lasprs 0.14.3

Library for Acoustic Signal Processing (Rust edition, with optional Python bindings via pyo3)
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135

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