daviddoji/Kalman-and-Bayesian-Filters-in-Python
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Roger Labbe committed 2015-04-20 21:25:06 -07:00
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commit 13e129165e
1 file changed
+111 -104
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08_Designing_Kalman_Filters.ipynb
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@@ -109,7 +109,7 @@
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" font-size: 16pt;\n",
" margin-bottom: 0em;\n",
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@@ -133,6 +133,11 @@
" overflow-y: scroll;\n",
" max-height: 50000px;\n",
" }\n",
" div.output_wrapper{\n",
" margin-top:0.2em;\n",
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"}\n",
"\n",
" code{\n",
" font-size: 70%;\n",
" }\n",
@@ -335,9 +340,9 @@
"outputs": [
{
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x52033c8>"
"<matplotlib.figure.Figure at 0x586de80>"
]
},
"metadata": {},
@@ -345,14 +350,17 @@
}
],
"source": [
"import numpy as np\n",
"import book_plots as bp\n",
"\n",
"pos = [4, 3]\n",
"sensor = PosSensor1(pos, (2, 1), 1)\n",
"\n",
"ps = []\n",
"for i in range(50):\n",
" pos = sensor.read() \n",
" bp.plot_measurements(pos[0], pos[1])\n",
" ps.append(sensor.read())\n",
"ps = np.asarray(ps)\n",
"bp.plot_measurements(ps[:, 0], ps[:, 1])\n",
"\n",
"plt.show()"
]
@@ -701,9 +709,9 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x5203588>"
"<matplotlib.figure.Figure at 0x586db70>"
]
},
"metadata": {},
@@ -772,9 +780,9 @@
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x8053208>"
"<matplotlib.figure.Figure at 0x8783198>"
]
},
"metadata": {},
@@ -911,7 +919,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc95c0>"
"<matplotlib.figure.Figure at 0x7723e48>"
]
},
"metadata": {},
@@ -999,7 +1007,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x8080160>"
"<matplotlib.figure.Figure at 0x87b5208>"
]
},
"metadata": {},
@@ -1272,14 +1280,14 @@
},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def simulate_system(Q, count):\n",
" obj = ConstantVelocityObject(x0=0, vel=1, noise_scale=Q)\n",
" obj = ConstantVelocityObject(x0=.0, vel=0.5, noise_scale=Q)\n",
" zs = []\n",
" xs = []\n",
" for i in range(count):\n",
@@ -1299,7 +1307,7 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": 63,
"metadata": {
"collapsed": false
},
@@ -1332,7 +1340,7 @@
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": 66,
"metadata": {
"collapsed": false
},
@@ -1344,13 +1352,13 @@
" \n",
" if zs is not None:\n",
" bp.plot_measurements(zs)\n",
" plt.legend(loc='best')\n",
" bp.show_legend()\n",
" plt.ylabel('meters')\n",
" plt.xlabel('time (sec)')\n",
" if title is not None:\n",
" plt.title(title)\n",
" plt.xlim((-1, len(xs)))\n",
" plt.ylim((-1, len(xs)))\n",
" #plt.ylim((-1, len(xs)))\n",
" #plt.axis('equal')\n",
" plt.show()"
]
@@ -1364,16 +1372,16 @@
},
{
"cell_type": "code",
"execution_count": 21,
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x80e42e8>"
"<matplotlib.figure.Figure at 0xa167400>"
]
},
"metadata": {},
@@ -1402,7 +1410,7 @@
},
{
"cell_type": "code",
"execution_count": 22,
"execution_count": 69,
"metadata": {
"collapsed": false
},
@@ -1421,16 +1429,16 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x811fcc0>"
"<matplotlib.figure.Figure at 0x87f5358>"
]
},
"metadata": {},
@@ -1454,16 +1462,16 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x81908d0>"
"<matplotlib.figure.Figure at 0xa16e4e0>"
]
},
"metadata": {},
@@ -1488,16 +1496,16 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x7fe11d0>"
"<matplotlib.figure.Figure at 0x8794860>"
]
},
"metadata": {},
@@ -1522,16 +1530,16 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x6fa1080>"
"<matplotlib.figure.Figure at 0xa1e50f0>"
]
},
"metadata": {},
@@ -1562,16 +1570,16 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x91e6d30>"
"<matplotlib.figure.Figure at 0x8794ba8>"
]
},
"metadata": {},
@@ -1596,16 +1604,16 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x6f56cc0>"
"<matplotlib.figure.Figure at 0xa29d978>"
]
},
"metadata": {},
@@ -1637,16 +1645,16 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x92850b8>"
"<matplotlib.figure.Figure at 0xa3468d0>"
]
},
"metadata": {},
@@ -1682,16 +1690,16 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x91e6cf8>"
"<matplotlib.figure.Figure at 0xa194240>"
]
},
"metadata": {},
@@ -1716,16 +1724,16 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9259ac8>"
"<matplotlib.figure.Figure at 0xa194be0>"
]
},
"metadata": {},
@@ -1733,9 +1741,9 @@
},
{
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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9941128>"
"<matplotlib.figure.Figure at 0x8791ef0>"
]
},
"metadata": {},
@@ -1768,16 +1776,16 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x99413c8>"
"<matplotlib.figure.Figure at 0x8791c88>"
]
},
"metadata": {},
@@ -1785,9 +1793,9 @@
},
{
"data": {
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truncated
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x8192828>"
"<matplotlib.figure.Figure at 0xa27b2e8>"
]
},
"metadata": {},
@@ -1820,7 +1828,7 @@
},
{
"cell_type": "code",
"execution_count": 33,
"execution_count": 80,
"metadata": {
"collapsed": false
},
@@ -1829,7 +1837,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x92baf60>"
"<matplotlib.figure.Figure at 0xa1499e8>"
]
},
"metadata": {},
@@ -1862,7 +1870,7 @@
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 81,
"metadata": {
"collapsed": false
},
@@ -1871,7 +1879,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x6f19be0>"
"<matplotlib.figure.Figure at 0xa185a20>"
]
},
"metadata": {},
@@ -1891,8 +1899,7 @@
" self.vel += self.acc\n",
" self.x += self.vel\n",
" return (self.x, self.vel, self.acc)\n",
"\n",
" \n",
" \n",
"R = 6.\n",
"Q = 0.02\n",
"def simulate_acc_system(R, Q, count):\n",
@@ -1921,16 +1928,16 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x92d9c18>"
"<matplotlib.figure.Figure at 0x88da5c0>"
]
},
"metadata": {},
@@ -1940,7 +1947,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x99557b8>"
"<matplotlib.figure.Figure at 0x9976da0>"
]
},
"metadata": {},
@@ -1968,16 +1975,16 @@
},
{
"cell_type": "code",
"execution_count": 36,
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9955780>"
"<matplotlib.figure.Figure at 0x9976a20>"
]
},
"metadata": {},
@@ -1987,7 +1994,7 @@
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9296e10>"
"<matplotlib.figure.Figure at 0xa36e4a8>"
]
},
"metadata": {},
@@ -2013,16 +2020,16 @@
},
{
"cell_type": "code",
"execution_count": 37,
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x80cf9b0>"
"<matplotlib.figure.Figure at 0x8948240>"
]
},
"metadata": {},
@@ -2032,7 +2039,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x80cffd0>"
"<matplotlib.figure.Figure at 0x8948400>"
]
},
"metadata": {},
@@ -2097,7 +2104,7 @@
},
{
"cell_type": "code",
"execution_count": 38,
"execution_count": 85,
"metadata": {
"collapsed": false
},
@@ -2113,7 +2120,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x6ffceb8>"
"<matplotlib.figure.Figure at 0x9b29b70>"
]
},
"metadata": {},
@@ -2201,7 +2208,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x92c65c0>"
"<matplotlib.figure.Figure at 0x9a13198>"
]
},
"metadata": {},
@@ -2254,7 +2261,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x80a9550>"
"<matplotlib.figure.Figure at 0x8942ba8>"
]
},
"metadata": {},
@@ -2314,7 +2321,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x8192f60>"
"<matplotlib.figure.Figure at 0x9af6748>"
]
},
"metadata": {},
@@ -2408,16 +2415,16 @@
},
{
"cell_type": "code",
"execution_count": 43,
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x6f8bf28>"
"<matplotlib.figure.Figure at 0x8948278>"
]
},
"metadata": {},
@@ -2425,9 +2432,9 @@
},
{
"data": {
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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x6f14f28>"
"<matplotlib.figure.Figure at 0x8780fd0>"
]
},
"metadata": {},
@@ -2435,9 +2442,9 @@
},
{
"data": {
"image/png": 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truncated
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA6gAAAE6CAYAAAAbcH9JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x8003400>"
"<matplotlib.figure.Figure at 0x9977e80>"
]
},
"metadata": {},
@@ -2493,16 +2500,16 @@
},
{
"cell_type": "code",
"execution_count": 44,
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x92ab978>"
"<matplotlib.figure.Figure at 0x87a3240>"
]
},
"metadata": {},
@@ -2510,9 +2517,9 @@
},
{
"data": {
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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x92ab828>"
"<matplotlib.figure.Figure at 0x87a33c8>"
]
},
"metadata": {},
@@ -2520,9 +2527,9 @@
},
{
"data": {
"image/png": 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truncated
"image/png": 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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x932be10>"
"<matplotlib.figure.Figure at 0xa13b240>"
]
},
"metadata": {},
@@ -2630,7 +2637,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9a457f0>"
"<matplotlib.figure.Figure at 0xa282b38>"
]
},
"metadata": {},
@@ -2775,7 +2782,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x800f9e8>"
"<matplotlib.figure.Figure at 0xa1ed3c8>"
]
},
"metadata": {},
@@ -2978,7 +2985,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9928438>"
"<matplotlib.figure.Figure at 0xa259f60>"
]
},
"metadata": {},
@@ -2988,7 +2995,7 @@
"data": {
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truncated
"text/plain": [
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]
},
"metadata": {},
@@ -3169,7 +3176,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9238e10>"
"<matplotlib.figure.Figure at 0xa14b048>"
]
},
"metadata": {},
@@ -3368,7 +3375,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9a96ba8>"
"<matplotlib.figure.Figure at 0xa2ccc88>"
]
},
"metadata": {},
@@ -3493,7 +3500,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x923fef0>"
"<matplotlib.figure.Figure at 0xa18ce80>"
]
},
"metadata": {},
@@ -3642,7 +3649,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9a88668>"
"<matplotlib.figure.Figure at 0xa2bf160>"
]
},
"metadata": {},
@@ -3714,7 +3721,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x92e2588>"
"<matplotlib.figure.Figure at 0x88c49b0>"
]
},
"metadata": {},
@@ -3724,7 +3731,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9ac2e10>"
"<matplotlib.figure.Figure at 0xa304fd0>"
]
},
"metadata": {},
@@ -3789,7 +3796,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x91f47b8>"
"<matplotlib.figure.Figure at 0xa185dd8>"
]
},
"metadata": {},
@@ -3799,7 +3806,7 @@
"data": {
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truncated
"text/plain": [
"<matplotlib.figure.Figure at 0x9ba8e10>"
"<matplotlib.figure.Figure at 0xa3380f0>"
]
},
"metadata": {},