PEP-8 compliance

06_Multivariate_Kalman_filter/Multivariate_Kalman_Filters.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:987eefe8f10a99155934ece7c91a37778a0d78fcab1413d90ca2a1b92905cd7b"
+  "signature": "sha256:546c300a4167bd8d99ab1fb7e4797514b2e0824b9845eb68a2349e78cf851afa"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -32,7 +32,7 @@
       "from __future__ import division, print_function\n",
       "import matplotlib.pyplot as plt\n",
       "import sys\n",
-      "sys.path.insert(0,'../code') # allow us to import book_format\n",
+      "sys.path.insert(0, '../code') # allow us to import book_format\n",
       "import book_format\n",
       "book_format.load_style()"
      ],
@@ -261,7 +261,7 @@
        "output_type": "pyout",
        "prompt_number": 1,
        "text": [
-        "<IPython.core.display.HTML at 0x7f7daa92d4e0>"
+        "<IPython.core.display.HTML at 0x7f4cf2ee74e0>"
        ]
       }
      ],
@@ -385,7 +385,7 @@
      "input": [
       "import mkf_internal\n",
       "import numpy as np\n",
-      "mkf_internal.plot_3d_covariance((2,17), np.array([[10.,0],[0,4.]]))"
+      "mkf_internal.plot_3d_covariance((2, 17), np.array([[10., 0], [0, 4.]]))"
      ],
      "language": "python",
      "metadata": {},
@@ -393,9 +393,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7daa91cf28>"
+        "<matplotlib.figure.Figure at 0x7f4cf2ed6f28>"
        ]
       }
      ],
@@ -519,7 +519,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print(multivariate_gaussian(x,mu,P))"
+      "print(multivariate_gaussian(x, mu, P))"
      ],
      "language": "python",
      "metadata": {},
@@ -553,7 +553,7 @@
      "collapsed": false,
      "input": [
       "import mkf_internal\n",
-      "mkf_internal.plot_3d_covariance((2,7), np.array([[8.,0],[0,4.]]))"
+      "mkf_internal.plot_3d_covariance((2, 7), np.array([[8., 0], [0, 4.]]))"
      ],
      "language": "python",
      "metadata": {},
@@ -561,9 +561,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d8677ffd0>"
+        "<matplotlib.figure.Figure at 0x7f4cced3a9e8>"
        ]
       }
      ],
@@ -584,28 +584,27 @@
      "input": [
       "import stats\n",
       "\n",
-      "P = np.array([[2,0],[0,2]])\n",
+      "P = np.array([[2, 0], [0, 2]])\n",
       "plt.subplot(131)\n",
-      "stats.plot_covariance_ellipse((2,7), cov=P, facecolor='g', alpha=0.2, \n",
+      "stats.plot_covariance_ellipse((2, 7), cov=P, facecolor='g', alpha=0.2, \n",
       "                              title='|2 0|\\n|0 2|', axis_equal=False)\n",
-      "plt.ylim((4,10))\n",
+      "plt.ylim((4, 10))\n",
       "plt.gca().set_aspect('equal', adjustable='box')\n",
       "\n",
       "plt.subplot(132)\n",
-      "P = np.array([[2,0],[0,9]])\n",
-      "plt.ylim((4,10))\n",
+      "P = np.array([[2, 0], [0, 9]])\n",
+      "plt.ylim((4, 10))\n",
       "plt.gca().set_aspect('equal', adjustable='box')\n",
-      "stats.plot_covariance_ellipse((2,7), P,  facecolor='g', alpha=0.2, \n",
+      "stats.plot_covariance_ellipse((2, 7), P,  facecolor='g', alpha=0.2, \n",
       "                              axis_equal=False, title='|2 0|\\n|0 9|')\n",
       "\n",
       "plt.subplot(133)\n",
-      "P = np.array([[2,1.2],[1.2,2]])\n",
-      "plt.ylim((4,10))\n",
+      "P = np.array([[2, 1.2], [1.2, 2]])\n",
+      "plt.ylim((4, 10))\n",
       "plt.gca().set_aspect('equal', adjustable='box')\n",
-      "stats.plot_covariance_ellipse((2,7), P,  facecolor='g', alpha=0.2, \n",
+      "stats.plot_covariance_ellipse((2, 7), P,  facecolor='g', alpha=0.2, \n",
       "                              axis_equal=False, title='|2 1.2|\\n|1.2 2|')\n",
       "\n",
-      "\n",
       "plt.tight_layout()\n",
       "plt.show()"
      ],
@@ -615,9 +614,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d86525630>"
+        "<matplotlib.figure.Figure at 0x7f4cce9dc668>"
        ]
       }
      ],
@@ -713,9 +712,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d865294a8>"
+        "<matplotlib.figure.Figure at 0x7f4cf2eebef0>"
        ]
       }
      ],
@@ -740,9 +739,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d864e0668>"
+        "<matplotlib.figure.Figure at 0x7f4cce9d9ef0>"
        ]
       }
      ],
@@ -767,9 +766,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d863083c8>"
+        "<matplotlib.figure.Figure at 0x7f4cceab8b70>"
        ]
       }
      ],
@@ -798,9 +797,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d8674d390>"
+        "<matplotlib.figure.Figure at 0x7f4cce89ff28>"
        ]
       }
      ],
@@ -863,9 +862,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d8656edd8>"
+        "<matplotlib.figure.Figure at 0x7f4cceb12f28>"
        ]
       }
      ],
@@ -1231,8 +1230,8 @@
       "        the various state variables to reasonable values; the defaults \n",
       "        below will not give you a functional filter.\n",
       "\n",
-      "        Parameters\n",
-      "        ----------\n",
+      "        **Parameters**\n",
+      "\n",
       "        dim_x : int\n",
       "            Number of state variables for the Kalman filter. For \n",
       "            example, if you are tracking the position and velocity \n",
@@ -1270,8 +1269,8 @@
       "        Add a new measurement (Z) to the kalman filter. If Z is\n",
       "        None, nothing is changed.\n",
       "\n",
-      "        Parameters\n",
-      "        ----------\n",
+      "        **Parameters**\n",
+      "\n",
       "        Z : np.array\n",
       "            measurement for this update.\n",
       "\n",
@@ -1298,16 +1297,17 @@
       "        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",
+      "        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",
       "\n",
       "\n",
       "    def predict(self, u=0):\n",
       "        \"\"\" Predict next position.\n",
-      "        Parameters\n",
-      "        ----------\n",
+      "        \n",
+      "        **Parameters**\n",
+      "\n",
       "        u : np.array\n",
       "            Optional control vector. If non-zero, it is multiplied\n",
       "            by B to create the control input into the system.\n",
@@ -1749,7 +1749,7 @@
       "import numpy as np\n",
       "from filterpy.kalman import KalmanFilter\n",
       "\n",
-      "dog_filter = KalmanFilter (dim_x=2, dim_z=1)\n",
+      "dog_filter = KalmanFilter(dim_x=2, dim_z=1)\n",
       "help(KalmanFilter.__init__)"
      ],
      "language": "python",
@@ -1801,12 +1801,12 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "dog_filter.x = np.array([[0], [0]])      # 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",
-      "dog_filter.Q = Q_discrete_white_noise(2,dt=0.1,var=0.1) # process noise\n",
-      "dog_filter.P *= 500.                     # covariance matrix "
+      "dog_filter.x = np.array([[0], [0]])        # 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",
+      "dog_filter.Q = Q_discrete_white_noise(2, dt=0.1, var=0.1) # process noise\n",
+      "dog_filter.P *= 500.                       # covariance matrix "
      ],
      "language": "python",
      "metadata": {},
@@ -1844,13 +1844,13 @@
       "from DogSensor import DogSensor\n",
       "from filterpy.kalman import KalmanFilter\n",
       "\n",
-      "def dog_tracking_filter(R,Q=0,cov=1.):\n",
-      "    dog_filter = KalmanFilter (dim_x=2, dim_z=1)\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",
       "                             [0]])    # (location and velocity)\n",
-      "    dog_filter.F = np.array([[1,1],\n",
-      "                             [0,1]])  # state transition matrix\n",
-      "    dog_filter.H = np.array([[1,0]])  # Measurement function\n",
+      "    dog_filter.F = np.array([[1, 1],\n",
+      "                             [0, 1]]) # state transition matrix\n",
+      "    dog_filter.H = np.array([[1, 0]]) # Measurement function\n",
       "    dog_filter.R *= R                 # measurement uncertainty\n",
       "    dog_filter.P *= cov               # covariance matrix \n",
       "    if np.isscalar(Q):\n",
@@ -1884,11 +1884,11 @@
       "    \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",
-      "        dog_filter.update (z)\n",
+      "        dog_filter.update(z)\n",
       "        dog_filter.predict()\n",
       "        \n",
       "    return (pos, zs, cov)"
@@ -1936,8 +1936,9 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "def plot_track(noise_var=None, count=0, R_var=0, Q_var=0, P=500., initial_x=None,\n",
-      "               data=None, plot_P=True, title='Kalman Filter'):\n",
+      "def plot_track(noise_var=None, count=0, R_var=0, Q_var=0, P=500., \n",
+      "               initial_x=None,data=None, plot_P=True, \n",
+      "               title='Kalman Filter'):\n",
       "    \n",
       "    ps, zs, cov = filter_dog(noise_var=noise_var, data=data, count=count,\n",
       "                             R=R_var, Q=Q_var, P=P, initial_x=initial_x)\n",
@@ -1946,7 +1947,7 @@
       "    plt.plot(range(1, count+1), zs, linestyle='dashed', label='measurement')\n",
       "    plt.plot(range(1, count+1), ps, label='filter')\n",
       "    plt.legend(loc=2)\n",
-      "    plt.ylim((0-10, count+10))\n",
+      "    plt.ylim((-10, count+10))\n",
       "    plt.title(title)\n",
       "    plt.show()\n",
       "    \n",
@@ -1981,7 +1982,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "plot_track (noise_var=10, R_var=10, Q_var=0.01, count=50)"
+      "plot_track(noise_var=10, R_var=10, Q_var=0.01, count=50)"
      ],
      "language": "python",
      "metadata": {},
@@ -1989,17 +1990,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d866b5e80>"
+        "<matplotlib.figure.Figure at 0x7f4ccec74b70>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d866ca6d8>"
+        "<matplotlib.figure.Figure at 0x7f4cd8030358>"
        ]
       }
      ],
@@ -2123,11 +2124,8 @@
      "input": [
       "def plot_kf_compare(x0, p0, R, Q, move):\n",
       "    # storage for filter output\n",
-      "    x1 = []\n",
-      "    x2 = []\n",
-      "\n",
-      "    p1 = []\n",
-      "    p2 = []\n",
+      "    x1,x2 = [], []\n",
+      "    p1, p2 = [], []\n",
       "\n",
       "    # initialize the filters\n",
       "    f = mkf_filter(R, Q)\n",
@@ -2140,10 +2138,10 @@
       "        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",
+      "        p2.append(f.P[0, 0])\n",
       "\n",
       "        u = move + randn()\n",
       "        pos = predict(pos[0], pos[1], u, Q)\n",
@@ -2166,17 +2164,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d866e2390>"
+        "<matplotlib.figure.Figure at 0x7f4cce993208>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d866cacf8>"
+        "<matplotlib.figure.Figure at 0x7f4cceb549b0>"
        ]
       }
      ],
@@ -2371,8 +2369,7 @@
       "\n",
       "def plot_compare_pos_vel(x0, p0, R, Q, move):\n",
       "    # storage for filter output\n",
-      "    x1 = []\n",
-      "    x2 = []\n",
+      "    x1, x2 = [], []\n",
       "\n",
       "    # initialize the filters\n",
       "    f1 = mkf_filter(R, Q)\n",
@@ -2410,9 +2407,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d8684a828>"
+        "<matplotlib.figure.Figure at 0x7f4cce993f28>"
        ]
       }
      ],
@@ -2480,17 +2477,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d91f1e828>"
+        "<matplotlib.figure.Figure at 0x7f4cce84dd30>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d867589b0>"
+        "<matplotlib.figure.Figure at 0x7f4cce848cf8>"
        ]
       }
      ],
@@ -2531,9 +2528,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d864949b0>"
+        "<matplotlib.figure.Figure at 0x7f4cd8046240>"
        ]
       }
      ],
@@ -2560,9 +2557,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d86703898>"
+        "<matplotlib.figure.Figure at 0x7f4cd8030978>"
        ]
       }
      ],
@@ -2589,9 +2586,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d86709dd8>"
+        "<matplotlib.figure.Figure at 0x7f4ccedb4320>"
        ]
       }
      ],
@@ -2647,8 +2644,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "P = np.array([[2, 0], \n",
-      "              [0, 6]])\n",
+      "P = np.array([[2., 0.], \n",
+      "              [0., 6.]])\n",
       "stats.plot_covariance_ellipse((0, 0), P,  facecolor='g', alpha=0.2,\n",
       "                              title='|2 0|\\n|0 6|')"
      ],
@@ -2658,9 +2655,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d86404cf8>"
+        "<matplotlib.figure.Figure at 0x7f4cceca15c0>"
        ]
       }
      ],
@@ -2677,8 +2674,8 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "P = np.array([[2, 2.4],\n",
-      "              [2.4, 6]])\n",
+      "P = np.array([[2.0, 2.4],\n",
+      "              [2.4, 6.0]])\n",
       "stats.plot_covariance_ellipse((0, 0), P,  facecolor='g', alpha=0.2, \n",
       "                              title='|2.0  2.4|\\n|2.4  6.0|')"
      ],
@@ -2688,9 +2685,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d86465470>"
+        "<matplotlib.figure.Figure at 0x7f4ccea60a20>"
        ]
       }
      ],
@@ -2760,17 +2757,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d8644a438>"
+        "<matplotlib.figure.Figure at 0x7f4ccea8e278>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d8645be10>"
+        "<matplotlib.figure.Figure at 0x7f4cceb56390>"
        ]
       }
      ],
@@ -2798,17 +2795,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d91f1eda0>"
+        "<matplotlib.figure.Figure at 0x7f4ccec8e4a8>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d86685710>"
+        "<matplotlib.figure.Figure at 0x7f4cce9d9550>"
        ]
       }
      ],
@@ -2839,9 +2836,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d86519940>"
+        "<matplotlib.figure.Figure at 0x7f4cce84bbe0>"
        ]
       }
      ],
@@ -2870,9 +2867,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d865e87f0>"
+        "<matplotlib.figure.Figure at 0x7f4cceb84e48>"
        ]
       }
      ],
@@ -2943,17 +2940,17 @@
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d8675a470>"
+        "<matplotlib.figure.Figure at 0x7f4cced2cb70>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d865e6a58>"
+        "<matplotlib.figure.Figure at 0x7f4cced4d710>"
        ]
       }
      ],
@@ -3018,7 +3015,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "plot_track_ellipses (noise=0., R=500., Q=.2, count=7, title='R = 500 m')"
+      "plot_track_ellipses(noise=0., R=500., Q=.2, count=7, title='R = 500 m')"
      ],
      "language": "python",
      "metadata": {},
@@ -3026,9 +3023,9 @@
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
        "text": [
-        "<matplotlib.figure.Figure at 0x7f7d8662c860>"
+        "<matplotlib.figure.Figure at 0x7f4ccebeceb8>"
        ]
       }
      ],