Figure_4c_boxplots_PNAS_PRL_Science_Nature.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\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",
    "#sys.path\n",
    "import scipy\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import datetime\n",
    "import math\n",
    "import time\n",
    "import itertools\n",
    "\n",
    "\n",
    "from pymongo import MongoClient\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",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "label_journal =  'PRE'  #  SCIENCE  NATURE   PNAS   PRL   PRE  JTB\n",
    "\n",
    "\n",
    "\n",
    "  \n",
    "    \n",
    "    \n",
    "#### this values must match the ones used in the \"queries_PRL_Nature_Science_PNAS_etc.ipynb\"  notebook, and they are only to read automatically the files generated there\n",
    "lista_years =[1990,1991,1992,1993,1994,1995]                      \n",
    "    \n",
    "    \n",
    "value_top = 1000            #   SAME VALUE FOR PNAS AND PRL  (1000),   SAME VALUE FOR NATURE AND SCIENCE (2000)    \n",
    "interval_intermediate=[95,105]    \n",
    "interval_low=[10,20]    \n",
    "\n",
    "size_random_sample_papers = 500\n",
    "if label_journal == 'NATURE'  or label_journal == 'SCIENCE':\n",
    "    value_top = 2000   \n",
    "    size_random_sample_papers = 200\n",
    "\n",
    "elif label_journal == 'PNAS':\n",
    "    value_top = 1000 \n",
    "    \n",
    "    \n",
    "elif label_journal == 'PRL':\n",
    "    value_top = 1000   \n",
    "    size_random_sample_papers = 500\n",
    "\n",
    "    \n",
    "elif label_journal == 'PRE':\n",
    "    value_top = 250    \n",
    "    interval_intermediate=[45,55]    \n",
    "    interval_low=[1,4]    \n",
    "    \n",
    "    \n",
    "elif label_journal == 'JTB':\n",
    "    value_top = 170    \n",
    "    interval_intermediate=[55,75]    \n",
    "    interval_low=[5,15]    \n",
    "    size_random_sample_papers = 200\n",
    "    \n",
    "   \n",
    "   \n",
    "    \n",
    "    \n",
    "   \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "dict_UT_ref_of_selected_top_JOURNAL_info = pickle.load(open('../data/dict_citing_UT_of_selected_UT_'+label_journal+'_top_info.pkl', 'rb'))\n",
    "dict_UT_ref_of_selected_intermediate_JOURNAL_info = pickle.load(open('../data/dict_citing_UT_of_selected_UT_'+label_journal+'_intermediate_info.pkl', 'rb'))\n",
    "dict_UT_ref_of_selected_low_JOURNAL_info = pickle.load(open('../data/dict_citing_UT_of_selected_UT_'+label_journal+'_low_info.pkl', 'rb'))\n",
    "dict_UT_ref_of_selected_RANDOM_SAMPLE_JOURNAL_info = pickle.load(open('../data/dict_citing_UT_of_selected_UT_'+label_journal+\"_RANDOM_SAMPLE\"+str(size_random_sample_papers)+'_info.pkl', 'rb'))                                                                        \n",
    "\n",
    "\n",
    "\n",
    "lista_dicts = [dict_UT_ref_of_selected_top_JOURNAL_info,dict_UT_ref_of_selected_intermediate_JOURNAL_info, dict_UT_ref_of_selected_low_JOURNAL_info,\\\n",
    "              dict_UT_ref_of_selected_RANDOM_SAMPLE_JOURNAL_info]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "list_top_UTs = pickle.load(open('../data/list_top_UTs'+str(lista_years[0])+'_'+str(lista_years[-1])+'_'+label_journal+'_'+str(value_top)+'.pkl', 'rb'))\n",
    "list_intermediate_UTs = pickle.load(open('../data/list_intermediate_UTs'+str(lista_years[0])+'_'+str(lista_years[-1])+'_'+label_journal+'_'+str(interval_intermediate[0])+'_'+str(interval_intermediate[-1])+'.pkl', 'rb'))\n",
    "list_low_UTs = pickle.load(open('../data/list_low_UTs'+str(lista_years[0])+'_'+str(lista_years[-1])+'_'+label_journal+'_'+str(interval_low[0])+'_'+str(interval_low[-1])+'.pkl', 'rb'))\n",
    "list_RANDOM_SAMPLE_UTs = pickle.load(open('../data/list_RANDOM_UTs'+str(lista_years[0])+'_'+str(lista_years[-1])+'_'+label_journal+'.pkl', 'rb'))\n",
    "\n",
    "lista_listas_to_look_for = [list_top_UTs, list_intermediate_UTs, list_low_UTs, list_RANDOM_SAMPLE_UTs] \n",
    "    \n",
    "\n",
    "master_dict_JOURNAL_UT_info  =  pickle.load(open('../data/dict_'+label_journal+'_UT_info_year'+str(lista_years[0])+'_'+str(lista_years[-1])+'_any_num_cit.pkl', 'rb'))\n",
    "\n",
    "    \n",
    "print ('done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(master_dict_JOURNAL_UT_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "### box plots with offset for the different traces, so it is easy to see\n",
    "\n",
    "data = []\n",
    "\n",
    "\n",
    "include_random = 'no'\n",
    "\n",
    "label_plot_yplus1 = 'yes'  #'yes'\n",
    "\n",
    "\n",
    "\n",
    "label_log_y = 'log10'  # or log or log10  or no  (ojo! only active if if label_plot_yplus1=1)  ## also comment lines in the layout about range!!\n",
    "\n",
    "\n",
    "\n",
    "initial_offset = -0.3\n",
    "delta_offset_value = 0.15   # this will also afect the width of the boxes\n",
    "\n",
    "\n",
    "\n",
    "range_y = [0.24,2.51]\n",
    "\n",
    "\n",
    "# this is for plotting number of citations (not log of numb cit)\n",
    "# if label_journal == \"PRL\":\n",
    "#     range_y = [.35,3.3 ]\n",
    "    \n",
    "# elif label_journal == \"PRE\":\n",
    "#     range_y = [.2,3.3 ]\n",
    "    \n",
    "# elif label_journal == \"PNAS\":\n",
    "#     range_y = [.25,3.3 ]\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "\n",
    "print (label_journal)\n",
    "    \n",
    "# lista_tickvals_y = [2,5,10,20,50,100,200,500,1000 ]    \n",
    "lista_tickvals_y = [.25,0.5,0.75,1,1.25,1.5,1.75,2,2.25,2.5,2.75]   # this is just the labels of the axis, not the range of values themselves!\n",
    "# if label_log_y != 'no' :\n",
    "#     lista_tickvals_y = [0,1,2,3,4,5,6,7,8,9 ]    \n",
    "\n",
    "\n",
    "\n",
    "boxpoints_option = False  #'outliers'   # 'outliers',  #'suspectedoutliers',  #False, #'all',  # False,# THIS DICTATES THE BEHAVIOUR OF THE WHISKERS!\n",
    "opacity_points = 0.     # to hide all points but keep the same whiskers, set it to 0.0\n",
    "opacity_box = 1.    \n",
    "\n",
    "\n",
    "\n",
    "width_line = 7\n",
    "width_whisker = 1.     # [0,1] value  (horizontal line of whisker)\n",
    "width_line_whiskers = 4.  # vertical line of whisker\n",
    "    \n",
    "    \n",
    "    \n",
    "#### for the colors of top, intermediate and low\n",
    "\n",
    "color_rand =  '#af8dc3'   #  purple    lista_colors[-1]\n",
    "\n",
    "\n",
    "\n",
    "if label_journal == \"PNAS\":\n",
    "    lista_colores = ['#330080','#6600ff','#c299ff']  # purples \n",
    "    color_rand = '#006d2c'\n",
    "    \n",
    "elif label_journal == \"PRE\":\n",
    "    lista_colores =  ['#804000','#ff8c1a','#ffe680']    #    ['#993404','#fe9929','#fee391']   # oranges\n",
    "    \n",
    "elif label_journal == \"PRL\":\n",
    "    lista_colores = ['#08519c', '#6baed6','#c6dbef']   ## blues\n",
    "\n",
    "elif label_journal == \"SCIENCE\":\n",
    "    lista_colores = ['#a50f15','#fb6a4a','#fcbba1']   # reds        \n",
    "        \n",
    "elif label_journal == \"NATURE\":                 \n",
    "     lista_colores = ['#006d2c','#66c2a4','#ccece6']      ## greens            \n",
    "\n",
    "elif label_journal == \"JTB\":\n",
    "    lista_colores = ['#252525','#636363','#bdbdbd']  # greys\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "            \n",
    "title_y='Number of citations'\n",
    "if label_plot_yplus1 =='yes' :            \n",
    "    title_y='Number of citations +1'\n",
    "    if label_log_y == 'log'  :\n",
    "        \n",
    "        title_y='Ln(Number of citations +1)'\n",
    "    if label_log_y == 'log10'  :\n",
    "        \n",
    "        title_y='log10(Number of citations +1)'        \n",
    "            \n",
    "            \n",
    "font_gral = 60\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#################\n",
    "#### TRACE:   THE TOP  values\n",
    "\n",
    "\n",
    "label_selected = 't'\n",
    "if label_selected == 't':  \n",
    "    dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_top_JOURNAL_info \n",
    "    lista_JOURNAL_to_look_for = list_top_UTs\n",
    "    \n",
    "    offset = initial_offset#-2.*offset_value\n",
    "\n",
    "\n",
    "   ### get the data\n",
    "master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low     \n",
    "\n",
    "    JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "    \n",
    "    list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "    for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "        try:\n",
    "            year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "            num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "            reset_year = year - JOURNAL_year\n",
    "\n",
    "            try:\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "            except KeyError:\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "        except: pass\n",
    "\n",
    "        \n",
    "\n",
    "cont  = 0        \n",
    "lista_val_line_x = []\n",
    "lista_val_line_y = []\n",
    "for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "    if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "#         print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "#                2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "        \n",
    "       \n",
    "        lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "        if label_plot_yplus1 =='yes':\n",
    "            lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "            \n",
    "            if label_log_y == 'log':\n",
    "                lista_y = [np.log(i) for i in lista_y]\n",
    "            elif label_log_y == 'log10':\n",
    "                lista_y = [np.log10(i) for i in lista_y]\n",
    "        \n",
    "        \n",
    "        lista_x = [(reset_year+offset)]* len(lista_y)\n",
    "        \n",
    "        \n",
    "        \n",
    "        lista_val_line_y.append(np.median(lista_y))\n",
    "        lista_val_line_x.append(reset_year+offset)\n",
    "        \n",
    "        \n",
    "        name = ''\n",
    "        flag_show_legend = False\n",
    "        if cont ==0:\n",
    "            flag_show_legend = True\n",
    "            name = '>'+str(value_top)+\" citations\"\n",
    "        \n",
    "        \n",
    "        \n",
    "        trace0 = go.Box(\n",
    "                y = lista_y,  \n",
    "                x = lista_x, \n",
    "                name = name,\n",
    "                opacity = opacity_box, \n",
    "                whiskerwidth= 0,#width_whisker ,\n",
    "                marker = dict(\n",
    "                            color = lista_colores[0],#color,#'##size = 0,  # this doesnt work!!!\n",
    "                            #opacity = opacity_points, \n",
    "                        ),\n",
    "                boxmean=True,\n",
    "                showlegend=flag_show_legend,\n",
    "                #boxpoints = False,  #boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "                line=dict(width=0)#width_line_whiskers),\n",
    "            )\n",
    "        \n",
    "       \n",
    "         \n",
    "                \n",
    "                \n",
    "        \n",
    "        cont  +=1\n",
    "        \n",
    "\n",
    "        data.append(trace0)    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "trace1 = go.Scatter(\n",
    "    x = lista_val_line_x,\n",
    "    y = lista_val_line_y,\n",
    "    mode = 'lines+markers',    \n",
    "    line = dict(\n",
    "        color = lista_colores[0],#color,\n",
    "        width = width_line,),\n",
    "    showlegend=False\n",
    "    \n",
    "    \n",
    "    \n",
    ")    \n",
    "data.append(trace1)\n",
    "        \n",
    "# print (offset)\n",
    "        \n",
    "\n",
    "    \n",
    "## i fit to an exponential decay:\n",
    "#  x = numpy.array([10, 19, 30, 35, 51])\n",
    "# >>> y = numpy.array([1, 7, 20, 50, 79])\n",
    "\n",
    "# scipy.optimize.curve_fit(lambda t,a,b: a*numpy.exp(b*t),  x,  y,  p0=(4, 0.1))  #  p0 is the initial guess for the papram\n",
    "# (array([ 4.88003249,  0.05531256]),\n",
    "#  array([[  1.01261314e+01,  -4.31940132e-02],\n",
    "#         [ -4.31940132e-02,   1.91188656e-04]]))   \n",
    "# y ≈ 4.88 exp(0.0553 x). much better.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print (\"fit to trace: highly-cited\")\n",
    "#results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y)\n",
    "# print (results)\n",
    "\n",
    "\n",
    "results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y, p0=(lista_val_line_y[0],0.1))\n",
    "# print (results)\n",
    "\n",
    "\n",
    "A = results[0][0]\n",
    "landa = results[0][1]\n",
    "tau = 1./landa\n",
    "half_life = tau* np.log(2) \n",
    "    \n",
    "print (\"A:\",A,\",   landa:\", landa,',   tau:', round(tau,1),\"years,     half-life:\",round(half_life,1), \" years\\n\")\n",
    "\n",
    "\n",
    "#τ is the time at which the population of the assembly is reduced to 1/e ≈ 0.367879441 times its initial value.\n",
    "### interpretaion of example results:\n",
    "# (array([ 4.88003249,  0.05531256]),\n",
    "#  array([[  1.01261314e+01,  -4.31940132e-02],\n",
    "#         [ -4.31940132e-02,   1.91188656e-04]]))\n",
    "# # y ≈ 4.88 exp(0.0553 x). much better.\n",
    "        \n",
    "  \n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "#################\n",
    "#### TRACE:   THE INTERMEDIATE  values\n",
    "\n",
    "\n",
    "label_selected = 'i'\n",
    "if label_selected == 'i':  \n",
    "    dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_intermediate_JOURNAL_info \n",
    "    lista_JOURNAL_to_look_for = list_intermediate_UTs\n",
    "   \n",
    "    offset += delta_offset_value \n",
    "\n",
    "\n",
    "   ### get the data\n",
    "master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "\n",
    "for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low     \n",
    "\n",
    "    JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "    \n",
    "    list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "    for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "        try:\n",
    "            year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "            num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "            reset_year = year - JOURNAL_year\n",
    "\n",
    "            try:\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "            except KeyError:\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "        except: pass\n",
    "\n",
    "\n",
    "        \n",
    "cont = 0\n",
    "lista_val_line_y = []\n",
    "lista_val_line_x = []        \n",
    "for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "    if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "#         print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "#                2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "        \n",
    "        lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "        if label_plot_yplus1 =='yes':\n",
    "            lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "            \n",
    "            if label_log_y == 'log':\n",
    "                lista_y = [np.log(i) for i in lista_y]\n",
    "            elif label_log_y == 'log10':\n",
    "                lista_y = [np.log10(i) for i in lista_y]        \n",
    "                \n",
    "                \n",
    "                \n",
    "        \n",
    "        lista_x = [(reset_year+offset)]* len(lista_y)\n",
    "\n",
    "        \n",
    "        lista_val_line_y.append(np.median(lista_y))\n",
    "        lista_val_line_x.append(reset_year+offset)\n",
    "        \n",
    "        name = ''\n",
    "        flag_show_legend = False\n",
    "        if cont ==0:\n",
    "            flag_show_legend = True\n",
    "            name = str(interval_intermediate[0])+'-'+str(interval_intermediate[-1])+\" citations\"\n",
    "        \n",
    "            \n",
    "            \n",
    "        trace0 = go.Box(\n",
    "                y = lista_y,  \n",
    "                x = lista_x,  \n",
    "                name = name,\n",
    "                opacity = opacity_box, \n",
    "                whiskerwidth= width_whisker ,\n",
    "                marker = dict(\n",
    "                            color = lista_colores[1],#color,#'##size = 0,  # this doesnt work!!!\n",
    "                            opacity = opacity_points, \n",
    "                        ),\n",
    "                boxmean=True,                \n",
    "                 showlegend=flag_show_legend,\n",
    "                #boxpoints = False,  #boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "                line=dict(width=0)#width_line_whiskers),\n",
    "            )\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        cont  +=1\n",
    "        \n",
    "        data.append(trace0)\n",
    "\n",
    "    \n",
    "        \n",
    "#   value_top = 170    \n",
    "#     interval_intermediate=[55,75]    \n",
    "#     interval_low=[5,15] \n",
    "\n",
    "    \n",
    "    \n",
    "trace1 = go.Scatter(\n",
    "    x = lista_val_line_x,\n",
    "    y = lista_val_line_y,\n",
    "    mode = 'lines+markers',    \n",
    "    line = dict(\n",
    "        color = lista_colores[1],#color,\n",
    "        width = width_line,),\n",
    "    showlegend=False,        \n",
    "    \n",
    ")    \n",
    "data.append(trace1)\n",
    "\n",
    "\n",
    "#print (offset)      \n",
    "\n",
    "        \n",
    "        \n",
    "print (\"fit to trace: medium-cited\")\n",
    "#results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y)\n",
    "# print (results)\n",
    "\n",
    "\n",
    "results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y, p0=(lista_val_line_y[0],0.1))\n",
    "# print (results)\n",
    "\n",
    "\n",
    "A = results[0][0]\n",
    "landa = results[0][1]\n",
    "tau = 1./landa\n",
    "half_life = tau* np.log(2) \n",
    "    \n",
    "print (\"A:\",A,\",   landa:\", landa,',   tau:', round(tau,1),\"years,     half-life:\",round(half_life,1), \" years\\n\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#################\n",
    "#### TRACE:   THE LOW  values\n",
    "\n",
    "   \n",
    "label_selected = 'l'\n",
    "if label_selected == 'l':  \n",
    "   \n",
    "    dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_low_JOURNAL_info \n",
    "    lista_JOURNAL_to_look_for = list_low_UTs\n",
    " \n",
    "           \n",
    "    offset += delta_offset_value \n",
    "\n",
    "\n",
    "   ### get the data\n",
    "master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "\n",
    "for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low     \n",
    "\n",
    "    JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "    \n",
    "    list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "    for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "        try:\n",
    "            year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "            num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "            reset_year = year - JOURNAL_year\n",
    "\n",
    "            try:\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "            except KeyError:\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "                master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "        except: pass\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "        \n",
    "cont = 0\n",
    "lista_val_line_y = []\n",
    "lista_val_line_x = []\n",
    "for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "    if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "#         print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "#                2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "        \n",
    "        lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "        if label_plot_yplus1 =='yes':\n",
    "            lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "            \n",
    "            if label_log_y == 'log':\n",
    "                lista_y = [np.log(i) for i in lista_y]\n",
    "            elif label_log_y == 'log10':\n",
    "                lista_y = [np.log10(i) for i in lista_y]        \n",
    "       \n",
    "    \n",
    "        \n",
    "        lista_x = [(reset_year+offset)]* len(lista_y)\n",
    "        \n",
    "        lista_val_line_y.append(np.median(lista_y))\n",
    "        lista_val_line_x.append(reset_year+offset)\n",
    "\n",
    "        \n",
    "        name = ''\n",
    "        flag_show_legend = False\n",
    "        if cont ==0:\n",
    "            flag_show_legend = True\n",
    "            name = str(interval_low[0])+'-'+str(interval_low[-1])+\" citations\"\n",
    "        \n",
    "        \n",
    "        trace0 = go.Box(\n",
    "                 y = lista_y,  \n",
    "                x = lista_x,    \n",
    "                name = name ,\n",
    "                opacity = opacity_box, \n",
    "                whiskerwidth= width_whisker ,\n",
    "                marker = dict(\n",
    "                            color = lista_colores[2],#color,#'##size = 0,  # this doesnt work!!!\n",
    "                            opacity = opacity_points, \n",
    "                        ),\n",
    "                boxmean=True,                \n",
    "                showlegend=flag_show_legend,\n",
    "                #boxpoints = False,  #boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "                line=dict(width=0)#width_line_whiskers),\n",
    "          \n",
    "            )\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        cont  +=1\n",
    "        \n",
    "        data.append(trace0)\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "trace1 = go.Scatter(\n",
    "    x = lista_val_line_x,\n",
    "    y = lista_val_line_y,\n",
    "    mode = 'lines+markers',    \n",
    "    line = dict(\n",
    "        color = lista_colores[2],#color,\n",
    "        width = width_line,),\n",
    "    showlegend=False,\n",
    "  \n",
    ")    \n",
    "data.append(trace1)\n",
    "\n",
    "# print (offset)        \n",
    "\n",
    "        \n",
    "print (\"fit to trace: poorly-cited\")\n",
    "#results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y)\n",
    "# print (results)\n",
    "\n",
    "\n",
    "results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y, p0=(lista_val_line_y[0],0.1))\n",
    "# print (results)\n",
    "\n",
    "\n",
    "A = results[0][0]\n",
    "landa = results[0][1]\n",
    "tau = 1./landa\n",
    "half_life = tau* np.log(2) \n",
    "    \n",
    "print (\"A:\",A,\",   landa:\", landa,',   tau:', round(tau,1),\"years,     half-life:\",round(half_life,1), \" years\\n\")\n",
    "\n",
    "\n",
    "        \n",
    "        \n",
    " \n",
    "     \n",
    "\n",
    "#################\n",
    "#### TRACE:   THE RANDOM DATA\n",
    "\n",
    "if include_random == 'yes':\n",
    "\n",
    "    label_selected = 'r'\n",
    "    if label_selected == 'r':  \n",
    "        dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_RANDOM_SAMPLE_JOURNAL_info \n",
    "        lista_JOURNAL_to_look_for = list_RANDOM_SAMPLE_UTs\n",
    "\n",
    "        \n",
    "        offset += delta_offset_value \n",
    "\n",
    "\n",
    "       ### get the data\n",
    "    master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "    for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low     \n",
    "\n",
    "        JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "        \n",
    "        list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "        for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "            try:\n",
    "                year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "                num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "                reset_year = year - JOURNAL_year\n",
    "\n",
    "                try:\n",
    "                    master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "                except KeyError:\n",
    "                    master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "                    master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "            except: pass\n",
    "\n",
    "\n",
    "    cont = 0\n",
    "    lista_val_line_y = []\n",
    "    lista_val_line_x = []        \n",
    "\n",
    "    for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "        if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "#             print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "#                    2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "            \n",
    "            lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "            if label_plot_yplus1 =='yes':\n",
    "                lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "                \n",
    "            if label_log_y == 'log':\n",
    "                lista_y = [np.log(i) for i in lista_y]\n",
    "            elif label_log_y == 'log10':\n",
    "                lista_y = [np.log10(i) for i in lista_y]        \n",
    "       \n",
    "                \n",
    "                \n",
    "            lista_x = [(reset_year+offset)]* len(lista_y)\n",
    "\n",
    "            lista_val_line_y.append(np.median(lista_y))\n",
    "            lista_val_line_x.append(reset_year+offset)\n",
    "\n",
    "            flag_show_legend = False\n",
    "            if cont ==0:\n",
    "                 flag_show_legend = True\n",
    "        \n",
    "\n",
    "            trace0 = go.Box(\n",
    "                    y = lista_y,  \n",
    "                    x = lista_x,                \n",
    "                    opacity = opacity_box, \n",
    "                    whiskerwidth= width_whisker ,\n",
    "                    marker = dict(\n",
    "                                color = color_rand,#color,#'##size = 0,  # this doesnt work!!!\n",
    "                                opacity = opacity_points, \n",
    "                            ),\n",
    "                    boxmean=True,\n",
    "                    showlegend=False,\n",
    "                    boxpoints = boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "                    line=dict(width=width_line_whiskers),\n",
    "                )\n",
    "\n",
    "\n",
    "           \n",
    "            cont  +=1\n",
    "\n",
    "\n",
    "\n",
    "            data.append(trace0)\n",
    "\n",
    "            \n",
    "\n",
    "\n",
    "\n",
    "    trace1 = go.Scatter(\n",
    "        x = lista_val_line_x,\n",
    "        y = lista_val_line_y,\n",
    "        mode = 'lines+markers',    \n",
    "        line = dict(\n",
    "            color = color_rand,\n",
    "            width = width_line,),\n",
    "        showlegend=False,\n",
    "\n",
    "\n",
    "    )    \n",
    "    data.append(trace1)\n",
    "\n",
    "\n",
    "   # print (offset)\n",
    "\n",
    "\n",
    "        \n",
    "           \n",
    "      \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "            \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "   \n",
    "layout = Layout(    \n",
    "    title = '',#citing papers of '+label_selected+' JOURNALJOURNALfo+\"<br>N:\"+n,\n",
    "    # showlegend=True,  IF I WRITE THIS INSIDE EACH TRACE, I PICK WHICH LENGENDS TO SHOW!\n",
    "    legend=dict(x=.7, \n",
    "                y=.95,\n",
    "               font=dict(            \n",
    "                    size=font_gral-5,           \n",
    "                ),\n",
    "               ),\n",
    "    yaxis=dict(\n",
    "        title=title_y, \n",
    "        range = range_y,      ###### un-comment this line if i want to plot linear y-axis!!!\n",
    "       #type='log',           ###### un-comment this line if i want to plot linear y-axis!!!\n",
    "        titlefont=dict(            \n",
    "            size=font_gral,\n",
    "            color='black'\n",
    "        #    color='lightgrey'\n",
    "        ),  \n",
    "        tickfont=dict(               \n",
    "            size=font_gral-15,\n",
    "            color='black'\n",
    "        ),\n",
    "        tickvals = lista_tickvals_y ,\n",
    "        showgrid=True,\n",
    "        gridcolor='#525252',\n",
    "        gridwidth=1,\n",
    "    ),  \n",
    "       \n",
    "    \n",
    "    \n",
    "    xaxis=dict(\n",
    "        title='Age difference', \n",
    "        titlefont=dict(            \n",
    "            size=font_gral,\n",
    "            color='black'\n",
    "        #    color='lightgrey'\n",
    "        ),  \n",
    "        tickfont=dict(               \n",
    "            size=font_gral -15, \n",
    "            color='black'\n",
    "        ),\n",
    "        tickvals = [0,1,2,3,4,5,6,7,8,9,10],\n",
    "    ),\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "        margin=go.Margin(\n",
    "        l=300,\n",
    "       # r=50,\n",
    "        b=120,\n",
    "        t=20,\n",
    "        #pad=80\n",
    "    ),\n",
    "\n",
    "    boxgap=0.01\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    ")\n",
    "    \n",
    "    \n",
    "fig = Figure(data, layout =  layout)\n",
    "\n",
    "fig_name='box_plot_offset_'+label_journal\n",
    "path='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/'\n",
    "\n",
    "\n",
    "offline.plot(fig, auto_open=True, image = 'png', image_filename=fig_name ,\n",
    "                 output_type='file',image_width=2200, image_height=1800, filename=path+fig_name+\".html\", validate=False)\n",
    "\n",
    "\n",
    "\n",
    "#### PNAS\n",
    "# fit to trace: highly-cited\n",
    "# A: 79.5775433126 ,   landa: 0.127033119861 ,   tau: 7.9 years,     half-life: 5.5  years\n",
    "\n",
    "# fit to trace: medium-cited\n",
    "# A: 52.3421128686 ,   landa: 0.0857932567604 ,   tau: 11.7 years,     half-life: 8.1  years\n",
    "\n",
    "# fit to trace: poorly-cited\n",
    "# A: 23.1535757648 ,   landa: 0.0282811138629 ,   tau: 35.4 years,     half-life: 24.5  years\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# SCIENCE\n",
    "# fit to trace: highly-cited\n",
    "# A: 73.5566847391 ,   landa: 0.14482791378 ,   tau: 6.9 years,     half-life: 4.8  years\n",
    "\n",
    "# fit to trace: medium-cited\n",
    "# A: 41.880926933 ,   landa: 0.0645084725871 ,   tau: 15.5 years,     half-life: 10.7  years\n",
    "\n",
    "# fit to trace: poorly-cited\n",
    "# A: 19.5927563684 ,   landa: -0.000444452670601 ,   tau: -2250.0 years,     half-life: -1559.6  years\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# NATURE\n",
    "# fit to trace: highly-cited\n",
    "# A: 59.5875784412 ,   landa: 0.0875669423552 ,   tau: 11.4 years,     half-life: 7.9  years\n",
    "\n",
    "# fit to trace: medium-cited\n",
    "# A: 41.6401832307 ,   landa: 0.0480770365393 ,   tau: 20.8 years,     half-life: 14.4  years\n",
    "\n",
    "# fit to trace: poorly-cited\n",
    "# A: 21.0361533805 ,   landa: 0.00208731952169 ,   tau: 479.1 years,     half-life: 332.1  years\n",
    "\n",
    "\n",
    "# PRE\n",
    "# fit to trace: highly-cited\n",
    "# A: 42.484635282 ,   landa: 0.104757926239 ,   tau: 9.5 years,     half-life: 6.6  years\n",
    "\n",
    "# fit to trace: medium-cited\n",
    "# A: 23.7676029139 ,   landa: 0.0834063429733 ,   tau: 12.0 years,     half-life: 8.3  years\n",
    "\n",
    "# fit to trace: poorly-cited\n",
    "# A: 5.46116383289 ,   landa: -0.0350143554733 ,   tau: -28.6 years,     half-life: -19.8  years\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# PRL\n",
    "# fit to trace: highly-cited\n",
    "# A: 60.1177575044 ,   landa: 0.292642091292 ,   tau: 3.4 years,     half-life: 2.4  years\n",
    "\n",
    "# fit to trace: medium-cited\n",
    "# A: 25.9369434312 ,   landa: 0.110985782474 ,   tau: 9.0 years,     half-life: 6.2  years\n",
    "\n",
    "# fit to trace: poorly-cited\n",
    "# A: 11.837182788 ,   landa: 0.0430482652405 ,   tau: 23.2 years,     half-life: 16.1  years\n",
    "\n",
    "\n",
    "\n",
    "# JTB\n",
    "# fit to trace: highly-cited\n",
    "# A: 36.0663881783 ,   landa: -0.0129775250214 ,   tau: -77.1 years,     half-life: -53.4  years\n",
    "\n",
    "# fit to trace: medium-cited\n",
    "# A: 28.7488070266 ,   landa: 0.0149116465487 ,   tau: 67.1 years,     half-life: 46.5  years\n",
    "\n",
    "# fit to trace: poorly-cited\n",
    "# A: 13.0394080734 ,   landa: -0.0284148992609 ,   tau: -35.2 years,     half-life: -24.4  years\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "### running KS test between pairs of distributions:\n",
    "for year in range(11):\n",
    "\n",
    "    for i in range (11):\n",
    "        if i >= year:\n",
    "            lista1 = master_dict_reset_year_list_citations_of_citing_papers[year]\n",
    "            lista2 = master_dict_reset_year_list_citations_of_citing_papers[i]\n",
    "\n",
    "        #     new_lista1 =  [np.log(item) for item in lista1]\n",
    "        #     new_lista2 =  [np.log(item) for item in lista2]\n",
    "\n",
    "\n",
    "            print (\"year\",year, ' vs ',i, ':',stats.ks_2samp(lista1,lista2 )[1])\n",
    "            # If the K-S statistic is small or the p-value is high, then we cannot reject the hypothesis that the distributions of the two samples are the same.\n",
    "    print (\" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # #  p-value of KS test 2-sample PNAS:  higly cited >1000 citation group: \n",
    "\n",
    "# year 0  vs  0 : 1.0\n",
    "# year 0  vs  1 : 0.000210917513462\n",
    "# year 0  vs  2 : 1.05582830755e-07\n",
    "# year 0  vs  3 : 3.71520743252e-13\n",
    "# year 0  vs  4 : 1.03645778229e-15\n",
    "# year 0  vs  5 : 1.73829823336e-19\n",
    "# year 0  vs  6 : 1.83609492139e-21\n",
    "# year 0  vs  7 : 3.12114316708e-23\n",
    "# year 0  vs  8 : 5.84619288202e-25\n",
    "# year 0  vs  9 : 9.79069887555e-28\n",
    "# year 0  vs  10 : 3.93470346264e-29\n",
    " \n",
    "# year 1  vs  1 : 1.0\n",
    "# year 1  vs  2 : 0.000158299312426\n",
    "# year 1  vs  3 : 1.29701471806e-22\n",
    "# year 1  vs  4 : 1.97770571732e-38\n",
    "# year 1  vs  5 : 1.03138839049e-62\n",
    "# year 1  vs  6 : 3.27068773788e-73\n",
    "# year 1  vs  7 : 7.80545764259e-87\n",
    "# year 1  vs  8 : 2.06661134251e-86\n",
    "# year 1  vs  9 : 3.48920492618e-102\n",
    "# year 1  vs  10 : 1.24863780558e-113\n",
    " \n",
    "# year 2  vs  2 : 1.0\n",
    "# year 2  vs  3 : 3.25924567916e-12\n",
    "# year 2  vs  4 : 1.48561343278e-28\n",
    "# year 2  vs  5 : 7.03419140466e-56\n",
    "# year 2  vs  6 : 4.09172338702e-68\n",
    "# year 2  vs  7 : 9.74690974768e-85\n",
    "# year 2  vs  8 : 1.53407623321e-83\n",
    "# year 2  vs  9 : 5.13659279589e-103\n",
    "# year 2  vs  10 : 1.05115221068e-117\n",
    " \n",
    "# year 3  vs  3 : 1.0\n",
    "# year 3  vs  4 : 1.00120022725e-05\n",
    "# year 3  vs  5 : 8.34676369215e-23\n",
    "# year 3  vs  6 : 7.44348565292e-32\n",
    "# year 3  vs  7 : 1.04575263071e-43\n",
    "# year 3  vs  8 : 3.75994651178e-42\n",
    "# year 3  vs  9 : 1.19875169438e-57\n",
    "# year 3  vs  10 : 1.59685682594e-66\n",
    " \n",
    "# year 4  vs  4 : 1.0\n",
    "# year 4  vs  5 : 2.84560788828e-07\n",
    "# year 4  vs  6 : 2.74269630683e-13\n",
    "# year 4  vs  7 : 5.21014652143e-22\n",
    "# year 4  vs  8 : 7.76746170027e-21\n",
    "# year 4  vs  9 : 1.9017863499e-32\n",
    "# year 4  vs  10 : 2.9430417224e-42\n",
    " \n",
    "# year 5  vs  5 : 1.0\n",
    "# year 5  vs  6 : 0.00611795450185\n",
    "# year 5  vs  7 : 4.52871757923e-07\n",
    "# year 5  vs  8 : 1.23691384453e-06\n",
    "# year 5  vs  9 : 1.47378336628e-13\n",
    "# year 5  vs  10 : 6.79173514922e-21\n",
    " \n",
    "# year 6  vs  6 : 1.0\n",
    "# year 6  vs  7 : 0.0323595549436\n",
    "# year 6  vs  8 : 0.0081530813193\n",
    "# year 6  vs  9 : 6.77596666376e-07\n",
    "# year 6  vs  10 : 1.1121276272e-12\n",
    " \n",
    "# year 7  vs  7 : 1.0\n",
    "# year 7  vs  8 : 0.35072365099\n",
    "# year 7  vs  9 : 0.00262698923883\n",
    "# year 7  vs  10 : 7.41960596944e-06\n",
    " \n",
    "# year 8  vs  8 : 1.0\n",
    "# year 8  vs  9 : 0.0475819264305\n",
    "# year 8  vs  10 : 1.96844116123e-05\n",
    " \n",
    "# year 9  vs  9 : 1.0\n",
    "# year 9  vs  10 : 0.155089817496\n",
    " \n",
    "# year 10  vs  10 : 1.0\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # #  p-value of KS test 2-sample PNAS:  mid-cited 95-105 citation group: \n",
    "\n",
    "\n",
    "# year 0  vs  0 : 1.0\n",
    "# year 0  vs  1 : 0.000346657538114\n",
    "# year 0  vs  2 : 6.62404878967e-11\n",
    "# year 0  vs  3 : 1.1735372575e-17\n",
    "# year 0  vs  4 : 7.13173537247e-22\n",
    "# year 0  vs  5 : 2.52897968279e-27\n",
    "# year 0  vs  6 : 2.33960216454e-31\n",
    "# year 0  vs  7 : 9.72341361601e-35\n",
    "# year 0  vs  8 : 2.1404083223e-37\n",
    "# year 0  vs  9 : 8.18990172896e-38\n",
    "# year 0  vs  10 : 4.63643582654e-41\n",
    " \n",
    "# year 1  vs  1 : 1.0\n",
    "# year 1  vs  2 : 5.2072604308e-09\n",
    "# year 1  vs  3 : 1.27422317623e-21\n",
    "# year 1  vs  4 : 1.02766593066e-30\n",
    "# year 1  vs  5 : 3.66738861947e-45\n",
    "# year 1  vs  6 : 2.43073957566e-53\n",
    "# year 1  vs  7 : 1.189568019e-57\n",
    "# year 1  vs  8 : 2.80484747483e-60\n",
    "# year 1  vs  9 : 2.48527039364e-61\n",
    "# year 1  vs  10 : 1.02672651051e-61\n",
    " \n",
    "# year 2  vs  2 : 1.0\n",
    "# year 2  vs  3 : 4.1479298197e-06\n",
    "# year 2  vs  4 : 7.99384247418e-11\n",
    "# year 2  vs  5 : 1.00598688623e-20\n",
    "# year 2  vs  6 : 1.55940978237e-27\n",
    "# year 2  vs  7 : 1.7130918772e-31\n",
    "# year 2  vs  8 : 3.7989772632e-34\n",
    "# year 2  vs  9 : 2.27693731105e-36\n",
    "# year 2  vs  10 : 4.76961337454e-38\n",
    " \n",
    "# year 3  vs  3 : 1.0\n",
    "# year 3  vs  4 : 0.0238203933017\n",
    "# year 3  vs  5 : 2.70286350838e-07\n",
    "# year 3  vs  6 : 4.03873266956e-12\n",
    "# year 3  vs  7 : 3.1035484211e-15\n",
    "# year 3  vs  8 : 8.8689167124e-18\n",
    "# year 3  vs  9 : 3.0544969574e-19\n",
    "# year 3  vs  10 : 3.43934626601e-22\n",
    " \n",
    "# year 4  vs  4 : 1.0\n",
    "# year 4  vs  5 : 0.000673033759815\n",
    "# year 4  vs  6 : 1.21721306642e-07\n",
    "# year 4  vs  7 : 2.44614614584e-08\n",
    "# year 4  vs  8 : 8.46894217783e-12\n",
    "# year 4  vs  9 : 3.01157646003e-14\n",
    "# year 4  vs  10 : 3.11955128165e-14\n",
    " \n",
    "# year 5  vs  5 : 1.0\n",
    "# year 5  vs  6 : 0.00589780550143\n",
    "# year 5  vs  7 : 0.00303557473815\n",
    "# year 5  vs  8 : 0.000258540315954\n",
    "# year 5  vs  9 : 1.27567039179e-07\n",
    "# year 5  vs  10 : 1.77525002758e-07\n",
    " \n",
    "# year 6  vs  6 : 1.0\n",
    "# year 6  vs  7 : 0.525025043231\n",
    "# year 6  vs  8 : 0.0917953714075\n",
    "# year 6  vs  9 : 0.00859236780187\n",
    "# year 6  vs  10 : 0.000447849707589\n",
    " \n",
    "# year 7  vs  7 : 1.0\n",
    "# year 7  vs  8 : 0.639813192153\n",
    "# year 7  vs  9 : 0.0134988869785\n",
    "# year 7  vs  10 : 0.0386136971503\n",
    " \n",
    "# year 8  vs  8 : 1.0\n",
    "# year 8  vs  9 : 0.0706528698986\n",
    "# year 8  vs  10 : 0.284923308628\n",
    " \n",
    "# year 9  vs  9 : 1.0\n",
    "# year 9  vs  10 : 0.410735357193\n",
    " \n",
    "# year 10  vs  10 : 1.0\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # #  p-value of KS test 2-sample PNAS:  poorly-cited 10-20 citation group: \n",
    "\n",
    "# year 0  vs  0 : 1.0\n",
    "# year 0  vs  1 : 0.321910833743\n",
    "# year 0  vs  2 : 0.800600001524\n",
    "# year 0  vs  3 : 0.443181313465\n",
    "# year 0  vs  4 : 0.40315511614\n",
    "# year 0  vs  5 : 0.0230638992049\n",
    "# year 0  vs  6 : 0.549012462462\n",
    "# year 0  vs  7 : 0.0959430636799\n",
    "# year 0  vs  8 : 0.613172478029\n",
    "# year 0  vs  9 : 0.185408178949\n",
    "# year 0  vs  10 : 0.0521202761056\n",
    " \n",
    "# year 1  vs  1 : 1.0\n",
    "# year 1  vs  2 : 0.238447923276\n",
    "# year 1  vs  3 : 0.00284872228279\n",
    "# year 1  vs  4 : 0.00187736860762\n",
    "# year 1  vs  5 : 2.49839968703e-06\n",
    "# year 1  vs  6 : 0.00540240869484\n",
    "# year 1  vs  7 : 0.000874010984586\n",
    "# year 1  vs  8 : 0.0873333450301\n",
    "# year 1  vs  9 : 0.00013761848733\n",
    "# year 1  vs  10 : 3.99692138851e-05\n",
    " \n",
    "# year 2  vs  2 : 1.0\n",
    "# year 2  vs  3 : 0.223667437532\n",
    "# year 2  vs  4 : 0.0412316346192\n",
    "# year 2  vs  5 : 3.24851205834e-05\n",
    "# year 2  vs  6 : 0.124351819987\n",
    "# year 2  vs  7 : 0.00634322132845\n",
    "# year 2  vs  8 : 0.154375886196\n",
    "# year 2  vs  9 : 0.00278514547228\n",
    "# year 2  vs  10 : 0.000534915445594\n",
    " \n",
    "# year 3  vs  3 : 1.0\n",
    "# year 3  vs  4 : 0.49924269147\n",
    "# year 3  vs  5 : 0.00339927087022\n",
    "# year 3  vs  6 : 0.57055632215\n",
    "# year 3  vs  7 : 0.0760320913733\n",
    "# year 3  vs  8 : 0.931907991639\n",
    "# year 3  vs  9 : 0.0381657327876\n",
    "# year 3  vs  10 : 0.0133309713848\n",
    " \n",
    "# year 4  vs  4 : 1.0\n",
    "# year 4  vs  5 : 0.128416922032\n",
    "# year 4  vs  6 : 0.958493488876\n",
    "# year 4  vs  7 : 0.57653356681\n",
    "# year 4  vs  8 : 0.663656576336\n",
    "# year 4  vs  9 : 0.254970067512\n",
    "# year 4  vs  10 : 0.108221945786\n",
    " \n",
    "# year 5  vs  5 : 1.0\n",
    "# year 5  vs  6 : 0.175355146916\n",
    "# year 5  vs  7 : 0.849113900902\n",
    "# year 5  vs  8 : 0.0272754410378\n",
    "# year 5  vs  9 : 0.980685059436\n",
    "# year 5  vs  10 : 0.734218792938\n",
    " \n",
    "# year 6  vs  6 : 1.0\n",
    "# year 6  vs  7 : 0.659437612892\n",
    "# year 6  vs  8 : 0.564305279925\n",
    "# year 6  vs  9 : 0.566571893762\n",
    "# year 6  vs  10 : 0.220440191632\n",
    " \n",
    "# year 7  vs  7 : 1.0\n",
    "# year 7  vs  8 : 0.278196294869\n",
    "# year 7  vs  9 : 0.536192159065\n",
    "# year 7  vs  10 : 0.279270771516\n",
    " \n",
    "# year 8  vs  8 : 1.0\n",
    "# year 8  vs  9 : 0.081941597619\n",
    "# year 8  vs  10 : 0.045927424104\n",
    " \n",
    "# year 9  vs  9 : 1.0\n",
    "# year 9  vs  10 : 0.606183844246\n",
    " \n",
    "# year 10  vs  10 : 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # #  p-value of KS test 2-sample PRE, >250 citations group:\n",
    "\n",
    "# year 0  vs  0 : 1.0\n",
    "# year 0  vs  1 : 0.51709066564\n",
    "# year 0  vs  2 : 0.0514679389187\n",
    "# year 0  vs  3 : 0.00692180847247\n",
    "# year 0  vs  4 : 0.00710737533515\n",
    "# year 0  vs  5 : 0.00460542067032\n",
    "# year 0  vs  6 : 5.40741706755e-05\n",
    "# year 0  vs  7 : 0.000463542444765\n",
    "# year 0  vs  8 : 3.31640589664e-05\n",
    "# year 0  vs  9 : 8.11946494924e-05\n",
    "# year 0  vs  10 : 2.13264295159e-05\n",
    " \n",
    "# year 1  vs  1 : 1.0\n",
    "# year 1  vs  2 : 0.000276911963314\n",
    "# year 1  vs  3 : 5.06503782435e-05\n",
    "# year 1  vs  4 : 5.09751692474e-05\n",
    "# year 1  vs  5 : 5.92843733064e-06\n",
    "# year 1  vs  6 : 5.24717217854e-10\n",
    "# year 1  vs  7 : 1.65087873865e-09\n",
    "# year 1  vs  8 : 1.32314213031e-10\n",
    "# year 1  vs  9 : 2.97862248361e-10\n",
    "# year 1  vs  10 : 1.95773615977e-13\n",
    " \n",
    "# year 2  vs  2 : 1.0\n",
    "# year 2  vs  3 : 0.482870450704\n",
    "# year 2  vs  4 : 0.227547706282\n",
    "# year 2  vs  5 : 0.0968981616195\n",
    "# year 2  vs  6 : 0.00025772437401\n",
    "# year 2  vs  7 : 0.00624652367325\n",
    "# year 2  vs  8 : 0.000316814345548\n",
    "# year 2  vs  9 : 0.00288939931427\n",
    "# year 2  vs  10 : 4.04438640525e-05\n",
    " \n",
    "# year 3  vs  3 : 1.0\n",
    "# year 3  vs  4 : 0.980138646443\n",
    "# year 3  vs  5 : 0.497937207215\n",
    "# year 3  vs  6 : 0.0103700085649\n",
    "# year 3  vs  7 : 0.10231043863\n",
    "# year 3  vs  8 : 0.014327743148\n",
    "# year 3  vs  9 : 0.0693743042052\n",
    "# year 3  vs  10 : 0.00144759912811\n",
    " \n",
    "# year 4  vs  4 : 1.0\n",
    "# year 4  vs  5 : 0.802559445596\n",
    "# year 4  vs  6 : 0.0488576595894\n",
    "# year 4  vs  7 : 0.222591497844\n",
    "# year 4  vs  8 : 0.0240301565208\n",
    "# year 4  vs  9 : 0.0956023816707\n",
    "# year 4  vs  10 : 0.00127924769725\n",
    " \n",
    "# year 5  vs  5 : 1.0\n",
    "# year 5  vs  6 : 0.063760172978\n",
    "# year 5  vs  7 : 0.369198106168\n",
    "# year 5  vs  8 : 0.0762577321672\n",
    "# year 5  vs  9 : 0.103908657036\n",
    "# year 5  vs  10 : 0.00339608560251\n",
    " \n",
    "# year 6  vs  6 : 1.0\n",
    "# year 6  vs  7 : 0.741201841833\n",
    "# year 6  vs  8 : 0.723611057391\n",
    "# year 6  vs  9 : 0.789698810357\n",
    "# year 6  vs  10 : 0.0779946041634\n",
    " \n",
    "# year 7  vs  7 : 1.0\n",
    "# year 7  vs  8 : 0.716556243384\n",
    "# year 7  vs  9 : 0.764406144419\n",
    "# year 7  vs  10 : 0.122174019579\n",
    " \n",
    "# year 8  vs  8 : 1.0\n",
    "# year 8  vs  9 : 0.900425722275\n",
    "# year 8  vs  10 : 0.267012134444\n",
    " \n",
    "# year 9  vs  9 : 1.0\n",
    "# year 9  vs  10 : 0.164937678702\n",
    " \n",
    "# year 10  vs  10 : 1.0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # #  p-value of KS test 2-sample PRE, mid-cited 45-55 citations group:\n",
    "\n",
    "# year 0  vs  0 : 1.0\n",
    "# year 0  vs  1 : 0.173736586788\n",
    "# year 0  vs  2 : 0.00495055763109\n",
    "# year 0  vs  3 : 0.000624429006997\n",
    "# year 0  vs  4 : 0.000690046643794\n",
    "# year 0  vs  5 : 0.000478833940901\n",
    "# year 0  vs  6 : 1.34447401913e-06\n",
    "# year 0  vs  7 : 2.81224034467e-07\n",
    "# year 0  vs  8 : 9.84831065443e-06\n",
    "# year 0  vs  9 : 1.62761153675e-08\n",
    "# year 0  vs  10 : 7.3176291294e-08\n",
    " \n",
    "# year 1  vs  1 : 1.0\n",
    "# year 1  vs  2 : 0.197081074161\n",
    "# year 1  vs  3 : 0.0150172929202\n",
    "# year 1  vs  4 : 0.0017386611302\n",
    "# year 1  vs  5 : 0.00385574332135\n",
    "# year 1  vs  6 : 4.01746181283e-07\n",
    "# year 1  vs  7 : 5.35037176585e-08\n",
    "# year 1  vs  8 : 4.53238800735e-07\n",
    "# year 1  vs  9 : 4.69057193599e-10\n",
    "# year 1  vs  10 : 1.20411847247e-09\n",
    " \n",
    "# year 2  vs  2 : 1.0\n",
    "# year 2  vs  3 : 0.357152730524\n",
    "# year 2  vs  4 : 0.112940720893\n",
    "# year 2  vs  5 : 0.354961296283\n",
    "# year 2  vs  6 : 9.65102433202e-05\n",
    "# year 2  vs  7 : 0.000241966280135\n",
    "# year 2  vs  8 : 0.000389146215715\n",
    "# year 2  vs  9 : 5.80347965289e-06\n",
    "# year 2  vs  10 : 5.10979091122e-06\n",
    " \n",
    "# year 3  vs  3 : 1.0\n",
    "# year 3  vs  4 : 0.467607865706\n",
    "# year 3  vs  5 : 0.929039815894\n",
    "# year 3  vs  6 : 0.0138493764536\n",
    "# year 3  vs  7 : 0.00882465622541\n",
    "# year 3  vs  8 : 0.0106845805468\n",
    "# year 3  vs  9 : 0.000174330542415\n",
    "# year 3  vs  10 : 0.000124277117491\n",
    " \n",
    "# year 4  vs  4 : 1.0\n",
    "# year 4  vs  5 : 0.426819288272\n",
    "# year 4  vs  6 : 0.00445589549017\n",
    "# year 4  vs  7 : 0.00854959053356\n",
    "# year 4  vs  8 : 0.00068009099017\n",
    "# year 4  vs  9 : 4.19631011382e-05\n",
    "# year 4  vs  10 : 9.47138241207e-06\n",
    " \n",
    "# year 5  vs  5 : 1.0\n",
    "# year 5  vs  6 : 0.0147751585864\n",
    "# year 5  vs  7 : 0.0164978795275\n",
    "# year 5  vs  8 : 0.0507594032608\n",
    "# year 5  vs  9 : 0.00174758192767\n",
    "# year 5  vs  10 : 0.00132947842804\n",
    " \n",
    "# year 6  vs  6 : 1.0\n",
    "# year 6  vs  7 : 0.890820648709\n",
    "# year 6  vs  8 : 0.79386859213\n",
    "# year 6  vs  9 : 0.631030276644\n",
    "# year 6  vs  10 : 0.315089407451\n",
    " \n",
    "# year 7  vs  7 : 1.0\n",
    "# year 7  vs  8 : 0.402462641025\n",
    "# year 7  vs  9 : 0.232022905926\n",
    "# year 7  vs  10 : 0.259501143441\n",
    " \n",
    "# year 8  vs  8 : 1.0\n",
    "# year 8  vs  9 : 0.339994369434\n",
    "# year 8  vs  10 : 0.387501199775\n",
    " \n",
    "# year 9  vs  9 : 1.0\n",
    "# year 9  vs  10 : 0.988245465301\n",
    " \n",
    "# year 10  vs  10 : 1.0\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # # # # #  p-value of KS test 2-sample PRE, 1-4 citations group:\n",
    "\n",
    "# year 0  vs  0 : 1.0\n",
    "# year 0  vs  1 : 0.779324705415\n",
    "# year 0  vs  2 : 0.449909696512\n",
    "# year 0  vs  3 : 0.585892142938\n",
    "# year 0  vs  4 : 0.914255026344\n",
    "# year 0  vs  5 : 0.147269101293\n",
    "# year 0  vs  6 : 0.600851233807\n",
    "# year 0  vs  7 : 0.559066975535\n",
    "# year 0  vs  8 : 0.487560822256\n",
    "# year 0  vs  9 : 0.396697114537\n",
    "# year 0  vs  10 : 0.519916998559\n",
    " \n",
    "# year 1  vs  1 : 1.0\n",
    "# year 1  vs  2 : 0.464353086552\n",
    "# year 1  vs  3 : 0.814702074918\n",
    "# year 1  vs  4 : 0.712065151425\n",
    "# year 1  vs  5 : 0.424931641219\n",
    "# year 1  vs  6 : 0.961174786319\n",
    "# year 1  vs  7 : 0.904028080816\n",
    "# year 1  vs  8 : 0.821002170201\n",
    "# year 1  vs  9 : 0.8504974226\n",
    "# year 1  vs  10 : 0.946156952098\n",
    " \n",
    "# year 2  vs  2 : 1.0\n",
    "# year 2  vs  3 : 0.867461716305\n",
    "# year 2  vs  4 : 0.608180422492\n",
    "# year 2  vs  5 : 0.788572338437\n",
    "# year 2  vs  6 : 0.953643594811\n",
    "# year 2  vs  7 : 0.955174245517\n",
    "# year 2  vs  8 : 0.716603354124\n",
    "# year 2  vs  9 : 0.90200135416\n",
    "# year 2  vs  10 : 0.956307934207\n",
    " \n",
    "# year 3  vs  3 : 1.0\n",
    "# year 3  vs  4 : 0.838789845978\n",
    "# year 3  vs  5 : 0.478171406848\n",
    "# year 3  vs  6 : 0.558830508542\n",
    "# year 3  vs  7 : 0.811075694966\n",
    "# year 3  vs  8 : 0.393954168423\n",
    "# year 3  vs  9 : 0.491291571594\n",
    "# year 3  vs  10 : 0.426055725244\n",
    " \n",
    "# year 4  vs  4 : 1.0\n",
    "# year 4  vs  5 : 0.29289252297\n",
    "# year 4  vs  6 : 0.807037371538\n",
    "# year 4  vs  7 : 0.498175846422\n",
    "# year 4  vs  8 : 0.676920690355\n",
    "# year 4  vs  9 : 0.701608347433\n",
    "# year 4  vs  10 : 0.717749222223\n",
    " \n",
    "# year 5  vs  5 : 1.0\n",
    "# year 5  vs  6 : 0.814671707811\n",
    "# year 5  vs  7 : 0.739380519191\n",
    "# year 5  vs  8 : 0.619699608533\n",
    "# year 5  vs  9 : 0.985603821487\n",
    "# year 5  vs  10 : 0.959093518591\n",
    " \n",
    "# year 6  vs  6 : 1.0\n",
    "# year 6  vs  7 : 0.974294588935\n",
    "# year 6  vs  8 : 0.895475793795\n",
    "# year 6  vs  9 : 0.970504614329\n",
    "# year 6  vs  10 : 0.98494572419\n",
    " \n",
    "# year 7  vs  7 : 1.0\n",
    "# year 7  vs  8 : 0.999987651055\n",
    "# year 7  vs  9 : 0.929712855395\n",
    "# year 7  vs  10 : 0.866516304392\n",
    " \n",
    "# year 8  vs  8 : 1.0\n",
    "# year 8  vs  9 : 0.982650737704\n",
    "# year 8  vs  10 : 0.881223018063\n",
    " \n",
    "# year 9  vs  9 : 1.0\n",
    "# year 9  vs  10 : 0.999555214362\n",
    " \n",
    "# year 10  vs  10 : 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # box plotting for citing papers of top/intermediate/low   JOURNALs:\n",
    "\n",
    "\n",
    "\n",
    "# label_selected = 't'  #  top  or intermediate or low  or random\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# offset = 0.\n",
    "# label_plot_yplus1  ='yes'  # plotin the y axis either number of citations, or number of citations  +1\n",
    "\n",
    "# opacity= .75\n",
    "\n",
    "# range_y = [0.9,4 ]\n",
    "\n",
    "# if label_journal == \"PRL\":\n",
    "#     range_y = [.35,4 ]\n",
    "    \n",
    "# elif label_journal == \"PRE\":\n",
    "#     range_y = [.5,3.6 ]\n",
    " \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# if label_selected == 't':  \n",
    "#     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_top_JOURNAL_info \n",
    "#     lista_JOURNAL_to_look_for = list_top_UTs\n",
    "    \n",
    "#     offset = 0.01\n",
    "    \n",
    "#     color= '#999999'   # grey       #lista_colors[0]\n",
    "# #     info = ', >='+str(value_top)+'citations'\n",
    "   \n",
    "    \n",
    "# elif label_selected == 'i':  \n",
    "#     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_intermediate_JOURNAL_info \n",
    "#     lista_JOURNAL_to_look_for = list_intermediate_UTs\n",
    "    \n",
    "#     color=  '#7fbf7b' #green     lista_colors[2]\n",
    "#     opacity= 1.\n",
    "    \n",
    "# #     info = linfo = ', '+str(interval_intermediate)+' citations'\n",
    "    \n",
    "# elif label_selected == 'l':  \n",
    "#     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_low_JOURNAL_info \n",
    "#     lista_JOURNAL_to_look_for = list_low_UTs\n",
    "    \n",
    "#     color= '#ef8a62'     #  salmon    lista_colors[1]\n",
    "    \n",
    "#     #info =  ', '+str(interval_low)+' citations'\n",
    "    \n",
    "#     opacity= 1.\n",
    "# elif label_selected == 'r':      \n",
    "#     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_RANDOM_SAMPLE_JOURNAL_info \n",
    "#     lista_JOURNAL_to_look_for = list_RANDOM_SAMPLE_UTs\n",
    "    \n",
    "#     color=  '#af8dc3'   #  purple    lista_colors[-1]\n",
    "#     offset = -0.01\n",
    "    \n",
    "#     #info =  ', random sample'            \n",
    "    \n",
    "    \n",
    "# else:\n",
    "#     exit()\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "# n= str(len(dict_UT_ref_of_selected_JOURNAL_info))    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low\n",
    "    \n",
    "   \n",
    "\n",
    "#     JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "#     #print (JOURNAL_UT, JOURNAL_year)\n",
    "#     list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "#     for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "#         try:\n",
    "#             year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "#             num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "#             reset_year = year - JOURNAL_year\n",
    "\n",
    "#             try:\n",
    "#                 master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "#             except KeyError:\n",
    "#                 master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "#                 master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "#         except: pass\n",
    "\n",
    "    \n",
    "        \n",
    "        \n",
    "    \n",
    "# for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "#     if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "#         print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "#                2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "#         title_y='Number citations of citing paper'\n",
    "#         lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "#         if label_plot_yplus1 =='yes':\n",
    "#             lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "#             title_y='Number citations of citing paper +1'\n",
    "            \n",
    "#         trace0 = go.Box(\n",
    "#                 y=lista_y,  \n",
    "#                # x=[(reset_year+offset)]*len(lista_y),   #  to fabricate some offset between the different traces\n",
    "#                 name = str(reset_year),#+' (N:'+str(len(master_dict_reset_year_list_citations_of_citing_papers[reset_year]))+\")\",\n",
    "#                 opacity = opacity, \n",
    "#                 marker = dict(\n",
    "#                         color = color,#'#808080',\n",
    "#                         ),\n",
    "#             boxmean=True,\n",
    "#             )\n",
    "\n",
    "#         data.append(trace0)\n",
    "        \n",
    "# #         print (lista_y)\n",
    "# #         print ([reset_year+offset]*len(lista_y))\n",
    "\n",
    "        \n",
    "        \n",
    " \n",
    "# font_gral = 60\n",
    "   \n",
    "# layout = Layout(    \n",
    "#     title = '',#citing papers of '+label_selected+' JOURNALJOURNALfo+\"<br>N:\"+n,\n",
    "#     showlegend=False,\n",
    " \n",
    "#     yaxis=dict(\n",
    "#         title=title_y, \n",
    "#         range = range_y,  #[0.1,3.5],   # ojo!!! if long scale, also log range, not linear!!!   #[.5,4.5], \n",
    "#         type='log',\n",
    "#         titlefont=dict(            \n",
    "#             size=font_gral,\n",
    "#             color='black'\n",
    "#         #    color='lightgrey'\n",
    "#         ),  \n",
    "#         tickfont=dict(               \n",
    "#             size=font_gral-15,\n",
    "#             color='black'\n",
    "#         ),\n",
    "#         showgrid=True,\n",
    "#         gridcolor='#525252',\n",
    "#         gridwidth=1,\n",
    "#     ),  \n",
    "       \n",
    "    \n",
    "    \n",
    "#     xaxis=dict(\n",
    "#         title='Age difference (citing paper - cited paper)', \n",
    "#         titlefont=dict(            \n",
    "#             size=font_gral,\n",
    "#             color='black'\n",
    "#         #    color='lightgrey'\n",
    "#         ),  \n",
    "#         tickfont=dict(               \n",
    "#             size=font_gral -15, \n",
    "#             color='black'\n",
    "#         ),\n",
    "#         tickvals = [0,1,2,3,4,5,6,7,8,9,10],\n",
    "#     ),\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "#         margin=go.Margin(\n",
    "#         l=200,\n",
    "#        # r=50,\n",
    "#         b=150,\n",
    "#         t=20,\n",
    "#         #pad=80\n",
    "#     ),\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "# )\n",
    "    \n",
    "\n",
    "# if label_selected == 't':  \n",
    "\n",
    "\n",
    "#     fig = Figure(data=data, layout=layout)\n",
    "\n",
    "#     fig_name='box_plot_'+label_selected+'_'+label_journal\n",
    "#     path='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/'\n",
    "#     offline.plot(fig, auto_open=True, image = 'png', image_filename=fig_name ,\n",
    "#                  output_type='file',image_width=2000, image_height=1400, filename=path+fig_name+\".html\", validate=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# landa =  0.02703153\n",
    "# tau = 1./landa\n",
    "# half_life = tau* np.log(2) \n",
    "    \n",
    "# print (\"half-life:\",round(half_life,1), \" years\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# ### REFACTORING CODE\n",
    "# ### box plots with offset for the different traces, so it is easy to see\n",
    "\n",
    "# data = []\n",
    "\n",
    "\n",
    "# include_random = 'no'\n",
    "\n",
    "# label_plot_yplus1 = 'yes'  #'yes'\n",
    "\n",
    "\n",
    "\n",
    "# label_log_y = 'log10'  # or log or log10  or no  (ojo! only active if if label_plot_yplus1=1)  ## also comment lines in the layout about range!!\n",
    "\n",
    "\n",
    "\n",
    "# initial_offset = -0.3\n",
    "# delta_offset_value = 0.15   # this will also afect the width of the boxes\n",
    "\n",
    "\n",
    "\n",
    "# range_y = [0.24,2.51]\n",
    "\n",
    "\n",
    "# # this is for plotting number of citations (not log of numb cit)\n",
    "# # if label_journal == \"PRL\":\n",
    "# #     range_y = [.35,3.3 ]\n",
    "    \n",
    "# # elif label_journal == \"PRE\":\n",
    "# #     range_y = [.2,3.3 ]\n",
    "    \n",
    "# # elif label_journal == \"PNAS\":\n",
    "# #     range_y = [.25,3.3 ]\n",
    "\n",
    "\n",
    "\n",
    " \n",
    "\n",
    "# print (label_journal)\n",
    "    \n",
    "# # lista_tickvals_y = [2,5,10,20,50,100,200,500,1000 ]    \n",
    "# lista_tickvals_y = [.25,0.5,0.75,1,1.25,1.5,1.75,2,2.25,2.5,2.75]   # this is just the labels of the axis, not the range of values themselves!\n",
    "# # if label_log_y != 'no' :\n",
    "# #     lista_tickvals_y = [0,1,2,3,4,5,6,7,8,9 ]    \n",
    "\n",
    "\n",
    "\n",
    "# boxpoints_option = False  #'outliers'   # 'outliers',  #'suspectedoutliers',  #False, #'all',  # False,# THIS DICTATES THE BEHAVIOUR OF THE WHISKERS!\n",
    "# opacity_points = 0.     # to hide all points but keep the same whiskers, set it to 0.0\n",
    "# opacity_box = 1.    \n",
    "\n",
    "\n",
    "\n",
    "# width_line = 7\n",
    "# width_whisker = 1.     # [0,1] value  (horizontal line of whisker)\n",
    "# width_line_whiskers = 4.  # vertical line of whisker\n",
    "    \n",
    "    \n",
    "    \n",
    "# #### for the colors of top, intermediate and low\n",
    "\n",
    "# color_rand =  '#af8dc3'   #  purple    lista_colors[-1]\n",
    "\n",
    "\n",
    "\n",
    "# if label_journal == \"PNAS\":\n",
    "#     lista_colores = ['#330080','#6600ff','#c299ff']  # purples \n",
    "#     color_rand = '#006d2c'\n",
    "    \n",
    "# elif label_journal == \"PRE\":\n",
    "#     lista_colores =  ['#804000','#ff8c1a','#ffe680']    #    ['#993404','#fe9929','#fee391']   # oranges\n",
    "    \n",
    "# elif label_journal == \"PRL\":\n",
    "#     lista_colores = ['#08519c', '#6baed6','#c6dbef']   ## blues\n",
    "\n",
    "# elif label_journal == \"SCIENCE\":\n",
    "#     lista_colores = ['#a50f15','#fb6a4a','#fcbba1']   # reds        \n",
    "        \n",
    "# elif label_journal == \"NATURE\":                 \n",
    "#      lista_colores = ['#006d2c','#66c2a4','#ccece6']      ## greens            \n",
    "\n",
    "# elif label_journal == \"JTB\":\n",
    "#     lista_colores = ['#252525','#636363','#bdbdbd']  # greys\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "            \n",
    "# title_y='Number of citations'\n",
    "# if label_plot_yplus1 =='yes' :            \n",
    "#     title_y='Number of citations +1'\n",
    "#     if label_log_y == 'log'  :\n",
    "        \n",
    "#         title_y='Ln(Number of citations +1)'\n",
    "#     if label_log_y == 'log10'  :\n",
    "        \n",
    "#         title_y='log10(Number of citations +1)'        \n",
    "            \n",
    "            \n",
    "# font_gral = 60\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# lista_traces = ['t','i','l','r']\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# #lista_dicts = [dict_UT_ref_of_selected_top_JOURNAL_info,dict_UT_ref_of_selected_intermediate_JOURNAL_info, dict_UT_ref_of_selected_low_JOURNAL_info,\\\n",
    "#               #dict_UT_ref_of_selected_RANDOM_SAMPLE_JOURNAL_info]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# #lista_listas_to_look_for = [list_top_UTs, list_intermediate_UTs, list_low_UTs, list_RANDOM_SAMPLE_UTs] \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# offset = initial_offset\n",
    "\n",
    "\n",
    "# for i in range(len(lista_traces)):\n",
    "    \n",
    "#     label_selected = lista_traces[i]\n",
    "    \n",
    "#     dict_UT_ref_of_selected_JOURNAL_info = lista_dicts[i]\n",
    "#     lista_JOURNAL_to_look_for = lista_listas_to_look_for[i]\n",
    "    \n",
    "#     if i>len(lista_traces):\n",
    "#         color_trace = lista_colores[i]\n",
    "#     else:\n",
    "#         color_trace = color_rand\n",
    "               \n",
    "               \n",
    "\n",
    "#        ### get the data\n",
    "#     master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "#     for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low     \n",
    "\n",
    "#         JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "\n",
    "#         list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "#         for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "#             try:\n",
    "#                 year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "#                 num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "#                 reset_year = year - JOURNAL_year\n",
    "\n",
    "#                 try:\n",
    "#                     master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "#                 except KeyError:\n",
    "#                     master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "#                     master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "#             except: pass\n",
    "\n",
    "\n",
    "\n",
    "#     cont  = 0        \n",
    "#     lista_val_line_x = []\n",
    "#     lista_val_line_y = []\n",
    "#     for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "#         if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "#     #         print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "#     #                2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "\n",
    "\n",
    "#             lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "#             if label_plot_yplus1 =='yes':\n",
    "#                 lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "\n",
    "#                 if label_log_y == 'log':\n",
    "#                     lista_y = [np.log(i) for i in lista_y]\n",
    "#                 elif label_log_y == 'log10':\n",
    "#                     lista_y = [np.log10(i) for i in lista_y]\n",
    "\n",
    "\n",
    "#             lista_x = [(reset_year+offset)]* len(lista_y)\n",
    "\n",
    "\n",
    "\n",
    "#             lista_val_line_y.append(np.median(lista_y))\n",
    "#             lista_val_line_x.append(reset_year+offset)\n",
    "\n",
    "#             flag_show_legend = False\n",
    "#             if cont ==0:\n",
    "#                 flag_show_legend = True\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#             trace0 = go.Box(\n",
    "#                     y = lista_y,  \n",
    "#                     x = lista_x,                \n",
    "#     #                 opacity = opacity_box, \n",
    "#                     whiskerwidth= 0,#width_whisker ,\n",
    "#                     marker = dict(\n",
    "#                                 color = color_trace,#color,#'##size = 0,  # this doesnt work!!!\n",
    "#                                 #opacity = opacity_points, \n",
    "#                             ),\n",
    "#                     boxmean=True,\n",
    "#                     showlegend=flag_show_legend,\n",
    "#                     #boxpoints = False,  #boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "#                     line=dict(width=0)#width_line_whiskers),\n",
    "#                 )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#             cont  +=1\n",
    "\n",
    "\n",
    "#             data.append(trace0)\n",
    "\n",
    "\n",
    "#     #   value_top = 170    \n",
    "#     #     interval_intermediate=[55,75]    \n",
    "#     #     interval_low=[5,15] \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#     trace1 = go.Scatter(\n",
    "#         x = lista_val_line_x,\n",
    "#         y = lista_val_line_y,\n",
    "#         mode = 'lines+markers',    \n",
    "#         line = dict(\n",
    "#             color = color_trace,#color,\n",
    "#             width = width_line,),\n",
    "#         showlegend=False\n",
    "\n",
    "\n",
    "\n",
    "#     )    \n",
    "#     data.append(trace1)\n",
    "               \n",
    "               \n",
    "#     offset += delta_offset_value   # ready for the next box trace\n",
    "#     # print (offset)\n",
    "\n",
    "\n",
    "\n",
    "#     ## i fit to an exponential decay:\n",
    "#     #  x = numpy.array([10, 19, 30, 35, 51])\n",
    "#     # >>> y = numpy.array([1, 7, 20, 50, 79])\n",
    "\n",
    "#     # scipy.optimize.curve_fit(lambda t,a,b: a*numpy.exp(b*t),  x,  y,  p0=(4, 0.1))  #  p0 is the initial guess for the papram\n",
    "#     # (array([ 4.88003249,  0.05531256]),\n",
    "#     #  array([[  1.01261314e+01,  -4.31940132e-02],\n",
    "#     #         [ -4.31940132e-02,   1.91188656e-04]]))   \n",
    "#     # y ≈ 4.88 exp(0.0553 x). much better.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#     print (\"fit to trace:\",label_selected)\n",
    "#     #results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y)\n",
    "#     # print (results)\n",
    "\n",
    "\n",
    "#     results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y, p0=(lista_val_line_y[0],0.1))\n",
    "#     # print (results)\n",
    "\n",
    "\n",
    "#     A = results[0][0]\n",
    "#     landa = results[0][1]\n",
    "#     tau = 1./landa\n",
    "#     half_life = tau* np.log(2) \n",
    "\n",
    "#     print (\"A:\",A,\",   landa:\", landa,',   tau:', round(tau,1),\"years,     half-life:\",round(half_life,1), \" years\\n\")\n",
    "\n",
    "\n",
    "#     #τ is the time at which the population of the assembly is reduced to 1/e ≈ 0.367879441 times its initial value.\n",
    "#     ### interpretaion of example results:\n",
    "#     # (array([ 4.88003249,  0.05531256]),\n",
    "#     #  array([[  1.01261314e+01,  -4.31940132e-02],\n",
    "#     #         [ -4.31940132e-02,   1.91188656e-04]]))\n",
    "#     # # y ≈ 4.88 exp(0.0553 x). much better.\n",
    "\n",
    "\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "# # #################\n",
    "# # #### TRACE:   THE INTERMEDIATE  values\n",
    "\n",
    "\n",
    "# # label_selected = 'i'\n",
    "# # if label_selected == 'i':  \n",
    "# #     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_intermediate_JOURNAL_info \n",
    "# #     lista_JOURNAL_to_look_for = list_intermediate_UTs\n",
    "   \n",
    "# #     offset += delta_offset_value \n",
    "\n",
    "\n",
    "# #    ### get the data\n",
    "# # master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "# # for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low     \n",
    "\n",
    "# #     JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "    \n",
    "# #     list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "# #     for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "# #         try:\n",
    "# #             year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "# #             num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "# #             reset_year = year - JOURNAL_year\n",
    "\n",
    "# #             try:\n",
    "# #                 master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "# #             except KeyError:\n",
    "# #                 master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "# #                 master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "# #         except: pass\n",
    "\n",
    "\n",
    "        \n",
    "# # cont = 0\n",
    "# # lista_val_line_y = []\n",
    "# # lista_val_line_x = []        \n",
    "# # for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "# #     if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "# # #         print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "# # #                2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "        \n",
    "# #         lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "# #         if label_plot_yplus1 =='yes':\n",
    "# #             lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "            \n",
    "# #             if label_log_y == 'log':\n",
    "# #                 lista_y = [np.log(i) for i in lista_y]\n",
    "# #             elif label_log_y == 'log10':\n",
    "# #                 lista_y = [np.log10(i) for i in lista_y]        \n",
    "                \n",
    "                \n",
    "                \n",
    "        \n",
    "# #         lista_x = [(reset_year+offset)]* len(lista_y)\n",
    "\n",
    "        \n",
    "# #         lista_val_line_y.append(np.median(lista_y))\n",
    "# #         lista_val_line_x.append(reset_year+offset)\n",
    "        \n",
    "         \n",
    "# #         trace0 = go.Box(\n",
    "# #                 y = lista_y,  \n",
    "# #                 x = lista_x,                \n",
    "# #                 opacity = opacity_box, \n",
    "# #                 whiskerwidth= width_whisker ,\n",
    "# #                 marker = dict(\n",
    "# #                             color = lista_colores[1],#color,#'##size = 0,  # this doesnt work!!!\n",
    "# #                             opacity = opacity_points, \n",
    "# #                         ),\n",
    "# #                 boxmean=True,\n",
    "# #                 showlegend=False,\n",
    "# #                 boxpoints = boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "# #                 line=dict(width=width_line_whiskers),\n",
    "# #             )\n",
    "\n",
    "        \n",
    "# #         if cont ==0:\n",
    "# #              trace0 = go.Box(\n",
    "# #                     y = lista_y,  \n",
    "# #                     x = lista_x, \n",
    "# #                      name = str(interval_intermediate[0])+'-'+str(interval_intermediate[-1])+\" citations\",\n",
    "# #                     opacity = opacity_box, \n",
    "# #                     whiskerwidth= width_whisker ,\n",
    "# #                     marker = dict(\n",
    "# #                             color = lista_colores[1],#color,#'#808080',\n",
    "# #                             opacity = opacity_points, \n",
    "# #                            # size = 0,\n",
    "# #                             ),\n",
    "# #                     boxmean=True,\n",
    "# #                     showlegend=True,\n",
    "# #                     boxpoints = boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "# #                     line=dict(width=width_line_whiskers),   \n",
    "# #                 )\n",
    "        \n",
    "# #         cont  +=1\n",
    "        \n",
    "# #         data.append(trace0)\n",
    "\n",
    "    \n",
    "        \n",
    "# # #   value_top = 170    \n",
    "# # #     interval_intermediate=[55,75]    \n",
    "# # #     interval_low=[5,15] \n",
    "\n",
    "    \n",
    "    \n",
    "# # trace1 = go.Scatter(\n",
    "# #     x = lista_val_line_x,\n",
    "# #     y = lista_val_line_y,\n",
    "# #     mode = 'lines+markers',    \n",
    "# #     line = dict(\n",
    "# #         color = lista_colores[1],#color,\n",
    "# #         width = width_line,),\n",
    "# #     showlegend=False,        \n",
    "    \n",
    "# # )    \n",
    "# # data.append(trace1)\n",
    "\n",
    "\n",
    "# # #print (offset)      \n",
    "\n",
    "        \n",
    "        \n",
    "# # print (\"fit to trace: medium-cited\")\n",
    "# # #results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y)\n",
    "# # # print (results)\n",
    "\n",
    "\n",
    "# # results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y, p0=(lista_val_line_y[0],0.1))\n",
    "# # # print (results)\n",
    "\n",
    "\n",
    "# # A = results[0][0]\n",
    "# # landa = results[0][1]\n",
    "# # tau = 1./landa\n",
    "# # half_life = tau* np.log(2) \n",
    "    \n",
    "# # print (\"A:\",A,\",   landa:\", landa,',   tau:', round(tau,1),\"years,     half-life:\",round(half_life,1), \" years\\n\")\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "# # #################\n",
    "# # #### TRACE:   THE LOW  values\n",
    "\n",
    "   \n",
    "# # label_selected = 'l'\n",
    "# # if label_selected == 'l':  \n",
    "   \n",
    "# #     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_low_JOURNAL_info \n",
    "# #     lista_JOURNAL_to_look_for = list_low_UTs\n",
    " \n",
    "           \n",
    "# #     offset += delta_offset_value \n",
    "\n",
    "\n",
    "# #    ### get the data\n",
    "# # master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "# # for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low     \n",
    "\n",
    "# #     JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "    \n",
    "# #     list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "# #     for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "# #         try:\n",
    "# #             year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "# #             num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "# #             reset_year = year - JOURNAL_year\n",
    "\n",
    "# #             try:\n",
    "# #                 master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "# #             except KeyError:\n",
    "# #                 master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "# #                 master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "# #         except: pass\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "        \n",
    "# # cont = 0\n",
    "# # lista_val_line_y = []\n",
    "# # lista_val_line_x = []\n",
    "# # for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "# #     if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "# # #         print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "# # #                2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "        \n",
    "# #         lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "# #         if label_plot_yplus1 =='yes':\n",
    "# #             lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "            \n",
    "# #             if label_log_y == 'log':\n",
    "# #                 lista_y = [np.log(i) for i in lista_y]\n",
    "# #             elif label_log_y == 'log10':\n",
    "# #                 lista_y = [np.log10(i) for i in lista_y]        \n",
    "       \n",
    "    \n",
    "        \n",
    "# #         lista_x = [(reset_year+offset)]* len(lista_y)\n",
    "        \n",
    "# #         lista_val_line_y.append(np.median(lista_y))\n",
    "# #         lista_val_line_x.append(reset_year+offset)\n",
    "\n",
    "        \n",
    "# #         trace0 = go.Box(\n",
    "# #                  y = lista_y,  \n",
    "# #                 x = lista_x,                \n",
    "# #                 opacity = opacity_box, \n",
    "# #                 whiskerwidth= width_whisker ,\n",
    "# #                 marker = dict(\n",
    "# #                             color = lista_colores[2],#color,#'##size = 0,  # this doesnt work!!!\n",
    "# #                             opacity = opacity_points, \n",
    "# #                         ),\n",
    "# #                 boxmean=True,\n",
    "# #                 showlegend=False,\n",
    "# #                 boxpoints = boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "# #                 line=dict(width=width_line_whiskers),\n",
    "          \n",
    "# #             )\n",
    "\n",
    "        \n",
    "        \n",
    "# #         if cont ==0:  # i only show the legend for one of them in each series\n",
    "# #              trace0 = go.Box(\n",
    "# #                     y = lista_y,  \n",
    "# #                     x = lista_x, \n",
    "# #                      name = str(interval_low[0])+'-'+str(interval_low[-1])+\" citations\",\n",
    "# #                     opacity = opacity_box, \n",
    "# #                     whiskerwidth= width_whisker ,\n",
    "# #                     marker = dict(\n",
    "# #                             color = lista_colores[2],#color,#'#808080',\n",
    "# #                             opacity = opacity_points, \n",
    "# #                            # size = 0,\n",
    "# #                             ),\n",
    "# #                     boxmean=True,\n",
    "# #                     showlegend=True,\n",
    "# #                     boxpoints = boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "# #                     line=dict(width=width_line_whiskers),   \n",
    "# #                 )\n",
    "        \n",
    "# #         cont  +=1\n",
    "        \n",
    "# #         data.append(trace0)\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "# # trace1 = go.Scatter(\n",
    "# #     x = lista_val_line_x,\n",
    "# #     y = lista_val_line_y,\n",
    "# #     mode = 'lines+markers',    \n",
    "# #     line = dict(\n",
    "# #         color = lista_colores[2],#color,\n",
    "# #         width = width_line,),\n",
    "# #     showlegend=False,\n",
    "  \n",
    "# # )    \n",
    "# # data.append(trace1)\n",
    "\n",
    "# # # print (offset)        \n",
    "\n",
    "        \n",
    "# # print (\"fit to trace: poorly-cited\")\n",
    "# # #results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y)\n",
    "# # # print (results)\n",
    "\n",
    "\n",
    "# # results = scipy.optimize.curve_fit(lambda t,A,landa: A*np.exp(-1.*landa*t),  lista_val_line_x,  lista_val_line_y, p0=(lista_val_line_y[0],0.1))\n",
    "# # # print (results)\n",
    "\n",
    "\n",
    "# # A = results[0][0]\n",
    "# # landa = results[0][1]\n",
    "# # tau = 1./landa\n",
    "# # half_life = tau* np.log(2) \n",
    "    \n",
    "# # print (\"A:\",A,\",   landa:\", landa,',   tau:', round(tau,1),\"years,     half-life:\",round(half_life,1), \" years\\n\")\n",
    "\n",
    "\n",
    "        \n",
    "        \n",
    " \n",
    "     \n",
    "\n",
    "# # #################\n",
    "# # #### TRACE:   THE RANDOM DATA\n",
    "\n",
    "# # if include_random == 'yes':\n",
    "\n",
    "# #     label_selected = 'r'\n",
    "# #     if label_selected == 'r':  \n",
    "# #         dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_RANDOM_SAMPLE_JOURNAL_info \n",
    "# #         lista_JOURNAL_to_look_for = list_RANDOM_SAMPLE_UTs\n",
    "\n",
    "        \n",
    "# #         offset += delta_offset_value \n",
    "\n",
    "\n",
    "# #        ### get the data\n",
    "# #     master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "# #     for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low     \n",
    "\n",
    "# #         JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "        \n",
    "# #         list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "\n",
    "\n",
    "# #         for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "\n",
    "# #             try:\n",
    "# #                 year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "# #                 num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "# #                 reset_year = year - JOURNAL_year\n",
    "\n",
    "# #                 try:\n",
    "# #                     master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "# #                 except KeyError:\n",
    "# #                     master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "# #                     master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "# #             except: pass\n",
    "\n",
    "\n",
    "# #     cont = 0\n",
    "# #     lista_val_line_y = []\n",
    "# #     lista_val_line_x = []        \n",
    "\n",
    "# #     for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "# #         if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "# # #             print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), \\\n",
    "# # #                    2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "            \n",
    "# #             lista_y = master_dict_reset_year_list_citations_of_citing_papers[reset_year]\n",
    "# #             if label_plot_yplus1 =='yes':\n",
    "# #                 lista_y = [i+1 for i in master_dict_reset_year_list_citations_of_citing_papers[reset_year]]\n",
    "                \n",
    "# #             if label_log_y == 'log':\n",
    "# #                 lista_y = [np.log(i) for i in lista_y]\n",
    "# #             elif label_log_y == 'log10':\n",
    "# #                 lista_y = [np.log10(i) for i in lista_y]        \n",
    "       \n",
    "                \n",
    "                \n",
    "# #             lista_x = [(reset_year+offset)]* len(lista_y)\n",
    "\n",
    "# #             lista_val_line_y.append(np.median(lista_y))\n",
    "# #             lista_val_line_x.append(reset_year+offset)\n",
    "\n",
    "\n",
    "\n",
    "# #             trace0 = go.Box(\n",
    "# #                     y = lista_y,  \n",
    "# #                     x = lista_x,                \n",
    "# #                     opacity = opacity_box, \n",
    "# #                     whiskerwidth= width_whisker ,\n",
    "# #                     marker = dict(\n",
    "# #                                 color = color_rand,#color,#'##size = 0,  # this doesnt work!!!\n",
    "# #                                 opacity = opacity_points, \n",
    "# #                             ),\n",
    "# #                     boxmean=True,\n",
    "# #                     showlegend=False,\n",
    "# #                     boxpoints = boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "# #                     line=dict(width=width_line_whiskers),\n",
    "# #                 )\n",
    "\n",
    "\n",
    "# #             if cont ==0:\n",
    "# #                  trace0 = go.Box(\n",
    "# #                         y = lista_y,  \n",
    "# #                         x = lista_x, \n",
    "# #                         name = \"random sample\",\n",
    "# #                         opacity = opacity_box, \n",
    "# #                         whiskerwidth= width_whisker ,\n",
    "# #                         marker = dict(\n",
    "# #                             color = color_rand,#color,#'#808080',\n",
    "# #                             opacity = opacity_points, \n",
    "# #                            # size = 0,\n",
    "# #                             ),\n",
    "# #                         boxmean=True,\n",
    "# #                         showlegend=True,\n",
    "# #                         boxpoints = boxpoints_option, #'outliers',  #'suspectedoutliers',  #False, #'all',  # False,#\n",
    "# #                         line=dict(width=width_line_whiskers),   \n",
    "\n",
    "# #                     )\n",
    "\n",
    "# #             cont  +=1\n",
    "\n",
    "\n",
    "\n",
    "# #             data.append(trace0)\n",
    "\n",
    "            \n",
    "\n",
    "\n",
    "\n",
    "# #     trace1 = go.Scatter(\n",
    "# #         x = lista_val_line_x,\n",
    "# #         y = lista_val_line_y,\n",
    "# #         mode = 'lines+markers',    \n",
    "# #         line = dict(\n",
    "# #             color = color_rand,\n",
    "# #             width = width_line,),\n",
    "# #         showlegend=False,\n",
    "\n",
    "\n",
    "# #     )    \n",
    "# #     data.append(trace1)\n",
    "\n",
    "\n",
    "# #    # print (offset)\n",
    "\n",
    "\n",
    "        \n",
    "           \n",
    "      \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "            \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "   \n",
    "# layout = Layout(    \n",
    "#     title = '',#citing papers of '+label_selected+' JOURNALJOURNALfo+\"<br>N:\"+n,\n",
    "#     # showlegend=True,  IF I WRITE THIS INSIDE EACH TRACE, I PICK WHICH LENGENDS TO SHOW!\n",
    "#     legend=dict(x=.7, \n",
    "#                 y=.95,\n",
    "#                font=dict(            \n",
    "#                     size=font_gral-5,           \n",
    "#                 ),\n",
    "#                ),\n",
    "#     yaxis=dict(\n",
    "#         title=title_y, \n",
    "#         range = range_y,      ###### un-comment this line if i want to plot linear y-axis!!!\n",
    "#        #type='log',           ###### un-comment this line if i want to plot linear y-axis!!!\n",
    "#         titlefont=dict(            \n",
    "#             size=font_gral,\n",
    "#             color='black'\n",
    "#         #    color='lightgrey'\n",
    "#         ),  \n",
    "#         tickfont=dict(               \n",
    "#             size=font_gral-15,\n",
    "#             color='black'\n",
    "#         ),\n",
    "#         tickvals = lista_tickvals_y ,\n",
    "#         showgrid=True,\n",
    "#         gridcolor='#525252',\n",
    "#         gridwidth=1,\n",
    "#     ),  \n",
    "       \n",
    "    \n",
    "    \n",
    "#     xaxis=dict(\n",
    "#         title='Age difference', \n",
    "#         titlefont=dict(            \n",
    "#             size=font_gral,\n",
    "#             color='black'\n",
    "#         #    color='lightgrey'\n",
    "#         ),  \n",
    "#         tickfont=dict(               \n",
    "#             size=font_gral -15, \n",
    "#             color='black'\n",
    "#         ),\n",
    "#         tickvals = [0,1,2,3,4,5,6,7,8,9,10],\n",
    "#     ),\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "#         margin=go.Margin(\n",
    "#         l=200,\n",
    "#        # r=50,\n",
    "#         b=120,\n",
    "#         t=20,\n",
    "#         #pad=80\n",
    "#     ),\n",
    "\n",
    "#     boxgap=0.01\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "# )\n",
    "    \n",
    "    \n",
    "# fig = Figure(data, layout =  layout)\n",
    "\n",
    "# fig_name='box_plot_offset_'+label_journal\n",
    "# path='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/'\n",
    "\n",
    "\n",
    "# offline.plot(fig, auto_open=True, image = 'png', image_filename=fig_name ,\n",
    "#                  output_type='file',image_width=2200, image_height=1800, filename=path+fig_name+\".html\", validate=False)\n",
    "\n",
    "\n",
    "\n",
    "# #### PNAS\n",
    "# # fit to trace: highly-cited\n",
    "# # A: 79.5775433126 ,   landa: 0.127033119861 ,   tau: 7.9 years,     half-life: 5.5  years\n",
    "\n",
    "# # fit to trace: medium-cited\n",
    "# # A: 52.3421128686 ,   landa: 0.0857932567604 ,   tau: 11.7 years,     half-life: 8.1  years\n",
    "\n",
    "# # fit to trace: poorly-cited\n",
    "# # A: 23.1535757648 ,   landa: 0.0282811138629 ,   tau: 35.4 years,     half-life: 24.5  years\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # SCIENCE\n",
    "# # fit to trace: highly-cited\n",
    "# # A: 73.5566847391 ,   landa: 0.14482791378 ,   tau: 6.9 years,     half-life: 4.8  years\n",
    "\n",
    "# # fit to trace: medium-cited\n",
    "# # A: 41.880926933 ,   landa: 0.0645084725871 ,   tau: 15.5 years,     half-life: 10.7  years\n",
    "\n",
    "# # fit to trace: poorly-cited\n",
    "# # A: 19.5927563684 ,   landa: -0.000444452670601 ,   tau: -2250.0 years,     half-life: -1559.6  years\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # NATURE\n",
    "# # fit to trace: highly-cited\n",
    "# # A: 59.5875784412 ,   landa: 0.0875669423552 ,   tau: 11.4 years,     half-life: 7.9  years\n",
    "\n",
    "# # fit to trace: medium-cited\n",
    "# # A: 41.6401832307 ,   landa: 0.0480770365393 ,   tau: 20.8 years,     half-life: 14.4  years\n",
    "\n",
    "# # fit to trace: poorly-cited\n",
    "# # A: 21.0361533805 ,   landa: 0.00208731952169 ,   tau: 479.1 years,     half-life: 332.1  years\n",
    "\n",
    "\n",
    "# # PRE\n",
    "# # fit to trace: highly-cited\n",
    "# # A: 42.484635282 ,   landa: 0.104757926239 ,   tau: 9.5 years,     half-life: 6.6  years\n",
    "\n",
    "# # fit to trace: medium-cited\n",
    "# # A: 23.7676029139 ,   landa: 0.0834063429733 ,   tau: 12.0 years,     half-life: 8.3  years\n",
    "\n",
    "# # fit to trace: poorly-cited\n",
    "# # A: 5.46116383289 ,   landa: -0.0350143554733 ,   tau: -28.6 years,     half-life: -19.8  years\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # PRL\n",
    "# # fit to trace: highly-cited\n",
    "# # A: 60.1177575044 ,   landa: 0.292642091292 ,   tau: 3.4 years,     half-life: 2.4  years\n",
    "\n",
    "# # fit to trace: medium-cited\n",
    "# # A: 25.9369434312 ,   landa: 0.110985782474 ,   tau: 9.0 years,     half-life: 6.2  years\n",
    "\n",
    "# # fit to trace: poorly-cited\n",
    "# # A: 11.837182788 ,   landa: 0.0430482652405 ,   tau: 23.2 years,     half-life: 16.1  years\n",
    "\n",
    "\n",
    "\n",
    "# # JTB\n",
    "# # fit to trace: highly-cited\n",
    "# # A: 36.0663881783 ,   landa: -0.0129775250214 ,   tau: -77.1 years,     half-life: -53.4  years\n",
    "\n",
    "# # fit to trace: medium-cited\n",
    "# # A: 28.7488070266 ,   landa: 0.0149116465487 ,   tau: 67.1 years,     half-life: 46.5  years\n",
    "\n",
    "# # fit to trace: poorly-cited\n",
    "# # A: 13.0394080734 ,   landa: -0.0284148992609 ,   tau: -35.2 years,     half-life: -24.4  years\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### box plots with offset for the different traces, so it is easy to see\n",
    "\n",
    "# # working example with random data\n",
    "\n",
    "# data = []\n",
    "\n",
    "\n",
    "# for i in range(10):\n",
    "#     y0 = np.random.randn(50)+i\n",
    "#     x0 = [i]* len(y0)\n",
    "#     trace0 = go.Box(\n",
    "#         y=y0,\n",
    "#         x = x0,\n",
    "#         marker = dict(\n",
    "#             color = 'rgb(214, 12, 140)',\n",
    "#         )\n",
    "#     )\n",
    "#     data.append(trace0)\n",
    "#     print (y0)\n",
    "#     print (x0)\n",
    "#     print (\"\\n\")\n",
    "\n",
    "\n",
    "# offset = .1    \n",
    "    \n",
    "# for i in range(10):\n",
    "#     y0 = np.random.randn(50)+i\n",
    "#     x0 = [i+offset]* len(y0)\n",
    "#     trace0 = go.Box(\n",
    "#         y=y0,\n",
    "#         x = x0,\n",
    "#         marker = dict(\n",
    "#             color = 'rgb(20, 112, 240)',\n",
    "#         )\n",
    "#     )\n",
    "#     data.append(trace0)\n",
    "#     print (y0)\n",
    "#     print (x0)\n",
    "#     print (\"\\n\")\n",
    "\n",
    "    \n",
    "# offset = -0.1        \n",
    "# for i in range(10):\n",
    "#     y0 = np.random.randn(50)+i\n",
    "#     x0 = [i+offset]* len(y0)\n",
    "#     trace0 = go.Box(\n",
    "#         y=y0,\n",
    "#         x = x0,\n",
    "#         marker = dict(\n",
    "#             color = 'rgb(110, 12, 240)',\n",
    "#         )\n",
    "#     )\n",
    "#     data.append(trace0)\n",
    "#     print (y0)\n",
    "#     print (x0)\n",
    "#     print (\"\\n\")\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "# font_gral = 60\n",
    "   \n",
    "# layout = Layout(    \n",
    "#     title = '',#citing papers of '+label_selected+' JOURNALJOURNALfo+\"<br>N:\"+n,\n",
    "#     showlegend=False,\n",
    " \n",
    "#     yaxis=dict(\n",
    "#         title=title_y, \n",
    "#         #range = range_y,   \n",
    "#         #type='log',\n",
    "#         titlefont=dict(            \n",
    "#             size=font_gral,\n",
    "#             color='black'\n",
    "#         #    color='lightgrey'\n",
    "#         ),  \n",
    "#         tickfont=dict(               \n",
    "#             size=font_gral-15,\n",
    "#             color='black'\n",
    "#         ),\n",
    "#         showgrid=True,\n",
    "#         gridcolor='#525252',\n",
    "#         gridwidth=1,\n",
    "#     ),  \n",
    "       \n",
    "    \n",
    "    \n",
    "#     xaxis=dict(\n",
    "#         title='Age difference (citing paper - cited paper)', \n",
    "#         titlefont=dict(            \n",
    "#             size=font_gral,\n",
    "#             color='black'\n",
    "#         #    color='lightgrey'\n",
    "#         ),  \n",
    "#         tickfont=dict(               \n",
    "#             size=font_gral -15, \n",
    "#             color='black'\n",
    "#         ),\n",
    "#         tickvals = [0,1,2,3,4,5,6,7,8,9,10],\n",
    "#     ),\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "#         margin=go.Margin(\n",
    "#         l=200,\n",
    "#        # r=50,\n",
    "#         b=150,\n",
    "#         t=20,\n",
    "#         #pad=80\n",
    "#     ),\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "# )\n",
    "    \n",
    "    \n",
    "# fig = Figure(data, layout =  layout)\n",
    "\n",
    "# fig_name='box_plot_offset'+label_selected+'_'+label_journal\n",
    "# path='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/'\n",
    "\n",
    "\n",
    "# offline.plot(fig, auto_open=True, image = 'png', image_filename=fig_name ,\n",
    "#                  output_type='file',image_width=2000, image_height=1400, filename=path+fig_name+\".html\", validate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# fig = Figure(data=data, layout=layout)\n",
    "\n",
    "# fig_name='box_plot_'+label_selected+'_'+label_journal\n",
    "# path='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/'\n",
    "# offline.plot(fig, auto_open=True, image = 'png', image_filename=fig_name ,\n",
    "#              output_type='file',image_width=2000, image_height=1400, filename=path+fig_name+\".html\", validate=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "trace1 = {\n",
    "  \"y\": [1, 2, 3, 2, 1, 10], \n",
    "  \"name\": \"default\", \n",
    "  \"type\": \"box\"\n",
    "}\n",
    "trace2 = {\n",
    "  \"y\": [1, 2, 3, 2, 1, 10], \n",
    "  \"boxpoints\": False, \n",
    "  \"name\": \"no outliers\", \n",
    "  \"type\": \"box\"\n",
    "}\n",
    "trace3 = {\n",
    "  \"y\": [1, 2, 3, 2, 1, 10], \n",
    "  \"boxpoints\": \"all\", \n",
    "  \"name\": \"jitter boxpoints\", \n",
    "  \"type\": \"box\"\n",
    "}\n",
    "data = Data([trace1, trace2, trace3])\n",
    "layout = {\"title\": \"Comparing boxplot \\\"boxpoints\\\" settings\"}\n",
    "fig = Figure(data=data, layout=layout)\n",
    "offline.plot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # violin plots:  NOT A GOOD IDEA BECAUSE DATA TOO SKEWED\n",
    "\n",
    "\n",
    "# label_selected = 'top'  #  top  or intermediate or low\n",
    "\n",
    "\n",
    "# if label_selected == 'top':  \n",
    "#     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_top_JOURNAL_info \n",
    "#     lista_JOURNAL_to_look_for = list_top_UTs\n",
    "#     info = ', >='+str(value_top)+'citations'\n",
    "   \n",
    "    \n",
    "# elif label_selected == 'intermediate':  \n",
    "#     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_intermediate_JOURNAL_info \n",
    "#     lista_JOURNAL_to_look_for = list_intermediate_UTs\n",
    "#     info = linfo = ', '+str(interval_intermediate)+' citations'\n",
    "    \n",
    "# elif label_selected == 'low':  \n",
    "#     dict_UT_ref_of_selected_JOURNAL_info = dict_UT_ref_of_selected_low_JOURNAL_info \n",
    "#     lista_JOURNAL_to_look_for = list_low_UTs\n",
    "#     info =  ', '+str(interval_low)+' citations'\n",
    "    \n",
    "    \n",
    "    \n",
    "# n= str(len(dict_UT_ref_of_selected_JOURNAL_info))    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# master_dict_reset_year_list_citations_of_citing_papers = {}\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# for JOURNAL_UT in lista_JOURNAL_to_look_for:   # either i focus on top JOURNAL papers, intermediate or low\n",
    "#     JOURNAL_year = master_dict_JOURNAL_UT_info[JOURNAL_UT]['year']\n",
    "#     #print (JOURNAL_UT, JOURNAL_year)\n",
    "#     list_citing_papers_of_JOURNAL = master_dict_JOURNAL_UT_info[JOURNAL_UT]['list_cit']\n",
    "    \n",
    "    \n",
    "#     for citing_paper in list_citing_papers_of_JOURNAL:\n",
    "        \n",
    "#         try:\n",
    "#             year = dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['year']\n",
    "#             num_citations_of_citing = len(dict_UT_ref_of_selected_JOURNAL_info[citing_paper]['list_cit'])\n",
    "\n",
    "            \n",
    "#             reset_year = year - JOURNAL_year\n",
    "            \n",
    "#             try:\n",
    "#                 master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "#             except KeyError:\n",
    "#                 master_dict_reset_year_list_citations_of_citing_papers[reset_year] = []\n",
    "#                 master_dict_reset_year_list_citations_of_citing_papers[reset_year].append(num_citations_of_citing)\n",
    "\n",
    "#         except: pass\n",
    "        \n",
    "# data = []        \n",
    "# for reset_year in sorted(master_dict_reset_year_list_citations_of_citing_papers):\n",
    "#     if (reset_year <=10)  and (reset_year >= 0):   ### i only plot the first 10 years\n",
    "#     # if (reset_year >= 0):  \n",
    "#         print (reset_year, np.median(master_dict_reset_year_list_citations_of_citing_papers[reset_year]),np.mean(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), 2.*np.std(master_dict_reset_year_list_citations_of_citing_papers[reset_year]), len(set(master_dict_reset_year_list_citations_of_citing_papers[reset_year])))\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "#         trace = {\n",
    "#                 \"type\": 'violin',\n",
    "#                 \"x\": reset_year,    \n",
    "#                 \"y\": master_dict_reset_year_list_citations_of_citing_papers[reset_year],  \n",
    "#                 \"name\": reset_year,\n",
    "#                 \"box\": {\n",
    "#                     \"visible\": True\n",
    "#                 },\n",
    "#             \"line\": { \n",
    "#                 \"color\": 'black'},\n",
    "#              \"meanline\": {\n",
    "#                     \"visible\": True\n",
    "#                 },\n",
    "#              \"fillcolor\": '#b3ffcc',\n",
    "#              #\"opacity\": 0.6,\n",
    "#             }\n",
    "#         data.append(trace)\n",
    "        \n",
    "        \n",
    "   \n",
    "\n",
    "        \n",
    "# fig = {\n",
    "#     \"data\": data,\n",
    "#     \"layout\" : {\n",
    "#         'title': 'citing papers of '+label_selected+' JOURNAL'+info+\"<br>N:\"+n,\n",
    "#         'showlegend':False,\n",
    "#         \"yaxis\": {\n",
    "#             \"zeroline\": False,\n",
    "#             'title':'Number citations of citing paper (as of 2017)',\n",
    "#             'range' : [0,1000],\n",
    "#            # 'type':'log',\n",
    "#         },\n",
    "#         \"xaxis\": {           \n",
    "#             'title':'Age difference (citing paper - JOURNAL paper)', \n",
    "#         }\n",
    "#     }\n",
    "# }\n",
    "\n",
    "\n",
    "# #iplot(fig, filename='violin/multiple', validate = False)\n",
    "# offline.plot(fig, auto_open=True, image = 'png', image_filename=\"histogram_num_cit_JOURNAL_1990_1995.html\" ,\n",
    "#              output_type='file',image_width=2000, image_height=1400, filename=path+fig_name+\".html\", validate=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cursor = papers_con.collection.find().sort([(\"issue.PY\",-1)]).limit(10) # i look for the 10 youngest papers (write +1 instead of -1 for the oldest)\n",
    "# for s in cursor:\n",
    "#     print(s)\n",
    "#     print('--------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### plot histograms for number of citations and number of references of the selected  JOURNAL\n",
    "\n",
    "\n",
    "# lista_years =[1990,1991,1992,1993,1994,1995]\n",
    "\n",
    "# lista_num_cit = []\n",
    "# lista_num_ref = []\n",
    "# for UT in master_dict_JOURNAL_UT_info:\n",
    "#     lista_num_cit.append(len(master_dict_JOURNAL_UT_info[UT]['list_cit']))\n",
    "#     lista_num_ref.append(master_dict_JOURNAL_UT_info[UT]['num_ref'])\n",
    "\n",
    "\n",
    "\n",
    "# trace1= Histogram(x=lista_num_cit, histnorm='probability')\n",
    "# #         x=x1, \n",
    "# #      marker=dict(\n",
    "# #         color='#17BECF',  #  'purple'     #EB89B5',\n",
    "# #     ),\n",
    "# #        # name='high cit',\n",
    "# #       # histnorm='probability',\n",
    "# #     #cumulative=dict(enabled=True)\n",
    "# # )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# ##   xbins.start, xbins.end and xbins.size\n",
    "\n",
    "\n",
    "\n",
    "# ##   xbins.start, xbins.end and xbins.size\n",
    "\n",
    "\n",
    "# layout = Layout(    \n",
    "#     title = label_journal+\" \"+str(lista_years),\n",
    "#      xaxis=dict(       \n",
    "#          title= 'Number of citations received as of 2017' , ),\n",
    "#      yaxis=dict(       \n",
    "#          title= 'PDF' ,\n",
    "#       type='log',),\n",
    "# )\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# # layout = Layout(    \n",
    "\n",
    "# #     bargap=0.2,\n",
    "\n",
    "\n",
    "\n",
    "# #     xaxis=dict(\n",
    "        \n",
    "# #         title= 'Year' , #v1_string.replace(\"_\",\" \"),   \n",
    "# #         titlefont=dict(           \n",
    "# #             size=font_axes,\n",
    "# #             color='black'\n",
    "# #         #    color='lightgrey'\n",
    "# #         ),  \n",
    "# #         tickfont=dict(               \n",
    "# #             size=font_ticks,\n",
    "# #             color='black'\n",
    "# #         ),\n",
    "# #         #type='log'\n",
    "# #     ),\n",
    "# #     yaxis=dict(\n",
    "# #         title='Number papers published',    \n",
    "# #         type='log',\n",
    "# #         titlefont=dict(            \n",
    "# #             size=font_axes,\n",
    "# #             color='black'\n",
    "# #         #    color='lightgrey'\n",
    "# #         ),  \n",
    "# #         tickfont=dict(               \n",
    "# #             size=font_ticks,\n",
    "# #             color='black'\n",
    "# #         ),\n",
    "        \n",
    "# #     ),                \n",
    "\n",
    "# #     margin=go.Margin(\n",
    "# #         l=250,\n",
    "# #        # r=50,\n",
    "# #         b=150,\n",
    "# #         #t=100,\n",
    "# #         #pad=80\n",
    "# #     ),\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "   \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "# #     bargroupgap=0.1\n",
    "# #)       \n",
    "\n",
    "# data = [trace1]#, trace2]#, trace3]\n",
    "\n",
    "# fig = Figure(data=data, layout=layout)\n",
    "\n",
    "# fig_name='histogram_num_cit_PNAS_1990_1995'\n",
    "# path='/home/staff/julia/at_Northwestern/In_Text_Citations/In-Text-Citations-New/plots/'\n",
    "\n",
    "# #iplot(fig, filename=fig_name)\n",
    "\n",
    "\n",
    "# offline.plot(fig, auto_open=True, image = 'png', image_filename=\"histogram_num_cit_PNAS_1990_1995.html\" ,\n",
    "#              output_type='file',image_width=2000, image_height=1400, filename=path+fig_name+\".html\", validate=False)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}