diff --git a/animations/multivariate_animations.ipynb b/animations/multivariate_animations.ipynb
index 0103cfd..5bdde96 100644
--- a/animations/multivariate_animations.ipynb
+++ b/animations/multivariate_animations.ipynb
@@ -6,252 +6,19 @@
"metadata": {
"collapsed": false
},
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- "\n"
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"from __future__ import division, print_function\n",
"%matplotlib inline\n",
"import sys\n",
"sys.path.insert(0,'..') # allow us to format the book\n",
"sys.path.insert(0,'../code') # allow us to format the book\n",
+ "sys.path.insert(0,'./code') # allow us to format the book\n",
"\n",
"# use same formatting as rest of book so that the plots are\n",
"# consistant with that look and feel.\n",
- "import book_format\n",
- "book_format.load_style('..')"
+ "#import book_format\n",
+ "#book_format.load_style('..')"
]
},
{
@@ -279,15 +46,23 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+ " if self._edgecolors == str('face'):\n"
+ ]
+ },
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -295,9 +70,9 @@
},
{
"data": {
- "image/png": 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GIzQaDe677z489dRT0XUMBkOP2yowDIPVq1ejqakJbDYbo0ePxubNmzFv3rz0vQuKisHu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\nEPNpWKmc029gr7zyStTW1mL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hcrwz/521mrK1l0Mro6x5AjYc2jjCwuGcSuUqz4TjhNJhJia0Y5OpqjVlczvL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\n",
"text/plain": [
- ""
+ ""
]
},
"metadata": {},
@@ -306,7 +81,7 @@
],
"source": [
"import numpy as np\n",
- "import mkf_internal\n",
+ "#import mkf_internal\n",
"import matplotlib.pyplot as plt\n",
"from gif_animate import animate\n",
"\n",
@@ -318,7 +93,7 @@
" stats.plot_covariance_ellipse((2,7), cov=P, facecolor='g', alpha=0.2, \n",
" title='|2.0 {:.1f}|\\n|{:.1f} 2.0|'.format(cov, cov))\n",
"fig = plt.figure()\n",
- "animate('multivariate_ellipse.gif', ellipse_animate, 30, 125, figsize=(3, 3))"
+ "animate('multivariate_ellipse.gif', ellipse_animate, 30, 125, figsize=(4, 4))"
]
},
{
@@ -330,16 +105,47 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
- "image/png": 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MmTNZtmyZBHwhuus734EpU5LJ0jKTqC1e3L36HnnErBPMpGNbtyYDqKIk5rlr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\ngImBiUzwT8BpOOlJ9qAoCp2RThy6Q3r5Esk4pqgEP70mbTg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"text/plain": [
- ""
+ "array([[ 0.0025, 0.005 ],\n",
+ " [ 0.005 , 0.01 ]])"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "f.Q"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "C:\\Anaconda3\\lib\\site-packages\\matplotlib\\collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
+ " if self._edgecolors == str('face'):\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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l6be/JjE2tMkkcM4vPmDUTa/xwY5NvPvNt6zPONZ0vb/5jXvIsUYDen3TZYRo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\nWQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ ""
]
},
"metadata": {},
@@ -358,13 +164,15 @@
"import filterpy.stats\n",
"from filterpy.common import Q_discrete_white_noise\n",
"from filterpy.kalman import KalmanFilter\n",
+ "import book_format\n",
+ "book_format.load_style('..')\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, 0]) # initial state (location and velocity)\n",
- " dog_filter.F = np.array([[1,1],\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.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",
@@ -377,7 +185,7 @@
"Q = .01\n",
"noise = 2.\n",
"P = 20.\n",
- "dog = DogSimulation(measurement_variance=R, process_variance=Q)\n",
+ "dog = DogSimulation(measurement_var=R, process_var=Q)\n",
"f = dog_tracking_filter(R=R, Q=Q, cov=P)\n",
"random.seed(200)\n",
"zs = []\n",
@@ -385,20 +193,20 @@
"def animate_track(frame):\n",
" if frame > 30: return\n",
" \n",
- " plt.gca().set_xlim(-100,100)\n",
" if frame == 0:\n",
- " stats.plot_covariance_ellipse ((0, f.x[0]), cov=f.P, axis_equal=True, \n",
- " facecolor='g', edgecolor=None, alpha=0.2)\n",
- " xs.append (f.x[0])\n",
+ " stats.plot_covariance_ellipse((0, f.x[0]), cov=f.P, axis_equal=True, \n",
+ " facecolor='g', edgecolor=None, alpha=0.2)\n",
+ " xs.append(f.x[0])\n",
" \n",
- " z = dog.move_and_sense()\n",
+ " z = dog.move_and_sense()[1]\n",
+ "\n",
" zs.append(z)\n",
" f.update(z)\n",
- " xs.append (f.x[0])\n",
+ " xs.append(f.x[0])\n",
" \n",
- " stats.plot_covariance_ellipse ((frame+1, f.x[0]), cov=f.P, axis_equal=True, \n",
- " facecolor='g', edgecolor=None, alpha=0.5,\n",
- " xlim=(-5,30), ylim=(-5,30))\n",
+ " stats.plot_covariance_ellipse((frame+1, f.x[0]), cov=f.P, axis_equal=True, \n",
+ " facecolor='g', edgecolor=None, alpha=0.5,\n",
+ " xlim=(-5,40), ylim=(-5,40))\n",
" \n",
" plt.plot(zs, color='r', linestyle='dashed')\n",
" plt.plot(xs, color='b')\n",
@@ -438,7 +246,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.4.3"
+ "version": "3.5.0"
}
},
"nbformat": 4,
diff --git a/animations/multivariate_ellipse.gif b/animations/multivariate_ellipse.gif
index 3c9fcd1..082f301 100644
Binary files a/animations/multivariate_ellipse.gif and b/animations/multivariate_ellipse.gif differ
diff --git a/animations/multivariate_track1.gif b/animations/multivariate_track1.gif
index 1a026d1..52062c3 100644
Binary files a/animations/multivariate_track1.gif and b/animations/multivariate_track1.gif differ