PEP-8 compliance
This commit is contained in:
Roger Labbe committed
1 parent
546eca9a6c
commit
dc4d234d04
1 file changed
+32
-31
@@ -1,7 +1,7 @@
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{
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"metadata": {
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"name": "",
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"signature": "sha256:cd758a1ca3157c15f8cb0a9161fe3c9e5fe8a7522243d185ae67fa44bdbd4dc6"
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"signature": "sha256:33a7a44dc4488b0b4ba93e74480b2d2d6f2db305a040407e469194f4c08082cf"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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@@ -32,7 +32,7 @@
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"from __future__ import division, print_function\n",
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"import matplotlib.pyplot as plt\n",
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"import sys\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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"import book_format\n",
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"book_format.load_style()"
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],
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@@ -261,7 +261,7 @@
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"output_type": "pyout",
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"prompt_number": 1,
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"text": [
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"<IPython.core.display.HTML at 0x7fa6499e67b8>"
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"<IPython.core.display.HTML at 0x7f30e36cb4e0>"
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]
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}
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],
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@@ -377,21 +377,22 @@
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"cell_type": "code",
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"collapsed": false,
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"input": [
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"circle1=plt.Circle((-4,0),5,color='#004080',fill=False,linewidth=20, alpha=.7)\n",
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"circle2=plt.Circle((4,0),5,color='#E24A33', fill=False, linewidth=5, alpha=.7)\n",
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"circle1=plt.Circle((-4, 0), 5, color='#004080', \n",
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" fill=False, linewidth=20, alpha=.7)\n",
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"circle2=plt.Circle((4, 0), 5, color='#E24A33', \n",
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" fill=False, linewidth=5, alpha=.7)\n",
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"\n",
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"fig = plt.gcf()\n",
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"ax = fig.gca()\n",
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"\n",
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"plt.axis('equal')\n",
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"plt.xlim((-10,10))\n",
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"plt.ylim((-10,10))\n",
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"plt.xlim((-10, 10))\n",
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"plt.ylim((-10, 10))\n",
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"\n",
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"plt.plot ([-4,0], [0,3], c='#004080')\n",
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"plt.plot ([4,0], [0,3], c='#E24A33')\n",
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"plt.plot ([-4, 0], [0, 3], c='#004080')\n",
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"plt.plot ([4, 0], [0, 3], c='#E24A33')\n",
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"plt.text(-4, -.5, \"A\", fontsize=16, horizontalalignment='center')\n",
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"plt.text(4, -.5, \"B\", fontsize=16, horizontalalignment='center')\n",
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"#plt.scatter ([-4],[0], 'r')\n",
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"\n",
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"ax.add_artist(circle1)\n",
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"ax.add_artist(circle2)\n",
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@@ -403,9 +404,9 @@
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{
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"metadata": {},
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"output_type": "display_data",
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"png": 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truncated
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"png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAFwCAYAAABdOnYEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4HNd1N/7v3Y5d9EUvRCFIAiQI9k6CpCiJKrR6s2PJ\nTiT3JJKdxHHiOD87shO/cYvf5HXiIsVFshJZktVFSSxiFztIggRAEgRA9LroZcvc3x8rUgBmQLQ7\nOzuz5/M8fETOLu5eQuDds2fOPZdxzjkIIYQQQgiJQCatJ0AIIYQQQohWKBgmhBBCCCERi4JhQggh\nhBASsSgYJoQQQgghEYuCYUIIIYQQErEoGCaEEEIIIRHLEooX2b17dyhehhBCCCGERLht27ZN6/kh\nCYYBoKioKFQvRXSmo2cIhyuacKSiGU2dAyF7XZvNiubmFrjd7pC9JvnYgqwErCtKx+oFaXA5rFpP\nJ6y43W688sor2Lx5s9ZTiUjc5wWaG8BbGoD2FqCtGby/V+tphQ2bzYam1jYkFy0CktPBUjOAjDlA\nbDwYY1pPj0Qwt9uNgwcPTvvrQhYMEzKaJHGcq+3AnrJ6nKlpBx39EnmqGjyoavDg93ursLYwDTct\nzUZeWpzW0yIRiPt8QEsDeGMt0FAH3toESAH1X9hkAnNEAVbbx79sNsBqB6zW4O/NVoAxwMQA9lFl\nI+cf/ZIASQJ8PsA3Ani9gO+jX96R4HXvCLh3RPzUpQB4SyPQ0gh+LniNxcSCZ+aCZeYAWTlgsQnC\nX5cQNVAwTEKqb9CL/eWN+OBMPdp6hrSeDgkDXn8A+8sbsb+8EflpcbhpaTbWFKbBZjFrPTViUFwK\nBDO/9VdUC36Z1QbEJwIxcYArBnDFgLlcwd87owFXNOBwgpnU37rDfT5goA8Y6AcG+4GBXvCBgY+u\n9QE9XeB9s898875eoPIseOVZAACLiQsGxZm5QM5cMGf0rF+DEDVQMExUxzlHdXMP9pypx7HKFvgC\nktZTui4mmhbncHKlpQdXdvbgfz6oQuniTGwpyUZqglPraWmCSsvE4iMjwNVq8JqLQN1l8GEBH8ZN\nJrC4hGDQG+8Gi3cDCe7gn53RYVMywKzWj+aY+PG1cc/hPh/Q0wV0d4J7uoCeTsDTBXg6wEeGZWNG\nT2Ht5H09QMVZ8IqzAGNgqZlgefOAvAVAYlLYfH8IoWCYqEaSOE5casVbx2pQ2xqe9XbRMdHwen1a\nT4OM0z/sw9vHa/HOiVosyU/GjtV5mJcZWbdci4qK0NnZqfU0dI339QA1F8FrLoI31M0u+2sygSUm\nAynpYCnpQHIakJQKZjFGvTuzWoGk1ODfadR1zjlYX0+wbrqtGWhvBuvpQjQAr9c79RfgHLzlozrs\nI3vB4hLA8+YHg+P0OWBmuhNEtEPBMBHOH5Bw+EIT3jpWgxbPoNbTITrGOVBW3Y6y6nYUZiXgzjV5\nWJxLGSUyMT7YD1w8D15VDt7WNONxmCsmeIs/Pdtwge90MMaA2Pjg5riC4N2K2MRE8J5udFWdD36P\nG68G/ytN/a4f7/EAZUfBy46C2R3g8xaCzS8G0rNDUjpCyGgUDBNhhrx+7D/bgHdO1MLTL37DBols\nlQ0eVDZ4kJMaix2r87ByXipMJgqKCYIbxK5UBQPghpppBWXXXA9+M3OBzBwgLoE+dE2AMQYWnwBW\nUHQ9QObekeAmxIbaaQfHfGQYKD8FXn4quAlv/mKwBYvB3Mkq/i0I+RgFw2TW+oe82HX6Kt4/dRX9\nw1RyQNRV19qL//fGGaQmOHHnqjxsWJQBi5kySZGGSwGgvga86hx4dRXgn+baY7GCzckHy5kLZOUC\ncYkU/M4Cs9mBOXPB5swF8FFw3NwQ/HBScxHcM7WSH97XC5w8BH7yEFhyWjBbvKA4+GGFEJVQMExm\nbNjrx3un6vD2sVoMef1aT4dEmFbPIJ597zzeOHoF964vwLqidMoURwA+2A9+vgw4f3LaHRCYKwa4\nVqealRuRZQ+hwmz2YAeJnLnAhpuDwfC1+u3mekylnyZvbwFvbwGO7AHLXwC2eAWQmUsfWohwFAyT\nafMHJOw714DXjlSjZ2AaGygMyGm3ID7ajniXHXEuOxKig/912i1w2Cxw2MyIsllgt5o/+nPw92YT\ng4kxXFvTOQc4ODgHfAEJI74Ahr1+jPgCGBrxY9j78Z/7h33o7h9Bz8AIPP3D6BnwwtM/Aq8/BH1R\nw1B7zxB+8c457DxRi/s3zsOSfKopNhrOOdBUD15+Avxy5bQ2wrGkVCB/AVjuPCA5jepRNcIS3EDC\nOrDl68CHBoMdPWougtdenjyrL0nglyvAL1cEx1m8CihcDGZ3hGbyxPAoGCZTJkkcx6pa8Mqhy2jt\njoyNcWYTQ1JcFNITXEhLdCI13oXUBCeSYqMQH22H3SpmB3QwdgsGcBazCVE2C+CyT/nrOecYGvHD\nMzCCjp4htHQNoNkziFbPAFo8g+jqk7dGMpqr7X34yR9PYUFWAh7cNC/iuk8YEfeOAFXl4OdOgHe2\nTfnrWEwcML+Y6k7DFItyAoUlYIUlwf/HNReD5S5Xr0yaMeaeTvD9O4HDu8EKF4MVrwBLTgvRzIlR\nUTBMJsU5x/m6Trx44BLqwrRFmggJ0XbkpMYiPy0O2ckxSE90ITkuShf1qIwxOB1WOB1WZLqjsSR/\nbAAw7PWjrXsQTZ0DqGvrRW1rL+ra+jBgwBrvqgYPvvvCMSybm4wHNs5DVjLVGuoNH+wHLzsKnDs5\n5dPTmN0BzFv0UUeCLMoA6wSz2YEFwQ1zfLAfuHQhGBi3TtIJxO8Dv7bpLjMHbOUGIDuf7gqRGaFg\nmNxQq2cQz++pwJmaDq2nIlSs04bi/HQUZCbA7TQhNy0W8dPIxOqNw2bBnJRYzEmJxdqidADBDzlt\n3UOoa+tFTUsvalt7cKWlB8NeY5RbnK5ux5krHbhpaTbu31AAp4PqQ8Md7/WAnzoCfqEMCEzh55Ax\nsNx5YEVLgJwCMAu9pekZc0YDS1aDLVkdzABXnQMulIEP9N3w63hjHXhjXTBDvHIDkF9IH4bItNDK\nQRSN+AJ48+gVvHO8NqxOjJupWKcNRdmJWJCdgKLsRKQnupCUlAQAEXuwAWMMqQlOpCY4sXpB8DZj\nQJJQ09KLqgYPKuu7UNXgwYhPv8GxxDl2nb6KY1UteLB0PjYuzKBNdmGId7aBnzwMfun8lNpxMacL\nWLQMbNHyYEkEMRyW4AZbuwV81Sawmovg504GO1PcAG9vAX/n5eBJgCvWAQtK6DAPMiUUDJMxOOc4\ndbkNv99biY5e/daZRtksKM51ozA7EUXZichwu+j22RSYTSYUZMSjICMed67Ogz8goba1FxX1Xai4\n2oWqhi74A5PvAg83vYNePLOzHPvONuDRbUXITY3VekoEAG9rAj92IHhE8hSwjDnBjgJziyjIiRDM\nbAY+6mfMu9rBy08Gj3i+QfkM7+4E3/0m2NH9wPJ1wQ9O1DmE3ADjfAr9TWZp9+7dKCoqUvtlyCy1\neAbw/J5KnNVpSURSrAPLClKwND8FhdkJk9b6ut1uAJGbGZ6JoRE/ztV24HR1G85e6dBlX2nGgK1L\ngqUT0VE2raejyOg/m9zTCX70A/BLFyZ/stkMVrQEbPHKYGcIojmtfz65dyR4yuCZo+Bdk79fsZhY\nsDVbgrXJVD5haG63GwcPHsS2bdum9XWUGSbwByS88eEVvHWsRnclEflpcVhWkIxlc1OQlRRN2V+V\nRdktWL0gDasXpCEgSbjU2I2y6nacqm5Dq06O3uYc2FNWj+NVLXhkywJsWJhBPzchwgf6gpngC6cn\nLYdgNjuweAXYkjVgrugQzZDoAbPZgeLlwMKlwRKKk4duuOGO9/WC73od7NQRYN3WYK9p+jdPRqFg\nOMLVtvbiVzvPob69X+upTFmmOxrrF6ZjbWE6kuKitJ5OxDKbTCjMTkRhdiIe3jwfV9v7cORCM45U\nNqNbB8dx9w358Mt3ynH8Yis+e8tCJERTz1K18OEh8FOHwc8cn7SnLItyAkvWgJWspD6y5IaYyQTM\nLQz2kW6sBT9xCLx+4rpi3tUO/taLYGlZwPqbwDJzQjhbEs4oGI5Q/oCE145U461jNQhI4V8DmhBt\nx7qidKwtSsec5Bj6VB9mGGPISYlFTkosHiqdjwtXu3CkogknLrWGfXeKsup2fLPxMP5kayHWL0yn\nny2BeCAAnDkGfuIg+MiN9yCwmFiwZeuD2T4r1XeSqWOMAVl5YFl54K1NwUzxlaoJexbzlgbwV34L\nllMAtvEWsMSkEM+YhBsKhiOQXrLBVrMJqxakYuOiTBRlJ1IXAJ0wmRiKc90oznXjsZsXoqy6DfvL\nG1FeG771rwPDPvzinXM4drGFssSC8IYa8H07J63pZM5osNWlwSCYNsWRWWKpGWB3PBjsUHJkD3jN\npQmfy+sug9fXgC1dA7ZqY7D8gkQkCoYjiF6ywSlxUdiyJBulxZmIcYbnBicyNXarGWsK07GmMB0t\nngHsPVOPA+VNYXvYB2WJZ4/39YIfen/SzXHM7gBbvi7YV9ZK/86JWMydArbjEfDmevBDu8Gb65Wf\nKAXATx0Gqs4Bm24Ndq6gf/cRh4LhCNHU2Y//fPMsrrbfuHm5VhgDluQl46al2Vicm0RZYANKS3Dh\nk1sKcd+GeTha1YLdp6+iNgxPNLyWJT5xqRWPb18Uth0nwg0PBICyo5CO7wd8N/iwYzaDLVkNtmI9\nmMMZugmSiMTSs4H7PwNWexn8yG7wznbF5/GBPvCdL4Nl5QGbt4Ml0jHekYSC4QhwoLwRv91VAa8/\n/Go3HTYzblqSjZuWZiM5jt4YI4HdakZpcSZKizNxpaUH752sw7GqlrC7W3Hqchvq2nrxpTLine truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa62d55b7b8>"
|
||||
"<matplotlib.figure.Figure at 0x7f30c7268748>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -693,7 +694,7 @@
|
||||
"input": [
|
||||
"from math import sin, cos, atan2\n",
|
||||
"\n",
|
||||
"def H_of (pos, pos_A, pos_B):\n",
|
||||
"def H_of(pos, pos_A, pos_B):\n",
|
||||
" \"\"\" Given the position of our object at 'pos' in 2D, and two \n",
|
||||
" transmitters A and B at positions 'pos_A' and 'pos_B', return \n",
|
||||
" the partial derivative of H\n",
|
||||
@@ -729,7 +730,7 @@
|
||||
" self.B = pos_b\n",
|
||||
" self.noise_factor = noise_factor\n",
|
||||
" \n",
|
||||
" def range_of (self, pos):\n",
|
||||
" def range_of(self, pos):\n",
|
||||
" \"\"\" returns tuple containing noisy range data to A and B\n",
|
||||
" given a position 'pos'\n",
|
||||
" \"\"\"\n",
|
||||
@@ -761,7 +762,7 @@
|
||||
"from filterpy.kalman import KalmanFilter\n",
|
||||
"import numpy as np\n",
|
||||
"\n",
|
||||
"pos_a = (100,-20)\n",
|
||||
"pos_a = (100, -20)\n",
|
||||
"pos_b = (-100, -20)\n",
|
||||
"\n",
|
||||
"f1 = KalmanFilter(dim_x=4, dim_z=2)\n",
|
||||
@@ -774,16 +775,16 @@
|
||||
"f1.R *= 1.\n",
|
||||
"f1.Q *= .1\n",
|
||||
"\n",
|
||||
"f1.x = np.array([[1,0,1,0]], dtype=float).T\n",
|
||||
"f1.x = np.array([[1, 0, 1, 0]], dtype=float).T\n",
|
||||
"f1.P = np.eye(4) * 5.\n",
|
||||
"\n",
|
||||
"# initialize storage and other variables for the run\n",
|
||||
"count = 30\n",
|
||||
"xs, ys = [],[]\n",
|
||||
"pxs, pys = [],[]\n",
|
||||
"xs, ys = [], []\n",
|
||||
"pxs, pys = [], []\n",
|
||||
"\n",
|
||||
"# create the simulated sensor\n",
|
||||
"d = DMESensor (pos_a, pos_b, noise_factor=3.)\n",
|
||||
"d = DMESensor(pos_a, pos_b, noise_factor=3.)\n",
|
||||
"\n",
|
||||
"# pos will contain our nominal position since the filter does not\n",
|
||||
"# maintain position.\n",
|
||||
@@ -791,31 +792,31 @@
|
||||
"\n",
|
||||
"for i in range(count):\n",
|
||||
" # move (1,1) each step, so just use i\n",
|
||||
" pos = [i,i]\n",
|
||||
" pos = [i, i]\n",
|
||||
" \n",
|
||||
" # compute the difference in range between the nominal track\n",
|
||||
" # and measured ranges\n",
|
||||
" ra,rb = d.range_of(pos)\n",
|
||||
" rx,ry = d.range_of((pos[0]+f1.x[0,0], pos[1]+f1.x[2,0]))\n",
|
||||
" z = np.array([[ra-rx],[rb-ry]])\n",
|
||||
" rx,ry = d.range_of((pos[0] + f1.x[0, 0], pos[1] + f1.x[2, 0]))\n",
|
||||
" z = np.array([[ra-rx], [rb-ry]])\n",
|
||||
"\n",
|
||||
" # compute linearized H for this time step\n",
|
||||
" f1.H = H_of (pos, pos_a, pos_b)\n",
|
||||
"\n",
|
||||
" # store stuff so we can plot it later\n",
|
||||
" xs.append (f1.x[0,0]+i)\n",
|
||||
" ys.append (f1.x[2,0]+i)\n",
|
||||
" pxs.append (pos[0])\n",
|
||||
" xs.append(f1.x[0, 0]+i)\n",
|
||||
" ys.append(f1.x[2, 0]+i)\n",
|
||||
" pxs.append(pos[0])\n",
|
||||
" pys.append(pos[1])\n",
|
||||
" \n",
|
||||
" # perform the Kalman filter steps\n",
|
||||
" f1.predict ()\n",
|
||||
" f1.predict()\n",
|
||||
" f1.update(z)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"p1, = plt.plot (xs, ys, 'r--')\n",
|
||||
"p2, = plt.plot (pxs, pys)\n",
|
||||
"plt.legend([p1,p2], ['filter', 'ideal'], loc=2)\n",
|
||||
"plt.plot(xs, ys, 'r--', label='filter')\n",
|
||||
"plt.plot(pxs, pys, label='ideal')\n",
|
||||
"plt.legend(loc=2)\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
@@ -824,9 +825,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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||||
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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fa62c29bf28>"
|
||||
"<matplotlib.figure.Figure at 0x7f30c5fa8e10>"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
Reference in new issue
Block a user