Trajectory-retrieval_original.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "#Due to leftoevererrors in Nathan's python installation, some cleaning up occurs here\n",
    "#sys.path.append(\"./code/\")\n",
    "#sys.path.remove('/usr/local/lib/python2.7/site-packages') \n",
    "\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import copy\n",
    "import datetime\n",
    "import pickle\n",
    "import gzip\n",
    "import os,glob\n",
    "import time\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "#sys.path\n",
    "\n",
    "import regex as re\n",
    "import datetime\n",
    "import math\n",
    "\n",
    "from IPython.core.display import display,HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))  # to make the notebook use the entire width of the browser\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This code is the MongoConnection class from the Amaral lab LabTools folder.\n",
    "\n",
    "from __future__ import print_function, unicode_literals\n",
    "import sys\n",
    "from pymongo import MongoClient\n",
    "\n",
    "\n",
    "class MongoConnection(object):\n",
    "    def __init__(self, cxnSettings, **kwargs):\n",
    "        self.settings = cxnSettings\n",
    "        self.mongoURI = self._constructURI()\n",
    "        self.connect(**kwargs)\n",
    "        self.ensure_index()\n",
    "\n",
    "    def _constructURI(self):\n",
    "        '''\n",
    "        Construct the mongo URI\n",
    "        '''\n",
    "        mongoURI = 'mongodb://'\n",
    "        #User/password handling\n",
    "        if 'user'in self.settings and 'password' in self.settings:\n",
    "            mongoURI += self.settings['user'] + ':' + self.settings['password']\n",
    "            mongoURI += '@'\n",
    "        elif 'user' in self.settings:\n",
    "            print('Missing password for given user, proceeding without either')\n",
    "        elif 'password' in self.settings:\n",
    "            print('Missing user for given passord, proceeding without either')\n",
    "        #Host and port\n",
    "        try:\n",
    "            mongoURI += self.settings['host'] + ':'\n",
    "        except KeyError:\n",
    "            print('Missing the hostname. Cannot connect without host')\n",
    "            sys.exit()\n",
    "        try:\n",
    "            mongoURI += str(self.settings['port'])\n",
    "        except KeyError:\n",
    "            print('Missing the port. Substituting default port of 27017')\n",
    "            mongoURI += str('27017')\n",
    "        return mongoURI\n",
    "\n",
    "    def connect(self, **kwargs):\n",
    "        '''\n",
    "        Establish the connection, database, and collection\n",
    "        '''\n",
    "        self.connection = MongoClient(self.mongoURI, **kwargs)\n",
    "        #########\n",
    "        try:\n",
    "            self.db = self.connection[self.settings['db']]\n",
    "        except KeyError:\n",
    "            print(\"Must specify a database as a 'db' key in the settings file\")\n",
    "            sys.exit()\n",
    "        #########\n",
    "        try:\n",
    "            self.collection = self.db[self.settings['collection']]\n",
    "        except KeyError:\n",
    "            print('Should have a collection.', end='')\n",
    "            print('Starting a collection in database', end='')\n",
    "            print(' for current connection as test.')\n",
    "            self.collection = self.db['test']\n",
    "\n",
    "    def tearDown(self):\n",
    "        '''\n",
    "        Closes the connection\n",
    "        '''\n",
    "        self.connection.close()\n",
    "\n",
    "    def ensure_index(self):\n",
    "        '''\n",
    "        Ensures the connection has all given indexes.\n",
    "        indexes: list of (`key`, `direction`) pairs.\n",
    "            See docs.mongodb.org/manual/core/indexes/ for possible `direction`\n",
    "            values.\n",
    "        '''\n",
    "        if 'indexes' in self.settings:\n",
    "            for index in self.settings['indexes']:\n",
    "                self.collection.ensure_index(index[0], **index[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def retrieve_trajectory(ut, plos_uts):\n",
    "    # Retrieve a citation trajectory for a single reference UT. This version collects -all- WoS citations for a given UT,\n",
    "    # PLOS or not, so this one takes a lot more time, but shows the underlaying shape of the citation profile.\n",
    "    # Requires inputting the list of all plos UTs ('paper_UT' column in the plos dataframe)\n",
    "    count = 0\n",
    "    cite_dates = {}\n",
    "    \n",
    "    doc = papers_con.collection.find_one({\"UT\":ut})\n",
    "    if 'citations' in doc:\n",
    "        for ref_ut in doc['citations']:\n",
    "            count+=1\n",
    "            ref = papers_con.collection.find_one({\"UT\":ref_ut})\n",
    "            year = float('nan')\n",
    "            date = float('nan')\n",
    "            plos_flag = False\n",
    "            if ref:\n",
    "                if 'issue' in ref:\n",
    "                    if 'PY' in ref['issue']:\n",
    "                        year = ref['issue']['PY']\n",
    "                    if 'PD' in ref['issue']:\n",
    "                        date = ref['issue']['PD']\n",
    "                    if ref_ut in plos_uts:\n",
    "                        plos_flag = True\n",
    "                    \n",
    "            if count%1000==0:\n",
    "                print(count)   \n",
    "            cite_dates[ref_ut] = [date, year, plos_flag]\n",
    "\n",
    "    return cite_dates\n",
    "\n",
    "\n",
    "def retrieve_multiple_trajectories(ut_list, plos_uts):\n",
    "    # Retrieve multiple trajectories for all reference UTs in ut_list. Returns a nested dictionary\n",
    "    # {Reference_UT_1: {Citation_UT_1: [Month, Year], ...}}\n",
    "    trajectories = {}\n",
    "    for ut in ut_list:\n",
    "        print(ut)\n",
    "        cite_dates = retrieve_trajectory(ut, plos_uts)\n",
    "        trajectories[ut] = cite_dates\n",
    "    return trajectories\n",
    "\n",
    "\n",
    "def retrieve_trajectory_plos_only(ut, plos_uts):\n",
    "    # Retrieve a citation trajectory for a single reference UT. This version collects only PLOS citations, so it's much faster\n",
    "    # Requires inputting the list of all plos UTs ('paper_UT' column in the plos dataframe)\n",
    "\n",
    "    count = 0\n",
    "    cite_dates = {}\n",
    "    if ut in plos_uts:\n",
    "        doc = papers_con.collection.find_one({\"UT\":ut})\n",
    "        if 'citations' in doc:\n",
    "            for ref_ut in doc['citations']:\n",
    "                count+=1\n",
    "                ref = papers_con.collection.find_one({\"UT\":ref_ut})\n",
    "                year = float('nan')\n",
    "                date = float('nan')\n",
    "                plos_flag = False\n",
    "                if ref:\n",
    "                    if 'issue' in ref:\n",
    "                        if 'PY' in ref['issue']:\n",
    "                            year = ref['issue']['PY']\n",
    "                        if 'PD' in ref['issue']:\n",
    "                            date = ref['issue']['PD']\n",
    "                        if ref_ut in plos_uts:\n",
    "                            plos_flag = True\n",
    "                if count%1000==0:\n",
    "                    print(count)   \n",
    "                cite_dates[ref_ut] = [date, year, plos_flag]\n",
    "\n",
    "    return cite_dates\n",
    "\n",
    "\n",
    "def retrieve_multiple_trajectories_plos_only(ut_list, plos_uts):\n",
    "    # Retrieve multiple trajectories for all reference UTs in ut_list. Returns a nested dictionary\n",
    "    # {Reference_UT_1: {Citation_UT_1: [Month, Year], ...}}\n",
    "\n",
    "    trajectories = {}\n",
    "    for ut in ut_list:\n",
    "        print(ut)\n",
    "        cite_dates = retrieve_trajectory_plos_only(ut, plos_uts)\n",
    "        trajectories[ut] = cite_dates\n",
    "    return trajectories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datetime_filler_traj(traj_dict):\n",
    "    # Generate a new dictionary from a trajectory dictionary with datetime objects. Includes the plos_flag value\n",
    "    \n",
    "\n",
    "    numeric = re.compile('[0-9]+')\n",
    "    alpha = re.compile('[A-Za-z]+')\n",
    "    month_dict = {'JAN':1, 'FEB': 2, 'MAR':3, 'APR':4, 'MAY':5, 'JUN': 6, \n",
    "                  'JUL': 7, 'AUG': 8, 'SEP': 9, 'OCT':10, 'NOV': 11, 'DEC': 12,\n",
    "                 'SPR': 3, 'SUM': 6, 'FAL': 9, 'WIN': 12}\n",
    "    formatted_traj_dict = {}\n",
    "    \n",
    "    for ref_ut in traj_dict:\n",
    "        formatted_traj_dict[ref_ut] = {}\n",
    "        for cite_ut in traj_dict[ref_ut]:\n",
    "            date = traj_dict[ref_ut][cite_ut][0]\n",
    "            year = traj_dict[ref_ut][cite_ut][1]\n",
    "            plos_flag = traj_dict[ref_ut][cite_ut][2]\n",
    "            \n",
    "            date_field = []\n",
    "\n",
    "            if type(date) == str:\n",
    "                \n",
    "                for i in re.findall(alpha, date):\n",
    "                    date_field.append(i)\n",
    "                for i in re.findall(numeric, date):\n",
    "                    date_field.append(i)\n",
    "                \n",
    "                if len(date_field) > 2: # No weirdness here, only 2 values max for date field\n",
    "                    print(date_field)\n",
    "                \n",
    "                if len(date_field[0])==1: # There's a single date that is just '0'. Likely a database error/nan\n",
    "                    print(date_field)\n",
    "\n",
    "                    \n",
    "            else:\n",
    "                date_field = ['JUNE', '15']\n",
    "                \n",
    "            month = -1\n",
    "            day = -1\n",
    "            \n",
    "            if date_field[0] in month_dict:\n",
    "                month = month_dict[date_field[0]]\n",
    "                if len(date_field)==2:\n",
    "                    if len(date_field[1])<3:\n",
    "                        day = int(date_field[1])\n",
    "            \n",
    "                        \n",
    "            if not math.isnan(year):\n",
    "                if month != -1:\n",
    "                    if day != -1:\n",
    "                        dt = datetime.datetime(year, month, day)\n",
    "                    else:\n",
    "                        dt = datetime.datetime(year, month, 15)\n",
    "                    \n",
    "                else:\n",
    "                    dt = datetime.datetime(year, 6, 15)\n",
    "\n",
    "\n",
    "            else:\n",
    "                dt = -1\n",
    "            \n",
    "            formatted_traj_dict[ref_ut][cite_ut] = [dt, plos_flag]\n",
    "\n",
    "    return formatted_traj_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_papers_settings = {\n",
    "    \"host\": \"chicago.chem-eng.northwestern.edu\",\n",
    "    \"port\": \"27017\",\n",
    "    \"db\": \"web_of_science_aux\",\n",
    "    \"collection\": \"merged_papers\",\n",
    "    \"user\": \"mongoreader\",\n",
    "    \"password\": \"emptycoffeecup\"\n",
    "}\n",
    "issues_settings = {\n",
    "    \"host\": \"chicago.chem-eng.northwestern.edu\",\n",
    "    \"port\": \"27017\",\n",
    "    \"db\": \"web_of_science_aux\",\n",
    "    \"collection\": \"issues\",\n",
    "    \"user\": \"mongoreader\",\n",
    "    \"password\": \"emptycoffeecup\"\n",
    "}\n",
    "journal_settings = {\n",
    "    \"host\": \"chicago.chem-eng.northwestern.edu\",\n",
    "    \"port\": \"27017\",\n",
    "    \"db\": \"web_of_science_aux\",\n",
    "    \"collection\": \"journals\",\n",
    "    \"user\": \"mongoreader\",\n",
    "    \"password\": \"emptycoffeecup\"\n",
    "}\n",
    "papers_con = MongoConnection(merged_papers_settings)\n",
    "issue_con = MongoConnection(issues_settings)\n",
    "journal_con = MongoConnection(journal_settings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/notebooks'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def section_regex_parser(ref_df):\n",
    "# Using regular expressions to sort sections. Reach a concensus on section identification by scanning both section_title and section_title_alt\n",
    "    import regex as re\n",
    "\n",
    "    sect_index_dict = {'intro': 0, 'methods': 1, 'results': 2, 'disc': 3, 'res_disc':4, 'concl':5, 'mixed':6, 'na':7}\n",
    "\n",
    "    intro_re = re.compile(r'(intro)')\n",
    "    method_re = re.compile(r'(method)')\n",
    "    results_re = re.compile(r'(results)')\n",
    "    disc_re = re.compile(r'(disc)')\n",
    "    concl_re = re.compile(r'(conclu)')\n",
    "    backgr_re = re.compile(r'(backgr)')\n",
    "    mater_re = re.compile(r'(mater)')\n",
    "    count = 0\n",
    "    judgement = []\n",
    "\n",
    "    for i in range(len(ref_df)):\n",
    "        count += 1\n",
    "        if count%100000 == 0:\n",
    "            print(count)\n",
    "        sect = ref_df.iloc[i]['section_title']\n",
    "        sect_alt = ref_df.iloc[i]['section_title_alt']\n",
    "        sect_tag = -1\n",
    "        sect_alt_tag = -1\n",
    "        sect_final = -1\n",
    "\n",
    "        if sect == None:\n",
    "            sect_tag = 'na'\n",
    "        else:\n",
    "            if re.search(intro_re, sect.lower()) or re.search(backgr_re, sect.lower()):\n",
    "                sect_tag = 'intro'\n",
    "\n",
    "            elif re.search(method_re,sect.lower()) or re.search(mater_re, sect.lower()):\n",
    "                if re.search(results_re, sect.lower()):\n",
    "                    sect_tag = 'mixed'\n",
    "\n",
    "                elif re.search(disc_re, sect.lower()):\n",
    "                    sect_tag = 'mixed'\n",
    "\n",
    "                else:\n",
    "                    sect_tag = 'methods'\n",
    "\n",
    "            elif re.search(results_re, sect.lower()):\n",
    "                if re.search(disc_re, sect.lower()):\n",
    "                    sect_tag = 'res_disc'\n",
    "                else:\n",
    "                    sect_tag = 'results'\n",
    "\n",
    "            elif re.search(disc_re, sect.lower()):\n",
    "                sect_tag = 'disc'\n",
    "\n",
    "            elif re.search(concl_re, sect.lower()):\n",
    "                sect_tag = 'concl'\n",
    "\n",
    "            else:\n",
    "                sect_tag = 'na'\n",
    "\n",
    "\n",
    "\n",
    "        if sect_alt == None:\n",
    "            sect_alt_tag = 'na'\n",
    "        else:\n",
    "            if re.search(intro_re, sect_alt.lower()) or re.search(backgr_re, sect_alt.lower()):\n",
    "                sect_alt_tag = 'intro'\n",
    "\n",
    "            elif re.search(method_re, sect_alt.lower()) or re.search(mater_re, sect_alt.lower()):\n",
    "                if re.search(results_re, sect_alt.lower()):\n",
    "                    sect_alt_tag = 'mixed'\n",
    "\n",
    "                elif re.search(disc_re, sect_alt.lower()):\n",
    "                    sect_alt_tag = 'mixed'\n",
    "\n",
    "                else:\n",
    "                    sect_alt_tag = 'methods'\n",
    "\n",
    "            elif re.search(results_re, sect_alt.lower()):\n",
    "                if re.search(disc_re, sect_alt.lower()):\n",
    "                    sect_alt_tag = 'res_disc'\n",
    "                else:\n",
    "                    sect_alt_tag = 'results'\n",
    "\n",
    "            elif re.search(disc_re, sect_alt.lower()):\n",
    "                sect_alt_tag = 'disc'\n",
    "\n",
    "            elif re.search(concl_re, sect_alt.lower()):\n",
    "                sect_alt_tag = 'concl'\n",
    "\n",
    "            else:\n",
    "                sect_alt_tag = 'na'\n",
    "\n",
    "\n",
    "\n",
    "        if sect_tag == sect_alt_tag: # Confident on label\n",
    "            sect_final = sect_tag\n",
    "            #ref_df.iloc[i]['regex_sect_index'] = sect_index_dict[sect_tag]\n",
    "\n",
    "        else:\n",
    "            if sect_tag == 'na':\n",
    "                sect_final = sect_alt_tag\n",
    "                #ref_df.iloc[i]['regex_sect_index'] = sect_index_dict[sect_alt_tag]\n",
    "            elif sect_alt_tag == 'na':\n",
    "                sect_final = sect_tag\n",
    "\n",
    "            elif sect_tag == 'mixed':\n",
    "                sect_final = sect_alt_tag\n",
    "\n",
    "            elif sect_alt_tag == 'mixed':\n",
    "                sect_final = sect_tag\n",
    "\n",
    "            elif sect_alt_tag == 'concl': # Conclusion is a plos-based standard that includes discussion/results and discussion\n",
    "                sect_final = sect_tag\n",
    "            else:\n",
    "                sect_final = sect_alt_tag\n",
    "\n",
    "        judgement.append(sect_final)\n",
    "    return judgement\n",
    "        #ref_df.iloc[i]['regex_sect_index'] = sect_index_dict[sect_final]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_df_zero_citations(df_pap_,df_ref_,df_pap_ref_):\n",
    "\n",
    "    list_ref_ut_ = list(df_ref_[\\\n",
    "                              (df_ref_['cite_count']>0)\\\n",
    "                             ].index.values)\n",
    "    df_ref_sel_ = df_ref_[df_ref_.index.isin(list_ref_ut_)]\n",
    "    df_pap_ref_sel_ = df_pap_ref_[df_pap_ref_['reference_UT'].isin(list_ref_ut_)]\n",
    "    ## identify the remaining pap-uts and cut the df_pap dataframe\n",
    "    list_pap_ut_ = df_pap_ref_sel_['paper_UT'].drop_duplicates().tolist()\n",
    "    df_pap_sel_ = df_pap_[df_pap_.index.isin(list_pap_ut_)]\n",
    "\n",
    "    ## in case we want to get rid again of the 'unique_occurrences'-column\n",
    "    # df_ref_sel_.drop('occurrences_unique', axis=1, inplace=True)\n",
    "    return df_pap_sel_,df_ref_sel_,df_pap_ref_sel_\n",
    "\n",
    "def filter_df_plos(df_pap_,df_ref_,df_pap_ref_, y1_,y2_, list_pap_art_type_, list_pap_field_ = None):\n",
    "    '''filter our three dataframes\n",
    "        - publication-year-interval >=y1_ <= y2_\n",
    "        - article-type: list_pap_art_type_\n",
    "\n",
    "    TODO:\n",
    "        - scientific field \n",
    "        - journal (plosone,plosbio, ....)\n",
    "    '''\n",
    "    ## identify paper-uts that satisfy the constraints\n",
    "    if list_pap_field_ == None: ## no contraint no field\n",
    "        list_pap_ut_ = list(df_pap_[\\\n",
    "                              (df_pap_['plos_pub_year']>=y1_)&(df_pap_['plos_pub_year']<=y2_)\\\n",
    "                                &(df_pap_['plos_article_type'].isin(list_pap_art_type_))\\\n",
    "                             ].index.values)\n",
    "    else: ## constraint on field\n",
    "        ## create list-field as string\n",
    "        x_tmp = df_pap_['plos_field'].values\n",
    "        y_tmp = [str(h) for h in x_tmp]\n",
    "        df_pap_.loc[:,'plos_field_str'] = y_tmp\n",
    "        field_sel_str_ = '|'.join(list_pap_field_)\n",
    "        list_pap_ut_ = list(df_pap_[\\\n",
    "                      (df_pap_['plos_pub_year']>=y1_)&(df_pap_['plos_pub_year']<=y2_)\\\n",
    "                        &(df_pap_['plos_article_type'].isin(list_pap_art_type_))\\\n",
    "                        &(df_pap_['plos_field_str'].str.contains(field_sel_str_))\\\n",
    "                     ].index.values)\n",
    "\n",
    "    ## cut the df_pap dataframe\n",
    "    df_pap_sel_ = df_pap_[df_pap_.index.isin(list_pap_ut_)]\n",
    "    try:\n",
    "        df_pap_sel_.drop('plos_field_str', axis=1, inplace=True)\n",
    "    except ValueError:\n",
    "        pass\n",
    "\n",
    "    ## cut the df_pap_ref dataframe\n",
    "    df_pap_ref_sel_ = df_pap_ref_[df_pap_ref_['paper_UT'].isin(list_pap_ut_)]\n",
    "\n",
    "    ## identify the remaining ref-uts and cut the df_ref dataframe\n",
    "    list_ref_ut_ = df_pap_ref_sel_['reference_UT'].drop_duplicates().tolist()\n",
    "    df_ref_sel_ = df_ref_[df_ref_.index.isin(list_ref_ut_)]\n",
    "    return df_pap_sel_,df_ref_sel_,df_pap_ref_sel_\n",
    "\n",
    "def filter_df_ref(df_pap_,df_ref_,df_pap_ref_, n_occ_min_,n_occ_max_, y1_,y2_, list_ref_art_type_, list_ref_field_ = None):\n",
    "    '''filter our three dataframes\n",
    "        - number of unique occurrences in plos-paers: n_thresh\n",
    "        - publication-year-interval >=y1_ <= y2_\n",
    "        - article-type: list_pap_art_type_\n",
    "\n",
    "    TODO:\n",
    "        - scientific field \n",
    "    '''\n",
    "    ## create a new column in df_ref with unique number of occurrences\n",
    "    df_tmp = df_pap_ref_[['paper_UT', 'reference_UT']].drop_duplicates() # remove multi-occurence of the same ref in the same paper\n",
    "    x_ = df_tmp.groupby(['reference_UT'])['reference_UT'].value_counts()\n",
    "    x_ut_ = [h[0] for h in x_.index.tolist()]\n",
    "    x_series_tmp = pd.Series(data=x_.values,index=x_ut_)\n",
    "    df_ref_.loc[:,'occurrences_unique'] = x_series_tmp\n",
    "\n",
    "    ## filter ref-uts and cut the df_ref dataframe\n",
    "    if list_ref_field_ == None: ## no contraint no field\n",
    "\n",
    "        list_ref_ut_ = list(df_ref_[\\\n",
    "                                  (df_ref_['occurrences_unique']>=n_occ_min_)&(df_ref_['occurrences_unique']<=n_occ_max_)\\\n",
    "                                  &(df_ref_['ref_pub_year']>=y1_)&(df_ref_['ref_pub_year']<=y2_)\\\n",
    "                                  &(df_ref_['ref_article_type'].isin(list_ref_art_type_))\\\n",
    "                                 ].index.values)\n",
    "\n",
    "    else: ## constraint on field\n",
    "        ## create list-field as string\n",
    "        x_tmp = df_ref_['ref_field'].values\n",
    "        y_tmp = [str(h) for h in x_tmp]\n",
    "        df_ref_.loc[:,'ref_field_str'] = y_tmp\n",
    "        field_sel_str_ = '|'.join(list_ref_field_)\n",
    "        list_ref_ut_ = list(df_ref_[\\\n",
    "                          (df_ref_['occurrences_unique']>=n_occ_min_)&(df_ref_['occurrences_unique']<=n_occ_max_)\\\n",
    "                          &(df_ref_['ref_pub_year']>=y1_)&(df_ref_['ref_pub_year']<=y2_)\\\n",
    "                          &(df_ref_['ref_article_type'].isin(list_ref_art_type_))\\\n",
    "                          &(df_ref_['ref_field_str'].str.contains(field_sel_str_))\\\n",
    "                         ].index.values)\n",
    "\n",
    "    df_ref_sel_ = df_ref_[df_ref_.index.isin(list_ref_ut_)]\n",
    "\n",
    "    ## cut the df_pap dataframe\n",
    "    try:\n",
    "        df_ref_sel_.drop('ref_field_str', axis=1, inplace=True)\n",
    "    except ValueError:\n",
    "        pass\n",
    "\n",
    "\n",
    "    ## cut the df_pap_ref dataframe\n",
    "    df_pap_ref_sel_ = df_pap_ref_[df_pap_ref_['reference_UT'].isin(list_ref_ut_)]\n",
    "\n",
    "    ## identify the remaining pap-uts and cut the df_pap dataframe\n",
    "    list_pap_ut_ = df_pap_ref_sel_['paper_UT'].drop_duplicates().tolist()\n",
    "    df_pap_sel_ = df_pap_[df_pap_.index.isin(list_pap_ut_)]\n",
    "\n",
    "    ## in case we want to get rid again of the 'unique_occurrences'-column\n",
    "    # df_ref_sel_.drop('occurrences_unique', axis=1, inplace=True)\n",
    "    return df_pap_sel_,df_ref_sel_,df_pap_ref_sel_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load up current dataframe\n",
    "\n",
    "ref_df_min = pickle.load(open('../data/ref_dataframe_min.pkl', 'rb'))\n",
    "plos_df = pickle.load(open('../data/plos_paper_dataframe.pkl', 'rb'))\n",
    "cite_df = pickle.load(open('../data/citation_dataframe.pkl', 'rb'))\n",
    "#ref_df = pd.concat([ref_df_p1, ref_df_p2])\n",
    "#suppl_dict= pickle.load(open('../suppl_dict.txt', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ref_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-9ac5b63379ae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mjudgement\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msection_regex_parser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mref_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mjudgement_2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0msect_index_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'intro'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'methods'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'results'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'disc'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'res_disc'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'concl'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mixed'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'na'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mjudgement\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mjudgement_2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msect_index_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ref_df' is not defined"
     ]
    }
   ],
   "source": [
    "judgement = section_regex_parser(ref_df)\n",
    "judgement_2 = []\n",
    "sect_index_dict = {'intro': 0, 'methods': 1, 'results': 2, 'disc': 3, 'res_disc':4, 'concl':5, 'mixed':6, 'na':7}\n",
    "for i in judgement:\n",
    "    judgement_2.append(sect_index_dict[i])\n",
    "ref_df['regex_sect_index'] = judgement_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_df_min.loc[:,'paper_UT'] = ref_df_min.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D RO MULTIDISCIPLINARY SCIENCES 124637\n",
      "D CU BIOLOGY 42796\n",
      "D TI PARASITOLOGY 5259\n",
      "D YU TROPICAL MEDICINE 3546\n",
      "D KM GENETICS & HEREDITY 2300\n",
      "D QU MICROBIOLOGY 1713\n",
      "D ZE VIROLOGY 1713\n",
      "D CO BIOCHEMICAL RESEARCH METHODS 1410\n",
      "D MC MATHEMATICAL & COMPUTATIONAL BIOLOGY 1410\n",
      "D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY 1022\n"
     ]
    }
   ],
   "source": [
    "# ## dont need for now\n",
    "\n",
    "\n",
    "# from collections import Counter\n",
    "# ## for the plos-papers\n",
    "# x = plos_df['plos_field'].values\n",
    "# list_fields_pap = []\n",
    "# for h in x:\n",
    "#     if isinstance(h, (list, tuple)):\n",
    "#         list_fields_pap += h\n",
    "#     else:\n",
    "#         list_fields_pap += [h]\n",
    "        \n",
    "# c_fields_pap = Counter(list_fields_pap)\n",
    "# for field_,n_field_ in c_fields_pap.most_common(10):\n",
    "#     print(field_,n_field_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## filtering plos-articles\n",
    "pap_pub_year_min = 2000\n",
    "pap_pub_year_max = 2010\n",
    "pap_art_type = ['@ Article','L Letter','K Article','N Note']\n",
    "\n",
    "list_pap_field = None ## if you want to keep all fields\n",
    "# list_pap_field = ['D CU BIOLOGY']\n",
    "\n",
    "## filtering reference-article\n",
    "n_thresh = 10\n",
    "n_max = 100000\n",
    "ref_pub_year_min = 1900\n",
    "ref_pub_year_max = 2000\n",
    "list_ref_art_type = ['@ Article','L Letter','K Article','N Note']\n",
    "list_ref_field = None ## if you want to keep all fields\n",
    "# list_ref_field = ['D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pap, df_ref, df_pap_ref = filter_df_plos(plos_df, cite_df, ref_df,\\\n",
    "                                            pap_pub_year_min, pap_pub_year_max,\\\n",
    "                                            pap_art_type,\\\n",
    "                                           list_pap_field_ = list_pap_field\\\n",
    "                                           ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "df_pap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pap, df_ref, df_pap_ref = filter_df_ref(df_pap, df_ref, df_pap_ref,\\\n",
    "                                           n_thresh, n_max,\\\n",
    "                                           ref_pub_year_min, ref_pub_year_max,\\\n",
    "                                           ref_art_type,\\\n",
    "                                          list_ref_field_ = list_ref_field\\\n",
    "                                          )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(df_pap_ref))\n",
    "print(len(df_pap_ref['reference_UT'].unique()))\n",
    "print(np.mean(df_pap_ref['cite_count']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
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     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_pap_ref['reference_UT'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = ref_df_min.join(cite_df, on='reference_UT')\n",
    "ref_df = result.join(plos_df, on='paper_UT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
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      "326394\n"
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    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1ce054d30>"
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     "execution_count": 38,
     "metadata": {},
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t2uuJk/zMAyl+V8J3KhUDy22TlpKmpVQqbqRfl9LmLqtdbmZXEfNDQsydtlsZ\nc3s2Rjnb0YmRVW2Cu09097+4+2Huvh4x2vKz5OO9zKyUDi8n/zyd0ozU4SW5/Cn514BjzaylniiR\nGX5qBVPFkyQzHQKTi6Tdu4xyKyE9aek3S0ifTlNswtPmli5/t0IJzWw7mnFONHdflowSuCb19s5Z\nydLbrdg+0xKKrbMh1N2u80KOJOn5ajbM8Xkmn7WBLYrUpZLrZiJ1J7rfKOEJbumnrrX2Pr1e6nWx\nWLFXkc8zE9WvbWZfL5iycjL7SXczKzYRbbr+Za33pCPwqNRbQ/OlzfHdUo7V5jCeuttgij0NtJgJ\nyb8diLmrCvlGkc+fSr1uar0AMLMdiNtwIU4mD6FunqzVgLtLvKWiUUrYxpXcFplJxY2YT6YxKhb/\nkpGO7yX5bJPc3lVIo4/DSkqe2PZ/qbdKPqazpCd9bpHO/kRm3Rn1O4MaMLOdiZEWTuv/5jRW+rcq\n7zxMyUMUmjJ3asnM7Ezid8GJJ0MeV+JXKxUDJwKZOaR2TT+9MI/dm1BWpZV8LpBcZMkcn/Op/7TJ\niUDm4mix3yYo/vuULvdy4tY8iN+V3Sp1i3wBxdrJ61E30vGdlnogQ2O4+yPE6K+MvDHWzHpSN09Y\nk56kKY2jDi9pwN3/DTye/LcL0Zgu+fYCMzvVzHZsRNGZoavFhuSmh4L2ypcoqfPPaDjZZ75yK3EL\n5HPEFQkD9k1uM8hXvwOIqzoOPN1M98uXYyR1jYtjzGytAml/0QL1gfpXl7NHPaWHOrf2UHYDjihy\nMnR66nWuWwLeSL0u1Cg4heLzdlRs3SSTlo4klrEbcEy+tEnn+KGpOjzRlLIroNRYMRj4NoVjxR2p\n16Vc5a6E9H6Sd9RVMtdisf2rmGmp1425DaWp3y9L0hExmtgvh5pZwZPiIh5OvT41X6KkU+mkfJ8n\n7qEujp5pZqs1oV6Z37E7iXWamT/nM3e/B7idWP6BxAS7zW1a6nXtNq7wtkgfZ2c3cv1V+rchczx1\nJNoUOSXbKrN/ODHHS2ualnrd2GPy+dTrYk9ArKR0DCvW3jgz9TrfU4HbupJ+q4iRns3WuZ1h8aTV\ni4n9+H1ikvpSJzCvSAxMynuciCvrEQ9dyVffb9F2ntAI0WbKTIh+dDICO5+TqbsY+lD6aZTJbcj/\nSPLZ0My+ny8TM/sGJT6h0cx+R7QZnBiNtpu7N2rC+zIYsHeREcmnUte+bUw7pqVNS70uFGPTsfP5\nvKmk2aiYGMY3AAAgAElEQVTDS/I5mri1wIh5PJ5JrjLnZWbbmdk/gCspPvluLlOTf9c1s7y3RSS3\n+vwvqdu2luMR6GbWmWj4lHJ7xdTU68F5U5UguU3tyuS/HYH7ck0CnYzW+GvqrZY4WSko6XC7jbqO\njTtz3d5jZj8hng5VrCMxLzNb38wuM7MeBdKsTuyHGROzklRsu1WAE/ON3J2r09TMfkw8TAGiQ/Tu\nHHmMTr3+Va7beZIh07+h+Lqv9Lq5nDjZztxCtVOOuq1DHHOZCcCHt4GrcxNSr3+UzF1Vj5n1JhpW\nRuHRIPcRV+YyJ/W35WvIm9lKJQxvL8UtwKykzMOSYy+7rA7An6m76v9fdx+bleYcMyt29fuU1Ova\nY61Cx2pz+Q1x9duAe8ys4Cg9M9vMzP6Q4+TjYeKYyTTIG3QuJut5OHUXKXJy9w+IkVCWpH0suWqd\nr05mZt+0eLhCLn8hblNx4A9Z2/YUYjSIEXNvlTKqOFcdKrGNK7It3P156k5yewMPJbElXz65RoFV\nOv5dQ0wybMAvk4tV2fXoRHRMbkhsq/vdvdio0kYzs8uTkdaFpJ+W2ahj0t2nEaNjDdi+MXk00kji\nARtGzEF2Wa4RPmb2a+JiBcStQne1XBUrKj3q+8I8y3oCMWqv0W2vUpjZNtR1PC8A9ivwBN8GKhwD\n/5hJBvzJYh6l7O/3Bm6gmddLOZL2dGa6lbWBEZbj4RHJ7/KFyX+XUbe8aZlzCgOutRxTBiRtm1so\nYR2Y2W+IjtPMwxF2d/eiT4KuACfOi+7N1QGYxPLMhbuF1D9HanFm9jczy9uJanG3wwmptwrF2PT5\nc2tfCG6XNGm95OTus81sN+LJLFsQVw3+bWbPA08SvdqZyWV7EbcObp35eiOL/RewX/L6QTMbTlx5\nyNyeMMndM/ezXwNcTfwA/N3M7iRuOZpPPM72WOJxtrcRjfRCdfoXdcN6bzKzK4lbGGqS994ts+H6\nR+JWjJ2JJya9bmY3EZP/d0zeP5p4NLUD17n76Dx5tbQziW25MTEB42tJ3d8lHhd9IHF7wRRiTqNB\njSynE3HV9nSLx94/TQzjnkvMQ9SHuJq3EbGOxrv7s1l5TCI6A75CjK76FPgPdU8/+dLdxzeyfo3x\nEHFP/2tmdiPRKbt28l7m5G85cEKuCSvd/b9m9jQxT09v4CUz+yuxrrsSj6zeD3ibaAwUOpF7mjj5\nXJk4QYPorMncmjjH3XPdVpmTuz9vZpcSE8CvCTxlZncDY4j13Z94AuB6xPaaSN0tWK3G3T8ysweA\nA4j9d6KZ/Y1YFx2IY/FIYn/MxIp8eS1Prq4+R2yPI4DdzeweYnkXEh3Fg4nj/0uaOM+du39hZscC\njxBXPa9OTrbvJxqqPZL6Z67qzqP+rYkZuwHnm9kM4mrxK0THawfiBH0/Yr+D2EeuSH23Esdqs3D3\nl83sJOIJU12BUWb2LDCK6PRYmry/JXG7wbZJHa/IymeZmR1PNERXAi5NOizvo249H0Xs5yOIp3s6\n+W+H/xUx6mp3YBgwxcz+Dvw7yW8V4vdpIHG7y/rE3JkXpTMxs0OJ/cyJ34/fZNX7CzM7jPjtWwm4\nxcwGuHspj2NPa/I2rtS2SBxNXAXfLFk/k5Pj7AViH1+H2Oe/Q2yb7BGvFf1tcPf3zOw0osNzZeB+\nM3uY6JibS8Tr46g73j+gfgdycziI2F5TiX3nVWKZOxHz8H2fuk7wT4nt0lgPESeim5jZpkknWLNy\nd7eYnP054rbdXwC7JW29D4jfmoOpu41oCXBUGaOQ2pqbiU6IzsTv1Utmdjt1y3ogcUvbDGL/rsjt\n0nncQt3k4iOAzcxssyLfedrd0w84qEgMdPdnzewvROdtV+A/ZnYrEfOWEx0JxyX1zbTB8jKzo4l1\nDTDO3QveYtdEZxLL35u41fCNpD39BlHfPYjb0zMP4DrX3SdlZ+LuY8zsFmJ0fXfgheT/zxHrYDvi\nnKcL0TbIjAJr8PuU/M5dQN050bVAHys+D2T29m2szDZ63cyuJ/bl1Yk28veJczoHftnMc4mV4nhi\nvsrXiCejvkY8xKMzEet/QN3ca29TeHTpsOTfabm2sbQAd9ef/vL+ESe31xINxZrkb3mOv8xnHwIn\nAh2y8tk1le7cPGV1JhrZ+co4Kiv9bQXqVEOM2uiUem9MnnI7EHOQ5Mvr3Kz0U5P3pxRYb6sTowYK\n1W8ZcGWBPHqm0t5UZDuVnLaEbd6buKKbq+41RGfn1sDYzHt58jk69Z3sbdcjlX+xfepJYJ08ZRxf\nII8pWWlvTuXbo8R1UWzfSee5OfGDl297fwkcW6S8TYkOrnzr/nWig7nguk/yuqjAuhnTmHVDPB11\nSYFlrCE6kHNur1KPn8akLZBHV6KDJ9+6WEo0zovGqCS/XkXyy+TxvxzfPS/1+S5lLMN3iIZWofU+\nBRiQ5/tjitQ3k89M4mpvxY/VEpezUdsb2JeY3LeUOs4EuubJ5xBiREO+9TyG6GzJ/P/BAnXqSMyH\nme94ya7XTVnf35ToTFlOXMjpXaCsX6fye6QR671i27iC26J7CfttDbA0z/fL+W0o6bgkruYX2j9q\niIc/bFIgj6Kxu5S05P+dyK7PZPLEhTL2jwGpPM+u1DFcSlqiQ+ODAstaQ3T07V4gj7Ljbir/nL/9\nqXR52znlbnviwkOh/es94gEjBX+vKa29XWjfmpqj/GJ/DdYrTYyBqXw6EJ1whX7Df17KtshKU3Db\nVmIfITrdny2y/y4CzihSVkfg3iLr4GfEwyky+e6fI5+bc3y3Udu3jPVU7/gjBisUOi86p0Bepezb\nFYlBWftmof32JQq3m7dMpf9tU/Y5/TX+T7c0SkHuPs/df0Kc5J0GPEY0oD4nAuynxMF+HbA/0dD7\nm7svz5VdkbIWADsSJ+ovEo39GvJcSXf3o4irzmOJJ2UsJuYaeBQ42N0P9Lj/3VN/ucpdTlxpOYu4\n+jSHCLp5v1PkM9x9obvvT4yWuovoJPqSmFvkbWJ9bevup+XLI1VO+t9Kpc2fSQxt7g+cTZzYzyeu\nqk8irgwN9rorFAXXQ77P3X060UF0CrF+XiW29zKiwfcOcdvfd9z9m+7+WXYeST7XE+v4IWLbL6Lw\n9i5W33zpS1nGJe5+EDEi45/ECJrFRCP1RmCQu99csLC4cj6ImDvjDWLU0OfEMXYWsJ3XjTYstg+e\nTcynNZq4KlzsWCi6btz9t8TJzzVE59s8Yp1PJzr7vufuu+fbXuWU1ci0Db8cVyV3JDq1Xib2rwXE\nqMUbgZ3c/ZJUWcXym+zu2xBX9+4ntu9CYv1+SIygOpO6EVNNXh53f5SIwecSI1U+JU4iZhKdAv8H\nbOnu+SZD/Q4xauBPxOidGcn3Fyev/0mM4Ojt7v/KKrsix2o5i0v562ckMRrox8RouOnUbZNZxJXw\na4j1sJHnuVLt7vcSsS8zsnIRMRrhaaKzY3fqz5+T94q3x0TvpxKN3d8T220W8bu5IMl/JNFZNcDd\nayeETm6fvJ24Yu/EU9sK3XJyCTFRtBNzR55cIG2uulZsG1dwW3ziMfpiH2JdTCZ+PzP7/TiiAz7n\nPJnN8dvg7tcRI94vJWJJpt3xETHa6xji9/H9QvmUUlYJaYcAhxP76vPEfrqEWM73k/qcCPQrEBdK\nq0B8/zmSuSqbUOey03rc4tqb6NB4itiHlhAx8Fkirm+eHbeaWKd0+lLbXk1eXo9JsAcTnTvTqVvO\nCcA5wDbu/mKJZTa13l7GX672fpNiYFY+y939GCJmjEzyWET89t4J7OzumZGixdZLesqJkm/TzKPo\nPuLus9x9Z+JiyoPUxaLPiTb1lcBW7n55wYJiXR5CjPQbTf11cDvRjrmKeKhXRr7fp3K2bd7tW6ba\n9eTuPyUujDxCtJkybad7iG15Yb5MUnmVXF4T0m5IdCDeQpyTZs4NFxIdZQ8S56BDkt/QfDIxs4a4\n9VZagbk36dxYREREpF0ws28TDXUHfu7ufyryFZEVnpl9F3iA2O93SjqiRFYoZnYXcbFqOdFR0VLz\nTbYIM7ufuLjlwLruPrfIV6QZJRev3iXuwrnH3Q9v5Sq1WxrhJSIiIlKa9IMDxrZaLURakLs/RIz4\nhhhVJbIi2o3oDLq3Cju7NqXuadMT1dnVJhxGTE+wnLhDRlqJOrxERESk3TOzXQp8Zmb2e+omi36+\nqbeKiaxgziJua/yOmTX2gTUircLi6ehfIZknqpWrUxYz+6qZbVTg842IW+xWSd5q1SccSu3orsxT\n1W9297dbuUrtmp7SKCIiIgJjzGwKMUfKJGLOjlWBvsQTpDJPZFpMzFEl0m64+z+Sp1PuR8yftn/r\n1kikLLsTnQ83uPuU1q5Mmb4G3Gxm44k5JScT8wKvS8xRejAxP5kTcxFf30r1lDqHEm2GucQ8ddKK\nNIeXiIiItHtmtpQY+W45Ps40lmYTD0UZ11L1EhGR9svMDieeTA+Ff5/GAgfpdkaR+tThJSIiIu2e\nme1OjF7ZDlifuHq+CjHS6w1gFHCdu3/RapUUEZF2xcy6EKO4vkmMOO4GdKXuqbXPE5Oij2y1Soq0\nYerwEhERERERERGRqqJJ60VEREREREREpKqow0tERERERERERKqKOrxERERERERERKSqqMNLRERE\nRERERESqijq8RERERERERESkqqjDS0REREREREREqoo6vEREREREREREpKqow0tERERERERERKqK\nOrxERERERERERKSqqMNLRERERERERESqijq8RERERERERESkqqjDS0REREREREREqoo6vERERERE\nREREpKqow0tERERERERERKqKOrxERERERERERKSqqMNLRERERERERESqijq8RERERERERESkqqjD\nS0REREREREREqoo6vEREREREREREpKqow0tERERERERERKqKOrxERERERERERKSqqMNLRERERERE\nRESqijq8RERERERERESkqqjDS0REREREREREqoo6vEREREREREREpKqow0tERERERERERKqKOrxE\nRERERERERKSqqMNLRERERERERESqijq8RERERERERESkqqjDS0REREREREREqoo6vERERERERERE\npKqow0tERERERERERKqKOrxERERERERERKSqqMNLRERERERERESqijq8RERERERERESkqqjDS0RE\nREREREREqoo6vEREREREREREpKqow0tERERERERERKqKOrxERERERERERKSqqMNLRERERERERESq\nijq8RERERERERESkqqjDS0REREREREREqoo6vEREREREREREpKqow0tERERERERERKqKOrxERERE\nRERERKSqqMNLRERERERERESqijq8RERERERERESkqqjDS0REREREREREqoo6vEREREREREREpKqo\nw0tERERERERERKqKOrxERERERERERKSqqMNLRERERERERESqijq8RERERERERESkqqjDS0RERERE\nREREqoo6vEREREREREREpKqow0tERERERERERKqKOrxERERERERERKSqqMNLRERERERERESqijq8\nRERERERERESkqqjDS0REREREREREqoo6vEREREREREREpKqow0tERERERERERKqKOrxERERERERE\nRKSqqMNLRERERERERESqijq8RERERERERESkqqjDS0REREREREREqoo6vEREREREREREpKqow0tE\nRERERERERKqKOrxERERERERERKSqqMNLRERERERERESqijq8RIows9fMbJcK5vcrM7uuUvmJSPug\nWCQibYFikYi0RWY21MzerHCeFY130vLU4SUrDDM7xcxeMLNFZnZTpdPn4+793X18kud5ZnZbGXXe\n1czez8rvEnc/obH1EZHWY2armNkNZjbNzD43s5fMbO8i37ndzGaY2Vwze8vMftiYshWLRCQXM+tt\nZl8WiwlmNt/M5iV/881smZn9qdzyFItExMx6mtlIM5tjZh+Z2TVmlrdvodB5WZLX8lRsmmdmZ5db\nJ3d/xt37pvKdama7lbFMN5vZBVl51sY7WTF1bO0KiJThQ+BCYC9gtWZI3xwM8FYqW0QqryMwHfi6\nu79vZvsCI8ysv7tPz/OdS4Dj3X2RmW0BPGVmL7n7yy1VaRSLRKrZtcB/iyVy9y6Z12bWGZgBjGjG\neuWiWCRSHf4CzALWA9YB/gmcTMSjXIqdlzmwlrsrPkhFaYSXrDDc/SF3fwSY0xzpzex4M3sjuarw\nmpltk7w/1cx2M7O9gF8DhyRXH15OPj8m9b13zeyE5P3VgceBDVNXK9ZProbenip3v6S8OWY2xsy2\nTH021cxON7OJZvaZmd1tZqskn61rZo8m7882s6dKWU4RaTx3X+juF7j7+8n/RwJTgSEFvvOGuy9K\n/ps52euVL71ikYiUysx+AHwG/KvMrx4EzHL3ZwvkrVgkIvlsCtzr7kvdfRYwGtgqX+ISzsuMEvsm\nzGwdM7vJzD5MjvUHkvdrR5BajDztATyaxJpfJO+PsBh1/5mZjTOzvsn7xwOHA79M0j+cvF87Ssxi\nlP9VSbkfmNmVZrZyumwz+7mZzUzSHJOq8z5m9nqS9/tm9vNSllWaTh1eIoCZfR84FzjC3dcE9gNm\np9O4+xPAxURw7+Lug5KPZgL7JN87FrjSzLZx94XAt4CPkvRruvvHmeyScrcA7gJ+CnQHRhGBOT36\n8vvAnsBmwEDgmOT904H3gXWBrxCNThFpQWa2HtAbeL1Iuj+b2QLgTeAj4qQvVzrFIhEpiZmtCZwP\n/Jw4WSzHUUDeWxEVi0SkiKuAH5jZama2EXFsj2pCfg5MM7PpSWfWugXS3kGMEutLHOtXZuWDux9F\njMj/dhJr/pB8/jhx0fErwEtEvMHdrwfuBC5L0u+fo9zfANsDA4jYs33yXsb6QBdgQ+BHwJ/NbK3k\nsxuI0f5rAv2BMUXWh1SIOrxEwg+JAPcSgLtPyYzgKMbdR7n7tOT108A/gK+XWO7BwGPuPsbda4A/\nEAF8p1SaP7n7THefCzwKbJO8vxTYANjM3WsKXaUVkcpLTsDuAG5x93cKpXX3U4A1gKHAA8DiPEkV\ni0SkVBcA17v7R+V8ycx6ArsAtxZIplgkIoU8TXTczCM6ll5IRnA1xqfAdkBPYsR8F6LzqQEzW5+4\nLfJEd5+XHOtPF8i73sUAd78lGa2/lIihA82sS+6vNnAYcL67z3b32cQFhyNTny8BLkzqNAr4AuiT\n+mwrM+vi7p+7+ysllilNpA4vqQpm9nhqePyhjchiE2ByI8v+lpn9OxlS+xlxhaNbiV/fEHgv85/k\nvvX3gY1SaWamXi8kTpoBLk/q/I/kloEzG1N/ESmfmRnR2bUY+L/U+3ljkYfniHhzUp6sFYtEpKjk\n9sI9iFEWuT4v1C46EnjG3d/L9d2EYpGI5JS0gUYD9wOrE8d3VzP7ffJ5Wedl7r7A3V9y9+Xu/gnw\nE2BPi7kGs20CzHH3eY2odwcz+30SH+YSU1I45cWn9Hyt7yXvZcx29+Wp/6fj04HAvsB7ZjbWzHYs\nt/7SOJq0XqqCu+/TxCzep8CcOumi0v9J5o24HzgCeNjdl5vZg9RdTSg28eJHxNWRtE2AD4pWxP0L\n4BfAL8ysHzDWzP7r7mOLL4aINNGNRANpn2QUAlByLOpI/nijWCQipdiVGA0xPTn5XANYycz6ufu2\nRWLRkcStiIUoFolIPl2J4/LPyUipz8zsZmJS+rMqcF4GEStyDc55n+hcW7OETq/seHMY8B1gN3ef\nntxu+BnlxaeexPQUJK9LGmHr7i8C3zWzlYgLpSOIOcakmWmEl6wwzGwlM1sVWAnoaGadkqBRifQ3\nEA2kwcl3e5nZJjnSzQQ2TRqXAKskf58mjbpvEfNKpNOvm8yzkcsIYF8zG2ZmHS0mVFwE/DvfcqWW\nb18zyzRG5wPLgOUFviIiFWBmfwW2BPZz9yVF0nY3s0PMrHNyZXEv4AfE04xyUSwSkVL8jeiQ2oaY\nS+avwGPUP+4bMLOdiBEJ9xfJX7FIRHJKbuebCvw4Od9aGzgamJjvO4XOy8xsezPbwsK6wJ+Ase4+\nP0fZHxNzhf3FzNZO4kS+W6Y/Br6a+n8XYmT+Z8nosUuo38k1Myt9truB35hZNzPrBpwD3F4gfWbZ\nVzazw5JOuhoiPtUU+55Uhjq8ZEXyG2Jo6JnEUzQWAmdXIr273w9cBNxlZvOAB4mrF1A/EN5HXAWY\nbWYTkquJpwL3mdkc4kT24VS+bxPBcYrF04bWzyr3HeIq6LXAJ8RQ1++4+7IcZWfrDfzTzOYDzxJX\nWfREIpFmZGY9gBOIk8yZJQzZd+L2xfeJJxNdBpzq8XTHhokVi0SkBO6+yN1nZf6IuWIWuXuxJ1Mf\nBfzd3RcUyV+xSEQKOQDYhzhO3yHmqCr05MFC52VfJW6RnAe8SnRyH1YgryOJDu23iE6qU/Ok+z1w\nThJrfk7MWzgd+BB4DXguK/2NxDxbcyx58iP1Y87vgAlJHScmry8qUM/0d48Epia3Up5QZPmkgixu\njZcMM7sR+DYw090H5ElzNTEfwQLgGE06JyKVplgkIm2BYpGItAWKRSLSGBrh1dDNxJMfckqGZvdy\n997AicQQchGRSlMsEpG2QLFIRNoCxSIRKZs6vLK4+zPE5HX57A/clqR9HljLzNZribqJSPuhWCQi\nbYFikYi0BYpFItIY6vAq30bEXCwZH1L/UckiIi1BsUhE2gLFIhFpCxSLRKQBdXiJiIiIiIiIiEhV\n6djaFVgBfQikH8u8cfJePWampwFUgXXWWYcuXbq0djWkwubPn89nnxUaFV+fu1vxVC2upFgEikcr\nOsWh6qVYJG2V4k77olgkbZViUfvSLLHI3fWX9QdsCkzK89k+wMjk9Y7Af/Kk89Zy3nnnqdwKOeGE\nEwp+/sILLzRb2Sq3+coutl3TkmN5hY1F3orxqBpjQmuUXcr+2t5igmKRYlE5FIvKV86+maaYsGKW\nq1jUMhSLyqdY1PbLbuuxSCO8spjZXcA3gHXNbDpwHrAKsUKvc/fHzWwfM3uXeOTtsa1X29ymTZum\nclvI/PnzVW47KLs1KBateOW2ZtntLSYoFrUcxaIVr9zWLFsxofrLbS2KRSteua1Zdns8LtvjMpdC\nHV5Z3P2wEtL8pCXqIiLtl2KRiLQFikUi0hYoFolIY2jS+ip0zDHHqNwW0qdPH5XbDsqWxmmPMaG1\nym5vMUGxSMqhWNRyFBOqv1xpPMWiltMej8v2uMylUIdXFfrGN76hclvIhhtuqHLbQdnSOO0xJrRW\n2e0tJigWSTkUi1qOYkL1lyuNp1jUctrjcdkel7kU6vCqQuPGjVO5LeSjjz5Sue2gbGmc9hgTWqvs\n9hYTFIukHIpFLUcxofrLlcZTLGo57fG4bI/LXAp1eImIiIiIiIiISFVRh1cVam/DZXVLY/WX29pl\nS+O0x5igofvVXW5rly2No1jUchQTqr9caTzFopbTHo/L9rjMpVCHl4iIiIiIiIiIVBV1eFWh9nZ/\n+Io8h9c111zDY4891uLl5rJw4UK+9a1vsd1223H77bc3utzJkyczbNgw1lprLX71q1/Vvv/MM88w\naNAgBg8ezKBBg1hnnXX44Q9/WPv5X//6VwYNGkT//v358Y9/XPv+HXfcwVZbbUWfPn34+9//Xvv+\ntGnTGDZsGP379+foo49uzCJLM2uPMWFFnauiGmMRwPjx4xk6dChbb701gwcPBuCLL77gwAMPZMCA\nAfTp04cLLrigNv1uu+3G4MGDGTx4ML169WLttddm4cKFXHXVVbXxq3///qy88sq1dTvmmGPYeuut\n6du3b73YJW2HYlHLKfXYbGzMaWq5uZQSc0opO1/7B+D6669n0KBBDBgwgMMOO4xly5YBsGjRIo49\n9lj69u3LrrvuysyZMwH4/PPP2XvvvenWrRuHHnpovbx+9rOf0bt3b/r378/gwYMZPXo0AEcccURt\n/Orbty8rr7wy06ZNK3t5pPkoFrWc9hyLnnrqKYYMGcKgQYPo27cvjzzySG2afLEoY/z48XTq1IkH\nHngAgJqaGk499VS22WYb+vbty2WXXVabNtP+6d27d732z4wZM9hvv/0YMmQIAwYM4Iknnih7WSrK\n3fXXDH+xalvH2LFjVW6FnHDCCQU///DDD5ut7JYu98Ybb/SzzjqryeXOmjXL3333XT/66KP9rrvu\nyplmzpw5vsEGG/i0adPc3f2GG27w/fbbz5csWeLu7p988om7uz/33HO+7bbb+hdffOEvvfSSb7zx\nxu7uvnz5ch84cKA/9dRT7u7+7W9/20eNGlXagnrx7ZqWHMutHlOa8tda8agaY0JrlF3K/qpY1NCk\nSZN866239g8++MDd6+LKrFmzfMKECe7uvmDBAl9//fX9f//7X4PvH3300X7JJZc0eP++++7zfv36\neU1NjS9atMjHjBnj7hGXttlmG3/yySeL1i1DsahlKBaVr5x9M21FjEWlxJxSys7X/nnzzTe9T58+\nvnDhQnd3P+igg/yee+5xd/eTTz7ZL7zwQnd3/8Mf/uC//OUv3d193rx5/vrrr/s555zjF198cb3y\n9thjD3/rrbcKLvN5553nJ554YsnLoFjUMhSLyqdYVH7Z8+bNq33vn//8p++www7uXjgWubt/8MEH\nfsABB3ivXr188uTJ7u4+fPhwP+KII9zdfdGiRb7xxhv7jBkz6rV/Pvjgg3rtn0MOOcSvu+46d3d/\n7bXXvE+fPiUvQ3PEIo3wqkLt7f7wtjKH10477cR7770HRA/7tttuC8Cjjz7KAQccwMEHH0zv3r05\n5ZRTar/To0cPli9fDsBFF11E//792W677Tj88MO57bbbAOjZs2dt7/tzzz3HQQcdxIYbbsiXX37J\nKaecwk477UT//v3r9d5nvPHGGwwePJjvf//79OvXj0WLFjFixAiGDh3KwIED+dGPfgTAXXfdxS9+\n8QeB/tYAACAASURBVAseeOABDj/8cCZMmMDXv/51Bg0axFZbbVV7hbCUe7S7d+9Or169mDRpEltv\nvXXONBdeeCGHHXYYPXv2pKamhnPPPZfhw4ez8sorA9CtWzcArrzySi6++GI6d+7MoEGD6NixI599\n9hmjRo2iX79+7LLLLgAMGTKEN998s2jdpGW1x5jQFuaqUCwKv/3tbzn77LPZaKONgLq40r17d4YM\nGQJAp06dMDOWLl1a77uvvPIK48aN47TTTmuQ76WXXspll11Ghw4d6NSpE8OGDQPAzFhllVUa5CWt\nT7GoeaVjDtCsMQfIGXOyY0KlY04hmbLztX+WLFnC6quvTocOHfj888+ZOnUqG2+8MXPmzOHJJ5/k\n17/+NRBtmbfeeguALl260K9fP15//fUGbam33nqLzTbbLG8cnDFjBn/96185//zzi9ZdWpZiUfNS\nLIqyu3TpAsTApmeffZY99tgDyB+LABYvXszJJ5/MZZddxpw5c/jqV78KwNKlS2vzmzJlCgBrrbVW\nvfbPRhttVK/9s2zZMtZcc00AXnzxRTbZZJOidW9WpfSK6W/FuXIglVVqL/Py5ct9o402qv3/qFGj\n/LjjjnN394suush33nlnX7BggS9dutTXW289d3f/+OOPffDgwe4eIwYOP/xwd3efMWOGd+rUyV95\n5RWfNWuWDxw4sDbf4cOH+wUXXODu7scff7w//vjj7u4+e/Zs79evX4N63XPPPb7xxhvX9viPGzfO\njznmGF+6dKm7Rw/8+PHj3d196NChPmXKFHd332GHHfy9995zd/eFCxf6smXLfM6cOb7rrruWtD5q\namp8rbXWqi0nbcqUKb7hhhv63Llza+vUt29fP+6443ybbbaprd+CBQu8W7duXlNTU/vdbt26+Rdf\nfOGHH364jxgxovb9008/3a+99tqS6uauK5myYilnf1UsCosXL/bOnTv7r371K99uu+38m9/8Zm0+\naWeffbbvvffeDd7fc889/bbbbmvw/p133unDhg3LWeb111/vAwYMyBn38lEskrZK7Z/KtX8WLlzo\ne+21l3/lK1/xNddc0//2t7+5e8SMk08+uTbdo48+6gcddFC9/Hr37l07Gt7dfe7cud6pUycfMmSI\nb7/99v7Pf/6zQR1OOOGE2nVVKsUiaasUixoXi/bZZx/v2rWrH3jggbXv5YtF7u4nnniiP/300/78\n88/70KFDa9+fOnWq9+zZ0zfaaCPv2bOnP/PMMw3Kym7/jBw50rt06eLrrbeeDx06tKxRbxrhJSVp\nb/eHt4U5vCZPnsxmm21W+/6rr75ae0Vu0qRJnHnmmay++up07NiRVVZZpfb9AQMGAHDBBRdw6aWX\nArD++uvTvXt3+vXrx8SJE+td2cvk++qrrzJixAjOPvtsBg0axO67786qq67aoH6TJk3i9NNPr+3x\nv/zyy3nhhRfYbrvtGDRoEC+++GLmh59p06bVLsPGG2/MT37yEx5//HE6derESiutxDrrrMNdd91V\n0np599136dGjBx07dmzw2dlnn80ZZ5zBWmutVbtMs2fP5swzz+TFF19k+vTpjBgxgnfeeYfNN9+c\nDh0iTL388susscYadO7cmddee6123WWWs3///iXVTVpOe4wJrT1XhWJRePvtt/nyyy/Zdttt+e9/\n/8uOO+7I7373u3ppzjvvPMaPH899991X7/0nnniCTz75hCOPPLLe+0uWLOGcc87hrLPOalDeDTfc\nwNVXX80TTzyRM+5J61Isaj7ZMeeZZ55p1pgza9asnDEne96cSsecQuszXXau9s+BBx7I8ccfz8yZ\nM7n22mt55ZVXAIq2ZRYtWsSnn35Kz549a9/r1KkTM2fOZMKECZx22mkcccQR9eryxhtvMHr0aH7x\ni1/kra+0HsWi5qNYVD8WjRw5kvfee48333yT//znP0DDWPTyyy8DMHz4cPr06cPQoUN55ZVXGDhw\nIABz585lv/324x//+AcffPABe+65Jx9++GG9Mm+44QauuOKK2vbPs88+y+WXX86HH37I5MmTgYhl\nrUmtMpEKyB6+PmHCBE488UQgGjQ77rgjUD8Yv/rqqwwYMICamhpmzJhRe9vNRx99xDrrrMPKK6+c\nM9/TTz+dCRMmsP/++3PrrbcWrNerr75ab0j7q6++yvTp0xukmz59Oj169Kj9//3338/YsWMZPnw4\nt956K/fee2+T1kfGCy+8wAsvvFA7RBhgwYIFHH744WyxxRYAbLnllixevJjFixfTuXPn2nSjR4/m\nsMMOA2LY7RprrAHAnDlzmDp1au3tjSLtmWJRWLBgAQMHDuSAAw4AYOutt653m8Opp57K//73P558\n8kk6depU+767c+aZZ3LFFVc0yPPqq6+uvW0h7fLLL2fEiBGMGzeOrl27llQ/kWqRHRsmTpzIz372\nM6B5Ys6kSZNyxpzsk8y20v7JnPQdeOCBAOy5556ce+65QP22DMBDDz1Ub6Lq119/nb59+9bLf9VV\nV629qDB48GDmz59f7/MzzzyT8847j9VWW62seous6BSLGlpjjTXYbLPNmDVrVsFYdNNNNzF79myu\nvvpq5syZQ6dOnRgyZAirrLIKO+20U+052u67786oUaM4+OCDgbr2z3333cf6668PwC233MKJJ55Y\nextk//79efrpp2tvkWwNGuFVhdrb/eFtYQ6vOXPmsPbaawPw5ptvMnLkSAYMGMDSpUuZN28e3bt3\nB+oCK9RdVVhppZUwMz7++GNqamo47bTTatOk8x07dixvv/02vXr1Yuutt+a5556rbejMmjWLWbNm\nNajfm2++yVZbbVX7/w022ICRI0cCsHz5ct544416dYEIuEuXLmXYsGGcdNJJLF68uMHyFpOvw+uM\nM87g4osvrnfls1+/frVXC6ZPn87o0aPZd9992XzzzZkyZQpLlizho48+4vbbb+ekk06q/U7mqsQv\nf/lLTjrpJMyspLpJy2mPMaG15/BSLAqbb7458+bNY+nSpXz55ZcMHz6cQw45hJqaGo444ghmzJjB\nI488Uq+zC6KhtuGGG7LbbrvVe3/OnDlcccUVXHLJJfXKPuuss3jssccYM2aMOrvaMMWi5pMdc8aM\nGdOsMWeDDTbIGXOyY0KlY04h6bKz2z9dunRh7ty5zJ49G4jREDvssAMAffv2rW3L3Hbbbay33nq1\nJ5aZdZbdlvrkk0+AmCNn+PDhHHvssbWfjR07lvfee6/ee9K2KBY1H8WiiEWZ/CCeuPjqq68ybNiw\ngrHohRdeYMqUKUydOpUhQ4bw8MMPc+yxx9K1a1feeustli9fzpIlS7jjjjtqOw7T7Z/08nXt2pVJ\nkyYB8PHHH/PEE0/UzqXWWjTCS6QC9tprL77zne/w/vvvs8UWW9CtWze6d+/eYHj6xIkTa4NZeij7\n+eefz4477simm25Knz596NWrFxBDT48++mj+/e9/s8kmm9Re6evXrx9HHXUUgwcPpkuXLjlv8Zk/\nfz6dOnWqHbYLcNVVV3HyySdz9tln07FjR8444wz69etXL8g+9thjXH311XTu3JmuXbty3XXXAbD/\n/7N35+FRlff7x+8HkIIgImiQXZRFErKCEZAiVShg3X4VsLVqoVZw+VYRRa1WW6sWqy1FrdRaBeoK\nqK3U5RI3aBXRImFHQARcQiVlFyVK8PP7I5PpJCSQbc45c877dV25ZGZO5p4zOed28mSeZ845R1Om\nTCn3duGKXnvtNY0ZM0a7du1S48aN9fe//13vvvuupNIFI7/66iuNHDmy3PecddZZ+sc//qHs7Gw1\nadJEjzzyiNLS0iRJV155Zfz6+++/P76w4q9+9StdfPHFuv7663XWWWdVurA0EEV0Uamjjz5aEydO\nVF5enho0aKCxY8fqjDPO0BNPPKGnnnoqviitc06TJk3S0KFDVVxcrF//+tfxF6KJ7rzzTv34xz+O\nd5Ck+Nv2u3fvroEDB8o5p5/97Gf8solIoXNKVfX6p0WLFrr77rs1YMAANWnSRN27d9fUqVMlSWPG\njNGIESPUq1cvde3aNf5OkbVr12rw4MHas2ePzExz587VBx98oN27d+u0006Tc04NGjTQ8OHDdfvt\nt8cfww033KB77rmHPwAikuiiUn/4wx/0xhtv6KijjlJaWppeeuml+LutquqiRGvWrFGPHj0kScOH\nD9dLL72kjIwMNW7cWGeeeabGjRt30Nc/119/vX70ox8pKytLjRo10qRJk8oNiPmiOgt98ZVaiyFG\n7SNvk5l7qIXzkvGRt5dccom9/PLLnudWh1+59Z3N4qzeCGMn+JFdneOVLkrNbLrIG3RRzdXk2ExU\nl/OjOp2TjNy6CkMP0kXeoItqji4KfnbQu4gpjUDAJL7VFgD8QhcB8BKdAyAI6KJwYUpjCEVtfngQ\n1vCqT//+9799ya0Ov3L9zkbtRLET/F7Dqz7RRcHLRu3QRd6py/lRnc5JRm5dRbEHUTt0kXfoovDn\nVhfv8AIAAAAAAECoMOAVQvPnzyfXIxU/eraiwsJCzZ49+5D3U93t3njjDV100UWHzE2W6uQ+9dRT\n6tOnj7Kzs9W3b1+tX79ekjR79mzl5uYqLy9Pubm5atasWXyxVTPTnXfeqaysLPXs2VN33nmnpNKP\nDv7Od76jI488Uj/72c/K5ezcuVNjxoxRZmamunfvrtdee62e9xZ1FcVO8Cu77NxMVuccKtdribnv\nvPOO+vfvr2bNmunPf/5zue1eeeUV5eTkKCMjQ/fff3/8+ilTpqhz587Ky8tTXl6epk2bJknas2eP\nLrzwQuXl5SkjI0OPPfZY/HuuvPJKdenSJf7R22WWL1+ugQMHKi8vTyNHjqz2pynBO3SRd6LWRYnZ\ns2bNUl5ennJycpSVlaV33nlHUunrlf79+ysvL09du3bVbbfddsB9PPnkk2rYsKEKCgoklX/98/Of\n/7zctqeffrry8vLUpUuX+KdXJ/rXv/6lb33rW/rb3/5W37uKOqKLvBPlLvrPf/6js88+W71791ZW\nVpbmzp0b36aq10VlKnbRa6+9pv79+ys7O1s5OTnxDyOTqn5dVCYoXcSAF5BEr7/+erww6mO7ZcuW\nKTc3tz4eWtJ06NBB//rXv7Rs2TKNHDlSv/zlLyVJo0aN0pIlS1RQUKDZs2erZcuW8U9XvPXWW/Xp\np59q+fLlev/99zVu3DhJUosWLfTwww/r//2//xf/VJQy5557rr773e9qxYoVWr16tfLz873dUSCA\notg57du316xZs5Sfn19uzY1Nmzbpqquu0ssvv6z33ntP99xzj3bt2iVJWrVqlR566CEVFBSooKBA\nP/nJTyRJd9xxhzp37qyCggK9+eabuuqqq+L395Of/ERTp07ViSeeGL+uuLhYF154of70pz+poKBA\nHTt21MMPP+zRngPBFcUuGjZsmAoKCrR06VJdffXVmjJliiTp8MMP1xtvvKGCggItWbJEd999t/bu\n3Rv/vuXLl2vu3Llq3rx5/NPkEl//JPaamem5555TQUGB/vnPf2ru3Llas2ZN/PbCwkLde++96tix\no3JycjzacyC4othF11xzjc466ywtXrxYTz31lK6++mpJ0saNG6t8XSRV3UUvvPCCli1bpltuuUUT\nJkyIb1/Z66IyQeoiBrxCKGrzw4O6hteCBQs0YcIEPfvss8rLy9OmTZu0cOFC9e/fX7m5uRoxYoT2\n7dtX6XYPPPCA+vbtq6ysLJ199tn6+uuvJZWWbHZ29gG5ld2vVDrCP2LECOXl5Sk9PV3vvfeeJGn6\n9Onq06ePsrKyNHr0aEnSX//61/g7swYOHFjj/S3z7W9/W4cffrik0hd5JSUlB2xz44036qabblLz\n5s21detWTZs2TZMnT47ffvTRR0uSjjnmGJ1wwglasWJFucf0wgsvqGnTpvrhD38oSWrUqJFatGhx\nyMcGb0WxE/xcqyKZnVNR2f1+73vfS2rnHGx/y3Ts2FEdO3Y84KPHp06dqgkTJujYY49V06ZNdfzx\nx2vDhg2SpNWrV8c/cjxRSUlJvEtWrlypY445Jn5b7969tX79evXp0yd+3YIFC3T88cfHP3K7c+fO\nWrVqVY32BclHF3knal1Uts+SdOSRR0qS9u3bp3fffVeDBw+WJDVu3FhNmjSRJC1atEh5eXlq2rSp\nJGn79u26+eabdfXVV6tTp05q3LixpPKvfzIzM+NZzjkdccQRkqRdu3bpyCOPVJcuXSRJX3/9ta64\n4grdfffd2r59u44//vga7wuSiy7yTpS7KPG1zOLFi9WxY0dJ0p/+9KcqXxdV1UX5+flq1aqVJKlp\n06blfq+r7HWRFMAuqs5HOfKVOh93i/pV24/CLTNs2DBbvXq1mZkVFRXZiSeeaJ9++qmZmV1++eX2\n8MMPH7Cdmdm2bdvi/x4zZoy99NJLZmaWk5NjW7duLZdR1f2WlJRYdnZ2/Hv37t1re/bssZkzZ9rg\nwYPtyy+/NDOzLVu22Oeff27p6em2b98+MzPbtWuXmZm9+uqrds0119Rq3z/++GPr1KmTvfXWW+Wu\nX7BggfXo0cNKSkrMzGzGjBk2cOBAGzlypGVlZdl1111Xbvv9+/fbkUceGX9sZmajR4+2iy++2E4/\n/XTLysqyJ554okaPjY/fRiqpyfEa1c4pLCy04447rtx17du3t6Kiovjl3r1728qVK83M7KijjrLe\nvXtbTk6OPfXUU/FtFi1aZK1bt7Z27dpZr169bNWqVeXu89JLL7UZM2bEL7/55puWmZlpxcXFVlhY\naCeeeKJNnDix2o+bLkJQ8fqn5l1UUlJiffr0sRYtWtiECRPK3Xbrrbdap06drHv37rZ9+3YzK319\nc95559m6dets1qxZduGFF5b7nspe/5iZPfjgg3bCCSdY27Ztbf369fHrx40bZ2+++aa9++67NmDA\ngBo9droIQUUX1byLXnzxRTviiCOsTZs2NmDAANu8ebOZVf266FBdZGa2c+dO69Wrl82cObPc9RVf\nF5kFr4t4h1cIRW1+eJDX8Fq7dq169OghqXQkf/To0Wrfvr0kKT09XUVFRQdsV1JSosmTJ+ukk05S\nTk6O5syZoyZNmqikpES7d+9W69aty+VWdb/PPfec0tPTNXz4cElSkyZN1KxZM91xxx2aOnVq/K+L\naWlpatiwoYqLi3Xttddq8eLF8b8KDB48uNw7r6o7L/3DDz/UkCFD9Pvf/16nnHJKudsmTpyou+66\nSw0bNpRU+vbZoqIi3XfffXrnnXf0wgsv6O23345vv379enXq1Cn+XJV9z969e/XSSy/pmWee0bhx\n4/TNN99U67HBO1HsBL/XqkhW5yRKvN/NmzcntXMOtb9lVq5cWe5dEDt37tT+/fvj79Dav3+/Nm3a\npG7dusnMtGbNGr333nt6/PHHdemll2rPnj1av369xo0bp5UrV+qjjz5Sly5dtHPnzgNyEteqGDBg\ngAYPHqyTTz5ZEyZMUFpamu9v3ceB6CLvRK2LEve5YcOGWrRokdatW6cnn3xShYWF8W1uu+02bdq0\nSf3794+vNXjTTTfpggsuULdu3Sp910jZ659GjRqVu37cuHFav369zj//fN1zzz2SSt+50aNHDw0Y\nMEBLly6t9B0o8B9d5J2odtGCBQt0zz33qLCwUB9++KEkae/evQd9XXSoLtq6dauGDBmiSy65ROef\nf3652yq+LnrwwQcD10UMeAFJsm3bNrVs2VINGpSeZqtXry73C9mKFSuUnp5+wHaTJk1ScXGxFi5c\nqKVLl6pFixbKyMjQ+++/f8A6Vge736VLl6pv377ltt2/f78KCwvVrVu3ctc3bdpUK1eu1CmnnKKx\nY8fqwQcfrPV+r1ixQkOHDtUf/vAHjRgxotxtzzzzjBo0aKBzzz03ft0XX3yhyy+/vNzbaxMXfa74\ndv6y7/nFL36hxo0bq1u3bmrUqJH2799f68cMhEFUO6fsMSQ+pq+++krNmzePX547d66GDh2qxo0b\nyzmntLQ0SVJGRoaaNm2qL7/8UrNmzdLIkSN17LHHqlGjRjrllFP06quvlstZs2bNAfsyefJkLV26\nVI888og2bNigM844o077AqS6KHeRJLVp00atW7fWtm3byl3vnFNGRoaKiopkZnrsscd03XXXqUuX\nLpoyZYomT55crnMqe/2TqGfPnvFf1qdNm6b7779fXbp00cSJEzV79mxNnz69zvsCpLIodtGMGTM0\nbtw4HXHEEWrWrJl69eqlN998U1999ZWaNWsW367sddFhhx120C765JNPNGjQIF122WUaP378AXkV\nXxc98sgjgesiBrxCKGrzw4O6htemTZvK3d6+ffv42i7vvfeeFi5cqDPPPPOA7VasWKFTTz1VjRo1\n0uTJk1VcXKy0tLRyI+7Vud9jjz223FoyW7duVcOGDdWkSRN9/PHH8euk0r8iNm3aVKNGjdKZZ56p\n4uLiGu+vJL399ts666yzNG3atPhfM8qUlJTo5ptvPuCvFOnp6fG/gi5ZskTr1q0r966wshd8idkZ\nGRnx73nwwQc1ePBgHXbYYQd9bPBeFDvBz7Uqktk5iRLvd/PmzUntnIPtb6KKvximpaVp79692rZt\nm3bv3q1bbrkl/kIt8d2iv/vd75Sfn6+0tDS1atUq/vg///xzPf300zr55JPj227cuFHHHHNMubUo\nyvZn//79Gj9+vK644gq1bNmyRvuC5KOLvBO1Lirb58S8mTNnysyUlZWljRs3xu+zsLBQf/nLXzRi\nxAg551RYWKgNGzZo48aNatOmjRYvXqwhQ4aUez4Se23z5s3xBaZ37Nihv/71rxo1apSk0rXByu6r\nd+/emjNnjsaMGVPjfUFy0UXeiWoXtWrVSitWrJAkffbZZ5o7d65OOukkpaWlqbi4+IDXRQfrorVr\n1+q0007TrbfeGv9wn0SVvS4KZBdVZ94jX8wNj6q6zBvfs2eP5efnW2Zmpi1cuNCKiorstNNOs8zM\nTBs0aJCtW7eu0u3mzZtnXbp0sVNOOcUmTpxoQ4cONTOz6667zmbPnn1AzsHu9+yzz7aMjAzLzc21\n559/3szMnnnmGevZs6fl5OTY9773PTMrXROrR48e1rt3b7v44ovtq6++sqKiIjv99NNrtM9du3a1\ntLQ0y83NtZycHDv33HPjt9133312/vnnH/A9X3zxhZ177rmWk5NjAwcOtOXLl5tZ6Zz1Dh062BFH\nHGGtW7e2/Pz8+PesWbPG+vXrZzk5OTZq1Kj4ehjVxVoVSCXVPV6j2DnTp0+3Dh06WNOmTS0tLc3O\nO++8+G1PPvmknXjiidarV69y6/yddNJJlpGRYdnZ2TZ27FjbvXu3mZkVFxfb+eefbxkZGZaVlWX3\n3ntv/HsyMzOtTZs21qRJE+vYsaMtWLDAzMxuu+0269mzp2VlZdldd91Vo8duRhchuHj9U7MuGjp0\nqHXv3t3y8vJs5MiR9vHHH5uZ2UMPPWQnnHCC5ebmWt++fW3OnDkHfO/evXutZcuW8ctVvf55/vnn\nrVu3bpadnW29e/eOrz1UUdu2bcutP1QddBGCii6qWRdt3brVhg4dapmZmZabm1tuza2qXheVqdhF\ngwcPtpYtW8Z/r+vXr1/8tqpeFyUKShf5Xjhh/fKzSOfNm0duPTnUSVdYWJi0bHKTl80LO2+EsRP8\nyK7O8Rq1TqCLUvOLLkqd7Nr+kkknpGYuXeQNuqjm6KLgZwe9i5jSCAAAAAAAgFBhwCuEojY/PKhr\neJEbnmzUThQ7wc+1KsgNfzZqhy7yDp0Q/lzUHl3knSiel1Hc5+pgwAsAAAAAAAChwoBXCM2fP59c\nj2zevPmA6woLCzV79uxDfm91t3vjjTd00UUXHTLXC9XJvf3229WjRw8ddthh2rt37wG3r1mzRkcd\ndVS5T2u88847lZOTo4yMDF1zzTXx66dMmaLOnTsrLy9PmZmZmjZtmiTptddeU//+/ZWdna2cnBy9\n++679bB3qG9R7AS/sumiqk2aNEkNGjTQ9u3by11fWRfdfPPNysrKUk5Ojl588cX49du2bdM555yj\njIwMDRkyJN5tO3fuVIsWLZSXl6fc3Fyde+65ddwzJANd5J2qzs1k95FfXVTd7Kefflq5ubnKzs7W\nr3/96/j1S5cuVevWrZWXl6e8vDxdccUVkqTZs2crNzc33i3NmjXT7bffXu4+H3jgATVs2FAFBQWS\npD179ujCCy9UXl6eMjIy9Nhjj9XjXqI+0EXeKCws1IMPPlit7Wr7+qcqQeiiqn4XmzVrlvLy8pST\nk6OsrCy98847kg7eN/Pnz1d+fr5ycnJ0+eWXx+8r8XexjIyM+O9ib731Vrn7Ouqoo3TJJZd49RRU\nigEvoJ69/vrr8Rcf9bHdsmXLlJubWx8PrU6++eabam139tln65lnntHxxx+vpk2blrvt888/1w03\n3KCePXsqJydHkvTyyy/rtddeU0FBgVatWqW33347XpqrVq3SQw89pIKCAs2dOzf+kbgtWrTQCy+8\noGXLlumWW27RhAkT6nFPgXCIehdJ0ty5c7Vu3Tp16NBBrVq1il9fWRf9+c9/VmFhoZYvX65XXnml\n3Au7iy++WKNGjdKqVavUsWNH/fWvf5VU2lFnnHGGCgoKtGTJEj333HP1tJdAuES5j1auXKm7775b\n//znP7VkyRK98MILWrlypaTSDrn00ktVUFCggoICTZ06VZI0atQoLVmyRAUFBZo9e7ZatmxZ7g+C\ny5cv1/z589W8eXP16tVLknTHHXeoc+fOKigo0JtvvqmrrroqCXsMBN/rr78eP8cOtV3Y+kaq+nex\nYcOGqaCgQEuXLtXVV1+tKVOmSKq6bz777DNdeeWVeu6557R06VIVFRXF/xiY+LvY+PHj47+LDRgw\nIH5fb7zxhpo2bapbb721np+JmmHAK4SiNj88SGt4LViwQBMmTNCzzz6rvLw8bdq0SQsXLlT/S9iT\ngwAAIABJREFU/v2Vm5urESNGaN++fZVu98ADD6hv377KysrS2Wefra+//lpSaclmZ2cfkNu/f39d\ndNFFys/PV/fu3eOFvW7dOp166qnq3bt3udH7V155Rf369dOgQYPUo0cPXXrppfH7mzdvngYNGqS8\nvDydc845+uqrryRJ+fn5Gj9+vPLz8/XYY4/ptddeU58+fZSdna2BAwdW+pxkZ2dr06ZNyszMPOC2\nyy67TL/5zW+0cePG+D7t27dPzZo1k1T6l4n//ve/OvbYYyVJq1ev1gknnHDAc52fnx//5bVp06Yq\nKSk55M8K3otiJwRlrQq6SPrwww/18MMPa+TIkQc87sq66PHHH9f48eMlSWlpadqzZ4+Kioq0evVq\n7dixQz/60Y8kSQMHDtT7778vqXxHIbjoIu9UtpaLF31UlhvEPpo1a5Z+9KMfqUWLFmrQoIHat2+v\n1atXSyrtkK5dux70Ob3xxht10003qXnz5pKk7du36+abb9bPf/5zderUSY0bN5YklZSUqEWLFpJK\nB9mOOeaYQ/244DG6KPnKemTu3LlJff0jVd437dq1S2rfPProo1X2TVkPVvW72JFHHimp9Hevd999\nV4MHDz5gnxL75sUXX9SQIUPi99upUyetWrUq/rjKfhdr3759pb+L3X777brgggvUuXPnQ/3Ykqs6\nH+XIV+p83C3qV20+CnfYsGG2evVqMzMrKiqyE0880T799FMzM7v88svt4YcfPmA7M7Nt27bF/z1m\nzBh76aWXzMwsJyfHtm7dWi7jm2++sebNm9tf/vIXMzN7/PHHbdSoUWZmtnv3bispKTEzs3/+85/x\n6++++27LzMy0HTt22L59+yw9Pd3ef/99W7t2rZ111ln2xRdfmJnZ9ddfb48++qh988031qxZM5sx\nY4aZmX3++eeWnp5u+/btMzOzXbt2mZnZq6++atdcc025x3fnnXfar371q3LX3X777fbUU0/Zli1b\nrEOHDuX2Oycnx9q2bWtHH320Pffcc/HbjjrqKOvdu7fl5OTYU089dcBzvXPnTuvVq5fNnDnzwB/E\nQfDx20gltf1I7ih30RdffGFnnHGG/fe//7Xf/va39otf/CL+mKvqosGDB9vjjz9uZmaTJ0+2Bg0a\n2J49e+zmm2+2u+++O77d/fffb9ddd52ZmY0fP966du1q6enpdumll9r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AEjKKJGo8kdwZgMOoXx+5kOPC8iq23Xh2LoyRs2F/1ctxDlol3pv+N2QKRS6iqGHq3C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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1ca8e44a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ## dont need for now\n",
    "\n",
    "# # Non-time-filtered regex-based exploration\n",
    "# import numpy as np\n",
    "# i=0\n",
    "# #k = np.log2(i)\n",
    "# bin_points = []\n",
    "# while i<16:\n",
    "#     k = 2**i\n",
    "#     i+=1\n",
    "#     bin_points.append(k)\n",
    "# bin_points\n",
    "\n",
    "\n",
    "# fig = plt.figure(figsize=(20,20))\n",
    "# fig.suptitle('Citation distribution across regex sections (normalized, log2 bins)', size=30)\n",
    "# bin_num = 8\n",
    "# subplot_num = 1\n",
    "# hist_list_raw = []\n",
    "# #hist_list = np.array([])\n",
    "# hist_list = []\n",
    "\n",
    "# for i in range(len(bin_points)-1):\n",
    "#     print(subplot_num)\n",
    "#     #print(i)\n",
    "#     if i == len(bin_points)-2:\n",
    "#         upper_bound = int(max(ref_df['cite_count'])+1)\n",
    "#         print(upper_bound)\n",
    "#     else:\n",
    "#         upper_bound = bin_points[i+1]\n",
    "#         #print('asdfasdf')\n",
    "        \n",
    "\n",
    "#     cite_count_log_list = ref_df.loc[ref_df['cite_count'].isin(range(bin_points[i], upper_bound))]\n",
    "#     title_text = str(bin_points[i]) + '-' + str(upper_bound-1) + ' citations'\n",
    "#     unique_refs = len(cite_count_log_list['reference_UT'].unique())\n",
    "#     unique_papers = len(cite_count_log_list['paper_UT'].unique())\n",
    "#     total_occs = len(cite_count_log_list)\n",
    "    \n",
    "#     ax = fig.add_subplot(4,4,subplot_num)\n",
    "    \n",
    "    \n",
    "#     hist1, bins1 = np.histogram(cite_count_log_list['sect_index'], bins= bin_num, range=[0,8])\n",
    "#     hist_list_raw.append(hist1)\n",
    "#     widths1 = np.diff(bins1)\n",
    "#     hist1 = hist1/float(len(cite_count_log_list))\n",
    "#     hist_list.append(hist1)\n",
    "#     #hist_list = np.append(hist_list, hist1, axis=1)\n",
    "#     ax.bar(bins1[:-1], hist1, widths1)\n",
    "    \n",
    "#     ax.title.set_text(title_text)\n",
    "    \n",
    "#     subtext1 = 'unique refs:  ' + str(unique_refs)\n",
    "#     subtext2 = 'total occs:   ' + str(total_occs)\n",
    "#     subtext3 = 'total papers:' + str(unique_papers)\n",
    "\n",
    "\n",
    "#     ax.text(7.3, 0.83, subtext1 + '\\n' + subtext2 + '\\n' + subtext3, style='italic', horizontalalignment='right',\n",
    "#         bbox={'facecolor':'white', 'alpha':0.6, 'pad':10})\n",
    "\n",
    "#     ax.grid()\n",
    "#     ax.set_ylim([0,1])\n",
    "#     ax.set_xlim([0,7])\n",
    "#     ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
    "#     subplot_num +=1\n",
    "    \n",
    "\n",
    "# # Plotting the distribution for all papers    \n",
    "# cite_count_all = ref_df.loc[(ref_df['cite_count']>=0)]\n",
    "# title_text = 'All citations'\n",
    "# unique_refs = len(cite_count_all['reference_UT'].unique())\n",
    "# unique_papers = len(cite_count_all['paper_UT'].unique())\n",
    "# total_occs = len(cite_count_all)\n",
    "\n",
    "# ax = fig.add_subplot(4,4,subplot_num)\n",
    "# hist1, bins1 = np.histogram(cite_count_all['sect_index'], bins= bin_num, range=[0,8])\n",
    "# hist_all_raw = hist1\n",
    "# widths1 = np.diff(bins1)\n",
    "# hist1 = hist1/float(len(cite_count_all))\n",
    "# hist_all = hist1\n",
    "# ax.bar(bins1[:-1], hist1, widths1)\n",
    "# ax.title.set_text(title_text)\n",
    "# subtext1 = 'unique refs:   ' + str(unique_refs)\n",
    "# subtext2 = 'total occs:   ' + str(total_occs)\n",
    "# subtext3 = 'total papers:   ' + str(unique_papers)\n",
    "\n",
    "# ax.text(7.3, 0.83, subtext1 + '\\n' + subtext2 + '\\n' + subtext3, style='italic', horizontalalignment='right',\n",
    "#     bbox={'facecolor':'white', 'alpha':0.6, 'pad':10})\n",
    "# ax.grid()\n",
    "# ax.set_ylim([0,1])\n",
    "# ax.set_xlim([0,7])\n",
    "# ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7, 8],['i','m','r','d','rd','c','mx','na',''])\n",
    "\n",
    "\n",
    "# fig.text(0.5, 0.08, 'Section Label', ha='center', size = 30)\n",
    "# fig.text(0.07, 0.55, 'Fraction of Total Occurences', va='center', rotation='vertical', size = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mid_cites = ref_df.loc[ref_df['cite_count'].isin(range(100, 110))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52408"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mid_cites['reference_UT'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_cites = ref_df[ref_df['cite_count']>=16384]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = ref_df[ref_df['paper_UT'] == '000271022300001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14212"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(high_cites['paper_UT'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "62"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(high_cites['reference_UT'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159057"
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "len(ref_df['paper_UT'].unique())"
   ]
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  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2607458"
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     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "len(ref_df['reference_UT'].unique())"
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  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "any_cites = ref_df[ref_df['cite_count']>=1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2607316"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "len(any_cites['reference_UT'].unique())"
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  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'000324515600133'"
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     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "any_cites['paper_UT'].unique()[0]"
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  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AB': 'Obstructive nephropathy is the most common presentation of urothelial carcinoma. The role of the urine in the obstructed kidney namely \"hydronephrotic urine\" in urothelial carcinoma has not been extensively explored. This study aims to evaluate whether hydronephrotic urine in the obstructed kidney could promote urothelial carcinoma. The hydronephrotic urine was collected from the obstructed kidneys of Sprague-Dawley rats induced by different periods of unilateral ureteral obstruction (UUO). By the inhibition of LY294002 and PD184352, we confirm that hydronephrotic urine promotes urothelial carcinoma cell (T24) and immortalized normal urothelial cells (E6) proliferation, migration and invasion in a dose-dependent manner through the activation of the mTORC2-AKT and ERK signaling pathways. Hydronephrotic urine also increases the expression of cyclin-D2, cyclin-B and CDK2. It also decreases the expression of p27 and p21 in both urothelial carcinoma cells and normal urothelial cells. By the protein array study, we demonstrate that many growth factors which promote tumor cell survival and metastasis are over-expressed in a time-dependent manner in the hydronephrotic urine, including beta-FGF, IFN-gamma, PDGF-BB, PIGF, TGF-beta, VEGF-A, VEGF-D and EGF. These results suggest that hydronephrotic urine promotes normal and malignant urothelial cells proliferation, migration and invasion, through the activation of the mTORC2-AKT and ERK signaling pathways. Further investigation using live animal models of tumor growth may be needed to clarify aspects of these statements.',\n",
       " 'AR': ['ARTN e74300', 'DOI 10.1371/journal.pone.0074300'],\n",
       " 'AU': [{'AD': {'AA': 'Natl Yang Ming Univ, Inst Clin Med, Taipei 112, Taiwan',\n",
       "    'AD': '1'},\n",
       "   'AF': 'Chi-Hao',\n",
       "   'AU': 'Chang, CH',\n",
       "   'LN': 'Chang',\n",
       "   'RO': 'Author'},\n",
       "  {'AD': {'AA': 'Taichung Vet Gen Hosp, Div Urol, Dept Surg, Taichung, Taiwan',\n",
       "    'AD': '2'},\n",
       "   'AF': 'Jian-Ri',\n",
       "   'AU': 'Li, JR',\n",
       "   'LN': 'Li',\n",
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       "  {'AD': [{'AA': 'Taichung Vet Gen Hosp, Div Nephrol, Dept Med, Taichung, Taiwan',\n",
       "     'AD': '3'},\n",
       "    {'AA': 'Chung Shan Med Univ, Sch Med, Taichung, Taiwan', 'AD': '5'}],\n",
       "   'AF': 'Kuo-Hsiung',\n",
       "   'AU': 'Shu, KH',\n",
       "   'LN': 'Shu',\n",
       "   'RO': 'Author'},\n",
       "  {'AD': [{'AA': 'Natl Yang Ming Univ, Inst Clin Med, Taipei 112, Taiwan',\n",
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       "   'AF': 'Yun-Ching',\n",
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       "   'RO': 'Author'},\n",
       "  {'AD': [{'AA': 'Natl Yang Ming Univ, Inst Clin Med, Taipei 112, Taiwan',\n",
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       "    {'AA': 'Taichung Vet Gen Hosp, Div Nephrol, Dept Med, Taichung, Taiwan',\n",
       "     'AD': '3'},\n",
       "    {'AA': 'Chung Shan Med Univ, Sch Med, Taichung, Taiwan', 'AD': '5'},\n",
       "    {'AA': 'China Med Univ, Sch Med, Grad Inst Clin Med Sci, Taichung, Taiwan',\n",
       "     'AD': '6'},\n",
       "    {'AA': 'Natl Chung Hsing Univ, Inst Biomed Sci, Taichung 40227, Taiwan',\n",
       "     'AD': '7'}],\n",
       "   'AF': 'Ming-Ju',\n",
       "   'AU': 'Wu, MJ',\n",
       "   'EM': 'wmj530@gmail.com',\n",
       "   'LN': 'Wu',\n",
       "   'RO': 'Author, Reprint Author'}],\n",
       " 'AV': 'Y',\n",
       " 'BP': '',\n",
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       "   'NC': 'Taichung Vet Gen Hosp',\n",
       "   'ND': ['Div Urol', 'Dept Surg'],\n",
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       "   'RS': 'DOI 10.1016/j.juro.2010.12.035'},\n",
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       "  {'/A': 'KIYOKAWA, E',\n",
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       "   '/W': 'JNCI-J NATL CANCER I',\n",
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       "   '/V': '55',\n",
       "   '/W': 'AM J KIDNEY DIS',\n",
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       "   'RS': 'DOI 10.1053/j.ajkd.2009.10.055'},\n",
       "  {'/A': 'LOEWITH, R',\n",
       "   '/P': '457',\n",
       "   '/V': '10',\n",
       "   '/W': 'MOL CELL',\n",
       "   '/Y': '2002',\n",
       "   'R9': '0091891694'},\n",
       "  {'/A': 'MANUCHA, W',\n",
       "   '/P': '1',\n",
       "   '/V': '31',\n",
       "   '/W': 'BIOCELL',\n",
       "   '/Y': '2007',\n",
       "   'R9': '0114991055',\n",
       "   'RS': 'UNSP 17665634'},\n",
       "  {'/A': 'NG, CK',\n",
       "   '/P': '27',\n",
       "   '/V': '29',\n",
       "   '/W': 'UROL ONCOL-SEMIN ORI',\n",
       "   '/Y': '2011',\n",
       "   'R9': '0133257671',\n",
       "   'RS': 'DOI 10.1016/j.urolonc.2008.10.023'},\n",
       "  {'/A': 'SARBASSOV, DD',\n",
       "   '/P': '1098',\n",
       "   '/V': '307',\n",
       "   '/W': 'SCIENCE',\n",
       "   '/Y': '2005',\n",
       "   'R9': '0105732338',\n",
       "   'RS': 'DOI 10.1126/science.1106148'},\n",
       "  {'/A': 'SARBASSOV, DD',\n",
       "   '/P': '1296',\n",
       "   '/V': '14',\n",
       "   '/W': 'CURR BIOL',\n",
       "   '/Y': '2004',\n",
       "   'R9': '0103158087'},\n",
       "  {'/A': 'SHOKEIR, AA',\n",
       "   '/P': '313',\n",
       "   '/V': '24',\n",
       "   '/W': 'INDIAN J UROL',\n",
       "   '/Y': '2008',\n",
       "   'R9': '0146067792',\n",
       "   'RS': 'DOI 10.4103/0970-1591.42611.PUBMED:19468460'},\n",
       "  {'/A': 'TAI, HC',\n",
       "   '/P': '403',\n",
       "   '/V': '22',\n",
       "   '/W': 'TRANSPL INT',\n",
       "   '/Y': '2009',\n",
       "   'R9': '0126997590',\n",
       "   'RS': 'DOI 10.1111/j.1432-2277.2008.00798.x'},\n",
       "  {'/A': 'TRUONG, LD',\n",
       "   '/P': '311',\n",
       "   '/V': '169',\n",
       "   '/W': 'CONTRIB NEPHROL',\n",
       "   '/Y': '2011',\n",
       "   'R9': '0140242778'},\n",
       "  {'/A': 'VERHOEST, G',\n",
       "   '/P': '495',\n",
       "   '/V': '29',\n",
       "   '/W': 'WORLD J UROL',\n",
       "   '/Y': '2011',\n",
       "   'R9': '0141675902',\n",
       "   'RS': 'DOI 10.1007/s00345-011-0710-3'},\n",
       "  {'/A': 'WAGNER, M',\n",
       "   '/P': '156',\n",
       "   '/V': '18',\n",
       "   '/W': 'EUR J PEDIATR SURG',\n",
       "   '/Y': '2008',\n",
       "   'R9': '0121078051',\n",
       "   'RS': 'DOI 10.1055/s-2008-1038445'},\n",
       "  {'/A': 'WU, MJ',\n",
       "   '/P': '69',\n",
       "   '/V': '30',\n",
       "   '/W': 'UROL ONCOL-SEMIN ORI',\n",
       "   '/Y': '2012',\n",
       "   'R9': '0145359611',\n",
       "   'RS': 'DOI 10.1016/j.urolonc.2009.11.009'},\n",
       "  {'/A': 'WU, MJ',\n",
       "   '/P': '1091',\n",
       "   '/V': '43',\n",
       "   '/W': 'AM J KIDNEY DIS',\n",
       "   '/Y': '2004',\n",
       "   'R9': '0102809515',\n",
       "   'RS': 'DOI 10.1053/j.ajkd.2004.03.016'},\n",
       "  {'/A': 'WU, MJ',\n",
       "   '/P': '2029',\n",
       "   '/V': '69',\n",
       "   '/W': 'KIDNEY INT',\n",
       "   '/Y': '2006',\n",
       "   'R9': '0110870449',\n",
       "   'RS': 'DOI 10.1038/sj.ki.5000161'},\n",
       "  {'/A': 'WULLSCHLEGER, S',\n",
       "   '/P': '30697',\n",
       "   '/V': '280',\n",
       "   '/W': 'J BIOL CHEM',\n",
       "   '/Y': '2005',\n",
       "   'R9': '0107786458',\n",
       "   'RS': 'DOI 10.1074/jbc.M505553200'},\n",
       "  {'/A': 'WULLSCHLEGER, S',\n",
       "   '/P': '471',\n",
       "   '/V': '124',\n",
       "   '/W': 'CELL',\n",
       "   '/Y': '2006',\n",
       "   'R9': '0109667375',\n",
       "   'RS': 'DOI 10.1016/j.cell.2006.01.016'},\n",
       "  {'/A': 'YANG, Q',\n",
       "   '/P': '2820',\n",
       "   '/V': '20',\n",
       "   '/W': 'GENE DEV',\n",
       "   '/Y': '2006',\n",
       "   'R9': '0112395049',\n",
       "   'RS': 'DOI 10.1101/gad.1461206'},\n",
       "  {'/A': 'ZHOU, HY',\n",
       "   '/P': '30',\n",
       "   '/V': '12',\n",
       "   '/W': 'CURR PROTEIN PEPT SC',\n",
       "   '/Y': '2011',\n",
       "   'R9': '0140045380'}],\n",
       " 'DT': '@ Article',\n",
       " 'EP': '',\n",
       " 'GB': {'GN': 'TCVHG-1003603C', 'GO': 'Taichung Veterans General Hospital'},\n",
       " 'GT': 'This work was supported by grant number TCVHG-1003603C from Taichung Veterans General Hospital. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.',\n",
       " 'ID': ['UNILATERAL URETERAL OBSTRUCTION',\n",
       "  'RICTOR-MTOR COMPLEX',\n",
       "  'BLADDER-CANCER',\n",
       "  'BINDING PARTNER',\n",
       "  'RAPAMYCIN TOR',\n",
       "  'GROWTH',\n",
       "  'RAPTOR',\n",
       "  'NEPHROPATHY',\n",
       "  'NUTRIENT',\n",
       "  'TARGET'],\n",
       " 'LA': 'EN English',\n",
       " 'NR': '37',\n",
       " 'PG': '12',\n",
       " 'RP': {'NC': 'Natl Yang Ming Univ',\n",
       "  'ND': 'Inst Clin Med',\n",
       "  'NF': 'Natl Yang Ming Univ, Inst Clin Med, Taipei 112, Taiwan',\n",
       "  'NU': 'Taiwan',\n",
       "  'NY': 'Taipei',\n",
       "  'NZ': '112         AC',\n",
       "  'RA': 'Wu, MJ'},\n",
       " 'SL': [],\n",
       " 'T9': '0157043669',\n",
       " 'TI': 'Hydronephrotic Urine in the Obstructed Kidney Promotes Urothelial Carcinoma Cell Proliferation, Migration, Invasion through the Activation of mTORC2-AKT and ERK Signaling Pathways',\n",
       " 'UT': '000324515600133',\n",
       " '_id': ObjectId('54dcfb0ad5ed380daeece7cb'),\n",
       " 'citations': ['000364303800096', '000369329600031'],\n",
       " 'issue': {'CF': 'D',\n",
       "  'GA': '219OE',\n",
       "  'IS': '9',\n",
       "  'J1': 'PLOS ONE',\n",
       "  'J2': 'PLOS ONE',\n",
       "  'J9': 'PLOS ONE',\n",
       "  'JI': 'PLoS One',\n",
       "  'LD': '20131016-08:58:49',\n",
       "  'PA': '1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA',\n",
       "  'PD': 'SEP 4',\n",
       "  'PI': 'SAN FRANCISCO',\n",
       "  'PT': 'J',\n",
       "  'PU': 'PUBLIC LIBRARY SCIENCE',\n",
       "  'PW': 'IP201343',\n",
       "  'PY': 2013,\n",
       "  'SC': 'D RO MULTIDISCIPLINARY SCIENCES',\n",
       "  'SN': '1932-6203',\n",
       "  'SO': 'PLOS ONE',\n",
       "  'SQ': 'C7035J0',\n",
       "  'UI': '0003245156',\n",
       "  'VL': '8'}}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = papers_con.collection.find_one({\"UT\":'000324515600133'})\n",
    "doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_traj = retrieve_trajectory('000089825700038')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "test_traj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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       "{'AB': 'Several years ago it was discovered that plant transformation with a transcribed sense transgene could shut down the expression of a homologous endogenous gene. Moreover, it was shown that the introduction into the cell of dsRNA (double-stranded RNA) containing nucleotide sequence complementary to an mRNA sequence causes selective degradation of the latter and thus silencing of a specific gene. This phenomenon, called RNA interference (RNAi) was demonstrated to be present in almost all eukaryotic organisms. RNAi is also capable of silencing transposons in germ line cells and fighting RNA virus infection. Enzymes involved in this process exhibit high homology across species. Some of these enzymes are involved in other cellular processes, for instance developmental timing, suggesting strong interconnections between RNAi and other metabolic pathways. RNAi is probably an ancient mechanism that evolved to protect eukaryotic cells against invasive forms of nucleic acids.',\n",
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       "   '/V': '101',\n",
       "   '/W': 'CELL',\n",
       "   '/Y': '2000',\n",
       "   'R9': '0076329892'}],\n",
       " 'DE': ['RNAi', 'PTGS', 'quelling', 'gene silencing', 'small RNAs'],\n",
       " 'DT': 'R Review',\n",
       " 'EP': '229',\n",
       " 'ID': ['DOUBLE-STRANDED-RNA',\n",
       "  'C-ELEGANS',\n",
       "  'MESSENGER-RNA',\n",
       "  'CAENORHABDITIS-ELEGANS',\n",
       "  'DNA METHYLATION',\n",
       "  'ANTISENSE RNA',\n",
       "  'NEUROSPORA-CRASSA',\n",
       "  'VIRUS-RESISTANCE',\n",
       "  'TRANSGENE',\n",
       "  'ARABIDOPSIS'],\n",
       " 'LA': 'EN English',\n",
       " 'NR': '62    ',\n",
       " 'PG': '13',\n",
       " 'RP': {'NC': 'Adam Mickiewicz Univ Poznan',\n",
       "  'ND': ['Inst Mol Biol & Biotechnol', 'Dept Gene Express'],\n",
       "  'NF': 'Adam Mickiewicz Univ Poznan, Inst Mol Biol & Biotechnol, Dept Gene Express, Miedzychodzka 5, PL-60371 Poznan, Poland',\n",
       "  'NN': 'Miedzychodzka 5',\n",
       "  'NU': 'Poland',\n",
       "  'NY': 'Poznan',\n",
       "  'NZ': 'PL-60371    BC',\n",
       "  'RA': 'Szweykowska-Kulinska, Z'},\n",
       " 'T9': '0093952244',\n",
       " 'TI': 'RNA interference and its role in the regulation of eucaryotic gene expression',\n",
       " 'UT': '000181970800023',\n",
       " '_id': ObjectId('54d2efecec29bd33ace8aabf'),\n",
       " 'citations': ['000314905200038',\n",
       "  '000291656700005',\n",
       "  '000291886100001',\n",
       "  '000249230500004',\n",
       "  '000274226200004',\n",
       "  '000188865200006',\n",
       "  '000188956800017',\n",
       "  '000265565900002',\n",
       "  '000221635900001',\n",
       "  '000259572700011',\n",
       "  '000222418100007',\n",
       "  '000186369300007',\n",
       "  '000252047200013',\n",
       "  '000275698400020',\n",
       "  '000277123400005',\n",
       "  '000296037300023',\n",
       "  '000374377300006'],\n",
       " 'issue': {'CF': 'D',\n",
       "  'GA': '662VZ',\n",
       "  'IL': '00031',\n",
       "  'IS': '1',\n",
       "  'J1': 'ACT BIOCH P',\n",
       "  'J2': 'ACTA BIOCHIM POL',\n",
       "  'J9': 'ACTA BIOCHIM POL',\n",
       "  'JI': 'Acta Biochim. Pol.',\n",
       "  'LD': '20030414-21:07:19',\n",
       "  'PA': 'PASTEURA 3, 02-093 WARSAW, POLAND',\n",
       "  'PI': 'WARSAW',\n",
       "  'PT': 'J',\n",
       "  'PU': 'ACTA BIOCHIMICA POLONICA',\n",
       "  'PW': 'IP200315',\n",
       "  'PY': 2003,\n",
       "  'SC': 'D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY',\n",
       "  'SN': '0001-527X',\n",
       "  'SO': 'ACTA BIOCHIMICA POLONICA',\n",
       "  'SQ': '00440J0',\n",
       "  'TV': 'Y',\n",
       "  'UI': '0001819708',\n",
       "  'VL': '50'}}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc = papers_con.collection.find_one({\"UT\":'000181970800023'})\n",
    "doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000223658400031\n",
      "0\n",
      "000187449400012\n",
      "0\n",
      "000234969600020\n",
      "0\n",
      "000256693400024\n",
      "0\n",
      "000259205600007\n",
      "0\n",
      "000247077500026\n",
      "0\n",
      "000247573900013\n",
      "0\n",
      "000245075900012\n",
      "0\n",
      "000235742700012\n",
      "0\n",
      "000245271200015\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "filtered_ref_uts = list(df_pap_ref['reference_UT'].unique()[0:10])\n",
    "filtered_trajs = retrieve_multiple_trajectories(filtered_ref_uts, all_plos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_traj_dict = datetime_filler_traj(filtered_trajs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'000187449400012': {'000279019700003': [datetime.datetime(2010, 6, 15, 0, 0),\n",
       "   False],\n",
       "  '000382413200005': [datetime.datetime(2016, 9, 15, 0, 0), False],\n",
       "  '000255355900029': [datetime.datetime(2008, 4, 15, 0, 0), False],\n",
       "  '000260148900008': [datetime.datetime(2008, 9, 26, 0, 0), False],\n",
       "  '000345558100114': [datetime.datetime(2014, 11, 3, 0, 0), True],\n",
       "  '000272478800021': [datetime.datetime(2009, 12, 1, 0, 0), False],\n",
       "  '000300193200012': [datetime.datetime(2012, 2, 15, 0, 0), False],\n",
       "  '000280672100009': [datetime.datetime(2010, 7, 15, 0, 0), False],\n",
       "  '000257826300006': [datetime.datetime(2008, 7, 15, 0, 0), False],\n",
       "  '000257648500016': [datetime.datetime(2008, 7, 15, 0, 0), False],\n",
       "  '000260693500001': [datetime.datetime(2008, 10, 15, 0, 0), False],\n",
       "  '000309310600007': [datetime.datetime(2012, 9, 15, 0, 0), False],\n",
       "  '000230809200006': [datetime.datetime(2005, 1, 15, 0, 0), False],\n",
       "  '000244942400014': [datetime.datetime(2007, 3, 15, 0, 0), False],\n",
       "  '000286925500022': [datetime.datetime(2011, 2, 3, 0, 0), False],\n",
       "  '000369111000001': [datetime.datetime(2016, 2, 1, 0, 0), False],\n",
       "  '000287989500010': [datetime.datetime(2011, 2, 25, 0, 0), False],\n",
       "  '000291644700014': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
       "  '000355053500017': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       "  '000336284900010': [datetime.datetime(2014, 1, 15, 0, 0), False],\n",
       "  '000268667600059': [datetime.datetime(2009, 8, 4, 0, 0), False],\n",
       "  '000319638600004': [datetime.datetime(2013, 3, 15, 0, 0), False],\n",
       "  '000263653100029': [datetime.datetime(2009, 3, 1, 0, 0), False],\n",
       "  '000292425800017': [datetime.datetime(2011, 7, 15, 0, 0), False],\n",
       "  '000275983300010': [datetime.datetime(2010, 3, 15, 0, 0), False],\n",
       "  '000297347700060': [datetime.datetime(2011, 11, 7, 0, 0), True],\n",
       "  '000258704300002': [datetime.datetime(2008, 8, 15, 0, 0), False],\n",
       "  '000261013800014': [datetime.datetime(2008, 11, 15, 0, 0), False],\n",
       "  '000388056200007': [datetime.datetime(2016, 11, 15, 0, 0), False],\n",
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       "  '000268071600020': [datetime.datetime(2009, 6, 15, 0, 0), False],\n",
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       "  '000331248500008': [datetime.datetime(2014, 3, 15, 0, 0), False],\n",
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       "  '000258244400001': [datetime.datetime(2008, 6, 15, 0, 0), False],\n",
       "  '000329233300001': [datetime.datetime(2014, 1, 15, 0, 0), False],\n",
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       "  '000236712700002': [datetime.datetime(2006, 1, 15, 0, 0), False],\n",
       "  '000261220000015': [datetime.datetime(2008, 12, 1, 0, 0), False],\n",
       "  '000250671900004': [datetime.datetime(2007, 11, 15, 0, 0), False],\n",
       "  '000289277000018': [datetime.datetime(2011, 4, 27, 0, 0), False],\n",
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       "  '000356924900002': [datetime.datetime(2015, 6, 28, 0, 0), False],\n",
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       "  '000385839300007': [datetime.datetime(2016, 6, 15, 0, 0), False],\n",
       "  '000260824600001': [datetime.datetime(2008, 12, 15, 0, 0), False],\n",
       "  '000239189600012': [datetime.datetime(2006, 8, 15, 0, 0), False],\n",
       "  '000340218300018': [datetime.datetime(2014, 7, 15, 0, 0), False],\n",
       "  '000227672500009': [datetime.datetime(2005, 4, 15, 0, 0), False],\n",
       "  '000289444300004': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
       "  '000272253800019': [datetime.datetime(2009, 12, 15, 0, 0), False],\n",
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       "  '000293561700013': [datetime.datetime(2011, 7, 15, 0, 0), False],\n",
       "  '000259783100001': [datetime.datetime(2008, 8, 15, 0, 0), False],\n",
       "  '000266008800009': [datetime.datetime(2009, 2, 16, 0, 0), False],\n",
       "  '000292006000002': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
       "  '000259718600013': [datetime.datetime(2008, 9, 15, 0, 0), False],\n",
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       "  '000228630800008': [datetime.datetime(2005, 5, 1, 0, 0), False],\n",
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       "  '000365128400002': [datetime.datetime(2015, 12, 15, 0, 0), False],\n",
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       "  '000388431000013': [datetime.datetime(2016, 12, 15, 0, 0), False],\n",
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       "  '000285834000005': [datetime.datetime(2010, 11, 15, 0, 0), False],\n",
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       "  '000300616700004': [datetime.datetime(2012, 2, 15, 0, 0), False],\n",
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       "  '000276651900016': [datetime.datetime(2010, 5, 15, 0, 0), False],\n",
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       "  '000310589900076': [datetime.datetime(2012, 10, 15, 0, 0), False],\n",
       "  '000284878700014': [datetime.datetime(2010, 12, 15, 0, 0), False],\n",
       "  '000253624000004': [datetime.datetime(2008, 2, 15, 0, 0), False],\n",
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       "  '000245259300007': [datetime.datetime(2007, 3, 15, 0, 0), False],\n",
       "  '000240936800001': [datetime.datetime(2006, 9, 15, 0, 0), False],\n",
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       "  '000236556100021': [datetime.datetime(2006, 4, 15, 0, 0), False],\n",
       "  '000301483700003': [datetime.datetime(2012, 4, 15, 0, 0), False],\n",
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       "  '000261565500007': [datetime.datetime(2008, 11, 15, 0, 0), False],\n",
       "  '000272126100004': [datetime.datetime(2009, 11, 15, 0, 0), False],\n",
       "  '000279735800004': [datetime.datetime(2010, 8, 15, 0, 0), False],\n",
       "  '000293115800013': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
       "  '000265058600001': [datetime.datetime(2009, 4, 15, 0, 0), False],\n",
       "  '000305301600008': [datetime.datetime(2012, 7, 15, 0, 0), False],\n",
       "  '000307500100032': [datetime.datetime(2012, 8, 13, 0, 0), True],\n",
       "  '000265499900023': [datetime.datetime(2009, 3, 31, 0, 0), True],\n",
       "  '000265448900005': [datetime.datetime(2009, 5, 15, 0, 0), False],\n",
       "  '000256923100007': [datetime.datetime(2008, 7, 15, 0, 0), False],\n",
       "  '000299395600001': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
       "  '000235686700001': [datetime.datetime(2005, 11, 15, 0, 0), False],\n",
       "  '000248012600005': [datetime.datetime(2007, 6, 15, 0, 0), False],\n",
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       "  '000239855900014': [datetime.datetime(2006, 8, 15, 0, 0), False],\n",
       "  '000363001100005': [datetime.datetime(2015, 8, 15, 0, 0), False],\n",
       "  '000387039100011': [datetime.datetime(2016, 11, 15, 0, 0), False],\n",
       "  '000330359900005': [datetime.datetime(2013, 7, 15, 0, 0), False],\n",
       "  '000242841700003': [datetime.datetime(2006, 11, 15, 0, 0), False],\n",
       "  '000232092600035': [datetime.datetime(2005, 10, 1, 0, 0), False],\n",
       "  '000244026800007': [datetime.datetime(2007, 2, 15, 0, 0), False],\n",
       "  '000252162700020': [datetime.datetime(2008, 1, 1, 0, 0), False],\n",
       "  '000299255400017': [datetime.datetime(2012, 1, 15, 0, 0), False],\n",
       "  '000298634000019': [datetime.datetime(2012, 1, 15, 0, 0), False],\n",
       "  '000252711500001': [datetime.datetime(2008, 3, 15, 0, 0), False],\n",
       "  '000305282900019': [datetime.datetime(2012, 4, 26, 0, 0), False],\n",
       "  '000291069700002': [datetime.datetime(2011, 5, 15, 0, 0), False],\n",
       "  '000226571300039': [datetime.datetime(2005, 2, 1, 0, 0), False],\n",
       "  '000361167300003': [datetime.datetime(2015, 8, 15, 0, 0), False],\n",
       "  '000303691400013': [datetime.datetime(2012, 4, 15, 0, 0), False],\n",
       "  '000227510900033': [datetime.datetime(2005, 3, 15, 0, 0), False],\n",
       "  '000262842100059': [datetime.datetime(2009, 2, 1, 0, 0), False],\n",
       "  '000361686500013': [datetime.datetime(2015, 10, 15, 0, 0), False],\n",
       "  '000298777300018': [datetime.datetime(2011, 12, 23, 0, 0), False],\n",
       "  '000275215400083': [datetime.datetime(2010, 1, 15, 0, 0), False],\n",
       "  '000246427900003': [datetime.datetime(2007, 4, 15, 0, 0), False],\n",
       "  '000308710700001': [datetime.datetime(2012, 10, 15, 0, 0), False],\n",
       "  '000284505500003': [datetime.datetime(2010, 6, 15, 0, 0), False],\n",
       "  '000328314700005': [datetime.datetime(2013, 11, 30, 0, 0), False],\n",
       "  '000319205900047': [datetime.datetime(2013, 6, 1, 0, 0), False],\n",
       "  '000292637000014': [datetime.datetime(2011, 7, 4, 0, 0), False],\n",
       "  '000323421000020': [datetime.datetime(2013, 9, 15, 0, 0), False],\n",
       "  '000377182900001': [datetime.datetime(2016, 5, 31, 0, 0), False],\n",
       "  '000308802900006': [datetime.datetime(2012, 3, 15, 0, 0), False],\n",
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       "  '000209501300239': [datetime.datetime(2012, 6, 15, 0, 0), False],\n",
       "  '000320485300003': [datetime.datetime(2013, 7, 15, 0, 0), False],\n",
       "  '000296422800002': [datetime.datetime(2011, 11, 15, 0, 0), False],\n",
       "  '000298908900004': [datetime.datetime(2011, 12, 15, 0, 0), False],\n",
       "  '000276159500042': [datetime.datetime(2010, 3, 30, 0, 0), False],\n",
       "  '000353180900055': [datetime.datetime(2015, 5, 15, 0, 0), False],\n",
       "  '000269112900014': [datetime.datetime(2009, 11, 15, 0, 0), False],\n",
       "  '000255398800039': [datetime.datetime(2008, 5, 1, 0, 0), False],\n",
       "  '000352443900001': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       "  '000239545700014': [datetime.datetime(2006, 8, 15, 0, 0), False],\n",
       "  '000299255400018': [datetime.datetime(2012, 1, 15, 0, 0), False],\n",
       "  '000327539800106': [datetime.datetime(2013, 11, 21, 0, 0), True],\n",
       "  '000267812600016': [datetime.datetime(2009, 7, 15, 0, 0), False],\n",
       "  '000269850900006': [datetime.datetime(2009, 8, 17, 0, 0), False],\n",
       "  '000247828200013': [datetime.datetime(2007, 7, 15, 0, 0), False],\n",
       "  '000239103400052': [datetime.datetime(2006, 7, 15, 0, 0), False],\n",
       "  '000308739800011': [datetime.datetime(2012, 9, 15, 0, 0), False],\n",
       "  '000319505200004': [datetime.datetime(2013, 5, 15, 0, 0), False],\n",
       "  '000261044900005': [datetime.datetime(2008, 11, 15, 0, 0), False],\n",
       "  '000329911300005': [datetime.datetime(2013, 11, 15, 0, 0), False],\n",
       "  '000288813900014': [datetime.datetime(2011, 3, 25, 0, 0), True],\n",
       "  '000292460600001': [datetime.datetime(2011, 5, 15, 0, 0), False],\n",
       "  '000244605200009': [datetime.datetime(2007, 3, 15, 0, 0), False],\n",
       "  '000295592100016': [datetime.datetime(2011, 9, 15, 0, 0), False],\n",
       "  '000233126300004': [datetime.datetime(2005, 11, 15, 0, 0), False],\n",
       "  '000274041800008': [datetime.datetime(2010, 1, 15, 0, 0), False],\n",
       "  '000251267000020': [datetime.datetime(2007, 12, 15, 0, 0), False],\n",
       "  '000263397300023': [datetime.datetime(2009, 2, 15, 0, 0), False],\n",
       "  '000304198100028': [datetime.datetime(2012, 6, 15, 0, 0), False],\n",
       "  '000282545500022': [datetime.datetime(2010, 9, 14, 0, 0), False],\n",
       "  '000296403800009': [datetime.datetime(2011, 8, 15, 0, 0), False],\n",
       "  '000290128500001': [datetime.datetime(2011, 4, 30, 0, 0), False],\n",
       "  '000337531700019': [datetime.datetime(2014, 6, 20, 0, 0), False],\n",
       "  '000227266600018': [datetime.datetime(2005, 2, 15, 0, 0), False],\n",
       "  '000298154400006': [datetime.datetime(2011, 12, 15, 0, 0), False],\n",
       "  '000264832700005': [datetime.datetime(2009, 6, 15, 0, 0), False],\n",
       "  '000277591200046': [datetime.datetime(2010, 5, 11, 0, 0), False],\n",
       "  '000221971200010': [datetime.datetime(2004, 7, 15, 0, 0), False],\n",
       "  '000276854000005': [datetime.datetime(2010, 5, 15, 0, 0), False],\n",
       "  '000253406700010': [datetime.datetime(2008, 3, 15, 0, 0), False],\n",
       "  '000243597600017': [datetime.datetime(2007, 2, 15, 0, 0), False],\n",
       "  '000291309700008': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
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       "  '000270830300013': [datetime.datetime(2009, 10, 15, 0, 0), False],\n",
       "  '000279719900003': [datetime.datetime(2010, 6, 15, 0, 0), False],\n",
       "  '000235332000001': [datetime.datetime(2006, 3, 15, 0, 0), False],\n",
       "  '000248068800016': [datetime.datetime(2007, 7, 15, 0, 0), False],\n",
       "  '000294461200014': [datetime.datetime(2011, 9, 15, 0, 0), False],\n",
       "  '000282059500032': [datetime.datetime(2010, 10, 1, 0, 0), False],\n",
       "  '000278749600008': [datetime.datetime(2010, 6, 15, 0, 0), False],\n",
       "  '000236713300005': [datetime.datetime(2006, 5, 15, 0, 0), False],\n",
       "  '000239189600014': [datetime.datetime(2006, 8, 15, 0, 0), False],\n",
       "  '000269623300023': [datetime.datetime(2009, 9, 15, 0, 0), False],\n",
       "  '000227579900003': [datetime.datetime(2005, 6, 15, 0, 0), False],\n",
       "  '000363096000011': [datetime.datetime(2015, 11, 15, 0, 0), False],\n",
       "  '000321411500005': [datetime.datetime(2013, 7, 15, 0, 0), False],\n",
       "  '000288014700036': [datetime.datetime(2011, 3, 15, 0, 0), False],\n",
       "  '000247578200020': [datetime.datetime(2007, 6, 27, 0, 0), False],\n",
       "  '000259511800022': [datetime.datetime(2008, 9, 1, 0, 0), False],\n",
       "  '000296199000021': [datetime.datetime(2011, 10, 15, 0, 0), False],\n",
       "  '000237583300004': [datetime.datetime(2006, 6, 15, 0, 0), False],\n",
       "  '000304282200034': [datetime.datetime(2012, 6, 1, 0, 0), False],\n",
       "  '000385004600012': [datetime.datetime(2016, 10, 1, 0, 0), False],\n",
       "  '000310781700005': [datetime.datetime(2012, 12, 15, 0, 0), False],\n",
       "  '000248959200017': [datetime.datetime(2007, 8, 15, 0, 0), False],\n",
       "  '000360794400001': [datetime.datetime(2015, 9, 7, 0, 0), False],\n",
       "  '000300319700014': [datetime.datetime(2012, 2, 15, 0, 0), False],\n",
       "  '000283997800051': [datetime.datetime(2010, 11, 9, 0, 0), False],\n",
       "  '000240897600018': [datetime.datetime(2006, 9, 22, 0, 0), False],\n",
       "  '000230545800008': [datetime.datetime(2005, 5, 15, 0, 0), False],\n",
       "  '000209374100159': [datetime.datetime(2013, 6, 15, 0, 0), False],\n",
       "  '000262285400012': [datetime.datetime(2009, 2, 15, 0, 0), False],\n",
       "  '000269959500008': [datetime.datetime(2009, 9, 15, 0, 0), False],\n",
       "  '000287361700053': [datetime.datetime(2011, 2, 9, 0, 0), True],\n",
       "  '000250549700009': [datetime.datetime(2007, 11, 15, 0, 0), False],\n",
       "  '000242838600016': [datetime.datetime(2006, 12, 15, 0, 0), False],\n",
       "  '000222819300002': [datetime.datetime(2004, 6, 15, 0, 0), False],\n",
       "  '000255397600012': [datetime.datetime(2008, 7, 15, 0, 0), False],\n",
       "  '000324323300004': [datetime.datetime(2013, 10, 15, 0, 0), False],\n",
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       "  ...},\n",
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       "   False],\n",
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       "  '000283132400007': [datetime.datetime(2010, 11, 15, 0, 0), False],\n",
       "  '000291603900025': [datetime.datetime(2011, 7, 15, 0, 0), False],\n",
       "  '000318703900009': [datetime.datetime(2013, 6, 15, 0, 0), False],\n",
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       "  '000295156600017': [datetime.datetime(2011, 10, 15, 0, 0), False],\n",
       "  '000322101300016': [datetime.datetime(2013, 8, 15, 0, 0), False],\n",
       "  '000244942400009': [datetime.datetime(2007, 3, 15, 0, 0), False],\n",
       "  '000243421900002': [datetime.datetime(2006, 12, 15, 0, 0), False],\n",
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       "  '000258217200030': [datetime.datetime(2008, 8, 1, 0, 0), False],\n",
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       "  '000240936800001': [datetime.datetime(2006, 9, 15, 0, 0), False],\n",
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       "  '000228187300004': [datetime.datetime(2005, 4, 4, 0, 0), False],\n",
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       "  '000330795800004': [datetime.datetime(2014, 2, 15, 0, 0), False],\n",
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       "  '000292660000016': [datetime.datetime(2011, 7, 15, 0, 0), False],\n",
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       "  '000279465000002': [datetime.datetime(2010, 8, 15, 0, 0), False],\n",
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       "  '000232067100004': [datetime.datetime(2005, 9, 15, 0, 0), False],\n",
       "  '000330948300006': [datetime.datetime(2013, 7, 25, 0, 0), False],\n",
       "  '000230979200009': [datetime.datetime(2005, 6, 15, 0, 0), False],\n",
       "  '000274139800022': [datetime.datetime(2010, 1, 15, 0, 0), False],\n",
       "  '000371778400017': [datetime.datetime(2016, 3, 15, 0, 0), False],\n",
       "  '000365976200003': [datetime.datetime(2015, 12, 15, 0, 0), False],\n",
       "  '000274915700006': [datetime.datetime(2010, 6, 15, 0, 0), False],\n",
       "  '000251586500009': [datetime.datetime(2008, 1, 15, 0, 0), False],\n",
       "  '000253747100002': [datetime.datetime(2008, 3, 15, 0, 0), False],\n",
       "  '000289959200010': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
       "  '000282230600002': [datetime.datetime(2010, 9, 15, 0, 0), False],\n",
       "  '000237239000004': [datetime.datetime(2006, 5, 15, 0, 0), False],\n",
       "  '000260659000054': [datetime.datetime(2008, 11, 1, 0, 0), False],\n",
       "  '000249056300018': [datetime.datetime(2007, 8, 15, 0, 0), False],\n",
       "  '000251768600005': [datetime.datetime(2007, 10, 15, 0, 0), False],\n",
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       "  '000261664600031': [datetime.datetime(2009, 1, 1, 0, 0), False],\n",
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       "  '000358692700004': [datetime.datetime(2015, 11, 15, 0, 0), False],\n",
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       "  '000250538000012': [datetime.datetime(2008, 1, 15, 0, 0), False],\n",
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       "  '000368140100092': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       "  '000318839000005': [datetime.datetime(2013, 1, 15, 0, 0), False],\n",
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       "  '000278439600070': [datetime.datetime(2010, 6, 1, 0, 0), False],\n",
       "  '000346418100026': [datetime.datetime(2014, 12, 15, 0, 0), False],\n",
       "  '000259656300017': [datetime.datetime(2008, 9, 1, 0, 0), False],\n",
       "  '000374600700007': [datetime.datetime(2016, 5, 15, 0, 0), False],\n",
       "  '000297318700008': [datetime.datetime(2011, 11, 15, 0, 0), False],\n",
       "  '000254274900013': [datetime.datetime(2008, 4, 15, 0, 0), False],\n",
       "  '000281122600032': [datetime.datetime(2010, 9, 1, 0, 0), False],\n",
       "  '000245352800020': [datetime.datetime(2007, 4, 15, 0, 0), False],\n",
       "  '000302632200004': [datetime.datetime(2012, 6, 15, 0, 0), False],\n",
       "  '000361653000008': [datetime.datetime(2015, 9, 15, 0, 0), False],\n",
       "  '000328882000011': [datetime.datetime(2013, 12, 23, 0, 0), True],\n",
       "  '000240562200010': [datetime.datetime(2006, 10, 15, 0, 0), False],\n",
       "  '000359383700094': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       "  '000305719700015': [datetime.datetime(2012, 6, 15, 0, 0), False],\n",
       "  '000259639500013': [datetime.datetime(2008, 10, 1, 0, 0), False],\n",
       "  '000251527100004': [datetime.datetime(2007, 12, 15, 0, 0), False],\n",
       "  '000266280100009': [datetime.datetime(2009, 6, 15, 0, 0), False],\n",
       "  '000250388000008': [datetime.datetime(2007, 11, 1, 0, 0), False],\n",
       "  '000317774500001': [datetime.datetime(2013, 6, 15, 0, 0), False],\n",
       "  '000235766600010': [datetime.datetime(2006, 3, 15, 0, 0), False],\n",
       "  '000357336600024': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       "  '000268846800001': [datetime.datetime(2009, 8, 15, 0, 0), False],\n",
       "  '000296256000003': [datetime.datetime(2011, 11, 1, 0, 0), False],\n",
       "  '000249800900052': [datetime.datetime(2007, 10, 1, 0, 0), False],\n",
       "  '000271833300004': [datetime.datetime(2009, 6, 15, 0, 0), False],\n",
       "  '000268770200002': [datetime.datetime(2009, 8, 6, 0, 0), False],\n",
       "  '000279022700001': [datetime.datetime(2009, 2, 15, 0, 0), False],\n",
       "  '000274909600004': [datetime.datetime(2010, 3, 15, 0, 0), False],\n",
       "  '000336611300001': [datetime.datetime(2014, 6, 15, 0, 0), False],\n",
       "  '000316667000018': [datetime.datetime(2013, 4, 15, 0, 0), False],\n",
       "  '000271589400015': [datetime.datetime(2009, 11, 15, 0, 0), False],\n",
       "  '000384280800001': [datetime.datetime(2016, 8, 26, 0, 0), False],\n",
       "  '000252396800010': [datetime.datetime(2007, 11, 15, 0, 0), False],\n",
       "  '000339127600061': [datetime.datetime(2014, 8, 15, 0, 0), False],\n",
       "  '000251361000012': [datetime.datetime(2007, 6, 15, 0, 0), False],\n",
       "  '000292929000022': [datetime.datetime(2011, 7, 15, 0, 0), False],\n",
       "  '000299014500021': [datetime.datetime(2012, 1, 15, 0, 0), False],\n",
       "  '000263653100025': [datetime.datetime(2009, 3, 1, 0, 0), False],\n",
       "  '000287270400015': [datetime.datetime(2011, 1, 15, 0, 0), False],\n",
       "  '000293286500038': [datetime.datetime(2011, 7, 29, 0, 0), True],\n",
       "  '000263566100010': [datetime.datetime(2009, 2, 19, 0, 0), False],\n",
       "  '000291324100001': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
       "  '000320367300001': [datetime.datetime(2013, 6, 13, 0, 0), False],\n",
       "  '000311620100001': [datetime.datetime(2013, 2, 1, 0, 0), False],\n",
       "  '000311287600012': [datetime.datetime(2012, 12, 1, 0, 0), False],\n",
       "  '000366433700040': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       "  '000310888400027': [datetime.datetime(2012, 11, 15, 0, 0), False],\n",
       "  '000300346300001': [datetime.datetime(2011, 12, 16, 0, 0), False],\n",
       "  '000296714500033': [datetime.datetime(2011, 11, 3, 0, 0), False],\n",
       "  '000316610700060': [datetime.datetime(2013, 4, 1, 0, 0), False],\n",
       "  '000361909100021': [datetime.datetime(2015, 8, 15, 0, 0), False],\n",
       "  '000351259800001': [datetime.datetime(2015, 3, 13, 0, 0), False],\n",
       "  '000292884600003': [datetime.datetime(2011, 7, 15, 0, 0), False],\n",
       "  '000307074200008': [datetime.datetime(2012, 9, 15, 0, 0), False],\n",
       "  '000273409400016': [datetime.datetime(2009, 1, 15, 0, 0), False],\n",
       "  '000287352600009': [datetime.datetime(2011, 2, 15, 0, 0), False],\n",
       "  '000314901900062': [datetime.datetime(2013, 1, 16, 0, 0), False],\n",
       "  '000351403200027': [datetime.datetime(2015, 4, 15, 0, 0), False],\n",
       "  '000233095200002': [datetime.datetime(2005, 11, 15, 0, 0), False],\n",
       "  '000280438700012': [datetime.datetime(2010, 7, 15, 0, 0), False],\n",
       "  '000307884900019': [datetime.datetime(2012, 9, 15, 0, 0), False],\n",
       "  '000317043900001': [datetime.datetime(2013, 4, 15, 0, 0), False],\n",
       "  '000344863100034': [datetime.datetime(2014, 11, 7, 0, 0), True],\n",
       "  '000255885000066': [datetime.datetime(2008, 5, 15, 0, 0), False],\n",
       "  '000354364700002': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       "  '000291997100001': [datetime.datetime(2011, 6, 10, 0, 0), False],\n",
       "  '000236802500002': [datetime.datetime(2006, 6, 15, 0, 0), False],\n",
       "  '000245197300013': [datetime.datetime(2007, 4, 1, 0, 0), False],\n",
       "  '000279387700014': [datetime.datetime(2010, 6, 15, 0, 0), False],\n",
       "  '000250901200003': [datetime.datetime(2007, 11, 1, 0, 0), False],\n",
       "  '000275139100004': [datetime.datetime(2010, 3, 15, 0, 0), False],\n",
       "  '000289300400011': [datetime.datetime(2011, 5, 15, 0, 0), False],\n",
       "  '000360780600004': [datetime.datetime(2015, 9, 15, 0, 0), False],\n",
       "  '000259301300005': [datetime.datetime(2008, 9, 15, 0, 0), False],\n",
       "  '000263213600026': [datetime.datetime(2009, 2, 1, 0, 0), False],\n",
       "  '000273151800012': [datetime.datetime(2009, 12, 15, 0, 0), False],\n",
       "  '000280151500001': [datetime.datetime(2010, 7, 22, 0, 0), False],\n",
       "  '000330788100007': [datetime.datetime(2014, 1, 15, 0, 0), False],\n",
       "  '000380797500066': [datetime.datetime(2016, 7, 21, 0, 0), False],\n",
       "  '000258392300042': [datetime.datetime(2008, 8, 15, 0, 0), False],\n",
       "  '000250466900012': [datetime.datetime(2007, 11, 15, 0, 0), False],\n",
       "  '000261220000027': [datetime.datetime(2008, 12, 1, 0, 0), False],\n",
       "  '000346363900005': [datetime.datetime(2014, 12, 15, 0, 0), False],\n",
       "  '000310888400023': [datetime.datetime(2012, 11, 15, 0, 0), False],\n",
       "  '000337608300018': [datetime.datetime(2014, 9, 1, 0, 0), False],\n",
       "  '000313677800001': [datetime.datetime(2013, 6, 15, 0, 0), False],\n",
       "  '000281926900007': [datetime.datetime(2010, 10, 15, 0, 0), False],\n",
       "  '000343688600018': [datetime.datetime(2014, 10, 15, 0, 0), False],\n",
       "  '000347955200001': [datetime.datetime(2015, 2, 15, 0, 0), False],\n",
       "  '000281340100016': [datetime.datetime(2010, 9, 15, 0, 0), False],\n",
       "  '000327173000018': [datetime.datetime(2013, 11, 4, 0, 0), False],\n",
       "  ...}}"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_traj_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'000279019700003': [datetime.datetime(2010, 6, 15, 0, 0), False],\n",
       " '000382413200005': [datetime.datetime(2016, 9, 15, 0, 0), False],\n",
       " '000255355900029': [datetime.datetime(2008, 4, 15, 0, 0), False],\n",
       " '000260148900008': [datetime.datetime(2008, 9, 26, 0, 0), False],\n",
       " '000345558100114': [datetime.datetime(2014, 11, 3, 0, 0), True],\n",
       " '000272478800021': [datetime.datetime(2009, 12, 1, 0, 0), False],\n",
       " '000300193200012': [datetime.datetime(2012, 2, 15, 0, 0), False],\n",
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       " '000257648500016': [datetime.datetime(2008, 7, 15, 0, 0), False],\n",
       " '000260693500001': [datetime.datetime(2008, 10, 15, 0, 0), False],\n",
       " '000309310600007': [datetime.datetime(2012, 9, 15, 0, 0), False],\n",
       " '000230809200006': [datetime.datetime(2005, 1, 15, 0, 0), False],\n",
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       " '000287989500010': [datetime.datetime(2011, 2, 25, 0, 0), False],\n",
       " '000291644700014': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
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       " '000275983300010': [datetime.datetime(2010, 3, 15, 0, 0), False],\n",
       " '000297347700060': [datetime.datetime(2011, 11, 7, 0, 0), True],\n",
       " '000258704300002': [datetime.datetime(2008, 8, 15, 0, 0), False],\n",
       " '000261013800014': [datetime.datetime(2008, 11, 15, 0, 0), False],\n",
       " '000388056200007': [datetime.datetime(2016, 11, 15, 0, 0), False],\n",
       " '000373502700010': [datetime.datetime(2016, 6, 15, 0, 0), False],\n",
       " '000268071600020': [datetime.datetime(2009, 6, 15, 0, 0), False],\n",
       " '000310168100003': [datetime.datetime(2012, 12, 15, 0, 0), False],\n",
       " '000331248500008': [datetime.datetime(2014, 3, 15, 0, 0), False],\n",
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       " '000288636400003': [datetime.datetime(2011, 3, 15, 0, 0), False],\n",
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       " '000278484000007': [datetime.datetime(2010, 6, 15, 0, 0), False],\n",
       " '000310095000024': [datetime.datetime(2012, 10, 15, 0, 0), False],\n",
       " '000330313800007': [datetime.datetime(2013, 11, 15, 0, 0), False],\n",
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       " '000287633200004': [datetime.datetime(2011, 3, 15, 0, 0), False],\n",
       " '000255914700003': [datetime.datetime(2008, 6, 15, 0, 0), False],\n",
       " '000271494100002': [datetime.datetime(2009, 12, 15, 0, 0), False],\n",
       " '000289468700005': [datetime.datetime(2011, 5, 15, 0, 0), False],\n",
       " '000250514900005': [datetime.datetime(2007, 11, 15, 0, 0), False],\n",
       " '000270830300008': [datetime.datetime(2009, 10, 15, 0, 0), False],\n",
       " '000289520900002': [datetime.datetime(2011, 6, 15, 0, 0), False],\n",
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       " '000209501300123': [datetime.datetime(2012, 6, 15, 0, 0), False],\n",
       " '000270936400008': [datetime.datetime(2009, 6, 15, 0, 0), False],\n",
       " '000263723800007': [datetime.datetime(2009, 3, 15, 0, 0), False],\n",
       " '000330715900003': [datetime.datetime(2013, 12, 15, 0, 0), False],\n",
       " '000332474100010': [datetime.datetime(2014, 4, 15, 0, 0), False],\n",
       " '000254302200008': [datetime.datetime(2007, 12, 15, 0, 0), False],\n",
       " '000240475300005': [datetime.datetime(2006, 9, 15, 0, 0), False],\n",
       " '000388853900004': [datetime.datetime(2016, 11, 15, 0, 0), False],\n",
       " '000289081500012': [datetime.datetime(2011, 4, 15, 0, 0), False],\n",
       " '000227860000002': [datetime.datetime(2005, 4, 15, 0, 0), False],\n",
       " '000309936600001': [datetime.datetime(2012, 7, 6, 0, 0), False],\n",
       " '000300405900003': [datetime.datetime(2012, 2, 1, 0, 0), False],\n",
       " '000323457700006': [datetime.datetime(2013, 9, 1, 0, 0), False],\n",
       " '000254856500023': [datetime.datetime(2008, 5, 15, 0, 0), False],\n",
       " '000312238000029': [datetime.datetime(2013, 1, 15, 0, 0), False],\n",
       " '000259755700038': [datetime.datetime(2008, 10, 1, 0, 0), False],\n",
       " '000349463600002': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       " '000284851800007': [datetime.datetime(2011, 1, 15, 0, 0), False],\n",
       " '000232658300009': [datetime.datetime(2005, 11, 15, 0, 0), False],\n",
       " '000326611900009': [datetime.datetime(2013, 11, 15, 0, 0), False],\n",
       " '000248727700009': [datetime.datetime(2007, 8, 6, 0, 0), False],\n",
       " '000262777300024': [datetime.datetime(2009, 2, 15, 0, 0), False],\n",
       " '000270218500002': [datetime.datetime(2009, 10, 15, 0, 0), False],\n",
       " '000332037400010': [datetime.datetime(2014, 2, 15, 0, 0), False],\n",
       " '000240200400010': [datetime.datetime(2006, 9, 15, 0, 0), False],\n",
       " '000249800900052': [datetime.datetime(2007, 10, 1, 0, 0), False],\n",
       " '000251953300004': [datetime.datetime(2007, 11, 15, 0, 0), False],\n",
       " '000250697800004': [datetime.datetime(2006, 10, 15, 0, 0), False],\n",
       " '000271833300004': [datetime.datetime(2009, 6, 15, 0, 0), False],\n",
       " '000235405800004': [datetime.datetime(2006, 2, 15, 0, 0), False],\n",
       " '000303908100006': [datetime.datetime(2012, 4, 15, 0, 0), False],\n",
       " '000259880700005': [datetime.datetime(2008, 9, 15, 0, 0), False],\n",
       " '000246794900014': [datetime.datetime(2007, 6, 15, 0, 0), False],\n",
       " '000271516500016': [datetime.datetime(2009, 12, 1, 0, 0), False],\n",
       " '000252396800010': [datetime.datetime(2007, 11, 15, 0, 0), False],\n",
       " '000276651900006': [datetime.datetime(2010, 5, 15, 0, 0), False],\n",
       " '000337902100006': [datetime.datetime(2014, 7, 15, 0, 0), False],\n",
       " '000289957100027': [datetime.datetime(2011, 5, 15, 0, 0), False],\n",
       " '000309180000007': [datetime.datetime(2012, 10, 15, 0, 0), False],\n",
       " '000261296200001': [datetime.datetime(2008, 12, 15, 0, 0), False],\n",
       " '000292877000013': [datetime.datetime(2011, 1, 15, 0, 0), False],\n",
       " '000249507500014': [datetime.datetime(2007, 2, 15, 0, 0), False],\n",
       " '000385015000007': [datetime.datetime(2015, 6, 15, 0, 0), False],\n",
       " '000262882800001': [datetime.datetime(2009, 2, 15, 0, 0), False],\n",
       " '000254884000002': [datetime.datetime(2008, 4, 15, 0, 0), False],\n",
       " '000318073000002': [datetime.datetime(2013, 6, 15, 0, 0), False],\n",
       " '000337212600004': [datetime.datetime(2014, 6, 15, 0, 0), False],\n",
       " '000303113600037': [datetime.datetime(2012, 5, 15, 0, 0), False],\n",
       " '000301098900014': [datetime.datetime(2012, 3, 7, 0, 0), False],\n",
       " '000221276900028': [datetime.datetime(2004, 5, 15, 0, 0), False],\n",
       " ...}"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_traj_dict['000187449400012']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
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       " 792,\n",
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       " 899,\n",
       " 900,\n",
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       " 904,\n",
       " 905,\n",
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       " 911,\n",
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       " 918,\n",
       " 919,\n",
       " 920,\n",
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       " 922,\n",
       " 923,\n",
       " 924,\n",
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       " 937,\n",
       " 938,\n",
       " 939,\n",
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       " 948,\n",
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       " 950,\n",
       " 951,\n",
       " 952,\n",
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       " 957,\n",
       " 958,\n",
       " 959,\n",
       " 960,\n",
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       " 963,\n",
       " 964,\n",
       " 965,\n",
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       " 976,\n",
       " 977,\n",
       " 978,\n",
       " 979,\n",
       " 980,\n",
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       " 983,\n",
       " 984,\n",
       " 985,\n",
       " 986,\n",
       " 987,\n",
       " 988,\n",
       " 989,\n",
       " 990,\n",
       " 991,\n",
       " 992,\n",
       " 993,\n",
       " 994,\n",
       " 995,\n",
       " 996,\n",
       " 997,\n",
       " 998,\n",
       " 999,\n",
       " ...]"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(range(len(dates)), key=lambda k: dates[k])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LioJVq2ywf+MN/pfYiSnnvmchTVhNPeqy1p2nj4tzB3nP0XnTprBgAXH4MoB3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1751fd588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for paper in test_traj_dict:\n",
    "    dates = []\n",
    "    count = []\n",
    "    plos_dates = []\n",
    "    plos_count = []\n",
    "    for index, cite in enumerate(test_traj_dict[paper]):\n",
    "        #print(type(test_traj_dict[paper][cite][0]))\n",
    "        if type(test_traj_dict[paper][cite][0]) != int:\n",
    "            dates.append(test_traj_dict[paper][cite][0])#if test_traj_dict[paper][cite][1] == True:    \n",
    "            count.append(index)\n",
    "            if test_traj_dict[paper][cite][1] == True:\n",
    "                #plos_dates.append(test_traj_dict[paper][cite][0])#if test_traj_dict[paper][cite][1] == True:    \n",
    "                plos_count.append(index)\n",
    "                \n",
    "    sorted_ind = sorted(range(len(dates)), key=lambda k: dates[k])\n",
    "    plos_dates = [dates[x] for x in plos_count]\n",
    "    plos_inds = [sorted_ind.index(i) for i in plos_count]\n",
    "\n",
    "    #print(plos_dates)\n",
    "    dates.sort()\n",
    "    #sorted(range(len(plos_dates)), key=lambda k: plos_dates[k])\n",
    "    #plos_dates.sort()\n",
    "    plt.plot(dates, count, color = 'b')\n",
    "    \n",
    "    plt.scatter(plos_dates, plos_inds, color='r')\n",
    "    break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "list indices must be integers or slices, not tuple",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-265-b8e96672c696>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: list indices must be integers or slices, not tuple"
     ]
    }
   ],
   "source": [
    "dates[0,5,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "000312649800013\n",
      "0\n",
      "000313598500007\n",
      "0\n",
      "000251143700003\n",
      "0\n",
      "000244396300011\n",
      "0\n",
      "000240028900008\n",
      "0\n",
      "000168445600006\n",
      "0\n",
      "000075418500008\n",
      "0\n",
      "000171979000013\n",
      "0\n",
      "000300634900018\n",
      "0\n",
      "000165834700003\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "all_plos = ref_df['paper_UT'].unique()\n",
    "mid_cite_ref_uts = mid_cites['reference_UT'].unique()[0:10]\n",
    "mid_cites_trajs = retrieve_multiple_trajectories(mid_cite_ref_uts, all_plos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'000075418500008': {'000076464500009': ['OCT', 1998, False],\n",
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       "  '000083280600003': ['NOV', 1999, False],\n",
       "  '000084250600005': ['DEC', 1999, False],\n",
       "  '000085249200001': ['FEB 10', 2000, False],\n",
       "  '000087258900009': [nan, 1999, False],\n",
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       "  '000278663500014': ['JUN', 2010, False],\n",
       "  '000280732400007': ['AUG', 2010, False],\n",
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       "  '000325441300001': ['AUG', 2013, False],\n",
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       "  '000332957000002': ['MAR', 2014, False],\n",
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       "  '000376199300001': [nan, 2016, False],\n",
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       "  '000231710000011': ['SEP 6', 2005, False],\n",
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       "  '000242813800008': ['NOV', 2006, False],\n",
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       "  '000250990000042': [nan, 2007, False],\n",
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       "  '000251576400038': [nan, 2007, False],\n",
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       "  '000257261500022': ['JUN', 2008, False],\n",
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       "  '000287340200005': ['FEB 4', 2011, False],\n",
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       "  '000295233300012': ['SEP 5', 2011, False],\n",
       "  '000295844100001': [nan, 2011, False],\n",
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       "  '000305684900014': ['MAY', 2012, False],\n",
       "  '000305684900018': ['MAY', 2012, False],\n",
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       "  '000307733800031': ['AUG 20', 2012, True],\n",
       "  '000308254200014': ['AUG', 2012, False],\n",
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       "  '000314391200005': ['JAN 29', 2013, False],\n",
       "  '000320888100013': ['JUL', 2013, False],\n",
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       "  '000338979100006': ['JUL 10', 2014, False],\n",
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       "  '000354195300003': [nan, 2015, False],\n",
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       "  '000275270800001': ['MAR', 2010, False],\n",
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       "  '000329448600001': [nan, 2013, False],\n",
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       "  '000376967600001': ['JUN', 2016, False],\n",
       "  '000377036500006': ['MAY 1', 2016, False],\n",
       "  '000378131100058': ['FEB', 2016, False],\n",
       "  '000378360400019': ['JUL', 2016, False],\n",
       "  '000378458000004': ['JUL', 2016, False],\n",
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       "  '000379095100002': ['JUL', 2016, False],\n",
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       "  '000379484400004': ['JUL', 2016, False],\n",
       "  '000379557800006': ['AUG', 2016, False],\n",
       "  '000380749200017': ['AUG 2', 2016, False],\n",
       "  '000380791900001': ['AUG 3', 2016, False],\n",
       "  '000380870400013': ['SEP', 2016, False],\n",
       "  '000381065600005': ['AUG', 2016, False],\n",
       "  '000381535900013': ['SEP', 2016, False],\n",
       "  '000381995400012': ['AUG', 2016, False],\n",
       "  '000382800100027': ['AUG', 2016, False],\n",
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       "  '000382857700014': ['JUL', 2016, False],\n",
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       "  '000385075000001': [nan, 2016, False],\n",
       "  '000385292700007': ['AUG 1', 2016, False],\n",
       "  '000385821600001': ['OCT 21', 2016, False],\n",
       "  '000386227500019': ['NOV', 2016, False],\n",
       "  '000387153900072': ['SEP 13', 2016, False],\n",
       "  '000388049300005': ['DEC', 2016, False],\n",
       "  '000388737100001': ['NOV 30', 2016, False],\n",
       "  '000390017000005': ['DEC', 2016, False],\n",
       "  '000390117000016': ['NOV', 2016, False]}}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mid_cites_trajs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
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       "      <td>introduction</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>445</td>\n",
       "      <td>74</td>\n",
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       "      <th>10</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000289279600018</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>269</td>\n",
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       "      <td>000289279600018</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>628</td>\n",
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       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
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       "      <td>000324515600133</td>\n",
       "      <td>000289279600018</td>\n",
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       "      <td>3</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>discussion</td>\n",
       "      <td>20179</td>\n",
       "      <td>3546</td>\n",
       "      <td>3</td>\n",
       "      <td>3844</td>\n",
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       "      <td>[D ZA UROLOGY &amp; NEPHROLOGY]</td>\n",
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       "      <td>3780</td>\n",
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       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
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       "      <th>13</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000287717700023</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>1635</td>\n",
       "      <td>261</td>\n",
       "      <td>0</td>\n",
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       "      <td>[D ZA UROLOGY &amp; NEPHROLOGY]</td>\n",
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       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
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       "      <td>-1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>2326</td>\n",
       "      <td>380</td>\n",
       "      <td>0</td>\n",
       "      <td>2326</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000257589600013</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>2177</td>\n",
       "      <td>351</td>\n",
       "      <td>0</td>\n",
       "      <td>2177</td>\n",
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       "      <td>R Review</td>\n",
       "      <td>D ZA UROLOGY &amp; NEPHROLOGY</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000257589600013</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>discussion</td>\n",
       "      <td>18913</td>\n",
       "      <td>3334</td>\n",
       "      <td>3</td>\n",
       "      <td>2578</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>D ZA UROLOGY &amp; NEPHROLOGY</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000246465200001</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>1638</td>\n",
       "      <td>262</td>\n",
       "      <td>0</td>\n",
       "      <td>1638</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>D DR CELL BIOLOGY</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000246465200001</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>2178</td>\n",
       "      <td>352</td>\n",
       "      <td>0</td>\n",
       "      <td>2178</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>D DR CELL BIOLOGY</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000246465200001</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>discussion</td>\n",
       "      <td>18914</td>\n",
       "      <td>3335</td>\n",
       "      <td>3</td>\n",
       "      <td>2579</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>D DR CELL BIOLOGY</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000238112100022</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>1754</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>1754</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D ZA UROLOGY &amp; NEPHROLOGY]</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000238112100022</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>1942</td>\n",
       "      <td>311</td>\n",
       "      <td>0</td>\n",
       "      <td>1942</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D ZA UROLOGY &amp; NEPHROLOGY]</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000266227800006</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>1755</td>\n",
       "      <td>284</td>\n",
       "      <td>0</td>\n",
       "      <td>1755</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>D ZA UROLOGY &amp; NEPHROLOGY</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000266227800006</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>2179</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>2179</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>D ZA UROLOGY &amp; NEPHROLOGY</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000266227800006</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>discussion</td>\n",
       "      <td>18915</td>\n",
       "      <td>3336</td>\n",
       "      <td>3</td>\n",
       "      <td>2580</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>D ZA UROLOGY &amp; NEPHROLOGY</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>-1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>2180</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>2180</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>-1</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>2327</td>\n",
       "      <td>381</td>\n",
       "      <td>0</td>\n",
       "      <td>2327</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>-1</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>discussion</td>\n",
       "      <td>18916</td>\n",
       "      <td>3337</td>\n",
       "      <td>3</td>\n",
       "      <td>2581</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000220879700004</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>2635</td>\n",
       "      <td>434</td>\n",
       "      <td>0</td>\n",
       "      <td>2635</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>[D CQ BIOCHEMISTRY &amp; MOLECULAR BIOLOGY, D DM O...</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>000324515600133</td>\n",
       "      <td>000177105200006</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>Introduction</td>\n",
       "      <td>introduction</td>\n",
       "      <td>2638</td>\n",
       "      <td>435</td>\n",
       "      <td>0</td>\n",
       "      <td>2638</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D CQ BIOCHEMISTRY &amp; MOLECULAR BIOLOGY, D DR C...</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>21733</td>\n",
       "      <td>3780</td>\n",
       "      <td>SEP 4</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D RO MULTIDISCIPLINARY SCIENCES</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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325576</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000247935300010</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>8575</td>\n",
       "      <td>1476</td>\n",
       "      <td>3</td>\n",
       "      <td>891</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D DR CELL BIOLOGY, D DE PLANT SCIENCES]</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325577</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>A1982PW56700007</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>8945</td>\n",
       "      <td>1536</td>\n",
       "      <td>3</td>\n",
       "      <td>1261</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[K DE PLANT SCIENCES, D DE PLANT SCIENCES]</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325578</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>A1980KX98200001</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>9288</td>\n",
       "      <td>1597</td>\n",
       "      <td>3</td>\n",
       "      <td>1604</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>[K DE PLANT SCIENCES, D DE PLANT SCIENCES]</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325579</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>A1980KX98200001</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>10268</td>\n",
       "      <td>1754</td>\n",
       "      <td>3</td>\n",
       "      <td>2584</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>[K DE PLANT SCIENCES, D DE PLANT SCIENCES]</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325580</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000301971900007</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>9290</td>\n",
       "      <td>1598</td>\n",
       "      <td>3</td>\n",
       "      <td>1606</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325581</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>-1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>9426</td>\n",
       "      <td>1618</td>\n",
       "      <td>3</td>\n",
       "      <td>1742</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325582</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000264005500007</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>9517</td>\n",
       "      <td>1631</td>\n",
       "      <td>3</td>\n",
       "      <td>1833</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325583</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000080163800019</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>9762</td>\n",
       "      <td>1671</td>\n",
       "      <td>3</td>\n",
       "      <td>2078</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D CQ BIOCHEMISTRY &amp; MOLECULAR BIOLOGY, D DE P...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325584</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000080163800019</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>10270</td>\n",
       "      <td>1755</td>\n",
       "      <td>3</td>\n",
       "      <td>2586</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D CQ BIOCHEMISTRY &amp; MOLECULAR BIOLOGY, D DE P...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325585</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000080163800019</td>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>11381</td>\n",
       "      <td>1948</td>\n",
       "      <td>3</td>\n",
       "      <td>3697</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D CQ BIOCHEMISTRY &amp; MOLECULAR BIOLOGY, D DE P...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325586</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000080163800019</td>\n",
       "      <td>33</td>\n",
       "      <td>4</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>13578</td>\n",
       "      <td>2304</td>\n",
       "      <td>3</td>\n",
       "      <td>5894</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D CQ BIOCHEMISTRY &amp; MOLECULAR BIOLOGY, D DE P...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325587</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000185745000010</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>9867</td>\n",
       "      <td>1687</td>\n",
       "      <td>3</td>\n",
       "      <td>2183</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325588</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000185745000010</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>10212</td>\n",
       "      <td>1744</td>\n",
       "      <td>3</td>\n",
       "      <td>2528</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325589</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>A1982PH38400012</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>9962</td>\n",
       "      <td>1702</td>\n",
       "      <td>3</td>\n",
       "      <td>2278</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[K DE PLANT SCIENCES, D DE PLANT SCIENCES]</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325590</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>A1989AE93800008</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>9994</td>\n",
       "      <td>1707</td>\n",
       "      <td>3</td>\n",
       "      <td>2310</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325591</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000173977100041</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>11265</td>\n",
       "      <td>1932</td>\n",
       "      <td>3</td>\n",
       "      <td>3581</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325592</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000175801600018</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>11712</td>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>4028</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D EI CHEMISTRY, PHYSICAL</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325593</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000220747700018</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>11714</td>\n",
       "      <td>2000</td>\n",
       "      <td>3</td>\n",
       "      <td>4030</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D EI CHEMISTRY, PHYSICAL, D PM MATERIALS SCIE...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325594</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000174377700007</td>\n",
       "      <td>40</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>12370</td>\n",
       "      <td>2105</td>\n",
       "      <td>3</td>\n",
       "      <td>4686</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325595</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000271586700013</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>12372</td>\n",
       "      <td>2106</td>\n",
       "      <td>3</td>\n",
       "      <td>4688</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D DE PLANT SCIENCES]</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325596</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000221871800012</td>\n",
       "      <td>42</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>12515</td>\n",
       "      <td>2129</td>\n",
       "      <td>3</td>\n",
       "      <td>4831</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325597</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000221871800012</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>13452</td>\n",
       "      <td>2284</td>\n",
       "      <td>3</td>\n",
       "      <td>5768</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325598</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000221871800012</td>\n",
       "      <td>42</td>\n",
       "      <td>3</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>13580</td>\n",
       "      <td>2305</td>\n",
       "      <td>3</td>\n",
       "      <td>5896</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325599</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>A1954UF73200026</td>\n",
       "      <td>43</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>12659</td>\n",
       "      <td>2148</td>\n",
       "      <td>3</td>\n",
       "      <td>4975</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325600</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>-1</td>\n",
       "      <td>44</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>12661</td>\n",
       "      <td>2149</td>\n",
       "      <td>3</td>\n",
       "      <td>4977</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325601</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>-1</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>13152</td>\n",
       "      <td>2230</td>\n",
       "      <td>3</td>\n",
       "      <td>5468</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325602</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000176187500027</td>\n",
       "      <td>46</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>13154</td>\n",
       "      <td>2231</td>\n",
       "      <td>3</td>\n",
       "      <td>5470</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>[D CQ BIOCHEMISTRY &amp; MOLECULAR BIOLOGY, D DE P...</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325603</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>A1993LX59200002</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>13361</td>\n",
       "      <td>2269</td>\n",
       "      <td>3</td>\n",
       "      <td>5677</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325604</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000183262600008</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>13364</td>\n",
       "      <td>2270</td>\n",
       "      <td>3</td>\n",
       "      <td>5680</td>\n",
       "      <td>...</td>\n",
       "      <td>R Review</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12325605</th>\n",
       "      <td>000321341000076</td>\n",
       "      <td>000321228000014</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>Discussion</td>\n",
       "      <td>None</td>\n",
       "      <td>13582</td>\n",
       "      <td>2306</td>\n",
       "      <td>3</td>\n",
       "      <td>5898</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>D DE PLANT SCIENCES</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>14583</td>\n",
       "      <td>2455</td>\n",
       "      <td>JUL 2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>[D RO MULTIDISCIPLINARY SCIENCES]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10848620 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 paper_UT     reference_UT  reference_rank  occurence  \\\n",
       "0         000324515600133  A1995QY75100004               1          1   \n",
       "1         000324515600133  A1995QY75100004               1          2   \n",
       "2         000324515600133  A1995QY75100004               1          3   \n",
       "3         000324515600133  000222499800017               2          1   \n",
       "4         000324515600133  000222499800017               2          2   \n",
       "5         000324515600133  000222499800017               2          3   \n",
       "6         000324515600133  000222499800017               2          4   \n",
       "7         000324515600133  000222499800017               2          5   \n",
       "8         000324515600133  000263911400006               3          1   \n",
       "9         000324515600133  000263911400006               3          2   \n",
       "10        000324515600133  000289279600018               4          1   \n",
       "11        000324515600133  000289279600018               4          2   \n",
       "12        000324515600133  000289279600018               4          3   \n",
       "13        000324515600133  000287717700023               5          1   \n",
       "14        000324515600133               -1               6          1   \n",
       "15        000324515600133  000257589600013               7          1   \n",
       "16        000324515600133  000257589600013               7          2   \n",
       "17        000324515600133  000246465200001               8          1   \n",
       "18        000324515600133  000246465200001               8          2   \n",
       "19        000324515600133  000246465200001               8          3   \n",
       "20        000324515600133  000238112100022               9          1   \n",
       "21        000324515600133  000238112100022               9          2   \n",
       "22        000324515600133  000266227800006              10          1   \n",
       "23        000324515600133  000266227800006              10          2   \n",
       "24        000324515600133  000266227800006              10          3   \n",
       "25        000324515600133               -1              11          1   \n",
       "26        000324515600133               -1              11          2   \n",
       "27        000324515600133               -1              11          3   \n",
       "28        000324515600133  000220879700004              12          1   \n",
       "29        000324515600133  000177105200006              17          1   \n",
       "...                   ...              ...             ...        ...   \n",
       "12325576  000321341000076  000247935300010              27          1   \n",
       "12325577  000321341000076  A1982PW56700007              28          1   \n",
       "12325578  000321341000076  A1980KX98200001              29          1   \n",
       "12325579  000321341000076  A1980KX98200001              29          2   \n",
       "12325580  000321341000076  000301971900007              30          1   \n",
       "12325581  000321341000076               -1              31          1   \n",
       "12325582  000321341000076  000264005500007              32          1   \n",
       "12325583  000321341000076  000080163800019              33          1   \n",
       "12325584  000321341000076  000080163800019              33          2   \n",
       "12325585  000321341000076  000080163800019              33          3   \n",
       "12325586  000321341000076  000080163800019              33          4   \n",
       "12325587  000321341000076  000185745000010              34          1   \n",
       "12325588  000321341000076  000185745000010              34          2   \n",
       "12325589  000321341000076  A1982PH38400012              35          1   \n",
       "12325590  000321341000076  A1989AE93800008              36          1   \n",
       "12325591  000321341000076  000173977100041              37          1   \n",
       "12325592  000321341000076  000175801600018              38          1   \n",
       "12325593  000321341000076  000220747700018              39          1   \n",
       "12325594  000321341000076  000174377700007              40          1   \n",
       "12325595  000321341000076  000271586700013              41          1   \n",
       "12325596  000321341000076  000221871800012              42          1   \n",
       "12325597  000321341000076  000221871800012              42          2   \n",
       "12325598  000321341000076  000221871800012              42          3   \n",
       "12325599  000321341000076  A1954UF73200026              43          1   \n",
       "12325600  000321341000076               -1              44          1   \n",
       "12325601  000321341000076               -1              45          1   \n",
       "12325602  000321341000076  000176187500027              46          1   \n",
       "12325603  000321341000076  A1993LX59200002              47          1   \n",
       "12325604  000321341000076  000183262600008              49          1   \n",
       "12325605  000321341000076  000321228000014              50          1   \n",
       "\n",
       "         section_title section_title_alt  paper_char_pos  paper_word_pos  \\\n",
       "0         Introduction      introduction             139              21   \n",
       "1           Discussion        discussion           16829            2992   \n",
       "2           Discussion        discussion           17257            3057   \n",
       "3         Introduction      introduction             266              42   \n",
       "4         Introduction      introduction             444              73   \n",
       "5           Discussion        discussion           16830            2993   \n",
       "6           Discussion        discussion           17110            3035   \n",
       "7           Discussion        discussion           17258            3058   \n",
       "8         Introduction      introduction             142              22   \n",
       "9         Introduction      introduction             445              74   \n",
       "10        Introduction      introduction             269              43   \n",
       "11        Introduction      introduction             628              98   \n",
       "12          Discussion        discussion           20179            3546   \n",
       "13        Introduction      introduction            1635             261   \n",
       "14        Introduction      introduction            2326             380   \n",
       "15        Introduction      introduction            2177             351   \n",
       "16          Discussion        discussion           18913            3334   \n",
       "17        Introduction      introduction            1638             262   \n",
       "18        Introduction      introduction            2178             352   \n",
       "19          Discussion        discussion           18914            3335   \n",
       "20        Introduction      introduction            1754             283   \n",
       "21        Introduction      introduction            1942             311   \n",
       "22        Introduction      introduction            1755             284   \n",
       "23        Introduction      introduction            2179             353   \n",
       "24          Discussion        discussion           18915            3336   \n",
       "25        Introduction      introduction            2180             354   \n",
       "26        Introduction      introduction            2327             381   \n",
       "27          Discussion        discussion           18916            3337   \n",
       "28        Introduction      introduction            2635             434   \n",
       "29        Introduction      introduction            2638             435   \n",
       "...                ...               ...             ...             ...   \n",
       "12325576    Discussion              None            8575            1476   \n",
       "12325577    Discussion              None            8945            1536   \n",
       "12325578    Discussion              None            9288            1597   \n",
       "12325579    Discussion              None           10268            1754   \n",
       "12325580    Discussion              None            9290            1598   \n",
       "12325581    Discussion              None            9426            1618   \n",
       "12325582    Discussion              None            9517            1631   \n",
       "12325583    Discussion              None            9762            1671   \n",
       "12325584    Discussion              None           10270            1755   \n",
       "12325585    Discussion              None           11381            1948   \n",
       "12325586    Discussion              None           13578            2304   \n",
       "12325587    Discussion              None            9867            1687   \n",
       "12325588    Discussion              None           10212            1744   \n",
       "12325589    Discussion              None            9962            1702   \n",
       "12325590    Discussion              None            9994            1707   \n",
       "12325591    Discussion              None           11265            1932   \n",
       "12325592    Discussion              None           11712            1999   \n",
       "12325593    Discussion              None           11714            2000   \n",
       "12325594    Discussion              None           12370            2105   \n",
       "12325595    Discussion              None           12372            2106   \n",
       "12325596    Discussion              None           12515            2129   \n",
       "12325597    Discussion              None           13452            2284   \n",
       "12325598    Discussion              None           13580            2305   \n",
       "12325599    Discussion              None           12659            2148   \n",
       "12325600    Discussion              None           12661            2149   \n",
       "12325601    Discussion              None           13152            2230   \n",
       "12325602    Discussion              None           13154            2231   \n",
       "12325603    Discussion              None           13361            2269   \n",
       "12325604    Discussion              None           13364            2270   \n",
       "12325605    Discussion              None           13582            2306   \n",
       "\n",
       "          sect_index  sect_char_pos                ...                  \\\n",
       "0                  0            139                ...                   \n",
       "1                  3            494                ...                   \n",
       "2                  3            922                ...                   \n",
       "3                  0            266                ...                   \n",
       "4                  0            444                ...                   \n",
       "5                  3            495                ...                   \n",
       "6                  3            775                ...                   \n",
       "7                  3            923                ...                   \n",
       "8                  0            142                ...                   \n",
       "9                  0            445                ...                   \n",
       "10                 0            269                ...                   \n",
       "11                 0            628                ...                   \n",
       "12                 3           3844                ...                   \n",
       "13                 0           1635                ...                   \n",
       "14                 0           2326                ...                   \n",
       "15                 0           2177                ...                   \n",
       "16                 3           2578                ...                   \n",
       "17                 0           1638                ...                   \n",
       "18                 0           2178                ...                   \n",
       "19                 3           2579                ...                   \n",
       "20                 0           1754                ...                   \n",
       "21                 0           1942                ...                   \n",
       "22                 0           1755                ...                   \n",
       "23                 0           2179                ...                   \n",
       "24                 3           2580                ...                   \n",
       "25                 0           2180                ...                   \n",
       "26                 0           2327                ...                   \n",
       "27                 3           2581                ...                   \n",
       "28                 0           2635                ...                   \n",
       "29                 0           2638                ...                   \n",
       "...              ...            ...                ...                   \n",
       "12325576           3            891                ...                   \n",
       "12325577           3           1261                ...                   \n",
       "12325578           3           1604                ...                   \n",
       "12325579           3           2584                ...                   \n",
       "12325580           3           1606                ...                   \n",
       "12325581           3           1742                ...                   \n",
       "12325582           3           1833                ...                   \n",
       "12325583           3           2078                ...                   \n",
       "12325584           3           2586                ...                   \n",
       "12325585           3           3697                ...                   \n",
       "12325586           3           5894                ...                   \n",
       "12325587           3           2183                ...                   \n",
       "12325588           3           2528                ...                   \n",
       "12325589           3           2278                ...                   \n",
       "12325590           3           2310                ...                   \n",
       "12325591           3           3581                ...                   \n",
       "12325592           3           4028                ...                   \n",
       "12325593           3           4030                ...                   \n",
       "12325594           3           4686                ...                   \n",
       "12325595           3           4688                ...                   \n",
       "12325596           3           4831                ...                   \n",
       "12325597           3           5768                ...                   \n",
       "12325598           3           5896                ...                   \n",
       "12325599           3           4975                ...                   \n",
       "12325600           3           4977                ...                   \n",
       "12325601           3           5468                ...                   \n",
       "12325602           3           5470                ...                   \n",
       "12325603           3           5677                ...                   \n",
       "12325604           3           5680                ...                   \n",
       "12325605           3           5898                ...                   \n",
       "\n",
       "          ref_article_type                                          ref_field  \\\n",
       "0                @ Article                          D ZA UROLOGY & NEPHROLOGY   \n",
       "1                @ Article                          D ZA UROLOGY & NEPHROLOGY   \n",
       "2                @ Article                          D ZA UROLOGY & NEPHROLOGY   \n",
       "3                @ Article                          D ZA UROLOGY & NEPHROLOGY   \n",
       "4                @ Article                          D ZA UROLOGY & NEPHROLOGY   \n",
       "5                @ Article                          D ZA UROLOGY & NEPHROLOGY   \n",
       "6                @ Article                          D ZA UROLOGY & NEPHROLOGY   \n",
       "7                @ Article                          D ZA UROLOGY & NEPHROLOGY   \n",
       "8                @ Article               [D YP TRANSPLANTATION, D YA SURGERY]   \n",
       "9                @ Article               [D YP TRANSPLANTATION, D YA SURGERY]   \n",
       "10               @ Article                        [D ZA UROLOGY & NEPHROLOGY]   \n",
       "11               @ Article                        [D ZA UROLOGY & NEPHROLOGY]   \n",
       "12               @ Article                        [D ZA UROLOGY & NEPHROLOGY]   \n",
       "13               @ Article                        [D ZA UROLOGY & NEPHROLOGY]   \n",
       "14                     NaN                                                NaN   \n",
       "15                R Review                          D ZA UROLOGY & NEPHROLOGY   \n",
       "16                R Review                          D ZA UROLOGY & NEPHROLOGY   \n",
       "17                R Review                                  D DR CELL BIOLOGY   \n",
       "18                R Review                                  D DR CELL BIOLOGY   \n",
       "19                R Review                                  D DR CELL BIOLOGY   \n",
       "20               @ Article                        [D ZA UROLOGY & NEPHROLOGY]   \n",
       "21               @ Article                        [D ZA UROLOGY & NEPHROLOGY]   \n",
       "22                R Review                          D ZA UROLOGY & NEPHROLOGY   \n",
       "23                R Review                          D ZA UROLOGY & NEPHROLOGY   \n",
       "24                R Review                          D ZA UROLOGY & NEPHROLOGY   \n",
       "25                     NaN                                                NaN   \n",
       "26                     NaN                                                NaN   \n",
       "27                     NaN                                                NaN   \n",
       "28                R Review  [D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY, D DM O...   \n",
       "29               @ Article  [D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY, D DR C...   \n",
       "...                    ...                                                ...   \n",
       "12325576         @ Article           [D DR CELL BIOLOGY, D DE PLANT SCIENCES]   \n",
       "12325577         @ Article         [K DE PLANT SCIENCES, D DE PLANT SCIENCES]   \n",
       "12325578          R Review         [K DE PLANT SCIENCES, D DE PLANT SCIENCES]   \n",
       "12325579          R Review         [K DE PLANT SCIENCES, D DE PLANT SCIENCES]   \n",
       "12325580         @ Article                                D DE PLANT SCIENCES   \n",
       "12325581               NaN                                                NaN   \n",
       "12325582         @ Article                                D DE PLANT SCIENCES   \n",
       "12325583         @ Article  [D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY, D DE P...   \n",
       "12325584         @ Article  [D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY, D DE P...   \n",
       "12325585         @ Article  [D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY, D DE P...   \n",
       "12325586         @ Article  [D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY, D DE P...   \n",
       "12325587         @ Article                                D DE PLANT SCIENCES   \n",
       "12325588         @ Article                                D DE PLANT SCIENCES   \n",
       "12325589         @ Article         [K DE PLANT SCIENCES, D DE PLANT SCIENCES]   \n",
       "12325590         @ Article                                D DE PLANT SCIENCES   \n",
       "12325591         @ Article                                D DE PLANT SCIENCES   \n",
       "12325592         @ Article                           D EI CHEMISTRY, PHYSICAL   \n",
       "12325593         @ Article  [D EI CHEMISTRY, PHYSICAL, D PM MATERIALS SCIE...   \n",
       "12325594         @ Article                                D DE PLANT SCIENCES   \n",
       "12325595         @ Article                              [D DE PLANT SCIENCES]   \n",
       "12325596         @ Article                                D DE PLANT SCIENCES   \n",
       "12325597         @ Article                                D DE PLANT SCIENCES   \n",
       "12325598         @ Article                                D DE PLANT SCIENCES   \n",
       "12325599         @ Article                                                 -1   \n",
       "12325600               NaN                                                NaN   \n",
       "12325601               NaN                                                NaN   \n",
       "12325602          R Review  [D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY, D DE P...   \n",
       "12325603         @ Article                                D DE PLANT SCIENCES   \n",
       "12325604          R Review                                D DE PLANT SCIENCES   \n",
       "12325605         @ Article                                D DE PLANT SCIENCES   \n",
       "\n",
       "          paper_cite_count  total_refs  paper_char_total  paper_word_total  \\\n",
       "0                        2          37             21733              3780   \n",
       "1                        2          37             21733              3780   \n",
       "2                        2          37             21733              3780   \n",
       "3                        2          37             21733              3780   \n",
       "4                        2          37             21733              3780   \n",
       "5                        2          37             21733              3780   \n",
       "6                        2          37             21733              3780   \n",
       "7                        2          37             21733              3780   \n",
       "8                        2          37             21733              3780   \n",
       "9                        2          37             21733              3780   \n",
       "10                       2          37             21733              3780   \n",
       "11                       2          37             21733              3780   \n",
       "12                       2          37             21733              3780   \n",
       "13                       2          37             21733              3780   \n",
       "14                       2          37             21733              3780   \n",
       "15                       2          37             21733              3780   \n",
       "16                       2          37             21733              3780   \n",
       "17                       2          37             21733              3780   \n",
       "18                       2          37             21733              3780   \n",
       "19                       2          37             21733              3780   \n",
       "20                       2          37             21733              3780   \n",
       "21                       2          37             21733              3780   \n",
       "22                       2          37             21733              3780   \n",
       "23                       2          37             21733              3780   \n",
       "24                       2          37             21733              3780   \n",
       "25                       2          37             21733              3780   \n",
       "26                       2          37             21733              3780   \n",
       "27                       2          37             21733              3780   \n",
       "28                       2          37             21733              3780   \n",
       "29                       2          37             21733              3780   \n",
       "...                    ...         ...               ...               ...   \n",
       "12325576                 2          50             14583              2455   \n",
       "12325577                 2          50             14583              2455   \n",
       "12325578                 2          50             14583              2455   \n",
       "12325579                 2          50             14583              2455   \n",
       "12325580                 2          50             14583              2455   \n",
       "12325581                 2          50             14583              2455   \n",
       "12325582                 2          50             14583              2455   \n",
       "12325583                 2          50             14583              2455   \n",
       "12325584                 2          50             14583              2455   \n",
       "12325585                 2          50             14583              2455   \n",
       "12325586                 2          50             14583              2455   \n",
       "12325587                 2          50             14583              2455   \n",
       "12325588                 2          50             14583              2455   \n",
       "12325589                 2          50             14583              2455   \n",
       "12325590                 2          50             14583              2455   \n",
       "12325591                 2          50             14583              2455   \n",
       "12325592                 2          50             14583              2455   \n",
       "12325593                 2          50             14583              2455   \n",
       "12325594                 2          50             14583              2455   \n",
       "12325595                 2          50             14583              2455   \n",
       "12325596                 2          50             14583              2455   \n",
       "12325597                 2          50             14583              2455   \n",
       "12325598                 2          50             14583              2455   \n",
       "12325599                 2          50             14583              2455   \n",
       "12325600                 2          50             14583              2455   \n",
       "12325601                 2          50             14583              2455   \n",
       "12325602                 2          50             14583              2455   \n",
       "12325603                 2          50             14583              2455   \n",
       "12325604                 2          50             14583              2455   \n",
       "12325605                 2          50             14583              2455   \n",
       "\n",
       "          plos_pub_date  plos_pub_year  plos_article_type  \\\n",
       "0                 SEP 4         2013.0          @ Article   \n",
       "1                 SEP 4         2013.0          @ Article   \n",
       "2                 SEP 4         2013.0          @ Article   \n",
       "3                 SEP 4         2013.0          @ Article   \n",
       "4                 SEP 4         2013.0          @ Article   \n",
       "5                 SEP 4         2013.0          @ Article   \n",
       "6                 SEP 4         2013.0          @ Article   \n",
       "7                 SEP 4         2013.0          @ Article   \n",
       "8                 SEP 4         2013.0          @ Article   \n",
       "9                 SEP 4         2013.0          @ Article   \n",
       "10                SEP 4         2013.0          @ Article   \n",
       "11                SEP 4         2013.0          @ Article   \n",
       "12                SEP 4         2013.0          @ Article   \n",
       "13                SEP 4         2013.0          @ Article   \n",
       "14                SEP 4         2013.0          @ Article   \n",
       "15                SEP 4         2013.0          @ Article   \n",
       "16                SEP 4         2013.0          @ Article   \n",
       "17                SEP 4         2013.0          @ Article   \n",
       "18                SEP 4         2013.0          @ Article   \n",
       "19                SEP 4         2013.0          @ Article   \n",
       "20                SEP 4         2013.0          @ Article   \n",
       "21                SEP 4         2013.0          @ Article   \n",
       "22                SEP 4         2013.0          @ Article   \n",
       "23                SEP 4         2013.0          @ Article   \n",
       "24                SEP 4         2013.0          @ Article   \n",
       "25                SEP 4         2013.0          @ Article   \n",
       "26                SEP 4         2013.0          @ Article   \n",
       "27                SEP 4         2013.0          @ Article   \n",
       "28                SEP 4         2013.0          @ Article   \n",
       "29                SEP 4         2013.0          @ Article   \n",
       "...                 ...            ...                ...   \n",
       "12325576          JUL 2         2013.0          @ Article   \n",
       "12325577          JUL 2         2013.0          @ Article   \n",
       "12325578          JUL 2         2013.0          @ Article   \n",
       "12325579          JUL 2         2013.0          @ Article   \n",
       "12325580          JUL 2         2013.0          @ Article   \n",
       "12325581          JUL 2         2013.0          @ Article   \n",
       "12325582          JUL 2         2013.0          @ Article   \n",
       "12325583          JUL 2         2013.0          @ Article   \n",
       "12325584          JUL 2         2013.0          @ Article   \n",
       "12325585          JUL 2         2013.0          @ Article   \n",
       "12325586          JUL 2         2013.0          @ Article   \n",
       "12325587          JUL 2         2013.0          @ Article   \n",
       "12325588          JUL 2         2013.0          @ Article   \n",
       "12325589          JUL 2         2013.0          @ Article   \n",
       "12325590          JUL 2         2013.0          @ Article   \n",
       "12325591          JUL 2         2013.0          @ Article   \n",
       "12325592          JUL 2         2013.0          @ Article   \n",
       "12325593          JUL 2         2013.0          @ Article   \n",
       "12325594          JUL 2         2013.0          @ Article   \n",
       "12325595          JUL 2         2013.0          @ Article   \n",
       "12325596          JUL 2         2013.0          @ Article   \n",
       "12325597          JUL 2         2013.0          @ Article   \n",
       "12325598          JUL 2         2013.0          @ Article   \n",
       "12325599          JUL 2         2013.0          @ Article   \n",
       "12325600          JUL 2         2013.0          @ Article   \n",
       "12325601          JUL 2         2013.0          @ Article   \n",
       "12325602          JUL 2         2013.0          @ Article   \n",
       "12325603          JUL 2         2013.0          @ Article   \n",
       "12325604          JUL 2         2013.0          @ Article   \n",
       "12325605          JUL 2         2013.0          @ Article   \n",
       "\n",
       "                                 plos_field  \n",
       "0           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "1           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "2           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "3           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "4           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "5           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "6           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "7           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "8           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "9           D RO MULTIDISCIPLINARY SCIENCES  \n",
       "10          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "11          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "12          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "13          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "14          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "15          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "16          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "17          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "18          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "19          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "20          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "21          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "22          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "23          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "24          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "25          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "26          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "27          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "28          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "29          D RO MULTIDISCIPLINARY SCIENCES  \n",
       "...                                     ...  \n",
       "12325576  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325577  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325578  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325579  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325580  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325581  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325582  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325583  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325584  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325585  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325586  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325587  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325588  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325589  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325590  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325591  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325592  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325593  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325594  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325595  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325596  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325597  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325598  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325599  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325600  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325601  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325602  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325603  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325604  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "12325605  [D RO MULTIDISCIPLINARY SCIENCES]  \n",
       "\n",
       "[10848620 rows x 31 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_df.loc[:,'paper_UT'] = ref_.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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