PDB_release_stats.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PDBeurope/pdbe-api-training/blob/master/PDB_release_stats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTGHhvOknJVl"
      },
      "source": [
        "1) Making imports and setting variables\n",
        "First, we import some packages that we will use, and set some variables.\n",
        "\n",
        "Note: Full list of valid URLs is available from https://www.ebi.ac.uk/pdbe/api/doc/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4J7Jbb4YmjRP",
        "outputId": "91201ca5-a566-4626-880a-7364a4685905"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: solrq in /opt/homebrew/lib/python3.10/site-packages (1.1.1)\n",
            "Note: you may need to restart the kernel to use updated packages.\n"
          ]
        }
      ],
      "source": [
        "import requests # used for getting data from a URL\n",
        "from pprint import pprint # pretty print\n",
        "import pandas as pd # used for turning results into mini databases\n",
        "\n",
        "# This is possible, but I would instead specify all the requirements beforehand so the \n",
        "# user can install into whatever environment they're using (or one you provide). A \n",
        "# good approach is to specify a requirements.txt file in the root of your GitHub repo. \n",
        "# This can be generated using :\n",
        "# pip freeze >> requirements.txt\n",
        "\n",
        "# and then removing rows you don't need. The user can then pip install all packages \n",
        "# using :\n",
        "# pip install -r requirements.txt\n",
        "%pip install solrq\n",
        "\n",
        "\n",
        "from solrq import Q # used to turn result queries into the right format\n",
        "import numpy as np\n",
        "import datetime\n",
        "today = datetime.date.today()\n",
        "year = today.year\n",
        "\n",
        "\n",
        "# Look into a package called pathlib. It allows you to handle paths and URLs in an \n",
        "# object-oriented fashion. Python objects can also be parsed into paths more easily. It \n",
        "# is also very useful if your code will be used across different OSs -- Windows paths \n",
        "# are formatted differently and pathlib will handle this for you, reducing bugs\n",
        "search_url = \"https://www.ebi.ac.uk/pdbe/search/pdb/select?\" # the rest of the URL used for PDBe's search API.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFz478jSnH82"
      },
      "source": [
        "2) a function to get data from the search API\n",
        "Let's start with defining a function that can be used to GET data from the PDBe search API."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "HkWQmWWOnIQ9"
      },
      "outputs": [],
      "source": [
        "# General formatting tip: Use speech marks for strings and quotation marks for single \n",
        "# characters (called chars). This is not particularly important in Python but is in \n",
        "# other languages like C, C++, Java\n",
        "\n",
        "def make_request(search_dict, number_of_rows=10, group_pdb=False):\n",
        "    \"\"\"\n",
        "    makes a get request to the PDBe API\n",
        "    :param dict search_dict: the terms used to search\n",
        "    :param number_of_rows: number or rows to return - limited to 10\n",
        "    :return dict: response JSON\n",
        "    \"\"\"\n",
        "    if 'rows' not in search_dict:\n",
        "        search_dict['rows'] = number_of_rows\n",
        "    search_dict['wt'] = 'json'\n",
        "    if group_pdb == True:\n",
        "      search_dict['group'] = 'true'\n",
        "      search_dict['group.field'] = 'pdb_id'\n",
        "      search_dict['group.ngroups'] = 'true'\n",
        "    response = requests.post(search_url, data=search_dict)\n",
        "\n",
        "    if response.status_code == 200:\n",
        "        return response.json()\n",
        "    else:\n",
        "        print(\"[No data retrieved - %s] %s\" % (response.status_code, response.text))\n",
        "\n",
        "    return {}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBMlvGx-nIgt"
      },
      "source": [
        "3) formatting the search terms\n",
        "This will allow us to use human readable search terms and this function will make a URL that the search API can handle."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "spc_Pz4gnUYl"
      },
      "outputs": [],
      "source": [
        "def format_search_terms(search_terms, filter_terms=None):\n",
        "    \"\"\"\n",
        "    Include a docstring here. The labels below are in Sphinc docstring format. \n",
        "\n",
        "    :param search_terms: _description_\n",
        "    :type search_terms: _type_\n",
        "    :param filter_terms: _description_, defaults to None\n",
        "    :type filter_terms: _type_, optional\n",
        "    :return: _description_\n",
        "    :rtype: _type_\n",
        "    \"\"\"\n",
        "    ret = {'q': str(search_terms)}\n",
        "    if filter_terms:\n",
        "        fl = '{}'.format(','.join(filter_terms))\n",
        "        ret['fl'] = fl\n",
        "    return ret"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "id": "NV0DT6henTu1"
      },
      "source": [
        "4) Getting useful data out of the search\n",
        "This function will run the search and will return a list of the results\n",
        "\n",
        "In the case of the two functions below, you only need the second one. You can add a \n",
        "condition to skip the group-specific lines of code. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "EBY0EJafnVzF"
      },
      "outputs": [],
      "source": [
        "def run_search(search_terms, filter_terms=None, number_of_rows=100, group_pdb=False):\n",
        "    search_term = format_search_terms(search_terms, filter_terms)\n",
        "    response = make_request(search_term, number_of_rows, group_pdb)\n",
        "    results = response.get('response', {}).get('docs', [])\n",
        "    return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "Ju_O-Sq6ddak"
      },
      "outputs": [],
      "source": [
        "def run_grouped_search(search_terms, filter_terms=None, number_of_rows=100, group_pdb=True):\n",
        "    search_term = format_search_terms(search_terms, filter_terms)\n",
        "    response = make_request(search_term, number_of_rows, group_pdb)\n",
        "    preresults = response.get('grouped', {}).get('pdb_id', {}).get('groups', [])\n",
        "    results = []\n",
        "    for group in preresults:\n",
        "      docs = group.get('doclist', {}).get('docs', [])\n",
        "      for doc in docs:\n",
        "        results.append(doc)\n",
        "    return results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VlpNyk4nU1k"
      },
      "source": [
        "Search for entries by year:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vh6O-zQLnVM9",
        "outputId": "0a580841-41a2-46b7-b61f-3ae9d8a7d1a7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'fl': 'pdb_id,experimental_method,release_year,resolution,em_resolution,assembly_mol_wt,journal,refinement_software,synchrotron_site,em_electron_detection,cath_homologous_superfamily,assembly_composition,processing_site',\n",
            " 'group': 'true',\n",
            " 'group.field': 'pdb_id',\n",
            " 'group.ngroups': 'true',\n",
            " 'q': 'status:REL',\n",
            " 'rows': 220000,\n",
            " 'wt': 'json'}\n",
            "'https://www.ebi.ac.uk/pdbe/search/pdb/select?'\n",
            "Number of results: 200069\n"
          ]
        }
      ],
      "source": [
        "search_terms = Q(status=\"REL\")\n",
        "filter_terms = ['pdb_id', 'experimental_method', 'release_year', 'resolution', 'em_resolution', 'assembly_mol_wt', 'journal', 'refinement_software', 'synchrotron_site', 'em_electron_detection', 'cath_homologous_superfamily', 'assembly_composition', 'processing_site']\n",
        "#all_results = run_search(search_terms, filter_terms, number_of_rows=100, group_pdb=False)\n",
        "all_results = run_grouped_search(search_terms, filter_terms, number_of_rows=220000, group_pdb=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Leb5FIYrx4x"
      },
      "source": [
        "6) Analysing and plotting the results\n",
        "We are going to use a Python package called Pandas to help us sort and visualise the results\n",
        "\n",
        "First we have to do a bit of housekeeping, some of the results are lists (a PDB entry can have more than one experimental method or organism for example) so we need to change them into strings so we can use them in a graph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "Xi1G0UUArwfb"
      },
      "outputs": [],
      "source": [
        "def change_lists_to_strings(results):\n",
        "    \"\"\"\n",
        "    input - list of results from search\n",
        "    output - list of results with lists changed into strings\n",
        "    \"\"\"\n",
        "    for row in results:\n",
        "        for data in row:\n",
        "            if type(row[data]) == list:\n",
        "                # if there are any numbers in the list change them into strings\n",
        "                row[data] = [str(a) for a in row[data]]\n",
        "                # unique and sort the list and then change the list into a string\n",
        "                row[data] = ','.join(sorted(list(set(row[data]))))\n",
        "                \n",
        "    return results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "U4CbGRyyr_a5"
      },
      "outputs": [],
      "source": [
        "results = change_lists_to_strings(all_results)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VH-JRNrlsQlZ"
      },
      "source": [
        "Notice that the only thing that changed is ['X-ray diffraction'] is now 'X-ray diffraction'\n",
        "\n",
        "If we wanted to know the experimental methods used to determine structures of Human Dihydrofolate reductase we could loop through the results and count how many entries use each experimental method.\n",
        "\n",
        "We can use a Python package called Pandas to do this for us. It changes the results into a mini database - called a DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XWMyPGyasQH5",
        "outputId": "c80f6aad-e826-4e93-f35a-56108597c78b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "           assembly_composition  assembly_mol_wt  \\\n",
            "0             protein structure           18.964   \n",
            "1             protein structure           21.155   \n",
            "2             protein structure           18.175   \n",
            "3       protein/protein complex          247.373   \n",
            "4             protein structure           80.061   \n",
            "...                         ...              ...   \n",
            "200064        protein structure           30.581   \n",
            "200065        protein structure           22.811   \n",
            "200066        protein structure           36.315   \n",
            "200067        protein structure          121.094   \n",
            "200068  protein/protein complex           31.722   \n",
            "\n",
            "                              cath_homologous_superfamily experimental_method  \\\n",
            "0                                                Lysozyme   X-ray diffraction   \n",
            "1                                            Glutaredoxin        Solution NMR   \n",
            "2                            Ubiquitin Conjugating Enzyme   X-ray diffraction   \n",
            "3                              Urease, gamma-like subunit   X-ray diffraction   \n",
            "4       Adenovirus DNA-binding, C-terminal domain supe...   X-ray diffraction   \n",
            "...                                                   ...                 ...   \n",
            "200064                                                NaN   X-ray diffraction   \n",
            "200065                                                NaN   X-ray diffraction   \n",
            "200066                                                NaN   X-ray diffraction   \n",
            "200067                                                NaN   X-ray diffraction   \n",
            "200068                                                NaN   X-ray diffraction   \n",
            "\n",
            "                                       journal pdb_id processing_site  \\\n",
            "0                                 Biochemistry   186l             BNL   \n",
            "1                                 Biochemistry   1a23             BNL   \n",
            "2       Acta Crystallogr. D Biol. Crystallogr.   1a3s             BNL   \n",
            "3                                 Biochemistry   1a5m             BNL   \n",
            "4                                J. Mol. Biol.   1adv             NaN   \n",
            "...                                        ...    ...             ...   \n",
            "200064                              J Med Chem   7mln            RCSB   \n",
            "200065                              J Mol Biol   7mmh            RCSB   \n",
            "200066                                 Sci Adv   7mne            RCSB   \n",
            "200067                           Int J Mol Sci   7mk8            RCSB   \n",
            "200068                                 Science   7mnt            RCSB   \n",
            "\n",
            "       refinement_software  release_year  resolution synchrotron_site  \\\n",
            "0                      TNT          1995        1.80              NaN   \n",
            "1                   X-PLOR          1998         NaN              NaN   \n",
            "2                      CNS          1998        2.80              NaN   \n",
            "3                   X-PLOR          1998        2.00              NaN   \n",
            "4                   X-PLOR          1996        3.20              NaN   \n",
            "...                    ...           ...         ...              ...   \n",
            "200064              REFMAC          2022        1.52              APS   \n",
            "200065              PHENIX          2022        1.75              NaN   \n",
            "200066              PHENIX          2022        1.60             SSRL   \n",
            "200067              PHENIX          2021        2.15             SSRL   \n",
            "200068              PHENIX          2022        2.45              APS   \n",
            "\n",
            "       em_electron_detection em_resolution  \n",
            "0                        NaN           NaN  \n",
            "1                        NaN           NaN  \n",
            "2                        NaN           NaN  \n",
            "3                        NaN           NaN  \n",
            "4                        NaN           NaN  \n",
            "...                      ...           ...  \n",
            "200064                   NaN           NaN  \n",
            "200065                   NaN           NaN  \n",
            "200066                   NaN           NaN  \n",
            "200067                   NaN           NaN  \n",
            "200068                   NaN           NaN  \n",
            "\n",
            "[200069 rows x 13 columns]\n"
          ]
        }
      ],
      "source": [
        "def pandas_dataset(list_of_results):\n",
        "    results = change_lists_to_strings(list_of_results) # we have added our function to change lists to strings\n",
        "    df = pd.DataFrame(results)\n",
        "\n",
        "    return df\n",
        "\n",
        "df = pandas_dataset(list_of_results=results)\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xF9LN4a-sZHC"
      },
      "source": [
        "**Total releases by experimental method**\n",
        "\n",
        "We can use the this to count how many PDB codes there are for each experimental method This groups PDB IDs by experimental method and then counts the number of unique PDB IDs per method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwKaU263sZ_5",
        "outputId": "d9fd7877-ce69-4eab-8ed7-5a1eecd12a7a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "experimental_method\n",
            "EPR,Hybrid,Solution NMR                                                      1\n",
            "EPR,Hybrid,X-ray diffraction                                                 7\n",
            "Electron Microscopy                                                      13623\n",
            "Electron Microscopy,Hybrid,Solid-state NMR                                   9\n",
            "Electron Microscopy,Hybrid,Solid-state NMR,Solution NMR                      1\n",
            "Electron Microscopy,Hybrid,Solid-state NMR,X-ray solution scattering         1\n",
            "Electron Microscopy,Hybrid,Solution NMR                                      2\n",
            "Electron Microscopy,Hybrid,X-ray solution scattering                         2\n",
            "Electron crystallography                                                   219\n",
            "Fiber diffraction                                                           29\n",
            "Fiber diffraction,Hybrid,Solid-state NMR                                     1\n",
            "Fluorescence transfer                                                        1\n",
            "Hybrid,Neutron Diffraction,Solution NMR                                      1\n",
            "Hybrid,Neutron Diffraction,X-ray diffraction                               134\n",
            "Hybrid,Solid-state NMR,Solution NMR                                          6\n",
            "Hybrid,Solution NMR,Theoretical Model                                        6\n",
            "Hybrid,Solution NMR,X-ray diffraction                                        1\n",
            "Hybrid,Solution NMR,X-ray solution scattering                               36\n",
            "Hybrid,X-ray diffraction,X-ray solution scattering                           6\n",
            "Infrared spectroscopy                                                        4\n",
            "Neutron Diffraction                                                         75\n",
            "Solid-state NMR                                                            135\n",
            "Solution NMR                                                             13743\n",
            "X-ray diffraction                                                       171952\n",
            "X-ray powder diffraction                                                    20\n",
            "X-ray solution scattering                                                   28\n",
            "Name: pdb_id, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "ds = df.groupby('experimental_method')['pdb_id'].nunique()\n",
        "print(ds)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40AcGEMqsqQB"
      },
      "source": [
        "We will make this into two functions so we can use them again"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "8uKWmEH4srZh"
      },
      "outputs": [],
      "source": [
        "def pandas_count(list_of_results, column_to_group_by):\n",
        "    df = pandas_dataset(list_of_results)\n",
        "    ds = df.groupby(column_to_group_by)['pdb_id'].nunique()\n",
        "    return ds\n",
        "    \n",
        "def pandas_min_max(list_of_results, column_to_group_by, get_min=True):\n",
        "    df = pandas_dataset(list_of_results)\n",
        "    if get_min:\n",
        "        ds = df.groupby(column_to_group_by)['pdb_id'].min()\n",
        "    else:\n",
        "        ds = df.groupby(column_to_group_by)['pdb_id'].max()\n",
        "    return ds\n",
        "    \n",
        "def pandas_plot(list_of_results, column_to_group_by, graph_type='bar'):\n",
        "    ds = pandas_count(list_of_results=list_of_results, column_to_group_by=column_to_group_by)\n",
        "    ds.plot(kind=graph_type, figsize=(8,5))\n",
        "\n",
        "def pandas_mean_plot(list_of_results, column_to_group_by, graph_type='bar'):\n",
        "    df = pandas_dataset(list_of_results)\n",
        "    df.groupby(column_to_group_by).mean().plot(kind=graph_type, figsize=(8,5))\n",
        "\n",
        "def pandas_plot_binned(list_of_results, column_to_group_by, bin_cat, bin_range, graph_type='bar'):\n",
        "    df = pandas_dataset(list_of_results)\n",
        "    df[bin_cat] = pd.cut(df[column_to_group_by],bin_range)\n",
        "    ds = df.groupby(bin_cat)\n",
        "    ds.plot(kind=graph_type, figsize=(8,5))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmIUP6i4tDfJ",
        "outputId": "45da0f4c-2f73-4e6f-eb82-1f87fd740e43"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "experimental_method\n",
              "EPR,Hybrid,Solution NMR                                                      1\n",
              "EPR,Hybrid,X-ray diffraction                                                 7\n",
              "Electron Microscopy                                                      13623\n",
              "Electron Microscopy,Hybrid,Solid-state NMR                                   9\n",
              "Electron Microscopy,Hybrid,Solid-state NMR,Solution NMR                      1\n",
              "Electron Microscopy,Hybrid,Solid-state NMR,X-ray solution scattering         1\n",
              "Electron Microscopy,Hybrid,Solution NMR                                      2\n",
              "Electron Microscopy,Hybrid,X-ray solution scattering                         2\n",
              "Electron crystallography                                                   219\n",
              "Fiber diffraction                                                           29\n",
              "Fiber diffraction,Hybrid,Solid-state NMR                                     1\n",
              "Fluorescence transfer                                                        1\n",
              "Hybrid,Neutron Diffraction,Solution NMR                                      1\n",
              "Hybrid,Neutron Diffraction,X-ray diffraction                               134\n",
              "Hybrid,Solid-state NMR,Solution NMR                                          6\n",
              "Hybrid,Solution NMR,Theoretical Model                                        6\n",
              "Hybrid,Solution NMR,X-ray diffraction                                        1\n",
              "Hybrid,Solution NMR,X-ray solution scattering                               36\n",
              "Hybrid,X-ray diffraction,X-ray solution scattering                           6\n",
              "Infrared spectroscopy                                                        4\n",
              "Neutron Diffraction                                                         75\n",
              "Solid-state NMR                                                            135\n",
              "Solution NMR                                                             13743\n",
              "X-ray diffraction                                                       171952\n",
              "X-ray powder diffraction                                                    20\n",
              "X-ray solution scattering                                                   28\n",
              "Name: pdb_id, dtype: int64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pandas_count(list_of_results=results, column_to_group_by='experimental_method')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IDY9qUxFPOxl"
      },
      "source": [
        "**Total releases by year**\n",
        "\n",
        "We can use the this to count how many PDB codes there are for each experimental method This groups PDB IDs by experimental method and then counts the number of unique PDB IDs per method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OTaYlSs1tFUZ",
        "outputId": "26b87ca7-8b62-4886-b030-46d0678cc8f4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "release_year\n",
              "1976       13\n",
              "1977       23\n",
              "1978        6\n",
              "1979       11\n",
              "1980       16\n",
              "1981       16\n",
              "1982       32\n",
              "1983       36\n",
              "1984       22\n",
              "1985       20\n",
              "1986       18\n",
              "1987       25\n",
              "1988       53\n",
              "1989       74\n",
              "1990      142\n",
              "1991      187\n",
              "1992      192\n",
              "1993      696\n",
              "1994     1289\n",
              "1995      941\n",
              "1996     1172\n",
              "1997     1564\n",
              "1998     2056\n",
              "1999     2356\n",
              "2000     2627\n",
              "2001     2813\n",
              "2002     2994\n",
              "2003     4147\n",
              "2004     5149\n",
              "2005     5334\n",
              "2006     6407\n",
              "2007     7134\n",
              "2008     6903\n",
              "2009     7289\n",
              "2010     7745\n",
              "2011     7927\n",
              "2012     8755\n",
              "2013     9339\n",
              "2014     9562\n",
              "2015     9252\n",
              "2016    10814\n",
              "2017    11065\n",
              "2018    11168\n",
              "2019    11495\n",
              "2020    13997\n",
              "2021    12587\n",
              "2022    14292\n",
              "2023      314\n",
              "Name: pdb_id, dtype: int64"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pandas_count(list_of_results=results, column_to_group_by='release_year')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "uUliIGdmtGoT",
        "outputId": "64c833af-7922-491e-db2f-0ee23ca4eb93"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pandas_plot(list_of_results=results, column_to_group_by='release_year')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "Zthb-z-0tIDh",
        "outputId": "5e4b312a-9762-45d3-850b-7819d3d2f5a5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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NfkBei9o1QSwHZWaTzWyemc0rKSlJx1uKiMghWL5xJ5+W7m7xXey1YtFwMLrZ67HZ6yLH+1nslRcBfVPq5XllByrPq6O8Ts65B5xzY5xzY7p3b7kjJEVEgurlpZswg3OG9jx45RYg6Ml8OlA7In0SMC2l/BpvVPtYYIfXHf8KcK6ZdfYGvp0LvOK9VmZmY71R7NekHEtERALm1aWbOD6/C93aZfsdSlrEoqHATE17AngfGGJmhWZ2PfBr4BwzWwmc7T0HeAlYDRQAfwO+DeCcKwXuAuZ6jzu9Mrw6D3r7rAL+0zQfTURE0mnNlnJWbNrJhGGto4sdalvm/l8zjxysgnPuinpeOquOug64sZ7jTAGm1FE+Dxh+sDhERCSzvbJ0EwDnDmsdXeyQnJ4W5G52ERGRz3h5ySZG5HYkr3Mbv0NJm+xoiIq4/y1zJXMREalTIuGYuaKYV5ZuItnxWr9NOypYtH57qxnFXisnGs6IVdMO2s0uIiKty56qGp5ZUMiUd9aweks5ACf078Iv/2s4g3q0r3OfV5clu9jHt6Lr5ZA5o9mVzEVEBIDinRU8/v46/j57Hdt2VzMityP3Xz6KPVU1/Oo/Kzjv/rf55mkDuenMQXsXGan18pJNDOrRjkE92vkUvT9i0VAwBsCJiEjLtmJTGQ+9vYZpizZQnUhwztE9+fqpAzg+vzO1a1+dPbQnv3xxOX+YWcALH23gronDOe3I5P0+SsurmLOmlBtOH+Dnx/BFLBrOiKlpSuYiIq3Uph0V3Dr1Q95euYWcaJjLT+jLtaf0p3+3tp+r261dNvd8ZRSXHpfHz55fwjVTPuDCkX34+QVH8+bHJdQkHBOG9fbhU/hL3ewiIuKb8so41z0yl3Vby7l1/BCuOrEfndpkHXS/kwd14z+3nMqf31zFn2au4s2Pi+neLpvcTjkMz+2QhsgzSywSojKewDmHnyt4azS7iEgrU5Nw3PzkQlZsKuMPV43mxjMGNSiR18qOhLnl7CN5+ZZTGZHbkdVbypkwvJevycwv2d7YgUqfp6epZS4i0src/eIyXltezF0Th3HGkMNfpnRA93b84+snMmdNKcNzOzZhhMGRE923pvn+gwLTSclcRKQVeez9tTz87lquO6U/V5+U3+jjmRljB3Rt9HGCKrY3mfvbMlc3u4hIKzFzRTF3TF/K2Uf35KdfPNrvcFqEWDSZRv0eBKdkLiLSCizfWMZN/1zA0b07cP/lowiHWt/17eZQ2zL3e3qakrmISAtXXFbB9Y/MpX0sykOTjqdttq6wNpVMaZnrX1REpAXbXRXn+kfnsX1PNU/fcBK9Osb8DqlFiUV0zVxERJpRTcJxy5OLWLphB7+/4liG9WmdI86bU+3UtIq4utlFRKQZ/N/LK3h12WZ+fsFQzjq69awxnk61U9P8XjlNyVxEpAWav66Uv85azVUn9uPaU/r7HU6Lte+aubrZRUSkCcVrEvzs+aX07hjjv8/XFLTmFEu5aYyflMxFRFqYv89ex/KNZfz8gqEaud7MNDVNRESaXMnOSv7/Vz/h1MHdOG94L7/DafHUzS4iIk3uVy8tpyJewx0XDWuVC5+k276paWqZi4hIE5izeivPLixi8mkDGNi9nd/htAqhkJEVDmlqmoiINF51TYL/mbaU3E453HjGIL/DaVVi0RCV6mYXEZHGevS9tXy8eSc/v2AobbI06C2dYtGwutlFRKRxNpdVcN9rKxk3pDvjh+nmMOkW+GRuZt8zs6VmtsTMnjCzmJn1N7M5ZlZgZv8ysyyvbrb3vMB7PT/lOD/xyj82s/GN+0giIq3LL19aTlU8wR0XatCbH2LRUHCnpplZLvBdYIxzbjgQBi4HfgPc65wbBGwDrvd2uR7Y5pXf69XDzIZ6+w0DJgB/MrPw4cYlItKavLdqC9MWbeCG0weQ362t3+G0SsmWebCvmUeAHDOLAG2AjcCZwFTv9UeBi73tid5zvNfPsuRXyInAk865SufcGqAAOKGRcYmItHi1g97yOufwbQ16800sEuBududcEfBb4FOSSXwHMB/Y7pyLe9UKgVxvOxdY7+0b9+p3TS2vYx8REanHw++uoaB4F3dcOGzvncgk/bKjISriAW2Zm1lnkq3q/kAfoC3JbvJmY2aTzWyemc0rKSlpzrcSEcloG7bv4b7XVnLWUT04e6gGvfkpJxr2fdW0xsxfOBtY45wrATCzZ4FTgE5mFvFa33lAkVe/COgLFHrd8h2BrSnltVL3+Qzn3APAAwBjxoxxjYhdRCRw4jUJ3inYwrMLinh12Sacg9svHOZ3WK1eJoxmb0wy/xQYa2ZtgD3AWcA8YCZwKfAkMAmY5tWf7j1/33v9DeecM7PpwD/N7B6SLfzBwAeNiEtEpMVwzrFsYxnPLihi2qINbNlVScecKJeMzuOqE4+gX9c2fofY6sWiId8HwB12MnfOzTGzqcACIA4sJNlqfhF40szu9soe8nZ5CHjczAqAUpIj2HHOLTWzp4Bl3nFudM75+xVHRMRnm3ZUMG1REc8uKOLjzTuJho0zhvTgS6PzOOOo7mRHdI08U8SiYd+npjXqNkHOuduB2/crXk0do9GdcxXAZfUc5xfALxoTi4hISzF1fiG3PfMR8YTj2H6duOvi4Vwwojed22b5HZrUIejd7CIi0sQemLWKX760glMGdeXui0fQX3PHM14sEqIynsA559tNe5TMRUQygHOOX/9nBX+dtZovHtObe748Ul3pAZHtTQusjCd8myKoZC4i4rN4TYLbnl3M1PmFXD32CO64aBjhkG7LGhQ50X1rmiuZi4i0QnuqavjOEwt4bXkxt5w9mJvPGqz7qwdMbG8y929Eu5K5iIhPduyu5uuPzWXeum3cNXEYV5+U73dIchhi0eT91/wcBKdkLiLig81lFUya8gGrSnbx+yuO5YJj+vgdkhym2pa5n9PTlMxFRNJszZZyrn5oDtvKq3j4ayfwhcHd/A5JGkEtcxGRVmbmx8Xc8uQiwiHjicljOSavk98hSSPFIrpmLiLSKtQkHPe/vpLfv7GSIT3b85evHqf1x1uI2qlpFXG1zEVEMl4i4Zg6v5BhuR0Y1qdjg/crLa/i5icX8vbKLVwyOo+7Lx5OTpbmkLcUtVPT/Fw5TclcRKSBHnt/LXe8sAyAUwd341unD+SkgV0POJVs0frtfPvv89myq4pffWkElx/fV1PPWph918zVzS4iktFWlezi1y+v4LQjuzN2QBemvLOWKx+cw4jcjnzz9AGcN7z3Z2704pzj73M+5c4XltKjfYyp3zpJ18dbqFjKTWP8omQuInIQ8ZoE33/qQ2LRML+99Bh6dIhx3Sn9eW5hEQ/MWs1N/1xIvy4f843TBnDZcXkknOOnzy3huYVFjBvSnfu+MopObbRISkulqWkiIgHw5zdX8eH67fzhymPp0SEGJP+AX3FCP748pi8zlm3iz2+t5ufPL+G+GZ/QMSfKmq3lfP+cI7npjEGEdGvWFk3d7CIiGW5J0Q7uf30lF43sU+eNXcIhY8Lw3owf1os5a0r5y1urWL6xjEevPYHTjuzuQ8SSbvumpqllLiKScSqqa/j+U4vo2i6LOycOO2BdM2PsgK6MHdA1TdFJpgiFjKxwSFPTREQy0T0zPuGTzbt45Nrjdc1bDigWDVHpYzd7yLd3FhHJYHNWb+Vvb6/mqhP7MW5ID7/DkQwXi4Z97WZXMhcR2c+uyjg/ePpD+nVpw3+ff7Tf4UgA+J3M1c0uIrKfu/+9jA3b9/DUN0+ibbb+TMrBxaIhX6emqWUuIpLijRWbeXLuer55+kDG5HfxOxwJiGTLXNfMRUR8V1pexY+mLuaoXu255ezBfocjARKL6Jq5iEhGuPvfy9ixp4p7vzKK7IgWQpGGy46GqIirZS4i4qt1W8t5flER157Sn6N7d/A7HAmYnGjY11XTlMxFRIC/vb2aSCjE17/Q3+9QJID8Hs2uZC4ird6WXZU8Pa+QL43O3XvvdZFDEYuGgjsAzsw6mdlUM1thZsvN7CQz62JmM8xspfezs1fXzOx3ZlZgZh+Z2eiU40zy6q80s0mN/VAiIofikXfXUlWTYPJpA/wORQIqFg37ejvXxrbM7wdeds4dBYwElgO3Aa875wYDr3vPAc4DBnuPycCfAcysC3A7cCJwAnB77RcAEZHmtqsyzmPvr2X80F4M6N7O73AkoGLRMHuqApjMzawjcBrwEIBzrso5tx2YCDzqVXsUuNjbngg85pJmA53MrDcwHpjhnCt1zm0DZgATDjcuEZFD8eQHn1JWEeeGcQP9DkUCLBYJURlP4Jzz5f0b0zLvD5QAD5vZQjN70MzaAj2dcxu9OpuAnt52LrA+Zf9Cr6y+chGRZlUVT/Dg22sYO6ALo/p28jscCbDsaHIqY6VP09Mak8wjwGjgz865Y4Fy9nWpA+CSX1Ga7GuKmU02s3lmNq+kpKSpDisirdS0RUVsKqvghtPVKpfGyYn6u6Z5Y5J5IVDonJvjPZ9KMrlv9rrP8X4We68XAX1T9s/zyuor/xzn3APOuTHOuTHdu3dvROgi0tolEo6/vLWKo3t34PQj9fdEGie2N5kHrGXunNsErDezIV7RWcAyYDpQOyJ9EjDN254OXOONah8L7PC6418BzjWzzt7At3O9MhGRZvPa8s2sKinnhtMHYGZ+hyMBF4sm06lfLfPGLgf0HeAfZpYFrAauJfkF4Skzux5YB3zZq/sScD5QAOz26uKcKzWzu4C5Xr07nXOljYxLRKReziVb5Xmdc/jiiN5+hyMtwN6WuU/T0xqVzJ1zi4Axdbx0Vh11HXBjPceZAkxpTCwiIg01d+02Fny6nf+9aBiRsO6dJY1X2zL3a3qafotFpNX5y1ur6NI2iy+P6XvwyiINEIsE9Jq5iEgQfbxpJ2+sKOZrJ+eTk6WV0aRpZPvcza5kLiKtyl/fWkWbrDDXnHSE36FIC1I7Nc2vldOUzEWk1SjavofpH27g8uP70alNlt/hSAuybzS7utlFRJrVg2+vBuDrp2qZU2lasQDfNEZEJDDWl+7myQ/Wc9GoPvTplON3ONLC+J3MGzvPXEQkY1VU1/DK0k08s6CId1aWEAmFdOtWaRZ7p6b51M2uZC4iLYpzjnnrtvHM/EJe/GgjOyvj5HbK4dvjBnHpcXnkd2vrd4jSAu2bmqaWuYjIYSvavoep8wp5dmEh67bupk1WmPOG9+aS43IZ278roZBu2SrNJxQyssKhYN4BTkQkE2zZVcmEe2exqyrOSQO68t0zBzNheC/aZutPnKRPLBqiUt3sIiKH54FZqymvivPCTV9geG5Hv8ORVioWDWs0u4jI4diyq5LH3l/LxFG5SuTiKyVzEZHD9LdZq6mKJ7jpzEF+hyKtXCwa0k1jREQOVbJVvo6LRvZhYPd2focjrVwsGmaPWuYiIofmb2+vpiJew01nDvY7FBFiEXWzi4gckq27KnnsvWSrfFAPtcrFf9nREBVxdbOLiDTY395eQ0W8hu/oWrlkiJxoWKumiYg0VGl5FY+9v5YLj+nDoB7t/Q5HBNBodhGRQ/K3t1ezp7qG756lVrlkDo1mFxFpoNLyKh57by0XqFUuGSYWDft2O1clcxEJlAffXs3u6hq+q2vlkmFi0TB7qpTMRUQOaFt5FY++t5YvjujN4J5qlUtmiUVCVMYTOOfS/t5K5iISGA++47XKz9K8csk82dHkMqiVPkxPUzIXkUBItsrXcf6I3hypVrlkoJyof2uaK5mLSCA89M4ayqvifFd3e5MMFdubzNUyFxH5nO27q3jkvbWcP7w3Q3qpVS6ZKRZNptRAtszNLGxmC83s397z/mY2x8wKzOxfZpbllWd7zwu81/NTjvETr/xjMxvf2JhEpGWZ8s4adlXGda1cMtrelrkP09OaomV+M7A85flvgHudc4OAbcD1Xvn1wDav/F6vHmY2FLgcGAZMAP5kZuEmiEtEWoCK6hoen72Oc4b2VKtcMlpty9yP6WmNSuZmlgd8EXjQe27AmcBUr8qjwMXe9kTvOd7rZ3n1JwJPOucqnXNrgALghMbEJSItx/RFG9i2u5prT8n3OxSRA4pFgnvN/D7gR0Bt5F2B7c65uPe8ECM+S78AABdBSURBVMj1tnOB9QDe6zu8+nvL69jnM8xsspnNM7N5JSUljQxdRDKdc44p767hqF7tOWlAV7/DETmg7CB2s5vZBUCxc25+E8ZzQM65B5xzY5xzY7p3756utxURn8xeXcqKTTu59pR8kh15IpmrdmqaHyunRRqx7ynARWZ2PhADOgD3A53MLOK1vvOAIq9+EdAXKDSzCNAR2JpSXit1HxFpxR5+dw2d20SZOKrOzjqRjLJvNHuAutmdcz9xzuU55/JJDmB7wzl3FTATuNSrNgmY5m1P957jvf6GS97zbjpwuTfavT8wGPjgcOMSkZZhfeluZizfzJUn9ts7Slgkk8V8vGlMY1rm9fkx8KSZ3Q0sBB7yyh8CHjezAqCU5BcAnHNLzewpYBkQB250zvlzp3oRyRiPvreWkBlXj833OxSRBgl8MnfOvQm86W2vpo7R6M65CuCyevb/BfCLpohFRIKvvDLOv+at57zhvejVMeZ3OCINsndqWpC62UVEmsuzCwrZWRHn2lP6+x2KSIPtm5oWoNHsIiLNIZFwPPzeWkbmdWR0v05+hyPSYKGQkRUOBWtqmohIc5i1soTVJeVce0p/TUeTwIlFQ1Sqm11EWruH311Lj/bZnD+it9+hiByyWDSsbnYRad0Kinfx1iclfHXsEWRF9OdJgkfJXERavUffW0tWOMQVJ/TzOxSRwxKLhoJ10xgRkaa0Y081zywo5MKRfejePtvvcEQOSywaZo9a5iLSWj09bz27q2q0OpoEWiyibnYRaaVqEo5H3lvLCfldGJ7b0e9wRA5bdjRERVzd7CLSCr22fDOF2/aoVS6BlxMN+7JqmpK5iPju4XfXkNsph3OG9vQ7FJFG8Ws0e3MstCIiclDOORZ8up1nFxQye3Upt513FJGw2hcSbH6NZlcyF5G0Wre1nOcWFvH8wiLWbt1NLBriS8fm8tWxR/gdmkijxaJhX27nqmQuIs1uW3kV/168kecWFLLg0+2YwckDu3LjGYOYMLwX7WNRv0MUaRKxaJg9VUrmItJCVMUTzPy4mGfmFzLz42KqaxxDerbntvOOYuKoPvTumON3iCJNLhYJURlP4JxL69oCSuYi0mSccyzdUMbU+YVM/3ADpeVVdGuXzaST8vmv0bkM7d1Bi6dIi5YdTS6DWhlPEPO200HJXEQarWRnJdMWFTF1fiErNu0kKxzinKE9ufS4PE4d3E0D26TVyInuW9NcyVxEAuG9gi08+M4a3vqkhJqEY1TfTtx18XAuPKY3ndpk+R2eSNrF9ibz9I5oVzIXkUO2bEMZv355BbM+KaFH+2wmnzaAS0bnMahHO79DE/FVLJrshUr3XHMlcxFpsMJtu7nn1U94blERHWJRfvbFo/nq2CPS2p0oksn2tszTPD1NyVxEDmr77ir+OLOAR99bhxl887SBfGvcQDrmaEqZSKralnm6p6cpmYtIvSqqa3jkvbX8aWYBuyrjXDI6j++dcyR9OmlamUhdYhFdMxeRDOGc46XFm/jFi8vYsKOCM4/qwY8nHMWQXu39Dk0ko2Wrm11EMkFB8S7umL6Udwq2MKxPB+75yijGDujqd1gigVA7NS3dK6cpmYsIAOWVcX7/RgEPvbOanGiYuyYO48oTjyAc0k1eRBpq32j29HazH/adHMysr5nNNLNlZrbUzG72yruY2QwzW+n97OyVm5n9zswKzOwjMxudcqxJXv2VZjap8R9LRBoq2aW+kbPveYu/vLWKi0fl8sYPx3H1SflK5CKHKJZy05h0akzLPA78wDm3wMzaA/PNbAbwNeB159yvzew24Dbgx8B5wGDvcSLwZ+BEM+sC3A6MAZx3nOnOuW2NiE1EGmBVSbJL/e2VWxjauwN/uPJYjjuii99hiQRW4JK5c24jsNHb3mlmy4FcYCIwzqv2KPAmyWQ+EXjMOeeA2WbWycx6e3VnOOdKAbwvBBOAJw43NhE5MOccf5xZwP2vryQWDfO/Fw3jqhP76barIo20d2paEEezm1k+cCwwB+jpJXqATUBPbzsXWJ+yW6FXVl+5iDQD5xy/fGk5f3t7DRcc05vbLxxG9/bZfocl0iLsm5oWkJZ5LTNrBzwD3OKcK0tdEck558zMNfY9Ut5rMjAZoF+/fk11WJFWwznHnf9exsPvruVrJ+dz+4VDtYqZSBMKhYyscCjtU9Ma1admZlGSifwfzrlnveLNXvc53s9ir7wI6Juye55XVl/55zjnHnDOjXHOjenevXtjQhdpdZxz3D59KQ+/u5brTumvRC7STGLREJUBGs1uwEPAcufcPSkvTQdqR6RPAqallF/jjWofC+zwuuNfAc41s87eyPdzvTIRaSKJhONnzy/hsffX8Y1T+/PzC45WIhdpJrFoOFDd7KcAVwOLzWyRV/bfwK+Bp8zsemAd8GXvtZeA84ECYDdwLYBzrtTM7gLmevXurB0MJyKNl0g4fvr8Yp74YD03nD6QH08YokQu0owClcydc+8A9f1FOKuO+g64sZ5jTQGmHG4sIlK3moTjtmc+4un5hdx0xiB+cO6RSuQizSwWDene7CLSNGoSjlunfsizC4q4+azB3HL2YCVykTSIRcPsCUrLXEQyV7wmwQ+f/pDnF23ge2cfyc1nD/Y7JJFWIxYJUDe7iGSmbeVV3PyvRcz6pIRbxw/hxjMG+R2SSKuSHQ1RVhFP63sqmYu0IEuKdnDD3+dTXFbJL/9rBFeeqPsxiKRbTjRMyc7KtL6nkrlIC/H0vPX87PkldGmbxVM3nMSovp38DkmkVQrUaHYRyQyV8RrufGEZ/5jzKScN6MrvrzyWbu10e1YRv2g0u0gLt6eqhrdXlnDGUT2INsGiJht37OFbf1/AovXb+eZpA7h1/BAtliLis1g0nPbbuSqZi6RJTcLxnScW8tryzYzM68g9XxnFwO7tDvt476/ayneeWMCeqhr+dNVozh/RuwmjFZHDFYuG2VMVoHuzi0jD/eblFby2fDOXH9+XdaW7+eLv3ubx2etI3k+p4ariCf7y1iq++tAcOuREmXbTKUrkIhkkFglRGU8c8v/txlDLXCQN/jX3Ux6YtZprTjqCOycO53vnHMmtUz/i588v4fXlm/m/S46hR4fYAY+xs6KaJz9Yz0PvrGFTWQXjh/Xkt5eNpH0smqZPISINkR1NLoNaGU8Q87abm5K5SDN7f9VWfvrcEk4d3I3/uWAoAD07xHj02uN5fPY6fvnScsbfN4tffWkEE4Z/voVdvLOCR95dy+Oz17GzIs5JA7ry60tGcPqR3XVHN5EMlBPdt6a5krlIC7BmSznf+sd88ru15Q9Xjv7M4DQz45qT8jl5YDe+/9Qibvj7Ai4ZnccdFw2lfSzKmi3lPDBrNc8sKKS6JsF5w3vxzdMGMlJTzkQyWmxvMk/fiHYlc5FmsmN3Ndc/MhcDHpo0ho45dXeHD+rRjme+dTK/f30lf5hZwOzVWxme24FXl20mGg5x6XF5TD51APnd2qb3A4jIYYlFk1/a0znXXMlcpBlU1yT49j/ns37bbv7x9bEc0fXAiTgaDvH9c4dw+pAefP+pRby/ais3jhvEpJPz6d5ec8ZFgmRvyzyN09OUzEWamHOO26cv5d2Crfz2spGc0L9Lg/c97ojOvPnDcdQknOaLiwRUbcs8ndPTlMxFmtjD767ln3M+5VvjBnLpcXmHvL+ZEQlrYJtIUMUi6b9mrq/+Ik1o5opi7n5xGeOH9eTWc4f4HY6I+CDbh252JXORJjJvbSk3/nMBR/fuwL1fGUUopNa1SGtUOzWtMo0D4JTMRZrA/HWlTJryAb06xHj4a8fTJktXsERaq32j2dXNLhIYCz7dxqQpc+nRIcYTk8ce9E5uItKyxVJuGpMuSuYijbBo/XYmPfQBXdtl8cQ3xtJTiVyk1VMyFwmQD9dv5+qH5tC5bTKR9+qoRC4iKVPT1M0uktk+KtzOVx+aQ6c2UZ6YPJY+nXL8DklEMsS+qWlqmYtkrCVFO/jqg3PomBPliW+MJVeJXERShEJGVjikqWkimWpJ0Q6uenAO7WPJRJ7XuY3fIYlIBuqQE4H0LWeuO8BJ65VIOLbvqWbLrkq27KykdHcV8RpHwjlqEg7noGbvtqMynuAPMwtomxXmiW+MpW8XJXIRqdu8n52T1vfLmGRuZhOA+4Ew8KBz7tc+hyQBlkg4NpVVsG7rbj4tLefT0t1s2lGZTNy7KinZWUlpeRXxxKF9dc7tlMM/v3Ei/boqkYtI5siIZG5mYeCPwDlAITDXzKY755b5G5lkguqaBLsra9hdHWd3VQ17qmrYU13jbSfLtu+u5tPS3azbWs660t0Ulu6hqmbfSNJwyOjZPptu7bPp2SHGsD4d6NYuO/lon023dll0bZtNNGyEzAiHjFDICBmErXbbaB+LENUCKCKSYTIimQMnAAXOudUAZvYkMBFISzJ/b9UWtuyqojqeIJ5IUF3jiNckiCfcZ7azIiGyIyGyo2FiKT9j0TDZkRDRSAjnHAkHNQlHIuFtu2TX7d7nXrdtTWp3biJZxzWgoVjjHBXVNVTGE1RWJ6iM11Dh/ayMJ6iMJ6hJJEgkSL6vw4vLUeNtQ3LZzaxwiKzIvkd27c9wiHAoRMjALLn4B3jbmPfzs8+Tr1tKeW28UJNIUJPY76dzxBOOiqoadlbG2VURZ2dFnF2VcXZWVHs/41TGGza9o21WmH5d23Jkj/acc3RP+nVtwxFd2tKvSxv6dIppFTIRabEyJZnnAutTnhcCJ6brze+bsZIP1pam6+2aXDRsZEeSXyhi0TBZkRARryVpBiEzQiG850bYkuMyqmsSVMVTHjXelwOvvLlFvNZvTjRMu+wI7WPJR7d2WeR3a5t8nh2hbXaENllh2mQlf+ZkhcmJhvdut8mK0DEnSuc20b1fOkREWpNMSeYNYmaTgckA/fr1a7Lj/vaykVTV1BANh4iEQ0RDRiQcIhI2oqHkz0jIqK5xVMRrkq3ilBZxbSu5qiaR7KK1ZPdsKOR119YmVK/71gyvvPax73lqK7g+IUvOY8yOhsiOhAk3w4Ie7jMtenDs6zVwKc+dV9d55ez3GiS7qcPeOQyZ7U3iIiLSNDIlmRcBfVOe53lln+GcewB4AGDMmDFNNui/oYOZsiJGViREh1i0qd46Y9W24Pd1louISKbKlIuIc4HBZtbfzLKAy4HpPsckIiISCBnRMnfOxc3sJuAVklPTpjjnlvocloiISCBkRDIHcM69BLzkdxwiIiJBkynd7CIiInKYlMxFREQCTslcREQk4JTMRUREAk7JXEREJOCUzEVERAJOyVxERCTgzDVkma4MZGYlwDq/42gm3YAtfgfRAum8Ng+d1+ajc9s8gnpej3DOda/rhcAm85bMzOY558b4HUdLo/PaPHRem4/ObfNoiedV3ewiIiIBp2QuIiIScErmmekBvwNooXRem4fOa/PRuW0eLe686pq5iIhIwKllLiIiEnBK5mlgZlPMrNjMlqSUjTSz981ssZm9YGYdvPKomT3qlS83s5+k7DPBzD42swIzu82Pz5JJDvG8ZpnZw175h2Y2LmWf47zyAjP7nZmZDx8nY5hZXzObaWbLzGypmd3slXcxsxlmttL72dkrN++8FZjZR2Y2OuVYk7z6K81skl+fKVMcxrk9yvt9rjSzH+53LP098BzGeb3K+11dbGbvmdnIlGMF87w65/Ro5gdwGjAaWJJSNhc43du+DrjL274SeNLbbgOsBfKBMLAKGABkAR8CQ/3+bAE6rzcCD3vbPYD5QMh7/gEwFjDgP8B5fn82n89rb2C0t90e+AQYCvwfcJtXfhvwG2/7fO+8mXce53jlXYDV3s/O3nZnvz9fwM5tD+B44BfAD1OOo78HjTuvJ9f+LgLnpfzOBva8qmWeBs65WUDpfsVHArO87RnAJbXVgbZmFgFygCqgDDgBKHDOrXbOVQFPAhObO/ZMdojndSjwhrdfMbAdGGNmvYEOzrnZLvm/+THg4uaOPZM55zY65xZ42zuB5UAuyd+3R71qj7LvPE0EHnNJs4FO3nkdD8xwzpU657aR/PeYkMaPknEO9dw654qdc3OB6v0Opb8HKQ7jvL7n/U4CzAbyvO3Anlclc/8sZd8vyWVAX297KlAObAQ+BX7rnCsl+Yu5PmX/Qq9MPqu+8/ohcJGZRcysP3Cc91ouyXNZS+c1hZnlA8cCc4CezrmN3kubgJ7edn2/m/qdPYAGntv66NzW4zDO6/Uke5YgwOdVydw/1wHfNrP5JLuFqrzyE4AaoA/QH/iBmQ3wJ8RAqu+8TiH5H3MecB/wHsnzLPUws3bAM8Atzrmy1Ne8XgxNhTlMOrfN41DPq5mdQTKZ/zhtQTaTiN8BtFbOuRXAuQBmdiTwRe+lK4GXnXPVQLGZvQuMIfltsW/KIfKAovRFHAz1nVfnXBz4Xm09M3uP5HW1bezrYgOdVyA5EJPkH8V/OOee9Yo3m1lv59xGrxu92Csvou7fzSJg3H7lbzZn3EFwiOe2PvWd81brUM+rmR0DPEhyjMxWrziw51Utc5+YWQ/vZwj4GfAX76VPgTO919qSHFC0guTArsFm1t/MsoDLgenpjjvT1XdezayNdz4xs3OAuHNumdcFV2ZmY71R7NcA0/yJPjN45+EhYLlz7p6Ul6YDtSPSJ7HvPE0HrvFGtY8Fdnjn9RXgXDPr7I0iPtcra7UO49zWR38PUhzqeTWzfsCzwNXOuU9S6gf3vPo9Aq81PIAnSF4DrybZ1Xs9cDPJluEnwK/ZdwOfdsDTJK/9LgNuTTnO+V79VcBP/f5cfj8O8bzmAx+THBjzGsnVh2qPMwZY4p3XP9Tu01ofwBdIdkd+BCzyHucDXYHXgZXeOezi1Tfgj975WwyMSTnWdUCB97jW78/m9+Mwzm0v73e7jOSgzUKSAzb196Bx5/VBkr1ytXXnpRwrkOdVd4ATEREJOHWzi4iIBJySuYiISMApmYuIiASckrmIiEjAKZmLiIgEnJK5iIhIwCmZi7RgZrbL7xhEpPkpmYsEnHfntVbxf9lbTVBE9tMq/gCItDRmlm9mH5vZYyTvXvdzM5trZh+Z2f/Ws8+tddUxs+fNbL6ZLTWzyV5Z2MweMbMlZrbYzL7nlQ80s5e9+m+b2VH1vFd7M1vj3S8bM+tQ+7y+Y5jZhWY2x8wWmtlrZtbTK7/DzB731il4vAlPo0iLoW+5IsE1mOT9pjsAl5Jccc+A6WZ2mkuu9w6AmZ3r1a+rznXOuVIzywHmmtkzJG9/m+ucG+7t38k71APADc65lWZ2IvAnvLUEUjnndprZmyQXunme5D2un3XOVZtZfcd4BxjrnHNm9nXgR8APvEMOBb7gnNvTBOdNpMVRMhcJrnXOudlm9luSi5gs9MrbkUzcs1LqnnuAOt81s//yyvt65R8DA8zs98CLwKve8pInA08n17UAIPsA8T1IMiE/D1wLfOMgx8gD/uWtbpUFrEk51nQlcpH6KZmLBFe599OAXznn/nqAunXWMbNxwNnASc653V5rOuac22ZmI4HxwA3Al4FbgO3OuVENCc459653OWAcEHbOLTGzDgc4xu+Be5xz07197qjjs4pIHXTNXCT4XgGu81q9mFlu7VKwDajTEdjmJfKjSC65i5l1A0LOuWdILiU72jlXBqwxs8u8OuYl/AN5DPgn8DDAQY7RkX1rR0/a/0AiUj8lc5GAc869SjJhvm9mi4GpQPsG1nkZiJjZcpJLxs72dskF3jSzRcDfgZ945VcB15vZhySX6Z14kPD+AXQmuVxtrfqOcQfJ7vf5wJYGnwAR0RKoItJ8zOxSYKJz7mq/YxFpyXTNXESahTd47jzgfL9jEWnplMxFpFHM7KfAZfsVP+2c+44f8Yi0RupmFxERCTgNgBMREQk4JXMREZGAUzIXEREJOCVzERGRgFMyFxERCbj/B72bfyK8nLkVAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pandas_plot(list_of_results=results, column_to_group_by='release_year', graph_type='line')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "k63zGdY_qagw"
      },
      "outputs": [],
      "source": [
        "def pandas_plot_multi_groupby(results, first_column_to_group_by, second_column_to_group_by, y_axis='pdb_id', graph_type='line', first_range_flag=False, first_lower=1971, first_upper=year):\n",
        "    \n",
        "    df = pandas_dataset(results)\n",
        "    if first_range_flag:\n",
        "      df = df[df[first_column_to_group_by].between(first_lower, first_upper)]\n",
        "    new_df = df.groupby([first_column_to_group_by, second_column_to_group_by])\n",
        "    ds = new_df.count().unstack().reset_index(first_column_to_group_by)\n",
        "    ds.plot(x=first_column_to_group_by, y=y_axis, kind=graph_type,figsize=(8,5)).legend(bbox_to_anchor=(1,1))\n",
        "    \n",
        "def pandas_plot_multi_groupby_filter(results, first_column_to_group_by, second_column_to_group_by, filter_column, filter_list, y_axis='pdb_id', graph_type='line', first_range_flag=False, first_lower=1971, first_upper=year):\n",
        "    \n",
        "    df = pandas_dataset(results)\n",
        "    if first_range_flag:\n",
        "      df = df[df[first_column_to_group_by].between(first_lower, first_upper)]\n",
        "    ds = df.loc[df[filter_column].isin(filter_list)]\n",
        "    new_df = ds.groupby([first_column_to_group_by, second_column_to_group_by])\n",
        "    ds = new_df.count().unstack().reset_index(first_column_to_group_by)\n",
        "    ds.plot(x=first_column_to_group_by, y=y_axis, kind=graph_type,figsize=(8,5)).legend(bbox_to_anchor=(1,1))\n",
        "    \n",
        "def pandas_plot_multi_groupby_binned(results, first_column_to_group_by, second_column_to_group_by, bin_cat, bin_range, y_axis='pdb_id', graph_type='line', first_range_flag=False, first_lower=1971, first_upper=year):\n",
        "    \n",
        "    df = pandas_dataset(results)\n",
        "    if first_range_flag:\n",
        "      df = df[df[first_column_to_group_by].between(first_lower, first_upper)]\n",
        "    df.dropna(subset=[first_column_to_group_by,second_column_to_group_by])\n",
        "    if df.dtypes[second_column_to_group_by] != 'float64':\n",
        "      df[second_column_to_group_by] = pd.to_numeric(df[second_column_to_group_by], errors='coerce')\n",
        "    df[bin_cat] = pd.cut(df[second_column_to_group_by],bin_range)\n",
        "    new_df = df.groupby([first_column_to_group_by, bin_cat])\n",
        "    ds = new_df.count().unstack().reset_index(first_column_to_group_by)\n",
        "    ds.plot(x=first_column_to_group_by, y=y_axis, kind=graph_type, figsize=(8,5)).legend(bbox_to_anchor=(1,1))\n",
        "    \n",
        "def pandas_box_plot(results, first_column_to_group_by, second_column_to_group_by, first_range_flag=False, first_lower=1971, first_upper=year):\n",
        "    df = pandas_dataset(results)\n",
        "    if first_range_flag:\n",
        "      df = df[df[first_column_to_group_by].between(first_lower, first_upper)]\n",
        "    df.boxplot(column=second_column_to_group_by,by=first_column_to_group_by,showfliers=False,figsize=(8,5))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pUH7Xrhj6HAG"
      },
      "source": [
        "**Experimental method by year**\n",
        "\n",
        "Create plot plotting experimental method by year. Added filtering to remove minor method types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "oWaGlKTwWywm",
        "outputId": "60824003-670c-448b-8c83-6782bee5a082"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "filter_column = 'experimental_method'\n",
        "filter_list = ['Electron Microscopy', 'X-ray diffraction', 'Solution NMR']\n",
        "pandas_plot_multi_groupby_filter(results, first_column_to_group_by='release_year', second_column_to_group_by='experimental_method', filter_column=filter_column, filter_list=filter_list)\n",
        "\n",
        "pandas_plot_multi_groupby_filter(results, first_column_to_group_by='release_year', second_column_to_group_by='experimental_method', first_range_flag=True, first_lower=2014, first_upper=2022, filter_column=filter_column, filter_list=filter_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUSF3LHC652z"
      },
      "source": [
        "**X-ray resolution bins by year**\n",
        "\n",
        "Create plot plotting X-ray resolution by year. Added filtering to remove minor method types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "NUe6UhCh69xz",
        "outputId": "e43cb24c-b637-4e26-b514-e7be90649843"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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ILMnEraJbNQlSYkEiTmacrKljxDOCu9j9/1uSdK1Jbf37ky3Pxg/XfsCR1CMQcAWY230uZvjN6FBdhM6mztg0fBPCToRh7sm5iBodBSsjK32H1W4pVAps/WcrZnWdBaFAqO9wWt3AgQOLT548KRw/fnwpAPj7+3vfuXPHsLy8nCuRSLpv2LAhfdKkSSXh4eFdAgMDy0JDQ4sXLVqUP2nSJFcnJ6euYrFYs2fPntsAkJWVxedyufUmVWPHjnW9cOGCSCaT8SQSSfd///vf9xcvXpy/evVqawBYtmxZ3s6dO80jIyNtuFwuNTQ01EZHR9/hcJr/IYxt1MowjZAqS8Xu5N04eucoytXleN7meUz1mYqhTkPB57S/bpOWplApkFqU+lAr0q2iWyisKKypY2FoUdPFVp0guZu5w5jfsuNAm6qgvABb/9mKPSl7AAAh3iGY3W12h04Srjy4grmn5sJB5IBtI7dBbCDWd0jtzrn757Dq3Cpkl2Vj9aDVGOUyqsWu3VY3ao2Pjzdes2aN5PDhw2lPe63PP//c2tnZWRkaGlrcErHVZW9v3y0hISGpbrcZ26iVYZpIo9Xg7N2z2J20G3/m/AkBR4AgtyBMlU6Fj6WPvsNrU4z5xuhu3f2R6eX55fkPJ0iyWzhw6wDK1eU1dRyEDo+0IjmbOrf6zuulylJsv7EdOxJ3oFJTiXHu4/BmjzdhJ7Rr1fu2BT1temLdkHVY8MsCzP9lPrYM36L3pLS9KFGWYM3FNTiUeggupi6IGh2F52ye03dYz0T//v0VCQkJJWq1GtVrDTXX8uXL81oorIfI5XISEBDgo1KpCIfDaXRrD2sZYpg6iiuLcfDWQcQmx+J+2X3YmtgixDsEkzwnwdzQXN/htXtaqsW90nu4WXTzoVakjJIMaGnVmEg+hw83sVtNguRh5gEvcy9IjCVPPZW9XF2O3cm7EfFPBEqUJRjhPAILnlvQKQcU/5LxC97+7W0E2Abg+6Hfw4BroO+Q2rSzWWfxyflPkF+Rj5l+M/FmjzdhyDNs8fu01ZahjoC1DDHME6QUpmB38m78dOcnVGgqEGgbiKWBSzHYcXCrt1J0JhzCgaOpIxxNHTHUaWhNeaWmEmnFaTUJ0s2im/gr5y/E3YmrqSMSiOBp5vnQoO3GTv1XaVQ4eOsgNl3bhLzyPPSz74e3nnurU08zH+o8FB/3+xjvx7+Ppb8txdrBa9nPej1kFTJ8+deX+DntZ3iae+LbF7+Fn9Vjd4pg2iH2U890amqtGr9m/oqY5Bhcyr0EQ65hTVeYt4W3vsPrVAy4BpBaSCG1kD5UXlxZ/MhYpGNpx7D35t6aOhJjCTzMPeBl5lWTIFVP/ddoNfg57WdsuLIBd+V38ZzNc1g9cDUCbAPqhtApBbsHo1RZii//+hIr/liBT/t/2q5nA7YkSilOZJzAF39+gRJlCeb3mI/Xu73eLpdXYBrGkiGmU5JVyHDg1gHsSdmDnLIc2Avt8bb/25jgOYENJm1jxAZi+Ev8H1o5ufbU/+oEKVWWir+y/3po6r+TqRMopUgvSYfUQorvh36PAfYD2vSq0foQ6hMKuVKO7658B6FAiPd6vdfpv0d5ijx89udn+CXzF/hZ+mHLiC3wMvfSd1hMK2HJENOpJBYkIiYpBsfSjkGpVaK3XW8s77UcAx0GtpsVhZlGTP2X3apZI6m4shgLei7ACJcRrMWjAXO7z4VcJcf2G9sh5Avx1vNv6TskvaCU4sfbP+Kri1+hUl2Jxf6L8Zrva6z7sINj/7pMh6fSqvBLxi+ISY7B5QeXYcQzwgTPCZgqnQp3M3d9h8e0ID6HD3czd7ibuWOUa8tNde4MCCFY4r8EpcpSbPlnC0QCEcK6huk7rGcqW56NVRdW4Y97f+A5m+ewqu8quIob3Di9U5HL5WTIkCFe58+fT+HxeBgwYIDnlStXTOrbPb7at99+a7ly5UoHiUSiAoC5c+c+WLJkyWMHgpeWlnLGjh3rlpGRYcDlcjFixIiiDRs23Kuv7nvvvWe7a9cuKw6Hg6+//jpz0qRJJdWzyW7fvm2YmZl5rbErUrNkiOmw8svzsf/mfuxL2YcH5Q/gKHLE0oClGO85vkMtpMcwLYUQgg9f+BBylRxrL62FSCDCy14v6zusVqelWuy/uR9rL62Flmrx717/xlTpVNaSWMf69eutgoODZdXT6t95552csrIyzpYtW6wbOm/s2LGy6OjozMbe5+23384dO3ZsaUVFBenXr5/X3r17TSdPnlxSu86lS5cMDx48aJGSknIjIyODP3z4cK9x48ZdFwqFNDk5OdHe3r5bU56NJUNMh3M9/zpikmJwPP04VFoV+nXph5V9V6K/fX/2y41hnoDL4eKL/l+gTFWGj89/DCFf2KFb2bJKsrDy/EpczLmI3na98VGfj+AgctB3WG3S3r17LWNjY+9Uvx83blxpXFycqCXvIRKJtGPHji0FAENDQ9q9e3dFVlbWI5sg7t+/32zixImFRkZGVCqVKp2dnSvPnj1rMmzYsLLm3PeJyRAhxBDA/wAY6Orvp5SuJIRsBzAIQPXqkTMppVdI1ai7dQBeAqDQlf+tu9YMAB/o6n9KKY1qTtAMU5dKo8KJjBPYnbQb1/KvwZhnjJe9XsZU6VTWzM0wTcTn8rF28Fq8ceoNvPf7ezDmGz80Nqsj0Gg12JW0C+svrwePw8NHfT7CRM+J7WLg+Id/fOiYKktt0VUyPcw9FJ/0++SxG8BWVFSQrKwsA29vb+Xj6jzOsWPHzLy8vIRubm4V3333XZaHh4eqMefl5+dzT506ZbZ06dLcusfu3bsneOGFF+TV77t06aLUJU2tkwwBqATwIqVUTgjhA4gnhBzTHVtKKd1fp/5oAJ66/3oD2AigNyHEAsBKAAEAKIBLhJAfKaWy5gTOMEDVjI99N/dhb8peFFQUwMXUBf/u9W+Mcx/XKfYKYpjWYsQzwndDv8PsE7Ox5OwS/DDshw6zHMGdojv48NyHuJZ3DYMcBuGDFz6ArYmtvsNq03JycngikajJG6FOnjy5aM6cOYVGRkb0P//5j9Wrr77qeuHChSduzqpSqTBx4kS3uXPn5vr6+jY5AWuqJyZDtGqJ6ursi6/7r6Flq8cBiNadd4EQYkYIsQMwGMApSmkhABBCTgEYBWB388NnOiNKKa7mXUVMcgxOpZ+CmqoxwH4ApvlMQ98ufVlXGMO0EJFAhE3DN2Hm8ZlY+OtCRIyIaNeLDaq0KkTdiMKGKxtgzDfGFwO+QJBrULtoDaqtoRac1mJiYqJVKpVN/uVqa2urqf568eLF+R9//HGj+iCnTZvm4ubmVrFixYoH9R23t7dX1u4+u3//vsDR0bHZSVOjHowQwiWEXAHwAFUJzZ+6Q58RQq4RQv5LCKlex90eQO1/qLu6sseVM0yjKDVK/Hj7R0z5aQqmH5uO3+/+jinSKfhpwk/YMGwDGxPEMK3A3NAcm4dvhpmBGd44/QZuF93Wd0jNklyYjNCfQrHu73UY4jgEh8cdxhi3Me0uEdIXa2trjUajIQqFoknfsIyMjJoVKmNiYszc3Nwqqt+7urrWm1m/9dZbXUpKSrgRERGPTfomTZpUdPDgQYvy8nKSnJwsSE9PNxw8eHCzusiARg6gppRqAPQkhJgBOEQI6QrgPQA5AAQANgN4F8DHzQ2kGiFkLoC5AODk5PS0l2M6gNyyXOxJ2YMDtw6gsKIQbmI3vN/7fQS7B7PNJRnmGZCYSLB5+GbMOD4Dc0/ORdToqHYzyFipUWLztc2I+CcCYgMx1g5ei+HOw/UdVrs0cODA4pMnTwrHjx9fCgD+/v7ed+7cMSwvL+dKJJLuGzZsSJ80aVJJeHh4l8DAwLLQ0NDi1atX25w4ccKMy+VSMzMz9fbt29MBIDs7m0cpfSSxun37Nn/9+vV2rq6uFX5+fr7A/0/H37Vrl/jixYsm33zzzf2AgICK8ePHF3p5eflxuVysXbs242k2j23yRq2EkBUAFJTSNbXKBgN4h1I6hhCyCcBZSulu3bEUVHWRDQYwmFI6T1f+UL36sI1aOy9KKf5+8DdikmLwS+Yv0FItBjkOwjTpNLxg9wL7NMcwenBTdhNhx8NgKjBF9OhoWBs3OKNa767lXcOKP1bgdvFtBLsHY1ngsnaxwnxb3ag1Pj7eeM2aNZLDhw+nPe21du/eLb59+7bBBx98UG832NOyt7fvlpCQkFR3naFmb9RKCLEGoKKUFhFCjAAMB/AVIcSOUpqtmz02HsB13Sk/AlhICIlF1QDqYl29EwA+J4RUb/s9AlWtSwxTo0JdgWNpxxCTHIPkwmSIBCJM952OEO+QdvNJlGE6Ki9zL2wcthGvn3wdc0/NxbaR22BmaKbvsB5Rri7HhisbEJ0YDWsja3w/9PsONxtOH/r3769ISEgoUavVeJpWGACYOnVq8ZNrNV31oosqlYpwOJxGt/Y05mnsAEQRQrioGmO0l1IaRwj5VZcoEQBXALyhq/8zqqbVp6Jqan0YAFBKCwkhnwC4qKv3cfVgaobJlmcjNiUWB28dRFFlETzMPLCizwoEuQaxrjCGaUO6W3fHdy9+hzdPv4n5v8zHlhFbYMI30XdYNRJyErDy3EpklmbiFa9XsMR/CZtZ2oLCw8ML9B1DQ6oXXWzqeY2ZTXYNwHP1lL/4mPoUwILHHIsEENnEGJkOilKKS7mXsCtpF37N+hUAMMRxCEJ9QhEgCWBdYQzTRvWy64U1g9Zg8dnF+Nev/8KGoRtgyDPUa0xlqjJ8c+kbxKbEwkHogIgREehl10uvMTHtB1uBmnnm1Fo1TmeeRtT1KFwvuA6xgRgz/WYixDsEXYRd9B0ewzCNMMRpCD7t/ymW/74cS39birVD1oLP4T/5xFZw7v45rDq3Ctll2XjV51X867l/sRZlpklYMsQ8M2WqMhy8dRA7E3fiftl9OJs648MXPsRY97Ew4hnpOzyGYZpojNsYyJVyfPbnZ/gg/gN8MeCLZ7q8RYmyBGsursGh1ENwMXVB9Oho9LTp+czuz3QcLBliWl1uWS52Je/C/pT9KFWV4nmb57Gs1zIMdhgMLoer7/AYhnkKU6RTIFfJse7vdRAJRHi/9/vPpIv7TOYZfHLhExRWFGJ219l4s+ebMOAaPPlEhqkHW6GOaTUphSlY/vtyjDowClE3otCnSx/semkXokZHYajTUJYIMUwH8Xq31zGr6yzsSdmDby9/26r3klXIsOx/y/DWmbdgbmiOXUG7EO4fzhKhZ0Aul5PAwEBvtVqNc+fOGfXs2VPq4eHh5+Xl5btlyxbz+s4pLy8nQUFBbk5OTl27d+8uTUlJeWTT1dpSU1P5vXv39nJ3d/fz8PDw++STT2zqqxcXFycSiUQ9pVKpr1Qq9X3nnXfsqmOUSqW+fD7/+ezs7EY3+LCWIaZFUUrxx/0/EHUjCheyL8CIZ4Qp0ikI9QllU+MZpgMLfz4cpcpSbP1nK4R8IWZ3m92i16eU4kTGCXzx5xcoUZZgQc8FmN11Nvhc/YxT6ozWr19vFRwcLOPxeBAKhdodO3akdevWrTI9PZ0fGBjoM2HChBIrKytN7XPWrVtnJRaL1ZmZmdc3b95svmTJEoeffvrpzuPuwefz8fXXX9/t37+/QiaTcZ577jnfl156qcTf37+ibt2AgAD5mTNnUmuXVc8ms7e379aUZ2PJENMilBolfrrzE6ITo5FalAobIxuEPx+Ol71ebheLnDEM83QIIXi/9/uQq+T45u9vIBKIMNl7cotcO0+Rh08vfIpfs35FV8uu2DpiKzzNPVvk2kzj7d271zI2NvYOAHTv3r2yutzFxUVlYWGhzs7O5tVNhuLi4sw++uij+wAQFhYme/fdd520Wi04nPo7ppydnVXOzs4qADA3N9e6u7uXZ2ZmCupLhloSS4aYp1JcWYy9KXsRkxyD/PJ8eJp74rP+n2G0y2j2iY1hOhkuh4vP+n+GMlUZPr3wKUz4JghyC2r29SilOHL7CFZfXA2lRom3/d/Gq76vgsfp3H+67i9/37Hy1q0WnS5n4Omp6PL5Z4/dC6yiooJkZWUZeHt7P7IZ6pkzZ4xVKhXx9fWtrHssNzdX4OrqqgSqWn2EQh/KaBEAACAASURBVKEmNzeXV3dl6PqkpKQIEhMTjQcNGiSv7/jly5eF3t7evhKJRLV27dqsgICAZidMnfsnimm2rJIs7EjagcOph1GuLkffLn3xWf/P0MeuD1sfiGE6MT6Hj68HfY35v8zH+/Hvw4RvgsGOg5t8nWx5NladX4U/7v+B522ex6q+q+AidmnxeJnGycnJ4YlEokcSmIyMDH5YWJhbREREGpfbcuNAi4uLORMnTnT/8ssvsywsLLR1j/ft27csIyPjmlgs1u7Zs0c8adIkj4yMjOv1XasxWDLENMmVB1cQnRiN0xmnweVwEeQahNf8XoOXuZe+Q2MYpo0w5Bli/Yvr8fqJ1/H22bfxw/AfEGgb2KhztVSL/Tf34+uEr0FBsbz3coR4hzzTKfttXUMtOK3FxMREq1QqH/pHKCws5IwePdpj5cqV94YOHVrvjvESiUSZlpYmcHd3V6lUKsjlcq5EImmwVaiyspIEBQW5v/LKK4UzZswoqq9O7QQpJCSkeMmSJU7Z2dmNanGqD/vpYp5Io9XgdMZpTP95OqYfm44L2Rcwu9tsnJh0Ap/2/5QlQgzDPMKEb4KNwzbCUeSIhb8sxD95/zzxnMySTMw+MRufXPgE3a2749C4Q5gqncoSoTbA2tpao9FoiEKhIEBVt1lQUJDHlClTCsLCwmSPOy8oKKgoMjLSEgC2bdtm3qdPn1IOh4O0tDR+nz59HvnjodVqMWXKFGcvL6+Kjz76KPdx183MzORptVX50JkzZ4y1Wi2elGQ1hLUMMY+lUClw5PYR7EjcgazSLNgL7fHvXv/GBI8JbHVXhmGeyMzQDJtHbMZrx17Dm7+8iW0jt9U78Fmj1WBX0i6sv7wefA4fq/quwgSPCazLvY0ZOHBg8cmTJ4Xjx48vjYyMNL948aJQJpPxYmJirAAgMjIyrW/fvuXh4eFdAgMDy0JDQ4sXLVqUP2nSJFcnJ6euYrFYs2fPntsAkJWVxedyuY9spHrq1Cnh4cOHLT09PculUqkvAKxatepeSEhI8erVq60BYNmyZXk7d+40j4yMtOFyudTQ0FAbHR1953GDshuDVG0l1jYFBATQhIQEfYfR6eSX5yMmKQZ7b+5FcWUxult1xwy/GWxtIIZhmiWrNAszjs0AAESNioKjqWPNsdtFt7Hi3Apcy7uGwQ6D8cELH0BiItFXqG0CIeQSpTSgdtnVq1fTe/Toka+vmAAgPj7eeM2aNZLDhw+nPe21Pv/8c2tnZ2dlaGhoq+xeb29v3y0hISGpbrfZ1atXrXr06OFStz5rGWJqpMpSEZ0Yjbg7cVBr1XjR6UXM8JuBntY92Sc0hmGazVHkiM3DNyPsRBjmnJqDqFFRsDCywPbr27Hx6kaY8E3w1YCvMNp1NPtd04b1799fkZCQUKJWq8HjPV36sHz58rwWCushcrmcBAQE+KhUKsLhcBrd2sOSoU6OUoo/c/5E1I0oxN+LhyHXEBM9J2K673Q4mzrrOzyGYToID3MP/DDsB8w+ORtzT82FAdcASYVJGOkyEu/1eg+WRpb6DpFphPDw8AJ9x9CQ6kUXm3oeS4Y6KZVWheNpxxGdGI3kwmRYGlpiYc+FCPEOgZmhmb7DYximA/Kz8sP6F9fjzdNvQiQQ4ZvB32Co81B9h8UwLBnqbEqVpdh/cz92Ju3EA8UDuIvdsarvKgS5BbG9fRiGaXWBtoE4Mv4ITAWmEAlE+g6HYQCwZKjTuC+/j51JO3Hg5gEo1Ar0tu2Nj/p8hH72/di0VYZhnil7ob2+Q2CYh7BkqIO7kX8D229sx6mMUyAgGOk6EjN8Z8DH0kffoTEMwzBMm8CaBDogLdXibNZZzDw+E1N+moL4e/GY7jsdxyYdw5cDvmSJEMMwDNNkcrmcBAYGeqvVapw7d86oZ8+eUg8PDz8vLy/fLVu2mNd3zrfffmtpbm7eQyqV+kqlUt+1a9daPek+AwYM8PT29vb18PDwmzZtmpNa/ehailqtFjNnznR0cnLq6uXl5RsfH28MADdu3DCQSqW+xsbGzzXl2TpNyxClFC8ffRkmfBOIBCKIBKKaPuvq1/q+FvKF7WZtnQp1BY7eOYroG9FIL0mHnYkdlgYsxUTPiRAKhPoOj2EYhmnH1q9fbxUcHCzj8XgQCoXaHTt2pHXr1q0yPT2dHxgY6DNhwoSSurvWA8DYsWNl0dHRmY29z5EjR25bWFhotVotRo8e7R4ZGWk+d+7ch1a53rdvn/jOnTuG6enp18+cOWMyf/58p2vXriX7+flVJicnJ7Jk6DHUWjWcRE4oVZYiT5GHO0V3UKoqRamyFFr6yB5wDzHhmzyUJFUnSnXL6is34Zu0+picwopC7Eneg9iUWBRWFMLX0herB67GcOfhnX53Z4ZhGKZl7N271zI2NvYOAHTv3r1mh3oXFxeVhYWFOjs7m1dfMtRU1fuOqVQqolKpSH1rTx05csQsNDS0gMPhYOjQoWUlJSW8jIwMvrOzs6o59+w0fyn5XD7+O+S/j5RrqRYKlQKlylKUKEtQqix97NfVr9nybNxU3qw6ript8L4EBEKBsMHk6XGtU6YCUxjxjB67CFlacRqiE6Nx9PZRVGoqMchhEGb4zUCAJIAtXMYwDNNB/RKd5Fh4T96ieyJZ2AsVQ1/zeewGsBUVFSQrK8vA29tbWffYmTNnjFUqFfH19a2s79xjx46ZeXl5Cd3c3Cq+++67LA8PjycmLP379/e8du2ayaBBg4rr2/ssOzub7+LiUhOLnZ2dslWTIUKIIYD/ATDQ1d9PKV1JCHEFEAvAEsAlANMppUpCiAGAaAD+AAoAhFBK03XXeg/AbAAaAG9RSk80J+iWxCEcCAVCCAVC2MGuyedrtBrIVfKaxKl2AlU7oapdnlGSUfNeoVY0eH0u4dbbhVemKsO5++cg4Agw1n0sXvN7DW5it+Z+GxiGYRjmsXJycngikeiRwTsZGRn8sLAwt4iIiDQu99EhJZMnTy6aM2dOoZGREf3Pf/5j9eqrr7peuHDh5pPuFx8ff0uhUJAJEya4HT161HTChAklLfQo9WpMy1AlgBcppXJCCB9APCHkGIAlAP5LKY0lhPyAqiRno+5VRin1IIRMAfAVgBBCiC+AKQD8AHQBcJoQ4kUpfeomNX3icrgQG4ghNhA363yVVgW5Ut6oFqnq/x4oHkBDNXizx5sI8Q5hK7cyDMN0Ig214LQWExMTrVKpfGjMR2FhIWf06NEeK1euvDd06NCy+s6ztbWt+Ru/ePHi/I8//tihsfc0NjamY8eOLTp06JBZ3WTIzs5OlZ6eLqh+n52dLWhuqxDQiGSIVu3kKte95ev+owBeBDBNVx4F4CNUJUPjdF8DwH4A3+k6/MYBiKWUVgJII4SkAugF4Hxzg+8I+Bw+zA3NYW5Y70B8hmEYhtE7a2trjUajIQqFghgbG9OKigoSFBTkMWXKlIL6urGq1e66iomJMXNzc6uoPubq6uqXlpZ2o3b94uJiTlFREdfZ2VmlUqlw7Ngxcb9+/R4ZjxIcHFy0YcMGmzlz5hSeOXPGRCQSaVo1GQIAQggXVV1hHgC+B3AbQBGltLrJ7C6A6lW07AFkAQClVE0IKUZVV5o9gAu1Llv7nNr3mgtgLgA4OTk18XEYhmEYhmkNAwcOLD558qRw/PjxpZGRkeYXL14UymQyXkxMjBUAREZGpvXt27c8PDy8S2BgYFloaGjx6tWrbU6cOGHG5XKpmZmZevv27ekAkJ2dzaOUPjK4taSkhBMUFOShVCoJpZT07du3ZOnSpXkAsHr1amsAWLZsWd7kyZOLf/rpJ7Gzs3NXIyMj7datW9Of5tlIVcNPIysTYgbgEIAPAWynlHroyh0BHKOUdiWEXAcwilJ6V3fsNoDeqGotukAp3akrj9Cds/9x9wsICKAJCQnNejCGYRiGaY8IIZcopQG1y65evZreo0ePfH3FBADx8fHGa9askRw+fDjtaa+1e/du8e3btw0++OCDBy0RW13GxsbPKRSKy3XLr169atWjRw+XuuVNmk1GKS0ihJwB0AeAGSGEp2sdcgBwT1ftHgBHAHcJITwAYlQNpK4ur1b7HIZhGIZpd7QKBYjR42f9diT9+/dXJCQklKjVavB4TzcZferUqcUtFNZDbty4YTBp0iR3S0vLJnWZPXEBHEKIta5FCIQQIwDDASQBOAPgZV21GQCO6L7+UfceuuO/6sYd/QhgCiHEQDcTzRPAX00JlmEYhmHaAlVODh58/TVuDR6C8kuX9B3OMxMeHl7wtIlQa6pedDErK+t6U85rzBPZAYjSjRviANhLKY0jhCQCiCWEfArgMoAIXf0IADt0A6QLUTWDDJTSG4SQvQASAagBLGjvM8kYhmGeBapSoeTnn6GWySAODgbPwkLfIXVaFYmJKNi+HSU/HwO0WohGjABX3LzZxEzb0ZjZZNcAPLKsNaX0Dqpmg9UtrwDwymOu9RmAz5oeJsMwTOejVShQtP8ACrZtgzo7GwCQ9/VamL40GuahoTDq3l3PEXYOVKuF/LffULhtOxR//QWOsTEsQqfBfPprEDg8Mg+IaYfablsXwzBMJ6UpLkbhrl2Q7dgJjUwGowB/2K36CPwuXSDbHYviQ4dQfORHGHbrBvNp02D60mhwDAz0HXaHoy0vR/GRH1EYFQVlWhp4trawWboUZpNfAVck0nd4TAtiyRDDMEwbocp9gMKoKBTFxkKrUEA4aBAs582F8fPP19Sx/fADWC9ejOIfj0C2KwbZ772HB199BbNXXob5lCng27OWiqelzs+HLCYGspjd0BQVwdDPD13WrIHpyBEgfL6+w2NaQevuIMowDMM8kTIjA9kfrsDtYcNQuH07hC++CNcjh+G46YeHEqFqXKEJLKZNg1vcUTht3wbjwEAUREQidfgIZC1YCPkff6Apy6YwVSpv3cL9999H6pAXkb/xBxj5+8N5RzRc9u+DeExQp0+E5HI5CQwM9Far1bh586bA19fXRyqV+np4ePhVrwFUV25uLrdv376ezs7OXfv27euZl5f36J4dtTT2ukuWLOliY2PTXSqV+kqlUt89e/aIAeD48eNCd3d3P09PT7+mPFuT1hl61tg6QwzDdGQViYnI37IFpSdOgvB4EE+cAMvZsyFwdHzyyXWo7t+HbM9eFO3bB01hIQSurjCfOhXiCeNZl04DKKUoO3cOhdu2oyw+HsTQEGYTJ8B8+nQYuLrqJaa2us7QF198Ya1Wq8mHH374oKKiglBKYWRkRIuLizm+vr5+f/zxR7KLi8tDU9rfeOMNBwsLC/Xnn3+es3z5cluZTMbduHHjY5fVaex1lyxZ0kUoFGo+/vjj3LrXSElJEYwZM8bz1q1bN+oea5F1hhiGYZinQymF4uJFFGzZirLffwfHxASWs2fB4rXXwLOu90Nwo/C7dIHN4nBYLZiP0uPHUbhrF3I//xwPvvkG4nHBsJg2DQaeni34JO2bVqlEydE4FG7fjspbt8C1toJ1eDjMQiaDZ862R6rP3r17LWNjY+8AgKGhYU1LSnl5OdFqtfWec/z4cbPffvstBQDmzZtXMGjQIG80sMZgY6/b0lgyxDAM8wxQrRbys7+hYPNmlF+5Aq6FBawXL4b51Cngmpq22H04AgHEwcEQBwej/J/rkMXEoPjAQRTtjoVxr14wnzYNoqEvdtouH7VMhqLYWBTuioEmPx8GXl6w++ILmAa9BI5A8OQLtAEnNn7jmJ+VYdyS17RydFaMfDP8sRvAVlRUkKysLANvb29ldVlqair/pZde8szKyjJYsWLF3bqtNwBQUFDAq94zzNHRUVVQUPDEvKMx1wWAiIgIm9jYWMsePXooNmzYkGVtbd3s5XrYmCGGYZhWRNVqFB89irRx43F3/nyoHzyA5MMP4PHrL7CaN7dFE6G6jLp1RZcvPofHb2dh887bUN29i3vh4UgdNhz5GzdCna/XXpdnqjItDdkffYTUIS8ib923MPT1gVNkBFyPHIbZhPHtJhHSl5ycHJ5IJFLXLvPw8FDdvHkzMSkp6XpMTIxVVlZWg4kOh8Np1Erdjbnu4sWLH2RkZPyTlJSUaGtrq5o/f37T+5ZrYS1DDMMwrUBbUYGigwdRGBEJ1b17MPD0QJfVX8F09Ohn3irDMzeH5euvwyIsDPLf/gfZrl3IW/ct8jZshOnIkTAPnQajnj073JYS1V2Shdu2Q372LAifD9PgsbCcMaNddxk21ILTWkxMTLRKpbLeBhQXFxeVVCotP336tKjuDvaWlpbq6p3rMzIy+BYWFur6rtHU6zo6OtZcZ+HChXljxox5qn9Q1jLEMAzTgjSlpcjftBmpQ4ch9+NPwLOygsOG7+F65AjEwcF67Z4iXC5ELw6BU8RWuB37GeZTp0B+9iwypk5D+qSXUXTgALQVFXqLr6VQlQrFR+OQ/vIryHxtBsqvXIHV/Pnw+PUXdPn003adCOmLtbW1RqPREIVCQQDg9u3bfLlcTgAgLy+Pe/HiRaGfn98jPzwjR44s2rRpkyUAbNq0yXLUqFFFAHDmzBnjCRMmuNSt39jrZmRk1PyPFBsba+bt7V3+NM/HWoYYhmFagDo/H4VR0ZDt3g2tXA6T/v1hOXcOjAMD22SLi4GrK2yXL4fNokUoPnq0as2i9z/Ag9X/gXjSJJhPndKsWW36pCkpQdG+fSjcsRPqnBwI3Nxg+/EqiIODwTE01Hd47d7AgQOLT548KRw/fnzptWvXjN59910HQggopVi4cGFOr169ygEgJCTEecGCBXkDBw5UrFq1KnvChAnuzs7OVvb29spDhw7dBoD09HQDIyOjR6azN/a6ixYtckhMTDQCAAcHB+W2bdsynubZ2NR6psOjWi2oSgWqVDbrVatUArrXmnKlqt66PEsLGEilMPTxgYGnJ/sF3Ako795FQUQEig8cBFWpIBo1ElZz5sDQ11ffoTVJdZeSLGY3Sk+dArRaCAcNgnnoNJj06wfCabsdCcq7d1EYHY3i/QegVShg/MILsAybCZMBA9p03I/TVqfWx8fHG69Zs0Zy+PDhtKe91rx58xxmzZpV0Lt376dq0akPm1rPtDuVd9KguJTwaILR1MRFVXW+VlVd/v/XgrrRXdSNw+WC8PkgAsHDr3w+yv7IhTZmd1U9DgcCN1cY+vjCUCqFoY8UBj4+bNpuB1GRchMFW7ei5OefQTgciMePh+XsWRC4uOg7tGYhhMCkVy+Y9OoFVW4uivbsgWzvPsjnzAXf2QnmU6fCbOLEVh3w3VSKy5dRuD2qKnnjcCAOegkWM2fC0MdH36F1SP3791ckJCSUqNVqPO3O9Zs2bbrbQmE95Pjx48K33nrLydzcvEm/+FnLEKMXir//RsHWCMh//bX+CjxenYSjKtngCAQgfEH9yUhjXnXXqXuM0+A5dcq4j19AlVIK1d27qEhKQmVyMiqSklGRnFyzySYA8GxtYSiVwsBHCkOpDwx9pOA7OLTLT7CdkeLvyyjYvLlqQK6xMcxDQmAxcwb4Eom+Q2txVKlEyclTkO3ahfLLl0GMjCAeOxbmodNg6O2tn5g0GpSeOo3C7dtRfuUKOKamMA8JgfmroR3m36Cttgx1BKxliNE7qtGg9NdfURgRWbXOilgMq/lvQjxuHDgi0f8nHDxegwlHW0YIgcDRsWqsxYgRNeVqmaxWcpSEyqQkyH//HdBULYvBEQphIPWuSY4MfXwg8PBg033bCEopyn7/HfmbN6M84RK4Zmaw+tdCWISGgmtmpu/wWg0RCCAeEwTxmCBUJCaiMCYGxUeOoGjvXhgF+MMiNBSiYcOeyaBwjbwMxQcPoDB6B1R374Lv5ATJBx9UTYs3MWn1+zMdG2sZYlqdtqICxYePoHDbNigzMsB3cIDFzJkwmzgBHOMWXTesXdFWVKDyVqouOUpGRVISKlJSQBWKqgo8Hgzc3au62Hx9YCD1gaHUG1yxWL+BdyJUo0HJ8eMo2LIVlcnJ4NnawnJWGMxefrnT/uxqiopQdPAQZLt3Q5WVBZ61NcxCQmA2+RXwbWxa/H6qnBwU7tiBor37oC0thZG/PyxmzoDoxRfb7YemJ2EtQ63ncS1DLBliWo1aJoNs927Idu6CprAQhl27wnL2LIiGDwd5yv7mjopqtVBlZqIiORkViUk1iZI6L6+mDr9LFxj4+vx/K5JUCl6XLm1yxlJ7pa2sRPHhIyiIiIAqMxMCNzdYvv561WadrLUOQFWiKP/9d8hiYlD2v98BHg+mI4bDPDQURs8//9Q/j+U3bqBw23aUHD8OUArTkSNgMXMmjLp3b6EnaLtYMtR6WDcZ88wos7JQuD0KRQcPgpaXw2TQQFjOmg3jXm1zinFbQjgcCFxcIHBxgemoUTXl6vx8VCSnoCIpsaoVKTkZ8l9+BXQfZjhicVULUvVYJB8fGLi5ddotF5pLIy9D0Z5YFG6PgjovD4Zdu8Lm23VVXUFsTNdDCJcL0eDBEA0eDGVGBmS7Y1F08CBKfj4GA6kU5tOmQjxmTJNa0Kq3LCnctg2KixfBMTGBxauvwmL6q+Db27fi0zCdHWsZYlpM+T//oCAiEqUnTwJcLsRjxsByVhhb4KyVaBUKVN66VdW9Vj0WKeUmqG7RPMLnw8DT86GB2gZSKbhCoZ4jb3vUhYUo3LEDsl0x0JaUwLjPC7CaOxfGL7zAEvgm0CoUKI6LgyxmNyqTk8ExNYXZhAkwnzYVAmfnx59XXo7iI0dQuD0KyvR08LrYwWL6azB7eRK4ItEzfIK2oa22DMnlcjJkyBCv8+fPp9y5c0cwfvx4d61WS9RqNZk7d+6DZcuW5dU9Z8mSJV127txpVb3y9KpVq+6FhIQUP+learUa3bp187W1tVWeOXMmte7x8vJy8vLLL7v+888/xmZmZup9+/bd8fb2Vh4/fly4YMECZw6HAza1nnlmqFYL+f/+h8KIyKpPckIhLGeFwXz69A4zs6Ot4hgbw6hHDxj16FFTRjUaKDMyUJGYhMrkqiRJfuYsig8crKnDd3J6aKq/oY8PeDY2nfKPvur+fRREbkPR/v2glZUQDRsGy7lzYNStm75Da5c4xsYwnzwZZq+8gvK//4ZsVwwKd+1CYVQUTAYOgPm0aRAOHFjTyqbOy0NhTAyKdsdCU1QEw27dYL/2a4hGjGBd6W3Q+vXrrYKDg2U8Hg9OTk6qS5cuJRsZGdHi4mKOr6+v3+TJk4vq21T1jTfeyP34449zm3KvTz/9VOLh4VEul8vrHRi2bt06K7FYrM7MzLy+efNm8yVLljj89NNPd0aNGiX/+eefbzV1ew7208Y0i1apRMnROBRsi4Qy9TZ4trawWbYMZpNfYS0PekS4XBi4ucHAzQ0YEwSgaiaU+kFeTXJUkZyMiqTEqhY8Ha65ua7lqCo5MpR6g2tpCY6xMYiBQYdLlCpTU1GwNQLFcXEAAPHYsbB8fTYM3N31HFnHQAiBsb8/jP39YfPgAYr27kPRnj24+8ab4Ds6wmzyK1CmpaPk6FFQtRqiYUOrxgO1wFgjpvXs3bvXMjY29g4AGBoa1nQrlZeXE61W22L3uX37Nv/EiRPi9957L/u///1vvZ+q4+LizD766KP7ABAWFiZ79913nbRaLTjN7M5myRDTJJrSUhTt2YPC6B1QP3gAAy8vdPnqy6rNJ9nA0jaJEAK+xAZ8iQ2EgwbVlGvkZai8mfLQmkiynTurFqqsjcsFx9gYHBOTqtfaX9d9rf7apOE6+vrUX37tGvI3b4b89C8gRkYwnzYVlmFh4NvZ6SWezoBvYwPrhQtgNW8uSk+fhmxXDPK+XgtiZASzyZNh8dr0BrvQmEcV7r/pqMopa9HpjHxbE4XFy16P3QC2oqKCZGVlGXh7e9f8gkhNTeW/9NJLnllZWQYrVqy4W1+rEABERETYxMbGWvbo0UOxYcOGLGtra01DsSxYsMBx9erVd4uLix87XTA3N1fg6uqqBAA+nw+hUKjJzc3l2dnZNWuV3Sf+RiKEOAKIBiABQAFsppSuI4R8BGAOgOo+wuWU0p9157wHYDYADYC3KKUndOWjAKwDwAWwlVL6ZXOCZp49VXY2CqOiUbRvH7RlZTDu8wLsPvsMJv37sU9y7RRXaALj55+H8fPP15RRlQqVaWmovHkLmuIiaBUKaMvK6n1V5ebUKlP8/5IAjUAMDBpOpmonVcZ1kqt6ziNGRo/9OaSUouzcORRs2QrFhQvgiMWwmj8f5tNfZauBP0OEz4fp6NEwHT0aysxMcMVitkxEO5KTk8MTiUQPJRoeHh6qmzdvJqanp/PHjh3r8eqrr8pq7yYPAIsXL36wevXq+4QQhIeH28+fP99x37596Y+7z+7du8VWVlbqAQMGKOLi4p7ZgLHGfDxTA3ibUvo3IUQE4BIh5JTu2H8ppWtqVyaE+AKYAsAPQBcApwkhXrrD3wMYDuAugIuEkB8ppYkt8SBM66hISUFBRARKfj5WNb111ChYzAqDkZ+fvkNjWgHh82Ho5QVDL68nV66DarXQKsqhVZQ9NoGiCgU0ZWX1v8pLoX6QW/W+TAGNQgGo6v2gWU/g5LGJkurBA1QmJYFnY6Pryp0MrpAt0qdPAicnfYfQrjXUgtNaTExMtEqlst4+KBcXF5VUKi0/ffq0KCwsTFb7WO3kaOHChXlPGssTHx8vPHXqlJm9vb24srKSU1ZWxhk3bpzrkSNHHtoPTSKRKNPS0gTu7u4qlUoFuVzOlUgkzd576YnJEKU0G0C27utSQkgSgIbmOI4DEEsprQSQRghJBdBLdyyVUnoHAAghsbq6LBlqYyilUJw/j4KISJT98UfVlgPTpsLitRkQOLDprUz9CIcDrtCkRRMNqlTWm1Q9LqGqqVNW9arOywPh86t2Lh8/nq3ozTDNZG1trdFoNEShUBBjY2N6+/ZtvkQiUQuFQpqXl8e9ePGicOnSQSxnYgAAIABJREFUpY8Mks7IyOA7OzurACA2NtbM29u7HADS0tL406ZNcz1//vzN2vW///77e99///09AIiLixN9/fXXkrqJEAAEBQUVRUZGWg4bNqxs27Zt5n369Clt7nghoIljhgghLgCeA/AngH4AFhJCXgOQgKrWIxmqEqULtU67i/9PnrLqlPduVtTNpJGXsU+EDaBqNUqOHUfBtkhUJiaBa2UF6/BwmE8J6dBbDjBtFxEIwBUI2M8fw7QBAwcOLD558qRw/PjxpdeuXTN69913HQghoJRi4cKFOb169SoHgJCQEOcFCxbkDRw4ULFo0SKHxMREIwBwcHBQbtu2LQMAsrKy+Fwut0lr+4SHh3cJDAwsCw0NLV60aFH+pEmTXJ2cnLqKxWLNnj17bj/NszV6nSFCiBDAbwA+o5QeJIRIAOSjahzRJwDsKKWzCCHfAbhAKd2pOy8CwDHdZUZRSl/XlU8H0JtSurDOfeYCmAsATk5O/hkZGU/zfDWoWo2UgEDwLCxg6OdbNaXY1xeGPr7g2Vh36nEv2rIyFO3fj4KoKKjvZ0Pg6gqLWWEQBweDY2Cg7/AYhmE6lba6zlB8fLzxmjVrJIcPH36kpaapPv/8c2tnZ2dlaGjoE9ccaqqUlBTBmDFjPFt8nSFCCB/AAQC7KKUHAYBSmlvr+BYAcbq39wA41jrdQVeGBsprUEo3A9gMVC262Jj4GoOqVLBeuAAViYmoSExC6anTNce4VlZV04mrEyRfH/AdHTt8gqTOy0Phjp2QxcZCW1ICI39/2H7wAYSDB7PVdhmGYZiH9O/fX5GQkFCiVqvBe8oZocuXL39kgcaWcPz4ceFbb73lZG5u3qTxQ42ZTUYARABIopSurVVupxtPBAATAFzXff0jgBhCyFpUDaD2BPAXAALg/9h78/g6yvtQ/3nnrDpHR9LRLsuSN3nBNhiMsFkNJCwmTUOWNiFpmqQkF9qSm+Y2ub+StE1ulubSXm57k95bGtKQ5TaBpG0g3CwQIBAgbJbBxhvesI0XyZZkbUdHZ5t5f3/MnE2bLUs+50j6Ph/OZ2beeWfmHQlLj77v933f5UqpJdgSdBvwoak0djoYZWXUfOITmWMzMkx87xvEdu22Z/DdvZveF1+ElP31M0IhZ4FMW478q1fjXbJkTkwEFn/zTXofeIDBnz7qzPFxAzUfv52yiy8udtMEQRCEEubTn/50b7HbMBmbN2+O7Nu3b8q5yGfzm/0q4A+BHUqpbU7Z54EPKqUuxu4mOwzcCaC13qWU+jF2YnQKuEtrbQIopT4JPI49tP4BrfWYEFahcJUHM5OCpbHiceL79hPbs9uOIO3ZQ99DD6HjccAeDuxbtTIbQbpgNb4Vy2dFV5LWmpGtW+n99gNEnn4a5fNR+b73UvOxj+FdvLjYzRMEQRCEonE2o8mex47qjOYXk1zzN8DfjFP+i8muKzaGz0fZhWspu3BtpkynUiQOHcp0r8X27GHw57+g/6Ef2RXcbnzLluV1sflWXVAyidraNBl68il6H/g2se2v46qqsudY+YMP4a6pKXbzBEEQBKHozP4+n/OMcrvtxS6XL6fy1lsBO8qSPHYsr4st8txzDDzyiHORwtvamp+ovXp1QSd4s2IxBh5+mN7vfpfkkbfwtLTQ8Nd/RdV734tRVlawdgiCIAhCqSMydA4opfC2tOBtaaFi881Adv2ndBdbfM8eRrZttycrdHA3NeVFkPyrV+NuaJjRRO1UXx99P/ghfT/4AWZfH/4LL6T+f/0DoRtvRLkmnNlcEARBEOYtIkMzRO76T6HrrsuUm/39TvRoTyYPKfL00+BMaeAKh7Ny5IiSp7V1yqO5Em+9xenvfpf+nzyMjsUov/Zaqj9+O4HLLpvzo+IEQRCE808kElHXX3/9ihdffHFvejTZ6dOnjQsuuGDtzTff3P/973//rdHXnDx50vWe97xn6fHjx33Nzc3xn/70p29OtjbZ4cOHPX/8x3/c8thjj705WVseeOCB8Fe/+tUFdXV1yS996Usn7rrrrkWGYTDecPqzQWToPOOqqiJ4xRUEr7giU2YNDxPbuy8vUbv3u9/LLD1gBIP4LliF/4LV2TykpUtRHs+Y+4+8/jq9336AoSeeAJfLXn379j/C19ZWsHcUBEEQ5j7/+I//WPuud72rL3dY/Wc+85nmDRs2DE10zRe/+MWm6667buhrX/va/s9//vONX/jCFxrvu+++MdPqpFm8eHHyTCIE8J3vfKf2vvvuO3LzzTdHAH7xi1/sP9NSH5MhMlQEjGCQwPpLCKy/JFNmJRIkDhzIJmrv3k3/v/87emQEsGfi9a1YkZEjV0UFfT98kGhHB0YoRM3Hbyf84T/E01BfrNcSBEEQ5jA//vGPax566KGMqDz33HOB7u5uz0033TTQ0dEx7qihxx57rOo3v/nNXoA777yz99prr13JOHMMpsmdMPEb3/hGzc9+9rOqkZER46233vLdcsst/f/8z/987LOf/WzT1q1by++8887FN998c/83v/nNY9N9N5GhEsHwejOJ1mm0aZI4fDivi23wscfo//GPATsHqf4v/oKq3//9khm9JgiCMF/QWtN7PEJVQwC3p3A5mY888kjLqVOnAjN5z/r6+ui73/3uCReAjcVi6ujRo76VK1cmAEzT5DOf+UzLgw8++ObPf/7ziomu6+3tdafXJmtpaUn29vZOyTt2794d2L59++6ysjKrra1t7Wc/+9mT9957b+ezzz5bce+99x7dtGlTdCr3mwiRoRJGuVz4li3Dt2wZlb/7TsAZyXb8BKnOE5RdfPG4XWeCIAjC+WPodIx9r3Sx75WTnD4xzOY717Lskrkdle/q6nKHQqHMrM5/+7d/W3fTTTf1L1u2LHm29zAMY8o5rFdfffVgTU2NCdDW1hY7ePCgr62t7ayfebaIDM0ylFJ4FzbL6vGCIAgFJDac5OCrp9j3yklO7O8HoHFpJZtuW0Hz8sJNmwIwWQTnfBEMBq1EIpEZ2fPSSy+Vb9mypfw73/lOfTQaNZLJpFFeXm7+0z/9U14XWE1NTSq9cv2RI0c81dXVU1omw+v1ZpblcrlcOplMnpcRQSJDgiAIgjAOqaTJkR297H25iyO7erFSmqqGABvftYTllzVSWTd/5myrq6szTdNU0WhUBQIB/eijj2YWa/3GN75R09HRERwtQgBOTk/N1772ta5vfvObNZs3b+4HePrppwPf+MY36h9++OHDBXyNCREZEgRBEAQHbWmO7+9n3ytdHHy1m8RIikCFlws3LWTFxgbqWkPzdrqSTZs2DfzqV78qf/e73z3h6DGAD3zgA4vuuuuu7k2bNkW/9KUvdb7nPe9ZtmjRotrm5ubEww8/fBDg8OHDvrKyshlbjH26KK1Lpi1jaG9v1x0dHcVuhiAIgjCHSSdC73v5JPu2nGS4P47H52LpJXWs3NBI88oqDNfU5n6bDkqprVrr9tyy7du3H163bl1PwRoxDs8//3zg3nvvbXjkkUcOnbn25Nx5550Lb7/99t6NGzeOzETbckehTVZv+/bttevWrVs8ulwiQ4IgCMK8ZHQitGEoWtdUc9X72li8rhaPV2btz+Xqq6+OdnR0DKZSKXLnGjoXZmI4fJrHHnus/FOf+lRrOByeUj5SLiJDgiAIwrxh/EToCjbdtoK29nrKyr1FbmFp8+lPf7q32G0YzebNmyP79u3bPZ17iAwJgiAIc5p0IvS+V05yeGdPJhF6w+8uYcWGBirrZnTKnvOFZVmWMgyjdHNbShzLshRgjXdOZEgQBEGYc4yXCF02uxOhd3Z3d6+uq6sbECGaOpZlqe7u7kpg53jnRYYEQRCEOUPPsQj7Xu5if8dJIn3ZROgVGxpYuDJc0ETomSSVSn2iq6vrX7q6utYCs/MliosF7EylUp8Y76TIkCAIgjCrGTodY/+Wk+x9uSuTCN2yppor3zt3EqEvvfTSU8C7it2OuYrIkCAIgjDrmDQR+tJ6ykKSCC2cPSJDgiAIwqxgjiRCCyWIyJAgCIJQsmhLc2J/P3vnTiK0UIKIDAmCIAglR8+xCPte6WL/FjsR2u1zseziOlZsnN2J0EJpIjIkCIIglATpROh9r3TRe3wY5cwIfcV7l7Hkojo8vtmfCC2UJmeUIaVUC/B9oAHQwP1a668rpaqBHwGLgcPA+7XWfcqOV34deAcQBT6mtX7VuddHgb9ybv1VrfX3ZvZ1BEEQhNlEPJrk4Kvd7H25K5MI3bBEEqGFwnI2kaEU8Bmt9atKqRCwVSn1BPAx4Cmt9T1KqbuBu4G/AG4BljufjcB9wEZHnr4ItGNL1Val1KNa676ZfilBEAShdEklTY7sdBKhd0gitFB8zihDWutOoNPZH1JK7QGagVuB65xq3wOewZahW4Hva6018JJSqkop1eTUfUJrfRrAEarNwIMz+D6CIAhCiZCIpeg/GaWvK0pf5zB9zv7AqSiWqSmr8LJ2UzMrNzZKIrRQVKaUM6SUWgxcArwMNDiiBNCF3Y0GtigdzbnsmFM2UbkgCIIwS9FaEx1M0NcVpb9rmNPOtq8rSqQvnqmnDEVlXRnhxgBLLqqheUWYhaskEVooDc5ahpRS5cB/AJ/WWg/mGrzWWiulZmStFKXUHcAdAK2trTNxS0EQBGGaWKbFYE+MPkd0stsoiZFUpp7H5yLcGGDBiirCDUHCTQHCDUEq68twuUV8hNLkrGRIKeXBFqEfaK1/4hSfVEo1aa07nW6wU075caAl5/KFTtlxst1q6fJnRj9La30/cD9Ae3u7LEYnCIJQQPK6tnKEJ921lSZQ4SXcFGDFZQ0Z4Qk3BQhW+aS7S5h1nM1oMgV8G9ijtf77nFOPAh8F7nG2P80p/6RS6iHsBOoBR5geB76mlAo79W4CPjczryEIgiCcLaO7tnLFZ7yuraoGu2urqiFIuDFAuDGAL+Ap4hsIwsxyNpGhq4A/BHYopbY5ZZ/HlqAfK6U+DhwB3u+c+wX2sPoD2EPr/whAa31aKfUVYItT78vpZGpBEARh5pmoa6v/ZJR4NNu15fa5CDcEWLC8inBjWniCVNaV4fJI15Yw91H2oK/SpL29XXd0dBS7GYIgCCXN6K6t/q4ofSej9J+KYqVGdW05olPVGKDa2ZZX+VCGdG2VCkqprVrr9mK3Yz4hM1ALgiDMAnTnDqKdx+gPbKDvVCw7VL1zOL9rS0FFXRnhxiCL1tZk5achgD8oXVuCMB4iQ4IgCCWIZVr0HIvQeWCAzld30XlomKgVBrYD4PYahBuDTtdWNtpTVReQri1BmCIiQ4IgCCVAIpbi5KFBOg/003lwgK5Dg6TiJgDlRj/N4R4aVijCxx4kHHuV8iUrUTd8EVrXFLnlgjD7ERkSBEEoAsP9cToPDmTkp+dYBG1pUFDTXM4FG2pp7P0RTd0/IHTpZnjnP4DbC6l3wqvfg2f/BzxwE6zYDG/7a2hcW+xXEoRZiyRQC4IgnGe0penritJ5sN/u9jrYz2BPDAC3x6BhSQVNbVU0LaukYWklvsRJeOiD0Pk63PQVuOKTdjJQLolhePmb8Nv/BbFBuPD34PrPQ/XSIryhMJNIAnXhERkSBEGYYVJJk+4jQ3mRn/RQ9rKQh6ZlVTS1VdK0rIra1nJcuUtSHNsKD30IEhF437dh5ebJHzbSB7/9Brz8z2AmYP1HYNP/BxVN5/ENhfOJyFDhERkSBEGYJrFIks43B+hyIj8njwxmhrRXNQRoWlaZkZ/K+rKJZ2je8e/w07ugvB4++CNoWH32jRg6aXedbf0uGG7YeAdc9WkIVE//BYVx6e/q5NC2DlZcfjXBqvCZLzhLRIYKj+QMCYIgTAGtNYM9MbvL6+AAnQcG6OscBsBwKepaQ1x03UKa2qpoXFpJoMJ75ptaFvzmHvjN30LrlfCB/wvB2qk1LNQAv3MvXHEXPHOPHS3q+C5c9Z9h45+Ar3zqLyvkkUzEObZ7J4e2dXB421b6Ok8AUBaqYNVV1xa5dcJ0kMiQIAjCJOQNcXcEKDqQAMBb5qZxaTrqU0n94go8XtfUHpCIwiN/DLt/Chd/OJsoPV1O7oZffxX2/hyCdbDpv8KlHwO3b/r3nkekoz+Htm3l6K4dpBJx3B4vLWsuZPHF7Sy55FLCjQtm9JkSGSo8IkOCIAg5TDrEvdpH07IqFrRV0risiuoFQYzpzNw8eAIe/CB0bp84UXq6HN0CT30JDj8Hla1w/efgog+AMUVpmydMFP2pamxiySXtLLm4nYWr1+Lxnj+pFBkqPCJDgiDMa840xH3Bskq7y2tZJaFq/8w9+PhWeHAKidLTQWt482l48kvQuQ3qVsHb/gpWvXPm5WsW0td1gkOvbeXwtg6O7t5ZkOjPZIgMFR6RIUEQ5g1THuJedp7SKnf+Bzzyp+eWKD0dtIY9j9rdZz37oPlSePsXYOl1hXl+iTBR9CfctIDFF19akOjPZIgMFR6RIUEQ5izJhEn3W0N0TTTE3RGfcYe4nw8sy06S/s090HoFfOBfp54oPROYKdj+oJ1oPXgMllwLN3zRlqM5SqlFfyZDZKjwiAwJgjAniEeT9ByN0H10yP68FaG/a5j0j7iqhkAm0fmMQ9zPB4koPPInsPsRJ1H674ufzJyMQccD8Ny9EO21u83e9tdQv6q47ZoBcqM/h17roL+rEyid6M9kiAwVHpEhQRBmHSNDCbrfykpP99EhBrtHMueDlV7qWkPUtoaoawnRtKySstAMjNA6V3ITpW/8Mlz5n0srVyc+BC/+E7zwj5Achotug+vuhvCiYrdsSuRFf3btIJVM2NGftRexxBGgqsbSn4xSZKjwiAwJglCyaK0Z7o/b4vPWEN1HI/QcHSLSF8/Uqaj1U9fiiI8jP2c1t0+hKGSi9HQZ7oXn/x5e+RZoC9pvh02ftXObSpBkIs6xXTs4tG0rh7bNrujPZIgMFR6RIUEQSgJ7MsMRO9LjRH16jg4xMpS0KygINwSobXGkpzVE7cJy/EFPcRs+GXmJ0g9BwyxZYX7guJ3b9Nq/gtsPl/8JXPUp8FcWu2VzJvozGSJDhUdkSBCEgmNZmv6uqNPNZX96jkVIjNjJzYahqG4O2uLjyE9NcxCvf5ZMmp+bKN1yuZ0oXV5X7FZNnZ4D8PTfwK6fgL8Krv4vsOEO8AYK1oS5Gv2ZDJGhwiMyJAjCecVMWZw+MZwnPr3HIqSSFgAuj0HtwnK7q6ul3BafBeW4POd5ZNf5Ii9R+g+cGaVn+S/qzu3w1FfgwBMQarJns17/EXCdn6hcOvpzaFsHx9LRH6+PljUXzpnoz2SIDBUekSGhqJhJi1TSxFvmLuzIHuG8kEyY9B6L5CQ3D3H6xDCWaf+c8fhddqSnJURtqy1A4cYAxvke0l4o8hKlvwRXfqq0EqWny5EX7Ikbj74E4SVw/V/C2veBMb3v35miP0svbqd5jkV/JkNkqPCIDAnnDcu0GB5IEDkdI9IXZ6jP3kZOxxjujzPUF2dk0F7jyeU2CFR6CVR4CVb6CFR6CVZ6CVT68srKQt7pLX8gzBjxkRQ96WjPOEPZ/UEPda12pCfd3VVZV4aaq9+/46/aIpSIwPv+BVbeUuwWnR+0hv2/gqe+DCd3QsNaezj+ipunJH59nccd+dk6L6M/kyEyVHhEhoRzQlua6FCCyOk4kb4c2ck5jg7EGf2/l8fvojzsJxT2UR72UV7tx+11MTKYYHgwTnQgwfBAguhAPDM5Xi5KQVnI68iSL7vNEaZApZdghW/2drOUIFMdyl7XGqI87Js/0b6dP7G7xoL18KFZlCg9HSzLziX69Veh75CdG/X2L8DiqyaobtK5by8HOl7iYMdLObM+Nzvyc+m8iv5MhshQ4TmjDCmlHgDeCZzSWq91yv4b8J+Abqfa57XWv3DOfQ74OGACn9JaP+6Ubwa+DriAf9Fa33OmxokMFQetNbFI0hYcJ6qTFpz0drg/nun6SOP2GJRX+23JqfJl98P+jPhMZXmDVNIkOpAgOphwJClOdNDZDmS3I0OJMdIF4Au4CVT6nAiTLUi5EpUWKI/fNX9+aedgmRbJhEUqbpKMmyQTJinnOD6S4nTnsJ3YPNuGshcSre1ZnGd7ovR0MJPw2v+FZ/4WIl3QdoMtRU3rSCbivLVjOwe2vMTBrS8zMjiA4XLTuvYilq6/bF5HfyZDZKjwnI0MbQIiwPdHyVBEa33vqLqrgQeBDcAC4ElghXN6H3AjcAzYAnxQa717smeLDM08WmsSI6mJRed0nEh/HNNJbk1juFS+2OQITnnYRyjsxxcsTt6PZWlGhvKFKToQdyJMOfI0GMdKjf3/3e01stJUkZWn7L699Qc9Be3i0VqTSmZlJZWwbGFxxMUuM0nGLXubOWdl6uTXdeo5ZeN9LfKYjUPZC0kiCj/9U9j1MKz7EPzu/5r9idLTIRGFLd9i5Omvc6jXxQF1IYe6IZVI4C0LsOSSdtouu5wlF1+KLxAsdmtLGpGhwnPGP9O11s8qpRaf5f1uBR7SWseBQ0qpA9hiBHBAa/0mgFLqIafupDIkTJ1ELJUvOKdjRPrj2f2+OMm4mXeNMhTBSi/lYT91i0IsubjOiexkpScQ8pZsrodhKIKVPoKVPuoITVhPa008msoRpqwopeWp93iEt3bHScbMMdcbhspElLLylLNf6cNwqRwRceRllLjkSkoqJyKTreMITcKEKfZiu70GHp8Lt9dlbz32cbDKh8frwu1z4fG68PiMbB2vC4/XyJxz++zyyrqy2TOUvdAMnoCHPgQntjkzSs+xROkpMth9igMdL3Fgy0mO7VmLtizK3RHWhE7TdvU6Wn7vC7hqZtds1sL8Yjo/6T6plPoI0AF8RmvdBzQDL+XUOeaUARwdVb5xGs+eV5imRXw4RTyaJB5NERtOZrqxRndfjcmzURAIeSkP+wg3BWlZXZ0RnJAT1QlUeOfOaJ5JUErhD3rwBz3UnGE9xmTcJDo4NroUHYgzPJhgqHeErjcHiEWSU2qDYShHOoyMdHi8LrxlboKVPtw5UmKLSr7cpGXF7TVy6mTFp1SFdU5x/FVbhOJDcNsPYdU7it2igqO1pvvIIQ5seZEDHS/TffhNAGoWtrLh1t+jrf1yGuorUM//vb322T/9P9jwn+DqP4dgTZFbLwhjOVcZug/4CvbfrV8B/idw+0w0SCl1B3AHQGtr60zcsiTQWpOMm8SjttTEhlPEh7NyE48miUXtslhafIZTxKLJcaMUafzlHltsaspY0FY1Jk8nWOXD5Z77ojPT2JGRAJV1k08uZ6YsRoaySd+WpUdFYGxZSUuNfC9mOTt/Ys8oHayF2x+HxrXFblHBsEyTY3t2caDjRQ52vMxg9ylQiuaVF7Dpw7fT1r6RcFNz/kXv+Du44i47r+qlf4Kt37PXZbviT8E3cRRXEArNOcmQ1vpkel8p9S3gZ87hcaAlp+pCp4xJykff+37gfrBzhs6lfecTy7SIj6QyopKO2MQyW1twcqUmLTmjE45zMdwKf8CDL+DGH/RQHvZT2+zGF/DgC9plvqB97HfKyqt8uL2uAr69MBqX23DE01/spgjnE63tGaWf+e/QshE+8IN5kSidjMU4vP1VDmx5kTdf6yAWGcLl8bDooku4/L23sezSDQQqqya/SXgRvOc+uOrP4NdfgWe+Bq98E1qvAMOd/bhy9g0PGC57UsfcOpm6zvlMXffU64/3vExdp3wed33ON85JhpRSTVrrTufwPcBOZ/9R4IdKqb/HTqBeDrwCKGC5UmoJtgTdBnxoOg2fDunE1HiuwOTIjb3NjdpkhSe9XMBEeP0ufMGs1ASrym2ZyZWaQPrYg98RHLfXmJcjmgSh5MlLlP4g/O7X53SidHSgn4NbX+HAlhc5smMbZjKJP1jO0ks30HbZ5Sy+aD0e/znIf/0quO0HcGwrPPs/4PQhsFLOJwmWae+bOftW0t5q68z3Px8o1+SylpanW/4Oll5bnDYKM8IZZUgp9SBwHVCrlDoGfBG4Til1MXY32WHgTgCt9S6l1I+xE6NTwF1aa9O5zyeBx7GH1j+gtd41428zCWbS4kdf25LpfjJTE//jMgyVjcIE3QQqvYSbAmMEJi08acHxBty45kHujSDMG3ITpW/4kh3dmIN/tPR1HudAx8sc2PISJ/btAa2pqKtn3Q230HbZ5TSvWoPhmqEo9MJL7bmYpoJlTSJOqbEfMzWqfsq+Zrz6Zs55KznBPUbXH1XXVz4zXxuhaMybSRe11jz+rV34ylyO0NgCkxae3GiOxzc/550pOKk4vPItOPJbe1XvimaoWGCvfZTe91cUu5XCfCWdKB0btGeUnkOJ0tqy6HpzPwcdAeo99hYAdYuX0tZ+OW2XXU7doiXyc7BIyND6wjNvxs0qpdh8x/xJdixptIY3fga/+mt75tqaNji2BYa7x9b1lttSVLEAQguy+5lPMwRq5uRf60IRyU2U/viv5kSitJlKcnTn6xzoeJmDHS8R6TuNMgwWXrCWi27YzLJLN1JZ31DsZgpCUZg3MiSUCJ3b4bHPw5Hnoe4C+PBPoO3t9rlUHIY6YbATBo/bXRSDJ2DI2R76DQx1gR41us7lzY8mVeTspwWqvMHu8xeEyRiTKP2vdtRylhKPDnPotQ4OdLzModc6SIxEcft8LFl3qT0B4vrLKCuXUV2CIL8dhMIw1AVPfQW2/QAC1fA7fw/rP5ovKG4fhBfbn4mwTIicypekweOOQJ2A41thzwkw4/nXKcMWojFRpmZHnpwyj4wKm7ckR+xo0K6fwEW32YnSs/D/h6HTPRzssBOgj+7agWWmCFRWseLyq2m77HJaL1wn638JwihEhoTzS3IEXvzf8Nw/gJmAKz8J13wWys4wHHciDJcjL03ApePX0RpG+vKjS7lRpp798OZvID449tqy6gkiTJLHNKcZ7ISHPugkSv9uqNuIAAAgAElEQVQ3uOrTs6brVWvN6eNHObDlJQ5seZGug/sBqGpsYv073kVb++U0rViJYcg0HIIwESJDwvlBa9j5H/Dkf4OBo7DqnfayBTXLzv+zlbKjT4FqaLxw4nrxoWyX3FDnWHk6vhWiPWOv84ay0aSKZkeUnH1vwI5eadMeAaNNe1hwpsw5HlNm2l+zMWVW9j55ZaPum3e/mXiuhsqF9verZrmd11W7fG7mZ514DR78oJ0ofdsPYNXvFLtFZyR3BfgDW16kv8ue6aSxbQVX3/YR2i67nOrmFkmAFoSzRGRImHmOboHHP2cnRTdeBO++D5ZcU+xWjcUXgroQ1K2YuE4mj2lUhCktUG8+Y28LNQ+KctnRMeWyu/6M3O2ofcM4y7L0PT3ZMrQdQdv3uD2EOI2/KitGuaJUsww8ZYX5Gswkux6Gh//ESZR+fHJ5LjKpRIIjO7aNuwJ8+zvfw7JLN1JeLUtdCMK5IDIkzBwDx+xI0I5/s/Nzbv0/9iR1szk8P9U8ptSILRoZaZmKqDjHY8rS9Yowh5WZgoG3oPegLUe9+6H3gN3NuP3BnIoKKltsKapNC5IjTRULi9P2ydAafvN39mzICzfYEaESTZQ+feI4rz/5S3b95ilikSFZAV4QzgMiQ8L0iUfgt1+HF75hH1/zWbj60/Nn7aG8PKY5hssN1Uvtz/Ib88/FI3A6LUkHs6K07UFIDGXruf1QvWyUKC2H2jYoCxf2fcDOY/vpXXY3bokmSpupJAe2vMT2J37J0V2vY7hctF12BWuvv5HWtRfhcnuK3URBmFOIDAnnjmXZ0YGnvgyRLlj7Pjv5tGruLLArTIKvHJrW2Z9ctLYjZb37HVE6YH9O7YY3fp4/NUKgJicnqS27X73k/Cx5MdjpzCj9WkkmSg+c6uL1Jx9j5zNPEh3op6Kugatv+whrr7+RYFURxFEQ5gkiQ8K5ceQFeOxz0LkNmtvhA/8XWjYUu1VCKaAUhBrsz+Kr88+ZSeg7bMtRrijt/xVs+9ecexi2VI8nShULzk1gTrwGD34IYgMllShtmSYHX32F15/4JYdffw2FYumlG1h3w2YWrbtERoEJQgEQGRKmxulD8MQXYM+j9uip934L1v5e6eWECKWJy2N3ldUuh5W35J+LDThydDBHlPbby7Uko9l6nsDYUW7pHKWJpj0owUTpwZ5udvz6V+z89eNE+k5TXl3DFe+7jQvfdjOhmtpiN08Q5hUiQ8LZERuE5+6Fl+6zV2q+/i/hik/aQ8kFYSbwV0LzpfYnF8uyR+ylc5J6HEk68SrsfiR/JF95Q1aM0qJ0Yhv85p6SSJS2LJPD21/l9Scf482tW9Bolqxbz9s//qcsXX/ZzC2GKgjClBAZEibHMuHV78Ovv2rPubPuQ/D2v7a7KgShEBgGVDbbn6XX5Z9Lxe1o5WhReuNnEO3N1ityovRwfx87fv0rdvz6cQa7TxGorOKyW9/HRW+/mcr6xqK0SRCELCJDwsQcfBoe/0s4tQtar4Cb/w2a1xe7VYKQxe2D+lX2ZzTR03aXWypm5y4VOFFaWxZv7Xyd15/8JQc6XsIyTVrXrmPTH9xO22UbZUSYIJQQIkPCWHr2w6/+CvY9BlWL4Pe/B6tvLalRN4JwRtKzkBeY6OAAu555ktefeoz+rk78oQouueVdXPT2zVQvaC54ewRBODMiQ0KW6Gl7Irot3wJ3GdzwJdj4xyU3B4sglBpaa47v2cX2J3/J/pd/i5lK0bxqDVf+3odYvvEq3F5vsZsoCMIkiAwJ9nDnLd+GZ/67vXjp+o/YCdIlOiOvIJQKsUiE3c8+xfYnH+P08aP4AkEuuuEWLrphM7Uti4rdPEEQzhKRofmM1vb8Lo//pZ10uuRauPlr0Li22C0ThJJFa03n/r28/uRj7H3hWVLJBI1tK7j5j/+MlVdeg8cnkVRBmG2IDM1XTu6Gxz8Pbz5tD0H+4EOwYrPkBQnCBMSjUfY8/wyvP/lLuo8cwuMvY/W1b+OiG26hYcmyYjdPEIRpIDI03xjugaf/BrZ+11477Ob/Dpd9AtyS0yAI43HyzQO8/uRj7Hn+GZLxGHWLl3LDJ+7igquvxVsm82wJwlxAZGi+kIrDy/8Mz94LiWG47D/BdXcXZbSNIJQ6yViMN154ltef/CVdB/fj9vpYeeU1rLvxFhqXrUBJBFUQ5hQiQ3MdrWHP/4Mn/tpeE2r5TXDTV6FuZbFbJgglR89bh9n+5GPsfvbXJEai1Cxs5fqP3cnqTdfjD5YXu3mCIJwnzihDSqkHgHcCp7TWa52yauBHwGLgMPB+rXWfsv9c+jrwDiAKfExr/apzzUeBv3Ju+1Wt9fdm9lWEMZzYZidHH3ke6lbBh/8D2m4odqsEoaRIJRLse+l5tj/5GCf27sbldrPi8qu56MZbaF65WqJAgjAPOJvI0HeB/w18P6fsbuAprfU9Sqm7neO/AG4BljufjcB9wEZHnr4ItAMa2KqUelRr3TdTLyLkMNgJv/4KbPuh3Q32O/8T1n8MXBIIFIQ0p08c4/UnH2PXb54iFhki3LSAaz98O6uvfTuBispiN08QhAJyxt+OWutnlVKLRxXfClzn7H8PeAZbhm4Fvq+11sBLSqkqpVSTU/cJrfVpAKXUE8Bm4MFpv8FU2PJtezFIfxWUVUFZ2N73V84NUUiOwAv/G57/BzATcOUn4ZrP2u8qCAJmKsmBLS+x/YlfcnTX6xguF22XXcG6G2+hZfWFKMModhMFQSgC52oADVrrTme/C2hw9puBozn1jjllE5UXjlQcfv7nE5/3VdjSMFqUysKjjked84WKPxxda9j5H/DEF2HwGKx6J9z4ZaiR4b6CANB/sosdTz3GzmeeJDrQT0VdA1ff9hHWXn8jwapwsZsnCEKRmXY4RGutlVJ6JhoDoJS6A7gDoLW1daZuCy4vfGYfxPphpB9G+pz9Pvs4d3+kD4b2ZPet5CQNdk0uUeMKlXPsKZv+ex3dAo9/Do5tgcaL4D3/DEuumf59BWGWkxiJcnDrK+x+9tccfv01FIqll25g3Q2bWbTuEgzDVewmCoJQIpyrDJ1USjVprTudbrBTTvlxoCWn3kKn7DjZbrV0+TPj3VhrfT9wP0B7e/uMSRZKQajB/kwFrSEZnViaRstVtNdeKXukD2ID2ClSE+DyTSxKk0mUvwqGOuGpL8GOf4PyBrj1/8C6D4L8gBfmMcl4jEOvdbD3hed489UtpJIJymtqueJ9t3Hh224mVFNb7CYKglCCnKsMPQp8FLjH2f40p/yTSqmHsBOoBxxhehz4mlIqHY++CfjcuTe7gCgF3qD9qVw4tWsty17r60xRqLRQDRyDkzvtskTkTA0Dt8/OCbr603Z3nSDMQ1LJJIe3v8reF57lYMfLJOMxApVVrH3bjay8chPNKy6QXCBBECblbIbWP4gd1alVSh3DHhV2D/BjpdTHgSPA+53qv8AeVn8Ae2j9HwForU8rpb4CbHHqfTmdTD2nMQwnsnMOCcxm0o4sTRSFslKw/g+haga7EgVhlmCmUry1czt7X3iOA1teJB4dxl8eYtXV17LyimtoWX0hhkuipIIgnB3KHvhVmrS3t+uOjo5iN0MQhBLAskyO7d7F3heeZd8rLxAbGsRbFmD5hitYecU1tF54MS73HBgVKsx7lFJbtdbtxW7HfEJ+cgiCULJoy+LEvjd444Vn2f/ybxnu78Pj87OsfSMrr7iGxevW4/bKunqCIEwPkSFBEEoKrTUnD+7njRefY++LzxHp7cHt8bLkknZWXrmJpevb8fj8xW6mME8xIwmSJ6OkTkVJnoySPDlM1TuW4m2RvM3ZjMiQIAhFR2tN95FD7HUEaOBkF4bLzeJ1l7Dpgx9lWftGWSFeKCjmcJLUyWFbeE5FSZ205ccazk61ovwuPA1BdNIsYkuFmUBkSBCEotF77Ch7X3yWvS88x+kTx1CGQevadWx8z/tZftmV+MtlcVTh/GJFk06Ex47ypBz5sSI50uNz4WkI4L+gGk9DEE9DAE9DAKPCK2vXzRFEhgRBKCj9J7vY+8Kz7H3hWbrfOgxK0XLBWta/410s33iVrAsmnBesaJKk07WVFp7kyWGsoRzp8bpwNwTwr6zOCI+7IYirUqRnriMyJAjCeWewp5t9Lz7HGy88x8k39wOwYMUFXP+xO1ix8SrKq2uK3EJhrmDFUvlRnnT31lAiU0d5Ddz1Afwrqh3hCeCpD+Cq8on0zFNEhgRBOC8M9/ex98Xn2fvic5zYuxuAhqVtbPrw7ay84moqauuL3EJhNmPFUnm5PGn5MQdzpMfjSM/yqkyUJyM9hkiPkEVkSBCEGSM6OMD+l19g74vPcWz3TrS2qG1dzFUf+ENWXnkN4cYFxW6iMMuw4qls11YmmXkYc2Cs9PiWVWWiPJ6GAK6wX6RHOCtEhgRBmBax4QgHtrzE3hee5ciObWjLIrxgIRvf+wFWXXkNNQtllnThzFhxMztc/VS2i8vsj2cruRWeugC+JZV2lKdBpEeYGUSGBEGYMonYCAc7Xmbvi89xeNtWzFSKyvoGLvvd97Lyyk3ULVoiuRfCGKy4iRVJYEaSpHpGnIjP8FjpcdnS411UgWdDNpHZXV060qO1ZmBggM7OTlpbWwkGg8VukjANRIYEQTgrkok4h17dkr8ifHUNF9/8TlZeeQ2Ny1aIAM0ztKWxokmsSBIzksQaTmAOJbGGk5hDCazh9LkEViSJTlr5N3ApPHVltvRcFsgkM7ury1Cu0vl/ybIsent76ezspKurK7MdGRkB4Pd///dZs2ZNkVspTAeRIUEQJiRvRfitr5CMjciK8HMcnbQy8mIOJ7GGEvnbSDIT3bGGkzDe8pYGGEEPrqAXI+TBV1OBUe7FKPfgKvdglHtxV/tx15SW9ACkUilOnTqVJz4nT54kmbSH4LtcLurr67ngggtobGykqamJhoaGIrdamC4iQ4Iwz7FMk8TICIlYlEQ0SnxkhOhAHwc7XslfEf6qTay6chMLV6/FMGRF+NmC1ho9krLlJUdi0pGbdCQnfU7Hx59NWXkNjHIvrnIPrrAfb2uFLTyO3OSKjlHmLpnurMmIxWKcPHmSzs7OjPx0d3djWXYEy+v10tjYyPr16zPiU1dXh8sl///PNUSGBGEWoi2LZDxGYmSE+EiUxEjUFppR2/iILTiJmFOWsx939lPx+LjPkBXhSxedsmyRyRWcSBJzOIE1lBwTycEaJ3yjwAh4MhLjWRjCX+4cB225sc85+97ZLQCRSCQT6UmLz+nTpzPng8EgjY2NLF++PCM+4XAYQyKf8wL56SYIBUJrjZlM2iIygaTEo9GcKI1zLjbiRGxypCc2Anq8/ol8DJcbXyCAt6wMb1kAb1mAQGUVVY0L8JUF8AYCeP3OuUAZXn8AX1kZ3kCQ+sVLZUX4IqJNi1T3CMmuYZKdwyS7hkmdjtnRm5HU+Be5DSc648FV6cPTXJ6J1rjKPXYkJ+TFCNr7syF6M1W01vT394/J7xkaGsrUqaqqoqmpiXXr1tHU1ERjYyOhUEhy3uYxIkPCrEJrjbYsLMtCmyaWZWb3nWNtWfa+aY06dvYtZ980sbSFZU50Lwvt1LVMZz/3XnrsfVPxOPGREZIjdndTfqQmimWeeUFHpYwceSmzhSUQIFRTm5WXgC026X1fWb7wpPfdHk8BvivCdDGHElnpccQneSoKpiO8zugqT2MQX060JiM6QQ9GyIPyuubVL3TTNMdNbI7FYgAopaitrWXJkiWZaE9jYyNlZWVFbrlQaogMCecdrTWxyBBDvT0M9XYz1NPDYG83Qz3dDPX2MDI4kCMtYwUm91hb1pkfWEAMlxvDMFAuF4Zh4Pb5MpLiC5QRqGicRF7GCoyvLIDbJ0sCzFV00rLXxMqJ9iS7hvMWBTUqvHgag5SvCONtDOJpCuKuLUO553d3TTKZHDexOZWyo2Qul4uGhgbWrFmTEZ/6+nq8Et0UzgKRIWHaJGIjDPU4ouMIz2BPet8+Hp2XYrjchGpqCNXUUduyCMOdLxWGy4UyXNkylwvDcKEMA8NlYBgup44x6ly6rpE5Vs757P1z72uMc23+sTLSz8tpn1NHEMZDa405kMiXns5hUj1RSPu828DTGMC/qhqPIz2exiCuYDaalx7SfWLXATo7OxkeHsbj8eR9vF7vuPvjnXO5ZkfkKBaL5UV6Ojs76e7uRjtdwz6fj8bGRtrb2zPRntraWklsFs4ZkSFhUsxUkqHe3qzo9HSP2u8hNhzJv0gpyqvChGrqqGtZxNJL2gnV1BGqrSVUU0tFbT2BikqRCWFOYCVMe7ZkR3oSzjY3r8dV5cPTFKRsTY0tPU1Be1h5Ts6OZVn09PTQeaCTEydOZEQgkbCXnXC73YRCIZLJJMlkkkQikZGDs0UpdVbSdDbnxqvndrunLFtDQ0NjxKevry9zvry8nKamJlauXJkRn3A4PCukTpg9iAzNYyzLJNrfnxPFGSs8wwP9YxJ1/aEKQjW1hOrqWbBqDRW1dfZxTS2hmjrKq2tk5JEw59CWxuyLjeriipLqHcnMtaO8LjyNAQIX1eZFewx//r8Hy7Lo7unOSM+JEyfo6urKzGXjdrtpbGzk4osvpqmpiQULFowb+UilUhk5SgvSmfYnOjcyMjLmnHkWOW6jORuhcrvdmdmbI5HsH1PhcJimpiYuueSSvMRmQTjfyG+sOUo6T2cy0Ymc7h2T0Ovx+W2pqa2jtnWJE8mpy0Z2qmvx+P1FeitBKAxWLDWmiyvZFUUnnH8vCtw1Zbb4XFyX7eIaZ40sy7I4deoUJ06cyIv4pMXH4/HQ2NjIJZdcwoIFC2hqajrrLh+3243b7T5vCcGmaU5JqCY7F4lE8o5DoRDLli3LS2z2y88WoUiIDJUYWmvQGo3O/LVph8K1HaBxzlkpk8jpXoZ6TjGYk5szlJOrk0pMnKfTvGpNJpKTKzy+YFDCz8K8QVuaVO9IvvR0Duetk6X8bjxNAQKX1uNtKre7uBoC4867Y5omPSd7MtGeicRn/fr1eRGfUp3LxuVy4XK5RFKEOc+0ZEgpdRgYAkwgpbVuV0pVAz8CFgOHgfdrrfuU/Rv268A7gCjwMa31q9N5/lSwLJPv/vmfOLKBIxuOYKDHlNvSgVMn202kR53LFZfxpCUzVX3meJJnTofcPJ3WxSxdf5nk6QhCDuZwcswormRXFFJORrMB7lpncdCNOQnNld5x/0AwTZOenp4xXV3p0U0ej4empibWr1+fF/EpVfERhPnMTESGrtda9+Qc3w08pbW+Ryl1t3P8F8AtwHLnsxG4z9kWBIWiYeny7LFSoBTKPnB+2Cn7P2ff3iicWnnn7GucOyvnulHl2ftO/sz0czL7OfdMtz3vXkphuFyUh6slT0cQxsGMJEgcHbI/xyL28PXBROa8EfTgaQpSfnlTRno89QGUZ3xRMU2T7u7uMRGftPikl21Ij25asGABNTU1Ij6CMEs4H789bwWuc/a/BzyDLUO3At/XdvjkJaVUlVKqSWvdeR7aMAZlGPzOp/5rIR4lCEIBsRImyRMREm8NkTg2ROKtoWw3lwJPQwD/sqrMKC5PYxBXaOK5Z9LikxvxyZ3PJld80hEfER9BmN1MV4Y08CullAa+qbW+H2jIEZwuIL2cbzNwNOfaY05ZQWRIEITZj7Y0qVPRbNTn6BDJk8OZuXtcVT68LSG8Vy7A2xLC01w+6ZpapmlmJvLLjfikR1F5vV6amppEfARhjjNdGbpaa31cKVUPPKGUeiP3pNZaO6J01iil7gDuAGhtbZ1m8wRBmM2kBuIkjw4RPzpE0unySo/oUn4X3pYQoQta8C4M4W0JTRrxSaVS40Z8RovPhg0bMl1d1dXVIj6CMA+YlgxprY8721NKqYeBDcDJdPeXUqoJOOVUPw605Fy+0Ckbfc/7gfsB2tvbp5lVLAjCbMGKpUgci2S6uhLHhrJ5Pi6FpymIb30tqskP9T50uYtkKslIMkky2U/yePe4Q7qj0Whm6Ya0+Ph8voz4pCM+Ij6CMH85ZxlSSgUBQ2s95OzfBHwZeBT4KHCPs/2pc8mjwCeVUg9hJ04PFCpfSBCE84/WGtM0z2rOmUQ8Qez0MPG+YWL9UeJDMZKxOElMUliYHo3p0aTCFilMklaK5OkkVo8F286+TUop/H4/DQ0NbNy4MRPxCYfDIj6CIGSYTmSoAXjYGeHkBn6otX5MKbUF+LFS6uPAEeD9Tv1fYA+rP4A9tP6PpvFsQRBmCK018XicSCTC0NAQkUiESCRCIpGY8ozGU14eQis8yoXH5cYT9OD1+/AGyvD7fdNaKmK2rcUlCEJxOWcZ0lq/Cawbp7wXePs45Rq461yfJwjC1LAsi5GRkYzgjN7m7qcnBRyNy+UaVzb8fj+hUGhcCfHgwoiY0J9C9aWgN45rBNy4cLvcBBorKFtYRXBRFWWLqnCFfSIsgiAUFZmYRhBmGaZpjpGZ8UQnEolgWdaY630+H+Xl5YRCIZqbmwmFQpSXl2fK0vs+n++MS0LopEWiM2KP6nJGd6V6Y9jzbHlw11fivSCEtzWEd2EIT2MA5ZLuKUEQSguRIUEoERKJxLhRm9HbaDQ67vWBQCAjM/X19Xlykys5Xu/EI64mI710RWY+n6NDJDuHwbS7xowKL96WEIHLGu3h7c3lYxYoFQRBKEXkJ5UgnEe01oyMjEzYPZW7TSQSY643DCMjM+FwmJaWljGCEwqFCAaDZ7Ww51QwhxJ58/kkjg2hY86wdq8L78JyQlc32+LTEsJV6ZvR5wuCIBQKkSFhXmNZFqZpYpomqVQqb3u2ZbnbtPjkik56OHcuHo8nIzONjY20tbWNG8kpKyub0VFPWmt0zMSMJLCGEpiRZGZrDiWwIknMSAJzMJEd1m6ApzFIYF2dPZ9Pawh3XWDM6uyCIAizFZEhoWhorYlGo0QikSlJx1QlZbJz4+XUTIeysrKMzCxatGjcbqpQKITPN3NRFK01Om7aMjOUzBcdR3LMSFZ0SI0z4ssAI+jFVe7BCHnxNATxNAbsWZwXTD6LsyAIwmxHZEg4r5imyeDgIH19fZw+fTpv29fXRzweP6f7GoaBy+XC7Xbjcrny9nO36STg8c5Ndt3Z1hm9P1OMKziRtOg40ZtMVGcywfHgKvfaglMfwAjZwmOXOdtyD0bAI5EeQRDmLSJDwrRJJBIZuRktPP39/XnRF8MwCIfDhMNhWltbCYfDVFRUTFk8ZuOEeXmCM0psshGcMwiOAqM8KzGeukqMckdwQnZZeiuCIwiCcHaIDAlnRGvN8PDwmKhOej8SieTV9/l8VFdX09jYyOrVqwmHw1RXV2fEZzaKzHho08KKmehYCitmYsVSWNFs19TorRlJQmqcbjmVG8Hx4KmtzEZtQvmiI4IjCIIw84gMCYDdnTUwMDCh8Iwe6VRRUUE4HKatrS0jOultIBAo0lucHVprdMKyJSZuS4yO5W+teFZyxq0XN9HJSfKNRgmOL1dwMnLj5OgERXAEQRCKicjQPCIej0/YnTUwMJDXneVyuTLdWYsWLcoTnqqqKjweT1HeYXQ0RsezURntCEv++bTEpOvZ13AWq0YonwvD70L53Rh+N0bAg1Htx/C7UX4Xhs/Z+t12PZ8bI+C2RUcERxAEYdYwr2Sos7MTwzAyn3TuyUT7sw2tNZFIZELhGR4ezqvv9/uprq5mwYIFrF27NiM/1dXVhEKhGf8aaEvbUjKS84ml0CMzFI1J41JZQfG7MXwujOoyPI645ItMjuz40vsulNclMiOUBLGkyd6uIXZ3DhJPmqxprmR1UwVB37z68S0I55V586/Jsiy++c1vTumasxGm3P2p1J3uvtaagYGBMcIzeo2piooKqqurWbFiRSa6kxaesrKyKX09tNbopIV2JCZXanRGbsz88px6Oj52vp08lD2Z35SjMbmS43ej3LNPZAUBoCcSZ/eJQXZ3Dma2b3ZHsEZFMpWCpbVBLmyuZG1zJWsWVLKmuYIKf3EitoIw21FTXWW6kLS3t+uOjo4ZuZdlWezbty8zt0x6sr1S2J/O9yDdnTU6byccDo/bnaVN7URjRglNumzExBpJZqRG55y3RlKZpRcmQnldGGVujDIXqswRmrLsR406zpRJNEaYR5iW5kjvcJ707D4xyKmh7FQTzVVlXNBUweoFFaxuqmDNggq8boNdJwbYcWyQnScG2Hl8gM6BWOaaxTUB1jqCtHZBJWubK6gKnNvyK0LxUEpt1Vq3F7sd84l5I0OljNbaFiTTwjJNzJRpb00LM5XKlKecrZWy0JZFyBskaPjtbqQ8ocmN0iTtbqj0+TNFZwyVlZQyJ+oymcz4c+u6US6RGUHIZSRh8kZXfrRnb9cQ0YT9b9FtKNrqyzPSk96ercT0ROLsOjHIzuO2HO04PsCxvpHM+YXhspwIUgUXNldSUy5Lp5QyIkOFZ97IkLY0p3+4B62xk2e1Bm2Xp/czZVqDNepYA9ao4wnOo7XzHJ3znFHHo+5xNgm9Z4ud+JuWFBdGmSdPasaN2KS3HgOlRGgE4VzoHoqPivYMcKhnONPNFfK784Rn9YIK2urL8blndobv/miCXScG2eEI0s7jAxzuzS7w21Tpz4seXdhcSX2Ff0bbIJw7IkOFZ97kDAEku0dQCrvDPWerDJVThi0DbsAwQCmMUdcoBRij7jHuPSc4b6SPs8/Mv++oa4yx91CGsnNkciI0Ep0RhMJgWppDPfndXHs6B+ke1c21ekEF77xoQUZ+FobLCvLHRlXAy1VttVzVVpspG4wl2XV8kF0nshGkJ/ecJP33cF3IZ0eQFlRkutqaKv3yx5EwL5g3kSFBEIRzIZpI8UbXUF5uzxtdg8SckY0el2J5fSgjPBc02dvKQOknMw/HU+zuTHex2dv9p4YykayaoJc1OYJ0YXNlwYRuPiORoWNYZN4AAA5QSURBVMIjMiQIgoDddd09FGfXqGjPoZ7hTPSkwu92pKcyIz9t9eV459AIxnSOU1qQdhwfYN/JIVKOIVWWeVjbXMHaBZWscQRpUXUAYx4MfjAtTSSesj+xFJF4kqFYijULKqkLzVwelshQ4ZlX3WSCIAiQ7uaKsCsn2rOnc5CeSHam9ZbqMlY3VXDrumYuaLIjP81Vcz8qUuZ1cUlrmEtaw5myeMpkX1fEzkFyutm+89vDJEw7Ohby2ZKYjh6tba5gSW05rhIRpETKygjMUDzpiIz9GYrlyk36OJlTP3sunfQ+mvv/8FJuWtNY4LcSZhKRIUGY55iWJpY0iSVN4ikrs9XaSV9ToHBy2simvuGU2ccqJ0Uup2461y3nOpVzHaOOR98nfeFZPSf3vjl1R5LOaK60+HQOsXdUN9eKhhDXr6zPRHtWNVVQWVb63VyFwud2ceHCSi5cWJkpS5oW+04Oseu4Pcx/x/EBfvDykczXNeB1sbqpIjuKbWElbXXluF1nF0XTWhNPWXmyMhWRGYplZSYx3pqAozAUlPvchPweyn1uyv1uqgJeFlYHCPncmTK7jptynydz3FZXfm5fWKFkkG6yGcSyNAnTImlaJFIWSVOTSFkkTJNEKv9cIlMnfxvPuS5pZuslTItkemvadbwuA6/bwOcevXWd4djZd7nweQy8LiNn68LrMvC41Jz/C7jU0FqTNDWxlCMmSYt4yiSWtPJEZcxxyq6b2ebVHXVdKv++8ZRJ8gxzR80lKss8rFmQzetZvaCCZXVzq5urmKRMi4Pdw3YXmxNB2nViMBNR8bkNLmiqYG1zBQGvO0dmkvly4whOavRsk+PgcamswDjCEsoRl8yxz025Uy80+pzfTZnHVTI/86SbrPDMGxkyLc2/dRwdJReahGlm5CM+jpzkSUvmmvGF5mz+4U4Fl6HwuFRGerwuA4+zdRmKlKWJp8xM29NbcwbaoRS2HLkNvG5XVqAyMmUL1lRlzDdOudtQziwDOm+WAytzbBdazn52JgNnmgKnzHKmLMjOapCzj86cZ8z9c+uO0wbnAo0tvHkzIuS0UWv7r+XYKImJpSzizjYtJ3FHVGKjxGQ63zqv28DvNvB7bMn1u132vlPm99iymzl259Yz8ur63AZK5c86Aflf/9yvZaZ81NfTuWjUdfnHjPM9y953gueMOmZMvex5j8tgZYPdzSWjowpPeuRd7ii2XScGSZl6rLiMkRnPKJlxj5GZmZ6WoBQQGSo8Be8mU0ptBr4OuIB/0VrfU5DnAnf/ZMeYcpehMpEQr9uF16Xwug08aQFx9oM+N1WufCHxurOi4snZ+kYd2yKjnK3LedbEddNl59rfnsqJKGUlyfklnBpbnpigLJ7zGV2eSFlEEyn6RyziSft56W267nyKOOSiFHmCkZYLn8eF321QWebBH/JlBWU8GXHqprf5UjP2Oq/LmBcJrMLsw+VMKtlWX86tFzcXuzmCMC4FlSGllAv4P8CNwDFgi1LqUa317vP9bMNQvHD32zKCYwvQuQtHKeN2GbhdBsWehT/dbRgfR6RyBStl6TH5IIazn84pMZzck7zckZx9Y1QOiZE+n3e9fcPc++flmqizbAM5dUflsniki1EQBGHWUejI0AbggNb6TQCl1EPArcB5lyGABVVTW5hUmB6GofAbdiQDJBlVEARBKE0KnTXYDBzNOT7mlGVQSt2hlOpQSnV0d3cXtHGCIAiCIMw/Sm4Ihdb6fq11u9a6va6urtjNEQRBEARhjlNoGToOtOQcL3TKBEEQBEEQikKhZWgLsFwptUQp5QVuAx4tcBsEQRAEQRAyFDSBWmudUkp9Engce2j9A1rrXYVsgyAIgiAIQi4Fn2dIa/0L4BeFfq4gCIIgCMJ4lFwCtSAIgiAIQiERGRIEQRAEYV4jMiQIgiAIwrxGZEgQBEEQhHlNSa9ar5TqBo7M8G1rgZ4ZvmcxmCvvAfIupcpceZe58h4g71KqzPS7LNJay6zDBaSkZeh8oJTq0Fq3F7sd02WuvAfIu5Qqc+Vd5sp7gLxLqTKX3mW+It1kgiAIgiDMa0SGBEEQBEGY18xHGbq/2A2YIebKe4C8S6kyV95lrrwHyLuUKnPpXeYl8y5nSBAEQRAEIZf5GBkSBEEQBEHIMOtlSCnVopR6Wim1Wym1Syn1Z055tVLqCaXUfmcbdspXKaVeVErFlVKfHed+LqXUa0qpn83md1FKHVZK7VBKbVNKdczi96hSSv27UuoNpdQepdQVs/FdlFIrne9F+jOolPr0bHwX59x/ce6xUyn1oFLKP0vf48+cd9hV6O/HOb7LHyilXnf+bb+glFqXc6/NSqm9SqkDSqm7Z/m7PKCUOqWU2lno95jJd5noPkIJorWe1R+gCVjv7IeAfcBq4O+Au53yu4G/dfbrgcuAvwE+O879/hz4IfCz2fwuwGGgdrZ/T4DvAZ9w9r1A1Wx9l5x7uoAu7LlEZt27AM3AIaDMOf4x8LFZ+B5rgZ1AAHvR6ieBthL/nlwJhJ39W4CXc/6fOggsdf6dbAdWz8Z3cY43AeuBnYV8h/PwfRn3PsV4J/lM/pn1kSGtdafW+lVnfwjYg/3D+v9v7/5D7a7rOI4/X7k03a/McNlmbIVkEaUGtWiN5WShlqvQWJSMXIkQhREZYsH8qwKRQCiKieGvFXNzDaI1KmxmTeZy060lZivdUme6tdLAVa/++Hyu3Lbdc8/d/Z5fO68HHO7Z53zO+77f57tzz/t8vt9zvkspL6TUnx+tc/bb3gocPjKWpDnApcCqLqR+lCZr6aWm6pA0k/JH8dY672XbB7tSRNWhbbIYeMJ2018o2lLDtUwBTpU0hdJM/LXD6b+iwTreRnnResn2v4FfAR/vQgmvOI5afmP7QB3fAsyp198D/NH2n2y/DPywxuiaBmvB9mbghS6lfpSmamkRJ/rMwDdDo0maC5wPPAjMsv10vekZYFYbIb4NXAf8txP5TUQDtRjYJGmbpKs7kmQbJlnHPOA54DaVXZerJE3tVK7jaWCbjFgGrG40uQmaTC229wE3AU8CTwN/t72pY8m2MMltshP4gKQzJJ0GXAKc3aFUx3UctawAflqvzwaeGnXbXnr4ojvJWvpKU7UcESf6zAnTDEmaBqwFrrV9aPRttk1pDlrd/8PAftvbOpdleyZbS7XA9gWUJdvPS1rYfKatNVDHFMpS+Xdtnw+8SFma7rqGtgmSTgYuA9Y0nmSbGniunE55hzwPeCMwVdKnO5RuqzwmVYft3cC3gE3ARmA78J/OZNvaRGuR9EHKi+5Xu5Zkm1LL0bW0ihP94YRohiS9mvIf7S7b6+rws5LOqrefBewfJ8z7gcsk/ZmyxHyhpDs7lPKYGqpl5N07tvcD91KW0bumoTr2Anttj7yTuofSHHVVU9ukuhj4ne1nm890fA3VchGwx/Zztg8D6yjHTHRNg8+TW22/2/ZC4ADlmI6ummgtkt5J2ZW/1PbzdXgf/7+qNaeOdVVDtfSFpmoZI070mYFvhiSJckzJbts3j7ppA7C8Xl8O/LhVHNvX255jey5lN8YvbXf13W5TtUiaKmn6yHVgCWWXQFc0uE2eAZ6S9NY6tBj4fcPpttRULaN8kh7tImuwlieB+ZJOqzEXU46F6Iomt4mkM+vPN1GOF7q72WzH/f0TqqXmuQ640vboxm0rcI6keXX1cVmN0TUN1tJzTdXSIk70G/fBUdyTuQALKEuVj1CWubdT9v2fAfwCeJzyKZHX1flvoKw4HAIO1uszjoi5iN58mqyRWiifKNlRL7uAGwaxjnrbecBDNdZ66ic2BrSWqcDzwMxBf64ANwJ/oDTZdwCnDGgd91Ma7B3A4gHYJqsoK1gjcx8aFesSysrWE91+znegltWU49EO1+21YhBrGStOt7dNLuNf8g3UERERMdQGfjdZRERExGSkGYqIiIihlmYoIiIihlqaoYiIiBhqaYYiIiJiqKUZioiIiKGWZijiBCDpn73OISJiUKUZihgQKobiOStpSq9ziIjhMRR/WCMGlaS5kh6TdDvl256/LmmrpEck3TjGfb5yrDmS1kvaJmmXpKvr2EmSfiBpp6RHJX2pjr9F0sY6/35J547xu6ZL2lPPv4SkGSP/HiuGpI9IelDSw5J+LmlWHV8p6Q5JD1C+1Toioivy7iui/51DOQ/SDOByykl3BWyQtND25pGJkpbU+ceac5XtFySdCmyVtBaYC8y2/Y56/9fWUN8HrrH9uKT3At8BLjwyMdv/kHQfcCnldCnLgHW2D0saK8avgfm2LemzwHXAl2vItwMLbP+rgcctIqItaYYi+t9fbG+RdBPlpLsP1/FplMZn86i5S1rM+aKkj9Xxs+v4Y8CbJd0C/ATYJGka5Sz0a8p5JgE4pUV+qygNzXrgM8DnxokxB/hRPev3ycCeUbE2pBGKiG5LMxTR/16sPwV8w/b3Wsw95hxJi4CLgPfZfqmu5rzG9gFJ7wI+BFwDfAK4Fjho+7x2krP9QN2dtwg4yfZOSTNaxLgFuNn2hnqflceoNSKia3LMUMTg+BlwVV11QdJsSWe2OWcmcKA2QucC8+vtrwdeZXst8DXgAtuHgD2SrqhzVBumVm4H7gZuAxgnxkxgX72+/LgeiYiIBqUZihgQtjdRGo7fSnoUuAeY3uacjcAUSbuBbwJb6l1mA/dJ2g7cCVxfxz8FrJC0A9gFLB0nvbuA04HVo8bGirGSsvtsG/C3th+AiIgOke1e5xARA07S5cBS21f2OpeIiInKMUMRMSn14OuLgUt6nUtExPFIMxQRbZF0A3DFEcNrbH+hF/lERDQlu8kiIiJiqOUA6oiIiBhqaYYiIiJiqKUZioiIiKGWZigiIiKGWpqhiIiIGGr/A1k4OtIcGkSVAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "bin_cat = 'resolution range'\n",
        "bin_range = [0, 1, 1.5, 2, 2.5, 3, 3.5, 4, float(\"inf\")]\n",
        "pandas_plot_multi_groupby_binned(results, first_column_to_group_by='release_year', second_column_to_group_by='resolution', bin_cat=bin_cat, bin_range=bin_range)\n",
        "pandas_plot_multi_groupby_binned(results, first_column_to_group_by='release_year', second_column_to_group_by='resolution', first_range_flag=True, first_lower=2014, first_upper=2022, bin_cat=bin_cat, bin_range=bin_range)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wZAX-Qfs94Jy"
      },
      "source": [
        "**EM resolution bins by year**\n",
        "\n",
        "Create plot plotting EM resolution by year. Added filtering to remove minor method types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "b6plFuv796ZU",
        "outputId": "4eefaca4-d96e-471e-abb6-cba365e04d7a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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77bepKSkpcSdOnEhetmyZw7179xo8zGzy5MlFCQkJ8QkJCfGhoaHN7qX0008/3U5MTIxPSkqKKygo0I6MjHwo2fr++++N79y50ystLe3mtm3b0ufOnesIAN7e3tWdtsqMc/4ngMJ6ZSc457Up23kAteOOzwA4wDmv5pynAkgBMLjmK4VzfodzLgNwoKYuIYQQ0ulyN4RBlp4O2w3roWVi0nwD8pBDhw6ZBwcHFwOAr69vdb9+/aoBwNnZWW5mZqbIzs7ukGk4teeUyeVyJpfLWUPJ608//WQyffr0AoFAgLFjx1aUlpYK09PTtdtz344IfjaAgzWf7XA/QaqVWVMGAJJ65UM64N6EEEJIk8pOnULxwYMwmz0bBkOHqjucdvvo748cUopS9DuyTzdTN+nHwz9u9NDYqqoqJpFIdD09PWX1r0VFRenL5XLm5eVV3VDbX3/91cTDw8OwT58+VV999ZXEzc1N3lw8gYGB7tevXzcYOXJkSUNnpWVnZ2s7Ozv/E4uNjY0sPT1d28nJqdm+G9OuVWaMseUAFAD2tqefen2+xRiLYYzF5Od3ykaWhBBCeghFfj6yl6+Abt++EIUsVHc43VZOTo7QyMjoock86enp2rNmzeqzc+fONC2th9+YBQcHF2dkZNxISkqKHzt2bOkrr7zi0pL7RUdHJ+fk5MTKZDLBkSNHenfAIzSrzSNEjLGZACYBGMv//0C0LAAOdarZ15ShifIHcM53ANgB3D/LrK3xEUII6dk457i7bDlUUinsPguDQEdH3SF1iKZGcjqLgYGBSiaTPTCIUlhYKHj66afdVq1alTV27NiKhtpZW1sraz8vWrTo3tq1a1u8tE9fX59Pnjy5+McffzR59tlnS+tes7Gxkaelpf3zB5qdna3TntEhoI0jRIyx8QCWAgjinEvrXPoZwIuMMV3GmAsAdwAXAVwC4M4Yc2GM6eD+xOuf2xM4IYQQ0pSifftQ8ddfsFyyBLpubuoOp1sTiURKpVLJpFIpA+6/Qps4caLbiy++WNDQK61adef17Nu3z6RPnz5Vtd+7uLh4169fUlIiqG0jl8vx66+/GovF4sr69YKCgor37t1rrlKp8McffxgYGRkp25sQNTtCxBjbD2AUAAvGWCaAVbi/qkwXwMmayU7nOedvc87jGGOHAMTj/qu0eZxzZU0/8wH8BkALQCTnPK49gRNCCCGNqU5JQV7YZzB4/HGYTn9Z3eE8EkaMGFFy4sQJwylTppRFRkaaXrp0ybCoqEi4b98+CwCIjIxMDQgIqAwJCbEdNGhQxfTp00vCwsIsf/vtNxMtLS1uYmKi+Oabb9IAIDs7W8g5f2i2dGlpqWDixIluMpmMcc5ZQEBA6ZIlS/IBICwsTAQAS5cuzQ8ODi45duyYsZOTk4+enp7q66+/Tmvv87H/f9ulefz9/XlMTIy6wyCEENKNqGQypE17EYqcHPT5+ScIRSJ1h9QqjLHLnHP/umWxsbFpfn5+zS5Z70zR0dH64eHhVocPH05tb1/79+83vn37tu6KFSvyOiK2+vT19ftLpdKr9ctjY2Mt/Pz8nBtqQztVE0IIeaTkb9qE6lu3YL91S7dLhjRZYGCgNCYmplShUEDYziNPXnrppZIOCusBcXFxulOnTnU1Nzdv9eszSogIIYQ8MirOX0Bh5G6YBAfDaMwYdYfzyAkJCSlQdwxN6dSNGQkhhJDuQFlSgrsffAAdJydYffC+usMh3QyNEBFCCOn2OOfIXr0ainv34Lx/PwT6HbpvIekBaISIEEJIt1f6888o+/U4RPPnQ6+fj7rDId0QJUSEEEK6NVlmJnLWfgy9gQNh/uYb6g6HdFOUEBFCCOm2uEKBu0vfBxiD7YYNYA0cH0E6Rnl5ORs0aJCnQqFAUlKSjpeXV1+xWOzl5ubmXbtHUH25ublaAQEB7k5OTj4BAQHu+fn5Tf4BtbTf0NBQW0tLS1+xWOwlFou9Dh48aAwAx48fN3R1dfV2d3d/aNPH5lBCRAghpNsq2LkTlVeuwHrlR9Cxt2u+AWmziIgIi6CgoCKhUAhHR0f55cuXExISEuIvX758a9OmTdZpaWkPnTa/atUqm1GjRpWlp6ffHDVqVNnKlSutm7pHS/sFgLfffjs3ISEhPiEhIX7atGklADB+/PjyX375Jbktz0eTqgkhhHRLldevI/+rLeg9YQJ6T56s7nAeeYcOHTI/cODAHQDo1avXP7s6V1ZWMpVK1WCb48ePm5w5cyYRAObMmVMwcuRITzRylmlr+u0MlBARQgjpdlQVFchasgRCS0tYr16FmmOkeoS7y5Y7VCcnd+gyOl13d6ntuk8bPTS2qqqKSSQSXU9PT1ltWUpKivaECRPcJRKJ7sqVKzOdnZ0f2gyxoKBAWHvGmIODg7ygoKDZvKMl/QLArl27LA8cOGDu5+cn3bp1q0QkEikbqtdS9MqMEEJIt5O7fgPkGRLYblgPrd691R3OIy8nJ0doZGSkqFvm5uYmT0pKir9169bNffv2WUgkkiaTHYFA0KLEtSX9Llq0KC89Pf3GrVu34q2treVz5851aPVD1UMjRIQQQrqVst9/R/H338P8zTdgMHiwusPpck2N5HQWAwMDlUwma3AQxdnZWS4Wiyt///13o/on35ubmyvS09O1nZyc5Onp6dpmZmaKhvpobb8ODg7/9DN//vz8SZMmubf2meqjESJCCCHdhjwvD9krPoKuV1+I3n1X3eH0GCKRSKlUKplUKmUAcPv2be3y8nIGAPn5+VqXLl0y9Pb2rqrf7qmnnirevn27OQBs377dfPz48cUAEBUVpf/ss88616/f0n7T09P/mWh94MABE09Pz8r2PiMlRIQQQroFrlIhe9lyqCorYffZZ2A6OuoOqUcZMWJEyYkTJwwB4Pr163oDBgzo6+np6TV8+HDP+fPn5wwePLgSAKZNm+b0559/6gPAmjVrsqOiono7OTn5nD59uveaNWuyASAtLU1XT0+P179HS/tduHChvYeHh5eHh4fXmTNnem/ZsqXdo2aM84fi0Rj+/v48JiZG3WEQQgjRAIV7vkXuunWwWvkRzF5+Wd3hdBrG2GXOuX/dstjY2DQ/P7976ooJAKKjo/XDw8OtDh8+nNrevubMmWM/e/bsgiFDhrR7ZKe+xMREnUmTJrknJyfH1b8WGxtr4efn59xQO5pDRAghRONVJSUhLzwchiNHwvSll9QdTo8UGBgojYmJKVUoFBAK25c+bN++PbODwnrA8ePHDRcsWOBoamra4rlKtSghIoQQotFUMhnuLlkKgaEhbD79pEctsdc0ISEhBeqOoSnjx48vT0pKim9LW0qICCGEaLT8L75EdWIi7LdthdDCQt3hkEcUTaomhBCisSrOnUPh7t0weelFGI0ere5wyCOMEiJCCCEaSVlcjLsffAgdFxdYLV2q7nDII45emRFCCNE4nHNkr1oNRUEBnLdsgUBPT90hkUccjRARQgjROCU/HkbZb79BtGAB9Hy81R0OAVBeXs4GDRrkqVAokJSUpOPl5dVXLBZ7ubm5eYeFhYkaahMaGmpraWnpKxaLvcRisdfBgweNW3IvhUKBvn37eo0ePdqtoeuVlZVs4sSJfRwdHX18fX3FiYmJOsD9VWaurq7e7u7urf5LQwkRIYQQjSLLyEDuJ59A398f5q/PVnc4pEZERIRFUFBQkVAohKOjo/zy5csJCQkJ8ZcvX761adMm67S0NO2G2r399tu5CQkJ8QkJCfHTpk0racm9PvnkEys3N7dG9yjatGmThbGxsSIjI+Pm/Pnzc0NDQ+2B+6vMfvnll+S2PB8lRIQQQjQGVyhwd+n7gJYWbMM2gGlpqTskUuPQoUPmwcHBxQDQq1cvXrvTdGVlJVOpVB12n9u3b2v/9ttvxm+++WajG1EePXrUZPbs2QUAMGvWrKKzZ88atTcGmkNECCFEY9zbvh2V167BNjwc2ra26g5HI/2x55ZDYVa5fkf2aWZnKB37Wt9Gj7+oqqpiEolE19PTU1ZblpKSoj1hwgR3iUSiu3LlykxnZ2d5Q2137dpleeDAAXM/Pz/p1q1bJSKRSNlULPPmzXMICwvLLCkpaTQbzs3N1XFxcZEBgLa2NgwNDZW5ublCGxubVm/IWItGiAghhGiEymvXcG/rNvSePBnGkyaqOxxSR05OjtDIyOiBZMPNzU2elJQUf+vWrZv79u2zkEgkDw2yLFq0KC89Pf3GrVu34q2treVz5851aOo++/fvN7awsFA8/vjj0o5+hubQCBEhhBC1U5ZXIGvp+xBaWcL6oxXqDkejNTWS01kMDAxUMpmswUEUZ2dnuVgsrvz999+NZs2aVVT3moODwz9J1Pz58/MnTZrk3tR9oqOjDU+ePGliZ2dnXF1dLaioqBA888wzLj/99NMD56dZWVnJUlNTdVxdXeVyuRzl5eVaVlZWbR4dAlowQsQYi2SM5THGbtYpM2OMnWSMJdf8alpTzhhjmxljKYyx64yxAXXazKipn8wYm9GeoAkhhDxacv+1DnKJBHYbNkCrd291h0PqEYlESqVSyaRSKQPuz/MpLy9nAJCfn6916dIlQ29v76r67dLT0/+ZaH3gwAETT0/PSgBITU3VHjZsmEf9+lu2bMnKzc29npWVdeObb765M3To0LL6yRAATJw4sTgyMtIcAHbv3m06bNiwMoGgfS+9WtL6GwDj65V9AOAPzrk7gD9qvgeApwG413y9BWAbcD+BArAKwBAAgwGsqk2iCCGE9GylJ06g5If/wfzNN6E/aJC6wyGNGDFiRMmJEycMAeD69et6AwYM6Ovp6ek1fPhwz/nz5+cMHjy4EgCmTZvm9Oeff+oDwMKFC+09PDy8PDw8vM6cOdN7y5YtEgCQSCTaWlpavDX3DwkJsd27d69xTb/3ioqKhI6Ojj4RERHW4eHh7T4slnHefDyMMWcARznnPjXfJwIYxTnPZozZADjNOfdkjG2v+by/br3aL875nJryB+o1xt/fn8fExLTx0QghhGg6eW4uUoOegba9PZz37wPT0VF3SGrHGLvMOfevWxYbG5vm5+fX6KqrrhAdHa0fHh5udfjw4YdGbFpr3bp1IicnJ9n06dNbtAy/NRITE3UmTZrknpycHFf/WmxsrIWfn59zQ+3aOofIinOeXfM5B4BVzWc7AHXfbWbWlDVWTgghpIfiKhWyP/wQKpkMtp99RsmQhgsMDJTGxMSUKhQKCIXtm4K8bNmy/A4K6wHHjx83XLBggaOpqWmr5xO1e1I155wzxlo17NUUxthbuP+6DY6Ojh3VLSGEEA1T9O23qDh7DtarV0O3j4u6wyEtEBISUqDuGJoyfvz48qSkpPi2tG3rDKTcmldlqPk1r6Y8C0DdJXX2NWWNlT+Ec76Dc+7POfcXiRrcCZwQQkg3V5WYhLzPN8Jw9GiYTAtWdziEtDkh+hlA7UqxGQB+qlP+Ws1qs6EASmperf0GYBxjzLRmMvW4mjJCCCE9jKq6Gnffew8CIyPYfPIxGGPqDomQ5l+ZMcb24/6kaAvGWCburxZbD+AQY+x1AOkAatP7XwBMAJACQApgFgBwzgsZYx8DuFRTby3nvLADn4MQQkg3wDlH3oYwVCcnw2H7vyE0N1d3SO0SVxAHb3M6fPZR0GxCxDl/qZFLYxuoywHMa6SfSACRrYqOEELII0NZWors5ctRdvJ3mL72KgxHjlR3SO3yR8YfCIkKwbrAdZjsOlnd4ZB2oqM7CCGEdLrKGzeR+txUlEWdhuUH78Pqww/VHVK7pJakYnn0cviY++Ap56fUHU6XKC8vZ4MGDfJUKP5/AVdhYaHAysrK97XXXmtwFVRubq5WQECAu5OTk09AQIB7fn5+i07rba7f0NBQW0tLS1+xWOwlFou9Dh48aAzcX2Xm6urq7e7u3uphO0qICCGEdBrOOQr37kX6yy+DK5Vw/u5bmM+c2a3nDVXIKxASFQJdLV18MfoL6Gj1jO0CIiIiLIKCgorqLrlfvHix3eDBg8saa7Nq1SqbUaNGlaWnp98cNWpU2cqVK61bcq/m+gWAt99+OzchISE+ISEhftq0aSXA/VVmv/zyS3ILH+kBlBARQgjpFMrycmSFhq7nBQEAACAASURBVCL340+gHzAMLv/7AXqPPabusNqFc46P/v4IaaVpCBsRBmuDFv18fyQcOnTIPDg4uLj2+7/++ks/Pz9f+8knnyxtrM3x48dN5syZUwAAc+bMKfj111+bPaWiJf12BjrclRBCSIerSkhA1sIQyDIzIVocCvPXXwdr51lTmuCbuG9wMv0kFg9cjCE2Q9QSw2/bvnS4J0nX78g+LRycpE+9E9LoobFVVVVMIpHoenp6ygBAqVRi8eLFDvv3779z7NixRg+fKygoEDo5OckBwMHBQV5QUNBk3tHSfgFg165dlgcOHDD38/OTbt26VSISiZRNP2XTuv/fTkIIIRqDc46iQ4eQFjwNqspKOP3nG1i8+eYjkQydzz6PL698iXFO4zDDu2edUZ6TkyM0MjL6Z/LQhg0bROPGjSt2dXWVt7QPgUDQ7KvSlva7aNGivPT09Bu3bt2Kt7a2ls+dO9ehqfotQSNEhBBCOoSqogLZq9eg9MgRGAwfDtuwDd1+WX2t7PJsLD2zFC69XbB2+Fq1zoFqaiSnsxgYGKhkMtk/We358+cNL126ZLh7925LqVQqkMvlAkNDQ+XWrVsf2HTZ3NxckZ6eru3k5CRPT0/XNjMza/JIjZb26+Dg8E8/8+fPz580aZJ7e5+REiJCCCHtVp2cjMyFIZClpcFiwbuwmDMHTKtFC4o0XrWyGotOL4JcJccXo7+AgbaBukPqciKRSKlUKplUKmX6+vr8559//ueA182bN5vHxMQY1E9aAOCpp54q3r59u/m6detytm/fbj5+/PhiAIiKitLfvHmz5Y8//phWt35L+61NsgDgwIEDJp6enpXtfcbuP4ZJCCFErYp/PIzUF4KhLC2FY+QuiObOfWSSIQD414V/Ia4gDp8GfgoX45575tqIESNKTpw4YdhcvWnTpjn9+eef+gCwZs2a7KioqN5OTk4+p0+f7r1mzZpsAEhLS9PV09Nr1TmodftduHChvYeHh5eHh4fXmTNnem/ZsqXdo2Y0QkQIIaRNVJWVyPn4E5T873/QHzwYdp+HQ/iInUH536T/4ofkH/BmvzcxxnGMusNRqwULFuSHh4dbTZkypaxeeQGAfw59PXjwYHrtZ2tra+W5c+eS6vd1/vx5g4ULF+bVL29pv4cPH05tsFE7UEJECCGk1arv3EHWwhBUp6TAYu47sJg375EaFQKAG/k3sO7COgy3HY55jzV4CEOPEhgYKI2JiSlVKBSouxdRW2zfvj2zg8J6wPHjxw0XLFjgaGpq2uRcpYZQQkQIIaRVSo4cRfaqVRDo6sJh504YBg5Xd0gdrqCyAItOL4KlviXWP74eWoJHK9lrq5CQkILma6nP+PHjy5OSkuLb0pYSIkIIIS2iqq5G7rp/ofjgQegNHAi7jZ9D28pK3WF1OIVKgaV/LkVxdTG+ffpbmPQyUXdIpAtQQkQIIaRZsvR0ZIYsQvWtWzB/43WIFi4E09ZWd1idYtOVTbiYcxGfDP8Efc37qjsc0kUoISKEENKk0uO/IXv5ckAohP22rTAaPVrdIXWa42nH8U3cN3jR80U84/aMusMhXYgSIkIIIQ1SyWTIC/sMRd99Bz0/P9h9sRHatrbqDqvTpBSlYOXfK/GY6DEsHbRU3eGQLkb7EBFCCHmILDMT6dNfQdF338Fsxgw4fbvnkU6GymRlCDkdAn2hPj4f9Tm0tR7N14HtUV5ezgYNGuSpUPz/Aq7CwkKBlZWV72uvvebYUJvQ0FBbS0tLX7FY7CUWi70OHjxo3Nx97Ozs+nl4eHiJxWIvHx+fBt9ZqlQqzJw508HR0dHHw8PDKzo6Wh8A4uLidMVisZe+vn7/1j4fjRARQgh5QNkff+Duh8sAzmEXsRm9n3xS3SF1KhVXYXn0cmSVZeHrp76Gpb6lukPSSBERERZBQUFFdZfcL1682G7w4MFlTTTD22+/nbt27drc1tzrzJkzSTY2No0unf/++++N79y50ystLe1mVFSUwdy5cx2vX7+e4O3tXZ2QkBDfloSIRogIIYQAALhcjtz1G5A5bz50HBzg8r8fHvlkCAC+vvE1oiRReG/QexhoNVDd4WisQ4cOmQcHBxfXfv/XX3/p5+fnaz/55JOlXR3LTz/9ZDJ9+vQCgUCAsWPHVpSWlgrT09PbNaxHI0SEEEIgz85G1qJQVF67BtOXX4blB+9DoKOj7rA63d9Zf+Orq19hgssEvCx+Wd3htEjhf5Mc5DkV+h3Zp7a1gdTseY9Gj7+oqqpiEolE19PTUwYASqUSixcvdti/f/+dY8eO9W6q7127dlkeOHDA3M/PT7p161aJSCRSNhfP2LFj3RljmDVrVv577713r/717OxsbWdnZ1nt9zY2NrK655u1BY0QEUJID1d+5gxSpzyL6uRk2H2xEdYrP+oRyVBmWSaW/rkU7qbuWDVslVpPsNd0OTk5QiMjo39eYW3YsEE0bty4YldX1yYTkEWLFuWlp6ffuHXrVry1tbV87ty5Ds3dKzo6OiE+Pv7WiRMnknfu3Gn566+/Nnt+WkegESJCCOmhuEKB/E2bUbBzJ3TFYth/+QV0nJ3VHVaXqFRUYtHpReDg+HLUl9DX7tABl07V1EhOZzEwMFDJZLJ/BlHOnz9veOnSJcPdu3dbSqVSgVwuFxgaGirrn0zv4ODwTxI1f/78/EmTJrk3dy8XFxc5ANjZ2SkmTpxYfO7cOYOnn366vG4dGxsbeVpa2j9Ze3Z2tk57RocAGiEihJAeSZ6bh4yZs1CwcydMgoPhfGB/j0mGOOf4+NzHSCxMxPrH18Ohd7ODFj2eSCRSKpVKJpVKGQD8/PPPqdnZ2TeysrJurFmzJvO5554rqJ8MAUDdeT0HDhww8fT0rASA1NRU7WHDhnnUr19aWiooKioS1H6Oiorq7evrW1m/XlBQUPHevXvNVSoV/vjjDwMjIyNlexMiGiEihJAepvzvv3F3yVKoKithG7YBxkFB6g6pSx1IPIAjd45g7mNzMcJ+hLrD6TZGjBhRcuLECcP6p93XN23aNKd58+bljxgxQrpw4UL7+Ph4PQCwt7eX7d69Ox0AJBKJtpaWFq/fNjMzU/jss8+6AYBSqWRTp04teP7550sBICwsTAQAS5cuzQ8ODi45duyYsZOTk4+enp7q66+/Tmvv8zHOH4pHY/j7+/OYmBh1h0EIIY8ErlTi3patuLdtG3TdXGH35ZfQdXVVd1hd6lreNcw6PgvD7YZj85jNEDDNe1HCGLvMOfevWxYbG5vm5+f30OTirhQdHa0fHh5udfjw4dT29rVu3TqRk5OTbPr06SUdEVt9+vr6/aVS6dX65bGxsRZ+fn7ODbWhESJCCOkBFPfuIWvJEkjPnYfxs8/C+qMVEOh3n3kzHeFe5T2Eng6FjaEN1j2+TiOTIU0WGBgojYmJKVUoFKi7F1FbLFu2LL+DwnpAXFyc7tSpU13Nzc1b/fqMEiJCCHnEVVy4iKz3FkNVVg6bTz+FydTn1B1Sl5Or5Fh8ejHK5eX495P/Rm+dJleKk0aEhIQUqDuGptRuzNiWtpQQEULII4qrVCjYsQP5myOg4+QEx693oZfnQ/NYe4TPYz7Hlbwr2PD4BniY9szfA9K0do0XMsYWMcbiGGM3GWP7GWO9GGMujLELjLEUxthBxphOTV3dmu9Taq47d8QDEEIIeZiisBCSt+Yg/8tN6D1hApy//77HJkNHbh/B3lt78arXq5jQZ4K6wyEaqs0JEWPMDsACAP6ccx8AWgBeBLABwBecczcARQBer2nyOoCimvIvauoRQgjpYNIrV5D67HOQXrwI69WrYftZGLQMDdQdllokFiZi7bm1GGg1EIsGLlJ3OESDtXdGmRCAHmNMCEAfQDaAMQD+W3P9PwCm1Hx+puZ71Fwfy2hbUEII6TBcpULBrl1If/U1sF66cD6wH6YvTuuxOzCXVJcgJCoEvXV6I3xkOLQFdII9aVybEyLOeRaAcAAZuJ8IlQC4DKCYc167M2UmALuaz3YAJDVtFTX1zdt6f0IIIf9PWVyMzLnzkPdZOIyeeAIu//0venl5qTsstVFxFT746wPkSHOwcfRGWOhZqDukbq+8vJwNGjTIU6G4/yNeS0troFgs9hKLxV5jxoxxa6hNZWUlmzhxYh9HR0cfX19fcWJiYrNnwvTv31/cXJ3jx48burm5eYvFYq+YmJheYrHYqy0n3NfV5knVjDFT3B/1cQFQDOB7AOPbE0xNv28BeAsAHB0d29sdIYQ88ipjY5G5aBEU+fdgtWIFTKe/3GNHhWpti92G6KxofDT0I/iJ/NQdziMhIiLCIigoqKh2yb2urq6quRVdmzZtsjA2NlZkZGTc3LFjh2loaKj9sWPH7jTV5urVqwnNxbJnzx6z0NDQ7Llz5xYCQEJCQnx7E6L2vDJ7AkAq5zyfcy4H8D8AwwGY1LxCAwB7ALVbeWcBcACAmuvGAB5avsc538E59+ec+4tEonaERwghjzbOOQr37EHaK6+CMQGc9+2F2SvTe3wydEZyBv+O/TeecX0GL3i8oO5wHhmHDh0yDw4OLm5Nm6NHj5rMnj27AABmzZpVdPbsWSOVStVkm9rE5ujRo0aDBw/2HD9+fB8XFxfvoKAgF5VKhY0bN1ocO3bM7NNPP7ULCgpyafMD1dOeZfcZAIYyxvQBVAIYCyAGQBSA5wEcADADwE819X+u+f5czfVTXJO3ySaEEA2mLC1F9vIVKDt5EoZjx8J23afQMjZWd1hql1GagQ//+hB9zfpixdAVnZoc3sssR+zvGRjyjCsMTXU77T71HT582CEvL69Dd9W0tLSUTpkypdFDY6uqqphEItH19PSU1ZbJZDKBj49PXy0tLf7ee+/lvPrqqw8lS7m5uTouLi4yANDW1oahoaEyNzdXaGNjo6hftyG3bt3Su3bt2h1nZ2f5wIEDxSdPnjQMDQ299/fffxtOmjSpZNasWUVted6GtDkh4pxfYIz9F8AVAAoAVwHsAHAMwAHG2Cc1ZbtqmuwC8C1jLAVAIe6vSCOEENJKlXFxyApZBHl2Nizffx9mM2f0+FEhAJDKpVgYtRACgQBfjP4CvYS9OvwenHNk3irC1d8zIIkvhFBXC30GWHZpQqQOOTk5QiMjoweSmOTk5OsuLi7y+Ph4nSeffNJzwIABld7e3tUded9+/fpVuLq6ygHA29tbevv27WbnILVVuzZm5JyvArCqXvEdAIMbqFsFgMYuCSGkjTjnKNq/H3n/Wg8tCws4fbsH+v3bNW3ikcE5x+qzq3Gn5A62PbENdoZ2zTdqBaVShZSYPFw9mYGCzHLoG+tg6JQ+8H7cDr0Munb1WlMjOZ3FwMBAJZPJHphm4+LiIgcALy8v2dChQ8suXryoXz8hsrKykqWmpuq4urrK5XI5ysvLtaysrFo0OgQAurq6/7xJ0tLSgkKh6LTMnw5yIYSQbkBZXoG7ixcjd+3H0A8YBpf//UDJUB3fxn+LX9N+xbv930WAbUCH9VtdqcDVExn4bsU5/L47HlzFMea1vnjtkwAMHO/c5cmQuohEIqVSqWRSqZQBQH5+vlZlZSUDgOzsbGFMTIyhr69vZf12EydOLI6MjDQHgN27d5sOGzasTCAQIDU1VXvYsGEatVMoHd1BCCEaTlVZiYzXZ6PqZhxEoaEwf+N1MAH9/2ytSzmXsPHyRox1HIvXfV5vvkELlBVW4XpUJuL+yoK8Sgk7T1OMmi6Go7dZj309OWLEiJITJ04YTpkypezatWu95s2b58QYA+ccISEhOQMHDqwCgJCQENtBgwZVTJ8+vWThwoX3pk6d6uLo6OhjbGysPHjw4G0AkEgk2lpaWho1j5hp8rxmf39/HhMTo+4wCCFEbbhSicyFC1H+xynYbd6E3k8+qe6QNEpORQ6mHZ2G3jq9sX/ifhjqGLarv3uZZbh6MgMpl/LAAbgNtET/Jx0hcjTqmIBbgDF2mXPuX7csNjY2zc/P716XBdGA6Oho/fDwcKvDhw+ntrevdevWiZycnGTTp08v6YjYgPur06RS6dWm6sTGxlr4+fk5N3SNRogIIUSD5W7YgPLf/4DV8uWUDNUjU8qw+MxiVCmqsPup3W1OhjjnkNwqxLWTGZDcKoK2rhb6jbaH7xh79DbX6+Cou6/AwEBpTExMqUKhQO1eRG21bNmy/A4KC3FxcbpTp051NTc3l7enH0qICCFEQxX+5z8o2vMtzGbMgNmrr6g7HI2z4eIGXM+/jo2jNqKPSZ9Wt1cqVEiOycW1kxkoyKqAvrEOhj3rCu/HbaGr3zPmBrVWSEjIQ/sHqpu3t3d1cxtEtgQlRIQQooFKT5xA7voNMBo3DpbvL1V3OBrnx+QfcSjpEGb5zMKTTq0bOauuVCDuryxcP5WJiuJqmNkaYOyMvnAfZAUtocbOzVKpVComEAg0d56LhlOpVAxAo7tCUkJECCEaRnr1Ku4uWQo9Pz/Yhm2gCdT1xBXE4ZPzn2CIzRAs6L+gxe3KCqsQe0qC+Oi7kFcpYS82xehXxXD06hYTpW/m5+d7iUSiEkqKWk+lUrH8/HxjADcbq0MJESGEaBBZejoy586D0MoK9lu3QNCr4zcX7M6KqoqwKGoRzPXMETYiDEJB8z/G8jPKcO33DCTH5AEA3P0t8dgTXTtRur0UCsUbOTk5X+fk5PiAtsxpCxWAmwqF4o3GKlBCRAghGkJRVATJW3MAzuG4YzuEZmbqDkmjKFVKLP1zKQoqC7Dn6T0w69X47w/nHBnx9ydKZybcnyjtO8YefmMcYGTW/ZLMgQMH5gEIUnccjzJKiAghRAOoqqqQOXce5NnZcPzmG+g4O6s7JI0TcTUC57PPY23AWnhbeDdYR6lQIflSLq6ezEDh3QoYGOtg2HOu8A6kidKkaZQQEUKImnGVCnff/wCV167B7ssvoT+AdqCu7/f037Hr5i487/E8nnV/9qHr1VI54v66i+unJKgokcHczgBjZ/aFu79GT5QmGoQSIkIIUbO8z8JR9ttvsHz/ffR+apy6w9E4d0ruYHn0cvSz6IcPB3/4wLXSgkpcP5V5f6J09f2J0mNm9IVD324xUZpoEEqICCFEjQr37kXh7t0wnT4dZjNnqDscjVMhr0BIVAh6CXth46iN0NG6f9h5fkbNjtKX88AAuA2qmSjt0H0mShPNQgkRIYSoSdmpU8j9dB0Mx4yB1bIPaUSjHs45VkSvQEZpBnY8uQNW+lZIv1mAqyczkJVYBO1eWvAbYw/fbjpRmmgWSogIIUQNKm/cQFboYvTy9obd5+FgWlrqDknjRN6MxO8Zv2Nx//dglGqPAzsv3p8obaKLgOfc4PW4LXT16McY6Rj0N4kQQrqYLDMTkrffgdDCAg7btkKgR+dl1Xfu7jn8+9JOTKueC77XFadKEmBuZ4gnZvaFG02UJp2AEiJCCOlCyuJiSN58C1yhgMOOHRBaWKg7JI2TnJGG/f85hVdyVkOo1IG5lyGemOEI+76m9FqRdBpKiAghpIuoZDJkzn8X8sxMOO6OhG4fF3WHpFHy0ktx+UQabl/JgzsGw6G/MQIn9IWFPU2UJp2PEiJCCOkCXKVC9gcfQhoTA9vPw6Hv76/ukDQCV3GkxxXg2skMZCUVQ6WtwHWbM3jhubEY5zNY3eGRHoQSIkII6QL5X3yJ0l9+gWhxKIwnTlR3OGqnlKuQeDEH136XoCi7AoamujAcUYHNlWsxc8BrGOczRt0hkh6GEiJCCOlkRQcOomDnTpi8OA3mbzR6tmSPUFkuQ9xfd3EjKhPSUhnM7Q3xxCwvVDrlYtbJ+RjiNARz/eaqO0zSA1FCRAghnaj8zBnkrF0Lw5EjYb1iRY+dFFxwtxzXT2Ui8UIOlHIVHLzM8MSTjrAXm6KgqgDTjobCSt8KGx7fAC0BbUFAuh4lRIQQ0kkq4+KQuSgUvcRi2G38HEzYs/7JrZ0fFPuHBJkJRdDSFsBzqDV8R9vD3NYQAKBQKbDkzBKUVJfguwnfwVjXWM1Rk56qZ/3XSQghXUSelQXJ229Dy8QY9v/eBoGBgbpD6jKyKgUSzuXgepQEJXmVMDDRxdApfeAdaIdehg+eOP/F5S8QkxuDdYHrIDYTqyliQighIoSQDqcsLUXGnDngVdVwioyEtqWlukPqEqX3KnHjdCbi/86GrFIBK5feGPJ6H/QZIIKW1sMbKR5PPY498XvwkvglTHadrIaICfl/lBARQkgH4jIZMt9dAFl6Bhx37oSuu7u6Q+pUnHNkp5Qg9pQEqdfyAcbgNkAE3zEOsO7T+Ouv5KJkrDy7Ev0t+2OJ/5IujJiQhlFCRAghHYRzjuyPPoL0wgXYhm2AwdAh6g6p0yjlKiRfzsX1U5nIzyiDrr4Q/cc5wWekXbMHrZbKSrHo9CIYaBvg85GfQ1tLu8n6hHSFdiVEjDETAF8D8AHAAcwGkAjgIABnAGkAgjnnRez+0opNACYAkAKYyTm/0p77E0KIJrkXEYGSn36GaOECGAcFqTucTiEtlSHuryzcOJOFylIZTK31MWq6JzyGWENbp/nVYSXVJVj651JklWVh11O7INIXdUHUhDSvvSNEmwAc55w/zxjTAaAPYBmAPzjn6xljHwD4AMD7AJ4G4F7zNQTAtppfCSGk2yv+4Qfc27oNxs9Phfnbb6s7nA6XLynD9VMSJF3KhUrB4eRjDr8xDq06X+zPzD+x+uxqFFUV4aNhH2GA1YBOjpqQlmtzQsQYMwYwAsBMAOCcywDIGGPPABhVU+0/AE7jfkL0DIA9nHMO4DxjzIQxZsM5z25z9IQQogHKo/9G9spVMAgMhM2qVY/MXkMqFUfa9Xu4fkqCrKRiCHUE8BpuC9/R9jC1bvmquXJZOcIuheHHlB/hZuKGLWO3oK95306MnJDWa88IkQuAfAC7GWN+AC4DWAjAqk6SkwPAquazHQBJnfaZNWWUEBFCuq2qhARkLVwIXXd32H35BZh2958PI6tU4NbZbFyPkqD0XhUMzXQR8Jwb+g63QS+D1j3f+ezzWPn3SuRKc/FGvzfwjt870NHS6aTICWm79iREQgADALzLOb/AGNuE+6/H/sE554wx3ppOGWNvAXgLABwdHdsRHiGEdC55Tg4kc96GwMgIDtv/DS1DQ3WH1C7FeVLciMrErXPZkFcpYeNmjIDn3ODiZwFBA8vmmyKVS/HF5S9wIPEAnHs7Y8/Te+An8uukyAlpv/YkRJkAMjnnF2q+/y/uJ0S5ta/CGGM2APJqrmcBcKjT3r6m7AGc8x0AdgCAv79/q5IpQgjpKsqyMkjemgNVeTmc9u2FtpVV8400EOccWYlFiD2VibQb9yAQMLj5W8JvjAMsnXq3qc8ruVew4u8VyCzLxCt9X8GCAQugJ9Tr4MgJ6VhtTog45zmMMQljzJNznghgLID4mq8ZANbX/PpTTZOfAcxnjB3A/cnUJTR/iBDSHXG5HFkLQ1B95w4ctv8bvTw91R1SqynkSiRdzMX1UxIUZFWgl6E2/J92hs9IOxgY67apz2plNSKuRGBP/B7YGtoi8qlI+Fv7d3DkhHSO9q4yexfA3poVZncAzAIgAHCIMfY6gHQAwTV1f8H9JfcpuL/sflY7700IIV2Oc47sVatRcfYsbNatg+Hw4eoOqVUqSqpx80wW4v7KQmWZHOZ2hhj9qhgeg60g1G77oao3793E8ujluFNyB8EewVjsvxj62vodGDkhnatdCRHn/BqAhtL/sQ3U5QDmted+hBCibve2bUPJ//4Hi3nzYPLcs+oOp8Xy0ksRe0qClJg8qFQczv0s4DfWAXYeJu1aFSdXyrEtdhsib0bCQs8C25/YjgC7gA6MnJCuQTtVE0JICxUfPox7myNgPGUKLOZr/v/fqZQq3Ll2f9l89u0SaOtqwWekHXxH28NY1P7Rm8TCRCyPXo7EokQEuQbh/cHvo7dO2+YdEaJulBARQkgLVJw7h+wVH0F/6FDYrF2j0XsNVVXIEf/3Xdw4nYnywmr0tuiFwBfcIQ6wga5e+//ZV6gUiLwZiW2x22CsY4zNozdjtOPoDoicEPWhhIgQQppRlZSEzHcXQNfFBfYRm8F0NHMfnaKcClyPykTCuWwoZCrYeZjg8WAPOPtaQCDomATuTvEdLI9ejpsFNzHeeTyWD1kOk14mHdI3IepECREhhDRBnpt3f68hPT047NgOLSMjdYf0AM45JLcKEftHJjLiCiAQMngMtobfGHtY2HdcrEqVEt/d+g6br2yGvrY+Phv5GcY7j++w/glRN0qICCGkEcryCkjeeRuqkhI4ffcttG1s1B3SP+QyJZIu5CD2VCaKsiug11sHgye7wPtxO+j37tgRLEmpBCv+XoEreVcwymEUVg1bBQs9iw69ByHqRgkRIYQ0gCsUyFq0CNWJSXD49zb08vJSd0gAgPKiKtw4nYW46CxUVyhg4WCIJ2b2hdtAK2hpt2436eaouAqHEg9h4+WNEDIhPg38FJP7TNbo+VOEtBUlRIQQUg/nHDlr1qLir79gvXYNDB9/XN0hIT+jDFdPpCPlSj7AOfo8JoLvGAfYuBl3SoKSXZ6Nj85+hAvZFxBgG4A1AWtgbWDd4fchRFNQQkQIIfUU7NiJ4u+/h/mcOTANDm6+QScqK6zChZ/uIPFCDnT0hPAbY49+o+zR26JzjsLgnONwymGEXQqDiquwcthKPO/+PI0KkUceJUSEEFJHyZGjyP/iC/SeNAmikIVqi6O6UoErx9MRe0oCcGDAU04YMN6pQ5bNNyZfmo/V51bjz8w/4W/lj4+Hfwx7I/tOux8hmoQSIkIIqVFx8SKyly2D/qBBsFn3qVpGRZRKFeL/uouLR1NRVS6H5xBrDHmmD4zMenXaPTnn+CX1F6y7sA7Vymp8MPgDvCR+CQLWsXOSCNFklBARQgiA6tu3kTn/XWg7OsL+qwgIunivIc455rFxkwAAIABJREFUUmPv4dyPt1GcK4WdpwkCnnNr84nzLVVYVYhPzn+Ck+kn4SvyxSfDP4GLsUun3pMQTUQJESGkx1Pk50Py1hwwHR04bN8OLWPjLr1/bmop/v4hGdkpJTC11sfEeb5w8jHv9BGqP9L/wNrza1EmK0PIgBDM9J4JLUHbD3glpDujhIgQ0qOppFJI3n4HisJCOO3ZAx17uy67d+m9Spw/fBvJMXnQM9LGyJc94TXcBgKtzn1VVVJdgvUX1+PonaPoa9YXX4/7Gu6m7p16T0I0HSVEhJAeiyuVyFr8Hqpu3YL9lq+g18+nS+5bVSHH5V/TcP10JgSMwX+CM/qPc4ROr87/J/mvzL+w+uxqFFYVYq7fXLzh+wa0Bdqdfl9CNB0lRISQHolzjtxPP0V5VBSsVn4Eo9GdfzipUqHCzTNZuHQsFdWVCvQdZoPBk/vA0FS30+9dLitHeEw4fkj+AW4mbogYGwEvc83YbJIQTUAJESGkRyqM3I2iffth9vpsmL38cqfei3OO21fyce7HFJTeq4KDlxkCnnPt0LPGmnIh+wI++vsj5EpzMdtnNuY9Ng86Wpp5QC0h6kIJESGkxyk9fhx5n30Go6fHw3Lx4k69V/btEpz9IRk5d0phbmeAye/6wdHbvFPvWUsql+LLK19if8J+OPV2wn/G/wePWT7WJfcmpLuhhIgQ0qNIr1zB3aXvQ2/AANiuXw8m6JwJzMV5Upz/8TZuX82HvrEORr8qhniYDQSCrtnb6FreNSyPXo6Msgy80vcVLBiwAHrCztndmpBHASVEhJAeozo1FZnvzIW2rS3st3wFgW7Hz935P/bOO8ySqk7Y76mbY+fcPd0TegITQaLEAUREjCvMrnwrpoVVYEFFBXUXn11QMaxKkAVRMeACIq6KCSUo2QEGJjA593RP5+6bQ1Wd74+qvn07zfRM5+7zPk89derUuXXP6Xv73vf+TqhULMv63+9l818PoTk1Tn3XfNZcOA+XZ3Kms6eNNHdvuJsHtjxAdbCaH779h5xSecqkPLdCMZNRQqRQKOYEemcnB6+6GhwO6u67F2dR0fheP2uw8ekmXv3DfrIpnWVnVXPqpfMJFEz8gOk+tnRs4YvPfZHdvbu5bPFlfObkzxBwBSbt+RWKmYwSIoVCMesxk0kOfvKT6O3t1P/4Adzz5o3btaUp2flKKy/93x6iXSnqV5ZwxvsWUlIdHLfnOBpZI8u9G+/l/k33U+Ir4Z4L7+GsmrMm7fkVitmAEiKFQjGrkYZB8+c+R2rjJmrvvAPf6tXjdu1DO7p54Ze7aNsfpbQuyPkfWkPt0uJxu/5o2NG9gy8+90W2dW3j3QvfzedP/Txh98Te7kOhmI0oIVIoFLOa1ttvJ/rnv1DxhS8QuvDCcblm9+E4Lzy2m30bOwgWebjww8tYfGolYpIGTAPops4DWx7g7tfvJuwO89213+X8eedP2vMrFLMNJUQKhWJWInWdtm98k+6f/JTiKz9E8Yf+eczXTEQyrP/dXrY824zTrXH6exew+vw6nO7Jvf/Xnt49fOm5L7GpYxNvb3g7XzztixR5x3dMlEIx11BCpFAoZh16VxeHPv0ZEi+9RNEVV1D+uc+N6XrZjMEbTx7ktT/tR8+YrDi7mpPfOR9/eHIXNzSlyc/e/Bl3bLgDr9PLN875BhfPv3hS66BQzFaUECkUillFcvMWmv7tOozOLqq+9lUK3/ve476WNCXbXz7My7/ZQ6w7zfzVpZzxvoUUVU7+zK2maBNfev5LvNr6KufVnsctb72FUl/ppNdDoZitKCFSKBSzhp7HfsXhL38ZR2kJ9Q8+iG/F8uO+1sFtXbzwy110HIxRXh/ibR89gerGqemWeq31Na5/+np0U+fWM2/l3QvfjRCTN15JoZgLjFmIhBAO4BXgkJTyUiHEfOAhoAR4FfhnKWVGCOEBfgK8BegE1kkp9431+RUKhUJmMrR+7Xa6f/5z/KefTs1/fwtn8fHN9uo8FOOFx3ZzYEsnoRIvF31sOYveUj6pA6bzeXzP4/zH8/9BTbCGuy+4m3nh8VsyQKFQ9DMeEaLrga1A3zzP24FvSykfEkL8D/Ax4B573y2lXCSE+Ee73LpxeH6FQjGH0dvbabrhUyRffZXij3yE8s98GuE89o+2eG+av/92L1ufb8btc/LWf1jEqvNqcbgm5tYeR0NKyf+88T98743vcUrlKXz7vG9T4CmYkrooFHOBMQmREKIWeCdwG/BpYcVwzwf6bh39Y+DLWEL0HjsN8ChwlxBCSCnlWOqgUCjmLsnXX6fp367HiESo/tY3KXjnO4/5Gtm0wYY/H2DDnw9g6iar1tZx8iUNeIOuCajx6MgYGW554RYe3/M47174br58xpdxOaauPgrFXGCsEaLvAJ8DQvZxCdAjpdTt4yagxk7XAAcBpJS6EKLXLt8xxjooFIo5SPfDj3D41ltxVVbS8PBDeJcsOabHm6Zk2wstvPzbPSR6Myw8qZwz3reAgjL/BNV4dPSkerj+6et5re01rjvxOv5l5b+o8UIKxSRw3EIkhLgUaJNSviqEOG+8KiSEuAq4CmDeOC6vr1AoZgdmJkPrf91Kzy9+QeDss6n5xtdxFBaO+vFSSg68aQ2Y7mqOU7kgzMVXraRq4dR3R+3r3cc1T17D4fhhNaVeoZhkxhIhOhN4txDiEsCLNYbou0ChEMJpR4lqgUN2+UNAHdAkhHACBViDqwcgpbwPuA/g5JNPVt1pCoUiR/bwYZquv57UGxspufpqyv7tOoRj9Isith+M8sIvd9G0rZtwmY+Lr1rBghPLpkUEZv3h9dzw9A04NSc/ePsPWFO+ZqqrpFDMKY5biKSUNwM3A9gRohullFcIIX4BfABrptmVwK/th/zGPn7RPv+UGj+kUChGS2L9eppu+BQymaTmju8SvuiiUT821p3i5d/sYdtLh/H4nZx1WSMrzq3B4ZyaAdOD+c3u33DLC7dQF6rj7gvupi5UN9VVUijmHBOxDtHngYeEELcCG4Af2Pk/AH4qhNgFdAH/OAHPrVAoZhlSSrp/9iCtt9+Ou7aW2h8/gGfRolE9Np3U2fDEft74y0FMKTnxwnm85R31ePzTY4CylJK7X7+bezfey2mVp/Hfa/9b3ZhVoZgixkWIpJTPAM/Y6T3AqcOUSQGXjcfzKRSKuYGZSnH4li/T++tfE1y7luqv344jFDrq4zIpnY1PN/H6nw+QTug0nlLB6e9ZQLjUNwm1Hh1pI82/P/fv/GHfH3h/4/v50ulfwqVND1FTKOYiaqVqhUIxLckeOkTTdf9G6s03Kb3uWko/8QmEduQurkxKZ/NfD7HhiQOk4lkaVpVy6qXzKZt3dImaTLpSXVz/1PW83v46N5x0Ax9d8dFpMY5JoZjLKCFSKBTTjvhLL3Hohk8hdZ3ae75HaO3aI5bPZgxbhPaTjGaZt7yEU981n4qG6df9tKd3D9f85Rrak+1869xvcVHD6MdCKRSKiUMJkUKhmDZIKen60QO0ffObuBfMp+6uu3A3NIxYXs8YbHm2mVf/tJ9kJEPdsiJOfdcCKhdM/RT64Xi55WU+9cyncGkufvj2H7KqbNVUV0mhUNgoIVIoFNMCM5Gg5Uv/TuT3vyf09rdT/ZXb0ALD31XeyJpsea6Z1/64j3hvhpolRZx61QqqF41+PaLJ5lc7f8V/vvif1IfrufvCu6kJ1hz9QQqFYtJQQqRQKKaczIEDNF17Helduyj7zKcp+fjHhx1TY+gmW19o4dU/7CPWnaZqUQFv++hyapZMzV3oR4MpTe7ccCf3b7qfM6rO4FvnfYuQe3qNaVIoFEqIFArFFBN79lkOfeZGEIK6++4jeNaZQ8oYhsn2Fw/zyu/3Ee1KUbmggPOvXEbtkqJpPRg5paf44nNf5In9T3DZ4su4+bSb1UwyhWKaooRIoVBMCVJKOu+9j/bvfhfPkiXU3nkH7rqBCxKahsn2l1t55fd7iXSkKG8Ic94VS6g7oXhaixBAR7KD65+6nk0dm7jx5Bv50AkfmvZ1VijmMkqIFArFpGPE4rTcfBPRP/+F8KWXUvVf/4nm618jyDQlO9e3sv7xvfS2JymbF+Kd1yymfkXJjJCKXd27uPapa+lMdvLttd/mgnkXTHWVFArFUVBCpFAoJpX0nr00XXcdmX37KL/p8xRfeWVOckxTsuvVVtY/vo+e1gQltUEu+cRKGlaVzggRAnih+QU+88xn8Dq9PHDxAywvXT7VVVIoFKNACZFCoZg0ok89RfPnPo9wu5n3gx8QOP00AKQp2b2hnb8/vpfuljjF1QEuvnoFC1aXIbSZIUIAj+54lFtfupUFhQu4+/y7qQpWTXWVFArFKFFCpFAoJhxpmnTcdTcd3/se3hUrqL3zDlxVVUgp2ft6B39/fA+dh+IUVfq56OPLWXRS+YwSIVOafOfV7/CjLT/izJoz+eY53yToDk51tRQKxTGghEihUEwoRiRC82c/R+yvf6Xg/e+n8pb/QLjd7H3Digh1HIxRWOHnbR87gUVvqUCbQSIEkNST3PzszTx54EnWLVnHTafehFNTH60KxUxD/dcqFIoJI71zJwevvZbsoWYqb/kPCtat48CWLtY/vpe2/VHCZT4u/PAyGk+pQHMc+T5l05H2RDvXPXUdb3a+yedP+TxXLLtixox1UigUA1FCpFAoJoTIH/9E8xe+gBbwM++BH9Hpn8+T33iN1r0RQiVezv/QUpacVjkjRQhgR/cOrnnyGnrTvdxx/h2cV3feVFdJoVCMASVECoViXJGGQft3vkPn9+/Hu2YN4vrb+NPT3bTsfoNgsYfzrljC0rdW4ZihIgTw3KHnuPGvNxJwBvjxxT9mWcmyqa6SQqEYI0qIFArFuKF3d9N842eJP/882fddxbaSs2n+8T4ChR7O/afFLHtrNQ7XzBUhgIe3PcxX//5VGosaufP8O6kMVE51lRQTjJ7JkIj0krS3RDRipaMREpFeTnz7pZTVz5/qairGiBIihUIxLqS2bqXp2uvoSAZoet93aO124TdTnL1uMSecVYXT5ZjqKo4JwzT41qvf4qdv/pRza8/l6+d8Hb/LP9XVUhwjUkoyySRJW2r6RCdhC05+niU8EbKp5LDXEpqGNxRm4VtOU0I0C1BCpFAoxkzvb3/LttvvZ2/DOjqDC/HpLs66rIHlZ1fjdM9sEQJIZBN8/tnP88zBZ7hi2RV89uTP4tBmfrtmA9I0ScVjAyQmGYkMEZ3880Y2O+y1HC4XvnAB/lABvnCYoqoa6zhcgDcUQrrcJA1JNJWmNxajo7OLjvY2ZDA8ya1WTARKiBQKxXEjs1m23XYPb2yBzhXX4/U7eOvF81lxbg0uz+wQhrZEG9c+eS3bu7dz86k388FlH5zqKs1qTMMYIDD9+0gugjMgohONIE1z2Gu5fT58oTC+cAGh4hLK6xfgC4fxhwtyouMLh/GFCvCHw7i8PoQQJBIJ2traaG1tpa2tjT1tbbRt2006nc5dO+x3U643s9DcR7i1HpYsnaw/kWKCUEKkUCiOi8ObDvLcd5+k1bkCd1mW0y9tYOX583B7Z8/Hyvau7Vzz5DVEM1HuPP9Ozqk9Z6qrNC2RUmIaOtl0Gj2dJpux9+k0embQ3j6fikX7RSevuyoVj434PN5gyBaZMEVV1VQvWWZJjS00vnzRCYVxut1HrHcmk6G9vZ29zS20vf5GToBisf46eL1eKioqWLVqFeXl5ZTrTZRvvAdfy8tQNB/eeTOs/MC4/S0VU8fs+eRSKBSTQuehGC8++Dr792RwUsrqxWlO/cTbcPtm18fJ35r+xmf/+llC7hA/ecdPWFK8ZKqrdFyYpoGeyQwVlHxxGVZgUqMsZ+1HitKMhNA0fKH+aE1ZwwJLauyIzhDRCYXRHMcXdTQMg66urpzw9G1dXV25Mk6nk7KyMhYuXEhFRYUlP+XlhEIha22pfc/D07fA/uchXAvvugPWfBAcruOqk2L6Mbs+wRQKxYTR2Rxj/eP72P1aGw49ycKe9ZzxhX+g4KSVU121cefBrQ/y9fVfZ0nREu664C7K/eWT9tyGrpOKRUnFYtY+3p9OJ+JHlZrB50YaL3MkhNBwejy4PB6c7oF7byhMqMRjnXd7Riw34Pygck63B7fXi9DGd8ahlJLe3t6c8PQJUEdHB4Zh2G0TlJSUUFlZmYv6VFRUUFRUhDZcfZpegaduhT1PQ7ASLvkmnPQhcHrGte6KqUcJkUKhOCLdh+Os/90+dr7SihOdhn1/ZnFVhPnf/xrOoqKprt64YpgGX1//dX6+7eesrVvL187+2nHPJDP0bE5kkrEoqWjUFp0oqbidn8uL2eITJZMcfkZTH87BkmHv3V4f/nDhqKVk4N5tl/Hi9HhwOJ3TfsXtRCIxJOLT1tY2cJxPOEx5eTmLFi3KRXxKS0txuUYR1WnZCE9/BXb8AfwlcNFtcMrHwOWbwFYpphIlRAqFYlh6WhO88vt97Pj7YRwujYWp16le/3MqrlxH+ae+inDOro+PeDbO5/72Of7W9Dc+dMKH+PRbPo1Dc6BnMv0iE4uRtMUllS8zdiQnGYvl8rPp1IjPpTkceIMhvIEg3mCIYHExpfPq8YVCeAMh61wwaO9DVn4whNvrG/eoynSnb5xPfsTnaON8KioqKCsrw+c7Dnlp2wbPfAXe/DV4C+D8f4fTrgZPaBxbpZiOCCnlVNdhRE4++WT5yiuvTHU1FIo5g54xaNreza5X2tixvhWHQ7B0mYvSR/4LR28b1bfdSviSS6a6msdF30DefJFJ2qLT2X2Yp3f8mVQ8ymLvfMKmj6QdxdHzIg6D0RxOvMEgvlC4X2AC/TLTJzL5ed5gCLfPN+0jMJONYRh0dnYOiPa0trbS3d2dK9M3zid/jM+AcT5joXM3/PV22PgIuINwxifh9E+Cr3CMLTt2hBCvSilPnvQnnuPMrp94CoXimIl1p9m3qYP9mzpo2taNnjVxeRysWlvDguQGer9xG66qKmofegjvksVTXd1REeloY98br7Hvjddo2bWDVCSCns2MWN7QJGGXSUPRPIq9pfhCISqCoQFRnAGCY8uNy+OdtWIjpRyyjZR/PGV7enoGRHyGG+dTVVXF6tWrcwI04jifsdBzAP76dXj95+Bww5n/Bm+9HgIl4/s8immPEiKFYo4hTUnbgSj7Nnawb1MHHQetrodQiZdlZ1Uzf2UplfU+2r9yK72/fIzAOWdT841v4CgomOKaj0w2leLg1k22BG2gu7kJgGBJKXUnrCRYVDywG8qO2GyIbObLG24l5C/k7gvvprGocYpbMpRoNMquXbvYtWsXnZ2d4yIjoyk7WYTDYSoqKo5vnM9YiLTAs9+EV38MQsCpV8FZn4JQxcQ+r2LactxCJISoA34CVAASuE9K+V0hRDHwMNAA7AMul1J2C+tn1HeBS4AE8GEp5Wtjq75CoRgNmZRO09Zu9m3qYN/mTpKRDEJA5cICznjfQhpWllJU5QddJ/r00zR96V5Sb75JySf+lbJrr0Uc53TniUJKSfv+vezfuIF9b7zGoW1bMHQdp8tN7fKVrL7wYhpWn0RxTd2wERwpJT9986d8c8M3WV6ynDsvuJNSX+kUtGQopmnS1NTErl272LlzJy0tLQAEg0Gqq6vRNC3XJiHEqLbpWLZvwLPX653cP3CsHZ7/Dqy/H0zdmjF29o1QUDO59VBMO8YSIdKBz0gpXxNChIBXhRB/Bj4MPCml/JoQ4ibgJuDzwDuARns7DbjH3isUigkg0pFk36ZOqytsRzemLnH7nMxbXkzDylLql5fgDVq/wrPNzbTfcT89jz6K0d6Bs7KS2rvuJHThhVPcin4Skd6cAO3fuIF4jzW2pLSunjUXv4uG1SdRu3T5URfj002dr/39azy8/WHeVv82bjvrNnzOqZ05FIvF2L17Nzt37mT37t0kk0mEENTV1XHBBRfQ2NhIRUXFrO2emxQSXfDCnfDyvaAnYfU/wbmfg6KGqa6ZYppw3EIkpWwBWux0VAixFagB3gOcZxf7MfAMlhC9B/iJtGKxLwkhCoUQVfZ1FArFGDFNSeueXvZt6mTfpg66muMAFFb4WXlerdUVtqgAh8MagyENg+jTT9Pz0MPEnn0WpCR4zjkUrltH8Jyzp3wWmaFnad6xLSdBrXt3g5R4Q2HqV66hYdWJ1K8+kVDx6CM7sUyMG/92I88fep6PrPgIN5x0A5qY/FlbpmnS3NzMzp072blzJ83NzQAEAgGWLFnCokWLWLhw4fHNklIMJBWBl74HL94N6Sis+Ac47yYonX7do4qpZVw+8YQQDcCJwMtARZ7kHMbqUgNLlg7mPazJzlNCpFAcJ+mkzoEtnezf1Mn+zZ2k4lk0TVDVWMCZH1hEw8pSCisGrqOTbW2j55eP0vOLR9FbWnCUlVJy9VUUfeADuGqmttug53CLNQ5o42sc2LyRbCqJ0DSqFy/lzMuuoGH1SZQvWIh2HDdWbYm1cM1T17C3Zy+3nHELH1g8ubdbiMfjA6JAiUQCIQQ1NTWsXbuWxsZGKisrx3/Q8FwlE4e/3wfPfxeS3bDsXXDeF6DihKmumWKaMmYhEkIEgV8CN0gpI/khXSmlFEIc0+g8IcRVwFUA8+bNG2v1FIpZR09rwhoLtKmTlp09mKbEE3BSv6KEhpWlzDuhGI9/4IBUaZrEn3+BnkceJvrU02AYBN76VipuvonQ2rWIiR7AOgKZZIIDmzfmusF6Wq3fRwXlFZxw9nnUrz6JectX4fEHxvQ8Wzq2cO1T15LW03zvwu9xRvUZ41H9I2KaJi0tLezcuZNdu3bR1GQN9Pb7/SxatIjGxkYWLlyI3398Cz8qRiCbgld+CM/9N8TbofEiWPsFqD5xqmummOaMSYiEEC4sGXpQSvmYnd3a1xUmhKgC2uz8Q0Bd3sNr7bwBSCnvA+4Dax2isdRPoZgNGIbJ4V29OQnqaU0AUFwdYM3b6mhYWUrFggI0bej4Er2jg57HfkXPL35B9uBBHMXFlHzkwxRedhnu+vrJbgrSNGnbtyc3Jb55x1ZMw8Dl8VK3YhUnXfJuGlafRGFl9biNl3ly/5Pc9OxNlPhKuP+i+1lYuHBcrjscyWQyFwXatWsX8bjVbVlTU8N5551HY2MjVVVVKgo0EegZ2PAT+Nu3INoM88+F878EdadOdc0UM4SxzDITwA+ArVLK/8479RvgSuBr9v7XefnXCiEewhpM3avGDykUw5OKZdm/xRoQfeDNLtIJHc0pqFlcxMrzamlYWUK4dPjxJVJKEi//ne6HHyL6lychm8V/6qmU3XA9obe9De0og47Hm1h314DB0MloBIDyhoWcfOn7aFh9EtVLluFwjm+UqjnWzH0b7+OxnY+xsmwld6y9gxLf+K4tI6Xk8OHDubFATU1NSCnx+XwsXLiQxsZGFi1aRCAwtgiX4ggYOmx8yFpUsecA1J0O778X5p8z1TVTzDDGEiE6E/hnYJMQ4nU77wtYIvSIEOJjwH7gcvvc77Gm3O/Cmnb/kTE8t0Ixq5BS0t3S1xXWweHdvUgJvpCL+WvKmL+ylNplRbi9I//L6t3d9P7fr+l5+GEy+/ahFRRQ/MEPUrjucjwLFkxaW/RslkPbtlgC9MZrtB/YB4C/oJD5a95C/eqTqF+5hkDhxNwHrS3Rxvc3fp9Hdz6KQHDFsiu4/qTr8TrHZ3p3KpUaEAXqu4VEVVUVZ599No2NjdTU1Kgo0ERjGrD5MXjmq9C12+oSu/TbsPACa10hheIYUbfuUCgGYRgG2WyWTCZDNpsdkB5p35cGcLlcOJ1OXC7XgPTgvSYcdDcnad0VpXlHhFhnFiE1SmvDzF9VRsPKUsrrQ4hhusL6kFKS3LCB7oceIvrHPyEzGXwnnkjhussJX3wx2iSs8SKlpKu5if12N9jBNzejZ9JoDic1S0+gYfVJNKw+ibJ5DRN6H67OZCc/3PxDHt7+MIZp8L7G93HVqquoDFSO6bpSSlpbW3MCdODAAaSUeL3eAVGgYDA4Ti1RHBEpYetvrRuvtm+FihXWGKEll8waEVK37pgalBApZhxSSnRdH7WkHGuZvtsHjBZN03C73bmVdbPZLLquo+v6cbfR4XAcUaYcAK2tmPv2QVc3TodGYOEigqtW4a2qPOJjh5WzYxSVVDzGgc1v5MYCRTvaASiqqskJUO0JK3B7J37aeG+6lwe2PMCDWx8kbaR514J3cfXqq6kL1R39wSOQSqXYu3dvrissGo0CUFlZmROg2tpaHNNswcpZjZSw8wl4+jZoeQNKF8N5N8MJ74VZFo1TQjQ1qFt3KKYE0zTp7u6mubmZ7u7uo4rLcJGYY6FPBPrEpW8fDAaH5A3eH+mc2+0e8UvRNE10Xaf1QA/7NrdzYGs7Hc0RJCaeoEbZ/CBldQHCFR5MaeRE6kj7TE8PkbY2MtEohqZhFhRgVlSgC2EJ2MY3rO0Y0TRtiDR5vV58Ph8+nw+v14uRTBBrb6Xn0AEiTQfByOJ2uZi3ZBmnvucy5q95CwXlk3fbg1gmxk+3/pSfbPkJ8Wyci+dfzCdWf4L5BfOP+VpSStrb23MCdODAAUzTxOPxsGDBgpwEhcPhCWiJ4ohICXuesUSoab21kOL77oWVl8FxLL+gUIyEihApJhzTNOnq6qK5uZmWlpbcls67i3jfF/JY5WS4Mk6nc1LHc/TdMb5vlehYt9XO8voQDatKaVhZSmldcNSzqIxYnMjjj9P9yMOk39yK8PspeOc7KVy3Dt+K5blyfZGzPnkajWAdqWwsGiHS3UMyESdrGMgjfPkIIXLydLTN7/fn0h6P55hfm0Q2wf9u+19+tOVH9KZ7uWDeBXxyzSdZXHRsN55Np9MDokCRiD3Yu7ycxsZGGhsbqaurU1GgqWT/i/DUrbD/OQjXwrmfhTVXgGNqlomYLFSEaGqYMxF+uvX0AAAgAElEQVQiKSWPPPIIBQUFlJWV5Ta1Euz4YpomnZ2dtLS0DBCgvqiO0+mkoqKCVatWUVVVRVVV1eTcyHGCkFKSiGToaonTdShO0/ZumrZ2oWdNnB4HdUuLOOXS+dSvKCFQ4Dmma6e2bqX7oYeJ/Pa3mIkEniVLqLzlPwi/6104hhmvIoTICeHxvK+jnR00bd3MoW1bOLhlEz32DVJLikusLrAVqylftASpOUgmk0fcYrEY7e3tJBKJo0b0RitQDo+Dp1ue5ue7fk5bto2za8/mmhOvYXnJ8iNevw8pJR0dHbmxQPv378cwDNxuNwsWLODcc89l0aJFFEzjm9jOGQ69Ck/dBrufhGAFvOMb8JYrwXls/0MKxbEwZyJE6XSaH/3oR3R0dAwY2xEMBgcIUmlpKWVlZQQCAXXfoKNgmiYdHR1D5CebzQKW/FRWVlJVVUV1dTVVVVWUlZXNyF/cA8SnOU5XS5xuO51O9L+fQsVeGlaW0LCqlOrFhThdx9ZWM5kk8vs/0P3ww6Q2bkR4PIQvuYSidZfjXb163N6TUkp6DjfTtHULh7ZtoWnrZnrbWgFw+/zULFnGvJVraFh9EiW188b0vIZhHFWg8rdEIkEymRwQQRyO/C69I0WhDMNgz5497Ny5k56eHgDKyspy3WDz5s3DOcW3KVHYHN5kDZbe/nvwl1h3nz/5Y+CeW4tXqgjR1DBnhKgP0zTp7e2lvb19yJb/S9bn8w0Qpb4tFArNSVEyDGOI/Bw+fDgnPy6Xa4j8lJaWzjj5GSw+3S3xXDpffDx+J8XVAYqrAhRVBXJpf9h9XO+P9M6ddD/8CL2//jVmNIp74UKK1l1OwXveg2McIhbSNOk4uJ+mrZtzEtR3c1RfuIDapcupXbacmmUrKKtvOK5bY4w3GT3Dr7f+mgc3PUh3tJuloaVcXHMxVZ6qYQWqb0ulUkOu5XK5mD9/fq4rrLCwcApapBiR9u2WCL35f+AtgLdeB6f9K3hCU12zKUEJ0dQw54RoJKSURCIR2tvb6ejoGCBKyWQyV87j8eSiSPlbQUHBrFl3xDAM2tvbcxGf5uZmDh8+nIusuVyuXHdXvvzMpPZPlfjkY6bTRJ94gu6HHib56qsIl4vQ299O0brL8Z188tiiMrpO297dlgBtswQoba+aHCopo3bZcmqXraBm2XKKq2unleQbpsEf9/2Re964h/2R/SwvWc61J17LmdVnjqqepmmSSqVygmSaJlVVVTO2W3ZW07UHnrkdNj0CLj+c/kk44xrwzW1hVUI0NSghOgpSSuLx+JBoUkdHR25BNrAkIV+U+tJFRUXTOkrSJz99UZ/m5mZaW1tz8uN2u6msrMyJT3V1NSUlJTNGfqaD+AwmvXcvPY/8gt5f/QqjpwdX/TyKLl9Hwfvfh7Po+BYrzGbSHN65naatW2jatoXmHVvR7S6noupaapeeQO2yFdQuW0G4rHw8mzNuSCl58sCT3P363ezq2cXiosVcs+Ya1tatnVbCphgjhm4tpPjiXbDhQXC44dR/gTNvgMD4riQ+U1FCNDUoIRoDiURiSDSpvb09N1sFrPVkSkpKhkSUiouLJ33cgq7rw8pP37o7brd7QNSnurqa4uLiGSE/+eLTnTfOp6slTjo+VHyKqizhmUjxGVC/TIboU0/R/dDDJF56CZxOQhdcQNE/rsN/2mnHvGBhOhGnefvWXBfY4d07MQ0dhKCsfn5/F9jS5RO2IvR4IaXk2UPPcteGu9jatZWGcAPXnHgNF9VfhCam/3tPMQzpKHTthe590G3v+457D4KpWyJ08kfhrE9DaPKWa5gJKCGaGpQQTQDpdHpYUeru7s6VEUJQUlIypPttvGZc6bpOW1vbgMHO+fLj8XiGyE9RUdG0l5/pLj6DyTQ10fPIL+j55S8xOjtxVVdTePnlFP7D+3GWlY36OolIL4e2bskJUPv+vUhpojkcVCxstAVoBdVLluENzIwVk6WUvNTyEne9fhcb2zdSG6zlE2s+wSXzL8GpqUHO0xrThGjL8MLTvRcSnQPL+4qgaL61hlCxvV94ARTUTHrVZwJKiKYGJUSTSDabHSBKfenOzk7yX4eioqJhZ755PMNPOdV1ndbW1gEDnltbWzFNE7Bm4+TLT1VV1bSXn2MSn6oARbbw9MnPZIvPgLrrOrFnnqH74UeIP/ccCEFw7VqK1l1O4MwzEaPoQo10tHPIlp+mrZvpsqfAO90eqhqX5MYAVS1agmsSbs8x3rzW+hp3briTV1pfoTJQydWrruY9i96DS1PjfKYN2SR07x9eeLr3g5E3C1A4oKB2oPD0CVBRw5wfE3SsKCGaGpQQTQN0Xaerq2tIRKmzs3PAbSTC4XBOksLhcG7gc1tb2wD5yY/69MnPdB2D0Sc++WN7Zor45CNNk8y+/UR+9zt6Hn0UvbUVZ0UFhR/4AIWXfQBX5cj307Ju7NpsrQFkD4KOtLcB4PEHqF6yzB7/s5yKBYvG/a7wk8nmjs3cteEunm9+nlJfKf+y8l/4wOIP4Ha4p7pqcw8pId4xgvDssyJA+biDluQUNwwUnuL5UFA36xdLnEyUEE0NSoimMYZh0NPTM+yA7mw2i8/nGyI/hYWF00IQRiLem6Z9f5S2/RHaDkRp2x8lGelf7mC6iw9Ys8PSO3aS2raV9NZtpLZtI71tG2YiAUIQOPssitatI3juuYhhxomZpkHHgf3W9HdbgBK91vo4/oJCavIGQJfOq58WU+DHyvau7dz1+l08c/AZijxFfHTFR1m3dB0+p1oYdULRM9aYne69ecKTt2ViA8uHqodGePqO/SWz5uap0x0lRFODEqIZiGmaJJNJ/H7/tJGE4UhEMrTtj9Bui0/7/gjxXkt+hICiqgBl80KU1YWsMT7TTHwA9O5u0tu2kdq6zRagraT37AU7cqcFAniWLsW7dCneZUvxn34G7tqB4yIMPUvrnt25VaAPbXuTdMKaAh8uK6fGHgBdu2wFRVU106r9Y2VPzx7ufv1untj/BCF3iA8v/zBXLLuCgCsw1VWbPSS7+wVncJSntwmk2V/W6e3vxhosPIX14Jp53a/DIaUEQyKzpr0ZSN3MO7bz+tLDnRucp5vIjDFs+eIPLsW3tHjc6q+EaGpQIxdnIJqmEQhMry+UZCxjR36itgBFcvfwQkBRhZ+apUWUzwtTVh+itDaI2zt93n7SNMkeOkTqza0DIj/64cO5Ms6KCrzLlhG88EK8S5fhXbYUV23tkBli2XSKlp07bAHaTPOO7eiZ/inwi884y4oALV0+bafAj5WDkYPc88Y9/G7v7/A6vFy96mo+tPxDhN3q5qjHhZGFlo3QunnoQOZUz8CygTJLcOpOh1UNAyM+wYpJuzO8lBJMkIYJpkTqfXuJNCXow4jJ0YRkOJHJ9J0bmM/x/tbXBMKlWZtT60+7HAi3A83vysuzyjgL1S1FZgPT5xtJMWNIxbM56emL/kQ7+1cHLij3UbWokPL6UC4C5PZNn7eamcmQ3rlzYORn23bMvnWlHA48C+bjP+WUXOTHs3QpzmLrF2AmlSTR00NHpIf4qy+T6OkhEekh3tND277dtO7eNWAK/MoLLrIWQVxywrSfAj9WWmIt3LvxXv5v1//h0lxcecKVfGTFRyjyzu52jzuZuHVn9wMvwf4XoGk9MpPBxA/CgwzPQ4YXwIJzkaFaCNYgA9VIfzk4fEhDgmHae4lsN5GHJZiHkboE0+wXk/xyg9IYVpkh18tP98mNLTuY/efGBYcYKCX5kuJxoAVc/ef6JMbdJyuOIfIy4FpD8jSEY/pONlFMLKrLTHFE0kl9iPxE2vtX7g6Xeimvt6I+5fMsAfL4p8/gSqOnh9S27QPH++zeDfbCk8Lvx7N4MdriRsz6OoyKCrKhIMlEnESvJTqJ3p5+6entyS14OBhPIEBxTV1uAHT14pkzBX6stCfa+f6m7/PojkcBuHzJ5Xx85ccp9ZVOcc2mJ9KQmImstcV1zO5ujKYdmIcPYnZ1YMbSmDKEIcOYjlJMGUIaE/R/5RAIhwBNQzgFQhPg1Kx93zmHhnAMPGflC0sg7HLCoVkRFqew9rlzWl55+3pOMVRwBoiLLSza7OlCHi2qy2xqUEKkyJFJ6XQctKSnr+urpzWROx8q9lpRn/qQJUHzQngD00N+pJRkDzWT3raV1NZtJN58k8iObSQ6O0k7HWScDrJFBRhlpWRDITIeF2lpkkgmSEZ6MfNm8/UhhIYvHMZfUIi/oJBAQSH+ggL8BUV2XgEBO+0LF+Ccg7eG6Ep18cNNP+Sh7Q9hmAbvbXwvV6+6msrAyLPqZhvSlJhJ3ZYbW3ASWYx4nvDk0lmMuI5M6SNeT2hpNI+JFvDgKAyjhfxoARdawInmcQ6UD8fwApMvKThsgemTFKfWLysas2rM2mxBCdHUMH36MRSTSjZt9MvPgQjt+6N0tyZy/e7BIg9l80IsOa0yJ0G+4NROjdYzGStK09FO77Zt9O7aSfTgAeJtrSQivaSlSdrpJONykHE4oNRvbXk4jBR+6cPvCRIsKKA8JzoDt0BBId5QaFbM8JoIetO9/HjLj/nZ1p+RNtJcuuBS/nX1v1IXqpvqqo0JKSUybdjiksVM2DKTJzdGntxYUR595PEqTg1HwInmMdCI4JKteMU+NGczGhE0VxatohatthHHgjVo809E+ObWnd0ViumCEqI5gJ4x6GiK5WZ6tR2I0t0Spy84GChwU1YfpvGUCsrmWdEff3hy5CebSRPr7CDR20ui1+qSSuRt8a4O4p0dJKJRsnp22Gs4AW9RAf6QtU5TsKqGQElpXkSn0I7qFODxB9Qv4kHIrIERs6MXsSxmzPqil6a0/lYCexNkzDTrW1/hpZaXSJkpri25knPqz6HMUwbbIS5arYtqwpqhbT8OrL3oO847NzDP2g+fN/C4/7GD8sjL0wCTnLj0i06f0AzO08EcwW4cAs3vsgTH78JVGbAiN34nWsCFI+BC87vQvKDFdqK1v4R26AU4+BJE7FXqgxWw7AyofyvMOwMqloOSboViWqC6zGYZetag81DcEh+766urJW4NfAR8IRflDWFrvE+9tQ9M4AyJbCpFpKONSHsbve1tRNpb6W1vI9reRm97a279ncG4hYYnq+NKpfFkdTy6gcfjJVheQaiunoLGRgpWrKRg2Qm4/eoXdT7SMK1IRiyTi270ic5weTIztLtw1iOw5MWWmwFCMyTPEh7hcQwv0+mYPQD6RXsA9Cug2+Psihda4lN/hrUvXqDW8lEcFdVlNjWoCNEMxtBNOg/FcoOd2/ZH6DoUx7Tlxxt0UV4fYv7qUjvyY8nPeEZIMqkkkfY+4WkdmG5rJRmNDCivaRoBr5+g20OVw02gsBxPPIE4eAh3bwS3buA2TLwNDfbsLmt6u3fp0mO699dsIjdGJZ7FjGUGRnMG5fVFPYZFE9aXfNCFFnThKfaiBd39eQEr3xF0owWcCE2Q1tP8asev+MmWn9Cd6uaMqjP4l+UfZ0nxEqubSAJSWtFGKXPHSHvadX6+3ZYhefnlTIDB1xvmWrnnONLz2nk5+bHkxhFwIbzO4x+sG++w5edFOPCCNR1eGiA0qFgBb7nSkp95Z6iblioUMwglRNMQKSXphE4ymiERsbZkNJs7TkYzxLrTdDbHMHXrW8Xjd1JeH2LNRfNy091Dxd4xyY+ZTpM4fJjepgP0Hmoi0nqYSGcHkZ4uYrEIsUSctDHwy1eTEp9u4EtlKM1k8WV0fBkdv5326AZ9NdJCIRyhEI6yUrznno/3hGV4ly61Zn3N4qhPbpxKzO6qiWUx4hmrq6ovL190EllbFAYhsKMXltS4KgOW0ARcA0XHzhM+56jfD1kjy692Pca9G++lLdHGaZWn8dUTb2dN+Zrx/WNMd6SEnv39098PvAgdO6xzDg/UngxnfcqKANWeCl61zpJCMVNRQjRJmIZJMpa1xCaSIRHN9AtO7jibEx5zmDU8hABvyI0/5MZf4Gb1+XWU14cprw8RKhkqP1LX0aNRzEgEIxLFjEYwItZmRqMYvRGSPV1Eu7uJRnuJJ+PEMmnihk5CSJJOB1nnwPENmmnagqNTYUoCmoOA20vQ5ycUssbrOMNhtIIwjlAYRziEFg7jCIctASoowBEKIQIBMAVmUh/SZWPEwYgnmInItJEXuekXncF56MN3VQuPw5YYN45iH+554bzIjQst4O6P5vhd1iyiEdBNnXg2TjTTRTwVJxqJWsfZKPGMvc/GiWasfSwTI5a1trZEG12pLtaUreErZ32F06pOm6g/2fTCNKF9a7/87H8Ros3WOU8BzDsNVv+TNQao+kRwTkx3s2lK4hmdSEonmsoSSVp7U0JxwEWR301xwE3Y60Kbg9PSJwMpJT2JLJ3xNO3RDB2xNB2xNJ2x/nSHnb79H1Zx5iK1xMRMRwnRGDCyJom8qE3fPhnJDshPRjMkY9lhZ6JoTmEJTtiSnJLaIP6QG1/Qiddt4nXouLUMXlI4swlIxjHjrZixGMahKMbWCLFIhN5oFCPSixmJYkSjmL29GIkEukMj4XaSdDlJul0k3f37hNuJPujO6w6PRtAVIujzUxUMEyosIlxSSri8ksLqWgKVVTgLwmjhMEJzYqZ0qzsnqSNTBmYyi5k0cvkyqZPt0Ekf1DGTGWTqMGbyIGbSGHnw6mzCqfVHaYKDoji5CI67P4rj1JBSktSTRDNRItk40Wxvv6xkYsQ6Y8QOx3J5+VKTk5tsjKSePHr1hJOgO0jAFSDkDhFwBajwV9AQWsjbGy5mbd05OGbzQnV6Bpo3WF1fB16ytr6Vn0NV9vgfewB0+QmjXuU5lTWIpnQiqay1T1r7aCo7JC+SyhIZdBxL64xmeKcmoMjvpijgptjvpijgojjgzglTbp93PugZfaRwtqEbJl3xTE5k8iWnvU9womk641aePsxnlCagOOChNOimLOShocRPaBqtuq84ftSg6jyklGTTRi5qk4zmiU2f9ET7u7AyyeHHarg8Gj6fhtcLXreJx6Hj1bJ4ZBK3mcStR3GlI7hTPWixXmQijhmLWZKTiGPG4sjk0b/MJGCEQ6QKQ6SCQVJeNwmXk6QmiJs68WyG7KAuLafbTbi4lILySsIVVRSWVBIOlRIKFuP3F+ISblts9AFSY/bl2fkyqSOzw/Xj5OEQaD5nbhPe/rTmdaL5HAifE83lgFny+Ww4JSlPlqQ7TcydIiZjxPR4v8wMt88OPI5n45jyKH9bIOAKEHQFrc09aJ+X9ruCCNODaXjJZN1kMm4SaReJpJPeuKQrkaUrnslt3YkM2bwIpduh4XFpeF0OvC4Nr9PRn3Y5+jenNiTf48wvo+HLS3uGu45TwzmRApaOwsG/90d/Dr0Cur3Kekkj1J+BWXc6sYpT6fVUE0nrtsj0CUt2YNQm3R+9yZebjH7k108TEPK6CHmdhPv2vv7jsNdJyOsi7LP3dhmA7oT1GnXFs3THM3QlMtY+npefyGCM8IPD5RDDCJPLFqbhRcrnnr4z4VJZIxet6RwUuekTnI5Ymk777zPcV57bqVEW9FASdFMatGSnNOihxE6X5aWL/O4Jj8qpQdVTw5wRItMwad7V2989lb/vTZOIpEnGdIwRujLcDh2PyOAhhcdI4NZjuNIRXMkeXIkuXNF2nJF23JkIDnP46eE5XC4cgQBaMIgWDCL9foyAD8PrRfd40D0udKcT3amRFYKsgKyUZE2DjK6TyaZJp1JEujow0wZuzYtb8+DSPPi8BRQUlhEMFhPwFeLzBPE4/bg0Dw7pRGTol5q0ceT7/QgGSYxjqNjYciN8w8iOa/p8iJrSJG2kSekpUnqKpJHMpYccGymS+gjHxgiPsY91c+QF9/rwODwDIjIhV2hIlCbkChFw2/v8fHcIl/CRzrjoHiQyXfEMnfEMXbG+dJqueIaeZHbEaEPY66Qk6KHY/iIsyftCNKQklTVIZU1SWYO03p/O5Q/J688/3o8WpyYGSNRwIuZxOezjPMlyQlDLEtDSBEgRIIVPpPGn2/EfXk9B+ysURbajYWKi0eRdxDb3Ct7QTmC9sYSmTIBISieWPvpr6HM58mSlT14GCc4gyckXnIB7hBlr44SUkkhKpycxSJSGFagM3YnsiLIA4HVpwwuT32114eUiUFZ+od+Fx3l8//9SSmJpPSc1nbE07fliExvYfRUd4fUKepw5sSnNl52Qh9KA29rb+aFpFjVTQjQ1TLoQCSEuBr4LOID7pZRfG6nseApRNpHk4c++iMBap0STJi4jiUtP2FsKl5HCZSRx6mlcRhqnmcZpZHCYaTSHA+H1IzweNI8P4fYgPF5we8DlwnS4MB0OTM2B1DRMNCQCE6tnSJoS05RI+z4/0jCt1ZENCXathNDQ0HJpgUATmpUvHGiaw9rbxw7ptFtzhL+324HmG0Zk8iXGFpjBZYTbMeHL5kspyZrZIcKR1JMD5SP/OC9/OHkZ7vEpI3X0ygxCIPA6vficPrwOL16nvTnsvEHHfaIzXJQmP+1yDFzROpHR6YwNkpq4/Yt2QJ4lOyN9AfR1n+TkJtiX9uREp8T+1d+3d01QNEZKScYwSWVN0nmSlMwYpDI66UwKPRlBT8Yw0/0bmXhuE9k4jmwCh5HA0fd/aiZxG1ak1SNT+GQSr0zhJ4VfDH9LFYCUdLHBbOTvcgkb5FJ2eU7A4QsNEpiB0pIfrckXm6DXOWF/t6nEMCWRZHbEiFNX3/sx73zkCCtuBz3OgZGnQcKUzprDjMux9ulhImzCfn+XBPLEZoSITmnQg3ca/SA7VpQQTQ2T2vEphHAAdwNvA5qA9UKI30gp35zo53a4PZwXGnxrBTdQMD5PoJsI3USTJiYSTZpITGtGkbClU8PqGhp0r5/+ewXZy+07NXBo1nL8ToF0CKQGJiamkOjCxNBMDJdEdxtk3Qa62yTrMsi4dDJug4wrS9qZxRAmhjTQTR1TmuhSxzCNgWnTRI/qmJFRlD1K2jSP7RryOG9J7dbcOSEZLCwF7oKjyktfOvf4YY7dmtv61SglSBNMw5penZ82TetYGmC3J5oy6Elm6Yln6WrLciiZpCsRsX+F63TZv8a7EzrdiQzJrAkIJMKeLW6lXQ6NQr+HQltq5lf7KAoUUhzwUBR0UxLwUBTwUhz0UBLwUDARoXwjO0BSyORJS3a4/IQlM5kYnkwcz5BydtlRdAnmcPrA7QdPANxBcAfAXQmugJ0OIN0BDGeArNNLVvOT0XykNR9p4SPlLsBVtZwFgQBrvC68Lm1aRQOmCw5NUGSLMqNc4SJrmPQkRhKm/vyueIZdbTG64xnieZMonJrI66bysLA8SNngiI4tOcUB98R2pyrmPJM9EuxUYJeUcg+AEOIh4D3AhAtRJpvilfQf0PUshm6g6zpmVkdKE1NKZJ/A2BJjYlrnhEQ6JYZLYjrBcJroThPdLck6LAnJukyyLknWZZJxWvu00yTt0kk7TLKaiS5NdGlgYJKVJgaGlTfsfOoj0Le+ignowNGHGg2Lw45KORBoUqAxzCatMlaavLxBaWmdd/alEQgJQtJ/DelAk45ceSElwn6cQ4JLCjwS3Ka9l+CR4DHBIyVeKfGa4JUSj5Q4kTgw0UihkcSKyVmbIy+t5cpZ6eHL9J/ryweJnjsevbQJIGxv80b3QljbSGSBHns7plpAbhnnI+4Z/hxYiwsamdE/rea0hSVoCYzbFphQlZV2+fOEJjC0XF9+nujgDoxqJWdB/5/SO/oaK8aIy6FRFvJQFhr9bLtU1qAnkcXr0tQsOcW0YrKFqAY4mHfcBEzKfF7N4eD51N/JOkwyXknGadoCY5JxmWTtfSa3l2RcJoZmLezmkhKnlDgluLDSLmnnY6WdUtrnIKRLivT+x7kkOBmYdg26njN3vbzH2em+fKfsf5wDiUP2fRFYeZqd50RaspHLtx6r0R+oGiumHckA7H3+MSBELj9XJj/PvseCRCCFrSTCVhih2fl2Ov+8cFjdkXZZKQQmDqSw1SavrIEDPe+89bjhyjpy9egv21cfbUD5/rKOIc/ncjoIuh0E3BoBT1/a2lwO0beSIP2DNeSgvJH2g8uO8FgY5fXyyw7zGACnd5DADN6CA0XHObX3ulPMDLwuB5UFM7c7SzF7mXZzBYUQVwFXAcybN6rf2KPC5XSxaN3/wyGcOIQDh3DitPf9eQ6cdtqZV05j+oTYdXsDa9Vnh6bl9g6HsNPCOs7bNIeGM6+spgmcmsDhyCuj2ap0tEiCfayC1wqFQqGYLUy2EB0C8m+HXWvn5ZBS3gfcB9ag6vF6YqFpXH3eh8frcgqFQqFQKGYRk/0jfz3QKISYL4RwA/8I/GaS66BQKBQKhUIxgEmNEEkpdSHEtcCfsIa+/FBKuWUy66BQKBQKhUIxmEkfQySl/D3w+8l+XoVCoVAoFIqRUONiFQqFQqFQzHmUECkUCoVCoZjzKCFSKBQKhUIx51FCpFAoFAqFYs6jhEihUCgUCsWcRwmRQqFQKBSKOY8SIoVCoVAoFHMeIeW43R1j3BFCtAP7x/mypUDHOF9zKpgt7QDVlunKbGnLbGkHqLZMV8a7LfVSyrJxvJ5iFExrIZoIhBCvSClPnup6jJXZ0g5QbZmuzJa2zJZ2gGrLdGU2tWUuo7rMFAqFQqFQzHmUECkUCoVCoZjzzEUhum+qKzBOzJZ2gGrLdGW2tGW2tANUW6Yrs6ktc5Y5N4ZIoVAoFAqFYjBzMUKkUCgUCoVCMYAZL0RCiDohxNNCiDeFEFuEENfb+cVCiD8LIXba+yI7f6kQ4kUhRFoIceMw13MIITYIIR6fyW0RQuwTQmwSQrwuhHhlBrejUAjxqBBimxBiqxDijJnYFiHEEvu16NsiQogbZmJb7HOfsq+xWQjxv0II7wxtx/V2G7ZM9utxnG25Qgix0f7ffkEIsTrvWhcLIbYLIXYJIW6a4UUDcOMAAAeDSURBVG35oRCiTQixebLbMZ5tGek6immKlHJGb0AVcJKdDgE7gBOArwM32fk3Abfb6XLgFOA24MZhrvdp4OfA4zO5LcA+oHSmvybAj4GP22k3UDhT25J3TQdwGGutkRnXFqAG2Av47ONHgA/PwHasADYDfsAJ/AVYNM1fk7cCRXb6HcDLee+p3cAC+//kDeCEmdgW+/gc4CRg82S2YQJel2GvMxVtUtvRtxkfIZJStkgpX7PTUWAr1gf2e7C+TLH377XLtEkp1wPZwdcSQtQC7wTun4SqD2E82zKVjFc7hBAFWB+MP7DLZaSUPZPSCJsJek0uAHZLKcd70dEjMs5tcQI+IYQTSyiaJ7j6OcaxHcuwvrgSUkod+Cvw/kloQo7jaMsLUspuO/8loNZOnwrsklLukVJmgIfsa0wa49gWpJR/A7omqepDGK+2HOE6imnIjBeifIQQDcCJwMtAhZSyxT51GKgYxSW+A3wOMCeifsfCOLRFAk8IIV4VQlw1IZUcBWNsx3ygHfiRsLox7xdCBCaqrkdjHF6TPv4R+N9xrdwxMpa2SCkPAd8EDgAtQK+U8okJq+wRGONrshk4WwhRIoTwA5cAdRNU1aNyHG35GPAHO10DHMw718QUfvGOsS3TivFqy6DrKKYhs0aIhBBB4JfADVLKSP45KaXEEoQjPf5SoE1K+erE1XJ0jLUtNmdJKU/CCt9eI4Q4Z/xremTGoR1OrLD5PVLKE4E4Vph60hmn1wQhhBt4N/CLca/kKBmH/5UirF/K84FqICCE+H8TVN0j1WNM7ZBSbgVuB54A/gi8DhgTU9sjc6xtEUKsxfri/fykVXKUqLYMbcuRrqOYPswKIRJCuLDebA9KKR+zs1uFEFX2+Sqg7SiXORN4txBiH1a4+XwhxM8mqMojMk5t6fsVj5SyDfgVVkh90hindjQBTVLKvl9Uj2IJ0qQyXq+JzTuA16SUreNf06MzTm25ENgrpWyXUmaBx7DGUPz/9u4vRMoqjOP495eSlbr2Dwt2CyskiSipizYQkTY2MkICDSNE0gpviiIKxIL1qi7CGyEojEJLCVNsITCpEE0yVHTVzURCKoQuMsX+XXjxdHHOwrTszI76OjNv7+8Dw7x75rzPnDMv7/LMOeedt2UKPE/ej4j7I2IucIa0xqOlLrQvku4hTesviIjTufgU/x3d6sllLVVQXzpCUX2pE8c6UOkTIkkirTE5FhFral4aBJbm7aXAZ43iRMTKiOiJiBmkKY2vI6Kl33qL6oukyZKmjmwD/aTpgZYo8Jj8Cvwi6c5c1Ad8X3BzGyqqLzWeok3TZQX25WegV9I1OWYfaW1ESxR5TCRNz8+3ktYPbSy2teO+/wX1JbdzK7AkImqTt33ATEm35VHIxTlGyxTYl7Yrqi8N4lgnig5Y2X0pD2AOadjyMGnI+xBpLcANwFfACdLVI9fn+jeTRh7OAWfzdteomPNoz1VmhfSFdKXJUH4MA6vK2I/82mxgf461jXwlR0n7Mhk4DUwr+7kCrAZ+ICXaG4BJJe3HblKSPQT0leCYrCONZI3U3V8Taz5phOvHVp/zl6Evm0jr087n47W8jH2pF6fVx8aP5h7+pWozMzOrvNJPmZmZmZldKidEZmZmVnlOiMzMzKzynBCZmZlZ5TkhMjMzs8pzQmRmZmaV54TI7H9A0p/tboOZWZk5ITIrCSWVOGclTWx3G8ysWirxz9WsrCTNkHRc0nrSr0K/IWmfpMOSVtfZ59Wx6kjaJumApGFJz+eyCZI+lHRU0hFJL+fyOyRtz/V3S5pV572mSjqZ79eEpK6Rv+vFkPS4pO8kHZT0paSbcvmApA2S9pB+/drMrGX8Lcys880k3TepC1hIulGvgEFJcyNi10hFSf25/lh1lkXE75KuBvZJ2gLMALoj4u68/7U51HvAiog4IekB4B3godENi4g/JO0EHiPdWmUxsDUizkuqF+MboDciQtKzwGvAKznkXcCciPingM/NzKxpTojMOt9PEbFX0tukG/UezOVTSMnPrpq6/Q3qvCjpiVx+Sy4/DtwuaS3wObBD0hTS3es3p3tTAjCpQfvWkZKabcAzwHPjxOgBPsl3C78SOFkTa9DJkJm1gxMis873V34W8GZEvNug7ph1JM0DHgYejIi/86jOVRFxRtK9wCPACuBJ4CXgbETMbqZxEbEnT+3NAyZExFFJXQ1irAXWRMRg3mdgjL6ambWU1xCZlccXwLI8+oKkbknTm6wzDTiTk6FZQG9+/UbgiojYArwO3BcR54CTkhblOspJUyPrgY3ABwDjxJgGnMrbSy/qkzAzK5gTIrOSiIgdpKTjW0lHgE+BqU3W2Q5MlHQMeAvYm3fpBnZKOgR8BKzM5U8DyyUNAcPAgnGa9zFwHbCppqxejAHSVNoB4LemPwAzs8tIEdHuNphZyUlaCCyIiCXtbouZ2cXwGiIzuyR5QfajwPx2t8XM7GI5ITKzpkhaBSwaVbw5Il5oR3vMzIrkKTMzMzOrPC+qNjMzs8pzQmRmZmaV54TIzMzMKs8JkZmZmVWeEyIzMzOrvH8BjfUlWpZw+lYAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "bin_cat = 'resolution range'\n",
        "bin_range = [-float(\"inf\"), 2, 2.5, 3, 3.5, 4, 4.5, 5, float(\"inf\")]\n",
        "pandas_plot_multi_groupby_binned(results, first_column_to_group_by='release_year', second_column_to_group_by='em_resolution', bin_cat=bin_cat, bin_range=bin_range)\n",
        "pandas_plot_multi_groupby_binned(results, first_column_to_group_by='release_year', second_column_to_group_by='em_resolution', first_range_flag=True, first_lower=2014, first_upper=2022, bin_cat=bin_cat, bin_range=bin_range)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQchBhE4c4Ss"
      },
      "source": [
        "**Assembly molecular weight by year**\n",
        "\n",
        "Create plot plotting Assembly molecular weight by year."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "56OMskklTGKc",
        "outputId": "64eff3ba-490b-4e44-8010-3f1941905eba"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.8/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "pandas_box_plot(results, first_column_to_group_by='release_year', second_column_to_group_by='assembly_mol_wt')\n",
        "pandas_box_plot(results, first_column_to_group_by='release_year', second_column_to_group_by='assembly_mol_wt', first_range_flag=True, first_lower=2014, first_upper=2022)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "authorship_tag": "ABX9TyMFyGwUtHw9XWMTUhkEo7/5",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.6"
    },
    "vscode": {
      "interpreter": {
        "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}