Sound_of_Waves.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bingsen-wang/EE-Fundamentals/blob/main/Sound_of_Waves.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jAMKoELNpsK-"
      },
      "source": [
        "<center><h1><b>Sound of Multiple Waves</h1></b>\n",
        "\n",
        "Dr. Bingsen Wang\n",
        "\n",
        "12/1/2023\n",
        "</center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hv0YxDrWU4h8"
      },
      "source": [
        "#Python Code\n",
        "The Python code generates the sound of varies waveforms.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "XeGsHYTt8IXU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "70a7c40b-a46b-43b5-958d-2f331ce633b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Building video SoundOfWavesFinal.mp4.\n",
            "MoviePy - Writing audio in SoundOfWavesFinalTEMP_MPY_wvf_snd.mp3\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MoviePy - Done.\n",
            "Moviepy - Writing video SoundOfWavesFinal.mp4\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": []
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moviepy - Done !\n",
            "Moviepy - video ready SoundOfWavesFinal.mp4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x900 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from logging import WARNING\n",
        "from os import linesep\n",
        "import matplotlib\n",
        "from scipy.io.wavfile import write\n",
        "from moviepy.editor import *\n",
        "from numpy import linspace,cos,pi,sin,arccos,fft,angle,abs,clip,heaviside,tan,arctan,int16,array\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import animation,rc\n",
        "rc('animation', html='jshtml')\n",
        "plt.rcParams.update({\"font.family\" : \"serif\",\"mathtext.fontset\" : \"cm\"})\n",
        "\n",
        "# functions\n",
        "def spectrum(xt): #spectrum - magnitude and angle\n",
        "  xfft = fft.rfft(xt)\n",
        "  xfft_angle = angle(xfft)\n",
        "  xfft_mag = abs(xfft)/len(xt)*2\n",
        "  xfft_mag[0]=xfft_mag[0]*0.5 #adjust the dc component\n",
        "  return xfft_mag,xfft_angle\n",
        "\n",
        "def waveforms(f,n,t): #n: type of the waveform\n",
        "  if n==0:\n",
        "    y = cos(2*pi*f*t)\n",
        "    return y,'Cosine'\n",
        "  elif n==1:\n",
        "    y = (heaviside(cos(2*pi*f*t),0)*2-1)*(heaviside(abs(cos(2*pi*f*t))-0.2,0))\n",
        "    return y,'Square'\n",
        "  elif n==2:\n",
        "    y = 2/pi*arctan(tan(pi*f*t))\n",
        "    return y,'Sawtooth Rising'\n",
        "  elif n==3:\n",
        "    y = -2/pi*arctan(tan(pi*f*t))\n",
        "    return y, 'Sawtooth Falling'\n",
        "  elif n==4:\n",
        "    y = 2/pi*arccos(cos(2*pi*f*t+pi))-1\n",
        "    return y,'Triangle'\n",
        "  elif n==5:\n",
        "    y = clip((2/pi*arccos(cos(2*pi*f*t+pi))-1)*2,-1,1)\n",
        "    return y,'Trapezoid'\n",
        "  else:\n",
        "    y = abs(sin(pi*f*t))-2/pi\n",
        "    return y,'Rectified Sine'\n",
        "\n",
        "# parameters\n",
        "Nf = 7 # number of frames\n",
        "f1 = [1, 2, 4] #fundamental frequency for plotting\n",
        "fsnd = [220, 440, 880] #fundamental frequency for sound\n",
        "sr = 44100 #sampling rate\n",
        "tpf = 1.0 # time per frame\n",
        "\n",
        "#time and frequency series\n",
        "t = linspace(0,1,2048) #time series for plotting\n",
        "tsnd=linspace(0,tpf,int(tpf*sr)) #time series for sampling\n",
        "fser = linspace(0,1,51) # frequency series\n",
        "soundTrack=[]\n",
        "for i in range(len(fsnd)):\n",
        "  for k in range(Nf):\n",
        "    soundTrack.extend(int16(waveforms(fsnd[i],k,tsnd)[0]*32767))\n",
        "\n",
        "fig = plt.figure(figsize=(9,9))\n",
        "fig.tight_layout()\n",
        "ax_frame = [[[0, 0.5, 1, .4], [-.02,1.1], [-1.1,1.1]], #time-domain waveform\n",
        "            [[0, 0,   1, .4], [-.02,1.1], [-.1,1.1]], # spectrum\n",
        "            ]# [pos-boundary, xlim, ylim] for subplots\n",
        "ax_lbl=[['$f(t)$', '$t$'],\n",
        "        ['$F_{fft}$', '$t$'],\n",
        "        ] #variables for yx axes of each subplot\n",
        "title_lst = ['','Spectrum']\n",
        "clst=['b','r'] #colors of lines\n",
        "tx_titles = []\n",
        "ax_lst=[] #axis list or the list of subplots\n",
        "ylabel_lst=[] #y-labels to update\n",
        "for k in range(len(ax_frame)):\n",
        "  xn,xm,yn,ym = ax_frame[k][1]+ax_frame[k][2]\n",
        "  ax=fig.add_axes(ax_frame[k][0],xlim=[xn,xm], ylim=[yn,ym],fc='none') #no fc\n",
        "  ax.axis('off') #turn off axis frames\n",
        "  ax_lst.append(ax)\n",
        "  ax.annotate(\"\", (xm, 0), (xn, 0),arrowprops={'arrowstyle':\"->\"}) #x-axis\n",
        "  ax.annotate(\"\", (0,ym), (0,yn),arrowprops={'arrowstyle':\"->\"}) #y-axis\n",
        "  if k==0:\n",
        "    line_wave = ax.plot([], [],color=clst[k], lw=2)[0]\n",
        "  else:\n",
        "    line_fft = ax.plot([], [], ls='',color=clst[k],marker='x',markersize=7)[0] #fft plot\n",
        "  ax.text(xm-.02,0,ax_lbl[k][-1],size=24,va='top',ha='right') #x-axis label\n",
        "  ylabel_lst.append(ax.text(0.01,ym,ax_lbl[k][0],size=24,va='bottom',color=clst[k])) #y label\n",
        "  tx_titles.append(ax.text(0.5,ax_frame[k][2][1]+0.1,title_lst[k],va='top',ha='center',size=20))\n",
        "\n",
        "# animation function. This is called sequentially\n",
        "def animate(i):\n",
        "  k = int(i/Nf)\n",
        "  ii = i%Nf\n",
        "  ft,wn = waveforms(f1[k],ii,t)\n",
        "  Ffft = spectrum(ft)\n",
        "  line_wave.set_data(t,ft)\n",
        "  line_fft.set_data(fser,Ffft[0][0:len(fser)])\n",
        "  tx_titles[0].set_text(wn+', '+str(fsnd[k])+' Hz')\n",
        "  return\n",
        "\n",
        "anim = animation.FuncAnimation(fig, animate, frames=Nf*len(fsnd), interval=50)\n",
        "# anim #uncomment to generate animation in the output area\n",
        "# to save the animation, uncomment the following three lines\n",
        "fn = \"SoundOfWaves\"\n",
        "writervideo = animation.FFMpegWriter(fps=1)\n",
        "anim.save(fn+'.mp4', writer=writervideo,dpi = 120)\n",
        "write(fn+'.wav', sr,array(soundTrack))\n",
        "audio = AudioFileClip(fn+'.wav',fps=sr)\n",
        "video = VideoFileClip(fn+'.mp4')\n",
        "final = video.set_audio(audio)\n",
        "# final.ipython_display()\n",
        "final.write_videofile(fn+\"Final.mp4\")"
      ]
    }
  ]
}