{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bcd07c1e-6722-44c7-b162-267f1341c2fe",
   "metadata": {},
   "source": [
    "# Test Sound Level Meter implementation - 1"
   ]
  },
  {
   "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",
    "# Only do the following if you are in develop mode\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._lasprs import StandardFilterDescriptor, SLMSettings, FreqWeighting, TimeWeighting, SLM\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.Overall()]+StandardFilterDescriptor.genOctaveFilterSet(16, 16e3)\n",
    "\n",
    "names = [str(o) for o in octaves]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab22d9cc-03ae-4d58-b730-599b659f9dc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = 48000\n",
    "\n",
    "tw = TimeWeighting.Impulse()\n",
    "# tw = TimeWeighting.Fast()\n",
    "N = fs\n",
    "if tw == TimeWeighting.Slow():\n",
    "    tau = 1.0\n",
    "elif tw == TimeWeighting.Fast():\n",
    "    tau = 1/8\n",
    "elif tw == TimeWeighting.Impulse():\n",
    "    tau = 35e-3\n",
    "\n",
    "else:\n",
    "    raise NotImplementedError()\n",
    "settings = SLMSettings(fs, FreqWeighting.Z, tw, filterDescriptors=octaves, Lref=1)\n",
    "slm = SLM(settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1960b80-99f9-4744-9b0f-6651ed2693cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = np.ones(N)\n",
    "# inp = np.zeros(N)\n",
    "inp[0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "532539b8-7a5e-47c2-afb1-a72d62c445d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.linspace(0, N/fs, N, endpoint=False)\n",
    "out = slm.run(inp, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f446ac32-4ac2-4e85-87c8-43a06be9fb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure()\n",
    "for o in out:\n",
    "    plt.plot(t,o)\n",
    "plt.ylim(-60, 0)\n",
    "plt.legend(names)"
   ]
  }
 ],
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