{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import librosa\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "block=1024\n",
    "length=block*20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "diracs = [ x/float(length) if (x % block) == 0 else 0.0 for x in range(0,length) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7fe1e18320>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ao7QyAOblsHPbuea3rGlr87qpXgZAnzYfAdmJSBpkANS8rBw65hzqKGl8BkCfHM4A/IMw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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8020199278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(diracs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "wave = [ (int(x/block) / float(length/block)) * math.sin(x*math.pi/(block/8)) for x in range(0,length)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7fe1e54780>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GLnU9iSqts1WJTEvOvVISstwExOcw47p3mNYk8Z3vhCR+333t221/7WshAd9+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7fe1e54cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wave)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "duration=20.0\n",
    "rate=22050\n",
    "nsamples=duration*rate\n",
    "repeats=int(nsamples/length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data=[diracs * repeats, wave * repeats]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ndata = np.uint8(np.array(data) * 127.0 + 128.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(430080, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndata = np.swapaxes(ndata, 0, 1)\n",
    "ndata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.io.wavfile import write\n",
    "write('/tmp/test.wav', rate, ndata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
