BreadBoard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'gammatonecl'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-345bbb3d1cdc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msignal\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msgn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgammatone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilters\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0moldfilt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mgammatonecl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilters\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnewfilt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtqdm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'gammatonecl'"
     ]
    }
   ],
   "source": [
    "import pyopencl as cl\n",
    "from pyopencl.elementwise import ElementwiseKernel\n",
    "import pyopencl.array as cla\n",
    "import pyopencl.clmath as clm\n",
    "from pyopencl.elementwise import ElementwiseKernel\n",
    "import numpy as np\n",
    "import math\n",
    "import time\n",
    "import soundfile\n",
    "from scipy import signal as sgn\n",
    "import gammatone.filters as oldfilt\n",
    "import gammatoneclS.filters as newfilt\n",
    "from matplotlib import pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from pyopencl.scan import GenericScanKernel\n",
    "\n",
    "ctx = cl.create_some_context()\n",
    "q = cl.CommandQueue(ctx)\n",
    "M_PI = 3.14159265358979323846\n",
    "BW_CORRECTION = 1.0190\n",
    "\n",
    "def erb(x):\n",
    "    return 24.7 * ( 4.37e-3 * x + 1.0 )\n",
    "\n",
    "def normalize(v, axis=-1):\n",
    "    norm = np.linalg.norm(v, axis=axis)\n",
    "    return v / norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "Error opening 't29_lwwj2n_m17_lgwe7s.wav': System error.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-8f531f6154bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msoundfile\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msoundfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m't29_lwwj2n_m17_lgwe7s.wav'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mascontiguousarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m9557352\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgammatonecl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilters\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_coefficients\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcenter_freqs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/PersonalProjects/gammatone/venv/lib/python3.8/site-packages/soundfile.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(file, frames, start, stop, dtype, always_2d, fill_value, out, samplerate, channels, format, subtype, endian, closefd)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m     \"\"\"\n\u001b[0;32m--> 256\u001b[0;31m     with SoundFile(file, 'r', samplerate, channels,\n\u001b[0m\u001b[1;32m    257\u001b[0m                    subtype, endian, format, closefd) as f:\n\u001b[1;32m    258\u001b[0m         \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/PersonalProjects/gammatone/venv/lib/python3.8/site-packages/soundfile.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file, mode, samplerate, channels, subtype, endian, format, closefd)\u001b[0m\n\u001b[1;32m    627\u001b[0m         self._info = _create_info_struct(file, mode, samplerate, channels,\n\u001b[1;32m    628\u001b[0m                                          format, subtype, endian)\n\u001b[0;32m--> 629\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode_int\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosefd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    630\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missuperset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'r+'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseekable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m             \u001b[0;31m# Move write position to 0 (like in Python file objects)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/PersonalProjects/gammatone/venv/lib/python3.8/site-packages/soundfile.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, file, mode_int, closefd)\u001b[0m\n\u001b[1;32m   1181\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid file: {0!r}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m         _error_check(_snd.sf_error(file_ptr),\n\u001b[0m\u001b[1;32m   1184\u001b[0m                      \"Error opening {0!r}: \".format(self.name))\n\u001b[1;32m   1185\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode_int\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_snd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSFM_WRITE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/PersonalProjects/gammatone/venv/lib/python3.8/site-packages/soundfile.py\u001b[0m in \u001b[0;36m_error_check\u001b[0;34m(err, prefix)\u001b[0m\n\u001b[1;32m   1355\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0merr\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1356\u001b[0m         \u001b[0merr_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_snd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msf_error_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1357\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefix\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0m_ffi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'utf-8'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'replace'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1358\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Error opening 't29_lwwj2n_m17_lgwe7s.wav': System error."
     ]
    }
   ],
   "source": [
    "import soundfile\n",
    "import numpy as np\n",
    "samples, fs = soundfile.read('t29_lwwj2n_m17_lgwe7s.wav')\n",
    "samples = np.ascontiguousarray(samples[:9557352, 0])\n",
    "from gammatonecl.filters import get_coefficients, center_freqs\n",
    "import time\n",
    "t0 = time.time()\n",
    "get_coefficients(samples, fs, center_freqs(fs, 16, 20))\n",
    "print(f'That took {time.time() - t0} seconds')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "that took 7.756906747817993 seconds\n"
     ]
    }
   ],
   "source": [
    "from gammatone import gtgram\n",
    "t0 = time.time()\n",
    "gtgram.gtgram_xe(samples, 44100, 16, 20)\n",
    "print(f'that took {time.time() - t0} seconds')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OpenCL implementation took 1.7675251960754395\n",
      "Serial implementation took 7.545799970626831\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": "<matplotlib.image.AxesImage at 0x7fd49c6fae50>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gammatonecl.cochleagram import cochleagram\n",
    "from gammatone.gtgram import gtgram\n",
    "import soundfile\n",
    "import numpy as np\n",
    "import time\n",
    "from matplotlib import pyplot as plt\n",
    "samples, fs = soundfile.read('04 Traffic and Weather.flac')\n",
    "t0 = time.time()\n",
    "samples = np.ascontiguousarray(samples[:9557352, 0])\n",
    "gram = cochleagram(samples, fs, 1, 16, 20)\n",
    "print(f'OpenCL implementation took {time.time() - t0}')\n",
    "plt.imshow(gram, aspect= gram.shape[1]/ gram.shape[0]/1.618)\n",
    "plt.show()\n",
    "t0 = time.time()\n",
    "gram = gtgram(samples, fs, 1, 1, 16, 20)\n",
    "print(f'Serial implementation took {time.time() - t0}')\n",
    "plt.imshow(gram, aspect=gram.shape[1]/ gram.shape[0]/1.618)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [],
   "source": [
    "samples, fs = soundfile.read('04 Traffic and Weather.flac')\n",
    "samples = np.ascontiguousarray(samples[:9557352, 0])\n",
    "samp_g = cla.to_device(q, samples)\n",
    "coseed = 1\n",
    "sineed = 0\n",
    "tpt = (M_PI + M_PI) / 44100\n",
    "tptbw = tpt * erb ( 5512.5 ) * BW_CORRECTION\n",
    "coscf = math.cos(tpt*5512.5)\n",
    "sincf = math.cos(tpt*5512.5)\n",
    "ts = np.arange(0, len(samples), dtype=np.float64)\n",
    "decay = np.exp(-tptbw)\n",
    "gain = np.float64((tptbw ** 4) / 3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "q_maker = ElementwiseKernel(ctx,\n",
    "                            \"double *qcos, double *qsin, double *t, double tpt, double cf\",\n",
    "                            \"qcos[i] = cos(tpt * cf * t[i]); qsin[i] = - sin(tpt * cf * t[i])\"\n",
    "                            )"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That took 0.027311325073242188 seconds.\n"
     ]
    }
   ],
   "source": [
    "t0 = time.time()\n",
    "ts_g = cla.to_device(q, ts)\n",
    "qcos = cla.empty_like(ts_g)\n",
    "qsin = cla.empty_like(ts_g)\n",
    "q_maker(qcos, qsin, ts_g, tpt, 5512.5)\n",
    "cosx = samp_g * qcos\n",
    "sinx = samp_g * qsin\n",
    "print(f'That took {time.time() - t0} seconds.')\n",
    "# qcos and qsin can be preprocessed very quickly\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "p0 = GenericScanKernel(ctx, np.float64,\n",
    "                            arguments='double *x, double *y',\n",
    "                            preamble=f'__constant double d = {decay};',\n",
    "                            neutral='0',\n",
    "                            input_expr='x[i]',\n",
    "                            scan_expr='b',\n",
    "                            output_statement=\"\"\"y[i] = item + d*prev_item\"\"\"\n",
    "                            )\n",
    "\n",
    "p1 = GenericScanKernel(ctx, np.float64,\n",
    "                       arguments='double *x',\n",
    "                       preamble='__constant double d = 0.9139569249435527;',\n",
    "                       neutral='0',\n",
    "                       input_expr='x[i]',\n",
    "                       scan_expr='b',\n",
    "                       output_statement=\"x[i]= item - d*d*prev_item\")\n",
    "\n",
    "p2 = GenericScanKernel(ctx, np.float64,\n",
    "                       arguments='double *x',\n",
    "                       preamble='__constant double d = 0.9139569249435527;',\n",
    "                       neutral='0',\n",
    "                       input_expr='x[i]',\n",
    "                       scan_expr='b',\n",
    "                       output_statement=\"x[i]= item + 2*d*d*d*prev_item\")\n",
    "\n",
    "p3 = GenericScanKernel(ctx, np.float64,\n",
    "                       arguments='double *x',\n",
    "                       preamble='__constant double d = 0.9139569249435527;',\n",
    "                       neutral='0',\n",
    "                       input_expr='x[i]',\n",
    "                       scan_expr='b',\n",
    "                       output_statement=\"x[i]= item - d*d*d*d*prev_item\")\n",
    "\n",
    "u0 = ElementwiseKernel(ctx,\n",
    "                       'double *p0, double *p1, double *p2, double *y',\n",
    "                       'y[i] = p0[i] + d * p1[i] + d*d*p2[i]',\n",
    "                       preamble='__constant double d = 0.9139569249435527;')\n",
    "\n",
    "bm_maker = ElementwiseKernel(ctx,\n",
    "                             'double *ur, double *ui, double *y',\n",
    "                             'y[i] = (ur[i] * ur[i] + ui[i] * ui[i]) * 2.1842638127896094e-05')\n",
    "\n",
    "\n",
    "shift = ElementwiseKernel(ctx,\n",
    "                          \"double *x\",\n",
    "                          \"x[i] = x[i+1]\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That took 0.17933344841003418 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f1f0392c340>]"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm_g = cla.empty_like(ts_g)\n",
    "p0r_g = cla.empty_like(ts_g)\n",
    "p0i_g = cla.empty_like(ts_g)\n",
    "ur_g = cla.empty_like(ts_g)\n",
    "ui_g = cla.empty_like(ts_g)\n",
    "t0 = time.time()\n",
    "p0(cosx, p0r_g)\n",
    "p0(sinx, p0i_g)\n",
    "p1(p0r_g)\n",
    "p1(p0i_g)\n",
    "p2(p0r_g)\n",
    "p2(p0i_g)\n",
    "p3(p0r_g)\n",
    "p3(p0i_g)\n",
    "p1r_g = p0r_g.copy(q)\n",
    "shift(p1r_g)\n",
    "p2r_g = p1r_g.copy(q)\n",
    "shift(p2r_g)\n",
    "\n",
    "p1i_g = p0i_g.copy(q)\n",
    "shift(p1i_g)\n",
    "p2i_g = p1i_g.copy(q)\n",
    "shift(p2i_g)\n",
    "u0(p0r_g, p1r_g, p2r_g, ur_g)\n",
    "u0(p0i_g, p1i_g, p2i_g, ui_g)\n",
    "bm_maker(ur_g, ui_g, bm_g)\n",
    "x = (bm_g**2).get()\n",
    "print(f'That took {time.time() - t0} seconds')\n",
    "plt.plot(x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "that took 0.5199120044708252 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f1f035ff850>]"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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9gzmtt7mm1XKPn91HjnPyHoOVapmcGpFzWCcM2CYQQJtrWnHD4+tML790TyO217VhqYWB46yMYhq1JJRoSNx9JLdJ4a3cd3Hd71fn9B1WVDV2YNXeZnznny5w/Lv8LKoFaiaBAHp+w9BJyc2csFfttX/u5pPzEzh0DeVFcrHynUfaw1G1c/30tejuG2QSiGi9amirg4LGTH/8yHLj4MzhK55bX5N9oQDo7suteisIwt7H3w5MAj5x9UMrHf38+jZr/bTNVFlE9MLpJK/PL79bVOltAD4W1av6XDAJpBGLaaCK+mZO2C9udG5O2531x3NuLPUf58/sdp2fpi+vwgBHKLVNtt8lrGUKJoE0HltRhSt/swwHm92bCSmVpZ4QPrjosTLchVUl2w9jT4OzN6z54E/oir+sr0HtsW6vw/ClkfaBxOvdxlAqYcMkkEZiMpYo9+k2k1icvpM4ceW1vvoYJjzqfC8Zt+QzZHFBUUnO63b2DuD/zduJyX9en/Nn+IG1+QTyXzYxuujySvs7V/hBZJJAR08/VlRam1s3X0+tOYCCohK0d9t7J6IbQzv7oXThd7lWD3z5j2tsjcOsmHH2tHt/dIuVfdLO3bczpCWAhMgkgZ/O2YZbnt6EegtX9/nWASbq4Rs7nB08K6aKRxbvjeykGHbyurGXyG2RuU+gujneBfOEie5wfrgKthLCkl2NaDjeg7rWE3joax93LKYweXJ1NT5x3rtRWPBeANHqTcI8l1509oChIpMEwmwgFu8hEp4eOs6715jy8eD9X/I4kvxYOaFH8SRnqWQ3woVA2K8PIlMdlAu/Vg2MdIduiTHTmB2iMKVkOpZ+cr/uICFn6q+ew+4bzT0+AkmgubPX8l2DYc/8ZgT1b7Cn4XhO93gEdHMjxc7faOOBY1izr9nGTwwuU0lARCaISKWIVIlIUZr3TxeROcb7G0SkIOX980WkU0R+bk/Y5uysb0fhvUvwUlkwRhQ1a/hVOk9hCRMeXY0rf+PO3LxBLQc4PZSCquKhNystdcJw29eeKMU3n9ow5LWRLnzCXirOmgREZDSAGQCuAzAewGQRGZ+y2K0AWlV1HIBHADyQ8v7DAN7IP1xr9jXGR3lctz+3jO/UwGhB0Grz5PXkPbcav3cf6cAfl1Xhtue32Pq5bUbvN0tJzIZDOKilYrPMlAQuB1ClqtWq2gdgNoCJKctMBPCs8fhlANeIsceJyL8COACgwp6QnRe0zO/ETvr1Ym9uKIrFFD+ZXY7tdW2+H0U06JzqSJC4H6HP5rl7rfw26Q6J780qsy2WMDGTBM4BUJv0vM54Le0yqjoAoB3AWSJyBoBfAPifTF8gIlNEpExEypqanL4rz72jPJ9it1c3xnjtaEcP5m09jCmzNrv6vUG92st1F1tb1YwP/3ohNlRHZ8rRxbuO5rReQHcN05xuGJ4G4BFVzThOsqoWq2qhqhaOHTvWkUBymmkoz3yRWD31BFN47xL86q878vtwcoSlqQuTFm3u7M35O//hnsU5r5sLxan5pjlr2SlBqwGwi5kkUA/gvKTn5xqvpV1GRMYAOBNAC4ArAPxWRA4C+AmAO0Xk9jxjdpz9V4VDP7C5sxfPb3BuVE+yLt8TwOX3Lcl53RaX2l9ODoTWN4jpy6tc+U6vWUnqQS0N5svMzWKbAFwkIhcgfrKfBOAbKcvMB3AzgFIANwJYpvG6kH9OLCAi0wB0qup0G+K25Fh3P473hGf8j9SdNYw7bxAa5ZNjjPk/XF+JxRQDDv7Rcm0EP9E3iO6+AZx1xul5f1ZQZE0CqjpgXL0vAjAawExVrRCRuwGUqep8AE8BeE5EqgAcQzxR+EauUysG5bhOvor97rPBbvxyo0gepnrwICTLwTQn+//5WwWeLfXfzGz/OmMtKo92DLmTPOyzk5kaNkJVFwBYkPLa1KTHPQBuyvIZ03KIzxGPrajC3oYOPDrpUq9Dsd2S3bk1fvnN0eO9WFPlTCeBmhbr4+mvqGzE6FGCf77oVJtV0M4Nbl/QJr7vydXVw977i8nq0JqWLrz1LaPx/ne9Neuyq/Y24S2jR+HKC8+yFGeyyqMdOa8bVKG/Yzid3y6sxLyth70OI6NMB6wfTz6qwA/+shlrq+y7C7P2mPs3G430t73l6U34j6c2uhuMCXbuC8e6+hy56q1rtfY77qhrP/n4qgdX4Ir/v3TI+yNF+K2ZG0/OlZDYjny7lQLhrw6KZBIwy8wB8WZFAwqKSrDfw4ni/bCPDsRieGNnA255Or8TpdVtWbzrKBptmNXMD39Dr33ynsV4YtXwq3a3fWV65vkWzIwAkGgDPNbNmx6zCXUSeHlzbsNFjJT5Kw63D5uab4ExaNv2uracvotyF4spvjerDF97otS2z7R0M6oPS2T5WrbbnYmX8sm5W2tbTS/77LqDeXxTXNgvEEI9lHRimshk+Ry4X/pD/ArF9PDDDp0khvUOcuZrLIpH4cWJscbinLlBaEx1QlT7wZsV9pP9SEKdBPJl16kisXOpKrYcMncV8+TqAxY+3z97rxenV6+uyH30ZzfFTPILQoK0kswC9hN5ItTVQbmya8dJPZzmltXihsfNVV2s2x+sbox2nBAHY2ppikyvT8JhrA4KwjY59buPlFzCXoJiScCkfEbVTOxC1U1dGZeracn8fhBY6V3SeLwHR9p78PHz3g0AuP+N3fizhRIQWWPmZOZWDvA6gdMpLAlkYhwRG6pbcKkd47tk2fGvenBF/t/hkYnT1wKwdhK5+qGVmDhj7cnnC3Y02BxVepm6ngahOsRJQbgxignEXkwCaaTuZDvq29MvmOSnc7ZhzqbMN8CEuVjZ2RvvkpfpHPLj2eVDupAm1nFbunFzEu0qLg9VTw6z1F420qQy4T1sAbA6KKN7S3bhXW8z/yf6xSs78PXLzje9fHdfbifBYfOKBWQnfc3HN+jlNsps+NKAa9VBEBe/zZyAHEa2i1xJYOpr5ue22d/UZbohN5OFFQ1o7OgZdrLeXpe9hJHqq4+txUsp9z/4OQn0DcTQ1JH7MMtuO26hYTpozOwnIcxrefPx4WWLyCWBUhODh9nWO8g4on67sBLffHKDLZ+75ZD/b0p7JSlJ/fylbbjsviVpBxHzo0eX7vM6BE9Z+ZVqWrpQcXj4hYzTicTLatUwDT6YELkkkKygqAR7j3YgFlOc6BvEjY+vw49eLMfyyqEDl1mpV7z/jT1pX69rPTHsSsyug8WLMXYy+dlL204+LjHuqPai6qS7b8Dy94axiscpVz244uQNlOkk9vfn1qcZLTTlWLBUNepUF1ETx/mehvANMBfpJAAA1z6yCrc8swnlh1pRVtOKv20bXm99z+u7TH/en1buH/G9MDcMZ3OnjTOpzSuvT/s7JWvp7MX4qYvw2IqRf490rOQAP5wQbM9ZHiVBK8dYqv7BGIpe2W5jNNESqiTQ2tWH+0qs70y5zjdglZ/r7p0218SgX2b9ZM5W/OjF8ozLHD0eb4fIlixSDSadBP8QgKohu7u0elUOstJulHwY/WzuNqysbMLsTbXpl7XhmAv7cRuqJHBPyS7XbzYaqQg5c80BHEwZtz7k+1IgJeZ6TvyM5UltLg8v3utFSJGQz7GQfMy9sqUOsQyll9TSd1t3HwqKSk4+n1y83tJ3h7G6MFRJoH8wjx/I5jP03emKt2G/pMhRfdsJ0wfX8R57e+8k5nrmTxOXyznuta31mL5seKnJrfOlla85lDLYYHJHkajuAqFKAn6mGt2dLJt/vH8Z5palL84nq2npwsemvZn2vXxuPIvFFDvrj6d9L9fhyIMql+qlH8/eit+9mW+paejRsb+pc8gVeyaO90ZKukIIXzmASeAkpxttT/QP4oWNQ+8ojvoQBcnKDmYfXbW6eeSxlT5616Ihz61c2WeaeOTnST2dosDOE2r/YGzE93oHRn4PADZUHxvxveE/bYbqIAs98qJaGmQSyEFHTz8KikrwXOnBtO9vOph+Bw7STVNREuRqXrOxP732AD7864XOBmNI3Duw96j52fYsje6Q5cSeXLWY+rFmf+pfvnqqt1Fx0mxrQd5XRsIkkINEz5OnR5i16KY/2TfTFZ3i1IVaFEpkZntn2XGSs2NKx3yuypO3IdfNeXFj9urJsGASMFjb6eK7VkRLj3krKCrBixszD7bnqvDnANPy/VNsq21DTbO1md4Aa8eSlav7YctmyHJeDWjoNSaBHCT2o3xn9DJ71eWrE6ZNfr/Eeh98p2ZQi0IOGGXyT6eqeXWDnDhjLeaYaOTPJlO4qftBxi6iFnaZku1HzC8cIkwCBiunl8Qul+8p6XGTd7P+8lX77rYNMqf6aIexnjeV2ZPhnoYO19oOMskU7/Cr+5TnGT433586jLsKk0AOTpUE8vucNVXN+QdDect0JRkGyysbR+wCm062njtOeHPXUdPLpv5aw55n+D3z7R3Em8UIwKmGxCiPBWTGVx9bO+LoobnUv6ZWA8wZYaiAZGaO2SAf1mZi//bTm0Z8LzHAn99kOras1PM7VYUYJqaSgIhMEJFKEakSkaI0758uInOM9zeISIHx+r+IyGYR2WH8f7W94dvHys5ywJgrmPtXZpmGvU5NArmciIsyVJNZ+W3CeHVnlh8GwUtn5b6h43klz7+9tTbzcOqZf03zQ0xERdYkICKjAcwAcB2A8QAmi8j4lMVuBdCqquMAPALgAeP1ZgBfUdVLANwM4Dm7AvdS8epT/Yajudt4w7EuotHNAb6V2kibPP92ppsGs1m1d+Qq2Khe1JkpCVwOoEpVq1W1D8BsABNTlpkI4Fnj8csArhERUdVyVU0M41gB4G0icrodgdstlx2ARc1w2N+U+aamqPYaCYrylBJnclJPPUJ/n2Fk2KjOvGYmCZwDILnytc54Le0yqjoAoB3AWSnL3ABgi6oOu21WRKaISJmIlDU1uTOscz4k5X/yLzM3gt2Soc4cAG57YYtd4ZADnhnhpk0yx5WGYRG5GPEqov9M976qFqtqoaoWjh071o2Qhnl6rfkhqBN13SwIEPlPrneA59ImcKJvED39gzl9n1+YSQL1AM5Len6u8VraZURkDIAzAbQYz88F8FcA31JVa9M8uWjBjgbL67iZBLYcyj7AWtjlVGVnc3ntIz7oQ58syo3aI0muvjth4QRtqjooJcF8ZOpCXDJt0QhLB4OZJLAJwEUicoGInAZgEoD5KcvMR7zhFwBuBLBMVVVE3g2gBECRqq61K+iRuH1h7mZvgq8+tg7t3faOpe+kgQwjSLrhq4+tA2D/uEBWTipu8CIFzCo9iGcslJzddsfcUyO/unHxlNc8Jj4wJtsCqjogIrcDWARgNICZqlohIncDKFPV+QCeAvCciFQBOIZ4ogCA2wGMAzBVRKYar12rqo12b4gX3K4O6h3w1wkok3G/esPS8sv3NKKlK/PAY9VN5nuFdPcF528VNFNfq/A6BLJR1iQAAKq6AMCClNemJj3uAXBTmvXuBXBvnjH6ltslj2Bfb2SWLQEAwF3zefLxmt0zu/mJmd5+Yax94x3D+XC5KBDGHZCC5fvPbfY6BEvsrrIN4yHIJJAHdg7yl3T9/Zk47bX7iPkxiIImqsczk0Ae3G4TiMLkJ/m45qGVXodAFDim2gQovfJDbcPuViRyk9slnTBfhkT1vh8mAQq1qB7Y+drf1IkLx54x5DVVRVuAuikD1n7/FZXZRytQBRo7elDZ0IFVe/0/uoEZTAIUamwTyM01D63Ekjuuwrj3nUoEGw4c8zAif1Aobnh8HWqPnfA6FNuwTSBAeEIjNx093jPk+UhzQyT0eTAZTTZOFATDlAAAJoFA2dMQ3p4Z5D/Fq6qxve5Um1e2qpXvzipzOCLrttW1ex2C7zEJBMh3nvHfQUbhtXJvE66ffmq0l1FZskBqHfn57327I3F5KYylcSYBCrUQHrNDubiB2ZJAqrPPOM2hSMhOTAJElNExY0iPUexpFUpMAkSU0bzy+MjxnEkvnJgEKNTCftpy4y7yUZIYLiL0lWtZhXH+hlDdJ8ALFUoVvkPWfXsaOjDtb7tw9Yff53Uonnt5c53XIdiOJQEiyqjBuF9gRz27Wx5s6U77ekFRCY4e78GrW+owLWBDnjonhm8AAAgBSURBVDMJUKhVNQ4fWTRM3KidSPQKaurotbRe1NoQ9h3txB1ztwVu4nsmAYqEJ1dXo3iVb6e49jWrXUMTwlh/nslgQLc3VG0CROm0d/fj3pLdAIApn7nQ42iCh11DzYllGVbDr1gSoNC7c94Or0MItFxLAlHz7Wc2eR1CTpgEKPQaUwZCI2tG8SwRavx5KfQqDod34L3fLqpMW/e+va4NM5ZX2fIdC3Y02PI55E9sE6DQiwW0wc6MFzcewr9deg5+PLscR9p7sHXqv+Ce13fjlS3x/uy3fW6cxxGS3zEJUOj19J8a576rd8DDSJzxtSdKTz7+xN2LPYxkqKh1EU322tZ6fPljH8DoALSqh6o6qLfff5NakL9cfNcir0OIjM01rV6H4Jkfz96KWaUHvQ7DlFAlgYUVrLsk8pOOnmDNSWyn1q4+dPT0Y8shfyfD0FQH9Q+yFECUyuu+65dMe9PT7/dSeW0bJjy6GvVtJ3D758bhP678O7z/XW/1OqxhTJUERGSCiFSKSJWIFKV5/3QRmWO8v0FECpLe+6XxeqWIfMG+0IfKNv8pURQ9uaba6xAia/W+ZtS3xecjnr68Clc9uBzlPiwVZE0CIjIawAwA1wEYD2CyiIxPWexWAK2qOg7AIwAeMNYdD2ASgIsBTADwmPF5tgtxBxCinIVx1Mug6umP4d8eW+d1GMOYqQ66HECVqlYDgIjMBjARwK6kZSYCmGY8fhnAdIl3DZgIYLaq9gI4ICJVxueVwmYc4ZBouL1Hwz2AXhAVFJUAAMa974xh81189kNj8asvpV5jO8tMEjgHQG3S8zoAV4y0jKoOiEg7gLOM19enrHtO6heIyBQAUwDg/PPPNxv7EB96/ztzWo+IyAsXve+MYXOgeNFm4IuGYVUtBlAMAIWFhTlV7Jz59rfg4P1fsjUuIqKwM9MwXA/gvKTn5xqvpV1GRMYAOBNAi8l1iYjII2aSwCYAF4nIBSJyGuINvfNTlpkP4Gbj8Y0Alml8QJP5ACYZvYcuAHARgI32hE5ERPnKWh1k1PHfDmARgNEAZqpqhYjcDaBMVecDeArAc0bD7zHEEwWM5eYi3og8AOA2VR10aFuIiMgi8dvsP4WFhVpWVuZ1GEREgSIim1W10Op6oRo2goiIrGESICKKMCYBIqIIYxIgIoow3zUMi0gTgJo8PuJsAM02hRM0Ud52gNsf5e2P8rYD8e1/h6qOtbqi75JAvkSkLJcW8jCI8rYD3P4ob3+Utx3Ib/tZHUREFGFMAkREERbGJFDsdQAeivK2A9z+KG9/lLcdyGP7Q9cmQERE5oWxJEBERCYxCRARRVggk0A+E9+HgYntv0NEdonIdhFZKiJ/50WcTsm2/UnL3SAiKiKh6TpoZttF5GvG718hIi+4HaOTTOz754vIchEpN/b/L3oRpxNEZKaINIrIzhHeFxH5g/G32S4inzT1waoaqH+ID2e9H8DfAzgNwDYA41OW+SGAPxmPJwGY43XcLm//5wC83Xj8g6htv7HcOwGsQnx600Kv43bxt78IQDmA9xjP3+d13C5vfzGAHxiPxwM46HXcNm7/ZwB8EsDOEd7/IoA3AAiATwHYYOZzg1gSODnxvar2AUhMfJ9sIoBnjccvA7jGmPg+DLJuv6ouV9Vu4+l6xGd0Cwszvz8A3APgAQA9bgbnMDPb/j0AM1S1FQBUtdHlGJ1kZvsVwLuMx2cCOOxifI5S1VWIz9cykokAZmncegDvFpH/m+1zg5gE0k18nzp5/ZCJ7wEkJr4PAzPbn+xWxK8OwiLr9hvF4PNUtcTNwFxg5rf/IIAPishaEVkvIhNci855ZrZ/GoBvikgdgAUAfuROaL5g9dwAwCcTzZMzROSbAAoBXOV1LG4RkVEAHgZwi8eheGUM4lVCn0W8BLhKRC5R1TZPo3LPZADPqOpDInIl4jMeflRVY14H5ldBLAnkM/F9GJjZfojI5wH8CsD1qtrrUmxuyLb97wTwUQArROQg4nWj80PSOGzmt68DMF9V+1X1AIC9iCeFMDCz/bcCmAsAqloK4K2ID64WBabODamCmATymfg+DLJuv4hcCuAJxBNAmOqEgSzbr6rtqnq2qhaoagHibSLXq2oY5iw1s+/PQ7wUABE5G/HqoWo3g3SQme0/BOAaABCRjyCeBJpcjdI78wF8y+gl9CkA7ap6JNtKgasO0jwmvg8Dk9v/IIAzALxktIcfUtXrPQvaRia3P5RMbvsiANeKyC4AgwD+S1VDUQo2uf0/A/BnEfkp4o3Et4TlAlBEXkQ8wZ9ttHncBeAtAKCqf0K8DeSLAKoAdAP4tqnPDcnfh4iIchDE6iAiIrIJkwARUYQxCRARRRiTABFRhDEJEBF5KNvAcCnLPiIiW41/e0Uk75sA2TuIiMhDIvIZAJ2Ij/vzUQvr/QjApar6nXy+nyUBIiIPpRsYTkQuFJGFIrJZRFaLyIfTrDoZwIv5fn/gbhYjIoqAYgDfV9V9InIFgMcAXJ1405gj5AIAy/L9IiYBIiIfEZEzAHwap+74B4DTUxabBOBlVR3M9/uYBIiI/GUUgDZV/USGZSYBuM2uLyMiIp9Q1eMADojITcDJaSM/nnjfaB94D4BSO76PSYCIyEPGwHClAD4kInUiciuAfwdwq4hsA1CBoTOoTQIw266B8dhFlIgowlgSICKKMCYBIqIIYxIgIoowJgEioghjEiAiijAmASKiCGMSICKKsP8FuOQj8wsNQSsAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gammatone import gtgram\n",
    "t0 = time.time()\n",
    "coefs = gtgram.make_erb_filters(44100, 5512.5)\n",
    "x = gtgram.erb_filterbank(samples, coefs)[0]\n",
    "x=np.power(x, 2)\n",
    "print(f'that took {time.time() - t0} seconds')\n",
    "plt.plot(x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "p0 = GenericScanKernel(ctx, np.float64,\n",
    "                            arguments='double *x, double *y, double d',\n",
    "                            neutral='0',\n",
    "                            input_expr='x[i]',\n",
    "                            scan_expr='b*d',\n",
    "                            output_statement=\"\"\"y[i] = item - d*d*prev_item\"\"\"\n",
    "                            )\n",
    "\n",
    "p1 = GenericScanKernel(ctx, np.float64,\n",
    "                       arguments='double *x, double d',\n",
    "                       neutral='0',\n",
    "                       input_expr='x[i]',\n",
    "                       scan_expr='b*d*d*d',\n",
    "                       output_statement=\"x[i]= item - d*d*d*d*prev_item\")\n",
    "\n",
    "u0 = ElementwiseKernel(ctx,\n",
    "                       'double *p0, double *p1, double *p2, double *y, double d',\n",
    "                       'y[i] = p0[i] + d * p1[i] + d*d*p2[i]')\n",
    "\n",
    "bm_maker = ElementwiseKernel(ctx,\n",
    "                             'double *ur, double *ui, double *y, double g',\n",
    "                             'y[i] = (ur[i] * ur[i] + ui[i] * ui[i]) * g')\n",
    "\n",
    "\n",
    "shift = ElementwiseKernel(ctx,\n",
    "                          \"double *x\",\n",
    "                          \"x[i] = x[i+1]\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "That took 0.07910656929016113 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7ff7243a2160>]"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bm_g = cla.empty_like(ts_g)\n",
    "p0r_g = cla.empty_like(ts_g)\n",
    "p0i_g = cla.empty_like(ts_g)\n",
    "ur_g = cla.empty_like(ts_g)\n",
    "ui_g = cla.empty_like(ts_g)\n",
    "t0 = time.time()\n",
    "p0(cosx, p0r_g, decay)\n",
    "p0(sinx, p0i_g, decay)\n",
    "p1(p0r_g, decay)\n",
    "p1(p0i_g, decay)\n",
    "p1r_g = p0r_g.copy(q)\n",
    "shift(p1r_g)\n",
    "p2r_g = p1r_g.copy(q)\n",
    "shift(p2r_g)\n",
    "\n",
    "p1i_g = p0i_g.copy(q)\n",
    "shift(p1i_g)\n",
    "p2i_g = p1i_g.copy(q)\n",
    "shift(p2i_g)\n",
    "u0(p0r_g, p1r_g, p2r_g, ur_g, decay)\n",
    "u0(p0i_g, p1i_g, p2i_g, ui_g, decay)\n",
    "bm_maker(ur_g, ui_g, bm_g, gain)\n",
    "cl_x = (bm_g**2).get()\n",
    "print(f'That took {time.time() - t0} seconds')\n",
    "plt.plot(cl_x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "that took 0.5595438480377197 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f559c335520>]"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [],
   "source": [
    "def get_coefficients(signal, fs, cfs):\n",
    "    samp_g = cla.to_device(q, signal)\n",
    "    tpt = (M_PI + M_PI) / fs\n",
    "    ts_g = cla.arange(q, 0, len(signal))\n",
    "    coefficients = []\n",
    "    if type(cfs) != list:\n",
    "        cfs = [cfs]\n",
    "    for cf in cfs:\n",
    "        # Calculating the parameters for the given center frequencies\n",
    "        tptbw = tpt * erb ( cf ) * BW_CORRECTION\n",
    "        decay = np.exp(-tptbw)\n",
    "        gain = np.float64((tptbw ** 4) / 3)\n",
    "        # Setting up memory for everything.\n",
    "        qcos = cla.empty_like(ts_g)\n",
    "        qsin = cla.empty_like(ts_g)\n",
    "        bm_g = cla.empty_like(ts_g)\n",
    "        p0r_g = cla.empty_like(ts_g)\n",
    "        p0i_g = cla.empty_like(ts_g)\n",
    "        ur_g = cla.empty_like(ts_g)\n",
    "        ui_g = cla.empty_like(ts_g)\n",
    "        # Preparing the imaginary/real cyclical effects\n",
    "        q_maker(qcos, qsin, ts_g, tpt, cf)\n",
    "        cosx = samp_g * qcos\n",
    "        sinx = samp_g * qsin\n",
    "        # Performing the filtering operation\n",
    "        p0(cosx, p0r_g, decay)\n",
    "        p0(sinx, p0i_g, decay)\n",
    "        p1(p0r_g, decay)\n",
    "        p1(p0i_g, decay)\n",
    "        # Preparing the memory to calculate basilar membrane displacement.\n",
    "        p1r_g = p0r_g.copy(q)\n",
    "        shift(p1r_g)\n",
    "        p2r_g = p1r_g.copy(q)\n",
    "        shift(p2r_g)\n",
    "        p1i_g = p0i_g.copy(q)\n",
    "        shift(p1i_g)\n",
    "        p2i_g = p1i_g.copy(q)\n",
    "        shift(p2i_g)\n",
    "        # Calculating Basilar Membrane displacement\n",
    "        u0(p0r_g, p1r_g, p2r_g, ur_g, decay)\n",
    "        u0(p0i_g, p1i_g, p2i_g, ui_g, decay)\n",
    "        bm_maker(ur_g, ui_g, qcos, qsin, bm_g, gain)\n",
    "        # Append to the list\n",
    "        cl_x = (bm_g**2).get()\n",
    "        coefficients.append(cl_x)\n",
    "\n",
    "    return np.row_stack(coefficients)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0625 0.125  0.1875 0.25   0.3125 0.375  0.4375 0.5    0.5625 0.625\n",
      " 0.6875 0.75   0.8125 0.875  0.9375 1.    ]\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([0.0625, 0.125 , 0.1875, 0.25  , 0.3125, 0.375 , 0.4375, 0.5   ,\n       0.5625, 0.625 , 0.6875, 0.75  , 0.8125, 0.875 , 0.9375, 1.    ])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.linspace(1/16, 1, 16))\n",
    "np.arange(1, 17) / 16"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "that took 0.2726454734802246 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": "[<matplotlib.lines.Line2D at 0x7f606825d5e0>]"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from gammatone import gtgram\n",
    "t0 = time.time()\n",
    "coefs = gtgram.make_erb_filters(44100, 5512.5)\n",
    "x = gtgram.erb_filterbank(samples, coefs)[0]\n",
    "print(f'that took {time.time() - t0} seconds')\n",
    "plt.plot(x)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16593.77787396 12473.81784111  9362.86116079  7013.7967549\n",
      "  5240.03255629  3900.67403364  2889.33258612  2125.67483041\n",
      "  1549.04151901  1113.62922426   784.85205743   536.59442499\n",
      "   349.13660164   207.58834311   100.70611685    20.        ]\n",
      "[16593.77806648 12473.8181289   9362.86148271  7013.79707405\n",
      "  5240.03285177  3900.67429497  2889.33280936  2125.67501562\n",
      "  1549.04166849  1113.62934146   784.85214626   536.59448939\n",
      "   349.1366453    207.5883694    100.70612872    20.        ]\n"
     ]
    }
   ],
   "source": [
    "from gammatone import gtgram\n",
    "print(gtgram.centre_freqs(44100, 16, 20))\n",
    "from gammatonecl.filters import center_freqs\n",
    "print(center_freqs(44100, 16, 20))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "arange requires a dtype argument",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-74b3e78e0453>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mt0\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mget_coefficients\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m44100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcenter_freqs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m44100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m16\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'That took {time.time() - t0} seconds'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/PersonalProjects/gammatone/gammatonecl/filters.py\u001b[0m in \u001b[0;36mget_coefficients\u001b[0;34m(signal, fs, cfs)\u001b[0m\n\u001b[1;32m     59\u001b[0m     \u001b[0msamp_g\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcla\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msignal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0mtpt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mM_PI\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mM_PI\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m     \u001b[0mts_g\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcla\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m     \u001b[0mcoefficients\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcfs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/PersonalProjects/gammatone/venv/lib/python3.8/site-packages/pyopencl/array.py\u001b[0m in \u001b[0;36marange\u001b[0;34m(queue, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2127\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mexplicit_dtype\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2129\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"arange requires a dtype argument\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2131\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mmath\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mceil\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: arange requires a dtype argument"
     ]
    }
   ],
   "source": [
    "from gammatonecl.filters import get_coefficients\n",
    "import time\n",
    "t0 = time.time()\n",
    "get_coefficients(samples, 44100, center_freqs(44100, 16, 20))\n",
    "print(f'That took {time.time() - t0} seconds')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "test_g = cla.arange(q, 0, 10, dtype=np.float64)\n",
    "y_g = cla.zeros_like(test_g)\n",
    "z_g = cla.zeros_like(test_g)\n",
    "\n",
    "test_scan = GenericScanKernel(ctx, np.float64,\n",
    "                              arguments='double *x, double *y, double *z',\n",
    "                              neutral='0',\n",
    "                              input_expr='x[i]',\n",
    "                              scan_expr='a + b',\n",
    "                              output_statement='y[i] = item, z[i] = y[i] + prev_item')\n",
    "\n",
    "test_scan(test_g, y_g, z_g)\n",
    "x = test_g.get()\n",
    "y = y_g.get()\n",
    "z = z_g.get()\n",
    "out = np.row_stack([x,z,y])\n",
    "print(out)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "elem = ElementwiseKernel(ctx,\n",
    "                         \"double *x, double *y\",\n",
    "                         \"y[i] = x[i-1] + y[i-1]\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "array([0., 0., 1., 2., 3., 4., 5., 6., 7., 8.])"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_g = cla.arange(q, 0, 10, dtype=np.float64)\n",
    "y_g = cla.zeros_like(test_g)\n",
    "elem(test_g, y_g)\n",
    "y_g"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cl' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-6cdce30cb599>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m multi_serial = cl.Program(ctx,\n\u001b[0m\u001b[1;32m      2\u001b[0m                           \"\"\"\n\u001b[1;32m      3\u001b[0m                           __kernel void gt(double signal, __global double *level, int siglen){\n\u001b[1;32m      4\u001b[0m                           \u001b[0mgid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_global_id\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cl' is not defined"
     ]
    }
   ],
   "source": [
    "multi_serial = cl.Program(ctx,\n",
    "                          \"\"\"\n",
    "                          __kernel void gt(double signal, __global double *level, int siglen){\n",
    "                          gid = get_global_id(0);\n",
    "\n",
    "                          for(uint t=0; t<siglen, t++){\n",
    "                                signal[t] = signal[t] + signal[t] * level[gid];\n",
    "                          }\n",
    "                          \"\"\").build()\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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