fred_zap_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "def subsample_average(x, width):\n",
    "\n",
    "    \"\"\"Downsamples x by averaging `width` points\"\"\"\n",
    "    avg = np.nanmean(x.reshape(-1, width), axis=1)\n",
    "    return avg\n",
    "\n",
    "\n",
    "def moving_average(x,window_size):\n",
    "    #moving average across data x within a window_size\n",
    "    half_window_size=int(window_size/2)\n",
    "    time_len=len(x)\n",
    "    moving_average_trace=[]\n",
    "    max_lim=(time_len-window_size/2)\n",
    "    min_lim=half_window_size\n",
    "    for i in range(time_len):\n",
    "        if i >=min_lim and i <=max_lim :\n",
    "            moving_average_trace.append(np.mean(x[i-half_window_size:i+half_window_size]))\n",
    "        elif i<min_lim:\n",
    "            moving_average_trace.append(np.mean(x[0:i+half_window_size]))\n",
    "        elif i>max_lim:\n",
    "            moving_average_trace.append(np.mean(x[i-half_window_size:time_len]))\n",
    "\n",
    "    return np.asarray(moving_average_trace)\n",
    "\n",
    "def get_yaml_config_params():\n",
    "    #select yaml file and load all parameters\n",
    "    #F = FileDialog()\n",
    "    #fname = F.getOpenFileName(caption='Select a Config File')[0]\n",
    "    fname_path = '/Users/stripathy/Documents/GitHub/valiante_lab_abf_process/config _sample2.yaml.txt'\n",
    "    #load yaml params files\n",
    "    with open(fname_path,'r') as f:\n",
    "        params = yaml.load(f)\n",
    "        \n",
    "    return params\n",
    "\n",
    "def plot_trace(imp,freq,moving_avg_wind,time,voltage,current,sharpness_thr,filtered_method,fig_idx,save_path):\n",
    "    \n",
    "    plt.figure(figsize=(20,20))\n",
    "    plt.subplot(3, 1, 1)\n",
    "    cen_freq,freq_3db,res_sharpness=plot_impedance_trace(imp,freq,moving_avg_wind,fig_idx,sharpness_thr,filtered_method)\n",
    "    \n",
    "    #plot voltage and current time trace\n",
    "    plt.subplot(3, 1, 2)\n",
    "    plt.plot(time, voltage*1e3)\n",
    "\n",
    "    plt.ylabel('Voltage (mV)')\n",
    "    \n",
    "    plt.subplot(3, 1, 3)\n",
    "    plt.plot(time, current*1e12)\n",
    "    plt.xlabel('time (s)')\n",
    "    plt.ylabel('Current(pA)')\n",
    "\n",
    "    plt.savefig(save_path+str(fig_idx)+'.png')\n",
    "    plt.close()\n",
    "    \n",
    "    \n",
    "    return cen_freq,freq_3db,res_sharpness\n",
    "    \n",
    "    \n",
    "    \n",
    "def plot_impedance_trace(imp,freq,moving_avg_wind,fig_idx,sharpness_thr,filtered_method):\n",
    "    #generate impedance trace over frequency with peak and cutoff frequency detection\n",
    "    imp=imp/1e6\n",
    "    plt.plot(freq,imp)\n",
    "    \n",
    "    prominence_factor=1.01\n",
    "    if filtered_method==1:\n",
    "       filtered_imp=moving_average(imp,moving_avg_wind)\n",
    "    elif filtered_method==2:\n",
    "       start_idx=np.argmin(freq-0.5)\n",
    "       freq=freq[start_idx:]\n",
    "       imp=imp[start_idx:]\n",
    "       filtered_imp=moving_average(imp,moving_avg_wind)\n",
    "\n",
    "#    filtered_imp = savgol_filter(imp, moving_avg_wind, 1)\n",
    "    plt.plot(freq,filtered_imp)\n",
    "    plt.ylim([np.min(imp)*0.9,np.max(imp)*1.1])\n",
    "    idx_max_mag=np.argmax(filtered_imp)\n",
    "    cen_freq=freq[idx_max_mag]\n",
    "\n",
    "    \n",
    "    left_imp_mean=np.median(filtered_imp[0:idx_max_mag-1])\n",
    "    right_imp_mean=np.median(filtered_imp[idx_max_mag+1:])\n",
    "    max_imp=filtered_imp[idx_max_mag]\n",
    "    \n",
    "    if (left_imp_mean*prominence_factor)>max_imp  or  (right_imp_mean*prominence_factor)>max_imp or cen_freq<0.5 :\n",
    "        cen_freq=0  \n",
    "        \n",
    "    if cen_freq>0:\n",
    "        res_sharpness=max_imp/filtered_imp[np.argmin(freq-0.5)]\n",
    "    else:\n",
    "        res_sharpness=0\n",
    "        \n",
    "    if sharpness_thr>res_sharpness:\n",
    "        cen_freq=0  \n",
    "    #find cutoff freq(3dB below max)\n",
    "    i_3db_cutoff=np.argmin(abs(filtered_imp-max_imp/np.sqrt(2)))\n",
    "    freq_3db=freq[i_3db_cutoff]\n",
    "#     if cen_freq>0:\n",
    "#         i_3db_cutoff=np.argmin(abs(filtered_imp-max_imp/np.sqrt(2)))\n",
    "#         freq_3db=freq[i_3db_cutoff]\n",
    "#     else:\n",
    "#         freq_3db=0\n",
    "        \n",
    "        \n",
    "    plt.xlabel('Frequency[Hz]')\n",
    "    plt.ylabel('Impedance[MOhms]')\n",
    "    if cen_freq is not None:\n",
    "        if freq_3db is not None:\n",
    "            plt.title('Trial {fig_idx}, '.format(fig_idx=fig_idx)+'Fr={:.2f} Hz, Cutoff Freq={:.2f}Hz, Sharpness={:.2f}'.format(cen_freq,freq_3db,res_sharpness))\n",
    "        else:\n",
    "            plt.title('Trial {fig_idx}, '.format(fig_idx=fig_idx)+'Fr={:.2f} Hz, Cutoff Freq=None'.format(cen_freq))\n",
    "    else:\n",
    "        plt.title('Trial {fig_idx}, No Resonance')\n",
    "    plt.legend(['Raw Trace','Moving Averaged'])\n",
    "    \n",
    "    \n",
    "    return cen_freq,freq_3db,res_sharpness\n",
    "\n",
    "     \n",
    "def cal_imp(abf,sweep_end_freq):\n",
    "    ref_freq=np.linspace(0.1,sweep_end_freq,1000)\n",
    "    recorded_var=abf.data\n",
    "    dataRate=abf.dataRate\n",
    "    total_time=np.arange(0,recorded_var.shape[1])/dataRate\n",
    "    n_sample=10000\n",
    "    adcUnits=abf.adcUnits\n",
    "    #find the corresponding index for voltage and current in data\n",
    "    current_idx=1\n",
    "    voltage_idx=0\n",
    "    for unit_idx,unit in enumerate(adcUnits):\n",
    "        if unit=='pA':\n",
    "            current_idx=unit_idx\n",
    "        elif unit=='mV':\n",
    "            voltage_idx=unit_idx\n",
    "#    hamming_window=np.hamming(len(time))\n",
    "#    hamming_window=gaussian(len(time), std=len(time)/2)\n",
    "    #count the number of data sets in one folder and segment data accordingly\n",
    "    sweepList=abf.sweepList \n",
    "    sweepTimesSec=np.asarray(sweepList)*abf.sweepLengthSec\n",
    "    voltage_array=[]\n",
    "    current_array=[]\n",
    "    time_array=[]\n",
    "    impedance_array=[]\n",
    "    for sweep_idx in sweepList: \n",
    "\n",
    "        start_idx=int(sweepTimesSec[sweep_idx]*dataRate)\n",
    "        if sweep_idx==sweepList[-1]:\n",
    "            end_idx=len(total_time)-1\n",
    "        else:\n",
    "            end_idx=int(sweepTimesSec[sweep_idx+1]*dataRate)-1\n",
    "            \n",
    "            \n",
    "        voltage=recorded_var[voltage_idx,start_idx:end_idx]*1e-3\n",
    "        N=voltage.shape[0]\n",
    "        width = int(N / n_sample)\n",
    "        pad = int(width*np.ceil(N/width) - N)\n",
    "        \n",
    "        \n",
    "        voltage_detrend=subsample_average(np.pad(voltage, (pad,0), 'constant', constant_values=np.nan), width)\n",
    "        current=recorded_var[current_idx,start_idx:end_idx]*1e-12\n",
    "        current_detrend=subsample_average(np.pad(current, (pad,0), 'constant', constant_values=np.nan), width)\n",
    "        voltage_array.append(voltage_detrend)\n",
    "        current_array.append(current_detrend)\n",
    "        time=total_time[start_idx:end_idx]-total_time[start_idx]\n",
    "        time=time[::width]\n",
    "        time_array.append(time)\n",
    "        #FFT on voltage and current\n",
    "        sp_V= np.fft.fft(voltage_detrend)\n",
    "        \n",
    "        \n",
    "        sp_I= np.fft.fft(current_detrend)\n",
    "        freq = np.fft.fftfreq(len(time), d=time[1])\n",
    "        half_freq=int(len(time)/2)\n",
    "        \n",
    "        #discard frequency above 20Hz and negative frequency\n",
    "        freq=freq[1:half_freq]\n",
    "        sp_V=sp_V[1:half_freq]\n",
    "        sp_I=sp_I[1:half_freq]\n",
    "        selected_freq=freq<21\n",
    "        impedance=np.abs(sp_V[selected_freq]/sp_I[selected_freq])\n",
    "        \n",
    "        f_v=interp1d(freq[selected_freq],sp_V[selected_freq])\n",
    "        f_i=interp1d(freq[selected_freq],sp_I[selected_freq])\n",
    "        f_imp=interp1d(freq[selected_freq],impedance,fill_value=\"extrapolate\")\n",
    "        interpolated_trace=f_imp(ref_freq)\n",
    "        impedance_array.append(interpolated_trace)\n",
    "#        plt.figure()\n",
    "#        plt.plot(time,recorded_var[voltage_idx,start_idx:end_idx])\n",
    "        \n",
    "\n",
    "    return impedance_array,ref_freq,voltage_array,current_array,time_array\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yaml\n",
    "def get_yaml_config_params():\n",
    "    #select yaml file and load all parameters\n",
    "    #F = FileDialog()\n",
    "    #fname = F.getOpenFileName(caption='Select a Config File')[0]\n",
    "    fname_path = '/Users/stripathy/Documents/GitHub/valiante_lab_abf_process/config _sample2.yaml.txt'\n",
    "    #load yaml params files\n",
    "    with open(fname_path,'r') as f:\n",
    "        params = yaml.load(f)\n",
    "        \n",
    "    return params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'data_file_path': '/Users/stripathy/Downloads/Analayzed/Sub threshold hold RMP-L23-Lihua',\n",
       " 'root_result_folder': 'result',\n",
       " 'sweep_end_freq': 20,\n",
       " 'moving_avg_wind': 41,\n",
       " 'is_resonance_filter': False,\n",
       " 'sharpness_thr': 0,\n",
       " 'filtered_method': 1}"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_yaml_config_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "# new_path = '/Users/stripathy/Downloads/Analayzed/'\n",
    "# curr_zap_abf_files = glob.glob(new_path + '**/*.abf', recursive= True)\n",
    "\n",
    "dir_list_regex = '/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub*/'\n",
    "dir_list = glob.glob(dir_list_regex, recursive= False)\n",
    "\n",
    "dir_list_regex = '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub*/'\n",
    "temp_list = glob.glob(dir_list_regex, recursive= False)\n",
    "\n",
    "dir_list = dir_list + temp_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold -80 mV-L23-Lihua/\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold -80 mV-L23-Lihua/9\n",
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      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold -80 mV-L23-Lihua/3\n",
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      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold RMP-L5-Lihua/\n",
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      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold RMP-L5-Lihua/14\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold -80 mV-L5-Lihua/\n",
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      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold RMP-L23-Lihua/\n",
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      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold RMP-L23-Lihua/13\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold RMP-L23-Lihua/5\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Lihua/Analayzed-Lihua-Zap data/Sub threshold hold RMP-L23-Lihua/14\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/19129064\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2019_11_28_0093\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2019_11_28_0032\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0050\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0016\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/L3/331\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/L3/332\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/L3/333\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0073\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2019_11_26_0037\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/19129047\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/20\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/18\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/9\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/11\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/7\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/16\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2019_11_28_0010\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_27_0042\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/6\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/17\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0101\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/1\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/10\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/19\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/8\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0108\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_27_0038\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0116\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2019_11_04_0001\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0065\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/L3-march/1\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/L3-march/3\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/L3-march/2\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0030\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0090\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0063\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0063/331\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0063/332\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0063/333\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0048\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2020_01_06_0082\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2019_11_04_0082\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/4\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/19129072\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/15\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/3\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/12\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/2\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/13\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/5\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L3-Homeira/14\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2016_03_01_0000\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2016_02_25_0255\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2016_02_25_0073\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2016_03_03_0002\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/18\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/9\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/7\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/6\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/1\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/19\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/8\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2016_03_01_0047\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/19129051\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2016_03_03_0103\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/19129057\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2016_02_25_0007\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2016_02_25_0082\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/Un healthy/2016_03_03_0054\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/Un healthy/2016_02_25_0134\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/19122003\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/4\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/3\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/2\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/5\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L23-Homeira/14\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L5-Homeira/\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L5-Homeira/L3/2020_01_27_0014\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L5-Homeira/2020_01_27_0045\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L5-Homeira/Unhealthy/C1\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L5-Homeira/2020_01_28_0029\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L5-Homeira/2020_01_27_0008\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L5-Homeira/2020_01_28_0006-Good\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_03_03_0100\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_03_01_0000\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_02_25_0255\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/6\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_03_01_0047\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_03_03_0103\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_02_25_0134\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2019_11_28_0032\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0101\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0108\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_27_0038\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0116\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2019_11_28_0007\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0065\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0030\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0090\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0048\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/32\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/35\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/34\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/33\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/L5-March 02-2020/3\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/20\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/18\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/27\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/9\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/11\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/7\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/29\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/16\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/6\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/28\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/17\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/1\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/10\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/19\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/26\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/8\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/21\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/36\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/31\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/30\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/24\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/23\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/4\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/15\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/3\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/12\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/13\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/5\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/14\n",
      "/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/22\n"
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       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_03_01_0000',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_02_25_0255',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/6',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_03_01_0047',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_03_03_0103',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2016_02_25_0134',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L23-Homeira/2',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2019_11_28_0032',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0101',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0108',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_27_0038',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0116',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2019_11_28_0007',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0065',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0030',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0090',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold  hold-80 mV-L3-Homeira/2020_01_06_0048',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/32',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/35',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/34',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/33',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/L5-March 02-2020/3',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/20',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/18',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/27',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/9',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/11',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/7',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/29',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/16',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/6',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/28',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/17',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/1',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/10',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/19',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/26',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/8',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/21',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/36',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/31',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/30',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/24',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/23',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/4',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/15',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/3',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/12',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/13',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/5',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/14',\n",
       " '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/22']"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pyabf\n",
    "import numpy as np\n",
    "# from pyqtgraph import FileDialog\n",
    "import os\n",
    "from scipy.interpolate import interp1d\n",
    "from scipy.signal import gaussian \n",
    "import yaml\n",
    "import pandas as  pd\n",
    "from scipy.signal import savgol_filter\n",
    "\n",
    "#place all abf files in a directory, specify the path in the yaml config file\n",
    "params=get_yaml_config_params()\n",
    "data_file_path=params['data_file_path']\n",
    "\n",
    "#data_file_path = '/Users/stripathy/Downloads/homeira_lihua_resonance_abf_files/Homeira/Layer 2 and 3 ZAP function Raw data/Subthreshold/Sub threshold hold RMP'\n",
    "sweep_end_freq=params['sweep_end_freq']\n",
    "moving_avg_wind=params['moving_avg_wind']\n",
    "root_result_folder=params['root_result_folder']\n",
    "is_sharpness_filter=params['is_resonance_filter']\n",
    "sharpness_thr=params['sharpness_thr']\n",
    "filtered_method=params['filtered_method']\n",
    "\n",
    "if not(is_sharpness_filter):\n",
    "    is_sharpness_filter=0\n",
    "    \n",
    "# rewrite folder findy thingy to look for abf files at leaf directories\n",
    "    \n",
    "folder_list=[]\n",
    "for curr_dir in dir_list:\n",
    "    \n",
    "    data_file_path = curr_dir\n",
    "    print(data_file_path)\n",
    "\n",
    "    root_result_path=os.path.join(data_file_path,root_result_folder)\n",
    "    #generating directory to save results\n",
    "    if not(os.path.exists(root_result_path)):\n",
    "        os.mkdir(root_result_path)\n",
    "\n",
    "\n",
    "    root_result_path=os.path.join(data_file_path,root_result_folder)\n",
    "    #generating directory to save results\n",
    "    if not(os.path.exists(root_result_path)):\n",
    "        os.mkdir(root_result_path)\n",
    "    root_imped_fig_path=os.path.join(root_result_path,'impedance_fig')\n",
    "    if not(os.path.exists(root_imped_fig_path)):\n",
    "        os.mkdir(root_imped_fig_path)\n",
    "\n",
    "\n",
    "    for curr_path, curr_dirs, curr_files in os.walk(data_file_path):\n",
    "        for f in curr_files:\n",
    "            if f.endswith('.abf'):\n",
    "                folder_list.append(curr_path)\n",
    "                print(curr_path)\n",
    "                break\n",
    "                #file_path = path + '/' + f\n",
    "                #print(file_path)\n",
    "                #os.remove(file_path)\n",
    "        #print(folder_name)\n",
    "#         path=os.path.join(data_file_path,folder_name)\n",
    "#         print(path)\n",
    "#         if os.path.isdir(path):\n",
    "#             folder_list.append(path)\n",
    "#             files = os.listdir(path)\n",
    "#             for f in files:\n",
    "#                 if f.endswith('.png'):\n",
    "#                     file_path = path + '/' + f\n",
    "#                     print(file_path)\n",
    "#                     os.remove(file_path)\n",
    "\n",
    "#     for idx_folder,folder_name in enumerate(os.listdir(data_file_path)):\n",
    "#         print(folder_name)\n",
    "#         path=os.path.join(data_file_path,folder_name)\n",
    "#         print(path)\n",
    "#         if os.path.isdir(path):\n",
    "#             folder_list.append(path)\n",
    "#             files = os.listdir(path)\n",
    "#             for f in files:\n",
    "#                 if f.endswith('.png'):\n",
    "#                     file_path = path + '/' + f\n",
    "#                     print(file_path)\n",
    "#                     os.remove(file_path)\n",
    "        \n",
    "folder_list\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the sheets containing gains and offsets provided by homeira\n",
    "# this is needed because we need to know what the RMP values are per cell\n",
    "\n",
    "summary_table_csv = '/Users/stripathy/rstudio_projects/valiante_ih/summary_tables/gain_offset_info_merged.csv'\n",
    "#excel_file = file_base_base_path + 'valiante_lab_ephys_mar_2020/L23/Homeira/Total2Homeira-Lastversion_March 10_2020.xlsx'\n",
    "cell_info = pd.read_csv(open(summary_table_csv, 'rb'))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[True, True, False]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "abf_path = '/Users/stripathy/Downloads/homeira_lihua_zap_files/Analayzed-Homeira-Zap data 3/Sub threshold hold RMP-L5- Homeira/20/2020_01_27_0000.abf'\n",
    "abf = pyabf.ABF(abf_path)\n",
    "\n",
    "impedance_array,ref_freq,voltage_array,current_array,time_array=cal_imp(abf,sweep_end_freq)\n",
    "\n",
    "from ipfx.feature_extractor import SpikeFeatureExtractor\n",
    "\n",
    "ext = SpikeFeatureExtractor(start = .1, end = 1, filter = .5)\n",
    "\n",
    "spike_detect_threshold = -20\n",
    "spiking_sweeps = [any(array > spike_detect_threshold * .001) for array in voltage_array]\n",
    "print(spiking_sweeps)\n",
    "    \n",
    "#results = ext.process(t=time_array[0], v=voltage_array[0], i=current_array[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 15312099 from cell 9\n",
      "15312099.abf\n",
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      "Processing: 15312069 from cell 8\n",
      "15312069.abf\n",
      "Processing: 14d02008 from cell 4\n",
      "14d02008.abf\n",
      "Processing: 14d02009 from cell 4\n",
      "14d02009.abf\n",
      "Processing: 14d02013 from cell 4\n",
      "14d02013.abf\n",
      "Processing: 14d02007 from cell 4\n",
      "14d02007.abf\n",
      "Processing: 14d02006 from cell 4\n",
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      "Processing: 14d02012 from cell 4\n",
      "14d02012.abf\n",
      "Processing: 14d02010 from cell 4\n",
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      "Processing: 14d02011 from cell 4\n",
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      "Processing: 14d02005 from cell 4\n",
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      "Processing: 14d02015 from cell 4\n",
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      "Processing: 14317352 from cell 3\n",
      "14317352.abf\n",
      "Processing: 14317351 from cell 3\n",
      "14317351.abf\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/stripathy/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2920: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/Users/stripathy/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py:85: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 14317350 from cell 3\n",
      "14317350.abf\n",
      "Processing: 14317349 from cell 3\n",
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      "Processing: 14306310 from cell 2\n",
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      "Processing: 14306307 from cell 2\n",
      "14306307.abf\n",
      "Processing: 14306300 from cell 2\n",
      "14306300.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 14306300.abf is intrinsic file\n",
      "Processing: 14306309 from cell 2\n",
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      "Processing: 14306308 from cell 2\n",
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      "Processing: 14624369 from cell 9\n",
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      "Processing: 14624368 from cell 9\n",
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      "Processing: 14n03358 from cell 15\n",
      "14n03358.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 14n03358.abf is intrinsic file\n",
      "Processing: 14n03415 from cell 15\n",
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      "Processing: 14n03414 from cell 15\n",
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      "Processing: 14918313 from cell 12\n",
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      "Processing: 14918305 from cell 12\n",
      "14918305.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 14918305.abf is intrinsic file\n",
      "Processing: 14918311 from cell 12\n",
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      "Processing: 14918310 from cell 12\n",
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      "Processing: 14515368 from cell 2\n",
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      "Processing: 14515366 from cell 2\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 14918348 from cell 13\n",
      "14918348.abf\n",
      "Processing: 14918349 from cell 13\n",
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      "Processing: 14918350 from cell 13\n",
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      "Processing: 14918356 from cell 13\n",
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      "Processing: 14918354 from cell 13\n",
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      "Processing: 14605301 from cell 5\n",
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      "Processing: 14605302 from cell 5\n",
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      "Processing: 14605303 from cell 5\n",
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      "Processing: 14605307 from cell 5\n",
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      "Processing: 14605312 from cell 5\n",
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      "Processing: 14605306 from cell 5\n",
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      "Processing: 14605310 from cell 5\n",
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      "Processing: 14605304 from cell 5\n",
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      "Processing: 14605305 from cell 5\n",
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      "Processing: 14605311 from cell 5\n",
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      "Processing: 14918371 from cell 14\n",
      "14918371.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 14918371.abf is intrinsic file\n",
      "Processing: 14918373 from cell 14\n",
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      "Processing: 14918377 from cell 14\n",
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      "Processing: 14918376 from cell 14\n",
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      "Processing: 14918378 from cell 14\n",
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      "Processing: 14918379 from cell 14\n",
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      "Processing: 14918381 from cell 14\n",
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      "Processing: 14918380 from cell 14\n",
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      "Processing: 15420039 from cell 9\n",
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      "Processing: 15420059 from cell 9\n",
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      "Processing: 15420058 from cell 9\n",
      "15420058.abf\n",
      "Processing: 15420048 from cell 9\n",
      "15420048.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 15420048.abf is intrinsic file\n",
      "Processing: 15420049 from cell 9\n",
      "15420049.abf\n",
      "Processing: 15420044 from cell 9\n",
      "15420044.abf\n",
      "Processing: 15420050 from cell 9\n",
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      "Processing: 15420053 from cell 9\n",
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      "Processing: 15330017 from cell 7\n",
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      "Processing: 15330016 from cell 7\n",
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      "Processing: 15330014 from cell 7\n",
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      "Processing: 15330015 from cell 7\n",
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      "Processing: 15330018 from cell 7\n",
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      "Processing: 15330024 from cell 7\n",
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      "Processing: 15330019 from cell 7\n",
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      "Processing: 15330022 from cell 7\n",
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      "Processing: 15330023 from cell 7\n",
      "15330023.abf\n",
      "Processing: 15330021 from cell 7\n",
      "15330021.abf\n",
      "Processing: 15224029 from cell 6\n",
      "15224029.abf\n",
      "Processing: 15224028 from cell 6\n",
      "15224028.abf\n",
      "Processing: 15224032 from cell 6\n",
      "15224032.abf\n",
      "Processing: 15224033 from cell 6\n",
      "15224033.abf\n",
      "Processing: 15224031 from cell 6\n",
      "15224031.abf\n",
      "Processing: 15224025 from cell 6\n",
      "15224025.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 15224025.abf is intrinsic file\n",
      "Processing: 15224030 from cell 6\n",
      "15224030.abf\n",
      "Processing: 15224034 from cell 6\n",
      "15224034.abf\n",
      "Processing: 15224035 from cell 6\n",
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      "Processing: 15224037 from cell 6\n",
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      "Processing: 14424348 from cell 1\n",
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      "Processing: 14424347 from cell 1\n",
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      "Processing: 14424346 from cell 1\n",
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      "Processing: 14n04308 from cell 10\n",
      "14n04308.abf\n",
      "Processing: 14n04309 from cell 10\n",
      "14n04309.abf\n",
      "Processing: 14n03359 from cell 10\n",
      "14n03359.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 14n03359.abf is intrinsic file\n",
      "Processing: 14n04307 from cell 10\n",
      "14n04307.abf\n",
      "Processing: 14n04306 from cell 10\n",
      "14n04306.abf\n",
      "Processing: 14n04312 from cell 10\n",
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      "Processing: 14n04304 from cell 10\n",
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      "Processing: 14n04310 from cell 10\n",
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      "Processing: 14n04311 from cell 10\n",
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      "Processing: 14n04305 from cell 10\n",
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      "Processing: 15330048 from cell 8\n",
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      "Processing: 15330049 from cell 8\n",
      "15330049.abf\n",
      "Processing: 15330028 from cell 8\n",
      "15330028.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 15330028.abf is intrinsic file\n",
      "Processing: 15330047 from cell 8\n",
      "15330047.abf\n",
      "Processing: 15330046 from cell 8\n",
      "15330046.abf\n",
      "Processing: 15330050 from cell 8\n",
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      "Processing: 15330044 from cell 8\n",
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      "Processing: 15330045 from cell 8\n",
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      "Processing: 15330051 from cell 8\n",
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      "Processing: 15330041 from cell 8\n",
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      "Processing: 14n03315 from cell 4\n",
      "14n03315.abf\n",
      "Processing: 14n03301 from cell 4\n",
      "14n03301.abf\n",
      "skipping file, 30 too many sweeps\n",
      "or is 14n03301.abf is intrinsic file\n",
      "Processing: 14n03316 from cell 4\n",
      "14n03316.abf\n",
      "Processing: 14n03317 from cell 4\n",
      "14n03317.abf\n",
      "Processing: 14n03319 from cell 4\n",
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      "Processing: 14n03318 from cell 4\n",
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      "Processing: 14n03324 from cell 4\n",
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      "Processing: 14n03320 from cell 4\n",
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      "Processing: 14n03321 from cell 4\n",
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      "Processing: 14n03323 from cell 4\n",
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      "Processing: 14911320 from cell 3\n",
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      "Processing: 14911318 from cell 3\n",
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      "Processing: 14911317 from cell 3\n",
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      "Processing: 14911316 from cell 3\n",
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      "Processing: 14617302 from cell 2\n",
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      "Processing: 14617307 from cell 2\n",
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      "Processing: 14617306 from cell 2\n",
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      "Processing: 14617304 from cell 2\n",
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      "Processing: 14617305 from cell 2\n",
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      "Processing: 14617308 from cell 2\n",
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      "Processing: 14617309 from cell 2\n",
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      "Processing: 14n10469 from cell 5\n",
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      "Processing: 15127072 from cell 20\n",
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      "Processing: 15105070 from cell 18\n",
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      "Processing: 15105073 from cell 18\n",
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      "Processing: 15105077 from cell 18\n",
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      "Processing: 15105075 from cell 18\n",
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      "Processing: 15105069 from cell 18\n",
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      "Processing: 15105068 from cell 18\n",
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      "Processing: 14d02125 from cell 9\n",
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      "Processing: 14d02131 from cell 9\n",
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      "Processing: 14d02130 from cell 9\n",
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      "Processing: 14d02132 from cell 9\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 14d18117 from cell 16\n",
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      "skipping file, 30 too many sweeps\n",
      "or is 14317333.abf is intrinsic file\n",
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      "Processing: 2020_03_02_0013 from cell 331\n",
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      "Processing: 2020_03_02_0024 from cell 332\n",
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      "different stimulus type error\n",
      "failing file: 2020_03_02_0024.abf\n",
      "Processing: 2020_03_02_0021 from cell 333\n",
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      "Processing: 2020_03_02_0022 from cell 333\n",
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      "Processing: 2019_11_26_0033 from cell 2019_11_26_0037\n",
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      "Processing: 2019_11_26_0034 from cell 2019_11_26_0037\n",
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      "Processing: 19129048 from cell 19129047\n",
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      "Processing: 2020_01_27_0043 from cell 20\n",
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      "Processing: 2020_01_06_0039 from cell 9\n",
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      "Processing: 2020_01_06_0062 from cell 11\n",
      "2020_01_06_0062.abf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 2020_01_06_0008 from cell 7\n",
      "2020_01_06_0008.abf\n",
      "Processing: 2020_01_06_0009 from cell 7\n",
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      "Processing: 2020_01_06_0097 from cell 16\n",
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      "Processing: 2020_01_06_0098 from cell 16\n",
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      "Processing: 2019_11_28_0006 from cell 2019_11_28_0010\n",
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      "Processing: 2019_11_28_0005 from cell 2019_11_28_0010\n",
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      "Processing: 2019_11_28_0004 from cell 2019_11_28_0010\n",
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      "Processing: 2019_11_28_0003 from cell 2019_11_28_0010\n",
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      "Processing: 2020_01_27_0043 from cell 2020_01_27_0042\n",
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      "Processing: 2019_11_28_0095 from cell 6\n",
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      "Processing: 2019_11_28_0096 from cell 6\n",
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      "Processing: 2019_11_28_0097 from cell 6\n",
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      "Processing: 2020_01_06_0102 from cell 17\n",
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      "Processing: 2020_01_06_0097 from cell 2020_01_06_0101\n",
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      "Processing: 2020_01_06_0098 from cell 2020_01_06_0101\n",
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      "Processing: 2019_11_04_0023 from cell 1\n",
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      "Processing: 2019_11_04_0022 from cell 1\n",
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      "Processing: 2019_11_04_0036 from cell 1\n",
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      "Processing: 2019_11_04_0020 from cell 1\n",
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      "Processing: 2019_11_04_0034 from cell 1\n",
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      "Processing: 2019_11_04_0009 from cell 1\n",
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      "Processing: 2019_11_04_0035 from cell 1\n",
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      "Processing: 2019_11_04_0021 from cell 1\n",
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      "Processing: 2019_11_04_0019 from cell 1\n",
      "2019_11_04_0019.abf\n",
      "Processing: 2019_11_04_0025 from cell 1\n",
      "2019_11_04_0025.abf\n",
      "different stimulus type error\n",
      "failing file: 2019_11_04_0012.abf\n",
      "Processing: 2020_01_06_0050 from cell 10\n",
      "2020_01_06_0050.abf\n",
      "Processing: 2020_01_27_0037 from cell 19\n",
      "2020_01_27_0037.abf\n",
      "Processing: 2020_01_06_0031 from cell 8\n",
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      "Processing: 2020_01_06_0030 from cell 8\n",
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      "Processing: 2020_01_06_0032 from cell 8\n",
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      "Processing: 2020_01_06_0102 from cell 2020_01_06_0108\n",
      "2020_01_06_0102.abf\n",
      "Processing: 2020_01_06_0103 from cell 2020_01_06_0108\n",
      "2020_01_06_0103.abf\n",
      "Processing: 2020_01_27_0037 from cell 2020_01_27_0038\n",
      "2020_01_27_0037.abf\n",
      "Processing: 2020_01_06_0109 from cell 2020_01_06_0116\n",
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      "Processing: 2020_01_06_0113 from cell 2020_01_06_0116\n",
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      "Processing: 2020_01_06_0112 from cell 2020_01_06_0116\n",
      "2020_01_06_0112.abf\n",
      "Processing: 2019_11_04_0023 from cell 2019_11_04_0001\n",
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      "Processing: 2019_11_04_0022 from cell 2019_11_04_0001\n",
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      "Processing: 2019_11_04_0036 from cell 2019_11_04_0001\n",
      "2019_11_04_0036.abf\n",
      "Processing: 2019_11_04_0020 from cell 2019_11_04_0001\n",
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      "Processing: 2019_11_04_0034 from cell 2019_11_04_0001\n",
      "2019_11_04_0034.abf\n",
      "Processing: 2019_11_04_0009 from cell 2019_11_04_0001\n",
      "2019_11_04_0009.abf\n",
      "Processing: 2019_11_04_0035 from cell 2019_11_04_0001\n",
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      "Processing: 2019_11_04_0021 from cell 2019_11_04_0001\n",
      "2019_11_04_0021.abf\n",
      "Processing: 2019_11_04_0019 from cell 2019_11_04_0001\n",
      "2019_11_04_0019.abf\n",
      "Processing: 2019_11_04_0025 from cell 2019_11_04_0001\n",
      "2019_11_04_0025.abf\n",
      "different stimulus type error\n",
      "failing file: 2019_11_04_0012.abf\n",
      "Processing: 2020_01_06_0066 from cell 2020_01_06_0065\n",
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      "Processing: 2020_03_02_0013 from cell 1\n",
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      "Processing: 2020_03_02_0026 from cell 3\n",
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      "Processing: 2020_03_02_0025 from cell 3\n",
      "2020_03_02_0025.abf\n",
      "Processing: 2020_03_02_0024 from cell 3\n",
      "2020_03_02_0024.abf\n",
      "different stimulus type error\n",
      "failing file: 2020_03_02_0024.abf\n",
      "Processing: 2020_03_02_0021 from cell 2\n",
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      "Processing: 2020_03_02_0022 from cell 2\n",
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      "Processing: 2020_01_06_0031 from cell 2020_01_06_0030\n",
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      "Processing: 2020_01_06_0032 from cell 2020_01_06_0030\n",
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      "Processing: 2020_01_06_0087 from cell 2020_01_06_0090\n",
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      "Processing: 2020_01_06_0062 from cell 2020_01_06_0063\n",
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      "Processing: 2020_03_02_0013 from cell 331\n",
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      "Processing: 2020_03_02_0012 from cell 331\n",
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      "Processing: 2020_03_02_0011 from cell 331\n",
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      "Processing: 2020_03_02_0026 from cell 332\n",
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      "Processing: 2020_03_02_0025 from cell 332\n",
      "2020_03_02_0025.abf\n",
      "Processing: 2020_03_02_0024 from cell 332\n",
      "2020_03_02_0024.abf\n",
      "different stimulus type error\n",
      "failing file: 2020_03_02_0024.abf\n",
      "Processing: 2020_03_02_0021 from cell 333\n",
      "2020_03_02_0021.abf\n",
      "Processing: 2020_03_02_0022 from cell 333\n",
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      "Processing: 2020_03_02_0018 from cell 333\n",
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      "Processing: 2020_01_06_0039 from cell 2020_01_06_0048\n",
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      "Processing: 2020_01_06_0080 from cell 2020_01_06_0082\n",
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      "Processing: 2019_11_04_0088 from cell 2019_11_04_0082\n",
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      "Processing: 2019_11_28_0006 from cell 4\n",
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      "Processing: 2019_11_28_0005 from cell 4\n",
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      "Processing: 2019_11_28_0004 from cell 4\n",
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      "Processing: 2019_11_28_0003 from cell 4\n",
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      "Processing: 2019_11_28_0002 from cell 4\n",
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      "Processing: 19129074 from cell 19129072\n",
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      "Processing: 2020_01_06_0087 from cell 15\n",
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      "Processing: 2019_11_26_0033 from cell 3\n",
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      "Processing: 2019_11_26_0032 from cell 3\n",
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      "Processing: 2019_11_26_0034 from cell 3\n",
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      "Processing: 2020_01_06_0066 from cell 12\n",
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      "Processing: 2019_11_04_0088 from cell 2\n",
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      "Processing: 2020_01_06_0073 from cell 13\n",
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      "Processing: 2019_11_28_0035 from cell 5\n",
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      "Processing: 2020_01_06_0080 from cell 14\n",
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      "Processing: 2020_01_06_0081 from cell 14\n",
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      "Processing: 2020_01_06_0079 from cell 14\n",
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      "Processing: 2016_03_01_0008 from cell 2016_03_01_0000\n",
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      "Processing: 2016_03_01_0011 from cell 2016_03_01_0000\n",
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      "Processing: 2016_03_01_0013 from cell 2016_03_01_0000\n",
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      "Processing: 2016_03_01_0007 from cell 2016_03_01_0000\n",
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      "Processing: 2016_03_01_0006 from cell 2016_03_01_0000\n",
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      "Processing: 2016_03_01_0012 from cell 2016_03_01_0000\n",
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      "Processing: 2016_03_01_0015 from cell 2016_03_01_0000\n",
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      "Processing: 2016_03_01_0014 from cell 2016_03_01_0000\n",
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      "Processing: 2016_02_25_0214 from cell 2016_02_25_0255\n",
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      "Processing: 2016_02_25_0215 from cell 2016_02_25_0255\n",
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      "Processing: 2016_02_25_0217 from cell 2016_02_25_0255\n",
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      "2016_02_25_0221.abf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 2016_02_25_0220 from cell 2016_02_25_0255\n",
      "2016_02_25_0220.abf\n",
      "Processing: 2016_02_25_0222 from cell 2016_02_25_0255\n",
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      "Processing: 2016_02_25_0079 from cell 2016_02_25_0073\n",
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      "Processing: 2016_03_03_0029 from cell 2016_03_03_0002\n",
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      "Processing: 2016_03_03_0028 from cell 2016_03_03_0002\n",
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      "Processing: 2016_03_03_0037 from cell 2016_03_03_0002\n",
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      "Processing: 2016_03_03_0036 from cell 2016_03_03_0002\n",
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      "Processing: 2016_03_03_0032 from cell 2016_03_03_0002\n",
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      "Processing: 2016_03_03_0033 from cell 2016_03_03_0002\n",
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      "Processing: 2016_03_03_0031 from cell 2016_03_03_0002\n",
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      "Processing: 2016_03_01_0108 from cell 18\n",
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      "Processing: 2016_03_01_0109 from cell 18\n",
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      "Processing: 2014_05_12_600__0039 from cell 9\n",
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      "Processing: 2014_04_24_600_0193 from cell 7\n",
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      "Processing: 2014_04_24_600_0136 from cell 6\n",
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      "Processing: 2014_04_24_600_0133 from cell 6\n",
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      "Processing: 2014_04_24_600_0139 from cell 6\n",
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      "Processing: 2014_04_08_600_0003 from cell 1\n",
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      "Processing: 2014_04_08_600_0010 from cell 1\n",
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      "Processing: 2014_04_08_600_0004 from cell 1\n",
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      "Processing: 2014_04_08_600_0005 from cell 1\n",
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      "Processing: 2014_04_08_600_0011 from cell 1\n",
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      "Processing: 2014_04_08_600_0007 from cell 1\n",
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      "Processing: 2014_04_08_600_0006 from cell 1\n",
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      "Processing: 2014_04_08_600_0008 from cell 1\n",
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      "Processing: 2014_04_08_600_0009 from cell 1\n",
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      "Processing: 2016_03_03_0015 from cell 19\n",
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      "Processing: 2016_03_03_0014 from cell 19\n",
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      "Processing: 2016_03_03_0007 from cell 19\n",
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      "Processing: 2016_03_03_0013 from cell 19\n",
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      "Processing: 2016_03_03_0012 from cell 19\n",
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      "Processing: 2016_03_03_0006 from cell 19\n",
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      "Processing: 2016_03_03_0010 from cell 19\n",
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      "Processing: 2016_03_03_0011 from cell 19\n",
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      "Processing: 2016_03_03_0008 from cell 19\n",
      "2016_03_03_0008.abf\n",
      "Processing: 2014_05_12_600__0017 from cell 8\n",
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      "Processing: 2014_05_12_600__0018 from cell 8\n",
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      "Processing: 2014_05_12_600__0024 from cell 8\n",
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      "Processing: 2014_05_12_600__0019 from cell 8\n",
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      "Processing: 2014_05_12_600__0022 from cell 8\n",
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      "Processing: 2014_05_12_600__0023 from cell 8\n",
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      "Processing: 2014_05_12_600__0021 from cell 8\n",
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      "Processing: 2014_05_12_600__0020 from cell 8\n",
      "2014_05_12_600__0020.abf\n",
      "Processing: 2016_03_01_0054 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_01_0055 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_01_0057 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_01_0056 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_01_0059 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_01_0061 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_01_0060 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_01_0062 from cell 2016_03_01_0047\n",
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      "Processing: 19129054 from cell 19129051\n",
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      "Processing: 19129052 from cell 19129051\n",
      "19129052.abf\n",
      "skipping file, 16 too many sweeps\n",
      "or is 19129052.abf is intrinsic file\n",
      "Processing: 2016_03_03_0116 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0115 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0114 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0110 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0111 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0107 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0113 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0112 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0108 from cell 2016_03_03_0103\n",
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      "Processing: 19129059 from cell 19129057\n",
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      "Processing: 2016_02_25_0016 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0017 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0015 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0014 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0010 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0011 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0013 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0012 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0009 from cell 2016_02_25_0007\n",
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      "Processing: 2016_02_25_0101 from cell 2016_02_25_0082\n",
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      "Processing: 2016_02_25_0100 from cell 2016_02_25_0082\n",
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      "Processing: 2016_02_25_0102 from cell 2016_02_25_0082\n",
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      "Processing: 2016_02_25_0089 from cell 2016_02_25_0082\n",
      "2016_02_25_0089.abf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 2016_02_25_0088 from cell 2016_02_25_0082\n",
      "2016_02_25_0088.abf\n",
      "Processing: 2016_02_25_0103 from cell 2016_02_25_0082\n",
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      "Processing: 2016_02_25_0098 from cell 2016_02_25_0082\n",
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      "Processing: 2016_02_25_0099 from cell 2016_02_25_0082\n",
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      "Processing: 2016_03_03_0062 from cell 2016_03_03_0054\n",
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      "Processing: 2016_03_03_0061 from cell 2016_03_03_0054\n",
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      "Processing: 2016_03_03_0060 from cell 2016_03_03_0054\n",
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      "Processing: 2016_03_03_0058 from cell 2016_03_03_0054\n",
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      "Processing: 2016_03_03_0064 from cell 2016_03_03_0054\n",
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      "Processing: 2016_03_03_0065 from cell 2016_03_03_0054\n",
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      "Processing: 2016_02_25_0176 from cell 14\n",
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      "Processing: 2020_01_27_0022 from cell 2020_01_27_0014\n",
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      "Processing: 2020_01_28_0035 from cell 2020_01_28_0029\n",
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      "Processing: 2020_01_27_0011 from cell 2020_01_27_0008\n",
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      "Processing: 2020_01_28_0008 from cell 2020_01_28_0006-Good\n",
      "2020_01_28_0008.abf\n",
      "skipping file, 17 too many sweeps\n",
      "or is 2020_01_28_0008.abf is intrinsic file\n",
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      "Processing: 2016_03_01_0032 from cell 2016_03_01_0000\n",
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      "Processing: 2016_03_01_0033 from cell 2016_03_01_0000\n",
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      "Processing: 2016_02_25_0239 from cell 2016_02_25_0255\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 2016_02_25_0235 from cell 2016_02_25_0255\n",
      "2016_02_25_0235.abf\n",
      "Processing: 2016_02_25_0234 from cell 2016_02_25_0255\n",
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      "Processing: 2016_02_25_0236 from cell 2016_02_25_0255\n",
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      "Processing: 2016_03_01_0132 from cell 6\n",
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      "Processing: 2016_03_01_0131 from cell 6\n",
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      "Processing: 2016_03_01_0130 from cell 6\n",
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      "Processing: 2016_03_01_0137 from cell 6\n",
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      "Processing: 2016_03_01_0136 from cell 6\n",
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      "Processing: 2016_03_01_0138 from cell 6\n",
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      "Processing: 2016_03_01_0129 from cell 6\n",
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      "Processing: 2016_03_01_0085 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_01_0084 from cell 2016_03_01_0047\n",
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      "Processing: 2016_03_03_0138 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0136 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0134 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0135 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0131 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0132 from cell 2016_03_03_0103\n",
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      "Processing: 2016_03_03_0133 from cell 2016_03_03_0103\n",
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      "Processing: 2016_02_25_0162 from cell 2016_02_25_0134\n",
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      "Processing: 2016_02_25_0163 from cell 2016_02_25_0134\n",
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      "Processing: 2016_02_25_0161 from cell 2016_02_25_0134\n",
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      "Processing: 2016_02_25_0160 from cell 2016_02_25_0134\n",
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      "Processing: 2016_02_25_0164 from cell 2016_02_25_0134\n",
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      "Processing: 2016_02_25_0158 from cell 2016_02_25_0134\n",
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      "Processing: 2016_02_25_0159 from cell 2016_02_25_0134\n",
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      "Processing: 2016_02_25_0157 from cell 2016_02_25_0134\n",
      "2016_02_25_0157.abf\n",
      "Processing: 2016_02_25_0156 from cell 2016_02_25_0134\n",
      "2016_02_25_0156.abf\n",
      "Processing: 2016_02_25_0200 from cell 2\n",
      "2016_02_25_0200.abf\n",
      "Processing: 2016_02_25_0201 from cell 2\n",
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      "Processing: 2016_02_25_0206 from cell 2\n",
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      "Processing: 2016_02_25_0205 from cell 2\n",
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      "Processing: 2016_02_25_0199 from cell 2\n",
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      "Processing: 2016_02_25_0208 from cell 2\n",
      "2016_02_25_0208.abf\n",
      "Processing: 2019_11_28_0036 from cell 2019_11_28_0032\n",
      "2019_11_28_0036.abf\n",
      "Processing: 2019_11_28_0037 from cell 2019_11_28_0032\n",
      "2019_11_28_0037.abf\n",
      "Processing: 2020_01_06_0099 from cell 2020_01_06_0101\n",
      "2020_01_06_0099.abf\n",
      "Processing: 2020_01_06_0104 from cell 2020_01_06_0108\n",
      "2020_01_06_0104.abf\n",
      "Processing: 2020_01_27_0039 from cell 2020_01_27_0038\n",
      "2020_01_27_0039.abf\n",
      "Processing: 2020_01_27_0036 from cell 2020_01_27_0038\n",
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      "Processing: 2020_01_27_0035 from cell 2020_01_27_0038\n",
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      "Processing: 2020_01_27_0041 from cell 2020_01_27_0038\n",
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      "Processing: 2020_01_06_0114 from cell 2020_01_06_0116\n",
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      "Processing: 2019_11_28_0009 from cell 2019_11_28_0007\n",
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      "Processing: 2019_11_28_0008 from cell 2019_11_28_0007\n",
      "2019_11_28_0008.abf\n",
      "Processing: 2020_01_06_0072 from cell 2020_01_06_0065\n",
      "2020_01_06_0072.abf\n",
      "Processing: 2020_01_06_0026 from cell 2020_01_06_0030\n",
      "2020_01_06_0026.abf\n",
      "Processing: 2020_01_06_0089 from cell 2020_01_06_0090\n",
      "2020_01_06_0089.abf\n",
      "Processing: 2020_01_06_0043 from cell 2020_01_06_0048\n",
      "2020_01_06_0043.abf\n",
      "Processing: 19228052 from cell 32\n",
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      "Processing: 19228053 from cell 32\n",
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      "Processing: 19228045 from cell 32\n",
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      "Processing: 19228051 from cell 32\n",
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      "Processing: 19228054 from cell 32\n",
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      "Processing: 19228055 from cell 32\n",
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      "Processing: 19319033 from cell 35\n",
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      "Processing: 19319032 from cell 35\n",
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      "Processing: 19319036 from cell 35\n",
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      "Processing: 19319035 from cell 35\n",
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      "Processing: 19228080 from cell 34\n",
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      "Processing: 19228064 from cell 33\n",
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      "Processing: 19228063 from cell 33\n",
      "19228063.abf\n",
      "Processing: 2020_03_02_0025 from cell 3\n",
      "2020_03_02_0025.abf\n",
      "Processing: 2020_03_02_0024 from cell 3\n",
      "2020_03_02_0024.abf\n",
      "Processing: 2020_01_27_0002 from cell 20\n",
      "2020_01_27_0002.abf\n",
      "skipping file, 17 too many sweeps\n",
      "or is 2020_01_27_0002.abf is intrinsic file\n",
      "Processing: 2020_01_27_0000 from cell 20\n",
      "2020_01_27_0000.abf\n",
      "Processing: 2019_11_26_0112 from cell 18\n",
      "2019_11_26_0112.abf\n",
      "Processing: 2019_11_26_0113 from cell 18\n",
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      "Processing: 2019_11_26_0116 from cell 18\n",
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      "Processing: 2019_11_26_0114 from cell 18\n",
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      "Processing: 2019_11_26_0115 from cell 18\n",
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      "Processing: 2020_01_28_0034 from cell 27\n",
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      "Processing: 2020_01_28_0033 from cell 27\n",
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      "Processing: 2020_01_28_0032 from cell 27\n",
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      "Processing: 18032014_0163 from cell 9\n",
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      "Processing: 18032014_0166 from cell 9\n",
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      "Processing: 18032014_0167 from cell 9\n",
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      "Processing: 18032014_0171 from cell 9\n",
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      "Processing: 18032014_0170 from cell 9\n",
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      "Processing: 18032014_0164 from cell 9\n",
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      "Processing: 18032014_0169 from cell 9\n",
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      "Processing: 18032014_0168 from cell 9\n",
      "18032014_0168.abf\n",
      "Processing: 2019_11_04_0023 from cell 11\n",
      "2019_11_04_0023.abf\n",
      "Processing: 2019_11_04_0022 from cell 11\n",
      "2019_11_04_0022.abf\n",
      "Processing: 2019_11_04_0036 from cell 11\n",
      "2019_11_04_0036.abf\n",
      "Processing: 2019_11_04_0020 from cell 11\n",
      "2019_11_04_0020.abf\n",
      "Processing: 2019_11_04_0034 from cell 11\n",
      "2019_11_04_0034.abf\n",
      "Processing: 2019_11_04_0009 from cell 11\n",
      "2019_11_04_0009.abf\n",
      "Processing: 2019_11_04_0035 from cell 11\n",
      "2019_11_04_0035.abf\n",
      "Processing: 2019_11_04_0021 from cell 11\n",
      "2019_11_04_0021.abf\n",
      "Processing: 2019_11_04_0019 from cell 11\n",
      "2019_11_04_0019.abf\n",
      "Processing: 2019_11_04_0025 from cell 11\n",
      "2019_11_04_0025.abf\n",
      "different stimulus type error\n",
      "failing file: 2019_11_04_0012.abf\n",
      "Processing: 18032014_0015 from cell 7\n",
      "18032014_0015.abf\n",
      "Processing: 18032014_0017 from cell 7\n",
      "18032014_0017.abf\n",
      "Processing: 18032014_0016 from cell 7\n",
      "18032014_0016.abf\n",
      "Processing: 18032014_0021 from cell 7\n",
      "18032014_0021.abf\n",
      "Processing: 18032014_0020 from cell 7\n",
      "18032014_0020.abf\n",
      "Processing: 18032014_0022 from cell 7\n",
      "18032014_0022.abf\n",
      "Processing: 18032014_0023 from cell 7\n",
      "18032014_0023.abf\n",
      "Processing: 18032014_0018 from cell 7\n",
      "18032014_0018.abf\n",
      "Processing: 18032014_0024 from cell 7\n",
      "18032014_0024.abf\n",
      "Processing: 18032014_0019 from cell 7\n",
      "18032014_0019.abf\n",
      "Processing: 19129017 from cell 29\n",
      "19129017.abf\n",
      "Processing: 19129016 from cell 29\n",
      "19129016.abf\n",
      "Processing: 19129015 from cell 29\n",
      "19129015.abf\n",
      "skipping file, 17 too many sweeps\n",
      "or is 19129015.abf is intrinsic file\n",
      "Processing: 2019_11_26_0069 from cell 16\n",
      "2019_11_26_0069.abf\n",
      "Processing: 2019_11_26_0068 from cell 16\n",
      "2019_11_26_0068.abf\n",
      "Processing: 2019_11_26_0071 from cell 16\n",
      "2019_11_26_0071.abf\n",
      "Processing: 2019_11_26_0070 from cell 16\n",
      "2019_11_26_0070.abf\n",
      "Processing: 2019_11_26_0067 from cell 16\n",
      "2019_11_26_0067.abf\n",
      "Processing: 17032014_0189 from cell 6\n",
      "17032014_0189.abf\n",
      "Processing: 17032014_0188 from cell 6\n",
      "17032014_0188.abf\n",
      "Processing: 17032014_0194 from cell 6\n",
      "17032014_0194.abf\n",
      "Processing: 17032014_0195 from cell 6\n",
      "17032014_0195.abf\n",
      "Processing: 17032014_0197 from cell 6\n",
      "17032014_0197.abf\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing: 17032014_0196 from cell 6\n",
      "17032014_0196.abf\n",
      "Processing: 17032014_0192 from cell 6\n",
      "17032014_0192.abf\n",
      "Processing: 17032014_0193 from cell 6\n",
      "17032014_0193.abf\n",
      "Processing: 17032014_0191 from cell 6\n",
      "17032014_0191.abf\n",
      "Processing: 17032014_0190 from cell 6\n",
      "17032014_0190.abf\n",
      "Processing: 19129006 from cell 28\n",
      "19129006.abf\n",
      "Processing: 19129009 from cell 28\n",
      "19129009.abf\n",
      "Processing: 19129008 from cell 28\n",
      "19129008.abf\n",
      "different stimulus type error\n",
      "failing file: 19129008.abf\n",
      "Processing: 2019_11_26_0096 from cell 17\n",
      "2019_11_26_0096.abf\n",
      "Processing: 2019_11_26_0097 from cell 17\n",
      "2019_11_26_0097.abf\n",
      "Processing: 2016_02_29_0013 from cell 1\n",
      "2016_02_29_0013.abf\n",
      "Processing: 2016_02_29_0007 from cell 1\n",
      "2016_02_29_0007.abf\n",
      "Processing: 2016_02_29_0006 from cell 1\n",
      "2016_02_29_0006.abf\n",
      "Processing: 2016_02_29_0012 from cell 1\n",
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      "Processing: 2016_02_29_0010 from cell 1\n",
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      "Processing: 2016_02_29_0005 from cell 1\n",
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      "Processing: 2016_02_29_0014 from cell 1\n",
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      "Processing: 2016_02_29_0049 from cell 10\n",
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      "Processing: 2016_02_29_0045 from cell 10\n",
      "2016_02_29_0045.abf\n",
      "Processing: 2016_02_29_0044 from cell 10\n",
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      "Processing: 2016_02_29_0050 from cell 10\n",
      "2016_02_29_0050.abf\n",
      "Processing: 2016_02_29_0046 from cell 10\n",
      "2016_02_29_0046.abf\n",
      "Processing: 2016_02_29_0047 from cell 10\n",
      "2016_02_29_0047.abf\n",
      "Processing: 2016_02_29_0043 from cell 10\n",
      "2016_02_29_0043.abf\n",
      "Processing: 2019_11_28_0121 from cell 19\n",
      "2019_11_28_0121.abf\n",
      "Processing: 2019_11_28_0120 from cell 19\n",
      "2019_11_28_0120.abf\n",
      "Processing: 2020_01_28_0018 from cell 26\n",
      "2020_01_28_0018.abf\n",
      "Processing: 2020_01_28_0019 from cell 26\n",
      "2020_01_28_0019.abf\n",
      "Processing: 2020_01_28_0017 from cell 26\n",
      "2020_01_28_0017.abf\n",
      "skipping file, 17 too many sweeps\n",
      "or is 2020_01_28_0017.abf is intrinsic file\n",
      "Processing: 18032014_0103 from cell 8\n",
      "18032014_0103.abf\n",
      "Processing: 18032014_0102 from cell 8\n",
      "18032014_0102.abf\n",
      "Processing: 18032014_0100 from cell 8\n",
      "18032014_0100.abf\n",
      "Processing: 18032014_0101 from cell 8\n",
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      "Processing: 18032014_0105 from cell 8\n",
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      "Processing: 18032014_0099 from cell 8\n",
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      "Processing: 18032014_0106 from cell 8\n",
      "18032014_0106.abf\n",
      "Processing: 18032014_0107 from cell 8\n",
      "18032014_0107.abf\n",
      "Processing: 18032014_0098 from cell 8\n",
      "18032014_0098.abf\n",
      "Processing: 2020_01_27_0007 from cell 21\n",
      "2020_01_27_0007.abf\n",
      "Processing: 19129004 from cell 36\n",
      "19129004.abf\n",
      "skipping file, 17 too many sweeps\n",
      "or is 19129004.abf is intrinsic file\n",
      "Processing: 19129006 from cell 36\n",
      "19129006.abf\n",
      "Processing: 19129009 from cell 36\n",
      "19129009.abf\n",
      "Processing: 19228031 from cell 31\n",
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      "Processing: 2020_01_27_0045 from cell 24\n",
      "2020_01_27_0045.abf\n",
      "Processing: 2020_01_27_0043 from cell 23\n",
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      "Processing: 2020_01_27_0044 from cell 23\n",
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      "Processing: 17032014_0075 from cell 4\n",
      "17032014_0075.abf\n",
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      "17032014_0078.abf\n",
      "Processing: 2019_11_26_0055 from cell 15\n",
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      "Processing: 2019_11_26_0054 from cell 15\n",
      "2019_11_26_0054.abf\n",
      "Processing: 17032014_0016 from cell 3\n",
      "17032014_0016.abf\n",
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      "Processing: 17032014_0013 from cell 3\n",
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      "Processing: 2019_11_04_0088 from cell 12\n",
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      "Processing: 2019_11_04_0115 from cell 13\n",
      "2019_11_04_0115.abf\n",
      "Processing: 2019_11_04_0114 from cell 13\n",
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      "Processing: 2019_11_04_0116 from cell 13\n",
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      "Processing: 17032014_0115 from cell 5\n",
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      "Processing: 17032014_0113 from cell 5\n",
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      "Processing: 17032014_0112 from cell 5\n",
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      "Processing: 17032014_0111 from cell 5\n",
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      "Processing: 17032014_0120 from cell 5\n",
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      "Processing: 17032014_0119 from cell 5\n",
      "17032014_0119.abf\n",
      "Processing: 17032014_0118 from cell 5\n",
      "17032014_0118.abf\n",
      "Processing: 2019_11_26_0005 from cell 14\n",
      "2019_11_26_0005.abf\n",
      "Processing: 2019_11_26_0002 from cell 14\n",
      "2019_11_26_0002.abf\n",
      "Processing: 2020_01_27_0017 from cell 22\n",
      "2020_01_27_0017.abf\n",
      "Processing: 2020_01_27_0018 from cell 22\n",
      "2020_01_27_0018.abf\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "impedance_file_array=[]\n",
    "#storing data from different files in a list\n",
    "v_file_array=[]\n",
    "i_file_array=[]\n",
    "\n",
    "max_file_num_for_analysis = 10\n",
    "max_sweep_count_per_file = 20\n",
    "spike_detect_threshold = 0\n",
    "\n",
    "max_sweeps_for_average = 5\n",
    "#reference freq for interpolation(data length consistency)\n",
    "\n",
    "index=['trial','center_freq','3dB_freq' ,'res_sharpness', 'abf_name', 'cell_ID', 'file_time']\n",
    "df_array=[]\n",
    "failing_file_vec = []\n",
    "datacount=0\n",
    "dict_list = dict()\n",
    "for folder_path in folder_list:\n",
    "    \n",
    "    file_list= glob.glob(folder_path + \"/*.abf\")\n",
    "    if len(file_list) == 0:\n",
    "        continue\n",
    "    \n",
    "    if file_list[0].endswith('.abf'):\n",
    "        folder_name=folder_path.split('/')[-1]\n",
    "        df = pd.DataFrame(columns=index)\n",
    "        impedance_all=[]\n",
    "        voltage_all = []\n",
    "        current_all = []\n",
    "        time_all = []\n",
    "        \n",
    "        for idx_file,file_name in enumerate(os.listdir(folder_path)):\n",
    "            if file_name.endswith(\".abf\") and idx_file < max_file_num_for_analysis:\n",
    "                fname=os.path.join(folder_path, file_name)\n",
    "                dir_info=fname.split('/')\n",
    "                #cell_ID = '1'\n",
    "                cell_ID=dir_info[-2]\n",
    "                abf_name=dir_info[-1].split('.')[0]\n",
    "                \n",
    "                if not(cell_ID==abf_name):\n",
    "                    print('Processing: '+abf_name + ' from cell ' + cell_ID)\n",
    "                    print(file_name)\n",
    "                    abf = pyabf.ABF(fname)\n",
    "                    file_time = abf.abfDateTime\n",
    "                    is_intrinsic = any(cell_info['cell_id'].isin([file_name]))\n",
    "                    if abf.sweepCount > max_sweep_count_per_file or is_intrinsic:\n",
    "                        print('skipping file, %d too many sweeps' % abf.sweepCount)\n",
    "                        print('or is %s is intrinsic file' % file_name)\n",
    "                        continue\n",
    "                    try: \n",
    "                        \n",
    "                        impedance_array,ref_freq,voltage_array,current_array,time_array=cal_imp(abf,sweep_end_freq)\n",
    "                        spiking_sweeps = [any(array > spike_detect_threshold * .001) for array in voltage_array]\n",
    "                                               \n",
    "                        \n",
    "                        impedance_all.append(np.asarray(impedance_array))\n",
    "                        voltage_all.append(np.asarray(voltage_array))\n",
    "                        current_all.append(np.asarray(current_array))\n",
    "                        time_all.append(np.asarray(time_array))\n",
    "                    \n",
    "                    except Exception:\n",
    "                        print('failing file: ' + file_name)\n",
    "                        failing_file_vec.append(file_name)\n",
    "                        continue\n",
    "                    \n",
    "                    for trial_idx,impedance in enumerate(impedance_array):\n",
    "                        \n",
    "                        # check if sweep has spikes\n",
    "                        has_spike = any(voltage_array[trial_idx] > spike_detect_threshold * .001)\n",
    "                        \n",
    "                        try:\n",
    "                            imped_fig_path=os.path.join(root_imped_fig_path,cell_ID)\n",
    "                            if not(os.path.exists(imped_fig_path)):\n",
    "                                os.mkdir(imped_fig_path)\n",
    "                            cen_freq,freq_3db,res_sharpness=plot_trace(impedance,ref_freq,moving_avg_wind,\n",
    "                                                                       time_array[trial_idx],voltage_array[trial_idx],\n",
    "                                                                       current_array[trial_idx],sharpness_thr,\n",
    "                                                                       filtered_method,\n",
    "                                                                       trial_idx,\n",
    "                                                                       os.path.join(imped_fig_path,abf_name))\n",
    "                        except Exception:\n",
    "                            print('failing file: ' + file_name)\n",
    "                            failing_file_vec.append(file_name)\n",
    "                            continue\n",
    "                        \n",
    "                        \n",
    "                        df=df.append({'trial':trial_idx,'center_freq':cen_freq,\n",
    "                                      '3dB_freq':freq_3db,'res_sharpness':res_sharpness,\n",
    "                                     'abf_name': file_name, 'cell_ID': cell_ID, \n",
    "                                     'file_time': file_time, 'has_spike':has_spike},ignore_index=True)\n",
    "        if len(impedance_all)==0 or len(df) == 0:\n",
    "            continue\n",
    "        \n",
    "        # reorder by abf file start time\n",
    "        df = df.sort_values(by=['file_time', 'trial'])\n",
    "        \n",
    "        #avg_df = df\n",
    "        avg_df = df.query('has_spike == 0')\n",
    "        \n",
    "        index_values = avg_df.index.to_numpy()\n",
    "        avg_df = avg_df.reset_index(drop = True)\n",
    "        if avg_df.shape[0] == 0:\n",
    "            continue\n",
    "        first_abf_file_name = avg_df.iloc[0]['abf_name']\n",
    "        \n",
    "        try:\n",
    "            voltage_all = np.concatenate(voltage_all)\n",
    "            current_all = np.concatenate(current_all)\n",
    "            time_all = np.concatenate(time_all)\n",
    "        except Exception:\n",
    "            print('different stimulus type error')\n",
    "            print('failing file: ' + file_name)\n",
    "            failing_file_vec.append(file_name)\n",
    "            continue\n",
    "        impedance_all = np.concatenate(impedance_all)\n",
    "        \n",
    "#         spiking_sweeps = ![any(array > spike_detect_threshold * .001) for array in voltage_all]\n",
    "#         keep_sweeps = np.logical_not(spiking_sweeps)\n",
    "        \n",
    "#         voltage_all = voltage_all[keep_sweeps]\n",
    "#         current_all = current_all[keep_sweeps]\n",
    "#         impedance_all = impedance_all[keep_sweeps]\n",
    "#         time_all = time_all[keep_sweeps]\n",
    "        \n",
    "        max_sweeps_for_average = 3\n",
    "        impedance_all = impedance_all[index_values[0:max_sweeps_for_average],: ]\n",
    "        voltage_all = voltage_all[index_values[0:max_sweeps_for_average],: ]\n",
    "        current_all = current_all[index_values[0:max_sweeps_for_average],: ]\n",
    "        time_all = time_all[index_values[0:max_sweeps_for_average],: ]\n",
    "        \n",
    "        #impedance_all=np.concatenate(impedance_all)\n",
    "        impedance_mean=np.median(impedance_all,axis=0)\n",
    "        voltage_mean=np.median(voltage_all,axis=0)\n",
    "        current_mean = np.median(current_all,axis=0)\n",
    "        \n",
    "        cen_freq,freq_3db,res_sharpness=plot_trace(impedance_mean,ref_freq,moving_avg_wind,time_array[0],voltage_mean, current_mean,sharpness_thr,filtered_method,-1,os.path.join(imped_fig_path,'avg'))\n",
    "        \n",
    "        df=df.append({'trial':'avg','center_freq':cen_freq,\n",
    "                      '3dB_freq':freq_3db,'res_sharpness':res_sharpness, \n",
    "                      'abf_name': first_abf_file_name, 'cell_ID': cell_ID, \n",
    "                     'file_time': file_time, 'has_spike':False},ignore_index=True)\n",
    "        df.set_index('trial')\n",
    "\n",
    "        df_array.append(df)\n",
    "        \n",
    "        avg_res_dict = {'impedance_mean':impedance_mean, 'ref_freq':ref_freq, \n",
    "                     'voltage_mean':voltage_mean, 'current_mean':current_mean, 'time_array':time_array[0]}\n",
    "        dict_list[first_abf_file_name] = avg_res_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trial</th>\n",
       "      <th>center_freq</th>\n",
       "      <th>3dB_freq</th>\n",
       "      <th>res_sharpness</th>\n",
       "      <th>abf_name</th>\n",
       "      <th>cell_ID</th>\n",
       "      <th>file_time</th>\n",
       "      <th>has_spike</th>\n",
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       "  </thead>\n",
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       "      <td>15312093.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:45:29.216</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.518318</td>\n",
       "      <td>0</td>\n",
       "      <td>15312094.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:45:54.278</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.195596</td>\n",
       "      <td>0</td>\n",
       "      <td>15312095.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:46:19.106</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.474474</td>\n",
       "      <td>0</td>\n",
       "      <td>15312096.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:46:44.575</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.733433</td>\n",
       "      <td>0</td>\n",
       "      <td>15312097.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:47:22.528</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.354955</td>\n",
       "      <td>0</td>\n",
       "      <td>15312098.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:47:54.324</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.593994</td>\n",
       "      <td>0</td>\n",
       "      <td>15312099.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:48:26.871</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.474474</td>\n",
       "      <td>0</td>\n",
       "      <td>15312100.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:49:28.527</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.514314</td>\n",
       "      <td>0</td>\n",
       "      <td>15312101.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:49:54.449</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.856957</td>\n",
       "      <td>0</td>\n",
       "      <td>15312102.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:51:05.073</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.856957</td>\n",
       "      <td>0</td>\n",
       "      <td>15312093.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:47:22.528</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.119820</td>\n",
       "      <td>0</td>\n",
       "      <td>15127120.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:45:47.196</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.912613</td>\n",
       "      <td>0</td>\n",
       "      <td>15127121.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:46:12.227</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.430531</td>\n",
       "      <td>0</td>\n",
       "      <td>15127122.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:46:37.773</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.773173</td>\n",
       "      <td>0</td>\n",
       "      <td>15127124.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:47:32.444</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.486386</td>\n",
       "      <td>0</td>\n",
       "      <td>15127125.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:50:58.813</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.342943</td>\n",
       "      <td>0</td>\n",
       "      <td>15127126.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:52:13.640</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.860861</td>\n",
       "      <td>0</td>\n",
       "      <td>15127127.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:52:40.170</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.498298</td>\n",
       "      <td>0</td>\n",
       "      <td>15127128.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:53:11.419</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.044144</td>\n",
       "      <td>0</td>\n",
       "      <td>15127129.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:53:38.887</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.462362</td>\n",
       "      <td>0</td>\n",
       "      <td>15127130.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:54:37.605</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>6.354855</td>\n",
       "      <td>0</td>\n",
       "      <td>15127120.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:46:37.773</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.522122</td>\n",
       "      <td>0</td>\n",
       "      <td>15127075.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:25:03.783</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.876777</td>\n",
       "      <td>0</td>\n",
       "      <td>15127076.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:25:46.517</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.243343</td>\n",
       "      <td>0</td>\n",
       "      <td>15127077.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:26:25.500</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.067968</td>\n",
       "      <td>0</td>\n",
       "      <td>15127078.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:27:07.297</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9.083884</td>\n",
       "      <td>0</td>\n",
       "      <td>15127079.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:27:32.437</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.972372</td>\n",
       "      <td>0</td>\n",
       "      <td>15127081.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:28:23.546</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.111812</td>\n",
       "      <td>0</td>\n",
       "      <td>15127082.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:28:52.139</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.765165</td>\n",
       "      <td>0</td>\n",
       "      <td>15127083.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:29:18.123</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1667</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.665666</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_04_0116.abf</td>\n",
       "      <td>13</td>\n",
       "      <td>2019-11-04 17:19:30.265</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1668</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7.430531</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_04_0116.abf</td>\n",
       "      <td>13</td>\n",
       "      <td>2019-11-04 17:19:30.265</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1669</th>\n",
       "      <td>3</td>\n",
       "      <td>1.39479</td>\n",
       "      <td>8.267167</td>\n",
       "      <td>1.15359</td>\n",
       "      <td>2019_11_04_0116.abf</td>\n",
       "      <td>13</td>\n",
       "      <td>2019-11-04 17:19:30.265</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1670</th>\n",
       "      <td>0</td>\n",
       "      <td>2.05215</td>\n",
       "      <td>7.271171</td>\n",
       "      <td>1.02903</td>\n",
       "      <td>2019_11_04_0117.abf</td>\n",
       "      <td>13</td>\n",
       "      <td>2019-11-04 17:22:58.562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1671</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.773273</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_04_0117.abf</td>\n",
       "      <td>13</td>\n",
       "      <td>2019-11-04 17:22:58.562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.677678</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_04_0114.abf</td>\n",
       "      <td>13</td>\n",
       "      <td>2019-11-04 17:22:58.562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1673</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.123824</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0111.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:05:56.804</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1674</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.980380</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0112.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:06:11.100</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1675</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.315015</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0113.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:06:31.413</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1676</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.139740</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0114.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:07:08.146</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1677</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.948549</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0115.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:07:30.365</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1678</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.721421</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0116.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:07:46.271</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1679</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.099900</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0117.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:08:03.239</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.825025</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0118.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:08:18.629</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.410711</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0119.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:08:33.770</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.601902</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0120.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:08:51.676</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>5.020220</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0111.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:08:18.629</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.044144</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0002.abf</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-11-26 12:58:09.140</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1685</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.593994</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0005.abf</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-11-26 13:01:46.609</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1686</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.589990</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0005.abf</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-11-26 13:01:46.609</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1687</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1.713514</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0005.abf</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-11-26 13:01:46.609</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1688</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1.534234</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0005.abf</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-11-26 13:01:46.609</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>3.008308</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0002.abf</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-11-26 12:58:09.140</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.960460</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0017.abf</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-01-27 16:33:52.156</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1691</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5.458458</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0017.abf</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-01-27 16:33:52.156</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1692</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4.522222</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0017.abf</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-01-27 16:33:52.156</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1693</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.585986</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0018.abf</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-01-27 16:35:14.046</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1694</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.629830</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0018.abf</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-01-27 16:35:14.046</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1695</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3.127828</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0018.abf</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-01-27 16:35:14.046</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>4.860861</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0017.abf</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-01-27 16:35:14.046</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1697 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trial center_freq  3dB_freq res_sharpness             abf_name cell_ID  \\\n",
       "0        0           0  6.394695             0         15312093.abf       9   \n",
       "1        0           0  0.518318             0         15312094.abf       9   \n",
       "2        0           0  1.195596             0         15312095.abf       9   \n",
       "3        0           0  1.474474             0         15312096.abf       9   \n",
       "4        0           0  1.733433             0         15312097.abf       9   \n",
       "5        0           0  1.354955             0         15312098.abf       9   \n",
       "6        0           0  1.593994             0         15312099.abf       9   \n",
       "7        0           0  1.474474             0         15312100.abf       9   \n",
       "8        0           0  1.514314             0         15312101.abf       9   \n",
       "9        0           0  0.856957             0         15312102.abf       9   \n",
       "10     avg           0  0.856957             0         15312093.abf       9   \n",
       "11       0           0  5.119820             0         15127120.abf       7   \n",
       "12       0           0  6.912613             0         15127121.abf       7   \n",
       "13       0           0  7.430531             0         15127122.abf       7   \n",
       "14       0           0  6.773173             0         15127124.abf       7   \n",
       "15       0           0  3.486386             0         15127125.abf       7   \n",
       "16       0           0  4.342943             0         15127126.abf       7   \n",
       "17       0           0  4.860861             0         15127127.abf       7   \n",
       "18       0           0  5.498298             0         15127128.abf       7   \n",
       "19       0           0  4.044144             0         15127129.abf       7   \n",
       "20       0           0  9.462362             0         15127130.abf       7   \n",
       "21     avg           0  6.354855             0         15127120.abf       7   \n",
       "22       0           0  9.522122             0         15127075.abf       6   \n",
       "23       0           0  5.876777             0         15127076.abf       6   \n",
       "24       0           0  4.243343             0         15127077.abf       6   \n",
       "25       0           0  8.067968             0         15127078.abf       6   \n",
       "26       0           0  9.083884             0         15127079.abf       6   \n",
       "27       0           0  6.972372             0         15127081.abf       6   \n",
       "28       0           0  7.111812             0         15127082.abf       6   \n",
       "29       0           0  8.765165             0         15127083.abf       6   \n",
       "...    ...         ...       ...           ...                  ...     ...   \n",
       "1667     1           0  3.665666             0  2019_11_04_0116.abf      13   \n",
       "1668     2           0  7.430531             0  2019_11_04_0116.abf      13   \n",
       "1669     3     1.39479  8.267167       1.15359  2019_11_04_0116.abf      13   \n",
       "1670     0     2.05215  7.271171       1.02903  2019_11_04_0117.abf      13   \n",
       "1671     1           0  1.773273             0  2019_11_04_0117.abf      13   \n",
       "1672   avg           0  0.677678             0  2019_11_04_0114.abf      13   \n",
       "1673     0           0  4.123824             0    17032014_0111.abf       5   \n",
       "1674     0           0  4.980380             0    17032014_0112.abf       5   \n",
       "1675     0           0  6.315015             0    17032014_0113.abf       5   \n",
       "1676     0           0  5.139740             0    17032014_0114.abf       5   \n",
       "1677     0           0  2.948549             0    17032014_0115.abf       5   \n",
       "1678     0           0  4.721421             0    17032014_0116.abf       5   \n",
       "1679     0           0  5.099900             0    17032014_0117.abf       5   \n",
       "1680     0           0  3.825025             0    17032014_0118.abf       5   \n",
       "1681     0           0  2.410711             0    17032014_0119.abf       5   \n",
       "1682     0           0  4.601902             0    17032014_0120.abf       5   \n",
       "1683   avg           0  5.020220             0    17032014_0111.abf       5   \n",
       "1684     0           0  4.044144             0  2019_11_26_0002.abf      14   \n",
       "1685     0           0  1.593994             0  2019_11_26_0005.abf      14   \n",
       "1686     1           0  2.589990             0  2019_11_26_0005.abf      14   \n",
       "1687     2           0  1.713514             0  2019_11_26_0005.abf      14   \n",
       "1688     3           0  1.534234             0  2019_11_26_0005.abf      14   \n",
       "1689   avg           0  3.008308             0  2019_11_26_0002.abf      14   \n",
       "1690     0           0  4.960460             0  2020_01_27_0017.abf      22   \n",
       "1691     1           0  5.458458             0  2020_01_27_0017.abf      22   \n",
       "1692     2           0  4.522222             0  2020_01_27_0017.abf      22   \n",
       "1693     0           0  3.585986             0  2020_01_27_0018.abf      22   \n",
       "1694     1           0  2.629830             0  2020_01_27_0018.abf      22   \n",
       "1695     2           0  3.127828             0  2020_01_27_0018.abf      22   \n",
       "1696   avg           0  4.860861             0  2020_01_27_0017.abf      22   \n",
       "\n",
       "                   file_time  has_spike  \n",
       "0    2015-03-12 15:45:29.216        0.0  \n",
       "1    2015-03-12 15:45:54.278        0.0  \n",
       "2    2015-03-12 15:46:19.106        0.0  \n",
       "3    2015-03-12 15:46:44.575        0.0  \n",
       "4    2015-03-12 15:47:22.528        0.0  \n",
       "5    2015-03-12 15:47:54.324        0.0  \n",
       "6    2015-03-12 15:48:26.871        0.0  \n",
       "7    2015-03-12 15:49:28.527        0.0  \n",
       "8    2015-03-12 15:49:54.449        0.0  \n",
       "9    2015-03-12 15:51:05.073        0.0  \n",
       "10   2015-03-12 15:47:22.528        0.0  \n",
       "11   2015-01-27 19:45:47.196        0.0  \n",
       "12   2015-01-27 19:46:12.227        0.0  \n",
       "13   2015-01-27 19:46:37.773        0.0  \n",
       "14   2015-01-27 19:47:32.444        0.0  \n",
       "15   2015-01-27 19:50:58.813        0.0  \n",
       "16   2015-01-27 19:52:13.640        0.0  \n",
       "17   2015-01-27 19:52:40.170        0.0  \n",
       "18   2015-01-27 19:53:11.419        0.0  \n",
       "19   2015-01-27 19:53:38.887        0.0  \n",
       "20   2015-01-27 19:54:37.605        0.0  \n",
       "21   2015-01-27 19:46:37.773        0.0  \n",
       "22   2015-01-27 17:25:03.783        0.0  \n",
       "23   2015-01-27 17:25:46.517        0.0  \n",
       "24   2015-01-27 17:26:25.500        0.0  \n",
       "25   2015-01-27 17:27:07.297        0.0  \n",
       "26   2015-01-27 17:27:32.437        0.0  \n",
       "27   2015-01-27 17:28:23.546        0.0  \n",
       "28   2015-01-27 17:28:52.139        0.0  \n",
       "29   2015-01-27 17:29:18.123        0.0  \n",
       "...                      ...        ...  \n",
       "1667 2019-11-04 17:19:30.265        0.0  \n",
       "1668 2019-11-04 17:19:30.265        0.0  \n",
       "1669 2019-11-04 17:19:30.265        0.0  \n",
       "1670 2019-11-04 17:22:58.562        0.0  \n",
       "1671 2019-11-04 17:22:58.562        0.0  \n",
       "1672 2019-11-04 17:22:58.562        0.0  \n",
       "1673 2014-03-17 15:05:56.804        0.0  \n",
       "1674 2014-03-17 15:06:11.100        0.0  \n",
       "1675 2014-03-17 15:06:31.413        0.0  \n",
       "1676 2014-03-17 15:07:08.146        0.0  \n",
       "1677 2014-03-17 15:07:30.365        0.0  \n",
       "1678 2014-03-17 15:07:46.271        0.0  \n",
       "1679 2014-03-17 15:08:03.239        0.0  \n",
       "1680 2014-03-17 15:08:18.629        0.0  \n",
       "1681 2014-03-17 15:08:33.770        0.0  \n",
       "1682 2014-03-17 15:08:51.676        0.0  \n",
       "1683 2014-03-17 15:08:18.629        0.0  \n",
       "1684 2019-11-26 12:58:09.140        0.0  \n",
       "1685 2019-11-26 13:01:46.609        0.0  \n",
       "1686 2019-11-26 13:01:46.609        0.0  \n",
       "1687 2019-11-26 13:01:46.609        0.0  \n",
       "1688 2019-11-26 13:01:46.609        0.0  \n",
       "1689 2019-11-26 12:58:09.140        0.0  \n",
       "1690 2020-01-27 16:33:52.156        0.0  \n",
       "1691 2020-01-27 16:33:52.156        0.0  \n",
       "1692 2020-01-27 16:33:52.156        0.0  \n",
       "1693 2020-01-27 16:35:14.046        0.0  \n",
       "1694 2020-01-27 16:35:14.046        0.0  \n",
       "1695 2020-01-27 16:35:14.046        0.0  \n",
       "1696 2020-01-27 16:35:14.046        0.0  \n",
       "\n",
       "[1697 rows x 8 columns]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat(df_array, ignore_index = True)\n",
    "#df.sort_values(by=['file_time', 'trial'])\n",
    "\n",
    "df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trial</th>\n",
       "      <th>center_freq</th>\n",
       "      <th>3dB_freq</th>\n",
       "      <th>res_sharpness</th>\n",
       "      <th>abf_name</th>\n",
       "      <th>cell_ID</th>\n",
       "      <th>file_time</th>\n",
       "      <th>has_spike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.856957</td>\n",
       "      <td>0</td>\n",
       "      <td>15312093.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-03-12 15:47:22.528</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>6.354855</td>\n",
       "      <td>0</td>\n",
       "      <td>15127120.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2015-01-27 19:46:37.773</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>6.514214</td>\n",
       "      <td>0</td>\n",
       "      <td>15127075.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2015-01-27 17:28:23.546</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>12.530030</td>\n",
       "      <td>0</td>\n",
       "      <td>14304312.abf</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-03-04 17:13:21.004</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>4.920621</td>\n",
       "      <td>0</td>\n",
       "      <td>15312067.abf</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-03-12 15:22:46.559</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>10.617718</td>\n",
       "      <td>0</td>\n",
       "      <td>14d02005.abf</td>\n",
       "      <td>4</td>\n",
       "      <td>2014-12-02 14:42:43.376</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>10.298999</td>\n",
       "      <td>0</td>\n",
       "      <td>14317349.abf</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-03-17 18:06:21.919</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>19.621522</td>\n",
       "      <td>0</td>\n",
       "      <td>14306307.abf</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-03-06 11:53:18.801</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>1.295195</td>\n",
       "      <td>0</td>\n",
       "      <td>14d02109.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-12-02 17:11:53.005</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>12.669469</td>\n",
       "      <td>0</td>\n",
       "      <td>15420098.abf</td>\n",
       "      <td>20</td>\n",
       "      <td>2015-04-20 16:39:22.467</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>19.960160</td>\n",
       "      <td>0</td>\n",
       "      <td>15407002.abf</td>\n",
       "      <td>18</td>\n",
       "      <td>2015-04-07 13:18:01.985</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>avg</td>\n",
       "      <td>0.956557</td>\n",
       "      <td>7.809009</td>\n",
       "      <td>1.04033</td>\n",
       "      <td>14624360.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2014-06-24 16:41:16.993</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>avg</td>\n",
       "      <td>0.956557</td>\n",
       "      <td>5.338939</td>\n",
       "      <td>1.07461</td>\n",
       "      <td>14911304.abf</td>\n",
       "      <td>11</td>\n",
       "      <td>2014-09-11 17:30:00.312</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.876877</td>\n",
       "      <td>0</td>\n",
       "      <td>14605377.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2014-06-05 18:16:35.219</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>6.056056</td>\n",
       "      <td>0</td>\n",
       "      <td>14n04363.abf</td>\n",
       "      <td>16</td>\n",
       "      <td>2014-11-04 18:24:54.654</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>avg</td>\n",
       "      <td>3.32703</td>\n",
       "      <td>16.075776</td>\n",
       "      <td>1.09685</td>\n",
       "      <td>14605350.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-06-05 17:27:45.913</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>avg</td>\n",
       "      <td>3.26727</td>\n",
       "      <td>16.593694</td>\n",
       "      <td>1.03166</td>\n",
       "      <td>15224013.abf</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-02-24 13:21:10.727</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>avg</td>\n",
       "      <td>1.13584</td>\n",
       "      <td>6.255255</td>\n",
       "      <td>1.2753</td>\n",
       "      <td>14512308.abf</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-05-12 17:29:43.359</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>7.052052</td>\n",
       "      <td>0</td>\n",
       "      <td>14715335.abf</td>\n",
       "      <td>10</td>\n",
       "      <td>2014-07-15 19:05:01.420</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>avg</td>\n",
       "      <td>3.28719</td>\n",
       "      <td>14.302903</td>\n",
       "      <td>1.09289</td>\n",
       "      <td>15420003.abf</td>\n",
       "      <td>19</td>\n",
       "      <td>2015-04-20 13:50:35.474</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>avg</td>\n",
       "      <td>4.82102</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.08085</td>\n",
       "      <td>14617313.abf</td>\n",
       "      <td>8</td>\n",
       "      <td>2014-06-17 13:04:33.452</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>avg</td>\n",
       "      <td>0.558158</td>\n",
       "      <td>4.482382</td>\n",
       "      <td>1.03622</td>\n",
       "      <td>14626301.abf</td>\n",
       "      <td>21</td>\n",
       "      <td>2014-06-26 13:00:16.803</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>19.980080</td>\n",
       "      <td>0</td>\n",
       "      <td>14515402.abf</td>\n",
       "      <td>4</td>\n",
       "      <td>2014-05-15 16:29:14.296</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>avg</td>\n",
       "      <td>0.936637</td>\n",
       "      <td>9.502202</td>\n",
       "      <td>1.0221</td>\n",
       "      <td>14n03406.abf</td>\n",
       "      <td>15</td>\n",
       "      <td>2014-11-03 18:02:38.599</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>1.474474</td>\n",
       "      <td>0</td>\n",
       "      <td>14515380.abf</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-05-15 15:18:07.108</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>10.617718</td>\n",
       "      <td>0</td>\n",
       "      <td>14918309.abf</td>\n",
       "      <td>12</td>\n",
       "      <td>2014-09-18 15:33:04.731</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>9.382683</td>\n",
       "      <td>0</td>\n",
       "      <td>14515362.abf</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-05-15 14:34:29.499</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>5.876777</td>\n",
       "      <td>0</td>\n",
       "      <td>14918347.abf</td>\n",
       "      <td>13</td>\n",
       "      <td>2014-09-18 17:26:25.060</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>avg</td>\n",
       "      <td>3.24735</td>\n",
       "      <td>15.717217</td>\n",
       "      <td>1.19533</td>\n",
       "      <td>14605301.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-06-05 15:38:45.952</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>12.988188</td>\n",
       "      <td>0</td>\n",
       "      <td>14918373.abf</td>\n",
       "      <td>14</td>\n",
       "      <td>2014-09-18 18:06:28.903</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1408</th>\n",
       "      <td>avg</td>\n",
       "      <td>1.25536</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.10158</td>\n",
       "      <td>19228080.abf</td>\n",
       "      <td>34</td>\n",
       "      <td>2019-02-28 19:32:35.953</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1431</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>1.693594</td>\n",
       "      <td>0</td>\n",
       "      <td>19228063.abf</td>\n",
       "      <td>33</td>\n",
       "      <td>2019-02-28 18:44:28.578</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>avg</td>\n",
       "      <td>4.02422</td>\n",
       "      <td>10.538038</td>\n",
       "      <td>1.38572</td>\n",
       "      <td>2020_03_02_0024.abf</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-03-02 16:37:40.718</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>2.769269</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0000.abf</td>\n",
       "      <td>20</td>\n",
       "      <td>2020-01-27 12:55:40.562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>8.824925</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0112.abf</td>\n",
       "      <td>18</td>\n",
       "      <td>2019-11-26 18:42:08.015</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1460</th>\n",
       "      <td>avg</td>\n",
       "      <td>2.31111</td>\n",
       "      <td>6.215415</td>\n",
       "      <td>1.27603</td>\n",
       "      <td>2020_01_28_0032.abf</td>\n",
       "      <td>27</td>\n",
       "      <td>2020-01-28 21:09:15.875</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1471</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>4.442543</td>\n",
       "      <td>0</td>\n",
       "      <td>18032014_0163.abf</td>\n",
       "      <td>9</td>\n",
       "      <td>2014-03-18 19:45:36.475</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1482</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>4.064064</td>\n",
       "      <td>0</td>\n",
       "      <td>18032014_0015.abf</td>\n",
       "      <td>7</td>\n",
       "      <td>2014-03-18 16:10:45.059</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1503</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>1.613914</td>\n",
       "      <td>0</td>\n",
       "      <td>19129016.abf</td>\n",
       "      <td>29</td>\n",
       "      <td>2019-01-29 13:35:30.375</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>4.004304</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0067.abf</td>\n",
       "      <td>16</td>\n",
       "      <td>2019-11-26 16:59:17.796</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1520</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>2.868869</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0188.abf</td>\n",
       "      <td>6</td>\n",
       "      <td>2014-03-17 17:10:24.598</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1523</th>\n",
       "      <td>avg</td>\n",
       "      <td>0.956557</td>\n",
       "      <td>3.446547</td>\n",
       "      <td>1.01393</td>\n",
       "      <td>2019_11_26_0096.abf</td>\n",
       "      <td>17</td>\n",
       "      <td>2019-11-26 17:55:14.921</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1534</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.518318</td>\n",
       "      <td>0</td>\n",
       "      <td>2016_02_29_0005.abf</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-02-29 14:19:48.359</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>6.215415</td>\n",
       "      <td>0</td>\n",
       "      <td>2016_02_29_0043.abf</td>\n",
       "      <td>10</td>\n",
       "      <td>2016-02-29 15:35:23.203</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>avg</td>\n",
       "      <td>1.01632</td>\n",
       "      <td>8.685485</td>\n",
       "      <td>1.03159</td>\n",
       "      <td>2019_11_28_0120.abf</td>\n",
       "      <td>19</td>\n",
       "      <td>2019-11-28 17:16:55.562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1555</th>\n",
       "      <td>avg</td>\n",
       "      <td>1.31512</td>\n",
       "      <td>6.334935</td>\n",
       "      <td>1.0477</td>\n",
       "      <td>2020_01_28_0018.abf</td>\n",
       "      <td>26</td>\n",
       "      <td>2020-01-28 18:31:27.390</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.697598</td>\n",
       "      <td>0</td>\n",
       "      <td>18032014_0098.abf</td>\n",
       "      <td>8</td>\n",
       "      <td>2014-03-18 18:22:32.478</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>2.052152</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0007.abf</td>\n",
       "      <td>21</td>\n",
       "      <td>2020-01-27 14:04:13.187</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1582</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>6.494294</td>\n",
       "      <td>0</td>\n",
       "      <td>19129006.abf</td>\n",
       "      <td>36</td>\n",
       "      <td>2019-01-29 12:38:47.046</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1600</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>1.972472</td>\n",
       "      <td>0</td>\n",
       "      <td>19228031.abf</td>\n",
       "      <td>31</td>\n",
       "      <td>2019-02-28 16:21:56.375</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1619</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>3.267267</td>\n",
       "      <td>0</td>\n",
       "      <td>19228001.abf</td>\n",
       "      <td>30</td>\n",
       "      <td>2019-02-28 13:19:22.046</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1623</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>8.127728</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0045.abf</td>\n",
       "      <td>24</td>\n",
       "      <td>2020-01-27 21:27:07.546</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1630</th>\n",
       "      <td>avg</td>\n",
       "      <td>0.896797</td>\n",
       "      <td>3.327027</td>\n",
       "      <td>1.08367</td>\n",
       "      <td>2020_01_27_0043.abf</td>\n",
       "      <td>23</td>\n",
       "      <td>2020-01-27 21:02:12.640</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>2.291191</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0075.abf</td>\n",
       "      <td>4</td>\n",
       "      <td>2014-03-17 14:00:42.725</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1652</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.518318</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0013.abf</td>\n",
       "      <td>3</td>\n",
       "      <td>2014-03-17 12:41:26.801</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1659</th>\n",
       "      <td>avg</td>\n",
       "      <td>4.88078</td>\n",
       "      <td>18.944244</td>\n",
       "      <td>1.19676</td>\n",
       "      <td>2019_11_04_0088.abf</td>\n",
       "      <td>12</td>\n",
       "      <td>2019-11-04 15:38:51.578</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>0.677678</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_04_0114.abf</td>\n",
       "      <td>13</td>\n",
       "      <td>2019-11-04 17:22:58.562</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>5.020220</td>\n",
       "      <td>0</td>\n",
       "      <td>17032014_0111.abf</td>\n",
       "      <td>5</td>\n",
       "      <td>2014-03-17 15:08:18.629</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1689</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>3.008308</td>\n",
       "      <td>0</td>\n",
       "      <td>2019_11_26_0002.abf</td>\n",
       "      <td>14</td>\n",
       "      <td>2019-11-26 12:58:09.140</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1696</th>\n",
       "      <td>avg</td>\n",
       "      <td>0</td>\n",
       "      <td>4.860861</td>\n",
       "      <td>0</td>\n",
       "      <td>2020_01_27_0017.abf</td>\n",
       "      <td>22</td>\n",
       "      <td>2020-01-27 16:35:14.046</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>184 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trial center_freq   3dB_freq res_sharpness             abf_name cell_ID  \\\n",
       "10     avg           0   0.856957             0         15312093.abf       9   \n",
       "21     avg           0   6.354855             0         15127120.abf       7   \n",
       "32     avg           0   6.514214             0         15127075.abf       6   \n",
       "37     avg           0  12.530030             0         14304312.abf       1   \n",
       "48     avg           0   4.920621             0         15312067.abf       8   \n",
       "59     avg           0  10.617718             0         14d02005.abf       4   \n",
       "64     avg           0  10.298999             0         14317349.abf       3   \n",
       "69     avg           0  19.621522             0         14306307.abf       2   \n",
       "80     avg           0   1.295195             0         14d02109.abf       5   \n",
       "91     avg           0  12.669469             0         15420098.abf      20   \n",
       "101    avg           0  19.960160             0         15407002.abf      18   \n",
       "111    avg    0.956557   7.809009       1.04033         14624360.abf       9   \n",
       "122    avg    0.956557   5.338939       1.07461         14911304.abf      11   \n",
       "125    avg           0   0.876877             0         14605377.abf       7   \n",
       "136    avg           0   6.056056             0         14n04363.abf      16   \n",
       "140    avg     3.32703  16.075776       1.09685         14605350.abf       6   \n",
       "151    avg     3.26727  16.593694       1.03166         15224013.abf      17   \n",
       "155    avg     1.13584   6.255255        1.2753         14512308.abf       1   \n",
       "165    avg           0   7.052052             0         14715335.abf      10   \n",
       "176    avg     3.28719  14.302903       1.09289         15420003.abf      19   \n",
       "187    avg     4.82102  20.000000       1.08085         14617313.abf       8   \n",
       "198    avg    0.558158   4.482382       1.03622         14626301.abf      21   \n",
       "209    avg           0  19.980080             0         14515402.abf       4   \n",
       "219    avg    0.936637   9.502202        1.0221         14n03406.abf      15   \n",
       "230    avg           0   1.474474             0         14515380.abf       3   \n",
       "240    avg           0  10.617718             0         14918309.abf      12   \n",
       "251    avg           0   9.382683             0         14515362.abf       2   \n",
       "262    avg           0   5.876777             0         14918347.abf      13   \n",
       "273    avg     3.24735  15.717217       1.19533         14605301.abf       5   \n",
       "283    avg           0  12.988188             0         14918373.abf      14   \n",
       "...    ...         ...        ...           ...                  ...     ...   \n",
       "1408   avg     1.25536  20.000000       1.10158         19228080.abf      34   \n",
       "1431   avg           0   1.693594             0         19228063.abf      33   \n",
       "1440   avg     4.02422  10.538038       1.38572  2020_03_02_0024.abf       3   \n",
       "1444   avg           0   2.769269             0  2020_01_27_0000.abf      20   \n",
       "1452   avg           0   8.824925             0  2019_11_26_0112.abf      18   \n",
       "1460   avg     2.31111   6.215415       1.27603  2020_01_28_0032.abf      27   \n",
       "1471   avg           0   4.442543             0    18032014_0163.abf       9   \n",
       "1482   avg           0   4.064064             0    18032014_0015.abf       7   \n",
       "1503   avg           0   1.613914             0         19129016.abf      29   \n",
       "1509   avg           0   4.004304             0  2019_11_26_0067.abf      16   \n",
       "1520   avg           0   2.868869             0    17032014_0188.abf       6   \n",
       "1523   avg    0.956557   3.446547       1.01393  2019_11_26_0096.abf      17   \n",
       "1534   avg           0   0.518318             0  2016_02_29_0005.abf       1   \n",
       "1544   avg           0   6.215415             0  2016_02_29_0043.abf      10   \n",
       "1547   avg     1.01632   8.685485       1.03159  2019_11_28_0120.abf      19   \n",
       "1555   avg     1.31512   6.334935        1.0477  2020_01_28_0018.abf      26   \n",
       "1566   avg           0   0.697598             0    18032014_0098.abf       8   \n",
       "1570   avg           0   2.052152             0  2020_01_27_0007.abf      21   \n",
       "1582   avg           0   6.494294             0         19129006.abf      36   \n",
       "1600   avg           0   1.972472             0         19228031.abf      31   \n",
       "1619   avg           0   3.267267             0         19228001.abf      30   \n",
       "1623   avg           0   8.127728             0  2020_01_27_0045.abf      24   \n",
       "1630   avg    0.896797   3.327027       1.08367  2020_01_27_0043.abf      23   \n",
       "1641   avg           0   2.291191             0    17032014_0075.abf       4   \n",
       "1652   avg           0   0.518318             0    17032014_0013.abf       3   \n",
       "1659   avg     4.88078  18.944244       1.19676  2019_11_04_0088.abf      12   \n",
       "1672   avg           0   0.677678             0  2019_11_04_0114.abf      13   \n",
       "1683   avg           0   5.020220             0    17032014_0111.abf       5   \n",
       "1689   avg           0   3.008308             0  2019_11_26_0002.abf      14   \n",
       "1696   avg           0   4.860861             0  2020_01_27_0017.abf      22   \n",
       "\n",
       "                   file_time  has_spike  \n",
       "10   2015-03-12 15:47:22.528        0.0  \n",
       "21   2015-01-27 19:46:37.773        0.0  \n",
       "32   2015-01-27 17:28:23.546        0.0  \n",
       "37   2014-03-04 17:13:21.004        0.0  \n",
       "48   2015-03-12 15:22:46.559        0.0  \n",
       "59   2014-12-02 14:42:43.376        0.0  \n",
       "64   2014-03-17 18:06:21.919        0.0  \n",
       "69   2014-03-06 11:53:18.801        0.0  \n",
       "80   2014-12-02 17:11:53.005        0.0  \n",
       "91   2015-04-20 16:39:22.467        0.0  \n",
       "101  2015-04-07 13:18:01.985        0.0  \n",
       "111  2014-06-24 16:41:16.993        0.0  \n",
       "122  2014-09-11 17:30:00.312        0.0  \n",
       "125  2014-06-05 18:16:35.219        0.0  \n",
       "136  2014-11-04 18:24:54.654        0.0  \n",
       "140  2014-06-05 17:27:45.913        0.0  \n",
       "151  2015-02-24 13:21:10.727        0.0  \n",
       "155  2014-05-12 17:29:43.359        0.0  \n",
       "165  2014-07-15 19:05:01.420        0.0  \n",
       "176  2015-04-20 13:50:35.474        0.0  \n",
       "187  2014-06-17 13:04:33.452        0.0  \n",
       "198  2014-06-26 13:00:16.803        0.0  \n",
       "209  2014-05-15 16:29:14.296        0.0  \n",
       "219  2014-11-03 18:02:38.599        0.0  \n",
       "230  2014-05-15 15:18:07.108        0.0  \n",
       "240  2014-09-18 15:33:04.731        0.0  \n",
       "251  2014-05-15 14:34:29.499        0.0  \n",
       "262  2014-09-18 17:26:25.060        0.0  \n",
       "273  2014-06-05 15:38:45.952        0.0  \n",
       "283  2014-09-18 18:06:28.903        0.0  \n",
       "...                      ...        ...  \n",
       "1408 2019-02-28 19:32:35.953        0.0  \n",
       "1431 2019-02-28 18:44:28.578        0.0  \n",
       "1440 2020-03-02 16:37:40.718        0.0  \n",
       "1444 2020-01-27 12:55:40.562        0.0  \n",
       "1452 2019-11-26 18:42:08.015        0.0  \n",
       "1460 2020-01-28 21:09:15.875        0.0  \n",
       "1471 2014-03-18 19:45:36.475        0.0  \n",
       "1482 2014-03-18 16:10:45.059        0.0  \n",
       "1503 2019-01-29 13:35:30.375        0.0  \n",
       "1509 2019-11-26 16:59:17.796        0.0  \n",
       "1520 2014-03-17 17:10:24.598        0.0  \n",
       "1523 2019-11-26 17:55:14.921        0.0  \n",
       "1534 2016-02-29 14:19:48.359        0.0  \n",
       "1544 2016-02-29 15:35:23.203        0.0  \n",
       "1547 2019-11-28 17:16:55.562        0.0  \n",
       "1555 2020-01-28 18:31:27.390        0.0  \n",
       "1566 2014-03-18 18:22:32.478        0.0  \n",
       "1570 2020-01-27 14:04:13.187        0.0  \n",
       "1582 2019-01-29 12:38:47.046        0.0  \n",
       "1600 2019-02-28 16:21:56.375        0.0  \n",
       "1619 2019-02-28 13:19:22.046        0.0  \n",
       "1623 2020-01-27 21:27:07.546        0.0  \n",
       "1630 2020-01-27 21:02:12.640        0.0  \n",
       "1641 2014-03-17 14:00:42.725        0.0  \n",
       "1652 2014-03-17 12:41:26.801        0.0  \n",
       "1659 2019-11-04 15:38:51.578        0.0  \n",
       "1672 2019-11-04 17:22:58.562        0.0  \n",
       "1683 2014-03-17 15:08:18.629        0.0  \n",
       "1689 2019-11-26 12:58:09.140        0.0  \n",
       "1696 2020-01-27 16:35:14.046        0.0  \n",
       "\n",
       "[184 rows x 8 columns]"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_avgs_only = df[df['trial'] == 'avg']\n",
    "df_avgs_only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_avgs_only.to_csv('output_files/fred_zap_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_avgs_only.to_csv('output_files/fred_zap_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'15312093.abf': {'impedance_mean': array([1.1898e+11, 1.6302e+11, 1.8531e+11, ..., 3.2750e+10, 3.2708e+10,\n",
       "         3.2670e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0811, -0.0811, -0.0812, ..., -0.081 , -0.0811, -0.0811],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15127120.abf': {'impedance_mean': array([9.9789e+10, 8.4493e+10, 7.5040e+10, ..., 3.7270e+10, 3.5838e+10,\n",
       "         3.4515e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0803, -0.0803, -0.0802, ..., -0.0803, -0.0803, -0.0803],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15127075.abf': {'impedance_mean': array([7.1668e+10, 6.8380e+10, 6.6078e+10, ..., 3.1064e+10, 2.7615e+10,\n",
       "         2.4380e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0823, -0.0823, -0.0823, ..., -0.0823, -0.0823, -0.0824],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14304312.abf': {'impedance_mean': array([3.4612e+10, 3.8014e+10, 3.9889e+10, ..., 1.9679e+10, 1.8943e+10,\n",
       "         1.8269e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0778, -0.0778, -0.0779, ..., -0.0777, -0.0778, -0.0777],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15312067.abf': {'impedance_mean': array([8.2063e+10, 8.1960e+10, 7.7146e+10, ..., 3.2876e+10, 3.1464e+10,\n",
       "         3.0657e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0802, -0.0803, -0.0803, ..., -0.0784, -0.0783, -0.0769],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d02005.abf': {'impedance_mean': array([6.8669e+10, 5.4902e+10, 5.1231e+10, ..., 2.5869e+10, 2.4774e+10,\n",
       "         2.3788e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0787, -0.0786, -0.0787, ..., -0.08  , -0.0799, -0.0797],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14317349.abf': {'impedance_mean': array([2.9909e+10, 2.8606e+10, 2.7420e+10, ..., 1.4046e+10, 1.3397e+10,\n",
       "         1.2861e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0742, -0.0742, -0.0742, ..., -0.0745, -0.0745, -0.0745],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14306307.abf': {'impedance_mean': array([6.3064e+10, 6.0126e+10, 6.3321e+10, ..., 4.0565e+10, 4.0391e+10,\n",
       "         3.8914e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0812, -0.0813, -0.0813, ..., -0.0811, -0.0811, -0.0811],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d02109.abf': {'impedance_mean': array([6.4732e+10, 7.3299e+10, 7.8277e+10, ..., 3.3501e+10, 3.1161e+10,\n",
       "         3.0155e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0827, -0.0827, -0.0827, ..., -0.0825, -0.0825, -0.0825],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15420098.abf': {'impedance_mean': array([2.3155e+10, 2.4488e+10, 2.4026e+10, ..., 1.4233e+10, 1.3225e+10,\n",
       "         1.2202e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0778, -0.0778, -0.0778, ..., -0.0781, -0.0781, -0.0781],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15407002.abf': {'impedance_mean': array([4.3349e+10, 4.4636e+10, 4.4512e+10, ..., 3.0368e+10, 2.8936e+10,\n",
       "         2.8099e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0738, -0.0737, -0.0737, ..., -0.074 , -0.074 , -0.074 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14624360.abf': {'impedance_mean': array([2.8858e+10, 2.7534e+10, 2.6992e+10, ..., 1.1515e+10, 1.1540e+10,\n",
       "         1.1564e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0728, -0.0728, -0.0728, ..., -0.0727, -0.0727, -0.0727],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14911304.abf': {'impedance_mean': array([7.6055e+10, 7.1360e+10, 6.5685e+10, ..., 2.1976e+10, 2.1441e+10,\n",
       "         2.0944e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0675, -0.0676, -0.0676, ..., -0.0677, -0.0678, -0.0677],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14605377.abf': {'impedance_mean': array([1.6442e+11, 1.7235e+11, 1.7528e+11, ..., 5.3140e+10, 5.1326e+10,\n",
       "         4.9667e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0755, -0.0755, -0.0755, ..., -0.0759, -0.0759, -0.0759],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14n04363.abf': {'impedance_mean': array([7.1654e+10, 6.7220e+10, 6.4449e+10, ..., 2.8112e+10, 2.6628e+10,\n",
       "         2.5489e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.076 , -0.076 , -0.076 , ..., -0.0761, -0.0762, -0.0762],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14605350.abf': {'impedance_mean': array([5.0115e+10, 5.2749e+10, 5.5849e+10, ..., 4.3274e+10, 4.0928e+10,\n",
       "         3.8641e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0765, -0.0766, -0.0765, ..., -0.0757, -0.0758, -0.0758],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15224013.abf': {'impedance_mean': array([5.6335e+10, 5.5853e+10, 5.7233e+10, ..., 3.8166e+10, 3.6403e+10,\n",
       "         3.4376e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0781, -0.0782, -0.0783, ..., -0.0782, -0.0782, -0.0781],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14512308.abf': {'impedance_mean': array([9.4350e+10, 6.8027e+10, 5.7703e+10, ..., 2.8943e+10, 2.9234e+10,\n",
       "         2.9024e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0723, -0.0724, -0.0724, ..., -0.072 , -0.072 , -0.0718],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14715335.abf': {'impedance_mean': array([6.0656e+10, 5.5562e+10, 5.2384e+10, ..., 2.2353e+10, 2.1507e+10,\n",
       "         2.0658e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.073 , -0.073 , -0.073 , ..., -0.0728, -0.0729, -0.0729],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15420003.abf': {'impedance_mean': array([2.3390e+10, 2.1429e+10, 2.2770e+10, ..., 1.4755e+10, 1.4448e+10,\n",
       "         1.4155e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0713, -0.0713, -0.0713, ..., -0.0713, -0.0713, -0.0713],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14617313.abf': {'impedance_mean': array([2.6292e+10, 2.6776e+10, 2.6935e+10, ..., 2.1650e+10, 2.1797e+10,\n",
       "         2.1298e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0707, -0.0707, -0.0708, ..., -0.0709, -0.071 , -0.0712],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14626301.abf': {'impedance_mean': array([1.8251e+11, 8.4715e+10, 7.7853e+10, ..., 4.6170e+10, 4.4365e+10,\n",
       "         4.0774e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0709, -0.071 , -0.071 , ..., -0.0708, -0.0709, -0.0709],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14515402.abf': {'impedance_mean': array([9.9999e+08, 1.0000e+09, 1.0000e+09, ..., 9.9997e+08, 9.9998e+08,\n",
       "         1.0000e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0743, -0.0743, -0.0744, ..., -0.0744, -0.0744, -0.0744],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14n03406.abf': {'impedance_mean': array([5.6427e+10, 5.5454e+10, 5.3598e+10, ..., 2.9956e+10, 2.9081e+10,\n",
       "         2.8295e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0777, -0.0777, -0.0777, ..., -0.0773, -0.0773, -0.0773],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14515380.abf': {'impedance_mean': array([2.2015e+11, 2.1583e+11, 2.0784e+11, ..., 3.0360e+10, 3.0147e+10,\n",
       "         2.7318e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0763, -0.0764, -0.0763, ..., -0.0762, -0.0761, -0.0761],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14918309.abf': {'impedance_mean': array([1.1862e+11, 1.2081e+11, 1.1591e+11, ..., 5.2019e+10, 5.0001e+10,\n",
       "         4.7979e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0725, -0.0726, -0.0725, ..., -0.0724, -0.0724, -0.0724],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14515362.abf': {'impedance_mean': array([6.7434e+10, 6.6453e+10, 6.9568e+10, ..., 3.2257e+10, 3.2280e+10,\n",
       "         3.2261e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.071 , -0.071 , -0.0709, ..., -0.0712, -0.0712, -0.0712],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14918347.abf': {'impedance_mean': array([5.8215e+10, 5.4505e+10, 5.1869e+10, ..., 1.5525e+10, 1.5484e+10,\n",
       "         1.5256e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0734, -0.0734, -0.0734, ..., -0.0735, -0.0735, -0.0735],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14605301.abf': {'impedance_mean': array([4.7791e+10, 4.2859e+10, 4.0899e+10, ..., 3.4222e+10, 3.2896e+10,\n",
       "         3.1666e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0693, -0.0693, -0.0694, ..., -0.0693, -0.0693, -0.0693],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14918373.abf': {'impedance_mean': array([7.1973e+10, 7.9215e+10, 8.1863e+10, ..., 4.3556e+10, 4.0234e+10,\n",
       "         3.7212e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0748, -0.0749, -0.0749, ..., -0.0747, -0.0748, -0.0748],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15420039.abf': {'impedance_mean': array([1.6580e+11, 1.5588e+11, 1.4400e+11, ..., 5.5539e+10, 5.2817e+10,\n",
       "         5.0309e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0827, -0.0827, -0.0827, ..., -0.083 , -0.083 , -0.083 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15330014.abf': {'impedance_mean': array([9.0294e+10, 8.1895e+10, 7.4863e+10, ..., 3.3468e+10, 3.2039e+10,\n",
       "         3.0670e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0821, -0.0821, -0.0821, ..., -0.082 , -0.082 , -0.082 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15224028.abf': {'impedance_mean': array([4.8315e+10, 4.3555e+10, 4.2255e+10, ..., 2.2381e+10, 2.1996e+10,\n",
       "         2.1572e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0877, -0.0877, -0.0877, ..., -0.0882, -0.0882, -0.0882],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14424346.abf': {'impedance_mean': array([1.0312e+11, 9.8399e+10, 9.9218e+10, ..., 3.8184e+10, 3.7293e+10,\n",
       "         3.6505e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0796, -0.0796, -0.0795, ..., -0.0794, -0.0794, -0.0794],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14n04304.abf': {'impedance_mean': array([2.9043e+10, 3.0278e+10, 2.9969e+10, ..., 1.6681e+10, 1.5526e+10,\n",
       "         1.5506e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0791, -0.0791, -0.0791, ..., -0.079 , -0.079 , -0.079 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15330041.abf': {'impedance_mean': array([7.6247e+10, 9.5033e+10, 9.3809e+10, ..., 5.0843e+10, 4.8037e+10,\n",
       "         4.6192e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0855, -0.0855, -0.0855, ..., -0.0856, -0.0857, -0.0857],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14n03315.abf': {'impedance_mean': array([1.1676e+11, 1.1696e+11, 1.1023e+11, ..., 3.9344e+10, 3.4256e+10,\n",
       "         3.1324e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0808, -0.0807, -0.0807, ..., -0.0803, -0.0801, -0.08  ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14911316.abf': {'impedance_mean': array([6.1938e+10, 5.9163e+10, 5.7311e+10, ..., 2.3088e+10, 2.4093e+10,\n",
       "         2.4430e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0815, -0.0815, -0.0816, ..., -0.0822, -0.0822, -0.0823],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14617302.abf': {'impedance_mean': array([4.7768e+10, 5.0498e+10, 5.4262e+10, ..., 4.8523e+10, 4.6165e+10,\n",
       "         4.4509e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.079 , -0.079 , -0.0788, ..., -0.0785, -0.0788, -0.0788],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14n10462.abf': {'impedance_mean': array([2.2767e+11, 1.8287e+11, 1.7104e+11, ..., 4.0354e+10, 4.0891e+10,\n",
       "         4.0859e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0798, -0.0798, -0.0798, ..., -0.079 , -0.079 , -0.079 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15127064.abf': {'impedance_mean': array([3.1658e+10, 5.8199e+10, 6.8288e+10, ..., 3.5098e+10, 2.9793e+10,\n",
       "         2.3505e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0733, -0.0733, -0.0733, ..., -0.0727, -0.0726, -0.0727],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15105068.abf': {'impedance_mean': array([2.5175e+10, 1.9790e+10, 1.9587e+10, ..., 1.6320e+10, 1.8224e+10,\n",
       "         1.9880e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0736, -0.0737, -0.0737, ..., -0.0735, -0.0736, -0.0737],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d02125.abf': {'impedance_mean': array([5.9395e+10, 5.7611e+10, 5.7254e+10, ..., 2.8933e+10, 2.7696e+10,\n",
       "         2.5269e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0749, -0.0749, -0.075 , ..., -0.0748, -0.0748, -0.0748],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d16019.abf': {'impedance_mean': array([2.4034e+10, 2.1751e+10, 2.3295e+10, ..., 2.7010e+10, 2.6356e+10,\n",
       "         2.5823e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0755, -0.0752, -0.0749, ..., -0.0753, -0.0753, -0.0754],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14318310.abf': {'impedance_mean': array([1.8237e+10, 1.7920e+10, 1.7804e+10, ..., 9.6828e+09, 9.4503e+09,\n",
       "         9.2346e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0717, -0.0717, -0.0718, ..., -0.0716, -0.0716, -0.0716],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d18117.abf': {'impedance_mean': array([2.3203e+10, 2.3024e+10, 2.3098e+10, ..., 1.5798e+10, 1.5543e+10,\n",
       "         1.5040e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0764, -0.0764, -0.0764, ..., -0.0763, -0.0763, -0.0763],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14317321.abf': {'impedance_mean': array([7.0149e+10, 6.2986e+10, 5.9503e+10, ..., 2.4534e+10, 2.4671e+10,\n",
       "         2.4610e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0746, -0.0746, -0.0746, ..., -0.0733, -0.0732, -0.0731],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15105004.abf': {'impedance_mean': array([2.6024e+10, 2.9453e+10, 3.0479e+10, ..., 1.4230e+10, 1.3502e+10,\n",
       "         1.2856e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0716, -0.0716, -0.0715, ..., -0.0721, -0.0721, -0.0721],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14224303.abf': {'impedance_mean': array([1.1417e+11, 1.2486e+11, 1.1294e+11, ..., 3.9020e+10, 3.8549e+10,\n",
       "         3.8355e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0668, -0.0667, -0.0668, ..., -0.0668, -0.0668, -0.0667],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d02170.abf': {'impedance_mean': array([3.3634e+10, 3.2628e+10, 3.2275e+10, ..., 2.8415e+10, 2.7921e+10,\n",
       "         2.7512e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0769, -0.0769, -0.0771, ..., -0.0774, -0.0774, -0.0774],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '15127044.abf': {'impedance_mean': array([5.3919e+10, 5.4578e+10, 5.3964e+10, ..., 3.8646e+10, 3.6430e+10,\n",
       "         3.3864e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0736, -0.0737, -0.0737, ..., -0.0735, -0.0735, -0.0735],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d02057.abf': {'impedance_mean': array([3.5155e+10, 3.3310e+10, 3.4419e+10, ..., 2.6796e+10, 2.6053e+10,\n",
       "         2.6010e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0728, -0.0729, -0.0729, ..., -0.073 , -0.0732, -0.0732],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14317335.abf': {'impedance_mean': array([1.3897e+10, 1.3415e+10, 1.3562e+10, ..., 9.9737e+09, 9.5701e+09,\n",
       "         9.1384e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0486, -0.0486, -0.0487, ..., -0.0489, -0.0489, -0.0489],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14304301.abf': {'impedance_mean': array([7.8931e+10, 6.4530e+10, 5.5445e+10, ..., 1.8600e+10, 1.8573e+10,\n",
       "         1.8414e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0625, -0.0625, -0.0625, ..., -0.0624, -0.0624, -0.0624],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d18091.abf': {'impedance_mean': array([6.7721e+10, 6.4432e+10, 6.4053e+10, ..., 3.7877e+10, 3.6865e+10,\n",
       "         3.5926e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0765, -0.0765, -0.0765, ..., -0.0765, -0.0765, -0.0765],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14224322.abf': {'impedance_mean': array([4.1825e+10, 3.5377e+10, 3.4071e+10, ..., 1.9278e+10, 1.8674e+10,\n",
       "         1.8078e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0697, -0.0697, -0.0697, ..., -0.0689, -0.0689, -0.0689],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d16111.abf': {'impedance_mean': array([3.3005e+10, 3.6116e+10, 3.8148e+10, ..., 2.4835e+10, 2.4575e+10,\n",
       "         2.4351e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0753, -0.0752, -0.0752, ..., -0.0751, -0.0752, -0.0753],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14224315.abf': {'impedance_mean': array([4.3248e+10, 4.5288e+10, 4.6883e+10, ..., 1.8477e+10, 1.8379e+10,\n",
       "         1.8232e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0689, -0.0688, -0.0688, ..., -0.0688, -0.0689, -0.069 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d16140.abf': {'impedance_mean': array([3.7807e+10, 4.3629e+10, 4.4776e+10, ..., 2.4909e+10, 2.4258e+10,\n",
       "         2.3645e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.071 , -0.071 , -0.0711, ..., -0.0708, -0.0708, -0.0708],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14317302.abf': {'impedance_mean': array([2.4490e+10, 3.0349e+10, 3.4197e+10, ..., 1.6954e+10, 1.7414e+10,\n",
       "         1.7469e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0735, -0.0735, -0.0736, ..., -0.0734, -0.0734, -0.0735],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '14d18008.abf': {'impedance_mean': array([2.3646e+10, 2.2746e+10, 2.1355e+10, ..., 1.4173e+10, 1.3510e+10,\n",
       "         1.2259e+10]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0772, -0.0773, -0.0773, ..., -0.077 , -0.0771, -0.0771],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0022,  0.0044, ..., 22.9944, 22.9966, 22.9988])},\n",
       " '19129065.abf': {'impedance_mean': array([5.3627e+08, 5.4531e+08, 5.1880e+08, ..., 1.9877e+08, 2.0352e+08,\n",
       "         2.0828e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1776, -0.1776, -0.1777, ..., -0.1755, -0.1754, -0.1756],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2019_11_28_0095.abf': {'impedance_mean': array([81879731.1357, 77819283.9685, 73758836.8013, ..., 38162932.0074,\n",
       "         37289989.1597, 36521904.208 ]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0667, -0.0667, -0.0667, ..., -0.0674, -0.0674, -0.0675],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2019_11_28_0034.abf': {'impedance_mean': array([2.9149e+08, 2.7664e+08, 2.6178e+08, ..., 1.1051e+08, 1.0962e+08,\n",
       "         1.0864e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0701, -0.0701, -0.0701, ..., -0.0705, -0.0705, -0.0704],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0007.abf': {'impedance_mean': array([1.2485e+08, 1.1983e+08, 1.1324e+08, ..., 4.2998e+07, 4.2863e+07,\n",
       "         4.2184e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0645, -0.0645, -0.0644, ..., -0.0645, -0.0645, -0.0644],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_03_02_0011.abf': {'impedance_mean': array([3.2570e+08, 3.1244e+08, 3.1854e+08, ..., 1.2656e+08, 1.2397e+08,\n",
       "         1.2371e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0718, -0.0718, -0.0718, ..., -0.0718, -0.0718, -0.0719],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_03_02_0017.abf': {'impedance_mean': array([1.5947e+08, 1.5661e+08, 1.5375e+08, ..., 8.1855e+07, 8.4755e+07,\n",
       "         8.8963e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0675, -0.0676, -0.0676, ..., -0.068 , -0.0681, -0.0681],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2019_11_26_0032.abf': {'impedance_mean': array([5.6188e+08, 5.4667e+08, 5.3146e+08, ..., 9.4993e+07, 9.5605e+07,\n",
       "         9.4155e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0694, -0.0694, -0.0694, ..., -0.0696, -0.0695, -0.0695],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '19129048.abf': {'impedance_mean': array([1.4502e+08, 1.4283e+08, 1.4064e+08, ..., 3.8825e+07, 3.7838e+07,\n",
       "         3.5508e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1625, -0.1623, -0.1623, ..., -0.1637, -0.1638, -0.1638],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2020_01_27_0043.abf': {'impedance_mean': array([1.8270e+08, 1.9014e+08, 2.1226e+08, ..., 4.7249e+07, 4.6875e+07,\n",
       "         4.6487e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0655, -0.0655, -0.0655, ..., -0.0641, -0.0641, -0.0641],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0109.abf': {'impedance_mean': array([1.2728e+08, 1.2678e+08, 1.2629e+08, ..., 7.8848e+07, 7.8433e+07,\n",
       "         7.7959e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0659, -0.0658, -0.0658, ..., -0.0661, -0.066 , -0.066 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0039.abf': {'impedance_mean': array([1.5839e+08, 1.4742e+08, 1.3645e+08, ..., 2.5440e+07, 2.4814e+07,\n",
       "         2.4283e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0609, -0.0608, -0.0608, ..., -0.0609, -0.0609, -0.0609],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0062.abf': {'impedance_mean': array([79925227.0402, 79272547.4857, 78619867.9311, ..., 32025164.1309,\n",
       "         32161036.3774, 32292787.676 ]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0671, -0.0671, -0.0671, ..., -0.0672, -0.0672, -0.0672],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0097.abf': {'impedance_mean': array([1.2398e+08, 1.1946e+08, 1.1538e+08, ..., 7.3567e+07, 7.4645e+07,\n",
       "         7.5422e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0654, -0.0655, -0.0656, ..., -0.0658, -0.0658, -0.0658],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2019_11_28_0002.abf': {'impedance_mean': array([1.0912e+08, 1.0377e+08, 9.8416e+07, ..., 5.4967e+07, 5.5526e+07,\n",
       "         5.5491e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0626, -0.0626, -0.0625, ..., -0.0622, -0.0622, -0.0622],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0102.abf': {'impedance_mean': array([5.8696e+08, 5.5623e+08, 5.4210e+08, ..., 9.4716e+07, 9.6082e+07,\n",
       "         9.5921e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0646, -0.0645, -0.0646, ..., -0.0649, -0.0649, -0.0649],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0050.abf': {'impedance_mean': array([5.2410e+07, 7.4018e+07, 1.1006e+08, ..., 5.7574e+07, 5.1545e+07,\n",
       "         4.8527e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0628, -0.0628, -0.0628, ..., -0.0628, -0.0628, -0.0627],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_27_0037.abf': {'impedance_mean': array([1.1641e+08, 1.0849e+08, 1.0057e+08, ..., 3.6103e+07, 3.5885e+07,\n",
       "         3.5507e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0717, -0.0717, -0.0717, ..., -0.0727, -0.0727, -0.0727],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0030.abf': {'impedance_mean': array([2.7771e+08, 2.5584e+08, 2.3398e+08, ..., 8.3360e+07, 8.7352e+07,\n",
       "         8.8475e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0619, -0.0619, -0.0618, ..., -0.0624, -0.0623, -0.0623],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0066.abf': {'impedance_mean': array([1.5230e+08, 1.2555e+08, 9.4395e+07, ..., 2.8737e+07, 2.8437e+07,\n",
       "         2.7150e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.068 , -0.0681, -0.0681, ..., -0.0682, -0.0682, -0.0681],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0031.abf': {'impedance_mean': array([2.6054e+08, 2.4967e+08, 2.3398e+08, ..., 8.5191e+07, 8.7453e+07,\n",
       "         8.9479e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0623, -0.0624, -0.0623, ..., -0.0625, -0.0625, -0.0625],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0087.abf': {'impedance_mean': array([1.8909e+08, 1.9938e+08, 1.8977e+08, ..., 6.3843e+07, 6.0144e+07,\n",
       "         5.7781e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0674, -0.0674, -0.0674, ..., -0.0673, -0.0673, -0.0673],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0079.abf': {'impedance_mean': array([1.0279e+08, 9.8771e+07, 9.4748e+07, ..., 3.3640e+07, 3.2863e+07,\n",
       "         3.2508e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0712, -0.0712, -0.0711, ..., -0.0714, -0.0714, -0.0715],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2019_11_04_0088.abf': {'impedance_mean': array([3.7328e+09, 3.2460e+09, 2.7591e+09, ..., 1.7563e+09, 1.7606e+09,\n",
       "         1.7673e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0064, -0.0064, -0.0064, ..., -0.0064, -0.0064, -0.0064],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '19129074.abf': {'impedance_mean': array([2.9764e+08, 3.0739e+08, 3.1714e+08, ..., 9.4937e+07, 9.1059e+07,\n",
       "         9.0737e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.2103, -0.2104, -0.2104, ..., -0.2106, -0.2107, -0.2108],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0.,  0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2020_01_06_0065.abf': {'impedance_mean': array([92876328.977 , 92295343.5965, 85417571.3091, ..., 33213369.892 ,\n",
       "         32375029.8797, 34584760.144 ]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0659, -0.0659, -0.0659, ..., -0.0668, -0.0667, -0.0667],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0073.abf': {'impedance_mean': array([1.2980e+08, 1.1701e+08, 1.2640e+08, ..., 4.3999e+07, 4.2917e+07,\n",
       "         4.1927e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0612, -0.0612, -0.0613, ..., -0.0612, -0.0612, -0.0612],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2016_03_01_0006.abf': {'impedance_mean': array([1.7845e+09, 1.6186e+09, 1.4527e+09, ..., 5.2376e+08, 5.1815e+08,\n",
       "         5.1318e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0022, -0.0022, -0.0022, ..., -0.0022, -0.0022, -0.0022],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_02_25_0212.abf': {'impedance_mean': array([2.4255e+09, 2.2601e+09, 2.0694e+09, ..., 9.9455e+08, 9.8932e+08,\n",
       "         9.8391e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.003, -0.003, -0.003, ..., -0.003, -0.003, -0.003], dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_02_25_0078.abf': {'impedance_mean': array([1.6467e+10, 1.3730e+10, 1.0994e+10, ..., 1.2050e+09, 1.2405e+09,\n",
       "         1.2710e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0021, -0.0021, -0.0021, ..., -0.0021, -0.0021, -0.0021],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_03_03_0028.abf': {'impedance_mean': array([5.5083e+09, 5.5693e+09, 5.5852e+09, ..., 5.2481e+08, 5.1750e+08,\n",
       "         5.1128e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0023, -0.0023, -0.0023, ..., -0.0022, -0.0022, -0.0022],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2014_05_12_600__0030.abf': {'impedance_mean': array([3.2662e+09, 3.4399e+09, 3.6136e+09, ..., 1.7579e+09, 1.7656e+09,\n",
       "         1.7728e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0033, -0.0033, -0.0033, ..., -0.0033, -0.0033, -0.0033],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2014_04_24_600_0184.abf': {'impedance_mean': array([4.8840e+09, 4.5450e+09, 4.2061e+09, ..., 9.3743e+08, 9.1907e+08,\n",
       "         8.9904e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.003, -0.003, -0.003, ..., -0.003, -0.003, -0.003], dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2014_04_24_600_0134.abf': {'impedance_mean': array([3.0851e+09, 2.9664e+09, 2.8477e+09, ..., 5.7485e+08, 5.7253e+08,\n",
       "         5.6795e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0032, -0.0032, -0.0032, ..., -0.0032, -0.0032, -0.0032],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2014_04_08_600_0003.abf': {'impedance_mean': array([2.6980e+09, 2.5793e+09, 2.5034e+09, ..., 7.2739e+08, 7.1125e+08,\n",
       "         6.9616e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0029, -0.0029, -0.0029, ..., -0.0028, -0.0028, -0.0028],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2016_03_03_0006.abf': {'impedance_mean': array([2.6824e+09, 2.4469e+09, 2.2114e+09, ..., 4.8867e+08, 5.0125e+08,\n",
       "         5.0317e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0023, -0.0023, -0.0023, ..., -0.0023, -0.0023, -0.0023],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2014_05_12_600__0015.abf': {'impedance_mean': array([2.4064e+11, 1.9979e+11, 1.5895e+11, ..., 7.1853e+09, 7.7409e+09,\n",
       "         8.2336e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0035, -0.0035, -0.0035, ..., -0.0034, -0.0034, -0.0034],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2016_03_01_0054.abf': {'impedance_mean': array([1.9879e+09, 1.8618e+09, 1.7357e+09, ..., 4.8359e+08, 4.7243e+08,\n",
       "         4.7183e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.003, -0.003, -0.003, ..., -0.003, -0.003, -0.003], dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '19129054.abf': {'impedance_mean': array([1.8786e+08, 1.3875e+08, 1.1359e+08, ..., 5.7183e+07, 5.7675e+07,\n",
       "         5.8167e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1374, -0.1374, -0.1374, ..., -0.1347, -0.1345, -0.1343],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0.,  0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2016_03_03_0107.abf': {'impedance_mean': array([4.2224e+09, 3.9131e+09, 3.6037e+09, ..., 5.7939e+08, 5.7704e+08,\n",
       "         5.7389e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0025, -0.0025, -0.0025, ..., -0.0025, -0.0025, -0.0025],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '19129059.abf': {'impedance_mean': array([2.2104e+08, 2.2005e+08, 2.1905e+08, ..., 8.6627e+07, 8.6370e+07,\n",
       "         8.6038e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1904, -0.1904, -0.1903, ..., -0.1885, -0.1886, -0.1886],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2016_02_25_0009.abf': {'impedance_mean': array([1.3767e+09, 1.3905e+09, 1.4620e+09, ..., 5.4476e+08, 5.3267e+08,\n",
       "         5.2786e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0023, -0.0023, -0.0023, ..., -0.0023, -0.0023, -0.0023],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_02_25_0088.abf': {'impedance_mean': array([2.2727e+09, 2.0686e+09, 1.8644e+09, ..., 7.4431e+08, 7.3938e+08,\n",
       "         7.3612e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0026, -0.0026, -0.0026, ..., -0.0026, -0.0026, -0.0026],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_03_03_0058.abf': {'impedance_mean': array([2.4221e+10, 1.9163e+10, 1.5189e+10, ..., 1.9573e+09, 1.9707e+09,\n",
       "         1.8941e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0017, -0.0017, -0.0017, ..., -0.0017, -0.0017, -0.0017],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_02_25_0136.abf': {'impedance_mean': array([5.2559e+10, 4.5235e+10, 3.7910e+10, ..., 1.9675e+09, 2.0394e+09,\n",
       "         2.0981e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0017, -0.0017, -0.0017, ..., -0.0016, -0.0016, -0.0016],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '19122004.abf': {'impedance_mean': array([2.9670e+08, 2.8964e+08, 2.8594e+08, ..., 1.1721e+08, 1.4309e+08,\n",
       "         1.6889e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.188 , -0.1877, -0.1873, ..., -0.1871, -0.1871, -0.1872],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([ 0., -0.,  0., ...,  0.,  0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2014_04_24_600_0058.abf': {'impedance_mean': array([4.8995e+09, 4.7766e+09, 4.6537e+09, ..., 7.2793e+08, 7.4045e+08,\n",
       "         7.5101e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0029, -0.0029, -0.0029, ..., -0.0029, -0.0029, -0.0029],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2014_04_08_600_0102.abf': {'impedance_mean': array([6.2973e+09, 6.0586e+09, 5.8199e+09, ..., 2.4587e+09, 2.4574e+09,\n",
       "         2.4558e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0029, -0.0029, -0.0029, ..., -0.0029, -0.0029, -0.0029],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2014_04_08_600_0055.abf': {'impedance_mean': array([2.1989e+09, 2.0217e+09, 1.8906e+09, ..., 6.7516e+08, 6.6824e+08,\n",
       "         6.6200e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0022, -0.0022, -0.0022, ..., -0.0022, -0.0022, -0.0022],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2014_04_24_600_0109.abf': {'impedance_mean': array([5.2955e+08, 5.0824e+08, 6.0281e+08, ..., 4.0967e+08, 4.0969e+08,\n",
       "         4.0987e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0029, -0.0029, -0.0029, ..., -0.0029, -0.0029, -0.0029],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2016_02_25_0176.abf': {'impedance_mean': array([2.2564e+09, 2.4066e+09, 2.5568e+09, ..., 8.2058e+08, 7.8973e+08,\n",
       "         7.7819e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0037, -0.0037, -0.0037, ..., -0.0039, -0.0039, -0.0039],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2020_01_27_0019.abf': {'impedance_mean': array([6.5125e+08, 5.6019e+08, 5.3006e+08, ..., 2.5380e+08, 2.5582e+08,\n",
       "         2.5744e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0805, -0.0805, -0.0805, ..., -0.0777, -0.0777, -0.0779],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_27_0046.abf': {'impedance_mean': array([76495723.667 , 78465368.1817, 76880997.3204, ..., 50750677.4079,\n",
       "         50293855.268 , 49897063.488 ]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0786, -0.0787, -0.0787, ..., -0.0787, -0.0787, -0.0788],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2016_02_29_0025.abf': {'impedance_mean': array([7.2634e+09, 6.5784e+09, 5.4537e+09, ..., 1.4054e+09, 1.2493e+09,\n",
       "         1.1107e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0033, -0.0033, -0.0033, ..., -0.0033, -0.0033, -0.0033],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2020_01_28_0035.abf': {'impedance_mean': array([1.4537e+08, 1.5340e+08, 1.5908e+08, ..., 3.8727e+07, 3.8272e+07,\n",
       "         3.7306e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0702, -0.0702, -0.0702, ..., -0.0694, -0.0694, -0.0694],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0029,  0.0058, ..., 29.9918, 29.9947, 29.9976])},\n",
       " '2020_01_27_0011.abf': {'impedance_mean': array([3.2587e+08, 3.2801e+08, 3.3823e+08, ..., 4.4502e+07, 4.3458e+07,\n",
       "         3.3978e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0768, -0.0767, -0.0768, ..., -0.0765, -0.0764, -0.0764],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_28_0014.abf': {'impedance_mean': array([1.0091e+08, 1.0110e+08, 1.0836e+08, ..., 4.2968e+07, 4.2337e+07,\n",
       "         4.1291e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0746, -0.0747, -0.0746, ..., -0.0744, -0.0743, -0.0743],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0029,  0.0058, ..., 29.9918, 29.9947, 29.9976])},\n",
       " '2016_03_03_0078.abf': {'impedance_mean': array([1.9931e+10, 1.5925e+10, 1.1919e+10, ..., 1.6808e+09, 1.5927e+09,\n",
       "         1.4754e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0026, -0.0026, -0.0026, ..., -0.0026, -0.0026, -0.0026],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_02_25_0234.abf': {'impedance_mean': array([2.3532e+09, 2.2754e+09, 2.2826e+09, ..., 9.5152e+08, 9.4495e+08,\n",
       "         9.3800e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0023, -0.0023, -0.0023, ..., -0.0023, -0.0023, -0.0023],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_03_01_0078.abf': {'impedance_mean': array([2.5166e+09, 2.2875e+09, 2.0583e+09, ..., 4.6595e+08, 4.5712e+08,\n",
       "         4.4916e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0033, -0.0033, -0.0033, ..., -0.0033, -0.0033, -0.0033],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_03_03_0131.abf': {'impedance_mean': array([3.9451e+09, 3.6337e+09, 3.3761e+09, ..., 7.2290e+08, 7.1275e+08,\n",
       "         7.0274e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.003, -0.003, -0.003, ..., -0.003, -0.003, -0.003], dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_02_25_0156.abf': {'impedance_mean': array([3.0075e+10, 2.4728e+10, 1.9380e+10, ..., 2.6092e+09, 2.5332e+09,\n",
       "         2.4432e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.003 , -0.003 , -0.003 , ..., -0.0031, -0.0031, -0.0031],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_02_25_0199.abf': {'impedance_mean': array([2.6026e+09, 1.9536e+09, 1.3047e+09, ..., 8.9665e+08, 8.8317e+08,\n",
       "         8.7269e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0052, -0.0052, -0.0052, ..., -0.0052, -0.0052, -0.0052],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2019_11_28_0036.abf': {'impedance_mean': array([2.2619e+08, 2.2052e+08, 2.1486e+08, ..., 1.1222e+08, 1.1032e+08,\n",
       "         1.0858e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0808, -0.0808, -0.0808, ..., -0.0818, -0.0818, -0.0818],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0099.abf': {'impedance_mean': array([1.5593e+08, 1.5471e+08, 1.5284e+08, ..., 9.0211e+07, 9.0962e+07,\n",
       "         9.1589e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0795, -0.0794, -0.0794, ..., -0.0795, -0.0795, -0.0795],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0104.abf': {'impedance_mean': array([3.5434e+08, 3.6034e+08, 3.3466e+08, ..., 1.0380e+08, 1.0837e+08,\n",
       "         1.1014e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0811, -0.0811, -0.0812, ..., -0.0819, -0.0818, -0.0818],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_27_0035.abf': {'impedance_mean': array([73058049.3351, 72203996.5801, 71349943.825 , ..., 37873447.6337,\n",
       "         37710439.3207, 36983974.872 ]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0745, -0.0746, -0.0746, ..., -0.0749, -0.075 , -0.075 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0114.abf': {'impedance_mean': array([2.9473e+08, 2.8982e+08, 2.8491e+08, ..., 2.2233e+08, 2.2031e+08,\n",
       "         2.1830e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0763, -0.0763, -0.0763, ..., -0.0763, -0.0763, -0.0763],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2019_11_28_0008.abf': {'impedance_mean': array([1.1300e+08, 1.1136e+08, 1.0972e+08, ..., 5.0890e+07, 5.2013e+07,\n",
       "         5.2974e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0819, -0.0819, -0.0819, ..., -0.0821, -0.0821, -0.0821],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0072.abf': {'impedance_mean': array([81046464.1699, 91189318.0969, 68718768.0045, ..., 29519283.364 ,\n",
       "         29004844.29  , 28570554.74  ]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0732, -0.0732, -0.0732, ..., -0.0732, -0.0733, -0.0732],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0026.abf': {'impedance_mean': array([1.9524e+09, 1.7713e+09, 1.5902e+09, ..., 3.8113e+08, 3.5448e+08,\n",
       "         3.3056e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0853, -0.0854, -0.0854, ..., -0.0889, -0.0888, -0.0887],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0089.abf': {'impedance_mean': array([1.5691e+08, 1.4652e+08, 1.3614e+08, ..., 6.1329e+07, 6.2661e+07,\n",
       "         6.4271e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0795, -0.0795, -0.0795, ..., -0.0783, -0.0784, -0.0784],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_06_0043.abf': {'impedance_mean': array([49008518.3158, 51148718.1605, 53288918.0052, ..., 26026630.3026,\n",
       "         26328864.6132, 26565436.9643]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0745, -0.0745, -0.0745, ..., -0.0744, -0.0745, -0.0745],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '19228045.abf': {'impedance_mean': array([2.2228e+08, 1.7862e+08, 1.3496e+08, ..., 4.8648e+07, 4.7408e+07,\n",
       "         4.6167e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.2031, -0.2031, -0.2033, ..., -0.2015, -0.2018, -0.202 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '19319032.abf': {'impedance_mean': array([3.1701e+09, 2.8221e+09, 2.4609e+09, ..., 7.7939e+08, 7.8646e+08,\n",
       "         7.7667e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1826, -0.1827, -0.1833, ..., -0.1773, -0.1772, -0.177 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '19228080.abf': {'impedance_mean': array([3.5113e+08, 3.9127e+08, 4.3141e+08, ..., 2.3253e+08, 2.3427e+08,\n",
       "         2.3435e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1741, -0.1739, -0.1738, ..., -0.1671, -0.1671, -0.1667],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([ 0., -0., -0., ..., -0.,  0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '19228063.abf': {'impedance_mean': array([5.4087e+09, 5.3824e+09, 4.7458e+09, ..., 1.7546e+09, 1.7220e+09,\n",
       "         1.7381e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1782, -0.1786, -0.1785, ..., -0.1781, -0.1779, -0.1778],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2020_03_02_0024.abf': {'impedance_mean': array([74440923.5104, 63245638.8143, 54575263.2185, ..., 37578349.1142,\n",
       "         37366432.0131, 36955011.2455]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0663, -0.0663, -0.0663, ..., -0.066 , -0.066 , -0.0659],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_27_0000.abf': {'impedance_mean': array([2.2071e+08, 2.0484e+08, 1.8897e+08, ..., 4.6865e+07, 4.8034e+07,\n",
       "         4.9132e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0659, -0.0659, -0.0659, ..., -0.0664, -0.0664, -0.0664],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2019_11_26_0112.abf': {'impedance_mean': array([1.6073e+08, 1.5595e+08, 1.5117e+08, ..., 7.5020e+07, 7.5377e+07,\n",
       "         7.5655e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0684, -0.0684, -0.0683, ..., -0.0683, -0.0683, -0.0682],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_28_0032.abf': {'impedance_mean': array([1.6749e+08, 1.2323e+08, 9.1375e+07, ..., 3.7504e+07, 3.7863e+07,\n",
       "         3.7130e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0758, -0.0758, -0.0758, ..., -0.0755, -0.0755, -0.0755],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0029,  0.0058, ..., 29.9918, 29.9947, 29.9976])},\n",
       " '18032014_0163.abf': {'impedance_mean': array([1.1512e+10, 1.0782e+10, 9.7485e+09, ..., 1.5428e+09, 1.5031e+09,\n",
       "         1.4766e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0062, -0.0062, -0.0062, ..., -0.0062, -0.0062, -0.0062],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '18032014_0015.abf': {'impedance_mean': array([9.4972e+09, 8.7658e+09, 7.8044e+09, ..., 2.0996e+09, 2.1172e+09,\n",
       "         2.1330e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0057, -0.0057, -0.0057, ..., -0.0058, -0.0058, -0.0058],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '19129016.abf': {'impedance_mean': array([5.8659e+08, 5.0695e+08, 4.8840e+08, ..., 6.1802e+07, 5.9424e+07,\n",
       "         5.9984e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1502, -0.1502, -0.1501, ..., -0.1445, -0.1445, -0.1447],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2019_11_26_0067.abf': {'impedance_mean': array([1.8648e+08, 1.8519e+08, 1.9007e+08, ..., 5.3117e+07, 5.2666e+07,\n",
       "         5.2210e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0645, -0.0645, -0.0646, ..., -0.0652, -0.0652, -0.0652],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '17032014_0188.abf': {'impedance_mean': array([2.1478e+09, 2.0184e+09, 1.8890e+09, ..., 5.2317e+08, 5.1624e+08,\n",
       "         5.0953e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.004 , -0.004 , -0.004 , ..., -0.004 , -0.0041, -0.004 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2019_11_26_0096.abf': {'impedance_mean': array([2.1211e+08, 1.9832e+08, 1.8453e+08, ..., 2.9267e+07, 2.9242e+07,\n",
       "         2.9188e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0673, -0.0673, -0.0673, ..., -0.0667, -0.0667, -0.0666],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2016_02_29_0005.abf': {'impedance_mean': array([2.6255e+10, 2.2061e+10, 1.7866e+10, ..., 1.5122e+09, 1.5563e+09,\n",
       "         1.6743e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0027, -0.0027, -0.0027, ..., -0.0027, -0.0027, -0.0027],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2016_02_29_0043.abf': {'impedance_mean': array([3.5671e+09, 3.3866e+09, 2.9862e+09, ..., 1.0116e+09, 1.0103e+09,\n",
       "         1.0084e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0026, -0.0026, -0.0026, ..., -0.0026, -0.0026, -0.0026],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0013,  0.0026, ..., 13.7972, 13.7985, 13.7998])},\n",
       " '2019_11_28_0120.abf': {'impedance_mean': array([3.2666e+08, 3.0207e+08, 2.7748e+08, ..., 1.3958e+08, 1.2713e+08,\n",
       "         1.1618e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0622, -0.0622, -0.0622, ..., -0.062 , -0.0619, -0.0619],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_28_0018.abf': {'impedance_mean': array([2.8069e+08, 2.1578e+08, 1.8101e+08, ..., 7.9983e+07, 7.9556e+07,\n",
       "         7.7867e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0537, -0.0537, -0.0537, ..., -0.0536, -0.0536, -0.0536],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0029,  0.0058, ..., 29.9918, 29.9947, 29.9976])},\n",
       " '18032014_0098.abf': {'impedance_mean': array([1.0954e+10, 1.1210e+10, 1.1467e+10, ..., 2.6313e+09, 2.6163e+09,\n",
       "         2.6010e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0032, -0.0032, -0.0032, ..., -0.0032, -0.0032, -0.0031],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2020_01_27_0007.abf': {'impedance_mean': array([2.9841e+08, 2.8742e+08, 2.7643e+08, ..., 5.0745e+07, 5.0803e+07,\n",
       "         5.0900e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.075 , -0.075 , -0.075 , ..., -0.0752, -0.0752, -0.0752],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '19129006.abf': {'impedance_mean': array([1.0362e+08, 9.2309e+07, 8.1002e+07, ..., 3.1242e+07, 3.0355e+07,\n",
       "         2.9909e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.1544, -0.1545, -0.1545, ..., -0.1548, -0.1548, -0.1548],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '19228031.abf': {'impedance_mean': array([3.7671e+08, 3.0515e+08, 2.7442e+08, ..., 5.0799e+07, 5.0806e+07,\n",
       "         4.9947e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.2001, -0.2001, -0.2001, ..., -0.2023, -0.2022, -0.202 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '19228001.abf': {'impedance_mean': array([4.7915e+08, 4.3658e+08, 3.9400e+08, ..., 7.7322e+07, 7.8080e+07,\n",
       "         7.8837e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.2587, -0.2586, -0.2583, ..., -0.2542, -0.2542, -0.254 ],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.   ,  0.002,  0.004, ..., 20.64 , 20.642, 20.644])},\n",
       " '2020_01_27_0045.abf': {'impedance_mean': array([98742151.6302, 95676216.1252, 92610280.6201, ..., 47201055.3644,\n",
       "         47772639.638 , 47589973.496 ]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0674, -0.0674, -0.0674, ..., -0.0678, -0.0678, -0.0678],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '17032014_0075.abf': {'impedance_mean': array([8.7669e+09, 8.3220e+09, 8.5635e+09, ..., 1.5687e+09, 1.5836e+09,\n",
       "         1.5977e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0063, -0.0063, -0.0063, ..., -0.0064, -0.0064, -0.0064],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '17032014_0013.abf': {'impedance_mean': array([2.5964e+10, 2.4671e+10, 2.3378e+10, ..., 1.4947e+09, 1.6408e+09,\n",
       "         1.7768e+09]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0057, -0.0057, -0.0057, ..., -0.0059, -0.0059, -0.0059],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2019_11_04_0114.abf': {'impedance_mean': array([1.1177e+08, 1.1331e+08, 1.1594e+08, ..., 2.8376e+07, 2.7557e+07,\n",
       "         2.6812e+07]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0682, -0.068 , -0.0679, ..., -0.0686, -0.0688, -0.0689],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '17032014_0111.abf': {'impedance_mean': array([37731976.1493, 35094012.6575, 34557922.0616, ...,  9276726.4476,\n",
       "          9069103.7388,  8874203.18  ]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0001, -0.0001, -0.0001, ..., -0.0001, -0.0001, -0.0001],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([0., 0., 0., ..., 0., 0., 0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0011,  0.0022, ..., 11.4972, 11.4983, 11.4994])},\n",
       " '2019_11_26_0002.abf': {'impedance_mean': array([7.2452e+08, 6.4513e+08, 5.6573e+08, ..., 1.1072e+08, 1.1504e+08,\n",
       "         1.2077e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0699, -0.0699, -0.0699, ..., -0.0695, -0.0695, -0.0694],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])},\n",
       " '2020_01_27_0017.abf': {'impedance_mean': array([6.1536e+08, 5.4973e+08, 5.1175e+08, ..., 1.7463e+08, 1.7698e+08,\n",
       "         1.8212e+08]),\n",
       "  'ref_freq': array([ 0.1   ,  0.1199,  0.1398, ..., 19.9602, 19.9801, 20.    ]),\n",
       "  'voltage_mean': array([-0.0635, -0.0635, -0.0635, ..., -0.0641, -0.0642, -0.0642],\n",
       "        dtype=float32),\n",
       "  'current_mean': array([-0., -0., -0., ..., -0., -0., -0.], dtype=float32),\n",
       "  'time_array': array([ 0.    ,  0.0019,  0.0038, ..., 19.9956, 19.9975, 19.9994])}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4545545545545546\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[21750686115.138885, 41938515328.60087]"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = dict_list['14d16019.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "filtered_imp=moving_average(impedance_mean,101 )\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "print(cen_freq)\n",
    "[min(impedance_mean), max(impedance_mean)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2380f71080>]"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [16, 10]\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2,  gridspec_kw={'width_ratios': [1, 1], 'height_ratios':[2, 1]})\n",
    "ax1.plot(time_array, voltage_mean * 1000)\n",
    "ax3.plot(time_array, current_mean * 1e12)\n",
    "#ax2.plot([cen_freq, cen_freq], [min(impedance_mean), max(impedance_mean)])\n",
    "ax2.plot(ref_freq, impedance_mean)\n",
    "ax2.plot(ref_freq,filtered_imp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_list = '14317335.abf' # most resonant L2.3 cell\n",
    "'19122003.abf' # small layer 2.3 cell\n",
    "\n",
    "# L3c\n",
    "'2020_03_02_0019.abf' # most resonant L3c cell\n",
    "'2020_01_06_0048.abf' # weakly resonance, L3c cell\n",
    "\n",
    "# L5\n",
    "'15420000.abf' # most resonant L5 cell\n",
    "'19129015.abf' # small L5 morphology, not resonant\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'legend.fontsize': 'x-large',\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "\n",
    "\n",
    "pylab.rcParams.update(params)\n",
    "pylab.rcParams['pdf.fonttype'] = 42\n",
    "pylab.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "row = dict_list['14317335.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [10, 3]\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "fig, ((ax1, ax2, ax3), (ax4, ax5, ax6)) = plt.subplots(2, 3,  sharey = False, sharex = True, gridspec_kw={'width_ratios': [1, 1, 1], 'height_ratios':[1, 1]})\n",
    "\n",
    "ax1.plot(time_array, voltage_mean * 1000, '#20205C', linewidth=.33)\n",
    "\n",
    "row = dict_list['19129006.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean * .25 * 1000 -19.7\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax4.plot(time_array, voltage_mean, \"#7C80BD\", linewidth=.33)\n",
    "\n",
    "row = dict_list['2020_03_02_0017.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax2.plot(time_array, (voltage_mean *1000), \"#225966\", linewidth=.33)\n",
    "\n",
    "row = dict_list['2020_01_06_0039.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax5.plot(time_array, (voltage_mean *1000), \"#75969E\", linewidth=.33)\n",
    "\n",
    "row = dict_list['15420003.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax3.plot(time_array, (voltage_mean *1000), \"#BB2E26\", linewidth=.33)\n",
    "\n",
    "row = dict_list['19129016.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = (voltage_mean * .25 * 1000) -22.7\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax6.plot(time_array, voltage_mean, \"#ED968C\", linewidth=.33)\n",
    "\n",
    "ax1.axes.set_xlim(0, 20.1)\n",
    "fig.savefig('figs/pretty_figs/zap_examples.pdf', format='pdf')\n",
    "\n",
    "\n",
    "#ax1.yaxis.set_ticks(np.arange(0, 10, 2))\n",
    "\n",
    "\n",
    "\n",
    "# ax3.plot(time_array, current_mean * 1e12)\n",
    "# #ax2.plot([cen_freq, cen_freq], [min(impedance_mean), max(impedance_mean)])\n",
    "# ax2.plot(ref_freq, impedance_mean)\n",
    "# ax2.plot(ref_freq,filtered_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as pylab\n",
    "\n",
    "\n",
    "\n",
    "row = dict_list['14317335.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean'] / 1E9\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "filtered_imp = filtered_imp / np.max(filtered_imp)\n",
    "\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [8, 2]\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey = True, sharex = True, gridspec_kw={'width_ratios': [1, 1, 1], 'height_ratios':[1]})\n",
    "\n",
    "ax1.plot(ref_freq, filtered_imp, '#20205C', linewidth=1.5)\n",
    "\n",
    "row = dict_list['19129006.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean'] * .25 * 1000 / .2 / 1E9\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean * .25 * 1000 -19.7\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "filtered_imp = filtered_imp / np.max(filtered_imp)\n",
    "\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax1.plot(ref_freq, filtered_imp, \"#7C80BD\", linewidth=1.5)\n",
    "\n",
    "row = dict_list['2020_03_02_0017.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean'] / 1E9\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "filtered_imp = filtered_imp / np.max(filtered_imp)\n",
    "\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax2.plot(ref_freq, filtered_imp, \"#225966\", linewidth=1.5)\n",
    "\n",
    "row = dict_list['2020_01_06_0039.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']  / 1E9\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "filtered_imp = filtered_imp / np.max(filtered_imp)\n",
    "\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax2.plot(ref_freq, filtered_imp, \"#75969E\", linewidth=1.5)\n",
    "\n",
    "row = dict_list['15420003.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean'] / 1E9\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "filtered_imp = filtered_imp / np.max(filtered_imp)\n",
    "\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax3.plot(ref_freq, filtered_imp, \"#BB2E26\", linewidth=1.5)\n",
    "\n",
    "row = dict_list['19129016.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean'] \n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = (voltage_mean * .25 * 1000) -22.7\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp = moving_average(impedance_mean,moving_avg_wind)\n",
    "filtered_imp = filtered_imp / np.max(filtered_imp)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax3.plot(ref_freq, filtered_imp, \"#ED968C\", linewidth=1.5)\n",
    "\n",
    "ax1.axes.set_xlim(0, 20.1)\n",
    "ax1.set_ylabel('Norm. Impedance')\n",
    "ax1.set_xlabel('Frequency (Hz)')\n",
    "\n",
    "fig.savefig('figs/pretty_figs/norm_impedance.pdf', format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 237.6x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'legend.fontsize': 'x-large',\n",
    "         'axes.labelsize': 'x-large',\n",
    "         'axes.titlesize':'x-large',\n",
    "         'xtick.labelsize':'x-large',\n",
    "         'ytick.labelsize':'x-large'}\n",
    "\n",
    "\n",
    "pylab.rcParams.update(params)\n",
    "pylab.rcParams['pdf.fonttype'] = 42\n",
    "pylab.rcParams['ps.fonttype'] = 42\n",
    "\n",
    "row = dict_list['14d16019.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [3.3, 3.5]\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "fig, ((ax1, ax2)) = plt.subplots(2, 1,  sharey = False, sharex = True )\n",
    "\n",
    "ax1.plot(time_array, voltage_mean * 1000, '#20205C', linewidth=.33)\n",
    "\n",
    "row = dict_list['14605301.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean']\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean * .25 * 1000 -19.7\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,moving_avg_wind)\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax2.plot(time_array, voltage_mean, \"#BB2E26\", linewidth=.33)\n",
    "\n",
    "ax1.axes.set_xlim(0, 20.1)\n",
    "fig.savefig('figs/pretty_figs/zap_int_examples.pdf', format='pdf')\n",
    "\n",
    "\n",
    "#ax1.yaxis.set_ticks(np.arange(0, 10, 2))\n",
    "\n",
    "\n",
    "\n",
    "# ax3.plot(time_array, current_mean * 1e12)\n",
    "# #ax2.plot([cen_freq, cen_freq], [min(impedance_mean), max(impedance_mean)])\n",
    "# ax2.plot(ref_freq, impedance_mean)\n",
    "# ax2.plot(ref_freq,filtered_imp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 576x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 237.6x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pylab as pylab\n",
    "\n",
    "\n",
    "\n",
    "row = dict_list['14d16019.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean'] / 1E9\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,101)\n",
    "filtered_imp = filtered_imp / np.max(filtered_imp)\n",
    "\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [3.3, 3]\n",
    "\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "fig, (ax1) = plt.subplots(1, 1, sharey = True, sharex = True)\n",
    "\n",
    "ax1.plot(ref_freq, filtered_imp, '#20205C', linewidth=1.5)\n",
    "\n",
    "row = dict_list['14605301.abf']\n",
    "\n",
    "impedance_mean = row['impedance_mean'] * .25 * 1000 / .2 / 1E9\n",
    "ref_freq = row['ref_freq']\n",
    "time_array = row['time_array']\n",
    "current_mean = row['current_mean']\n",
    "voltage_mean = row['voltage_mean']\n",
    "voltage_mean = voltage_mean * .25 * 1000 -19.7\n",
    "voltage_mean = voltage_mean - np.mean(voltage_mean)\n",
    "\n",
    "filtered_imp=moving_average(impedance_mean,101)\n",
    "filtered_imp = filtered_imp / np.max(filtered_imp)\n",
    "\n",
    "\n",
    "idx_max_mag=np.argmax(filtered_imp)\n",
    "cen_freq=ref_freq[idx_max_mag]\n",
    "\n",
    "ax1.plot(ref_freq, filtered_imp, \"#BB2E26\", linewidth=1.5)\n",
    "\n",
    "\n",
    "ax1.axes.set_xlim(0, 20.1)\n",
    "ax1.set_ylabel('Norm. Impedance')\n",
    "ax1.set_xlabel('Frequency (Hz)')\n",
    "\n",
    "fig.savefig('figs/pretty_figs/int_norm_impedance.pdf', format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 400,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyabf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob\n",
    "\n",
    "from scripts.util_functions import guess_response_gain, get_stim_gain, get_stim_info, get_stim_dict\n",
    "# read final csv that has the ouput of metadata gathering process\n",
    "csv_meta_save_path = 'output_files/cell_final_raw_meta_df.csv'\n",
    "cell_final_raw_meta_df = pd.read_csv(open(csv_meta_save_path, 'rb'))  \n",
    "\n",
    "#from scripts.abf_plotting import plot_ephys_from_abf\n",
    "from scripts.abf_to_sweep_set import cell_id_to_sweep_set\n",
    "from scripts.abf_feature_extraction import get_baseball_card_fig, get_lsa_results\n",
    "\n",
    "abf_file_name = \"14605300.abf\"\n",
    "#use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "print(num_sweeps)\n",
    "use_sweeps = [0, num_sweeps - 11, num_sweeps - 7]\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [3, 3]\n",
    "\n",
    "plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "fig.savefig('figs/pretty_figs/layer5_int_voltage.pdf', format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "abf_file_name = \"14d16016.abf\"\n",
    "#use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "print(num_sweeps)\n",
    "use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "(fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "fig.savefig('figs/pretty_figs/layer23_int_voltage.pdf', format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "abf_file_name = \"14605300.abf\"\n",
    "#use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "print(num_sweeps)\n",
    "use_sweeps = [0, num_sweeps - 11, num_sweeps - 7]\n",
    "\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "(fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "fig.savefig('figs/pretty_figs/layer5_int_voltage.pdf', format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17\n",
      "30\n",
      "17\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# abf_file_name = \"2019_11_26_0110.abf\"\n",
    "\n",
    "abf_file_name = \"19320017.abf\"\n",
    "use_sweeps = [num_sweeps - 6, num_sweeps - 4, num_sweeps - 2]\n",
    "\n",
    "#use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "use_sweeps = [num_sweeps - 6, num_sweeps - 4, num_sweeps - 2]\n",
    "\n",
    "print(num_sweeps)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "(fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "fig.savefig('figs/pretty_figs/layer23_pyr_voltage.pdf', format='pdf')\n",
    "\n",
    "# abf_file_name = \"2019_11_26_0110.abf\"\n",
    "\n",
    "abf_file_name = \"15420096.abf\"\n",
    "\n",
    "#use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "use_sweeps = [num_sweeps - 8, num_sweeps - 4, num_sweeps -1]\n",
    "\n",
    "print(num_sweeps)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "(fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "fig.savefig('figs/pretty_figs/layer5_pyr_voltage.pdf', format='pdf')\n",
    "\n",
    "abf_file_name = \"2020_01_06_0082.abf\"\n",
    "use_sweeps = [num_sweeps - 6, num_sweeps - 4, num_sweeps - 2]\n",
    "\n",
    "#use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "use_sweeps = [num_sweeps - 6, num_sweeps - 4, num_sweeps - 2]\n",
    "\n",
    "print(num_sweeps)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "(fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "fig.savefig('figs/pretty_figs/layer3c_pyr_voltage.pdf', format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13\n",
      "17\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 576x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 144x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# abf_file_name = \"2019_11_26_0110.abf\"\n",
    "\n",
    "abf_file_name = \"2019_11_28_0119.abf\"\n",
    "#use_sweeps = [0, num_sweeps - 4, num_sweeps - 2]\n",
    "\n",
    "#use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "use_sweeps = [0, num_sweeps - 5, num_sweeps - 1]\n",
    "\n",
    "print(num_sweeps)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "(fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "fig.savefig('figs/pretty_figs/bipolar_cell_voltage.pdf', format='pdf')\n",
    "\n",
    "abf_file_name = \"19129015.abf\"\n",
    "\n",
    "\n",
    "#use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "\n",
    "num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "use_sweeps = [0, num_sweeps - 8, num_sweeps - 6]\n",
    "\n",
    "print(num_sweeps)\n",
    "\n",
    "plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "(fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "fig.savefig('figs/pretty_figs/l5_cell_small_voltage.pdf', format='pdf')\n",
    "\n",
    "# abf_file_name = \"15420096.abf\"\n",
    "\n",
    "# #use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "# meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "\n",
    "# num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "# use_sweeps = [num_sweeps - 8, num_sweeps - 4, num_sweeps -1]\n",
    "\n",
    "# print(num_sweeps)\n",
    "\n",
    "# plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "# (fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "# fig.savefig('figs/pretty_figs/layer5_pyr_voltage.pdf', format='pdf')\n",
    "\n",
    "# abf_file_name = \"2020_01_06_0082.abf\"\n",
    "# use_sweeps = [num_sweeps - 6, num_sweeps - 4, num_sweeps - 2]\n",
    "\n",
    "# #use_sweeps = [0, num_sweeps - 9, num_sweeps - 1]\n",
    "# meta_row = cell_final_raw_meta_df.loc[cell_final_raw_meta_df['cell_id'] == abf_file_name]\n",
    "\n",
    "# num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "# use_sweeps = [num_sweeps - 6, num_sweeps - 4, num_sweeps - 2]\n",
    "\n",
    "# print(num_sweeps)\n",
    "\n",
    "# plt.rcParams['figure.figsize'] = [2, 3]\n",
    "\n",
    "# (fig, ax1, ax2) = plot_ephys_from_abf(abf_file_name, cell_final_raw_meta_df, show_sweeps = use_sweeps)\n",
    "# fig.savefig('figs/pretty_figs/layer3c_pyr_voltage.pdf', format='pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ipfx.sweep import Sweep, SweepSet\n",
    "import pyabf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def plot_ephys_from_abf(abf_file_name, meta_dict, show_sweeps = [0, -1]):\n",
    "    \n",
    "    curr_file = abf_file_name\n",
    "\n",
    "    #curr_file = '15o08020.abf'\n",
    "\n",
    "    meta_row = meta_dict.loc[meta_dict['cell_id'] == curr_file]\n",
    "    \n",
    "    file_path = meta_row['full_path'].values[0]\n",
    "    stim_file_path = meta_row['stim_path'].values[0]\n",
    "    #fn = file_base_base_path + file_base_path + curr_file\n",
    "    # 2016_02_04_0042.abf - example from cluster 4 - burst firing\n",
    "    # 13d02049.abf - example from cluster 1\n",
    "\n",
    "    resp_abf = pyabf.ABF(file_path)\n",
    "    stim_abf = pyabf.ABF(stim_file_path) # for some files we're using stim traces from a different file\n",
    "\n",
    "    num_sweeps = int(meta_row['num_sweeps'].values[0])\n",
    "    stim_channel_num = int(meta_row['stim_chan'].values[0])\n",
    "    response_chan_num = int(meta_row['resp_chan'].values[0])\n",
    "    stim_gain = meta_row['stim_gain'].values[0]\n",
    "    stim_name = meta_row['stim_name'].values[0]\n",
    "    response_gain = meta_row['resp_gain'].values[0]\n",
    "    \n",
    "    stim_start_time = meta_row['stim_start_time'].values[0]\n",
    "    stim_end_time = meta_row['stim_end_time'].values[0]\n",
    "    resp_offset = meta_row['resp_offset'].values[0]\n",
    "    \n",
    "    #stim_end = 2\n",
    "\n",
    "    sweep_num = 0\n",
    "    sweep_plot_list = show_sweeps\n",
    "\n",
    "    fig = plt.figure(figsize=(8, 5))\n",
    "    fig, (ax1, ax2) = plt.subplots(2, sharex=True, gridspec_kw={'height_ratios':[3, 1]})\n",
    "\n",
    "    for i in sweep_plot_list:\n",
    "        sweep_num = i\n",
    "        resp_abf.setSweep(sweep_num, channel=response_chan_num)\n",
    "        # plot the ADC (voltage recording)\n",
    "        #ax1 = fig.add_subplot(211)\n",
    "        #ax1.set_title(\"ADC (recorded waveform)\")\n",
    "        resp_vec = resp_abf.sweepY*response_gain + resp_offset\n",
    "        ax1.plot(resp_abf.sweepX, resp_vec, linewidth = 1)\n",
    "\n",
    "        # plot the DAC (clamp current)\n",
    "        #ax2 = fig.add_subplot(212, sharex=ax1)  # <-- this argument is new\n",
    "        #ax2.set_title(\"DAC (stimulus waveform)\")\n",
    "        stim_abf.setSweep(sweep_num, channel=stim_channel_num)\n",
    "\n",
    "        if stim_name == 'sweepY':\n",
    "            stim_vec = stim_abf.sweepY * stim_gain\n",
    "        else:\n",
    "            stim_vec = stim_abf.sweepC * stim_gain\n",
    "        #abf.setSweep(sweep_num, channel=1)\n",
    "        ax2.plot(stim_abf.sweepX, stim_vec, linewidth = 1)\n",
    "\n",
    "    # decorate the plots\n",
    "    #ax1.set_ylabel(resp_abf.sweepLabelY)\n",
    "    #ax2.set_xlabel(resp_abf.sweepLabelX)\n",
    "    #ax2.set_ylabel(resp_abf.sweepLabelC)\n",
    "    ax1.axes.set_xlim(0, stim_end_time + .1)  # <-- adjust axis like this\n",
    "    ax1.axes.set_ylim(-120, 50)\n",
    "    return(fig, ax1, ax2)\n",
    "    #plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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