fermipy_stats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fermipy Usage Statistics\n",
    "This notebook polls the SAO/NASA Astrophysics Data System [(ADS)](https://ui.adsabs.harvard.edu/) to determine usage rates of the [Fermipy](https://fermipy.readthedocs.io/en/master/) python package.\n",
    "\n",
    "## Setting up the API\n",
    "In the fields below, `token` should be replaced with [your own API token](https://ui.adsabs.harvard.edu/user/settings/token). If you haven't worked with the ADS API before, it's recommended that you read the [README](https://github.com/adsabs/adsabs-dev-api/blob/master/README.md) before beginning. You may want to familiarize yourself with their other API Jupyter notebooks, especially the ones on [searching](https://github.com/adsabs/adsabs-dev-api/blob/master/Search_API.ipynb), [interacting with libraries](https://github.com/adsabs/adsabs-dev-api/blob/master/Libraries_API.ipynb), and [using the API with Python](https://github.com/adsabs/adsabs-dev-api/blob/master/Converting_curl_to_python.ipynb).\n",
    "\n",
    "Note that much of the code in this notebook was adapted from the example notebook for [maintaining a mission bibliography](https://github.com/adsabs/adsabs-dev-api/blob/master/API_documentation_Python/API_examples/maintain_mission_bibliography.ipynb) on the ADS website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the requests package and set your token in a variable for later use\n",
    "import requests\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "token=\"i4bKvOj8azhzoZp1wi1eSV3B9BTuJQUhSo7qboHp\"\n",
    "base_url = \"https://api.adsabs.harvard.edu/v1/search/query?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usage statistics are compiled in *very* simple fashion: count the number of refereed publications that reference 'Fermipy' in a given year vs. the number of publications that only reference the FermiTools (formerly ScienceTools). This will give us a rough lower bound for Fermipy usage amongst the community. If you were to enter the queries into the ADS online search, they would look like this:\n",
    "\n",
    "query1 = 'pubdate:[2023-01 TO 2024-01] abs:fermi OR keyword:(\"gamma ray\") body:(fermi AND (sciencetools OR fermitools) NOT fermipy) property:refereed'\n",
    "\n",
    "query2 = 'pubdate:[2023-01 TO 2024-01] abs:fermi OR keyword:(\"gamma ray\") body:(fermi AND fermipy) property:refereed'\n",
    "\n",
    "After you enter those queries into the ADS search bar, you'll notice that the url changes to reflect the specfic paramters of your search. You'll want to copy everything after the \"search/q=\" part of these urls for use in the next step:\n",
    "\n",
    "query1 = 'pubdate%3A%5B2023-01%20TO%202024-01%5D%20abs%3Afermi%20OR%20keyword%3A(\"gamma%20ray\")%20body%3A(fermi%20AND%20(sciencetools%20OR%20fermitools)%20NOT%20fermipy)%20property%3Arefereed&sort=date%20desc%2C%20bibcode%20desc&p_=0'\n",
    "\n",
    "query2 = 'pubdate%3A%5B2008-01%20TO%202009-01%5D%20abs%3Afermi%20OR%20keyword%3A(\"gamma%20ray\")%20body%3A(fermi%20AND%20fermipy)%20property%3Arefereed&sort=date%20desc%2C%20bibcode%20desc&p_=0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2008-2009:\n",
      "  Fermitools: 1\n",
      "  Fermipy: 0\n",
      "2009-2010:\n",
      "  Fermitools: 8\n",
      "  Fermipy: 0\n",
      "2010-2011:\n",
      "  Fermitools: 19\n",
      "  Fermipy: 0\n",
      "2011-2012:\n",
      "  Fermitools: 20\n",
      "  Fermipy: 0\n",
      "2012-2013:\n",
      "  Fermitools: 32\n",
      "  Fermipy: 0\n",
      "2013-2014:\n",
      "  Fermitools: 24\n",
      "  Fermipy: 0\n",
      "2014-2015:\n",
      "  Fermitools: 35\n",
      "  Fermipy: 0\n",
      "2015-2016:\n",
      "  Fermitools: 49\n",
      "  Fermipy: 0\n",
      "2016-2017:\n",
      "  Fermitools: 41\n",
      "  Fermipy: 2\n",
      "2017-2018:\n",
      "  Fermitools: 36\n",
      "  Fermipy: 8\n",
      "2018-2019:\n",
      "  Fermitools: 35\n",
      "  Fermipy: 16\n",
      "2019-2020:\n",
      "  Fermitools: 42\n",
      "  Fermipy: 27\n",
      "2020-2021:\n",
      "  Fermitools: 44\n",
      "  Fermipy: 43\n",
      "2021-2022:\n",
      "  Fermitools: 46\n",
      "  Fermipy: 52\n",
      "2022-2023:\n",
      "  Fermitools: 54\n",
      "  Fermipy: 45\n",
      "2023-2024:\n",
      "  Fermitools: 62\n",
      "  Fermipy: 69\n"
     ]
    }
   ],
   "source": [
    "startYear = 2008\n",
    "endYear = 2024\n",
    "\n",
    "year = []\n",
    "toolct = [] # array to store count of FermiTools-only papers\n",
    "frmpyct = [] # attay to store count of fermipy papers\n",
    "for i in range(startYear,endYear): \n",
    "    #This block gets the papers that do NOT use fermipy\n",
    "    results = requests.get(\"https://api.adsabs.harvard.edu/v1/search/query?\" \\\n",
    "                           'q=pubdate%3A%5B'+str(i)+'-01%20TO%20'+str(i+1)+'-01%5D%20abs%3Afermi%20OR%20keyword%3A(\"gamma%20ray\")%20body%3A(fermi%20AND%20(sciencetools%20OR%20fermitools)%20NOT%20fermipy)%20property%3Arefereed&sort=date%20desc%2C%20bibcode%20desc' \\\n",
    "                           \"&sort=date%20desc%2C%20bibcode%20desc\" \\\n",
    "                           \"&fl=bibcode,title\" \\\n",
    "                           \"&rows=500\", \\\n",
    "                           headers={'Authorization': 'Bearer ' + token})\n",
    "    res = results.json()['response']['docs']\n",
    "    bibcodes = [d['bibcode'] for d in res]\n",
    "    year.append(i)\n",
    "    toolct.append(len(bibcodes))\n",
    "    #print(str(i)+\"-\"+str(i+1)+\":\",str(len(bibcodes)))\n",
    "\n",
    "    # This block gets the papers that DO use fermipy\n",
    "    results2 = requests.get(\"https://api.adsabs.harvard.edu/v1/search/query?\" \\\n",
    "                           'q=pubdate%3A%5B'+str(i)+'-01%20TO%20'+str(i+1)+'-01%5D%20abs%3Afermi%20OR%20keyword%3A(\"gamma%20ray\")%20body%3A(fermi%20AND%20fermipy)%20property%3Arefereed&sort=date%20desc%2C%20bibcode%20desc' \\\n",
    "                           \"&sort=date%20desc%2C%20bibcode%20desc\" \\\n",
    "                           \"&fl=bibcode,title\" \\\n",
    "                           \"&rows=500\", \\\n",
    "                           headers={'Authorization': 'Bearer ' + token})\n",
    "    res2 = results2.json()['response']['docs']\n",
    "    bibcodes2 = [d['bibcode'] for d in res2]\n",
    "    frmpyct.append(len(bibcodes2))\n",
    "    print(str(i)+\"-\"+str(i+1)+\":\")\n",
    "    print(\"  Fermitools: \"+str(len(bibcodes)))\n",
    "    print(\"  Fermipy: \"+str(len(bibcodes2)))\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AqRF2AACAqXHOTh5kZmYqPT3d1WXACby9veXp6enqMgAATkDYyQXDMJSQkKCLFy+6uhQ4UYkSJWS1WrnXEgCYDGEnF64HnaCgIPn5+fHH0GQMw1BKSooSExMlSeXKlXNxRQAARyLs3EZmZqYt6JQuXdrV5cBJfH19JUmJiYkKCgrikBYAmAgnKN/G9XN0/Pz8XFwJnO36Z8x5WQBgLoSdXOLQlfnxGQOAORF2AACAqRF24HQWi0UrVqxwdRkAgLsUJyjnV3RgAe8vKc9v6dWrlxYtWpSt/dChQ6pWrZojqsqVs2fPqmTJkgW2PwAAbkTYMbkOHTpowYIFdm1ly5bN83bS09Pl7e2drxqsVmu+3gcAgCNwGMvkfHx8ZLVa7RZPT099+eWXatCggYoWLaoqVapowoQJysjIsL3PYrFo7ty56tq1q4oVK6bXXntN0dHRqlu3rubPn69KlSqpePHieuWVV5SZmamYmBhZrVYFBQXp9ddft6vhxsNYx44dk8Vi0dKlS9WsWTMVLVpUtWvX1oYNGyT9ec+batWq6Y033rDbxt69e+Xh4aEjR4449fcFADAfws5daO3atXr66ac1cOBA7d+/X/PmzdPChQuzhZTx48era9eu2rNnj55//nlJ0pEjR/T1119rzZo1WrJkiebPn69OnTrp1KlT2rhxo6ZNm6YxY8Zo69att6zhH//4h4YOHapdu3apWbNm6tKli86dOyeLxaLnn38+22zU/Pnz9cADD6hq1aqO/WUAAEyPw1gmt2rVKhUvXtz2+pFHHtFvv/2mkSNH6tlnn5UkValSRZMmTdLw4cM1fvx4W9+oqChbyLkuKytL8+fPl7+/v2rVqqXWrVvr4MGDWr16tTw8PFSjRg1NmzZNGzZsUJMmTXKsq3///nrsscckSXPmzNGaNWv04Ycfavjw4Xruuec0btw4bdu2Tffff7/S09P18ccfa/r06Y781QAA7lRuz1/Nx3mnjkTYMbnWrVtrzpw5ttfFihVTtWrVtH37druZnMzMTF27dk0pKSm2m+s1bNgw2/ZCQ0Pl7+9vex0cHCxPT095eHjYtV1/9EJOmjZtavvZy8tLDRs21IEDByT9+biGTp06af78+br//vu1atUqXbt2TU888UQeRw8AAGHH9K6HmxtlZWVpwoQJ6tatW7b+RYsWtXvvX/31JGWLxXLTtqysrDzXeuNN/V544QX17NlTM2fO1IIFC9SjRw/uYg0AyBfCzl2ofv36OnjwYIFefv5XW7du1YMPPihJysjI0I4dO9S/f3/b+o4dO6pYsWKaM2eOvv76a33//feuKhUAUMgRdu5C48aNU2RkpCpWrKgnnnhCHh4e2r17t/bs2aPXXnutQGp45513FB4erpo1a2rmzJm6cOGC3flBnp6e6tWrl0aNGqVq1arZHfYCACAvuBrrLtS+fXutWrVKsbGxatSokZo0aaIZM2aocuXKBVbD1KlTNW3aNN1333364Ycf9N///ldlypSx69O7d2+lpaVlO0kaAIC8sBiGYbi6CFdLTk5WYGCgkpKSFBAQYLfu2rVrio+PV1hYmN35LMifY8eOKSwsTLt27VLdunVv2ffHH39Uq1atdOrUKQUHBzu9Nj5rAMgjF1+Ndau/3zfiMBbcTmpqqk6ePKmxY8eqe/fuBRJ0AADmxWEsuJ0lS5aoRo0aSkpKUkxMjKvLAQAUcszsoECFhobqdkdOe/XqpV69ehVMQQAA02NmBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphB05nsVi0YsWKAtlXaGioZs2aVSD7AgAUDtxnJ59CR35VoPs7NrVTnt/Tq1cvLVq0KFv7oUOHCvSJ52fPnlXJkiW1cOFCPffcc7fsu379erVq1apgCgMA3BUIOybXoUMHLViwwK6tbNmyed5Oenq6vL2981WD1WqVJPXo0UMdOnSwtXfr1k0RERGaOHGira1UqVL52gcAADnhMJbJ+fj4yGq12i2enp768ssv1aBBAxUtWlRVqlTRhAkTlJGRYXufxWLR3Llz1bVrVxUrVkyvvfaaoqOjVbduXc2fP1+VKlVS8eLF9corrygzM1MxMTGyWq0KCgrS66+/blfD9cNYvr6+dnUUKVJEfn5+ttc+Pj564YUXVLJkSfn5+emRRx7RoUOH7Lb1+eefq3bt2vLx8VFoaKjefPPNW44/OjpalSpVko+Pj0JCQjRw4EDH/XIBAIUCYecutHbtWj399NMaOHCg9u/fr3nz5mnhwoXZQsr48ePVtWtX7dmzR88//7wk6ciRI/r666+1Zs0aLVmyRPPnz1enTp106tQpbdy4UdOmTdOYMWO0devWPNfVq1cv/fzzz1q5cqW2bNkiwzDUsWNHpaenS5J27Nih7t276+9//7v27Nmj6OhojR07VgsXLrzp9j777DPNnDlT8+bN06FDh7RixQrVqVMnz3UBAAo3DmOZ3KpVq1S8eHHb60ceeUS//fabRo4cqWeffVaSVKVKFU2aNEnDhw/X+PHjbX2joqJsIee6rKwszZ8/X/7+/qpVq5Zat26tgwcPavXq1fLw8FCNGjU0bdo0bdiwQU2aNMl1nYcOHdLKlSv1448/qlmzZpKk//znP6pYsaJWrFihJ554QjNmzFCbNm00duxYSVL16tW1f/9+TZ8+/abP0jpx4oSsVqsefvhheXt7q1KlSrr//vtzXRMAwBwIOybXunVrzZkzx/a6WLFiqlatmrZv3243k5OZmalr164pJSVFfn5+kqSGDRtm215oaKj8/f1tr4ODg+Xp6SkPDw+7tsTExDzVeeDAAXl5ealx48a2ttKlS6tGjRo6cOCArU/Xrl3t3te8eXPNmjVLmZmZ8vT0tFv3xBNPaNasWapSpYo6dOigjh07qnPnzvLy4p89ANxN+NY3uevh5kZZWVmaMGGCunXrlq1/0aJF7d77V389Sdlisdy0LSsrK0915vQkdMMwZLFYsv18u/dJUsWKFXXw4EHFxsbq22+/Vd++fTV9+nRt3Lgx3ydbAwAKH8LOXah+/fo6ePBggV5+fju1atVSRkaGfvrpJ9thrHPnzunXX39VzZo1bX02bdpk977NmzerevXq2WZ1rvP19VWXLl3UpUsX9evXT/fcc4/27Nmj+vXrO3dAAAC34dITlL///nt17txZISEh2W48l56erhEjRqhOnToqVqyYQkJC9Mwzz+jMmTN220hNTdWAAQNUpkwZFStWTF26dNGpU6cKeCSFy7hx4/Tvf/9b0dHR2rdvnw4cOKBly5ZpzJgxLqspPDxcXbt21YsvvqhNmzbpl19+0dNPP63y5cvbDl0NHTpU3333nSZNmqRff/1VixYt0uzZszVs2LCbbnPhwoX68MMPtXfvXh09elQfffSRfH19Vbly5YIcGgDAxVwadq5cuaL77rtPs2fPzrYuJSVFO3fu1NixY7Vz50598cUX+vXXX9WlSxe7foMGDdLy5cu1dOlSbdq0SZcvX1ZkZKQyMzMLahiFTvv27bVq1SrFxsaqUaNGatKkiWbMmOHyELBgwQI1aNBAkZGRatq0qQzD0OrVq22HnOrXr69PPvlES5cuVUREhMaNG6eJEyfe9ORkSSpRooTef/99NW/eXPfee6++++47ffnllypdunQBjgoA4GoW41YnPRQgi8Wi5cuX69FHH82xz/bt23X//ffr+PHjqlSpkpKSklS2bFl99NFH6tGjhyTpzJkzqlixolavXq327dvnat/JyckKDAxUUlKSAgIC7NZdu3ZN8fHxCgsLszufBebDZw0AeRQdmMt+SU7Z/a3+ft+oUN1nJykpSRaLRSVKlJD0531X0tPT1a5dO1ufkJAQRUREaPPmzTluJzU1VcnJyXYLAAAwp0ITdq5du6aRI0cqKirKlt4SEhJUpEgRlSxZ0q5vcHCwEhISctzWlClTFBgYaFsqVqzo1NoBAIDrFIqwk56err///e/KysrSu+++e9v+N7tE+UajRo1SUlKSbTl58qQjywUAAG7E7cNOenq6unfvrvj4eMXGxtodk7NarUpLS9OFCxfs3pOYmKjg4OAct+nj46OAgAC7BQAAmJNbh53rQefQoUP69ttvs11F06BBA3l7eys2NtbWdvbsWe3du9d2rxZHcZPzuOFEfMYAYE4uvang5cuXdfjwYdvr+Ph4xcXFqVSpUgoJCdHjjz+unTt3atWqVcrMzLSdh1OqVCkVKVJEgYGB6t27t4YOHarSpUurVKlSGjZsmOrUqaOHH37YITVev+w5JSVFvr6+Dtkm3FNKSoqk7HeJBgAUbi4NOz///LNat25tez1kyBBJ0rPPPqvo6GitXLlSklS3bl27961fv16tWrWSJM2cOVNeXl7q3r27rl69qjZt2mjhwoU53lE3rzw9PVWiRAnbs578/PxueT4QCh/DMJSSkqLExESVKFHCYf92AADuwW3us+NKt7tO3zAMJSQk6OLFiwVfHApMiRIlZLVaCbMAkFuF5D47PBsrFywWi8qVK6egoCClp6e7uhw4gbe3NzM6AGBShJ088PT05A8iAACFjFtfjQUAAHCnCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUXBp2vv/+e3Xu3FkhISGyWCxasWKF3XrDMBQdHa2QkBD5+vqqVatW2rdvn12f1NRUDRgwQGXKlFGxYsXUpUsXnTp1qgBHAQAA3JlLw86VK1d03333afbs2TddHxMToxkzZmj27Nnavn27rFar2rZtq0uXLtn6DBo0SMuXL9fSpUu1adMmXb58WZGRkcrMzCyoYQAAADdmMQzDcHURkmSxWLR8+XI9+uijkv6c1QkJCdGgQYM0YsQISX/O4gQHB2vatGnq06ePkpKSVLZsWX300Ufq0aOHJOnMmTOqWLGiVq9erfbt2+dq38nJyQoMDFRSUpICAgKcMj4AAEwnOjCX/ZKcsvvc/v1223N24uPjlZCQoHbt2tnafHx81LJlS23evFmStGPHDqWnp9v1CQkJUUREhK3PzaSmpio5OdluAQAA5uS2YSchIUGSFBwcbNceHBxsW5eQkKAiRYqoZMmSOfa5mSlTpigwMNC2VKxY0cHVAwAAd+G2Yec6i8Vi99owjGxtf3W7PqNGjVJSUpJtOXnypENqBQAA7sdtw47VapWkbDM0iYmJttkeq9WqtLQ0XbhwIcc+N+Pj46OAgAC7BQAAmJOXqwvISVhYmKxWq2JjY1WvXj1JUlpamjZu3Khp06ZJkho0aCBvb2/Fxsaqe/fukqSzZ89q7969iomJcVntAAAUOBefLOzOXBp2Ll++rMOHD9tex8fHKy4uTqVKlVKlSpU0aNAgTZ48WeHh4QoPD9fkyZPl5+enqKgoSVJgYKB69+6toUOHqnTp0ipVqpSGDRumOnXq6OGHH3bVsAAAgBtxadj5+eef1bp1a9vrIUOGSJKeffZZLVy4UMOHD9fVq1fVt29fXbhwQY0bN9Y333wjf39/23tmzpwpLy8vde/eXVevXlWbNm20cOFCeXp6Fvh4AADI9QyLdFfOsriC29xnx5W4zw4AwGFcFXZccRiL++wAAAC4HmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYGmEHAACYmperCwAAwGmiA3PZL8m5dcClmNkBAACmRtgBAACmluews2bNGm3atMn2+p133lHdunUVFRWlCxcuOLQ4AACAO5XnsPOPf/xDycnJkqQ9e/Zo6NCh6tixo44ePaohQ4Y4vEAAAIA7kecTlOPj41WrVi1J0ueff67IyEhNnjxZO3fuVMeOHR1eIAAAwJ3I88xOkSJFlJKSIkn69ttv1a5dO0lSqVKlbDM+AAAA7iLPMzstWrTQkCFD1Lx5c23btk3Lli2TJP3666+qUKGCwwsEAAC4E3me2Zk9e7a8vLz02Wefac6cOSpfvrwk6euvv1aHDh0cXiAAAMCdyPPMTqVKlbRq1aps7TNnznRIQQAAAI6UrzsoZ2Vl6fDhw0pMTFRWVpbdugcffNAhhQEAADhCnsPO1q1bFRUVpePHj8swDLt1FotFmZmZDisOAADgTuU57Lz88stq2LChvvrqK5UrV04Wi8UZdQEAADhEnsPOoUOH9Nlnn6latWrOqAcAAMCh8nw1VuPGjXX48GFn1JJNRkaGxowZo7CwMPn6+qpKlSqaOHGi3XlChmEoOjpaISEh8vX1VatWrbRv374CqQ8AALi/PM/sDBgwQEOHDlVCQoLq1Kkjb29vu/X33nuvw4qbNm2a5s6dq0WLFql27dr6+eef9dxzzykwMFCvvvqqJCkmJkYzZszQwoULVb16db322mtq27atDh48KH9/f4fVAgAACqc8h53HHntMkvT888/b2iwWiwzDcPgJylu2bFHXrl3VqVMnSVJoaKiWLFmin3/+WdKfszqzZs3S6NGj1a1bN0nSokWLFBwcrMWLF6tPnz4OqwUAABRO+Xo2VkFp0aKF5s6dq19//VXVq1fXL7/8ok2bNmnWrFm2WhISEmyPrJAkHx8ftWzZUps3bybsAACAvIedypUrO6OOmxoxYoSSkpJ0zz33yNPTU5mZmXr99df15JNPSpISEhIkScHBwXbvCw4O1vHjx3PcbmpqqlJTU22veaYXAADmla+bCh45ckSzZs3SgQMHZLFYVLNmTb366quqWrWqQ4tbtmyZPv74Yy1evFi1a9dWXFycBg0apJCQED377LO2fn+9/P36IbWcTJkyRRMmTHBorQAAwD3l+WqstWvXqlatWtq2bZvuvfdeRURE6KefflLt2rUVGxvr0OL+8Y9/aOTIkfr73/+uOnXqqGfPnho8eLCmTJkiSbJarZL+f4bnusTExGyzPTcaNWqUkpKSbMvJkycdWjcAAHAfeZ7ZGTlypAYPHqypU6dmax8xYoTatm3rsOJSUlLk4WGfxzw9PW2XnoeFhclqtSo2Nlb16tWTJKWlpWnjxo2aNm1ajtv18fGRj4+Pw+oEAADuK89h58CBA/rkk0+ytT///PO2E4cdpXPnznr99ddVqVIl1a5dW7t27dKMGTNsV4JZLBYNGjRIkydPVnh4uMLDwzV58mT5+fkpKirKobUAAIDCKc9hp2zZsoqLi1N4eLhde1xcnIKCghxWmCS9/fbbGjt2rPr27avExESFhISoT58+GjdunK3P8OHDdfXqVfXt21cXLlxQ48aN9c0333CPHQAAICkfYefFF1/USy+9pKNHj6pZs2ayWCzatGmTpk2bpqFDhzq0OH9/f82aNeuWM0YWi0XR0dGKjo526L4BAIA55DnsjB07Vv7+/nrzzTc1atQoSVJISIiio6M1cOBAhxcIAABwJ/IcdiwWiwYPHqzBgwfr0qVLksQhIwAA4LbydZ+d6wg5AADA3eUq7NSvX1/fffedSpYsqXr16t3yhn07d+50WHEAAAB3Kldhp2vXrrb70nTt2vWWYQcAAMCd5CrsjB8/3vYzVz0BAIDCJM+Pi6hSpYrOnTuXrf3ixYuqUqWKQ4oCAABwlDyHnWPHjikzMzNbe2pqqk6dOuWQogAAABwl11djrVy50vbz2rVrFRgYaHudmZmp7777TmFhYY6tDgAA4A7lOuw8+uijkv68z86zzz5rt87b21uhoaF68803HVocAADAncp12LnxSePbt29XmTJlnFYUAACAo+T5poLx8fHOqAMAAMAp8nUH5StXrmjjxo06ceKE0tLS7NbxfCwAAOBO8hx2du3apY4dOyolJUVXrlxRqVKl9Mcff8jPz09BQUGEHQAA4FbyfOn54MGD1blzZ50/f16+vr7aunWrjh8/rgYNGuiNN95wRo0AAAD5luewExcXp6FDh8rT01Oenp5KTU1VxYoVFRMTo3/+85/OqBEAACDf8hx2vL29bc/GCg4O1okTJyRJgYGBtp8BAADcRZ7P2alXr55+/vlnVa9eXa1bt9a4ceP0xx9/6KOPPlKdOnWcUSMAAEC+5XlmZ/LkySpXrpwkadKkSSpdurReeeUVJSYmat68eQ4vEAAA4E7keWanYcOGtp/Lli2r1atXO7QgAAAAR8rzzM5DDz2kixcvZmtPTk7WQw895IiaAAAAHCbPYWfDhg3ZbiQoSdeuXdMPP/zgkKIAAAAcJdeHsXbv3m37ef/+/UpISLC9zszM1Jo1a1S+fHnHVgcAAHCHch126tatK4vFIovFctPDVb6+vnr77bcdWhwAwESiA3PZL8m5deCuk+uwEx8fL8MwVKVKFW3btk1ly5a1rStSpIiCgoLk6enplCIBAADyK9dhp3LlypKkrKwspxUDAADgaLkKOytXrtQjjzwib29vrVy58pZ9u3Tp4pDCAAAAHCFXYefRRx9VQkKCgoKC9Oijj+bYz2KxKDMz01G1AQAA3LFchZ0bD11xGAsAABQmeb7PDgAAQGGS67Czbt061apVS8nJydnWJSUlqXbt2vr+++8dWhwAAMCdynXYmTVrll588UUFBARkWxcYGKg+ffpo5syZDi0OAADgTuU67Pzyyy/q0KFDjuvbtWunHTt2OKQoAAAAR8l12Pntt9/k7e2d43ovLy/9/vvvDikKAADAUXIddsqXL689e/bkuH737t0qV66cQ4oCAABwlFyHnY4dO2rcuHG6du1atnVXr17V+PHjFRkZ6dDiAAAA7lSuHxcxZswYffHFF6pevbr69++vGjVqyGKx6MCBA3rnnXeUmZmp0aNHO7NWAACAPMt12AkODtbmzZv1yiuvaNSoUTIMQ9Kfd01u37693n33XQUHBzutUAAAgPzIddiR/nwY6OrVq3XhwgUdPnxYhmEoPDxcJUuWdFZ9AAAAdyRPYee6kiVLqlGjRo6uBQAAwOF4XAQAADA1wg4AADC1fB3GAgAACL22OFf9jjm3jNvK1cxO/fr1deHCBUnSxIkTlZKS4tSibnT69Gk9/fTTKl26tPz8/FS3bl27x1IYhqHo6GiFhITI19dXrVq10r59+wqsPgAA4N5yFXYOHDigK1euSJImTJigy5cvO7Wo6y5cuKDmzZvL29tbX3/9tfbv368333xTJUqUsPWJiYnRjBkzNHv2bG3fvl1Wq1Vt27bVpUuXCqRGAADg3nJ1GKtu3bp67rnn1KJFCxmGoTfeeEPFixe/ad9x48Y5rLhp06apYsWKWrBgga0tNDTU9rNhGJo1a5ZGjx6tbt26SZIWLVqk4OBgLV68WH369HFYLQAAoHDK1czOwoULVbp0aa1atUoWi0Vff/21li9fnm1ZsWKFQ4tbuXKlGjZsqCeeeEJBQUGqV6+e3n//fdv6+Ph4JSQkqF27drY2Hx8ftWzZUps3b3ZoLQAAoHDK1cxOjRo1tHTpUkmSh4eHvvvuOwUFBTm1MEk6evSo5syZoyFDhuif//yntm3bpoEDB8rHx0fPPPOMEhISJCnbnZuDg4N1/PjxHLebmpqq1NRU2+vk5GTnDAAAALhcnq/GysrKckYdOe6rYcOGmjx5siSpXr162rdvn+bMmaNnnnnG1s9isdi9zzCMbG03mjJliiZMmOCcogEAgFvJ1312jhw5ogEDBujhhx9W27ZtNXDgQB05csTRtalcuXKqVauWXVvNmjV14sQJSZLVapUk2wzPdYmJibd8TteoUaOUlJRkW06ePOngygEAgLvIc9hZu3atatWqpW3btunee+9VRESEfvrpJ9WuXVuxsbEOLa558+Y6ePCgXduvv/6qypUrS5LCwsJktVrt9puWlqaNGzeqWbNmOW7Xx8dHAQEBdgsAADCnPB/GGjlypAYPHqypU6dmax8xYoTatm3rsOIGDx6sZs2aafLkyerevbu2bdum9957T++9956kPw9fDRo0SJMnT1Z4eLjCw8M1efJk+fn5KSoqymF1AACAwivPYefAgQP65JNPsrU///zzmjVrliNqsmnUqJGWL1+uUaNGaeLEiQoLC9OsWbP01FNP2foMHz5cV69eVd++fXXhwgU1btxY33zzjfz9/R1aCwAAKJzyHHbKli2ruLg4hYeH27XHxcU55QqtyMhIRUZG5rjeYrEoOjpa0dHRDt83AAAo/PIcdl588UW99NJLOnr0qJo1ayaLxaJNmzZp2rRpGjp0qDNqBAAAyLc8h52xY8fK399fb775pkaNGiVJCgkJUXR0tAYOHOjwAgEAAO5EnsOOxWLR4MGDNXjwYNvzpzg/BgAAuKs8h50bEXIAAIC7y9dNBQEAAAoLwg4AADA1wg4AADA1wg4AADC1fIWd/v376/z5846uBQAAwOFyHXZOnTpl+3nx4sW6fPmyJKlOnTo8NRwAALitXF96fs8996h06dJq3ry5rl27ppMnT6pSpUo6duyY0tPTnVkjAAC4jdBri3PV75hzy3BLuZ7ZSUpK0qeffqoGDRooKytLHTt2VPXq1ZWamqq1a9cqISHBmXUCAADkS67DTnp6uu6//34NHTpUvr6+2rVrlxYsWCBPT0/Nnz9fVatWVY0aNZxZKwAAQJ7l+jBWQECA6tWrp+bNmystLU0pKSlq3ry5vLy8tGzZMlWoUEHbtm1zZq0AAAB5luuZnTNnzmjMmDHy8fFRRkaGGjZsqAceeEBpaWnauXOnLBaLWrRo4cxaAQAA8izXYadMmTLq3LmzpkyZIj8/P23fvl0DBgyQxWLRsGHDFBAQoJYtWzqzVgAAgDzL900FAwMD1b17d3l7e2vdunWKj49X3759HVkbAADAHcvXU893796t8uXLS5IqV64sb29vWa1W9ejRw6HFAQAA3Kl8hZ2KFSvaft67d6/DigEAFIDowFz2S3JuHUAB4dlYAADA1Ag7AADA1Ag7AADA1Ag7AADA1PJ1gjIAOFPoyK9y1e/Y1E5OrgSAGTCzAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI377AAA4ECh1xbnuu8x55WBGzCzAwAATI2wAwAATI2wAwAATI2wAwAATI0TlAHAlaIDc9kvybl1mFRuTxY+5twy4GLM7AAAAFMj7AAAAFPjMBYASLk/nCRxSAkoZJjZAQAApkbYAQAApsZhLABAgeDKKLhKoZrZmTJliiwWiwYNGmRrMwxD0dHRCgkJka+vr1q1aqV9+/a5rkgAAOBWCk3Y2b59u9577z3de++9du0xMTGaMWOGZs+ere3bt8tqtapt27a6dOmSiyoFAADupFAcxrp8+bKeeuopvf/++3rttdds7YZhaNasWRo9erS6desmSVq0aJGCg4O1ePFi9enTx1UlAyhkeFI1YF6FYmanX79+6tSpkx5++GG79vj4eCUkJKhdu3a2Nh8fH7Vs2VKbN2/OcXupqalKTk62WwAAgDm5/czO0qVLtXPnTm3fvj3buoSEBElScHCwXXtwcLCOHz+e4zanTJmiCRMmOLZQAADgltx6ZufkyZN69dVX9fHHH6to0aI59rNYLHavDcPI1najUaNGKSkpybacPHnSYTUDAAD34tYzOzt27FBiYqIaNGhga8vMzNT333+v2bNn6+DBg5L+nOEpV66crU9iYmK22Z4b+fj4yMfHx3mFAwAAt+HWMztt2rTRnj17FBcXZ1saNmyop556SnFxcapSpYqsVqtiY2Nt70lLS9PGjRvVrFkzF1YOAADchVvP7Pj7+ysiIsKurVixYipdurStfdCgQZo8ebLCw8MVHh6uyZMny8/PT1FRUa4oGQAAuBm3Dju5MXz4cF29elV9+/bVhQsX1LhxY33zzTfy9/d3dWkAAMANFLqws2HDBrvXFotF0dHRio6Odkk9AADAvbn1OTsAAAB3irADAABMjbADAABMjbADAABMrdCdoAwAZpLbB5Aec24ZgKkxswMAAEyNsAMAAEyNw1gAbil05Fe56ndsaicnVwJH4dAZ7jbM7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFPjPjtAPnDvGQAoPJjZAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApkbYAQAApubl6gIA5E7oyK9y3ffY1E5OrAQAChdmdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKkRdgAAgKm5ddiZMmWKGjVqJH9/fwUFBenRRx/VwYMH7foYhqHo6GiFhITI19dXrVq10r59+1xUMQAAcDduHXY2btyofv36aevWrYqNjVVGRobatWunK1eu2PrExMRoxowZmj17trZv3y6r1aq2bdvq0qVLLqwcAAC4C7e+qeCaNWvsXi9YsEBBQUHasWOHHnzwQRmGoVmzZmn06NHq1q2bJGnRokUKDg7W4sWL1adPH1eUDQAA3Ihbz+z8VVJSkiSpVKlSkqT4+HglJCSoXbt2tj4+Pj5q2bKlNm/enON2UlNTlZycbLcAAABzcuuZnRsZhqEhQ4aoRYsWioiIkCQlJCRIkoKDg+36BgcH6/jx4zlua8qUKZowYYLzikWByu1jFHiEAgDcnQrNzE7//v21e/duLVmyJNs6i8Vi99owjGxtNxo1apSSkpJsy8mTJx1eLwAAcA+FYmZnwIABWrlypb7//ntVqFDB1m61WiX9OcNTrlw5W3tiYmK22Z4b+fj4yMfHx3kFAwAAt+HWYccwDA0YMEDLly/Xhg0bFBYWZrc+LCxMVqtVsbGxqlevniQpLS1NGzdu1LRp01xR8l2Nw0kAAHfk1mGnX79+Wrx4sf773//K39/fdo5OYGCgfH19ZbFYNGjQIE2ePFnh4eEKDw/X5MmT5efnp6ioKBdXDwAA3IFbh505c+ZIklq1amXXvmDBAvXq1UuSNHz4cF29elV9+/bVhQsX1LhxY33zzTfy9/cv4GoBAIA7cuuwYxjGbftYLBZFR0crOjra+QUBAIBCp9BcjQUAAJAfhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqXq4uAM4ROvKrXPU7NrWTkysBAMC1mNkBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmRtgBAACmZpqw8+677yosLExFixZVgwYN9MMPP7i6JAAA4AZMEXaWLVumQYMGafTo0dq1a5ceeOABPfLIIzpx4oSrSwMAAC7m5eoCHGHGjBnq3bu3XnjhBUnSrFmztHbtWs2ZM0dTpkxxaW2hI7/Kdd9jUzs5sRIAAO5OhX5mJy0tTTt27FC7du3s2tu1a6fNmze7qCoAAOAuCv3Mzh9//KHMzEwFBwfbtQcHByshIeGm70lNTVVqaqrtdVJSkiQpOTnZ4fVlpabkuq8j95/b/Rb2fbpqv+68T1ft927Zp6v2W9j36ar9uvM+XbXfwr7Pm23XMIxbdzQKudOnTxuSjM2bN9u1v/baa0aNGjVu+p7x48cbklhYWFhYWFhMsJw8efKWWaHQz+yUKVNGnp6e2WZxEhMTs832XDdq1CgNGTLE9jorK0vnz59X6dKlZbFYnFqv9GcSrVixok6ePKmAgACn789V7pZxSozVrO6Wsd4t45QYq9kYhqFLly4pJCTklv0KfdgpUqSIGjRooNjYWP3tb3+ztcfGxqpr1643fY+Pj498fHzs2kqUKOHMMm8qICDAtP8Ab3S3jFNirGZ1t4z1bhmnxFjNJDAw8LZ9Cn3YkaQhQ4aoZ8+eatiwoZo2bar33ntPJ06c0Msvv+zq0gAAgIuZIuz06NFD586d08SJE3X27FlFRERo9erVqly5sqtLAwAALmaKsCNJffv2Vd++fV1dRq74+Pho/Pjx2Q6lmc3dMk6JsZrV3TLWu2WcEmO9W1kM43bXawEAABRehf6mggAAALdC2AEAAKZG2AEAAKZG2AEAAKZG2MmHKVOmqFGjRvL391dQUJAeffRRHTx40K6PYRiKjo5WSEiIfH191apVK+3bt8+uT2pqqgYMGKAyZcqoWLFi6tKli06dOmXX59dff1XXrl1VpkwZBQQEqHnz5lq/fr3Tx3ido8b63nvvqVWrVgoICJDFYtHFixez7evChQvq2bOnAgMDFRgYqJ49e960nzMU1DiPHTum3r17KywsTL6+vqpatarGjx+vtLQ0Zw/RpiA/0+tSU1NVt25dWSwWxcXFOWFUN1fQY/3qq6/UuHFj+fr6qkyZMurWrZuzhpZNQY7Vld9Ljhjn+fPnNWDAANWoUUN+fn6qVKmSBg4caHtO4nWu/E6SCm6s7vC95GyEnXzYuHGj+vXrp61btyo2NlYZGRlq166drly5YusTExOjGTNmaPbs2dq+fbusVqvatm2rS5cu2foMGjRIy5cv19KlS7Vp0yZdvnxZkZGRyszMtPXp1KmTMjIytG7dOu3YsUN169ZVZGRkjg85ddexpqSkqEOHDvrnP/+Z476ioqIUFxenNWvWaM2aNYqLi1PPnj2dOr7rCmqc//vf/5SVlaV58+Zp3759mjlzpubOnXvL34ujFeRnet3w4cNvezt3ZyjIsX7++efq2bOnnnvuOf3yyy/68ccfFRUV5dTx3aggx+rK7yVHjPPMmTM6c+aM3njjDe3Zs0cLFy7UmjVr1Lt3b7t9ufI7qSDH6g7fS053Z4/hhGEYRmJioiHJ2Lhxo2EYhpGVlWVYrVZj6tSptj7Xrl0zAgMDjblz5xqGYRgXL140vL29jaVLl9r6nD592vDw8DDWrFljGIZh/P7774Yk4/vvv7f1SU5ONiQZ3377bUEMLZv8jPVG69evNyQZFy5csGvfv3+/IcnYunWrrW3Lli2GJON///ufcwZzC84a583ExMQYYWFhDqs9r5w91tWrVxv33HOPsW/fPkOSsWvXLmcMI1ecNdb09HSjfPnyxgcffODU+vPCWWN1t++lOx3ndZ988olRpEgRIz093TAM9/tOMgznjfVmXP295GjM7DjA9enAUqVKSZLi4+OVkJCgdu3a2fr4+PioZcuW2rx5syRpx44dSk9Pt+sTEhKiiIgIW5/SpUurZs2a+ve//60rV64oIyND8+bNU3BwsBo0aFBQw7OTn7HmxpYtWxQYGKjGjRvb2po0aaLAwMA8bcdRnDXOnPZ1fT+u4Myx/vbbb3rxxRf10Ucfyc/Pz3FF55Ozxrpz506dPn1aHh4eqlevnsqVK6dHHnkk2yGiguSssbrb95KjxpmUlKSAgAB5ef15r113+066XqPk+LHm1MeV30uORti5Q4ZhaMiQIWrRooUiIiIkyTaV+9enrgcHB9vWJSQkqEiRIipZsmSOfSwWi2JjY7Vr1y75+/uraNGimjlzptasWeOSB5fmd6y5kZCQoKCgoGztQUFBBXbI7jpnjvOvjhw5orfffttlz3Fz5lgNw1CvXr308ssvq2HDho4rOp+cOdajR49KkqKjozVmzBitWrVKJUuWVMuWLXX+/HkHjSD3nDlWd/pectQ4z507p0mTJqlPnz62Nnf6TpKcO9a/cvX3kjOY5nERrtK/f3/t3r1bmzZtyrbOYrHYvTYMI1vbX93YxzAM9e3bV0FBQfrhhx/k6+urDz74QJGRkdq+fbvKlSvnuIHkgqPHertt5Hc7d8rZ47zuzJkz6tChg5544gm98MIL+drGnXLmWN9++20lJydr1KhRd1ynIzhzrFlZWZKk0aNH67HHHpMkLViwQBUqVNCnn356yz8szuDMsbrT95IjxpmcnKxOnTqpVq1aGj9+/C23cavtOJuzx3qdO3wvOQMzO3dgwIABWrlypdavX68KFSrY2q1WqyRlS9aJiYm2BG61WpWWlqYLFy7k2GfdunVatWqVli5dqubNm6t+/fp699135evrq0WLFjlzaNncyVhzw2q16rfffsvW/vvvv+dpO3fK2eO87syZM2rdurWaNm2q9957786Kzidnj3XdunXaunWrfHx85OXlpWrVqkmSGjZsqGeffdYBI8g9Z4/1+h/4WrVq2dp8fHxUpUoVnThx4k5Kz7OC+Fzd4XvJEeO8dOmSOnTooOLFi2v58uXy9va22447fCdJzh/rde7wveQshJ18MAxD/fv31xdffKF169YpLCzMbn1YWJisVqtiY2NtbWlpadq4caOaNWsmSWrQoIG8vb3t+pw9e1Z79+619UlJSZEkeXjYf0weHh62/5N0NkeMNTeaNm2qpKQkbdu2zdb2008/KSkpKU/bya+CGqcknT59Wq1atVL9+vW1YMGCbJ+vsxXUWP/1r3/pl19+UVxcnOLi4rR69WpJ0rJly/T66687ZjC3UVBjbdCggXx8fOwuC05PT9exY8dUuXLlOx9ILhTUWF39veSocSYnJ6tdu3YqUqSIVq5cqaJFi9ptx9XfSVLBjVVy/feS0xXEWdBm88orrxiBgYHGhg0bjLNnz9qWlJQUW5+pU6cagYGBxhdffGHs2bPHePLJJ41y5coZycnJtj4vv/yyUaFCBePbb781du7caTz00EPGfffdZ2RkZBiG8edVD6VLlza6detmxMXFGQcPHjSGDRtmeHt7G3FxcYVqrGfPnjV27dplvP/++7YrOXbt2mWcO3fO1qdDhw7Gvffea2zZssXYsmWLUadOHSMyMtJU4zx9+rRRrVo146GHHjJOnTplt6+CUpCf6Y3i4+ML/Gqsghzrq6++apQvX95Yu3at8b///c/o3bu3ERQUZJw/f95UY3X195IjxpmcnGw0btzYqFOnjnH48GG77Vz//jUM134nFeRY3eF7ydkIO/kg6abLggULbH2ysrKM8ePHG1ar1fDx8TEefPBBY8+ePXbbuXr1qtG/f3+jVKlShq+vrxEZGWmcOHHCrs/27duNdu3aGaVKlTL8/f2NJk2aGKtXry6IYRqG4bixjh8//rbbOXfunPHUU08Z/v7+hr+/v/HUU0/l6tLtwjTOBQsW5LivglKQn+mNXBF2CnKsaWlpxtChQ42goCDD39/fePjhh429e/cW0EgLdqyu/F5yxDivX1Z/syU+Pt7Wz5XfSQU5Vnf4XnI2i2EYRs7zPgAAAIWbyQ7KAQAA2CPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsAAAAUyPsACgUDMPQww8/rPbt22db9+677yowMLDAH7oJoHAg7AAoFCwWixYsWKCffvpJ8+bNs7XHx8drxIgReuutt1SpUiWH7jM9Pd2h2wPgGoQdAIVGxYoV9dZbb2nYsGGKj4+XYRjq3bu32rRpo/vvv18dO3ZU8eLFFRwcrJ49e+qPP/6wvXfNmjVq0aKFSpQoodKlSysyMlJHjhyxrT927JgsFos++eQTtWrVSkWLFtXHH3/simECcDCejQWg0Hn00Ud18eJFPfbYY5o0aZK2b9+uhg0b6sUXX9Qzzzyjq1evasSIEcrIyNC6deskSZ9//rksFovq1KmjK1euaNy4cTp27Jji4uLk4eGhY8eOKSwsTKGhoXrzzTdVr149+fj4KCQkxMWjBXCnCDsACp3ExERFRETo3Llz+uyzz7Rr1y799NNPWrt2ra3PqVOnVLFiRR08eFDVq1fPto3ff/9dQUFB2rNnjyIiImxhZ9asWXr11VcLcjgAnIzDWAAKnaCgIL300kuqWbOm/va3v2nHjh1av369ihcvblvuueceSbIdqjpy5IiioqJUpUoVBQQEKCwsTJKyndTcsGHDgh0MAKfzcnUBAJAfXl5e8vL68yssKytLnTt31rRp07L1K1eunCSpc+fOqlixot5//32FhIQoKytLERERSktLs+tfrFgx5xcPoEARdgAUevXr19fnn3+u0NBQWwC60blz53TgwAHNmzdPDzzwgCRp06ZNBV0mABfhMBaAQq9fv346f/68nnzySW3btk1Hjx7VN998o+eff16ZmZkqWbKkSpcurffee0+HDx/WunXrNGTIEFeXDaCAEHYAFHohISH68ccflZmZqfbt2ysiIkKvvvqqAgMD5eHhIQ8PDy1dulQ7duxQRESEBg8erOnTp7u6bAAFhKuxAACAqTGzAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATI2wAwAATO3/AJZv+5DO2IvnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "width = 0.35\n",
    "p1 = plt.bar(year, toolct, width)\n",
    "p2 = plt.bar(year, frmpyct, width, bottom=toolct)\n",
    "\n",
    "plt.title('FermiTools + Fermipy Usage')\n",
    "plt.ylabel('# of Citations')\n",
    "plt.xlabel('Year')\n",
    "plt.legend((p2[0], p1[0]), ('Fermipy', 'FermiTools'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5036144578313253\n"
     ]
    }
   ],
   "source": [
    "# What fraction of papers used fermipy since 2020?\n",
    "\n",
    "print(sum(frmpyct[-4:])/(sum(toolct[-4:])+sum(frmpyct[-4:])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "Half of all published studies in the refereed literature since 2020 that analyse *Fermi/LAT* data utilize Fermipy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}