzz89RBgAAKlKW\nUP3JJ5/ERx99FIVCISZOnNhzv1AoxK5du2LGjBk5ygAAQEXKEqpnzpwZpVIpR1MAADDkDNg51QAA\ncK4QqgEAIJFQDQAAiYRqAABIJFQDAEAioRoAABIJ1QAAkEioBgCAREI1AAAkEqoBACCRUA0AAImE\nagAASCRUAwBAIqEaAAASZQvVixcvjgkTJsSIESNi9OjRceedd8aXX36Zq3kAAKhY2UL11KlTY9Om\nTfHVV1/Fe++9F+VyOWbNmhV//fVXrhIAAFCRhuVqaMmSJT3X48aNi+eeey5uuumm+Oabb6Kuri5X\nGQAAqDgDsqe6q6srNm7cGHV1dTF+/PiBKAEAABWjUC6Xy7kaW7duXTz55JPR1dUVV199dezYsSMm\nTJhwyns6OjpylQMAgP+5YrH4j3u9rlQ3NTVFVVVVr6+Wlpae9y9atCj2798fzc3Ncd1110VjY2N0\ndnbm/0kAAKCC9LpS3d7eHu3t7b02UFtbG9XV1f+4/+eff8bIkSNj7dq1cf/99/fct1INAMBQdrqV\n6l7/ULGmpiZqamr+VbFSqRTlcjlKpVKfnQAAgKEsy+kfhw4dirfeeitmz54do0aNih9//DFWrVoV\nF154YcybNy9HCQAAqFhZTv8YPnx4NDc3R2NjY9TV1cXChQujWCzG3r1747LLLstRAgAAKlbW0z8A\nAOBcNCDnVAMAwLlEqAYAgERCNQAAJBKqAQAgkVANAACJhGoAAEgkVAMAQCKhGgAAEv0HENXkpcqZ\nEhkAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x836e550>"
+       "<matplotlib.figure.Figure at 0x7ef21d0>"
       ]
      },
      "metadata": {},
@@ -760,7 +748,7 @@
    "source": [
     "We are now ready to learn about Gaussians. Let's remind ourselves of the motivation for this chapter.\n",
     "\n",
-    "    We desire a unimodal, continuous way to represent probabilities that models how the real world works, and that is very computationally efficient to calculate."
+    "> We desire a unimodal, continuous way to represent probabilities that models how the real world works, and that is very computationally efficient to calculate."
    ]
   },
   {
@@ -781,7 +769,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x83b5128>"
+       "<matplotlib.figure.Figure at 0x7f01828>"
       ]
      },
      "metadata": {},
@@ -790,7 +778,8 @@
    ],
    "source": [
     "from filterpy.stats import plot_gaussian\n",
-    "plot_gaussian(mean=1.8, variance=0.1414**2, xlabel='Student Height')"
+    "plot_gaussian(mean=1.8, variance=0.1414**2, \n",
+    "              xlabel='Student Height')"
    ]
   },
   {
@@ -818,7 +807,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x847d668>"
+       "<matplotlib.figure.Figure at 0x8011ef0>"
       ]
      },
      "metadata": {},
@@ -827,8 +816,9 @@
    ],
    "source": [
     "import book_plots\n",
+    "belief = [ 0.,0., 0., 0.1, 0.15, 0.5, 0.2, .15, 0, 0]\n",
     "with figsize(y=1.5):\n",
-    "    book_plots.bar_plot([ 0.,0., 0., 0.1, 0.15, 0.5, 0.2, .15, 0, 0])"
+    "    book_plots.bar_plot(belief)"
    ]
   },
   {
@@ -856,7 +846,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8458ac8>"
+       "<matplotlib.figure.Figure at 0x8085a90>"
       ]
      },
      "metadata": {},
@@ -937,7 +927,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8400eb8>"
+       "<matplotlib.figure.Figure at 0x8003ef0>"
       ]
      },
      "metadata": {},
@@ -975,7 +965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },
@@ -984,15 +974,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Probability of value in range 21.5 to 22.5 is 19.74%\n",
-      "Probability of value in range 23.5 to 24.5 is 12.10%\n"
+      "Probability of range 21.5 to 22.5 is 19.74%\n",
+      "Probability of range 23.5 to 24.5 is 12.10%\n"
      ]
     }
    ],
    "source": [
-    "print('Probability of value in range 21.5 to 22.5 is {:.2f}%'.format(\n",
+    "print('Probability of range 21.5 to 22.5 is {:.2f}%'.format(\n",
     "      norm_cdf((21.5, 22.5), 22,4)*100))\n",
-    "print('Probability of value in range 23.5 to 24.5 is {:.2f}%'.format(\n",
+    "print('Probability of range 23.5 to 24.5 is {:.2f}%'.format(\n",
     "      norm_cdf((23.5, 24.5), 22,4)*100))"
    ]
   },
@@ -1065,7 +1055,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7dad710>"
+       "<matplotlib.figure.Figure at 0x7945e80>"
       ]
      },
      "metadata": {},
@@ -1112,7 +1102,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8458be0>"
+       "<matplotlib.figure.Figure at 0x7feb0b8>"
       ]
      },
      "metadata": {},
@@ -1157,7 +1147,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAADaCAYAAAB3s44kAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x95b2eb8>"
+       "<matplotlib.figure.Figure at 0x9142748>"
       ]
      },
      "metadata": {},
@@ -1167,7 +1157,7 @@
    "source": [
     "import math\n",
     "from IPython.html.widgets import interact, interactive, fixed\n",
-    "import IPython.html.widgets as widgets\n",
+    "from IPython.html.widgets import FloatSliderWidget\n",
     "\n",
     "set_figsize(y=3)\n",
     "def plt_g(mu,variance):\n",
@@ -1177,7 +1167,7 @@
     "    plt.ylim((0, 1))\n",
     "\n",
     "interact (plt_g, mu=(0, 10), \n",
-    "          variance=widgets.FloatSliderWidget(value=0.6, min=0.2, max=4));"
+    "          variance=FloatSliderWidget(value=0.6, min=0.2, max=4));"
    ]
   },
   {
@@ -1265,17 +1255,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "probability density of 1.5 is       0.1311\n",
-      "probability density of 2.5 is also  0.1311\n",
-      "whereas probability density of 2 is 0.1330\n"
+      "pdf of 1.5 is       0.1311\n",
+      "pdf of 2.5 is also  0.1311\n",
+      "pdf of 2 is         0.1330\n"
      ]
     }
    ],
    "source": [
     "n23 = norm(2, 3)\n",
-    "print('probability density of 1.5 is       %.4f' % n23.pdf(1.5))\n",
-    "print('probability density of 2.5 is also  %.4f' % n23.pdf(2.5))\n",
-    "print('whereas probability density of 2 is %.4f' % n23.pdf(2))"
+    "print('pdf of 1.5 is       %.4f' % n23.pdf(1.5))\n",
+    "print('pdf of 2.5 is also  %.4f' % n23.pdf(2.5))\n",
+    "print('pdf of 2 is         %.4f' % n23.pdf(2))"
    ]
   },
   {
@@ -1298,13 +1288,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-1.84551343  0.16224968  1.24528372  1.66242862  1.29968878  3.39047438\n",
-      "  1.27725073  8.76198727 -0.60691441 -2.13881535 -0.27072415  2.00097679\n",
-      "  0.62953538  8.63718322 -0.88693697]\n"
+      "[ 1.665  5.067 -3.73   1.994 -2.977 -3.43   3.368\n",
+      " -0.468  4.66  -1.389  0.625  2.565  4.079  1.163\n",
+      " -0.795]\n"
      ]
     }
    ],
    "source": [
+    "np.set_printoptions(precision=3, linewidth=50)\n",
     "print(n23.rvs(size=15))"
    ]
   },
@@ -1403,7 +1394,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAADaCAYAAAD9nBdlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x960f6d8>"
+       "<matplotlib.figure.Figure at 0x79563c8>"
       ]
      },
      "metadata": {},
@@ -1412,7 +1403,8 @@
    ],
    "source": [
     "xs = np.arange(10,100, 0.05)\n",
-    "plt.plot(xs, [gaussian(x, 90, 30) for x in xs], label='var=0.2')\n",
+    "ys = [gaussian(x, 90, 30) for x in xs]\n",
+    "plt.plot(xs, ys, label='var=0.2')\n",
     "plt.xlim((0,120))\n",
     "plt.ylim(0, 0.09);"
    ]
@@ -1437,7 +1429,6 @@
    "outputs": [],
    "source": [
     "from numpy.random import randn\n",
-    "\n",
     "def sense():\n",
     "    return 10 + randn()*2"
    ]
@@ -1458,9 +1449,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x966d4a8>"
+       "<matplotlib.figure.Figure at 0x9200780>"
       ]
      },
      "metadata": {},
@@ -1493,8 +1484,9 @@
     "import math\n",
     "\n",
     "def rand_student_t(df, mu=0, std=1):\n",
-    "    \"\"\"return random number distributed by student's t distribution with\n",
-    "    `df` degrees of freedom with the specified mean and standard deviation.\n",
+    "    \"\"\"return random number distributed by student's t \n",
+    "    distribution with `df` degrees of freedom with the \n",
+    "    specified mean and standard deviation.\n",
     "    \"\"\"\n",
     "    x = random.gauss(0, std)\n",
     "    y = 2.0*random.gammavariate(0.5*df, 2.0)\n",
@@ -1510,9 +1502,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAADaCAYAAADqvoesAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuMAAADaCAYAAAAMl0zoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9682da0>"
+       "<matplotlib.figure.Figure at 0x923c550>"
       ]
      },
      "metadata": {},

05-Multivariate-Gaussians.ipynb

@@ -381,7 +381,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x40504a8>"
+       "<matplotlib.figure.Figure at 0x439c2e8>"
       ]
      },
      "metadata": {},
@@ -545,7 +545,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x40503c8>"
+       "<matplotlib.figure.Figure at 0x439c198>"
       ]
      },
      "metadata": {},
@@ -582,9 +582,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79624a8>"
+       "<matplotlib.figure.Figure at 0x853f1d0>"
       ]
      },
      "metadata": {},
@@ -621,7 +621,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x875deb8>"
+       "<matplotlib.figure.Figure at 0x8980128>"
       ]
      },
      "metadata": {},
@@ -663,7 +663,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8c21208>"
+       "<matplotlib.figure.Figure at 0x91b9710>"
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      },
      "metadata": {},
@@ -696,7 +696,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9050470>"
+       "<matplotlib.figure.Figure at 0x8980320>"
       ]
      },
      "metadata": {},
@@ -776,7 +776,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7da30f0>"
+       "<matplotlib.figure.Figure at 0x7cceb38>"
       ]
      },
      "metadata": {},
@@ -811,7 +811,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7909550>"
+       "<matplotlib.figure.Figure at 0x89edc88>"
       ]
      },
      "metadata": {},
@@ -854,7 +854,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90cffd0>"
+       "<matplotlib.figure.Figure at 0x91c7e80>"
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      },
      "metadata": {},
@@ -908,7 +908,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82c0ba8>"
+       "<matplotlib.figure.Figure at 0x8107f28>"
       ]
      },
      "metadata": {},
@@ -939,7 +939,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x90efe10>"
+       "<matplotlib.figure.Figure at 0x89b6fd0>"
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      "metadata": {},
@@ -972,7 +972,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x910edd8>"
+       "<matplotlib.figure.Figure at 0x941f1d0>"
       ]
      },
      "metadata": {},
@@ -1010,7 +1010,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9116240>"
+       "<matplotlib.figure.Figure at 0x89b26d8>"
       ]
      },
      "metadata": {},
@@ -1041,7 +1041,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa3ed908>"
+       "<matplotlib.figure.Figure at 0xa6eebe0>"
       ]
      },
      "metadata": {},
@@ -1081,7 +1081,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x911b6d8>"
+       "<matplotlib.figure.Figure at 0xa716be0>"
       ]
      },
      "metadata": {},
@@ -1117,7 +1117,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa45aa90>"
+       "<matplotlib.figure.Figure at 0xa731208>"
       ]
      },
      "metadata": {},
@@ -1150,7 +1150,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x915fc18>"
+       "<matplotlib.figure.Figure at 0xa699668>"
       ]
      },
      "metadata": {},
@@ -1199,7 +1199,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa42e358>"
+       "<matplotlib.figure.Figure at 0xa7560f0>"
       ]
      },
      "metadata": {},
@@ -1229,7 +1229,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa42ed30>"
+       "<matplotlib.figure.Figure at 0xa7562e8>"
       ]
      },
      "metadata": {},
@@ -1264,7 +1264,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82dab70>"
+       "<matplotlib.figure.Figure at 0x853fcf8>"
       ]
      },
      "metadata": {},
@@ -1293,7 +1293,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8768630>"
+       "<matplotlib.figure.Figure at 0x91a6fd0>"
       ]
      },
      "metadata": {},
@@ -1322,7 +1322,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x907b3c8>"
+       "<matplotlib.figure.Figure at 0xa679470>"
       ]
      },
      "metadata": {},
@@ -1351,7 +1351,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x82e1320>"
+       "<matplotlib.figure.Figure at 0x808f0b8>"
       ]
      },
      "metadata": {},

07-Kalman-Filter-Math.ipynb

@@ -364,15 +364,15 @@
     "Let's rewrite this using our newly learned terminology.\n",
     "\n",
     "```python\n",
-    "def update(prior_probability, measure, prob_hit, prob_miss):\n",
-    "    posterior_probability = np.zeros(len(prior_probability))\n",
+    "def update(prior, measure, prob_hit, prob_miss):\n",
+    "    posterior = np.zeros(len(prior))\n",
     "    for i in range(len(hallway)):\n",
     "        if hallway[i] == measure:\n",
-    "            posterior_probability[i] = prior_probability[i] * p_hit\n",
+    "            posterior[i] = prior[i] * p_hit\n",
     "        else:\n",
-    "            posterior_probability[i] = prior_probability[i] * p_miss\n",
+    "            posterior[i] = prior[i] * p_miss\n",
     "\n",
-    "    return posterior_probability / sum(posterior_probability)\n",
+    "    return posterior / sum(posterior)\n",
     "```\n",
     "\n",
     "\n",
@@ -588,7 +588,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ab96d8>"
+       "<matplotlib.figure.Figure at 0x6dd0e80>"
       ]
      },
      "metadata": {},
@@ -622,7 +622,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4020dd8>"
+       "<matplotlib.figure.Figure at 0x436c9e8>"
       ]
      },
      "metadata": {},
@@ -652,7 +652,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b8beb8>"
+       "<matplotlib.figure.Figure at 0x7ed9128>"
       ]
      },
      "metadata": {},
@@ -1022,9 +1022,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7b94240>"
+       "<matplotlib.figure.Figure at 0x7ee7c50>"
       ]
      },
      "metadata": {},
@@ -1034,7 +1034,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7cb7f60>"
+       "<matplotlib.figure.Figure at 0x8012470>"
       ]
      },
      "metadata": {},
@@ -1069,7 +1069,8 @@
     "        pos = predict(pos[0], pos[1], u, Q)\n",
     "        f.predict(u=u)\n",
     "\n",
-    "    plt.scatter(range(len(x_kf)), x_kf, c='r', s=80, label='Kalman')\n",
+    "    plt.scatter(range(len(x_kf)), x_kf, c='r',\n",
+    "                s=80, label='Kalman')\n",
     "    plt.title('State')\n",
     "    plt.plot(x_uni, label='Univariate')\n",
     "    plt.xlim([0, 50])\n",
@@ -1331,82 +1332,19 @@
    "metadata": {},
    "source": [
     "### Numerical Solutions\n",
-    "Finally, there are numerical techniques to find $\\Phi$. As filters get larger finding analytical solutions becomes very tedious (though packages like SymPy make it easier). C. F. van Loan [3] has developed a technique that finds both $\\Phi$ and $Q$ numerically.\n",
+    "Finally, there are numerical techniques to find $\\Phi$. As filters get larger finding analytical solutions becomes very tedious (though packages like SymPy make it easier). C. F. van Loan [3] has developed a technique that finds both $\\Phi$ and $Q$ numerically. Given the continuous model\n",
     "\n",
+    "$$ x' = Fx + Gu$$\n",
+    "\n",
+    "where u is the unity white noise, we compute and return the $\\sigma$ and $Q_k$ that discretizes that equation.\n",
+    "    \n",
     "I have implemented van Loan's method in `FilterPy`. You may use it as follows:\n",
     "\n",
     "    from filterpy.common import van_loan_discretization\n",
     "  \n",
     "    F = np.array([[0,1],[-1,0]], dtype=float)\n",
     "    G = np.array([[0.],[2.]]) # white noise scaling\n",
-    "    phi, Q = van_loan_discretization(F, G, dt=0.1)\n",
-    "    \n",
-    "See the docstring documentation for van_loan_discretization for more information, which I have embedded below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on function van_loan_discretization in module filterpy.common.discretization:\n",
-      "\n",
-      "van_loan_discretization(F, G, dt)\n",
-      "    Discretizes a linear differential equation which includes white noise\n",
-      "    according to the method of C. F. van Loan [1]. Given the continuous\n",
-      "    model\n",
-      "    \n",
-      "        x' =  Fx + Gu\n",
-      "    \n",
-      "    where u is the unity white noise, we compute and return the sigma and Q_k\n",
-      "    that discretizes that equation.\n",
-      "    \n",
-      "    \n",
-      "    **Example**::\n",
-      "    \n",
-      "        Given y'' + y = 2u(t), we create the continuous state model of\n",
-      "    \n",
-      "        x' = | 0 1| * x + |0|*u(t)\n",
-      "             |-1 0|       |2|\n",
-      "    \n",
-      "        and a time step of 0.1:\n",
-      "    \n",
-      "    \n",
-      "        >>> F = np.array([[0,1],[-1,0]], dtype=float)\n",
-      "        >>> G = np.array([[0.],[2.]])\n",
-      "        >>> phi, Q = van_loan_discretization(F, G, 0.1)\n",
-      "    \n",
-      "        >>> phi\n",
-      "        array([[ 0.99500417,  0.09983342],\n",
-      "               [-0.09983342,  0.99500417]])\n",
-      "    \n",
-      "        >>> Q\n",
-      "        array([[ 0.00133067,  0.01993342],\n",
-      "               [ 0.01993342,  0.39866933]])\n",
-      "    \n",
-      "        (example taken from Brown[2])\n",
-      "    \n",
-      "    \n",
-      "    **References**\n",
-      "    \n",
-      "    [1] C. F. van Loan. \"Computing Integrals Involving the Matrix Exponential.\"\n",
-      "        IEEE Trans. Automomatic Control, AC-23 (3): 395-404 (June 1978)\n",
-      "    \n",
-      "    [2] Robert Grover Brown. \"Introduction to Random Signals and Applied\n",
-      "        Kalman Filtering.\" Forth edition. John Wiley & Sons. p. 126-7. (2012)\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from filterpy.common import van_loan_discretization\n",
-    "help(van_loan_discretization)"
+    "    phi, Q = van_loan_discretization(F, G, dt=0.1)"
    ]
   },
   {
@@ -1552,7 +1490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "collapsed": false
    },
@@ -1579,14 +1517,15 @@
        "⎣    6           2                 ⎦       "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "import sympy\n",
-    "from sympy import init_printing, Matrix,MatMul, integrate, symbols\n",
+    "from sympy import (init_printing, Matrix,MatMul, \n",
+    "                   integrate, symbols)\n",
     "\n",
     "init_printing(use_latex='mathjax')\n",
     "dt, phi = symbols('\\Delta{t} \\Phi_s')\n",
@@ -1613,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "collapsed": false
    },
@@ -1634,7 +1573,7 @@
        "[\\Delta{t}⋅\\Phi_s]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1649,7 +1588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "collapsed": false
    },
@@ -1678,7 +1617,7 @@
        "⎣    2                 ⎦       "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1728,7 +1667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "collapsed": false
    },
@@ -1750,7 +1689,7 @@
        "⎣    2                 ⎦    "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1790,7 +1729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "collapsed": false
    },
@@ -1817,7 +1756,7 @@
        "⎣    2                             ⎦    "
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1851,6 +1790,30 @@
     "FilterPy offers several routines to compute the $\\mathbf{Q}$ matrix. The function `Q_continuous_white_noise()` computes $\\mathbf{Q}$ for a given value for $\\Delta t$ and the spectral density."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.33333333  0.5       ]\n",
+      " [ 0.5         1.        ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from filterpy.common import Q_continuous_white_noise\n",
+    "from filterpy.common import Q_discrete_white_noise\n",
+    "\n",
+    "Q = Q_continuous_white_noise(dim=2, dt=1, spectral_density=1)\n",
+    "print(Q)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -1859,45 +1822,18 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[ 0.33333333,  0.5       ],\n",
-       "       [ 0.5       ,  1.        ]])"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.05        0.125       0.16666667]\n",
+      " [ 0.125       0.33333333  0.5       ]\n",
+      " [ 0.16666667  0.5         1.        ]]\n"
+     ]
     }
    ],
    "source": [
-    "import filterpy.common as common\n",
-    "\n",
-    "common.Q_continuous_white_noise(dim=2, dt=1, spectral_density=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.05      ,  0.125     ,  0.16666667],\n",
-       "       [ 0.125     ,  0.33333333,  0.5       ],\n",
-       "       [ 0.16666667,  0.5       ,  1.        ]])"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "common.Q_continuous_white_noise(dim=3, dt=1, spectral_density=1)"
+    "Q = Q_continuous_white_noise(dim=3, dt=1, spectral_density=1)\n",
+    "print(Q)"
    ]
   },
   {
@@ -1907,6 +1843,27 @@
     "The function `Q_discrete_white_noise()` computes $\\mathbf{Q}$ assuming a piecewise model for the noise."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.25  0.5 ]\n",
+      " [ 0.5   1.  ]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "Q = Q_discrete_white_noise(2, var=1.)\n",
+    "print(Q)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 18,
@@ -1915,43 +1872,18 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[ 0.25,  0.5 ],\n",
-       "       [ 0.5 ,  1.  ]])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.25  0.5   0.5 ]\n",
+      " [ 0.5   1.    1.  ]\n",
+      " [ 0.5   1.    1.  ]]\n"
+     ]
     }
    ],
    "source": [
-    "common.Q_discrete_white_noise(2, var=1.)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.25,  0.5 ,  0.5 ],\n",
-       "       [ 0.5 ,  1.  ,  1.  ],\n",
-       "       [ 0.5 ,  1.  ,  1.  ]])"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "common.Q_discrete_white_noise(3, var=1.)"
+    "Q = Q_discrete_white_noise(3, var=1.)\n",
+    "print(Q)"
    ]
   },
   {
@@ -1965,26 +1897,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "collapsed": false
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[ 0.00000002,  0.00000078,  0.00002083],\n",
-       "       [ 0.00000078,  0.00004167,  0.00125   ],\n",
-       "       [ 0.00002083,  0.00125   ,  0.05      ]])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 0.00000002  0.00000078  0.00002083]\n",
+      " [ 0.00000078  0.00004167  0.00125   ]\n",
+      " [ 0.00002083  0.00125     0.05      ]]\n"
+     ]
     }
    ],
    "source": [
-    "common.Q_continuous_white_noise(dim=3, dt=0.05, spectral_density=1)"
+    "Q = Q_continuous_white_noise(\n",
+    "    dim=3, dt=0.05, spectral_density=1)\n",
+    "print(Q)"
    ]
   },
   {
@@ -2064,16 +1995,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
    },
    "outputs": [
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c71c18>"
+       "<matplotlib.figure.Figure at 0x7fbc160>"
       ]
      },
      "metadata": {},
@@ -2097,7 +2029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "collapsed": false
    },
@@ -2106,7 +2038,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAusAAAEtCAYAAACxoC4JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c71b38>"
+       "<matplotlib.figure.Figure at 0x7fbc7f0>"
       ]
      },
      "metadata": {},
@@ -2118,7 +2050,7 @@
     "t = np.linspace(-1, 2, 20)\n",
     "plt.plot(t, np.exp(t))\n",
     "t = np.linspace(0, 1, 2)\n",
-    "plt.plot([1,2, 4])\n",
+    "plt.plot([1, 2, 4])\n",
     "book_plots.set_labels(x='x', y='y');"
    ]
   },
@@ -2131,7 +2063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "collapsed": true
    },
@@ -2148,7 +2080,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "collapsed": false
    },
@@ -2178,7 +2110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "collapsed": false
    },
@@ -2187,7 +2119,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a834a8>"
+       "<matplotlib.figure.Figure at 0x8ddb940>"
       ]
      },
      "metadata": {},
@@ -2207,7 +2139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "collapsed": false
    },
@@ -2288,7 +2220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "collapsed": false
    },
@@ -2299,8 +2231,8 @@
     "    y is the initial value for y\n",
     "    x is the initial value for x\n",
     "    dx is the difference in x (e.g. the time step)\n",
-    "    f is a callable function (y, x) that you supply to compute dy/dx for\n",
-    "      the specified values.\n",
+    "    f is a callable function (y, x) that you supply \n",
+    "    to compute dy/dx for the specified values.\n",
     "    \"\"\"\n",
     "    \n",
     "    k1 = dx * f(y, x)\n",
@@ -2326,7 +2258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "collapsed": false
    },
@@ -2342,7 +2274,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa34f550>"
+       "<matplotlib.figure.Figure at 0x9dbbc50>"
       ]
      },
      "metadata": {},
@@ -2396,7 +2328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
@@ -2405,7 +2337,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa31f048>"
+       "<matplotlib.figure.Figure at 0x8d8b748>"
       ]
      },
      "metadata": {},
@@ -2621,7 +2553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "collapsed": false
    },
@@ -2671,8 +2603,8 @@
     "     [500, 500]], dtype=float)\n",
     "\n",
     "def satellite_range(pos, sat_pos):\n",
-    "    \"\"\" Compute distance between position 'pos' and the list of positions\n",
-    "    in sat_pos\"\"\"\n",
+    "    \"\"\" Compute distance between position 'pos' and \n",
+    "    the list of positions in sat_pos\"\"\"\n",
     "\n",
     "    N = len(sat_pos)\n",
     "    rng = np.zeros(N)\n",
@@ -2706,8 +2638,8 @@
     "\n",
     "\n",
     "def lop_ils(zs, sat_pos, pos_est, hx, eps=1.e-6):\n",
-    "    \"\"\" iteratively estimates the solution to a set of measurement, given\n",
-    "    known transmitter locations\"\"\"\n",
+    "    \"\"\" iteratively estimates the solution to a set of\n",
+    "    measurement, given known transmitter locations\"\"\"\n",
     "    pos = np.array(pos_est)\n",
     "\n",
     "    with book_format.numpy_precision(precision=4):\n",
@@ -2733,7 +2665,7 @@
     "\n",
     "    return pos, converged\n",
     "\n",
-    "# compute measurement of where you are with respect to each sensor\n",
+    "# compute range to each sensor\n",
     "rz = satellite_range(user_pos, sat_pos)\n",
     "\n",
     "pos, converted = lop_ils(rz, sat_pos, (900, 90), hx=hx_ils)\n",
@@ -2754,7 +2686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
@@ -2798,7 +2730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "collapsed": false
    },

09-Nonlinear-Filtering.ipynb

@@ -374,9 +374,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEPCAYAAACTNwgMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3X1YVGX+P/D3ADI8hAOpg4MYojJKmWSoqaikhYtlGtmT\n9LCMra4rbrVtYZqbfl2XotLLb8Xq5hYZOlbWr6zNJW0zSrBvwrqWRY2uZK08hAaEwIzAzO8P9r47\nMwzPMzwM79d1eTmcOXPmZg5zzufc53N/bpXNZrOBiIiIiIhczqu3G0BERERE5KkYbBMRERERuQmD\nbSIiIiIiN2GwTURERETkJgy2iYiIiIjcxKen3qi6urqn3oqIiIiIyOU0Gk2nX8OebSIiIiIiN2Gw\nTURERETkJj2WRqLUlS546jsKCgoAAJMnT+7lllB3cV96Du5Lz8F96Tm4Lz1Dd1Oh2bNNREREROQm\nDLaJiIiIiNyEwTYRERERkZsw2CYiIiIichMG20REREREbtIr1UiIiIiI+iKbzYaGhgZYrdZubysi\nIgIAYDabu70tch9fX194ebmv/5nBNhERERGaA22z2QxfX18MGjQIKpWqW9vz8/NzUcvIXcQ+9/Pz\n6/b+bg2DbSKiHmI0GgEAycnJLtme1WrFqVOnYDKZUFFRAQAoKyvDuHHjMGbMGLf21BB5ooaGBvj6\n+sLb27u3m0I9RKVSwdfXFxcvXoRarXbLezDYJiLqZ5qamlBYWIgPPvgAW7Zswfnz5+2eHzJkCH7/\n+9/juuuuQ2xsbJcDB1dfHBD1dVarFYMGDertZlAP8/b2RkNDg9u2z2CbiKiHdDdoNRqN8PPzQ2Vl\nJVJTU2GxWJyud/78eaxduxZqtRrZ2dlYsGAB/P39u/XeRAOFu1IJaOBq9x7jhg0b4OXlZfcvLCys\nxTojRoxAQEAA5syZg6+++sptDSYi6o+MRiNSU1Nlb3FXXvfFF1+gtLQUy5YtazXQVrJYLLjjjjvw\nt7/9DU1NTZ1uc3JyMnu1iYi6qUM92+PHj8dHH30kf1bekszIyMCWLVuwc+dO6PV6bNy4EQkJCfjm\nm29wySWXuLzBRER9RVfTLJy9riPb0uv1+M1vfgObzdbh97LZbLjnnntw4cIFqNXqDrVV2Za22sU0\nEyKi9nUo2Pb29oZWq22x3GazYevWrVizZg2SkpIAADt37oRWq4XRaMTy5ctd21oiol5gNBqRl5eH\nuLg4AGjxWPzcXtAp1klNTYXJZALwc6CalZXVYv2srCzo9XpkZmbCarXi0Ucf7VCPtiOLxYJjx47h\n448/Rnp6OuLj4xEXF4e8vLwWbTMajUhPT2/1c1C22R0YwBORp+lQsH369GmMGDECarUa11xzDdLT\n0xEZGYni4mKUl5dj3rx5cl0/Pz/Mnj0b+fn5DLaJyCPk5eXBZDLJADs3Nxe5ublYu3Yt4uLiZKAs\nAmkAyMzMlK8XAaxOpwMA7Nu3D5WVlfI1EyZMQElJCWJjY5GVlYXS0lLodDoUFhYiPz8fJpMJ8+bN\nw4svvtjl38FoNGLp0qV27dq3bx8AIDo6GkBzcJ+fn4/GxkZERUXJYD8uLg4JCQnydY4BuzJoFxwv\nPhzTZ1oLpsW2GGwTudZXX32FjRs34v/+7/9QVlaGSy+9FFFRUZgzZw7Wr1/f283zaO0G29OmTcPO\nnTsxfvx4lJeXY9OmTZgxYwa+/PJLlJWVAQBCQ0PtXqPValFSUuKeFhMR9RDRo20ymaDX62XPr06n\nQ1FREbKysmAwGKDX62EymWSPdWFhIfbt2yeD2KKiItTV1aGkpASlpaWorKxEXV0dDh06hPDwcFm2\nT2xHPBavLS0txYULF/Djjz92+Xc5f/48AgMDERISAuDngD8yMlK+b2lpKdRqNRobG1FSUoLi4mLk\n5+fjr3/9KwAgKioKVVVVKCsrQ0lJCUwmE4qKihAcHIyqqirU1dUhLCwM8fHxdp+fCMrF5xgXFwej\n0SgvUgwGA4NrIjc6cuQI5syZg/DwcCxduhQjRoxASUkJCgoKkJGRwWDbzVS2ziT/Aairq0NkZCQe\nffRRXHPNNZg5cya+++47hIeHy3WWLl2K0tJS/P3vf5fLqqur5eOTJ0+6oOlERO3LyckBACQmJra5\nzvHjx3HmzBmcO3cOsbGxAIAzZ84gIiJCLh86dKicEe7dd9/FoEGDMGzYMNTU1CAoKAgAUFNTg6qq\nKjQ2NspB5VarFQEBATCbzXLMi0gHUavVGDRoEBoaGtDU1CTXVU6GERQUBIPBgMcff7xbn8W6deuw\ndetWmM1mNDY2yve/6aabUFhYiJKSEln2TLkOAPj4+MigGvh5sg6z2Yzg4GDU1NSgqakJfn5+aGho\nQFhYGJYuXSo/16KiIrvXic8LAIYOHQqgeba9mJgYHD9+HAAQExNjt986si+JuiMiIgLDhg3r7Wa4\n3I033ojPPvsMJpNJXnALFRUV/fZ3vnjxIry9vV1SF72iogJnzpxx+lxUVJR8rNFoOr3tTs94EBAQ\ngCuuuAKnTp2St0TLy8vt1ikvL8fw4cM73RgiIlfKycnBO++8g5deegkrV66UwVpOTg5WrlyJlStX\nIiMjA++8844MCL///nsUFhbKg674v6amBsXFxThz5gw++ugjNDU1oaGhAd9//z1qamrkOj/++COs\nVivUajWsVisaGxthtVpRV1dnV8PXx8cHl1xyiV3QKdZtaGhAVVUVfvzxR5w7dw7ff/89fH19u/15\neHt7twiiLRYLzpw5g9jYWDQ1NeHChQuoq6uze50ItMU2RE1a5RTUQUFBGDlypNxmRUUFjh8/jo8+\n+gjHjh2D2WyG2WxGTU0Nampq5N1PcWFz7tw5uxNdYWEhjh8/joyMDLnvjh8/jnfeeQcZGRlyXxJR\n+/7973/j8ssvbxFoA2gRaB84cADx8fEICgpCUFAQ5s+fLy+AhZSUFPj7+6OkpAQ333wzgoKCoNVq\n8cgjj7SY5v7111/HlClToNFoMHjwYFx++eXYtGmT3Trffvst7rjjDgwZMgQBAQGYOnWqTHMTPvro\nI3h5ecFoNGLDhg247LLLEBAQgLNnz3bno+kRna6zbTabUVRUhLlz5yIyMhLDhw/HgQMH5AHTbDbj\n8OHDeOaZZ1rdxuTJk7veYup1BQUFALgfPYGn7EtlPrBy8GJubi7q6upQXl6O8vJyaDQajB49Grm5\nufjyyy+hVqtRV1cnUyCsVitCQ0MREBBgtzwsLAxqtRrBwcGoq6tDXV0dQkNDERwcjJMnT2Lo0KEY\nM2YMDh06BKvVCpVKhbFjx6KkpAS1tbWy7J4yAFer1bKXvLi42K7CSEBAACwWi5zNLCAgwCW1f4cM\nGYLQ0FBUVlaivr4eXl5eUKvV+PLLL3Hq1CkZhPv5+SEkJATBwcHyd6isrMTgwYMxduxYfP3112hq\napIXAE1NTYiNjZXpLgBQX1+P/fv3y8BdnORFj77FYkFVVRWSkpJknrbJZML27dtRV1eHgIAAaLVa\nmEwmaDQa5ObmAgCmTJkCACgtLYXJZPLI9BNP+V72R8oLSE8SGRmJw4cP4/PPP8fEiRNbXc9oNOKe\ne+7BvHnz8OSTT8JsNuOFF17ArFmzcPToUYwbN06ua7VakZiYiGuuuQabN2/GwYMHsXnzZowZMwYr\nVqwAAHzwwQe48847cf311+PJJ5+Et7c3vv76a7txHj/88ANmzJiB2tpa3H///Rg2bBiys7Nxyy23\nYPfu3bjzzjvt2pieng5vb2/87ne/g81mQ2BgoEs+o6CgoFa/c8rsjK5oN9h++OGHsXDhQowcORI/\n/PAD/vjHP6K+vh6//OUvAQAPPvgg0tPTMX78eERFRWHTpk0ICgryyAMgEfVtWVlZKCoqkrnBAKDT\n6WSe8aFDh+TgQ7VajYCAAHmHTgxYBICSkhIZFEZGRsptCCJIr6ysRENDAyoqKmSwDkCmW4SFhcn3\nVqvVmDFjBvLz8+V2165di7y8PFRVVaGyshJNTU3w9vaGxWJBSEgIFi1aJC8c6urqcOmll3Y5b3vI\nkCGwWCyIjo5GaWmp/GyKiopQUVEhA21vb2/MmDEDBoMBWVlZKCkpgY9P86mitrYWVVVVGDx4sGxj\nZWUlLBYLCgsLUVtbK9+vqakJ9fX1AJonCbFYLGhsbJQXHlarFZWVlbj33nuhVqsRGRkpP4fGxkZY\nLBaZVx4SEiLz3wHIHPCsrCy7SjAiR1ysI/B8RANdWloaDh48iKuvvhqxsbGYNWsW5s6di+uuu05e\n1NfW1mLVqlUwGAxynAYA3HfffRg3bhw2btyI3bt3y+UNDQ24/fbbsW7dOgDA8uXLERsbixdffFEG\n2++99x40Gg3ef//9VjsMnnzySZSVleGjjz7C7Nmz7bb10EMP4dZbb5XHIAC4cOECioqK+tVEXe0G\n22fPnsWSJUtw7tw5DBs2DNOnT8enn34qbxempaWhvr4eqampqKysxLRp03DgwAGXXWkQETmW3gN+\nrhACQJbHy8rKQkVFBSoqKmRAKYJkkfrw1Vdf4auvvkJwcDCCg4PlAD+1Wo3a2lqZ3uDv74+QkBA5\n2K+oqAjFxcVobGyUB36LxQIvLy/5OCwsDNHR0XYDDoODg1FZWQm1Wg2DwQCgOUVCEL9DSEiI7NGN\njo6WgwbF7z537lysXLmyxe3Xjrrvvvvw7rvvwtfXV/5OJpNJfg46nU5eoJhMJuTl5cmfRbvT0tLk\n+iUlJQgODrYbBBoQECAvNETQ7OPjIz9bABg8eLD8vOrq6tDU1ASLxYKTJ0/Cx8dHfg61tbVy4Kho\nQ1ZWFvbs2YPc3FzEx8fLz1l8hsrPUxCDMQEG3TRwzZkzB5988gkyMjLwwQcf4OjRoLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5d217b8>"
+       "<matplotlib.figure.Figure at 0x6e01f28>"
       ]
      },
      "metadata": {},
@@ -394,11 +394,12 @@
     "xs = r * np.cos(a)\n",
     "ys = r * np.sin(a)\n",
     "\n",
-    "plt.scatter(xs, ys, label='Measurements', color='k', marker='.', s=2)\n",
-    "plt.scatter(sum(xs)/N, sum(ys)/N, c='r', marker='*', s=200, label='Mean')\n",
-    "plt.scatter(0, 50, c='k', marker='o', s=400, label='Intuition')\n",
+    "plt.scatter(xs, ys, label='Sensor', color='k', marker='.', s=2)\n",
+    "xs, ys = sum(xs)/N, sum(ys)/N\n",
+    "plt.scatter(xs, ys, c='r', marker='*', s=200, label='Mean')\n",
+    "plt.scatter(0, 50, c='k', marker='o', s=300, label='Intuition')\n",
     "plt.axis('equal')\n",
-    "plt.legend(scatterpoints=1);"
+    "plt.legend();"
    ]
   },
   {
@@ -431,9 +432,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAEWCAYAAAA9wcIeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuwAAAEWCAYAAAA9wcIeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5d198d0>"
+       "<matplotlib.figure.Figure at 0x435cac8>"
       ]
      },
      "metadata": {},
@@ -465,9 +466,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7910fd0>"
+       "<matplotlib.figure.Figure at 0x435c8d0>"
       ]
      },
      "metadata": {},
@@ -507,9 +508,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa0a8898>"
+       "<matplotlib.figure.Figure at 0xa248cf8>"
       ]
      },
      "metadata": {},
@@ -549,9 +550,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7917a58>"
+       "<matplotlib.figure.Figure at 0xa243438>"
       ]
      },
      "metadata": {},
@@ -583,14 +584,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "input  mean, variance: -0.0002, 1.0020\n",
-      "output mean, variance: -0.1246, 2.4094\n"
+      "input  mean, variance: -0.0008, 1.0010\n",
+      "output mean, variance: -0.1237, 2.4052\n"
      ]
     }
    ],
    "source": [
-    "print('input  mean, variance: %.4f, %.4f'% (np.average(data), np.std(data)**2))\n",
-    "print('output mean, variance: %.4f, %.4f'% (np.average(y), np.std(y)**2))"
+    "print('input  mean, variance: %.4f, %.4f' % \n",
+    "      (np.mean(data), np.var(data)))\n",
+    "print('output mean, variance: %.4f, %.4f' % \n",
+    "      (np.mean(y), np.var(y)))"
    ]
   },
   {
@@ -611,9 +614,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xa277b00>"
+       "<matplotlib.figure.Figure at 0x9f25ef0>"
       ]
      },
      "metadata": {},
@@ -623,7 +626,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "output mean, variance: 0.0003, 2.2545\n"
+      "output mean, variance: 0.0012, 2.2522\n"
      ]
     }
    ],
@@ -634,7 +637,8 @@
     "with figsize(y=5):\n",
     "    plot_nonlinear_func(data, g3, gaussian)\n",
     "out = g3(data)\n",
-    "print ('output mean, variance: %.4f, %.4f'% (np.average(out), np.std(out)**2))"
+    "print('output mean, variance: %.4f, %.4f' % \n",
+    "      (np.mean(out), np.var(out)))"
    ]
   },
   {
@@ -655,8 +659,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "linear    output mean, variance: 0.0157, 7496.7472\n",
-      "nonlinear output mean, variance: -9.1550, 30561.5318\n"
+      "linear    output mean, variance: 0.0714, 7489.0102\n",
+      "nonlinear output mean, variance: -9.1074, 30507.3721\n"
      ]
     }
    ],
@@ -691,9 +695,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x886fef0>"
+       "<matplotlib.figure.Figure at 0x7931f60>"
       ]
      },
      "metadata": {},
@@ -742,7 +746,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79dfcc0>"
+       "<matplotlib.figure.Figure at 0x7947c18>"
       ]
      },
      "metadata": {},
@@ -772,7 +776,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x808f7b8>"
+       "<matplotlib.figure.Figure at 0x7b4bbe0>"
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      },
      "metadata": {},
@@ -801,7 +805,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x5d60438>"
+       "<matplotlib.figure.Figure at 0x7fc6908>"
       ]
      },
      "metadata": {},
@@ -830,7 +834,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtcAAAEWCAYAAACt0rvRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7c305c0>"
+       "<matplotlib.figure.Figure at 0x7fe6278>"
       ]
      },
      "metadata": {},

10-Unscented-Kalman-Filter.ipynb

@@ -294,9 +294,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVPX+P/DXGZZhH3AUBFlVUERUFBVNE9xKw6VIs265\ndHPJNM32bqXeFkuvdauvrVfBtFKzXDL3RFwQFVxBEdxAZFGRfYc5vz/8MTnOgALDnGF4PR8PCj7n\nc+a8DuCZN5/5zOcIoiiKICIiIiKiRpNJHYCIiIiIqKVjUU1ERERE1EQsqomIiIiImohFNRERERFR\nE7GoJiIiIiJqIhbVRERERERNxKKaJHf16lXIZDJMmzZN6ihEREREjcKimoyGIAhSR7iv2j8AwsLC\npI5CRERERsRc6gBE7u7uSE5OhkKhkDrKfdUW/i3hDwAiIiIyHBbVJDlzc3P4+flJHeOB8AakRERE\npAunf5DkdM2pnjp1KmQyGWJiYrBx40b069cPtra2UCqVePrpp5GZman1OKGhoZDJZLhy5Qr+85//\noEuXLrC2toanpydee+01FBcXa+1T31SORYsWQSaT4cCBAwCAqKgodOzYEQCwf/9+yGQy9cfixYv1\n8a0gIiKiFooj1WQ0dE2p+Prrr7F161aMGzcOYWFhiIuLw/r163H69GmcOnUKlpaWWvvMmzcPhw8f\nxlNPPQWFQoHt27fjs88+w6FDh3DgwAGtfR50KkdQUBDmzZuHL774At7e3pg6dap6W2hoaIPOlYiI\niEwLi2oyart27UJ8fDwCAgLUbf/4xz/wyy+/YMuWLZgwYYLWPnFxcTh9+jTc3d0BAB999BEiIiKw\nZcsWfPbZZ3jrrbcalaVnz56YP3++uqh+//33G3dSREREZHI4/YOM2ssvv6xRUAPA9OnTAQDHjx/X\nuc+8efPUBTVwZ4rHp59+CkEQsGrVqibl4ZxqIiIi0oVFNRm14OBgrbbagjkvL0/nPkOGDNFq8/Pz\ng7OzMy5duoSSkhL9hiQiIqJWj0U1GTVHR0etNnPzO7OWampqdO7j4uJSb3thYaGe0hERERHdwaKa\nTE5OTk697Q4ODhrt1dXVOvvn5+frNxgRERGZLBbVZHL279+v1XbhwgXk5OSgc+fOsLW1Vbc7OTnh\n2rVrOh9H15xtMzMzAHWPkhMREVHrxKKaTM4XX3yhUSjX1NTgzTffBACNtbABICQkBGlpadixY4dG\n+w8//IAjR45oLbfn5OQEAHUW4kRERNQ6cUk9MjkPPfQQevXqhYkTJ8LBwQE7duxAYmIi+vXrh1df\nfVWj7+uvv45du3bh8ccfx8SJE9GuXTskJCQgISEB4eHh2LZtm0Z/Ozs7DBw4ELGxsRg7diyCgoJg\nYWGBIUOGYPDgwYY8TSIiIjIiHKkmoyQIwgPflOXe/f773//i7bffRnR0NL744gvk5+djwYIF+Ouv\nv2BhYaHRPzQ0FFu3bkWvXr2wceNGREZGwtHREUePHkWfPn10ZlizZg3Gjx+PI0eO4KOPPsLChQsR\nHR3d6HMlIiKilk8QufAumYjQ0FAcOHAAV69ehaenp9RxiIiIqBXhSDWZlMaMbhMRERE1FYtqMil8\n4YWIiIikwKKaTEZj52ETERERNZXB51QXFBQY8nBERJJSKBRSRyAiIgPgSDURERERUROxqCYiIiIi\naiJJb/5iKi+LxsfHAwCCg4MlTqIfPB/jxvMxfpzmRkTU+nCkmoiIiIioiVhUExERERE1EYtqIiIi\nIqImYlFNRERERNRELKqJiIiIiJpI0tU/iJqTKIooryyVOgYRERG1AiyqySQVFN/G15sXISs3He5t\n/NDXeziOJ8ego2tXKBUuUscjIiIiE8OimkyCKIq4mZ8JexsnWMttsHH/98jKTQcAZNxOQcbtFACA\npbkcr0z8BB3a+aj3VYkqxJ7djavZF9Cny8Pw9wqS5ByIiIio5WJRTS1eWUUp/rdtCVIzzsLOWoFR\n/Z/C6UtxOvtWVldgy6HV+Gf4W/jj8BrcKshGasZZVFVXAgCOnY/GhLCZGNxjlCFPgYiIiFo4QRRF\n0ZAHvPtOY6mpqYY8NJmgGlU19p1fj6z8Kw3az9HGGfmlN+rcPqDTY/BtzxFrahxfX1/156Zy51gi\nIqofR6qpxcrMu4Sjl3eiqDyvwfvWV1ADwJFLfyIl5yQ6OfeAd9tusLKwaWxMIiIiagUkHak2lRGc\n+Ph4AEBwcLDESfTDGM8n4cJB7Dy6Hva2jhg/aCrKK8vw9aaFUImqZj92Rzd/vBzxIWQys2Y/1oMw\nxp9PU5ja+QCmeZ0jIqL6caSajN6NvOtYs+tzqEQVcvIy8PmGt1Cjqr7vfq5KTwzsPhLbDv8MQRBQ\nXlVSZ1+XNu4I9OmHv05shnhPoX458zxSrp1FV69eTT4XIiIiMk0sqsno7T6+UWNEWldB3dtvEHp2\nHoh1e/8PZZWl6ObVG5NHLYCN3A7WVc4QIMDb1x3L172O0opi9X62VvZY/Pz/YG5uAZkgg5NDO/wa\n/Z3W4+87uQUOtk5o38bdaEasiYiIyHiwqCajU11ThYNndqC0vBgBPsGIT46pt3+fLg9jyqMLAADd\nvIJQXFaosRa1TLhz49B2jq6YNvp1fLN5sbpIH9E3ApYWcnXfwT1GQWHrhN8PrMLtwr/nXSenncQn\naSehsG0DX49AtLF3Rp8uD8NV6aG38yYiIqKWi0U1GZ0N0d8hLmkvAGDXsQ319rU0l2PcoCnqr+WW\n1pBbWtfZv4tnTyx4ailiE3ejQzsfDAp8VKtPj04h6N6xH/4dNUujsAaAgpLb6iL/wOk/8cYzn6Gt\nov0DnxsRERGZJpnUAYhqlVYUIz3nIo4m/VVnn5F9n0RwlyGQycxgaS7H08NfgqOdskHH8XTpjEnD\nZmNwj1EQBEFnH5kgQz//sHofp7yyFH/Fb1KvcU1EREStF0eqySjsP/kHfj+wst4+Xi6+GBXyNMxk\nZnh6+EsQIcLSXF7vPk3xUPdHcOD0dpSWF9XZ53DiLhxO3IXOHQIwfcw7sJbbNlseIiIiMl4sqkly\nOXnXsflgZL19FHZKvBD+Nsz+/5sELcwtmz2Xwq4N5k/4GCdTDsPZqQP8vYNwMSMR/9v2iVbfi9eT\n8EfsWkwMm9nsuYiIiMj4cPoHSaaqugrJaafw6U/z611vWunggplj/wWFXRsDprujfRsPjAqZhD5d\nBsNGbocenUIwbtBUnX1jz+5CTt51wwYkIiIio8CRapJEjaoG32/9EBeuna6zT2ivMXg05CnIza1g\nZmY8v6oDug/HrmMbUF5ZqtGuElXYfDASM8b8q8652kRERGSaOFJNkog9u6vegtrTxRejQibBRm5n\nVAU1ANjI7TB11Ks6V/1IuhKPHXHrJEhFREREUjKuaoVahZLyIvwZ94vObbPGvQ9vVz/YyO0MnKph\nunn3wftT+0AURXy24U2kZaeot+08th6ubT0R5PuQhAmJiIjIkARRFEVDHrCgoED9eWpqqiEPTRK6\nWZiBgymbUFldgcqacq3tjjbOCOgQgk7OPSRI1zSFZbex40wkKqrL1G1WFrYY13sW5OZ1r5lNpsvX\n11f9uUKhkDAJEREZCqd/kEHEXd6B4ooCnQV1kFcYxgbNaJEFNQA4WLfBUP+nIBP+vn15eVUJEq78\nBQP/zUpEREQSkXT6R3BwsJSH15v4+HgAPJ+63MzPQt7hHJ3bnB3d8Gz4bFiYW+jlWLoY5ucTDNiU\nY8fRv+dTX7xxCsXVuejlOxBBvoP0dktz/r4Zv7tfkSMiotaBI9XUrERRROLl4zq3CRDwzIiXm7Wg\nNqThwRFwdnTTaMu+fQ07j67H8nWv4dqNSxIlIyIioubGopqazfm0k/hg9YvYdHCVzu1hvceho1tX\nA6dqPhbmFnjukVdgbWmjta2yugKr/lyKotJ8CZIRERFRc2NRTc2iuqYKa3b9F7cKsrW2+bkHImLI\nCxg7aLIEyZqXV3tfLHhqKdopXLW25Rbm4OM1c5Fw4YAEyYiIiKg5cUk9ahYXM5JQXKY9r7R9Gw/M\nifhAgkSG49LGHW8/9yXOXU3Az3v+D6UVxeptJeVF+HHn55BbWKN7x74SpiQiIiJ94kg1NYukq/E6\n23t0CjFwEmmYm1mgR6cQfPBCJLxdu2hsEyHiz7ifuTIIERGRCWFRTXpX15sTPZw7YVifxyVIJB0L\ncwu89PhiDOoxSqP9+s0rSE4/JVEqIiIi0jcW1aR3J1MPI7fw7yX0zM0ssPj5H/D608thLdd+E5+p\nk1tYYWLYTK1R+t8PrERZRYlEqYiIiEifWFST3qhUNdiw71tE7fiPRrufeyCc7NtJlMp4DA9+QuPr\nnNsZeOu75/DDHx/j0vUkiVIRERGRPrCoJr2oqq7ET3u+wqGzO7W2BfANeQAA7/Z+6OoVpNEmiiqc\nvXwMX/72Hg6c3i5RMiIiImoqFtXUZMeTY7A4ciaOJ+/X2uam9EI//zDDhzJSz42cBy8XX612UVRh\n4/7vEXNqmwSpiIiIqKkE0cBLENx9+97U1FRDHpqawcWc04i9+IdWu9zcBsE+w+HdthvMZFy58W41\nqmokXP0LKdknoBJrNLaZycwxptd0OFgrJUpH+uDr+/cfTgqFQsIkRERkKCyqqdGy8q9g77lfIIoq\njXZrCzs8EvgcC8P7qFHV4MrNszh2eReqVVUa2zyVXeHrEgQ3x44QBEGihNRYLKqJiFofSYtqU3my\niY+/syZzcHCwxEn040HORxRFfLxmLnLyMjTah/QKx4jgJ+Fg69isGRvC2H8+x85HY+3uL3Ru6+jm\nj5lj39NYNcXYz6ehTO18ANO8zhERUf04p5oa5XLmOY2CWoCAaaPfQMSQF4yqoG4J+Line truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAFfCAYAAACBcDBIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6ba9c88>"
+       "<matplotlib.figure.Figure at 0x6e2a400>"
       ]
      },
      "metadata": {},
@@ -357,7 +357,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAr8AAAD9CAYAAAC1Fh+gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x40df240>"
+       "<matplotlib.figure.Figure at 0x432c668>"
       ]
      },
      "metadata": {},
@@ -403,14 +403,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in mean x=0.201, y=42.679\n"
+      "Difference in mean x=-0.014, y=43.369\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78f07f0>"
+       "<matplotlib.figure.Figure at 0x866a7b8>"
       ]
      },
      "metadata": {},
@@ -465,7 +465,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78f4eb8>"
+       "<matplotlib.figure.Figure at 0x866ed30>"
       ]
      },
      "metadata": {},
@@ -503,7 +503,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7832c18>"
+       "<matplotlib.figure.Figure at 0x7a8a1d0>"
       ]
      },
      "metadata": {},
@@ -548,7 +548,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a7df28>"
+       "<matplotlib.figure.Figure at 0x7b8afd0>"
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      },
      "metadata": {},
@@ -599,7 +599,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x77a19e8>"
+       "<matplotlib.figure.Figure at 0x7c58898>"
       ]
      },
      "metadata": {},
@@ -648,7 +648,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8cf5518>"
+       "<matplotlib.figure.Figure at 0x87249e8>"
       ]
      },
      "metadata": {},
@@ -656,7 +656,8 @@
     }
    ],
    "source": [
-    "from filterpy.kalman import unscented_transform, MerweScaledSigmaPoints\n",
+    "from filterpy.kalman import unscented_transform\n",
+    "from filterpy.kalman import MerweScaledSigmaPoints as SigmaPoints\n",
     "import scipy.stats as stats\n",
     "\n",
     "def f_nonlinear_xy(x, y):\n",
@@ -669,7 +670,7 @@
     "\n",
     "### create sigma points - we will learn about this later\n",
     "### in the chapter\n",
-    "points = MerweScaledSigmaPoints(n=2, alpha=.1, beta=2., kappa=1.)\n",
+    "points = SigmaPoints(n=2, alpha=.1, beta=2., kappa=1.)\n",
     "Wm, Wc = points.weights()\n",
     "sigmas = points.sigma_points(mean, p)\n",
     "\n",
@@ -863,7 +864,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8411358>"
+       "<matplotlib.figure.Figure at 0x86c39b0>"
       ]
      },
      "metadata": {},
@@ -986,7 +987,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x928b9e8>"
+       "<matplotlib.figure.Figure at 0x9721470>"
       ]
      },
      "metadata": {},
@@ -1016,7 +1017,8 @@
     "kf.Q[0:2, 0:2] = Q_discrete_white_noise(2, dt=1, var=0.02)\n",
     "kf.Q[2:4, 2:4] = Q_discrete_white_noise(2, dt=1, var=0.02)\n",
     "\n",
-    "zs = [np.array([i + randn()*sigma_x, i + randn()*sigma_y]) for i in range(100)]               \n",
+    "zs = [np.array([i + randn()*sigma_x, \n",
+    "                i + randn()*sigma_y]) for i in range(100)]               \n",
     "xs, _, _, _ = kf.batch_filter(zs)\n",
     "plt.plot(xs[:, 0], xs[:, 2]);"
    ]
@@ -1042,7 +1044,8 @@
    "outputs": [],
    "source": [
     "def f_cv(x, dt):\n",
-    "    \"\"\" state transition function for a constant velocity aircraft\"\"\"\n",
+    "    \"\"\" state transition function for a \n",
+    "    constant velocity aircraft\"\"\"\n",
     "    \n",
     "    F = np.array([[1, dt, 0,  0],\n",
     "                  [0,  1, 0,  0],\n",
@@ -1087,7 +1090,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The rest of the code is the same as for the linear kalman filter. Let's just implement it! I'll use the same measurements as used by the linear Kalman filter, and compute the standard deviation of the difference between the two solution. I will use the `batch_filter` method to keep the code succinct; for clarity I put the equivalent iterative loop in a comment. As with all the filters we have done to date you ju"
+    "The rest of the code is the same as for the linear kalman filter. Let's just implement it! I'll use the same measurements as used by the linear Kalman filter, and compute the standard deviation of the difference between the two solution."
    ]
   },
   {
@@ -1109,7 +1112,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92817b8>"
+       "<matplotlib.figure.Figure at 0x970b0f0>"
       ]
      },
      "metadata": {},
@@ -1117,13 +1120,13 @@
     }
    ],
    "source": [
-    "from filterpy.kalman import MerweScaledSigmaPoints\n",
     "from filterpy.kalman import UnscentedKalmanFilter as UKF\n",
     "\n",
     "import numpy as np\n",
     "\n",
-    "sigmas = MerweScaledSigmaPoints(4, alpha=.1, beta=2., kappa=1.)\n",
-    "ukf = UKF(dim_x=4, dim_z=2, fx=f_cv, hx=h_cv, dt=dt, points=sigmas)\n",
+    "sigmas = SigmaPoints(4, alpha=.1, beta=2., kappa=1.)\n",
+    "ukf = UKF(dim_x=4, dim_z=2, fx=f_cv,\n",
+    "          hx=h_cv, dt=dt, points=sigmas)\n",
     "ukf.x = np.array([0., 0., 0., 0.])\n",
     "ukf.R = np.diag([0.09, 0.09]) \n",
     "ukf.Q[0:2, 0:2] = Q_discrete_white_noise(2, dt=1, var=0.02)\n",
@@ -1179,7 +1182,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92333c8>"
+       "<matplotlib.figure.Figure at 0x970dba8>"
       ]
      },
      "metadata": {},
@@ -1223,8 +1226,8 @@
    "outputs": [],
    "source": [
     "def f_radar(x, dt):\n",
-    "    \"\"\" state transition function for a constant velocity aircraft\n",
-    "    with state vector [x, velocity, altitude]'\"\"\"\n",
+    "    \"\"\" state transition function for a constant velocity \n",
+    "    aircraft with state vector [x, velocity, altitude]'\"\"\"\n",
     "    \n",
     "    F = np.array([[1, dt, 0],\n",
     "                  [0,  1, 0],\n",
@@ -1299,7 +1302,7 @@
     "\n",
     "    \n",
     "    def reading_of(self, ac_pos):\n",
-    "        \"\"\" Returns actual range and bearing to aircraft as tuple. \n",
+    "        \"\"\" Returns (range, bearing) to aircraft. \n",
     "        Bearing is in radians.\n",
     "        \"\"\"\n",
     "        \n",
@@ -1310,13 +1313,13 @@
     "\n",
     "\n",
     "    def noisy_reading(self, ac_pos):\n",
-    "        \"\"\" Compute range and bearing to aircraft with simulated noise\"\"\"\n",
+    "        \"\"\" Compute range and bearing to aircraft with \n",
+    "        simulated noise\"\"\"\n",
     "        \n",
     "        rng, brg = self.reading_of(ac_pos)      \n",
     "        rng += randn() * self.range_std\n",
     "        brg += randn() * self.bearing_std \n",
-    "        return rng, brg\n",
-    "        \n",
+    "        return rng, brg       \n",
     "   \n",
     "class ACSim(object):   \n",
     "    def __init__(self, pos, vel, vel_std):\n",
@@ -1326,12 +1329,11 @@
     "        \n",
     "        \n",
     "    def update(self, dt):\n",
-    "        \"\"\" compute next position. Incorporates random variation in\n",
-    "        velocity. Returns new position.\"\"\"\n",
+    "        \"\"\" Compute and returns next position. Incorporates \n",
+    "        random variation invelocity. \"\"\"\n",
     "        \n",
     "        vel = self.vel*dt + (randn() * self.vel_std) * dt      \n",
-    "        self.pos += vel\n",
-    "        \n",
+    "        self.pos += vel     \n",
     "        return self.pos"
    ]
   },
@@ -1356,7 +1358,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a6f0b8>"
+       "<matplotlib.figure.Figure at 0x8724940>"
       ]
      },
      "metadata": {},
@@ -1366,7 +1368,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7821208>"
+       "<matplotlib.figure.Figure at 0x6dfb0f0>"
       ]
      },
      "metadata": {},
@@ -1376,7 +1378,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x771d780>"
+       "<matplotlib.figure.Figure at 0x7c03080>"
       ]
      },
      "metadata": {},
@@ -1387,6 +1389,7 @@
     "import book_plots\n",
     "from filterpy.common import Q_discrete_white_noise\n",
     "import math\n",
+    "from ukf_internal import plot_radar\n",
     "\n",
     "dt = 12. # 12 seconds between readings\n",
     "range_std = 5 # meters\n",
@@ -1396,7 +1399,7 @@
     "radar_pos = (0., 0.)\n",
     "h_radar.radar_pos = radar_pos\n",
     "\n",
-    "points = MerweScaledSigmaPoints(n=3, alpha=.1, beta=2., kappa=0.)\n",
+    "points = SigmaPoints(n=3, alpha=.1, beta=2., kappa=0.)\n",
     "kf = UKF(3, 2, dt, fx=f_radar, hx=h_radar, points=points)\n",
     "\n",
     "kf.Q[0:2, 0:2] = Q_discrete_white_noise(2, dt=dt, var=0.1)\n",
@@ -1407,7 +1410,8 @@
     "kf.P = np.diag([300**2, 30**2, 150**2])\n",
     "\n",
     "random.seed(200)\n",
-    "radar = RadarStation(pos=(0, 0), range_std=range_std, bearing_std=bearing_std)\n",
+    "pos = (0, 0)\n",
+    "radar = RadarStation(pos, range_std, bearing_std)\n",
     "ac = ACSim(ac_pos, (100, 0), 0.02)\n",
     "\n",
     "time = np.arange(0, 360 + dt, dt)\n",
@@ -1418,7 +1422,7 @@
     "    kf.predict()\n",
     "    kf.update([r[0], r[1]]) \n",
     "    xs.append(kf.x)    \n",
-    "ukf_internal.plot_radar(xs, time)"
+    "plot_radar(xs, time)"
    ]
   },
   {
@@ -1454,7 +1458,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8da5828>"
+       "<matplotlib.figure.Figure at 0x7aa2780>"
       ]
      },
      "metadata": {},
@@ -1487,7 +1491,7 @@
     "    kf.update([r[0], r[1]]) \n",
     "    xs.append(kf.x)\n",
     "\n",
-    "ukf_internal.plot_radar(xs, time, plot_x=False, plot_vel=False, plot_alt=True)\n",
+    "plot_radar(xs, time, plot_x=False, plot_vel=False, plot_alt=True)\n",
     "print('Actual altitude: {:.1f}'.format(ac.pos[1]))\n",
     "print('UKF altitude   : {:.1f}'.format(xs[-1][2]))"
    ]
@@ -1521,7 +1525,8 @@
    "outputs": [],
    "source": [
     "def f_cv_radar(x, dt):\n",
-    "    \"\"\" state transition function for a constant velocity aircraft\"\"\"\n",
+    "    \"\"\" state transition function for a constant velocity \n",
+    "    aircraft\"\"\"\n",
     "    F = np.array([[1, dt, 0, 0],\n",
     "                  [0,  1, 0, 0],\n",
     "                  [0,  0, 1, dt],\n",
@@ -1529,7 +1534,7 @@
     "    return np.dot(F, x)\n",
     "    \n",
     "def cv_UKF(fx, hx, R_std):\n",
-    "    points = MerweScaledSigmaPoints(n=4, alpha=.1, beta=2., kappa=-1.)\n",
+    "    points = SigmaPoints(n=4, alpha=.1, beta=2., kappa=-1.)\n",
     "    kf = UKF(4, len(R_std), dt, fx=fx, hx=hx, points=points)\n",
     "\n",
     "    kf.Q[0:2, 0:2] = Q_discrete_white_noise(2, dt=dt, var=0.1)\n",
@@ -1553,7 +1558,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8da5358>"
+       "<matplotlib.figure.Figure at 0x7aa2080>"
       ]
      },
      "metadata": {},
@@ -1584,7 +1589,7 @@
     "    kf.update([r[0], r[1]]) \n",
     "    xs.append(kf.x)\n",
     "\n",
-    "ukf_internal.plot_radar(xs, time, plot_x=False, plot_vel=False, plot_alt=True)\n",
+    "plot_radar(xs, time, plot_x=False, plot_vel=False, plot_alt=True)\n",
     "print('Actual altitude: {:.1f}'.format(ac.pos[1]))\n",
     "print('UKF altitude   : {:.1f}'.format(xs[-1][2]))"
    ]
@@ -1624,7 +1629,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a69d30>"
+       "<matplotlib.figure.Figure at 0x8c384a8>"
       ]
      },
      "metadata": {},
@@ -1634,7 +1639,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Velocity standard deviation 3.4 m/s\n"
+      "Velocity std 3.4 m/s\n"
      ]
     }
    ],
@@ -1642,7 +1647,8 @@
     "range_std = 500.\n",
     "bearing_std = math.degrees(0.5)\n",
     "random.seed(200)\n",
-    "radar = RadarStation(pos=(0, 0), range_std=range_std, bearing_std=bearing_std)\n",
+    "pos = (0, 0)\n",
+    "radar = RadarStation(pos, range_std, bearing_std)\n",
     "ac = ACSim(ac_pos, (100, 0), 0.02)\n",
     "\n",
     "kf = cv_UKF(f_cv_radar, h_radar, R_std=[range_std, bearing_std])\n",
@@ -1656,8 +1662,8 @@
     "    xs.append(kf.x)\n",
     "\n",
     "xs = np.asarray(xs)\n",
-    "ukf_internal.plot_radar(xs, time, plot_x=False, plot_vel=True, plot_alt=False)\n",
-    "print('Velocity standard deviation {:.1f} m/s'.format(np.std(xs[10:, 1])))"
+    "plot_radar(xs, time, plot_x=False, plot_vel=True, plot_alt=False)\n",
+    "print('Velocity std {:.1f} m/s'.format(np.std(xs[10:, 1])))"
    ]
   },
   {
@@ -1672,7 +1678,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x796b208>"
+       "<matplotlib.figure.Figure at 0x8bbfa20>"
       ]
      },
      "metadata": {},
@@ -1682,7 +1688,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Velocity standard deviation 1.3 m/s\n"
+      "Velocity std 1.3 m/s\n"
      ]
     }
    ],
@@ -1718,8 +1724,8 @@
     "    kf.update([r[0], r[1], vx, vz]) \n",
     "    xs.append(kf.x)\n",
     "xs = np.asarray(xs)\n",
-    "ukf_internal.plot_radar(xs, time, plot_x=False, plot_vel=True, plot_alt=False)\n",
-    "print('Velocity standard deviation {:.1f} m/s'.format(np.std(xs[10:,1])))"
+    "plot_radar(xs, time, plot_x=False, plot_vel=True, plot_alt=False)\n",
+    "print('Velocity std {:.1f} m/s'.format(np.std(xs[10:,1])))"
    ]
   },
   {
@@ -1766,7 +1772,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAEWCAYAAACpJ2vsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a52fd0>"
+       "<matplotlib.figure.Figure at 0x7c937b8>"
       ]
      },
      "metadata": {},
@@ -1810,7 +1816,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAugAAAEWCAYAAAA0KmJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x795d278>"
+       "<matplotlib.figure.Figure at 0x7c93c18>"
       ]
      },
      "metadata": {},
@@ -1825,7 +1831,8 @@
     "sb_pos = [400, 0]\n",
     "\n",
     "def bearing(sensor, target_pos):\n",
-    "    return math.atan2(target_pos[1] - sensor[1], target_pos[0] - sensor[0])\n",
+    "    return math.atan2(target_pos[1] - sensor[1], \n",
+    "                      target_pos[0] - sensor[0])\n",
     "\n",
     "def measurement(A_pos, B_pos, pos):\n",
     "    angle_a = bearing(A_pos, pos)\n",
@@ -1842,10 +1849,11 @@
     "    pos = (x[0], x[2])\n",
     "    return measurement(sa_pos, sb_pos, pos)\n",
     "\n",
-    "def moving_target_filter(target_pos, std_noise, Q, dt=0.1, kappa=0.0):\n",
-    "    points = MerweScaledSigmaPoints(n=4, alpha=.1, beta=2., kappa=kappa)\n",
-    "    f = UKF(dim_x=4, dim_z=2, dt=dt, hx=hx_VOR, fx=fx_VOR, points=points)\n",
-    "    f.x = np.array([target_pos[0], 1., target_pos[1], 1.])\n",
+    "def moving_target_filter(pos, std_noise, Q, dt=0.1, kappa=0.0):\n",
+    "    points = SigmaPoints(n=4, alpha=.1, beta=2., kappa=kappa)\n",
+    "    f = UKF(dim_x=4, dim_z=2, dt=dt, \n",
+    "            hx=hx_VOR, fx=fx_VOR, points=points)\n",
+    "    f.x = np.array([pos[0], 1., pos[1], 1.])\n",
     "\n",
     "    q = Q_discrete_white_noise(2, dt, Q)\n",
     "    f.Q[0:2, 0:2] = q\n",
@@ -1907,7 +1915,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79bd5c0>"
+       "<matplotlib.figure.Figure at 0x7c58eb8>"
       ]
      },
      "metadata": {},
@@ -1955,7 +1963,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79b8438>"
+       "<matplotlib.figure.Figure at 0x7bf8c50>"
       ]
      },
      "metadata": {},
@@ -2006,9 +2014,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x79e2908>"
+       "<matplotlib.figure.Figure at 0x7b8e320>"
       ]
      },
      "metadata": {},
@@ -2047,7 +2055,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d33128>"
+       "<matplotlib.figure.Figure at 0x97119e8>"
       ]
      },
      "metadata": {},
@@ -2079,7 +2087,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7816d30>"
+       "<matplotlib.figure.Figure at 0x7ad12e8>"
       ]
      },
      "metadata": {},
@@ -2311,9 +2319,9 @@
    "outputs": [],
    "source": [
     "def predict(self, sigma_points_fn):\n",
-    "    \"\"\" Performs the predict step of the UKF. On return, self.xp and\n",
-    "    self.Pp contain the predicted state (xp) and covariance (Pp). 'p'\n",
-    "    stands for prediction.\n",
+    "    \"\"\" Performs the predict step of the UKF. On return, \n",
+    "    self.xp and self.Pp contain the predicted state (xp) \n",
+    "    and covariance (Pp). 'p' stands for prediction.\n",
     "    \"\"\"\n",
     "\n",
     "    # calculate sigma points for given mean and covariance\n",
@@ -2378,7 +2386,7 @@
     "    for i in range(self._num_sigmas):\n",
     "        sigmas_h[i] = self.hx(sigmas_f[i])\n",
     "\n",
-    "    # mean and covariance of prediction passed through inscented transform\n",
+    "    # mean and covariance of prediction passed through UT\n",
     "    zp, Pz = unscented_transform(sigmas_h, self.W, self.W, self.R)\n",
     "\n",
     "    # compute cross variance of the state and the measurements\n",
@@ -2446,7 +2454,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7a3bf28>"
+       "<matplotlib.figure.Figure at 0x8bbff28>"
       ]
      },
      "metadata": {},
@@ -2456,7 +2464,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8dfd630>"
+       "<matplotlib.figure.Figure at 0x8715e48>"
       ]
      },
      "metadata": {},
@@ -2466,7 +2474,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8dbe080>"
+       "<matplotlib.figure.Figure at 0x90ae198>"
       ]
      },
      "metadata": {},
@@ -2483,7 +2491,7 @@
     "radar_pos = (0., 0.)\n",
     "h_radar.radar_pos = radar_pos\n",
     "\n",
-    "points = MerweScaledSigmaPoints(n=3, alpha=.1, beta=2., kappa=0.)\n",
+    "points = SigmaPoints(n=3, alpha=.1, beta=2., kappa=0.)\n",
     "kf = UKF(3, 2, dt, fx=f_radar, hx=h_radar, points=points)\n",
     "\n",
     "kf.Q[0:2 ,0:2] = Q_discrete_white_noise(2, dt=dt, var=0.1)\n",
@@ -2493,7 +2501,7 @@
     "kf.x = np.array([0., 90., 1100.])\n",
     "kf.P = np.diag([300**2, 30**2, 150**2])\n",
     "\n",
-    "radar = RadarStation(pos=(0, 0), range_std=range_std, bearing_std=bearing_std)\n",
+    "radar = RadarStation((0, 0), range_std, bearing_std)\n",
     "ac = ACSim(ac_pos, (100, 0), 0.02)\n",
     "\n",
     "random.seed(200)\n",
@@ -2545,14 +2553,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Difference in position in meters: [-0.7717 -0.1195  0.0426 -1.1727 -1.0871]\n"
+      "Difference in position in meters:\n",
+      "\t [-0.7717 -0.1195  0.0426 -1.1727 -1.0871]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x78d8a20>"
+       "<matplotlib.figure.Figure at 0x7c62e10>"
       ]
      },
      "metadata": {},
@@ -2562,7 +2571,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9500128>"
+       "<matplotlib.figure.Figure at 0x97ab1d0>"
       ]
      },
      "metadata": {},
@@ -2572,7 +2581,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwcAAAEtCAYAAABd8Bw2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9223080>"
+       "<matplotlib.figure.Figure at 0x9748e48>"
       ]
      },
      "metadata": {},
@@ -2628,7 +2637,8 @@
     }
    ],
    "source": [
-    "ukf_internal.print_sigmas(mean=0, cov=3, alpha=1)"
+    "from ukf_internal import print_sigmas\n",
+    "print_sigmas(mean=0, cov=3, alpha=1)"
    ]
   },
   {
@@ -2675,7 +2685,7 @@
     }
    ],
    "source": [
-    "ukf_internal.print_sigmas(mean=0, cov=3, alpha=200)"
+    "print_sigmas(mean=0, cov=3, alpha=200)"
    ]
   },
   {
@@ -2718,7 +2728,7 @@
     }
    ],
    "source": [
-    "ukf_internal.print_sigmas(mean=0, cov=13, alpha=.1, kappa=0)"
+    "print_sigmas(mean=0, cov=13, alpha=.1, kappa=0)"
    ]
   },
   {
@@ -2748,7 +2758,7 @@
     }
    ],
    "source": [
-    "ukf_internal.print_sigmas(mean=0, cov=3, alpha=.1)"
+    "print_sigmas(mean=0, cov=3, alpha=.1)"
    ]
   },
   {
@@ -2771,7 +2781,7 @@
     }
    ],
    "source": [
-    "ukf_internal.print_sigmas(mean=0, cov=3, alpha=1)"
+    "print_sigmas(mean=0, cov=3, alpha=1)"
    ]
   },
   {
@@ -2794,7 +2804,7 @@
     }
    ],
    "source": [
-    "ukf_internal.print_sigmas(mean=0, cov=3, alpha=.1, beta=2, kappa=1)"
+    "print_sigmas(mean=0, cov=3, alpha=.1, beta=2, kappa=1)"
    ]
   },
   {
@@ -2831,7 +2841,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x94a4048>"
+       "<matplotlib.figure.Figure at 0x8bdd9b0>"
       ]
      },
      "metadata": {},
@@ -2935,7 +2945,8 @@
     "\n",
     "        sinh, sinhb = sin(hdg), sin(hdg + beta)\n",
     "        cosh, coshb = cos(hdg), cos(hdg + beta)\n",
-    "        return x + np.array([-r*sinh + r*sinhb, r*cosh - r*coshb, beta])\n",
+    "        return x + np.array([-r*sinh + r*sinhb, \n",
+    "                              r*cosh - r*coshb, beta])\n",
     "    else:\n",
     "        # moving in straight line\n",
     "        return x + np.array([dist*cos(hdg), dist*sin(hdg), 0])"
@@ -3099,9 +3110,8 @@
    "outputs": [],
    "source": [
     "def Hx(x, landmarks):\n",
-    "    \"\"\" takes a state variable and returns the measurement that would\n",
-    "    correspond to that state.\n",
-    "    \"\"\"\n",
+    "    \"\"\" takes a state variable and returns the measurement\n",
+    "    that would correspond to that state. \"\"\"\n",
     "    hx = []\n",
     "    for lmark in landmarks:\n",
     "        px, py = lmark\n",
@@ -3165,8 +3175,8 @@
     "With that done we are now ready to implement the UKF. You've seen this sort of code many times, so I will not describe it in detail. There is one new thing here that is important to discuss. When we construct the sigma points and filter we have to provide it the functions that we have written to compute the residuals and means.\n",
     "\n",
     "```python\n",
-    "points = MerweScaledSigmaPoints(n=3, alpha=.00001, beta=2, kappa=0, \n",
-    "                                subtract=residual_x)\n",
+    "points = SigmaPoints(n=3, alpha=.00001, beta=2, kappa=0, \n",
+    "                     subtract=residual_x)\n",
     "\n",
     "ukf = UKF(dim_x=3, dim_z=2, fx=fx, hx=Hx, dt=dt, points=points,\n",
     "         x_mean_fn=state_mean, z_mean_fn=z_mean,\n",
@@ -3189,49 +3199,58 @@
     "dt = 1.0\n",
     "wheelbase = 0.5\n",
     "\n",
-    "def run_localization(cmds, landmarks, sigma_vel, sigma_steer, sigma_range, \n",
-    "                     sigma_bearing, ellipse_step=1, step=10):\n",
+    "def run_localization(\n",
+    "    cmds, landmarks, sigma_vel, sigma_steer, sigma_range, \n",
+    "    sigma_bearing, ellipse_step=1, step=10):\n",
     "\n",
-    "    points = MerweScaledSigmaPoints(n=3, alpha=.00001, beta=2, kappa=0, \n",
+    "    points = SigmaPoints(n=3, alpha=.00001, beta=2, kappa=0, \n",
     "                                    subtract=residual_x)\n",
-    "    ukf = UKF(dim_x=3, dim_z=2*len(landmarks), fx=fx, hx=Hx, dt=dt,\n",
-    "              points=points, x_mean_fn=state_mean, z_mean_fn=z_mean,\n",
-    "             residual_x=residual_x, residual_z=residual_h)\n",
+    "    ukf = UKF(dim_x=3, dim_z=2*len(landmarks), fx=fx, hx=Hx,\n",
+    "              dt=dt, points=points, x_mean_fn=state_mean, \n",
+    "              z_mean_fn=z_mean, residual_x=residual_x, \n",
+    "              residual_z=residual_h)\n",
+    "\n",
     "    ukf.x = np.array([2, 6, .3])\n",
     "    ukf.P = np.diag([.1, .1, .05])\n",
-    "    ukf.R = np.diag([sigma_range**2, sigma_bearing**2]*len(landmarks))\n",
+    "    ukf.R = np.diag([sigma_range**2, \n",
+    "                     sigma_bearing**2]*len(landmarks))\n",
     "    ukf.Q = np.eye(3)*0.0001\n",
     "    \n",
     "    sim_pos = ukf.x.copy()\n",
     "    \n",
     "    # plot landmarks\n",
     "    if len(landmarks) > 0:\n",
-    "        plt.scatter(landmarks[:, 0], landmarks[:, 1], marker='s', s=60)\n",
+    "        plt.scatter(landmarks[:, 0], landmarks[:, 1], \n",
+    "                    marker='s', s=60)\n",
     "    \n",
     "    track = []\n",
     "    for i, u in enumerate(cmds):     \n",
-    "        sim_pos = move(sim_pos, u, dt/step, wheelbase) # simulate robot\n",
+    "        sim_pos = move(sim_pos, u, dt/step, wheelbase) \n",
     "        track.append(sim_pos)\n",
     "\n",
     "        if i % step == 0:\n",
     "            ukf.predict(fx_args=u)\n",
     "\n",
     "            if i % ellipse_step == 0:\n",
-    "                plot_covariance_ellipse((ukf.x[0], ukf.x[1]), ukf.P[0:2, 0:2], std=6,\n",
-    "                                        facecolor='b', alpha=0.08)\n",
+    "                plot_covariance_ellipse(\n",
+    "                    (ukf.x[0], ukf.x[1]), ukf.P[0:2, 0:2], std=6,\n",
+    "                     facecolor='b', alpha=0.08)\n",
     "\n",
     "            x, y = sim_pos[0], sim_pos[1]\n",
     "            z = []\n",
     "            for lmark in landmarks:\n",
-    "                d = sqrt((lmark[0] - x)**2 + (lmark[1] - y)**2) + randn()*sigma_range\n",
+    "                dx, dy = lmark[0] - x, lmark[1] - y\n",
+    "                d = sqrt(dx**2 + dy**2) + randn()*sigma_range\n",
     "                bearing = atan2(lmark[1] - y, lmark[0] - x)\n",
-    "                a = normalize_angle(bearing - sim_pos[2] + randn()*sigma_bearing)\n",
+    "                a = (normalize_angle(bearing - sim_pos[2] + \n",
+    "                     randn()*sigma_bearing))\n",
     "                z.extend([d, a])            \n",
     "            ukf.update(z, hx_args=(landmarks,))\n",
     "\n",
     "            if i % ellipse_step == 0:\n",
-    "                plot_covariance_ellipse((ukf.x[0], ukf.x[1]), ukf.P[0:2, 0:2], std=6,\n",
-    "                                        facecolor='g', alpha=0.4)\n",
+    "                plot_covariance_ellipse(\n",
+    "                    (ukf.x[0], ukf.x[1]), ukf.P[0:2, 0:2], std=6,\n",
+    "                     facecolor='g', alpha=0.4)\n",
     "    track = np.array(track)\n",
     "    plt.plot(track[:, 0], track[:,1], color='k', lw=2)\n",
     "    plt.axis('equal')\n",
@@ -3252,7 +3271,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d4cf98>"
+       "<matplotlib.figure.Figure at 0x7ac1cf8>"
       ]
      },
      "metadata": {},
@@ -3269,8 +3288,9 @@
    "source": [
     "landmarks = np.array([[5, 10], [10, 5], [15, 15]])\n",
     "cmds = [np.array([1.1, .01])] * 200\n",
-    "ukf = run_localization(cmds, landmarks, sigma_vel=0.1, sigma_steer=np.radians(1),\n",
-    "                      sigma_range=0.3, sigma_bearing=0.1)\n",
+    "ukf = run_localization(\n",
+    "    cmds, landmarks, sigma_vel=0.1, sigma_steer=np.radians(1),\n",
+    "    sigma_range=0.3, sigma_bearing=0.1)\n",
     "print(ukf.P.diagonal())"
    ]
   },
@@ -3294,7 +3314,7 @@
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -3305,23 +3325,27 @@
     "sigma_range=0.3\n",
     "sigma_bearing=0.1\n",
     "\n",
+    "def turn(v, t0, t1, steps):\n",
+    "  return [[v, a] for a in np.linspace(\n",
+    "                 np.radians(t0), np.radians(t1), steps)]  \n",
+    "    \n",
     "# accelerate from a stop\n",
     "cmds = [[v, .0] for v in np.linspace(0.001, 1.1, 30)]\n",
     "cmds.extend([cmds[-1]]*50)\n",
     "\n",
     "# turn left\n",
     "v = cmds[-1][0]\n",
-    "cmds.extend([[v, a] for a in np.linspace(0, np.radians(2), 15)])\n",
+    "cmds.extend(turn(v, 0, 2, 15))\n",
     "cmds.extend([cmds[-1]]*100)\n",
     "\n",
     "#turn right\n",
-    "cmds.extend([[v, a] for a in np.linspace(np.radians(2), -np.radians(2), 15)])\n",
+    "cmds.extend(turn(v, 2, -2, 15))\n",
     "cmds.extend([cmds[-1]]*200)\n",
     "\n",
-    "cmds.extend([[v, a] for a in np.linspace(-np.radians(2), 0, 15)])\n",
+    "cmds.extend(turn(v, -2, 0, 15))\n",
     "cmds.extend([cmds[-1]]*150)\n",
     "\n",
-    "cmds.extend([[v, a] for a in np.linspace(0, np.radians(1), 25)])\n",
+    "cmds.extend(turn(v, 0, 1, 25))\n",
     "cmds.extend([cmds[-1]]*100)"
    ]
   },
@@ -3337,7 +3361,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEjCAYAAADjWc1BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d4cb70>"
+       "<matplotlib.figure.Figure at 0x7c9c048>"
       ]
      },
      "metadata": {},
@@ -3352,9 +3376,10 @@
     }
    ],
    "source": [
-    "ukf = run_localization(cmds, landmarks, sigma_vel=0.1, sigma_steer=np.radians(1),\n",
-    "                      sigma_range=0.3, sigma_bearing=0.1, step=1,\n",
-    "                      ellipse_step=20)\n",
+    "ukf = run_localization(\n",
+    "    cmds, landmarks, sigma_vel=0.1, sigma_steer=np.radians(1),\n",
+    "    sigma_range=0.3, sigma_bearing=0.1, step=1,\n",
+    "    ellipse_step=20)\n",
     "print('final covariance', ukf.P.diagonal())"
    ]
   },
@@ -3376,7 +3401,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEjCAYAAADwjo21AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7821748>"
+       "<matplotlib.figure.Figure at 0x7b07cf8>"
       ]
      },
      "metadata": {},
@@ -3391,9 +3416,10 @@
     }
    ],
    "source": [
-    "ukf = run_localization(cmds, landmarks[0:2], sigma_vel=0.1, sigma_steer=np.radians(1),\n",
-    "                      sigma_range=0.3, sigma_bearing=0.1, step=1,\n",
-    "                      ellipse_step=20)\n",
+    "ukf = run_localization(\n",
+    "    cmds, landmarks[0:2], sigma_vel=0.1, sigma_steer=np.radians(1),\n",
+    "    sigma_range=0.3, sigma_bearing=0.1, step=1,\n",
+    "    ellipse_step=20)\n",
     "print('final covariance', ukf.P.diagonal())"
    ]
   },

11-Extended-Kalman-Filters.ipynb

@@ -307,7 +307,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b0ad68>"
+       "<matplotlib.figure.Figure at 0x6dda518>"
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      "metadata": {},
@@ -355,7 +355,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b921d0>"
+       "<matplotlib.figure.Figure at 0x6dbefd0>"
       ]
      },
      "metadata": {},
@@ -433,7 +433,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x4330160>"
+       "<matplotlib.figure.Figure at 0x434c6d8>"
       ]
      },
      "metadata": {},
@@ -671,8 +671,8 @@
    "outputs": [],
    "source": [
     "def hx(x):\n",
-    "    \"\"\" compute measurement for slant range that would correspond \n",
-    "    to state x.\n",
+    "    \"\"\" compute measurement for slant range that\n",
+    "    would correspond to state x.\n",
     "    \"\"\"\n",
     "    \n",
     "    return (x[0]**2 + x[2]**2) ** 0.5"
@@ -697,8 +697,8 @@
     "import math\n",
     "\n",
     "class RadarSim(object):\n",
-    "    \"\"\" Simulates the radar signal returns from an object flying \n",
-    "    at a constant altityude and velocity in 1D. \n",
+    "    \"\"\" Simulates the radar signal returns from an object\n",
+    "    flying at a constant altityude and velocity in 1D. \n",
     "    \"\"\"\n",
     "    \n",
     "    def __init__(self, dt, pos, vel, alt):\n",
@@ -708,8 +708,8 @@
     "        self.dt = dt\n",
     "        \n",
     "    def get_range(self):\n",
-    "        \"\"\" Returns slant range to the object. Call once for each\n",
-    "        new measurement at dt time from last call.\n",
+    "        \"\"\" Returns slant range to the object. Call once \n",
+    "        for each new measurement at dt time from last call.\n",
     "        \"\"\"\n",
     "        \n",
     "        # add some process noise to the system\n",
@@ -777,9 +777,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7251358>"
+       "<matplotlib.figure.Figure at 0x7539b70>"
       ]
      },
      "metadata": {},
@@ -787,9 +787,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x722cef0>"
+       "<matplotlib.figure.Figure at 0x758e898>"
       ]
      },
      "metadata": {},
@@ -797,9 +797,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwMAAAEtCAYAAABUG7xMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtcFPX+P/DXLPeLsIIsF1G8JEaUJJgKZt4QsIOS3yxD\ngyi/RSfzxPGUffv+KqyOndM5fvuq2UXON4JDknWUTlom5KUkpASNvIGiJgqKInKRVa47vz8Whh1Y\ncBVwF/b1fDz2wbxnPjPzmd1x3fd8Pp8ZQRRFEUREREREZHYUxq4AEREREREZB5MBIiIiIiIzxWSA\niIiIiMhMMRkgIiIiIjJTTAaIiIiIiMwUkwEiIiIiIjPFZICIiIiIyEwZLRnYu3cv5s2bB29vbygU\nCqSmpnYqc+LECfzHf/wHBg8eDAcHBwQFBaGoqEha3tDQgGXLlsHNzQ2Ojo6IiopCWVmZbBtVVVWI\niYmBUqmEUqlEbGwsampq+vz4iIiIiIhMndGSAbVajXHjxmHt2rWws7ODIAiy5b/99humTJmC0aNH\nY8+ePTh69ChWrVoFR0dHqUxCQgIyMjKwadMmZGdno7a2FpGRkdBoNFKZRYsWoaCgAJmZmdixYwcO\nHjyImJiY23acRERERESmSjCFJxAPGjQI77//PmJjY6V5ixYtgoWFBdLS0vSuU1NTA5VKhZSUFERH\nRwMASktL4ePjg2+//RZhYWEoLCyEv78/cnJyEBwcDADIycnB1KlTUVRUBF9f374/OCIiIiIiE2WS\nYwY0Gg2+/vpr+Pn5ISIiAiqVChMnTsQXX3whlTlw4ACampoQFhYmzfP29oafnx9yc3MBALm5uXB0\ndJQSAQAICQmBg4ODVIaIiIiIyFyZZDJw6dIl1NXV4e2330ZERAR27tyJ6OhoLF68GNu3bwcAlJeX\nw8LCAq6urrJ13d3dUV5eLpVxc3OTLRcEASqVSipDRERERGSuLI1dAX3a+vw/9NBDSEhIAACMGzcO\n+fn5WL9+PR588MEu173VXk8cVExERERE/Zmzs/NNr2OSLQNDhgyBpaUl7rrrLtn8O++8E2fPngUA\neHh4oKWlBZWVlbIyFy9ehIeHh1SmoqJCtlwURVy6dEkqQ0RERERkrkwyGbC2tsZ9990nu40ooL3V\n6IgRIwAAQUFBsLKyQlZWlrS8tLQURUVFCAkJAQAEBwejrq5ONj4gNzcXarVaKkNEREREZK6M1k1I\nrVajuLgYgLZbUElJCQoKCuDq6ophw4ZhxYoVePTRRzF16lTMmDEDe/bsweeff46vvvoKgLYZZMmS\nJVixYgVUKhVcXFywfPlyBAQEIDQ0FACkAcjx8fFISkqCKIqIj4/H3LlzMWbMmC7rditNLES3U35+\nPgBgwoQJRq4JkWF4zlJ/wvOV+pOednU3WstAXl4eAgMDERgYiPr6eiQmJiIwMBCJiYkAgKioKCQl\nJWH16tUYN24c3n//faSlpWHOnDnSNtasWYP58+dj4cKFuP/+++Hk5IRt27bJnlmQnp6OgIAAhIeH\nIyIiAuPHj+/ydqVERERERObEJJ4zYAp0syq2DJCp41Ur6m94zlJ/wvOV+pOe/oY1yTEDRERERETU\n95gMEBERERGZKSYDRERERERmiskAEREREZGZYjJARERERGSmmAwQEREREZkpJgNERERERGaKyQAR\nERERkZliMkBEREREZKaYDBARERERmSkmA0REREREZorJABERERGRmWIyQERERERkppgMEBERERGZ\nKSYDRERERERmiskAEREREZGZYjJARERERGSmmAwQEREREZkpJgNERERERGaKyQARERERkZliMkBE\nREREZKaYDBARERERmSkmA0REREREZspoycDevXsxb948eHt7Q6FQIDU1VbY8Li4OCoVC9goJCZGV\naWhowLJly+Dm5gZHR0dERUWhrKxMVqaqqgoxMTFQKpVQKpWIjY1FTU1Nnx8fEREREZGpM1oyoFar\nMW7cOKxduxZ2dnYQBEG2XBAEzJ49G+Xl5dJr+/btsjIJCQnIyMjApk2bkJ2djdraWkRGRkKj0Uhl\nFi1ahIKCAmRmZmLHjh04ePAgYmJibssxEhERERGZMktj7XjOnDmYM2cOAG0rQEeiKMLa2hoqlUrv\n+jU1NUhOTkZKSgpmzZoFAEhLS4OPjw927tyJsLAwFBYWIjMzEzk5OZg0aRIAYMOGDZg6dSpOnDgB\nX1/fvjk4IiIiIqJ+wGTHDAiCgB9//BHu7u4YO3YsnnnmGVRUVEjLDxw4gKamJoSFhUnzvL294efn\nh9zcXABAbm4uHB0dERwcLJUJCQmBg4ODVIaIiIiIyFwZrWXgRiIiIvDwww9j5MiR+O233/Dqq69i\n5syZOHDgAKytrVFeXg4LCwu4urrK1nN3d0d5eTkAoLy8HG5ubrLlgiBApVJJZYiIiIiIzJXJJgML\nFy6Upv39/REUFAQfHx988803mD9/fpfriaLY433n5+f3eBtEtwPPVepveM5Sf8LzlfqDMWPG9Gh9\nk+0m1JGnpye8vb1x8uRJAICHhwdaWlpQWVkpK3fx4kV4eHhIZXS7FgHaZOHSpUtSGSIiIiIic2Wy\nLQMdVVRUoKysDJ6engCAoKAgWFlZISsrC9HR0QCA0tJSFBUVSbcgDQ4ORl1dHXJzc6VxA7m5uVCr\n1Z1uU6prwoQJfXw0RD3TdrWK5yr1FzxnqT/h+Ur9SU9vmW+0ZECtVqO4uBgAoNFoUFJSgoKCAri6\nusLFxQWJiYlYsGABPDw8cObMGbzyyitwd3eXugg5OztjyZIlWLFiBVQqFVxcXLB8+XIEBAQgNDQU\nAODn54eIiAjEx8cjKSkJoigiPj4ec+fO7XGTChERERFRf2e0bkJ5eXkIDAxEYGAg6uvrkZiYiMDA\nQCQmJsLCwgJHjhxBVFQUxo4di7i4OOkuQQ4ODtI21qxZg/nz52PhwoW4//774eTkhG3btsmeWZCe\nno6AgACEh4cjIiIC48ePR1pamjEOmYiIiIjIpAhib4y4HQB0m1icnZ2NWBOiG2MTNvU3PGepP+H5\nSv1JT3/D9psBxERERERE1LuYDBARERERmSkmA0REREREZorJABERERGRmWIyQERERERkppgMEBER\nERGZKSYDRERERERmismAHs3NfPQCEREREQ18TAb0aGgydg2IiIiIiPoekwE9mAwQERERkTlgMqBH\nfYOxa0BERERE1PeYDOhR32jsGhARERER9T0mA3qwmxARERERmQMmA3qwZYCIiIiIzAGTAT2YDBAR\nERGROWAyoAcHEBMRERGROWAyoAfHDBARERGROWAyoAe7CRERERGROWAyoAeTASIiIiIyB0wG9OCY\nASIiIiIyB0wG9OCYASIiIiIyB0wG9GA3ISIiIiIyB0wG9GAyQERERETmwGjJwN69ezFv3jx4e3tD\noVAgNTW1y7Lx8fFQKBT4n//5H9n8hoYGLFu2DG5ubnB0dERUVBTKyspkZaqqqhATEwOlUgmlUonY\n2FjU1NR0WzcmA0RERERkDoyWDKjVaowbNw5r166FnZ0dBEHQW27z5s3Iy8uDl5dXpzIJCQnIyMjA\npk2bkJ2djdraWkRGRkKj0UhlFi1ahIKCAmRmZmLHjh04ePAgYmJiuq1bA5MBIiIiIjIDlsba8Zw5\nczBnzhwAQFxcnN4yJSUlSEhIwK5duxARESFbVlNTg+TkZKSkpGDWrFkAgLS0NPj4+GDnzp0ICwtD\nYWEhMjMzkZOTg0mTJgEANmzYgKlTp+LEiRPw9fXVu1+2DBARERGROTDZMQPNzc2Ijo7Ga6+9hrFj\nx3ZafuDAATQ1NSEsLEya5+3tDT8/P+Tm5gIAcnNz4ejoiODgYKlMSEgIHBwcpDL6MBkgIiIiInNg\nsslAYmIiVCoV4uPj9S4vLy+HhYUFXF1dZfPd3d1RXl4ulXFzc5MtFwQBKpVKKqMPkwEiIiIiMgdG\n6ybUne+//x6pqakoKCiQzRdF8YbrGlLmRi6UVyI//0yPt0PU1/Lz841dBaKbwnOW+hOer9QfjBkz\npkfrm2TLwA8//IALFy7A09MTVlZWsLKyQklJCV5++WUMHz4cAODh4YGWlhZUVlbK1r148SI8PDyk\nMhUVFbLloiji0qVLUhl9GptM8m0hIiIiIupVJtky8Nxzz+GRRx6RYlEUER4ejkWLFuHpp58GAAQF\nBcHKygpZWVmIjo4GAJSWlqKoqAghISEAgODgYNTV1SE3N1caN5Cbmwu1Wi2V0cfOYTAmTJjQV4dH\n1GNtV6t4nlJ/wXOW+hOer9Sf3OiW+TditGRArVajuLgYAKDRaFBSUoKCggK4urpi2LBhnfr6W1lZ\nwcPDQ2oKcXZ2xpIlS7BixQqoVCq4uLhg+fLlCAgIQGhoKADAz88PERERiI+PR1JSEkRRRHx8PObO\nndttkwrHDBARERGROTBaf5i8vDwEBgYiMDAQ9fX1SExMRGBgIBITEw3expo1azB//nwsXLgQ999/\nP5ycnLBt2zbZ8wjS09MREBCA8PBwREREYPz48UhLS+t2uw1Nt3xYRERERET9hsEtA5cvX0ZOTg4K\nCwtx+fJlCIKAIUOGwM/PDyEhIRgyZMhN7Xj69Omyh4PdyG+//dZpnrW1NdatW4d169Z1uZ5Sqbzh\nj/+O6htuqjgRERERUb/UbTLQ0NCAjRs34pNPPkFOTk63GwoJCcGTTz6Jxx9/HDY2Nr1ayduN3YSI\niIiIyBx02U3oww8/xOjRo/Hcc89h8ODBWLNmDbKzs1FWVoZr165BrVajtLQU2dnZWLNmDQYPHoyl\nS5di9OjR+Oijj27nMfQ6JgNEREREZA66bBlYtWoV/vSnP+Gpp56Cs7Oz3jJ2dnbw8Line truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x730dda0>"
+       "<matplotlib.figure.Figure at 0x75d6748>"
       ]
      },
      "metadata": {},
@@ -1019,7 +1019,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAEWCAYAAAA+Wg5zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e030b8>"
+       "<matplotlib.figure.Figure at 0x75015f8>"
       ]
      },
      "metadata": {},
@@ -1376,8 +1376,8 @@
     "from math import sqrt\n",
     "\n",
     "def H_of(x, landmark_pos):\n",
-    "    \"\"\" compute Jacobian of H matrix where h(x) computes the range and\n",
-    "    bearing to a landmark for state x \"\"\"\n",
+    "    \"\"\" compute Jacobian of H matrix where h(x) computes \n",
+    "    the range and bearing to a landmark for state x \"\"\"\n",
     "\n",
     "    px = landmark_pos[0]\n",
     "    py = landmark_pos[1]\n",
@@ -1409,8 +1409,8 @@
    "source": [
     "from math import atan2\n",
     "def Hx(x, landmark_pos):\n",
-    "    \"\"\" takes a state variable and returns the measurement that would\n",
-    "    correspond to that state.\n",
+    "    \"\"\" takes a state variable and returns the measurement\n",
+    "    that would correspond to that state.\n",
     "    \"\"\"\n",
     "    px = landmark_pos[0]\n",
     "    py = landmark_pos[1]\n",
@@ -1463,28 +1463,32 @@
     "from filterpy.kalman import ExtendedKalmanFilter as EKF\n",
     "from numpy import dot, array, sqrt\n",
     "class RobotEKF(EKF):\n",
-    "    def __init__(self, dt, wheelbase, sigma_vel, sigma_steer):\n",
+    "    def __init__(self, dt, wheelbase, std_vel, std_steer):\n",
     "        EKF.__init__(self, 3, 2, 2)\n",
     "        self.dt = dt\n",
     "        self.wheelbase = wheelbase\n",
-    "        self.sigma_vel = sigma_vel\n",
-    "        self.sigma_steer = sigma_steer\n",
+    "        self.std_vel = std_vel\n",
+    "        self.std_steer = std_steer\n",
     "\n",
-    "        a, x, y, v, w, theta, time = symbols('a, x, y, v, w, theta, t')\n",
+    "        a, x, y, v, w, theta, time = symbols(\n",
+    "            'a, x, y, v, w, theta, t')\n",
     "        d = v*time\n",
     "        beta = (d/w)*sympy.tan(a)\n",
     "        r = w/sympy.tan(a)\n",
     "    \n",
-    "        self.fxu = Matrix([[x-r*sympy.sin(theta)+r*sympy.sin(theta+beta)],\n",
-    "                           [y+r*sympy.cos(theta)-r*sympy.cos(theta+beta)],\n",
-    "                           [theta+beta]])\n",
+    "        self.fxu = Matrix(\n",
+    "            [[x-r*sympy.sin(theta)+r*sympy.sin(theta+beta)],\n",
+    "             [y+r*sympy.cos(theta)-r*sympy.cos(theta+beta)],\n",
+    "             [theta+beta]])\n",
     "\n",
     "        self.F_j = self.fxu.jacobian(Matrix([x, y, theta]))\n",
     "        self.V_j = self.fxu.jacobian(Matrix([v, a]))\n",
     "\n",
     "        # save dictionary and it's variables for later use\n",
-    "        self.subs = {x: 0, y: 0, v:0, a:0, time:dt, w:wheelbase, theta:0}\n",
-    "        self.x_x, self.x_y, self.v, self.a, self.theta = x, y, v, a, theta\n",
+    "        self.subs = {x: 0, y: 0, v:0, a:0, \n",
+    "                     time:dt, w:wheelbase, theta:0}\n",
+    "        self.x_x, self.x_y, = x, y \n",
+    "        self.v, self.a, self.theta = v, a, theta\n",
     "\n",
     "    def predict(self, u=0):\n",
     "        self.x = self.move(self.x, u, self.dt)\n",
@@ -1497,7 +1501,8 @@
     "        V = array(self.V_j.evalf(subs=self.subs)).astype(float)\n",
     "\n",
     "        # covariance of motion noise in control space\n",
-    "        M = array([[self.sigma_vel*u[0]**2, 0], [0, self.sigma_steer**2]])\n",
+    "        M = array([[self.std_vel*u[0]**2, 0], \n",
+    "                   [0, self.std_steer**2]])\n",
     "\n",
     "        self.P = dot(F, self.P).dot(F.T) + dot(V, M).dot(V.T)\n",
     "\n",
@@ -1538,7 +1543,7 @@
    "outputs": [],
    "source": [
     "def residual(a, b):\n",
-    "    \"\"\" compute residual (a-b) between two measurement containing \n",
+    "    \"\"\" compute residual (a-b) between measurements containing \n",
     "    [range, bearing]. Bearing is normalized to [-pi, pi)\"\"\"\n",
     "    y = a - b\n",
     "    y[1] = y[1] % (2 * np.pi)    # force in range [0, 2 pi)\n",
@@ -1567,16 +1572,35 @@
     "from math import sqrt, tan, cos, sin, atan2\n",
     "dt = 1.0\n",
     "\n",
-    "def run_localization(landmarks, sigma_vel, sigma_steer, sigma_range, sigma_bearing):\n",
-    "    ekf = RobotEKF(dt, wheelbase=0.5, sigma_vel=sigma_vel, sigma_steer=sigma_steer)\n",
+    "\n",
+    "def z_landmark(lmark, sim_pos, std_rng, std_brg):\n",
+    "    x, y = sim_pos[0, 0], sim_pos[1, 0]\n",
+    "    d = np.sqrt((lmark[0] - x)**2 + (lmark[1] - y)**2)  \n",
+    "    a = atan2(lmark[1] - y, lmark[0] - x) - sim_pos[2, 0]\n",
+    "    z = np.array([[d + randn()*std_rng],\n",
+    "                  [a + randn()*std_brg]])\n",
+    "    return z\n",
+    "\n",
+    "def ekf_update(ekf, z, landmark):\n",
+    "    ekf.update(z, HJacobian=H_of, Hx=Hx, \n",
+    "               residual=residual,\n",
+    "               args=(landmark), hx_args=(landmark))\n",
+    "    \n",
+    "                \n",
+    "def run_localization(landmarks, std_vel, std_steer, \n",
+    "                     std_range, std_bearing):\n",
+    "    ekf = RobotEKF(dt, wheelbase=0.5, std_vel=std_vel, \n",
+    "                   std_steer=std_steer)\n",
     "    ekf.x = array([[2, 6, .3]]).T\n",
     "    ekf.P = np.diag([.1, .1, .1])\n",
-    "    ekf.R = np.diag([sigma_range**2, sigma_bearing**2])\n",
+    "    ekf.R = np.diag([std_range**2, std_bearing**2])\n",
     "\n",
     "    sim_pos = ekf.x.copy() # simulated position\n",
-    "    u = array([1.1, .01]) # steering command (vel, steering angle radians)\n",
+    "    # steering command (vel, steering angle radians)\n",
+    "    u = array([1.1, .01]) \n",
     "\n",
-    "    plt.scatter(landmarks[:, 0], landmarks[:, 1], marker='s', s=60)\n",
+    "    plt.scatter(landmarks[:, 0], landmarks[:, 1],\n",
+    "                marker='s', s=60)\n",
     "    for i in range(200):\n",
     "        sim_pos = ekf.move(sim_pos, u, dt/10.) # simulate robot\n",
     "        plt.plot(sim_pos[0], sim_pos[1], ',', color='g')\n",
@@ -1584,20 +1608,19 @@
     "        if i % 10 == 0:\n",
     "            ekf.predict(u=u)\n",
     "\n",
-    "            plot_covariance_ellipse((ekf.x[0,0], ekf.x[1,0]), ekf.P[0:2, 0:2], std=6,\n",
-    "                                    facecolor='b', alpha=0.08)\n",
+    "            plot_covariance_ellipse(\n",
+    "                (ekf.x[0,0], ekf.x[1,0]), ekf.P[0:2, 0:2], \n",
+    "                 std=6, facecolor='b', alpha=0.08)\n",
     "\n",
     "            x, y = sim_pos[0, 0], sim_pos[1, 0]\n",
     "            for lmark in landmarks:\n",
-    "                d = np.sqrt((lmark[0] - x)**2 + (lmark[1] - y)**2)  \n",
-    "                a = atan2(lmark[1] - y, lmark[0] - x) - sim_pos[2, 0]\n",
-    "                z = np.array([[d + randn()*sigma_range], [a + randn()*sigma_bearing]])\n",
+    "                z = z_landmark(lmark, sim_pos,\n",
+    "                               std_range, std_bearing)\n",
+    "                ekf_update(ekf, z, lmark)\n",
     "\n",
-    "                ekf.update(z, HJacobian=H_of, Hx=Hx, residual=residual,\n",
-    "                           args=(lmark), hx_args=(lmark))\n",
-    "\n",
-    "            plot_covariance_ellipse((ekf.x[0,0], ekf.x[1,0]), ekf.P[0:2, 0:2], std=6,\n",
-    "                                    facecolor='g', alpha=0.4)\n",
+    "            plot_covariance_ellipse(\n",
+    "                (ekf.x[0,0], ekf.x[1,0]), ekf.P[0:2, 0:2],\n",
+    "                std=6, facecolor='g', alpha=0.4)\n",
     "    plt.axis('equal')\n",
     "    plt.show()\n",
     "    return ekf"
@@ -1613,9 +1636,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d92588>"
+       "<matplotlib.figure.Figure at 0x4ffecc0>"
       ]
      },
      "metadata": {},
@@ -1625,7 +1648,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.02359264  0.04263383  0.00222258]\n"
+      "[ 0.02356385  0.04184256  0.00219828]\n"
      ]
     }
    ],
@@ -1633,8 +1656,9 @@
     "landmarks = array([[5, 10], [10, 5], [15, 15], [20, 5]])\n",
     "landmarks = array([[5, 10], [10, 5], [15, 15]])\n",
     "\n",
-    "ekf = run_localization(landmarks, sigma_vel=0.1, sigma_steer=np.radians(1),\n",
-    "                      sigma_range=0.3, sigma_bearing=0.1)\n",
+    "ekf = run_localization(\n",
+    "    landmarks, std_vel=0.1, std_steer=np.radians(1),\n",
+    "    std_range=0.3, std_bearing=0.1)\n",
     "print(ekf.P.diagonal())"
    ]
   },
@@ -1647,9 +1671,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7392978>"
+       "<matplotlib.figure.Figure at 0x4ffe780>"
       ]
      },
      "metadata": {},
@@ -1659,15 +1683,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.02021647  0.02064576  0.0015385 ]\n"
+      "[ 0.02010224  0.02051038  0.00154269]\n"
      ]
     }
    ],
    "source": [
     "landmarks = array([[5, 10], [10, 5], [15, 15], [20, 5]])\n",
     "\n",
-    "ekf = run_localization(landmarks, sigma_vel=0.1, sigma_steer=np.radians(1),\n",
-    "                      sigma_range=0.3, sigma_bearing=0.1)\n",
+    "ekf = run_localization(\n",
+    "    landmarks, std_vel=0.1, std_steer=np.radians(1),\n",
+    "    std_range=0.3, std_bearing=0.1)\n",
     "print(ekf.P.diagonal())"
    ]
   },
@@ -1692,9 +1717,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ee55c0>"
+       "<matplotlib.figure.Figure at 0x4ffe080>"
       ]
      },
      "metadata": {},
@@ -1704,13 +1729,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.02085948  0.04494663  0.00022663]\n"
+      "[ 0.01975584  0.04575349  0.00022389]\n"
      ]
     }
    ],
    "source": [
-    "ekf = run_localization(landmarks[0:2], sigma_vel=1.e-10, sigma_steer=1.e-10,\n",
-    "                       sigma_range=1.4, sigma_bearing=.05)\n",
+    "ekf = run_localization(\n",
+    "    landmarks[0:2], std_vel=1.e-10, std_steer=1.e-10,\n",
+    "    std_range=1.4, std_bearing=.05)\n",
     "print(ekf.P.diagonal())"
    ]
   },
@@ -1731,9 +1757,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEWCAYAAACkORurAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7392320>"
+       "<matplotlib.figure.Figure at 0x9f749e8>"
       ]
      },
      "metadata": {},
@@ -1743,13 +1769,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.3006648   0.78199632  0.0036382 ]\n"
+      "[ 0.26397302  0.7937892   0.00351629]\n"
      ]
     }
    ],
    "source": [
-    "ekf = run_localization(landmarks[0:1], sigma_vel=1.e-10, sigma_steer=1.e-10,\n",
-    "                       sigma_range=1.4, sigma_bearing=.05)\n",
+    "ekf = run_localization(\n",
+    "    landmarks[0:1], std_vel=1.e-10, std_steer=1.e-10,\n",
+    "    std_range=1.4, std_bearing=.05)\n",
     "print(ekf.P.diagonal())"
    ]
   },
@@ -1770,9 +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 0x9d524a8>"
+       "<matplotlib.figure.Figure at 0x9ea99e8>"
       ]
      },
      "metadata": {},
@@ -1782,7 +1809,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.008653    0.00889465  0.00077193]\n"
+      "[ 0.00871873  0.00883399  0.00077378]\n"
      ]
     }
    ],
@@ -1790,8 +1817,9 @@
     "landmarks = array([[5, 10], [10,  5], [15, 15], [20,  5], [15, 10], \n",
     "                   [10,14], [23, 14], [25, 25], [10, 20]])\n",
     "\n",
-    "ekf = run_localization(landmarks, sigma_vel=0.1, sigma_steer=np.radians(1),\n",
-    "                      sigma_range=0.3, sigma_bearing=0.1)\n",
+    "ekf = run_localization(\n",
+    "    landmarks, std_vel=0.1, std_steer=np.radians(1),\n",
+    "    std_range=0.3, std_bearing=0.1)\n",
     "print(ekf.P.diagonal())"
    ]
   },
@@ -1832,9 +1860,9 @@
    "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 0x9e28748>"
+       "<matplotlib.figure.Figure at 0x73db128>"
       ]
      },
      "metadata": {},
@@ -1844,7 +1872,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.30, std=1.12\n",
+      "actual mean=1.30, std=1.13\n",
       "EKF    mean=1.00, std=0.95\n"
      ]
     }
@@ -1872,9 +1900,9 @@
    "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 0x9d0ca58>"
+       "<matplotlib.figure.Figure at 0xa0ce160>"
       ]
      },
      "metadata": {},
@@ -1884,7 +1912,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "actual mean=1.30, std=1.13\n",
+      "actual mean=1.30, std=1.14\n",
       "UKF    mean=1.30, std=1.08\n"
      ]
     }

12-Particle-Filters.ipynb

@@ -322,9 +322,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAEWCAYAAAAw37JZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x45601d0>"
+       "<matplotlib.figure.Figure at 0x791cba8>"
       ]
      },
      "metadata": {},
@@ -398,7 +398,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8901f60>"
+       "<matplotlib.figure.Figure at 0x84fcda0>"
       ]
      },
      "metadata": {},
@@ -476,7 +476,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8cb9f28>"
+       "<matplotlib.figure.Figure at 0x8c9e5f8>"
       ]
      },
      "metadata": {},
@@ -497,8 +497,10 @@
     "area = 2*2\n",
     "pi = area*(in_circle_count / N)\n",
     "\n",
-    "plt.scatter(ps[in_circle,0], ps[in_circle,1], marker=',', edgecolor='k', s=1)\n",
-    "plt.scatter(ps[~in_circle,0], ps[~in_circle,1], marker=',', edgecolor='r', s=1)\n",
+    "plt.scatter(ps[in_circle,0], ps[in_circle,1], \n",
+    "            marker=',', edgecolor='k', s=1)\n",
+    "plt.scatter(ps[~in_circle,0], ps[~in_circle,1], \n",
+    "            marker=',', edgecolor='r', s=1)\n",
     "plt.axis('equal')\n",
     "\n",
     "print('mean pi(N={})= {:.4f}'.format(N, pi))\n",
@@ -529,7 +531,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAEWCAYAAAAn/SKQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x892a5f8>"
+       "<matplotlib.figure.Figure at 0x8500da0>"
       ]
      },
      "metadata": {},
@@ -563,7 +565,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8ccd438>"
+       "<matplotlib.figure.Figure at 0x937e780>"
       ]
      },
      "metadata": {},
@@ -619,7 +621,8 @@
     "\n",
     "class ParticleFilter(object):\n",
     "\n",
-    "    def __init__(self, N, x_dim, y_dim, landmarks, measure_std_error):\n",
+    "    def __init__(self, N, x_dim, y_dim, \n",
+    "                 landmarks, measure_std_error):\n",
     "        self.N = N\n",
     "        self.x_dim = x_dim\n",
     "        self.y_dim = y_dim\n",
@@ -687,7 +690,8 @@
     "def update(self, z):\n",
     "    self.weights.fill(1.)\n",
     "    for i, landmark in enumerate(self.landmarks):\n",
-    "        dist = np.linalg.norm(self.particles[:, 0:2] - landmark, axis=1)\n",
+    "        y = self.particles[:, 0:2] - landmark\n",
+    "        dist = np.linalg.norm(y , axis=1)\n",
     "        self.weights *= scipy.stats.norm(dist, self.R).pdf(z[i])\n",
     "    self.weights /= sum(self.weights) # normalize\n",
     "```\n",
@@ -796,14 +800,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "final position error, variance: [ 18.00486271  18.00176074] [ 0.00458291  0.00448719]\n"
+      "final position error, variance:\n",
+      "\t [ 18.00486271  18.00176074] [ 0.00458291  0.00448719]\n"
      ]
     },
     {
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtsAAAEWCAYAAAC37ltfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x89c06a0>"
+       "<matplotlib.figure.Figure at 0x9361c50>"
       ]
      },
      "metadata": {},
@@ -820,15 +825,19 @@
     "            initial_x=None):\n",
     "    landmarks = np.array([[-1, 2], [5, 10], [12,14], [18,21]])\n",
     "    NL = len(landmarks)\n",
-    "    pf = RobotLocalizationParticleFilter(N, 20, 20, landmarks, sensor_std_err)\n",
+    "    pf = RobotLocalizationParticleFilter(\n",
+    "        N, 20, 20, landmarks, sensor_std_err)\n",
+    "    \n",
     "    if initial_x is not None:\n",
-    "        pf.create_gaussian_particles(mean=initial_x, var=(5, 5, np.pi/4))\n",
+    "        pf.create_gaussian_particles(mean=initial_x, \n",
+    "                                     var=(5, 5, np.pi/4))\n",
     "        \n",
     "    if plot_particles:\n",
     "        alpha = .20\n",
     "        if N > 5000:\n",
     "            alpha *= np.sqrt(5000)/np.sqrt(N)           \n",
-    "        plt.scatter(pf.particles[:, 0], pf.particles[:, 1], alpha=alpha, color='g')\n",
+    "        plt.scatter(pf.particles[:, 0], pf.particles[:, 1], \n",
+    "                    alpha=alpha, color='g')\n",
     "    \n",
     "    xs = []\n",
     "    pos = np.array([0., 0.])\n",
@@ -846,8 +855,10 @@
     "        mu, var = pf.estimate()\n",
     "        xs.append(mu)\n",
     "        if plot_particles:\n",
-    "            plt.scatter(pf.particles[:, 0], pf.particles[:, 1], color='k', marker=',', s=1)\n",
-    "        p1 = plt.scatter(pos[0], pos[1], marker='+', color='k', s=180, lw=3)\n",
+    "            plt.scatter(pf.particles[:, 0], pf.particles[:, 1], \n",
+    "                        color='k', marker=',', s=1)\n",
+    "        p1 = plt.scatter(pos[0], pos[1], marker='+',\n",
+    "                         color='k', s=180, lw=3)\n",
     "        p2 = plt.scatter(mu[0], mu[1], marker='s', color='r')\n",
     "    \n",
     "    xs = np.array(xs)\n",
@@ -855,7 +866,7 @@
     "    plt.legend([p1, p2], ['Actual', 'PF'], loc=4, numpoints=1)\n",
     "    plt.xlim(*xlim)\n",
     "    plt.ylim(*ylim)\n",
-    "    print('final position error, variance:', mu, var)\n",
+    "    print('final position error, variance:\\n\\t', mu, var)\n",
     "\n",
     "from numpy.random import seed\n",
     "seed(2) \n",
@@ -901,14 +912,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "final position error, variance: [ 8.06115913  7.89412269] [ 0.0029203   0.00299334]\n"
+      "final position error, variance:\n",
+      "\t [ 8.06115913  7.89412269] [ 0.0029203   0.00299334]\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d2ceb8>"
+       "<matplotlib.figure.Figure at 0x938c198>"
       ]
      },
      "metadata": {},
@@ -917,7 +929,8 @@
    ],
    "source": [
     "seed(2)\n",
-    "run_pf1(N=5000, iters=8, plot_particles=True, xlim=(0,8), ylim=(0,8))"
+    "run_pf1(N=5000, iters=8, plot_particles=True, \n",
+    "        xlim=(0,8), ylim=(0,8))"
    ]
   },
   {
@@ -954,14 +967,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "final position error, variance: [ 8.03991809  7.93388899] [ 0.00275743  0.00305754]\n"
+      "final position error, variance:\n",
+      "\t [ 8.03991809  7.93388899] [ 0.00275743  0.00305754]\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8d2c0b8>"
+       "<matplotlib.figure.Figure at 0x8f07b38>"
       ]
      },
      "metadata": {},
@@ -970,7 +984,8 @@
    ],
    "source": [
     "seed(2) \n",
-    "run_pf1(N=100000, iters=8, plot_particles=True, xlim=(0,8), ylim=(0,8))"
+    "run_pf1(N=100000, iters=8, plot_particles=True, \n",
+    "        xlim=(0,8), ylim=(0,8))"
    ]
   },
   {
@@ -982,7 +997,8 @@
     "Another approach is to be smarter about generating the initial particle cloud. Suppose we know that the robot is near (0,0). This is not exact, as the simulation actually places the robot at (1,1), but it is close. If instead of creating a uniform cloud of particles over the entire map we made a normally distributed cloud near (0, 0) there is a much greater chance of the particles matching the robot's position. The particle filter code includes the method `create_gaussian_particles()`; feel free to alter the code above to use this function as in the code snippet below. However, we will be using this in the next section to help with a different problem, so feel free to wait.\n",
     "\n",
     "```python\n",
-    "pf = RobotLocalizationParticleFilter(N, 20, 20, landmarks, sensor_std_err)\n",
+    "pf = RobotLocalizationParticleFilter(\n",
+    "         N, 20, 20, landmarks, sensor_std_err)\n",
     "pf.create_gaussian_particles(mean=(0, 0), var=(10,10))\n",
     "```"
    ]
@@ -1007,14 +1023,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "final position error, variance: [ 13.65600711 -15.87383315] [ 51.52147194  34.40736373]\n"
+      "final position error, variance:\n",
+      "\t [ 13.65600711 -15.87383315] [ 51.52147194  34.40736373]\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a512e8>"
+       "<matplotlib.figure.Figure at 0x9574a90>"
       ]
      },
      "metadata": {},
@@ -1046,14 +1063,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "final position error, variance: [ 17.92304664  18.07173658] [ 0.00451171  0.00429415]\n"
+      "final position error, variance:\n",
+      "\t [ 17.92304664  18.07173658] [ 0.00451171  0.00429415]\n"
      ]
     },
     {
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f92dd8>"
+       "<matplotlib.figure.Figure at 0xaf8ee80>"
       ]
      },
      "metadata": {},
@@ -1122,7 +1140,8 @@
     "def update(self, z):\n",
     "    self.weights.fill(1.)\n",
     "    for i, landmark in enumerate(self.landmarks):\n",
-    "        dist = np.linalg.norm(self.particles[:, 0:2] - landmark, axis=1)\n",
+    "        y = self.particles[:, 0:2] - landmark\n",
+    "        dist = np.linalg.norm(y, axis=1)\n",
     "        self.weights *= scipy.stats.norm(dist, self.R).pdf(z[i])\n",
     "    self.weights /= sum(self.weights) # normalize\n",
     "```\n",
@@ -1180,7 +1199,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAacAAABACAYAAAC+2bgWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOP8CSdsd9j7kQ7EAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8f9c550>"
+       "<matplotlib.figure.Figure at 0x93c19b0>"
       ]
      },
      "metadata": {},
@@ -1223,7 +1242,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8fbe978>"
+       "<matplotlib.figure.Figure at 0xb381b38>"
       ]
      },
      "metadata": {},
@@ -1297,7 +1316,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8a5a0f0>"
+       "<matplotlib.figure.Figure at 0x95855c0>"
       ]
      },
      "metadata": {},
@@ -1342,7 +1361,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAABCCAYAAACSCTtfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x890cfd0>"
+       "<matplotlib.figure.Figure at 0x7c1a358>"
       ]
      },
      "metadata": {},
@@ -1363,7 +1382,7 @@
     "```python\n",
     "def stratified_resample(weights):\n",
     "    N = len(weights)\n",
-    "    # make N subdivisions, and chose a random position within each one\n",
+    "    # make N subdivisions, chose a random position within each one\n",
     "    positions = (random(N) + range(N)) / N\n",
     "\n",
     "    indexes = np.zeros(N, 'i')\n",
@@ -1405,7 +1424,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZMAAABCCAYAAACSCTtfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8936898>"
+       "<matplotlib.figure.Figure at 0x85686a0>"
       ]
      },
      "metadata": {},
@@ -1427,7 +1446,8 @@
     "def systematic_resample(weights):\n",
     "    N = len(weights)\n",
     "\n",
-    "    # make N subdivisions, choose positions with a consistent random offset\n",
+    "    # make N subdivisions, choose positions \n",
+    "    # with a consistent random offset\n",
     "    positions = (np.arange(N) + random()) / N\n",
     "\n",
     "    indexes = np.zeros(N, 'i')\n",
@@ -1470,7 +1490,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8915780>"
+       "<matplotlib.figure.Figure at 0x853c3c8>"
       ]
      },
      "metadata": {},
@@ -1480,7 +1500,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x8933390>"
+       "<matplotlib.figure.Figure at 0xb39c978>"
       ]
      },
      "metadata": {},
@@ -1490,7 +1510,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x940a518>"
+       "<matplotlib.figure.Figure at 0x935bc50>"
       ]
      },
      "metadata": {},
@@ -1500,7 +1520,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x93a8748>"
+       "<matplotlib.figure.Figure at 0x8f37780>"
       ]
      },
      "metadata": {},

13-Smoothing.ipynb

@@ -296,7 +296,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAucAAAEWCAYAAADFITnpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x40f05f8>"
+       "<matplotlib.figure.Figure at 0x436c978>"
       ]
      },
      "metadata": {},
@@ -306,7 +306,8 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "data = [10.1, 10.2, 9.8, 10.1, 10.2, 10.3, 10.1, 9.9, 10.2, 10.0, 9.9, 11.4]\n",
+    "data = [10.1, 10.2, 9.8, 10.1, 10.2, 10.3, \n",
+    "        10.1, 9.9, 10.2, 10.0, 9.9, 11.4]\n",
     "plt.plot(data);"
    ]
   },
@@ -340,7 +341,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAAEWCAYAAABYLDBhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b34828>"
+       "<matplotlib.figure.Figure at 0x6e6ccc0>"
       ]
      },
      "metadata": {},
@@ -372,7 +373,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x709c6d8>"
+       "<matplotlib.figure.Figure at 0x7457a20>"
       ]
      },
      "metadata": {},
@@ -514,9 +515,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEPCAYAAAAOOgGEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt4AAAEPCAYAAAB1HsNIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x711b6a0>"
+       "<matplotlib.figure.Figure at 0x74bcfd0>"
       ]
      },
      "metadata": {},
@@ -526,6 +527,7 @@
    "source": [
     "import numpy as np\n",
     "from numpy import random\n",
+    "from numpy.random import randn\n",
     "import matplotlib.pyplot as plt\n",
     "from filterpy.kalman import KalmanFilter\n",
     "import book_plots as bp\n",
@@ -534,7 +536,7 @@
     "    random.seed(123)\n",
     "    fk = KalmanFilter(dim_x=2, dim_z=1)\n",
     "\n",
-    "    fk.x = np.array([0., 1.])      # initial state (location and velocity)\n",
+    "    fk.x = np.array([0., 1.])      # state (x and dx)\n",
     "\n",
     "    fk.F = np.array([[1., 1.],\n",
     "                     [0., 1.]])    # state transition matrix\n",
@@ -545,7 +547,7 @@
     "    fk.Q = Q                       # process uncertainty\n",
     "\n",
     "    # create noisy data\n",
-    "    zs = np.asarray([t + random.randn()*noise for t in range (40)])\n",
+    "    zs = np.asarray([t + randn()*noise for t in range (40)])\n",
     "\n",
     "    # filter data with Kalman filter, than run smoother on it\n",
     "    mu, cov, _, _ = fk.batch_filter(zs)\n",
@@ -560,9 +562,10 @@
     "    if not show_velocity:\n",
     "        bp.plot_measurements(zs, lw=1)\n",
     "    plt.plot(M[:, index], c='b', label='RTS')\n",
-    "    plt.plot(mu[:, index], c='g', linestyle='--', label='KF output')\n",
+    "    plt.plot(mu[:, index], c='g', ls='--', label='KF output')\n",
     "    if not show_velocity:\n",
-    "        plt.plot([0, len(zs)], [0, len(zs)], 'k', linewidth=2, label='track') \n",
+    "        N = len(zs)\n",
+    "        plt.plot([0, N], [0, N], 'k', lw=2, label='track') \n",
     "    plt.legend(loc=4)\n",
     "    plt.show()\n",
     "    \n",
@@ -587,9 +590,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtMAAAEPCAYAAACA4Ej4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x710d470>"
+       "<matplotlib.figure.Figure at 0x746f080>"
       ]
      },
      "metadata": {},
@@ -616,9 +619,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEPCAYAAAAOOgGEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x728a160>"
+       "<matplotlib.figure.Figure at 0x76109e8>"
       ]
      },
      "metadata": {},
@@ -655,9 +658,9 @@
     },
     {
      "data": {
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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7301b38>"
+       "<matplotlib.figure.Figure at 0x7664d68>"
       ]
      },
      "metadata": {},
@@ -697,7 +700,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtoAAADaCAYAAACYceUaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQ0AAAmENgAAJBDaAACQ4H9iXqVlk6CqDgAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x73628d0>"
+       "<matplotlib.figure.Figure at 0x76f1e10>"
       ]
      },
      "metadata": {},
@@ -746,7 +749,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEWCAYAAAAq41LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7274780>"
+       "<matplotlib.figure.Figure at 0x7616ef0>"
       ]
      },
      "metadata": {},

14-Adaptive-Filtering.ipynb

@@ -313,7 +313,7 @@
    "source": [
     "from math import sin, cos, radians\n",
     "\n",
-    "def angle_between(x,y):\n",
+    "def angle_between(x, y):\n",
     "  return min(y-x, y-x+360, y-x-360, key=abs)\n",
     "\n",
     "class ManeuveringTarget(object): \n",
@@ -349,7 +349,8 @@
     "\n",
     "    def set_commanded_heading(self, hdg_degrees, steps):\n",
     "        self.cmd_hdg = hdg_degrees\n",
-    "        self.hdg_delta = angle_between(self.cmd_hdg, self.hdg) / steps\n",
+    "        self.hdg_delta = angle_between(self.cmd_hdg, \n",
+    "                                       self.hdg) / steps\n",
     "        if abs(self.hdg_delta) > 0:\n",
     "            self.hdg_step = steps\n",
     "        else:\n",
@@ -409,7 +410,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuoAAAE6CAYAAABAsXcUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b07198>"
+       "<matplotlib.figure.Figure at 0x6e4c908>"
       ]
      },
      "metadata": {},
@@ -469,9 +470,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6b059b0>"
+       "<matplotlib.figure.Figure at 0x6e319b0>"
       ]
      },
      "metadata": {},
@@ -561,7 +562,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x71e6470>"
+       "<matplotlib.legend.Legend at 0x752fe10>"
       ]
      },
      "execution_count": 7,
@@ -570,9 +571,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x70c12b0>"
+       "<matplotlib.figure.Figure at 0x74060f0>"
       ]
      },
      "metadata": {},
@@ -615,9 +616,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7201eb8>"
+       "<matplotlib.figure.Figure at 0x7410240>"
       ]
      },
      "metadata": {},
@@ -638,7 +639,8 @@
     "bp.plot_track(t, xs2)\n",
     "bp.plot_filter(t, kxs2[:, 0], label='KF')\n",
     "plt.legend(loc=4)\n",
-    "bp.set_labels(title='Effects of Acceleration', x='time (sec)', y='X')"
+    "bp.set_labels(title='Effects of Acceleration', \n",
+    "              x='time (sec)', y='X')"
    ]
   },
   {
@@ -657,9 +659,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvIAAAE6CAYAAAB0yLYIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x70ce080>"
+       "<matplotlib.figure.Figure at 0x7458128>"
       ]
      },
      "metadata": {},
@@ -669,7 +671,7 @@
    "source": [
     "# reinitialize filter\n",
     "initialize_filter(cvfilter)\n",
-    "cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=2.0) # var was 0.02\n",
+    "cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=2.0)\n",
     "\n",
     "# recompute track\n",
     "kxs2, _, _, _ = cvfilter.batch_filter(z_xs2)\n",
@@ -696,9 +698,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x72da278>"
+       "<matplotlib.figure.Figure at 0x75eb080>"
       ]
      },
      "metadata": {},
@@ -708,7 +710,7 @@
    "source": [
     "# reinitialize filter\n",
     "initialize_filter(cvfilter)\n",
-    "cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=20.0) # var was 0.02\n",
+    "cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=20.0)\n",
     "\n",
     "# recompute track\n",
     "cvfilter.x = array([0., 0.])\n",
@@ -761,9 +763,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7306198>"
+       "<matplotlib.figure.Figure at 0x764e3c8>"
       ]
      },
      "metadata": {},
@@ -778,7 +780,8 @@
     "bp.plot_track(t, xs2)\n",
     "bp.plot_filter(t, kxs2[:, 0], label='KF')\n",
     "plt.legend(loc=4)\n",
-    "bp.set_labels(title='Constant Acceration Kalman Filter', x='time (sec)', y='X')"
+    "bp.set_labels(title='Constant Acceration Kalman Filter',\n",
+    "              x='time (sec)', y='X')"
    ]
   },
   {
@@ -819,9 +822,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x730a3c8>"
+       "<matplotlib.figure.Figure at 0x758def0>"
       ]
      },
      "metadata": {},
@@ -849,9 +852,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d9fb38>"
+       "<matplotlib.figure.Figure at 0x8110860>"
       ]
      },
      "metadata": {},
@@ -957,9 +960,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvsAAAE6CAYAAACI3Z3CAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d58710>"
+       "<matplotlib.figure.Figure at 0x8235d68>"
       ]
      },
      "metadata": {},
@@ -985,7 +988,8 @@
     "    epss.append(eps)\n",
     "\n",
     "plt.plot(t, epss)\n",
-    "bp.set_labels(title='Epsilon vs time', x='time (sec)', y='$\\epsilon$')"
+    "bp.set_labels(title='Epsilon vs time', \n",
+    "              x='time (sec)', y='$\\epsilon$')"
    ]
   },
   {
@@ -1008,9 +1012,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e7a1d0>"
+       "<matplotlib.figure.Figure at 0x827da58>"
       ]
      },
      "metadata": {},
@@ -1093,9 +1097,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e44630>"
+       "<matplotlib.figure.Figure at 0x8267e10>"
       ]
      },
      "metadata": {},
@@ -1136,11 +1140,11 @@
     "\n",
     "        if abs(y[0]) > std_scale*std:\n",
     "            phi += Q_scale_factor\n",
-    "            cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=phi)\n",
+    "            cvfilter.Q = Q_discrete_white_noise(2, dt, phi)\n",
     "            count += 1\n",
     "        elif count > 0:\n",
     "            phi -= Q_scale_factor\n",
-    "            cvfilter.Q = Q_discrete_white_noise(dim=2, dt=dt, var=phi)\n",
+    "            cvfilter.Q = Q_discrete_white_noise(2, dt, phi)\n",
     "            count -= 1\n",
     "\n",
     "    xs = np.asarray(xs)\n",
@@ -1193,9 +1197,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ec3860>"
+       "<matplotlib.figure.Figure at 0x81390b8>"
       ]
      },
      "metadata": {},
@@ -1222,9 +1226,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7daf208>"
+       "<matplotlib.figure.Figure at 0x8087d68>"
       ]
      },
      "metadata": {},
@@ -1251,9 +1255,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e9b860>"
+       "<matplotlib.figure.Figure at 0x81aa5c0>"
       ]
      },
      "metadata": {},
@@ -1261,9 +1265,9 @@
     },
     {
      "data": {
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truncated
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7d97940>"
+       "<matplotlib.figure.Figure at 0x8076f28>"
       ]
      },
      "metadata": {},
@@ -1293,9 +1297,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91433c8>"
+       "<matplotlib.figure.Figure at 0x9431898>"
       ]
      },
      "metadata": {},
@@ -1303,9 +1307,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x91437f0>"
+       "<matplotlib.figure.Figure at 0x9473be0>"
       ]
      },
      "metadata": {},
@@ -1313,9 +1317,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x9094748>"
+       "<matplotlib.figure.Figure at 0x94984e0>"
       ]
      },
      "metadata": {},
@@ -1323,9 +1327,9 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x71fc8d0>"
+       "<matplotlib.figure.Figure at 0x82d1a20>"
       ]
      },
      "metadata": {},
@@ -1333,9 +1337,9 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e63b00>"
+       "<matplotlib.figure.Figure at 0x808bba8>"
       ]
      },
      "metadata": {},
@@ -1406,9 +1410,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x92decf8>"
+       "<matplotlib.figure.Figure at 0x95a65c0>"
       ]
      },
      "metadata": {},
@@ -1568,9 +1572,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f4cba8>"
+       "<matplotlib.figure.Figure at 0x76322b0>"
       ]
      },
      "metadata": {},
@@ -1599,9 +1603,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7e2b710>"
+       "<matplotlib.figure.Figure at 0x9468710>"
       ]
      },
      "metadata": {},

code/DogSimulation.py

@@ -13,7 +13,7 @@ import math
 class DogSimulation(object):
 
     def __init__(self, x0=0, velocity=1,
-                 measurement_variance=0.0, process_variance=0.0):
+                 measurement_var=0.0, process_var=0.0):
         """ x0 - initial position
             velocity - (+=right, -=left)
             measurement_variance - variance in measurement m^2
@@ -21,8 +21,8 @@ class DogSimulation(object):
         """
         self.x = x0
         self.velocity = velocity
-        self.measurement_noise = math.sqrt(measurement_variance)
-        self.process_noise = math.sqrt(process_variance)
+        self.measurement_noise = math.sqrt(measurement_var)
+        self.process_noise = math.sqrt(process_var)
 
 
     def move(self, dt=1.0):

code/book_plots.py

@@ -58,6 +58,27 @@ def set_limits(x, y):
     plt.gca().set_xlim(x)
     plt.gca().set_ylim(y)
 
+def plot_predictions(p, rng=None):
+    if rng is None:
+        rng = range(len(p))
+    plt.scatter(rng, p, marker='v', s=40, edgecolor='r',
+                facecolor='None', lw=2, label='prediction')
+
+
+
+def plot_kf_output(xs, filter_xs, zs, title=None, aspect_equal=True):
+    plot_filter(filter_xs[:, 0])
+    plot_track(xs[:, 0])
+
+    if zs is not None:
+        plot_measurements(zs)
+    show_legend()
+    set_labels(title=title, x='meters', y='time (sec)')
+    if aspect_equal:
+        plt.gca().set_aspect('equal')
+    plt.xlim((-1, len(xs)))
+    plt.show()
+
 
 def plot_measurements(xs, ys=None, color='k', lw=2, label='Measurements',
                       lines=False, **kwargs):
@@ -84,9 +105,11 @@ def plot_measurements(xs, ys=None, color='k', lw=2, label='Measurements',
             plt.plot(xs, color=color, lw=lw, ls='--', label=label, **kwargs)
     else:
         if ys is not None:
-            plt.scatter(xs, ys, edgecolor=color, facecolor='none', lw=2, label=label, **kwargs)
+            plt.scatter(xs, ys, edgecolor=color, facecolor='none',
+                        lw=2, label=label, **kwargs)
         else:
-            plt.scatter(range(len(xs)), xs, edgecolor=color, facecolor='none', lw=2, label=label, **kwargs)
+            plt.scatter(range(len(xs)), xs, edgecolor=color, facecolor='none',
+                        lw=2, label=label, **kwargs)
 
 
 def plot_residual_limits(Ps, stds=1.):

code/kf_internal.py

@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Jul 25 17:13:23 2015
+
+@author: rlabbe
+"""
+
+import book_plots as bp
+import matplotlib.pyplot as plt
+
+def plot_dog_track(xs, measurement_var, process_var):
+    N = len(xs)
+    bp.plot_track([0, N-1], [1, N])
+    bp.plot_measurements(xs, label='Sensor')
+    bp.set_labels('variance = {}, process variance = {}'.format(
+              measurement_var, process_var), 'time', 'pos')
+    plt.ylim([0, N])
+    bp.show_legend()
+    plt.show()
+
+
+def print_gh(predict, update, z):
+    predict_template = 'PREDICT: {: 6.2f} {: 6.2f}'
+    update_template = 'UPDATE: {: 6.2f} {: 6.2f}\tZ: {:.2f}'
+
+    print(predict_template.format(predict[0], predict[1]),end='\t')
+    print(update_template.format(update[0], update[1], z))
+
+
+def print_variance(positions):
+    print('Variance:')
+    for i in range(0, len(positions), 5):
+        print('\t{:.4f} {:.4f} {:.4f} {:.4f} {:.4f}'.format(
+                *[v[1] for v in positions[i:i+5]]))

code/mkf_internal.py

@@ -13,6 +13,50 @@ from matplotlib import cm
 from mpl_toolkits.mplot3d import Axes3D
 from numpy.random import multivariate_normal
 
+
+
+def zs_var_240():
+    return [3.59, 1.73, -2.575, 4.38, 9.71, 2.88, 10.08,
+      8.97, 3.74, 12.81, 11.15, 9.25, 3.93, 11.11,
+      19.29, 16.20, 19.63, 9.54, 26.27, 23.29, 25.18,
+      26.21, 17.1, 25.27, 26.86,33.70, 25.92, 28.82,
+      32.13, 25.0, 38.56, 26.97, 22.49, 40.77, 32.95,
+      38.20, 40.93, 39.42, 35.49, 36.31, 31.56, 50.29,
+      40.20, 54.49, 50.38, 42.79, 37.89, 56.69, 41.47, 53.66]
+
+def zs_var_375():
+    return [-6.947, 12.467, 6.899, 2.643, 6.980, 5.820, 5.788, 10.614, 5.210,
+      14.338, 11.401, 19.138, 14.169, 19.572, 25.471, 13.099, 27.090,
+      12.209, 14.274, 21.302, 14.678, 28.655, 15.914, 28.506, 23.181,
+      18.981, 28.197, 39.412, 27.640, 31.465, 34.903, 28.420, 33.889,
+      46.123, 31.355, 30.473, 49.861, 41.310, 42.526, 38.183, 41.383,
+      41.919, 52.372, 42.048, 48.522, 44.681, 32.989, 37.288, 49.141,
+      54.235, 62.974, 61.742, 54.863, 52.831, 61.122, 61.187, 58.441,
+      47.769, 56.855, 53.693, 61.534, 70.665, 60.355, 65.095, 63.386]
+
+
+
+def plot_track_ellipses(N, zs, ps, cov, title):
+    bp.plot_measurements(range(1,N + 1), zs)
+    plt.plot(range(1, N + 1), ps, c='b', lw=2, label='filter')
+    plt.legend(loc='best')
+    plt.title(title)
+
+    for i,p in enumerate(cov):
+        plot_covariance_ellipse(
+              (i+1, ps[i]), cov=p, variance=4,
+               axis_equal=False, ec='g', alpha=0.5)
+
+        if i == len(cov)-1:
+            s = ('$\sigma^2_{pos} = %.2f$' % p[0,0])
+            plt.text (20, 5, s, fontsize=18)
+            s = ('$\sigma^2_{vel} = %.2f$' % p[1, 1])
+            plt.text (20, 0, s, fontsize=18)
+    plt.xlim(-10, 80)
+    plt.gca().set_aspect('equal')
+    plt.show()
+
+
 def show_residual_chart():
     est_y = ((164.2-158)*.8 + 158)
 
@@ -23,7 +67,7 @@ def show_residual_chart():
 
     ax.annotate('', xy=[1,159], xytext=[1,164.2],
                 arrowprops=dict(arrowstyle='-',
-                                ec='k', lw=1, shrinkA=8, shrinkB=8))
+                                ec='k', lw=3, shrinkA=8, shrinkB=8))
 
     ax.annotate('', xy=(1., est_y), xytext=(0.9, est_y),
                 arrowprops=dict(arrowstyle='->', ec='#004080',

code/nonlinear_plots.py

@@ -266,7 +266,7 @@ def plot_cov_ellipse_colormap(cov=[[1,1],[1,1]]):
 
 
 
-def plot_multiple_gaussians(xs, ps, x_range, y_range, N):
+def plot_gaussians(xs, ps, x_range, y_range, N):
     """ given a list of 2d states (x,y) and 2x2 covariance matrices, produce
     a surface plot showing all of the gaussians"""
 

code/ukf_internal.py

@@ -301,7 +301,7 @@ def plot_rts_output(xs, Ms, t):
     plt.legend(loc=4)
 
     np.set_printoptions(precision=4)
-    print('Difference in position in meters:', xs[-6:-1, 0] - Ms[-6:-1, 0])
+    print('Difference in position in meters:\n\t', xs[-6:-1, 0] - Ms[-6:-1, 0])
 
 
 def plot_scatter_of_bearing_error():

pdf/6x9build_book.bat

@@ -1,4 +1,5 @@
-copy /Y  ..\01-g-h-filter.ipynb book.ipynb
+copy /Y  ..\14-Adaptive-Filtering.ipynb book.ipynb
+REM python merge_book.py
 
 ipython nbconvert --to latex --template book6x9 book.ipynb
 ipython to_pdf.py