01_inspection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 1: Inspecting the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first assignment, we will get to know the dataset that we will be using throughout. You can find the assignment tasks at the bottom of this document.\n",
    "\n",
    "Our dataset consists of short texts (article abstracts) from the [PubMed](https://www.ncbi.nlm.nih.gov/pubmed/) database of scientific publications in the Life Science domain. As the full dataset consists of millions of documents, we are using just a small subset, namely all publications that contain the word \"emotion\" in their title or abstract. You can download that dataset in the form of four files (`emotion_Summaries.pkl.bz2`, etc.) from Canvas. Save these four files in a directory called `data` (do not unpack them, but save them as .bz2 files!), which should be a sub-directory of the one that contains this notebook file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing some Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import namedtuple, defaultdict, Counter\n",
    "import pickle, bz2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "Summaries_file = 'data/emotion_Summaries.pkl.bz2'\n",
    "Summaries = pickle.load( bz2.BZ2File( Summaries_file, 'rb' ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make it easier to access the data, we convert here paper entries into [named tuples](http://docs.python.org/3/library/collections.html#collections.namedtuple). This will allow us to refer to fields by keyword (like `var.year`), rather than index (like `var[2]`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "paper = namedtuple( 'paper', ['title', 'authors', 'year', 'doi'] )\n",
    "\n",
    "for (id, paper_info) in Summaries.items():\n",
    "    Summaries[id] = paper( *paper_info )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "paper(title='Effect of Prolonged Exposure, intensified Prolonged Exposure and STAIR+Prolonged Exposure in patients with PTSD related to childhood abuse: a randomized controlled trial.', authors=['Oprel DAC', 'Hoeboer CM', 'Schoorl M', 'de Kleine RA', 'Cloitre M', 'Wigard IG', 'van Minnen A', 'van der Does W'], year=2021, doi='10.1080/20008198.2020.1851511')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summaries[34630934]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Effect of Prolonged Exposure, intensified Prolonged Exposure and STAIR+Prolonged Exposure in patients with PTSD related to childhood abuse: a randomized controlled trial.'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summaries[34630934].title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two lines below will install [matplotlib](http://matplotlib.org/) and [NumPy](http://www.numpy.org/), if they are not installed already (if that doesn't work, you might have to download and install them from [here](http://matplotlib.org/downloads.html) and [here](http://sourceforge.net/projects/numpy/files/NumPy/1.10.1/)). We will need these libraries for drawing diagrams and analyzing the data below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in /home/rss220/.local/lib/python3.8/site-packages (3.7.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/rss220/.local/lib/python3.8/site-packages (from matplotlib) (1.4.4)\n",
      "Requirement already satisfied: importlib-resources>=3.2.0; python_version < \"3.10\" in /usr/local/lib/python3.8/dist-packages (from matplotlib) (5.12.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/rss220/.local/lib/python3.8/site-packages (from matplotlib) (9.5.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib) (23.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /home/rss220/.local/lib/python3.8/site-packages (from matplotlib) (1.0.7)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /home/rss220/.local/lib/python3.8/site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: numpy>=1.20 in /home/rss220/.local/lib/python3.8/site-packages (from matplotlib) (1.24.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/rss220/.local/lib/python3.8/site-packages (from matplotlib) (4.39.3)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/rss220/.local/lib/python3.8/site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: zipp>=3.1.0; python_version < \"3.10\" in /usr/local/lib/python3.8/dist-packages (from importlib-resources>=3.2.0; python_version < \"3.10\"->matplotlib) (3.15.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7->matplotlib) (1.14.0)\n",
      "Requirement already satisfied: numpy in /home/rss220/.local/lib/python3.8/site-packages (1.24.2)\n"
     ]
    }
   ],
   "source": [
    "! pip install --user matplotlib\n",
    "! pip install --user numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# show plots inline within the notebook\n",
    "%matplotlib inline\n",
    "# set plots' resolution\n",
    "plt.rcParams['savefig.dpi'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Papers per Year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will try to find out how many papers in the dataset were published per year. We are using the [Counter](https://docs.python.org/3/library/collections.html#collections.Counter) class for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of papers in the dataset per year for the past decade:\n",
      "[(2013, 2395), (2014, 2713), (2015, 2951), (2016, 3050), (2017, 3249), (2018, 3493), (2019, 3750), (2020, 4364), (2021, 4719), (2022, 8)]\n"
     ]
    }
   ],
   "source": [
    "paper_years = [ p.year for p in Summaries.values() ]\n",
    "papers_per_year = sorted( Counter(paper_years).items() )\n",
    "\n",
    "print('Number of papers in the dataset per year for the past decade:')\n",
    "print(papers_per_year[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering results, to obain only papers since 1940:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of papers in the dataset published since 1940:\n",
      "46466\n"
     ]
    }
   ],
   "source": [
    "papers_per_year_since_1940 = [ (y,count) for (y,count) in papers_per_year if y >= 1940 ]\n",
    "years_since_1940 = [ y for (y,count) in papers_per_year_since_1940 ]\n",
    "nr_papers_since_1940 = [ count for (y,count) in papers_per_year_since_1940 ]\n",
    "\n",
    "print('Number of papers in the dataset published since 1940:')\n",
    "print(sum(nr_papers_since_1940))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a bar plot to visualize the results (using [matplotlib.pyplot.bar](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x=years_since_1940, height=nr_papers_since_1940, width=1.0)\n",
    "plt.xlim(1940, 2021)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('nr. of papers');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can get the same result by plotting it as a histogram with [matplotlib.pyplot.hist](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( x=[p.year for p in Summaries.values()], bins=range(1940,2023) );\n",
    "plt.xlim(1940, 2022)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('number of papers');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Papers per Author"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will obtain the distribution characterizing the number of papers published by an author."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# flattening the list of lists of authors:\n",
    "flat_author_list = [ auth for paper in Summaries.values() for auth in paper.authors ]\n",
    "\n",
    "nr_papers_by_author = Counter( flat_author_list )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of authors in the dataset with distinct names:\n",
      "99675\n"
     ]
    }
   ],
   "source": [
    "print('Number of authors in the dataset with distinct names:')\n",
    "print(len(nr_papers_by_author))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 50 authors with greatest number of papers:\n",
      "[('Wang Y', 240), ('Gross JJ', 221), ('Zhang J', 173), ('Li Y', 159), ('Zhang Y', 156), ('Wang X', 151), ('Li X', 141), ('Liu Y', 133), ('Wang J', 133), ('Wang L', 127), ('Li J', 123), ('Gur RC', 122), ('Davidson RJ', 120), ('Zhang X', 119), ('Liu X', 118), ('Phillips ML', 112), ('Chen Y', 111), ('Li H', 105), ('Pine DS', 99), ('Yang J', 98), ('de Gelder B', 98), ('Barrett LF', 98), ('Gratz KL', 97), ('Phan KL', 95), ('Habel U', 91), ('Yang Y', 90), ('Liu J', 87), ('Wang H', 85), ('Zhang L', 85), ('Tull MT', 85), ('Zhang W', 83), ('Chen J', 83), ('Vuilleumier P', 82), ('Gur RE', 80), ('Adolphs R', 79), ('Wang Z', 78), ('Li S', 78), ('Aleman A', 76), ('Kensinger EA', 76), ('Scherer KR', 75), ('Chen X', 75), ('Kuppens P', 75), ('Zvolensky MJ', 73), ('Li L', 72), ('Levenson RW', 72), ('Leibenluft E', 72), ('Tranel D', 71), ('Williams LM', 71), ('Li W', 69), ('Schneider F', 69)]\n"
     ]
    }
   ],
   "source": [
    "print('Top 50 authors with greatest number of papers:')\n",
    "print(sorted(nr_papers_by_author.items(), key=lambda i:i[1], reverse=True)[:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can create a histogram to visualize the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( x=list(nr_papers_by_author.values()), bins=range(101), log=True )\n",
    "plt.xlabel('number of papers authored')\n",
    "plt.ylabel('number of authors');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authors per Paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can make a similar analysis for the number of authors per paper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TvS8HAACohSyGYRj27ODkVPGkk8ViUUlJiV0FvPvuu5o1a5aysrIUGhqquXPnWi959e7dW0FBQVq8eLEk6dixY+XOCEVFRWnz5s3WGsqzaNEi/f73v9eJEyf01FNPad++fSooKFBgYKAee+wxTZkyxa7LZvn5+fLx8VFeXt4dd7ktaPLaX+6EKnUsqZ+jSwCAWqGy7992ByRcRUBCTSIgAUDVqOz79019We3ly5dvZncAAIBbkt0BqaSkRK+88oqaNm0qT09P/fjjj5KkqVOn6v3336/yAgEAAGqa3Yu0X3vtNX3wwQeaOXOmRo0aZW3v0KGDkpOTNXLkyCotEIBjL2tyeQ/AncjuGaQlS5ZowYIFGjp0qJydna3tISEhOnDgQJUWBwAA4Ah2B6RTp05ZP1L/30pLS1VUVFQlRQEAADiS3QGpffv2+te//lWm/e9//7s6d+5cJUUBAAA4kt1rkKZNm6bhw4fr1KlTKi0t1UcffaSDBw9qyZIlWrNmTXXUCAAAUKPsnkH67W9/q08//VQbN26Uh4eHpk2bpv379+vTTz/Vb37zm+qoEQAAoEbZPYMkSffdd582bNhQ1bUAAADcEm4oIEnSnj17tH//fklX1yWFhYVVWVEAAACOZHdAOnnypIYMGaJt27apfv36kqTc3Fz16NFDy5cv1913313VNQIAANQou9cgPfPMMyoqKtL+/ft14cIFXbhwQfv371dpaameeeaZ6qgRAACgRtk9g7RlyxZt375dbdq0sba1adNG77zzju67774qLQ4AAMAR7J5BCgwMLPeGkCUlJQoICKiSogAAABzJ7oA0a9YsjR07Vnv27LG27dmzR+PHj9ebb75ZpcUBAAA4gsUwDMOeHRo0aKBLly6puLhYdepcvUJ37WcPDw+bvhcuXKi6Sm8x+fn58vHxUV5enry9vR1dTo1y5BenoubxZbUAapPKvn/bvQYpOTn5ZuoCAAC45dkdkIYPH14ddQAAANwybvhGkZJ0+fJlXblyxabtTrvcBAAAah+7F2kXFBRozJgxaty4sTw8PNSgQQObBwAAwO3O7oD00ksv6YsvvtC8efPk6uqq//3f/9WMGTMUEBCgJUuWVEeNAAAANcruS2yffvqplixZot69e2vEiBG677771KpVKzVv3lxLly7V0KFDq6NOAACAGmP3DNKFCxfUokULSVfXG137KH+vXr20devWqq0OAADAAewOSC1atNDRo0clSW3bttXKlSslXZ1ZuvbltQAAALczuwPSiBEj9PXXX0uSJk+erJSUFLm5uSk2NlYTJ06s8gIBAABqmt1rkGJjY60/R0dHa//+/crIyFCrVq3UqVOnKi0OAADAEW7qPkiSFBQUpKCgoCooBQAA4NZg9yU2SUpNTdXDDz+sli1bqmXLlnr44Ye1cePGqq4NAADAIewOSH/5y1/Ut29feXl5afz48Ro/fry8vb310EMPKSUlpTpqBAAAqFF2X2J7/fXX9dZbb2nMmDHWtnHjxqlnz556/fXXNXr06CotEAAAoKbZPYOUm5urvn37lml/4IEHlJeXVyVFAQAAOJLdAenRRx/Vxx9/XKb9k08+0cMPP1wlRQEAADiS3ZfY2rdvr9dee02bN29WZGSkJGnHjh3atm2bXnjhBc2dO9fad9y4cVVXKQAAQA2xGIZh2LNDcHBw5Qa2WPTjjz/eUFG3g/z8fPn4+CgvL0/e3t6OLqdGBU1e6+gSUIOOJfVzdAkAUGUq+/5t9wzSta8ZAQAAqK1u6D5IAAAAtRkBCQAAwMThASklJUVBQUFyc3NTRESEdu3aVWHf7777TgMHDlRQUJAsFouSk5NvaMzLly9r9OjRatSokTw9PTVw4EBlZ2dX5WkBAIDbmEMD0ooVKxQXF6eEhARlZGQoJCREMTExysnJKbf/pUuX1KJFCyUlJcnf3/+Gx4yNjdWnn36qVatWacuWLTp9+rQGDBhQLecIAABuP5UKSAMGDFB+fr4kacmSJSosLKySg8+ZM0ejRo3SiBEj1L59e82fP1/u7u5auHBhuf27deumWbNmafDgwXJ1db2hMfPy8vT+++9rzpw5+vWvf62wsDAtWrRI27dv144dO6rkvAAAwO2tUgFpzZo1KigokCSNGDGiSu6YfeXKFaWnpys6OvrnYpycFB0drbS0tGobMz09XUVFRTZ92rZtq2bNml33uIWFhcrPz7d5AACA2qlSH/Nv27at4uPjdf/998swDK1cubLCewcMGzasUgc+d+6cSkpK5OfnZ9Pu5+enAwcOVGqMGxkzKytLLi4uql+/fpk+WVlZFY6dmJioGTNm3FBdAADg9lKpgDR//nzFxcVp7dq1slgsmjJliiwWS5l+Foul0gHpdhMfH6+4uDjr8/z8fAUGBjqwIgAAUF0qFZB69OhhXZ/j5OSkQ4cOqXHjxjd1YF9fXzk7O5f59Fh2dnaFC7CrYkx/f39duXJFubm5NrNIv3RcV1fXCtc9AQCA2sXuT7EdPXpUd911100f2MXFRWFhYUpNTbW2lZaWKjU11fodb9UxZlhYmOrWrWvT5+DBg8rMzLzh4wIAgNrF7q8aad68uXJzc/X+++9r//79kq5+ge3IkSPl4+Nj11hxcXEaPny4unbtqvDwcCUnJ6ugoEAjRoyQdHU9U9OmTZWYmCjp6iLs77//3vrzqVOntHfvXnl6eqpVq1aVGtPHx0cjR45UXFycGjZsKG9vb40dO1aRkZHq3r27vS8HAACohewOSHv27FFMTIzq1aun8PBwSdJbb72l119/XevXr1eXLl0qPdagQYN09uxZTZs2TVlZWQoNDdW6deusi6wzMzPl5PTzJNfp06fVuXNn6/M333xTb775pqKiorR58+ZKjXmtXicnJw0cOFCFhYWKiYnRX/7yF3tfCgAAUEtZDMMw7NnhvvvuU6tWrfTee++pTp2r+aq4uFjPPPOMfvzxR23durVaCr3VVPbbgGujoMlrHV0CatCxpH6OLgEAqkxl379vaAbpv8ORJNWpU0cvvfSSunbtemPVAgAA3ELsXqTt7e2tzMzMMu0nTpyQl5dXlRQFAADgSHYHpEGDBmnkyJFasWKFTpw4oRMnTmj58uV65plnNGTIkOqoEQAAoEbZfYntzTfftN4Qsri4WJJUt25dPf/880pKSqryAgEAAGqa3QHJxcVFb7/9thITE3XkyBFJUsuWLeXu7l7lxQEAADiC3QHpGnd3d3Xs2LEqawEAALgl2L0GCQAAoLYjIAEAAJjc8CU2AHcGR94YlJtUAnAUZpAAAABMbmgG6YcfftCmTZuUk5Oj0tJSm23Tpk2rksIAAAAcxe6A9N577+n555+Xr6+v/P39ZbFYrNssFgsBCQAA3PbsDkivvvqqXnvtNU2aNKk66gEAAHA4uwPSv//9b/3ud7+rjlpgB0cunAUAoLaze5H27373O61fv746agEAALgl2D2D1KpVK02dOlU7duxQx44dVbduXZvt48aNq7LiAAAAHMFiGIZhzw7BwcEVD2ax6Mcff7zpom4H+fn58vHxUV5enry9vWv8+Fxiw52A+yABqGqVff+2ewbp6NGjN1UYAADAre6mbhRpGIbsnIACAAC45d1QQFqyZIk6duyoevXqqV69eurUqZM+/PDDqq4NAADAIey+xDZnzhxNnTpVY8aMUc+ePSVJX375pZ577jmdO3dOsbGxVV4kAABATbI7IL3zzjuaN2+ehg0bZm179NFHde+992r69OkEJAAAcNuz+xLbmTNn1KNHjzLtPXr00JkzZ6qkKAAAAEeyOyC1atVKK1euLNO+YsUKtW7dukqKAgAAcCS7L7HNmDFDgwYN0tatW61rkLZt26bU1NRygxMAAMDtxu4ZpIEDB2rnzp3y9fXV6tWrtXr1avn6+mrXrl167LHHqqNGAACAGmX3DJIkhYWF6a9//WtV1wIAAHBLqFRAys/Pt96OOz8//7p9HfG1GwAAAFWpUgGpQYMGOnPmjBo3bqz69evLYrGU6WMYhiwWi0pKSqq8SAAAgJpUqYD0xRdfqGHDhpKkTZs2VWtBAAAAjlapgBQVFWX9OTg4WIGBgWVmkQzD0IkTJ6q2OgAAAAew+1NswcHBOnv2bJn2CxcuKDg4uEqKAgAAcCS7A9K1tUZmFy9elJubW5UUBQAA4EiV/ph/XFycJMlisWjq1Klyd3e3bispKdHOnTsVGhpa5QUCAADUtEoHpK+++krS1Rmkb7/9Vi4uLtZtLi4uCgkJ0Ysvvlj1FQIAANSwSgeka59eGzFihN5++23udwQAAGotu9cgJScnq7i4uEz7hQsXfvEmkgAAALcDuwPS4MGDtXz58jLtK1eu1ODBg2+oiJSUFAUFBcnNzU0RERHatWvXdfuvWrVKbdu2lZubmzp27KjPPvvMZrvFYin3MWvWLGufoKCgMtuTkpJuqH4AAFC72B2Qdu7cqfvvv79Me+/evbVz5067C1ixYoXi4uKUkJCgjIwMhYSEKCYmRjk5OeX23759u4YMGaKRI0fqq6++Uv/+/dW/f3/t27fP2ufMmTM2j4ULF8pisWjgwIE2Y/35z3+26Td27Fi76wcAALWP3QGpsLCw3EtsRUVF+s9//mN3AXPmzNGoUaM0YsQItW/fXvPnz5e7u7sWLlxYbv+3335bffv21cSJE9WuXTu98sor6tKli959911rH39/f5vHJ598ovvvv18tWrSwGcvLy8umn4eHh931AwCA2sfugBQeHq4FCxaUaZ8/f77CwsLsGuvKlStKT09XdHT0zwU5OSk6OlppaWnl7pOWlmbTX5JiYmIq7J+dna21a9dq5MiRZbYlJSWpUaNG6ty5s2bNmlVu8LumsLBQ+fn5Ng8AAFA7VfpTbNe8+uqrio6O1tdff60+ffpIklJTU7V7926tX7/errHOnTunkpIS+fn52bT7+fnpwIED5e6TlZVVbv+srKxy+3/wwQfy8vLSgAEDbNrHjRunLl26qGHDhtq+fbvi4+N15swZzZkzp9xxEhMTNWPGjMqeGgAAuI3ZHZB69uyptLQ0zZo1SytXrlS9evXUqVMnvf/++2rdunV11HhTFi5cqKFDh5a5y/e1G19KUqdOneTi4qI//vGPSkxMlKura5lx4uPjbfbJz89XYGBg9RUOAAAcxu6AJEmhoaFaunTpTR/c19dXzs7Oys7OtmnPzs6Wv79/ufv4+/tXuv+//vUvHTx4UCtWrPjFWiIiIlRcXKxjx46pTZs2Zba7urqWG5wAAEDtY/capP92+fLlm1qX4+LiorCwMKWmplrbSktLlZqaqsjIyHL3iYyMtOkvSRs2bCi3//vvv6+wsDCFhIT8Yi179+6Vk5OTGjdubNc5AACA2sfuGaRLly7ppZde0sqVK3X+/Pky20tKSuwaLy4uTsOHD1fXrl0VHh6u5ORkFRQUaMSIEZKkYcOGqWnTpkpMTJQkjR8/XlFRUZo9e7b69eun5cuXa8+ePWUWjufn52vVqlWaPXt2mWOmpaVZb1fg5eWltLQ0xcbG6qmnnlKDBg3sqh8AANQ+dgekiRMnatOmTZo3b56efvpppaSk6NSpU/qf//mfG7rR4qBBg3T27FlNmzZNWVlZCg0N1bp166wLsTMzM+Xk9PNEV48ePbRs2TJNmTJFL7/8slq3bq3Vq1erQ4cONuMuX75chmFoyJAhZY7p6uqq5cuXa/r06SosLFRwcLBiY2Nt1hgBAIA7l8UwDMOeHZo1a6YlS5aod+/e8vb2VkZGhlq1aqUPP/xQf/vb38rc1bq2ys/Pl4+Pj/Ly8hzyvXRBk9fW+DGBmnYsqZ+jSwBQy1T2/dvuNUgXLlyw3nDR29tbFy5ckCT16tVLW7duvcFyAQAAbh12B6QWLVro6NGjkqS2bdtq5cqVkqRPP/1U9evXr9LiAAAAHMHugDRixAh9/fXXkqTJkycrJSVFbm5uio2N1cSJE6u8QAAAgJpm9yLt2NhY68/R0dE6cOCA0tPT1apVK3Xq1KlKiwMAAHAEu2aQioqK1KdPH/3www/WtubNm2vAgAGEIwAAUGvYFZDq1q2rb775prpqAQAAuCXYvQbpqaee0vvvv18dtQAAANwS7F6DVFxcrIULF2rjxo0KCwuTh4eHzfY5c+ZUWXEAAACOYHdA2rdvn7p06SJJOnTokM02i8VSNVUBAAA4UKUC0jfffKMOHTrIyclJmzZtqu6aAAAAHKpSa5A6d+6sc+fOSbp6o8jyvqQWAACgtqhUQKpfv7717tnHjh1TaWlptRYFAADgSJW6xDZw4EBFRUWpSZMmslgs6tq1q5ydncvt++OPP1ZpgQAAADWtUgFpwYIFGjBggA4fPqxx48Zp1KhR8vLyqu7aAAAAHKLSn2Lr27evJCk9PV3jx48nIAEAgFrL7o/5L1q0qDrqAAAAuGXYfSdtAACA2o6ABAAAYEJAAgAAMCEgAQAAmBCQAAAATOz+FBsA1JSgyWsdduxjSf0cdmwAjscMEgAAgAkBCQAAwISABAAAYEJAAgAAMCEgAQAAmBCQAAAATAhIAAAAJgQkAAAAEwISAACACQEJAADAhIAEAABgQkACAAAwISABAACYEJAAAABMCEgAAAAmt0RASklJUVBQkNzc3BQREaFdu3Zdt/+qVavUtm1bubm5qWPHjvrss89stv/+97+XxWKxefTt29emz4ULFzR06FB5e3urfv36GjlypC5evFjl5wYAAG4/Dg9IK1asUFxcnBISEpSRkaGQkBDFxMQoJyen3P7bt2/XkCFDNHLkSH311Vfq37+/+vfvr3379tn069u3r86cOWN9/O1vf7PZPnToUH333XfasGGD1qxZo61bt+rZZ5+ttvMEAAC3D4thGIYjC4iIiFC3bt307rvvSpJKS0sVGBiosWPHavLkyWX6Dxo0SAUFBVqzZo21rXv37goNDdX8+fMlXZ1Bys3N1erVq8s95v79+9W+fXvt3r1bXbt2lSStW7dODz30kE6ePKmAgIAy+xQWFqqwsND6PD8/X4GBgcrLy5O3t/cNn/+NCpq8tsaPCdxJjiX1c3QJAKpBfn6+fHx8fvH926EzSFeuXFF6erqio6OtbU5OToqOjlZaWlq5+6Slpdn0l6SYmJgy/Tdv3qzGjRurTZs2ev7553X+/HmbMerXr28NR5IUHR0tJycn7dy5s9zjJiYmysfHx/oIDAy0+3wBAMDtwaEB6dy5cyopKZGfn59Nu5+fn7KyssrdJysr6xf79+3bV0uWLFFqaqreeOMNbdmyRQ8++KBKSkqsYzRu3NhmjDp16qhhw4YVHjc+Pl55eXnWx4kTJ+w+XwAAcHuo4+gCqsPgwYOtP3fs2FGdOnVSy5YttXnzZvXp0+eGxnR1dZWrq2tVlQgAAG5hDp1B8vX1lbOzs7Kzs23as7Oz5e/vX+4+/v7+dvWXpBYtWsjX11eHDx+2jmFeBF5cXKwLFy5cdxwAAHBncGhAcnFxUVhYmFJTU61tpaWlSk1NVWRkZLn7REZG2vSXpA0bNlTYX5JOnjyp8+fPq0mTJtYxcnNzlZ6ebu3zxRdfqLS0VBERETdzSgAAoBZw+Mf84+Li9N577+mDDz7Q/v379fzzz6ugoEAjRoyQJA0bNkzx8fHW/uPHj9e6des0e/ZsHThwQNOnT9eePXs0ZswYSdLFixc1ceJE7dixQ8eOHVNqaqp++9vfqlWrVoqJiZEktWvXTn379tWoUaO0a9cubdu2TWPGjNHgwYPL/QQbAAC4szh8DdKgQYN09uxZTZs2TVlZWQoNDdW6deusC7EzMzPl5PRzjuvRo4eWLVumKVOm6OWXX1br1q21evVqdejQQZLk7Oysb775Rh988IFyc3MVEBCgBx54QK+88orNGqKlS5dqzJgx6tOnj5ycnDRw4EDNnTu3Zk8eAADckhx+H6TbVWXvo1BduA8SUL24DxJQO90W90ECAAC4FRGQAAAATAhIAAAAJgQkAAAAEwISAACACQEJAADAhIAEAABgQkACAAAwISABAACYEJAAAABMCEgAAAAmBCQAAACTOo4uAABuRY78Qmi+KBdwPGaQAAAATAhIAAAAJgQkAAAAEwISAACACQEJAADAhIAEAABgQkACAAAwISABAACYEJAAAABMCEgAAAAmBCQAAAATAhIAAIAJAQkAAMCEgAQAAGBCQAIAADAhIAEAAJgQkAAAAEwISAAAACYEJAAAABMCEgAAgAkBCQAAwKSOowsAANgKmrzWYcc+ltTPYccGbiXMIAEAAJjcEgEpJSVFQUFBcnNzU0REhHbt2nXd/qtWrVLbtm3l5uamjh076rPPPrNuKyoq0qRJk9SxY0d5eHgoICBAw4YN0+nTp23GCAoKksVisXkkJSVVy/kBAIDbi8MD0ooVKxQXF6eEhARlZGQoJCREMTExysnJKbf/9u3bNWTIEI0cOVJfffWV+vfvr/79+2vfvn2SpEuXLikjI0NTp05VRkaGPvroIx08eFCPPvpombH+/Oc/68yZM9bH2LFjq/VcAQDA7cFiGIbhyAIiIiLUrVs3vfvuu5Kk0tJSBQYGauzYsZo8eXKZ/oMGDVJBQYHWrFljbevevbtCQ0M1f/78co+xe/duhYeH6/jx42rWrJmkqzNIEyZM0IQJE26o7vz8fPn4+CgvL0/e3t43NMbNcOQaBQC1F2uQUNtV9v3boTNIV65cUXp6uqKjo61tTk5Oio6OVlpaWrn7pKWl2fSXpJiYmAr7S1JeXp4sFovq169v056UlKRGjRqpc+fOmjVrloqLiysco7CwUPn5+TYPAABQOzn0U2znzp1TSUmJ/Pz8bNr9/Px04MCBcvfJysoqt39WVla5/S9fvqxJkyZpyJAhNklx3Lhx6tKlixo2bKjt27crPj5eZ86c0Zw5c8odJzExUTNmzLDn9AAAwG2qVn/Mv6ioSE888YQMw9C8efNstsXFxVl/7tSpk1xcXPTHP/5RiYmJcnV1LTNWfHy8zT75+fkKDAysvuIBAIDDODQg+fr6ytnZWdnZ2Tbt2dnZ8vf3L3cff3//SvW/Fo6OHz+uL7744hfXCUVERKi4uFjHjh1TmzZtymx3dXUtNzgBAIDax6FrkFxcXBQWFqbU1FRrW2lpqVJTUxUZGVnuPpGRkTb9JWnDhg02/a+Fox9++EEbN25Uo0aNfrGWvXv3ysnJSY0bN77BswEAALWFwy+xxcXFafjw4eratavCw8OVnJysgoICjRgxQpI0bNgwNW3aVImJiZKk8ePHKyoqSrNnz1a/fv20fPly7dmzRwsWLJB0NRw9/vjjysjI0Jo1a1RSUmJdn9SwYUO5uLgoLS1NO3fu1P333y8vLy+lpaUpNjZWTz31lBo0aOCYFwIAANwyHB6QBg0apLNnz2ratGnKyspSaGio1q1bZ12InZmZKSennye6evTooWXLlmnKlCl6+eWX1bp1a61evVodOnSQJJ06dUr/+Mc/JEmhoaE2x9q0aZN69+4tV1dXLV++XNOnT1dhYaGCg4MVGxtrs8YIAADcuRx+H6TbFfdBAlAbcR8k1Ha3xX2QAAAAbkUEJAAAABMCEgAAgAkBCQAAwISABAAAYOLwj/kDAG4djvyELJ+gw62EGSQAAAATAhIAAIAJAQkAAMCEgAQAAGBCQAIAADAhIAEAAJgQkAAAAEwISAAAACYEJAAAABMCEgAAgAkBCQAAwISABAAAYEJAAgAAMKnj6AIAAJCkoMlrHXbsY0n9HHZs3JqYQQIAADAhIAEAAJgQkAAAAEwISAAAACYEJAAAABMCEgAAgAkf8wcA3PG4xQDMmEECAAAwISABAACYEJAAAABMCEgAAAAmLNIGAMCBWCB+a2IGCQAAwISABAAAYMIlNgAA7lBc3qsYM0gAAAAmBCQAAACTW+ISW0pKimbNmqWsrCyFhITonXfeUXh4eIX9V61apalTp+rYsWNq3bq13njjDT300EPW7YZhKCEhQe+9955yc3PVs2dPzZs3T61bt7b2uXDhgsaOHatPP/1UTk5OGjhwoN5++215enraVXuHhM/l5Opu/0kDAHAHc9TlvdLCS5Xq5/AZpBUrViguLk4JCQnKyMhQSEiIYmJilJOTU27/7du3a8iQIRo5cqS++uor9e/fX/3799e+ffusfWbOnKm5c+dq/vz52rlzpzw8PBQTE6PLly9b+wwdOlTfffedNmzYoDVr1mjr1q169tlnq/18AQDArc9iGIbhyAIiIiLUrVs3vfvuu5Kk0tJSBQYGauzYsZo8eXKZ/oMGDVJBQYHWrFljbevevbtCQ0M1f/58GYahgIAAvfDCC3rxxRclSXl5efLz89PixYs1ePBg7d+/X+3bt9fu3bvVtWtXSdK6dev00EMP6eTJkwoICPjFuvPz8+Xj46PACSuZQQIA4DZRWnhJJ5KfUF5enry9vSvs59BLbFeuXFF6erri4+OtbU5OToqOjlZaWlq5+6SlpSkuLs6mLSYmRqtXr5YkHT16VFlZWYqOjrZu9/HxUUREhNLS0jR48GClpaWpfv361nAkSdHR0XJyctLOnTv12GOPlTluYWGhCgsLrc/z8vIkVX6qDgAAON619+1fmh9yaEA6d+6cSkpK5OfnZ9Pu5+enAwcOlLtPVlZWuf2zsrKs26+1Xa9P48aNbbbXqVNHDRs2tPYxS0xM1IwZM8q0n5r3+wrODgAA3Kp++ukn+fj4VLj9llikfTuIj4+3mbkqLS3VhQsX1KhRI1ksFrvGys/PV2BgoE6cOHHd6b3ahvPmvO8EnDfnfSe4nc/bMAz99NNPv7icxqEBydfXV87OzsrOzrZpz87Olr+/f7n7+Pv7X7f/tf/Nzs5WkyZNbPqEhoZa+5gXgRcXF+vChQsVHtfV1VWurq42bfXr17/+Cf4Cb2/v2+4fVlXgvO8snPedhfO+s9yu5329maNrHPopNhcXF4WFhSk1NdXaVlpaqtTUVEVGRpa7T2RkpE1/SdqwYYO1f3BwsPz9/W365Ofna+fOndY+kZGRys3NVXp6urXPF198odLSUkVERFTZ+QEAgNuTwy+xxcXFafjw4eratavCw8OVnJysgoICjRgxQpI0bNgwNW3aVImJiZKk8ePHKyoqSrNnz1a/fv20fPly7dmzRwsWLJAkWSwWTZgwQa+++qpat26t4OBgTZ06VQEBAerfv78kqV27durbt69GjRql+fPnq6ioSGPGjNHgwYMr9Qk2AABQuzk8IA0aNEhnz57VtGnTlJWVpdDQUK1bt866yDozM1NOTj9PdPXo0UPLli3TlClT9PLLL6t169ZavXq1OnToYO3z0ksvqaCgQM8++6xyc3PVq1cvrVu3Tm5ubtY+S5cu1ZgxY9SnTx/rjSLnzp1bI+fs6uqqhISEMpfsajvOm/O+E3DenPed4E44b4ffBwkAAOBW4/A7aQMAANxqCEgAAAAmBCQAAAATAhIAAIAJAckBUlJSFBQUJDc3N0VERGjXrl2OLqlaJSYmqlu3bvLy8lLjxo3Vv39/HTx40NFl1bikpCTrbShqu1OnTumpp55So0aNVK9ePXXs2FF79uxxdFnVqqSkRFOnTlVwcLDq1aunli1b6pVXXvnF73u63WzdulWPPPKIAgICZLFYrN+DeY1hGJo2bZqaNGmievXqKTo6Wj/88INjiq1C1zvvoqIiTZo0SR07dpSHh4cCAgI0bNgwnT592nEFV5Ff+n3/t+eee04Wi0XJyck1Vl91IiDVsBUrViguLk4JCQnKyMhQSEiIYmJiytzZuzbZsmWLRo8erR07dmjDhg0qKirSAw88oIKCAkeXVmN2796t//mf/1GnTp0cXUq1+/e//62ePXuqbt26+uc//6nvv/9es2fPVoMGDRxdWrV64403NG/ePL377rvav3+/3njjDc2cOVPvvPOOo0urUgUFBQoJCVFKSkq522fOnKm5c+dq/vz52rlzpzw8PBQTE6PLly/XcKVV63rnfenSJWVkZGjq1KnKyMjQRx99pIMHD+rRRx91QKVV65d+39d8/PHH2rFjR+26l6CBGhUeHm6MHj3a+rykpMQICAgwEhMTHVhVzcrJyTEkGVu2bHF0KTXip59+Mlq3bm1s2LDBiIqKMsaPH+/okqrVpEmTjF69ejm6jBrXr18/4w9/+INN24ABA4yhQ4c6qKLqJ8n4+OOPrc9LS0sNf39/Y9asWda23Nxcw9XV1fjb3/7mgAqrh/m8y7Nr1y5DknH8+PGaKaoGVHTeJ0+eNJo2bWrs27fPaN68ufHWW2/VeG3VgRmkGnTlyhWlp6crOjra2ubk5KTo6GilpaU5sLKalZeXJ0lq2LChgyupGaNHj1a/fv1sfu+12T/+8Q917dpVv/vd79S4cWN17txZ7733nqPLqnY9evRQamqqDh06JEn6+uuv9eWXX+rBBx90cGU15+jRo8rKyrL5t+7j46OIiIg76m+cdPXvnMViuenv7LzVlZaW6umnn9bEiRN17733OrqcKuXwO2nfSc6dO6eSkhLrXcKv8fPz04EDBxxUVc0qLS3VhAkT1LNnT5u7n9dWy5cvV0ZGhnbv3u3oUmrMjz/+qHnz5ikuLk4vv/yydu/erXHjxsnFxUXDhw93dHnVZvLkycrPz1fbtm3l7OyskpISvfbaaxo6dKijS6sxWVlZklTu37hr2+4Ely9f1qRJkzRkyJDb8otc7fHGG2+oTp06GjdunKNLqXIEJNSo0aNHa9++ffryyy8dXUq1O3HihMaPH68NGzbYfM1NbVdaWqquXbvq9ddflyR17txZ+/bt0/z582t1QFq5cqWWLl2qZcuW6d5779XevXs1YcIEBQQE1Orzhq2ioiI98cQTMgxD8+bNc3Q51So9PV1vv/22MjIyZLFYHF1OleMSWw3y9fWVs7OzsrOzbdqzs7Pl7+/voKpqzpgxY7RmzRpt2rRJd999t6PLqXbp6enKyclRly5dVKdOHdWpU0dbtmzR3LlzVadOHZWUlDi6xGrRpEkTtW/f3qatXbt2yszMdFBFNWPixImaPHmyBg8erI4dO+rpp59WbGys9Yu27wTX/o7dqX/jroWj48ePa8OGDbV+9uhf//qXcnJy1KxZM+vfuOPHj+uFF15QUFCQo8u7aQSkGuTi4qKwsDClpqZa20pLS5WamqrIyEgHVla9DMPQmDFj9PHHH+uLL75QcHCwo0uqEX369NG3336rvXv3Wh9du3bV0KFDtXfvXjk7Ozu6xGrRs2fPMrdxOHTokJo3b+6gimrGpUuXbL5YW5KcnZ1VWlrqoIpqXnBwsPz9/W3+xuXn52vnzp21+m+c9HM4+uGHH7Rx40Y1atTI0SVVu6efflrffPONzd+4gIAATZw4UZ9//rmjy7tpXGKrYXFxcRo+fLi6du2q8PBwJScnq6CgQCNGjHB0adVm9OjRWrZsmT755BN5eXlZ1yL4+PioXr16Dq6u+nh5eZVZZ+Xh4aFGjRrV6vVXsbGx6tGjh15//XU98cQT2rVrlxYsWKAFCxY4urRq9cgjj+i1115Ts2bNdO+99+qrr77SnDlz9Ic//MHRpVWpixcv6vDhw9bnR48e1d69e9WwYUM1a9ZMEyZM0KuvvqrWrVsrODhYU6dOVUBAgPr37++4oqvA9c67SZMmevzxx5WRkaE1a9aopKTE+neuYcOGcnFxcVTZN+2Xft/mIFi3bl35+/urTZs2NV1q1XP0x+juRO+8847RrFkzw8XFxQgPDzd27Njh6JKqlaRyH4sWLXJ0aTXuTviYv2EYxqeffmp06NDBcHV1Ndq2bWssWLDA0SVVu/z8fGP8+PFGs2bNDDc3N6NFixbG//t//88oLCx0dGlVatOmTeX+9zx8+HDDMK5+1H/q1KmGn5+f4erqavTp08c4ePCgY4uuAtc776NHj1b4d27Tpk2OLv2m/NLv26w2fczfYhi17DavAAAAN4k1SAAAACYEJAAAABMCEgAAgAkBCQAAwISABAAAYEJAAgAAMCEgAQAAmBCQAAAATAhIQC3Wu3dvTZgwwdFlWBmGoWeffVYNGzaUxWLR3r17a/T406dPV2hoaI0esyIWi0WrV692dBkAKsB3sQGoMevWrdPixYu1efNmtWjRQr6+vtV2LIvFoo8//viW/Q6wM2fOqEGDBo4uA0AFCEgA7FJSUiKLxVLmm+sr48iRI2rSpIl69OhRDZU5hmEYKikpUZ069v059ff3r6aK7mxFRUWqW7euo8tALcAlNqCa9e7dW+PGjdNLL72khg0byt/fX9OnT7duP3bsWJnLTbm5ubJYLNq8ebMkafPmzbJYLPr888/VuXNn1atXT7/+9a+Vk5Ojf/7zn2rXrp28vb315JNP6tKlSzbHLy4u1pgxY+Tj4yNfX19NnTpV//0VjIWFhXrxxRfVtGlTeXh4KCIiwnpcSVq8eLHq16+vf/zjH2rfvr1cXV2VmZlZ7rlu2bJF4eHhcnV1VZMmTTR58mQVFxdLkn7/+99r7NixyszMlMViUVBQULljnD9/XkOGDFHTpk3l7u6ujh076m9/+5tNn6CgICUnJ9u0hYaGWl/Xa2M/9thj5R7rww8/VFBQkHx8fDR48GD99NNPNq/HuHHj1LhxY7m5ualXr17avXu3dfu138U///lPhYWFydXVVV9++aW+/vpr3X///fLy8pK3t7fCwsK0Z8+ecs9Rsr3Edu3fwEcffaT7779f7u7uCgkJUVpaWoX7S1JmZqZ++9vfytPTU97e3nriiSeUnZ1t3X7tkuL1zre0tFSJiYkKDg5WvXr1FBISor///e/XPW5QUJBeeeUVDRkyRB4eHmratKlSUlJs+syZM0cdO3aUh4eHAgMD9ac//UkXL160br/272r16tVq3bq13NzcFBMToxMnTtiM88knn6hLly5yc3NTixYtNGPGDOu/qWuv47x58/Too4/Kw8NDr7322nVrByrNoV+VC9wBoqKiDG9vb2P69OnGoUOHjA8++MCwWCzG+vXrDcMwrN8E/tVXX1n3+fe//23zTeDXvlG7e/fuxpdffmlkZGQYrVq1MqKioowHHnjAyMjIMLZu3Wo0atTISEpKsjm2p6enMX78eOPAgQPGX//6V8Pd3d1YsGCBtc8zzzxj9OjRw9i6datx+PBhY9asWYarq6tx6NAhwzAMY9GiRUbdunWNHj16GNu2bTMOHDhgFBQUlDnPkydPGu7u7saf/vQnY//+/cbHH39s+Pr6GgkJCYZhGEZubq7x5z//2bj77ruNM2fOGDk5OeW+XidPnjRmzZplfPXVV8aRI0eMuXPnGs7OzsbOnTutfcr7xvCQkBDrsXJycgxJxqJFi2yOlZCQYHh6ehoDBgwwvv32W2Pr1q2Gv7+/8fLLL1vHGTdunBEQEGB89tlnxnfffWcMHz7caNCggXH+/Hmb30WnTp2M9evXG4cPHzbOnz9v3HvvvcZTTz1l7N+/3zh06JCxcuVKY+/evRX9szAkGR9//LFhGD//G2jbtq2xZs0a4+DBg8bjjz9uNG/e3CgqKip3/5KSEiM0NNTo1auXsWfPHmPHjh1GWFiYERUVZe1TmfN99dVXjbZt2xrr1q0zjhw5YixatMhwdXU1Nm/eXGHtzZs3N7y8vIzExETj4MGD1t/RtX/ThmEYb731lvHFF18YR48eNVJTU402bdoYzz//vHX7tX9XXbt2NbZv327s2bPHCA8PN3r06GHts3XrVsPb29tYvHixceTIEWP9+vVGUFCQMX36dJvXsXHjxsbChQuNI0eOGMePH6+wbsAeBCSgmkVFRRm9evWyaevWrZsxadIkwzDsC0gbN2609klMTDQkGUeOHLG2/fGPfzRiYmJsjt2uXTujtLTU2jZp0iSjXbt2hmEYxvHjxw1nZ2fj1KlTNvX16dPHiI+PNwzj6huZpOu+2RuGYbz88stGmzZtbI6VkpJieHp6GiUlJYZhXH3TbN68+XXHKU+/fv2MF154wfr8lwKSYdgGkGsSEhIMd3d3Iz8/39o2ceJEIyIiwjAMw7h48aJRt25dY+nSpdbtV65cMQICAoyZM2cahvHz72L16tU2Y3t5eRmLFy+u9DmVF5D+93//17r9u+++MyQZ+/fvL3f/9evXG87OzkZmZmaZfXbt2lWp8718+bLh7u5ubN++3WbskSNHGkOGDKmw9ubNmxt9+/a1aRs0aJDx4IMPVrjPqlWrjEaNGlmfX/t3tWPHDmvb/v37DUnWMNynTx/j9ddftxnnww8/NJo0aWJ9LsmYMGFChccFbhSX2IAa0KlTJ5vnTZo0UU5Ozk2N4+fnJ3d3d7Vo0cKmzTxu9+7dZbFYrM8jIyP1ww8/qKSkRN9++61KSkp0zz33yNPT0/rYsmWLjhw5Yt3HxcWlzDmY7d+/X5GRkTbH6tmzpy5evKiTJ09W+hxLSkr0yiuvqGPHjmrYsKE8PT31+eefV3hZz15BQUHy8vKyPv/v38WRI0dUVFSknj17WrfXrVtX4eHh2r9/v804Xbt2tXkeFxenZ555RtHR0UpKSrJ5/Srrv1/jJk2aSFKF/07279+vwMBABQYGWtvat2+v+vXr29R6vfM9fPiwLl26pN/85jc2v/8lS5b8Yv2RkZFlnv/3cTdu3Kg+ffqoadOm8vLy0tNPP63z58/bXAKuU6eOunXrZn3etm1bm/q//vpr/fnPf7apbdSoUTpz5ozNOObfBVAVWKQN1ADzolGLxaLS0lJJsi52Nv5rXVBRUdEvjmOxWK47bmVcvHhRzs7OSk9Pl7Ozs802T09P68/16tWzCT7VadasWXr77beVnJxsXcMyYcIEXblyxdrHycnJ5vWSKn7NzG72NbvGw8PD5vn06dP15JNPau3atfrnP/+phIQELV++XI899lilxzT/fiXdUG0VjXlt3GtjXlsTtHbtWjVt2tSmn6ur6w0f89ixY3r44Yf1/PPP67XXXlPDhg315ZdfauTIkbpy5Yrc3d0rNc7Fixc1Y8YMDRgwoMw2Nzc368/m3wVQFQhIgIPdddddkq5+7Ltz586SVKX3B9q5c6fN8x07dqh169ZydnZW586dVVJSopycHN133303dZx27drp//7v/2QYhvXNfdu2bfLy8tLdd99d6XG2bdum3/72t3rqqackXQ0Ihw4dUvv27a197rrrLp05c8b6PD8/X0ePHrUZp27duiopKbHrHFq2bCkXFxdt27ZNzZs3l3Q1eO3evbtS95O65557dM899yg2NlZDhgzRokWL7ApI9mjXrp1OnDihEydOWGeRvv/+e+Xm5tq8Vtfz34vuo6Ki7Dr+jh07yjxv166dJCk9PV2lpaWaPXu29f8ArFy5sswYxcXF2rNnj8LDwyVJBw8eVG5urnWcLl266ODBg2rVqpVdtQFVgYAEOFi9evXUvXt3JSUlKTg4WDk5OZoyZUqVjZ+Zmam4uDj98Y9/VEZGht555x3Nnj1b0tU39KFDh2rYsGGaPXu2OnfurLNnzyo1NVWdOnVSv379Kn2cP/3pT0pOTtbYsWM1ZswYHTx4UAkJCYqLi7PrlgCtW7fW3//+d23fvl0NGjTQnDlzlJ2dbfOm/+tf/1qLFy/WI488ovr162vatGllZsCCgoKUmpqqnj17ytXVtVL3HPLw8NDzzz+viRMnqmHDhmrWrJlmzpypS5cuaeTIkRXu95///EcTJ07U448/ruDgYJ08eVK7d+/WwIEDK33e9oqOjlbHjh01dOhQJScnq7i4WH/6058UFRVV6UtOXl5eevHFFxUbG6vS0lL16tVLeXl52rZtm7y9vTV8+PAK9922bZtmzpyp/v37a8OGDVq1apXWrl0rSWrVqpWKior0zjvv6JFHHtG2bds0f/78MmPUrVtXY8eO1dy5c1WnTh2NGTNG3bt3twamadOm6eGHH1azZs30+OOPy8nJSV9//bX27dunV1999QZeNaDyWIME3AIWLlyo4uJihYWFacKECVX6x3/YsGH6z3/+o/DwcI0ePVrjx4/Xs88+a92+aNEiDRs2TC+88ILatGmj/v37a/fu3WrWrJldx2natKk+++wz7dq1SyEhIXruuec0cuRIu8PelClT1KVLF8XExKh3797y9/cvc7PH+Ph4RUVF6eGHH1a/fv3Uv39/tWzZ0qbP7NmztWHDBgUGBlpn5iojKSlJAwcO1NNPP60uXbro8OHD+vzzz68bsJydnXX+/HkNGzZM99xzj5544gk9+OCDmjFjhl3nbg+LxaJPPvlEDRo00K9+9StFR0erRYsWWrFihV3jvPLKK5o6daoSExPVrl079e3bV2vXrlVwcPB193vhhRe0Z88ede7cWa+++qrmzJmjmJgYSVJISIjmzJmjN954Qx06dNDSpUuVmJhYZgx3d3dNmjRJTz75pHr27ClPT0+b+mNiYrRmzRqtX79e3bp1U/fu3fXWW29ZZ/eA6mQxzBfyAQC4jqCgIE2YMOGmvsZm8eLFmjBhgnJzc6usLqAqMYMEAABgQkACAAAw4RIbAACACTNIAAAAJgQkAAAAEwISAACACQEJAADAhIAEAABgQkACAAAwISABAACYEJAAAABM/j80qOMbUw4a/AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=[ len(p.authors) for p in Summaries.values() ], bins=range(20), align='left', density=True)\n",
    "plt.xlabel('number of authors in one paper')\n",
    "plt.ylabel('fraction of papers')\n",
    "plt.xlim(-0.5, 15.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Words in Titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can have a first look into the words we find in titles (applying a very naive word splitting method for now):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of distinct words in the paper titles:\n",
      "40245\n"
     ]
    }
   ],
   "source": [
    "words = [ word.lower() for paper in Summaries.values() for word in paper.title.split(' ') ]\n",
    "word_counts = Counter(words)\n",
    "\n",
    "print('Number of distinct words in the paper titles:')\n",
    "print(len(word_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your name:** ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1\n",
    "\n",
    "Create a Python dictionary object that returns a set of all names of first authors (that is, the authors that appear first in the list of authors) for a given year. Name this dictionary `first_authors_at_year`. (You can use a [*defaultdict*](https://docs.python.org/3/library/collections.html#collections.defaultdict) with a default value of [*set*](https://docs.python.org/3/library/stdtypes.html#set-types-set-frozenset).) Demonstrate the working of this dictionary by showing the set of first authors for the year 1965."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# your code here  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "\n",
    "Based on the dictionary `first_authors_at_year` from exercise 1 above, create a plot for the years from 1965 until 2021 that shows how many authors published at least one paper as first author in the given particular year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# your code here  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3\n",
    "\n",
    "Print out the top 10 most often occurring words in the papers' titles together with their frequency, in descending order with respect to the frequency. Filter out words that are only one, two or three characters long, like `a` or `of` or `the`, to only show words that have four or more characters. Use the data structures created above (in the sections before the tasks)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# your code here  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4\n",
    "\n",
    "Calculate and plot (e.g. using [plt.plot](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot)) a graph of the frequency of the 100 most frequent words in titles of papers, from most frequent to least frequent, again only counting words of four or more characters. (For readability, don't show the actual words.) Make again use of the data structures created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# your code here   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5\n",
    "\n",
    "What do you notice when looking at the top 10 most frequent words? Explain all types of words we find in this top-10 and why they show up here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**your answer:** \n",
    "\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Submit the answers to the assignment via Canvas as a modified version of this Notebook file (file with `.ipynb` extension) that includes your code and your answers.\n",
    "\n",
    "Before submitting, restart the kernel and re-run the complete code (**Kernel > Restart & Run All**), and then check whether your assignment code still works as expected.\n",
    "\n",
    "Don't forget to add your name, and remember that the assignments have to be done **individually**, and that code sharing or copying are **strictly forbidden** and will be punished."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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