{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synchronized Swept Sine, Sweep generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pylab as plt\n",
    "import numpy as np\n",
    "import scipy\n",
    "import soundfile as sf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Synchronized Swept Sine Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1 = 20        # start frequency\n",
    "f2 = 20000       # end frequency\n",
    "fs = 48000      # sampling frequency\n",
    "T = 6          # time duration of the sweep\n",
    "fade = [48000, 480]   # samlpes to fade-in and fade-out the input signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# -- generation of the signal\n",
    "L = T/np.log(f2/f1)                    # parametre of exp.swept-sine\n",
    "\n",
    "t = np.linspace(0,T,fs*T)  # time axis\n",
    "s = np.sin(2*np.pi*f1*L*np.exp(t/L))       # generated swept-sine signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fade-in the input signal\n",
    "if fade[0]>0:\n",
    "    s[0:fade[0]] = s[0:fade[0]] * ((-np.cos(np.arange(fade[0])/fade[0]*np.pi)+1) / 2)\n",
    "\n",
    "# fade-out the input signal\n",
    "if fade[1]>0:\n",
    "    s[-fade[1]:] = s[-fade[1]:] *  ((np.cos(np.arange(fade[1])/fade[1]*np.pi)+1) / 2)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "288000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add 6 seconds of tail to the signal\n",
    "s = np.hstack((s, np.zeros(6*fs)))\n",
    "s *= 10**(-12/20) # leave 12dB of headroom for avoid damaging speakers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = np.vstack((np.concatenate((s, np.zeros_like(s))), np.concatenate((np.zeros_like(s), s)))).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sf.write('sweep.wav', st, fs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fake Sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 0.8*s + np.roll(s, 4800)\n",
    "#y = s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10a128550>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(s)\n",
    "plt.plot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nonlinear deconvolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "sweep = s[:T*fs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "wet_sweep = sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute the inverse filter and obtain the rir\n",
    "s_inv = np.flip(sweep, 0) / np.exp(t/L)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "h = scipy.signal.fftconvolve(wet_sweep, s_inv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11cf93070>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(h) # we leave 100ms before the impulse for the pre-ringing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_ir(ir):\n",
    "    n_fft = max(2048, len(ir))  # Ensure enough frequency resolution\n",
    "    freq_response = np.fft.rfft(ir, n=n_fft)\n",
    "    frequencies = np.fft.rfftfreq(n_fft, d=1/fs)\n",
    "    \n",
    "    # Convert magnitude to Decibels (dB)\n",
    "    magnitude_db = 20 * np.log10(np.abs(freq_response) + 1e-10)  # Avoid log(0)\n",
    "    \n",
    "    # Extract Phase and convert to degrees\n",
    "    phase_deg = np.angle(freq_response, deg=True)\n",
    "    \n",
    "    # 3. Plotting the results (Avoiding plt.figure() per guidelines)\n",
    "    plt.figure(figsize=(14, 6), dpi=80)\n",
    "    # Magnitude plot\n",
    "    plt.semilogx(frequencies, magnitude_db, color='b', linewidth=1.5)\n",
    "    #plt.set_title('Audio Frequency Response')\n",
    "    #plt.set_ylabel('Magnitude (dB)')\n",
    "    plt.grid(True, which=\"both\", ls=\"-\", color='0.65')\n",
    "    plt.xlim(20, 20000)  # Audio frequency range: 20 Hz to 20 kHz\n",
    "    plt.ylim(bottom=max(magnitude_db) - 60, top=max(magnitude_db) + 5) # Show top 60 dB range\n",
    "    plt.xlabel('Hz')\n",
    "    plt.ylabel('dB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1120x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ir(h)"
   ]
  }
 ],
 "metadata": {
  "hide_input": false,
  "kernelspec": {
   "display_name": "rirs",
   "language": "python",
   "name": "rirs"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}