testing_soundtools_bout_f0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computer: lintu.ucsd.edu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/anaconda/lib/python2.7/site-packages/pytz/__init__.py:29: UserWarning: Module argparse was already imported from /usr/local/anaconda/lib/python2.7/argparse.pyc, but /usr/local/anaconda/lib/python2.7/site-packages is being added to sys.path\n",
      "  from pkg_resources import resource_stream\n"
     ]
    },
    {
     "data": {
      "image/png": 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1KDAVFRWoqKiwPc/KylIkDr0+xtbaBgAubnve7jW92+Va+96AUP7G8xSLK+by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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d30602310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pdb\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import scipy.signal as sg\n",
    "import math\n",
    "import scipy as sp\n",
    "import socket\n",
    "import os\n",
    "import wave\n",
    "import struct\n",
    "\n",
    "matplotlib.style.use('ggplot')\n",
    "\n",
    "comp_name=socket.gethostname()\n",
    "print 'Computer: ' + comp_name\n",
    "if  comp_name == 'chim':\n",
    "    sys.path.append('C:\\Users\\GentnerLab\\Documents\\Experiment\\scripts\\sound_tools')\n",
    "    experiment_folder = os.path.join('C:\\Users\\GentnerLab\\Documents\\Experiment')\n",
    "    r = os.path.join('C:\\Users\\GentnerLab\\Documents\\Experiment')\n",
    "    raw_data_folder = os.path.join(experiment_folder, 'raw_data')\n",
    "\n",
    "if 'lintu' in comp_name:\n",
    "    sys.path.append('/mnt/cube/earneodo/repos/soundflow/sound_tools')\n",
    "    experiment_folder = os.path.join('/mnt/cube/earneodo/bci_zf/')\n",
    "    raw_data_folder = os.path.join(experiment_folder, 'raw_data')\n",
    "#test soundtools\n",
    "from soundtools import WavData, Chunk\n",
    "    \n",
    "#file structure\n",
    "bird_id = 'z002'\n",
    "sess = 4\n",
    "rec = 'a'\n",
    "run = 2\n",
    "bout = '4369_020'\n",
    "\n",
    "raw_file_folder = os.path.join(raw_data_folder, bird_id, str(sess).zfill(3))\n",
    "raw_file_name = rec + '_' + str(run).zfill(2) + '_song.wav'\n",
    "if bout is not None:\n",
    "    raw_file_folder = os.path.join(raw_file_folder, 'bouts')\n",
    "    raw_file_name = rec + '_' + str(run).zfill(2) + '_song_' + bout + '.wav'\n",
    "raw_file_path = os.path.join(raw_file_folder, raw_file_name)\n",
    "sound = WavData(raw_file_path)\n",
    "\n",
    "\n",
    "#get a piece, filter it, plot it\n",
    "window_t = 234700 #time window in msec\n",
    "window_size = 1000 # window len in msec\n",
    "\n",
    "window_t_samples = int(round(window_t*sound.s_f/1000.))\n",
    "window_samples = int(round(window_size*sound.s_f/1000.))\n",
    "window_samples = min(window_samples, sound.n_samples - window_t_samples)\n",
    "\n",
    "\n",
    "if bout is not None:\n",
    "    segment = [0, None]\n",
    "else:\n",
    "    segment=[window_t_samples, window_t_samples + window_samples]\n",
    "\n",
    "window = Chunk(sound, segment=segment)\n",
    "plt.plot(np.transpose(window.data))\n",
    "# window2 = window2 = Chunk(sound, segment=segment, chan_list = [0, 1])\n",
    "# plt.plot(np.transpose(window2.data));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0d2e762290>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1KDAVFRWoqKiwPc/KylIkDr0+xtbaBgAubnve7jW92+Va+96AUP7G8xSLK+by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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d608a6c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "window = Chunk(sound)\n",
    "plt.plot(np.transpose(window.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f0d2e118a90>]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D4Y0P7LMtfnEEbMj3g3ptd/lS7XHnJNh+PDugdbvLe6IUYelKsBtuDPn1cl06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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0d2e71e310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#filter the signal\n",
    "s_f = window.sound.s_f\n",
    "\n",
    "filt_lo = 22000 #Hz\n",
    "filt_hi = 100 #Hz\n",
    "hp_b, hp_a = sg.butter(4, filt_hi/(s_f/2.), btype='high')\n",
    "lp_b, lp_a = sg.butter(4, filt_lo/(s_f/2.), btype='low')\n",
    "\n",
    "def band_pass_filter(chunk, hp_b, hp_a, lp_b, lp_a):    \n",
    "    chunk_hi = sg.filtfilt(hp_b, hp_a, chunk)\n",
    "    chunk_filt = sg.filtfilt(lp_b, lp_a, chunk_hi)\n",
    "    return chunk_filt\n",
    "    \n",
    "window.apply_filter(band_pass_filter, hp_b, hp_a, lp_b, lp_a)\n",
    "plt.plot(np.transpose(window.data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# variables and parameters\n",
    "y = window.data[0,:]\n",
    "fs = window.sound.s_f\n",
    "t = np.arange(0,y.shape[0], dtype=np.float)/fs\n",
    "\n",
    "n_window = 1024\n",
    "n_overlap = n_window-1\n",
    "sigma = 7./1000.*fs\n",
    "\n",
    "# pad the window so the output is the same size as the input\n",
    "assert(n_window%2 == 0)\n",
    "before_padding = n_window/2\n",
    "after_padding = n_window/2 -1 \n",
    "x = np.pad(y, (before_padding, after_padding), 'constant', constant_values = (0,0))\n",
    "\n",
    "# Get the spectrogram of the padded segment\n",
    "# overlap is n_window - 1, so that it slides across each time point\n",
    "# window is gaussian so it ends up doing a sliding gabor transform, which is what I need to compute the fundamental\n",
    "# f, t, Sxx = sg.spectrogram(x, fs, nperseg = n_window, noverlap=n_overlap, window = sg.gaussian(n_window, sigma), scaling='spectrum')\n",
    "\n",
    "# ax1 = plt.subplot(311)\n",
    "# plt.plot(t, y)\n",
    "# plt.subplot(212, sharex=ax1)\n",
    "# plt.pcolormesh(t, f, np.log(Sxx))\n",
    "# plt.ylim(0, 7000)\n",
    "\n",
    "# plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def pad_input(sliding_func):\n",
    "    def out_padded(*args, **kwargs):\n",
    "        n_window = kwargs['nperseg']\n",
    "        n_overlap = kwargs['noverlap']\n",
    "        \n",
    "        # pad the window so the output is the same size as the input\n",
    "        assert(n_window%2 == 0)\n",
    "        before_padding = n_window/2\n",
    "        after_padding = n_window/2 -1 \n",
    "        x = np.pad(args[0], (before_padding, after_padding), 'constant', constant_values = (0,0))\n",
    "        # make the new input to the function\n",
    "        args = (x,) + args[1:]\n",
    "        return sliding_func(*args, **kwargs)\n",
    "    \n",
    "    return out_padded\n",
    "\n",
    "from scipy.signal import spectrogram\n",
    "\n",
    "spectrogram = pad_input(spectrogram)\n",
    "\n",
    "f, t, Sxx = spectrogram(y, fs, nperseg = n_window, noverlap=n_overlap, window = sg.gaussian(n_window, sigma), scaling='spectrum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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xcZ1H6yizb6w4dBD1/TVopfsCq9QfX6aXVcUhfQLrI3cGt919va6IvyjRl996CXXjv2nd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FAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0b1c531890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = plt.subplot(311)\n",
    "plt.plot(t, y)\n",
    "plt.subplot(212, sharex=ax1)\n",
    "plt.pcolormesh(t, f, np.log(Sxx))\n",
    "plt.ylim(0, 7000)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28901,)\n",
      "(28901,)\n",
      "(513, 28901)\n"
     ]
    }
   ],
   "source": [
    "print y.shape\n",
    "print t.shape\n",
    "print Sxx.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  0.,  0.],\n",
       "       [ 1.,  1.,  1.,  0.,  0.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "span_before = np.zeros((513,2))\n",
    "span_before.shape\n",
    "a = np.ones((2,3))\n",
    "np.hstack((a, np.zeros((2,2))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.],\n",
       "       [ 0.,  0.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'get_f0' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-584151ccb578>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Now compute the fundamental across the spectrogram\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpitch_list\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mget_f0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_slice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSxx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf_range\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6000\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpow_thresh\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mn_slice\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpitch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'get_f0' is not defined"
     ]
    }
   ],
   "source": [
    "# Now compute the fundamental across the spectrogram\n",
    "pitch_list = [get_f0(n_slice, f, Sxx, f_range=[300, 6000], pow_thresh = 1000)[0] for n_slice in range(0, t.shape[0])]\n",
    "plt.plot(pitch_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fr = np.array(pitch_list, dtype=np.int)\n",
    "\n",
    "sz = y.shape[0]\n",
    "for i in range(0, sz-350):\n",
    "    if fr[i]==0 and fr[i+1]==0 and fr[i-1]>0:\n",
    "        for j in range(0, 350):\n",
    "            fr[i+j] = 0\n",
    "            fr[i-j] = 0\n",
    "\n",
    "\n",
    "for i in range(0, sz-100):\n",
    "    sumarfrec = 0\n",
    "    contador = 1\n",
    "    for j in range(0, 100):\n",
    "        if fr[i] > 0:\n",
    "            sumarfrec = sumarfrec + fr[i+j]\n",
    "            if fr[i+j] > 0:\n",
    "                contador = contador + 1\n",
    "    fr[i] = sumarfrec/(contador * 1.)\n",
    "\n",
    "plt.plot(pitch_list)\n",
    "plt.plot(fr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "point = 14900\n",
    "plt.plot(pitch_list)\n",
    "plt.plot(point, pitch_list[point], '*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 15))\n",
    "ax1 = plt.subplot(311)\n",
    "plt.plot(t, y)\n",
    "plt.subplot(312, sharex=ax1)\n",
    "plt.pcolormesh(t, f, np.log(Sxx))\n",
    "plt.ylim(0, 7000)\n",
    "plt.subplot(313, sharex=ax1)\n",
    "#plt.plot(t, pitch_list)\n",
    "plt.plot(t, fr)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Compute the fundamental in one spectral slice\n",
    "t_slice = 180\n",
    "n_slice = int(round(t_slice*fs/1000.))\n",
    "n_slice = point\n",
    "\n",
    "search_slice = (f>450) & (f<6000)\n",
    "\n",
    "plt.plot(f, Sxx[:, n_slice])\n",
    "#plt.plot(f[search_slice][peak_ind], Sxx[search_slice, n_slice][peak_ind], 'x')\n",
    "plt.xlim(0, 6000)\n",
    "\n",
    "f0, p0 = get_f0(n_slice, f, Sxx, f_range=[400, 6000], pow_thresh = 1200)\n",
    "plt.plot(f0, p0, 'o')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print get_f0(n_slice, f, Sxx, f_range=[450, 6000])[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Compute the fundamental on a spectral slice\n",
    "def get_f0(t_n, f, sp_matrix, f_range = [0, None], pow_thresh = 12000):\n",
    "    \n",
    "    f_slice = f > f_range[0]\n",
    "    if f_range[1] is not None:\n",
    "        f_slice = f_slice & (f < f_range[1])\n",
    "    \n",
    "    peaks_ind = sg.find_peaks_cwt(sp_matrix[f_slice, t_n], np.arange(0.1,0.5))\n",
    "    \n",
    "    relevant_peaks = np.where(sp_matrix[f_slice, t_n][peaks_ind] > pow_thresh)[0]\n",
    "\n",
    "    if not relevant_peaks.shape[0] == 0:\n",
    "        first_peak = relevant_peaks[0]\n",
    "        f_0 = f[f_slice][peaks_ind][first_peak]\n",
    "        p_0 = Sxx[f_slice, t_n][peaks_ind][first_peak]\n",
    "    else:\n",
    "        f_0 = 0\n",
    "        p_0 = 0\n",
    "\n",
    "    \n",
    "    return f_0, p_0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "12000*12000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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