Fixed animations.
Not sure why, but the two line title was being cut off when displayed. I must have changed somethign in the matplotlib sytle at some point that broke this.
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Roger Labbe committed
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commit
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3 files changed
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@@ -6,252 +6,19 @@
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],
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"outputs": [],
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"source": [
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"from __future__ import division, print_function\n",
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"%matplotlib inline\n",
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"import sys\n",
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"sys.path.insert(0,'..') # allow us to format the book\n",
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"sys.path.insert(0,'../code') # allow us to format the book\n",
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"sys.path.insert(0,'./code') # allow us to format the book\n",
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"\n",
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"# use same formatting as rest of book so that the plots are\n",
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"# consistant with that look and feel.\n",
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"import book_format\n",
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"book_format.load_style('..')"
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"#import book_format\n",
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"#book_format.load_style('..')"
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]
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},
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{
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@@ -279,15 +46,23 @@
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"cell_type": "code",
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"execution_count": 6,
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"name": "stderr",
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"text": [
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"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",
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"metadata": {},
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@@ -295,9 +70,9 @@
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"image/png": 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truncated
|
||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAEZCAYAAACad+BMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x7a0df28>"
|
||||
"<matplotlib.figure.Figure at 0x7f9e320>"
|
||||
]
|
||||
},
|
||||
"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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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x9986710>"
|
||||
"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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truncated
|
||||
"text/plain": [
|
||||
"<matplotlib.figure.Figure at 0x842bda0>"
|
||||
]
|
||||
},
|
||||
"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,
|
||||
|
||||
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Reference in new issue
Block a user