doc/examples/modal-room-acoustics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modal Room Acoustics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0 = 1, 3, 1.80  # source position\n",
    "L = 6, 6, 3  # dimensions of room\n",
    "deltan = 0.01  # absorption factor of walls\n",
    "N = 20  # maximum order of modes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can experiment with different combinations of modes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#N = [[1], 0, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sound Field for One Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = 500  # frequency\n",
    "omega = 2 * np.pi * f  # angular frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = sfs.util.xyz_grid([0, L[0]], [0, L[1]], L[2] / 2, spacing=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p = sfs.fd.source.point_modal(omega, x0, grid, L, N=N, deltan=deltan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For now, we apply an arbitrary scaling factor to make the plot look good\n",
    "\n",
    "TODO: proper normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p *= 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs.plot2d.amplitude(p, grid);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency Response at One Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = np.linspace(20, 200, 180)  # frequency\n",
    "omega = 2 * np.pi * f  # angular frequency\n",
    "\n",
    "receiver = 1, 1, 1.8\n",
    "\n",
    "p = [sfs.fd.source.point_modal(om, x0, receiver, L, N=N, deltan=deltan)\n",
    "     for om in omega]\n",
    "     \n",
    "plt.plot(f, sfs.util.db(p))\n",
    "plt.xlabel('frequency / Hz')\n",
    "plt.ylabel('level / dB')\n",
    "plt.grid()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}