Updated for filterpy version 1.4.4

I changed the IMM filter to use the predict/update cycle, requiring
a code change in the Adaptive Filtering chapter.

14-Adaptive-Filtering.ipynb

@@ -194,7 +194,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -297,7 +297,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -343,7 +343,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -384,7 +384,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -422,7 +422,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -485,7 +485,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -554,7 +554,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -653,7 +653,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -703,7 +703,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -784,7 +784,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -879,7 +879,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -906,7 +906,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -933,7 +933,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -943,7 +943,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -975,7 +975,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -985,7 +985,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -995,7 +995,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1005,7 +1005,7 @@
     },
     {
      "data": {
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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1015,7 +1015,7 @@
     },
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1086,7 +1086,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 4 Axes>"
       ]
@@ -1214,7 +1214,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1245,7 +1245,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1300,7 +1300,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1350,7 +1350,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1426,7 +1426,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1802,7 +1802,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -1813,8 +1813,9 @@
    ],
    "source": [
     "import copy\n",
-    "from filterpy.kalman import IMMEstimator\n",
     "from scipy.linalg import block_diag\n",
+    "from filterpy.kalman import IMMEstimator\n",
+    "from filterpy.common import Saver\n",
     "\n",
     "N = 600\n",
     "dt = 1.\n",
@@ -1856,19 +1857,15 @@
     "bank = IMMEstimator(filters, mu, M)\n",
     "\n",
     "xs, probs = [], []\n",
-    "cvxs, caxs = [], []\n",
     "for i, z in enumerate(zs):\n",
     "    z = np.array([z]).T\n",
+    "    bank.predict()\n",
     "    bank.update(z)\n",
     "\n",
     "    xs.append(bank.x.copy())\n",
-    "    cvxs.append(ca.x.copy())\n",
-    "    caxs.append(cano.x.copy())\n",
     "    probs.append(bank.mu.copy())\n",
     "\n",
     "xs = np.array(xs)\n",
-    "cvxs = np.array(cvxs)\n",
-    "caxs = np.array(caxs)\n",
     "probs = np.array(probs)\n",
     "plt.subplot(121)\n",
     "plt.plot(xs[:, 0], xs[:, 3], 'k')\n",
@@ -1897,7 +1894,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 900x400 with 1 Axes>"
       ]
@@ -1949,7 +1946,7 @@
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[1;32m<ipython-input-35-e96cb02ea05b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m                   [0.03, 0.97]])\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mimm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mIMMEstimator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mca\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrans\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32mC:\\Users\\rlabbe\\Dropbox\\filterpy\\filterpy\\kalman\\IMM.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filters, mu, M)\u001b[0m\n\u001b[0;32m    112\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfilters\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 114\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'All filters must have the same state dimension'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    115\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_shape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mC:\\Users\\rlabbe\\Dropbox\\filterpy\\filterpy\\kalman\\IMM.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filters, mu, M)\u001b[0m\n\u001b[0;32m    141\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mx_shape\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    142\u001b[0m                 raise ValueError(\n\u001b[1;32m--> 143\u001b[1;33m                     'All filters must have the same state dimension')\n\u001b[0m\u001b[0;32m    144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    145\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilters\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mValueError\u001b[0m: All filters must have the same state dimension"
      ]
     }

book_format.py

@@ -45,7 +45,7 @@ def test_filterpy_version():
     from distutils.version import LooseVersion
 
     v = filterpy.__version__
-    min_version = "1.4.0"
+    min_version = "1.4.4"
     if LooseVersion(v) < LooseVersion(min_version):
        raise Exception("Minimum FilterPy version supported is {}.\n"
                        "Please install a more recent version.\n"