Updated animations
I haven't run them in a long time, and the notebooks no longer work with the new structure or changes to matplotlib.
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Roger Labbe committed
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"# 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(directory='..')\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"book_format.set_style()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def update(mu1, var1, mu2, var2):\n",
|
||||
" mean = (var1*mu2 + var2*mu1) / (var1+var2)\n",
|
||||
" variance = 1 / (1/var1 + 1/var2)\n",
|
||||
@@ -51,10 +65,8 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_3d_covariance(ax, mean, cov):\n",
|
||||
@@ -111,37 +123,97 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'gif_animate'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-3-42bb287cbba9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
|
||||
]
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(25, 20.4)"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"voltage"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 648x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stats.plot_gaussian_pdf(25, 20.4);"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x1fea90c9248>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ax1.plot([3,2,4])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 648x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from __future__ import print_function, division\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy.random as random\n",
|
||||
"import math\n",
|
||||
"import stats\n",
|
||||
"from gif_animate import animate\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"from kf_book.gif_animate import animate\n",
|
||||
"\n",
|
||||
"sensor_error = 1.2**2\n",
|
||||
"movement_error = .2\n",
|
||||
"movement = 0\n",
|
||||
"voltage = (25,20) #who knows what the first value is?\n",
|
||||
"voltage = (25, 20) #who knows what the first value is?\n",
|
||||
"\n",
|
||||
"volts = [14.44, 18.74, 17.21, 15.98, 16.76, 14.8, 16.01, 16.90, 15.41, 17.31,\n",
|
||||
" 16.39, 17.96]\n",
|
||||
@@ -151,40 +223,42 @@
|
||||
"i = 0\n",
|
||||
"pred = 0\n",
|
||||
"def volt_animate(frame):\n",
|
||||
" global i, ps, voltage, Z, x, pred\n",
|
||||
" global i, ps, voltage, Z, x, pred, ax1, ax2\n",
|
||||
" \n",
|
||||
" step = frame % 4\n",
|
||||
" plt.subplot(211)\n",
|
||||
" plt.ylim([14,26])\n",
|
||||
" ax1.set_ylim([14, 26])\n",
|
||||
" ax1.set_xlim([0, 10])\n",
|
||||
"\n",
|
||||
" if step == 0:\n",
|
||||
" prev = voltage[0]\n",
|
||||
" voltage = predict(voltage[0], voltage[1], movement, movement_error)\n",
|
||||
" pred = voltage[0]\n",
|
||||
" plt.plot ([i, i+1], [prev, pred], c='g') \n",
|
||||
" ax1.plot([i, i+1], [prev, pred], c='g') \n",
|
||||
" \n",
|
||||
" elif step == 1:\n",
|
||||
" Z = volts[i]\n",
|
||||
" i += 1\n",
|
||||
" \n",
|
||||
" plt.scatter(i, Z, marker='+', s=64, color='r')\n",
|
||||
" i += 1 \n",
|
||||
" ax1.scatter(i, Z, marker='+', s=64, color='r')\n",
|
||||
"\n",
|
||||
" elif step == 2:\n",
|
||||
" plt.plot ([i,i], [pred, Z], c='r', alpha=0.3)\n",
|
||||
" ax1.plot([i,i], [pred, Z], c='r', alpha=0.3)\n",
|
||||
"\n",
|
||||
" else:\n",
|
||||
" voltage = update(voltage[0], voltage[1], Z, sensor_error)\n",
|
||||
" ps.append(voltage[0])\n",
|
||||
" plt.plot(ps, c='b')\n",
|
||||
" ax1.plot(ps, c='b')\n",
|
||||
" \n",
|
||||
" plt.subplot(212)\n",
|
||||
" plt.cla()\n",
|
||||
" stats.plot_gaussian(voltage[0], voltage[1], xlim=[0,32])\n",
|
||||
" plt.ylim([0,1])\n",
|
||||
" plt.tight_layout()\n",
|
||||
" ax2.cla()\n",
|
||||
" stats.plot_gaussian_pdf(voltage[0], voltage[1], xlim=[0,32], ax=ax2)\n",
|
||||
" ax2.set_xlim([10, 25])\n",
|
||||
" ax2.set_ylim([0, 1])\n",
|
||||
" #plt.tight_layout()\n",
|
||||
" \n",
|
||||
"N=12\n",
|
||||
"animate('05_volt_animate.gif', volt_animate, N*3, 350)"
|
||||
"fig = plt.figure()\n",
|
||||
"ax1 = fig.add_subplot(211)\n",
|
||||
"ax2 = fig.add_subplot(212)\n",
|
||||
"animate('05_volt_animate.gif', volt_animate, N*3, 350, fig);"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -196,21 +270,20 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'gif_animate'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-4-0138411cd6ba>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmath\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;31m# assume dog is always moving 1m to the right\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
|
||||
]
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 648x288 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -219,12 +292,12 @@
|
||||
"import numpy.random as random\n",
|
||||
"import math\n",
|
||||
"import stats\n",
|
||||
"from gif_animate import animate\n",
|
||||
"from kf_book.gif_animate import animate\n",
|
||||
"\n",
|
||||
"# assume dog is always moving 1m to the right\n",
|
||||
"movement = 1\n",
|
||||
"movement_error = .05\n",
|
||||
"sensor_error = 4.5\n",
|
||||
"sensor_error = 2.5\n",
|
||||
"pos = (0, 100) # gaussian N(0,100)\n",
|
||||
"\n",
|
||||
"# this is the recorded output of a run of the dog sensor with an initial\n",
|
||||
@@ -238,7 +311,7 @@
|
||||
"N=20\n",
|
||||
"\n",
|
||||
"def dog_animate(frame):\n",
|
||||
" global pos, zs, ps, N, ZS\n",
|
||||
" global pos, zs, ps, N, ZS, ax1, ax2, fig\n",
|
||||
" pos = predict(pos[0], pos[1], movement, movement_error) \n",
|
||||
" Z = ZS[frame]\n",
|
||||
" zs.append(Z)\n",
|
||||
@@ -247,23 +320,23 @@
|
||||
" ps.append(pos[0])\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" plt.subplot(211)\n",
|
||||
" plt.plot(zs,c='r', linestyle='dashed')\n",
|
||||
"\n",
|
||||
" plt.xlim([0,N*1.2])\n",
|
||||
" plt.ylim([0,N*1.2])\n",
|
||||
" ax1.plot(zs,c='r', linestyle='dashed')\n",
|
||||
" ax1.set_xlim([0, N*1.2])\n",
|
||||
" ax1.set_ylim([0, N*1.2])\n",
|
||||
"\n",
|
||||
" if len(ps) > 1:\n",
|
||||
" plt.plot(ps, c='#8EBA42')\n",
|
||||
" ax1.plot(ps, c='#8EBA42')\n",
|
||||
" \n",
|
||||
" plt.subplot(212)\n",
|
||||
" plt.cla()\n",
|
||||
" stats.plot_gaussian(pos[0], pos[1], xlim=[0,N*1.2])\n",
|
||||
" plt.ylim(0, 1)\n",
|
||||
" plt.tight_layout()\n",
|
||||
" ax2.cla()\n",
|
||||
" stats.plot_gaussian_pdf(pos[0], pos[1], xlim=[0,N*1.2], ax=ax2)\n",
|
||||
" ax2.set_ylim(0, 1)\n",
|
||||
" fig.tight_layout()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"animate('05_dog_track.gif', dog_animate, N, 200)"
|
||||
"fig = plt.figure()\n",
|
||||
"ax1 = fig.add_subplot(211)\n",
|
||||
"ax2 = fig.add_subplot(212)\n",
|
||||
"animate('05_dog_track.gif', dog_animate, N, 200, fig=fig)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -275,6 +348,11 @@
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
@@ -285,7 +363,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.10"
|
||||
"version": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
@@ -10,63 +10,64 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "AttributeError",
|
||||
"evalue": "module 'book_format' has no attribute 'load_style'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-1-730769ca9272>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minsert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'../code'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbook_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mbook_format\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'..'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;31mAttributeError\u001b[0m: module 'book_format' has no attribute 'load_style'"
|
||||
]
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
" <style>\n",
|
||||
" .output_wrapper, .output {\n",
|
||||
" height:auto !important;\n",
|
||||
" max-height:100000px;\n",
|
||||
" }\n",
|
||||
" .output_scroll {\n",
|
||||
" box-shadow:none !important;\n",
|
||||
" webkit-box-shadow:none !important;\n",
|
||||
" }\n",
|
||||
" </style>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
]
|
||||
},
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from __future__ import division, print_function\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import sys\n",
|
||||
"sys.path.insert(0,'..') # allow us to format the book\n",
|
||||
"sys.path.insert(0,'../code') \n",
|
||||
"import book_format\n",
|
||||
"book_format.load_style(directory='..')"
|
||||
"book_format.set_style()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This notebook creates the animations for the Discrete Bayesian filters chapter. It is not really intended to be a readable part of the book, but of course you are free to look at the source code, and even modify it. However, if you are interested in running your own animations, I'll point you to the examples subdirectory of the book, which contains a number of python scripts that you can run and modify from an IDE or the command line. This module saves the animations to GIF files, which is quite slow and not very interactive. "
|
||||
"This notebook creates the animations for the Discrete Bayesian filters chapter. It is not really intended to be a readable part of the book, but of course you are free to look at the source code, and even modify it. However, if you are interested in running your own animations, I'll point you to the examples subdirectory of the book, which contains a number of python scripts that you can run and modify from an IDE or the command line. This module saves the animations to GIF files, which is quite slow and not very interactive. \n",
|
||||
"\n",
|
||||
"On Windows you need to install ffmpeg with\n",
|
||||
"\n",
|
||||
" $ conda install -c conda-forge ffmpeg\n",
|
||||
" \n",
|
||||
"I don't know what to do for linux or MaxOS."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'book_plots'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-2-1d7fb353c3e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mbook_plots\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbar_plot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'matplotlib'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'inline'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'book_plots'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from matplotlib import animation\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import numpy as np\n",
|
||||
"from book_plots import bar_plot\n",
|
||||
"from kf_book.book_plots import bar_plot\n",
|
||||
"%matplotlib inline\n",
|
||||
"\n",
|
||||
"# the predict algorithm of the discrete bayesian filter\n",
|
||||
@@ -101,9 +102,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
@@ -133,24 +132,35 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'gif_animate'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-4-c8909b74d11e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mpos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1.0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mbar_animate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mglobal\u001b[0m \u001b[0mpos\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
|
||||
]
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 432x288 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 468x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from gif_animate import animate\n",
|
||||
"from kf_book.gif_animate import animate\n",
|
||||
"\n",
|
||||
"pos = [1.0,0,0,0,0,0,0,0,0,0]\n",
|
||||
"def bar_animate(nframe):\n",
|
||||
@@ -177,28 +187,18 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'animate' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-5-0c3b6443d1db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[0manimate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'02_simulate.gif'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbar_animate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m40\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m85\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'animate' is not defined"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
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|
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"<Figure size 468x180 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
@@ -236,7 +236,7 @@
|
||||
"metadata": {
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||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python [default]",
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@@ -250,9 +250,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.10"
|
||||
"version": "3.7.4"
|
||||
}
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||||
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||||
"nbformat": 4,
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||||
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||||
"nbformat_minor": 1
|
||||
}
|
||||
@@ -3,22 +3,42 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
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"\n",
|
||||
" <style>\n",
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||||
" .output_wrapper, .output {\n",
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||||
" height:auto !important;\n",
|
||||
" max-height:100000px;\n",
|
||||
" }\n",
|
||||
" .output_scroll {\n",
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" box-shadow:none !important;\n",
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" webkit-box-shadow:none !important;\n",
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" </style>\n",
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" "
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"text/plain": [
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],
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||||
"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.set_style()"
|
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]
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},
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{
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@@ -31,9 +51,7 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
|
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},
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import filterpy.stats as stats\n",
|
||||
@@ -47,27 +65,32 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
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"metadata": {
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"collapsed": false
|
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},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'gif_animate'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-3-c00fcbdb2b07>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;31m#import mkf_internal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mellipse_animate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
|
||||
]
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<Figure size 900x400 with 0 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
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{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 400x400 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"#import mkf_internal\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from gif_animate import animate\n",
|
||||
"from kf_book.gif_animate import animate\n",
|
||||
"\n",
|
||||
"def ellipse_animate(frame):\n",
|
||||
" \n",
|
||||
@@ -90,43 +113,17 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "NameError",
|
||||
"evalue": "name 'f' is not defined",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-4-a2279ef61a95>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mQ\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
|
||||
"\u001b[1;31mNameError\u001b[0m: name 'f' is not defined"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"f.Q"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'gif_animate'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"\u001b[1;32m<ipython-input-5-518c7c0f45f6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcm\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmpl_toolkits\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmplot3d\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAxes3D\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mgif_animate\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0manimate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 7\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mDogSimulation\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDogSimulation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
||||
"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gif_animate'"
|
||||
]
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
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||||
"<Figure size 550x550 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -135,14 +132,12 @@
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"from matplotlib import cm\n",
|
||||
"from mpl_toolkits.mplot3d import Axes3D\n",
|
||||
"from gif_animate import animate\n",
|
||||
"from kf_book.gif_animate import animate\n",
|
||||
"import numpy.random as random\n",
|
||||
"from DogSimulation import DogSimulation\n",
|
||||
"from kf_book.DogSimulation import DogSimulation\n",
|
||||
"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",
|
||||
@@ -223,9 +218,9 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.10"
|
||||
"version": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
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@@ -4,17 +4,41 @@
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" webkit-box-shadow:none !important;\n",
|
||||
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|
||||
" </style>\n",
|
||||
" "
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
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|
||||
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|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"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,'../kf_book') \n",
|
||||
"# use same formattibng as rest of book so that the plots are\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(directory='..')"
|
||||
"book_format.set_style()"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -109,7 +133,7 @@
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 400x400 with 1 Axes>"
|
||||
]
|
||||
@@ -119,7 +143,8 @@
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from pf_internal import plot_pf\n",
|
||||
"from kf_book.pf_internal import plot_pf\n",
|
||||
"from kf_book.gif_animate import animate\n",
|
||||
"\n",
|
||||
"seed(1234)\n",
|
||||
"N = 3000\n",
|
||||
@@ -159,7 +184,6 @@
|
||||
" plt.legend(scatterpoints=1, loc=2)\n",
|
||||
" plt.tight_layout()\n",
|
||||
"\n",
|
||||
"from gif_animate import animate\n",
|
||||
"animate('particle_filter_anim.gif', animatepf, \n",
|
||||
" frames=40, interval=800, figsize=(4, 4))"
|
||||
]
|
||||
@@ -188,7 +212,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.10"
|
||||
"version": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 295 KiB After Width: | Height: | Size: 299 KiB |
Reference in new issue
Block a user