0_osc_csv_to_spec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os, shutil, re, datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import signal\n",
    "from scipy.io import wavfile\n",
    "from scipy.signal import resample\n",
    "import librosa\n",
    "import librosa.display\n",
    "from helpers import *\n",
    "import pylab\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------\n",
      "CSV Directory:   /Users/kyy/openbci_stream/osc_data/0104_1522\n",
      "AUDIO Directory: /Users/kyy/cerebro_train/audio/0104_1522\n",
      "------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "\n",
    "FNAME = \"0104_1522\"\n",
    "CH1_MAX = 270\n",
    "CH2_MAX = 270\n",
    "CH3_MAX = 220\n",
    "CH4_MAX = 500\n",
    "SAMPLE_RATE = 400\n",
    "WINDOW = 2 # seconds of data per label\n",
    "NB_CHANNELS = 2\n",
    "\n",
    "SAVED_DATA= \"/Users/kyy/openbci_stream/osc_data/\"\n",
    "\n",
    "VALID_LABELS = ['zero', 'one', 'two', 'three', 'four', \n",
    "            'five', 'six', 'seven', 'eight', 'nine',\n",
    "            'left', 'right', 'stop', 'go', 'up', \n",
    "            'down', 'oneFinger', 'twoFinger', 'fourFinger']\n",
    "\n",
    "CSV_ROOT = os.path.join(SAVED_DATA, FNAME) \n",
    "AUD_ROOT = os.path.join(os.getcwd(), \"audio\", FNAME)\n",
    "\n",
    "print('-' * 42)\n",
    "print(\"CSV Directory:   {}\".format(CSV_ROOT))\n",
    "print(\"AUDIO Directory: {}\".format(AUD_ROOT))\n",
    "print('-' * 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def preprocess(samples, sample_rate, multiplier=1):\n",
    "    sr = sample_rate * multiplier\n",
    "    padded = np.zeros(sr)\n",
    "    samples = samples[:sr]\n",
    "    padded[:samples.shape[0]] = samples\n",
    "    return padded\n",
    "\n",
    "def make_dir(path):\n",
    "    if not os.path.isdir(path):\n",
    "        os.mkdir(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_labels(input_dir, output_path):\n",
    "    for label in [d for d in os.listdir(input_dir) if d in VALID_LABELS]:\n",
    "        print(\"  Label Processing: {}\".format(label))\n",
    "        label_path = os.path.join(input_dir, label)\n",
    "        o_label_path = os.path.join(output_dir, label)\n",
    "        make_dir(o_label_path)\n",
    "        for channel in range(1, NB_CHANNELS + 1):\n",
    "            ch_dir = os.path.join(o_label_path, \"ch{}\".format(channel))\n",
    "            make_dir(ch_dir)\n",
    "        process_files(label_path, o_label_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process_files(label_path, o_label_path):\n",
    "    for file in [f for f in os.listdir(label_path) if f.endswith(\".txt\")]:\n",
    "        csv_path = os.path.join(label_path, file)\n",
    "        df = pd.read_csv(csv_path, ',')\n",
    "        for channel in range(1, NB_CHANNELS + 1):\n",
    "            ch_name = 'ch{}'.format(channel)\n",
    "            ch_df = df[ch_name] \n",
    "            o_file_path = os.path.join(o_label_path, ch_name, file) \n",
    "            csv2wav(ch_name, ch_df, o_file_path)\n",
    "            \n",
    "            \n",
    "#         wavpath = os.path.join(channel_path, file)\n",
    "#         sample_rate, samples = wavfile.read(wavpath)\n",
    "#         if MOVE_WINDOW == True:\n",
    "#             items = make_windowed_spectrograms(samples, sample_rate, o_channel_path, items)\n",
    "#         else:\n",
    "#             items = make_spectrograms(samples, sample_rate, o_channel_path, items)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "def csv2wav(ch_name, ch_data, o_file_path):\n",
    "    \n",
    "    data = np.array(ch_data, dtype='float64')\n",
    "#     if ch_name == \"ch1\":\n",
    "#         data /= CH1_MAX\n",
    "#     if ch_name == \"ch2\":\n",
    "#         data /= CH2_MAX\n",
    "#     if ch_name == \"ch3\":\n",
    "#         data /= CH3_MAX\n",
    "#     if ch_name == \"ch4\":\n",
    "#         data /= CH4_MAX\n",
    "    print(np.max(np.abs(data)))\n",
    "    data = data / np.max(np.abs(data))\n",
    "    wav_path = o_file_path[:-4] + \".wav\"\n",
    "    wavfile.write(wav_path, 400, data)\n",
    "    \n",
    "#     data = signal.resample(data, (8000 // SAMPLE_RATE) * SAMPLE_RATE * WINDOW)\n",
    "#     make_spectrograms(data, 8000, o_file_path[:-4] + \".png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def make_spectrograms(samples, sample_rate, o_file_path): \n",
    "    changed = preprocess(samples, sample_rate, 1)\n",
    "    S = librosa.feature.melspectrogram(changed, sr=sample_rate, n_mels=128, fmax=512)\n",
    "    log_S = librosa.power_to_db(S, ref=np.max)\n",
    "    fig = plt.figure(figsize=(1.28, 1.28), dpi=100, frameon=False)\n",
    "    ax = plt.Axes(fig, [0., 0., 1., 1.])\n",
    "    ax.set_axis_off()\n",
    "    fig.add_axes(ax)\n",
    "    plt.axis('off')\n",
    "    librosa.display.specshow(log_S)                          \n",
    "    plt.savefig(o_file_path)\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not os.path.isdir(AUD_ROOT):\n",
    "    os.mkdir(AUD_ROOT)\n",
    "\n",
    "dir_pairs = {\n",
    "    CSV_ROOT : AUD_ROOT}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start: 2019-01-04 08:36:55.682561\n",
      "  Label Processing: oneFinger\n",
      "734.38\n",
      "734.38\n",
      "1085.94\n",
      "1085.94\n",
      "937.5\n",
      "937.5\n",
      "718.75\n",
      "718.75\n",
      "898.44\n",
      "898.44\n",
      "1085.94\n",
      "1085.94\n",
      "914.06\n",
      "914.06\n",
      "921.88\n",
      "921.88\n",
      "1015.62\n",
      "1015.62\n",
      "656.25\n",
      "656.25\n",
      "656.25\n",
      "656.25\n",
      "992.19\n",
      "992.19\n",
      "710.94\n",
      "710.94\n",
      "710.94\n",
      "710.94\n",
      "1148.44\n",
      "1148.44\n",
      "1226.56\n",
      "1226.56\n",
      "1203.12\n",
      "1203.12\n",
      "742.19\n",
      "742.19\n",
      "703.12\n",
      "703.12\n",
      "1289.06\n",
      "1289.06\n",
      "656.25\n",
      "656.25\n",
      "1171.88\n",
      "1171.88\n",
      "1187.5\n",
      "1187.5\n",
      "687.5\n",
      "687.5\n",
      "734.38\n",
      "734.38\n",
      "1093.75\n",
      "1093.75\n",
      "703.12\n",
      "703.12\n",
      "1070.31\n",
      "1070.31\n",
      "750.0\n",
      "750.0\n",
      "1054.69\n",
      "1054.69\n",
      "726.56\n",
      "726.56\n",
      "703.12\n",
      "703.12\n",
      "1046.88\n",
      "1046.88\n",
      "703.12\n",
      "703.12\n",
      "859.38\n",
      "859.38\n",
      "851.56\n",
      "851.56\n",
      "695.31\n",
      "695.31\n",
      "828.12\n",
      "828.12\n",
      "640.62\n",
      "640.62\n",
      "640.62\n",
      "640.62\n",
      "914.06\n",
      "914.06\n",
      "757.81\n",
      "757.81\n",
      "695.31\n",
      "695.31\n",
      "695.31\n",
      "695.31\n",
      "703.12\n",
      "703.12\n",
      "703.12\n",
      "703.12\n",
      "796.88\n",
      "796.88\n",
      "1031.25\n",
      "1031.25\n",
      "898.44\n",
      "898.44\n",
      "812.5\n",
      "812.5\n",
      "734.38\n",
      "734.38\n",
      "  Label Processing: twoFinger\n",
      "773.44\n",
      "773.44\n",
      "1101.56\n",
      "1101.56\n",
      "960.94\n",
      "960.94\n",
      "789.06\n",
      "789.06\n",
      "1046.88\n",
      "1046.88\n",
      "914.06\n",
      "914.06\n",
      "1046.88\n",
      "1046.88\n",
      "1101.56\n",
      "1101.56\n",
      "1843.75\n",
      "1843.75\n",
      "695.31\n",
      "695.31\n",
      "703.12\n",
      "703.12\n",
      "1132.81\n",
      "1132.81\n",
      "750.0\n",
      "750.0\n",
      "742.19\n",
      "742.19\n",
      "1218.75\n",
      "1218.75\n",
      "1140.62\n",
      "1140.62\n",
      "1187.5\n",
      "1187.5\n",
      "679.69\n",
      "679.69\n",
      "687.5\n",
      "687.5\n",
      "1257.81\n",
      "1257.81\n",
      "687.5\n",
      "687.5\n",
      "1515.62\n",
      "1515.62\n",
      "1156.25\n",
      "1156.25\n",
      "664.06\n",
      "664.06\n",
      "687.5\n",
      "687.5\n",
      "1226.56\n",
      "1226.56\n",
      "734.38\n",
      "734.38\n",
      "1070.31\n",
      "1070.31\n",
      "726.56\n",
      "726.56\n",
      "1039.06\n",
      "1039.06\n",
      "718.75\n",
      "718.75\n",
      "718.75\n",
      "718.75\n",
      "992.19\n",
      "992.19\n",
      "734.38\n",
      "734.38\n",
      "898.44\n",
      "898.44\n",
      "953.12\n",
      "953.12\n",
      "742.19\n",
      "742.19\n",
      "859.38\n",
      "859.38\n",
      "687.5\n",
      "687.5\n",
      "742.19\n",
      "742.19\n",
      "859.38\n",
      "859.38\n",
      "789.06\n",
      "789.06\n",
      "726.56\n",
      "726.56\n",
      "726.56\n",
      "726.56\n",
      "796.88\n",
      "796.88\n",
      "742.19\n",
      "742.19\n",
      "804.69\n",
      "804.69\n",
      "1101.56\n",
      "1101.56\n",
      "929.69\n",
      "929.69\n",
      "820.31\n",
      "820.31\n",
      "867.19\n",
      "867.19\n",
      "  Label Processing: fourFinger\n",
      "726.56\n",
      "726.56\n",
      "851.56\n",
      "851.56\n",
      "859.38\n",
      "859.38\n",
      "703.12\n",
      "703.12\n",
      "1015.62\n",
      "1015.62\n",
      "843.75\n",
      "843.75\n",
      "945.31\n",
      "945.31\n",
      "960.94\n",
      "960.94\n",
      "1804.69\n",
      "1804.69\n",
      "695.31\n",
      "695.31\n",
      "671.88\n",
      "671.88\n",
      "1078.12\n",
      "1078.12\n",
      "703.12\n",
      "703.12\n",
      "734.38\n",
      "734.38\n",
      "1859.38\n",
      "1859.38\n",
      "1156.25\n",
      "1156.25\n",
      "1242.19\n",
      "1242.19\n",
      "679.69\n",
      "679.69\n",
      "687.5\n",
      "687.5\n",
      "1210.94\n",
      "1210.94\n",
      "671.88\n",
      "671.88\n",
      "1320.31\n",
      "1320.31\n",
      "1289.06\n",
      "1289.06\n",
      "687.5\n",
      "687.5\n",
      "710.94\n",
      "710.94\n",
      "1140.62\n",
      "1140.62\n",
      "703.12\n",
      "703.12\n",
      "1429.69\n",
      "1429.69\n",
      "695.31\n",
      "695.31\n",
      "1000.0\n",
      "1000.0\n",
      "726.56\n",
      "726.56\n",
      "726.56\n",
      "726.56\n",
      "1054.69\n",
      "1054.69\n",
      "710.94\n",
      "710.94\n",
      "812.5\n",
      "812.5\n",
      "796.88\n",
      "796.88\n",
      "703.12\n",
      "703.12\n",
      "898.44\n",
      "898.44\n",
      "687.5\n",
      "687.5\n",
      "710.94\n",
      "710.94\n",
      "796.88\n",
      "796.88\n",
      "773.44\n",
      "773.44\n",
      "742.19\n",
      "742.19\n",
      "718.75\n",
      "718.75\n",
      "765.62\n",
      "765.62\n",
      "726.56\n",
      "726.56\n",
      "882.81\n",
      "882.81\n",
      "804.69\n",
      "804.69\n",
      "804.69\n",
      "804.69\n",
      "781.25\n",
      "781.25\n",
      "734.38\n",
      "734.38\n",
      "End: 2019-01-04 08:36:56.304538\n",
      "Finished in 00:00:00:00.622\n"
     ]
    }
   ],
   "source": [
    "for input_dir in dir_pairs:\n",
    "    output_dir = dir_pairs[input_dir]\n",
    "    if not os.path.isdir(output_dir):\n",
    "        os.mkdir(output_dir)\n",
    "    timer(process_labels, input_dir, output_dir)"
   ]
  },
  {
   "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": []
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}