Multiple_file_plotting_and_CSV.ipynb

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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import glob\n",
    "import sounddevice as sd\n",
    "from matplotlib import pyplot as plt\n",
    "from datetime import datetime\n",
    "from scipy.io.wavfile import write"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving all files in a folder to CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "give_input_folder_path_here=str(os.getcwd())+'\\\\DATA'\n",
    "give_output_CSV_folder_path_here=str(os.getcwd())+'\\\\DATA'\n",
    "give_output_Wav_folder_path_here=str(os.getcwd())+'\\\\DATA'\n",
    "date_format = '%Y-%m-%d %H:%M:%S.%f' \n",
    "# Change  date_format according to the value you get because the timestamp is generated by the server and may vary for every server"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample Rate of microphone for Sample1 are: 17474.86336993008\n",
      "Sample Rate of microphone for Sample2 are: 17456.645158918654\n"
     ]
    }
   ],
   "source": [
    "for filename in glob.glob(os.path.join(give_input_folder_path_here, '*.txt')):\n",
    "    with open(os.path.join(os.getcwd(), filename), 'r') as f:\n",
    "        f_name=filename.split('\\\\')[-1]\n",
    "        f_name=f_name.split('.')[0]\n",
    "        aa=str(filename[:-4])\n",
    "        string=f.read()\n",
    "        string=string.replace(\"\\x00\",\"\")\n",
    "        pattern = \"\\[(.*?)\\]\"\n",
    "        timestamps = re.findall(pattern, string)\n",
    "        totaltime=datetime.strptime(timestamps[-1], date_format)-datetime.strptime(timestamps[0], date_format)\n",
    "        timeinsec=totaltime.total_seconds()\n",
    "        pattern = \"\\((.*?)\\)\"\n",
    "        Microphone_temp = re.findall(pattern, string)\n",
    "        Microphone_temp=[re.sub('\\[(.*?)\\]', '', x) for x in Microphone_temp]\n",
    "        Microphone=[x for x in Microphone_temp if len(x)<12]\n",
    "        Microphone_numpy=np.array(Microphone)\n",
    "        Microphone_numpy = Microphone_numpy.astype(int)\n",
    "        Sr=len(Microphone_temp)/timeinsec\n",
    "        print(\"Sample Rate of microphone for {} are: {}\".format(f_name,Sr))\n",
    "        sound_df=pd.DataFrame(Microphone_numpy,columns=['Microphone'])\n",
    "        sound_df.to_csv(give_output_CSV_folder_path_here+\"\\\\\"+f_name+'_Microphone.csv',encoding='utf-8')\n",
    "        scaled = np.int16(Microphone_numpy/np.max(np.abs(Microphone_numpy)) * 32767)\n",
    "        write(give_output_Wav_folder_path_here+\"\\\\\"+f_name+'_Microphone.wav', int(Sr), scaled)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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