22-EPM_Whole_ON_OFF_Summary_Within_Overlap.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import pearsonr\n",
    "\n",
    "import importlib\n",
    "import tifffile as tiff\n",
    "from scipy.signal import chirp, find_peaks, peak_widths\n",
    "\n",
    "\n",
    "import cv2\n",
    "from tqdm import tqdm\n",
    "from numpy import linalg as LA\n",
    "from scipy.ndimage.measurements import center_of_mass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label statemment\n",
    "\n",
    "#### Location\n",
    "- Area:Mouse 1 Center In Center (10) #\n",
    "- Area:Mouse 1 Center In Close Arm (11) #\n",
    "- Area:Mouse 1 Center In Open Arm (12) #\n",
    " \n",
    "- AreaStay:Mouse 1 Nose In Close Arm while TailBase In Open Arm (12)\n",
    "- AreaStay:Mouse 1 Nose In Open Arm while TailBase In Close Arm (11)\n",
    " \n",
    "#### Action\n",
    "- Mouse 1 Grooming (31)\n",
    "- Mouse 1 HeadDip [None] (32) #\n",
    "- Mouse 1 SAP [Center In Close Arm] (33)\n",
    "- Mouse 1 SAP [Center In Open Arm] (34)\n",
    "- Mouse 1 SAP [None] (35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to define neuron type\n",
    "# similarity\n",
    "#----------------------------------------------------------------------------------\n",
    "def similarity(x, y):\n",
    "    simi = 2 * np.dot(x, y) / (LA.norm(x) ** 2 + LA.norm(y) ** 2)\n",
    "    return simi\n",
    "\n",
    "\n",
    "\n",
    "# shuffle\n",
    "#----------------------------------------------------------------------------------\n",
    "def random_shuffle(X):\n",
    "    new_X = []\n",
    "    for i in range(X.shape[1]):\n",
    "        x = X[:, i].copy()\n",
    "        shuffle(x)\n",
    "        new_X.append(x)\n",
    "    return np.vstack(new_X).T\n",
    "\n",
    "\n",
    "# collect samples\n",
    "#----------------------------------------------------------------------------------\n",
    "def collect_samples(arr, sample_size, n_samples):\n",
    "    samples = np.zeros((n_samples, sample_size), np.int32)\n",
    "\n",
    "    for sample_n in range(0, n_samples):\n",
    "        sample = get_sample(arr,\n",
    "                            n_iter=sample_n,\n",
    "                            sample_size=sample_size)\n",
    "        samples[sample_n] = sample\n",
    "\n",
    "    return samples\n",
    "\n",
    "\n",
    "# get samples\n",
    "#----------------------------------------------------------------------------------\n",
    "\n",
    "def get_sample(arr, n_iter=None, sample_size=10, fast=True):\n",
    "    return np.random.choice(arr, sample_size, replace=False)\n",
    "\n",
    "\n",
    "# define ON/OFF neuron\n",
    "#----------------------------------------------------------------------------------\n",
    "def clsfy(B, C):\n",
    "    b = B.copy()\n",
    "    simi = similarity(B, C)\n",
    "    x = LA.norm(b) ** 2 + LA.norm(C) ** 2\n",
    "    bs = collect_samples(b, len(b), 5000)\n",
    "    a = 2 * np.dot(C, bs.T) / x\n",
    "    if simi >= np.percentile(a, 99.95):\n",
    "        neuron_type = 'ON'\n",
    "    elif simi <= np.percentile(a, 0.05):\n",
    "        neuron_type = 'OFF'\n",
    "    else:\n",
    "        neuron_type = 'Other'\n",
    "    return simi, a, neuron_type\n",
    "\n",
    "\n",
    "\n",
    "# to check on/off neuron of certain status\n",
    "#----------------------------------------------------------------------------------\n",
    "def status_ONOFF(df, which_status_to_check_ON_OFF, ON_OFF, fps):\n",
    "    \n",
    "    # set up which_status_to_check_ON_OFF\n",
    "    \n",
    "    if which_status_to_check_ON_OFF == 'close':\n",
    "        which_status_to_check_ON_OFF = str(11)\n",
    "    elif which_status_to_check_ON_OFF == 'open':\n",
    "        which_status_to_check_ON_OFF = str(12)\n",
    "    else:\n",
    "        raise ValueError('Not approved status. choose from: open, close')\n",
    "\n",
    "    frame_label_list = df['Frame_Label'].values.tolist()\n",
    "    \n",
    "    # to separate status label, eg:20214111\n",
    "    n = 2\n",
    "    \n",
    "    label_list = []\n",
    "    for line in frame_label_list:\n",
    "        line = str(line)\n",
    "        line_list= [line[i:i+n] for i in range(0, len(line), n)]\n",
    "        if which_status_to_check_ON_OFF in line_list:\n",
    "            label_list.append(1)\n",
    "        else:\n",
    "            label_list.append(0)\n",
    "            \n",
    "    label_list_array = np.asarray(label_list)\n",
    "    \n",
    "    # get dataframe array; drop last column: frame_label\n",
    "    df_array = df.iloc[:, :-1].to_numpy()\n",
    "    # get n_neuron\n",
    "    T, n_neuron = df.iloc[:, :-1].shape\n",
    "    \n",
    "    # get on/off neuron type\n",
    "    neuron_type_list = []\n",
    "    if sum(label_list_array == 1) / fps >= 4:\n",
    "        for i in range(n_neuron):\n",
    "            simi, a, t = clsfy(label_list_array, df_array[:, i])\n",
    "            neuron_type_list.append(t)\n",
    "            \n",
    "    # get on/off neurom dataframe\n",
    "    df_columns  = df.iloc[:, :-1].columns\n",
    "    new_df_column = []\n",
    "    for i in range(len(neuron_type_list)):\n",
    "        if neuron_type_list[i] == ON_OFF:\n",
    "            neuron_keep = df_columns[i]\n",
    "            new_df_column.append(neuron_keep)\n",
    "\n",
    "    if 'Frame_Label' in new_df_column:\n",
    "        new_df_column.remove('Frame_Label')\n",
    "        \n",
    "    new_df = df[new_df_column]\n",
    "    new_df['Frame_Label'] = frame_label_list\n",
    "        \n",
    "    #shape(frames, neurons+frame_label)\n",
    "    return(new_df, new_df_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to keep certain status frame interval\n",
    "#----------------------------------------------------------------------------------\n",
    "def check_frame_interval_with_label(df, which_status_to_check_frame):\n",
    "    \n",
    "    # set up which_status_to_check_frame\n",
    "    \n",
    "    if which_status_to_check_frame == 'close':\n",
    "        which_status_to_check_frame = str(11)\n",
    "    elif which_status_to_check_frame == 'open':\n",
    "        which_status_to_check_frame = str(12)\n",
    "    else:\n",
    "        raise ValueError('Not approved status. choose from: open, close')\n",
    "\n",
    "    frame_label_list = df['Frame_Label'].values.tolist()\n",
    "    \n",
    "    # to separate status label, eg:20214111\n",
    "    n = 2\n",
    "    \n",
    "    check_frame_label_list = []\n",
    "    for line in frame_label_list:\n",
    "        line = str(line)\n",
    "        line_list= [line[i:i+n] for i in range(0, len(line), n)]\n",
    "        if which_status_to_check_frame in line_list:\n",
    "            check_frame_label_list.append(1)\n",
    "        else:\n",
    "            check_frame_label_list.append(0)       \n",
    "\n",
    "    df_keep = df.copy()\n",
    "    df_keep['status'] = check_frame_label_list\n",
    "    df_keep = df_keep[(df_keep['status'] == 1)]\n",
    "    df_keep = df_keep.drop('status', axis = 1)\n",
    "    #df_keep = df_keep.drop('Frame_Label', axis = 1) \n",
    "    # for whole session, frame_label doesn't need to be kept\n",
    "    # but for on/off, it need to be kept\n",
    "    \n",
    "    print('the shape of the dataframe in the status is:', df_keep.shape)\n",
    "    \n",
    "    # shape(frames, neurons+frame_label)\n",
    "    return(df_keep)\n",
    "\n",
    "\n",
    "\n",
    "# calculate event rate & amplitude\n",
    "#----------------------------------------------------------------------------------\n",
    "def get_coordinate(corrdinate_file_path):\n",
    "    rawdata = []\n",
    "    with open(corrdinate_file_path, 'r') as f:\n",
    "        for i, line in enumerate(f):\n",
    "            if i == 9:\n",
    "                start_frame = int(line.split(':')[1])\n",
    "            if i == 20:\n",
    "                temp = line.split('\\t')\n",
    "                left, right, top, bottom = float(temp[0].split(':')[2]), float(temp[1].split(':')[1]), \\\n",
    "                                           float(temp[2].split(':')[1]), float(temp[3].split(':')[1].split('\\n')[0])\n",
    "            lines = line.replace('\"', '').replace(\"'\", \"\").split('   ')\n",
    "            try:\n",
    "                rawdata.append([int(lines[0]), int(lines[1]), int(lines[2]),\n",
    "                                float(lines[5]), float(lines[6]), float(lines[9])])\n",
    "            except:\n",
    "                pass\n",
    "    rawdata = np.array(rawdata)\n",
    "    return (rawdata, start_frame, left, right, top, bottom)\n",
    "\n",
    "\n",
    "\n",
    "# distinguish prefered side of open arm or close arm, also duration in each side\n",
    "#----------------------------------------------------------------------------------\n",
    "def prefered_side(original_df, which_status_df_to_split, corrdinate_file_path, fps):\n",
    "    \n",
    "    if which_status_df_to_split.shape[0] == 0:\n",
    "        prefered_df = which_status_df_to_split\n",
    "        non_prefered_df = which_status_df_to_split\n",
    "        x1 = np.nan\n",
    "        x2 = np.nan\n",
    "    else:\n",
    "        coor, start_frame, left, right, top, bottom = get_coordinate(corrdinate_file_path)\n",
    "        center = [(left + right) / 2, (top + bottom) / 2]\n",
    "\n",
    "        a = np.asarray(np.arange(which_status_df_to_split.shape[0])/fps * 29.97 + (150-(coor[0, 0]-start_frame)))\n",
    "        # just in case the recording is not to 600s\n",
    "        new_a = a[(a <= len(coor))]\n",
    "        coordinate = coor[np.floor(new_a).astype('int'), 1:3]\n",
    "\n",
    "        # just in case the recording is not to 600s\n",
    "        which_status_df_to_split_kept = which_status_df_to_split.head(len(new_a))\n",
    "        \n",
    "        if coordinate[0].max()-coordinate[0].min() > coordinate[1].max()-coordinate[1].min():\n",
    "            # if this, then the mouse is along right&left arm, becaseu it's motion along x is wider than y\n",
    "            x1 = which_status_df_to_split_kept[(coordinate[:, 0] > center[0])].shape[0]/fps\n",
    "            x2 = which_status_df_to_split_kept[(coordinate[:, 0] < center[0])].shape[0]/fps\n",
    "            if x1 > x2:\n",
    "                prefered_df = which_status_df_to_split_kept[(coordinate[:, 0] > center[0])]\n",
    "                non_prefered_df = which_status_df_to_split_kept[(coordinate[:, 0] < center[0])]\n",
    "            else:\n",
    "                prefered_df = which_status_df_to_split_kept[(coordinate[:, 0] < center[0])]\n",
    "                non_prefered_df = which_status_df_to_split_kept[(coordinate[:, 0] > center[0])]\n",
    "        else:\n",
    "            # else, then the mouse is along up&down arm, \n",
    "            x1 = which_status_df_to_split_kept[(coordinate[:, 1] > center[1])].shape[0]/fps\n",
    "            x2 = which_status_df_to_split_kept[(coordinate[:, 1] < center[1])].shape[0]/fps\n",
    "            if x1 > x2:\n",
    "                prefered_df = which_status_df_to_split_kept[(coordinate[:, 1] > center[1])]\n",
    "                non_prefered_df = which_status_df_to_split_kept[(coordinate[:, 1] < center[1])]\n",
    "            else:\n",
    "                prefered_df = which_status_df_to_split_kept[(coordinate[:, 1] < center[1])]\n",
    "                non_prefered_df = which_status_df_to_split_kept[(coordinate[:, 1] > center[1])]\n",
    "    \n",
    "    # get status add original dataframe \n",
    "    print(prefered_df.shape[0],'frames are prefered')\n",
    "    \n",
    "    prefered_df_index = prefered_df.index.values.tolist()\n",
    "    non_prefered_df_index = non_prefered_df.index.values.tolist()\n",
    "    \n",
    "    # re-assign original_df to other names, don't make changes to original_df\n",
    "    prefer_original_df = original_df.copy()\n",
    "    nonprefer_original_df = original_df.copy()\n",
    "    \n",
    "    prefer_original_df['prefered'] = 0\n",
    "    nonprefer_original_df['non_prefered'] = 0\n",
    "        \n",
    "    prefer_original_df.loc[prefered_df_index, 'prefered'] = 1  \n",
    "    nonprefer_original_df.loc[non_prefered_df_index, 'non_prefered'] = 1 \n",
    "    \n",
    "    print('prefered',np.sum(prefer_original_df['prefered']))\n",
    "    print('non_prefered',np.sum(nonprefer_original_df['non_prefered']))\n",
    "    \n",
    "    return(x1, x2, prefered_df, non_prefered_df, prefer_original_df, nonprefer_original_df)\n",
    "\n",
    "\n",
    "\n",
    "# to check on/off neuron of certain status\n",
    "### in this case, we only check social(11)/obj(12)\n",
    "#----------------------------------------------------------------------------------\n",
    "def status_ONOFF_Prefered(df, status_to_check, fps, ON_OFF):\n",
    "    \n",
    "    # choose from 'prefered','non_prefered' for status_to_check\n",
    "    label_list_array = np.asarray(df[status_to_check].values.tolist())\n",
    "    frame_label_list = df['Frame_Label'].values.tolist()\n",
    "    \n",
    "    # get dataframe array; drop last column: frame_label, prefered, non-prefered\n",
    "    df_array = df.iloc[:, :-2].to_numpy()\n",
    "    # get n_neuron\n",
    "    T, n_neuron = df.iloc[:, :-2].shape\n",
    "    \n",
    "    # get on/off neuron type\n",
    "    neuron_type_list = []\n",
    "    if sum(label_list_array == 1) / fps >= 4:\n",
    "        for i in range(n_neuron):\n",
    "            simi, a, t = clsfy(label_list_array, df_array[:, i])\n",
    "            neuron_type_list.append(t)\n",
    "            \n",
    "    # get on/off neurom dataframe\n",
    "    df_columns  = df.iloc[:, :-2].columns\n",
    "    new_df_column = []\n",
    "    for i in range(len(neuron_type_list)):\n",
    "        if neuron_type_list[i] == ON_OFF:\n",
    "            neuron_keep = df_columns[i]\n",
    "            new_df_column.append(neuron_keep)\n",
    "    \n",
    "    if 'Frame_Label' in new_df_column:\n",
    "        new_df_column.remove('Frame_Label')\n",
    "    elif 'status' in new_df_column:\n",
    "        new_df_column.remove('status')\n",
    "    \n",
    "    new_df = df[new_df_column]\n",
    "    new_df['Frame_Label'] = frame_label_list\n",
    "    \n",
    "    # shape(frames, keep_neurons+frame_label)\n",
    "    return(new_df, new_df_column)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate ON/OFF summary file, save to csv\n",
    "#----------------------------------------------------------------------------------\n",
    "def generate_ONOFF_Summary(df, all_ONOFF_neuron_list, columns_name, mouse_name, project):\n",
    "        \n",
    "    all_neuron_list = df.columns.to_list()\n",
    "    if 'Frame_Label' in all_neuron_list:\n",
    "        all_neuron_list.remove('Frame_Label')\n",
    "    \n",
    "    label_list = []\n",
    "    for i in all_neuron_list:\n",
    "        label = []\n",
    "        for a in range(len(all_ONOFF_neuron_list)):\n",
    "            if i in all_ONOFF_neuron_list[a]:\n",
    "                label.append(1)\n",
    "            else:\n",
    "                label.append(0)\n",
    "        label_list.append(label)\n",
    "        \n",
    "    dic_ONOFF_summary = {}\n",
    "    for i in range(len(all_neuron_list)):\n",
    "        dic_ONOFF_summary[all_neuron_list[i]] = label_list[i]\n",
    "\n",
    "    df_ONOFF_summary = pd.DataFrame.from_dict(dic_ONOFF_summary).T\n",
    "    df_ONOFF_summary.columns = columns_name\n",
    "    df_ONOFF_summary.to_csv(project+'_A_Summary_'+mouse_name+'_ONOFF.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def degree(N, Na, Nb, Overlap):\n",
    "    \n",
    "    if Na == 0 and Nb == 0:\n",
    "        observed_degree = 0\n",
    "        predicted_degree = 0\n",
    "    else:\n",
    "        observed_degree = 2*Overlap/(Na+Nb)\n",
    "        predicted_degree = 2*Na*Nb/(N*(Na+Nb))\n",
    "    \n",
    "    return(observed_degree, predicted_degree)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get all WT and NULL files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_path = r'/Users/xiaoqiansun/Desktop/MedLu/EPM/EPM_Data'\n",
    "root_path_incorrect = r'/Users/xiaoqiansun/Desktop/MedLu/EPM/EPM_Data/EPM-incorrect trace'\n",
    "\n",
    "WT_list = ['NC128_WT', 'NC139_WT', 'NC158_WT', 'NC166_WT', \n",
    "           'NC228_WT', 'NC230_WT', 'NC238_WT', 'NC297_WT', \n",
    "           'NC298_WT', 'NC308_WT', 'NC314_WT', 'NC315_WT', \n",
    "           'NC324_WT', 'NC326_WT', 'NC463_WT', 'NC476_WT']\n",
    "WT_incorrect_neuron_list = ['NC128_WT.txt', 'NC139_WT.txt', 'NC158_WT.txt', 'NC166_WT.txt',\n",
    "                            'NC228_WT.txt', 'NC230_WT.txt', 'NC238_WT.txt', 'NC297_WT.txt', \n",
    "                            'NC298_WT.txt', 'NC308_WT.txt', 'NC314_WT.txt', 'NC315_WT.txt', \n",
    "                            'NC324_WT.txt', 'NC326_WT.txt',  'NC463_WT.txt', 'NC476_WT.txt']\n",
    "\n",
    "\n",
    "#-----------------------------------------------------------------------------------------------\n",
    "\n",
    "NULL_list = ['NC102_NULL', 'NC103_NULL', 'NC114_NULL', 'NC149_NULL', \n",
    "             'NC159_NULL', 'NC227_NULL', 'NC232_NULL', 'NC296_NULL', \n",
    "             'NC303_NULL', 'NC313_NULL', 'NC462_NULL', 'NC464_NULL', \n",
    "             'NC468_NULL','NC475_NULL']\n",
    "NULL_incorrect_neuron_list = ['NC102_NULL.txt', 'NC103_NULL.txt', 'NC114_NULL.txt', 'NC149_NULL.txt', \n",
    "                              'NC159_NULL.txt', 'NC227_NULL.txt', 'NC232_NULL.txt', 'NC296_NULL.txt',\n",
    "                              'NC303_NULL.txt', 'NC313_NULL.txt', 'NC462_NULL.txt', 'NC464_NULL.txt', \n",
    "                              'NC468_NULL.txt','NC475_NULL.txt']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Summary File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print('WT')\n",
    "\n",
    "for i in range(len(WT_list)):\n",
    "    \n",
    "    mouse_name = WT_list[i]\n",
    "    \n",
    "    mice = os.path.join(root_path, mouse_name)\n",
    "    trace = os.path.join(root_path, mouse_name, \"trace.csv\")\n",
    "    position = os.path.join(root_path, mouse_name, \"cells position.csv\")\n",
    "    event = os.path.join(root_path, mouse_name, \"events.xlsx\")\n",
    "    incorrect = os.path.join(root_path_incorrect, WT_incorrect_neuron_list[i])\n",
    "    coordinate = os.path.join(root_path, mouse_name,'{}.TXT'.format(mouse_name.split('_')[0]))\n",
    "    \n",
    "    # defube fps based on mouse_name\n",
    "    if mouse_name.split('_')[0] in ['NC128','NC102', 'NC013']:\n",
    "        fps = 20\n",
    "    else:\n",
    "        fps = 15\n",
    "        \n",
    "    df = process_(trace, incorrect, position)\n",
    "    df_frame_label = add_event_label(df, event, mice)\n",
    "    \n",
    "    # ON/OFF\n",
    "    #####---------------------------------------------------------\n",
    "    df_close_on , close_on_list  = status_ONOFF(df_frame_label, 'close', 'ON',  fps)\n",
    "    df_close_off, close_off_list = status_ONOFF(df_frame_label, 'close', 'OFF', fps)\n",
    "    \n",
    "    df_open_on , open_on_list  = status_ONOFF(df_frame_label, 'open', 'ON',  fps)\n",
    "    df_open_off, open_off_list = status_ONOFF(df_frame_label, 'open', 'OFF', fps)\n",
    "    \n",
    "    # Prefered ON/OFF\n",
    "    #####---------------------------------------------------------\n",
    "    Open_df_with_label  = check_frame_interval_with_label(df_frame_label, 'open')\n",
    "    Close_df_with_label = check_frame_interval_with_label(df_frame_label, 'close')\n",
    "    \n",
    "    # get open prefered session\n",
    "    open_x1, open_x2, open_p, open_np, OpenPrefered_Check, OpenNonPrefered_Check = prefered_side(df_frame_label, \n",
    "                                                                          Open_df_with_label,\n",
    "                                                                          coordinate,\n",
    "                                                                          fps)\n",
    "    # get close prefered session\n",
    "    close_x1, close_x2, close_p, close_np, ClosePrefered_Check, CloseNonPrefered_Check = prefered_side(df_frame_label,\n",
    "                                                                            Close_df_with_label,\n",
    "                                                                            coordinate,\n",
    "                                                                            fps)\n",
    "    #EPM openPrefered-NonPreferedON/closePrefered-NonPreferedON neuron list\n",
    "    openPreferedON,      openPreferedON_list      = status_ONOFF_Prefered(OpenPrefered_Check,     'prefered',     fps, 'ON')\n",
    "    openNonPreferedON,   openNonPreferedON_list   = status_ONOFF_Prefered(OpenNonPrefered_Check,  'non_prefered', fps, 'ON')\n",
    "    closePreferedON,     closePreferedON_list     = status_ONOFF_Prefered(ClosePrefered_Check,    'prefered',     fps, 'ON')\n",
    "    closeNonPreferedON,  closeNonPreferedON_list  = status_ONOFF_Prefered(CloseNonPrefered_Check, 'non_prefered', fps, 'ON')\n",
    "    openPreferedOFF,     openPreferedOFF_list     = status_ONOFF_Prefered(OpenPrefered_Check,     'prefered',     fps, 'OFF')\n",
    "    openNonPreferedOFF,  openNonPreferedOFF_list  = status_ONOFF_Prefered(OpenNonPrefered_Check,  'non_prefered', fps, 'OFF')\n",
    "    closePreferedOFF,    closePreferedOFF_list    = status_ONOFF_Prefered(ClosePrefered_Check,    'prefered',     fps, 'OFF')\n",
    "    closeNonPreferedOFF, closeNonPreferedOFF_list = status_ONOFF_Prefered(CloseNonPrefered_Check, 'non_prefered', fps, 'OFF')\n",
    "    \n",
    "    # generate on/off summary file for each mouse\n",
    "    all_ONOFF_neuron_list = [open_on_list, open_off_list, close_on_list, close_off_list,\n",
    "                             openPreferedON_list,    openPreferedOFF_list,\n",
    "                             openNonPreferedON_list, openNonPreferedOFF_list,\n",
    "                             closePreferedON_list,   closePreferedOFF_list,\n",
    "                             closeNonPreferedON_list,closeNonPreferedOFF_list]\n",
    "    columns_name = ['OpenON','OpenOFF', 'CloseON', 'CloseOFF',\n",
    "                    'openPreferedON', 'openPreferedOFF', 'openNonPreferedON','openNonPreferedOFF',\n",
    "                    'closePreferedON','closePreferedOF', 'closeNonPreferedON','closeNonPreferedOFF']\n",
    "    generate_ONOFF_Summary(df, all_ONOFF_neuron_list, columns_name, mouse_name,'EPM')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NULL\n",
      "57 neurons are not correct\n",
      "136 neurons are not our target\n",
      "141 neurons are removing\n",
      " \n",
      "The df shape of trace df with label: (11800, 40)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiaoqiansun/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:113: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/Users/xiaoqiansun/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:29: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/Users/xiaoqiansun/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:116: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/Users/xiaoqiansun/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:117: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the shape of the dataframe in the status is: (3092, 40)\n",
      "the shape of the dataframe in the status is: (8112, 40)\n",
      "2064 frames are prefered\n",
      "prefered 2064\n",
      "non_prefered 1028\n",
      "5176 frames are prefered\n",
      "prefered 5176\n",
      "non_prefered 2936\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/xiaoqiansun/opt/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:169: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "74 neurons are not correct\n",
      "139 neurons are not our target\n",
      "156 neurons are removing\n",
      " \n",
      "The df shape of trace df with label: (11800, 25)\n",
      "the shape of the dataframe in the status is: (1059, 25)\n",
      "the shape of the dataframe in the status is: (9919, 25)\n",
      "607 frames are prefered\n",
      "prefered 607\n",
      "non_prefered 452\n",
      "7588 frames are prefered\n",
      "prefered 7588\n",
      "non_prefered 1337\n",
      "122 neurons are not correct\n",
      "55 neurons are not our target\n",
      "149 neurons are removing\n",
      " \n",
      "The df shape of trace df with label: (8925, 32)\n",
      "the shape of the dataframe in the status is: (945, 32)\n",
      "the shape of the dataframe in the status is: (7611, 32)\n",
      "605 frames are prefered\n",
      "prefered 605\n",
      "non_prefered 340\n",
      "5117 frames are prefered\n",
      "prefered 5117\n",
      "non_prefered 2218\n",
      "83 neurons are not correct\n",
      "156 neurons are not our target\n",
      "171 neurons are removing\n",
      " \n",
      "The df shape of trace df with label: (8925, 10)\n",
      "the shape of the dataframe in the status is: (0, 10)\n",
      "the shape of the dataframe in the status is: (8925, 10)\n",
      "0 frames are prefered\n",
      "prefered 0\n",
      "non_prefered 0\n",
      "8925 frames are prefered\n",
      "prefered 8925\n",
      "non_prefered 0\n"
     ]
    }
   ],
   "source": [
    "print('NULL')\n",
    "\n",
    "for i in range(len(NULL_list)):\n",
    "    \n",
    "    mouse_name = NULL_list[i]\n",
    "    \n",
    "    mice = os.path.join(root_path, mouse_name)\n",
    "    trace = os.path.join(root_path, mouse_name, \"trace.csv\")\n",
    "    position = os.path.join(root_path, mouse_name, \"cells position.csv\")\n",
    "    event = os.path.join(root_path, mouse_name, \"events.xlsx\")\n",
    "    incorrect = os.path.join(root_path_incorrect, NULL_incorrect_neuron_list[i])\n",
    "    coordinate = os.path.join(root_path, mouse_name,'{}.TXT'.format(mouse_name.split('_')[0]))\n",
    "    \n",
    "    # defube fps based on mouse_name\n",
    "    if mouse_name.split('_')[0] in ['NC128','NC102', 'NC013']:\n",
    "        fps = 20\n",
    "    else:\n",
    "        fps = 15\n",
    "        \n",
    "    df = process_(trace, incorrect, position)\n",
    "    df_frame_label = add_event_label(df, event, mice)\n",
    "    \n",
    "    # ON/OFF\n",
    "    #####---------------------------------------------------------\n",
    "    df_close_on , close_on_list  = status_ONOFF(df_frame_label, 'close', 'ON',  fps)\n",
    "    df_close_off, close_off_list = status_ONOFF(df_frame_label, 'close', 'OFF', fps)\n",
    "    \n",
    "    df_open_on , open_on_list  = status_ONOFF(df_frame_label, 'open', 'ON',  fps)\n",
    "    df_open_off, open_off_list = status_ONOFF(df_frame_label, 'open', 'OFF', fps)\n",
    "    \n",
    "    # Prefered ON/OFF\n",
    "    #####---------------------------------------------------------\n",
    "    Open_df_with_label  = check_frame_interval_with_label(df_frame_label, 'open')\n",
    "    Close_df_with_label = check_frame_interval_with_label(df_frame_label, 'close')\n",
    "    \n",
    "    # get open prefered session\n",
    "    open_x1, open_x2, open_p, open_np, OpenPrefered_Check, OpenNonPrefered_Check = prefered_side(df_frame_label, \n",
    "                                                                          Open_df_with_label,\n",
    "                                                                          coordinate,\n",
    "                                                                          fps)\n",
    "    # get close prefered session\n",
    "    close_x1, close_x2, close_p, close_np, ClosePrefered_Check, CloseNonPrefered_Check = prefered_side(df_frame_label,\n",
    "                                                                            Close_df_with_label,\n",
    "                                                                            coordinate,\n",
    "                                                                            fps)\n",
    "    #EPM openPrefered-NonPreferedON/closePrefered-NonPreferedON neuron list\n",
    "    openPreferedON,      openPreferedON_list      = status_ONOFF_Prefered(OpenPrefered_Check,     'prefered',     fps, 'ON')\n",
    "    openNonPreferedON,   openNonPreferedON_list   = status_ONOFF_Prefered(OpenNonPrefered_Check,  'non_prefered', fps, 'ON')\n",
    "    closePreferedON,     closePreferedON_list     = status_ONOFF_Prefered(ClosePrefered_Check,    'prefered',     fps, 'ON')\n",
    "    closeNonPreferedON,  closeNonPreferedON_list  = status_ONOFF_Prefered(CloseNonPrefered_Check, 'non_prefered', fps, 'ON')\n",
    "    openPreferedOFF,     openPreferedOFF_list     = status_ONOFF_Prefered(OpenPrefered_Check,     'prefered',     fps, 'OFF')\n",
    "    openNonPreferedOFF,  openNonPreferedOFF_list  = status_ONOFF_Prefered(OpenNonPrefered_Check,  'non_prefered', fps, 'OFF')\n",
    "    closePreferedOFF,    closePreferedOFF_list    = status_ONOFF_Prefered(ClosePrefered_Check,    'prefered',     fps, 'OFF')\n",
    "    closeNonPreferedOFF, closeNonPreferedOFF_list = status_ONOFF_Prefered(CloseNonPrefered_Check, 'non_prefered', fps, 'OFF')\n",
    "    \n",
    "    # generate on/off summary file for each mouse\n",
    "    all_ONOFF_neuron_list = [open_on_list, open_off_list, close_on_list, close_off_list,\n",
    "                             openPreferedON_list,    openPreferedOFF_list,\n",
    "                             openNonPreferedON_list, openNonPreferedOFF_list,\n",
    "                             closePreferedON_list,   closePreferedOFF_list,\n",
    "                             closeNonPreferedON_list,closeNonPreferedOFF_list]\n",
    "    columns_name = ['OpenON','OpenOFF', 'CloseON', 'CloseOFF',\n",
    "                    'openPreferedON', 'openPreferedOFF', 'openNonPreferedON','openNonPreferedOFF',\n",
    "                    'closePreferedON','closePreferedOF', 'closeNonPreferedON','closeNonPreferedOFF']\n",
    "    generate_ONOFF_Summary(df, all_ONOFF_neuron_list, columns_name, mouse_name,'EPM')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Within Test Degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_path_on_off_summary = r'/Users/xiaoqiansun/Desktop/MedLu/EPM/EPM_Data/ON_OFF_Summary'\n",
    "\n",
    "WT_degree_dic_list = []\n",
    "\n",
    "for mouse_name in WT_list:\n",
    "    \n",
    "    project = 'EPM'\n",
    "    EPM_file_name = project+'_A_Summary_'+mouse_name+'_ONOFF.csv'\n",
    "\n",
    "    on_off_df = pd.read_csv(os.path.join(root_path_on_off_summary, EPM_file_name))\n",
    "    columns = on_off_df.columns.to_list()\n",
    "    columns[0] = 'Neurons'\n",
    "    on_off_df.columns = columns\n",
    "    on_off_df = on_off_df.set_index('Neurons')\n",
    "    \n",
    "    OpenON_CloseON_O, OpenON_CloseON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['OpenON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['CloseON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['OpenON'] == 1) & (on_off_df['CloseON'] == 1)]))\n",
    "    #------------\n",
    "    OpenPON_OpenNPON_O, OpenPON_OpenNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['openNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openPreferedON'] == 1) & (on_off_df['openNonPreferedON'] == 1)]))\n",
    "    #------------\n",
    "    ClosePON_CloseNPON_O, ClosePON_CloseNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['closePreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closeNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['closePreferedON'] == 1) & (on_off_df['closeNonPreferedON'] == 1)]))\n",
    "    #------------\n",
    "    OpenON_ClosePON_O, OpenON_ClosePON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['OpenON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closePreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['OpenON'] == 1) & (on_off_df['closePreferedON'] == 1)]))\n",
    "    \n",
    "    OpenON_CloseNPON_O, OpenON_CloseNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['OpenON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closeNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['OpenON'] == 1) & (on_off_df['closeNonPreferedON'] == 1)]))\n",
    "    #------------\n",
    "    OpenPON_ClosePON_O, OpenPON_ClosePON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closePreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openPreferedON'] == 1) & (on_off_df['closePreferedON'] == 1)]))\n",
    "    \n",
    "    OpenPON_CloseNPON_O, OpenPON_CloseNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closeNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openPreferedON'] == 1) & (on_off_df['closeNonPreferedON'] == 1)]))\n",
    "    #------------\n",
    "    OpenNPON_ClosePON_O, OpenNPON_ClosePON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closePreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openNonPreferedON'] == 1) & (on_off_df['closePreferedON'] == 1)]))\n",
    "    \n",
    "    OpenNPON_CloseNPON_O, OpenNPON_CloseNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closeNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openNonPreferedON'] == 1) & (on_off_df['closeNonPreferedON'] == 1)]))\n",
    "    \n",
    "    degree_dic = {'mouse_name': mouse_name,\n",
    "                  'OpenON_CloseON_O':    OpenON_CloseON_O,\n",
    "                  'OpenON_CloseON_P':    OpenON_CloseON_P,\n",
    "                  'OpenPON_OpenNPON_O':  OpenPON_OpenNPON_O,\n",
    "                  'OpenPON_OpenNPON_P':  OpenPON_OpenNPON_P,\n",
    "                  'ClosePON_CloseNPON_O':ClosePON_CloseNPON_O,\n",
    "                  'ClosePON_CloseNPON_P':ClosePON_CloseNPON_P,\n",
    "                  ' ': np.nan,\n",
    "                  'OpenON_ClosePON_O':OpenON_ClosePON_O,\n",
    "                  'OpenON_ClosePON_P':OpenON_ClosePON_P,\n",
    "                  'OpenON_CloseNPON_O':OpenON_CloseNPON_O,\n",
    "                  'OpenON_CloseNPON_P':OpenON_CloseNPON_P,\n",
    "                  'OpenPON_ClosePON_O':OpenPON_ClosePON_O,\n",
    "                  'OpenPON_ClosePON_P':OpenPON_ClosePON_P,\n",
    "                  'OpenPON_CloseNPON_O':OpenPON_CloseNPON_O,\n",
    "                  'OpenPON_CloseNPON_P':OpenPON_CloseNPON_P,\n",
    "                  'OpenNPON_ClosePON_O':OpenNPON_ClosePON_O,\n",
    "                  'OpenNPON_ClosePON_P':OpenNPON_ClosePON_P,\n",
    "                  'OpenNPON_CloseNPON_O':OpenNPON_CloseNPON_O,\n",
    "                  'OpenNPON_CloseNPON_P':OpenNPON_CloseNPON_P}\n",
    "    degree_df = pd.DataFrame.from_dict(degree_dic, orient='index').T\n",
    "    WT_degree_dic_list.append(degree_df)\n",
    "\n",
    "WT_df_degree = pd.concat(WT_degree_dic_list)\n",
    "WT_df_degree.to_csv('EPM_WT_Degree.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "root_path_on_off_summary = r'/Users/xiaoqiansun/Desktop/MedLu/EPM/EPM_Data/ON_OFF_Summary'\n",
    "\n",
    "NULL_degree_dic_list = []\n",
    "\n",
    "for mouse_name in NULL_list:\n",
    "    \n",
    "    project = 'EPM'\n",
    "    EPM_file_name = project+'_A_Summary_'+mouse_name+'_ONOFF.csv'\n",
    "\n",
    "    on_off_df = pd.read_csv(os.path.join(root_path_on_off_summary, EPM_file_name))\n",
    "    columns = on_off_df.columns.to_list()\n",
    "    columns[0] = 'Neurons'\n",
    "    on_off_df.columns = columns\n",
    "    on_off_df = on_off_df.set_index('Neurons')\n",
    "    \n",
    "    OpenON_CloseON_O, OpenON_CloseON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['OpenON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['CloseON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['OpenON'] == 1) & (on_off_df['CloseON'] == 1)]))\n",
    "    #------------\n",
    "    OpenPON_OpenNPON_O, OpenPON_OpenNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['openNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openPreferedON'] == 1) & (on_off_df['openNonPreferedON'] == 1)]))\n",
    "    #------------\n",
    "    ClosePON_CloseNPON_O, ClosePON_CloseNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['closePreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closeNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['closePreferedON'] == 1) & (on_off_df['closeNonPreferedON'] == 1)]))\n",
    "    #------------\n",
    "    OpenON_ClosePON_O, OpenON_ClosePON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['OpenON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closePreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['OpenON'] == 1) & (on_off_df['closePreferedON'] == 1)]))\n",
    "    \n",
    "    OpenON_CloseNPON_O, OpenON_CloseNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['OpenON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closeNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['OpenON'] == 1) & (on_off_df['closeNonPreferedON'] == 1)]))\n",
    "    #------------\n",
    "    OpenPON_ClosePON_O, OpenPON_ClosePON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closePreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openPreferedON'] == 1) & (on_off_df['closePreferedON'] == 1)]))\n",
    "    \n",
    "    OpenPON_CloseNPON_O, OpenPON_CloseNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closeNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openPreferedON'] == 1) & (on_off_df['closeNonPreferedON'] == 1)]))\n",
    "    #------------\n",
    "    OpenNPON_ClosePON_O, OpenNPON_ClosePON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closePreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openNonPreferedON'] == 1) & (on_off_df['closePreferedON'] == 1)]))\n",
    "    \n",
    "    OpenNPON_CloseNPON_O, OpenNPON_CloseNPON_P = degree(len(on_off_df.index),\n",
    "                                                     len(on_off_df[on_off_df['openNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[on_off_df['closeNonPreferedON'] == 1]),\n",
    "                                                     len(on_off_df[(on_off_df['openNonPreferedON'] == 1) & (on_off_df['closeNonPreferedON'] == 1)]))\n",
    "    \n",
    "    degree_dic = {'mouse_name': mouse_name,\n",
    "                  'OpenON_CloseON_O':    OpenON_CloseON_O,\n",
    "                  'OpenON_CloseON_P':    OpenON_CloseON_P,\n",
    "                  'OpenPON_OpenNPON_O':  OpenPON_OpenNPON_O,\n",
    "                  'OpenPON_OpenNPON_P':  OpenPON_OpenNPON_P,\n",
    "                  'ClosePON_CloseNPON_O':ClosePON_CloseNPON_O,\n",
    "                  'ClosePON_CloseNPON_P':ClosePON_CloseNPON_P,\n",
    "                  ' ': np.nan,\n",
    "                  'OpenON_ClosePON_O':OpenON_ClosePON_O,\n",
    "                  'OpenON_ClosePON_P':OpenON_ClosePON_P,\n",
    "                  'OpenON_CloseNPON_O':OpenON_CloseNPON_O,\n",
    "                  'OpenON_CloseNPON_P':OpenON_CloseNPON_P,\n",
    "                  'OpenPON_ClosePON_O':OpenPON_ClosePON_O,\n",
    "                  'OpenPON_ClosePON_P':OpenPON_ClosePON_P,\n",
    "                  'OpenPON_CloseNPON_O':OpenPON_CloseNPON_O,\n",
    "                  'OpenPON_CloseNPON_P':OpenPON_CloseNPON_P,\n",
    "                  'OpenNPON_ClosePON_O':OpenNPON_ClosePON_O,\n",
    "                  'OpenNPON_ClosePON_P':OpenNPON_ClosePON_P,\n",
    "                  'OpenNPON_CloseNPON_O':OpenNPON_CloseNPON_O,\n",
    "                  'OpenNPON_CloseNPON_P':OpenNPON_CloseNPON_P}\n",
    "    degree_df = pd.DataFrame.from_dict(degree_dic, orient='index').T\n",
    "    NULL_degree_dic_list.append(degree_df)\n",
    "\n",
    "NULL_df_degree = pd.concat(NULL_degree_dic_list)\n",
    "NULL_df_degree.to_csv('EPM_NULL_Degree.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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