136 lines
3.3 KiB
Plaintext
136 lines
3.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "bcd07c1e-6722-44c7-b162-267f1341c2fe",
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"metadata": {},
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"source": [
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"# Standard filterbank frequency response and impulse response"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f0c4d6ef-69b2-4b5c-92fd-d987a11f3cbd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Prerequisites, uncomment below in case of errors. Also for ipympl, restart Jupyter Lab if it was not installed\n",
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"\n",
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"!cd .. && maturin develop -F python-bindings\n",
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"#!pip install ipympl scipy matplotlib"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "23b45cf6-85bc-4eaa-8f27-77c5b354e772",
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"metadata": {},
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"outputs": [],
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"source": [
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"# If this does not work, install ipympl and reboot Jupyter Lab\n",
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"%matplotlib widget"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "96090ac9-2033-411d-8e45-0c6b375a268b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from numpy import log10\n",
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"from lasprs import StandardFilterDescriptor\n",
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"def level(a):\n",
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" return 20*np.log10(np.abs(a))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "52343e3d-63ef-4fc2-ad7e-85ca124efdac",
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"metadata": {},
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"outputs": [],
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"source": [
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"freq = np.logspace(log10(2), log10(2e4), 200)\n",
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"octaves = StandardFilterDescriptor.genFilterSetInRange(1, 10, 16e3, False)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2788f062-34ab-4573-9849-459fdccc97d3",
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"metadata": {},
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"outputs": [],
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"source": [
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"hs = [ o.genFilter().tf(0,freq) for o in octaves]\n",
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"names = [str(o) for o in octaves]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3366f635-29fe-4757-ab0c-9d4929f8cddb",
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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure()\n",
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"# plt.subplot(211)\n",
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"plt.title('Frequency response of filters - magnitude')\n",
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"\n",
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"for h in hs:\n",
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" plt.semilogx(freq, level(h))\n",
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"plt.legend(names)\n",
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"plt.ylabel('Magnitude [dB]')\n",
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"plt.ylim(-50, 1)\n",
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"plt.xlabel('Freq. [Hz]')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b242187a-3449-4c30-8e26-28e75d59c414",
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"metadata": {},
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"outputs": [],
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"source": [
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"fs = 48000\n",
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"tend = 0.01\n",
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"t = np.linspace(0, tend, int(tend*fs), endpoint=False)\n",
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"impulse = np.zeros(t.size)\n",
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"impulse[0] = 1\n",
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"plt.figure()\n",
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"plt.title('Filter impulse response')\n",
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"for o in octaves:\n",
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" i = o.genFilter().bilinear(fs).filter(impulse)\n",
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" plt.plot(t, i)\n",
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"plt.legend(names, loc='upper right')\n",
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"plt.ylabel('Filter output [-]')\n",
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"plt.xlabel('Time [s]')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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