Figure_1A.ipynb

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       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
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       "<style>.container { width:100% !important; }</style>"
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       "<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",
    "import operator\n",
    "import random\n",
    "from  scipy import stats\n",
    "\n",
    "\n",
    "\n",
    "from scipy.stats import norm\n",
    "from sklearn.neighbors import KernelDensity\n",
    "\n",
    "\n",
    "import itertools\n",
    "#sys.path\n",
    "\n",
    "\n",
    "import regex as re\n",
    "import datetime\n",
    "import math\n",
    "import time\n",
    "import itertools\n",
    "\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "from plotly import tools\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "import plotly\n",
    "plotly.tools.set_credentials_file(username='juliettapc', api_key='nM6iUdx6dGaOiPXQTwpP')   # go to: https://plot.ly/settings/api#/   for a new key if needed\n",
    "\n",
    "########## to be able to plot offline (without sending the plots to the plotly server every time)\n",
    "import plotly.offline as offline\n",
    "from plotly.graph_objs import *\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "\n",
    "init_notebook_mode(connected=True)\n",
    "################\n",
    "\n",
    "\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"
   ]
  },
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     "text": [
      "CPU times: user 4.48 s, sys: 3.4 s, total: 7.88 s\n",
      "Wall time: 31.7 s\n",
      "done loading pickles (5787634, 34)\n",
      "(156558, 34)\n"
     ]
    },
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occurence</th>\n",
       "      <th>reference_UT</th>\n",
       "      <th>reference_rank</th>\n",
       "      <th>regex_sect_index</th>\n",
       "      <th>cite_count</th>\n",
       "      <th>ref_pub_year</th>\n",
       "      <th>paper_cite_count</th>\n",
       "      <th>plos_pub_year</th>\n",
       "      <th>sect_char_pos</th>\n",
       "      <th>sect_char_total</th>\n",
       "      <th>...</th>\n",
       "      <th>plos_article_type</th>\n",
       "      <th>num_cit_young_ref_by2009</th>\n",
       "      <th>num_cit_young_ref_by2010</th>\n",
       "      <th>num_cit_young_ref_by2011</th>\n",
       "      <th>num_cit_young_ref_by2012</th>\n",
       "      <th>num_cit_young_ref_by2013</th>\n",
       "      <th>num_cit_young_ref_by2009after8</th>\n",
       "      <th>num_cit_young_ref_by2008after8</th>\n",
       "      <th>num_cit_young_ref_by2010after8</th>\n",
       "      <th>num_cit_young_ref_by2007after8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A1995QY75100004</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>139</td>\n",
       "      <td>4029</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>A1995QY75100004</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>494</td>\n",
       "      <td>5398</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>000263911400006</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>142</td>\n",
       "      <td>4029</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>000289279600018</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>269</td>\n",
       "      <td>4029</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>000289279600018</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>3844</td>\n",
       "      <td>5398</td>\n",
       "      <td>...</td>\n",
       "      <td>@ Article</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   occurence     reference_UT  reference_rank  regex_sect_index  cite_count  \\\n",
       "0          1  A1995QY75100004               1                 0        60.0   \n",
       "1          2  A1995QY75100004               1                 3        60.0   \n",
       "4          1  000263911400006               3                 0         5.0   \n",
       "5          1  000289279600018               4                 0        29.0   \n",
       "6          3  000289279600018               4                 3        29.0   \n",
       "\n",
       "   ref_pub_year  paper_cite_count  plos_pub_year  sect_char_pos  \\\n",
       "0        1995.0                 2         2013.0            139   \n",
       "1        1995.0                 2         2013.0            494   \n",
       "4        2009.0                 2         2013.0            142   \n",
       "5        2011.0                 2         2013.0            269   \n",
       "6        2011.0                 2         2013.0           3844   \n",
       "\n",
       "   sect_char_total              ...               plos_article_type  \\\n",
       "0             4029              ...                       @ Article   \n",
       "1             5398              ...                       @ Article   \n",
       "4             4029              ...                       @ Article   \n",
       "5             4029              ...                       @ Article   \n",
       "6             5398              ...                       @ Article   \n",
       "\n",
       "  num_cit_young_ref_by2009 num_cit_young_ref_by2010  num_cit_young_ref_by2011  \\\n",
       "0                      NaN                      NaN                       NaN   \n",
       "1                      NaN                      NaN                       NaN   \n",
       "4                      0.0                      0.0                       NaN   \n",
       "5                      0.0                      0.0                       0.0   \n",
       "6                      0.0                      0.0                       0.0   \n",
       "\n",
       "   num_cit_young_ref_by2012  num_cit_young_ref_by2013  \\\n",
       "0                       NaN                       NaN   \n",
       "1                       NaN                       NaN   \n",
       "4                       NaN                       NaN   \n",
       "5                       3.0                       NaN   \n",
       "6                       3.0                       NaN   \n",
       "\n",
       "   num_cit_young_ref_by2009after8 num_cit_young_ref_by2008after8  \\\n",
       "0                             NaN                            NaN   \n",
       "1                             NaN                            NaN   \n",
       "4                             5.0                            5.0   \n",
       "5                            39.0                           39.0   \n",
       "6                            39.0                           39.0   \n",
       "\n",
       "   num_cit_young_ref_by2010after8 num_cit_young_ref_by2007after8  \n",
       "0                             NaN                            NaN  \n",
       "1                             NaN                            NaN  \n",
       "4                             5.0                            5.0  \n",
       "5                            39.0                           39.0  \n",
       "6                            39.0                           39.0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "                                  \n",
    "\n",
    "####  NEW FILE INCLUDING EARLY CITATIONS OF YOUNG REFERENCES:   ../data/df_reference_cite_plos_no_self-cit_one_ref_per_sect_ONLY_ARTICLES_early_cit.pkl\n",
    "                                            \n",
    "#%time df_merged = pickle.load(open('../data/df_reference_cite_plos_merged_simplified_added_more_columns_no_self-cit_one_ref_per_sect_ONLY_ARTICLES.pkl', 'rb'))\n",
    "\n",
    "\n",
    "\n",
    "%time df_merged = pickle.load(open('../data/df_reference_cite_plos_no_self-cit_one_ref_per_sect_ONLY_ARTICLES_early_cit_and_after_accretion_time.pkl', 'rb'))\n",
    "print (\"done loading pickles\", df_merged.shape)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "df_merged = df_merged[df_merged['cite_count'] != -1]   # i dont know why, but there are 7 occurrences with value -1\n",
    "\n",
    "\n",
    "\n",
    "plos_df = df_merged.drop_duplicates(subset=['paper_UT'])\n",
    "print (plos_df.shape)\n",
    "\n",
    "\n",
    "\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original size: (5787630, 34)\n",
      "[2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017]\n",
      "size of preselection1 (by plos years): (5787630, 34)\n",
      "size of preselection1 (by isolated/group ref): (5787630, 34) \n",
      " size of preselection2 (by plos journal): (5787630, 34) \n",
      " size of preselection2 (by plos field): (5787630, 34) \n",
      "  No preselection by age of references: (5787630, 34)\n",
      "\n",
      "Tot # records included: 5787630    # number of plos papers: 156558    # unique ref: 2320774 \n",
      "\n",
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/staff/julia/.pyenv/versions/3.6.4/envs/psy/lib/python3.6/site-packages/plotly/graph_objs/_deprecations.py:396: DeprecationWarning:\n",
      "\n",
      "plotly.graph_objs.Margin is deprecated.\n",
      "Please replace it with one of the following more specific types\n",
      "  - plotly.graph_objs.layout.Margin\n",
      "\n",
      "\n",
      "/home/staff/julia/.pyenv/versions/3.6.4/envs/psy/lib/python3.6/site-packages/plotly/offline/offline.py:463: UserWarning:\n",
      "\n",
      "Your filename `/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/fig1a` didn't end with .html. Adding .html to the end of your file.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'file:///home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/fig1a.html'"
      ]
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     "execution_count": 3,
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   ],
   "source": [
    " ### DONE.  MULTIPLOT FOR BARPLOTS OF NUMBER OF RECORDS AND NUMBER OF PAPERS PER JOURNAL\n",
    "\n",
    "    \n",
    "### FIGURE 1A\n",
    "    \n",
    "    ### NOTE: THIS FIGURE REQUIRES HEAVY TOUCH-UPS ON INKSCAPE (FOR THE BRONEN Y AXIS)\n",
    "    \n",
    "    \n",
    "v1_string =  'cite_count'#  log2_num_cit_ref'  ######  log2_num_cit_ref' # #     #'ref_pub_year'     cite_count    diff_year_plos_ref \n",
    "       \n",
    "\n",
    "years=[2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017] \n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "string_filtering_x = 'paper_cite_count'   # bins by plos' citations on the x-axis ###      \n",
    "  \n",
    "string_references_age = \"\"   #young\"#old\"  # young # all   for the selection of what references i include\n",
    "    \n",
    "string_isolated_ref = \"\"   #\"\"   #\"  #\"\"   # 0  or 1 (or empty string, to include all ref)\n",
    "  \n",
    "string_self_ref =0    #\"\"      # \"\"   #1   # 0  or 1 (or empty string, to include all ref)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "######### plos ONE categories. \n",
    "string_code_categ=\"\" #  ojo!!! the codes are strings, not integers. if i want to include multiple subjects:  \"1 2 8\"\n",
    " \n",
    "#  '0': 'Biology and life sciences'             6,032,537\n",
    "#  '1': 'Computer and information sciences'     1,207,799\n",
    "#  '10': 'Social sciences'                      755,899\n",
    "#  '2': 'Earth sciences'                        533,155\n",
    "#  '3': 'Ecology and environmental sciences'    624,142\n",
    "#  '4': 'Engineering and technology'            382,247 \n",
    "#  '5': 'Medicine and health sciences'          4,535,926   \n",
    "#  '6': 'People and places'                     691,523\n",
    "#  '7': 'Physical sciences'                     2,100,827\n",
    "#  '8': 'Research and analysis methods'         3,871,470\n",
    "#  '9': 'Science policy'                        43,360 \n",
    "\n",
    "\n",
    "######### plos journals \n",
    "string_journal=\"\"\n",
    "\n",
    "    # PLOS ONE       6,367,070\n",
    "    # PLOS GENET      149,923\n",
    "    # PLO NE TR D     138,289   # (neglected tropical diseases)\n",
    "    # PLOS PATHOG     109,803\n",
    "    # PLOS COMPUT      77,924\n",
    "    # PLOS BIOL        56,754\n",
    "    # PLOS MED         24,506\n",
    "\n",
    " \n",
    "\n",
    "######### WoS subject categories. \n",
    "string_plos_field=\"\"#['D CU BIOLOGY']\"\n",
    "\n",
    "# ['D RO MULTIDISCIPLINARY SCIENCES']                                                                                                       4464540\n",
    "# ['D CU BIOLOGY']                                                                                                                          1055045\n",
    "# ['D RO MULTIDISCIPLINARY SCIENCES', 'D CU BIOLOGY']                                                                                        847485\n",
    "# ['D KM GENETICS & HEREDITY']                                                                                                               149923\n",
    "# ['D YU TROPICAL MEDICINE', 'D TI PARASITOLOGY']                                                                                            138289\n",
    "# ['D ZE VIROLOGY', 'D QU MICROBIOLOGY', 'D TI PARASITOLOGY']                                                                                109803\n",
    "# ['D CO BIOCHEMICAL RESEARCH METHODS', 'D MC MATHEMATICAL & COMPUTATIONAL BIOLOGY']                                                          77687\n",
    "# ['D CQ BIOCHEMISTRY & MOLECULAR BIOLOGY', 'D CU BIOLOGY']                                                                                   56754\n",
    "# ['D PY MEDICINE, GENERAL & INTERNAL']                                                                                                       24506\n",
    "# ['D CO BIOCHEMICAL RESEARCH METHODS', 'D MC MATHEMATICAL & COMPUTATIONAL BIOLOGY', 'D PO MATHEMATICS, INTERDISCIPLINARY APPLICATIONS']        237\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "      \n",
    "    \n",
    "    \n",
    "print (\"original size:\",df_merged.shape)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "##### preselection by plos year\n",
    "print (years)\n",
    "preselection_df = df_merged[df_merged['plos_pub_year'].isin(years)]  \n",
    "print (\"size of preselection1 (by plos years):\",preselection_df.shape)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#### i remove self-citations\n",
    "if (string_self_ref==0) or  ( string_self_ref == 1 ): \n",
    "    preselection_df = preselection_df[preselection_df['self_citation']== string_self_ref ]  \n",
    "    if string_self_ref ==0:\n",
    "        string_self_ref = \", no self-cit\"\n",
    "    elif string_self_ref ==1:\n",
    "        string_self_ref = \", only self-cit\"\n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "        \n",
    "######### preselection by isolated or group references:\n",
    "if (string_isolated_ref==0) or  ( string_isolated_ref == 1 ): \n",
    "    preselection_df0 = preselection_df[preselection_df['isolated_citation']== string_isolated_ref ]  \n",
    "    \n",
    "    if string_isolated_ref ==0:\n",
    "        string_isolated_ref = \", group ref\"\n",
    "    elif string_isolated_ref ==1:\n",
    "        string_isolated_ref = \", isolated ref\"\n",
    "else:    \n",
    "    preselection_df0 = preselection_df   \n",
    "    print (\"size of preselection1 (by isolated/group ref):\",preselection_df0.shape, string_isolated_ref)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "######### preselection by plos ONE subject category:\n",
    "if string_code_categ==\"\": \n",
    "    preselection_df111 = preselection_df0\n",
    "else:    \n",
    "    if \" \" not in string_code_categ:  # to include one single category\n",
    "        preselection_df111 = preselection_df0[preselection_df0['categ_codes'].str.contains(string_code_categ)]        \n",
    "        string_code_categ = \" \"+dict_code_categ[string_code_categ]  \n",
    "        \n",
    "    else:  # if multiple codes-categories\n",
    "        list_codes = string_code_categ.split(\" \")\n",
    "        print (list_codes)\n",
    "\n",
    "        if len(list_codes) >= 2:              \n",
    "            preselection_df111 = preselection_df0[ preselection_df0['categ_codes'].str.contains('|'.join(list_codes)) ]  # to look for partial matches from a list of strings!!!!!\n",
    "            \n",
    "       \n",
    "        string_code_categ = \"\" \n",
    "        for code in list_codes:\n",
    "            string_code_categ += \"-\"+dict_code_categ[code] \n",
    "            \n",
    "            \n",
    "    print (\" size of preselection (by plos ONE subject category):\",preselection_df111.shape, string_code_categ)\n",
    "           \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "######### preselection by plos journal:\n",
    "if string_journal==\"\": \n",
    "    preselection_df1 = preselection_df111\n",
    "else:    \n",
    "    preselection_df1 = preselection_df111[preselection_df111['plos_j1']== string_journal ]  \n",
    "print (\" size of preselection2 (by plos journal):\",preselection_df1.shape, string_journal)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "######### preselection by plos field:\n",
    "if string_plos_field==\"\": \n",
    "    preselection_df2 = preselection_df1\n",
    "else:    \n",
    "    preselection_df2 = preselection_df1[preselection_df1['plos_field']== string_plos_field ]  \n",
    "print (\" size of preselection2 (by plos field):\",preselection_df2.shape, string_plos_field)\n",
    "\n",
    "string_references_age\n",
    "\n",
    "\n",
    "preselection_df3 = preselection_df2\n",
    "   \n",
    "  \n",
    "\n",
    "\n",
    "\n",
    "if v1_string ==  'cite_count'  or       v1_string ==  'log_num_cit_ref'   or v1_string == 'log2_num_cit_ref':\n",
    "     \n",
    "      \n",
    "    \n",
    "    string_age_selection=''\n",
    "\n",
    "    ##### preselection only young/old references:        \n",
    "    if string_references_age == \"young\":\n",
    "        time_window = 1\n",
    "        string_age_selection=\"only young references from >=\"+ str((min(years)-time_window))\n",
    "        preselection_df3 = preselection_df2[preselection_df2['ref_pub_year'] >= (min(years)-time_window) ]   \n",
    "        print (\"  size of preselection3 (only young references):\",preselection_df3.shape, string_age_selection)\n",
    "        \n",
    "    elif string_references_age == \"old\":\n",
    "        time_window = 10\n",
    "        string_age_selection=\"only old references from <=\"+str((min(years)-time_window))\n",
    "        preselection_df3 = preselection_df2[preselection_df2['ref_pub_year'] <= (min(years)-time_window) ]   \n",
    "        print (\"  size of preselection3 (only young references):\",preselection_df3.shape,string_age_selection )\n",
    "        \n",
    "    else:\n",
    "        string_age_selection=\"young&old\"       \n",
    "        print (\"  No preselection by age of references:\",preselection_df3.shape )\n",
    "          \n",
    "    \n",
    "    \n",
    "\n",
    "N_plos=len(preselection_df3.paper_UT.unique())        ## this values are overall, for the title   \n",
    "N_all=len(preselection_df3)\n",
    " \n",
    "\n",
    "print (\"\\nTot # records included:\",len(preselection_df3),\"   # number of plos papers:\",len(preselection_df3.paper_UT.unique()), \"   # unique ref:\", len(preselection_df3.reference_UT.unique()),'\\n')\n",
    "\n",
    " \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "   \n",
    "list_colors = [\"#FF4040\",\"#4EEE94\",\"#87CEFA\",\"#FFA500\",\"#EE82EE\",\"#8B8B83\", \"#0000FF\"]\n",
    "list_journals = ['PLOS GENET', 'PLO NE TR D','PLOS PATHOG', 'PLOS COMPUT', 'PLOS BIOL',  'PLOS MED', 'PLOS ONE']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "fig = tools.make_subplots(rows=1, cols=2, vertical_spacing=0.001, horizontal_spacing=0.001)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "data = []\n",
    "\n",
    "list_num_papers = []\n",
    "list_num_records = []\n",
    "cont = 0\n",
    "for journal in list_journals:\n",
    "    df_selection = preselection_df3[preselection_df3['plos_j1'] == journal]        \n",
    "    \n",
    "    trace1 = go.Bar(\n",
    "        x=['# papers'],\n",
    "        y=[ len(df_selection.paper_UT.unique())],\n",
    "        marker=dict( color=list_colors[cont]),\n",
    "        #name=journal.replace('PLO NE TR D','PLOS Neglected<br>  Tropical Diseases').replace('PLOS BIOL','PLOS Biology').replace('PLOS COMPUT','PLOS Computational<br>  Biology').replace('PLOS GENET','PLOS Genetics').replace('PLOS MED','PLOS Medicine').replace('PLOS PATHOG','PLOS Pathology'),        \n",
    "        name=journal.replace('PLO NE TR D','Neglected<br>  Tropical Diseases').replace('PLOS BIOL','Biology').replace('PLOS COMPUT','Computational<br>  Biology').replace('PLOS GENET','Genetics').replace('PLOS MED','Medicine').replace('PLOS PATHOG','Pathology'),\n",
    "        showlegend=True\n",
    "    )\n",
    "   \n",
    "    fig.append_trace(trace1, 1, 1)\n",
    "    cont +=1\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "cont = 0    \n",
    "for journal in list_journals:\n",
    "    df_selection = preselection_df3[preselection_df3['plos_j1'] == journal]\n",
    "    \n",
    "    \n",
    "    trace2 = go.Bar(\n",
    "        x=['# records'],   \n",
    "        y=[len(df_selection)],\n",
    "        marker=dict( color=list_colors[cont]),\n",
    "        #name=journal.replace('PLO NE TR D','PLOS Neglected\\n Tropical Diseases').replace('PLOS BIOL','PLOS Biology').replace('PLOS COMPUT','PLOS Computational Biology').replace('PLOS GENET','PLOS Genetics').replace('PLOS MED','PLOS Medicine').replace('PLOS PATHOG','PLOS Pathology'),\n",
    "        showlegend=False,\n",
    "        xaxis='x2',\n",
    "        yaxis='y2'\n",
    "    )\n",
    "    fig.append_trace(trace2, 1, 2)\n",
    "    cont  +=1\n",
    "\n",
    " \n",
    "\n",
    "\n",
    "#fig['layout'].update(height=400, width=1200, legend=dict(orientation=\"h\"), margin=go.Margin( l=200))#, b=150),)# r=50, b=150,  #t=100, # pad=4 ), )\n",
    "fig['layout'].update( legend=dict(x=1.2, y=1.0), margin=go.Margin( l=250))#, b=150),)# r=50, b=150,  #t=100, # pad=4 ), )\n",
    "\n",
    "\n",
    "\n",
    "fig['layout'].update(barmode='stack')\n",
    "\n",
    "\n",
    "fig['layout']['yaxis2'].update(side='right')\n",
    "fig['layout']['yaxis'].update(title='Count')\n",
    "\n",
    "# fig['layout']['yaxis'].update(range=[0, 15000])  \n",
    "# fig['layout']['yaxis2'].update(range=[0, 800000])  \n",
    "\n",
    "\n",
    "#fig['layout']['yaxis'].update(type='log')\n",
    "#fig['layout']['yaxis2'].update(type='log')\n",
    "# fig['layout']['yaxis'].update(range=[0, 4])  ### ojo!! rango logaritmico if axis in log type!  0, 5.3\n",
    "# fig['layout']['yaxis'].update(range=[0, 6.84])  ### ojo!! rango logaritmico!   0, 6.84\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "font_gral=55 # 20 if i wanna see it on the browser, 40 if i care about the png output\n",
    "fig['layout']['font']['size'] = font_gral\n",
    "\n",
    "\n",
    "# Altering axis\n",
    "fig['layout']['xaxis']['tickfont']['size']  = font_gral + 10\n",
    "fig['layout']['xaxis2']['tickfont']['size'] = font_gral + 10\n",
    "fig['layout']['yaxis']['tickfont']['size']  = font_gral + 10\n",
    "fig['layout']['yaxis2']['tickfont']['size'] = font_gral + 10\n",
    "\n",
    "\n",
    "\n",
    "fig['layout']['xaxis'].update(domain=[0, 0.45]) # this is to force the relative location of the two panels with respect to each other\n",
    "fig['layout']['yaxis'].update(domain=[0, 1])\n",
    "\n",
    "fig['layout']['xaxis2'].update( domain=[0.55, 1])\n",
    "fig['layout']['yaxis2'].update(domain=[0, 1],  anchor='x2')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "offline.plot(fig, auto_open=True, image = 'svg', image_filename=\"fig1a\" ,image_width=2000, image_height=1000, \n",
    "              filename='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/fig1a', validate=True)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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