PEP-8 compliance.
This commit is contained in:
Roger Labbe committed
1 parent
b1ce657cca
commit
6e73cd9786
1 file changed
+153
-155
@@ -1,7 +1,7 @@
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{
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"metadata": {
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"name": "",
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"signature": "sha256:a78f0d79d16739c5c601c8274800d5e2241197cf7747eca727508403262be931"
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"signature": "sha256:75c473df8ee167446e82e1b07397a1f60535a0ab7d37591e1626e3798a42d126"
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},
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"nbformat": 3,
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"nbformat_minor": 0,
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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 0x7fc18a3cc7b8>"
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"<IPython.core.display.HTML at 0x7fa058b4a4e0>"
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]
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}
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],
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@@ -341,10 +341,10 @@
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"collapsed": false,
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"input": [
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"pos = [4,3]\n",
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"s = PosSensor1 (pos, (2,1), 1)\n",
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"sensor = PosSensor1 (pos, (2,1), 1)\n",
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"\n",
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"for i in range (50):\n",
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" pos = s.read() \n",
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" pos = sensor.read() \n",
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" plt.scatter(pos[0], pos[1])\n",
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"plt.show()"
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],
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@@ -354,9 +354,9 @@
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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": 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truncated
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"text": [
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"<matplotlib.figure.Figure at 0x7fc16df405f8>"
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"<matplotlib.figure.Figure at 0x7fa03c6c5550>"
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]
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}
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],
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@@ -428,13 +428,13 @@
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"from filterpy.kalman import KalmanFilter\n",
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"import numpy as np\n",
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"\n",
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"f1 = KalmanFilter(dim_x=4, dim_z=2)\n",
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"tracker = KalmanFilter(dim_x=4, dim_z=2)\n",
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"dt = 1. # time step\n",
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"\n",
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"f1.F = np.array ([[1, dt, 0, 0],\n",
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" [0, 1, 0, 0],\n",
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" [0, 0, 1, dt],\n",
|
||||
" [0, 0, 0, 1]])"
|
||||
"tracker.F = np.array ([[1, dt, 0, 0],\n",
|
||||
" [0, 1, 0, 0],\n",
|
||||
" [0, 0, 1, dt],\n",
|
||||
" [0, 0, 0, 1]])"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -453,7 +453,7 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"f1.u = 0."
|
||||
"tracker.u = 0."
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -509,9 +509,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"f1.H = np.array ([[1/0.3048, 0, 0, 0],\n",
|
||||
" [0, 0, 1/0.3048, 0]])\n",
|
||||
"print(f1.H)"
|
||||
"tracker.H = np.array ([[1/0.3048, 0, 0, 0],\n",
|
||||
" [0, 0, 1/0.3048, 0]])\n",
|
||||
"print(tracker.H)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -544,9 +544,9 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"f1.R = np.array([[5,0],\n",
|
||||
" [0, 5]])\n",
|
||||
"print (f1.R)"
|
||||
"tracker.R = np.array([[5,0],\n",
|
||||
" [0, 5]])\n",
|
||||
"print(tracker.R)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -578,8 +578,8 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"f1.Q = np.eye(4) * 0.1\n",
|
||||
"print(f1.Q)"
|
||||
"tracker.Q = np.eye(4) * 0.1\n",
|
||||
"print(tracker.Q)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -614,11 +614,11 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"f1.x = np.array([[0,0,0,0]]).T\n",
|
||||
"f1.P = np.eye(4) * 500.\n",
|
||||
"print(f1.x)\n",
|
||||
"tracker.x = np.array([[0, 0, 0, 0]]).T\n",
|
||||
"tracker.P = np.eye(4) * 500.\n",
|
||||
"print(tracker.x)\n",
|
||||
"print()\n",
|
||||
"print (f1.P)"
|
||||
"print (tracker.P)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -660,39 +660,39 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"f1 = KalmanFilter(dim_x=4, dim_z=2)\n",
|
||||
"tracker = KalmanFilter(dim_x=4, dim_z=2)\n",
|
||||
"dt = 1.0 # time step\n",
|
||||
"\n",
|
||||
"f1.F = np.array ([[1, dt, 0, 0],\n",
|
||||
" [0, 1, 0, 0],\n",
|
||||
" [0, 0, 1, dt],\n",
|
||||
" [0, 0, 0, 1]])\n",
|
||||
"f1.u = 0.\n",
|
||||
"f1.H = np.array ([[1/0.3048, 0, 0, 0],\n",
|
||||
" [0, 0, 1/0.3048, 0]])\n",
|
||||
"tracker.F = np.array ([[1, dt, 0, 0],\n",
|
||||
" [0, 1, 0, 0],\n",
|
||||
" [0, 0, 1, dt],\n",
|
||||
" [0, 0, 0, 1]])\n",
|
||||
"tracker.u = 0.\n",
|
||||
"tracker.H = np.array ([[1/0.3048, 0, 0, 0],\n",
|
||||
" [0, 0, 1/0.3048, 0]])\n",
|
||||
"\n",
|
||||
"f1.R = np.eye(2) * 5\n",
|
||||
"f1.Q = np.eye(4) * .1\n",
|
||||
"tracker.R = np.eye(2) * 5\n",
|
||||
"tracker.Q = np.eye(4) * .1\n",
|
||||
"\n",
|
||||
"f1.x = np.array([[0,0,0,0]]).T\n",
|
||||
"f1.P = np.eye(4) * 500.\n",
|
||||
"tracker.x = np.array([[0,0,0,0]]).T\n",
|
||||
"tracker.P = np.eye(4) * 500.\n",
|
||||
"\n",
|
||||
"# initialize storage and other variables for the run\n",
|
||||
"count = 30\n",
|
||||
"xs, ys = [],[]\n",
|
||||
"pxs, pys = [],[]\n",
|
||||
"\n",
|
||||
"s = PosSensor1 ([0,0], (2,1), 1.)\n",
|
||||
"sensor = PosSensor1 ([0,0], (2,1), 1.)\n",
|
||||
"\n",
|
||||
"for i in range(count):\n",
|
||||
" pos = s.read()\n",
|
||||
" z = np.array([[pos[0]],[pos[1]]])\n",
|
||||
" pos = sensor.read()\n",
|
||||
" z = np.array([[pos[0]], [pos[1]]])\n",
|
||||
"\n",
|
||||
" f1.predict ()\n",
|
||||
" f1.update (z)\n",
|
||||
" tracker.predict ()\n",
|
||||
" tracker.update (z)\n",
|
||||
"\n",
|
||||
" xs.append (f1.x[0,0])\n",
|
||||
" ys.append (f1.x[2,0])\n",
|
||||
" xs.append (tracker.x[0,0])\n",
|
||||
" ys.append (tracker.x[2,0])\n",
|
||||
" pxs.append (pos[0]*.3048)\n",
|
||||
" pys.append(pos[1]*.3048)\n",
|
||||
"\n",
|
||||
@@ -707,9 +707,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAF2CAYAAACLY5UZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc16df40668>"
|
||||
"<matplotlib.figure.Figure at 0x7fa03c6c55c0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -730,58 +730,57 @@
|
||||
"input": [
|
||||
"import stats\n",
|
||||
"\n",
|
||||
"f1 = KalmanFilter(dim_x=4, dim_z=2)\n",
|
||||
"tracker = KalmanFilter(dim_x=4, dim_z=2)\n",
|
||||
"dt = 1.0 # time step\n",
|
||||
"\n",
|
||||
"f1.F = np.array ([[1, dt, 0, 0],\n",
|
||||
" [0, 1, 0, 0],\n",
|
||||
" [0, 0, 1, dt],\n",
|
||||
" [0, 0, 0, 1]])\n",
|
||||
"f1.u = 0.\n",
|
||||
"f1.H = np.array ([[1/0.3048, 0, 0, 0],\n",
|
||||
" [0, 0, 1/0.3048, 0]])\n",
|
||||
"tracker.F = np.array([[1, dt, 0, 0],\n",
|
||||
" [0, 1, 0, 0],\n",
|
||||
" [0, 0, 1, dt],\n",
|
||||
" [0, 0, 0, 1]])\n",
|
||||
"tracker.u = 0.\n",
|
||||
"tracker.H = np.array([[1/0.3048, 0, 0, 0],\n",
|
||||
" [0, 0, 1/0.3048, 0]])\n",
|
||||
"\n",
|
||||
"f1.R = np.eye(2) * 5\n",
|
||||
"f1.Q = np.eye(4) * .1\n",
|
||||
"tracker.R = np.eye(2) * 5\n",
|
||||
"tracker.Q = np.eye(4) * .1\n",
|
||||
"\n",
|
||||
"f1.x = np.array([[0,0,0,0]]).T\n",
|
||||
"f1.P = np.eye(4) * 500.\n",
|
||||
"tracker.x = np.array([[0, 0, 0, 0]]).T\n",
|
||||
"tracker.P = np.eye(4) * 500.\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",
|
||||
"s = PosSensor1 ([0,0], (2,1), 1.)\n",
|
||||
"sensor = PosSensor1([0,0], (2,1), 1.)\n",
|
||||
"\n",
|
||||
"for i in range(count):\n",
|
||||
" pos = s.read()\n",
|
||||
" z = np.array([[pos[0]],[pos[1]]])\n",
|
||||
" pos = sensor.read()\n",
|
||||
" z = np.array([[pos[0]], [pos[1]]])\n",
|
||||
"\n",
|
||||
" f1.predict ()\n",
|
||||
" f1.update (z)\n",
|
||||
" tracker.predict()\n",
|
||||
" tracker.update(z)\n",
|
||||
"\n",
|
||||
" xs.append (f1.x[0,0])\n",
|
||||
" ys.append (f1.x[2,0])\n",
|
||||
" pxs.append (pos[0]*.3048)\n",
|
||||
" xs.append(tracker.x[0,0])\n",
|
||||
" ys.append(tracker.x[2,0])\n",
|
||||
" pxs.append(pos[0]*.3048)\n",
|
||||
" pys.append(pos[1]*.3048)\n",
|
||||
"\n",
|
||||
" # plot covariance of x and y\n",
|
||||
" cov = np.array([[f1.P[0,0], f1.P[2,0]], \n",
|
||||
" [f1.P[0,2], f1.P[2,2]]])\n",
|
||||
" cov = np.array([[tracker.P[0,0], tracker.P[2,0]], \n",
|
||||
" [tracker.P[0,2], tracker.P[2,2]]])\n",
|
||||
" \n",
|
||||
" #e = stats.sigma_ellipse (cov=cov, x=f1.x[0,0], y=f1.x[2,0])\n",
|
||||
" #stats.plot_sigma_ellipse(ellipse=e)\n",
|
||||
" stats.plot_covariance_ellipse((f1.x[0,0], f1.x[2,0]), cov=cov,\n",
|
||||
" facecolor='g', alpha=0.2)\n",
|
||||
" stats.plot_covariance_ellipse(\n",
|
||||
" (tracker.x[0,0], tracker.x[2,0]), cov=cov,\n",
|
||||
" facecolor='g', alpha=0.2)\n",
|
||||
"\n",
|
||||
" \n",
|
||||
"p1, = plt.plot (xs, ys, 'r--')\n",
|
||||
"p2, = plt.plot (pxs, pys)\n",
|
||||
"plt.legend([p1,p2], ['filter', 'measurement'], loc=2)\n",
|
||||
"p1, = plt.plot (xs, ys, 'r--', label='filter')\n",
|
||||
"p2, = plt.plot (pxs, pys, label='measurement')\n",
|
||||
"plt.legend(loc=2)\n",
|
||||
"plt.show()\n",
|
||||
"print(\"final P is:\")\n",
|
||||
"print(f1.P)"
|
||||
"print(tracker.P)"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -789,9 +788,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc16dbcb7b8>"
|
||||
"<matplotlib.figure.Figure at 0x7fa03c3f2048>"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -830,7 +829,7 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"print(np.diag(f1.P))"
|
||||
"print(np.diag(tracker.P))"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -856,7 +855,7 @@
|
||||
"cell_type": "code",
|
||||
"collapsed": false,
|
||||
"input": [
|
||||
"c = f1.P[0:2,0:2]\n",
|
||||
"c = tracker.P[0:2,0:2]\n",
|
||||
"print(c)\n",
|
||||
"stats.plot_covariance_ellipse((0,0), cov=c, facecolor='g', alpha=0.2)"
|
||||
],
|
||||
@@ -874,9 +873,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc16db65550>"
|
||||
"<matplotlib.figure.Figure at 0x7fa034824c18>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -953,9 +952,9 @@
|
||||
" dt = 0.1\n",
|
||||
" kf = KalmanFilter(dim_x=2, dim_z=2)\n",
|
||||
"\n",
|
||||
" kf.F = array ([[1., dt], [0., 1.]])\n",
|
||||
" kf.H = array ([[1., 0.], [1., 0.]])\n",
|
||||
" kf.x = array ([[0.], [1.]])\n",
|
||||
" kf.F = array([[1., dt], [0., 1.]])\n",
|
||||
" kf.H = array([[1., 0.], [1., 0.]])\n",
|
||||
" kf.x = array([[0.], [1.]])\n",
|
||||
" kf.Q *= array([[(dt**3)/3, (dt**2)/2],\n",
|
||||
" [(dt**2)/2, dt ]]) * 0.02\n",
|
||||
" kf.P *= 100\n",
|
||||
@@ -1007,9 +1006,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc166081978>"
|
||||
"<matplotlib.figure.Figure at 0x7fa0347fd0b8>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1126,9 +1125,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc1660760f0>"
|
||||
"<matplotlib.figure.Figure at 0x7fa0347d9cc0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1227,12 +1226,12 @@
|
||||
" self.noise = noise\n",
|
||||
" \n",
|
||||
" \n",
|
||||
" def step (self, dt):\n",
|
||||
" self.x = rk4 (self.x, self.t, dt, fx)\n",
|
||||
" self.y = rk4 (self.y, self.t, dt, fy)\n",
|
||||
" def step(self, dt):\n",
|
||||
" self.x = rk4(self.x, self.t, dt, fx)\n",
|
||||
" self.y = rk4(self.y, self.t, dt, fy)\n",
|
||||
" self.t += dt \n",
|
||||
" return (self.x +random.randn()*self.noise[0], \n",
|
||||
" self.y+random.randn()*self.noise[1])"
|
||||
" return (self.x + random.randn()*self.noise[0], \n",
|
||||
" self.y + random.randn()*self.noise[1])"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -1280,17 +1279,17 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc166065cf8>"
|
||||
"<matplotlib.figure.Figure at 0x7fa03c050668>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc166065208>"
|
||||
"<matplotlib.figure.Figure at 0x7fa03c050748>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1407,27 +1406,26 @@
|
||||
"\n",
|
||||
" g = 9.8 # gravitational constant\n",
|
||||
"\n",
|
||||
" f1 = KalmanFilter(dim_x=5, dim_z=2)\n",
|
||||
" kf = KalmanFilter(dim_x=5, dim_z=2)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" ay = .5*dt**2\n",
|
||||
"\n",
|
||||
" f1.F = np.array ([[1, dt, 0, 0, 0], # x = x0+dx*dt\n",
|
||||
" [0, 1, 0, 0, 0], # dx = dx\n",
|
||||
" [0, 0, 1, dt, ay], # y = y0 +dy*dt+1/2*g*dt^2\n",
|
||||
" [0, 0, 0, 1, dt], # dy = dy0 + ddy*dt \n",
|
||||
" [0, 0, 0, 0, 1]]) # ddy = -g.\n",
|
||||
" kf.F = np.array([[1, dt, 0, 0, 0], # x = x0+dx*dt\n",
|
||||
" [0, 1, 0, 0, 0], # dx = dx\n",
|
||||
" [0, 0, 1, dt, ay], # y = y0 +dy*dt+1/2*g*dt^2\n",
|
||||
" [0, 0, 0, 1, dt], # dy = dy0 + ddy*dt \n",
|
||||
" [0, 0, 0, 0, 1]]) # ddy = -g.\n",
|
||||
" \n",
|
||||
" # compute velocity in x and y\n",
|
||||
" omega = radians(omega)\n",
|
||||
" vx = cos(omega) * v0\n",
|
||||
" vy = sin(omega) * v0\n",
|
||||
" \n",
|
||||
" f1.Q *= 0.\n",
|
||||
"\n",
|
||||
" f1.x = np.array([[x, vx, y, vy, -g]]).T\n",
|
||||
" kf.Q *= 0.\n",
|
||||
" kf.x = np.array([[x, vx, y, vy, -g]]).T\n",
|
||||
" \n",
|
||||
" return f1"
|
||||
" return kf"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -1453,19 +1451,19 @@
|
||||
"\n",
|
||||
"ball = BallTrajectory2D(x0=x, y0=y, theta_deg=theta, \n",
|
||||
" velocity=v0, noise=[0,0])\n",
|
||||
"f1 = ball_kf(x,y,theta,v0,dt)\n",
|
||||
"kf = ball_kf(x, y, theta, v0, dt)\n",
|
||||
"t = 0\n",
|
||||
"while f1.x[2,0] > 0:\n",
|
||||
"while kf.x[2, 0] > 0:\n",
|
||||
" t += dt\n",
|
||||
" f1.predict() \n",
|
||||
" x,y = ball.step(dt)\n",
|
||||
" p1 = plt.scatter(f1.x[0,0], f1.x[2,0], \n",
|
||||
" kf.predict() \n",
|
||||
" x, y = ball.step(dt)\n",
|
||||
" p1 = plt.scatter(kf.x[0, 0], kf.x[2, 0], \n",
|
||||
" color='black', marker='+', s=75)\n",
|
||||
" p2 = plt.scatter(x, y, \n",
|
||||
" color='green', marker='o', s=75, \n",
|
||||
" alpha=0.5)\n",
|
||||
"\n",
|
||||
"plt.legend([p1,p2], ['Kalman filter', 'idealized'])\n",
|
||||
"plt.legend([p1, p2], ['Kalman filter', 'idealized'])\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
@@ -1474,9 +1472,9 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc165ecdc18>"
|
||||
"<matplotlib.figure.Figure at 0x7fa0346450f0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
@@ -1595,28 +1593,28 @@
|
||||
"def ball_kf(x, y, omega, v0, dt, r=0.5, q=0.):\n",
|
||||
"\n",
|
||||
" g = 9.8 # gravitational constant\n",
|
||||
" f1 = KalmanFilter(dim_x=5, dim_z=2)\n",
|
||||
" kf = KalmanFilter(dim_x=5, dim_z=2)\n",
|
||||
"\n",
|
||||
" ay = .5*dt**2\n",
|
||||
" f1.F = np.array ([[1, dt, 0, 0, 0], # x = x0+dx*dt\n",
|
||||
" kf.F = np.array ([[1, dt, 0, 0, 0], # x = x0+dx*dt\n",
|
||||
" [0, 1, 0, 0, 0], # dx = dx\n",
|
||||
" [0, 0, 1, dt, ay], # y = y0 +dy*dt+1/2*g*dt^2\n",
|
||||
" [0, 0, 0, 1, dt], # dy = dy0 + ddy*dt \n",
|
||||
" [0, 0, 0, 0, 1]]) # ddy = -g.\n",
|
||||
"\n",
|
||||
" f1.H = np.array([[1, 0, 0, 0, 0],\n",
|
||||
" kf.H = np.array([[1, 0, 0, 0, 0],\n",
|
||||
" [0, 0, 1, 0, 0]])\n",
|
||||
" \n",
|
||||
" f1.R *= r\n",
|
||||
" f1.Q *= q\n",
|
||||
" kf.R *= r\n",
|
||||
" kf.Q *= q\n",
|
||||
"\n",
|
||||
" omega = radians(omega)\n",
|
||||
" vx = cos(omega) * v0\n",
|
||||
" vy = sin(omega) * v0\n",
|
||||
"\n",
|
||||
" f1.x = np.array([[x, vx, y, vy, -9.8]]).T\n",
|
||||
" kf.x = np.array([[x, vx, y, vy, -9.8]]).T\n",
|
||||
" \n",
|
||||
" return f1"
|
||||
" return kf"
|
||||
],
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
@@ -1642,26 +1640,26 @@
|
||||
"\n",
|
||||
"ball = BallTrajectory2D(x0=x, y0=y, theta_deg=theta, velocity=v0, \n",
|
||||
" noise=[.2,.2])\n",
|
||||
"f1 = ball_kf(x,y,theta,v0,dt)\n",
|
||||
"kf = ball_kf(x,y,theta,v0,dt)\n",
|
||||
"\n",
|
||||
"t = 0\n",
|
||||
"xs = []\n",
|
||||
"ys = []\n",
|
||||
"while f1.x[2,0] > 0:\n",
|
||||
"while kf.x[2,0] > 0:\n",
|
||||
" t += dt\n",
|
||||
" x,y = ball.step(dt)\n",
|
||||
" z = np.array([[x,y]]).T\n",
|
||||
" z = np.array([[x, y]]).T\n",
|
||||
"\n",
|
||||
" f1.update(z)\n",
|
||||
" xs.append(f1.x[0,0])\n",
|
||||
" ys.append(f1.x[2,0])\n",
|
||||
" kf.update(z)\n",
|
||||
" xs.append(kf.x[0,0])\n",
|
||||
" ys.append(kf.x[2,0])\n",
|
||||
" \n",
|
||||
" f1.predict() \n",
|
||||
" kf.predict() \n",
|
||||
" \n",
|
||||
" p1 = plt.scatter(x, y, color='green', marker='.', s=75, alpha=0.5)\n",
|
||||
"\n",
|
||||
"p2, = plt.plot (xs, ys,lw=2)\n",
|
||||
"plt.legend([p2,p1], ['Kalman filter', 'Measurements'])\n",
|
||||
"plt.legend([p2, p1], ['Kalman filter', 'Measurements'])\n",
|
||||
"plt.show()"
|
||||
],
|
||||
"language": "python",
|
||||
@@ -1670,13 +1668,13 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc16dc0b7f0>"
|
||||
"<matplotlib.figure.Figure at 0x7fa0346ad2e8>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 22
|
||||
"prompt_number": 23
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -1811,13 +1809,13 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc166042208>"
|
||||
"<matplotlib.figure.Figure at 0x7fa03c32db38>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 23
|
||||
"prompt_number": 24
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -1859,7 +1857,7 @@
|
||||
" self.noise = noise\n",
|
||||
"\n",
|
||||
"\n",
|
||||
" def drag_force (self, velocity):\n",
|
||||
" def drag_force(self, velocity):\n",
|
||||
" \"\"\" Returns the force on a baseball due to air drag at\n",
|
||||
" the specified velocity. Units are SI\n",
|
||||
" \"\"\"\n",
|
||||
@@ -1878,7 +1876,7 @@
|
||||
"\n",
|
||||
" # force due to air drag\n",
|
||||
" v_x_wind = self.v_x - vel_wind\n",
|
||||
" v = sqrt (v_x_wind**2 + self.v_y**2)\n",
|
||||
" v = sqrt(v_x_wind**2 + self.v_y**2)\n",
|
||||
" F = self.drag_force(v)\n",
|
||||
"\n",
|
||||
" # Euler's equations for velocity\n",
|
||||
@@ -1891,7 +1889,7 @@
|
||||
"language": "python",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"prompt_number": 24
|
||||
"prompt_number": 25
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -1949,13 +1947,13 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc16603e978>"
|
||||
"<matplotlib.figure.Figure at 0x7fa03c322fd0>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 25
|
||||
"prompt_number": 26
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -2005,7 +2003,7 @@
|
||||
" p1 = plt.scatter(x, y, color='green', marker='o', s=75, alpha=0.5)\n",
|
||||
"\n",
|
||||
" p2, = plt.plot (xs, ys,lw=2)\n",
|
||||
" plt.legend([p1,p2], ['Measurements', 'Kalman filter'])\n",
|
||||
" plt.legend([p1, p2], ['Measurements', 'Kalman filter'])\n",
|
||||
" plt.show()\n",
|
||||
" \n",
|
||||
"plot_ball_with_q(0.01)\n",
|
||||
@@ -2017,21 +2015,21 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc16d9f6b70>"
|
||||
"<matplotlib.figure.Figure at 0x7fa034705b38>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc166012dd8>"
|
||||
"<matplotlib.figure.Figure at 0x7fa03c0a0860>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 26
|
||||
"prompt_number": 27
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
@@ -2055,21 +2053,21 @@
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc16606d390>"
|
||||
"<matplotlib.figure.Figure at 0x7fa03c3a9cc0>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"metadata": {},
|
||||
"output_type": "display_data",
|
||||
"png": 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truncated
|
||||
"png": 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truncated
|
||||
"text": [
|
||||
"<matplotlib.figure.Figure at 0x7fc165f6de10>"
|
||||
"<matplotlib.figure.Figure at 0x7fa034680ac8>"
|
||||
]
|
||||
}
|
||||
],
|
||||
"prompt_number": 27
|
||||
"prompt_number": 28
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
|
||||
Reference in new issue
Block a user