149 lines
3.5 KiB
Plaintext
149 lines
3.5 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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"# Test Sound Level Meter implementation - 1"
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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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"# Only do the following if you are in develop mode\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._lasprs import StandardFilterDescriptor, SLMSettings, FreqWeighting, TimeWeighting, SLM\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.Overall()]+StandardFilterDescriptor.genOctaveFilterSet(16, 16e3)\n",
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"\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": "ab22d9cc-03ae-4d58-b730-599b659f9dc0",
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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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"\n",
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"tw = TimeWeighting.Impulse()\n",
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"# tw = TimeWeighting.Fast()\n",
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"N = fs\n",
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"if tw == TimeWeighting.Slow():\n",
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" tau = 1.0\n",
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"elif tw == TimeWeighting.Fast():\n",
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" tau = 1/8\n",
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"elif tw == TimeWeighting.Impulse():\n",
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" tau = 35e-3\n",
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"\n",
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"else:\n",
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" raise NotImplementedError()\n",
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"settings = SLMSettings(fs, FreqWeighting.Z, tw, filterDescriptors=octaves, Lref=1)\n",
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"slm = SLM(settings)"
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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": "e1960b80-99f9-4744-9b0f-6651ed2693cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"inp = np.ones(N)\n",
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"# inp = np.zeros(N)\n",
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"inp[0] = 1"
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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": "532539b8-7a5e-47c2-afb1-a72d62c445d4",
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"metadata": {},
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"outputs": [],
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"source": [
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"t = np.linspace(0, N/fs, N, endpoint=False)\n",
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"out = slm.run(inp, True)"
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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": "f446ac32-4ac2-4e85-87c8-43a06be9fb14",
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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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"for o in out:\n",
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" plt.plot(t,o)\n",
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"plt.ylim(-60, 0)\n",
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"plt.legend(names)"
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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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