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libiir/python/filter_analysis.ipynb

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Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
{
"metadata": {
"name": "",
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"signature": "sha256:1cb8a128cf0baef93386ff39d25370890fe1a95c2ff36083b1a83c2cad364c1c"
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"Filter analysis laboratory"
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First configure the filter as desired. The filter is independent of sample rate, but is specified as a ratio to it. We use a specific value here simply to aid comprehension and label all the graph axes as desired. Setting it to 1 would be the same as specifying everything as a ratio."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import iir\n",
"\n",
"# change this to alter the sample rate we are displaying at\n",
"sample_rate = 100 # Hz\n",
"nyquist_freq = sample_rate / 2\n",
"\n",
"# build the filter object, fi, here\n",
"corner_freq = 10 # Hz\n",
"order = 4\n",
Add some more to the Python bindings
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"gain = 1.0\n",
"filter_desc = \"butterworth_lowpass(%d, %f, %f)\" % (order, gain, corner_freq / nyquist_freq)\n",
"print(filter_desc)\n",
"fi = iir.Filter(filter_desc)"
Add an iPython notebook for filter analysis
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],
"language": "python",
"metadata": {},
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"butterworth_lowpass(4, 1.000000, 0.200000)\n"
]
}
],
"prompt_number": 65
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we set up the plotting library and helper functions."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
Add some more to the Python bindings
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"import numpy as np\n",
Add an iPython notebook for filter analysis
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"import pylab\n",
"\n",
"# number of points to plot on graphs (arbitrary)\n",
"num_points = 1000\n",
"\n",
"# helper function: calls the Filter.response() method on each member of the array\n",
"respfunc = pylab.vectorize(lambda f: fi.response(f/sample_rate))"
],
"language": "python",
"metadata": {},
"outputs": [],
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"prompt_number": 66
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Filter characteristics"
]
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"-3dB points"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"halfgain = np.array(fi.responseMinus3dB(gain)) * sample_rate\n",
"print(halfgain)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[ 9.99999995]\n"
]
}
],
"prompt_number": 67
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Impulse response"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# start at t=0, impulse at t=tau, end at t=10\u00d7tau\n",
"tau = sample_rate * (1 / corner_freq) # number of samples\n",
"\n",
"x = pylab.hstack([pylab.zeros(tau), pylab.ones(9*tau)])\n",
"t = pylab.linspace(0, tau / 10, x.size)\n",
"fi2 = iir.Filter(fi)\n",
"\n",
"y = np.array(x.size, dtype=np.double)\n",
"y.resize(x.size)\n",
"for i in range(0, y.size):\n",
" y[i] = fi2.f(x[i])\n",
"\n",
"fig, axes = plt.subplots(figsize=(10,6), dpi=100)\n",
"axes.plot(t, y, 'r')\n",
"axes.plot(t, x)\n",
"axes.set_xlabel('Time (s)')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"<matplotlib.text.Text at 0x7f40b82e4a58>"
]
},
{
"metadata": {},
"output_type": "display_data",
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"text": [
"<matplotlib.figure.Figure at 0x7f40b846a320>"
]
}
],
"prompt_number": 68
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Impulse response"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = pylab.hstack([pylab.zeros(tau), 1, pylab.zeros(9*tau - 1)])\n",
"t = pylab.linspace(0, tau / 10, x.size)\n",
"fi2 = iir.Filter(fi)\n",
"\n",
"y = np.array(x.size, dtype=np.double)\n",
"y.resize(x.size)\n",
"for i in range(0, y.size):\n",
" y[i] = fi2.f(x[i])\n",
"\n",
"fig, axes = plt.subplots(figsize=(10,6), dpi=100)\n",
"axes.plot(t, y, 'r')\n",
"axes.plot(t, x)\n",
"axes.set_xlabel('Time (s)')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
"<matplotlib.text.Text at 0x7f40b83cce80>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAl0AAAF/CAYAAABzDnyLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAH39JREFUeJzt3X2UXXV97/H3yUyChQIBYqFCaJBnuJVgFIIKHQRpQCDW\npjyIIASVrprC6tUlUpcS/8CWLkx5SrkQeYhcAVvtcgGSRLgwSOUhqCSAEiCh6UoIhAchIKCZmZz7\nx++czJ7DmZlzZu/fPufMfr/W2mv2mbPn7F/YJPnk+/t99wZJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiQVzA3ARuCJEY65EngWWAkcmsegJEmSxpsjCUFquNB1AnBXZf9w4OE8BiVJkjQeTWP40PV/gFMT\nr1cBu8YekCRJUruZEPnzdwfWJV6vB/aIfE5JkqS2Ezt0AZRqXpdzOKckSVJb6Y78+c8DUxOv96h8\nb4i99967vGbNmshDkSRJysQaYJ9mfyh2pet24KzK/kzgdUK34xBr1qyhXC6P2+2448rcemvrxxFr\nu/jii1s+BjevXxE3r11nb16/zt2AvccSitJWum4F/gKYQli7dTEwsfLetYTOxROA1cBbwDkpz9eR\n+vrCJkmSiitt6Dq9gWPmpTxHxzN0SZKkPBbSF954D109PT2tHoJS8Pp1Lq9dZ/P6FU9tZ2GrlCtz\npOPSjBlwzjkwr/A1P0mSOl+pVIIxZCgrXTnYvDlskiSpuAxdORjv04uSJGl0hq4cGLokSZKhKweG\nLkmSZOjKgaFLkiQZunJg6JIkSYauHNi9KEmSDF05sNIlSZIMXTkwdEmSJENXZOUyDAwYuiRJKjpD\nV2TVsGXokiSp2AxdkRm6JEkSGLqiq3Yt2r0oSVKxGbois9IlSZLA0BWdoUuSJIGhKzpDlyRJAkNX\ndIYuSZIEhq7oqmHLhfSSJBWboSuyatiy0iVJUrEZuiLr64NttjF0SZJUdIauyPr6YNttDV2SJBWd\noSsyQ5ckSQJDV3SGLkmSBIau6Pr6YLvt7F6UJKnoDF2Rbd4cQpeVLkmSis3QFZnTi5IkCQxd0Rm6\nJEkSGLqiM3RJkiQwdEXX1wfveQ9s2QIDA60ejSRJahVDV2R9fTBpUtisdkmSVFyGrsg2b4aJE8Nm\n6JIkqbgMXZH19Rm6JEmSoSs6Q5ckSQJDV3SGLkmSBIau6FxIL0mSwNAVXbLS5fMXJUkqLkNXZHYv\nSpIkMHRF55ouSZIEhq7oDF2SJAkMXdEZuiRJEhi6okt2L7qQXpKk4jJ0ReZCekmSBIau6JxelCRJ\nYOiKztAlSZLA0BWdoUuSJEE2oWsWsAp4FriwzvtTgKXACuBJ4OwMztkxDF2SJAnSh64u4GpC8DoI\nOB04sOaYecBjwHSgB/gO0J3yvB3D7kVJkgTpQ9dhwGpgLdAH3AbMrjnmBWCHyv4OwKtAf8rzdgy7\nFyVJEqSvOO0OrEu8Xg8cXnPMIuBeYAOwPXBKynN2FKcXJUkSpK90lRs45h8J67neR5hiXEgIX4Vg\n6JIkSZC+0vU8MDXxeiqh2pX0EeCSyv4a4L+B/YFfJA+aP3/+1v2enh56enpSDq09GLokSepsvb29\n9Pb2pv6cUsqf7waeBo4hTB8uJyymfypxzAJgE/AtYFfgl8AHgN8mjimXy40UzTrPfvvBHXfAjTfC\njjvCRRe1ekSSJCmNUqkEY8hQaStd/YTuxGWETsbrCYHrvMr71wLfBm4EVhKmM7/K0MA1rtm9KEmS\nIJtbNyypbEnXJvZfAU7K4DwdKdm9+Ic/tHo0kiSpVbwjfWSu6ZIkSWDois7QJUmSwNAVnaFLkiSB\noSs6Q5ckSQJDV1Tl8mDosntRkqRiM3RFNDAApRJ0dVnpkiSp6AxdEVWrXGDokiSp6AxdERm6JElS\nlaErIkOXJEmqMnRFVH0EELiQXpKkojN0RWSlS5IkVRm6Iqo+dxEMXZIkFZ2hKyIrXZIkqcrQFZGh\nS5IkVRm6IjJ0SZKkKkNXRHYvSpKkKkNXRFa6JElSlaErIrsXJUlSlaErIitdkiSpytAVkaFLkiRV\nGboiMnRJkqQqQ1dEdi9KkqQqQ1dEVrokSVKVoSui2u7F/n4ol1s7JkmS1BqGroiSla5SCbq6QvCS\nJEnFY+iKKBm6wClGSZKKzNAVkaFLkiRVGboiSnYvgh2MkiQVmaErouRCerDSJUlSkRm6InJ6UZIk\nVRm6IjJ0SZKkKkNXRIYuSZJUZeiKqF7ociG9JEnFZOiKqF73opUuSZKKydAVkd2LkiSpytAVkWu6\nJElSlaErIkOXJEmqMnRFZOiSJElVhq6I7F6UJElVhq6I7F6UJElVhq6I7F6UJElVhq6IXNMlSZKq\nDF0RGbokSVKVoSsiQ5ckSaoydEVUbyG93YuSJBWToSsiK12SJKnK0BWR3YuSJKkqi9A1C1gFPAtc\nOMwxPcBjwJNAbwbn7AhWuiRJUlV3yp/vAq4GjgWeBx4FbgeeShwzGVgI/CWwHpiS8pwdw9AlSZKq\n0la6DgNWA2uBPuA2YHbNMZ8BfkQIXACvpDxnxzB0SZKkqrSha3dgXeL1+sr3kvYFdgbuA34BnJny\nnB3D7kVJklSVdnqx3MAxE4EPAscA2wIPAQ8T1oCNa1a6JElSVdrQ9TwwNfF6KoPTiFXrCFOK71S2\nnwGHUBO65s+fv3W/p6eHnp6elENrPbsXJUnqfL29vfT29qb+nFLKn+8GniZUsTYAy4HTGbqQ/gDC\nYvu/BLYBHgFOBX6TOKZcLjdSNOss3d3wzjuDweu66+DRR2HRotaOS5IkjV2pVIIxZKi0la5+YB6w\njNDJeD0hcJ1Xef9awu0klgKPA1uARQwNXONSuQwDAyF4VVnpkiSpuNKGLoAllS3p2prXl1W2wujr\nC4GrlMjBEye6kF6SpKLyjvSR1HYuQnhtpUuSpGIydEVS27kITi9KklRkhq5IajsXwdAlSVKRGboi\nsdIlSZKSDF2RGLokSVKSoSuS4UKX3YuSJBWToSsSuxclSVKSoSsSpxclSVKSoSsSuxclSVKSoSsS\nK12SJCnJ0BWJoUuSJCUZuiKxe1GSJCUZuiKxe1GSJCUZuiJxIb0kSUoydEXimi5JkpRk6IrE0CVJ\nkpIMXZEYuiRJUpKhK5J6C+m7uqBchoGB1oxJkiS1jqErknqVrlLJapckSUVl6IqkXvciGLokSSoq\nQ1ck9SpdYOiSJKmoDF2RGLokSVKSoSuSkUKXjwKSJKl4DF2R1OteBB8FJElSURm6InF6UZIkJRm6\nIrF7UZIkJRm6IrHSJUmSkgxdkRi6JElSkqErErsXJUlSkqErErsXJUlSkqErEqcXJUlSkqErErsX\nJUlSkqErEitdkiQpydAViaFLkiQlGboisXtRkiQlGboisXtRkiQlGboicXpRkiQlGboisXtRkiQl\nGboisdIlSZKSDF2RGLokSVKSoSsSuxclSVKSoSsSuxclSVKSoSsSpxclSVKSoSsSuxclSVKSoSsS\nK12SJCnJ0BWJC+klSVKSoSsSK12SJCkpi9A1C1gFPAtcOMJxHwb6gU9ncM62N1zosntRkqRiShu6\nuoCrCcHrIOB04MBhjrsUWAqUUp6z7Q0MQKkEXV3vfs9KlyRJxZQ2dB0GrAbWAn3AbcDsOsf9PfBD\n4OWU5+sIw3UugqFLkqSiShu6dgfWJV6vr3yv9pjZwDWV1+WU52x7w00tgqFLkqSi6k75840EqMuB\nr1WOLTHM9OL8+fO37vf09NDT05NyaK0zWuiye1GSpM7R29tLb29v6s9Ju75qJjCfsKYL4CJgC2H9\nVtVzifNMAd4GvgDcnjimXC6PnwLYiy/C9Onha60lS+CKK2Dp0vzHJUmS0iuVSjCGDJW20vULYF9g\nGrABOJWwmD7p/Yn9G4E7GBq4xh2nFyVJUq20oasfmAcsI3QoXg88BZxXef/alJ/fkQxdkiSpVtrQ\nBbCksiUNF7bOyeB8bc/uRUmS
"text": [
"<matplotlib.figure.Figure at 0x7f40b83edf28>"
]
}
],
"prompt_number": 69
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Linear Bode plot"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"freq = pylab.linspace(0, nyquist_freq, num_points)\n",
"resp = respfunc(freq)\n",
"\n",
"fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(10, 10), dpi=100)\n",
"\n",
"axes[0].plot(freq, abs(resp), 'r')\n",
"axes[0].set_xlabel('Frequency (Hz)')\n",
"axes[0].set_ylabel('Magnitude')\n",
"\n",
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"axes[1].plot(freq, pylab.angle(resp), 'r')\n",
Add an iPython notebook for filter analysis
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"axes[1].set_xlabel('Frequency (Hz)')\n",
"axes[1].set_ylabel('Phase (radians)')\n",
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"axes[1].set_ylim([-pylab.pi, pylab.pi])\n",
"axes[1].set_yticks(pylab.linspace(-pylab.pi, pylab.pi, 9))\n",
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
"axes[1].set_yticklabels([\"-\u03c0\", \"-\u00be\u03c0\", \"-\u00bd\u03c0\", \"-\u00bc\u03c0\", \"0\", \"\u00bc\u03c0\", \"\u00bd\u03c0\", \"\u00be\u03c0\", \"\u03c0\"])\n",
"\n",
"None"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAJeCAYAAADxx6J0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAh6v9pzHgEGEyQNtgAHARw2o
"text": [
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"<matplotlib.figure.Figure at 0x7f40b83d4390>"
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
]
}
],
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"prompt_number": 70
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Logarithmic Bode plot"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"freq = pylab.logspace(-3, pylab.log10(nyquist_freq), num_points)\n",
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
"resp = respfunc(freq)\n",
"\n",
"fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(10, 10), dpi=100)\n",
"\n",
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"axes[0].plot(freq, 10 * pylab.log10(abs(resp)), 'r')\n",
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
"axes[0].set_xlabel('Frequency (Hz)')\n",
"axes[0].set_xscale('log')\n",
"axes[0].set_ylabel('Magnitude (dB)')\n",
"axes[0].axhline(y=-3)\n",
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"for f in halfgain:\n",
" axes[0].axvline(f)\n",
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
"\n",
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"axes[1].plot(freq, pylab.angle(resp), 'r')\n",
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
"axes[1].set_xlabel('Frequency (Hz)')\n",
"axes[1].set_xscale('log')\n",
"axes[1].set_ylabel('Phase (radians)')\n",
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"axes[1].set_ylim([-pylab.pi, pylab.pi])\n",
"axes[1].set_yticks(pylab.linspace(-pylab.pi, pylab.pi, 9))\n",
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
"axes[1].set_yticklabels([\"-\u03c0\", \"-\u00be\u03c0\", \"-\u00bd\u03c0\", \"-\u00bc\u03c0\", \"0\", \"\u00bc\u03c0\", \"\u00bd\u03c0\", \"\u00be\u03c0\", \"\u03c0\"])\n",
"\n",
"None"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
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Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
"text": [
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"<matplotlib.figure.Figure at 0x7f40b8467b70>"
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
]
}
],
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"prompt_number": 71
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
Add some more to the Python bindings
2014-07-08 11:42:13 +01:00
"prompt_number": 71
Add an iPython notebook for filter analysis
2014-07-07 17:45:33 +01:00
}
],
"metadata": {}
}
]
}