Reverted silly rename of .x to .X

x is a vector, not a matrix, hence it should be lower case like
it always was. I don't know what I was thinking when I renamed it.

06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:a4dbdbf1a3ffcafdf9db53065cd9b449ac720ba488ba785046ba9d0f074b2a92"
+  "signature": "sha256:f358f0c9f6a227b3d0277e5be5d0a9a1dd6cd4b2acf4cfb48fa4f661a0e1add0"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -259,13 +259,13 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 2,
+       "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f279583df28>"
+        "<IPython.core.display.HTML at 0x7f90b149ad68>"
        ]
       }
      ],
-     "prompt_number": 2
+     "prompt_number": 1
     },
     {
      "cell_type": "heading",
@@ -391,13 +391,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAqsAAAFdCAYAAAAkOCRoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27ae3f37b8>"
+        "<matplotlib.figure.Figure at 0x7f90cd8cbef0>"
        ]
       }
      ],
-     "prompt_number": 3
+     "prompt_number": 2
     },
     {
      "cell_type": "markdown",
@@ -445,7 +445,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 4
+     "prompt_number": 3
     },
     {
      "cell_type": "markdown",
@@ -467,7 +467,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 5
+     "prompt_number": 4
     },
     {
      "cell_type": "markdown",
@@ -485,7 +485,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 6
+     "prompt_number": 5
     },
     {
      "cell_type": "markdown",
@@ -504,7 +504,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 7
+     "prompt_number": 6
     },
     {
      "cell_type": "markdown",
@@ -530,7 +530,7 @@
        ]
       }
      ],
-     "prompt_number": 8
+     "prompt_number": 7
     },
     {
      "cell_type": "markdown",
@@ -559,13 +559,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f279639df98>"
+        "<matplotlib.figure.Figure at 0x7f90a973acc0>"
        ]
       }
      ],
-     "prompt_number": 9
+     "prompt_number": 8
     },
     {
      "cell_type": "markdown",
@@ -613,13 +613,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27897c2c88>"
+        "<matplotlib.figure.Figure at 0x7f90a93e9278>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "markdown",
@@ -711,13 +711,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27897dff98>"
+        "<matplotlib.figure.Figure at 0x7f90a947f8d0>"
        ]
       }
      ],
-     "prompt_number": 11
+     "prompt_number": 10
     },
     {
      "cell_type": "markdown",
@@ -738,13 +738,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f278858dbe0>"
+        "<matplotlib.figure.Figure at 0x7f90a93ecac8>"
        ]
       }
      ],
-     "prompt_number": 12
+     "prompt_number": 11
     },
     {
      "cell_type": "markdown",
@@ -765,13 +765,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f278858b7b8>"
+        "<matplotlib.figure.Figure at 0x7f90a93b2940>"
        ]
       }
      ],
-     "prompt_number": 13
+     "prompt_number": 12
     },
     {
      "cell_type": "markdown",
@@ -796,13 +796,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27895e5208>"
+        "<matplotlib.figure.Figure at 0x7f90a94aa5c0>"
        ]
       }
      ],
-     "prompt_number": 14
+     "prompt_number": 13
     },
     {
      "cell_type": "markdown",
@@ -861,13 +861,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2796381978>"
+        "<matplotlib.figure.Figure at 0x7f90a96dc748>"
        ]
       }
      ],
-     "prompt_number": 15
+     "prompt_number": 14
     },
     {
      "cell_type": "heading",
@@ -993,7 +993,7 @@
        ]
       }
      ],
-     "prompt_number": 16
+     "prompt_number": 15
     },
     {
      "cell_type": "markdown",
@@ -1022,7 +1022,7 @@
        ]
       }
      ],
-     "prompt_number": 17
+     "prompt_number": 16
     },
     {
      "cell_type": "markdown",
@@ -1051,7 +1051,7 @@
        ]
       }
      ],
-     "prompt_number": 18
+     "prompt_number": 17
     },
     {
      "cell_type": "markdown",
@@ -1095,7 +1095,7 @@
        ]
       }
      ],
-     "prompt_number": 19
+     "prompt_number": 18
     },
     {
      "cell_type": "markdown",
@@ -1128,7 +1128,7 @@
        ]
       }
      ],
-     "prompt_number": 20
+     "prompt_number": 19
     },
     {
      "cell_type": "markdown",
@@ -1163,7 +1163,7 @@
        ]
       }
      ],
-     "prompt_number": 21
+     "prompt_number": 20
     },
     {
      "cell_type": "markdown",
@@ -1246,7 +1246,7 @@
       "            Default value of 0 indicates it is not used.\n",
       "        \"\"\"\n",
       "\n",
-      "        self.X = np.zeros((dim_x,1)) # state\n",
+      "        self.x = np.zeros((dim_x,1)) # state\n",
       "        self.P = np.eye(dim_x)       # uncertainty covariance\n",
       "        self.Q = np.eye(dim_x)       # process uncertainty\n",
       "        self.u = np.zeros((dim_x,1)) # motion vector\n",
@@ -1295,7 +1295,7 @@
       "        K = dot(P, H.T).dot(linalg.inv(S))\n",
       "\n",
       "        # predict new x with residual scaled by the kalman gain\n",
-      "        self.X += + dot(K, y)\n",
+      "        self.x += + dot(K, y)\n",
       "\n",
       "        I_KH = self._I - dot (K, H)\n",
       "        self.P = dot(I_KH).dot(P).dot(I_KH.T) + dot(K, R).dot(K.T)\n",
@@ -1310,13 +1310,13 @@
       "            to create the control input into the system.\n",
       "        \"\"\"\n",
       "  \n",
-      "        self.X = dot(self.F, self.X) + dot(self.B, u)\n",
+      "        self.x = dot(self.F, self.x) + dot(self.B, u)\n",
       "        self.P = self.F.dot(self.P).dot(self.F.T) + self.Q"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 22
+     "prompt_number": 21
     },
     {
      "cell_type": "markdown",
@@ -1340,7 +1340,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 23
+     "prompt_number": 22
     },
     {
      "cell_type": "heading",
@@ -1682,7 +1682,7 @@
        ]
       }
      ],
-     "prompt_number": 24
+     "prompt_number": 23
     },
     {
      "cell_type": "heading",
@@ -1747,7 +1747,7 @@
         "    You will have to assign reasonable values to all of these before\n",
         "    running the filter. All must have dtype of float\n",
         "    \n",
-        "    X : ndarray (dim_x, 1), default = [0,0,0...0]\n",
+        "    x : ndarray (dim_x, 1), default = [0,0,0...0]\n",
         "        state of the filter\n",
         "    \n",
         "    P : ndarray (dim_x, dim_x), default identity matrix\n",
@@ -1771,7 +1771,7 @@
        ]
       }
      ],
-     "prompt_number": 25
+     "prompt_number": 24
     },
     {
      "cell_type": "markdown",
@@ -1786,7 +1786,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "dog_filter.X = np.array([[0], [0]])     # initial state (location and velocity)\n",
+      "dog_filter.x = np.array([[0], [0]])     # initial state (location and velocity)\n",
       "dog_filter.F = np.array([[1,1], [0,1]]) # state transition matrix\n",
       "dog_filter.H = np.array([[1,0]])        # Measurement function\n",
       "dog_filter.R *= 5                       # measurement noise\n",
@@ -1796,7 +1796,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 26
+     "prompt_number": 25
     },
     {
      "cell_type": "markdown",
@@ -1816,7 +1816,7 @@
       "\n",
       "    pos = (0,500)\n",
       "    \n",
-      "Multidimensional Kalman filters stores the state variables in $\\mathbf{x}$ and their *covariance* in $\\mathbf{P}$. These are $\\verb,f.X,$ and $\\verb,f.P,$ in the code above. Notionally, this is similar as the one dimension case, but instead of having a mean and variance we have a mean and covariance. For the multidimensional case, we have\n",
+      "Multidimensional Kalman filters stores the state variables in $\\mathbf{x}$ and their *covariance* in $\\mathbf{P}$. These are `f.x` and `.P` in the code above. Notionally, this is similar as the one dimension case, but instead of having a mean and variance we have a mean and covariance. For the multidimensional case, we have\n",
       "\n",
       "$$\\mathcal{N}(\\mu,\\sigma^2)=\\mathcal{N}(\\mathbf{x},\\mathbf{P})$$\n",
       "\n",
@@ -1842,7 +1842,7 @@
       "\n",
       "def dog_tracking_filter(R,Q=0,cov=1.):\n",
       "    dog_filter = KalmanFilter (dim_x=2, dim_z=1)\n",
-      "    dog_filter.X = np.array([[0], \n",
+      "    dog_filter.x = np.array([[0], \n",
       "                             [0]])   # initial state (location and velocity)\n",
       "    dog_filter.F = np.array([[1,1],\n",
       "                             [0,1]])  # state transition matrix\n",
@@ -1872,14 +1872,14 @@
       "\n",
       "    dog_filter = dog_tracking_filter(R=R, Q=Q, cov=P)\n",
       "    if initial_x is not None:\n",
-      "        dog_filter.X = initial_x\n",
+      "        dog_filter.x = initial_x\n",
       "\n",
       "    pos = [None] * count\n",
       "    cov = [None] * count\n",
       "    \n",
       "    for t in range(count):\n",
       "        z = zs[t]\n",
-      "        pos[t] = dog_filter.X[0,0]\n",
+      "        pos[t] = dog_filter.x[0,0]\n",
       "        cov[t] = dog_filter.P\n",
       "    \n",
       "        # perform the kalman filter steps\n",
@@ -1891,7 +1891,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 27
+     "prompt_number": 26
     },
     {
      "cell_type": "markdown",
@@ -1964,7 +1964,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 28
+     "prompt_number": 27
     },
     {
      "cell_type": "markdown",
@@ -1985,21 +1985,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27896dd4e0>"
+        "<matplotlib.figure.Figure at 0x7f90a96e46d8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27963ac198>"
+        "<matplotlib.figure.Figure at 0x7f90a9588ef0>"
        ]
       }
      ],
-     "prompt_number": 29
+     "prompt_number": 28
     },
     {
      "cell_type": "markdown",
@@ -2075,7 +2075,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 30
+     "prompt_number": 29
     },
     {
      "cell_type": "markdown",
@@ -2104,7 +2104,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 31
+     "prompt_number": 30
     },
     {
      "cell_type": "markdown",
@@ -2127,7 +2127,7 @@
       "\n",
       "    # initialize the filters\n",
       "    f = mkf_filter(R, Q)\n",
-      "    f.X[0,0] = 0.\n",
+      "    f.x[0,0] = 0.\n",
       "    f.P[0,0] = p0\n",
       "    pos = (x0, p0)\n",
       "    for i in range(50):\n",
@@ -2136,7 +2136,7 @@
       "        f.update(z)\n",
       "\n",
       "        x1.append(pos[0])\n",
-      "        x2.append(f.X[0,0])\n",
+      "        x2.append(f.x[0,0])\n",
       "\n",
       "        p1.append(pos[1])\n",
       "        p2.append(f.P[0,0])\n",
@@ -2162,21 +2162,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27898b01d0>"
+        "<matplotlib.figure.Figure at 0x7f90a96e3860>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2789734128>"
+        "<matplotlib.figure.Figure at 0x7f90a92a3898>"
        ]
       }
      ],
-     "prompt_number": 32
+     "prompt_number": 31
     },
     {
      "cell_type": "markdown",
@@ -2338,7 +2338,7 @@
      "language": "python",
      "metadata": {},
      "outputs": [],
-     "prompt_number": 33
+     "prompt_number": 32
     },
     {
      "cell_type": "markdown",
@@ -2370,12 +2370,12 @@
       "\n",
       "    # initialize the filters\n",
       "    f1 = mkf_filter(R, Q)\n",
-      "    f1.X[0,0] = 0.\n",
+      "    f1.x[0,0] = 0.\n",
       "    f1.P[0,0] = p0\n",
       "    \n",
       "    f2 = pos_vel_filter(R, Q)\n",
-      "    f2.X[0,0] = 0.\n",
-      "    f2.X[1,0] = 1.\n",
+      "    f2.x[0,0] = 0.\n",
+      "    f2.x[1,0] = 1.\n",
       "    f2.P *= p0\n",
       "    \n",
       "    for i in range(50):\n",
@@ -2387,8 +2387,8 @@
       "        f1.update(z)\n",
       "        f2.update(z)\n",
       "        \n",
-      "        x1.append(f1.X[0,0])\n",
-      "        x2.append(f2.X[0,0])\n",
+      "        x1.append(f1.x[0,0])\n",
+      "        x2.append(f2.x[0,0])\n",
       "\n",
       "    plt.plot(x1, label='1D Filter')\n",
       "    plt.scatter(range(len(x2)), x2, c='r', label='2D Filter')\n",
@@ -2404,13 +2404,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f278856bcc0>"
+        "<matplotlib.figure.Figure at 0x7f90a9576be0>"
        ]
       }
      ],
-     "prompt_number": 34
+     "prompt_number": 33
     },
     {
      "cell_type": "markdown",
@@ -2464,21 +2464,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f279633f7f0>"
+        "<matplotlib.figure.Figure at 0x7f90a9731ef0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f278855ff28>"
+        "<matplotlib.figure.Figure at 0x7f90a952d9e8>"
        ]
       }
      ],
-     "prompt_number": 35
+     "prompt_number": 34
     },
     {
      "cell_type": "markdown",
@@ -2587,13 +2587,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f278855f630>"
+        "<matplotlib.figure.Figure at 0x7f90a9483780>"
        ]
       }
      ],
-     "prompt_number": 36
+     "prompt_number": 35
     },
     {
      "cell_type": "markdown",
@@ -2657,13 +2657,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27898734a8>"
+        "<matplotlib.figure.Figure at 0x7f90a9678630>"
        ]
       }
      ],
-     "prompt_number": 37
+     "prompt_number": 36
     },
     {
      "cell_type": "markdown",
@@ -2686,13 +2686,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27965cfe80>"
+        "<matplotlib.figure.Figure at 0x7f90a93fc780>"
        ]
       }
      ],
-     "prompt_number": 38
+     "prompt_number": 37
     },
     {
      "cell_type": "markdown",
@@ -2756,13 +2756,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2788546e10>"
+        "<matplotlib.figure.Figure at 0x7f90a97a4e80>"
        ]
       }
      ],
-     "prompt_number": 39
+     "prompt_number": 38
     },
     {
      "cell_type": "markdown",
@@ -2785,13 +2785,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f279633f400>"
+        "<matplotlib.figure.Figure at 0x7f90a974e208>"
        ]
       }
      ],
-     "prompt_number": 40
+     "prompt_number": 39
     },
     {
      "cell_type": "markdown",
@@ -2819,13 +2819,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2789744f60>"
+        "<matplotlib.figure.Figure at 0x7f90a95e02b0>"
        ]
       }
      ],
-     "prompt_number": 41
+     "prompt_number": 40
     },
     {
      "cell_type": "markdown",
@@ -2850,13 +2850,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsoAAAGDCAYAAAAyKTZ5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27897ec198>"
+        "<matplotlib.figure.Figure at 0x7f90ac362da0>"
        ]
       }
      ],
-     "prompt_number": 42
+     "prompt_number": 41
     },
     {
      "cell_type": "markdown",
@@ -2888,7 +2888,7 @@
       "    for t in range (count):\n",
       "        z = dog.sense()\n",
       "        f.update (z)\n",
-      "        ps.append (f.X[0,0])\n",
+      "        ps.append (f.x[0,0])\n",
       "        cov.append(f.P)\n",
       "        zs.append(z)\n",
       "        f.predict()\n",
@@ -2922,21 +2922,21 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f2789798d68>"
+        "<matplotlib.figure.Figure at 0x7f90a93e3cc0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f2789730c18>"
+        "<matplotlib.figure.Figure at 0x7f90a952d860>"
        ]
       }
      ],
-     "prompt_number": 43
+     "prompt_number": 42
     },
     {
      "cell_type": "markdown",
@@ -3005,13 +3005,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f27885178d0>"
+        "<matplotlib.figure.Figure at 0x7f90a936de48>"
        ]
       }
      ],
-     "prompt_number": 44
+     "prompt_number": 43
     },
     {
      "cell_type": "markdown",
@@ -3071,7 +3071,7 @@
       "        self.dim_x = dim_x\n",
       "        self.dim_z = dim_z\n",
       "\n",
-      "        self.X = np.zeros((dim_x,1)) # state\n",
+      "        self.x = np.zeros((dim_x,1)) # state\n",
       "        self.P = np.eye(dim_x)       # uncertainty covariance\n",
       "        self.Q = np.eye(dim_x)       # process uncertainty\n",
       "        self.u = 0                   # control input vector\n",
@@ -3085,7 +3085,7 @@
       "\n",
       "More than anything this method exists to document for you what the variable names are in the filter. To do anything useful with this filter you will have to modify most of these values. Some are set to useful values. For example, `R` is set to an identity matrix; if you want the diagonals of `R` to be 10. you may write (as we did earlier in this chapter) `my_filter.R += 10.`.\n",
       "\n",
-      "The names used for each variable matches the math symbology used in this chapter. Thus, `self.P` is the covariance matrix, `self.X` is the state, and so on.\n",
+      "The names used for each variable matches the math symbology used in this chapter. Thus, `self.P` is the covariance matrix, `self.x` is the state, and so on.\n",
       "\n",
       "The predict function implements the predict step of the Kalman equations, which are \n",
       "\n",
@@ -3099,12 +3099,12 @@
       "The corresponding code is\n",
       "\n",
       "    def predict(self):        \n",
-      "        self.X = self.F.dot(self.X) + self.B.dot(self.u)\n",
+      "        self.x = self.F.dot(self.x) + self.B.dot(self.u)\n",
       "        self.P = self.F.dot(self.P).dot(self.F.T) + self.Q\n",
       "\n",
       "I haven't discussed the use of numpy much until now, but this method illustrates the power of that package. We use numpy's `array` class to store our data and perform the linear algebra for our filters. `array` implements matrix multiplication using the `.dot()` method; if you use `*` you will get element-wise multiplication. As a heavy user of linear algebra this design is somewhat distressing as I use matrix multiplication far more often than element-wise multiplication. However, this design is due to historical developments in the library and we must live with it. The Python community has recognized this problem, and in Python 3.5 we will have the `@` operator to implement matrix multiplication. \n",
       "\n",
-      "With that in mind, the Python code `self.F.dot(self.X)` implements the math expression $\\mathbf{F x}$.\n",
+      "With that in mind, the Python code `self.F.dot(self.x)` implements the math expression $\\mathbf{F x}$.\n",
       "\n",
       "Numpy's `array` implements matrix transposition by using the `.T` property. Therefore, `F.T` is the python implementation of $\\mathbf{F}^\\mathsf{T}$.\n",
       "\n",
@@ -3144,7 +3144,7 @@
       "            R = np.eye(self.dim_z) * R\n",
       "\n",
       "        # error (residual) between measurement and prediction\n",
-      "        self.residual = Z - self.H.dot(self.X)\n",
+      "        self.residual = Z - self.H.dot(self.x)\n",
       "\n",
       "        # project system uncertainty into measurement space\n",
       "        self.S = self.H.dot(self.P).dot(self.H.T) + R\n",
@@ -3153,7 +3153,7 @@
       "        self.K = self.P.dot(self.H.T).dot(linalg.inv(self.S))\n",
       "\n",
       "        # predict new x with residual scaled by the kalman gain\n",
-      "        self.X += self.K.dot(self.residual)\n",
+      "        self.x += self.K.dot(self.residual)\n",
       "\n",
       "        KH = self.K.dot(self.H)\n",
       "        I_KH = self._I - KH\n",

08_Designing_Kalman_Filters/Designing_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
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   "name": "",
-  "signature": "sha256:7abdbb6792be112481394d0099141b4a4c633b66c486d753cbcc2f6d854636d7"
+  "signature": "sha256:0ef850590dea64bc14dae4522330405274d565dbfaca3167ffa776ae92b11960"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7fe3077d45f8>"
+        "<IPython.core.display.HTML at 0x7ffbea79f7b8>"
        ]
       }
      ],
@@ -354,9 +354,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2eac40400>"
+        "<matplotlib.figure.Figure at 0x7ffbd2314470>"
        ]
       }
      ],
@@ -614,9 +614,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "f1.X = np.array([[0,0,0,0]]).T\n",
+      "f1.x = np.array([[0,0,0,0]]).T\n",
       "f1.P = np.eye(4) * 500.\n",
-      "print(f1.X)\n",
+      "print(f1.x)\n",
       "print()\n",
       "print (f1.P)"
      ],
@@ -674,7 +674,7 @@
       "f1.R = np.eye(2) * 5\n",
       "f1.Q = np.eye(4) * .1\n",
       "\n",
-      "f1.X = np.array([[0,0,0,0]]).T\n",
+      "f1.x = np.array([[0,0,0,0]]).T\n",
       "f1.P = np.eye(4) * 500.\n",
       "\n",
       "# initialize storage and other variables for the run\n",
@@ -691,8 +691,8 @@
       "    f1.predict ()\n",
       "    f1.update (z)\n",
       "\n",
-      "    xs.append (f1.X[0,0])\n",
-      "    ys.append (f1.X[2,0])\n",
+      "    xs.append (f1.x[0,0])\n",
+      "    ys.append (f1.x[2,0])\n",
       "    pxs.append (pos[0]*.3048)\n",
       "    pys.append(pos[1]*.3048)\n",
       "\n",
@@ -707,9 +707,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2ea87cf98>"
+        "<matplotlib.figure.Figure at 0x7ffbd23144e0>"
        ]
       }
      ],
@@ -744,7 +744,7 @@
       "f1.R = np.eye(2) * 5\n",
       "f1.Q = np.eye(4) * .1\n",
       "\n",
-      "f1.X = np.array([[0,0,0,0]]).T\n",
+      "f1.x = np.array([[0,0,0,0]]).T\n",
       "f1.P = np.eye(4) * 500.\n",
       "\n",
       "# initialize storage and other variables for the run\n",
@@ -761,8 +761,8 @@
       "    f1.predict ()\n",
       "    f1.update (z)\n",
       "\n",
-      "    xs.append (f1.X[0,0])\n",
-      "    ys.append (f1.X[2,0])\n",
+      "    xs.append (f1.x[0,0])\n",
+      "    ys.append (f1.x[2,0])\n",
       "    pxs.append (pos[0]*.3048)\n",
       "    pys.append(pos[1]*.3048)\n",
       "\n",
@@ -770,15 +770,15 @@
       "    cov = np.array([[f1.P[0,0], f1.P[2,0]], \n",
       "                    [f1.P[0,2], f1.P[2,2]]])\n",
       " \n",
-      "    #e = stats.sigma_ellipse (cov=cov, x=f1.X[0,0], y=f1.X[2,0])\n",
+      "    #e = stats.sigma_ellipse (cov=cov, x=f1.x[0,0], y=f1.x[2,0])\n",
       "    #stats.plot_sigma_ellipse(ellipse=e)\n",
-      "    stats.plot_covariance_ellipse((f1.X[0,0], f1.X[2,0]), cov=cov,\n",
+      "    stats.plot_covariance_ellipse((f1.x[0,0], f1.x[2,0]), cov=cov,\n",
       "                                  facecolor='g', alpha=0.2)\n",
       "\n",
       "        \n",
       "p1, = plt.plot (xs, ys, 'r--')\n",
       "p2, = plt.plot (pxs, pys)\n",
-      "plt.legend([p1,p2], ['filter', 'measurement'], 2)\n",
+      "plt.legend([p1,p2], ['filter', 'measurement'], loc=2)\n",
       "plt.show()\n",
       "print(\"final P is:\")\n",
       "print(f1.P)"
@@ -786,20 +786,12 @@
      "language": "python",
      "metadata": {},
      "outputs": [
-      {
-       "output_type": "stream",
-       "stream": "stderr",
-       "text": [
-        "/home/rlabbe/anaconda/lib/python3.4/site-packages/matplotlib/cbook.py:133: MatplotlibDeprecationWarning: The \"loc\" positional argument to legend is deprecated. Please use the \"loc\" keyword instead.\n",
-        "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
-       ]
-      },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe3077c9908>"
+        "<matplotlib.figure.Figure at 0x7ffbd2314588>"
        ]
       },
       {
@@ -884,7 +876,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAssAAAF2CAYAAACYtNXtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2ea871710>"
+        "<matplotlib.figure.Figure at 0x7ffbd1f39978>"
        ]
       }
      ],
@@ -963,7 +955,7 @@
       "\n",
       "    kf.F = array ([[1., dt], [0., 1.]])\n",
       "    kf.H = array ([[1., 0.], [1., 0.]])\n",
-      "    kf.X = array ([[0.], [1.]])\n",
+      "    kf.x = array ([[0.], [1.]])\n",
       "    kf.Q *= array([[(dt**3)/3, (dt**2)/2],\n",
       "                   [(dt**2)/2,  dt      ]]) * 0.02\n",
       "    kf.P *= 100\n",
@@ -982,7 +974,7 @@
       "        kf.predict()\n",
       "        kf.update(z)\n",
       "\n",
-      "        xs.append(kf.X.T[0])\n",
+      "        xs.append(kf.x.T[0])\n",
       "        zs.append(z.T[0])\n",
       "        nom.append(i)\n",
       "\n",
@@ -1017,7 +1009,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAF2CAYAAACVqqWqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2fa3ac8>"
+        "<matplotlib.figure.Figure at 0x7ffbc6425908>"
        ]
       }
      ],
@@ -1088,7 +1080,7 @@
       "\n",
       "kf.F = array ([[1., dt], [0., 1.]])\n",
       "kf.H = array ([[1., 0.]])\n",
-      "kf.X = array ([[0.], [1.]])\n",
+      "kf.x = array ([[0.], [1.]])\n",
       "kf.Q *= 0.01\n",
       "kf.P *= 100\n",
       "kf.R[0,0] = wheel_sigma**2\n",
@@ -1104,7 +1096,7 @@
       "    kf.predict()\n",
       "    kf.update(z)\n",
       "\n",
-      "    xs.append(kf.X.T[0])\n",
+      "    xs.append(kf.x.T[0])\n",
       "    zs.append(z.T[0])\n",
       "    nom.append(i)\n",
       "\n",
@@ -1136,7 +1128,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2fa39e8>"
+        "<matplotlib.figure.Figure at 0x7ffbc641b080>"
        ]
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      ],
@@ -1284,7 +1276,7 @@
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2f9d780>"
+        "<matplotlib.figure.Figure at 0x7ffbc6428940>"
        ]
       },
       {
@@ -1292,7 +1284,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2f9dba8>"
+        "<matplotlib.figure.Figure at 0x7ffbc6428748>"
        ]
       }
      ],
@@ -1427,7 +1419,7 @@
       "    \n",
       "    f1.Q *= 0.\n",
       "\n",
-      "    f1.X = np.array([[x, vx, y, vy, -g]]).T\n",
+      "    f1.x = np.array([[x, vx, y, vy, -g]]).T\n",
       "    \n",
       "    return f1"
      ],
@@ -1456,11 +1448,11 @@
       "ball = BallTrajectory2D(x0=x, y0=y, theta_deg=theta, velocity=v0, noise=[0,0])\n",
       "f1 = ball_kf(x,y,theta,v0,dt)\n",
       "t = 0\n",
-      "while f1.X[2,0] > 0:\n",
+      "while f1.x[2,0] > 0:\n",
       "    t += dt\n",
       "    f1.predict() \n",
       "    x,y = ball.step(dt)\n",
-      "    p1 = plt.scatter(f1.X[0,0], f1.X[2,0], color='black', marker='+', s=75)\n",
+      "    p1 = plt.scatter(f1.x[0,0], f1.x[2,0], color='black', marker='+', s=75)\n",
       "    p2 = plt.scatter(x, y,                 color='green', marker='o', s=75, alpha=0.5)\n",
       "\n",
       "plt.legend([p1,p2], ['Kalman filter', 'idealized'])\n",
@@ -1474,7 +1466,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2df0198>"
+        "<matplotlib.figure.Figure at 0x7ffbc6272128>"
        ]
       }
      ],
@@ -1578,7 +1570,7 @@
       "    vx = cos(omega) * v0\n",
       "    vy = sin(omega) * v0\n",
       "\n",
-      "    f1.X = np.array([[x, vx, y, vy, -g]]).T\n",
+      "    f1.x = np.array([[x, vx, y, vy, -g]]).T\n",
       "    \n",
       "    \n",
       "With all the steps done we are ready to implement our filter and test it. First, the implementation:"
@@ -1612,7 +1604,7 @@
       "    vx = cos(omega) * v0\n",
       "    vy = sin(omega) * v0\n",
       "\n",
-      "    f1.X = np.array([[x, vx, y, vy, -9.8]]).T\n",
+      "    f1.x = np.array([[x, vx, y, vy, -9.8]]).T\n",
       "    \n",
       "    return f1"
      ],
@@ -1644,14 +1636,14 @@
       "t = 0\n",
       "xs = []\n",
       "ys = []\n",
-      "while f1.X[2,0] > 0:\n",
+      "while f1.x[2,0] > 0:\n",
       "    t += dt\n",
       "    x,y = ball.step(dt)\n",
       "    z = np.array([[x,y]]).T\n",
       "\n",
       "    f1.update(z)\n",
-      "    xs.append(f1.X[0,0])\n",
-      "    ys.append(f1.X[2,0])\n",
+      "    xs.append(f1.x[0,0])\n",
+      "    ys.append(f1.x[2,0])\n",
       "    \n",
       "    f1.predict() \n",
       "    \n",
@@ -1669,7 +1661,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAF2CAYAAAB+nR6pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2f03be0>"
+        "<matplotlib.figure.Figure at 0x7ffbd1fe0438>"
        ]
       }
      ],
@@ -1810,7 +1802,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e3026860>"
+        "<matplotlib.figure.Figure at 0x7ffbc6473400>"
        ]
       }
      ],
@@ -1912,17 +1904,17 @@
       "xs2 = []\n",
       "ys2 = []\n",
       "\n",
-      "while f1.X[2,0] > 0:\n",
+      "while f1.x[2,0] > 0:\n",
       "    t += dt\n",
       "    x,y = ball.update(dt)\n",
       "    z = np.array([[x, y]]).T\n",
       "\n",
       "    f1.update(z)\n",
       "    f2.update(z)\n",
-      "    xs.append(f1.X[0,0])\n",
-      "    ys.append(f1.X[2,0])\n",
-      "    xs2.append(f2.X[0,0])\n",
-      "    ys2.append(f2.X[2,0])    \n",
+      "    xs.append(f1.x[0,0])\n",
+      "    ys.append(f1.x[2,0])\n",
+      "    xs2.append(f2.x[0,0])\n",
+      "    ys2.append(f2.x[2,0])    \n",
       "    f1.predict() \n",
       "    f2.predict()\n",
       "    \n",
@@ -1943,7 +1935,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2f02240>"
+        "<matplotlib.figure.Figure at 0x7ffbc647a908>"
        ]
       }
      ],
@@ -1983,14 +1975,14 @@
       "    xs = []\n",
       "    ys = []\n",
       "\n",
-      "    while f1.X[2,0] > 0:\n",
+      "    while f1.x[2,0] > 0:\n",
       "        t += dt\n",
       "        x,y = ball.update(dt)\n",
       "        z = np.array([[x,y]]).T\n",
       "\n",
       "        f1.update(z)\n",
-      "        xs.append(f1.X[0,0])\n",
-      "        ys.append(f1.X[2,0]) \n",
+      "        xs.append(f1.x[0,0])\n",
+      "        ys.append(f1.x[2,0]) \n",
       "        f1.predict() \n",
       "\n",
       "\n",
@@ -2011,7 +2003,7 @@
        "output_type": "display_data",
        "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAF2CAYAAABtSl5dAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlYlOX6wPHvO8MwwCCrqCkuCBIuIeJeJmppbmWZVuax\nY6umWeYxzU6ZZlnmUTOr02kTy0xzKbPU3MUt9x0E3FDcEAGRZYZZ3t8f/JhEdmXn/lyXlzDzzjv3\n+zDAzTP3cz9KdHS0ihBCCCGEEDWcpqIDEEIIIYQQojKQxFgIIYQQQggkMRZCCCGEEAKQxFgIIYQQ\nQghAEmMhhBBCCCEASYyFEEIIIYQAJDEWQgghhBACKEZiPH78eLp06ULbtm155JFH2LhxIwDz5s2j\nZcuWtGnThjZt2vDAAw+UebBCCCGEEEKUFaWoDT5OnDhB06ZNcXR0ZMeOHYwYMYI9e/bw7bffcv78\neT7++OPyilUIIYQQQogy41DUAUFBQQCoqorZbMZgMKAoiv02IYQQQgghqoMiE2OAKVOmsHz5cpyc\nnPjf//6Hs7MziqKwefNmOnbsyF133cVrr71G9+7dyzpeIYQQQgghykSRpRQ5LBYLS5Ys4bvvvmP1\n6tXEx8fj7e1NrVq12LRpExMmTGDFihX4+fmVdcxCCCGEEEKUumLNGAM4ODgwdOhQFi5cyK5du+jW\nrZv9vp49e9KhQwe2b9+eJzE+f/58qQUrhBBCCCFEYRo2bHjbjy12YpzjduqKmzdvXuLHiLy8vb1Z\nsWIFYWFhFR1KlSdjWbpkPEuXjGfpkbEsXTKepUvGs3R5e3uzffv2OzpHoe3aEhMTWbp0KWlpaVgs\nFhYvXkxSUhIhISGsX7+e1NRUbDYbW7ZsYc+ePXTp0uWOghFCCCGEEKKiFDpjrNFo+P3335k1axZm\ns5mAgAC++OILPDw8+OOPP5g0aRJWq5UmTZrwySefSH2xEEIIIYSosgpNjL28vFiwYEG+933yySdl\nEpAonJSllB4Zy9Il41m6ZDxLj4xl6ZLxLF0ynpWLbAldxcg3UOmRsSxdMp6lS8az9MhYli4Zz9Il\n41m5SGIshBBCCCEEt9GVQgghhBA1j9ls5tSpU6iqisViqehwqoXk5GQAGc9i0ul0eHh4lOlzSGIs\nhBBCiEKZzWauXbtGnTp10GjkzWZRMVJSUsjIyMDFxaXMnkNe3UIIIYQoVHJysiTFosK5u7uTlpZW\nps8hr3AhhBBCFEpRFEmKRYVTFAVFUcr0OeRVLoQQQgghBJIYCyGEEEIIAUhiLIQQQgghBCBdKYQQ\nQghRDqw2K8cSj3H02lFQwN/Nn3b12qHT6Co6NCHsZMZYCCGEEHckNSuV3Zd3s/fyXm5k3chz/3XT\ndT49+CkrT6/kSsYVrqRfYeO5jczZP4dL6ZfKLK4lS5bg6+vLoEGD7Lelp6cTEBCAr68vFy5cKLPn\nrgk6duzI7NmzKzqMUiUzxkIIIYS4LVnWLH6O+Zkz18/YuwWsjVtLM/dmPB74ODqNDlVV+T7qeyyq\nBYPOYH+ss84ZVVVZGLWQsaFj7TPHJquJPZf3EJ0cjaqq1HGuQ7eG3XDXu992nBcvXuTy5cvUq1eP\nNWvWULt2bUmKS0FZd4ioCDJjLIQQQogSy0l442/E46JzwdnBGWcHZ1wcXDiTeoZFJxYBcCb1DMnG\nZDRK3pRDURRMVhMHEw4CkGxM5tODnxIRH8F103VSs1I5kXyCeYfmcTTx6G3H+vDDD7Ny5UoAfvnl\nFwYMGICqqgCkpqYyYcIEWrduTfPmzRkyZAixsbH2x548eZJnn32WNm3a0LRpU8LCwli0aFGu85vN\nZiZPnkzbtm3x9/enS5cufPXVV/b7z58/n2eGetasWXTq1CnXeXJmuI8dO0b//v3x9/enQ4cOHD9+\nHICsrCzef/992rZtS7NmzXjkkUfYt2+f/fEdO3ZkzJgxtGzZkpdeeolp06YRGBjIW2+9led5wsLC\n8Pf3p1u3bvz000+57vf19eXHH3/k0UcfJSAggP79+3Py5Mlcz+Pr60t8fDyzZ8/G19cXX19f5syZ\nU+wxqawkMRZCCCFEiZ2/cZ6LaRfRafPWCDtqHTl7/SyX0y9zLPEYzg7OBZ7H2cGZkyknUVWVRdHZ\nCaeTg5P9fgeNA84Ozqw8uZK0rNvb3OGRRx7h119/JTExkRMnTtClSxcAbDYbw4YN4/Tp0yxYsIDV\nq1fj5+fHU089RUZGBgBJSUm0adOG8PBwIiIiGDFiBBMnTmTr1q3288+fP59ly5Yxd+5cIiIimDFj\nBm5ubkXGVdCM67///W/GjBnD5s2beffdd9Hpssf49ddfZ8uWLXz++eds3LiRHj16MGTIEC5fvmw/\nn6enJwsWLGDNmjWYzWYWL17MwoULuXbtGgALFy5k6tSpjBs3jq1bt/LGG2/w7rvvsnbt2lwxfPPN\nN0yaNIlVq1aRnp7O1KlT7fetWbOGgwcPctdddzFy5EgOHTrEoUOHGDFixB2PSUWTUgohhBBClNje\nK3txcSh4a14XnQu7L+/GQXFARS3wOFVVUVCIT4vnWua1XOUWN3PQOhBxIYK+fn1LHGvLli0xmUzM\nmTOHPn364OCQnf5s376dAwcOcODAAXx8fACYNm0ay5YtY8OGDTzyyCN06NCBDh062M/19NNP88MP\nP7Bx40bCwsKA7BnhunXr2hPuBg0aFCuunFnrW73yyiv07NkTgEaNGgFw+vRpVq5cyR9//EHr1q0B\nGDt2LL/88gsrVqxg1KhRAPTo0YN27drh7e1Njx49CA0Nxdvbm/j4eLy9vZkzZw6vvvoqAwYMALJn\nhyMiIvjxxx/p3bu3PYZnn32Wjh07AvDUU08xb948+31eXl4AaLVaDAYDtWvXznMNtzsmFU0SYyGE\nEEKUmMVmKbTGVEHBYrPQsX5H9iXsw1Xnmu9xGZYMgn2COZlyEkeNY4Hn02l0XM64fNvxDhgwgJkz\nZ/Lrr7+SlZUFQEpKCqqqct999+U61mg0cu7cOQAyMzP55JNPWL9+PVeuXMFsNmM0GmnZsqX9+Mcf\nf5zly5dz//33c++999KxY0f69++Po2PB11OYnIT0ZseOHQNg8ODBeWKNi4uzf+7k5GT/P+djvV6P\n0WgkMTGRK1euMHPmzFyL5sxmMw0bNsx13qZNm9o/9vDwICUlpUTXUNpjUl4kMRZCCCFEiTVxb0Js\nSmyBZRIZlgwC3AOoZ6hHQ9eGJGQm5GnNZlWteOg9aO7VnFRTKlbVio6C27dpFe1tx/vUU0/h7u5O\nu3bt2Llzp/12Nzc31qxZk+d4Dw8PIHsGefv27bzzzjs0bdoUrVbLSy+9hM1msx8bEhLCnj172L59\nO7t27eLtt99m0aJFLFu2DMi/ZOLmx9+qsJKDX3/9FYMh96x6rVq1Cjw+x82z0++99559JjdHzix6\nQZ+XVFFjUllJYiyEEKJSU1WVmJQYjlw9AkCwTzCBHoHVckV8VRJaJ5Qt57dkl0Lc8rVQVRUXBxda\n1W4FwNDmQ1l0YhHnb5xHr9WjKApGi5HaTrUZ1nwYGkVDa5/WbDq/qcDnSzen80DDB2473rp16zJ8\n+PBct7m7u5OamorZbCYgICDfx+3Zs4fnnnvOXtqQmZlJfHw8rVq1ynWcq6srvXv3pnfv3rRt25ZR\no0aRkpKCh4eHPdFNS/u7Rjo+Pr5Er+GcGeorV67QvXv3Yj/uZrVr16ZevXrExcUxdOjQ2zrHzXQ6\nHWazucD7CxuTykoSYyGEEKWutJLZVFMq4ZHhpJhS7PWsUUlReOg9GN5iOG76yr+Yp7rSaXQ8dfdT\n/HjiRxQUHLXZb5GbrCYUFJ5p8QxaTfYMr16r59mWz3Il/QoHrx7EarNyT+17aFirof014aJzoZV3\nK45eO5pnFtpqy55Zvqf2PaV6DV26dCE0NJQRI0bw7rvv0rhxY+Li4li1ahWjRo3Cz8+Ppk2b8ttv\nv9G5c2esVitz5szBarXmOs/XX3+Nt7c3wcHBqKrKypUradCggT0BdHNzw8/Pj+XLl/PWW29x/Phx\n1q1bV6IE0d/fn4cffpiJEycydepUWrRoweXLl1m9ejV9+/bNt/wiP6+++ipTp06lbt26dOvWjfT0\ndHbs2IGzs3OePxyK0rRpUyIiIhg2bBienp7odDq0Wm2xxqSyksRYCCFEqSqtZFZVVcIjwzFZTbkW\nZBl0BkxWE+GR4YwJGSMzxxWosVtjxrYZy/aL2zmXeg5FUWji1oR769+bb4lFXUNdeht653OmbA/7\nP4xG0XA08SgW1YJG0WBTbTRwbcCQu4fYE+2SKOj1oSgKWq2WhQsX8uGHHzJ27FhSUlLw8fGha9eu\neHp6AjBlyhQmTJjAww8/jJubGy+++CI3buTexKRWrVp89dVXnDlzBq1WS0hICAsWLMh1zMcff8yE\nCRNYvHgxoaGhDBw4kE2b8s6QF/Z6njt3LrNmzWLKlCkkJCTg5eVFp06dSrSw7ZlnnsHJyYmvvvqK\nDz74ABcXF+655x5eeeWVQh+XX1xvvvkmEydOpGvXrmRmZvKvf/2L119/HSjemFRGSnR0dMFLRUvB\n+fPnad68eVk+RY3h7e0NYG+5Im6fjGXpkvEsXVV5PFVVZd6heZispjx9a22qDZ2iZ1izF7mekcX1\ndNP//8siJd1ESprJflu60YLimMnFrBg83RwwGFQMBhsGg4qLi4pGA2lZaQwJGkKgZ2CB8VTlsaxM\nrl69au/aUB4yLZlEJ0WTZcsiwD0AL2evcntuUbkV9lr09vZm+/bteRYSloTMGAshhChScUojrDYb\nO88eJepMOuYMF1JTNfZ/N24opKVpyMpSmMnCEjxz3kVFipKdHBsMBra77yC4/iU8XZ3wrKLine truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2ea92fcf8>"
+        "<matplotlib.figure.Figure at 0x7ffbc6484828>"
        ]
       },
       {
@@ -2019,7 +2011,7 @@
        "output_type": "display_data",
        "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2ddcef0>"
+        "<matplotlib.figure.Figure at 0x7ffbc63b4b38>"
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        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2fb7be0>"
+        "<matplotlib.figure.Figure at 0x7ffbc6459358>"
        ]
       },
       {
@@ -2057,7 +2049,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7fe2e2cc9e80>"
+        "<matplotlib.figure.Figure at 0x7ffbc615dfd0>"
        ]
       }
      ],

11_Designing_Nonlinear_Kalman_Filters/Designing_Nonlinear_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:2fac195271ec54000f2c80a5066167e42e5fea10f2ec75c3e505927d8bf9b6d1"
+  "signature": "sha256:1c019f940c5d07d4049402e4d9271803fc1fd66617cda061f1a54a65015769fc"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7ff94701a128>"
+        "<IPython.core.display.HTML at 0x7f571d5ff7b8>"
        ]
       }
      ],
@@ -406,7 +406,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff92e4599e8>"
+        "<matplotlib.figure.Figure at 0x7f57051417b8>"
        ]
       }
      ],
@@ -774,7 +774,7 @@
       "f1.R *= 1.\n",
       "f1.Q *= .1\n",
       "\n",
-      "f1.X = np.array([[1,0,1,0]], dtype=float).T\n",
+      "f1.x = np.array([[1,0,1,0]], dtype=float).T\n",
       "f1.P = np.eye(4) * 5.\n",
       "\n",
       "# initialize storage and other variables for the run\n",
@@ -796,15 +796,15 @@
       "    # compute the difference in range between the nominal track and measured \n",
       "    # ranges\n",
       "    ra,rb = d.range_of(pos)\n",
-      "    rx,ry = d.range_of((pos[0]+f1.X[0,0], pos[1]+f1.X[2,0]))\n",
+      "    rx,ry = d.range_of((pos[0]+f1.x[0,0], pos[1]+f1.x[2,0]))\n",
       "    z = np.array([[ra-rx],[rb-ry]])\n",
       "\n",
       "    # compute linearized H for this time step\n",
       "    f1.H = H_of (pos, pos_a, pos_b)\n",
       "\n",
       "    # store stuff so we can plot it later\n",
-      "    xs.append (f1.X[0,0]+i)\n",
-      "    ys.append (f1.X[2,0]+i)\n",
+      "    xs.append (f1.x[0,0]+i)\n",
+      "    ys.append (f1.x[2,0]+i)\n",
       "    pxs.append (pos[0])\n",
       "    pys.append(pos[1])\n",
       "    \n",
@@ -824,13 +824,13 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7ff92d1d2f98>"
+        "<matplotlib.figure.Figure at 0x7f56ffe59fd0>"
        ]
       }
      ],
-     "prompt_number": 10
+     "prompt_number": 9
     },
     {
      "cell_type": "heading",

12_HInfinity_Filters/HInfinity_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:14e099a145d5cd78a060a4c1c38c37a41238078ce93f0a43c52e3fcd938220e9"
+  "signature": "sha256:c5c10c287a1d7f362dbed42c41eebc10b9bae86b387a0b10952a0824fcb7e359"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -260,7 +260,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f4aa3b661d0>"
+        "<IPython.core.display.HTML at 0x7fbdc658afd0>"
        ]
       }
      ],
@@ -299,7 +299,7 @@
       "             [0., 1.]])\n",
       "\n",
       "f.H = array([[0., 1.]])\n",
-      "#f.X = array([[0., 0.]]).T\n",
+      "#f.x = array([[0., 0.]]).T\n",
       "f.G = array([[dt**2 / 2, dt]]).T\n",
       "\n",
       "f.P = 0.01\n",
@@ -316,8 +316,8 @@
       "for i in range(1,40):\n",
       "    f.update (5)\n",
       "    #print(f.x.T)\n",
-      "    xs.append(f.X[0,0])\n",
-      "    vs.append(f.X[1,0])\n",
+      "    xs.append(f.x[0,0])\n",
+      "    vs.append(f.x[1,0])\n",
       "    f.predict(u=u)\n",
       "\n",
       "plt.subplot(211)\n",
@@ -336,7 +336,7 @@
        "output_type": "display_data",
        "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f4a86c94f98>"
+        "<matplotlib.figure.Figure at 0x7fbdaa100c50>"
        ]
       }
      ],