Updated for Python 3.6

13-Smoothing.ipynb

@@ -20,7 +20,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from __future__ import division, print_function\n",
     "%matplotlib inline"
    ]
   },
@@ -36,7 +35,7 @@
        "        <style>\n",
        "        .output_wrapper, .output {\n",
        "            height:auto !important;\n",
-       "            max-height:100000px; \n",
+       "            max-height:100000px;\n",
        "        }\n",
        "        .output_scroll {\n",
        "            box-shadow:none !important;\n",
@@ -81,7 +80,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -128,7 +127,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -158,7 +157,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -267,11 +266,11 @@
     "    x, P, Pp = Xs.copy(), Ps.copy(), Ps.copy\n",
     "\n",
     "    for k in range(n-2,-1,-1):\n",
-    "        Pp[k] = dot(F, P[k]).dot(F.T) + Q # predicted covariance\n",
+    "        Pp[k] = F @ P[k] @ F.T + Q # predicted covariance\n",
     "\n",
-    "        K[k]  = dot(P[k], F.T).dot(inv(Pp[k]))\n",
-    "        x[k] += dot(K[k], x[k+1] - dot(F, x[k]))\n",
-    "        P[k] += dot(K[k], P[k+1] - Pp[k]).dot(K[k].T)\n",
+    "        K[k]  = P[k] @ F.T @inv(Pp[k])\n",
+    "        x[k] += K[k] @ (x[k+1] - (F @ x[k]))     \n",
+    "        P[k] += K[k] @ (P[k+1] - Pp[k]) @ K[k].T\n",
     "    return (x, P, K, Pp)\n",
     "```\n",
     "        \n",
@@ -287,7 +286,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -302,6 +301,7 @@
     "from numpy.random import randn\n",
     "import matplotlib.pyplot as plt\n",
     "from filterpy.kalman import KalmanFilter\n",
+    "from filterpy.common import Q_discrete_white_noise\n",
     "import kf_book.book_plots as bp\n",
     "\n",
     "def plot_rts(noise, Q=0.001, show_velocity=False):\n",
@@ -314,9 +314,9 @@
     "                     [0., 1.]])    # state transition matrix\n",
     "\n",
     "    fk.H = np.array([[1., 0.]])    # Measurement function\n",
-    "    fk.P = 10.                     # covariance matrix\n",
+    "    fk.P*= 10.                     # covariance matrix\n",
     "    fk.R = noise                   # state uncertainty\n",
-    "    fk.Q = Q                       # process uncertainty\n",
+    "    fk.Q = Q_discrete_white_noise(dim=2, dt=1., var=Q)  # process uncertainty\n",
     "\n",
     "    # create noisy data\n",
     "    zs = np.asarray([t + randn()*noise for t in range (40)])\n",
@@ -360,7 +360,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -387,7 +387,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -425,7 +425,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -463,7 +463,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 800x200 with 1 Axes>"
       ]
@@ -505,12 +505,12 @@
      "output_type": "stream",
      "text": [
       "standard deviation fixed-lag: 2.616\n",
-      "standard deviation kalman: 3.562\n"
+      "standard deviation kalman: 3.564\n"
      ]
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -541,7 +541,7 @@
     "kf.H = np.array([[1.,0.]])\n",
     "kf.P *= 200\n",
     "kf.R *= 5.\n",
-    "kf.Q *= 0.001\n",
+    "kf.Q = Q_discrete_white_noise(dim=2, dt=1., var=0.001)\n",
     "\n",
     "N = 4 # size of lag\n",
     "\n",
@@ -563,8 +563,8 @@
     "plt.plot(kf_x[:, 0], label='KF', ls='--')\n",
     "plt.legend(loc=4)\n",
     "\n",
-    "print('standard deviation fixed-lag: {:.3f}'.format(np.mean(fls_res)))\n",
-    "print('standard deviation kalman: {:.3f}'.format(np.mean(kf_res)))"
+    "print(f'standard deviation fixed-lag: {np.mean(fls_res):.3f}')\n",
+    "print(f'standard deviation kalman: {np.mean(kf_res):.3f}')"
    ]
   },
   {
@@ -610,7 +610,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   }
  },
  "nbformat": 4,