From 7c9bedca388535cd98f4b20a60d20747b72c0ad8 Mon Sep 17 00:00:00 2001 From: Roger Labbe Date: Wed, 25 Nov 2015 12:01:32 -0800 Subject: [PATCH] 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. --- animations/multivariate_animations.ipynb | 330 +++++------------------ animations/multivariate_ellipse.gif | Bin 187740 -> 156712 bytes animations/multivariate_track1.gif | Bin 458286 -> 450782 bytes 3 files changed, 69 insertions(+), 261 deletions(-) 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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dPYk3btwYcz21Wg2DwdBjmdFojD7Wl1xrRZrpdptOjx8/tZggyG/EJcKRKEry\n4vOqsf9oQ7pieRGAooSe7/IEoe/04eLyMlSoFNAqRP3Wnkq0zWmcn2Hj7Uk8efJk1NfXw+/3R5fV\n1dVBp9P1uTtMxebyBvBzqwlNtmYU8EnSYT3X2LHuxOvwBNHe6UNJYQnKlPJew0qlX7+B7a8n8fXX\nXx/9ft68eeDz+ViwYAGOHz+OnTt34oUXXqBHiJPk8QV/6RvcCi4vBHWKejAlvBvsDaG904fiwhKU\nKhT0Xq1Z1O8ucSI9iQsLC1FXV4eFCxdiwoQJkMlkWLp0KRYvXpzaqocBfzAcbfLNzg9kpRUp0BVW\nfYcXul/CWqqiF6BnU7+BTaQnMQCMHz8eX3zxxcArG8a6b0wl4LSCsH3QDYKwltGwDgp0LvEgEwyF\n0WzywOjtRIhxo1iV2Y783Ty+ENq6t6xyOQ3rIEEDO4iEwhH83GKG8Ze7yJWoMnuvm24eXwitRi90\nomKUyuUoU9PWLoMFDewgEQ5HcKrVjFabAV44oZJysnKvGY8vhLZfwlqmUNCwDjI0sINAJEJwqs2C\nFqsR9qAFaln2wtra3Y6UhnVQooHNMkIITrdZ0GbtgNXfiTK1IKM3U+7m9nZtWYt/OcBUTsM6KNHA\nZtnZdhvarCaYfB0o0wiRl5f5X0mPUzf0M+ugRgObRS0ddrRazDC621Gs4oNDw0r1gzbdyRKDxYXm\nTgv0zlboVAVZ6dLQPd2wmE6KyBl0C5sFZrsHjUYzWp0tUCt44PMy/+8mDWtuolvYDHO4/TjTbkaz\nvQVySR5Egsw3K3P7wmjv9HcdYCpS0Pvd5BC6hc0gjy+IU20mtNhbUChiIC3M/LWkTk8YFhtBSWEp\nypVFNKw5hm5hM8QfCOFUqxkt9lbk88IokmZ+frDV4YfNAah5GlSqFdDI6SVyuabfLWx7ezvuuusu\nKJVKFBQUYNy4cfjyyy/7XL+xsbHX1qh79uxJaeG5JBSOdE2MsLcBbD80itRcJpcIs90Psy0MTYEG\nOpmIhjVHxdzC2mw2XHXVVbjmmmvw0UcfoaioCA0NDVAqlf0OvHv3blRXV0e/l0qlA682B0Ui5Jcp\nh+3wExdKszA/uNPqg9MFVEjK4fC3QJKlOwNQAxczsOvWrYNOp8OWLVuiy+LtHCGTyeIK9lBGCMEZ\nvQVttg44glaUaQQZn3JoMHvh9TDR7oYNLmNGX59KrZi7xLt27cKkSZMwZ84cqFQqXHrppXj99dfj\nGviWW24Bc91HAAASBUlEQVSBSqXC1VdfjR07dqSk2FzTaOiaxWT2daJULUBehm+52N7pgc/DRrmk\nHBcVF0FWmPldcSq1Yv4FNTQ0YOPGjRg5ciT27NmDRYsW4YknnogZWpFIhPXr12P79u34+OOPMWPG\nDMyZMwdbt25NefGDWWunA60WCwyuzM9iIoSgrcODoD8f5dJSXFRM+wYPFQwhhPT1YH5+PiZNmoT9\n+/dHly1fvhzvvfcefvwx/r62Dz30EOrr63H06NHosnO7yJ06dSrRugc1q8uPZrMDBl87iqRsFHAz\nG9YOawhMiAc1X4UShQB8Lj0ZkG3n9iUWi5M/lRbzL0mr1aKqqqrHsjFjxqC5uTmhF5k4ceKQC2Vf\nXL4g9FY3jD4jZGJWRsMaIQRGawhMuAAaQddd5GhYh5aYv82rrroKJ0+e7LHs559/Rnl5eUIvcuTI\nEWi12j4fz7Uetn2N7QuEcKKpE0JOIy4Tjkq6JWl37+CqsVX9rPkP4QhBi8ENuVTYdTPlEjl4vdyf\nNZd7++Zy7anqSxwzsIsXL8aUKVOwZs0azJ49G4cPH8arr76KtWvXRtdZtmwZDh48iE8//RQAUFtb\ni/z8fFxyySVgsVj44IMPsHHjRqxbty4lBQ9WoXAEp9ssaHXokccNokgqyOhrtxjc4LMlKJFocFGx\nnN5MeYiK+VudMGECdu3ahSeffBKrVq1CWVkZVq9ejQceeCC6zvmtThmGwerVq9HU1AQ2m43Ro0dj\n8+bNmDdvXvreRZYRQnCmzYI2uxEB4kKpInNhDYYiaDa4UciRdoW1RA5OHr0/61DV7z/DN9xwA264\n4YY+Hz+/1en8+fMxf/78gVeWQ5qMdrTbzbD5zSjP4LlWXyCMVqMHcl4RdBIlRhXLM37qiMosut80\nQEaLC21mC9pd7ShV8zPWMaL7wnO1UAOdVIERWllW+kBRmUUDOwA2lw9NHVa0OlqhUfDA42ZmV9Tu\nCqDDHICusATFMhnK1RJ658BhggY2Sb5gGGf0ZrTYWyARszN2XavF7ofFFkKpuAylRTJ6n5thhgY2\nCaFwBK0mNwo4euTzwlBIMnOpnNHshdvDoFxSjnKVDCqZMCOvSw0eNLAJIoSg1exBh8cCHdgoy8AR\nYUII9J1eBP15KBeXYIRWTucFD1M0sAlqNNjQ6bbDHbGjWFWW9s+OkQhBa4cbrAgflbJijNTJIOLT\nu54PVzSwCTBaXNBbrLAGzFAp8tJ+CqV7QkQBqxAlUh1G6mTg8zLfA4oaPGhg4+T0+KNHhOUSNvLT\nfPomGCJo1Lsg4SpQLFZhVLEc+Rw6IWK4o2fZ4xAMhbs69Dv0EBeyweel98fmD0bQbg5CwVOjTKbB\n6FIFDSsFgAa2X11dI6xosxuAPF/SE/rj5XQHYTSHochXokyhwkV09hJ1DrpL3I+WDgcMdjMcAQvK\ndek9jWKy+WCzh6HmqaEsFGKEVkonRFA90MDGYHF40Wa2weBqR0kaW7wQQtBu8iLg62rnYvO1QC7i\n0rBSF6D7Wn3wBUJoNFjR6mxFkYybtmmHoXAETe1uRII8lEvLMLpECbmInrahepfyvsQAcOzYMUyb\nNg18Ph/FxcVYtWpVygrOhEiEoEFvhd7ZDh4vAokoPW1B/YEwGvUuCNgSVMpKUVWmpL2XqJhS3pfY\n4XBg5syZmD59Og4dOoQTJ07g7rvvhkAgwJIlS1L+BtKhucOOdrsJnrATFar0fG7tvhmVUqCBVqLA\nCK2UXsdK9SvlfYm3bt0Kn8+H2tpacLlcVFVV4eTJk9iwYUNOBNZs90BvsaHDY0SZhp+WS9Ysdj8s\n9hCKC0uglXZdbUMvjaPikfK+xAcOHMDUqVPB5f7jc1hNTQ30ej2amppSU3Wa+AMhNBq7JkcoZVxw\nU3zPVkIIDCYvbA6CMnEZKlVKVGqlNKxU3GK2OeXxeGAYBkuWLIn2dHr44Yfx/PPPY+HChb0+p6am\nBqWlpdi0aVN0WXNzM8rLy3HgwAFcccUVAAZnm9PGDhdanUZE8twokqR2CmAkQtBhC4EJcaEqUEIr\nE0DMp7fMGC5S1eY05i5xJBLBpEmT8NxzzwEAqqurcerUKbz++ut9BjZXT0WYnX5YvC54Iy5oC1Mb\n1mCIwGgNgs+IoBQoUKzgo4A2SaOSEPOvJpm+xGq1GgaDoccyo9EYfaw32W6J6QuEcLzRCFtBBKMV\nCgj5sQObSBvS7oNLk9RKaMRFGKmT9TvNkLYKzfzY6R4/VW1OY36GTaYv8eTJk1FfXw+/3x9dVldX\nB51OF/eNtDKt0WCDwWmEoAD9hjURnVYfDJ0BFBeWoFyhxhg6J5gaoJiBXbx4Mb7++musWbMGp0+f\nxvbt2/Hqq6/22B1etmwZrr/++uj38+bNA5/Px4IFC3D8+HHs3LkTL7zwwqA9QmywuGB0WOEK2qGS\np+ai8O6m3h43GxXSCozUqDBCR5ukUQOX8r7EhYWFqKurw8KFCzFhwgTIZDIsXboUixcvTt+7SJLX\nH0RLhw0GZzs0ytScwvH5w2jtcKOQI4NWpkKlVkovOKdSJuV9iQFg/Pjx+OKLLwZWWZoRQtBosMHo\nNkIoYEFQMPCDQDZnAJ0WP1QCLTQSOSo1UroLTKXUsD1UabC40OGwwh1yoFIlGtBYkQiBweyFz8dC\nmbgCOrkEJcrCnD1iTg1ewzKwwVAYerMDBpcBWtXAdoX9gTBaOzzgs0UYIdWgXC2lDdKotBmWgW03\nu2D2WMErAPi85H8ENmcAHRY/lHwVNGIFKrXSXu8YR1GpMuz+uvyBEDpsLli8ZpRqktsShiMEZnsI\nPGEEZeJy6GQSlCjF9CgwlXbDLrDtFhfMHjMEfFZSc4Ud7gDaOoMQsgsxUlaJUqUYcnFmGolT1LAK\nrC8QQofNCYvPggpdYg3AQ+EIjGYffD4GKq4aMr4Q48qV9CgwlVHDKrB6kxMmjxmFwjxwEmhT6nQH\nYbT4IOJIMEqugtl/FhJBPg0rlXHDJrC+QAiddifsfhsq47y9RjhCYDR74fUy0IlKoCyUoFwtwff2\ntjRXS1G9GzaBdbj9cAVcEPLZcd3D1eHuOgIszBNjhEyJkiIJlNLM3VmdonozbALr9PjhDnogEMV+\nyy5PEJ1WHxDhQCssgVIkRrlaAi49XUMNAsPmr9DlDcATdENZ0PsRXY8vhA6LD5FwHor4Gsj4EmgV\nIijoEWBqEBkWgSWEIBwhCEfCyGP3PFfq9YVgsvnhDzAo4qsg50uglglRJBHQ86rUoNPvh7lnnnkG\nLBarx5dWq+1z/cbGxgvWZ7FY2LNnT0oLTwTDMMjnsMHL46Hd5IXZ7kdrhxs/Nzmg7whCyJbjIvlI\njNXpcHGlCiqZkIaVGpTi2sKOGTMG+/bti37PZvd/OmP37t2orq6Ofi+VShOvLoU0MiHcvmLY/HaE\nvGGI8nhQS/jgc7mQiQqgkgnpPWyoQS+uwLLZ7Ji9iHsjk8kSfk46ycV8cPPVcHqkCIYi4PM4EBXk\n04NJVE6Ja5PS0NAAnU6HyspK3H777Th79my/z7nlllugUqlw9dVXY8eOHQMuNBWEBfnQyEUoVYmh\nEPNpWKmc029gr7zyStTW1mL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"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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\niOHLJjGbVcxWBVVVUIDktFo0NhH223jgF3+KEAK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+ "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 3c9fcd1da4cb53dd69378c357658aeda09071bd4..082f301f75c2f54656f835a1b20abfc6484880d6 100644 GIT binary patch literal 156712 zcmd?wXHZiOqxSo?k^n0ZAoLEQBOnIp)dUbwP@|%Npa!u3B1U>Qgx-Vn9ues%(p7|n z-VM?fFd!RXDRZ*p#Y}g1_wevA{Z(JrLVxGP+{DDh{QP_`Yhd{8aPK5* zV0vkI_AT?(`||JwYtvGsLx^Xt}+t?i$G{`~p- z!TfhS$^Jk9+x;EsOXs3xcG#` zq~w&;C^SDcBM~K>nVXkiP*_w9FzvWvzFz(8)Z7%T^f5R2u7!FVINb4uup z&aUpBf>Ip6p}8nH6eR{bfJ3%LbMTspz>K4X*bI#kLX_;@_baPwA5!_zF>E5ysL$qN z@MtW6qi`%o?IO7k^M|8C^ 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