AoFAaltoCS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb0bf193",
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.options import Options\n",
    "from webdriver_manager.chrome import ChromeDriverManager\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5fde90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_faculty():\n",
    "    url = 'https://www.aalto.fi/en/department-of-computer-science/faculty-members'\n",
    "    print(f'Scraping names of Aalto CS faculty members from {url}')\n",
    "    response = requests.request('GET', url)\n",
    "    if response.status_code!=200:\n",
    "        return None\n",
    "    html = response.text\n",
    "    l = []\n",
    "\n",
    "    restree = lxml_html.fromstring(hack_html(html))\n",
    "    tree    = lxml_etree.ElementTree(restree)\n",
    "    for a in restree.xpath(\"//a[@class='aalto-profile-card__name-link']\"):\n",
    "        n = a.text.strip()\n",
    "        if n not in l:\n",
    "            l.append(n)\n",
    "    return l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc484e2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def hack_html(html):\n",
    "    p = html.find('\\n')\n",
    "    if p>0:\n",
    "        html = '<?xml version=\"1.0\"?>'+html[p:]\n",
    "    return html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6142febe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scraping names of Aalto CS faculty members from https://www.aalto.fi/en/department-of-computer-science/faculty-members\n",
      "Tuomas Aura\n",
      "Chris Brzuska\n",
      "Parinya Chalermsook\n",
      "Stephane Deny\n",
      "Mario Di Francesco\n",
      "Fabian Fagerholm\n",
      "Vikas Kumar Garg\n",
      "Christian Guckelsberger\n",
      "Petter Holme\n",
      "Eero Hyvönen\n",
      "Perttu Hämäläinen\n",
      "Alex Jung\n",
      "Juho Kannala\n",
      "Petteri Kaski\n",
      "Samuel Kaski\n",
      "Sándor Kisfaludi-Bak\n",
      "Maarit Korpi-Lagg\n",
      "Juhi Kulshrestha\n",
      "Russell W. F. Lai\n",
      "Jouko Lampinen\n",
      "Casper Lassenius\n",
      "Jaakko Lehtinen\n",
      "Janne Lindqvist\n",
      "Harri Lähdesmäki\n",
      "Lauri Malmi\n",
      "Heikki Mannila\n",
      "Pekka Marttinen\n",
      "Ilkka Niemelä\n",
      "Marko Nieminen\n",
      "Pekka Orponen\n",
      "Alexandru Paler\n",
      "Jussi Rintanen\n",
      "Juho Rousu\n",
      "Jari Saramäki\n",
      "Arno Solin\n",
      "Jukka Suomela\n",
      "Linh Truong\n",
      "Jara Joel Olavi Uitto\n",
      "Aki Vehtari\n",
      "Johanna Viitanen\n",
      "Petri Vuorimaa\n",
      "Robin Welsch\n",
      "Antti Ylä-Jääski\n",
      "Bo Zhao\n",
      "Mikko Kiviharju\n",
      "Tero Ilmari Ojanperä\n",
      "Nitin Sawhney\n",
      "Talayeh Aledavood\n",
      "Lachlan Gunn\n",
      "Lassi Haaranen\n",
      "Arto Hellas\n",
      "Vesa Hirvisalo\n",
      "Jaakko Hollmen\n",
      "Wilhelmiina Hämäläinen\n",
      "Tommi Junttila\n",
      "Barbara Esther Keller\n",
      "Ari Korhonen\n",
      "Sari Kujala\n",
      "Jorma Laaksonen\n",
      "Riku Linna\n",
      "Mika P. Nieminen\n",
      "Kerttu Pollari-Malmi\n",
      "Risto Sarvas\n",
      "Otto Seppälä\n",
      "Juha Sorva\n",
      "Sanna Suoranta\n",
      "Jari-Pekka Vanhanen\n",
      "N Asokan\n",
      "Jari Collin\n",
      "Aristides Gionis\n",
      "Petri Myllymäki\n",
      "Marko Turpeinen\n",
      "Tapio Lokki\n",
      "Mikko Sams\n",
      "Simo Särkkä\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import os\n",
    "from lxml import html  as lxml_html\n",
    "from lxml import etree as lxml_etree\n",
    "import numpy as np\n",
    "\n",
    "people = fetch_faculty()\n",
    "\n",
    "fundingval = np.zeros(len(people))\n",
    "cntr=0\n",
    "for p in people: \n",
    "    print(p)\n",
    "    # Extract first name and last name\n",
    "    name_parts = p.split()\n",
    "    # Take the first two parts as first name and last name\n",
    "    first_name = name_parts[0]\n",
    "    last_name = name_parts[-1] # ' '.join(name_parts[1:])\n",
    "# Construct the URL with the updated first name and last name\n",
    "    url = f\"https://akareport.aka.fi/ibi_apps/WFServlet?ekaLataus=0&IBIF_ex=x_RahPaatYht_report2&UILANG=en&SANAHAKU=&ETUNIMI={first_name}&SUKUNIMI={last_name}&SUKUPUOLI=FOC_NONE&HAKU=FOC_NONE&ORGANIS=FOC_NONE&TUTKDI=FOC_NONE&TMK=FOC_NONE&PAATVUOSI_A=2001&PAATVUOSI_L=2023&LAJITTELU=PAATOS&TULOSTE=HTML\"\n",
    "    response = requests.get(url) #\"https://akareport.aka.fi/ibi_apps/WFServlet?ekaLataus=0&IBIF_ex=x_RahPaatYht_report2&UILANG=en&SANAHAKU=&ETUNIMI=Alex&SUKUNIMI=Jung&SUKUPUOLI=FOC_NONE&HAKU=FOC_NONE&ORGANIS=FOC_NONE&TUTKDI=FOC_NONE&TMK=FOC_NONE&PAATVUOSI_A=2011&PAATVUOSI_L=2023&LAJITTELU=PAATOS&TULOSTE=HTML\")  \n",
    "    soup = BeautifulSoup(response.content, 'html.parser')\n",
    "    # Find all <td> elements with the specified attributes\n",
    "    td_elements = soup.find_all('td', {'align': 'RIGHT', 'class': 'riviots7', 'nowrap': ''})\n",
    "    tmp = 0 \n",
    "    for td_element in td_elements:\n",
    "            # Extract the text content of the <td> element\n",
    "            td_text = td_element.text.strip()\n",
    "            tmp = tmp + float(td_text.replace(' ', ''))\n",
    "    fundingval[cntr]= tmp\n",
    "    cntr= cntr+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c9e21c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of Tax-Payer Money via Research Council of Finland')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yMgpMhubNm6dW1/Dhw8XS0lLt++75ZOh5mZmZkpGRIUOGDJHatWurLQMgLi4ukpqaqiq7deuWmJiYSGhoqKqsQYMG4u7urtbvqamp4uTkpHMyVOwPk+WQXIckNKlVqxYsLCzw/vvvY8OGDRpn02jjrbfe0nrdatWqoWbNmmplffv2RWpqKk6dOlWo9rUVFRWF1q1bw9PTU6180KBBePToEY4dO6ZW3rVrV7XnAQEBAFDgYYG0tDT8/vvvePvtt2Fra6sqNzU1xYABA3Djxg2tD7XpIjk5GR988AE8PT1hZmYGc3NzeHl5AUCeIfFevXph2LBh+PDDD/Hpp5/io48+Qtu2bVXLRQSZmZlqj+c1bNgQ5ubmsLOzQ+fOneHq6ordu3erzk+LiopCmzZt4ODgAFNTU5ibm2PatGm4c+eO2kzHMWPGIC4uDr/99huAZ4c0vvnmGwQFBan6b9euXVAoFOjfv79aTK6urqhZs2aeGU+Ojo5o1aqVzn34zjvvwMLCAt999x1++eUX3Lp1K98h9aioKFStWjXPOQiDBg2CiCAqKkqtvFOnTjA1NVU9f35f+ueff/DXX3+pzrfK/To7duyIpKQk1X7TokUL1KxZE1999ZWqvpUrV0KhUOD9998v8DV26NABrq6uCAsLU5Xt3bsXN2/efOHhxIcPH2LSpEnw8/ODmZkZzMzMYGtri7S0tDz7WH6WL18Oc3NzWFhYwN/fH7t378b333+POnXqqNbZtWsXqlevjlq1aqn1Q/v27dUO3dWrVw/As/1569atGmdRaVsXAJw+fRpdu3ZF6dKlVfvswIEDkZWVhUuXLqnVq2kf2717N/z9/dUmreTnRfuDPhw7dgyPHz/Osw97enqiVatW2L9/f6HrflH8ly5dwuXLlzFkyBBYWloWup38GPK1AcDGjRsRExOj9jAzK/h0Yk2/F0+ePNE4szu3bdu2oUmTJrC1tVV9d69bt07jZ6ply5ZqJ3+7uLjA2dlZ1e9paWmIiYlBz5491frdzs4OXbp0eeHrft5rkQylpaXhzp07cHd3z3cdX19f/Prrr3B2dsaIESPg6+sLX19fLFmyRKe2dJkJ4Orqmm/ZnTt3dGpXV3fu3NEYa04fPd9+6dKl1Z4rlUoAwOPHj/Nt4969exARndp5WdnZ2WjXrh22b9+OiRMnYv/+/Thx4oTqGL6meIODg5GRkQEzMzOMHj1abVl0dDTMzc3VHs8f08/5sjh9+jRu3ryJM2fOoEmTJgCAEydOoF27dgCANWvW4LfffkNMTAw+/vjjPPF069YN3t7eqh/2r7/+GmlpaRgxYoRqnX///RciAhcXlzxxHT9+XO28KEC3/TE3GxsbvPvuu1i/fj3WrVuHNm3aqBLK5+l7X8o5Z2bChAl5XuPw4cMBQO11jh49Gvv378fFixeRkZGBNWvW4O2339b4+crNzMwMAwYMQHh4uGp68ddffw03Nze0b9++wG379u2LL7/8EkOHDsXevXtx4sQJxMTEoGzZsgV+JnLr1asXYmJicPToUaxatQp2dnbo3bs3/v77b9U6//77L86cOZOnH+zs7CAiqn5o3rw5duzYgczMTAwcOBAeHh6oXr262lR9betKSEhAs2bNkJiYiCVLluDw4cOIiYlR7ZfPvz5N7/1///2n9aSRwny3AM/OB42Pj9eqjZx9ML/99GW+h14U/3///QfAcDNcDfnaAKBKlSqoW7eu2uNFCvOebt++Hb169UK5cuXw7bff4tixY4iJiUFwcDCePHnywjZy2slp4969e8jOzi7wd1YXxWo2WX4iIiKQlZX1wunwzZo1Q7NmzZCVlYWTJ09i2bJlGDt2LFxcXNC7d2+t2tLl2gW3bt3Ktyznjc7JaNPT09XWe/5HT1elS5dGUlJSnvKbN28CAMqUKfNS9QPP/mM0MTExeDu5nTt3Dn/88Qe+/vprBAUFqcr/+ecfjeunpaVhwIAB8Pf3x7///ouhQ4eqnbBXp04dxMTEqG3zfFKd82WhyebNm2Fubo5du3ap/Xei6RpKJiYmGDFiBD766CMsWLAAy5cvR+vWrdUuMVCmTBkoFAocPnxY9QWT2/Nlul5LI7fg4GCsXbsWZ86cwXfffZfvevrel3LWnzJlSp4TYHPk7pO+ffti0qRJ+Oqrr9CwYUPcunVLLYEsyODBgzF//nxs3rwZ7777Lnbu3ImxY8eq/af/vJSUFOzatQvTp0/H5MmTVeXp6em4e/euVu0Cz2YP5ew3jRo1QpUqVRAYGIiQkBDViaBlypSBlZUV1q9fr7GO3H3brVs3dOvWDenp6Th+/DhCQ0PRt29feHt7o1GjRlrXtWPHDqSlpWH79u1qCXBcXJzG7TTtY2XLlsWNGzde3AkvoX379li2bBmOHz/+wllNOd+n+e2nufvR0tJS48m+hf3OzZl4Y6j+0OW1vcq+/fZb+Pj4YMuWLWr71PO/fdpydHSEQqEo8HdWF8V+ZCghIQETJkyAg4MD/ve//2m1jampKRo0aKD6TyjnkJW2/7Fo6/z58/jjjz/UyjZt2gQ7Ozu88cYbAKCavXDmzBm19TRNb86dFb9I69atERUVpfrByrFx40ZYW1vrZcqkjY0NGjRogO3bt6vFlZ2djW+//RYeHh7w9/d/6XZyy/kQPZ8U5J5JmNsHH3yAhIQEbN++HevWrcPOnTuxaNEi1XI7O7s8/xVZWFjoFI+ZmZnaj+vjx4/xzTffaFx/6NChsLCwQL9+/XDx4kWMHDlSbXnnzp0hIkhMTMwTV926dVGjRg2tY3uRRo0aITg4GD169ECPHj3yXa9169a4cOFCnkO7GzduhEKhQMuWLXVqt1KlSqhYsSL++OMPja+xbt26asPjlpaWqsPbCxcuRK1atVQjcy9SpUoVNGjQAGFhYdi0aRPS09MxePDgArdRKBQQkTz72Nq1aws9AxF49s/YwIEDERERoTpM3blzZ1y+fBmlS5fW2A+aZl0qlUoEBgZi7ty5AKC6zo62dWn6DIkI1qxZo/Vr6dChAy5dupTnEKk+hYSEwMbGBsOHD9eYvIiIamp9o0aNYGVlhW+//VZtnRs3bqhOGcjh7e2NS5cuqf0I37lzB0ePHi1UnP7+/vD19cX69esL/cNeEF1e26tMoVCoLgSZ49atWxpnk2nDxsYG9evXx/bt29VGlh48eICff/5Z5/qK1cjQuXPnVMfBk5OTcfjwYYSFhcHU1BTh4eEFXsNh5cqViIqKQqdOnVC+fHk8efJE9R9UznFvOzs7eHl54aeffkLr1q3h5OSEMmXKFHoauLu7O7p27YoZM2bAzc0N3377LSIjIzF37lzVdTrq1auHSpUqYcKECcjMzISjoyPCw8Nx5MiRPPXVqFED27dvx4oVK1CnTh2YmJjkO2Ixffp07Nq1Cy1btsS0adPg5OSE7777DhEREZg3b57GqaqFERoairZt26Jly5aYMGECLCwssHz5cpw7dw7ff//9S41caFK5cmX4+vpi8uTJEBE4OTnh559/VpsmnmPt2rX49ttvERYWhmrVqqFatWoYOXIkJk2ahCZNmmh9HY6CdOrUCQsXLkTfvn3x/vvv486dO/jiiy80juoAQKlSpTBw4ECsWLECXl5eeY5tN2nSBO+//z4GDx6MkydPonnz5rCxsUFSUhKOHDmCGjVqYNiwYS8dd45169a9cJ2QkBBs3LgRnTp1wqxZs+Dl5YWIiAgsX74cw4YNK1TCu2rVKnTo0AHt27fHoEGDUK5cOdy9exd//vknTp06lWfK/PDhwzFv3jzExsZi7dq1OrUVHByM//3vf7h58yYaN26s8WKfudnb26N58+aYP3++6vMfHR2NdevWoVSpUrq+VDWzZ8/Gli1bMHXqVPz6668YO3YsfvzxRzRv3hwhISEICAhAdnY2EhISsG/fPowfPx4NGjTAtGnTcOPGDbRu3RoeHh64f/8+lixZAnNzcwQGBgKA1nW1bdsWFhYW6NOnDyZOnIgnT55gxYoVuHfvntavY+zYsdiyZQu6deuGyZMno379+nj8+DGio6PRuXNnnRNkTXx8fFQjerVq1VJddBEALly4gPXr10NE0KNHD5QqVQpTp07FRx99hIEDB6JPnz64c+cOZs6cCUtLS0yfPl1V74ABA7Bq1Sr0798f7733Hu7cuYN58+bB3t6+0LF+9dVX6NKlCxo2bIiQkBCUL18eCQkJ2Lt3b4GjrtrQ5bW9yjp37ozt27dj+PDhePvtt3H9+nXMnj0bbm5uaoeOdTF79my8+eabaNu2LcaPH4+srCzMnTsXNjY2Oo3iAiheU+tzHhYWFuLs7CyBgYHy2WefSXJycp5tnp/hdezYMenRo4d4eXmJUqmU0qVLS2BgoOzcuVNtu19//VVq164tSqVSAKjOhs89E+dFbYk8O5O+U6dO8sMPP0i1atXEwsJCvL29ZeHChXm2v3TpkrRr107s7e2lbNmyMmrUKImIiMgzw+Xu3bvy9ttvS6lSpUShUKi1CQ2z4M6ePStdunQRBwcHsbCwkJo1a6rNDBD5v5k027ZtUyvXNJMgP4cPH5ZWrVqJjY2NWFlZScOGDeXnn3/WWJ8+ZpNduHBB2rZtK3Z2duLo6CjvvPOOJCQkqPXBmTNnxMrKKs9shidPnkidOnXE29tb7t27p3Pbmqxfv14qVaokSqVSKlSoIKGhobJu3TqNswRFnl2OAIB8/vnnBdbZoEEDVZ/6+vrKwIED1WYiBQYGSrVq1QqMLbeC9uHcnp9NJiJy7do16du3r5QuXVrMzc2lUqVKMn/+fLVp6wW9x5r2zz/++EN69eolzs7OYm5uLq6urtKqVStZuXKlxrhatGghTk5O8ujRI+1e8P+XkpIiVlZWGi9tIKJ5NtmNGzfkrbfeEkdHR7Gzs5M333xTzp0798IZMjmgYWp9jg8//FAAqC6D8PDhQ/nkk0+kUqVKYmFhIQ4ODlKjRg0JCQlRzVjctWuXdOjQQcqVK6f6/uvYsaMcPnxYrW5t6hIR+fnnn6VmzZpiaWkp5cqVkw8//FB2796dpx8K2sfu3bsnY8aMkfLly4u5ubk4OztLp06d5K+//hIR3feH/Fy+fFmGDx8ufn5+olQqxcrKSqpWrSrjxo3L8/lau3atBAQEqF57t27d5Pz583nq3LBhg1SpUkUsLS2latWqsmXLlnxnk2kb/7Fjx6RDhw7i4OAgSqVSfH19JSQkRLX8ZabWa/vaCjObLL91C5pN9vx3iKbXpumz8vnnn4u3t7colUqpUqWKrFmzRuPvZ36fH0117ty5U9Uv5cuXl88//1xjnS+i+P8NE5GRjB8/HitWrMD169c1niRIeSUnJ8PLywujRo3CvHnzijocInrNFKvDZETF2fHjx3Hp0iUsX74c//vf/5gIaeHGjRu4cuUK5s+fDxMTE4wZM6aoQyKi1xCTISIjadSoEaytrdG5c2d8+umnRR1OsbB27VrMmjUL3t7e+O6771CuXLmiDomIXkM8TEZEREQlWrGfWk9ERET0MpgMERERUYnGZIiIiIhKtNf+BOrs7GzcvHkTdnZ2er8AIBERERmGiODBgwdwd3eHiYlhx25e+2To5s2bee7cTkRERMXD9evXDXYj3ByvfTKUc4+j69evv9Tl1omIiMh4UlNT4enpqXavQkN57ZOhnENj9vb2TIaIiIiKGWOc4sITqImIiKhEYzJEREREJdprf5jM0GbmGr6bzot5ExERFTscGSIiIqISjckQERERlWhMhoiIiKhEYzJEREREJRqTISIiIirRmAwRERFRicZkiIiIiEo0JkNERERUojEZIiIiohKNyRARERGVaEyGiIiIqERjMkREREQlGpMhIiIiKtGYDBEREVGJxmSIiIiISjQmQ0RERFSivXQy9O+//yIhIUEfsRAREREZndbJ0IMHD9C/f394eXkhKCgIT58+xYgRI+Dm5gYfHx8EBgYiNTXVkLESERER6Z3WydBHH32E2NhYTJgwAQkJCejVqxcOHTqEw4cP4+DBg7h79y7mzp1ryFiJiIiI9E4hIqLNiuXLl8eGDRvQsmVL3Lx5Ex4eHvjpp5/QpUsXAMAvv/yCcePG4a+//jJowLpKTU2Fg4MDUlJSYG9vr/f6ZyoUqr+na9eVRERE9AKG/v3OTeuRoeTkZPj5+QEA3N3dYWVlhUqVKqmWV6tWDdevX9d/hEREREQGpHUyVLp0afz333+q5926dUOpUqVUzx8+fAilUqnX4IiIiIgMTetkKCAgADExMarnmzZtgrOzs+p5TEwMqlSpot/oiIiIiAxM62Tou+++w7vvvpvvchcXF8yZM0enxg8dOoQuXbrA3d0dCoUCO3bsUFsuIpgxY4bqsFyLFi1w/vx5ndogIiIiKojWyZCTk5PaYbHndejQAS1atNCp8bS0NNSsWRNffvmlxuXz5s3DwoUL8eWXXyImJgaurq5o27YtHjx4oFM7RERERPkxK8rGO3TogA4dOmhcJiJYvHgxPv74Y/Ts2RMAsGHDBri4uGDTpk343//+Z8xQiYiI6DWl9chQRkYGJk6cCD8/P9SvXx9hYWFqy//991+YmprqLbD4+HjcunUL7dq1U5UplUoEBgbi6NGjemuHiIiISjatR4bmzJmDjRs3YsKECbh//z5CQkJw/PhxrFq1SrWOlpcs0sqtW7cAPDsXKTcXFxdcu3Yt3+3S09ORnp6ues6rYhMREVFBdDqBeu3atZgwYQI+/fRTxMbG4sCBAxg8eLAqCVLkugChvjxfp4gU2E5oaCgcHBxUD09PT73HRERERK8PrZOhxMREVK9eXfXc19cXBw8exLFjxzBgwABkZWXpNTBXV1cA/zdClCM5OTnPaFFuU6ZMQUpKiurBC0ESERFRQbROhlxdXXH58mW1Mnd3d0RFRSEmJgZBQUF6DczHxweurq6IjIxUlT19+hTR0dFo3LhxvtsplUrY29urPYiIiIjyo3Uy1KpVK2zatClPeU5CdPXqVZ0bf/jwIeLi4hAXFwfg2UnTcXFxSEhIgEKhwNixY/HZZ58hPDwc586dw6BBg2BtbY2+ffvq3BYRERGRJlqfQD116tR8b8Jarlw5HDp0CPv27dOp8ZMnT6Jly5aq5+PGjQMABAUF4euvv8bEiRPx+PFjDB8+HPfu3UODBg2wb98+2NnZ6dQOERERUX60vmt9ccW71hMRERU/xrxrvU4XXRQR/Prrrzh69Chu3boFhUIBFxcXNGnSBK1btzbIbDIiIiIiQ9JpNtkbb7yBDh06IDw8HFeuXME///yD8PBwvPnmm6hbty4SExMNGSsRERGR3mk9MjR8+HA4OTnh+vXrcHNzU1uWlJSE/v37Y8SIEXlutkpERET0KtM6Gdq/fz9+++23PIkQALi5ueGLL75As2bN9BocERERkaFpfZjMysoKd+/ezXf5vXv3YGVlpZegiIiIiIxF62Sod+/eCAoKwg8//ICUlBRVeUpKCn744QcMHjyY1/8hIiKiYkfrw2QLFixAZmYm+vXrh8zMTFhYWAB4dlVoMzMzDBkyBPPnzzdYoERERESGoPN1hlJTUxEbG6u6Z5irqyvq1Knzyt72gtcZIiIiKn5e2esMAYC9vb3aVaOJiIiIijOtzxnKcePGDTx8+DBPeUZGBg4dOqSXoIiIiIiMRetkKCkpCfXr14eXlxdKlSqFoKAgtaTo7t27HDEiIiKiYkfrZGjy5MkwNTXF77//jj179uDChQto0aIF7t27p1rnNb/NGREREb2GtE6Gfv31VyxZsgR169ZFmzZtcOTIEXh4eKBVq1aq6w/x3mRERERU3GidDKWkpMDR0VH1XKlU4ocffoC3tzdatmyJ5ORkgwRIREREZEhaJ0MVKlTAmTNn1MrMzMywbds2VKhQAZ07d9Z7cERERESGpnUy1KFDB6xevTpPeU5CVKtWLX3GRURERGQUWl9naM6cOXj06JHmSszMsH37dty4cUNvgREREREZg9YjQ2ZmZnmuAPnbb78hPT0dAGBqagovLy/9RkdERERkYDpfdDG3Dh06IDExUV+xEBERERndSyVDvK4QERERFXcvlQwRERERFXcvlQytWrUKLi4u+oqFiIiIyOh0vmt9bn379tVXHERERERFQm+HyS5fvoxWrVrpqzoiIiIio9BbMvTw4UNER0frqzoiIiIio9D6MNnSpUsLXM4p9kRERFQcaZ0MjR07Fm5ubrCwsNC4/OnTp3oLioiIiMhYtE6GvLy8MHfuXPTq1Uvj8ri4ONSpU0dvgREREREZg9bnDNWpUwexsbH5LlcoFLwIIxERERU7Wo8MzZo1K98btQJA1apVER8fr5egiIiIiIxF62SoatWqBS43NzfnjVqJiIio2CnURRezsrJw+/ZtKBQKlC5dGqampvqOi4iIiMgodLrOUHh4OJo0aQJra2u4u7vDzc0N1tbWaNKkCXbs2GGgEImIiIgMR+tkaNWqVejduzcCAgKwZcsWHDlyBIcPH8aWLVsQEBCA3r17Y82aNYaMlYiIiEjvtD5MNn/+fCxfvhxDhgzJs6x79+6oV68e5syZg/fee0+vARIREREZktYjQ4mJiWjatGm+yxs3boybN2/qJSgiIiIiY9E6GapWrRpWr16d7/I1a9agWrVqegmKiIiIyFi0Pky2YMECdOrUCXv27EG7du3g4uIChUKBW7duITIyEteuXcMvv/xiyFiJiIiI9E7rZCgwMBDnzp3DihUrcPz4cdy6dQsA4Orqis6dO+ODDz6At7e3oeIkIiIiMgidrjPk7e2NuXPnGioWIiIiIqMr1EUXr127hlu3bkGhUMDFxYVXniYiIqJiS6eLLi5atAienp6oUKECGjVqhIYNG6JChQrw9PTE4sWLDRQiERERkeFoPTI0e/ZsfPHFF/joo4/Qvn17uLi4QESQnJyMvXv3YsaMGXj48CE++eQTQ8ZLREREpFdaJ0OrV6/Ghg0b0L17d7Vyd3d31KpVC/7+/hg5ciSTISIiIipWtD5MdufOHVSqVCnf5f7+/rh3755egiIiIiIyFq2Tofr162POnDnIzMzMsywzMxOfffYZ6tevr9fgiIiIiAxN68Nky5YtQ7t27eDs7IzAwEC1iy4eOnQISqUSkZGRhoyViIiISO+0HhmqUaMGLl26hDlz5sDe3h7x8fG4cuUK7O3tMWfOHPz111+8HQcREREVOwoRkaIOwpBSU1Ph4OCAlJQU2Nvb673+mQqF6u/pr3dXEhERGY2hf79z0+k6Q0RERESvG52SoeXLl6NNmzbo1asXoqKi1Jbdvn0bFSpU0GtwRERERIamdTK0dOlSfPjhh6hcuTKUSiU6duyI0NBQ1fKsrCxcu3bNIEESERERGYrWs8lWrVqFNWvWoG/fvgCA4cOHo3v37nj8+DFmzZplsACJiIiIDEnrZCg+Ph6NGzdWPW/UqBGioqLQunVrZGRkYOzYsYaIj4iIiMigtE6GypQpg+vXr8Pb21tVVq1aNURFRaFVq1ZITEw0RHxEREREBqX1OUNNmzbFjz/+mKe8atWq2L9/P/bs2aPXwIiIiIiMQeuRocmTJyM2NlbjsmrVquHAgQP44Ycf9BYYERERkTFoPTIUEBCAwYMH57u8WrVqmD59ul6CypGZmYlPPvkEPj4+sLKyQoUKFTBr1ixkZ2frtR0iIiIqubQeGSoKc+fOxcqVK7FhwwZUq1YNJ0+exODBg+Hg4IAxY8YUdXhERET0Gnilk6Fjx46hW7du6NSpEwDA29sb33//PU6ePFnEkREREdHr4pW+HUfTpk2xf/9+XLp0CQDwxx9/4MiRI+jYsWO+26SnpyM1NVXtQURERJSfV3pkaNKkSUhJSUHlypVhamqKrKwszJkzB3369Ml3m9DQUMycOdOIURIREVFx9kqPDG3ZsgXffvstNm3ahFOnTmHDhg344osvsGHDhny3mTJlClJSUlSP69evGzFiIiIiKm50HhmqXbs2FApFnnKFQgFLS0v4+flh0KBBaNmy5UsH9+GHH2Ly5Mno3bs3AKBGjRq4du0aQkNDERQUpHEbpVIJpVL50m0TERFRyaDzyNCbb76JK1euwMbGBi1btkSLFi1ga2uLy5cvo169ekhKSkKbNm3w008/vXRwjx49gomJeoimpqacWk9ERER6o/PI0O3btzF+/HhMnTpVrfzTTz/FtWvXsG/fPkyfPh2zZ89Gt27dXiq4Ll26YM6cOShfvjyqVauG06dPY+HChQgODn6peomIiIhyKEREdNnAwcEBsbGx8PPzUyv/559/UKdOHaSkpOCvv/5CvXr18ODBg5cK7sGDB5g6dSrCw8ORnJwMd3d39OnTB9OmTYOFhYVWdaSmpsLBwQEpKSmwt7d/qXg0mZnrkOF03bqSiIiI8mHo3+/cdB4ZsrS0xNGjR/MkQ0ePHoWlpSUAIDs7Wy/n7djZ2WHx4sVYvHjxS9dFREREpInOydCoUaPwwQcfIDY2FvXq1YNCocCJEyewdu1afPTRRwCAvXv3onbt2noPloiIiEjfdD5MBgDfffcdvvzyS1y8eBEAUKlSJYwaNQp9+/YFADx+/Fg1u6yo8TAZERFR8fNKHyYDgH79+qFfv375Lreysip0QERERETGVOgrUD99+hTJycl5prmXL1/+pYMiIiIiMhadk6G///4bwcHBOHr0qFq5iEChUCArK0tvwREREREZms7J0KBBg2BmZoZdu3bBzc1N49WoiYiIiIoLnZOhuLg4xMbGonLlyoaIh4iIiMiodL4dR9WqVXH79m1DxEJERERkdDonQ3PnzsXEiRNx8OBB3LlzB6mpqWoPIiIiouJE58Nkbdq0AQC0bt1arZwnUBMREVFxpHMydODAAUPEQURERFQkdE6GAgMDDREHERERUZHQKhk6c+YMqlevDhMTE5w5c6bAdQMCAvQSGBEREZExaJUM1apVC7du3YKzszNq1aoFhUIBTbc04zlDREREVNxolQzFx8ejbNmyqr+JiIiIXhdaJUNeXl4AgIyMDMyYMQNTp05FhQoVDBoYERERkTHodJ0hc3NzhIeHGyoWIiIiIqPT+aKLPXr0wI4dOwwQChEREZHx6Ty13s/PD7Nnz8bRo0dRp04d2NjYqC0fPXq03oIjIiIiMjSFaJoWVgAfH5/8K1MocOXKlZcOSp9SU1Ph4OCAlJQU2Nvb673+mQqF6u/punUlERER5cPQv9+56TwyxNlkRERE9DrR+ZwhIiIioteJziNDwcHBBS5fv359oYMhIiIiMjadk6F79+6pPc/IyMC5c+dw//59tGrVSm+BERERERmDzsmQpusMZWdnY/jw4bwQIxERERU7ejlnyMTEBCEhIVi0aJE+qiMiIiIyGr2dQH358mVkZmbqqzoiIiIio9D5MNm4cePUnosIkpKSEBERgaCgIL0FRkRERGQMOidDp0+fVntuYmKCsmXLYsGCBS+caUZERET0qtE5GTpw4IAh4iAiIiIqErzoIhEREZVoWo0M1a5dG4pc9+AqyKlTp14qICIiIiJj0ioZ6t69u+rvJ0+eYPny5ahatSoaNWoEADh+/DjOnz+P4cOHGyRIIiIiIkPRKhmaPn266u+hQ4di9OjRmD17dp51rl+/rt/oiIiIiAxM53OGtm3bhoEDB+Yp79+/P3788Ue9BEVERERkLDonQ1ZWVjhy5Eie8iNHjsDS0lIvQREREREZi85T68eOHYthw4YhNjYWDRs2BPDsnKH169dj2rRpeg+QiIiIyJB0ToYmT56MChUqYMmSJdi0aRMAoEqVKvj666/Rq1cvvQdIREREZEg6J0MA0KtXLyY+RERE9FrgRReJiIioRGMyRERERCUakyEiIiIq0ZgMERERUYnGZIiIiIhKNK1mk40bN07rChcuXFjoYIiIiIiMTatk6PTp02rPY2NjkZWVhUqVKgEALl26BFNTU9SpU0f/ERIREREZkFbJ0IEDB1R/L1y4EHZ2dtiwYQMcHR0BAPfu3cPgwYPRrFkzw0RJREREZCAKERFdNihXrhz27duHatWqqZWfO3cO7dq1w82bN/Ua4MtKTU2Fg4MDUlJSYG9vr/f6ZyoUqr+n69aVRERElA9D/37npvMJ1Kmpqfj333/zlCcnJ+PBgwd6CYqIiIjIWHROhnr06IHBgwfjhx9+wI0bN3Djxg388MMPGDJkCHr27GmIGImIiIgMRud7k61cuRITJkxA//79kZGR8awSMzMMGTIE8+fP13uARERERIak8zlDOdLS0nD58mWICPz8/GBjY6Pv2PSC5wwREREVP6/0OUM5kpKSkJSUBH9/f9jY2KCQORURERFRkdI5Gbpz5w5at24Nf39/dOzYEUlJSQCAoUOHYvz48XoPkIiIiMiQdE6GQkJCYG5ujoSEBFhbW6vK3333XezZs0evwREREREZms4nUO/btw979+6Fh4eHWnnFihVx7do1vQVGREREZAw6jwylpaWpjQjluH37NpRKpV6Cyi0xMRH9+/dH6dKlYW1tjVq1aiE2Nlbv7RAREVHJpHMy1Lx5c2zcuFH1XKFQIDs7G/Pnz0fLli31Gty9e/fQpEkTmJubY/fu3bhw4QIWLFiAUqVK6bUdIiIiKrl0Pkw2f/58tGjRAidPnsTTp08xceJEnD9/Hnfv3sVvv/2m1+Dmzp0LT09PhIWFqcq8vb312gYRERGVbDqPDFWtWhVnzpxB/fr10bZtW6SlpaFnz544ffo0fH199Rrczp07UbduXbzzzjtwdnZG7dq1sWbNmgK3SU9PR2pqqtqDiIiIKD86X3QxISEBnp6eUOS62GDuZeXLl9dbcJaWlgCAcePG4Z133sGJEycwduxYrFq1CgMHDtS4zYwZMzBz5sw85bzoIhERUfFhzIsu6pwMmZqaIikpCc7Ozmrld+7cgbOzM7KysvQWnIWFBerWrYujR4+qykaPHo2YmBgcO3ZM4zbp6elIT09XPU9NTYWnpyeTISIiomLklb4CtYhoHBV6+PChaiRHX9zc3FC1alW1sipVqiAhISHfbZRKJezt7dUeRERERPnR+gTqcePGAXg2e2zq1Klq0+uzsrLw+++/o1atWnoNrkmTJrh48aJa2aVLl+Dl5aXXdoiIiKjk0joZOn36NIBnI0Nnz56FhYWFapmFhQVq1qyJCRMm6DW4kJAQNG7cGJ999hl69eqFEydOYPXq1Vi9erVe2yEiIqKSS+tk6MCBAwCAwYMHY8mSJUY5/FSvXj2Eh4djypQpmDVrFnx8fLB48WL069fP4G0TERFRyaDzCdTFjaFPwOIJ1ERERPpnzBOodb7oYlpaGj7//HPs378fycnJyM7OVlt+5coVvQVHREREZGg6J0NDhw5FdHQ0BgwYADc3N40zy4iIiIiKC52Tod27dyMiIgJNmjQxRDxERERERqXzdYYcHR3h5ORkiFiIiIiIjE7nZGj27NmYNm0aHj16ZIh4iIiIiIxK58NkCxYswOXLl+Hi4gJvb2+Ym5urLT916pTegiMiIiIyNJ2Toe7duxsgDCIiIqKioXMyNH36dEPEQURERFQkdD5niIiIiOh1ovPIUFZWFhYtWoStW7ciISEBT58+VVt+9+5dvQVHREREZGg6jwzNnDkTCxcuRK9evZCSkoJx48ahZ8+eMDExwYwZMwwQIhEREZHh6JwMfffdd1izZg0mTJgAMzMz9OnTB2vXrsW0adNw/PhxQ8RIREREZDA6J0O3bt1CjRo1AAC2trZISUkBAHTu3BkRERH6jY6IiIjIwHROhjw8PJCUlAQA8PPzw759+wAAMTExUCqV+o2OiIiIyMB0ToZ69OiB/fv3AwDGjBmDqVOnomLFihg4cCCCg4P1HiARERGRIek8m+zzzz9X/f3222/D09MTv/32G/z8/NC1a1e9BkdERERkaDolQxkZGXj//fcxdepUVKhQAQDQoEEDNGjQwCDBERERERmaTofJzM3NER4ebqhYiIiIiIyuUOcM7dixwwChEBERERmfzucM+fn5Yfbs2Th69Cjq1KkDGxsbteWjR4/WW3BEREREhqYQEdFlAx8fn/wrUyhw5cqVlw5Kn1JTU+Hg4ICUlBTY29vrvf6ZCoXq7+m6dSURERHlw9C/37npPDIUHx9viDiIiIiIigTvWk9EREQlms4jQ+PGjdNYrlAoYGlpCT8/P3Tr1g1OTk4vHRwRERGRoemcDJ0+fRqnTp1CVlYWKlWqBBHB33//DVNTU1SuXBnLly/H+PHjceTIEVStWtUQMRMRERHpjc6Hybp164Y2bdrg5s2biI2NxalTp5CYmIi2bduiT58+SExMRPPmzRESEmKIeImIiIj0SufZZOXKlUNkZGSeUZ/z58+jXbt2SExMxKlTp9CuXTvcvn1br8EWBmeTERERFT/GnE2m88hQSkoKkpOT85T/999/SE1NBQCUKlUKT58+ffnoiIiIiAysUIfJgoODER4ejhs3biAxMRHh4eEYMmQIunfvDgA4ceIE/P399R0rERERkd7pfAL1qlWrEBISgt69eyMzM/NZJWZmCAoKwqJFiwAAlStXxtq1a/UbKREREZEB6HzOUI6HDx/iypUrEBH4+vrC1tZW37HpBc8ZIiIiKn5e6StQ57C1tUVAQIA+YyEiIiIyOl6BmoiIiEo0JkNERERUojEZIiIiohKNyRARERGVaFqdQL106VKdKx48eDDs7Ox03o6IiIjImLRKhsaOHQsPDw+YmppqVen169fRuXNnJkNERET0ytN6av3Jkyfh7Oys1bpMgoiIiKi40OqcoenTp+t0UcWPPvoITk5OhQ6KiIiIyFh0ugK1iCAhIQHOzs6wsrIyZFx6wytQExERFT+v7F3rRQQVK1bEjRs3DBUPERERkVHplAyZmJigYsWKuHPnjqHiISIiIjIqna8zNG/ePHz44Yc4d+6cIeIhIiIiMiqdb9Tav39/PHr0CDVr1oSFhUWec4fu3r2rt+CIiIiIDE3nZGjx4sUGCIOIiIioaOicDAUFBRkiDiIiIqIioXMylJCQUODy8uXLFzoYIiIiImPTORny9vaGIte1dZ6XlZX1UgERERERGZPOydDp06fVnmdkZOD06dNYuHAh5syZo7fAiIiIiIxB52SoZs2aecrq1q0Ld3d3zJ8/Hz179tRLYERERETGoPN1hvLj7++PmJgYfVVHREREZBQ6jwylpqaqPRcRJCUlYcaMGahYsaLeAiMiIiIyBp2ToVKlSuU5gVpE4Onpic2bN+stMCIiIiJj0DkZOnDggNpzExMTlC1bFn5+fjAz07k6IiIioiKlc/YSGBhoiDiIiIiIioTWJ1APHz4cDx8+VD3/5ptv1J7fv38fHTt21G90zwkNDYVCocDYsWMN2o6+zVQoVA8iIiJ6tWidDK1atQqPHj1SPR8xYgSSk5NVz9PT07F37179RpdLTEwMVq9ejYCAAIO1QURERCWP1smQiBT43JAePnyIfv36Yc2aNXB0dDRau8bE0SMiIqKiobfrDBnSiBEj0KlTJ7Rp0+aF66anpyM1NVXtQURERJSfV3761+bNm3Hq1CmtL+gYGhqKmTNnGjgqIiIiel3olAxNmzYN1tbWAICnT59izpw5cHBwAAC184n05fr16xgzZgz27dsHS0tLrbaZMmUKxo0bp3qempoKT09PvcdGRERErwetk6HmzZvj4sWLqueNGzfGlStX8qyjT7GxsUhOTkadOnVUZVlZWTh06BC+/PJLpKenw9TUVG0bpVIJpVKp1ziIiIjo9aV1MnTw4EEDhqFZ69atcfbsWbWywYMHo3Llypg0aVKeRIiIiIhIV6/0OUN2dnaoXr26WpmNjQ1Kly6dp5yIiIioMIrFbDIiIiIiQ3mlR4Y0KYrDdURERPT64sgQERERlWhMhoiIiKhEK9Rhsvv37+PEiRNITk5Gdna22rKBAwfqJTAiIiIiY9A5Gfr555/Rr18/pKWlwc7ODopc99JSKBRMhoiIiKhY0fkw2fjx4xEcHIwHDx7g/v37uHfvnupx9+5dQ8RIREREZDA6J0OJiYkYPXq06rYcRERERMWZzslQ+/btcfLkSUPEQkRERGR0Op8z1KlTJ3z44Ye4cOECatSoAXNzc7XlXbt21VtwRERERIamczL03nvvAQBmzZqVZ5lCoUBWVtbLR0VERERkJDonQ89PpSciIiIqznjRRSIiIirRCpUMRUdHo0uXLvDz80PFihXRtWtXHD58WN+xERERERmczsnQt99+izZt2sDa2hqjR4/GyJEjYWVlhdatW2PTpk2GiJGIiIjIYHQ+Z2jOnDmYN28eQkJCVGVjxozBwoULMXv2bPTt21evARIREREZks4jQ1euXEGXLl3ylHft2hXx8fF6CYqIiIjIWHROhjw9PbF///485fv374enp6degiIiIiIyFp0Pk40fPx6jR49GXFwcGjduDIVCgSNHjuDrr7/GkiVLDBEj/X8zc90UFwCmixRRJERERK8PnZOhYcOGwdXVFQsWLMDWrVsBAFWqVMGWLVvQrVs3vQdIREREZEg6J0MA0KNHD/To0UPfsRAREREZHS+6SERERCWaViNDTk5OuHTpEsqUKQNHR0conjt3Jbe7d+/qLTgiIiIiQ9MqGVq0aBHs7OwAAIsXLzZkPERERERGpVUyFBQUpPFvIiIiouJOq2QoNTVV6wrt7e0LHQwRERGRsWmVDJUqVarA84QAQESgUCiQlZWll8CIiIiIjEGrZOjAgQOGjoOIiIioSGiVDAUGBho6DiIiIqIioVUydObMGa0rDAgIKHQwRERERMamVTJUq1YtKBQKyAvuhcVzhoiIiKi40SoZio+PN3QcREREREVCq2TIy8vL0HEQERERFQmtkqGdO3eiQ4cOMDc3x86dOwtct2vXrnoJjIiIiMgYtEqGunfvjlu3bsHZ2Rndu3fPdz2eM0RERETFjVbJUHZ2tsa/iYiIiIo7k6IOgIiIiKgoaTUyBAAbN27Uar2BAwcWOhgiIiIiY9M6GRo0aBBsbW1hZmaW7/WGFAoFk6EiNvO5e8hNf8G1oYiIiEo6rZOhKlWq4N9//0X//v0RHBzMK00TERHRa0Hrc4bOnz+PiIgIPH78GM2bN0fdunWxYsUKpKamGjI+IiIiIoPS6QTqBg0aYNWqVUhKSsLo0aOxdetWuLm5oV+/fkhPTzdUjEREREQGo/VhstysrKwwcOBAeHt7Y/r06di8eTO+/PJLKJVKfcdHBsDzioiIiP6PzlPrExMT8dlnn6FixYro3bs36tWrh/Pnz8PR0dEQ8REREREZlNYjQ1u3bkVYWBiio6PRvn17LFiwAJ06dYKpqakh4yMiIiIyKK2Tod69e6N8+fIICQmBi4sLrl69iq+++irPeqNHj9ZrgFQ0eCiNiIhKCq2TofLly0OhUGDTpk35rqNQKJgMERERUbGidTJ09epVA4ZBREREVDR4bzIiIiIq0bRKhpYuXYonT55oXenKlSvx4MGDQgdFxcNMhULtQUREVBxplQyFhITolNxMnDgR//33X6GDIiIiIjIWrc4ZEhG0bt0aZmbanWL0+PHjlwqKiIiIyFi0ym6mT5+uU6XdunWDk5NToQIiIiIiMiaDJENERERExQVnkxEREVGJxmSIiIiISrRC3bWeSBe5p93zth5ERPSq4cgQERERlWivdDIUGhqKevXqwc7ODs7OzujevTsuXrxY1GERERHRa0Tnw2Tjxo3TWK5QKGBpaQk/Pz+9Ta2Pjo7GiBEjUK9ePWRmZuLjjz9Gu3btcOHCBdjY2Lx0/fTq4KE0IiIqKjonQ6dPn8apU6eQlZWFSpUqQUTw999/w9TUFJUrV8by5csxfvx4HDlyBFWrVn2p4Pbs2aP2PCwsDM7OzoiNjUXz5s1fqm4iIiIioBCHybp164Y2bdrg5s2biI2NxalTp5CYmIi2bduiT58+SExMRPPmzRESEqL3YFNSUgCAF3QsoXgvNCIiMgSdR4bmz5+PyMhI2Nvbq8rs7e0xY8YMtGvXDmPGjMG0adPQrl07vQYqIhg3bhyaNm2K6tWr57teeno60tPTVc9TU1P1GgcRERG9XnQeGUpJSUFycnKe8v/++0+VeJQqVQpPnz59+ehyGTlyJM6cOYPvv/++wPVCQ0Ph4OCgenh6euo1DiIiInq9FOowWXBwMMLDw3Hjxg0kJiYiPDwcQ4YMQffu3QEAJ06cgL+/v96CHDVqFHbu3IkDBw7Aw8OjwHWnTJmClJQU1eP69et6i4OIiIhePzofJlu1ahVCQkLQu3dvZGZmPqvEzAxBQUFYtGgRAKBy5cpYu3btSwcnIhg1ahTCw8Nx8OBB+Pj4vHAbpVIJpVL50m0TERFRyaBzMmRra4s1a9Zg0aJFuHLlCkQEvr6+sLW1Va1Tq1YtvQQ3YsQIbNq0CT/99BPs7Oxw69YtAICDgwOsrKz00gYRERGVbIW+HYetrS0CAgL0GUseK1asAAC0aNFCrTwsLAyDBg0yaNtUPD0/y4zXLCIiohfRORlKS0vD559/jv379yM5ORnZ2dlqy69cuaK34IQ/ZGQATJiIiCg3nZOhoUOHIjo6GgMGDICbmxsUvN4LERERFWM6J0O7d+9GREQEmjRpYoh4iF4JHD0iIio5dE6GHB0deQVoIjBhIiJ6Xeh8naHZs2dj2rRpePTokSHiISIiIjIqnUeGFixYgMuXL8PFxQXe3t4wNzdXW37q1Cm9BUdU3Gk7epR7PY4wEREZl87JUM5VponIuJgwEREZhs7J0PTp0w0RBxHpAc9jIiLSnc7nDBERERG9TrQaGXJycsKlS5dQpkwZODo6Fnhtobt37+otOCIiIiJD0yoZWrRoEezs7AAAixcvNmQ8REREREalVTIUFBSk8W8iIiKi4k6rZCg1NVXrCu3t7QsdDBEREZGxaZUMlSpVSut7kGVlZb1UQERERETGpFUydODAAdXfV69exeTJkzFo0CA0atQIAHDs2DFs2LABoaGhhomSiPSG0++JiNRplQwFBgaq/p41axYWLlyIPn36qMq6du2KGjVqYPXq1TyniIiIiIoVnS+6eOzYMaxcuTJPed26dTF06FC9BEVERUub0SOOMBHR60LnZMjT0xMrV67EggUL1MpXrVoFT09PvQVGRMUf781GRMWBzsnQokWL8NZbb2Hv3r1o2LAhAOD48eO4fPkyfvzxR70HSERERGRIOt+Oo2PHjvj777/RtWtX3L17F3fu3EG3bt1w6dIldOzY0RAxEhERERmMziNDAODh4YHPPvtM37EQERERGV2hkqH79+/jxIkTSE5ORnZ2ttqygQMH6iUwIiIiImPQORn6+eef0a9fP6SlpcHOzk7tYowKhYLJEBERERUrOp8zNH78eAQHB+PBgwe4f/8+7t27p3rwjvVERERU3OicDCUmJmL06NGwtrY2RDxERERERqVzMtS+fXucPHnSELEQERERGZ3O5wx16tQJH374IS5cuIAaNWrA3NxcbXnXrl31FhwRERGRoemcDL333nsAnt2j7HkKhYJ3rSciIqJiRedk6Pmp9ERERETFmc7nDBERERG9TnQeGdJ0eCy3adOmFToYIiIiImPTORkKDw9Xe56RkYH4+HiYmZnB19eXyRARGQTvbE9EhqJzMnT69Ok8ZampqRg0aBB69Oihl6CIiIiIjEUv5wzZ29tj1qxZmDp1qj6qIyIqlJkKhepBRKStQt2oVZP79+8jJSVFX9URERnE84mSpkNu2qxDRK8PnZOhpUuXqj0XESQlJeGbb77Bm2++qbfAiIheZUyqiF4fOidDixYtUntuYmKCsmXLIigoCFOmTNFbYERERETGoFUydObMGVSvXh0mJiaIj483dExERCWGtqNHnE1HZDhaJUO1a9dGUlISnJ2dUaFCBcTExKB06dKGjo2IiHSgTcLEpIooL61mk5UqVUo1InT16lXekoOIiIheG1qNDL311lsIDAyEm5sbFAoF6tatC1NTU43rXrlyRa8BEhERERmSVsnQ6tWr0bNnT/zzzz8YPXo03nvvPdjZ2Rk6NiIiIiKD03o2Wc60+djYWIwZM4bJEBHRa4qXBKCSRuep9WFhYYaIg4iIihEmTPQ60dsVqImIiHJjwkTFBZMhIiIqMkyY6FXAZIiIiF5pvDAlGRqTISIiKjGYMJEmTIaIiIhyYcJU8mh1BWoiIiKi1xVHhoiIiHTEE79fLxwZIiIiohKNyRARERGVaDxMRkREZADaHErj4bZXA5MhIiKiV5g+kyrOlNOMh8mIiIioROPIEBEREamUxNGjYjEytHz5cvj4+MDS0hJ16tTB4cOHizokIiKiEmumQqF6vA5e+WRoy5YtGDt2LD7++GOcPn0azZo1Q4cOHZCQkFDUoREREdFr4JVPhhYuXIghQ4Zg6NChqFKlChYvXgxPT0+sWLGiqEMjIiKi18ArnQw9ffoUsbGxaNeunVp5u3btcPTo0SKKioiIiF4nr/QJ1Ldv30ZWVhZcXFzUyl1cXHDr1i2N26SnpyM9PV31PCUlBQCQmppqkBif5Po7vzYMsU5+671q6zy/Xkl7/eyjF6/z/HrsI/ZRYdZ5fj32UdH30cvKqVeMcRK3vMISExMFgBw9elSt/NNPP5VKlSpp3Gb69OkCgA8++OCDDz74eA0e169fN3i+8UqPDJUpUwampqZ5RoGSk5PzjBblmDJlCsaNG6d6np2djbt376J06dJQGOCs99TUVHh6euL69euwt7fXe/2kjv1tPOxr42FfGxf723hepq9FBA8ePIC7u7uBovs/r3QyZGFhgTp16iAyMhI9evRQlUdGRqJbt24at1EqlVAqlWplpUqVMmSYAAB7e3t+qIyI/W087GvjYV8bF/vbeArb1w4ODgaIJq9XOhkCgHHjxmHAgAGoW7cuGjVqhNWrVyMhIQEffPBBUYdGREREr4FXPhl69913cefOHcyaNQtJSUmoXr06fvnlF3h5eRV1aERERPQaeOWTIQAYPnw4hg8fXtRhaKRUKjF9+vQ8h+bIMNjfxsO+Nh72tXGxv42nuPS1QqSE3HiEiIiISINX+qKLRERERIbGZIiIiIhKNCZDREREVKIxGSIiIqISjcnQS1q+fDl8fHxgaWmJOnXq4PDhw0UdUrF36NAhdOnSBe7u7lAoFNixY4fachHBjBkz4O7uDisrK7Ro0QLnz58vmmCLudDQUNSrVw92dnZwdnZG9+7dcfHiRbV12N/6sWLFCgQEBKguPteoUSPs3r1btZz9bDihoaFQKBQYO3asqoz9rT8zZsyAQqFQe7i6uqqWF4e+ZjL0ErZs2YKxY8fi448/xunTp9GsWTN06NABCQkJRR1asZaWloaaNWviyy+/1Lh83rx5WLhwIb788kvExMTA1dUVbdu2xYMHD4wcafEXHR2NESNG4Pjx44iMjERmZibatWuHtLQ01Trsb/3w8PDA559/jpMnT+LkyZNo1aoVunXrpvpRYD8bRkxMDFavXo2AgAC1cva3flWrVg1JSUmqx9mzZ1XLikVfG/zuZ6+x+vXrywcffKBWVrlyZZk8eXIRRfT6ASDh4eGq59nZ2eLq6iqff/65quzJkyfi4OAgK1euLIIIXy/JyckCQKKjo0WE/W1ojo6OsnbtWvazgTx48EAqVqwokZGREhgYKGPGjBER7tf6Nn36dKlZs6bGZcWlrzkyVEhPnz5FbGws2rVrp1berl07HD16tIiiev3Fx8fj1q1bav2uVCoRGBjIfteDlJQUAICTkxMA9rehZGVlYfPmzUhLS0OjRo3YzwYyYsQIdOrUCW3atFErZ3/r399//w13d3f4+Pigd+/euHLlCoDi09fF4grUr6Lbt28jKysLLi4uauUuLi64detWEUX1+svpW039fu3ataII6bUhIhg3bhyaNm2K6tWrA2B/69vZs2fRqFEjPHnyBLa2tggPD0fVqlVVPwrsZ/3ZvHkzTp06hZiYmDzLuF/rV4MGDbBx40b4+/vj33//xaefforGjRvj/PnzxaavmQy9JIVCofZcRPKUkf6x3/Vv5MiROHPmDI4cOZJnGftbPypVqoS4uDjcv38fP/74I4KCghAdHa1azn7Wj+vXr2PMmDHYt28fLC0t812P/a0fHTp0UP1do0YNNGrUCL6+vtiwYQMaNmwI4NXvax4mK6QyZcrA1NQ0zyhQcnJyngyY9CdnhgL7Xb9GjRqFnTt34sCBA/Dw8FCVs7/1y8LCAn5+fqhbty5CQ0NRs2ZNLFmyhP2sZ7GxsUhOTkadOnVgZmYGMzMzREdHY+nSpTAzM1P1KfvbMGxsbFCjRg38/fffxWbfZjJUSBYWFqhTpw4iIyPVyiMjI9G4ceMiiur15+PjA1dXV7V+f/r0KaKjo9nvhSAiGDlyJLZv346oqCj4+PioLWd/G5aIID09nf2sZ61bt8bZs2cRFxenetStWxf9+vVDXFwcKlSowP42oPT0dPz5559wc3MrPvt2kZ26/RrYvHmzmJuby7p16+TChQsyduxYsbGxkatXrxZ1aMXagwcP5PTp03L69GkBIAsXLpTTp0/LtWvXRETk888/FwcHB9m+fbucPXtW+vTpI25ubpKamlrEkRc/w4YNEwcHBzl48KAkJSWpHo8ePVKtw/7WjylTpsihQ4ckPj5ezpw5Ix999JGYmJjIvn37RIT9bGi5Z5OJsL/1afz48XLw4EG5cuWKHD9+XDp37ix2dnaq38Li0NdMhl7SV199JV5eXmJhYSFvvPGGakoyFd6BAwcEQJ5HUFCQiDybqjl9+nRxdXUVpVIpzZs3l7NnzxZt0MWUpn4GIGFhYap12N/6ERwcrPquKFu2rLRu3VqVCImwnw3t+WSI/a0/7777rri5uYm5ubm4u7tLz5495fz586rlxaGvFSIiRTMmRURERFT0eM4QERERlWhMhoiIiKhEYzJEREREJRqTISIiIirRmAwRERFRicZkiIiIiEo0JkNERERUojEZItJAoVBgx44dOm3j7e2NxYsXGySe5x08eBAKhQL37983SnsA8OjRI7z11luwt7c3atuG6tfn6y3Me64PRdVuYV29ehUKhQJxcXFFHQqR3jAZIiqGGjdujKSkJDg4OBitzQ0bNuDw4cM4evSo0dvOzVDJQ1JSktrdt4sihsIoTHLSrl07mJqa4vjx4y/dvr4T83/++QeDBw+Gh4cHlEolfHx80KdPH5w8eVK1zoEDB9CyZUs4OTnB2toaFStWRFBQEDIzM/USA5U8TIaIXmFZWVnIzs7OU25hYQFXV1coFAqjxXL58mVUqVIF1atXN3rbxuDq6gqlUlnUYRhcQkICjh07hpEjR2LdunVFHY6akydPok6dOrh06RJWrVqFCxcuIDw8HJUrV8b48eMBAOfPn0eHDh1Qr149HDp0CGfPnsWyZctgbm6u8bNCpJWivh8Ika6ys7Nl7ty54uPjI5aWlhIQECDbtm1TLc/MzJTg4GDx9vYWS0tL8ff3l8WLF+epZ926dVK1alWxsLAQV1dXGTFihGoZAFmzZo10795drKysxM/PT3766acC4/Ly8pJZs2ZJnz59xMbGRtzc3GTp0qVq6yxYsECqV68u1tbW4uHhIcOGDZMHDx6oloeFhYmDg4P8/PPPUqVKFTE1NZUrV67kaSvn/m337t0TEZGrV69K586dpVSpUmJtbS1Vq1aViIiIfGP95ptvpE6dOmJraysuLi7Sp08f+ffff/NdPzAwUO3eZYGBgVrXc+7cOenYsaPY2dmJra2tNG3aVP755x9VvbnvFyUi0q1bN9V96HL6ddGiRaq/c8fh5eUl8fHxolAoJCYmRq2epUuXSvny5SU7O1vja8pdr8iz9zw8PFxERNLT02XEiBGqeyl5eXnJZ599lm8MOXbu3ClvvPGGKJVK8fHxkRkzZkhGRoZq+aVLl6RZs2aiVCqlSpUqsm/fPrV2Ndm9e7c0adJEHBwcxMnJSTp16qTqv5y4Nb03+ZkxY4b07t1b/vzzT7Gzs5OHDx/q1F58fLwAkNOnT6v+hoZ7CD558kRGjRolZcuWFaVSKU2aNJETJ07kG1d2drZUq1ZN6tSpI1lZWXmW5+zrixYtEm9v7wJfI5GuODJExc4nn3yCsLAwrFixAufPn0dISAj69++P6OhoAEB2djY8PDywdetWXLhwAdOmTcNHH32ErVu3qupYsWIFRowYgffffx9nz57Fzp074efnp9bOzJkz0atXL5w5cwYdO3ZEv379cPfu3QJjmz9/PgICAnDq1ClMmTIFISEhiIyMVC03MTHB0qVLce7cOWzYsAFRUVGYOHGiWh2PHj1CaGgo1q5di/Pnz8PZ2fmFfTJixAikp6er/lOeO3cubG1t813/6dOnmD17Nv744w/s2LED8fHxGDRoUL7rb9++He+99x4aNWqEpKQkbN++Xat6EhMT0bx5c1haWiIqKgqxsbEIDg4u9OGMmJgYAEBYWBiSkpIQExMDb29vtGnTBmFhYWrrhoWFYdCgQYUawVq6dCl27tyJrVu34uLFi/j222/h7e2dbwwAsHfvXvTv3x+jR4/GhQsXsGrVKnz99deYM2cOgGf7Zc+ePVWHp1auXIlJkya9MJa0tDSMGzcOMTEx2L9/P0xMTNCjRw/VKMiJEycAAL/++qvae6OJiCAsLAz9+/dH5cqV4e/vr/a50Ka93Dw9PfHjjz8CAC5evIikpCQsWbIEADBx4kT8+OOP2LBhA06dOgU/Pz+0b98+389QXFwczp8/j/Hjx8PEJO9PU6lSpQA8G8FLSkrCoUOHXtBzRDoo6myMSBcPHz4US0tLOXr0qFr5kCFDpE+fPvluN3z4cHnrrbdUz93d3eXjjz/Od30A8sknn6i1q1AoZPfu3flu4+XlJW+++aZa2bvvvisdOnTId5utW7dK6dKlVc/DwsIEgMTFxeW7jUjekaEaNWrIjBkzCtymICdOnBAAaqNUzxszZswLRx2er2fKlCni4+MjT58+1bi+riNDIqJxJGXLli3i6OgoT548ERGRuLg4USgUEh8fn2+sBdU7atQoadWqVb6jSppiaNasmWr0KMc333wjbm5uIiKyd+9eMTU1levXr6uW7969+4UjQ89LTk4WAKq7fuceqXmRffv2SdmyZVWjVYsWLZImTZq8VHvP74sizz4v5ubm8t1336nKnj59Ku7u7jJv3jyN7WzZskUAyKlTpwqMJzMzUwYNGiQAxNXVVbp37y7Lli2TlJSUF718onxxZIiKlQsXLuDJkydo27YtbG1tVY+NGzfi8uXLqvVWrlyJunXromzZsrC1tcWaNWuQkJAAAEhOTsbNmzfRunXrAtsKCAhQ/W1jYwM7OzskJycXuE2jRo3yPP/zzz9Vzw8cOIC2bduiXLlysLOzw8CBA3Hnzh2kpaWp1rGwsFBrWxujR4/Gp59+iiZNmmD69Ok4c+ZMgeufPn0a3bp1g5eXF+zs7NCiRQsAUPWRtl5UT1xcHJo1awZzc3Od6tVV9+7dYWZmhvDwcADA+vXr0bJlS9Vojq4GDRqEuLg4VKpUCaNHj8a+ffteuE1sbCxmzZqltl++9957SEpKwqNHj/Dnn3+ifPny8PDwUG3z/P6iyeXLl9G3b19UqFAB9vb28PHxAaD7ewUA69atw7vvvgszMzMAQJ8+ffD777/j4sWLem3v8uXLyMjIQJMmTVRl5ubmqF+/vtrnITcRAYAXjuSZmpoiLCwMN27cwLx58+Du7o45c+agWrVqSEpK0jpGotyYDFGxkjNUHxERgbi4ONXjwoUL+OGHHwAAW7duRUhICIKDg7Fv3z7ExcVh8ODBePr0KQDAyspKq7ae/wFXKBSFOkEz58v92rVr6NixI6pXr44ff/wRsbGx+OqrrwAAGRkZqvWtrKx0PrQzdOhQXLlyBQMGDMDZs2dRt25dLFu2TOO6aWlpaNeuHWxtbfHtt98iJiZGlUTk9JE2tKnnRX1tYmKi+hHMkbsvtGVhYYEBAwYgLCwMT58+xaZNmxAcHKxzPTneeOMNxMfHY/bs2Xj8+DF69eqFt99+u8BtsrOzMXPmTLX98uzZs/j7779haWmZ53UCL/7hB4AuXbrgzp07WLNmDX7//Xf8/vvvAHR7rwDg7t272LFjB5YvXw4zMzOYmZmhXLlyyMzMxPr16/XaXn6JjYjk+5r9/f0BIN9k6XnlypXDgAED8NVXX6n+SVq5cqXWMRLlxmSIipWqVatCqVQiISEBfn5+ag9PT08AwOHDh9G4cWMMHz4ctWvXhp+fn9qokZ2dHby9vbF//369x/f8VOXjx4+jcuXKAJ7NlMnMzMSCBQvQsGFD+Pv74+bNm3pr29PTEx988AG2b9+O8ePHY82aNRrX++uvv3D79m18/vnnaNasGSpXrvzCEa/C1hMQEIDDhw/nm+CULVtW7b/5rKwsnDt3rsB2zc3NkZWVlad86NCh+PXXX7F8+XJkZGSgZ8+eOr+m3Ozt7fHuu+9izZo12LJlC3788UfV+S6aYnjjjTdw8eLFPPuln58fTExMULVqVSQkJKi958eOHSswhjt37uDPP//EJ598gtatW6NKlSq4d++e2joWFhYAoLFPcvvuu+/g4eGBP/74Qy1hW7x4MTZs2IDMzEyt2nuepvb9/PxgYWGBI0eOqMoyMjJw8uRJVKlSRWM9tWrVQtWqVbFgwQKN/3QUNHXf0dERbm5uaiOsRLowK+oAiHRhZ2eHCRMmICQkBNnZ2WjatClSU1Nx9OhR2NraIigoCH5+fti4cSP27t0LHx8ffPPNN4iJiVEN9wPAjBkz8MEHH8DZ2RkdOnTAgwcP8Ntvv2HUqFEvFd9vv/2GefPmoXv37oiMjMS2bdsQEREBAPD19UVmZiaWLVuGLl264LffftPbf7Jjx45Fhw4d4O/vj3v37iEqKirfH53y5cvDwsICy5YtwwcffIBz585h9uzZOrepTT0jR47EsmXL0Lt3b0yZMgUODg44fvw46tevj0qVKqFVq1YYN24cIiIi4Ovri0WLFr3wejU5iWyTJk2gVCrh6OgIAKhSpQoaNmyISZMmITg4WOsRQE0WLVoENzc31KpVCyYmJti2bRtcXV1VJ/FqimHatGno3LkzPD098c4778DExARnzpzB2bNn8emnn6JNmzaoVKkSBg4ciAULFiA1NRUff/xxgXE4OjqidOnSWL16Ndzc3JCQkIDJkyerrePs7AwrKyvs2bMHHh4esLS01HgNqHXr1uHtt99G9erV1cq9vLwwadIkREREoEuXLi9s73leXl5QKBTYtWsXOnbsCCsrK9ja2mLYsGH48MMP4eTkhPLly2PevHl49OgRhgwZorEehUKBsLAwtGnTBs2bN8dHH32EypUr4+HDh/j555+xb98+REdHY9WqVYiLi0OPHj3g6+uLJ0+eYOPGjTh//ny+o6FEL1SkZywRFUJ2drYsWbJEKlWqJObm5lK2bFlp3769REdHi8izKb2DBg0SBwcHKVWqlAwbNkwmT54sNWvWVKtn5cqVqjrc3Nxk1KhRqmXQcFKrg4ODhIWF5RuXl5eXzJw5U3r16iXW1tbi4uKSZ0r/woULxc3NTaysrKR9+/ayceNGtZNPc6bWv8jzJ62OHDlSfH19RalUStmyZWXAgAFy+/btfLfftGmTeHt7i1KplEaNGsnOnTtfeBKuphOotannjz/+kHbt2om1tbXY2dlJs2bN5PLlyyLy7KTaYcOGiZOTkzg7O0toaOgLT6DeuXOn+Pn5iZmZmdq0dpFnl0sAUOAU7vzqzf2er169WmrVqiU2NjZib28vrVu3VjuxN78Y9uzZI40bNxYrKyuxt7eX+vXry+rVq1XLL168KE2bNhULCwvx9/eXPXv2vPAE6sjISKlSpYoolUoJCAiQgwcP5tlmzZo14unpKSYmJhpPcj958mSB/dKlSxfp0qWLVu1pOmF71qxZ4urqKgqFQvXePX78WEaNGiVlypTRamp97j4aOHCguLu7i4WFhXh5eUmfPn1U/X/q1Cnp37+/+Pj4iFKplNKlS0vz5s1l586dL6ybKD8KEQ0HsomIiqE5c+Zg8+bNOHv2bFGHQkTFCM8ZIqJi7+HDh4iJicGyZcswevToog6HiIoZJkNEVOyNHDkSTZs2RWBg4EvNIiOikomHyYiIiKhE48gQERERlWhMhoiIiKhEYzJEREREJRqTISIiIirRmAwRERFRicZkiIiIiEo0JkNERERUojEZIiIiohKNyRARERGVaP8PUSslDbrJkMEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "\n",
    "fundingsums = np.flip(np.sort(fundingval))\n",
    "fundingsums = fundingsums[0:50] \n",
    "\n",
    "plt.bar(range(len(fundingsums)),fundingsums/1e06, color ='maroon', \n",
    "        width = 0.4)\n",
    "plt.ylim(0,11)\n",
    "\n",
    "plt.xlabel(\"each bar is a faculty listed at Aalto CS\")\n",
    "plt.ylabel(\"funding [Million Eur] granted during 2001-2023\")\n",
    "plt.title(\"Distribution of Tax-Payer Money via Research Council of Finland\")\n",
    "#plt.title(\"Distribution of AoF gits in fields Applied mathematics, Computational data analysis, Computational science, Computer engineering, computer architecture, Computer science, Information systems science, Signal processing, Systems biology, bioinformatics\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "954a67e2",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}